diff --git "a/1383.jsonl" "b/1383.jsonl" new file mode 100644--- /dev/null +++ "b/1383.jsonl" @@ -0,0 +1,693 @@ +{"seq_id": "42146568180", "text": "from setuptools import setup\n\npackage_name = 'voice'\n\nsetup(\n name=package_name,\n version='0.0.0',\n packages=[package_name],\n data_files=[\n ('share/ament_index/resource_index/packages',\n ['resource/' + package_name]),\n ('share/' + package_name, ['package.xml']),\n ],\n install_requires=['setuptools'],\n zip_safe=True,\n maintainer='r1',\n maintainer_email='r1@todo.todo',\n description='TODO: Package description',\n license='TODO: License declaration',\n tests_require=['pytest'],\n entry_points={\n 'console_scripts': [\n 'voice_sub = voice.detection_subscriber:main',\n # 'main = voice.continuous_listener:main',\n ],\n },\n)\n \n", "repo_name": "Shivansh2703/r-1", "sub_path": "src/voice/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "setuptools.setup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "34999843596", "text": "import os\n\nfrom django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\nfrom django.contrib.staticfiles import views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^users/', include('users.urls')),\n url(r'', include('quizzes.urls')),\n]\n\nif settings.DEBUG:\n import debug_toolbar\n urlpatterns += [\n url(r'^static/(?P.*)$', views.serve, {\n 'document_root': os.path.join(\n settings.BASE_DIR, 'core/static'\n )\n }),\n url(r'^__debug__/', include(debug_toolbar.urls))\n ]\n", "repo_name": "srgypetrov/test.testing_service", "sub_path": "core/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.staticfiles.views.serve", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.staticfiles.views", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "debug_toolbar.urls", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "4365114684", "text": "from django.contrib import admin\nfrom django.urls import include, path, re_path\nfrom drf_yasg import openapi\nfrom drf_yasg.views import get_schema_view\nfrom rest_framework import permissions\n\nfrom conf import DEBUG\n\nif DEBUG:\n permission_classes = (permissions.AllowAny,)\nelse:\n permission_classes = (permissions.IsAdminUser,)\n\nopenapi_info = openapi.Info(\n title='{{ cookiecutter.project_name|capitalize }} API',\n default_version='v1',\n description='Server API for data store',\n)\nschema_view = get_schema_view(\n openapi_info,\n public=True,\n permission_classes=permission_classes,\n)\n\nurlpatterns = [\n path('api/', include('api.urls')),\n path('admin/', admin.site.urls),\n path('auth/', include('rest_framework.urls')),\n path('accounts/', include('django.contrib.auth.urls')),\n re_path(\n r'^api/swagger(?P\\.json|\\.yaml)$',\n schema_view.without_ui(cache_timeout=0),\n name='schema-json',\n ),\n re_path(\n r'^api/swagger/$', schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'\n ),\n]\n", "repo_name": "ProjectTemplates/django-webpack-app", "sub_path": "{{cookiecutter.project_name}}/core/server/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "conf.DEBUG", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 12, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.Info", "line_number": 14, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 14, "usage_type": "name"}, {"api_name": "drf_yasg.views.get_schema_view", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.re_path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.re_path", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "37077142134", "text": "from ANPR import ANPR\nimport cv2 as cv\nimport argparse\nimport os\nfrom imutils import paths\nimport imutils\ndir_path = 'D:/Car_Parking/Images/self_cap_Data/test_Data/data/images/train'\n\n\ndef cleanup_text(text):\n # strip out non ASCII text so we can draw the text on the image\n # using openCV\n return \"\".join([c if ord(c) < 128 else \"\" for c in text]).strip()\n\n\ndef get_license_plate_text():\n # construct the argument parser and parse the arguments\n ap = argparse.ArgumentParser()\n\n # ap.add_argument(\"-i\", \"--input\", required=True,\n # \thelp=\"path to input directory of images\")\n ap.add_argument(\"-c\", \"--clear-border\", type=int, default=True,\n help=\"whether or to clear border pixels before OCR'ing\")\n ap.add_argument(\"-p\", \"--psm\", type=int, default=7,\n help=\"default PSM mode for OCR'ing license plates\")\n ap.add_argument(\"-d\", \"--debug\", type=int, default=-1,\n help=\"whether or not to show additional visualizations\")\n args = vars(ap.parse_args())\n # initialize our ANPR class\n anpr = ANPR(debug=args[\"debug\"] > 0)\n # grab all image paths in the input directory\n # imagePaths = sorted(list(paths.list_images(args[\"input\"])))\n # loop over all image paths in the input directory\n for imagePath in os.listdir(dir_path):\n # load the input image from disk and resize it\n print(os.path.join(dir_path, imagePath))\n image = cv.imread(os.path.join(dir_path, imagePath))\n image = imutils.resize(image, width=600)\n # apply automatic license plate recognition\n (lpText, lpCnt) = anpr.find_and_ocr(image, psm=args[\"psm\"],\n clearBorder=args[\"clear_border\"] > 0)\n # only continue if the license plate was successfully OCR'd\n if lpText is not None and lpCnt is not None:\n # fit a rotated bounding box to the license plate contour and\n # draw the bounding box on the license plate\n box = cv.boxPoints(cv.minAreaRect(lpCnt))\n box = box.astype(\"int\")\n cv.drawContours(image, [box], -1, (0, 255, 0), 2)\n # compute a normal (unrotated) bounding box for the license\n # plate and then draw the OCR'd license plate text on the\n # image\n (x, y, w, h) = cv.boundingRect(lpCnt)\n cv.putText(image, cleanup_text(lpText), (x, y-15),\n cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)\n # show the output ANPR image\n print(\"[INFO] {}\".format(lpText))\n cv.imshow(\"Output ANPR\", image)\n return lpText\n # cv.waitKey(0)\n else:\n print(\"License Plate not found\")\n", "repo_name": "TranDuyNghia1402/Car-Parking", "sub_path": "storage.py", "file_name": "storage.py", "file_ext": "py", "file_size_in_byte": 2735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "ANPR.ANPR", "line_number": 30, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 37, "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": "imutils.resize", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.boxPoints", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.minAreaRect", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "29239786857", "text": "import unittest\nfrom mock import patch, Mock\n\nfrom maps.infra.monitoring.sla_calculator.core.graphite import graphite_statuses\n\n\nclass GraphiteStatusesTest(unittest.TestCase):\n @patch('requests.get')\n def test(self, get_method):\n SAMPLING_RATE = 15\n response = Mock()\n response.json.return_value = [{\n \"target\": \"test_target\",\n \"datapoints\": [\n # Spend 2 + 1 intervals under the treshold\n [\n # value\n 1.0,\n # timestamp\n 10 * SAMPLING_RATE\n ],\n [\n # This is still not more than 2. So this is counted as bad request.\n 2.0,\n (10 + 2) * SAMPLING_RATE\n ],\n # Following two intervals are good\n [\n 3.0,\n (10 + 2 + 1) * SAMPLING_RATE\n ],\n [\n 4.0,\n (10 + 2 + 1 + 1) * SAMPLING_RATE\n ]]}]\n get_method.return_value = response\n\n statuses = graphite_statuses('test_target', '2017-01-01', more_than=2)\n statuses.set_index('status', inplace=True)\n\n self.assertAlmostEqual(statuses['amount'][200], 2)\n self.assertAlmostEqual(statuses['amount'][500], 3)\n", "repo_name": "Alexander-Berg/2022-tests-examples", "sub_path": "maps/tests/graphite_test.py", "file_name": "graphite_test.py", "file_ext": "py", "file_size_in_byte": 1380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "attribute"}, {"api_name": "mock.Mock", "line_number": 11, "usage_type": "call"}, {"api_name": "maps.infra.monitoring.sla_calculator.core.graphite.graphite_statuses", "line_number": 38, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "677151651", "text": "import json\nfrom itertools import islice\n\n# 461. Hamming Distance - Easy\n# The Hamming distance between two integers is the number of positions\n# at which the corresponding bits are different.\n# Given two integers x and y, return the Hamming distance between them.\n#\n# https://leetcode.com/problems/hamming-distance/\n\nclass Solution:\n def hammingDistance(self, x: int, y: int) -> int:\n x = x ^ y # total XOR\n res = 0\n while x: # go through all bits and count 1\n if x & 1:\n res += 1 \n x = x >> 1\n\n return res\n \n\nif __name__ == '__main__': \n with open('OUTPUT/IN', 'r') as f_in, open('OUTPUT/OUT', \"w\") as f_out:\n while True:\n n_args = 2\n args_raw = [x.rstrip() for x in islice(f_in, n_args)]\n if not args_raw:\n break\n\n exec = Solution()\n res = exec.hammingDistance(int(args_raw[0]), int(args_raw[1])) \n\n f_out.write(json.dumps(res) + '\\n')\n", "repo_name": "yuvSid/interviewPrepare", "sub_path": "python/hamming_distance.py", "file_name": "hamming_distance.py", "file_ext": "py", "file_size_in_byte": 1010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "itertools.islice", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "71618006482", "text": "#-------------------------------------------------------------------------------\n# 先頭が # で始まっている行はコメントである．\n\n# 当面，次の行は必ず書く．\nimport flask\nfrom flask import Flask, redirect, url_for, request, render_template, \\\n flash, abort, make_response\napp = Flask(__name__)\n\n# 次のように書くことによって，ブラウザからの要求を処理することができる:\n#\n# @app.route('相対URL')\n# def 名前():\n# .... 関数本体 ....\n#\n# 関数本体にはさまざまなことを書くことができる．少しずつ紹介する．\n\n# 下の関数は，http://localhost:8088/hello というアクセスを処理する．\n@app.route('/hello')\ndef func_hello():\n return 'こんにちは．'\n\n# @route('/hello')\n# 「http://localhost:8088」を除いた部分 (/hello) が相対URLとして\n# 指定されている．\n#\n# def func_hello():\n# def の後ろに書く名前は，何でも良い．ただし，\n# - 半角英字で始まる半角英数字\n# - 1つのファイル中で複数回同じ名前を使ってはいけない．\n# 最後のコロンを忘れやすいので注意する．\n#\n# 関数本体は，半角スペース4つを行頭に置く．\n# もっとも簡単なものは，return '文字列'\n# return 'こんにちは．'\n#\n\n# 下の関数は，http://localhost:8088/bye というアクセスを処理する．\n@app.route('/bye')\ndef func_bye():\n return 'さようなら．'\n\n# 下の関数は，http://localhost:8088/Tsurumi/University/LAIS\n# というアクセスを処理する．\n@app.route('/Tsurumi/University/LAIS')\ndef func_tu():\n return '鶴見大学ドキュメンテーション学科'\n\n# 下のように，HTML文書を返すこともできる．\n# ただし，普通はこのような書き方はせず，templateというものを使う．後述．\n# ここでは，HTML文書も返せるということを示すために書いている．\n@app.route('/rich_hello')\ndef func_rich_hello():\n html_page = '''\n\n \n \n \n \n

ようこそ

\n
\n

こんにちは．どうぞよろしく

\n
\n \n\n'''\n return flask.render_template_string(html_page)\n\n# ファイル末尾には，当面，必ず下の行を書く．\napp.run(host='localhost', port=8088, debug=True)\n", "repo_name": "dbe2-2023/dbe2-2023.github.io", "sub_path": "content/docs/flask/020basic/hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 2472, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template_string", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "71559460563", "text": "from django.core.exceptions import ValidationError\n\nfrom accounts.models import SimpleUserProfile\n\n\ndef validate_user_is_service(user_profile: SimpleUserProfile):\n if not user_profile.is_service:\n raise ValidationError(\n \"This user profile is not a service\"\n )\n", "repo_name": "IldarSaygafarov2/api_hisay", "sub_path": "api/validators.py", "file_name": "validators.py", "file_ext": "py", "file_size_in_byte": 289, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "accounts.models.SimpleUserProfile", "line_number": 6, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "21313293062", "text": "#!/python3\nimport chess_lib\nimport chess\nimport time\n# import numpy as np\n\nstr_seq = \"\"\"e2e4\ne7e5\nd1h5\nb8c6\nf1c4\ng8f6\nh5f7\"\"\"\n\nseq = [x.strip() for x in str_seq.split(\"\\n\")]\n\ndef main():\n a = chess_lib.Board()\n a.start_game()\n i = 0\n while not a.game_has_ended():\n display_bard(a)\n moves = a.get_legal_moves(False)\n print(\"Legal Moves: \")\n for index, move in enumerate(moves):\n print(\"\\t\", index, move)\n\n if i >= len(seq):\n move_index = int(input(\"which move? \"))\n else:\n for index, move in enumerate(moves):\n if repr(move) == seq[i]:\n move_index = index\n break\n i += 1\n a.put_move(moves[move_index])\n\n print(\"winner: \", a.winner())\n \ndef display_bard(board):\n board_arr = board.get_board()\n print(\"+ - - - - - - - - +\")\n for i in range(len(board_arr)-1, -1, -1):\n print(\"| \",end=\"\")\n for j in range(len(board_arr[i])):\n id = board_arr[i][j]\n piece = board.get_piece(id)\n if piece == None:\n print(\" \", end=\"\")\n else:\n print(piece.get_char(0 if id < 0 else 1), end=\"\")\n print(\" \", end=\"\")\n print(\"|\")\n print(\"+ - - - - - - - - +\")\n\n\ndef volume_test():\n moves = 10000\n i = 0\n prev_time = time.time()\n while i < moves:\n a = chess_lib.Board()\n a.start_game()\n while not a.game_has_ended():\n if i >= moves:\n break\n\n move = a.get_legal_moves(False)[0]\n a.put_move(move)\n i += 1\n\n delta = time.time() - prev_time\n print(\"total time =\", delta)\n print(\"speed:\", moves / delta)\n\ndef chess_test():\n moves = 10000\n i = 0\n prev_time = time.time()\n while i < moves:\n a = chess.Board()\n while not a.is_game_over():\n if i >= moves:\n break\n\n move = list(a.legal_moves)[0]\n a.push(move)\n i += 1\n\n delta = time.time() - prev_time\n print(\"total time =\", delta)\n print(\"speed:\", moves / delta)\n\n\nif __name__ == \"__main__\":\n # volume_test()\n chess_test()\n # main()\n\n\n", "repo_name": "danielkopp4/ChessBot_old", "sub_path": "ChessLibary/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "chess_lib.Board", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "chess_lib.Board", "line_number": 62, "usage_type": "call"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}, {"api_name": "chess.Board", "line_number": 81, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "4989302443", "text": "import matplotlib.pyplot as plt\nfrom read_file import select_original_breakpoints\nfrom collections import defaultdict\n\n\nif __name__ == '__main__':\n N = 5\n slopes, intervals = select_original_breakpoints(N, 'segm/segmented_curves_filtered.txt')\n\n slopes_hist = defaultdict(lambda: [])\n intervals_hist = defaultdict(lambda: [])\n\n for sample in slopes:\n for i in range(N):\n slopes_hist[i].append(sample[i])\n\n for sample in intervals:\n for i in range(N):\n intervals_hist[i].append(sample[i])\n\n fig, axs = plt.subplots(5, 2, figsize=(6.5, 10), sharex='col')\n for i in range(N):\n axs[i][0].hist(slopes_hist[i])\n axs[i][0].set_xlabel('$\\\\alpha_%d$' % (i + 1))\n axs[i][0].set_ylabel('Occurrences', fontsize=12)\n for i in range(N):\n axs[i][1].hist(intervals_hist[i])\n axs[i][1].set_xlabel('$l_%d$' % (i + 1), fontsize=12)\n\n plt.tight_layout()\n plt.savefig('histogram_by_var.pdf')\n", "repo_name": "carolmb/viewing-profiles-of-scientific-articles", "sub_path": "plots/histogram_by_vars.py", "file_name": "histogram_by_vars.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "read_file.select_original_breakpoints", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "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"}]} +{"seq_id": "42794387505", "text": "\"\"\"empty message\n\nRevision ID: af553fa94b1f\nRevises: \nCreate Date: 2018-06-12 13:41:49.968147\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'af553fa94b1f'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('organizations',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('organization_name', sa.String(length=10), nullable=False),\n sa.Column('organization_code', sa.String(length=10), nullable=True),\n sa.Column('create_time', sa.TIMESTAMP(), nullable=True),\n sa.Column('update_time', sa.TIMESTAMP(), nullable=True),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_organizations')),\n sa.UniqueConstraint('organization_code', name=op.f('uq_organizations_organization_code')),\n sa.UniqueConstraint('organization_name', name=op.f('uq_organizations_organization_name'))\n )\n op.create_table('roles',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('role_name', sa.String(length=50), nullable=True),\n sa.Column('role_permissions', sa.Integer(), nullable=True),\n sa.Column('create_time', sa.TIMESTAMP(), nullable=True),\n sa.Column('update_time', sa.TIMESTAMP(), nullable=True),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_roles')),\n sa.UniqueConstraint('role_name', name=op.f('uq_roles_role_name'))\n )\n op.create_table('users',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('user_name', sa.String(length=50), nullable=False),\n sa.Column('password_md5', sa.String(length=50), nullable=False),\n sa.Column('real_name', sa.String(length=50), nullable=False),\n sa.Column('email', sa.String(length=50), nullable=True),\n sa.Column('organization_id', sa.Integer(), nullable=True),\n sa.Column('admin_flag', sa.Boolean(), nullable=True),\n sa.Column('create_time', sa.TIMESTAMP(), nullable=True),\n sa.Column('update_time', sa.TIMESTAMP(), nullable=True),\n sa.ForeignKeyConstraint(['organization_id'], ['organizations.id'], name=op.f('fk_users_organization_id_organizations')),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_users')),\n sa.UniqueConstraint('user_name', name=op.f('uq_users_user_name'))\n )\n op.create_table('users_roles',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('role_id', sa.Integer(), nullable=True),\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['role_id'], ['roles.id'], name=op.f('fk_users_roles_role_id_roles')),\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], name=op.f('fk_users_roles_user_id_users')),\n sa.PrimaryKeyConstraint('id', name=op.f('pk_users_roles'))\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('users_roles')\n op.drop_table('users')\n op.drop_table('roles')\n op.drop_table('organizations')\n # ### end Alembic commands ###\n", "repo_name": "shenbing/QCM", "sub_path": "migrations/versions/af553fa94b1f_.py", "file_name": "af553fa94b1f_.py", "file_ext": "py", "file_size_in_byte": 3025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 37, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 37, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 37, "usage_type": "name"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 38, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 40, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 50, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 50, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 50, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 51, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 51, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 51, "usage_type": "name"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 52, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 52, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 52, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 54, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 54, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 58, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 58, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 58, "usage_type": "name"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 59, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 59, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 59, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 60, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 60, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 60, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 67, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 67, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 68, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 68, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 69, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 69, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 70, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "15343806464", "text": "from itertools import permutations\nfrom random import choice\n\ndef Check_AB(X, Y):\n A = len([1 for i in range(len(Y)) if Y[i]==X[i]])\n B = len(set(Y)&set(X))-A\n return A,B\n\nData = list(permutations([ _ for _ in range(10)],4))\nguessTimes=0\nwhile True:\n comGuess = list(choice(Data)) #電腦猜的數\n guessTimes+=1\n print('第{}次猜題:{}'.format(guessTimes,comGuess))\n a, b =map(int,input('請輸入A、B值(空格分隔):').split())\n Data = [data for data in Data if (Check_AB(data,comGuess)==(a,b))]\n print(len(Data))\n if len(Data)==1:\n break\nprint('你的答案是:{}'.format(Data[0]))\n\n\n\n\n\n\n", "repo_name": "JianDa0127/GitHub_JianDa", "sub_path": "Guess Num/1-1_User出題_AI表內隨機猜.py", "file_name": "1-1_User出題_AI表內隨機猜.py", "file_ext": "py", "file_size_in_byte": 635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "itertools.permutations", "line_number": 9, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "22506453060", "text": "\"\"\"Testing sound device.\"\"\"\n\nfrom __future__ import print_function, absolute_import\n\nimport sounddevice as sd\nimport numpy as np\n\n\ndef play_sound(data, fs):\n sd.play(data, fs)\n status = sd.wait()\n\n return status\n\n\n# fs = 48000\n# sound_1 = np.ones((fs,), dtype=np.float64)*1000\n#\n# play_sound(sound_1, fs)\n\nfs = 44100\nsound_2 = np.ones((fs*2,), dtype=np.float64)*1000\n\nplay_sound(sound_2, fs)\n", "repo_name": "SensorsINI/jaer-control", "sub_path": "scripts/test_sound_device.py", "file_name": "test_sound_device.py", "file_ext": "py", "file_size_in_byte": 404, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sounddevice.play", "line_number": 10, "usage_type": "call"}, {"api_name": "sounddevice.wait", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "39776514094", "text": "\"\"\"testdb 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, include\nfrom django.contrib import admin\nfrom dbconn import views\n\nurlpatterns = [\n\turl(r'^$', views.homepage), # \"^\"符號表示字串開頭，\"$\"表示字串結尾\n\turl(r'^post/(\\w+)$', views.showpost),\n url(r'^currency/(?P[A-Z]{3})/$', views.USD),\n url(r'^oilprice/$', views.Oilprice),\n url(r'^rate/$', views.Rate),\n url(r'^invoice/$', views.Invoice),\n url(r'^admin/', include(admin.site.urls)),\n]\n", "repo_name": "multw/twinformation", "sub_path": "testdb/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "dbconn.views.homepage", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dbconn.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "dbconn.views.showpost", "line_number": 22, "usage_type": "attribute"}, {"api_name": "dbconn.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "dbconn.views.USD", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dbconn.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "dbconn.views.Oilprice", "line_number": 24, "usage_type": "attribute"}, {"api_name": "dbconn.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "dbconn.views.Rate", "line_number": 25, "usage_type": "attribute"}, {"api_name": "dbconn.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "dbconn.views.Invoice", "line_number": 26, "usage_type": "attribute"}, {"api_name": "dbconn.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "13041892763", "text": "import numpy\nfrom numpy import testing as nptest\nfrom scipy.signal import gausspulse\n\nfrom .. import qtransform\nfrom ...table import EventTable\nfrom ...segments import Segment\nfrom ...timeseries import TimeSeries\n\n__author__ = 'Alex Urban '\n\n\n# -- global variables ---------------------------------------------------------\n\n# create noise and a glitch template at 1000 Hz\nNOISE = TimeSeries(\n numpy.random.normal(size=4096 * 10), sample_rate=4096, epoch=-5)\nGLITCH = TimeSeries(\n gausspulse(NOISE.times.value, fc=500)*10, sample_rate=4096)\nDATA = NOISE + GLITCH\n\n# global test objects\nSEARCH = Segment(-0.25, 0.25)\nQGRAM, FAR = qtransform.q_scan(DATA, search=SEARCH)\nQSPECGRAM = QGRAM.interpolate()\n\n\n# -- test utilities -----------------------------------------------------------\n\ndef test_far():\n # test that FAR is better than 1 / Hubble time\n assert FAR < 1 / (1.37e10 * 365 * 86400)\n\n\ndef test_monotonicity():\n # test that Q-plane frequencies are strictly increasing\n freq = QGRAM.plane.frequencies\n assert (freq[1:] > freq[:-1]).all()\n\n\ndef test_q_scan():\n # scan with the TimeSeries method\n ts_qspecgram = DATA.q_transform(whiten=False)\n\n # test spectrogram output\n assert ts_qspecgram.q == QSPECGRAM.q\n assert ts_qspecgram.shape == QSPECGRAM.shape\n assert ts_qspecgram.dtype == numpy.dtype('float32')\n nptest.assert_allclose(ts_qspecgram.value, QSPECGRAM.value)\n\n\ndef test_unnormalised_q_scan():\n # scan with norm=False\n ts_qspecgram = DATA.q_transform(whiten=False, norm=False)\n\n # test spectrogram output\n assert ts_qspecgram.q == QSPECGRAM.q\n assert ts_qspecgram.dtype == numpy.dtype('float64')\n\n\ndef test_q_scan_fd():\n # create test object from frequency-domain input\n fdata = DATA.fft()\n fs_qgram, far = qtransform.q_scan(\n fdata, duration=abs(DATA.span), sampling=DATA.sample_rate.value,\n search=SEARCH, epoch=fdata.epoch.value)\n fs_qspecgram = fs_qgram.interpolate()\n\n # test that the output is the same\n assert far == FAR\n assert fs_qspecgram.q == QSPECGRAM.q\n assert fs_qspecgram.dtype == numpy.dtype('float32')\n assert fs_qspecgram.shape == QSPECGRAM.shape\n nptest.assert_allclose(fs_qspecgram.value, QSPECGRAM.value, rtol=3e-2)\n\n\ndef test_qtable():\n # test EventTable output\n qtable = QGRAM.table()\n imax = qtable['energy'].argmax()\n assert isinstance(qtable, EventTable)\n assert qtable.meta['q'] == QGRAM.plane.q\n nptest.assert_almost_equal(qtable['time'][imax], QGRAM.peak['time'])\n nptest.assert_almost_equal(qtable['duration'][imax], 1/1638.4)\n nptest.assert_almost_equal(qtable['frequency'][imax],\n QGRAM.peak['frequency'])\n nptest.assert_almost_equal(\n qtable['bandwidth'][imax],\n 2 * numpy.pi ** (1/2.) * qtable['frequency'][imax] / QGRAM.plane.q)\n nptest.assert_almost_equal(qtable['energy'][imax], QGRAM.peak['energy'])\n\n # it's enough to check consistency between the shape of time and\n # frequency columns, because of the way they're calculated\n assert qtable['time'].shape == qtable['frequency'].shape\n\n # test that too high an SNR threshold returns an empty table\n assert len(QGRAM.table(snrthresh=1e9)) == 0\n", "repo_name": "gwpy/gwpy", "sub_path": "gwpy/signal/tests/test_qtransform.py", "file_name": "test_qtransform.py", "file_ext": "py", "file_size_in_byte": 3257, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 358, "dataset": "github-code", "pt": "3", "api": [{"api_name": "timeseries.TimeSeries", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "timeseries.TimeSeries", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.signal.gausspulse", "line_number": 19, "usage_type": "call"}, {"api_name": "segments.Segment", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.dtype", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 74, "usage_type": "name"}, {"api_name": "table.EventTable", "line_number": 81, "usage_type": "argument"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "34885445874", "text": "import calendar\nimport webbrowser\nimport tempfile\nfrom datetime import date, timedelta\nfrom jinja2 import Template\nimport pandas as pd\n\nweekly_agenda_template = \"\"\"\n\n\n\n Daily Agenda for {{ from_date }} to {{ to_date }}\n\n\n\n
    \n {% for key, value in tasks.iterrows() %}\n
  • \n
    Date: {{ value['due'] }}
    \n
    {{ value['task'] }}
    \n
    • \n {% endfor %}\n
\n\n {# a comment #}\n\n\n\"\"\"\n\n\ndef weekly_agenda(tasks, get_config=lambda x, d: d, today=None):\n weekdays = list(calendar.day_name)\n if today is None:\n today = date.today()\n\n df = pd.DataFrame([x.as_dict() for x in tasks.ls])\n print(df)\n df['month'] = pd.DatetimeIndex(df['due']).month\n df['year'] = pd.DatetimeIndex(df['due']).year\n df['day'] = pd.DatetimeIndex(df['due']).day\n df['dayofweek'] = pd.DatetimeIndex(df['due']).dayofweek\n # df['weekofyear'] = pd.DatetimeIndex(df['due']).isocalendar().week\n\n from_date = min(df[\"due\"])\n to_date = max(df[\"due\"])\n template = Template(weekly_agenda_template)\n x = template.render(tasks=df,\n from_date=from_date,\n to_date=to_date,\n today=today)\n #print(x)\n html_report_file = None\n with tempfile.NamedTemporaryFile(\"w\", suffix=\".html\", delete=False) as tf:\n tf.write(x)\n html_report_file = 'file://' + tf.name\n webbrowser.open_new(html_report_file)\n\n\nif __name__ == '__main__':\n test_tasks = []\n t = date.today()\n delta = timedelta(days=1)\n for i in range(3):\n td = t + (delta * i)\n test_tasks.append({ \"task\": \"blah\" + str(i), \"due\": td})\n test_tasks.append({ \"task\": \"bluh\" + str(i), \"due\": td})\n weekly_agenda(test_tasks)\n", "repo_name": "abhishekmishra/idetodo", "sub_path": "view_calendar.py", "file_name": "view_calendar.py", "file_ext": "py", "file_size_in_byte": 1885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "calendar.day_name", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.DatetimeIndex", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DatetimeIndex", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DatetimeIndex", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DatetimeIndex", "line_number": 41, "usage_type": "call"}, {"api_name": "jinja2.Template", "line_number": 46, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 53, "usage_type": "call"}, {"api_name": "webbrowser.open_new", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "11577292671", "text": "\"\"\"Handles performing evaluations on results.\"\"\"\n\nfrom typing import Dict\n\nimport lark as _lark\nfrom pavilion import utils\nfrom pavilion.parsers import (check_expression, get_expr_parser,\n EvaluationExprTransformer,\n VarRefVisitor, match_examples,\n BAD_EXAMPLES)\nfrom ..errors import ParserValueError, StringParserError, ResultError\nfrom .base import BASE_RESULTS\n\n\ndef check_evaluations(evaluations: Dict[str, str]):\n \"\"\"Check all evaluations for basic errors.\n\n :raises ResultError: For detected problems.\n \"\"\"\n\n for key, expr in evaluations.items():\n if key in BASE_RESULTS:\n raise ResultError(\n \"Key '{}' in result.evaluate section is reserved.\"\n .format(key))\n\n try:\n check_expression(expr)\n except StringParserError as err:\n raise ResultError(\n \"Error parsing evaluate expression for key '{}':\\n{}\\n{}\"\n .format(key, err.message, err.context)\n )\n\ndef evaluate_results(results: dict, evaluations: Dict[str, str],\n base_log: utils.IndentedLog = None):\n \"\"\"Perform result evaluations using an expression parser. The variables\n in such expressions are pulled from the results data structure, and the\n results are stored there too.\n :param results: The result dict. Will be modified in place.\n :param evaluations: A dictionary of evals to perform.\n :param base_log: The optional logger function from (result.get_result_logger)\n :return:\n \"\"\"\n\n base_log = base_log or utils.IndentedLog()\n base_log(\"Evaluating result evaluations.\")\n\n log = utils.IndentedLog()\n\n if 'result' not in results and 'result' not in evaluations:\n evaluations['result'] = 'return_value == 0'\n\n try:\n parse_evaluation_dict(evaluations, results, log)\n except StringParserError as err:\n raise ResultError(\"\\n\".join([err.message, err.context]))\n except ValueError as err:\n # There was a reference loop.\n raise ResultError(err.args[0])\n finally:\n base_log.indent(log)\n\n\ndef parse_evaluation_dict(eval_dict: Dict[str, str], results: dict,\n log: utils.IndentedLog) -> None:\n \"\"\"Parse the dictionary of evaluation expressions, given that some of them\n may contain references to each other. Each evaluated value will be stored\n under its corresponding key in the results dict.\n\n :raises StringParserError: When there's an error parsing or resolving\n one of the expressions. The error will already contain key information.\n :raises ValueError: When there's a reference loop.\n \"\"\"\n\n parser = get_expr_parser()\n transformer = EvaluationExprTransformer(results)\n var_ref_visitor = VarRefVisitor()\n\n unresolved = {}\n\n for key, expr in eval_dict.items():\n log(\"Parsing the evaluate expression '{}'\".format(expr))\n try:\n tree = parser.parse(expr)\n except (_lark.UnexpectedCharacters, _lark.UnexpectedToken) as err:\n # Try to figure out why the error happened based on examples.\n err_type = match_examples(err, parser.parse, BAD_EXAMPLES, expr)\n log(\"Error parsing expression, failing.\")\n log(err_type)\n log(err.get_context(expr))\n raise StringParserError(\n \"Error evaluating expression '{}' for key '{}':\\n{}\"\n .format(expr, key, err_type), err.get_context(expr))\n\n var_refs = var_ref_visitor.visit(tree)\n\n unresolved[key] = (tree, var_refs, expr)\n\n log(\"Resolving evaluations.\")\n\n while unresolved:\n resolved = []\n for key, (tree, var_refs, expr) in unresolved.items():\n for var in var_refs:\n if var in unresolved:\n break\n else:\n log(\"Resolving evaluation '{}': '{}'\".format(key, expr))\n try:\n results[key] = transformer.transform(tree)\n except ParserValueError as err:\n log(\"Error resolving evaluation: {}\".format(err.args[0]))\n log(err.get_context(expr))\n\n # Any value errors should be converted to this error type.\n raise StringParserError(err.args[0], err.get_context(expr))\n resolved.append(key)\n log(\"Value resolved to: '{}'\".format(results[key]))\n\n if not resolved:\n # Pass up the unresolved\n raise ValueError(\"Reference loops found amongst evaluation keys \"\n \"{}.\".format(tuple(unresolved.keys())))\n\n for key in resolved:\n del unresolved[key]\n\n log(\"Finished resolving expressions\")\n", "repo_name": "hpc/pavilion2", "sub_path": "lib/pavilion/result/evaluations.py", "file_name": "evaluations.py", "file_ext": "py", "file_size_in_byte": 4816, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.Dict", "line_number": 15, "usage_type": "name"}, {"api_name": "base.BASE_RESULTS", "line_number": 22, "usage_type": "name"}, {"api_name": "errors.ResultError", "line_number": 23, "usage_type": "call"}, {"api_name": "pavilion.parsers.check_expression", "line_number": 28, "usage_type": "call"}, {"api_name": "errors.StringParserError", "line_number": 29, "usage_type": "name"}, {"api_name": "errors.ResultError", "line_number": 30, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 35, "usage_type": "name"}, {"api_name": "pavilion.utils.IndentedLog", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pavilion.utils", "line_number": 36, "usage_type": "name"}, {"api_name": "pavilion.utils.IndentedLog", "line_number": 46, "usage_type": "call"}, {"api_name": "pavilion.utils", "line_number": 46, "usage_type": "name"}, {"api_name": "pavilion.utils.IndentedLog", "line_number": 49, "usage_type": "call"}, {"api_name": "pavilion.utils", "line_number": 49, "usage_type": "name"}, {"api_name": "errors.StringParserError", "line_number": 56, "usage_type": "name"}, {"api_name": "errors.ResultError", "line_number": 57, "usage_type": "call"}, {"api_name": "errors.ResultError", "line_number": 60, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 65, "usage_type": "name"}, {"api_name": "pavilion.utils.IndentedLog", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pavilion.utils", "line_number": 66, "usage_type": "name"}, {"api_name": "pavilion.parsers.get_expr_parser", "line_number": 76, "usage_type": "call"}, {"api_name": "pavilion.parsers.EvaluationExprTransformer", "line_number": 77, "usage_type": "call"}, {"api_name": "pavilion.parsers.VarRefVisitor", "line_number": 78, "usage_type": "call"}, {"api_name": "lark.UnexpectedCharacters", "line_number": 86, "usage_type": "attribute"}, {"api_name": "lark.UnexpectedToken", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pavilion.parsers.match_examples", "line_number": 88, "usage_type": "call"}, {"api_name": "pavilion.parsers.BAD_EXAMPLES", "line_number": 88, "usage_type": "argument"}, {"api_name": "errors.StringParserError", "line_number": 92, "usage_type": "call"}, {"api_name": "errors.ParserValueError", "line_number": 112, "usage_type": "name"}, {"api_name": "errors.StringParserError", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "74211628881", "text": "from django.shortcuts import render\nimport requests\nfrom django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nimport json\n\n\n\ndef index(request):\n return render(request,\"index.html\",{})\n# Create your views here.\n\ndef get_weather(request):\n result = {'success': True, 'msg': ''}\n try:\n lat = request.POST['lat']\n lon = request.POST['lon']\n URL = \"https://api.met.no/weatherapi/locationforecast/2.0/compact\"\n PARAMS = {'lat': lat, 'lon': str(lon)}\n HEADERS = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36',\n 'From': 'youremail@domain.example'\n }\n res = requests.get(url = URL, headers= HEADERS, params = PARAMS)\n json_data = json.loads(res.text)\n result['data'] = json_data\n except Exception as e:\n result['success'] = False\n result['msg'] = str(e)\n print(str(e))\n return JsonResponse(result)\n", "repo_name": "chidodev/neliti_task_2", "sub_path": "neliti/task2/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "14898401197", "text": "import io\nfrom logging import StreamHandler, getLogger\nimport sys\n\nfrom qiskit import BasicAer\nfrom qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister\nfrom qiskit.compiler import transpile\nfrom qiskit.compiler import assemble\nfrom qiskit.qobj import QobjHeader\nfrom qiskit.test import QiskitTestCase\n\n\nclass StreamHandlerRaiseException(StreamHandler):\n \"\"\"Handler class that will raise an exception on formatting errors.\"\"\"\n\n def handleError(self, record):\n raise sys.exc_info()\n\n\nclass TestBasicAerQobj(QiskitTestCase):\n \"\"\"Tests for all the Terra simulators.\"\"\"\n\n def setUp(self):\n super().setUp()\n logger = getLogger()\n self.addCleanup(logger.setLevel, logger.level)\n logger.setLevel(\"DEBUG\")\n\n self.output = io.StringIO()\n logger.addHandler(StreamHandlerRaiseException(self.output))\n\n qr = QuantumRegister(1)\n cr = ClassicalRegister(1)\n self.qc1 = QuantumCircuit(qr, cr, name=\"circuit0\")\n self.qc1.h(qr[0])\n\n def test_qobj_headers_in_result(self):\n \"\"\"Test that the qobj headers are passed onto the results.\"\"\"\n custom_qobj_header = {\"x\": 1, \"y\": [1, 2, 3], \"z\": {\"a\": 4}}\n\n for backend in BasicAer.backends():\n with self.subTest(backend=backend):\n new_circ = transpile(self.qc1, backend=backend)\n qobj = assemble(new_circ, shots=1024)\n\n # Update the Qobj header.\n qobj.header = QobjHeader.from_dict(custom_qobj_header)\n # Update the Qobj.experiment header.\n qobj.experiments[0].header.some_field = \"extra info\"\n\n result = backend.run(qobj).result()\n self.assertEqual(result.header.to_dict(), custom_qobj_header)\n self.assertEqual(result.results[0].header.some_field, \"extra info\")\n", "repo_name": "peiyi1/nassc_code", "sub_path": "qiskit-terra/test/python/basicaer/test_basicaer_qobj_headers.py", "file_name": "test_basicaer_qobj_headers.py", "file_ext": "py", "file_size_in_byte": 1859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.StreamHandler", "line_number": 13, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 17, "usage_type": "call"}, {"api_name": "qiskit.test.QiskitTestCase", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 29, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 32, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 33, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 34, "usage_type": "call"}, {"api_name": "qiskit.BasicAer.backends", "line_number": 41, "usage_type": "call"}, {"api_name": "qiskit.BasicAer", "line_number": 41, "usage_type": "name"}, {"api_name": "qiskit.compiler.transpile", "line_number": 43, "usage_type": "call"}, {"api_name": "qiskit.compiler.assemble", "line_number": 44, "usage_type": "call"}, {"api_name": "qiskit.qobj.QobjHeader.from_dict", "line_number": 47, "usage_type": "call"}, {"api_name": "qiskit.qobj.QobjHeader", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "35300717134", "text": "from .shell import run_shell\n\ndef img2mov(ifile, ofile, framerate=1):\n '''Convert images into a movie.\n\n ifile can be in a glob pattern, e.g. *.png.\n\n See: http://trac.ffmpeg.org/wiki/Create%20a%20video%20slideshow%20from%20images'''\n\n cmd = ' '.join(['ffmpeg',\n '-framerate {}'.format(framerate),\n '-pattern_type glob',\n '-i \"{}\"'.format(ifile),\n '-pix_fmt yuv420p',\n '{}'.format(ofile)])\n run_shell(cmd)\n", "repo_name": "wy2136/wython", "sub_path": "misc/ffmpeg.py", "file_name": "ffmpeg.py", "file_ext": "py", "file_size_in_byte": 456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "shell.run_shell", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "72596581842", "text": "from copy import deepcopy\nimport logging\nfrom typing import Any, Optional, Union\n\nfrom pydicom.dataset import Dataset\nfrom pydicom.sr.coding import Code\n\nlogger = logging.getLogger(__name__)\n\n\nclass CodedConcept(Dataset):\n\n \"\"\"Coded concept of a DICOM SR document content module attribute.\"\"\"\n\n def __init__(\n self,\n value: str,\n scheme_designator: str,\n meaning: str,\n scheme_version: Optional[str] = None\n ) -> None:\n \"\"\"\n Parameters\n ----------\n value: str\n code\n scheme_designator: str\n designator of coding scheme\n meaning: str\n meaning of the code\n scheme_version: Union[str, None], optional\n version of coding scheme\n\n \"\"\"\n super(CodedConcept, self).__init__()\n if len(value) > 16:\n if value.startswith('urn') or '://' in value:\n self.URNCodeValue = str(value)\n else:\n self.LongCodeValue = str(value)\n else:\n self.CodeValue = str(value)\n if len(meaning) > 64:\n raise ValueError('Code meaning can have maximally 64 characters.')\n self.CodeMeaning = str(meaning)\n self.CodingSchemeDesignator = str(scheme_designator)\n if scheme_version is not None:\n self.CodingSchemeVersion = str(scheme_version)\n # TODO: Enhanced Code Sequence Macro Attributes\n\n def __hash__(self) -> int:\n return hash(self.scheme_designator + self.value)\n\n def __eq__(self, other: Any) -> bool:\n \"\"\"Compares `self` and `other` for equality.\n\n Parameters\n ----------\n other: Union[highdicom.sr.CodedConcept, pydicom.sr.coding.Code]\n code\n\n Returns\n -------\n bool\n whether `self` and `other` are considered equal\n\n \"\"\"\n if isinstance(other, (Code, CodedConcept)):\n this = Code(\n self.value,\n self.scheme_designator,\n self.meaning,\n self.scheme_version\n )\n return Code.__eq__(this, other)\n return super().__eq__(other)\n\n def __ne__(self, other: Any) -> bool:\n \"\"\"Compares `self` and `other` for inequality.\n\n Parameters\n ----------\n other: Union[CodedConcept, pydicom.sr.coding.Code]\n code\n\n Returns\n -------\n bool\n whether `self` and `other` are not considered equal\n\n \"\"\"\n return not (self == other)\n\n @classmethod\n def from_dataset(\n cls,\n dataset: Dataset,\n copy: bool = True\n ) -> 'CodedConcept':\n \"\"\"Construct a CodedConcept from an existing dataset.\n\n Parameters\n ----------\n dataset: pydicom.dataset.Dataset\n Dataset representing a coded concept.\n copy: bool\n If True, the underlying dataset is deep-copied such that the\n original dataset remains intact. If False, this operation will\n alter the original dataset in place.\n\n Returns\n -------\n highdicom.sr.CodedConcept:\n Coded concept representation of the dataset.\n\n Raises\n ------\n TypeError:\n If the passed dataset is not a pydicom dataset.\n AttributeError:\n If the dataset does not contain the required elements for a\n coded concept.\n\n \"\"\"\n if not isinstance(dataset, Dataset):\n raise TypeError(\n 'Dataset must be a pydicom.dataset.Dataset.'\n )\n code_value_kws = ['CodeValue', 'LongCodeValue', 'URNCodeValue']\n num_code_values = sum(hasattr(dataset, kw) for kw in code_value_kws)\n if num_code_values != 1:\n raise AttributeError(\n 'Dataset should have exactly one of the following attributes: '\n f'{\", \".join(code_value_kws)}.'\n )\n for kw in ['CodeMeaning', 'CodingSchemeDesignator']:\n if not hasattr(dataset, kw):\n raise AttributeError(\n 'Dataset does not contain the following attribute '\n f'required for coded concepts: {kw}.'\n )\n if copy:\n concept = deepcopy(dataset)\n else:\n concept = dataset\n concept.__class__ = cls\n return concept\n\n @classmethod\n def from_code(cls, code: Union[Code, 'CodedConcept']) -> 'CodedConcept':\n \"\"\"Construct a CodedConcept for a pydicom Code.\n\n Parameters\n ----------\n code: Union[pydicom.sr.coding.Code, highdicom.sr.CodedConcept]\n Code.\n\n Returns\n -------\n highdicom.sr.CodedConcept:\n CodedConcept dataset for the code.\n\n \"\"\"\n if isinstance(code, cls):\n return code\n return cls(*code)\n\n @property\n def value(self) -> str:\n \"\"\"str: value of either `CodeValue`, `LongCodeValue` or `URNCodeValue`\n attribute\"\"\"\n return getattr(\n self, 'CodeValue',\n getattr(\n self, 'LongCodeValue',\n getattr(\n self, 'URNCodeValue',\n None\n )\n )\n )\n\n @property\n def meaning(self) -> str:\n \"\"\"str: meaning of the code\"\"\"\n return self.CodeMeaning\n\n @property\n def scheme_designator(self) -> str:\n \"\"\"str: designator of the coding scheme (e.g. ``\"DCM\"``)\"\"\"\n\n return self.CodingSchemeDesignator\n\n @property\n def scheme_version(self) -> Optional[str]:\n \"\"\"Union[str, None]: version of the coding scheme (if specified)\"\"\"\n return getattr(self, 'CodingSchemeVersion', None)\n", "repo_name": "ImagingDataCommons/highdicom", "sub_path": "src/highdicom/sr/coding.py", "file_name": "coding.py", "file_ext": "py", "file_size_in_byte": 5768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 142, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "pydicom.dataset.Dataset", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 54, "usage_type": "name"}, {"api_name": "pydicom.sr.coding.Code", "line_number": 68, "usage_type": "name"}, {"api_name": "pydicom.sr.coding.Code", "line_number": 69, "usage_type": "call"}, {"api_name": "pydicom.sr.coding.Code.__eq__", "line_number": 75, "usage_type": "call"}, {"api_name": "pydicom.sr.coding.Code", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 78, "usage_type": "name"}, {"api_name": "pydicom.dataset.Dataset", "line_number": 97, "usage_type": "name"}, {"api_name": "pydicom.dataset.Dataset", "line_number": 125, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 143, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 150, "usage_type": "name"}, {"api_name": "pydicom.sr.coding.Code", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 195, "usage_type": "name"}]} +{"seq_id": "2086722500", "text": "from unicodedata import category\nfrom coins.models import Balance\nfrom coins.serializers import BalanceSerializer, TransactionSerializer\nfrom coins.models import Coin, Transactions\nfrom rest_framework.test import APITestCase\nfrom django.urls import reverse\nfrom django.contrib.auth.models import User\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework import status\nfrom django.contrib.auth.hashers import make_password\nfrom rest_framework.test import APITestCase\nfrom rest_framework.test import APIClient\n\n\nclass CoinApiTest(APITestCase):\n\n def setUp(self) -> None:\n self.client = APIClient()\n self.user = User.objects.create(\n username='admin',\n email='a@admin.com',\n password=make_password('a123456')\n )\n\n self.user2 = User.objects.create(\n username='test',\n email='test@test.com',\n password=make_password('test')\n )\n\n self.token = Token.objects.create(user=self.user)\n # self.client.credentials(Authorization='Token ' + self.token.key)\n self.client.force_authenticate(user=self.user)\n self.coin = Coin.objects.all().first()\n\n def test_create_deposit(self, **kwargs):\n url = reverse('transactions')\n\n data = {\n 'operation': Transactions.DEPOSIT,\n 'transmitter': self.user.id,\n 'coin': self.coin.id,\n 'amount': 100\n }\n\n response = self.client.post(url, data)\n\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n \n transaction = TransactionSerializer(\n Transactions.objects.get(\n transmitter=self.user,\n operation=Transactions.DEPOSIT\n )\n )\n\n balance = BalanceSerializer(\n Balance.objects.get(\n owner=self.user,\n coin=self.coin,\n category=Balance.REGULAR\n )\n )\n self.assertEqual(\n response.data,\n {\n 'transaction': transaction.data,\n 'balance': balance.data\n }\n )\n\n def test_create_withdrawal(self, **kwargs):\n url = reverse('transactions')\n\n data = {\n 'operation': Transactions.WITHDRAWAL,\n 'transmitter': self.user.id,\n 'coin': self.coin.id,\n 'amount': 100\n }\n\n response = self.client.post(url, data)\n\n self.assertEqual(response.status_code, status.HTTP_406_NOT_ACCEPTABLE)\n \n balance = Balance.objects.get(\n owner=self.user,\n category=Balance.REGULAR,\n coin=self.coin\n )\n\n balance.balance = 200.00000\n balance.save()\n\n response = self.client.post(url, data)\n\n balance = Balance.objects.get(\n owner=self.user,\n category=Balance.REGULAR,\n coin=self.coin\n )\n\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)\n self.assertEqual(response.data, BalanceSerializer(balance).data)\n\n", "repo_name": "LucaPicc/basic-wallet", "sub_path": "backend/backend/coins/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "rest_framework.test.APITestCase", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.auth.hashers.make_password", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.auth.hashers.make_password", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects.create", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 31, "usage_type": "name"}, {"api_name": "coins.models.Coin.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "coins.models.Coin.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "coins.models.Coin", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 37, "usage_type": "call"}, {"api_name": "coins.models.Transactions.DEPOSIT", "line_number": 40, "usage_type": "attribute"}, {"api_name": "coins.models.Transactions", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 48, "usage_type": "name"}, {"api_name": "coins.serializers.TransactionSerializer", "line_number": 50, "usage_type": "call"}, {"api_name": "coins.models.Transactions.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "coins.models.Transactions.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "coins.models.Transactions", "line_number": 51, "usage_type": "name"}, {"api_name": "coins.models.Transactions.DEPOSIT", "line_number": 53, "usage_type": "attribute"}, {"api_name": "coins.models.Transactions", "line_number": 53, "usage_type": "name"}, {"api_name": "coins.serializers.BalanceSerializer", "line_number": 57, "usage_type": "call"}, {"api_name": "coins.models.Balance.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "coins.models.Balance.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "coins.models.Balance", "line_number": 58, "usage_type": "name"}, {"api_name": "coins.models.Balance.REGULAR", "line_number": 61, "usage_type": "attribute"}, {"api_name": "coins.models.Balance", "line_number": 61, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 73, "usage_type": "call"}, {"api_name": "coins.models.Transactions.WITHDRAWAL", "line_number": 76, "usage_type": "attribute"}, {"api_name": "coins.models.Transactions", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_406_NOT_ACCEPTABLE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 84, "usage_type": "name"}, {"api_name": "coins.models.Balance.objects.get", "line_number": 86, "usage_type": "call"}, {"api_name": "coins.models.Balance.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "coins.models.Balance", "line_number": 86, "usage_type": "name"}, {"api_name": "coins.models.Balance.REGULAR", "line_number": 88, "usage_type": "attribute"}, {"api_name": "coins.models.Balance", "line_number": 88, "usage_type": "name"}, {"api_name": "coins.models.Balance.objects.get", "line_number": 97, "usage_type": "call"}, {"api_name": "coins.models.Balance.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "coins.models.Balance", "line_number": 97, "usage_type": "name"}, {"api_name": "coins.models.Balance.REGULAR", "line_number": 99, "usage_type": "attribute"}, {"api_name": "coins.models.Balance", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 103, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 103, "usage_type": "name"}, {"api_name": "coins.serializers.BalanceSerializer", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "7601714413", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy_splash import SplashRequest\n\n\nclass QuotesSpider(scrapy.Spider):\n name = 'frases_splash'\n start_urls = ['http://quotes.toscrape.com/js']\n\n def start_requests(self):\n yield SplashRequest(url=self.start_urls[0], callback=self.parse)\n\n def parse(self, response):\n frases = response.css(\"div.quote\")\n for frase in frases:\n yield self.procesar_frase(frase)\n siguiente_pagina = response.urljoin(response.css(\"li.next>a::attr(href)\").extract_first())\n if siguiente_pagina:\n yield SplashRequest(siguiente_pagina)\n\n def procesar_frase(self, frase):\n texto_frase = frase.css(\"span.text::text\").extract_first()\n autor = frase.css(\"small.author::text\").extract_first()\n etiquetas = frase.css(\"div.tags>a::text\").extract()\n return {\n \"autor\": autor,\n \"frase\": texto_frase,\n \"etiquetas\": etiquetas\n }\n", "repo_name": "manugarri/curso_data_science", "sub_path": "Secciones/Seccion7.WebScraping/scraping_javascript/frases/frases/spiders/frases_splash.py", "file_name": "frases_splash.py", "file_ext": "py", "file_size_in_byte": 977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 110, "dataset": "github-code", "pt": "2", "api": [{"api_name": "scrapy.Spider", "line_number": 6, "usage_type": "attribute"}, {"api_name": "scrapy_splash.SplashRequest", "line_number": 11, "usage_type": "call"}, {"api_name": "scrapy_splash.SplashRequest", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "3104962584", "text": "from django.contrib import admin\nfrom .models import RegisteredUser\n# Register your models here.\nadmin.site.site_header=\"Cov Testing Dashboard\"\nadmin.site.site_title=\"Dashboard\"\nadmin.site.index_title=\"Dashboard for Cov test report Shangri-la\"\n\n\nclass Dashboard(admin.ModelAdmin):\n list_display = ('name','email','age','gender','address','postcode','ttn','testResult')\n # change_list_template = \"sdsd\"\n list_filter = ('testResult','age','postcode',)\n # change_list_template = 'virusTestingApp/registeredUsers.html'\n\n # //http://127.0.0.1:8000/dashboard/virusTestingApp/registereduser/\n\nadmin.site.register(RegisteredUser,Dashboard)\n", "repo_name": "sk814/angular_api_mobile_cw3_frontend", "sub_path": "pro1/COVTesting/virusTestingApp/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "django.contrib.admin.site", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 17, "usage_type": "call"}, {"api_name": "models.RegisteredUser", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "6080471087", "text": "import os\nimport sys\nimport importlib\nfrom dataset.pascal_voc import PascalVoc\nfrom dataset.iterator import DetIter\nfrom detect.detector import Detector\nfrom config.config import cfg\nimport logging\n\ndef evaluate_net(net, dataset, devkit_path, mean_pixels, data_shape,\n model_prefix, epoch, ctx, year=None, sets='test',\n batch_size=1, nms_thresh=0.5, force_nms=False):\n \"\"\"\n Evaluate entire dataset, basically simple wrapper for detections\n\n Parameters:\n ---------\n dataset : str\n name of dataset to evaluate\n devkit_path : str\n root directory of dataset\n mean_pixels : tuple of float\n (R, G, B) mean pixel values\n data_shape : int\n resize input data shape\n model_prefix : str\n load model prefix\n epoch : int\n load model epoch\n ctx : mx.ctx\n running context, mx.cpu() or mx.gpu(0)...\n year : str or None\n evaluate on which year's data\n sets : str\n evaluation set\n batch_size : int\n using batch_size for evaluation\n nms_thresh : float\n non-maximum suppression threshold\n force_nms : bool\n force suppress different categories\n \"\"\"\n # set up logger\n logging.basicConfig()\n logger = logging.getLogger()\n logger.setLevel(logging.INFO)\n\n if dataset == \"pascal\":\n if not year:\n year = '2007'\n imdb = PascalVoc(sets, year, devkit_path, shuffle=False, is_train=False)\n data_iter = DetIter(imdb, batch_size, data_shape, mean_pixels,\n rand_samplers=[], rand_mirror=False, is_train=False, shuffle=False)\n sys.path.append(os.path.join(cfg.ROOT_DIR, 'symbol'))\n net = importlib.import_module(\"symbol_\" + net) \\\n .get_symbol(imdb.num_classes, nms_thresh, force_nms)\n model_prefix += \"_\" + str(data_shape)\n detector = Detector(net, model_prefix, epoch, data_shape, mean_pixels, batch_size, ctx)\n logger.info(\"Start evaluation with {} images, be patient...\".format(imdb.num_images))\n detections = detector.detect(data_iter)\n imdb.evaluate_detections(detections)\n else:\n raise NotImplementedError(\"No support for dataset: \" + dataset)\n", "repo_name": "burness/mxnet-101", "sub_path": "day7/ssd/evaluate/evaluate_net.py", "file_name": "evaluate_net.py", "file_ext": "py", "file_size_in_byte": 2211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 60, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.basicConfig", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 46, "usage_type": "attribute"}, {"api_name": "dataset.pascal_voc", "line_number": 48, "usage_type": "name"}, {"api_name": "dataset.pascal_voc.PascalVoc", "line_number": 51, "usage_type": "call"}, {"api_name": "dataset.iterator.DetIter", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.config.cfg.ROOT_DIR", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 54, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 55, "usage_type": "call"}, {"api_name": "detect.detector.Detector", "line_number": 58, "usage_type": "call"}, {"api_name": "dataset.pascal_voc", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "22682048094", "text": "from calendar import Calendar\nfrom dataclasses import asdict\nfrom datetime import datetime, timedelta, time\n\nfrom dao.event import eventDAO\nfrom db import transactional\nfrom domain.calendar import CalendarItem, CalendarView\nfrom domain.event import Event\nfrom utils import (\n format_date,\n generate_pregnancy_weeks,\n get_pregnancy_examinations\n)\n\n\ndef calendar_range(firstweekday: int, year: int, month: int):\n c = Calendar(firstweekday)\n return list(c.itermonthdates(year, month))\n\n\ndef list_range(firstweekday: int, calendar_id: int, year: int, month: int):\n time_range = calendar_range(firstweekday, year, month)\n rows = eventDAO.find_by_time_range(calendar_id,\n time_range[0],\n time_range[-1])\n items = {format_date(d): CalendarItem(date=d, events=[])\n for d in time_range}\n if rows is not None:\n events = [Event(*row) for row in rows]\n\n for event in events:\n\n end = event.end_date\n if end > time_range[-1]:\n end = time_range[-1]\n start = event.start_date\n if start < time_range[0]:\n start = time_range[0]\n delta = end - start\n for d in range(delta.days + 1):\n dd = start + timedelta(d)\n items[format_date(dd)].events.append(event.id)\n return CalendarView(events=events, items=items)\n\n\n@transactional\ndef menstruation_start(calendar_id: int, start: datetime,\n menstruation_period: int, full_period: int):\n\n end = start + timedelta(days=menstruation_period)\n\n full_period_delta = timedelta(days=full_period)\n # create current period\n menstruation_event = Event(\n id=None,\n title=\"经期\",\n description=\"\",\n calendar_id=calendar_id,\n create_time=datetime.now(),\n modified_time=datetime.now(),\n start_date=start,\n end_date=end,\n start_time=None,\n end_time=None,\n recurrence=0,\n state=1,\n )\n\n eventDAO.save(**asdict(menstruation_event))\n\n # predict ovulation preid\n ovulation_predict_delta = full_period / 2\n ovulation_start = ovulation_predict_delta - 5\n ovulation_end = ovulation_predict_delta + 4\n menstruation_event = Event(\n id=None,\n title=\"排卵期\",\n description=\"\",\n calendar_id=calendar_id,\n create_time=datetime.now(),\n modified_time=datetime.now(),\n start_date=start + timedelta(days=ovulation_start),\n end_date=start + timedelta(days=ovulation_end),\n start_time=None,\n end_time=None,\n recurrence=0,\n state=1,\n )\n eventDAO.save(**asdict(menstruation_event))\n # predict next period\n predict_menstruation = Event(\n id=None,\n title=\"经期(计算)\",\n description=\"\",\n calendar_id=calendar_id,\n create_time=datetime.now(),\n modified_time=datetime.now(),\n start_date=start + full_period_delta,\n end_date=end + full_period_delta,\n start_time=None,\n end_time=None,\n recurrence=0,\n state=1,\n )\n eventDAO.save(**asdict(predict_menstruation))\n\n\n@transactional\ndef normal(calendar_id: int, title: str, description: str,\n start_date: str, end_date: str,\n start_time: str, end_time: str) -> int:\n event = Event(\n id=None,\n title=title,\n description=description,\n calendar_id=calendar_id,\n create_time=datetime.now(),\n modified_time=datetime.now(),\n start_date=start_date,\n end_date=end_date,\n start_time=start_time,\n end_time=end_time,\n recurrence=0,\n state=1,\n )\n return eventDAO.save(**asdict(event))\n\n\n@transactional\ndef pregnancy_start(calendar_id: int, start: datetime):\n weeks = generate_pregnancy_weeks(start)\n events = []\n for w in weeks:\n wd = get_pregnancy_examinations(w[0])\n if wd is None:\n wd = \"\"\n event = Event(\n id=None,\n title=f'第 {w[0]} 周',\n description=wd,\n calendar_id=calendar_id,\n create_time=datetime.now(),\n modified_time=datetime.now(),\n start_date=w[1],\n end_date=w[2],\n start_time=time(hour=0, minute=0, second=0),\n end_time=time(hour=23, minute=59, second=59),\n recurrence=0,\n state=1\n )\n events.append(asdict(event))\n eventDAO.batch_save(events)\n", "repo_name": "lostsquirrel/calendar", "sub_path": "service/event.py", "file_name": "event.py", "file_ext": "py", "file_size_in_byte": 4565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "calendar.Calendar", "line_number": 17, "usage_type": "call"}, {"api_name": "dao.event.eventDAO.find_by_time_range", "line_number": 23, "usage_type": "call"}, {"api_name": "dao.event.eventDAO", "line_number": 23, "usage_type": "name"}, {"api_name": "utils.format_date", "line_number": 26, "usage_type": "call"}, {"api_name": "domain.calendar.CalendarItem", "line_number": 26, "usage_type": "call"}, {"api_name": "domain.event.Event", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "utils.format_date", "line_number": 42, "usage_type": "call"}, {"api_name": "domain.calendar.CalendarView", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 52, "usage_type": "call"}, {"api_name": "domain.event.Event", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "dao.event.eventDAO.save", "line_number": 69, "usage_type": "call"}, {"api_name": "dao.event.eventDAO", "line_number": 69, "usage_type": "name"}, {"api_name": "dataclasses.asdict", "line_number": 69, "usage_type": "call"}, {"api_name": "domain.event.Event", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 83, "usage_type": "call"}, {"api_name": "dao.event.eventDAO.save", "line_number": 89, "usage_type": "call"}, {"api_name": "dao.event.eventDAO", "line_number": 89, "usage_type": "name"}, {"api_name": "dataclasses.asdict", "line_number": 89, "usage_type": "call"}, {"api_name": "domain.event.Event", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 96, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 97, "usage_type": "name"}, {"api_name": "dao.event.eventDAO.save", "line_number": 105, "usage_type": "call"}, {"api_name": "dao.event.eventDAO", "line_number": 105, "usage_type": "name"}, {"api_name": "dataclasses.asdict", "line_number": 105, "usage_type": "call"}, {"api_name": "db.transactional", "line_number": 46, "usage_type": "name"}, {"api_name": "domain.event.Event", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "name"}, {"api_name": "dao.event.eventDAO.save", "line_number": 126, "usage_type": "call"}, {"api_name": "dao.event.eventDAO", "line_number": 126, "usage_type": "name"}, {"api_name": "dataclasses.asdict", "line_number": 126, "usage_type": "call"}, {"api_name": "db.transactional", "line_number": 108, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 130, "usage_type": "name"}, {"api_name": "utils.generate_pregnancy_weeks", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.get_pregnancy_examinations", "line_number": 134, "usage_type": "call"}, {"api_name": "domain.event.Event", "line_number": 137, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 142, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 143, "usage_type": "name"}, {"api_name": "datetime.time", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 147, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 151, "usage_type": "call"}, {"api_name": "dao.event.eventDAO.batch_save", "line_number": 152, "usage_type": "call"}, {"api_name": "dao.event.eventDAO", "line_number": 152, "usage_type": "name"}, {"api_name": "db.transactional", "line_number": 129, "usage_type": "name"}]} +{"seq_id": "19119000526", "text": "\"\"\"Serializers for indicator app\"\"\"\n\nfrom json import loads\n\nfrom rest_framework.serializers import ModelSerializer\n\nfrom console_api.search.models import History\nfrom console_api.users.models import User\n\n\nclass SearchHistorySerializer(ModelSerializer):\n \"\"\"Serializer for History model\"\"\"\n\n class Meta:\n \"\"\"Metainformation about the serializer\"\"\"\n\n model = History\n\n fields = [\n \"id\",\n \"search_type\",\n \"query_text\",\n \"query_data\",\n \"results\",\n \"created_by\",\n ]\n\n\nclass SearchHistoryListSerializer(ModelSerializer):\n \"\"\"Serializer for History objects list\"\"\"\n\n def to_representation(self, instance):\n \"\"\"Convert representation from null to valid value\"\"\"\n\n data = super().to_representation(instance)\n\n status = data[\"status\"]\n created_by = data[\"created-by\"]\n\n data[\"status\"] = \"detected\" if loads(status) else \"not-detected\"\n data[\"created-by\"] = {\n \"id\": created_by,\n \"login\": User.objects.get(id=created_by).login,\n }\n\n return data\n\n class Meta:\n \"\"\"Metainformation about the serializer\"\"\"\n\n model = History\n\n fields = [\n \"id\",\n \"status\",\n \"created-at\",\n \"created-by\",\n \"query\",\n ]\n\n extra_kwargs = {\n \"status\": {\"source\": \"results\"},\n \"created-at\": {\"source\": \"created_at\"},\n \"created-by\": {\"source\": \"created_by\"},\n \"query\": {\"source\": \"query_text\"},\n }\n", "repo_name": "hulahoo/console-check-sonar", "sub_path": "src/console_api/search/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 11, "usage_type": "name"}, {"api_name": "console_api.search.models.History", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 29, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "console_api.users.models.User.objects.get", "line_number": 43, "usage_type": "call"}, {"api_name": "console_api.users.models.User.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "console_api.users.models.User", "line_number": 43, "usage_type": "name"}, {"api_name": "console_api.search.models.History", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "16915505775", "text": "from django.http import JsonResponse\nfrom django.shortcuts import get_object_or_404, redirect, render\nfrom django.urls import reverse\n\nfrom .forms import DynamicInputs\nfrom .models import DynamicInputsDatas\n\n\ndef dynamic_inputs(request):\n template = 'dynamic_inputs/add.html'\n form = DynamicInputs()\n if request.method == 'POST':\n if 'submit' in request.POST:\n form = DynamicInputs(request.POST)\n if form.is_valid():\n inst = DynamicInputsDatas(data=form.cleaned_data)\n inst.save()\n return redirect(reverse('list'))\n if 'add_input' in request.POST:\n form = DynamicInputs(request.POST, add_new_input=True)\n context = {'form': form, }\n return render(request, template, context=context)\n\ndef data_list(request):\n template = 'dynamic_inputs/list.html'\n datas = DynamicInputsDatas.objects.all()\n context = {\n 'datas': datas,\n }\n return render(request, template, context=context)\n\n\ndef concrete_data(request, id: int):\n data = get_object_or_404(DynamicInputsDatas, id=id)\n return JsonResponse(data.data)\n", "repo_name": "YaraslavBondar/boya22", "sub_path": "dynamic_inputs/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1132, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "forms.DynamicInputs", "line_number": 11, "usage_type": "call"}, {"api_name": "forms.DynamicInputs", "line_number": 14, "usage_type": "call"}, {"api_name": "models.DynamicInputsDatas", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 18, "usage_type": "call"}, {"api_name": "forms.DynamicInputs", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "models.DynamicInputsDatas.objects.all", "line_number": 26, "usage_type": "call"}, {"api_name": "models.DynamicInputsDatas.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.DynamicInputsDatas", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 34, "usage_type": "call"}, {"api_name": "models.DynamicInputsDatas", "line_number": 34, "usage_type": "argument"}, {"api_name": "django.http.JsonResponse", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "12798121917", "text": "import argparse\nimport lut\n\ndef assembly_to_machine(line):\n \"\"\"Converts a line of $NAME assembly code to 9-bit machine instruction\"\"\"\n\n opcode = '0000'\n\n # Grab the first 3 chars of the line, and use LUT to generate opcode\n inst_name= line[:3]\n opcode = lut.LUT[inst_name] \n\n # Store space-delimited elements of the instruction\n elements = line.split()\n\n # only the halt instruction has no operands\n if len(elements) == 1:\n return '111111111'\n\n # immediate instruction: use 5 bits of numerical constant\n if inst_name in lut.IMM:\n\n # Check that no registers are specified. Assumes base 10\n #if \"$\" not in elements[1]:\n immval = int(elements[1])\n tail = format(immval, '05b')\n return opcode + tail\n # register instruction: uses 4 bits of register + 0 bit on tail\n else:\n\n # Check that no immediate value is specified. Assumes base 10\n #if \"$r\" in elements[1]:\n regval = int(elements[1])\n tail = format(regval, '04b')\n return opcode + tail + '0'\n\n\ndef main():\n \"\"\" Drives the program. \"\"\"\n\n parser = argparse.ArgumentParser(description='Convert $NAME assembly to' \\\n + ' machine code.')\n parser.add_argument('file_in', metavar='in', type=str, help='name of input' \\\n + ' file containing $NAME assembly code')\n parser.add_argument('file_out', metavar='outfile', type=str, help='name of'\\\n + ' output file to write machine instructions')\n\n results = parser.parse_args()\n \n with open(results.file_in, 'r') as fi, open(results.file_out, 'w') as fo:\n lines = [line.rstrip() for line in fi]\n\n line_ct = 0\n for inst in lines:\n if inst[0] == '#':\n continue\n else:\n fo.write(assembly_to_machine(inst))\n fo.write('\\n')\n line_ct += 1\n\n print(f'Wrote {line_ct} instructions to {results.file_out}.\\n')\n\n\nif __name__ == \"__main__\":\n main()\n\n\n", "repo_name": "ackamal/cse141l-lab2", "sub_path": "src/assembler/assembler.py", "file_name": "assembler.py", "file_ext": "py", "file_size_in_byte": 2008, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "lut.LUT", "line_number": 11, "usage_type": "attribute"}, {"api_name": "lut.IMM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "5358066032", "text": "# Chocolate Scraping with Beautiful Soup\n# Project Chocolate Scraping with Beautiful Soup\n\nimport seaborn as sns\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom bs4 import BeautifulSoup\n\nwebpage_response = requests.get(\"https://s3.amazonaws.com/codecademy-content/courses/beautifulsoup/cacao/index.html\")\nwebpage = webpage_response.content\nsoup = BeautifulSoup(webpage,\"html.parser\")\n#print(soup)\nsoup.find_all(attrs={\"class\": \"Rating\"})\n\nratings = []\nfor elements in soup.find_all(attrs={\"class\": \"Rating\"})[1:]:\n ratings.append(float(elements.get_text()))\n\nplt.hist(ratings)\nplt.show()\n\nsoup.select(\".Company\")\n\ncompanies = []\nfor company in soup.select(\".Company\")[1:]:\n companies.append(company.get_text())\n\ncocoa_percents = []\ncocoa_percent_tags = soup.select(\".CocoaPercent\")\n\nfor td in cocoa_percent_tags[1:]:\n percent = float(td.get_text().strip('%'))\n cocoa_percents.append(percent)\n\nd = {\"Company\": companies, \"Ratings\": ratings, \"CocoaPercentage\":cocoa_percents}\ncacao_df = pd.DataFrame.from_dict(d)\n\nmean_vals = cacao_df.groupby(\"Company\").Ratings.mean()\nten_best = mean_vals.nlargest(10)\nprint(ten_best)\n\nplt.clf()\nplt.scatter(cacao_df.CocoaPercentage, cacao_df.Ratings)\n\nz = np.polyfit(cacao_df.CocoaPercentage, cacao_df.Ratings, 1)\nline_function = np.poly1d(z)\nplt.plot(cacao_df.CocoaPercentage, line_function(cacao_df.CocoaPercentage), \"r--\")\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "meoclark/Data-Science-DropBox", "sub_path": "Beautiful_Soap/BS1.py", "file_name": "BS1.py", "file_ext": "py", "file_size_in_byte": 1460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "14009283571", "text": "from pathlib import Path\n\nfrom typer import Typer, Option\n\nfrom trecover.config import var, log\n\ncli = Typer(name='Download-cli', add_completion=False, help='Download train data or pre-trained model')\n\n\n@cli.command(name='data', help='Download train data')\ndef download_data(link: str = Option(var.TRAIN_DATA_URL, help='Link to the train data on Yandex disk or GitHub'),\n save_dir: Path = Option(var.DATA_DIR, help='Path where to store downloaded data'),\n yandex_disk: bool = Option(False, is_flag=True, help='If the link is to Yandex disk')\n ) -> None:\n \"\"\"\n Download train data from Yandex disk or GitHub.\n\n Parameters\n ----------\n link : str, default=var.TRAIN_DATA_URL\n Sharing link to the train data on Yandex disk or GitHub.\n save_dir : Path, default=var.DATA_DIR\n Path where to store downloaded data.\n yandex_disk : bool, default=False\n If the link is to Yandex disk.\n\n \"\"\"\n\n from trecover.utils.cli import download_archive\n\n download_archive(link=link, save_dir=save_dir, yandex_disk=yandex_disk)\n\n\n@cli.command(name='artifacts', help='Download model artifacts by specified version or archive_link')\ndef download_artifacts(version: str = Option('latest', help=\"Artifacts' version\"),\n archive_link: str = Option(None, help='Link to the artifacts archive on Yandex disk or GitHub'),\n save_dir: Path = Option(var.INFERENCE_DIR, help='Path where to save downloaded artifacts'),\n yandex_disk: bool = Option(False, is_flag=True, help='If the archive_link is to Yandex disk'),\n show: bool = Option(False, is_flag=True, help=\"Print available artifacts' versions\")\n ) -> None:\n \"\"\"\n Download model artifacts by specified version or archive_link to Yandex disk or GitHub.\n\n Parameters\n ----------\n version : str, default='latest'\n Artifacts' version.\n archive_link : str, default=None\n Sharing link to the model artifacts archive on Yandex disk or GitHub.\n save_dir : Path, default=var.INFERENCE_DIR\n Path where to save downloaded artifacts.\n yandex_disk : bool, default=False\n If the link is to Yandex disk.\n show : bool, default=False\n Print available artifacts' versions.\n\n \"\"\"\n\n from rich.prompt import Confirm\n from trecover.utils.cli import download_archive, download_from_github\n\n if show:\n log.project_console.print(var.CHECKPOINT_URLS.keys())\n\n elif archive_link:\n download_archive(link=archive_link, save_dir=save_dir, yandex_disk=yandex_disk)\n\n elif version in var.CHECKPOINT_URLS:\n download_from_github(direct_link=var.CHECKPOINT_URLS[version]['model'], save_dir=save_dir)\n download_from_github(direct_link=var.CHECKPOINT_URLS[version]['config'], save_dir=save_dir)\n\n elif Confirm.ask(prompt='[bright_blue]Specified version was not found. Continue downloading the latest version?',\n default=True,\n console=log.project_console):\n download_from_github(direct_link=var.CHECKPOINT_URLS['latest']['model'], save_dir=save_dir)\n download_from_github(direct_link=var.CHECKPOINT_URLS['latest']['config'], save_dir=save_dir)\n\n\nif __name__ == '__main__':\n cli()\n", "repo_name": "alex-snd/TRecover", "sub_path": "src/trecover/app/cli/download.py", "file_name": "download.py", "file_ext": "py", "file_size_in_byte": 3341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typer.Typer", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 12, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 11, "usage_type": "call"}, {"api_name": "trecover.config.var.TRAIN_DATA_URL", "line_number": 11, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 11, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 12, "usage_type": "call"}, {"api_name": "trecover.config.var.DATA_DIR", "line_number": 12, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 12, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 13, "usage_type": "call"}, {"api_name": "trecover.utils.cli.download_archive", "line_number": 31, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 35, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 36, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 37, "usage_type": "call"}, {"api_name": "trecover.config.var.INFERENCE_DIR", "line_number": 37, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 37, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 38, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 39, "usage_type": "call"}, {"api_name": "trecover.config.log.project_console.print", "line_number": 63, "usage_type": "call"}, {"api_name": "trecover.config.log.project_console", "line_number": 63, "usage_type": "attribute"}, {"api_name": "trecover.config.log", "line_number": 63, "usage_type": "name"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS.keys", "line_number": 63, "usage_type": "call"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS", "line_number": 63, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 63, "usage_type": "name"}, {"api_name": "trecover.utils.cli.download_archive", "line_number": 66, "usage_type": "call"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS", "line_number": 68, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 68, "usage_type": "name"}, {"api_name": "trecover.utils.cli.download_from_github", "line_number": 69, "usage_type": "call"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS", "line_number": 69, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 69, "usage_type": "name"}, {"api_name": "trecover.utils.cli.download_from_github", "line_number": 70, "usage_type": "call"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS", "line_number": 70, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 70, "usage_type": "name"}, {"api_name": "rich.prompt.Confirm.ask", "line_number": 72, "usage_type": "call"}, {"api_name": "rich.prompt.Confirm", "line_number": 72, "usage_type": "name"}, {"api_name": "trecover.config.log.project_console", "line_number": 74, "usage_type": "attribute"}, {"api_name": "trecover.config.log", "line_number": 74, "usage_type": "name"}, {"api_name": "trecover.utils.cli.download_from_github", "line_number": 75, "usage_type": "call"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS", "line_number": 75, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 75, "usage_type": "name"}, {"api_name": "trecover.utils.cli.download_from_github", "line_number": 76, "usage_type": "call"}, {"api_name": "trecover.config.var.CHECKPOINT_URLS", "line_number": 76, "usage_type": "attribute"}, {"api_name": "trecover.config.var", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "75090366456", "text": "from codes import urequests\r\nfrom codes import mywifi\r\nimport json\r\nfrom machine import UART\r\n\r\nwifi = mywifi.WIFI()#ssid,password\r\n\r\n#\r\n\r\nuart = UART(2, 9600)\r\nurl=\"http://planesystem.xyz/control_led/\"\r\n\r\nret = {}\r\nret['led1'] = 10 #亮度1\r\nret['led2'] = 15 #亮度1\r\nret['led3'] = 20 #亮度1\r\n\r\n#上传灯信息\r\nr=urequests.post(url,data=json.dumps(ret))\r\nprint(r.json()[\"res\"])\r\nr.close()\r\n#获得时间计算后的当前开关信息（0表示白天，1表示黑天可以开灯）\r\nuart.write(str(r))\r\n#记得每次cl\r\n#\r\n#\r\n# r.close()", "repo_name": "ywz978020607/History_mpy", "sub_path": "Micropython_esp32_8266/esp32_自组网/版本二 esp自带wifi组网/codes/test_django.py", "file_name": "test_django.py", "file_ext": "py", "file_size_in_byte": 542, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 36, "dataset": "github-code", "pt": "22", "api": [{"api_name": "codes.mywifi.WIFI", "line_number": 6, "usage_type": "call"}, {"api_name": "codes.mywifi", "line_number": 6, "usage_type": "name"}, {"api_name": "machine.UART", "line_number": 10, "usage_type": "call"}, {"api_name": "codes.urequests.post", "line_number": 19, "usage_type": "call"}, {"api_name": "codes.urequests", "line_number": 19, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "13349311341", "text": "#!/usr/bin/env python3\n\nimport datetime\nimport os\nimport subprocess\nimport uuid\nimport json\nimport logging\nimport sys\nimport time\nimport traceback\n\nimport requests\nimport yaml\nimport datadog\n\nimport libs.acme_tiny as acme_tiny\n\n\n# Rancher env variables:\n# - CATTLE_URL\n# - CATTLE_ACCESS_KEY\n# - CATTLE_SECRET_KEY\ndef rancher_get_certs():\n r = requests.get(os.environ['CATTLE_URL'] + \"/certificates\",\n auth=(os.environ['CATTLE_ACCESS_KEY'], os.environ['CATTLE_SECRET_KEY']))\n if r.status_code != 200:\n raise Exception('Rancher returned non-200 code: ' + str(r.status_code) + ' - ' + r.text)\n return r.json()[\"data\"]\n\n\ndef rancher_save_cert(name, private_key, cert, link=None):\n\n payload = {'key': private_key, 'cert': cert}\n\n if link is None: # New certificate\n payload[\"name\"] = name\n r = requests.post(os.environ['CATTLE_URL'] + \"/certificates\", data=json.dumps(payload),\n headers={'Content-Type': 'application/json'},\n auth=(os.environ['CATTLE_ACCESS_KEY'], os.environ['CATTLE_SECRET_KEY']))\n\n else: # Update existing certificate\n r = requests.put(link, data=json.dumps(payload),\n headers={'Content-Type': 'application/json'},\n auth=(os.environ['CATTLE_ACCESS_KEY'], os.environ['CATTLE_SECRET_KEY']))\n\n if r.status_code not in [200, 201]:\n raise Exception('Rancher returned non-200 code: ' + str(r.status_code) + ' - ' + r.text)\n\n\ndef openssl(args, input=None):\n proc = subprocess.Popen([\"openssl\"] + args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout, stderr = proc.communicate(input)\n if proc.returncode != 0:\n raise IOError(\"OpenSSL Error: {0}\".format(stderr.decode(\"utf-8\")))\n return stdout\n\n\ndef make_cert(config, logger, name, domains, link=None):\n logger.info(\"Creating certificate {0} for domains: {1}\".format(name, ', '.join(domains)))\n\n if len(domains) < 1:\n raise Exception(\"No domains for certificate\")\n\n private_key_file = \"/tmp/\" + uuid.uuid4().hex\n csr_file = \"/tmp/\" + uuid.uuid4().hex\n\n logger.debug(\"Generating private key to \" + private_key_file + \"...\")\n openssl([\"genrsa\", \"-out\", private_key_file, str(config[\"key_length\"])])\n\n with open(private_key_file, 'r') as f:\n private_key = f.read()\n\n logger.debug(\"Generating CSR to \" + csr_file + \"...\")\n if len(domains) == 1:\n openssl([\"req\", \"-new\", \"-sha256\", \"-key\", private_key_file, \"-out\", csr_file, \"-subj\", \"/CN=\" + domains[0]])\n else:\n csr_config_file = \"/tmp/\" + uuid.uuid4().hex\n logger.debug(\"Generating CSR config to \" + csr_config_file + \"...\")\n with open(\"/etc/ssl/openssl.cnf\", \"r\") as f:\n openssl_config = f.read()\n openssl_config += \"\\n[SAN]\\nsubjectAltName=DNS:\" + ',DNS:'.join(domains) + \"\\n\"\n with open(csr_config_file, \"w\") as f:\n f.write(openssl_config)\n openssl([\"req\", \"-new\", \"-sha256\", \"-key\", private_key_file, \"-out\", csr_file, \"-subj\", \"/\", \"-reqexts\", \"SAN\", \"-config\", csr_config_file])\n logger.debug(\"Deleting CSR config file...\")\n os.remove(csr_config_file)\n\n logger.debug(\"Deleting private key file...\")\n os.remove(private_key_file)\n\n logger.info(\"Signing CSR using acme_tiny...\")\n tiny_kwargs = {}\n if \"ca\" in config:\n tiny_kwargs[\"CA\"] = config[\"ca\"]\n else:\n tiny_kwargs[\"directory_url\"] = config[\"ca_directory\"]\n cert = acme_tiny.get_crt(config[\"account_key\"], csr_file, config[\"acme_dir\"], log=logger, **tiny_kwargs)\n\n logger.debug(\"Deleting CSR file...\")\n os.remove(csr_file)\n\n # TODO: Backup certificate & key ?\n\n logger.info(\"Saving cert in Rancher...\")\n rancher_save_cert(name, private_key, cert, link)\n\n\ndef load_config(logger):\n\n with open(\"config/config.yml\", \"r\") as f:\n config = yaml.safe_load(f)\n\n # Validation\n if \"ca\" in config and \"ca_directory\" in config:\n raise Exception(\"The config should have either ca_directory or ca (deprecated) but not both.\")\n if \"ca\" in config:\n logger.warning(\"The config 'ca' is deprecated, please use 'ca_directory' instead.\")\n if \"chain\" in config:\n logger.warning(\"The config 'chain' is not used anymore.\")\n\n # Strip cert names and domains\n for cert in config[\"certs\"]:\n cert[\"name\"] = cert[\"name\"].strip()\n for i in range(len(cert[\"domains\"])):\n cert[\"domains\"][i] = cert[\"domains\"][i].strip()\n\n return config\n\n\ndef contains_sublist(lst, sublst):\n for e in sublst:\n if e not in lst:\n return False\n return True\n\n\ndef check_certs(config, logger):\n now = datetime.datetime.now()\n\n logger.info(\"Getting certificates from Rancher...\")\n rancher_certs = rancher_get_certs()\n\n rancher_certs_by_name = {}\n for cert in rancher_certs:\n rancher_certs_by_name[cert[\"name\"].strip()] = cert\n\n # Log which certs are in Rancher and in the config\n logger.debug(\"Found certs from Rancher:\")\n for cert in rancher_certs:\n logger.debug(\"- \" + cert[\"name\"] + \": \" + ', '.join(cert[\"subjectAlternativeNames\"]))\n logger.debug(\"Found certs from config:\")\n for cert in config[\"certs\"]:\n logger.debug(\"- \" + cert[\"name\"] + \": \" + ', '.join(cert[\"domains\"]))\n\n to_do = [] # List of (remaining_days, name, domains, link) for certs to make\n\n logger.info(\"Checking certs:\")\n for cert_config in config[\"certs\"]:\n name = cert_config[\"name\"]\n domains = cert_config[\"domains\"]\n if name not in rancher_certs_by_name:\n logger.info(\"- Cert \" + name + \" does not exists\")\n to_do.append((0, name, domains, None))\n else:\n rancher_cert = rancher_certs_by_name[name]\n link = rancher_cert[\"links\"][\"self\"]\n if contains_sublist(rancher_cert[\"subjectAlternativeNames\"], domains):\n cert_exp = datetime.datetime.strptime(rancher_cert[\"expiresAt\"], \"%a %b %d %H:%M:%S %Z %Y\")\n remaining_days = (cert_exp - now).days\n logger.info(\"- Cert {0} expires in {1} days\".format(name, remaining_days))\n if remaining_days < 30:\n to_do.append((remaining_days, name, domains, link))\n else:\n logger.info(\"- Cert \" + name + \" is missing domains\")\n to_do.append((0, name, domains, link))\n\n # Renew certs in the order they expire\n to_do.sort()\n for (_, name, domains, link) in to_do:\n make_cert(config, logger, name, domains, link)\n\n return len(to_do)\n\n\ndef setup_logging():\n # Configure the logger to send <= info messages to stdout and >= warning messages to stderr\n class InfoFilter(logging.Filter):\n def filter(self, rec):\n return rec.levelno in (logging.DEBUG, logging.INFO)\n logger = logging.getLogger(__name__)\n h1 = logging.StreamHandler(sys.stdout)\n h1.setLevel(logging.DEBUG)\n h1.addFilter(InfoFilter())\n h2 = logging.StreamHandler()\n h2.setLevel(logging.WARNING)\n logger.addHandler(h1)\n logger.addHandler(h2)\n # Configure logger level\n logger.setLevel(logging.DEBUG if (\"LOG_DEBUG\" in os.environ) else logging.INFO)\n\n\ndef single_run():\n logger = logging.getLogger(__name__)\n start_time = datetime.datetime.now()\n\n logger.info(\"*** Rancher Auto Certs started \" + start_time.strftime(\"%Y-%m-%d %H:%M\") + \" ***\")\n\n config = load_config(logger)\n logger.debug(\"Using CA %s and directory %s\", config.get(\"ca\"), config.get(\"ca_directory\"))\n logger.debug(\"Using account key: \" + config[\"account_key\"])\n\n nb_certs = check_certs(config, logger)\n\n logger.info(\"*** {0} cert(s) created in {1} ***\".format(nb_certs, datetime.datetime.now() - start_time))\n\n return nb_certs\n\n\ndef daemon():\n datadog.initialize(\n statsd_host=os.getenv(\"DOGSTATSD_HOST\", \"127.0.0.1\"),\n statsd_port=int(os.getenv(\"DOGSTATSD_PORT\", \"8125\")),\n )\n\n while True:\n try:\n nb_certs = single_run()\n datadog.statsd.event(\n \"Rancher Auto Certs executed successfully\",\n \"{} certificate(s) created or renewed\".format(nb_certs),\n alert_type='success',\n )\n datadog.statsd.service_check('rancher_auto_certs.status', datadog.statsd.OK)\n except Exception as e:\n traceback.print_exc()\n datadog.statsd.event(\n \"Rancher Auto Certs encountered an error\",\n \"Please check container logs.\\n{}: {}\".format(type(e).__name__, str(e)),\n alert_type='error',\n )\n datadog.statsd.service_check('rancher_auto_certs.status', datadog.statsd.CRITICAL)\n time.sleep(24 * 60 * 60)\n\n\ndef main():\n setup_logging()\n if \"--daemon\" in sys.argv:\n daemon()\n else:\n single_run()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "jonremy/rancher-auto-certs", "sub_path": "app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 38, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 38, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 40, "usage_type": "attribute"}, {"api_name": "requests.put", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 52, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 65, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 66, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 78, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 87, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 90, "usage_type": "call"}, {"api_name": "libs.acme_tiny.get_crt", "line_number": 98, "usage_type": "call"}, {"api_name": "libs.acme_tiny", "line_number": 98, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 101, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 169, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 169, "usage_type": "attribute"}, {"api_name": "logging.Filter", "line_number": 188, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 190, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 190, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 191, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 192, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 192, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 193, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 195, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 200, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 200, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 200, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 205, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 205, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 215, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 215, "usage_type": "attribute"}, {"api_name": "datadog.initialize", "line_number": 221, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 222, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 223, "usage_type": "call"}, {"api_name": "datadog.statsd.event", "line_number": 229, "usage_type": "call"}, {"api_name": "datadog.statsd", "line_number": 229, "usage_type": "attribute"}, {"api_name": "datadog.statsd.service_check", "line_number": 234, "usage_type": "call"}, {"api_name": "datadog.statsd", "line_number": 234, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 236, "usage_type": "call"}, {"api_name": "datadog.statsd.event", "line_number": 237, "usage_type": "call"}, {"api_name": "datadog.statsd", "line_number": 237, "usage_type": "attribute"}, {"api_name": "datadog.statsd.service_check", "line_number": 242, "usage_type": "call"}, {"api_name": "datadog.statsd", "line_number": 242, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 243, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 248, "usage_type": "attribute"}]} +{"seq_id": "21650371318", "text": "import plotly.express as px\n\n# Data Set\nx = [1, 2, 3, 4, 5]\ny = [3, 5, 2, 7, 4]\n\n# Scatter Plot\nfig = px.scatter(x=x, y=y)\n\n# Adding title and axis layout\nfig.update_layout(\n title=\"Scatter Plot\",\n xaxis_title=\"X\",\n yaxis_title=\"Y\"\n)\n\n# Displaying the plot\nfig.show()\n", "repo_name": "Kairos-T/Data-Visualisations-AI-SIG", "sub_path": "scatterplot.py", "file_name": "scatterplot.py", "file_ext": "py", "file_size_in_byte": 277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "2", "api": [{"api_name": "plotly.express.scatter", "line_number": 8, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "31086942427", "text": "import math\nimport time\nimport os.path\n\nimport numpy as np\n\nimport keras\nfrom keras.callbacks import TensorBoard, History\nfrom keras.utils import np_utils\nfrom sklearn import metrics\n\nimport keras.backend as K\nimport tensorflow as tf\n\nfrom myclassifier.batchgenerator import PaddedBatchGenerator\n\nimport matplotlib.pyplot as plt\nimport itertools\n\nfrom lib.buildmodels import build_model\n\ndef train_and_evaluate(train, test, model, batch_size=4, epochs=25, name=\"model\"):\n\n ## create the batches for train annd test\n paddedTrainBatch = PaddedBatchGenerator(train['samples'], train['labels'], batch_size)\n paddedTestBatch = PaddedBatchGenerator(test['samples'], test['labels'], batch_size)\n\n ## compile the model\n model.compile(optimizer = \"Adam\", loss = \"categorical_crossentropy\", metrics = [\"accuracy\"])\n\n ## train the model\n model.fit(paddedTrainBatch, epochs=epochs, verbose=2)\n\n ## get predictions and labels\n actual, predicted = get_labels_and_prediction_without_padding(model, paddedTestBatch)\n\n ## calculate confusion matrix\n m = metrics.confusion_matrix(actual, predicted, labels=np.arange(4))\n\n ## get frame level accuracy\n frame_result = model.evaluate(paddedTestBatch, verbose=2)\n\n print('labels: ', actual)\n print('predicted:', predicted)\n\n ## calculate file level accuracy\n count = 0\n for i in range(len(actual)):\n if actual[i] == predicted[i]:\n count += 1\n\n return round(1 - frame_result[1], 4), round(1 - count / len(actual), 4), m\n\ndef handle_k_fold(name, models_rnn, k_fold, nodes, dropout, l2, epochs, trains, tests):\n print('--------------------------------------------------')\n errs_frame = []\n errs_file = []\n matrixes = None\n\n ## calculates data for all folds\n for i in range(k_fold):\n ## build new model for each fold\n rnn = build_model(models_rnn(20, nodes, dropout, l2))\n\n ## get error rate per frame and per file, and the confusion matrix\n err_frame, err_file, matrix = train_and_evaluate(trains[i], tests[i], rnn, epochs=epochs, name=name)\n errs_frame.append(err_frame)\n errs_file.append(err_file)\n\n ## sum all folds' confusion matrix\n if matrixes is not None:\n matrixes += matrix\n else:\n matrixes = matrix\n\n print('result for model:', name)\n print('frame avg err:', round(np.mean(errs_frame), 4))\n print('frame std err:', round(np.std(errs_frame), 4))\n print('file avg err: ', round(np.mean(errs_file), 4))\n print('file std err: ', round(np.std(errs_file), 4))\n print(matrixes)\n print('--------------------------------------------------')\n\n## calculate labels and prediction for each file and removed padding\ndef get_labels_and_prediction_without_padding(model, paddedTestBatch):\n actual_labels = []\n predicted_labels = []\n ## flattening the matrix while categorize the class\n ## output should be 1D array of class indexes\n for i in range(len(paddedTestBatch)):\n ## get examples and labels\n examples, targets = paddedTestBatch[i]\n\n ## get predictions\n prediction = model.predict(examples)\n\n ## get the class index while removing paddings\n for j in range(len(targets)):\n actual_label = []\n predicted_label = []\n for k in range(len(targets[j])):\n if np.sum(targets[j][k]) != 0:\n print(prediction[j][k])\n actual_label.append(np.argmax(targets[j][k]))\n predicted_label.append(np.argmax(prediction[j][k]))\n actual_labels.append(np.bincount(np.array(actual_label)).argmax())\n predicted_labels.append(np.bincount(np.array(predicted_label)).argmax())\n\n return actual_labels, predicted_labels\n", "repo_name": "mohit03031999/Speaker-Verification-Using-Recurrent-Neural-Network", "sub_path": "myclassifier/recurrent.py", "file_name": "recurrent.py", "file_ext": "py", "file_size_in_byte": 3800, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "myclassifier.batchgenerator.PaddedBatchGenerator", "line_number": 25, "usage_type": "call"}, {"api_name": "myclassifier.batchgenerator.PaddedBatchGenerator", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "lib.buildmodels.build_model", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "73501033646", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.select import Select\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\n\ndriver = webdriver.Chrome(executable_path=\"/Volumes/Macintosh HD/For Mac/python project/Browserdrivers/chromedriver\")\n\n# driver.get(\"https://echoecho.com/htmlforms11.htm\")\n\ndriver.get(\"https://www.wikipedia.org/\")\ndriver.maximize_window()\n\nwait = WebDriverWait(driver,10)\n#\n# driver.find_element(by=By.NAME,value=\"dropdownmenu\").send_keys(\"Milk\")\n\n## use to select the dropdown list\ndropdown = driver.find_element(by=By.ID,value=\"searchLanguage\")\nselect = Select(dropdown)\nselect.select_by_value(\"pl\")\n\noption = driver.find_elements(by=By.TAG_NAME,value=\"option\")\n\nfor op in option:\n print(\"Text is :\",op.text,\"Lang is :\"+op.get_attribute(\"lang\"))\n\n# print(\"Total dropdown values are,\",len(option))\n\nprint(\"------------------------------------------------------------\")\n\n## find the link by use the tag name\nlinks = driver.find_elements(by=By.TAG_NAME,value=\"a\")\nprint(len(links))\nfor link in links:\n print(\"Text is:\",link.text,\" --URL is :\"+link.get_attribute(\"href\"))\n\n## get the value from specfiy block\nprint(\"---------------------------------------------\")\nblock = driver.find_element(by=By.XPATH,value=\"//*[@class='other-projects']/div[1]\")\n\nprint(block.find_elements(by=By.TAG_NAME,value=\"a\").__getitem__(0).text)\n\n\ntime.sleep(1)\n\ndriver.quit()\ndriver.close()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "BalajiDhanaraj/Selenium_with_Python", "sub_path": "main_page/HandlingWebElement.py", "file_name": "HandlingWebElement.py", "file_ext": "py", "file_size_in_byte": 1554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.select.Select", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 24, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 34, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 43, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 43, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "20248712020", "text": "import json\n\nfrom rest_framework import status, viewsets, mixins\nfrom rest_framework.decorators import action\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom common.permissions import ManagerPermission\nfrom common.constants import CANCELED, DONE, DONT_ENOUGH_MONEY, DONT_AVAILABLE\nfrom payments.models import CreditCard, Order, Cart\nfrom payments.serializers import CreditCardSerializer, OrderSerializer, TransactionSerializer, CartSerializer\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass CreditCardView(viewsets.ViewSet, mixins.CreateModelMixin):\n permission_classes = (IsAuthenticated, )\n\n def create(self, request, *args, **kwargs):\n logger.info(f'create credit card: {request.data}')\n data = request.data\n data['user'] = request.user.pk\n serializer = CreditCardSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n if serializer.errors:\n card = CreditCard.objects.filter(user=request.user).first()\n if card:\n card.balance += request.data['balance']\n card.save()\n return Response(CreditCardSerializer(card).data, status=status.HTTP_200_OK)\n logger.error(f'create credit card: {request.data} - {str(serializer.errors)}')\n return Response({'error': serializer.errors},\n status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n\nclass CartView(APIView):\n permission_classes = (IsAuthenticated, )\n\n def get(self, request):\n try:\n logger.info('get cart')\n cart = Cart.objects.personal(user=request.user)\n return Response(CartSerializer(cart).data, status=status.HTTP_200_OK)\n except Exception as e:\n logger.error(f'get cart - {str(e)}')\n return Response({'error': str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n def post(self, request):\n try:\n logger.info('post items to cart')\n data = json.loads(request.body)\n Cart.objects.add_product(user=request.user, product_id=data['product_id'], amount=data.get('amount', 1))\n return Response({'info': 'added'}, status=status.HTTP_200_OK)\n except Exception as e:\n logger.error(f\"post item to cart - str(e)\")\n return Response({'error': str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n\nclass CardDetails(APIView):\n def post(self, request):\n try:\n logger.info(f'delete item from cart {str(request.data)}')\n data = json.loads(request.body)\n Cart.objects.remove_product(user=request.user, product_id=data['product_id'])\n return Response({'info': 'deleted'}, status=status.HTTP_200_OK)\n except Exception as e:\n logger.error(f'delete item from cart {str(request.data)} - {str(e)}')\n return Response({'error': str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n\nclass TransactionView(viewsets.ViewSet, mixins.CreateModelMixin):\n permission_classes = (IsAuthenticated, )\n\n def create(self, request):\n try:\n logger.info(f'create transaction: {request.data}')\n cart = Cart.objects.get(user=request.user)\n card = CreditCard.objects.get(user=request.user)\n has_balance = cart.check_balance()\n if has_balance == DONT_ENOUGH_MONEY:\n raise Exception('У вас недостаточно средств на карте')\n is_available = cart.check_availability()\n if is_available[0] == DONT_AVAILABLE:\n raise Exception('Данных товаров нет в наличии')\n available = is_available[1]\n new_cart = Cart.objects.create(total_sum=cart.total_sum)\n for cart_item in cart.cart_items.all():\n new_cart.cart_items.add(cart_item.id)\n new_cart.save()\n data = {\"cart\": new_cart.id, 'availability': available}\n serializer = TransactionSerializer(data=data)\n if serializer.is_valid():\n serializer.save()\n cart.withdraw_money()\n cart.empty_cart()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except CreditCard.DoesNotExist:\n logger.error(f'create transaction: {request.data} - credit card doesn\\'t exist')\n return Response({\"error\": \"Нет кредитной карты! Добавьте ее\"}, status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n logger.error(f'create transaction: {request.data} - {str(e)}')\n return Response({\"error\": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n\nclass OrderView(viewsets.ModelViewSet):\n permission_classes = (IsAuthenticated,)\n queryset = Order.objects.all()\n serializer_class = OrderSerializer\n\n @action(methods=['GET'], detail=False, url_path='managers', url_name='managers',\n permission_classes=(ManagerPermission,))\n def managers_orders(self, request):\n logger.info(f'managers\\' orders')\n queryset = Order.objects.assignee_orders(assignee=request.user)\n serializer = OrderSerializer(queryset, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n @action(methods=['PUT'], detail=True, url_path='cancel', url_name='cancel',\n permission_classes=(IsAuthenticated,))\n def user_cancel(self, request, pk):\n logger.info('user cancel order')\n order = Order.objects.get(id=pk)\n order.status = CANCELED\n order.assignee = None\n order.save()\n return Response({'info': 'canceled'}, status=status.HTTP_200_OK)\n\n @action(methods=['PUT'], detail=True, url_path='complete', url_name='complete',\n permission_classes=(IsAuthenticated,))\n def complete_order(self, request, pk):\n logger.info(f'order completed {pk}')\n order = Order.objects.get(id=pk)\n order.status = DONE\n order.save()\n return Response({'info': 'completed'}, status=status.HTTP_200_OK)", "repo_name": "ayazhanutemurat/DjanoProject", "sub_path": "market_place/payments/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 20, "usage_type": "name"}, {"api_name": "payments.serializers.CreditCardSerializer", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 29, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 29, "usage_type": "name"}, {"api_name": "payments.models.CreditCard.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "payments.models.CreditCard.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "payments.models.CreditCard", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 35, "usage_type": "call"}, {"api_name": "payments.serializers.CreditCardSerializer", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 38, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 42, "usage_type": "name"}, {"api_name": "payments.models.Cart.objects.personal", "line_number": 47, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "payments.models.Cart", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 48, "usage_type": "call"}, {"api_name": "payments.serializers.CartSerializer", "line_number": 48, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 51, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 51, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 56, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects.add_product", "line_number": 57, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "payments.models.Cart", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 58, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 58, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 61, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 61, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 64, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 68, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects.remove_product", "line_number": 69, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "payments.models.Cart", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 73, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 73, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 77, "usage_type": "name"}, {"api_name": "payments.models.Cart.objects.get", "line_number": 82, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "payments.models.Cart", "line_number": 82, "usage_type": "name"}, {"api_name": "payments.models.CreditCard.objects.get", "line_number": 83, "usage_type": "call"}, {"api_name": "payments.models.CreditCard.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "payments.models.CreditCard", "line_number": 83, "usage_type": "name"}, {"api_name": "common.constants.DONT_ENOUGH_MONEY", "line_number": 85, "usage_type": "name"}, {"api_name": "common.constants.DONT_AVAILABLE", "line_number": 88, "usage_type": "name"}, {"api_name": "payments.models.Cart.objects.create", "line_number": 91, "usage_type": "call"}, {"api_name": "payments.models.Cart.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "payments.models.Cart", "line_number": 91, "usage_type": "name"}, {"api_name": "payments.serializers.TransactionSerializer", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 101, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 101, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 101, "usage_type": "name"}, {"api_name": "payments.models.CreditCard.DoesNotExist", "line_number": 102, "usage_type": "attribute"}, {"api_name": "payments.models.CreditCard", "line_number": 102, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 104, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 104, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 107, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 107, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 107, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 110, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 110, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 111, "usage_type": "name"}, {"api_name": "payments.models.Order.objects.all", "line_number": 112, "usage_type": "call"}, {"api_name": "payments.models.Order.objects", "line_number": 112, "usage_type": "attribute"}, {"api_name": "payments.models.Order", "line_number": 112, "usage_type": "name"}, {"api_name": "payments.serializers.OrderSerializer", "line_number": 113, "usage_type": "name"}, {"api_name": "payments.models.Order.objects.assignee_orders", "line_number": 119, "usage_type": "call"}, {"api_name": "payments.models.Order.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "payments.models.Order", "line_number": 119, "usage_type": "name"}, {"api_name": "payments.serializers.OrderSerializer", "line_number": 120, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 121, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 121, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 121, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 115, "usage_type": "call"}, {"api_name": "common.permissions.ManagerPermission", "line_number": 116, "usage_type": "name"}, {"api_name": "payments.models.Order.objects.get", "line_number": 127, "usage_type": "call"}, {"api_name": "payments.models.Order.objects", "line_number": 127, "usage_type": "attribute"}, {"api_name": "payments.models.Order", "line_number": 127, "usage_type": "name"}, {"api_name": "common.constants.CANCELED", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 131, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 131, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 131, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 123, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 124, "usage_type": "name"}, {"api_name": "payments.models.Order.objects.get", "line_number": 137, "usage_type": "call"}, {"api_name": "payments.models.Order.objects", "line_number": 137, "usage_type": "attribute"}, {"api_name": "payments.models.Order", "line_number": 137, "usage_type": "name"}, {"api_name": "common.constants.DONE", "line_number": 138, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 140, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 140, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 140, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 133, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 134, "usage_type": "name"}]} +{"seq_id": "26481334275", "text": "#This module helps communicate with raspberry pi for SOIL SENSORS, WATER FLOW SENSOR,\r\nimport RPi.GPIO as GPIO\r\nimport time, sys\r\nimport os\r\nimport requests\r\nimport json\r\nimport weather_api as wa\r\nimport datetime\r\n\r\n\r\nFLOW_SENSOR_GPIO = 13\r\n#MQTT_SERVER = \"192.168.1.220\"\r\n\r\nGPIO.setmode(GPIO.BCM)\r\nGPIO.setup(FLOW_SENSOR_GPIO, GPIO.IN, pull_up_down = GPIO.PUD_UP)\r\n\r\nglobal count\r\ncount = 0\r\n\r\ndef countPulse(channel):\r\n\tglobal count\r\n\tif start_counter == 1:\r\n\t\tcount = count+1\r\n\r\nGPIO.add_event_detect(FLOW_SENSOR_GPIO, GPIO.FALLING, callback=countPulse)\r\n\r\nwhile True:\r\n\ttry:\r\n\r\n\t\tt = time.localtime()\r\n\t\tcurrent_time = time.strftime(\"%H:%M:%S\", t)\r\n\r\n\t\tnow = datetime.datetime.now()\r\n\t\tcurrent_date = now.strftime(\"%Y-%m-%d\")\r\n\r\n\r\n\t\tstart_counter = 1\r\n\t\ttime.sleep(1)\r\n\t\tstart_counter = 0\r\n\t\tflow = (count / 7.5) # Pulse frequency (Hz) =7.5Q, Q is flow rate in L/min.\r\n\t\tprint(\"The flow is: %.3f Liter/min\" % (flow))\r\n\t\tprint(\"Sending API Requests...\")\r\n\t\t#os.system (\"'curl -X PUT -d '{\"time\": current_time ,\"flow\": flow}' 'INSERT API LINK HERE'\")\r\n\r\n\t\t#publish.single(\"/Garden.Pi/WaterFlow\", flow, hostname=MQTT_SERVER)\r\n\t\t\r\n\r\n\t\theaders = {\r\n\t\t \t'Content-Type': 'application/x-www-form-urlencoded',\r\n\t\t }\r\n\r\n\t\tdata = {\"date\": current_date ,\"water_l\":\"270 m\",\"moisture\":\"20 %\",\"pump\":\"ON\",\"time\": current_time ,\"flow\": flow, \"weather\": wa.weather_patiala, \"temp\": wa.weather_patiala[\"main\"][\"temp\"], \"humidity\": wa.weather_patiala[\"main\"][\"humidity\"]}\r\n\t\tdata_json = json.dumps(data)\r\n\t\tresponse = requests.put('INSERT API LINK HERE', headers=headers, data=data_json)\r\n\t\tprint(\"Request Sent to Patiala Firebase API\")\r\n\r\n\t\tdata_chd = {\"date\": current_date ,\"time\": current_time ,\"flow\": flow, \"weather\": wa.weather_chandigarh, \"temp\": wa.weather_chandigarh[\"main\"][\"temp\"], \"humidity\": wa.weather_chandigarh[\"main\"][\"humidity\"]}\r\n\t\tdata_json_chd = json.dumps(data_chd)\r\n\t\tresponse = requests.put('INSERT API LINK HERE', headers=headers, data=data_json_chd)\r\n\t\tprint(\"Request Sent to Chandigarh Firebase API\")\r\n\r\n\r\n\r\n\t\tdata_amritsar = {\"date\": current_date ,\"time\": current_time ,\"flow\": flow, \"weather\": wa.weather_amritsar, \"temp\": wa.weather_amritsar[\"main\"][\"temp\"], \"humidity\": wa.weather_amritsar[\"main\"][\"humidity\"]}\r\n\t\tdata_json_amritsar = json.dumps(data_amritsar)\r\n\t\tresponse = requests.put('INSERT API LINK HERE', headers=headers, data=data_json_amritsar)\r\n\t\tprint(\"Request Sent to Amritsar Firebase API\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t# response = requests.put('INSERT API LINK HERE', headers=headers, data=data_json) old url\r\n\r\n\t\tcount = 0\r\n\t\ttime.sleep(60)\r\n\t\tcontinue\r\n\t\t\r\n\texcept KeyboardInterrupt:\r\n\t\tprint(\"KeyboardInterrupt has been caught.\")\r\n\t\tGPIO.cleanup() #shouldn't be used i guess\r\n\t\tsys.exit()\r\n", "repo_name": "SUNS-TIET/SMARTswitch", "sub_path": "raspi/raspberrypi_module.py", "file_name": "raspberrypi_module.py", "file_ext": "py", "file_size_in_byte": 2711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "RPi.GPIO.setmode", "line_number": 14, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 14, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 14, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 15, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 15, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 15, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.PUD_UP", "line_number": 15, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.add_event_detect", "line_number": 25, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 25, "usage_type": "name"}, {"api_name": "RPi.GPIO.FALLING", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.localtime", "line_number": 30, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "weather_api.weather_patiala", "line_number": 52, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 54, "usage_type": "call"}, {"api_name": "weather_api.weather_chandigarh", "line_number": 57, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 59, "usage_type": "call"}, {"api_name": "weather_api.weather_amritsar", "line_number": 64, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 66, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 82, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 82, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "31702734635", "text": "import matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\n\npath = \"cleaned/train.csv\"\ndf = pd.read_csv(path)\n\ndf.describe()\n\n# Remove Columns that will not be used for classification\nd_col = [\"ID\", \"Customer_ID\", \"Month\", \"Name\", \"SSN\", \"Monthly_Inhand_Salary\"]\n\nfor _ in d_col:\n if _ in df.columns:\n df = df.drop(_, axis=1)\n\ndf.info()\n\n\n# See Nominal values\nfor col in df:\n if df[col].dtypes == object:\n print(col)\n print(\"**\" * 20)\n print(df[col].value_counts(dropna=False))\n print(\"**\" * 20)\n\n\ndf[\"Credit_Score\"]\n\n\n# Conversion of Nominal data into Numeric\ny_, label = pd.factorize(df[\"Credit_Score\"])\ndf[df.select_dtypes([\"object\"]).columns] = df[\n df.select_dtypes([\"object\"]).columns\n].apply(lambda x: pd.factorize(x)[0])\n\ndf.describe()\n\n# finding Columns with Outliers using IQR method\ndef find_outliers(df, threshold=1.5):\n cols = []\n\n for _ in df.columns:\n q1 = np.percentile(df[_], 25)\n q3 = np.percentile(df[_], 75)\n iqr = q3 - q1\n lower_limit = q1 - threshold * iqr\n upper_limit = q3 + threshold * iqr\n\n if any((df[_] < lower_limit) | (df[_] > upper_limit)):\n cols.append(_)\n return cols\n\n\noutlier_columns = find_outliers(df)\nprint(outlier_columns)\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Generate a color palette with a unique color for each box plot\nnum_plots = len(outlier_columns)\npalette = sns.color_palette(\"PiYG\", num_plots)\n\nfig, axes = plt.subplots(nrows=num_plots, ncols=1, figsize=(10, 2 * num_plots))\n\nfor i, column in enumerate(outlier_columns):\n ax = axes[i]\n sns.boxplot(x=df[column], ax=ax, color=palette[i])\n ax.set_title(f\"Box plot of {column}\", fontsize=12)\n ax.set_ylabel(\"\")\n ax.grid(True, axis=\"y\")\nplt.text(\n 0.9,\n 0.1,\n \"Roll: 18, 25\",\n ha=\"right\",\n va=\"bottom\",\n transform=plt.gca().transAxes,\n color=\"red\",\n fontsize=24,\n)\nplt.tight_layout()\nplt.savefig(\"outlier_box.png\", dpi=300)\nplt.show()\n\n\n# Limit the Outliers to Upper limit and Lower Limit\nthreshold = 1.5\ndf2 = df.copy()\nfor col in outlier_columns:\n q1 = np.percentile(df[col], 25)\n q3 = np.percentile(df[col], 75)\n iqr = q3 - q1\n lower_limit = q1 - threshold * iqr\n upper_limit = q3 + threshold * iqr\n\n df2[col] = np.where(\n df[col] > upper_limit,\n upper_limit,\n np.where(df[col] < lower_limit, lower_limit, df[col]),\n )\n\n\"\"\"for _ in outlier_columns:\n Q1 = df[_].quantile(0.25)\n Q3 = df[_].quantile(0.75)\n IQR = Q3 - Q1\n df = df.drop(df.loc[df[_] > (Q3 + 1.5 * IQR)].index)\n df = df.drop(df.loc[df[_] < (Q1 - 1.5 * IQR)].index)\ndf.info()\"\"\"\n\ndf[\"Annual_Income\"]\n\n# Box plot after handeling outliers\nfig, axes = plt.subplots(\n nrows=len(outlier_columns), ncols=1, figsize=(10, 2.5 * len(outlier_columns))\n)\n\nfor i, column in enumerate(outlier_columns):\n ax = axes[i]\n sns.boxplot(x=df2[column], ax=ax)\n ax.set_xlabel(\"Index\", fontsize=12)\n ax.set_ylabel(column, fontsize=12)\n ax.set_title(f\"Box plot of {column}\", fontsize=14)\n\nplt.tight_layout()\nplt.show()\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set the color palette\npalette = sns.color_palette(\"PiYG\", 14)\n\nfig, axes = plt.subplots(2, 1, figsize=(6, 10))\n\n# Plot \"Before\" distribution\nsns.histplot(df[\"Annual_Income\"], kde=True, ax=axes[0], color=palette[0], alpha=0.5)\naxes[0].set_title(\"Before\")\n\n# Plot \"After\" distribution\nsns.histplot(df2[\"Annual_Income\"], kde=True, ax=axes[1], color=palette[0], alpha=0.5)\naxes[1].set_title(\"After\")\n\n# Adjust alpha value for plot elements\nfor ax in axes:\n ax.set_facecolor((1, 1, 1, 1)) # Set background alpha value\n ax.grid(alpha=0.2) # Adjust gridlines alpha value\nplt.text(\n 0.9,\n 0.1,\n \"Roll: 18, 25\",\n ha=\"right\",\n va=\"bottom\",\n transform=plt.gca().transAxes,\n color=\"red\",\n fontsize=14,\n)\nplt.tight_layout()\nplt.savefig(\"limit.png\", dpi=300)\nplt.show()\n\ncorr = df.corr()\n\nplt.figure(figsize=(20, 20))\nmatrix = np.triu(corr)\nsns.heatmap(corr, cmap=\"PiYG\", annot=True, mask=matrix)\nplt.tight_layout()\nplt.text(\n 0.9,\n 0.9,\n \"Roll: 18, 25\",\n ha=\"right\",\n va=\"top\",\n transform=plt.gca().transAxes,\n color=\"red\",\n fontsize=34,\n)\nplt.savefig(\"matrix.png\", dpi=300)\nplt.show()\n\n\n# Training Data\ny = df[\"Credit_Score\"]\nX = df.drop(\"Credit_Score\", axis=1)\n\nfrom sklearn import tree\n\nclf = tree.DecisionTreeClassifier(criterion=\"entropy\")\n\n\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=0.33, random_state=100\n)\n\n\nclf = clf.fit(X_train, y_train)\n\npredicted = clf.predict(X_test)\npred_label = label[predicted]\ny_label = label[y_test]\n\nprint(pred_label)\n\n\nprint(y_label)\n\nfrom sklearn.metrics import (\n accuracy_score,\n confusion_matrix,\n ConfusionMatrixDisplay,\n f1_score,\n classification_report,\n)\n\nconf_mat = confusion_matrix(y_label, pred_label)\nC = conf_mat / conf_mat.astype(np.float).sum(axis=1)\ndisp = ConfusionMatrixDisplay(confusion_matrix=C, display_labels=label)\nfig, ax = plt.subplots(figsize=(8, 6))\n\n# Use only the green color from the \"PiYG\" palette\ncmap = plt.cm.get_cmap(\"PiYG\")\ncmap = cmap(np.linspace(0.5, 1, cmap.N))\ncmap = cmap[:, 1:2]\ncmap = plt.cm.colors.ListedColormap(cmap)\n\ndisp.plot(ax=ax, cmap=\"Greens\", xticks_rotation=\"vertical\")\n\nplt.title(\"Confusion Matrix\")\nplt.tight_layout()\nplt.text(\n 0.9,\n 0.1,\n \"Roll: 18, 25\",\n ha=\"right\",\n va=\"bottom\",\n transform=plt.gca().transAxes,\n color=\"red\",\n fontsize=18,\n)\nplt.savefig(\"entropy.png\", dpi=300)\n\n\nprint(classification_report(y_test, predicted))\n\n\nclf_gini = tree.DecisionTreeClassifier(criterion=\"gini\", random_state=0)\n\n\n# fit the model\nclf_gini.fit(X_train, y_train)\n\n\ny_pred_gini = clf_gini.predict(X_test)\n\n\n\nconf_mat = confusion_matrix(y_test, y_pred_gini)\nC = conf_mat / conf_mat.astype(np.float).sum(axis=1)\ndisp = ConfusionMatrixDisplay(confusion_matrix=C, display_labels=label)\nfig, ax = plt.subplots(figsize=(8, 6))\ndisp.plot(ax=ax, cmap=\"Greens\", xticks_rotation=\"vertical\")\n\nplt.title(\"Confusion Matrix\")\nplt.tight_layout()\nplt.text(\n 0.9,\n 0.1,\n \"Roll: 18, 25\",\n ha=\"right\",\n va=\"bottom\",\n transform=plt.gca().transAxes,\n color=\"red\",\n fontsize=18,\n)\nplt.savefig(\"entropy2.png\", dpi=300)\n\n\nprint(classification_report(y_test, y_pred_gini))\n", "repo_name": "Pilot-Khadka/Machine_Learning_Projects", "sub_path": "Decision Tree -Credit Score classigicaiton/1.py", "file_name": "1.py", "file_ext": "py", "file_size_in_byte": 6409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 47, "usage_type": "call"}, {"api_name": "seaborn.color_palette", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "seaborn.boxplot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "seaborn.boxplot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "seaborn.color_palette", "line_number": 135, "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": "seaborn.histplot", "line_number": 140, "usage_type": "call"}, {"api_name": "seaborn.histplot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "numpy.triu", "line_number": 168, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 191, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 191, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 196, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 221, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.ConfusionMatrixDisplay", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 226, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.colors.ListedColormap", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 229, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 248, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 251, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 251, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 263, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.ConfusionMatrixDisplay", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 283, "usage_type": "call"}]} +{"seq_id": "70301674003", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.forms import ModelForm\n\nfrom build_world.fields import EntityCharField, EntityTextField\nimport merge_in_memory as mim_module\n\nclass Entity(models.Model):\n choices = (('world', 'World'), ('story', 'Story'), ('section', 'Section'))\n etype = models.CharField('Entity type', max_length='32', choices=choices, db_column='type',\n db_index=True, help_text='Specify the type of entity (eg. World, Story) you are creating.', default='world')\n parent = models.ForeignKey('self', null=True, blank=True,\n help_text='Indicate to which entity (eg. World, Story, Section) this belongs.')\n founder = models.ForeignKey(User, related_name='founders')\n owner = models.ForeignKey(User, null=True, blank=True)\n name = EntityCharField(max_length=100)\n body = EntityTextField(blank=True)\n description = EntityTextField(max_length=1000, blank=True,\n help_text='Describe your project. This description will be visible to everyone.')\n notes = EntityTextField(blank=True, help_text='This is a place for storing any notes you might want to keep about the project.')\n private = models.BooleanField(default=False)\n active_version = models.ForeignKey('EntityVersion', related_name='active_version', null=True, blank=True, default=None)\n\n def __unicode__(self):\n return self.etype + \": \" + self.name\n\n @models.permalink\n def get_absolute_url(self, attr=None):\n if attr and self.hasattr(attr):\n return ('entity_attr', (), {\n 'etype':self.etype, \n 'pk':self.id, \n 'attr':attr })\n else:\n return ('entity', (), {\n 'etype':self.etype, \n 'pk':self.id })\n\n def has_same_data(self, other, reverse=False, **kwargs):\n \"\"\"Takes in another entity and possibly some diffs to apply. Applies the diffs to self temporarily and checks if the primary attributes of the two entities are identical.\n \"\"\"\n if type(self) != type(other):\n return False\n\n merger = mim_module.Merger()\n body = self.body\n description = self.description\n notes = self.notes\n\n if 'body_diff' in kwargs:\n body = merger.diff_apply(body, kwargs['body_diff'], reverse)\n if 'descr_diff' in kwargs:\n description = merger.diff_apply(description, kwargs['descr_diff'], reverse)\n if 'notes_diff' in kwargs:\n notes = merger.diff_apply(notes, kwargs['notes_diff'], reverse)\n\n # TEST OUTPUT\n return self.body + \"
\" + body + \"
\" + other.body + \"
\" + kwargs['body_diff']\n return body\n return other.body\n\n if body == other.body and description == other.description and notes == other.notes:\n return True\n\n return False\n\n def get_to_version(self, version):\n \"\"\"Takes a version and tracks the current entity forward or back to\n that version by applying diffs.\n \"\"\"\n try:\n curr_version_num = self.active_version.version_num\n except AttributeError:\n curr_version_num = 0\n\n try:\n target_version_num = version.version_num\n except AttributeError:\n target_version_num = 0\n\n # Get a list of versions that are in between this entity and the target\n # version.\n if curr_version_num <= target_version_num:\n reverse = False\n versions = EntityVersion.objects.filter(\n entity=self, \n version_num__gt=curr_version_num, \n version_num__lte=target_version_num)\n else:\n reverse = True\n versions = EntityVersion.objects.filter(\n entity=self, \n version_num__lte=curr_version_num, \n version_num__gt=target_version_num)\n for ver in versions:\n self.apply_version(ver, reverse=reverse)\n\n def apply_version(self, version, reverse=False):\n \"\"\"Takes a version and applies its diffs to the current entity.\"\"\"\n self.apply_diff_strings(reverse, body_diff=version.body,\n descr_diff=version.description, notes_diff=version.notes)\n\n\n def apply_diff_strings(self, reverse=False, **kwargs):\n \"\"\"Takes a set of diffs and applies them to the relevant attributes.\"\"\"\n merger = mim_module.Merger()\n if 'body_diff' in kwargs:\n self.body = merger.diff_apply(self.body, kwargs['body_diff'], reverse)\n if 'descr_diff' in kwargs:\n self.description = merger.diff_apply(self.description, kwargs['descr_diff'], reverse)\n if 'notes_diff' in kwargs:\n self.notes = merger.diff_apply(self.notes, kwargs['notes_diff'], reverse)\n\n def make_version_with_diffs(self, other, version=None):\n \"\"\"Take in another entity and possibly a version. Return a version\n containing the diff of self and the other entity (in that order).\n \"\"\"\n merger = mim_module.Merger()\n if type(self) != type(other):\n return False\n if not version:\n version = EntityVersion()\n version.body = merger.diff_make(self.body, other.body)\n version.description = merger.diff_make(self.description, other.description)\n version.notes = merger.diff_make(self.notes, other.notes)\n return version\n \nclass MemberRelation(models.Model):\n \"\"\"Stores information about which user can contribute to which entities, and at what permission levels, and so on.\n \"\"\"\n entity = models.ForeignKey(Entity)\n user = models.ForeignKey(User)\n ranks = (('chief_contrib', 'Chief Contributor'), ('chief_editor', 'Chief Editor'), ('editor', 'Editor'),\n ('contributor', 'Contributor'), ('artist', 'Artist'))\n relation = models.CharField(max_length=64, choices=ranks)\n\n def __unicode__(self):\n return self.user.username + \", \" + self.relation + \" of \" + str(self.entity)\n\nclass Version(models.Model):\n \"\"\"Stores the changes a person has made to an entity, or the data for a new\n entity that has yet to be accepted.\n NOTE: This relies on the merge_in_memory package, found at: \n https://github.com/danielmoniz/merge_in_memory\n \"\"\"\n version_num = models.IntegerField(null=True, blank=True)\n\n class Meta:\n abstract = True\n\n def __unicode__(self):\n return self.version_num\n\nclass EntityVersion(Version):\n \"\"\"Stores entity data in a version.\"\"\"\n entity = models.ForeignKey(Entity, null=True)\n modifies = models.ForeignKey('self', null=True, blank=True)\n active = models.BooleanField(default=False)\n body = EntityTextField(null=True, blank=True)\n description = EntityTextField(null=True, blank=True)\n notes = EntityTextField(null=True, blank=True)\n edited = models.BooleanField(default=False)\n accepted = models.BooleanField(default=False)\n user = models.ForeignKey(User)\n\n def __unicode__(self):\n output_list = [\n str(self.entity), \n \"modified by \" + str(self.user),\n str(self.version_num)]\n if self.active:\n output_list.append('ACTIVE')\n else:\n output_list.append('ACTIVE')\n\n return ', '.join(output_list)\n\n def make_version_from_entities(self, entity1, entity2):\n \"\"\"Take in two entities and modify this version's relevant fields to\n reflect the changes in the entities.\n NOTE: This is primarily used with a real entity and a dummy entity.\n Therefore only the text fields need modifying.\n \"\"\"\n # Generate the diffs and call entity1.make_version_with_diffs\n pass\n\n\n\n# FORMS ================================================================\nclass MemberRelationForm(ModelForm):\n \"\"\"Allow users to create/modify/modify the permissions of others.\"\"\"\n class Meta:\n model = MemberRelation\n exclude = ('entity')\n \nclass EntityForm(ModelForm):\n \"\"\"A class for modifying an Entity.\"\"\"\n class Meta:\n model = Entity\n \nclass EntityCreateForm(EntityForm):\n \"\"\"A class for creating a new Entity. Leaves out both Founder and Owner.\n \"\"\"\n class Meta(EntityForm.Meta):\n exclude = ('founder', 'owner', 'active_version',)\n\nclass WorldForm(EntityForm):\n \"\"\"A class describing a form for modifying a World.\"\"\"\n class Meta(EntityForm.Meta):\n exclude = ('parent', 'founder', 'body', 'active_version')\n\nclass WorldNonOwnerForm(WorldForm):\n \"\"\"A class describing a form for modifying a World as a non-owner.\"\"\"\n class Meta(WorldForm.Meta):\n exclude = ('parent', 'founder', 'body', 'owner', 'active_version')\n\nclass StoryForm(EntityForm):\n \"\"\"A class describing a form for modifying a Story.\"\"\"\n class Meta(EntityForm.Meta):\n exclude = ('founder', 'active_version')\n\nclass StoryNonOwnerForm(StoryForm):\n \"\"\"A class describing a form for modifying a Story as a non-owner.\"\"\"\n class Meta(StoryForm.Meta):\n exclude = ('founder', 'owner', 'active_version')\n\nclass SectionForm(EntityForm):\n \"\"\"A class describing a form for modifying a Section.\"\"\"\n class Meta(EntityForm.Meta):\n exclude = ('founder', 'active_version')\n\nclass SectionNonOwnerForm(SectionForm):\n \"\"\"A class describing a form for modifying a Section as a non-owner.\"\"\"\n class Meta(SectionForm.Meta):\n exclude = ('founder', 'owner', 'active_version')\n", "repo_name": "danielmoniz/Rainbow", "sub_path": "build_world/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 9467, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "build_world.fields.EntityCharField", "line_number": 16, "usage_type": "call"}, {"api_name": "build_world.fields.EntityTextField", "line_number": 17, "usage_type": "call"}, {"api_name": "build_world.fields.EntityTextField", "line_number": 18, "usage_type": "call"}, {"api_name": "build_world.fields.EntityTextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.permalink", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "merge_in_memory.Merger", "line_number": 45, "usage_type": "call"}, {"api_name": "merge_in_memory.Merger", "line_number": 106, "usage_type": "call"}, {"api_name": "merge_in_memory.Merger", "line_number": 118, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 128, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 128, "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.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.CharField", "line_number": 135, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 135, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 140, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 140, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 146, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 146, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 156, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 156, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 157, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 157, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 158, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 158, "usage_type": "name"}, {"api_name": "build_world.fields.EntityTextField", "line_number": 159, "usage_type": "call"}, {"api_name": "build_world.fields.EntityTextField", "line_number": 160, "usage_type": "call"}, {"api_name": "build_world.fields.EntityTextField", "line_number": 161, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 162, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 162, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 163, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 163, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 164, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 164, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 164, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 190, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 196, "usage_type": "name"}]} +{"seq_id": "20696489157", "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 ('AreaOftalmologia', '0004_auto_20151203_1931'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='persona',\n old_name='seegundo_nombre',\n new_name='segundo_nombre',\n ),\n migrations.AlterField(\n model_name='tipo_examen',\n name='nombre',\n field=models.CharField(max_length=50),\n ),\n ]\n", "repo_name": "Everlm/IPS", "sub_path": "AreaOftalmologia/migrations/0005_auto_20151205_1041.py", "file_name": "0005_auto_20151205_1041.py", "file_ext": "py", "file_size_in_byte": 572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "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.RenameField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "41706710038", "text": "from typing import List\r\n\r\n\r\nclass Solution:\r\n def findDuplicate(self, nums: List[int]) -> int:\r\n nums_dict = dict()\r\n for num in nums:\r\n if num not in nums_dict:\r\n nums_dict[num] = True\r\n else:\r\n return num\r\n", "repo_name": "sgonzalezr94/Neetcode-Challenges", "sub_path": "Arrays_and_Hashing/1.containsDuplicate.py", "file_name": "1.containsDuplicate.py", "file_ext": "py", "file_size_in_byte": 278, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "17428566782", "text": "import asyncio\nimport base64\nimport json\nimport logging\nimport time\nfrom typing import Annotated, Any, AsyncGenerator, Literal, Optional, Tuple, Union\nfrom fastapi import WebSocket\nfrom vocode.streaming.agent.base_agent import AgentResponse, AgentResponseMessageChunk\nfrom vocode.streaming.models.agent import (\n EndInputStream,\n InputStreamChunk,\n InputStreamMessage,\n)\nfrom vocode.streaming.synthesizer import miniaudio_worker\nimport websockets\nfrom websockets.client import WebSocketClientProtocol\nimport aiohttp\nfrom opentelemetry.trace import Span\nfrom pydantic import BaseModel, Field\nfrom elevenlabs import generate\n\nfrom vocode import getenv\nfrom vocode.streaming.synthesizer.base_synthesizer import (\n BaseSynthesizer,\n SynthesisResult,\n encode_as_wav,\n tracer,\n)\nfrom vocode.streaming.models.synthesizer import (\n ElevenLabsSynthesizerConfig,\n SynthesizerType,\n)\nfrom vocode.streaming.agent.bot_sentiment_analyser import BotSentiment\nfrom vocode.streaming.models.message import BaseMessage\nfrom vocode.streaming.utils.mp3_helper import decode_mp3\nfrom vocode.streaming.synthesizer.miniaudio_worker import MiniaudioWorker\nfrom vocode.streaming.utils.worker import (\n AsyncQueueWorker,\n AsyncWorker,\n InterruptibleAgentResponseEvent,\n)\n\nlogger = logging.getLogger(__name__)\n\nADAM_VOICE_ID = \"pNInz6obpgDQGcFmaJgB\"\nELEVEN_LABS_BASE_URL = \"https://api.elevenlabs.io/v1/\"\nELEVEN_LABS_WEBSOCKET_BASE_URL = \"wss://api.elevenlabs.io/v1/\"\n\n\nclass ElevenLabsInputStreamWorker(AsyncWorker[AgentResponse]):\n def __init__(\n self,\n input_queue: asyncio.Queue[InterruptibleAgentResponseEvent[AgentResponse]],\n output_queue: asyncio.Queue[bytes | None],\n api_key: str,\n voice_id: str,\n model_id: str,\n voice_settings: Optional[dict] = None,\n ):\n super().__init__(input_queue, output_queue)\n self.api_key = api_key\n self.voice_id = voice_id\n self.model_id = model_id\n self.bos = dict(\n text=\" \",\n voice_settings={\n \"stability\": 0.5,\n \"similarity_boost\": True,\n },\n # generation_config=dict(\n # chunk_length_schedule=[50],\n # ),\n xi_api_key=self.api_key,\n )\n if voice_settings:\n self.bos[\"voice_settings\"] = voice_settings\n self.eos = dict(text=\"\")\n self.buffered_message = \"\"\n\n def get_message_so_far(self):\n # print(\"[SYNTHESIZER] returning buffered message\", self.buffered_message)\n return self.buffered_message\n\n async def _run_loop(self) -> None:\n url = (\n ELEVEN_LABS_WEBSOCKET_BASE_URL\n + f\"text-to-speech/{self.voice_id}/stream-input?model_type={self.model_id}\"\n )\n\n async with websockets.connect(\n url,\n # extra_headers={\"xi-api-key\": self.api_key},\n ) as websocket:\n try:\n await websocket.send(json.dumps(self.bos))\n except Exception as e:\n logger.error(e)\n return\n while True:\n item: InterruptibleAgentResponseEvent[\n AgentResponse\n ] = await self.input_queue.get()\n payload = item.payload\n # print(\"[SYNTHESIZER]\", payload)\n input_stream_message: InputStreamMessage\n if not isinstance(payload, AgentResponseMessageChunk):\n break\n else:\n input_stream_message = payload.chunk\n\n if isinstance(input_stream_message, InputStreamChunk):\n msg = dict(\n text=input_stream_message.text, try_trigger_generation=True\n )\n await websocket.send(json.dumps(msg))\n item.is_interruptible = False\n elif isinstance(input_stream_message, EndInputStream):\n await websocket.send(json.dumps(self.eos))\n item.is_interruptible = False\n break\n\n while True:\n try:\n response = await websocket.recv()\n except websockets.exceptions.ConnectionClosed:\n break\n try:\n data = json.loads(response)\n if data[\"audio\"]:\n self.output_queue.put_nowait(base64.b64decode(data[\"audio\"]))\n normalized_alignment = data.get(\"normalizedAlignment\")\n if normalized_alignment:\n text = \"\".join(data[\"normalizedAlignment\"][\"chars\"])\n self.buffered_message += text\n except json.JSONDecodeError:\n continue\n\n self.output_queue.put_nowait(None) # sentinel\n\n # await asyncio.gather(sender(websocket), receiver(websocket))\n # await sender(websocket)\n\n\nclass ElevenLabsSynthesizer(BaseSynthesizer[ElevenLabsSynthesizerConfig]):\n def __init__(\n self,\n synthesizer_config: ElevenLabsSynthesizerConfig,\n logger: Optional[logging.Logger] = None,\n aiohttp_session: Optional[aiohttp.ClientSession] = None,\n ):\n super().__init__(synthesizer_config, aiohttp_session)\n\n import elevenlabs\n\n self.elevenlabs = elevenlabs\n\n self.api_key = synthesizer_config.api_key or getenv(\"ELEVEN_LABS_API_KEY\")\n self.voice_id = synthesizer_config.voice_id or ADAM_VOICE_ID\n self.stability = synthesizer_config.stability\n self.similarity_boost = synthesizer_config.similarity_boost\n self.model_id = synthesizer_config.model_id\n self.optimize_streaming_latency = synthesizer_config.optimize_streaming_latency\n self.words_per_minute = 150\n self.experimental_streaming = synthesizer_config.experimental_streaming\n\n async def create_input_streamed_speech(\n self,\n chunk_size: int,\n input_queue: asyncio.Queue[InterruptibleAgentResponseEvent[AgentResponse]],\n ):\n voice = self.get_voice()\n miniaudio_worker_input_queue: asyncio.Queue[bytes | None] = asyncio.Queue()\n input_stream_worker = ElevenLabsInputStreamWorker(\n input_queue=input_queue,\n output_queue=miniaudio_worker_input_queue,\n api_key=self.api_key,\n voice_id=self.voice_id,\n model_id=self.model_id or \"eleven_monolingual_v1\",\n voice_settings=voice.settings.dict() if voice.settings else None,\n )\n miniaudio_worker = MiniaudioWorker(\n synthesizer_config=self.synthesizer_config,\n chunk_size=chunk_size,\n input_queue=miniaudio_worker_input_queue,\n output_queue=asyncio.Queue(),\n )\n input_stream_worker.start()\n miniaudio_worker.start()\n\n async def chunk_generator():\n try:\n # Await the output queue of the MiniaudioWorker and yield the wav chunks in another loop\n while True:\n # Get the wav chunk and the flag from the output queue of the MiniaudioWorker\n # print(\"[MINIAUDIO WORKER] getting chunk\")\n wav_chunk, is_last = await miniaudio_worker.output_queue.get()\n if self.synthesizer_config.should_encode_as_wav:\n wav_chunk = encode_as_wav(wav_chunk, self.synthesizer_config)\n\n yield SynthesisResult.ChunkResult(wav_chunk, is_last)\n\n if is_last:\n break\n except asyncio.CancelledError:\n pass\n finally:\n input_stream_worker.terminate()\n miniaudio_worker.terminate()\n\n return SynthesisResult(\n chunk_generator(),\n lambda seconds: input_stream_worker.get_message_so_far(),\n )\n\n def get_voice(self):\n voice = self.elevenlabs.Voice(voice_id=self.voice_id)\n if self.stability is not None and self.similarity_boost is not None:\n voice.settings = self.elevenlabs.VoiceSettings(\n stability=self.stability, similarity_boost=self.similarity_boost\n )\n return voice\n\n async def create_speech(\n self,\n message: BaseMessage,\n chunk_size: int,\n bot_sentiment: Optional[BotSentiment] = None,\n ) -> SynthesisResult:\n voice = self.get_voice()\n url = ELEVEN_LABS_BASE_URL + f\"text-to-speech/{self.voice_id}\"\n\n if self.experimental_streaming:\n url += \"/stream\"\n\n if self.optimize_streaming_latency:\n url += f\"?optimize_streaming_latency={self.optimize_streaming_latency}\"\n headers = {\"xi-api-key\": self.api_key}\n body = {\n \"text\": message.text,\n \"voice_settings\": voice.settings.dict() if voice.settings else None,\n }\n if self.model_id:\n body[\"model_id\"] = self.model_id\n\n create_speech_span = tracer.start_span(\n f\"synthesizer.{SynthesizerType.ELEVEN_LABS.value.split('_', 1)[-1]}.create_total\",\n )\n\n session = self.aiohttp_session\n\n response = await session.request(\n \"POST\",\n url,\n json=body,\n headers=headers,\n timeout=aiohttp.ClientTimeout(total=15),\n )\n if not response.ok:\n raise Exception(f\"ElevenLabs API returned {response.status} status code\")\n if self.experimental_streaming:\n return SynthesisResult(\n self.experimental_mp3_streaming_output_generator(\n response, chunk_size, create_speech_span\n ), # should be wav\n lambda seconds: self.get_message_cutoff_from_voice_speed(\n message, seconds, self.words_per_minute\n ),\n )\n else:\n audio_data = await response.read()\n create_speech_span.end()\n convert_span = tracer.start_span(\n f\"synthesizer.{SynthesizerType.ELEVEN_LABS.value.split('_', 1)[-1]}.convert\",\n )\n output_bytes_io = decode_mp3(audio_data)\n\n result = self.create_synthesis_result_from_wav(\n file=output_bytes_io,\n message=message,\n chunk_size=chunk_size,\n )\n convert_span.end()\n\n return result", "repo_name": "marinho-gomes/vocode-python-marinho", "sub_path": "vocode/streaming/synthesizer/eleven_labs_synthesizer.py", "file_name": "eleven_labs_synthesizer.py", "file_ext": "py", "file_size_in_byte": 10517, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "vocode.streaming.utils.worker.AsyncWorker", "line_number": 50, "usage_type": "name"}, {"api_name": "vocode.streaming.agent.base_agent.AgentResponse", "line_number": 50, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 53, "usage_type": "attribute"}, {"api_name": "vocode.streaming.utils.worker.InterruptibleAgentResponseEvent", "line_number": 53, "usage_type": "name"}, {"api_name": "vocode.streaming.agent.base_agent.AgentResponse", "line_number": 53, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 54, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 58, "usage_type": "name"}, {"api_name": "websockets.connect", "line_number": 90, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 95, "usage_type": "call"}, {"api_name": "vocode.streaming.utils.worker.InterruptibleAgentResponseEvent", "line_number": 100, "usage_type": "name"}, {"api_name": "vocode.streaming.agent.base_agent.AgentResponse", "line_number": 101, "usage_type": "name"}, {"api_name": "vocode.streaming.models.agent.InputStreamMessage", "line_number": 105, "usage_type": "name"}, {"api_name": "vocode.streaming.agent.base_agent.AgentResponseMessageChunk", "line_number": 106, "usage_type": "argument"}, {"api_name": "vocode.streaming.models.agent.InputStreamChunk", "line_number": 111, "usage_type": "argument"}, {"api_name": "json.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "vocode.streaming.models.agent.EndInputStream", "line_number": 117, "usage_type": "argument"}, {"api_name": "json.dumps", "line_number": 118, "usage_type": "call"}, {"api_name": "websockets.exceptions", "line_number": 125, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 128, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 130, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 135, "usage_type": "attribute"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.BaseSynthesizer", "line_number": 144, "usage_type": "name"}, {"api_name": "vocode.streaming.models.synthesizer.ElevenLabsSynthesizerConfig", "line_number": 144, "usage_type": "name"}, {"api_name": "vocode.streaming.models.synthesizer.ElevenLabsSynthesizerConfig", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 148, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 148, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 149, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 149, "usage_type": "attribute"}, {"api_name": "vocode.getenv", "line_number": 157, "usage_type": "call"}, {"api_name": "asyncio.Queue", "line_number": 169, "usage_type": "attribute"}, {"api_name": "vocode.streaming.utils.worker.InterruptibleAgentResponseEvent", "line_number": 169, "usage_type": "name"}, {"api_name": "vocode.streaming.agent.base_agent.AgentResponse", "line_number": 169, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 172, "usage_type": "attribute"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker", "line_number": 181, "usage_type": "name"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker.MiniaudioWorker", "line_number": 181, "usage_type": "call"}, {"api_name": "asyncio.Queue", "line_number": 185, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker.start", "line_number": 188, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker", "line_number": 188, "usage_type": "name"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker.output_queue.get", "line_number": 196, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker.output_queue", "line_number": 196, "usage_type": "attribute"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker", "line_number": 196, "usage_type": "name"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.encode_as_wav", "line_number": 198, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.SynthesisResult.ChunkResult", "line_number": 200, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.SynthesisResult", "line_number": 200, "usage_type": "name"}, {"api_name": "asyncio.CancelledError", "line_number": 204, "usage_type": "attribute"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker.terminate", "line_number": 208, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.miniaudio_worker", "line_number": 208, "usage_type": "name"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.SynthesisResult", "line_number": 210, "usage_type": "call"}, {"api_name": "vocode.streaming.models.message.BaseMessage", "line_number": 225, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 227, "usage_type": "name"}, {"api_name": "vocode.streaming.agent.bot_sentiment_analyser.BotSentiment", "line_number": 227, "usage_type": "name"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.tracer.start_span", "line_number": 245, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.tracer", "line_number": 245, "usage_type": "name"}, {"api_name": "vocode.streaming.models.synthesizer.SynthesizerType.ELEVEN_LABS.value.split", "line_number": 246, "usage_type": "call"}, {"api_name": "vocode.streaming.models.synthesizer.SynthesizerType.ELEVEN_LABS", "line_number": 246, "usage_type": "attribute"}, {"api_name": "vocode.streaming.models.synthesizer.SynthesizerType", "line_number": 246, "usage_type": "name"}, {"api_name": "aiohttp.ClientTimeout", "line_number": 256, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.SynthesisResult", "line_number": 261, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.tracer.start_span", "line_number": 272, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.tracer", "line_number": 272, "usage_type": "name"}, {"api_name": "vocode.streaming.models.synthesizer.SynthesizerType.ELEVEN_LABS.value.split", "line_number": 273, "usage_type": "call"}, {"api_name": "vocode.streaming.models.synthesizer.SynthesizerType.ELEVEN_LABS", "line_number": 273, "usage_type": "attribute"}, {"api_name": "vocode.streaming.models.synthesizer.SynthesizerType", "line_number": 273, "usage_type": "name"}, {"api_name": "vocode.streaming.utils.mp3_helper.decode_mp3", "line_number": 275, "usage_type": "call"}, {"api_name": "vocode.streaming.synthesizer.base_synthesizer.SynthesisResult", "line_number": 228, "usage_type": "name"}]} +{"seq_id": "20581042515", "text": "import facebook\nimport os\nfrom notipy.cli import Notipy\nimport pypapath\nimport testInternet\n\nNO = '\\033[0m' # white (normal)\nRED = '\\033[31m' # red\n \ndef handle():\n try:\n BASE_DIR = os.path.dirname(os.path.dirname(__file__))\n HOME_DIR = os.environ['HOME']\n os.chdir(HOME_DIR)\n print('''Do you have access token?\n If no, then goto https://developers.facebook.com/tools/explorer \n and click on 'Get Token' button then click on 'Get User Access Token' \n and on the User Data Permissions mark on 'pubish_actions'\n ''')\n imageName = input('Enter the image name with extension: ')\n imageLocation = input('Enter the image location: ')\n os.chdir(imageLocation)\n caption = input('Enter the caption to the image: ')\n token = input('Enter Facebook access token: ')\n graph = facebook.GraphAPI(access_token = token)\n graph.put_photo(image=open(imageName, 'rb'), message= caption)\n print('Your photo is uploaded!')\n Notipy().send('Your photo is uploaded!')\n except FileNotFoundError:\n print(RED + 'No such file or directory!' + NO)\n except:\n print(RED + '\\nClosing' + NO)\n \nif not testInternet.is_connected():\n print(RED + 'Internet is not working well! Check your connection.' + NO)\n exit(0)\nhandle()\n", "repo_name": "sawin0/pypa", "sub_path": "pypa/pict.py", "file_name": "pict.py", "file_ext": "py", "file_size_in_byte": 1341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 22, "usage_type": "call"}, {"api_name": "facebook.GraphAPI", "line_number": 25, "usage_type": "call"}, {"api_name": "notipy.cli.Notipy", "line_number": 28, "usage_type": "call"}, {"api_name": "testInternet.is_connected", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "25150981122", "text": "import multiprocessing as mp\nimport time\ndef test(res = 0):\n print(\"Test start with res = \", res)\n time.sleep(res)\n print(\"Test stop\")\n\ndef check():\n try:\n proc = mp.Process(target=test,args=([10]))\n proc.start()\n proc.join(timeout=3)\n if proc.is_alive():\n print(\"process is alive\")\n proc.terminate()\n else:\n print(\"process ended normally\")\n except Exception as e:\n print(e)\n\nif __name__ == \"__main__\":\n check()", "repo_name": "krishnabhunia/VSCode-Programs", "sub_path": "mul_test.py", "file_name": "mul_test.py", "file_ext": "py", "file_size_in_byte": 504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "time.sleep", "line_number": 5, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "35786375023", "text": "import os\nimport torch\nfrom collections import OrderedDict\n\n\nclass ModelsFactory(object):\n def __init__(self):\n pass\n\n @staticmethod\n def get_by_name(model_name, *args, **kwargs):\n model = None\n\n if model_name == \"imitator\":\n from .imitator import Imitator\n model = Imitator(*args, **kwargs)\n\n elif model_name == \"swapper\":\n from .imitator import Swapper\n model = Swapper(*args, **kwargs)\n\n elif model_name == \"viewer\":\n from .imitator import Viewer\n model = Viewer(*args, **kwargs)\n\n else:\n raise ValueError(f\"Model {model_name} not recognized.\")\n\n print(f\"Model {model.name} was created\")\n return model\n\n\nclass BaseModel(object):\n def __init__(self, opt):\n self._name = \"BaseModel\"\n\n self._opt = opt\n self._save_dir = opt.meta_data.checkpoints_dir\n\n @property\n def name(self):\n return self._name\n\n def load_network(self, network, network_label, epoch_label, need_module=False):\n load_filename = \"net_iter_%s_id_%s.pth\" % (epoch_label, network_label)\n load_path = os.path.join(self._save_dir, load_filename)\n\n self.load_params(network, load_path, need_module)\n\n def load_params(self, network, load_path, need_module=False):\n assert os.path.exists(\n load_path), \"Weights file not found. Have you trained a model!? We are not providing one %s\" % load_path\n\n def load(model, orig_state_dict):\n state_dict = OrderedDict()\n for k, v in orig_state_dict.items():\n # remove \"module\"\n name = k[7:] if \"module\" in k else k\n state_dict[name] = v\n\n # load params\n # model.load_state_dict(state_dict)\n model.load_state_dict(state_dict, strict=False)\n\n save_data = torch.load(load_path, map_location=\"cpu\")\n if need_module:\n # network.load_state_dict(save_data)\n network.load_state_dict(save_data, strict=False)\n else:\n load(network, save_data)\n\n print(\"Loading net: %s\" % load_path)\n\n\nclass BaseRunnerModel(BaseModel):\n\n def __init__(self, opt):\n super(BaseRunnerModel, self).__init__(opt)\n\n self._name = \"BaseRunnerModel\"\n\n def source_setup(self, *args, **kwargs):\n raise NotImplementedError\n\n def swap_params(self, *args, **kwargs):\n raise NotImplementedError\n\n def make_inputs_for_tsf(self, *args, **kwargs):\n raise NotImplementedError\n\n def post_update(self, *args, **kwargs):\n raise NotImplementedError\n\n", "repo_name": "iPERDance/iPERCore", "sub_path": "iPERCore/models/base_model.py", "file_name": "base_model.py", "file_ext": "py", "file_size_in_byte": 2646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2412, "dataset": "github-code", "pt": "2", "api": [{"api_name": "imitator.Imitator", "line_number": 16, "usage_type": "call"}, {"api_name": "imitator.Swapper", "line_number": 20, "usage_type": "call"}, {"api_name": "imitator.Viewer", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "74467415095", "text": "## Import Important Library\n\nimport unicodecsv\nfrom datetime import datetime\nfrom collections import Counter\nimport time\n\n## Filenames\nchicago = 'chicago.csv'\nnew_york_city = 'new_york_city.csv'\nwashington = 'washington.csv'\n\n## Date and Month Lists\nmonth_list = [('january', 1), ('february', 2), ('march', 3), ('april', 4), ('may', 5), ('june', 6)]\nday_list = [('monday', 1), ('tuesday', 2), ('wednesday', 3), ('thursday', 4), ('friday', 5), ('saturday', 6), ('sunday', 7)]\n\ndef read_csv(filename):\n '''Import CSV files and convert them to dictionaries\n\n Args:\n Bikshare csv Filename\n Returns:\n List of Dictionaries containing the bikeshare data\n '''\n with open(filename,'rb') as f:\n reader = unicodecsv.DictReader(f)\n return list(reader)\n\ndef change_timeclass(date_time):\n '''Function to change str to datetime class\n\n Args:\n (str) each row in bikeshare data that will be converted to datetime\n Returns:\n (datetime) each row in bikeshare data\n '''\n return datetime.strptime(date_time, '%Y-%m-%d %H:%M:%S')\n\ndef change_intclass(integer):\n '''Function to change str to int class and empty str to None\n\n Args:\n (str) each row in bikeshare data that will be converted to integer\n Returns:\n (int) each row in bikeshare data\n '''\n if integer == '':\n return None\n else:\n return int(float(integer))\n\ndef fix_data_type(city_file):\n '''Function to fix data type for each column in bikeshare file\n\n Args:\n Bikeshare city file ; str data\n Returns:\n Bikeshare city file with correct type for each data (str, int, datetime)\n '''\n for row in city_file:\n '''Fixing data type'''\n row['Start Time'] = change_timeclass(row['Start Time'])\n row['End Time'] = change_timeclass(row['End Time'])\n row['Trip Duration'] = change_intclass(row['Trip Duration'])\n\ndef get_city():\n '''Asks the user for a city and returns the filename for that city's bike share data.\n\n Args:\n none.\n Returns:\n (str) Filename for city's bikeshare data.\n '''\n city = input('\\nHello! Let\\'s explore some US bikeshare data!\\n'\n 'Would you like to see data for Chicago, New York, or Washington?\\n')\n\n city_list = ['chicago', 'new york', 'washington']\n ## Handle Invalid Raw Input\n if city.lower() not in city_list:\n print('\\nInvalid Input! Please choose Chicago, New York, or Washington.\\n')\n return get_city()\n\n return city\n\ndef get_time_period():\n '''Asks the user for a time period and returns the specified filter.\n\n Args:\n none.\n Returns:\n (str) Time Period for filtering city's bikeshare data.\n '''\n time_period = input('\\nWould you like to filter the data by month, day, or not at'\n ' all? Type \"none\" for no time filter.\\n')\n\n period_list = ['month', 'day', 'none']\n ## Handle Invalid Raw Input\n if time_period.lower() not in period_list:\n print('\\nInvalid Input! Please choose month, day, or none.\\n')\n return get_time_period()\n\n return time_period\n\ndef get_what_month():\n '''Asks the user for month name and returns the specified filter.\n\n Args:\n none.\n Returns:\n (str) Month name for filtering city's bikeshare data.\n '''\n month_filter = input('\\nWhich month? January, February, March, April, May, June.\\n')\n\n month_list = ['january', 'february', 'march', 'april', 'may', 'june']\n ## Handle Invalid Raw Input\n if month_filter.lower() not in month_list:\n print('\\nInvalid Input! Please choose a month.\\n')\n return get_what_month()\n\n return month_filter\n\ndef get_what_day():\n '''Asks the user for day name and returns the specified filter.\n\n Args:\n none.\n Returns:\n (str) Day name for filtering city's bikeshare data.\n '''\n day_filter = input('\\nWhich day?\\n')\n\n day_list = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n ## Handle Invalid Raw Input\n if day_filter.lower() not in day_list:\n print('\\nInvalid Input! Please choose a day.\\n')\n return get_what_day()\n\n return day_filter\n\ndef popular_month(city_file):\n '''Get Only Month from Start Time\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (str) Popular Month for given filter'''\n\n month_count = []\n for data in city_file:\n month_count.append(data['Start Time'].month)\n\n result = Counter(month_count).most_common()\n for month in month_list:\n if month[1] == result[0][0]:\n month_result = month[0]\n return month_result\n\ndef popular_day(city_file):\n '''Get Popular Day from Start Time\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (str) Popular Day for given filter '''\n\n day_count = []\n for data in city_file:\n day_count.append(data['Start Time'].isoweekday())\n\n result = Counter(day_count).most_common()\n for day in day_list:\n if day[1] == result[0][0]:\n day_result = day[0]\n return day_result\n\ndef popular_hour(city_file):\n '''Get Popular Hour from Start Time\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (int) Popular Hour for given filter '''\n\n hour_count = []\n for data in city_file:\n hour_count.append(data['Start Time'].hour)\n\n result = Counter(hour_count).most_common()\n return result[0][0]\n\ndef trip_duration(city_file):\n '''Get Statistic from Trip_Duration\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (int) Total Trip Duration and Average Trip Duration for given filter '''\n\n total_duration, trip_count = 0, 0\n for data in city_file:\n total_duration += data['Trip Duration']\n trip_count += 1\n\n average_duration = total_duration / trip_count\n return (total_duration, average_duration)\n\ndef popular_stations(city_file):\n '''Get Popular Start and End Station\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (str) Popular Start and End Station for given filter '''\n\n start_station, end_station = [], []\n for data in city_file:\n start_station.append(data['Start Station'])\n end_station.append(data['End Station'])\n\n start_result = Counter(start_station).most_common()\n end_result = Counter(end_station).most_common()\n\n return (start_result[0][0], end_result[0][0])\n\ndef popular_trip(city_file):\n '''Get Popular Trip\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (str) Popular Trip for given filter '''\n\n trip = []\n for data in city_file:\n trip.append((data['Start Station'], data['End Station']))\n\n trip_result = Counter(trip).most_common()\n return trip_result[0][0]\n\ndef users(city_file):\n '''Get Total Count of Each User Type\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (int) Total Count of Each User Type for given filter '''\n\n sub_count, cust_count = 0, 0\n for data in city_file:\n if data['User Type'] == 'Subscriber':\n sub_count += 1\n else:\n cust_count += 1\n\n return (sub_count, cust_count)\n\ndef gender(city_file):\n '''Get Total Count of Each Gender\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (int) Total Count of Each Gender for given filter '''\n\n male_count, female_count = 0, 0\n for data in city_file:\n if data['Gender'] == 'Male':\n male_count += 1\n elif data['Gender'] == 'Female':\n female_count += 1\n\n return (male_count, female_count)\n\ndef birth_years(city_file):\n '''Get Birth Years Statistic\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (int) Oldest User, Youngest User, Popular Birth Year for given filter '''\n\n birth_year = []\n for data in city_file:\n if type(data['Birth Year']) == int:\n birth_year.append(data['Birth Year'])\n\n year_result = Counter(birth_year).most_common()\n oldest_result = min(birth_year)\n youngest_result = max(birth_year)\n\n return (oldest_result, youngest_result, year_result[0][0])\n\ndef display_data(city_file,start_row,end_row):\n '''Displays five lines of data if the user specifies that they would like to.\n After displaying five lines, ask the user if they would like to see five more,\n continuing asking until they say stop.\n\n Args:\n (str) Rows of bikeshare data in list\n Returns:\n (str) Five rows from bikeshare data\n '''\n display = input('\\nWould you like to view individual trip data? '\n 'Type \\'yes\\' or \\'no\\'.\\n')\n\n ## Handle Invalid Raw Input\n if display.lower() == 'yes':\n print(city_file[start_row:end_row])\n start_row += 5\n end_row += 5\n display_data(city_file,start_row,end_row)\n elif display.lower() != 'yes' and display.lower() != 'no':\n print('\\nInvalid Input!')\n display_data(city_file,start_row,end_row)\n\ndef restart():\n '''Ask if the user want to restar the program or not\n\n Args:\n None\n Returns:\n None\n '''\n\n answer = input('\\nWould you like to restart? Type \\'yes\\' or \\'no\\'.\\n')\n\n ## Handle Invalid Raw Input\n if answer.lower() == 'yes':\n statistics()\n elif answer.lower() != 'yes' and answer.lower() != 'no':\n print('\\nInvalid Input\\n')\n restart()\n\ndef statistics():\n '''Calculates and prints out the descriptive statistics about a city and time period\n specified by the user via raw input.\n\n Args:\n none.\n Returns:\n none.\n '''\n # Filter by city (Chicago, New York, Washington)\n city = get_city().lower()\n\n # Open the correct CSV file base on city filter and fix the data type\n if city == 'chicago':\n city_data = read_csv(chicago)\n fix_data_type(city_data)\n for row in city_data:\n row['Birth Year'] = change_intclass(row['Birth Year'])\n\n elif city == 'new york':\n city_data = read_csv(new_york_city)\n fix_data_type(city_data)\n for row in city_data:\n row['Birth Year'] = change_intclass(row['Birth Year'])\n\n else:\n city_data = read_csv(washington)\n fix_data_type(city_data)\n\n # Filter by time period (month, day, none)\n time_period = get_time_period().lower()\n\n ## Statistic for \"none\" filter\n if time_period == 'none':\n\n print('Calculating the Statistic...\\n')\n\n start_time = time.time()\n\n # What is the most popular month for start time?\n pop_month = popular_month(city_data)\n print('\\nPopular Month is {}'.format(pop_month.title()))\n\n # What is the most popular day of week (Monday, Tuesday, etc.) for start time?\n pop_day = popular_day(city_data)\n print('\\nPopular Day is {}'.format(pop_day.title()))\n\n # What is the most popular hour of day for start time?\n pop_hour = popular_hour(city_data)\n print('\\nPopular Hour is {}'.format(pop_hour))\n\n # What is the total trip duration and average trip duration?\n trip_result = trip_duration(city_data)\n print('\\nTotal Trip Duration: {}'\n '\\nAverage Trip Duration: {}'.format(trip_result[0],trip_result[1]))\n\n # What is the most popular start station and most popular end station?\n station_result = popular_stations(city_data)\n print('\\nPopular Start Station: {}'\n '\\nPopular End Station: {}'.format(station_result[0],station_result[1]))\n\n # What is the most popular trip?\n most_trip = popular_trip(city_data)\n print('\\nPopular Trip is {}'.format(most_trip))\n\n # What are the counts of each user type?\n user_result = users(city_data)\n print('\\nSubscriber: {}'\n '\\nCustomer: {}'.format(user_result[0], user_result[1]))\n\n if city == 'chicago' or city == 'new york':\n\n # What are the counts of gender?\n gender_result = gender(city_data)\n print('\\nMale: {}'\n '\\nFemale: {}'.format(gender_result[0], gender_result[1]))\n\n # What are the earliest (i.e. oldest user), most recent (i.e. youngest user), and\n # most popular birth years?\n birthyear_result = birth_years(city_data)\n print('\\nOldest User: {}'\n '\\nYoungest User: {}'\n '\\nPopular Birth Year: {}'.format(birthyear_result[0], birthyear_result[1], birthyear_result[2]))\n\n print(\"\\nThat took %s seconds.\" % (time.time() - start_time))\n\n # Display five lines of data at a time if user specifies that they would like to\n start_row, end_row = 0, 4\n display_data(city_data,start_row,end_row)\n\n ## Statistic for \"month\" filter\n if time_period == 'month':\n\n # Filter by what month?\n month_filter = get_what_month().lower()\n\n # List of row based on selected month filter\n selected_data = []\n for month in month_list:\n if month[0] == month_filter:\n month_index = month[1]\n for data in city_data:\n if data['Start Time'].month == month_index:\n selected_data.append(data)\n\n print('\\nCalculating the Statistic...')\n\n start_time = time.time()\n\n # What is the most popular day of week (Monday, Tuesday, etc.) for start time?\n pop_day = popular_day(selected_data)\n print('\\nPopular Day is {}'.format(pop_day.title()))\n\n # What is the most popular hour of day for start time?\n pop_hour = popular_hour(selected_data)\n print('\\nPopular Hour is {}'.format(pop_hour))\n\n # What is the total trip duration and average trip duration?\n trip_result = trip_duration(selected_data)\n print('\\nTotal Trip Duration: {}'\n '\\nAverage Trip Duration: {}'.format(trip_result[0],trip_result[1]))\n\n # What is the most popular start station and most popular end station?\n station_result = popular_stations(selected_data)\n print('\\nPopular Start Station: {}'\n '\\nPopular End Station: {}'.format(station_result[0],station_result[1]))\n\n # What is the most popular trip?\n most_trip = popular_trip(selected_data)\n print('\\nPopular Trip is {}'.format(most_trip))\n\n # What are the counts of each user type?\n user_result = users(selected_data)\n print('\\nSubscriber: {}'\n '\\nCustomer: {}'.format(user_result[0], user_result[1]))\n\n if city == 'chicago' or city == 'new york':\n\n # What are the counts of gender?\n gender_result = gender(selected_data)\n print('\\nMale: {}'\n '\\nFemale: {}'.format(gender_result[0], gender_result[1]))\n\n # What are the earliest (i.e. oldest user), most recent (i.e. youngest user), and\n # most popular birth years?\n birthyear_result = birth_years(selected_data)\n print('\\nOldest User: {}'\n '\\nYoungest User: {}'\n '\\nPopular Birth Year: {}'.format(birthyear_result[0], birthyear_result[1], birthyear_result[2]))\n\n print(\"\\nThat took %s seconds.\" % (time.time() - start_time))\n\n # Display five lines of data at a time if user specifies that they would like to\n start_row, end_row = 0, 4\n display_data(selected_data,start_row,end_row)\n\n ## Statistic for \"day\" filter\n if time_period == 'day':\n\n # Filter by what day?\n day_filter = get_what_day().lower()\n\n # List of row based on selected day filter\n selected_data = []\n for day in day_list:\n if day[0] == day_filter:\n day_index = day[1]\n for data in city_data:\n if data['Start Time'].isoweekday() == day_index:\n selected_data.append(data)\n\n print('\\nCalculating the Statistic...')\n\n start_time = time.time()\n\n # What is the most popular hour of day for start time?\n pop_hour = popular_hour(selected_data)\n print('\\nPopular Hour is {}'.format(pop_hour))\n\n # What is the total trip duration and average trip duration?\n trip_result = trip_duration(selected_data)\n print('\\nTotal Trip Duration: {}'\n '\\nAverage Trip Duration: {}'.format(trip_result[0],trip_result[1]))\n\n # What is the most popular start station and most popular end station?\n station_result = popular_stations(selected_data)\n print('\\nPopular Start Station: {}'\n '\\nPopular End Station: {}'.format(station_result[0],station_result[1]))\n\n # What is the most popular trip?\n most_trip = popular_trip(selected_data)\n print('\\nPopular Trip is {}'.format(most_trip))\n\n # What are the counts of each user type?\n user_result = users(selected_data)\n print('\\nSubscriber: {}'\n '\\nCustomer: {}'.format(user_result[0], user_result[1]))\n\n if city == 'chicago' or city == 'new york':\n\n # What are the counts of gender?\n gender_result = gender(selected_data)\n print('\\nMale: {}'\n '\\nFemale: {}'.format(gender_result[0], gender_result[1]))\n\n # What are the earliest (i.e. oldest user), most recent (i.e. youngest user), and\n # most popular birth years?\n birthyear_result = birth_years(selected_data)\n print('\\nOldest User: {}'\n '\\nYoungest User: {}'\n '\\nPopular Birth Year: {}'.format(birthyear_result[0], birthyear_result[1], birthyear_result[2]))\n\n print(\"\\nThat took %s seconds.\" % (time.time() - start_time))\n\n # Display five lines of data at a time if user specifies that they would like to\n start_row, end_row = 0, 4\n display_data(selected_data,start_row,end_row)\n\n # Restart?\n restart()\n\nif __name__ == \"__main__\":\n\tstatistics()\n", "repo_name": "henhal12/udacity-data-analysis-nanodegree-term-one", "sub_path": "project2-bikeshare/bikeshare.py", "file_name": "bikeshare.py", "file_ext": "py", "file_size_in_byte": 18106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "unicodecsv.DictReader", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 152, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 170, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 188, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 220, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 221, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 237, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 287, "usage_type": "call"}, {"api_name": "time.time", "line_number": 371, "usage_type": "call"}, {"api_name": "time.time", "line_number": 418, "usage_type": "call"}, {"api_name": "time.time", "line_number": 441, "usage_type": "call"}, {"api_name": "time.time", "line_number": 484, "usage_type": "call"}, {"api_name": "time.time", "line_number": 507, "usage_type": "call"}, {"api_name": "time.time", "line_number": 546, "usage_type": "call"}]} +{"seq_id": "44298672989", "text": "import os, time\nimport argparse\nimport json, jsonlines\nfrom tqdm import tqdm\nimport logging\n\n# from transformers import AutoTokenizer, AutoModelForCausalLM\n# import transformers\nimport torch\nfrom vllm import LLM, SamplingParams\n\nfrom data import load_dataset\nfrom build_prompt import build_demo, build_prompt\n\n\n# Configure the logger\nlogging.basicConfig(\n level=logging.INFO, \n format='%(asctime)s - %(name)s:%(lineno)s - %(levelname)s - %(message)s',\n # filename='app.log', # Uncomment this if you want to log to a file\n # filemode='w', # Overwrites the log file every time\n) \n\nlogger = logging.getLogger(__name__)\n\n\ndef main(args):\n\n # Load Data\n dataset_name = args.input_path.split(\"/\")[-2]\n input_data = load_dataset(args.input_path, dataset_name=dataset_name)\n\n if args.debug:\n logger.info(\"Debug mode. Only process the first two examples. \")\n input_data = input_data[:2]\n else:\n if args.max_samples > 0:\n logger.info(f'\"--max_samples\" is set. Only process the first {args.max_samples} examples. ')\n input_data = input_data[:args.max_samples]\n else:\n logger.info(f\"Process all {len(input_data)} examples. \")\n \n sampling_params = SamplingParams(\n temperature=0.0,\n top_p=1.0,\n max_tokens=args.max_tokens,\n stop=\"Problem No.{}\".format(len(args.demo_indices)+2),\n # n=5,\n # use_beam_search=True,\n )\n llm = LLM(model=args.model_name_or_path, tensor_parallel_size=args.num_gpus, max_num_batched_tokens=args.max_tokens_total)\n \n # \n output_path = args.output_path\n \n output_path = output_path.rstrip(\".json\")\n output_path = output_path + \".demo-{}.json\".format(\"_\".join([str(_) for _ in args.demo_indices]))\n\n all_responses = []\n if os.path.exists(output_path) and not args.debug and not args.overwrite_output:\n with open(output_path, \"r\") as f:\n all_responses = [json.loads(line) for line in f.readlines() if line.strip()]\n logger.info(f\"Continue from {len(all_responses)}-th example.\")\n logger.info(f\"Number of examples to be processed: {len(input_data)-len(all_responses)}\")\n else:\n logger.info(f\"Start from the beginning.\")\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\n \n prompt_demo = build_demo(\n demo_indices=args.demo_indices,\n demonstration_path=args.demonstration_path,\n dataset_name=dataset_name,\n mode=args.mode,\n )\n\n if args.mode == \"cot\":\n system_message = \"You are a helpful, pattern-following assistant that helps people solve problems. \"\n elif args.mode == \"prolog\":\n system_message = \"You are a helpful, pattern-following assistant that helps people solve problems using Prolog. \"\n \n prompt_list = []\n for example in tqdm(input_data[len(all_responses):], total=len(input_data[len(all_responses):])):\n prompt = build_prompt(\n example=example,\n prompt_demo=prompt_demo,\n dataset_name=dataset_name,\n mode=args.mode,\n instruct=args.instruct,\n )\n # import pdb; pdb.set_trace()\n # prompt = f\"[INST] <>\\\\n{system_message}\\\\n<>\\\\n\\\\n{prompt}[/INST]\"\n prompt_list.append(prompt)\n\n if args.debug:\n # for i, _ in enumerate(all_responses):\n # logger.info(f\"****** Input-{i+1} ****** \\n\")\n # print(json.dumps(input_data[i]))\n # logger.info(f\"***** Response-{i+1} ***** \\n\")\n # print(json.dumps(_))\n pass\n else:\n\n outputs = llm.generate(prompt_list, sampling_params)\n # import pdb; pdb.set_trace()\n\n # Print the outputs.\n # for output in outputs:\n # prompt = output.prompt\n # generated_text = output.outputs[0].text\n # print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\n all_responses = [\n {\n \"prompt\": output.prompt,\n \"response\": [_.text for _ in output.outputs]\n }\n for output in outputs\n ]\n\n with open(output_path, \"w\") as f:\n f.writelines([json.dumps(_)+\"\\n\" for _ in all_responses])\n logger.info(\"*\"*20)\n logger.info(f\"Finished. Output saved to {output_path}. \")\n logger.info(\"*\"*20)\n \n \nif __name__ == \"__main__\":\n argparser = argparse.ArgumentParser()\n argparser.add_argument('--input_path', type=str, required=True)\n argparser.add_argument('--demonstration_path', type=str, required=True)\n argparser.add_argument('--output_path', type=str, required=True)\n argparser.add_argument('--model_name_or_path', type=str, required=True)\n argparser.add_argument('--debug', action='store_true')\n argparser.add_argument('--demo_indices', nargs='+', type=int, required=True)\n argparser.add_argument('--max_tokens_total', type=int, default=16384, help=\"Max number of total tokens (prompt + generated). Default is Code-LLaMA max-len = 16384 \")\n argparser.add_argument('--max_tokens', type=int, default=2048, help=\"Max number of generated tokens. Default is 2048. \")\n argparser.add_argument('--num_gpus', type=int, default=1, help=\"Number of GPUs to use. \")\n argparser.add_argument('--self_debug', action='store_true', help=\"Adopt self-debugging mode, which execute the GPT's response and prompt GPT with error messages if the execution fails. \")\n argparser.add_argument('--self_debug_limit', type=int, default=3, help=\"The maximum number of self-debugging trials. Default is 3. \")\n argparser.add_argument('--max_samples', type=int, default=-1, help=\"The maximum number of samples to be processed. Default is -1. \")\n argparser.add_argument('--mode', type=str, required=True, choices=['cot', 'prolog', 'direct'])\n argparser.add_argument('--overwrite_output', action='store_true', help=\"Whether to overwrite the output file, or to read the output file and continue from the break point. \")\n argparser.add_argument('--instruct', action='store_true', help=\"Whether to use instruction-style prompt. If False, use ICL-style prompt. \")\n args = argparser.parse_args()\n\n if args.debug:\n logger.info(\"Debug mode. \")\n main(args)", "repo_name": "DAMO-NLP-SG/CaRing", "sub_path": "src/run_generation.py", "file_name": "run_generation.py", "file_ext": "py", "file_size_in_byte": 6246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.basicConfig", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "data.load_dataset", "line_number": 31, "usage_type": "call"}, {"api_name": "vllm.SamplingParams", "line_number": 43, "usage_type": "call"}, {"api_name": "vllm.LLM", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 62, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "build_prompt.build_demo", "line_number": 69, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 82, "usage_type": "call"}, {"api_name": "build_prompt.build_prompt", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 120, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "2157695047", "text": "# Import libraries\nimport numpy as np # Numeric and matrix computation\nimport pandas as pd # Optional: good package for manipulating data\nimport sklearn as sk # Package with learning algorithms implemented\n\n\ndef test_profe():\n url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/ionosphere.data\"\n df = pd.read_csv(url,header =None)\n print(type(df))\n print(df.head())\n\n # No preprocessing needed. Numerical and scaled data\n # Separate data from labels\n\n y=df[34].values\n # print(y)\n X=df.values[:,0:34]\n\n from sklearn.model_selection import cross_val_score\n #from sklearn.linear_model import LogisticRegression\n from sklearn.naive_bayes import GaussianNB\n from sklearn.ensemble import VotingClassifier\n from sklearn.tree import DecisionTreeClassifier\n from sklearn.neighbors import KNeighborsClassifier\n from sklearn.model_selection import GridSearchCV\n\n cv=50\n\n clf1 = GaussianNB()\n\n params = {'n_neighbors':list(range(1,30,2)), 'weights':('distance','uniform')}\n knc = KNeighborsClassifier()\n clf = GridSearchCV(knc, param_grid=params,cv=cv,n_jobs=-1) # If cv is integer, by default is Stratifyed\n clf.fit(X, y)\n print(\"Best Params fo Knn=\",clf.best_params_, \"Accuracy=\", clf.best_score_)\n parval=clf.best_params_\n clf2 = KNeighborsClassifier(n_neighbors=parval['n_neighbors'],weights=parval['weights'])\n\n clf3 = DecisionTreeClassifier(criterion='entropy')\n\n\n for clf, label in zip([clf1, clf2, clf3], ['Naive Bayes','Knn (3)', 'Dec. Tree', ]):\n scores = cross_val_score(clf, X, y, cv=cv, scoring='accuracy')\n print(\"Accuracy: %0.3f [%s]\" % (scores.mean(), label))\n\n\ndef test_naive():\n path = '../dataset_diabetes/diabetic_data_output.csv'\n df = pd.read_csv(path)\n #print (df.corr())\n def get_redundant_pairs(df):\n '''Get diagonal and lower triangular pairs of correlation matrix'''\n pairs_to_drop = set()\n cols = df.columns\n for i in range(0, df.shape[1]):\n for j in range(0, i + 1):\n pairs_to_drop.add((cols[i], cols[j]))\n return pairs_to_drop\n\n def get_top_abs_correlations(df, n=5):\n au_corr = df.corr().abs().unstack()\n labels_to_drop = get_redundant_pairs(df)\n au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)\n return au_corr[0:n]\n\n print(\"Top Absolute Correlations\")\n print(get_top_abs_correlations(df, 10))\n #print (df.values[:,[1,6,7,8,9]])\n # insulin\n # admission_type_id, discharge_disposition_id\n # admission_source_id\n # time_in_hospital\n # print(df[\"insulin\"].values)\n #\n # print(df.values[:,0:34])\n # print(type(df.values))\n # print(type(df.columns.values.tolist().index(\"insulin\")))\n # print(df.columns.values.tolist().index(\"insulin\"))\n # print(type(np.where(df.columns.values == \"insulin\")))\n # print(np.where(df.columns.values == \"insulin\"))\n\nif __name__ == \"__main__\":\n test_naive()", "repo_name": "paulovick/MD-Projecte2-diabetes", "sub_path": "test/naive_bayes.py", "file_name": "naive_bayes.py", "file_ext": "py", "file_size_in_byte": 3006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "30643267450", "text": "from Configuration import Configuration\r\nfrom predictors import Predictor\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Activation\r\nfrom keras.layers.recurrent import LSTM\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom Service.Utilities import Tools\r\nimport numpy\r\nimport pandas\r\nimport traceback\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nimport test\r\n#https://www.datacamp.com/community/tutorials/deep-learning-python#predict\r\n# pip install tensorflow keras\r\n\r\nclass dlPredictor(Predictor.Predictor):\r\n \r\n def __init__(self, dataManager,hist=-1,context=None):\r\n super().__init__(\"dl\",dataManager,hist)\r\n self.model = None\r\n self.features = 7\r\n \r\n self.scaler = MinMaxScaler(feature_range=(-1, 1))\r\n \r\n \r\n @staticmethod\r\n def timeseries_to_supervised(data, lag=1):\r\n df = pandas.DataFrame(data)\r\n columns = [df.shift(i) for i in range(1, lag+1)]\r\n columns.append(df)\r\n df = pandas.concat(columns, axis=1)\r\n df.fillna(0, inplace=True)\r\n return df\r\n \r\n @staticmethod\r\n def difference(dataset, interval=1):\r\n diff = list()\r\n for i in range(interval, len(dataset)):\r\n value = dataset[i] - dataset[i - interval]\r\n diff.append(value)\r\n return pandas.Series(diff)\r\n \r\n @staticmethod\r\n def inverse_difference(history, yhat, interval=1):\r\n return yhat + history[-interval]\r\n \r\n def scale(self,train, test):\r\n \r\n self.scaler = self.scaler.fit(train)\r\n \r\n train = train.reshape(train.shape[0], train.shape[1])\r\n train_scaled = self.scaler.transform(train)\r\n \r\n test = test.reshape(test.shape[0], test.shape[1])\r\n test_scaled = self.scaler.transform(test)\r\n return train_scaled, test_scaled\r\n \r\n def invert_scale(self,X, value):\r\n new_row = [x for x in X] + [value]\r\n array = numpy.array(new_row)\r\n array = array.reshape(1, len(array))\r\n inverted = self.scaler.inverse_transform(array)\r\n return inverted[0, -1]\r\n \r\n def fit_lstm(self,train, batch_size, nb_epoch, neurons):\r\n X, y = train[:, 0:-1], train[:, -1]\r\n X = X.reshape(X.shape[0], 1, X.shape[1])\r\n if self.model is None:\r\n \r\n self.model = Sequential()\r\n self.model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))\r\n self.model.add(Dense(1))\r\n self.model.compile(loss='mean_squared_error', optimizer='adam')\r\n \r\n for i in range(nb_epoch):\r\n self.model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)\r\n self.model.reset_states()\r\n \r\n def forecast_lstm(self,batch_size, X):\r\n X = X.reshape(1, 1, len(X))\r\n yhat = self.model.predict(X, batch_size=batch_size)\r\n return yhat[0,0]\r\n \r\n def runAll(self):\r\n self.preprocess()\r\n \r\n def predict(self,sticker,timestamp,context):\r\n highhist = context[0]\r\n lowhist = context[1]\r\n closehist = context[2]\r\n volumehist = context[3]\r\n openhist = context[4]\r\n \r\n raw_values = closehist\r\n diff_values = dlPredictor.difference(raw_values, 1)\r\n supervised = dlPredictor.timeseries_to_supervised(diff_values, 1)\r\n supervised_values = supervised.values\r\n train, test = supervised_values[0:-1], supervised_values[-1:]\r\n train_scaled, test_scaled = self.scale(train, test)\r\n self.fit_lstm(train_scaled, 1, 50, 5)\r\n \r\n train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)\r\n self.model.predict(train_reshaped, batch_size=1)\r\n \r\n X, y = test_scaled[0, 0:-1], test_scaled[0, -1]\r\n yhat = self.forecast_lstm(1, X)\r\n \r\n yhat = self.invert_scale(X, yhat)\r\n \r\n yhat = self.inverse_difference(raw_values, yhat, len(test_scaled)+1)\r\n \r\n prediction = int(numpy.sign(yhat))\r\n \r\n confidence=1.0\r\n skip = False # if True, then not confident\r\n \r\n del X\r\n del y\r\n del diff_values\r\n del train\r\n del test\r\n del train_scaled\r\n del test_scaled\r\n \r\n del highhist\r\n del lowhist\r\n del closehist\r\n del volumehist\r\n del openhist\r\n \r\n \r\n return (prediction,confidence,skip)", "repo_name": "binun/predict", "sub_path": "predictors/dlPredictor.py", "file_name": "dlPredictor.py", "file_ext": "py", "file_size_in_byte": 4496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "predictors.Predictor.Predictor", "line_number": 16, "usage_type": "attribute"}, {"api_name": "predictors.Predictor", "line_number": 16, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 41, "usage_type": "call"}, {"api_name": "test.reshape", "line_number": 54, "usage_type": "call"}, {"api_name": "test.shape", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.recurrent.LSTM", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "11953601828", "text": "import time\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nISOTIMEFORMAT='%Y%m%d'\ndef sentemail():\n caodate=str(time.strftime(ISOTIMEFORMAT, time.localtime()))\n host = 'smtp.163.com'\n # 设置发件服务器地址\n port = 465\n # 设置发件服务器端口号。注意，这里有SSL和非SSL两种形式\n sender = 'zq476668643@163.com'\n # 设置发件邮箱，一定要自己注册的邮箱\n pwd = 'zq242333'\n # 设置发件邮箱的密码，163邮箱的授权码，等会登陆会用到\n receiver0 = '476668643@qq.com'\n # 设置邮件接收人，可以是扣扣邮箱\n receiver1 = '2652842878@qq.com'\n body = '

'+caodate+'

zhongfs

'\n # 设置邮件正文，这里是支持HTML的\n msg = MIMEText(body, 'html')\n # 设置正文为符合邮件格式的HTML内容\n message = MIMEMultipart()\n message['subject'] = caodate+'下载附件通知'\n # 设置邮件标题\n message['from'] = sender\n # 设置发送人\n message['to'] = receiver0\n # 设置接收人\n message.attach(msg)\n # filename='xfurlwett-'+caodate+'.txt'\n filename = '1.txt'\n # 构造附件1，传送当前目录下的 filename 文件\n att1 = MIMEText(open(filename, 'rb').read(), 'base64', 'utf-8')\n att1[\"Content-Type\"] = 'application/octet-stream'\n # 这里的filename可以任意写，写什么名字，邮件中显示什么名字\n att1[\"Content-Disposition\"] = 'attachment; filename=\"'+filename+'\"'\n message.attach(att1)\n try:\n s = smtplib.SMTP_SSL(host, port) # 注意！如果是使用SSL端口，这里就要改为SMTP_SSL\n s.login(sender, pwd) # 登陆邮箱\n s.sendmail(sender, receiver0, message.as_string())# 发送邮件！\n #s.sendmail(sender, receiver1, msg.as_string())\n print ('Done.sent email success')\n except smtplib.SMTPException:\n print ('Error.sent email fail')\nif __name__ == '__main__':\n sentemail()", "repo_name": "github3332422/case", "sub_path": "email_send/mail_163_annex.py", "file_name": "mail_163_annex.py", "file_ext": "py", "file_size_in_byte": 1997, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "time.strftime", "line_number": 7, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 7, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 21, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 23, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 34, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 40, "usage_type": "call"}, {"api_name": "smtplib.SMTPException", "line_number": 45, "usage_type": "attribute"}]} +{"seq_id": "21378934204", "text": "import asyncio\nfrom asyncio.events import AbstractEventLoop\nfrom signal import SIGINT, SIGTERM\n\nfrom app.configs.log import logger\n\n\ndef handler(sig):\n \"\"\"\n signal 처리를 위한 handler 함수\n :param sig: 프로세스에서 받은 시그널\n :return: None\n \"\"\"\n logger.info(f\"[sig handelr] recv signal : {sig}\")\n loop = asyncio.get_running_loop()\n\n for task in asyncio.all_tasks(loop=loop):\n task.cancel()\n\n logger.info(f\"[sig handelr] all tasks canceled\")\n\n loop.remove_signal_handler(SIGTERM)\n loop.add_signal_handler(SIGINT, lambda: None)\n\n\ndef set_signal(loop: AbstractEventLoop):\n \"\"\"\n 현재 동작중인 eventloop에 signal 처리 등록\n :param loop:\n :return:\n \"\"\"\n for sig in [SIGTERM, SIGINT]:\n loop.add_signal_handler(sig, handler, sig)\n", "repo_name": "f-lab-edu/ComMoni", "sub_path": "agent/app/sig/setting.py", "file_name": "setting.py", "file_ext": "py", "file_size_in_byte": 818, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "app.configs.log.logger.info", "line_number": 14, "usage_type": "call"}, {"api_name": "app.configs.log.logger", "line_number": 14, "usage_type": "name"}, {"api_name": "asyncio.get_running_loop", "line_number": 15, "usage_type": "call"}, {"api_name": "asyncio.all_tasks", "line_number": 17, "usage_type": "call"}, {"api_name": "app.configs.log.logger.info", "line_number": 20, "usage_type": "call"}, {"api_name": "app.configs.log.logger", "line_number": 20, "usage_type": "name"}, {"api_name": "signal.SIGTERM", "line_number": 22, "usage_type": "argument"}, {"api_name": "signal.SIGINT", "line_number": 23, "usage_type": "argument"}, {"api_name": "asyncio.events.AbstractEventLoop", "line_number": 26, "usage_type": "name"}, {"api_name": "signal.SIGTERM", "line_number": 32, "usage_type": "name"}, {"api_name": "signal.SIGINT", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "25496804941", "text": "import redis\n#连接\nr = redis.StrictRedis(host=\"crawler-platform02-redis-service.int.yidian-inc.com\",port=6379)\n\n#方法1：根据数据类型的不同，调用响应的方法\n#设置新增\nr.set(\"p1\",\"good\")\n#取值\nprint(r.get(\"p1\"))\n\n#方法2：pipline\n#缓冲多条命令，然后一次执行，减少服务器--客户端的TCP数据包\n# pipe = r.pipeline()\n# pipe.set(\"p2\",\"nice\")\n# pipe.set(\"p3\",\"cool\")\n# #保存至redis\n# pipe.execute()\n\nclass SunwenboRedis():\n def __init__(self,host=\"10.138.11.201\",port=6379):\n self.__redis = redis.StrictRedis(host=host,port=port,db=2)\n def set(self,key,value):\n self.__redis.set(key,value)\n def get(self,key):\n if self.__redis.exists(key):\n return self.__redis.get(key)\n else:\n return \"\"\n def delete(self,key):\n self.__redis.delete(key)\n def all(self):\n self.__redis.keys()\n return self.__redis.keys()\n\nabc = SunwenboRedis()\nabc.set(\"bbb\",\"222\")\nabc.set(\"abc\",\"222\")\nprint(abc.get(\"bbb\"))\n\nabc.delete(\"bbb\")\nprint(\"#########\")\n\nprint(abc.get(\"bbb\"))\nprint(abc.all())", "repo_name": "sunwenbo/python", "sub_path": "10.MongoDB 和Redis/4.Redis与python交互.py", "file_name": "4.Redis与python交互.py", "file_ext": "py", "file_size_in_byte": 1100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "redis.StrictRedis", "line_number": 3, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "20044702512", "text": "from pathlib import Path\n\nimport bisect\nfrom PIL import Image\nfrom tqdm import tqdm\nimport pandas as pd\nimport imageio\n\nimport torch\nimport torchvision.transforms as tfms\nfrom torch.utils.data import Dataset, ConcatDataset, Subset\n\nfrom utils import logger\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nDATA_BASE = Path('./data').resolve()\n## doyun' dev computer\n#MGH_DATA_BASE = DATA_BASE.joinpath('cxr/mgh/covid19_v4').resolve()\n#MGH_DATA_BASE = DATA_BASE.joinpath('cxr/mgh/v4').resolve()\n#MGH_DATA_BASE = DATA_BASE.joinpath('cxr/mgh/v4_crop').resolve()\n#LOF_DATA_BASE = DATA_BASE.joinpath('cxr/mgh/outlier_cat329').resolve()\n#ATLAS_DATA_BASE = DATA_BASE.joinpath('cxr/mgh/v4_crop').resolve()\n## LMIC devbox computer\n#MGH_DATA_BASE = DATA_BASE.joinpath('CheXpert-v1.0/external_data').resolve()\n#MGH_DATA_BASE = DATA_BASE.joinpath('NIH/external_data_pa').resolve()\n#MGH_DATA_BASE = DATA_BASE.joinpath('MIMIC_v1/external_data_pa').resolve()\n#MGH_DATA_BASE = DATA_BASE.joinpath('covid19_v4').resolve()\n#MGH_DATA_BASE = DATA_BASE.joinpath('v4').resolve()\nMGH_DATA_BASE = DATA_BASE.joinpath('v4_crop').resolve()\nLOF_DATA_BASE = DATA_BASE.joinpath('outlier').resolve()\nATLAS_DATA_BASE = DATA_BASE.joinpath('v4_crop').resolve()\n\nlabel_name = ['Bone>Fracture>.', 'Bone>Non-fracture>.', 'Diaphragm>Diaphragm>.',\n 'Foreign body>.>.', 'Hilar/mediastinum>Aorta>.',\n 'Hilar/mediastinum>Cardiomegaly>.', 'Hilar/mediastinum>Hilar area>.',\n 'Hilar/mediastinum>Mediastinum>.',\n 'Lung density>Decreased density (Lucency)>Cavity/Cyst',\n 'Lung density>Decreased density (Lucency)>Emphysema',\n 'Lung density>Increased lung density>Atelectasis',\n 'Lung density>Increased lung density>Nodule/mass',\n 'Lung density>Increased lung density>Other interstitial opacity',\n 'Lung density>Increased lung density>Pulmonary edema',\n 'Lung density>Increased lung density>pneumonia',\n 'Lung volume>Decreased lung volume>.',\n 'Lung volume>Increased lung volume>.', 'Pleura>Other pleural lesions>.',\n 'Pleura>Pleural effusion>.', 'Pleura>Pneumothorax>.']\n\nfolder_name = ['b_f', 'b_nf', 'd_d', 'fb', 'hm_a',\n 'hm_c', 'hm_ha', 'hm_m', 'ld_dd_cc', 'ld_dd_e',\n 'ld_ild_a', 'ld_ild_nm', 'ld_ild_oio', 'ld_ild_pe', 'ld_ild_p',\n 'lv_dlv', 'lv_ilv', 'p_opl', 'p_pe', 'p_p']\n\nClean_Neg = False\nClean_Neg_list = [('Bone>Fracture>.','clean_negative3.2.1_Bone_Bone_Fracture_BLANK.csv'),\n ('Bone>Non-fracture>.','clean_negative3.2.1_Bone_Bone_Non-fracture_BLANK.csv'),\n ('Diaphragm>Diaphragm>.','clean_negative3.2.1_Below diaphragm _Diaphragm_Diaphragm_BLANK.csv'),\n ('Foreign body>.>.','clean_negative3.2.1_Whole CXR_Foreign body_BLANK_BLANK.csv'),\n ('Hilar/mediastinum>Aorta>.','clean_negative3.2.1_Hilar mediastinum_Hilar mediastinum_Aorta_BLANK.csv'),\n ('Hilar/mediastinum>Cardiomegaly>.','clean_negative3.2.1_Hilar mediastinum_Hilar mediastinum_Cardiomegaly_BLANK.csv'),\n ('Hilar/mediastinum>Hilar area>.','clean_negative3.2.1_Hilar mediastinum_Hilar mediastinum_Hilar area_BLANK.csv'),\n ('Hilar/mediastinum>Mediastinum>.','clean_negative3.2.1_Hilar mediastinum_Hilar mediastinum_Mediastinum_BLANK.csv'),\n ('Lung density>Decreased density (Lucency)>Cavity/Cyst','clean_negative3.2.1_Lung_Lung density_Decreased density (Lucency)_Cavity Cyst.csv'),\n ('Lung density>Decreased density (Lucency)>Emphysema','clean_negative3.2.1_Lung_Lung density_Decreased density (Lucency)_Emphysema.csv'),\n ('Lung density>Increased lung density>Atelectasis','clean_negative3.2.1_Lung_Lung density_Increased lung density_Atelectasis.csv'),\n ('Lung density>Increased lung density>Nodule/mass','clean_negative3.2.1_Lung_Lung density_Increased lung density_Nodule mass.csv'),\n ('Lung density>Increased lung density>Other interstitial opacity','clean_negative3.2.1_Lung_Lung density_Increased lung density_Other interstitial opacity.csv'),\n ('Lung density>Increased lung density>Pulmonary edema','clean_negative3.2.1_Lung_Lung density_Increased lung density_Pulmonary edema.csv'),\n ('Lung density>Increased lung density>pneumonia','clean_negative3.2.1_Lung_Lung density_Increased lung density_pneumonia.csv'),\n ('Lung volume>Decreased lung volume>.','clean_negative3.2.1_Lung_Lung volume_Decreased lung volume_BLANK.csv'),\n ('Lung volume>Increased lung volume>.','clean_negative3.2.1_Lung_Lung volume_Increased lung volume_BLANK.csv'),\n ('Pleura>Other pleural lesions>.','clean_negative3.2.1_Pleura_Pleura_Other pleural lesions_BLANK.csv'),\n ('Pleura>Pleural effusion>.','clean_negative3.2.1_Pleura_Pleura_Pleural effusion_BLANK.csv'),\n ('Pleura>Pneumothorax>.','clean_negative3.2.1_Pleura_Pleura_Pneumothorax_BLANK.csv')]\n\ndef _tb_load_manifest(file_path, num_labels=31, name_labels=None, name_paths=None, mode='single', ext_data=False, fl_balance=False, r_seed=-1):\n if not file_path.exists():\n logger.error(f\"manifest file {file_path} not found.\")\n raise RuntimeError\n\n logger.debug(f\"loading dataset manifest {file_path} ...\")\n df = pd.read_csv(str(file_path)).fillna(0)\n\n if (not ext_data) and (True): # using the clean-set\n # cleanset\n if True:\n ## MGH validation set\n df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)]\n if r_seed != -1:\n df = df.sample(n=1000, replace=True, random_state=r_seed)\n\n if (False):\n df = df.loc[(df['Hilar/mediastinum>Cardiomegaly>.']==1) \n | (df['Lung density>Increased lung density>Atelectasis'] == 1)\n | (df['Lung density>Increased lung density>Pulmonary edema'] == 1)\n | (df['Lung density>Increased lung density>pneumonia'] == 1)\n | (df['Pleura>Pleural effusion>.'] == 1)]\n df.reset_index(drop=True, inplace=True)\n\n ## MGH testset\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][0:250]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][250:500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][500:750]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][750:]\n \n ## CheXpert trainset\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][0:250]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][250:500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][500:750]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][750:1000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1000:1250]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1250:1500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1500:1750]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1750:2000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2000:2250]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2250:2500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2500:2750]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2750:3000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][3000:3250]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][3250:3500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][3500:3750]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][3750:4000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][4000:4250]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][4250:4500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][4500:]\n\n ## NIH trainset\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][0:500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][500:1000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1000:1500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1500:2000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2000:2500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2500:3000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][3000:3500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][3500:4000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][4000:]\n\n ## MIMIC trainset\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][0:500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][500:1000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1000:1500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][1500:2000]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2000:2500]\n #df = df.loc[(df['bad_age'] == 0) & (df['bad_quality'] == 0)][2500:]\n\n if (Clean_Neg):\n #hilar area special care\n for cl_feature, cl_file in Clean_Neg_list:\n df_case = pd.read_csv(MGH_DATA_BASE.joinpath('clean_nagative_data_v5_deblank/'+cl_file), names=['ACC'])\n #df_case = pd.read_csv(MGH_DATA_BASE.joinpath('clean_nagative_data_v5/'+cl_file))\n #df_case = pd.read_csv(MGH_DATA_BASE.joinpath('clean_negative_data_v5_321/'+cl_file), names=['ACC'])\n df[f'{cl_feature}'] = df[f'{cl_feature}'].replace(0, -2)\n #df.loc[df.AccessionNumber.isin(df_case.ACC), f'{cl_feature}'] = 0\n df.loc[(df.AccessionNumber.isin(df_case.ACC))&(df[f'{cl_feature}']==-2), f'{cl_feature}'] = 0\n\n if (fl_balance):\n for k, feature in enumerate(label_name):\n num_p = df.loc[(df[f'{feature}'] == 1)].shape[0]\n num_n = df.loc[(df[f'{feature}'] == 0)].shape[0]\n ratio_pn = num_p / num_n\n ratio_th = 5\n if (ratio_pn < (1.0/ratio_th)):\n df[f'{feature}'] = df[f'{feature}'].replace(0, -1)\n df_n = df.loc[(df[f'{feature}'] == -1)].sample(n=(num_p*ratio_th), random_state=2020)\n df[f'{feature}'].loc[df['AccessionNumber'].isin(df_n['AccessionNumber'])] = 0\n\n pos = df[f'{feature}'].loc[df[f'{feature}']==1].shape[0]\n neg = df[f'{feature}'].loc[df[f'{feature}']==0].shape[0]\n dontcare = df[f'{feature}'].loc[df[f'{feature}']==-1].shape[0]\n\n logger.info(f'[{k:02d}-{feature}] pos: {pos}, neg: {neg}, dont-care: {dontcare}')\n\n if name_labels == None:\n df = df[~(df.iloc[:, -(num_labels+1):-1] == -1).all(1)]\n else:\n df = df[~(df[name_labels] == -1).all(1)]\n df.reset_index(drop=True, inplace=True)\n\n if (Clean_Neg):\n for cl_feature, cl_file in Clean_Neg_list:\n df[f'{cl_feature}'] = df[f'{cl_feature}'].replace(-2, -1)\n\n if False:\n for k, feature in enumerate(label_name):\n num_p = df.loc[(df[f'{feature}'] == 1)].shape[0]\n num_n = df.loc[(df[f'{feature}'] == 0)].shape[0]\n num_i = df.loc[(df[f'{feature}'] == -1)].shape[0]\n\n print(f'{feature}-{num_p}-{num_p/df.shape[0]}-{num_i}-{num_i/df.shape[0]}-{num_n}-{num_n/df.shape[0]}')\n exit(-1)\n\n if (True): # in order to add clinical information to network\n df['ScaledSex'] = df.sex.replace(0, -1)\n weight_gender = 10\n weight_age = 100\n min_age = 11.0\n max_age = 100.0\n #df.PatientAge = (df.PatientAge-min(df.PatientAge))/(max(df.PatientAge)-min(df.PatientAge))\n df['ScaledAge'] = (df.PatientAge-min_age)/(max_age-min_age)\n df.ScaledAge = weight_age * (df.ScaledAge - 0.5)\n df['ScaledSex'] = weight_gender * df.ScaledSex\n\n df.reset_index(drop=True, inplace=True)\n\n else:\n try:\n df['ScaledSex'] = df.sex.replace(0, -1)\n weight_gender = 10\n weight_age = 100\n min_age = 11.0\n max_age = 117.0\n #df.PatientAge = (df.PatientAge-min(df.PatientAge))/(max(df.PatientAge)-min(df.PatientAge))\n df['ScaledAge'] = (df.PatientAge-min_age)/(max_age-min_age)\n df.ScaledAge = weight_age * (df.ScaledAge - 0.5)\n df['ScaledSex'] = weight_gender * df.ScaledSex\n except:\n df['ScaledAge'] = 0\n df['ScaledSex'] = 0\n\n\n if (mode == 'single') | (mode == 'extd'):\n LABELS = df.columns[-(num_labels+1):-1] if name_labels == None else name_labels\n labels = df[LABELS].astype(int)\n paths = df['PATH'] if name_paths == None else df[name_paths]\n ages = df['ScaledAge'].astype(float)\n genders = df['ScaledSex'].astype(float)\n df_tmp = pd.concat([paths, ages, genders, labels], axis=1)\n elif mode == 'double':\n LABELS = df.columns[-(num_labels+2):-2] if name_labels == None else name_labels\n labels = df[LABELS].astype(int)\n paths = df[df.columns[-2:]] if name_paths == None else df[name_paths]\n df_tmp = pd.concat([paths, labels], axis=1)\n else:\n raise RuntimeError\n\n entries = df_tmp\n\n logger.debug(f\"{len(entries)} entries are loaded.\")\n return entries\n\n# data augmentation - 512\ntrain_transforms = tfms.Compose([\n tfms.ToPILImage(),\n tfms.Resize(562, Image.LANCZOS),\n tfms.RandomRotation((-10, 10)),\n tfms.RandomCrop((512, 512)),\n tfms.RandomHorizontalFlip(p=0.01), #with 1% horizontal flip\n tfms.ToTensor(),\n])\n\ntest_transforms = tfms.Compose([\n tfms.ToPILImage(),\n tfms.Resize((512, 512), Image.LANCZOS),\n tfms.ToTensor(),\n])\n\ndef get_image(img_path, transforms):\n image = imageio.imread(img_path)\n image_tensor = transforms(image)\n image_tensor = image_tensor[:1, :, :]\n #print(f'{img_path}-{image_tensor.shape}')\n return image_tensor\n\n\nclass CxrDataset(Dataset):\n transforms = train_transforms\n\n def __init__(self, base_path, manifest_file, num_labels=31, name_labels=None, name_paths=None, mode='single', ext_data=False, csv_path=None, fl_balance=False, r_seed=-1, *args, **kwargs):\n super().__init__(*args, **kwargs)\n manifest_path = base_path.joinpath(manifest_file).resolve() if csv_path == None else csv_path.joinpath(manifest_file).resolve()\n self.entries = _tb_load_manifest(manifest_path, num_labels=num_labels, name_labels=name_labels, name_paths=name_paths, mode=mode, ext_data=ext_data, fl_balance=fl_balance, r_seed = r_seed)\n self.base_path = base_path\n self.mode = mode\n self.name_labels = name_labels\n\n def __getitem__(self, index):\n # need to debug\n def get_entries(index):\n df = self.entries.loc[index]\n if (self.mode == 'single') | (self.mode == 'extd'):\n paths = self.base_path.joinpath(df[0]).resolve()\n label = df[3:].tolist() if self.name_labels == None else df[self.name_labels].tolist()\n age = df[1]\n gender = df[2]\n return paths, label, age, gender\n else:\n paths = [self.base_path.joinpath(df[0]).resolve(), self.base_path.joinpath(df[1]).resolve()]\n label = df[2:].tolist() if self.name_labels == None else df[self.name_labels].tolist()\n return paths, label\n\n if (self.mode == 'single') | (self.mode == 'extd'):\n img_path, label, age, gender = get_entries(index)\n image_tensor = get_image(img_path, CxrDataset.transforms)\n target_tensor = torch.FloatTensor(label)\n clinic_tensor = torch.FloatTensor([age, gender])\n #clinic_tensor = torch.FloatTensor([age])\n return image_tensor, target_tensor, clinic_tensor\n elif self.mode == 'double':\n img_paths, label = get_entries(index)\n image_tensor0 = get_image(img_paths[0], CxrDataset.transforms)\n image_tensor1 = get_image(img_paths[1], CxrDataset.transforms)\n target_tensor = torch.FloatTensor(label)\n return image_tensor0, image_tensor1, target_tensor\n else:\n raise RuntimeError\n\n\n def __len__(self):\n return len(self.entries)\n\n def get_label_counts(self, indices=None):\n df = self.entries if indices is None else self.entries.loc[indices]\n counts = [df[x].value_counts() for x in self.labels]\n new_df = pd.concat(counts, axis=1).fillna(0).astype(int)\n return new_df\n\n @property\n def labels(self):\n #if self.mode == 'single':\n # return self.entries.columns[1:].values.tolist()\n #elif self.mode == 'extd':\n if (self.mode == 'single') | (self.mode == 'extd'):\n return self.entries.columns[3:].values.tolist()\n else:\n return self.entries.columns[2:].values.tolist()\n\n @staticmethod\n def train():\n CxrDataset.transforms = train_transforms\n\n @staticmethod\n def eval():\n CxrDataset.transforms = test_transforms\n\nclass CxrConcatDataset(ConcatDataset):\n\n #def __init__(self, *args, **kwargs):\n # super().__init__(*args, **kwargs)\n # self.get_label_counts()\n\n def get_label_counts(self, indices=None):\n if indices is None:\n indices = list(range(self.__len__()))\n dataset_indices = [bisect.bisect_right(self.cumulative_sizes, idx) for idx in indices]\n sample_indices = [(i if d == 0 else i - self.cumulative_sizes[d - 1]) for i, d in zip(indices, dataset_indices)]\n nested_indices = [[] for d in self.datasets]\n for d, s in zip(dataset_indices, sample_indices):\n nested_indices[d].append(s)\n dfs = []\n for d, dataset in enumerate(self.datasets):\n dfs.append(dataset.get_label_counts(nested_indices[d]))\n df = pd.concat(dfs, sort=False).groupby(level=0).sum().astype(int)\n for dataset in self.datasets:\n assert len(df.columns) == len(dataset.labels), \"label names should be matched!\"\n return df\n\n @property\n def labels(self):\n return self.datasets[0].labels\n\n\nclass CxrSubset(Subset):\n\n #def __init__(self, *args, **kwargs):\n # super().__init__(*args, **kwargs)\n # self.get_label_counts()\n\n def get_label_counts(self, indices=None):\n if indices is None:\n indices = list(range(self.__len__()))\n\n df = self.dataset.get_label_counts([self.indices[x] for x in indices])\n return df\n\n @property\n def labels(self):\n return self.dataset.labels\n\n\ndef CxrRandomSplit(dataset, lengths):\n from torch._utils import _accumulate\n if sum(lengths) > len(dataset):\n raise ValueError(\"Sum of input lengths must less or equal to the length of the input dataset!\")\n indices = torch.randperm(sum(lengths)).tolist()\n return [CxrSubset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]\n\n\n\n# Initiating dataset\ndef copy_mgh_dataset(src_path, csv_path, csv_file, t_view='AP', t_path='PATH', cont_op=False):\n if (cont_op):\n csvs = MGH_DATA_BASE.joinpath(csv_file).resolve()\n else:\n csvs = csv_path.joinpath(csv_file)\n\n for m in [csvs.resolve()]:\n print(f'>>> processing {m}')\n\n df = pd.read_csv(str(m))\n failures = []\n failed_files = []\n for i in tqdm(range(len(df)), total=len(df)):\n fs = [df.iloc[i][str(t_path)]]\n\n for k, f in enumerate(fs):\n r, df = anonymization(df, i, t_view, t_path)\n t = MGH_DATA_BASE.joinpath(r).resolve()\n\n if Path(t).is_file():\n print(f'skip the existed file: {t}')\n else:\n try:\n resized = resize_image(f)\n Path.mkdir(t.parent, parents=True, exist_ok=True)\n resized.save(t, 'PNG')\n except:\n failures.append(i)\n failed_files.append(r)\n #breakpoint()\n\n df = df if cont_op else gen_labels(df)\n print(f'before failures: {df.shape}')\n df = df.drop(failures)\n print(f'after failures: {df.shape}')\n #breakpoint()\n t = MGH_DATA_BASE.joinpath(csv_file).resolve()\n #t = MGH_DATA_BASE.joinpath('post2015_mgh_cxr_all_dataset_v3').resolve()\n df.to_csv(t, float_format='%.0f', index=False)\n\n # 1. file make for two inputs is okay?\n # 2. make csv files for one or two inputs are okay?\n # 3. implementation of Data batch + augmentation\n\ndef resize_image(f_name):\n fp = src_path.joinpath(f_name).resolve()\n img = Image.open(fp)\n w, h = img.size\n rs = (512, int(h/w*512)) if w < h else (int(w/h*512), 512)\n resized = img.resize(rs, Image.LANCZOS)\n\n return resized\n\n# anonymizing a file name\ndef anonymization(df, i, t_view, t_path):\n r = f'mgh_{t_view}_{df.iloc[i][0]:08d}.png'\n df.loc[i, t_path] = r\n\n return r, df\n\ndef gen_labels(df):\n # view positions\n df.insert(7, 'ap', 0)\n df['ap'].loc[df['ViewPosition'] == 'AP'] = 1\n df.insert(8, 'pa', 0)\n df['pa'].loc[df['ViewPosition'] == 'PA'] = 1\n df.insert(9, 'll', 0)\n df['ll'].loc[df['ViewPosition'] == 'LL'] = 1\n # sex\n df.insert(11, 'sex', 0)\n df['sex'].loc[df['PatientSex'] == 'M'] = 1\n # manufacturer\n df.insert(14, 'varian', 0)\n df['varian'].loc[df['Manufacturer'] == 'Varian'] = 1\n df.insert(15, 'agfa', 0)\n df['agfa'].loc[df['Manufacturer'] == 'Agfa'] = 1\n df.insert(16, 'ge', 0)\n df['ge'].loc[(df['Manufacturer'] == 'GE Healthcare') | (df['Manufacturer'] == '\"GE Healthcare\"') | (df['Manufacturer'] == 'GE MEDICAL SYSTEMS')] = 1\n df.insert(17, 'others', 0)\n df['others'].loc[(df['varian'] + df['agfa'] + df['ge']) == 0] = 1\n\n return df\n\nif __name__ == \"__main__\":\n if (False):\n src_path = Path('/mnt/hdd/data_storage/mgh_cxr_img').resolve()\n csv_path = Path('/mnt/hdd/data_storage/mgh_cxr_list/clean-lists/20200316-dataset-mgh-v4').resolve()\n if src_path.exists():\n # for AP\n #csv_file = 'example-10-ap.csv'\n csv_file = 'post2015_mgh_cxr_ap_dataset_v4.csv'\n copy_mgh_dataset(src_path, csv_path, csv_file, t_view='AP', t_path='PATH')\n # for PA LL\n #csv_file = 'example-10-pa-ll.csv'\n csv_file = 'post2015_mgh_cxr_pa_ll_dataset_v4.csv'\n copy_mgh_dataset(src_path, csv_path, csv_file, t_view='PA', t_path='PATH1')\n copy_mgh_dataset(src_path, csv_path, csv_file, t_view='LL', t_path='PATH2', cont_op=True)\n #csv_file = 'example-10-pa.csv'\n csv_file = 'post2015_mgh_cxr_pa_dataset_v4.csv'\n copy_mgh_dataset(src_path, csv_path, csv_file, t_view='PA', t_path='PATH')\n #csv_file = 'example-10-ll.csv'\n csv_file = 'post2015_mgh_cxr_ll_dataset_v4.csv'\n copy_mgh_dataset(src_path, csv_path, csv_file, t_view='LL', t_path='PATH')\n else:\n assert False, (f'{src_path} is not existed.')\n else:\n inc_labels = [1, 2, 3, 6, 7, 8, 11, 13, 14, 15, 18, 19, 21, 22, 28]\n inc_rate = [2, 8, 4, 2, 4, 2, 2, 8, 16, 8, 8, 4, 8, 8, 8]\n for k, feature in enumerate(label_name):\n num_p = df.loc[(df[f'{feature}'] == 1)].shape[0]\n", "repo_name": "MGH-LMIC/First-Aid-CXR-AI", "sub_path": "data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 24062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "warnings.simplefilter", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.logger.error", "line_number": 79, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 79, "usage_type": "name"}, {"api_name": "utils.logger.debug", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 82, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 150, "usage_type": "call"}, {"api_name": "utils.logger.info", "line_number": 172, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 172, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 233, "usage_type": "call"}, {"api_name": "utils.logger.debug", "line_number": 239, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 239, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 243, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 243, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 244, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 244, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 245, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 245, "usage_type": "name"}, {"api_name": "PIL.Image.LANCZOS", "line_number": 245, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 245, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomRotation", "line_number": 246, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 246, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 247, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 247, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 248, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 248, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 249, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 249, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 252, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 252, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 253, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 253, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 254, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 254, "usage_type": "name"}, {"api_name": "PIL.Image.LANCZOS", "line_number": 254, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 254, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 255, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 255, "usage_type": "name"}, {"api_name": "imageio.imread", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 266, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 295, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 303, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 336, "usage_type": "name"}, {"api_name": "bisect.bisect_right", "line_number": 345, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.randperm", "line_number": 385, "usage_type": "call"}, {"api_name": "torch._utils._accumulate", "line_number": 386, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 400, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 403, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 410, "usage_type": "call"}, {"api_name": "pathlib.Path.mkdir", "line_number": 415, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 415, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 437, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 437, "usage_type": "name"}, {"api_name": "PIL.Image.LANCZOS", "line_number": 440, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 440, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 476, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 477, "usage_type": "call"}]} +{"seq_id": "25014272985", "text": "\"\"\"\nModule containing numba compiled filtering functions for usage in symbolic\ndynamics implementations.\n\nAuthors:\\n\n- Philipp Schuette\\n\n\"\"\"\n\nfrom typing import Tuple, no_type_check\n\nimport numba as nb # type: ignore\nimport numpy as np\nfrom numpy.typing import NDArray\n\nfrom pyzeta.core.pyzeta_types.general import tBoolMat, tMat, tWordVec\nfrom pyzeta.core.pyzeta_types.special import tLetter, tMask, tWord\n\n##############################################\n# Numba Versions of Symbolic Dynamic Methods #\n##############################################\n\n\n@nb.jit(\n nb.uint32[:, :](nb.uint32[:, :], nb.bool_[:, :]),\n nopython=True,\n fastmath=True,\n cache=True,\n)\n@no_type_check\ndef matMul(left: tMat, right: tBoolMat) -> tMat:\n \"\"\"\n Multiply two matrices `left` and `right` fast. Performs no checks.\n\n :param left: First matrix to multiply\n :param right: Second matrix to multiply\n :return: Matrix product\n \"\"\"\n dim: np.uint8 = left.shape[0]\n result: NDArray[np.uint32] = np.zeros((dim, dim), dtype=np.uint32)\n for i in range(dim):\n for j in range(dim):\n for k in range(dim):\n result[i][j] += left[i][k] * right[k][j]\n return result\n\n\n@nb.jit(\n nb.uint8[:, :](nb.uint8, nb.bool_[:, :]),\n nopython=True,\n fastmath=True,\n cache=True,\n)\n@no_type_check\ndef getWordsFast(n: int, adj: tBoolMat) -> tMat:\n \"\"\"\n Generate all words of length `n` for alphabet with adjacency matrix `adj`.\n\n :param n: Length of words to be generated\n :param adj: Adjacency matrix over some alphabet\n :return: Array containing all words of length `n`\n \"\"\"\n alphabetSize: np.uint8 = adj.shape[0]\n adjPower: NDArray[np.uint32] = np.eye(alphabetSize, dtype=np.uint32)\n for i in range(1, n):\n adjPower = matMul(adjPower, adj)\n wordNum: nb.uint8 = np.sum(adjPower)\n adjNonNull = np.nonzero(adj)\n # array for storing generated words; initialise with all entries set to -1\n result: tWordVec = np.full((wordNum, n), -1, dtype=tLetter)\n tmp: tWordVec\n\n # initialize words of length 1:\n for i in range(alphabetSize):\n result[i][0] = i\n adjPower = np.eye(alphabetSize, dtype=np.uint32)\n\n for length in range(1, n):\n wordNum = np.sum(adjPower)\n adjPower = matMul(adjPower, adj)\n tmp = np.copy(result)\n pos: np.uint8 = 0\n for i in range(wordNum):\n word: tWord = tmp[i]\n adjRow: tWord = adjNonNull[1][adjNonNull[0] == word[length - 1]]\n for letter in adjRow:\n for k in range(length):\n result[pos][k] = word[k]\n result[pos][k + 1] = letter\n pos += 1\n\n return result\n\n\n@nb.jit(\n nb.uint8[:, :](nb.uint8, nb.bool_[:, :], nb.uint8[:, :]),\n nopython=True,\n fastmath=True,\n cache=True,\n)\n@no_type_check\ndef appendWordsFast(n: int, adj: tBoolMat, words: tWordVec) -> tWordVec:\n \"\"\"\n Append to given words according to a given adjacency matrix to obtain words\n of a given length\n\n :param n: Length of words to be generated\n :param adj: Adjacency matrix determining valid words\n :param words: Array containing (all) words of some length <`n`\n :return: Array containing all words of length `n`\n \"\"\"\n alphabetSize: np.uint8 = adj.shape[0]\n givenWordNum: np.uint8 = words.shape[0]\n givenWordLen: np.uint8 = words.shape[1]\n adjPower: NDArray[np.uint32] = np.eye(alphabetSize, dtype=np.uint32)\n for i in range(1, n):\n adjPower = matMul(adjPower, adj)\n wordNum: nb.uint8 = np.sum(adjPower)\n adjNonNull = np.nonzero(adj)\n\n # array for storing generated words; initialise with all entries set to -1\n result: tWordVec = np.full((wordNum, n), -1, dtype=tLetter)\n tmp: tWordVec\n\n result[:givenWordNum, :givenWordLen] = words\n adjPower = np.eye(alphabetSize, dtype=np.uint32)\n for length in range(1, givenWordLen):\n adjPower = matMul(adjPower, adj)\n\n for length in range(givenWordLen, n):\n wordNum = np.sum(adjPower)\n adjPower = matMul(adjPower, adj)\n tmp = np.copy(result)\n pos: nb.uint8 = 0\n for i in range(wordNum):\n word: tWord = tmp[i]\n adjRow: tWord = adjNonNull[1][adjNonNull[0] == word[length - 1]]\n for letter in adjRow:\n for k in range(length):\n result[pos][k] = word[k]\n result[pos][k + 1] = letter\n pos += 1\n\n return result\n\n\n@nb.guvectorize(\n [(nb.uint8[:], nb.bool_[:])], \"(n)->()\", nopython=True, cache=True\n)\n@no_type_check\ndef isPrimeFast(word: tWord, res: bool):\n \"\"\"\n Check if given `word` is prime. Accepts arrays of symbolic words.\n\n :param word: Word (or array of words) of letters from a given alphabet\n :return: `True` iff word is prime\n \"\"\"\n n: np.uint8 = len(word)\n kPerm: bool = True\n res[0] = True\n\n for k in range(1, n // 2 + 1):\n if n % k == 0:\n for m in range(1, n // k):\n if np.any(word[:k] != word[m * k : (m + 1) * k]):\n kPerm = False\n break\n if kPerm:\n res[0] = False\n break\n kPerm = True\n\n\n@nb.jit(\n nb.bool_(nb.uint8[:], nb.uint8[:, :]),\n fastmath=True,\n nopython=True,\n cache=True,\n)\n@no_type_check\ndef containsPermFast(word: tWord, wordsToCheck: tWordVec) -> bool:\n \"\"\"\n Return `True` if `wordList` contains a permutation of `word`.\n\n :param word: Word over a given alphabet\n :param wordsToCheck: array of words over the same alphabet\n :return: `True` iff `wordsToCheck` contain permutations of `word`\n \"\"\"\n n: np.uint8 = len(word)\n for _ in range(n):\n word = np.roll(word, 1)\n for checkWord in wordsToCheck:\n if np.all(word == checkWord):\n return True\n return False\n\n\n@nb.jit(nb.bool_[:](nb.uint8[:, :]), fastmath=True, nopython=True, cache=True)\n@no_type_check\ndef filterPermsFast(words: tWordVec) -> tMask:\n \"\"\"\n Filter out all permutations from a list of `words` after the first\n occurrence.\n\n :param words: array of words over a given alphabet\n :return: Mask that implements this filter on the list\n \"\"\"\n size: np.int8 = len(words)\n mask: tMask = np.zeros(size, dtype=np.bool_)\n mask[0] = True\n for i in range(1, size):\n mask[i] = not containsPermFast(words[i], words[:i])\n return mask\n\n\n@nb.guvectorize(\n [(nb.uint8[:], nb.bool_[:, :], nb.bool_[:])],\n \"(n),(m,m)->()\",\n nopython=True,\n cache=True,\n)\n@no_type_check\ndef isCyclRedFast(word: tWord, adj: tBoolMat, res: Tuple[bool]) -> None:\n \"\"\"\n Return `True` if last to first letter of `word` defines a valid transtion\n (i.e. word is cyclically reduced). Accepts arrays of symbolic words.\n\n :param word: Word (or array of words) over a given alphabet\n :return: `True` if word is cyclically reduced, `False` otherwise\n \"\"\"\n n: np.uint8 = len(word)\n if adj[word[n - 1]][word[0]] == 0:\n res[0] = False\n else:\n res[0] = True\n\n\n@nb.guvectorize(\n [(nb.uint8[:], nb.bool_[:])], \"(n)->()\", nopython=True, cache=True\n)\n@no_type_check\ndef isPeriodicFast(word: tWord, res: Tuple[bool]) -> None:\n \"\"\"\n Check if given `word` is periodic. Accepts arrays of symbolic words.\n\n :param word: Word (or array of words) over a given alphabet\n :return: `True` if word is periodic, `False` otherwise\n \"\"\"\n n: np.uint8 = len(word)\n if word[0] == word[n - 1]:\n res[0] = True\n else:\n res[0] = False\n", "repo_name": "Spectral-Analysis-UPB/PyZeta", "sub_path": "pyzeta/core/dynamics/symbolic_dynamics/helpers/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 7584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pyzeta.core.pyzeta_types.general.tMat", "line_number": 30, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tBoolMat", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.typing.NDArray", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.uint32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 23, "usage_type": "call"}, {"api_name": "numba.uint32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numba.bool_", "line_number": 24, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 29, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tBoolMat", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.typing.NDArray", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.uint32", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 63, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 67, "usage_type": "call"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.full", "line_number": 69, "usage_type": "call"}, {"api_name": "pyzeta.core.pyzeta_types.special.tLetter", "line_number": 69, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 83, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 84, "usage_type": "name"}, {"api_name": "numba.jit", "line_number": 47, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numba.bool_", "line_number": 48, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 53, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tMat", "line_number": 54, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tBoolMat", "line_number": 101, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.typing.NDArray", "line_number": 114, "usage_type": "name"}, {"api_name": "numpy.uint32", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 114, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 118, "usage_type": "call"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.full", "line_number": 121, "usage_type": "call"}, {"api_name": "pyzeta.core.pyzeta_types.special.tLetter", "line_number": 121, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 132, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 135, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 136, "usage_type": "name"}, {"api_name": "numba.jit", "line_number": 94, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numba.bool_", "line_number": 95, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 100, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 150, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 164, "usage_type": "call"}, {"api_name": "numba.guvectorize", "line_number": 146, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numba.bool_", "line_number": 147, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 149, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 180, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 180, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.roll", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 192, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 173, "usage_type": "call"}, {"api_name": "numba.bool_", "line_number": 174, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 174, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 179, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tWordVec", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.int8", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pyzeta.core.pyzeta_types.special.tMask", "line_number": 208, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 208, "usage_type": "attribute"}, {"api_name": "numba.jit", "line_number": 197, "usage_type": "call"}, {"api_name": "numba.bool_", "line_number": 197, "usage_type": "attribute"}, {"api_name": "numba.uint8", "line_number": 197, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 198, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tMask", "line_number": 199, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 222, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.general.tBoolMat", "line_number": 222, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 222, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numba.guvectorize", "line_number": 215, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 216, "usage_type": "attribute"}, {"api_name": "numba.bool_", "line_number": 216, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 221, "usage_type": "name"}, {"api_name": "pyzeta.core.pyzeta_types.special.tWord", "line_number": 241, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 241, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 248, "usage_type": "attribute"}, {"api_name": "numba.guvectorize", "line_number": 237, "usage_type": "call"}, {"api_name": "numba.uint8", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numba.bool_", "line_number": 238, "usage_type": "attribute"}, {"api_name": "typing.no_type_check", "line_number": 240, "usage_type": "name"}]} +{"seq_id": "8150084277", "text": "import numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport time\nfrom typing import Union\n\nfrom models.BaseGAN import BaseGAN\nfrom visualizers.BaseSampler import BaseSampler\nfrom utils.common import random_batch_getter\n\n\nclass GAN(BaseGAN):\n def __init__(self,\n input_dim, latent_factor=5,\n D=None, G=None, d_optimizer=None, g_optimizer=None):\n super().__init__(input_dim, latent_factor)\n super()._setup_models(D, G, d_optimizer, g_optimizer)\n\n def _build_discriminator(self):\n return keras.Sequential([\n layers.Dense(32, input_shape=(self.input_dim,)), layers.LeakyReLU(),\n layers.Dense(16), layers.LeakyReLU(),\n layers.Dense(1)\n ])\n\n def _build_generator(self):\n return keras.Sequential([\n layers.Dense(32, input_shape=(self.latent_factor,)), layers.LeakyReLU(),\n layers.Dense(16), layers.LeakyReLU(),\n layers.Dense(self.input_dim)\n ])\n\n def _build_d_optimizer(self) -> tf.keras.optimizers.Optimizer:\n return tf.keras.optimizers.SGD(0.01)\n\n def _build_g_optimizer(self) -> tf.keras.optimizers.Optimizer:\n return tf.keras.optimizers.SGD(0.01)\n\n ####################################################\n # losses and training\n ####################################################\n\n cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n\n @staticmethod\n def _generator_loss(fake_output):\n return GAN.cross_entropy(tf.ones_like(fake_output), fake_output)\n\n @staticmethod\n def _discriminator_loss(real_output, fake_output):\n real_loss = GAN.cross_entropy(tf.ones_like(real_output), real_output)\n fake_loss = GAN.cross_entropy(tf.zeros_like(fake_output), fake_output)\n return real_loss + fake_loss\n\n @tf.function\n def _train_step_discriminator(self, real_x):\n print('Tracing d_step...')\n discriminator, generator = self.discriminator, self.generator\n noise = tf.random.normal([len(real_x), self.latent_factor])\n\n with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n fake_x = generator(noise, training=True) # training=True for differentiable D\n real_output = discriminator(real_x, training=True)\n fake_output = discriminator(fake_x, training=True)\n\n disc_loss = self._discriminator_loss(real_output, fake_output)\n\n disc_grads = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n self.d_optimizer.apply_gradients(zip(disc_grads, discriminator.trainable_variables))\n return disc_loss\n\n @tf.function\n def _train_step_both(self, real_x):\n print('Tracing both_step...') # tf.function trace for only a few times\n discriminator, generator = self.discriminator, self.generator\n noise = tf.random.normal([len(real_x), self.latent_factor])\n\n with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n fake_x = generator(noise, training=True) # training=True for differentiable D\n real_output = discriminator(real_x, training=True)\n fake_output = discriminator(fake_x, training=True)\n\n gen_loss = self._generator_loss(fake_output)\n disc_loss = self._discriminator_loss(real_output, fake_output)\n\n # update gradient\n disc_grads = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n gen_grads = gen_tape.gradient(gen_loss, generator.trainable_variables)\n self.d_optimizer.apply_gradients(zip(disc_grads, discriminator.trainable_variables))\n self.g_optimizer.apply_gradients(zip(gen_grads, generator.trainable_variables))\n\n return disc_loss, gen_loss\n\n def train(\n self, dataset: Union[tf.Tensor, np.ndarray], epochs, batch_size=64,\n sample_interval=20, sampler: BaseSampler = None, sample_number=300,\n metrics=None, dg_train_ratio=1\n ):\n dataset = self._check_dataset(dataset)\n seed = tf.random.normal([sample_number, self.latent_factor])\n n_samples = dataset.shape[0]\n n_batch = n_samples // batch_size\n metrics = metrics or []\n losses, metric_values = [], [[] for m in metrics]\n\n batch_getter = random_batch_getter(dataset, batch_size)\n\n for epoch in range(epochs):\n start = time.time()\n kwargs = {'model': self, 'dataset': dataset, 'epoch': epoch}\n total_d_loss = total_g_loss = .0\n\n # in each batch, train D for dg_train_ratio times and G once\n with tf.profiler.experimental.Trace('train', step_num=epoch, _r=1):\n for i in range(n_batch):\n for _ in range(dg_train_ratio - 1):\n self._train_step_discriminator(next(batch_getter))\n pass\n\n d_loss, g_loss = self._train_step_both(next(batch_getter))\n total_d_loss += d_loss\n total_g_loss += g_loss\n\n if epoch % sample_interval == 0 and sampler is not None:\n sampler(self.generator(seed), epoch)\n for i, v in enumerate(metric_values):\n v.append(metrics[i](**kwargs))\n\n total_g_loss /= n_batch\n total_d_loss /= n_batch\n losses.append((total_d_loss, total_g_loss))\n self.print_epoch(epoch, epochs, time.time() - start, total_d_loss, total_g_loss)\n\n # last sample\n sampler(self.generator(seed), epochs - 1)\n self.trained_epoch += epochs\n\n return np.array(losses), np.array(metric_values)\n\n def _train_deprecated(self, dataset, epochs, batch_size=32, sample_interval=20, sampler: BaseSampler = None,\n sample_number=300,\n dg_train_ratio=1):\n \"\"\"\n Deprecated. dataset with tf.data.Dataset without tf.function is slow when dg_train_ratio > 1\n \"\"\"\n seed = tf.random.normal([sample_number, self.latent_factor])\n dataset = dataset.shuffle(len(dataset)).repeat(dg_train_ratio).batch(batch_size, drop_remainder=True)\n n_batch = len(dataset)\n losses = [] # save tuple (d_loss, g_loss) of each epoch\n\n for epoch in range(epochs):\n start = time.time()\n\n with tf.profiler.experimental.Trace('train', step_num=epoch, _r=1):\n total_g_loss = total_d_loss = 0.0\n i = dg_train_ratio - 1\n for through_dataset in range(dg_train_ratio):\n for batch in dataset:\n if i == 0:\n # s = time.time()\n d_loss, g_loss = self._train_step_both(batch)\n # print(f'train step_d cost {time.time() - s:.3f} s')\n total_d_loss += d_loss\n total_g_loss += g_loss\n i = dg_train_ratio - 1 # reset counter\n else:\n self._train_step_discriminator(batch)\n i -= 1\n\n if epoch % sample_interval == 0 and sampler is not None:\n sampler(self.generator(seed), epoch)\n\n total_g_loss /= n_batch\n total_d_loss /= n_batch\n losses.append((total_d_loss, total_g_loss))\n self.print_epoch(epoch, epochs, time.time() - start, total_d_loss, total_g_loss)\n\n self.trained_epoch += epochs\n\n return np.array(losses)\n\n ####################################################\n # save and load\n ####################################################\n\n # using inherited config save and load\n", "repo_name": "PurplePower/GAN_fitting", "sub_path": "models/GAN.py", "file_name": "GAN.py", "file_ext": "py", "file_size_in_byte": 7794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "models.BaseGAN.BaseGAN", "line_number": 13, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 21, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LeakyReLU", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 23, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LeakyReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 24, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 28, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 29, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LeakyReLU", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 30, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LeakyReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.SGD", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.SGD", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.BinaryCrossentropy", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.ones_like", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.zeros_like", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.random.normal", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.random.normal", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 73, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 96, "usage_type": "name"}, {"api_name": "tensorflow.Tensor", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 96, "usage_type": "attribute"}, {"api_name": "visualizers.BaseSampler.BaseSampler", "line_number": 97, "usage_type": "name"}, {"api_name": "tensorflow.random.normal", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 101, "usage_type": "attribute"}, {"api_name": "utils.common.random_batch_getter", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.profiler.experimental.Trace", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.profiler", "line_number": 115, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 139, "usage_type": "call"}, {"api_name": "visualizers.BaseSampler.BaseSampler", "line_number": 141, "usage_type": "name"}, {"api_name": "tensorflow.random.normal", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 147, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.profiler.experimental.Trace", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.profiler", "line_number": 155, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "21258228525", "text": "import string, random\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.http import HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom details.models import Userdetails, Delivery, Item\nfrom details.forms import Add_detailsForm,Add_coordinatesForm\n\n\n# Create your views here.\ndef user_details(request, amount, template_name='user_registration.html'):\n\tform = Add_detailsForm(request.POST or None)\n\tctx = {}\n\tctx['form']=form\n\tform.fields['amount'].initial = amount\n\n\tif request.method == \"POST\":\n\t\tif form.is_valid():\n\t\t\tinstance = form.save()\n\t\t\t# return HttpResponseRedirect(instance.get_absolute_url())\n\t\t\treturn redirect(\"/location/%s\" % str(instance.slug))\n\t#else:\n\t\t#form = Add_detailsForm()\n\treturn render(request, template_name, ctx)\n\ndef user_location(request, slug, template_name='user_location.html'):\n\ttry:\n\t\tctx = {}\n\t\tuser_info = Userdetails.objects.get(slug=slug)\n\t\tform = Add_coordinatesForm(request.POST or None)\n\n\t\tfirst_name = user_info.first_name\n\t\tlast_name = user_info.last_name\n\t\temail = user_info.email\n\t\tctx['form']=form\n\n\t\tif form.is_valid():\n\t\t\tinstance = form.save(commit=False)\n\t\t\tinstance.save()\n\t\t\tdescription='none'\n\t\t\ttypes='MERCHANT'\n\t\t\treference='none'\n\t\t\ttotal_amount = float(user_info.amount) + float(request.POST['amount'])\n\t\t\treturn redirect('http://45.55.252.17/pickanddrop/pesapal-iframe.php?first_name=%s&last_name=%s&amount=%s&email=%s&description=%s&type=%s&reference=%s'%(first_name, \n\t\t\t\tlast_name,total_amount,email,description,types,reference))\n\texcept:\n\t\traise \n\n\treturn render(request, template_name, ctx)\n\ndef token_generator(request, template_name='token_generator.html'):\n\n\tdef id_generator(size=6, chars=string.ascii_uppercase + string.digits):\n\t\treturn ''.join(random.choice(chars) for _ in range(size))\n\ttoken = id_generator(7)\n\tctx = {}\n\tctx['token']=token\n\treturn render(request, template_name, ctx)\n", "repo_name": "skafis/pickanddrop", "sub_path": "details/class_views/users.py", "file_name": "users.py", "file_ext": "py", "file_size_in_byte": 1904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "details.forms.Add_detailsForm", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "details.models.Userdetails.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "details.models.Userdetails.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "details.models.Userdetails", "line_number": 28, "usage_type": "name"}, {"api_name": "details.forms.Add_coordinatesForm", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 52, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 52, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "39931810554", "text": "#!/usr/bin/env python3\n\nimport streamlink\nimport time\nfrom facenet_pytorch import MTCNN\nimport io\nimport requests\nimport concurrent.futures\nimport requests\nimport time\nimport torch\nimport sys\nimport logging\nimport sys\nimport streamlink\nimport os.path\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom methods.constants import IMG_HEIGHT, IMG_WIDTH\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\ntry:\n import cv2\nexcept ImportError:\n sys.stderr.write(\"This example requires opencv-python is installed\")\n raise\n\nlog = logging.getLogger(__name__)\nGREEN = (0, 255, 0)\nEKS_IP = \"http://ac1079231337f47aabdc6aa6e7a2be07-233993352.us-east-2.elb.amazonaws.com\" \n# EKS_IP = \"http://127.0.0.1\" # \n\ndef stream_to_url(url, quality='best'):\n if \"twitch\" in url:\n streams = streamlink.streams(url)\n if streams:\n return streams[quality].to_url()\n else:\n raise ValueError(\"No streams were available\")\n else:\n return url\n\n\ndef add_rect2frame(frame, boxes):\n for box in boxes:\n box = [int(i) for i in box]\n x1, y1, x2, y2 = box\n cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 3)\n\n\ndef augment(frame, boxes, asset):\n for box in boxes:\n x0, y0, x1, y1 = [int(i) for i in box]\n if x1 - x0 <= 0:\n continue\n if y1 - y0 <= 0:\n continue\n asset_patch = cv2.resize(asset, (x1 - x0, y1 - y0))\n a = (asset_patch[:, :, 3] / 255.)[:, :, None]\n frame[y0: y1, x0: x1] = (1-a) * frame[y0: y1, x0: x1] + a * asset_patch[:, :, :3]\n\n\ndef detect_faces(frame, mtcnn):\n boxes, _ = mtcnn.detect(frame)\n add_rect2frame(frame, boxes)\n return frame\n\n\ndef numpy_to_binary(arr):\n is_success, buffer = cv2.imencode(\".png\", arr)\n io_buf = io.BytesIO(buffer)\n return io_buf.read()\n\n\ndef detect_faces_online(frame, add2frame=False, timeout=5):\n X_sz, Y_sz = frame.shape[:2]\n W_new, H_new = IMG_WIDTH, IMG_HEIGHT\n resized = cv2.resize(frame, (W_new, H_new), interpolation=cv2.INTER_LINEAR)\n r = requests.put(\n f\"{EKS_IP}:9001/predictions/all_det\",\n numpy_to_binary(resized), \n timeout=timeout\n ).content\n\n scale_X = Y_sz / W_new\n scale_Y = X_sz / H_new\n\n boxes = json.loads(r.decode())\n #assert 0, boxes\n if isinstance(boxes, dict):\n print(boxes)\n exit(1)\n boxes = []\n boxes = [[x1 * scale_X, y1 * scale_Y , x2* scale_X, y2 * scale_Y] for x1, y1, x2, y2 in boxes]\n if add2frame:\n add_rect2frame(frame, boxes)\n return boxes\n\n\ndef write_on_line(text):\n sys.stdout.write(f'\\r{text}')\n sys.stdout.flush()\n\n\ndef main(url, fpath_asset=None, x0=None, y0=None, x1=None, y1=None, quality='best', fps=300.0):\n stream_url = stream_to_url(url)\n log.info(\"Loading stream {0}\".format(stream_url))\n cap = cv2.VideoCapture(stream_url)\n w, h = cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n print(\"shape=\", (h, w))\n if x0 is None:\n x0 = 0.0\n if x1 is None:\n x1 = 1.0\n if y0 is None:\n y0 = 0.0\n if y1 is None:\n y1 = 1.0\n x0, x1 = int(round(x0 * w)), int(round(x1 * w))\n y0, y1 = int(round(y0 * h)), int(round(y1 * h))\n print(y0, y1, x0, x1)\n if fpath_asset is None:\n fpath_asset = \"img.png\"\n img_data = requests.get(\"http://assets.stickpng.com/images/58e8ff52eb97430e819064cf.png\").content\n with open(fpath_asset, 'wb') as handler:\n handler.write(img_data)\n\n asset = cv2.imread(fpath_asset, cv2.IMREAD_UNCHANGED)\n\n frame_time = int((1.0 / fps) * 1000.0)\n CONNECTIONS = 200\n multithreader = concurrent.futures.ThreadPoolExecutor(max_workers=CONNECTIONS)\n tic = None \n cnt = 0\n futures = []\n frames_queue = []\n beginning = True\n boxes = InertialBoxes(b=0.90)\n lst_image = np.ones((IMG_WIDTH, IMG_HEIGHT, 3))\n while True:\n try:\n tic_ = time.time()\n ret, frame = cap.read()\n print(f\"Getting frame {time.time() - tic_}\")\n if ret is None:\n break\n frame = frame[y0:y1, x0:x1]\n assert len(frame.ravel()) > 0\n frames_queue.append(frame)\n # frame = detect_faces_online(frame)\n futures.append(multithreader.submit(detect_faces_online, frame))\n print(\"A\", time.time() -tic_)\n try:\n print(\"Futures# = \", len(futures))\n if len(futures) < 100 and beginning:\n print('=================')\n continue\n beginning = False\n if tic is None:\n tic = time.time()\n future = next(concurrent.futures.as_completed(futures[0:1]))\n print(\"B\", time.time() -tic_)\n boxes_new = future.result()\n print(\"B.1\")\n boxes.tick()\n for box in boxes_new:\n boxes.handle_box(box)\n futures.pop(0)\n frame = frames_queue.pop(0)\n print(\"C\", time.time() -tic_)\n # add_rect2frame(frame, boxes)\n augment(frame, [box[\"coords\"] for box in boxes.info], asset)\n print(\"D\", time.time() -tic_)\n cv2.imshow('frame', cv2.resize(frame, (1920//2, 1080//2)))\n lst_image = frame\n cnt += 1\n print(\"E\", time.time() -tic_)\n\n except Exception as e:\n #print(e.args)\n print(\"Err#1\")\n cv2.imshow('frame', cv2.resize(lst_image, (1920//2, 1080//2)))\n cnt += 1\n #exit(1)\n #time.sleep(100)\n\n # time.sleep(100)\n toc = time.time()\n print(f\"FPS = {cnt / (toc - tic)}\")\n if cv2.waitKey(max(1, min(1, frame_time - 1000 * int(toc - tic_)))) & 0xFF == ord('q'):\n break\n print(\"F\", time.time() -tic_)\n except KeyboardInterrupt:\n break\n\n cv2.destroyAllWindows()\n cap.release()\n\n\ndef stream_without_torch(url, quality='best', fps=300.0):\n stream_url = stream_to_url(url)\n log.info(\"Loading stream {0}\".format(stream_url))\n cap = cv2.VideoCapture(stream_url)\n\n frame_time = int((1.0 / fps) * 1000.0)\n tic = time.time()\n cnt = 0\n tic = time.time()\n while True:\n try:\n ret, frame = cap.read()\n \n if ret:\n tic_ = time.time()\n cv2.imshow('frame', frame)\n \n cnt += 1\n # time.sleep(100)\n toc = time.time()\n print(f\"FPS = {cnt / (toc - tic)}\")\n if cv2.waitKey(max(0, frame_time - 1000 * int(toc - tic_))) & 0xFF == ord('q'):\n break\n else:\n break\n except KeyboardInterrupt:\n break\n\n cv2.destroyAllWindows()\n cap.release()\n\n\nclass InertialBoxes:\n def __init__(self, b=0.90, tol=10, min_freshness=0.10, max_freshness=1.0):\n self.b = b\n self.tol = tol\n self.info = []\n self.min_freshness = min_freshness\n self.max_freshness = max_freshness\n\n def add_new_box(self, coords):\n x0, y0, x1, y1 = coords\n self.info.append({\n \"center\": (0.5 * (x1+x0), 0.5 * (y1+y0)),\n \"coords\": coords,\n \"freshness\": 1,\n })\n\n def update_box(self, i, coords):\n box = self.info[i]\n b = self.b\n box[\"coords\"] = tuple(b * np.array(box[\"coords\"]) + (1 - b) * np.array(coords))\n x0, y0, x1, y1 = box[\"coords\"]\n box[\"center\"] = (0.5 * (x1+x0), 0.5 * (y1+y0))\n box[\"freshness\"] = min(box[\"freshness\"] + 1, self.max_freshness)\n\n def tick(self):\n for i, box in enumerate(self.info):\n box[\"freshness\"] *= self.b\n self.info = [box for box in self.info if box[\"freshness\"] > self.min_freshness]\n\n def is_close(self, coords0, coords1):\n return abs(np.array(coords0) - np.array(coords1)).mean() < self.tol\n\n def handle_box(self, coords):\n found_box = False\n for i, box in enumerate(self.info):\n if self.is_close(coords, box[\"coords\"]):\n found_box = True\n self.update_box(i, coords)\n break\n if not found_box:\n self.add_new_box(coords)\n\n\nif __name__ == \"__main__\":\n import argparse\n logging.basicConfig(level=logging.INFO)\n\n parser = argparse.ArgumentParser(description=\"Face detection on streams via Streamlink\")\n parser.add_argument(\"url\", help=\"Stream to play\")\n parser.add_argument(\"--path_asset\", default=None)\n parser.add_argument(\"--x0\", default=None, type=float)\n parser.add_argument(\"--y0\", default=None, type=float)\n parser.add_argument(\"--x1\", default=None, type=float)\n parser.add_argument(\"--y1\", default=None, type=float)\n\n opts = parser.parse_args()\n \n TWITCH_URL = opts.url if opts.url else \"https://www.twitch.tv/valhalla_cup\"\n main(TWITCH_URL, opts.path_asset, opts.x0, opts.y0, opts.x1, opts.y1)\n # stream_without_torch(TWITCH_URL)\n", "repo_name": "khlin216/torchserve-streamer", "sub_path": "torchserve/streamer.py", "file_name": "streamer.py", "file_ext": "py", "file_size_in_byte": 9203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.cuda.is_available", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}, {"api_name": "streamlink.streams", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 70, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 71, "usage_type": "call"}, {"api_name": "methods.constants.IMG_WIDTH", "line_number": 77, "usage_type": "name"}, {"api_name": "methods.constants.IMG_HEIGHT", "line_number": 77, "usage_type": "name"}, {"api_name": "cv2.resize", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 78, "usage_type": "attribute"}, {"api_name": "requests.put", "line_number": 79, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 88, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 102, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 109, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 128, "usage_type": "attribute"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 132, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 132, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 139, "usage_type": "call"}, {"api_name": "methods.constants.IMG_WIDTH", "line_number": 139, "usage_type": "name"}, {"api_name": "methods.constants.IMG_HEIGHT", "line_number": 139, "usage_type": "name"}, {"api_name": "time.time", "line_number": 142, "usage_type": "call"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "time.time", "line_number": 152, "usage_type": "call"}, {"api_name": "time.time", "line_number": 160, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.as_completed", "line_number": 161, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 161, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 161, "usage_type": "name"}, {"api_name": "time.time", "line_number": 162, "usage_type": "call"}, {"api_name": "time.time", "line_number": 170, "usage_type": "call"}, {"api_name": "time.time", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 174, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 174, "usage_type": "call"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 182, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 182, "usage_type": "call"}, {"api_name": "time.time", "line_number": 188, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 190, "usage_type": "call"}, {"api_name": "time.time", "line_number": 192, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 196, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 203, "usage_type": "call"}, {"api_name": "time.time", "line_number": 206, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "time.time", "line_number": 214, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 215, "usage_type": "call"}, {"api_name": "time.time", "line_number": 219, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 221, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 262, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 277, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 277, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 279, "usage_type": "call"}]} +{"seq_id": "27970964811", "text": "import os\nimport sys\nimport subprocess\nfrom time import ctime\nimport time\nimport socket\nfrom utils.common_utils import Common\n\nroot_dir = os.path.dirname(os.path.dirname(__file__))\n\n\nclass AppiumUtils(Common):\n\n def appium_start(self, _host, _port):\n \"\"\"\n start an appium server, if you want start any auto test from appium, you need\n start appium server first!\n :param host: the localhost number, eg:127.0.0.1\n :param port: the appium server lisenning port, eg:4723 but can't set 4724, be\n -cause bootstrap-port need set next one by port\n :return:\n \"\"\"\n print(\"=======Start appium server[port:%s] at: %s=======\" % (str(_port), ctime()))\n bp = str(_port + 1)\n cmd = \"appium -a \" + str(_host) + \" -p \" + str(_port) + \" -bp \" + bp\n print(\"Cmd:%s\" % cmd)\n log_path = os.path.join(root_dir, 'log', 'appium_' + str(_port) + '.log')\n print(\"Log path:%s\" % log_path)\n subprocess.Popen(cmd, shell=True, stdout=open(log_path, 'a'), stderr=subprocess.STDOUT)\n time.sleep(5)\n print(\"=======Start appium server[port:%s] success!!!=======\" % str(_port))\n\n def start_appium_server(self, _host, _port):\n if self.is_appium_port_idle(_host, _port):\n self.appium_start(_host, _port)\n return True\n else:\n print(\"=======Start appium server[host:%s/port:%s] fail!!!=======\" % (str(_host), str(_port)))\n return False\n\n def release_appium_server_port(self, _port):\n server_pid = self.get_netstat_pid_by_port(_port)\n if server_pid is not None:\n kill_cmd = \"kill \" + str(server_pid)\n os.popen(kill_cmd)\n print(\"Appium server: port=%d/pid=%d kill done ...\" % (_port, server_pid))\n else:\n print(\"Appium server port:%d is idle and available!\" % _port)\n\n def is_appium_port_idle(self, _host, _port):\n s_client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n s_client.connect((_host, _port))\n s_client.shutdown(socket.SHUT_RDWR)\n except OSError:\n print(\"Appium server port:%d is not be used!\" % _port)\n return True\n else:\n print(\"Appium server port:%d is using!\" % _port)\n return False\n\n def get_netstat_pid_by_port(self, _port):\n pid = None\n cmd = \"netstat -nlptu | awk '{print $4,$7}' | grep \" + str(_port)\n shell_dict = self.shell_cmd(cmd)\n tag = \":\" + str(_port)\n for line in shell_dict['std_out'].split('\\n'):\n if tag in line:\n pid = line.split(' ')[1].split('/')[0]\n return int(pid)\n\n def multi_start_appium_server(self):\n pass\n\n def multi_connect_device(self):\n pass\n\n\n", "repo_name": "BoJunZeng/appium_demo", "sub_path": "utils/appium_utils.py", "file_name": "appium_utils.py", "file_ext": "py", "file_size_in_byte": 2820, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "utils.common_utils.Common", "line_number": 12, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 45, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 51, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 51, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 51, "usage_type": "attribute"}, {"api_name": "socket.SHUT_RDWR", "line_number": 54, "usage_type": "attribute"}]} +{"seq_id": "16000556185", "text": "import os\nfrom datetime import timedelta\nfrom pathlib import Path\n\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n\nSECRET_KEY = 'django-insecure-#c)nh44kcl3k6(ww^#vy^fp+(%1#g5%n-e8cs9##-_&7rmt=$n'\n\nDEBUG = os.getenv('DEBUG')\n\nALLOWED_HOSTS = os.getenv('ALLOWED_HOSTS').split(',')\n\n\nINSTALLED_APPS = [\n 'frontend.apps.FrontendConfig',\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'rest_framework',\n 'drf_yasg',\n 'phonenumber_field'\n]\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'config.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [\n os.path.join(BASE_DIR, 'api/docs'),\n os.path.join(BASE_DIR, 'frontend/templates')\n ],\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.contrib.messages.context_processors.messages'\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'config.wsgi.application'\n\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': BASE_DIR / 'db.sqlite3',\n }\n}\n\nLOGGING = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"formatters\": {\n \"verbose\": {\n \"format\": \"{levelname} {asctime} {message}\",\n \"style\": \"{\",\n },\n \"simple\": {\n \"format\": \"{levelname} {message}\",\n \"style\": \"{\",\n },\n },\n \"handlers\": {\n \"console\": {\n \"class\": \"logging.StreamHandler\",\n \"formatter\": \"verbose\",\n },\n },\n \"loggers\": {\n \"frontend\": {\n \"handlers\": [\"console\"],\n \"level\": \"INFO\",\n \"propagate\": False,\n },\n },\n}\n\nREST_FRAMEWORK = {\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework_simplejwt.authentication.JWTAuthentication',\n ),\n 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',\n 'PAGE_SIZE': 10\n}\n\nSIMPLE_JWT = {\n 'ACCESS_TOKEN_LIFETIME': timedelta(days=10),\n 'AUTH_HEADER_TYPES': ('Bearer',),\n}\n\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\nLOGIN_URL = 'frontend:signup'\nLOGOUT_REDIRECT_URL = 'frontend:main'\nAUTH_USER_MODEL = 'frontend.CustomUser'\n\nPHONENUMBER_DB_FORMAT = 'NATIONAL'\nPHONENUMBER_DEFAULT_REGION = 'RU'\n\nLANGUAGE_CODE = 'ru'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\nSTATICFILES_DIRS = (\n os.path.join(BASE_DIR, 'api/docs'),\n os.path.join(BASE_DIR, 'frontend/static')\n)\nSTATIC_ROOT = os.path.join(BASE_DIR, 'static')\nSTATIC_URL = '/static/'\n\n\nDEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'\n", "repo_name": "clownvkkaschenko/ReferralSystem", "sub_path": "referral_system/config/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 3698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}]} +{"seq_id": "10438954259", "text": "import numpy as np\nfrom xml.etree.ElementTree import ElementTree\nimport re\nfrom matplotlib import pyplot as plt\n\nclass SVGReader(object):\n def __init__(self):\n # load XML-tree\n self.ns = 'http://www.w3.org/2000/svg' # XML namespace\n self.et = ElementTree()\n self.svgpath = None\n\n def init(self, data):\n self.data = data\n self.tree = self.et.parse(data)\n if (self.tree.get('width')[-2:] in ['mm', 'px']): # units millimeter or pixels\n viewbox = self.tree.get('viewBox').split(' ')\n xmin, ymin, xmax, ymax = viewbox\n if (self.tree.get('width')[-2:] is 'mm'):\n self.width_px = float(xmax) - float(xmin)\n self.height_px = float(ymax) - float(ymin)\n self.meter_to_pixel = self.width_px/float(self.tree.get('width')[:-2])\n else: # [px]\n self.width_px = float(xmax) - float(xmin)\n self.height_px = float(ymax) - float(ymin)\n else:\n # if no unit mentioned, it is px\n self.width_px = float(self.tree.get('width')) # get width from svg\n self.height_px = float(self.tree.get('height')) # get height from svg\n\n self.position = [0, 0] # default, [px]\n self.obstacles = []\n\n def convert_path_to_points(self):\n\n # find svg-paths, describing the shapes e.g. using Bezier curves\n # for rectangle check on straight lines --> is control point on line\n # between start and end?\n try:\n # search for the word path in the outer branch of the SVG-file\n self.svgpath = self.tree.findall(\"{%s}path\" %self.ns)\n if not self.svgpath:\n # if not yet found, search for the word path in the next branch\n self.svgpath = self.tree.find(\"{%s}g\" %self.ns).findall(\"{%s}path\" %self.ns)\n if not self.svgpath:\n # if not yet found, search for the word path in the next branch\n self.svgpath = self.tree.find(\"{%s}g\" %self.ns).find(\"{%s}g\" %self.ns).findall(\"{%s}path\" %self.ns)\n except: # error occured, e.g. no found, this is possible when you only have basic shapes\n print('No shapes found which are described by a path, probably you only have basic shapes')\n return\n if not self.svgpath: # no path found, this is possible when you only have basic shapes\n print('No shapes found which are described by a path, probably you only have basic shapes')\n return\n\n self.n_paths = len(self.svgpath) # number of paths which build up the figure\n\n # initialize output file\n counter = 0\n # loop over paths\n while counter < self.n_paths:\n # look for all MCmc with a number behind it, line runs until a space or minus sign is found\n lines = re.findall('[MCmc][\\s.,0-9-]+', self.svgpath[counter].get('d'))\n points = []\n for line in lines:\n if line:\n # line [0] contains Mx,y, the startpoint\n test1=line[1:].replace(\",\",\" \") # replace comma by: space\n test2 = test1.replace(\"-\",\" -\") # replace minus sign by: space minus\n test3 = test2.replace(\"c\",\" c \") # replace c by: space c space\n # splits the line at each space, to create separate points\n newpoints = np.array(list(map(eval, test3.strip().split(' '))))\n if line[0] == 'c': # lower case c means relative coordinates, upper case C is absolute coordinates\n newpoints[0:6:2] = newpoints[0:6:2] + points[-2] # relative to absolute coordinates for x\n newpoints[1:6:2] = newpoints[1:6:2] + points[-1] # relative to absolute coordinates for y\n # for the first line (Mx,y) there is no 'c', so the starting point (x,y)\n # is added to points in the first iteration\n points.extend(newpoints) # add newpoints to points\n counter += 1\n # save points to file\n f = open(\"environment.txt\", \"a\")\n f.write( \"path_\"+ str(counter) + \"=\"+ str(np.array(points)) + \"\\n\" )\n f.close()\n\n def convert_basic_shapes(self):\n # code for basic shapes and \n\n # Todo: these shapes can also have a transform, find it and\n # add this transform to self.transform\n\n # find svg-paths, describing the rectangles\n try:\n # search for the word rect in the outer branch of the SVG-file\n self.rectangles = self.tree.findall(\"{%s}rect\" %self.ns)\n if not self.rectangles:\n # if not yet found, search for the word rect in the next branch\n self.rectangles = self.tree.find(\"{%s}g\" %self.ns).findall(\"{%s}rect\" %self.ns)\n if not self.rectangles:\n # if not yet found, search for the word rect in the next branch\n self.rectangles = self.tree.find(\"{%s}g\" %self.ns).find(\"{%s}g\" %self.ns).findall(\"{%s}rect\" %self.ns)\n self.n_rect = len(self.rectangles) # number of paths which build up the figure\n if self.n_rect == 0:\n print('No rectangles found')\n except:\n print('No shapes found which are described by a rect')\n\n # find svg-paths, describing the circles\n try:\n # search for the word circ in the outer branch of the SVG-file\n self.circles = self.tree.findall(\"{%s}circle\" %self.ns)\n if not self.circles:\n # if not yet found, search for the word circ in the next branch\n self.circles = self.tree.find(\"{%s}g\" %self.ns).findall(\"{%s}circle\" %self.ns)\n if not self.circles:\n # if not yet found, search for the word circ in the next branch\n self.circles = self.tree.find(\"{%s}g\" %self.ns).find(\"{%s}g\" %self.ns).findall(\"{%s}circle\" %self.ns)\n self.n_circ = len(self.circles) # number of paths which build up the figure\n if self.n_circ == 0:\n print('No circles found')\n except:\n print('No shapes found which are described by a circle')\n\n for rectangle in self.rectangles:\n obstacle = {}\n obstacle['shape'] = 'rectangle'\n pos = [float(rectangle.get('x')), float(rectangle.get('y'))] # Note: [x,y] is the top left corner\n # axis are placed in the top left corner and point to the right(x) and downward(y)\n obstacle['pos'] = [pos[0]+float(rectangle.get('width'))*0.5, pos[1]+float(rectangle.get('height'))*0.5]\n obstacle['pos'] += self.transform # apply transform\n obstacle['width'] = float(rectangle.get('width'))\n obstacle['height'] = float(rectangle.get('height'))\n obstacle['velocity'] = [0, 0]\n obstacle['bounce'] = False\n self.obstacles.append(obstacle)\n\n for circle in self.circles:\n obstacle = {}\n obstacle['shape'] = 'circle'\n obstacle['pos'] = [float(circle.get('cx')), float(circle.get('cy'))] # Note: [x,y] is the top left corner\n obstacle['pos'] += self.transform # apply transform\n obstacle['radius'] = float(circle.get('r'))\n obstacle['velocity'] = [0, 0]\n obstacle['bounce'] = False\n self.obstacles.append(obstacle)\n\n def convert_lines(self):\n # code for basic shapes and \n\n # find svg-polylines\n # example: \n try:\n # search for the word polyline in the outer branch of the SVG-file\n self.polylines = self.tree.findall(\"{%s}polyline\" %self.ns)\n if not self.polylines:\n # if not yet found, search for the word polyline in the next branch\n self.polylines = self.tree.find(\"{%s}g\" %self.ns).findall(\"{%s}polyline\" %self.ns)\n if not self.polylines:\n # if not yet found, search for the word polyline in the next branch\n self.polylines = self.tree.find(\"{%s}g\" %self.ns).find(\"{%s}g\" %self.ns).findall(\"{%s}polyline\" %self.ns)\n self.n_polylines = len(self.polylines) # number of paths which build up the figure\n if self.n_polylines == 0:\n print('No polylines found')\n except:\n print('No shapes found which are described by a polyline')\n\n # find svg-lines\n # example: \n try:\n # search for the word line in the outer branch of the SVG-file\n self.lines = self.tree.findall(\"{%s}line\" %self.ns)\n if not self.lines:\n # if not yet found, search for the word line in the next branch\n self.lines = self.tree.find(\"{%s}g\" %self.ns).findall(\"{%s}line\" %self.ns)\n if not self.lines:\n # if not yet found, search for the word line in the next branch\n self.lines = self.tree.find(\"{%s}g\" %self.ns).find(\"{%s}g\" %self.ns).findall(\"{%s}line\" %self.ns)\n self.n_lines = len(self.lines) # number of paths which build up the figure\n if self.n_lines == 0:\n print('No lines found')\n except:\n print('No shapes found which are described by a line')\n\n for polyline in self.polylines:\n try:\n stroke_width = float(polyline.get('stroke-width')) # stroke-width given as basic element\n except: # stroke-width wrapped in style element\n style = polyline.get('style').split(';')\n for element in style:\n if 'stroke-width' in element:\n stroke_width = float(element.split(':')[1])\n vertices = polyline.get('points').split(' ')\n vertices[:] = (v for v in vertices if v != '') # remove all empty strings\n vertices = np.array(list(map(eval, vertices))) # gives array of arrays [[x,y],[],...]\n vertices += self.transform\n\n # make rectangle of each vertex couple\n for l in range(len(vertices)-1):\n obstacle = {}\n obstacle['shape'] = 'rectangle'\n obstacle['velocity'] = [0, 0]\n obstacle['bounce'] = False\n\n # Note: to avoid explicitly checking if the line goes from\n # left to right / right to left\n # bottom to top / top to bottom\n # we use w and h separate from obstacle width and height\n line = np.array(vertices[l+1]) - np.array(vertices[l])\n if line[0] == 0: # vertical line\n obstacle['width'] = stroke_width\n obstacle['height'] = abs(line[1])\n h = line[1]\n w = cmp(h,0)*stroke_width # give stroke_width same sign as h\n elif line[1] == 0: # horizontal line\n obstacle['width'] = abs(line[0])\n obstacle['height'] = stroke_width\n w = line[0]\n h = cmp(w,0)*stroke_width # give stroke_width same sign as w\n else:\n raise RuntimeError('Diagonal lines are not yet supported')\n obstacle['pos'] = [vertices[l][0] + w*0.5, vertices[l][1] + h*0.5]\n self.obstacles.append(obstacle)\n\n for line in self.lines:\n obstacle = {}\n obstacle['shape'] = 'rectangle'\n obstacle['velocity'] = [0, 0]\n obstacle['bounce'] = False\n\n try:\n stroke_width = float(line.get('stroke-width')) # stroke-width given as basic element\n except: # stroke-width wrapped in style element\n style = line.get('style').split(';')\n for element in style:\n if 'stroke-width' in element:\n stroke_width = float(element.split(':')[1])\n x1, y1 = float(line.get('x1')), float(line.get('y1'))\n x2, y2 = float(line.get('x2')), float(line.get('y2'))\n # add transform\n x1 += self.transform[0]\n y1 += self.transform[1]\n x2 += self.transform[0]\n y2 += self.transform[1]\n if x1 == x2: # vertical line\n obstacle['width'] = stroke_width\n obstacle['height'] = abs(y2-y1)\n h = y2-y1 # signed value\n w = cmp(h,0)*stroke_width\n elif y1 == y2: # horizontal line\n obstacle['width'] = abs(x2-x1)\n obstacle['height'] = stroke_width\n w = x2-x1 # signed value\n h = cmp(w,0)*stroke_width\n else:\n raise RuntimeError('Diagonal lines are not yet supported')\n\n # don't use width and height since then you have to check if x1 > x2 etc,\n # to decide if the line goes from left to right or the other way around\n obstacle['pos'] = [x1 + w*0.5, y1 + h*0.5]\n self.obstacles.append(obstacle)\n\n def compute_transform(self):\n # Note: only works for translation for the moment\n # check if figure is transformed, e.g. a translation\n try:\n trans1 = self.tree.find(\"{%s}g\" %self.ns).get('transform')\n trans1 = trans1.split('translate')\n trans1.remove('')\n self.transform1 = np.array(map(eval, trans1)[0])\n except:\n print('No transform1 found')\n try:\n trans2 = self.tree.find(\"{%s}g\" %self.ns).find(\"{%s}g\" %self.ns).get('transform')\n trans2 = trans2.split('translate')\n trans2.remove('')\n self.transform2 = np.array(map(eval, trans2)[0])\n except:\n print('No transform2 found')\n if hasattr(self, 'transform1') and hasattr(self, 'transform2'):\n self.transform = self.transform1 + self.transform2 # coordinate frame transformation\n elif hasattr(self, 'transform1') :\n self.transform = self.transform1\n elif hasattr(self, 'transform2') :\n self.transform = self.transform2\n else:\n self.transform = [0, 0] # no transforms found\n\n def reconstruct(self, file):\n # help function, re-draws the loaded figure, allowing to check if it has the desired shapes\n points = []\n with open(file, \"r\") as f:\n for line in f:\n for word in line.split(' '):\n if (word != ', ' and word != ', ' and word != '' and word != ' '):\n points.append(word)\n f.close()\n newpoints = []\n for point in points:\n if point[-1:] == '\\n':\n point = point[:-1]\n if point[-1:] == ']':\n point = point[:-1]\n if point[0] != 'p':\n newpoints.append(point)\n x = []\n y = []\n for i in range(0,len(newpoints),2):\n x.append(newpoints[i])\n y.append(newpoints[i+1])\n\n plt.plot(x,y)\n plt.show()\n\n def build_environment(self):\n\n # Todo: write code here to check which elements are in the svg:\n # path, rectangle, circ, line, polyline,...\n # and call the appropriate functions, instead of calling them all\n\n self.compute_transform() # assigns values to self.transform\n\n self.convert_basic_shapes() # looks for rect and circle shapes\n self.convert_path_to_points() # looks for shapes defined by a Bezier path\n self.convert_lines() # looks for shapes defined by polyline and line\n # if you found some paths, they are transformed to an obstacle and\n # added to self.obstacles\n\n def get_gcode_description (self):\n # Note: for now this function only works for lines and paths. With capital M and lower-case c.\n # The paths are supposed to represent circle segments, if they don't,\n # a circle approximation of the curve is used.\n\n children = self.tree.getchildren() # the xml-tree\n self.commands = [] # holds the GCode commands\n\n for idx, child in enumerate(children):\n if 'line' in child.tag:\n # child is a line\n # example: \n x1, y1 = float(child.get('x1')), float(child.get('y1')) # start\n x2, y2 = float(child.get('x2')), float(child.get('y2')) # end\n # add transform\n x1 += self.transform[0]\n y1 += self.transform[1]\n x2 += self.transform[0]\n y2 += self.transform[1]\n\n y1 = -y1 # flip axis: in svg top left corner is [0,0], y-axis points downwards\n y2 = -y2 # make y-axis point upwards\n\n # make line GCode segment\n if not self.commands:\n # this is the first command, so make it a G00\n self.commands.append('G00 X'+str(x1)+' Y'+str(y1))\n self.commands.append('G01 X'+str(x2)+' Y'+str(y2)) # only add endpoint, startpoint comes from previous command\n\n elif 'path' in child.tag:\n path = child.get('d')\n # d='Mx,y c x1 y1 x2 y2 x y'\n # Mx,y = the startpoint or endpoint of the curve\n # c starts a curve, lower case means relative coordinates i.e. relative to Mx, y\n # x1 y1 is the control point that is closest to Mx, y\n # x2 y2 is the second control point\n # x y is the endpoint of the curve\n # d= can contain multiple c-commands = curves\n if path[0] == 'M':\n path = path[1:].replace(\",\",\" \") # replace comma by: space\n path = path.replace(\"-\",\" -\") # replace minus sign by: space minus\n path = path.replace(\"c\",\" c \") # replace c by: space c space\n path = path.split(' c ')\n\n filtered_path = []\n for curve in path:\n curve = curve.split(' ')\n # remove all empty strings\n curve = [e for e in curve if e!= '']\n curve = [e for e in curve if e!= ' ']\n filtered_path.append(curve) # save filtered path\n\n # the first border point of the curve (later decide if this is start or end)\n curve_point1 = filtered_path[0]\n curve_point1[0] = float(curve_point1[0])\n curve_point1[1] = -float(curve_point1[1]) # minus: let y-axis point up\n filtered_path.pop(0) # remove first point from path\n\n if filtered_path:\n # there are curves in the path,\n # loop over all curves\n circle_points = []\n for curve in filtered_path:\n if not circle_points:\n # first circle point, move starting from Mx, y = curve_point1\n # note: minus sign for y-direction\n circle_points.append([float(curve[-2])+curve_point1[0], -float(curve[-1])+curve_point1[1]])\n else:\n # this was not the first circle point, move relative from previous point\n # note: minus sign for y-direction\n circle_points.append([float(curve[-2])+circle_points[-1][0], -float(curve[-1])+circle_points[-1][1]])\n\n # add endpoint of curve\n circle_points.insert(0,curve_point1)\n\n # For now this function supposes that each curve consists of minimum three points.\n # If not, you need to approximate the circle shape in another way\n if len(circle_points) > 2:\n # find circle through first three points\n # by finding the intersection of the two perpendicular bisectors through [p1p2] and [p2p3]\n p1, p2, p3 = circle_points[:3]\n mid1 = [(p2[0]+p1[0])*0.5, (p2[1]+p1[1])*0.5] # midpoint of first bisector\n mid2 = [(p3[0]+p2[0])*0.5, (p3[1]+p2[1])*0.5]\n cx, cy = [], [] # will hold circle center\n\n if (p1[0] == p2[0] and p2[1] == p3[1]):\n # vertical and horizontal bisectors\n cx = mid1[0]\n cy = mid2[1]\n elif (p1[1] == p2[1] and p2[0] == p3[0]):\n # horizontal and vertical bisectors\n cx = mid2[0]\n cy = mid1[1]\n elif p2[0] == p1[0]:\n # vertical bisector1\n cx = mid1[0]\n rico2 = (p3[1]-p2[1])/(p3[0]-p2[0])\n normal2 = -1/rico2\n cy = mid2[1] + normal2*(x-mid2[0])\n elif p2[1] == p1[1]:\n # horizontal bisector1\n cy = mid1[1]\n rico2 = (p3[1]-p2[1])/(p3[0]-p2[0])\n normal2 = -1/rico2\n cx = (cy-mid2[1])/normal2 + mid2[0]\n elif p2[0] == p3[0]:\n # vertical bisector2\n cx = mid2[0]\n rico1 = (p2[1]-p1[1])/(p2[0]-p1[0])\n normal1 = -1/rico1\n cy = mid1[1] + normal1*(x-mid1[0])\n elif p2[1] == p3[1]:\n # horizontal bisector2\n cy = mid2[1]\n rico1 = (p2[1]-p1[1])/(p2[0]-p1[0])\n normal1 = -1/rico1\n cx = (cy-mid1[1])/normal1 + mid1[0]\n else:\n # two diagonal bisectors\n rico1 = (p2[1]-p1[1])/(p2[0]-p1[0])\n normal1 = -1/rico1\n # y = mid1[1] + normal1*(x-mid1[0]) [1]\n rico2 = (p3[1]-p2[1])/(p3[0]-p2[0])\n normal2 = -1/rico2\n # y = mid2[1] + normal2*(x-mid2[0]) [2]\n\n # [1] = [2] --> x =\n if normal2 != normal1:\n cx = (mid1[1]-mid2[1]-normal1*mid1[0]+normal2*mid2[0])/(normal2-normal1)\n else:\n raise RuntimeError('Normals are equal, something went wrong')\n\n cy = mid1[1] + normal1*(cx-mid1[0])\n\n # compute radius\n r = np.sqrt((p1[0]-cx)**2+(p1[1]-cy)**2)\n\n # plot solution\n # plt.figure(11)\n # eval = np.linspace(0,2*np.pi,100)\n # plt.plot(cx+r*np.cos(eval),cy+r*np.sin(eval),'g-')\n # plt.plot(cx,cy,'gx')\n # # plt.plot(mid1[0],mid1[1],'rx')\n # # plt.plot(mid2[0],mid2[1],'rx')\n # plt.plot(p2[0],p2[1],'rx')\n # plt.plot(p1[0],p1[1],'rx')\n # plt.plot(p3[0],p3[1],'rx')\n\n else:\n raise RuntimeError('Curve must consist of more than two points for the moment')\n\n # given radius and center\n # compute I an J from center\n\n # compute end point of curves\n curve_point2 = np.array(curve_point1)\n for curve in filtered_path:\n # add relative positions of all curves\n curve_point2 += np.array([float(curve[-2]), -float(curve[-1])])\n\n # now decide what is start and end of curve, because start must connect to end of previous command\n prev_seg_end = [float(self.commands[-1].split(' ')[1].split('X')[1]),\n float(self.commands[-1].split(' ')[2].split('Y')[1])]\n dist1 = np.sqrt((curve_point1[0]-prev_seg_end[0])**2+(curve_point1[1]-prev_seg_end[1])**2)\n dist2 = np.sqrt((curve_point2[0]-prev_seg_end[0])**2+(curve_point2[1]-prev_seg_end[1])**2)\n # curve start point is closest to the end of the previous segment\n if dist1 < dist2:\n start = curve_point1\n end_curve = curve_point2\n control_point = [start[0]+float(filtered_path[0][0]), start[1]-float(filtered_path[0][1])]\n else:\n start = curve_point2\n end_curve = curve_point1\n control_point = [start[0]-float(filtered_path[-1][4])+float(filtered_path[-1][2]),\n start[1]+float(filtered_path[-1][5])-float(filtered_path[-1][3])]\n\n # compute I and J\n I = cx - start[0]\n J = cy - start[1]\n\n # determine if circle goes clockwise or counter-clockwise\n # by taking the vector product of the center to the start point & start to control point\n v1 = [start[0]-cx, start[1]-cy]\n v2 = [control_point[0]-start[0], control_point[1]-start[1]]\n vector_product = v1[0]*v2[1] - v2[0]*v1[1]\n\n if vector_product < 0:\n # clockwise arc\n self.commands.append('G02 X'+str(end_curve[0])+' Y'+str(end_curve[1])+ ' I'+str(I)+' J'+str(J))\n else:\n # counter-clockwise arc\n self.commands.append('G03 X'+str(end_curve[0])+' Y'+str(end_curve[1])+ ' I'+str(I)+' J'+str(J))\n else:\n # there are no curves in the path, probably it just contains a move command\n # so it represents a GCode line segment\n self.commands.append('G01 X'+str(end_curve[0])+' Y'+str(end_curve[1]))\n else:\n raise RuntimeError('Only absolute positioning of the start of the curve is supported for now')\n\n # Write .nc file\n old_name = self.data.name.split('/')[-1][:-4]\n f = open(old_name+'_gcode.nc', 'w')\n for command in self.commands:\n f.write(command + '\\n')\n f.close()", "repo_name": "meco-group/omg-tools", "sub_path": "omgtools/gui/svg_reader.py", "file_name": "svg_reader.py", "file_ext": "py", "file_size_in_byte": 27698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 530, "dataset": "github-code", "pt": "2", "api": [{"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 10, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 310, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 489, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 490, "usage_type": "call"}]} +{"seq_id": "71393680688", "text": "import os\nimport json\nimport logging\nimport requests\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\nfrom socketserver import ThreadingMixIn\nimport io\nfrom keras.preprocessing.image import (\n ImageDataGenerator,\n load_img,\n array_to_img,\n img_to_array,\n)\n\n# Constants\nFORMAT = os.getenv(\"FORMAT\", \"JPEG\")\nARG_TYPE = os.getenv(\"ARG_TYPE\", \"bytes\")\n\n# Environment Variables\nhost_target = os.environ.get(\"AIS_TARGET_URL\")\nTRANSFORM = os.environ.get(\"TRANSFORM\")\nif not host_target:\n raise EnvironmentError(\"AIS_TARGET_URL environment variable missing\")\nif not TRANSFORM:\n raise EnvironmentError(\n \"TRANSFORM environment variable missing. Check documentation for examples (link)\"\n )\ntransform_dict = json.loads(TRANSFORM)\n\n\nclass Handler(BaseHTTPRequestHandler):\n def log_request(self, code=\"-\", size=\"-\"):\n \"\"\"Override log_request to not log successful requests.\"\"\"\n pass\n\n def _set_headers(self):\n \"\"\"Set standard headers for responses.\"\"\"\n self.send_response(200)\n self.send_header(\"Content-Type\", \"application/octet-stream\")\n self.end_headers()\n\n def transform(self, data: bytes) -> bytes:\n \"\"\"Process image data as bytes using the specified transformation.\"\"\"\n try:\n img = load_img(io.BytesIO(data))\n img = img_to_array(img)\n datagen = ImageDataGenerator()\n img = datagen.apply_transform(x=img, transform_parameters=transform_dict)\n img = array_to_img(img)\n buf = io.BytesIO()\n img.save(buf, format=FORMAT)\n return buf.getvalue()\n except Exception as e:\n logging.error(\"Error processing data: %s\", str(e))\n raise\n\n def do_PUT(self):\n \"\"\"PUT handler supports `hpush` operation.\"\"\"\n try:\n content_length = int(self.headers[\"Content-Length\"])\n post_data = self.rfile.read(content_length)\n processed_data = self.transform(post_data)\n if processed_data is not None:\n self._set_headers()\n self.wfile.write(processed_data)\n else:\n self.send_response(500)\n self.end_headers()\n self.wfile.write(b\"Data processing failed\")\n except Exception as e:\n logging.error(\"Error processing PUT request: %s\", str(e))\n self.send_response(500)\n self.end_headers()\n self.wfile.write(b\"Data processing failed\")\n\n def do_GET(self):\n \"\"\"GET handler supports `hpull` operation.\"\"\"\n try:\n if self.path == \"/health\":\n self._set_headers()\n self.wfile.write(b\"Running\")\n return\n\n query_path = host_target + self.path\n\n if ARG_TYPE == \"url\": # need this for webdataset\n result = self.transform(query_path)\n else:\n input_bytes = requests.get(query_path).content\n result = self.transform(input_bytes)\n\n if result is not None:\n self._set_headers()\n self.wfile.write(result)\n else:\n self.send_response(500)\n self.end_headers()\n self.wfile.write(b\"Data processing failed\")\n except Exception as e:\n logging.error(\"Error processing GET request: %s\", str(e))\n self.send_response(500)\n self.end_headers()\n self.wfile.write(b\"Data processing failed\")\n\n\nclass ThreadedHTTPServer(ThreadingMixIn, HTTPServer):\n \"\"\"Handle requests in a separate thread.\"\"\"\n\n\ndef run(addr=\"0.0.0.0\", port=80):\n server = ThreadedHTTPServer((addr, port), Handler)\n logging.info(f\"Starting HTTP server on {addr}:{port}\")\n server.serve_forever()\n\n\nif __name__ == \"__main__\":\n run(addr=\"0.0.0.0\", port=80)\n", "repo_name": "NVIDIA/ais-etl", "sub_path": "transformers/keras_preprocess/http-multithreaded-server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 31, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 45, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.array_to_img", "line_number": 49, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 100, "usage_type": "call"}, {"api_name": "socketserver.ThreadingMixIn", "line_number": 106, "usage_type": "name"}, {"api_name": "http.server.HTTPServer", "line_number": 106, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "18943642230", "text": "from collections import defaultdict\nfrom sys import stdin, argv\nfrom urllib.parse import urlparse\nfrom urllib.request import urlopen\n\ndef tsv(items):\n items = sorted([(k, v) for (k, v) in items.items()], key=lambda x: x[1])\n for k, v in items:\n print(\"{}\\t{}\".format(k, v))\n\ndef try_visit(url):\n try:\n req = urlopen(url, timeout=5)\n return req.status < 400\n except:\n return False\n\ndef main(type):\n counts = defaultdict(lambda: 0)\n for line in stdin:\n url = urlparse(line)\n if type == 'paths':\n counts[url.path] += 1\n elif type == 'hosts':\n counts[url.netloc] += 1\n elif type == 'visit':\n counts[line] = try_visit(line)\n\n tsv(counts)\n\nif __name__ == '__main__':\n if len(argv) < 2 or argv[1] not in ['paths', 'hosts', 'visit']:\n print(\"Supply 'hosts' or 'paths' as the argument to count\")\n exit(1)\n main(argv[1])", "repo_name": "sinkingpoint/blog-codes", "sub_path": "google-urls/stats.py", "file_name": "stats.py", "file_ext": "py", "file_size_in_byte": 940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "urllib.request.urlopen", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 20, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "23420092660", "text": "import pytest\n\nfrom inka.models.notes.basic_note import BasicNote\nfrom inka.models.notes.cloze_note import ClozeNote\n\n\n@pytest.fixture\ndef basic_note() -> BasicNote:\n return BasicNote(\"front content\", \"back content\", [\"tag1\", \"tag2\"], \"deck name\")\n\n\ndef test_search_query(basic_note):\n basic_note.front_html = \"

front content

\"\n expected = '\"

front content

\"'\n\n assert basic_note.search_query == expected\n\n\ndef test_convert_fields_to_html_when_function_passed(basic_note):\n new_text = \"new text\"\n\n basic_note.convert_fields_to_html(lambda text: new_text)\n\n assert basic_note.front_html == new_text\n assert basic_note.back_html == new_text\n\n\ndef test_update_fields_with_when_function_passed(basic_note):\n new_text = \"new text\"\n\n basic_note.update_fields_with(lambda text: new_text)\n\n assert basic_note.updated_front_md == new_text\n assert basic_note.updated_back_md == new_text\n\n\ndef test_get_raw_fields(basic_note):\n fields = basic_note.get_raw_fields()\n\n assert len(fields) == 2\n assert fields[0] == basic_note.raw_front_md\n assert fields[1] == basic_note.raw_back_md\n\n\ndef test_get_raw_question_field(basic_note):\n field = basic_note.get_raw_question_field()\n\n assert field == basic_note.raw_front_md\n\n\ndef test_get_html_fields(basic_note, config):\n front_name = \"myFront\"\n back_name = \"myBack\"\n config.update_option_value(\"anki\", \"front_field\", front_name)\n config.update_option_value(\"anki\", \"back_field\", back_name)\n expected = {front_name: basic_note.front_html, back_name: basic_note.back_html}\n\n assert basic_note.get_html_fields(config) == expected\n\n\ndef test_get_anki_note_type(basic_note, config):\n expected = \"my super type\"\n config.update_option_value(\"anki\", \"basic_type\", expected)\n\n assert basic_note.get_anki_note_type(config) == expected\n\n\ndef test_eq_when_same():\n first_note = BasicNote(\"front\", \"back\", [\"tag1\"], \"deck\")\n second_note = BasicNote(\"front\", \"back\", [\"tag1\"], \"deck\")\n\n assert first_note == second_note\n\n\n@pytest.mark.parametrize(\n \"second_note\",\n (\n BasicNote(\"oops\", \"back\", [\"tag1\"], \"deck\"),\n BasicNote(\"front\", \"oops\", [\"tag1\"], \"deck\"),\n BasicNote(\"front\", \"back\", [\"tag1\", \"tag2\"], \"deck\"),\n BasicNote(\"front\", \"back\", [\"tag1\"], \"my deck\"),\n None,\n ClozeNote(\"front\", [\"tag1\"], \"my deck\"),\n \"short string\",\n ),\n)\ndef test_eq_when_not_equal(second_note):\n first_note = BasicNote(\"front\", \"back\", [\"tag1\"], \"deck\")\n\n assert first_note != second_note\n", "repo_name": "keiqu/inka", "sub_path": "tests/test_basic_note.py", "file_name": "test_basic_note.py", "file_ext": "py", "file_size_in_byte": 2553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "22", "api": [{"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 9, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 7, "usage_type": "attribute"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 8, "usage_type": "name"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 69, "usage_type": "call"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 70, "usage_type": "call"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 88, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 75, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 75, "usage_type": "attribute"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 78, "usage_type": "call"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 79, "usage_type": "call"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 80, "usage_type": "call"}, {"api_name": "inka.models.notes.basic_note.BasicNote", "line_number": 81, "usage_type": "call"}, {"api_name": "inka.models.notes.cloze_note.ClozeNote", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "69947656698", "text": "# Import libraries \n\nimport numpy as np \n\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns \n\nimport os\n\nimport warnings\n\n\n\n\nwarnings.filterwarnings(\"ignore\")\n# Set the size of the plots \n\nplt.rcParams[\"figure.figsize\"] = (18,8)\n\nsns.set(rc={'figure.figsize':(18,8)})\ndata = pd.read_csv(\"../input/pubg-finish-placement-prediction/train_V2.csv\")\n\nprint(\"Finished loading the data\")\ndata.shape\ndata.info()\ndata.head()\ndata.drop(columns=['rankPoints'], inplace=True)\n# Check to see what we are dealing with regarding missing and null values \n\ndata.isnull().values.any()\ndata.isnull().sum()\ndata.dropna(inplace=True)\n\ndata.isnull().values.any()\n# Check to see win percentage distribution \n\nsns.distplot(data['winPlacePerc']).set_title('Distribution of Winning Percentile');\nprint('Mean: {:.4f}, Median {:.4f}'.format(data['winPlacePerc'].mean(), data['winPlacePerc'].median()))\ndata['matchMean'] = data.groupby('matchId')['winPlacePerc'].transform('mean')\n\ndata['matchMedian'] = data.groupby('matchId')['winPlacePerc'].transform('median')\nsns.distplot(data['matchMean'], kde=False).set_title('Mean for Winning Percentile grouped by match');\nsns.distplot(data['matchMedian'], kde=False).set_title('Median for Winning Percentile grouped by match');\n# Get values\n\nprint('Mean: {:.4f}, Median {:.4f}'.format(data['matchMean'].mean(), data['matchMedian'].median()))\n# Can do this with matchType and then derive the team and match size\n\ndata['matchType'].unique()\nsns.countplot('matchType', data=data);\ndata['teamSize'] = data.groupby('groupId')['groupId'].transform('count')\n\ndata['maxTeamSize'] = data.groupby('matchId')['teamSize'].transform('max')\n\ndata['matchSize'] = data.groupby('matchId')['Id'].transform('nunique')\nsns.distplot(data['matchSize'], kde=False).set_title('Distribution of Players per Game');\n# Let's see the largest team size\n\ndata['maxTeamSize'].max()\nsns.distplot(data['teamSize'], kde=False);\ntypes = ['solo', 'solo-fpp', 'duo', 'duo-fpp', 'squad', 'squad-fpp']\n\ndata = data.loc[data['matchType'].isin(types)]\nsns.countplot('matchType', data=data);\nsns.distplot(data['matchSize'], kde=False).set_title('Distribution of Players per Game');sns.distplot(data['matchSize'], kde=False).set_title('Distribution of Players per Game');\ndata['matchSize'].min()\nsns.distplot(data['teamSize'], kde=False);\n# Also look at top 10% and bottom 10% of players \n\ntop_10 = data[data['winPlacePerc'] >= 0.9]\n\nbottom_10 = data[data['winPlacePerc'] <= 0.1]\ndata['boosts'].unique()\nsns.scatterplot(x=\"boosts\", y=\"winPlacePerc\", data=data, color='seagreen');\nsns.scatterplot(x=\"boosts\", y=\"winPlacePerc\", data=top_10, color='seagreen');\nsns.scatterplot(x=\"boosts\", y=\"winPlacePerc\", data=bottom_10, color='seagreen');\nsns.scatterplot(x=\"heals\", y=\"winPlacePerc\", data=data, color='seagreen');\nsns.scatterplot(x=\"heals\", y=\"winPlacePerc\", data=top_10, color='seagreen');\nsns.scatterplot(x=\"heals\", y=\"winPlacePerc\", data=bottom_10, color='seagreen');\ntop_10[['boosts', 'heals']].describe()\nbottom_10[['boosts', 'heals']].describe()\n# Count \n\nsns.countplot(data['kills'], color='red');\nsns.lineplot(x=\"kills\", y='winPlacePerc', data=data, color='red');\nsns.scatterplot(x=\"kills\", y=\"winPlacePerc\", data=data, color='red');\nsns.scatterplot(x=\"kills\", y=\"winPlacePerc\", data=top_10, color='red');\nsns.scatterplot(x=\"kills\", y=\"winPlacePerc\", data=bottom_10, color='red');\nzero_kills = data.copy()\n\nzero_kills = zero_kills[zero_kills['kills']==0]\n# Same reason as previous line\n\nsns.scatterplot(x=\"kills\", y='winPlacePerc', data=zero_kills);\nsns.lineplot(x=\"killPlace\", y='winPlacePerc', data=zero_kills);\ndata.head()\ndata[data['groupId'] == '4d4b580de459be'][['matchType', 'kills', 'killPlace', 'winPlacePerc']]\ndata[data['matchType'] == 'duo-fpp'].head()\ndata[data['groupId'] == '8e0a0ea95d3596'][['matchType', 'kills', 'killPlace', 'winPlacePerc']]\nsns.scatterplot(x=\"damageDealt\", y=\"winPlacePerc\", data=data);\nsns.scatterplot(x=\"damageDealt\", y=\"winPlacePerc\", data=top_10);\nsns.scatterplot(x=\"damageDealt\", y=\"winPlacePerc\", data=bottom_10);\nsns.scatterplot(x=\"matchDuration\", y=\"winPlacePerc\", data=data, color='yellow');\nsns.scatterplot(x=\"matchDuration\", y=\"winPlacePerc\", data=top_10, color='yellow');\nsns.scatterplot(x=\"matchDuration\", y=\"winPlacePerc\", data=bottom_10, color='yellow');\nsns.scatterplot(x=\"killPoints\", y=\"winPlacePerc\", data=data, color='orange');\nsns.scatterplot(x=\"killPoints\", y=\"winPlacePerc\", data=top_10, color='orange');\nsns.scatterplot(x=\"killPoints\", y=\"winPlacePerc\", data=bottom_10, color='orange');\nsns.lineplot(x=\"killPoints\", y='kills', data=data, color='orange');\nsns.lineplot(x=\"kills\", y='killPoints', data=data, color='orange');\nsns.lineplot(x=\"winPoints\", y='winPlacePerc', data=data, color='brown');\nsns.scatterplot(x=\"winPoints\", y=\"winPlacePerc\", data=data, color='brown');\nsns.scatterplot(x=\"winPoints\", y=\"winPlacePerc\", data=top_10, color='brown');\nsns.scatterplot(x=\"winPoints\", y=\"winPlacePerc\", data=bottom_10, color='brown');", "repo_name": "aorursy/new-nb-5", "sub_path": "mjenkins1_pubg-presentation-intro.py", "file_name": "mjenkins1_pubg-presentation-intro.py", "file_ext": "py", "file_size_in_byte": 4995, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "warnings.filterwarnings", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 21, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "seaborn.set", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 40, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 45, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 46, "usage_type": "call"}, {"api_name": "seaborn.countplot", "line_number": 53, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 59, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 63, "usage_type": "call"}, {"api_name": "seaborn.countplot", "line_number": 67, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 68, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 70, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 77, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 78, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 79, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 80, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 81, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 82, "usage_type": "call"}, {"api_name": "seaborn.countplot", "line_number": 87, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 88, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 89, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 90, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 91, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 97, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 98, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 103, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 104, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 105, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 106, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 107, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 108, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 109, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 110, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 111, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 112, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 113, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 114, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 115, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 116, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "74983369335", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\ndef run(event,context):\n driver = webdriver.PhantomJS(service_args=['--ssl-protocol=any'])\n driver.implicitly_wait(10)\n driver.get('http://www.python.org/')\n assert \"Python\" in driver.title\n elem = driver.find_element_by_name(\"q\")\n elem.send_keys(\"pycon\")\n elem.send_keys(Keys.RETURN)\n assert \"No results found.\" not in driver.page_source\n print(driver.title)\n driver.quit() \n", "repo_name": "jaklinger/nesta_dataflow", "sub_path": "collect_data/utils/immerseuk/gtr/gtr_extrainfo_aws.py", "file_name": "gtr_extrainfo_aws.py", "file_ext": "py", "file_size_in_byte": 486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "selenium.webdriver.PhantomJS", "line_number": 5, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 5, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "1394902563", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Apr 10 13:39:46 2020\r\n\r\n@author: James\r\n\"\"\"\r\n\r\n\r\nimport numpy as np\r\nfrom scipy.integrate import solve_ivp\r\nimport matplotlib.pyplot as plt\r\n\r\n####################\r\n# Solver Constants #\r\n####################\r\n\r\nsolver = \"RK45\" # Radau, DOP853 # RK45 Works with rotating frame but not inertial\r\n\r\nT_Min = 0\r\n\r\nT_Max = 500\r\n\r\nResolution = 1000\r\n\r\naxesLimits = 8\r\n\r\ninertial = True\r\n\r\n\r\n\r\n######################\r\n# Physical Constants #\r\n######################\r\n\r\nG = 4*np.pi**2 # Solar system units: unit time = 1 year, unit length = 1 AU\r\n\r\n\r\n\r\n#################\r\n# Sun Constants #\r\n#################\r\n\r\nMsun = 1\r\n\r\nsunPos = [0,0,0]\r\n\r\n\r\n\r\n###############################\r\n# Asteroid Initial Conditions #\r\n###############################\r\n\r\nasteroidAngle = np.pi/180 * 30\r\n\r\nRAst = 5.2\r\n\r\nx0 = RAst * np.cos(asteroidAngle)\r\ny0 = RAst * np.sin(asteroidAngle)\r\nz0 = 0\r\n\r\ntheta0 = 0 * np.pi/180 \r\n\r\nw = np.sqrt( G*Msun/(RAst**3) )\r\n\r\norbitalSpeed = w * RAst \r\n\r\nvx0 = -orbitalSpeed*np.sin(asteroidAngle + theta0)\r\nvy0 = orbitalSpeed*np.cos(asteroidAngle + theta0)\r\nvz0 = 0\r\n\r\ninertial_y0 = [x0, y0, z0, vx0, vy0, vz0]\r\n\r\nrotating_y0 = [x0, y0, z0, vx0 + w*y0, vy0 - w*x0, vz0]\r\n\r\n\r\n\r\ndef inertialField(t, vec):\r\n \r\n retVec = np.zeros(6)\r\n \r\n # Unpack asteroid position & velocity from the last step\r\n x = vec[0]\r\n y = vec[1]\r\n z = vec[2]\r\n \r\n vx = vec[3]\r\n vy = vec[4]\r\n vz = vec[5]\r\n \r\n # Return the new position ODEs [ dr_i/dt = v_i ]\r\n retVec[0] = vx\r\n retVec[1] = vy\r\n retVec[2] = vz\r\n \r\n # Asteroid parameters wrt the Sun\r\n r_s = np.sqrt( (x-sunPos[0])**2 + (y-sunPos[1])**2 + (z-sunPos[2])**2 )\r\n phi_s = np.arctan2(y-sunPos[1], x-sunPos[0])\r\n theta_s = np.arccos( z/r_s )\r\n \r\n # New velocity ODEs [ d(v_i)/dt = F_i/m ]\r\n k = ( -G*Msun/r_s**2)\r\n retVec[3] = k*np.cos(phi_s)*np.sin(theta_s) # ax\r\n retVec[4] = k*np.sin(phi_s)*np.sin(theta_s) # ay\r\n retVec[5] = k*np.cos(theta_s) # az\r\n \r\n return retVec\r\n\r\ndef rotatingField(t, vec):\r\n \r\n retVec = np.zeros(6)\r\n \r\n # Unpack asteroid position & velocity from the last step\r\n x = vec[0]\r\n y = vec[1]\r\n z = vec[2]\r\n \r\n vx = vec[3]\r\n vy = vec[4]\r\n vz = vec[5]\r\n \r\n # Return the new position ODEs [ dr_i/dt = v_i ]\r\n retVec[0] = vx\r\n retVec[1] = vy\r\n retVec[2] = vz\r\n \r\n # Asteroid parameters wrt the Sun\r\n r_s = np.sqrt( (x-sunPos[0])**2 + (y-sunPos[1])**2 + (z-sunPos[2])**2 )\r\n phi_s = np.arctan2(y-sunPos[1], x-sunPos[0])\r\n theta_s = np.arccos( z/r_s )\r\n \r\n # New velocity ODEs [ d(v_i)/dt = F_i/m ]\r\n k = ( -G*Msun/r_s**2)\r\n retVec[3] = k*np.cos(phi_s)*np.sin(theta_s) + w**2 * x + 2*w*vy # ax\r\n retVec[4] = k*np.sin(phi_s)*np.sin(theta_s) + w**2 * y - 2*w*vx # ay\r\n retVec[5] = k*np.cos(theta_s) # az\r\n \r\n return retVec\r\n\r\nsol = solve_ivp(\r\n inertialField if inertial else rotatingField,\r\n [T_Min, T_Max],\r\n inertial_y0 if inertial else rotating_y0,\r\n dense_output=False,\r\n vectorized=False,\r\n method=solver,\r\n t_eval=np.linspace(T_Min, T_Max, (T_Max - T_Min)*Resolution)\r\n )\r\n\r\n\r\n\r\n\r\n# All operations and functions are in a vectorised form\r\n\r\nt = sol.t\r\n \r\nx = sol.y[0]\r\ny = sol.y[1]\r\nz = sol.y[2]\r\n \r\nvx = sol.y[3] \r\nvy = sol.y[4]\r\nvz = sol.y[5]\r\n \r\ntheta = w*t if inertial else -w*t\r\n \r\nxArray = np.cos(theta) * x + np.sin(theta) * y\r\n \r\nyArray = - np.sin(theta) * x + np.cos(theta) * y\r\n \r\nkineticEnergy = (1/2)*(vx**2 + vy**2 + vz**2) if inertial else (1/2)*( (vx - w*y)**2 + (vy + w*x)**2 + vz**2)\r\n \r\npotentialEnergy = -G*Msun / np.sqrt( (x - sunPos[0])**2 + (y - sunPos[1])**2 + (z - sunPos[2])**2 )\r\n \r\nenergyArray = kineticEnergy + potentialEnergy\r\n\r\nplt.plot(sol.y[0], sol.y[1])\r\n\r\n# plt.plot(xArray, yArray)\r\n\r\n# plt.plot(xArray if inertial else sol.y[0], yArray if inertial else sol.y[1], label=\"Relative Position\")\r\n\r\nplt.xlim(-axesLimits, axesLimits)\r\nplt.ylim(-axesLimits, axesLimits)\r\n\r\nplt.xlabel(\"x / AU\")\r\nplt.ylabel(\"y / AU\")\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\r\n\r\n\r\n", "repo_name": "jalsop24/Trojan-Asteroids", "sub_path": "singleBodyTest.py", "file_name": "singleBodyTest.py", "file_ext": "py", "file_size_in_byte": 4175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.pi", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 135, "usage_type": "call"}, {"api_name": "scipy.integrate.solve_ivp", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}]} +{"seq_id": "70007202297", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Apr 12 23:29:37 2018\r\n\r\n@author: saikiran\r\n\"\"\"\r\n\r\nfrom mpi4py import MPI\r\nimport numpy as np\r\ndef divide_data(data,n):\r\n split_data = np.split(data,n)\r\n return split_data\r\n \r\n \r\ncomm = MPI.COMM_WORLD\r\nrank = comm.rank\r\nprint(\"my rank is:\", rank)\r\nstart_time = MPI.Wtime()\r\nprint(\"start time is:\",start_time)\r\nn=4\r\nnp.random.seed(0)\r\nvector2 = np.random.rand(1,4)\r\nv2=vector2\r\n\r\nsize = len(v2[0])\r\n\r\nfor i in range(n):\r\n vector1 = np.random.rand(4,4)\r\n vector1=np.ravel(vector1)\r\n v1 = divide_data(vector1,n)\r\n if rank==0:\r\n vec1 = np.reshape(v1[i],(int(size/n),size))\r\n \r\n if i==0:\r\n data=vec1*v2\r\n print(\"my vector product is:\",data)\r\n end_time = MPI.Wtime()\r\n print(\"end time is:\",end_time)\r\n print(\"total execution time is :\",end_time-start_time)\r\n \r\n destination_process= i+1\r\n if destination_process==n:\r\n print(\"Data has been sent to all processes succesfully\")\r\n else:\r\n comm.send(v1[i+1],dest=destination_process, tag=8)\r\n print(\"sending vector1 data {} data to process{}\" .format(v1[i+1],destination_process))\r\n final_vector=comm.recv(source = i+1)\r\n print(\"received data is\",final_vector)\r\n append_data = np.append(data,final_vector,axis=0)\r\n data = append_data\r\n print(\"my final_vector product data is :\",data)\r\n \r\n if rank==i+1:\r\n vector3 = comm.recv(source=0,tag=8)\r\n vector3 = np.reshape(vector3,(int(size/n),size))\r\n print(\"received vector1 data is\",vector3)\r\n data2 = np.multiply(vector3,v2)\r\n print(\"my vector product is:\", data2)\r\n destination_process = 0\r\n comm.send(data2, dest=destination_process)\r\n print(\"sending vector average data {} data to process{}\" .format(data2,destination_process))\r\n end_time = MPI.Wtime()\r\n print(\"end time is:\",end_time)\r\n print(\"total execution time is :\",end_time-start_time)\r\n \r\nif rank==n-1:\r\n print(\"vector multiplication using point to point is completed successfully\")", "repo_name": "SaikiranGS/Distributed-programming", "sub_path": "Distributed_vector_computation/vector_multiplication.py", "file_name": "vector_multiplication.py", "file_ext": "py", "file_size_in_byte": 2190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.split", "line_number": 11, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 15, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 15, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Wtime", "line_number": 18, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.ravel", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 32, "usage_type": "call"}, {"api_name": "mpi4py.MPI.Wtime", "line_number": 37, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 57, "usage_type": "call"}, {"api_name": "mpi4py.MPI.Wtime", "line_number": 62, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "23955722750", "text": "from cStringIO import StringIO\nfrom gettext import gettext as _\nimport copy\nimport httplib\nimport json\nimport logging\nimport os\nimport re\nimport traceback\nimport urlparse\n\nfrom nectar.downloaders.threaded import HTTPThreadedDownloader\nfrom nectar.listener import AggregatingEventListener\nfrom nectar.report import DownloadReport\nfrom nectar.request import DownloadRequest\nfrom pulp.server import exceptions as pulp_exceptions\n\nfrom pulp.plugins.util import misc\n\nfrom pulp_docker.common import constants, error_codes\nfrom pulp_docker.plugins import models\nfrom pulp_docker.plugins import auth_util\n\n\n_logger = logging.getLogger(__name__)\n\n\nclass V1Repository(object):\n \"\"\"\n This class represents a Docker v1 repository.\n \"\"\"\n ANCESTRY_PATH = '/v1/images/%s/ancestry'\n DOCKER_TOKEN_HEADER = 'x-docker-token'\n DOCKER_ENDPOINT_HEADER = 'x-docker-endpoints'\n IMAGES_PATH = '/v1/repositories/%s/images'\n TAGS_PATH = '/v1/repositories/%s/tags'\n API_VERSION_CHECK_PATH = '/v1/_ping'\n\n def __init__(self, name, download_config, registry_url, working_dir):\n \"\"\"\n Initialize the V1Repository.\n\n :param name: name of a docker repository\n :type name: basestring\n :param download_config: download configuration object\n :type download_config: nectar.config.DownloaderConfig\n :param registry_url: URL for the docker registry\n :type registry_url: basestring\n :param working_dir: full path to the directory where files should\n be saved\n :type working_dir: basestring\n \"\"\"\n self.name = name\n self.download_config = download_config\n self.registry_url = registry_url\n self.listener = AggregatingEventListener()\n self.downloader = HTTPThreadedDownloader(self.download_config, self.listener)\n self.working_dir = working_dir\n self.token = None\n self.endpoint = None\n\n def _get_single_path(self, path):\n \"\"\"\n Retrieve a single path within the upstream registry, and return its\n body after deserializing it as json\n\n :param path: a full http path to retrieve that will be urljoin'd to the\n upstream registry url.\n :type path: basestring\n\n :return: whatever gets deserialized out of the response body's json\n \"\"\"\n # if talking to docker hub, we'll get an endpoint specified, and then we'll have to get\n # tags from that endpoint instead of talking to the original feed URL.\n if self.endpoint:\n # we assume the same scheme that the registry URL used\n registry_url_parts = urlparse.urlsplit(self.registry_url)\n parts = urlparse.SplitResult(scheme=registry_url_parts.scheme, netloc=self.endpoint,\n path=path, query=None, fragment=None)\n url = urlparse.urlunsplit(parts)\n else:\n url = urlparse.urljoin(self.registry_url, path)\n request = DownloadRequest(url, StringIO())\n if path.endswith('/images'):\n # this is required by the docker index and indicates that it should\n # return an auth token\n if request.headers is None:\n request.headers = {}\n request.headers[self.DOCKER_TOKEN_HEADER] = 'true'\n # endpoints require auth\n if self.endpoint:\n self.add_auth_header(request)\n\n report = self.downloader.download_one(request)\n if report.state == report.DOWNLOAD_FAILED:\n raise IOError(report.error_msg)\n\n self._parse_response_headers(report.headers)\n return json.loads(report.destination.getvalue())\n\n def _parse_response_headers(self, headers):\n \"\"\"\n Some responses can include header information that we need later. This\n grabs those values and stores them for later use.\n\n :param headers: dictionary-like object where keys are HTTP header names\n and values are their values.\n :type headers: dict\n \"\"\"\n # this is used for authorization on an endpoint\n if self.DOCKER_TOKEN_HEADER in headers:\n self.token = headers[self.DOCKER_TOKEN_HEADER]\n # this tells us what host to use when accessing image files\n if self.DOCKER_ENDPOINT_HEADER in headers:\n self.endpoint = headers[self.DOCKER_ENDPOINT_HEADER]\n\n def api_version_check(self):\n \"\"\"\n Make a call to the registry URL's /v1/_ping API call to determine if the registry supports\n API v1.\n\n :return: True if the v1 API is found, else False\n :rtype: bool\n \"\"\"\n _logger.debug('Determining if the registry URL can do v1 of the Docker API.')\n\n try:\n self._get_single_path(self.API_VERSION_CHECK_PATH)\n except IOError:\n return False\n\n return True\n\n def add_auth_header(self, request):\n \"\"\"\n Given a download request, add an Authorization header if we have an\n auth token available.\n\n :param request: a download request\n :type request: nectar.request.DownloadRequest\n \"\"\"\n if self.token:\n if request.headers is None:\n request.headers = {}\n # this emulates what docker itself does\n request.headers['Authorization'] = 'Token %s' % self.token\n\n def get_image_ids(self):\n \"\"\"\n Get a list of all images in the upstream repository. This is\n conceptually a little ambiguous, as there can be images in a repo that\n are neither tagged nor in the ancestry for a tagged image.\n\n :return: list of image IDs in the repo\n :rtype: list\n\n :raises pulp_exceptions.PulpCodedException: if fetching the IDs fails\n \"\"\"\n path = self.IMAGES_PATH % self.name\n\n _logger.debug('retrieving image ids from remote registry')\n try:\n raw_data = self._get_single_path(path)\n except IOError as e:\n _logger.debug(traceback.format_exc())\n raise pulp_exceptions.PulpCodedException(error_code=error_codes.DKR1007,\n repo=self.name,\n registry=self.registry_url,\n reason=str(e))\n\n return [item['id'] for item in raw_data]\n\n def get_image_url(self):\n \"\"\"\n Get a URL for the registry or the endpoint, for use in retrieving image\n files. The \"endpoint\" is a host name that might be returned in a header\n when retrieving repository data above.\n\n :return: a url that is either the provided registry url, or if an\n endpoint is known, that same url with the host replaced by\n the endpoint\n :rtype: basestring\n \"\"\"\n if self.endpoint:\n parts = list(urlparse.urlsplit(self.registry_url))\n parts[1] = self.endpoint\n return urlparse.urlunsplit(parts)\n else:\n return self.registry_url\n\n def get_tags(self):\n \"\"\"\n Get a dictionary of tags from the upstream repo.\n\n :return: a dictionary where keys are tag names, and values are either\n full image IDs or abbreviated image IDs.\n :rtype: dict\n \"\"\"\n repo_name = self.name\n # this is a quirk of the docker registry API.\n if '/' not in repo_name:\n repo_name = 'library/' + repo_name\n\n path = self.TAGS_PATH % repo_name\n\n _logger.debug('retrieving tags from remote registry')\n raw_data = self._get_single_path(path)\n # raw_data will sometimes be a list of dicts, and sometimes just a dict,\n # depending on what version of the API we're talking to.\n if isinstance(raw_data, list):\n return dict((tag['name'], tag['layer']) for tag in raw_data)\n return raw_data\n\n def get_ancestry(self, image_ids):\n \"\"\"\n Retrieve the \"ancestry\" file for each provided image ID, and save each\n in a directory whose name is the image ID.\n\n :param image_ids: list of image IDs for which the ancestry file\n should be retrieved\n :type image_ids: list\n\n :raises IOError: if a download fails\n \"\"\"\n requests = []\n for image_id in image_ids:\n path = self.ANCESTRY_PATH % image_id\n url = urlparse.urljoin(self.get_image_url(), path)\n destination = os.path.join(self.working_dir, image_id, 'ancestry')\n misc.mkdir(os.path.split(destination)[0])\n\n request = DownloadRequest(url, destination)\n self.add_auth_header(request)\n requests.append(request)\n\n _logger.debug('retrieving ancestry files from remote registry')\n self.downloader.download(requests)\n if len(self.listener.failed_reports):\n raise IOError(self.listener.failed_reports[0].error_msg)\n\n def create_download_request(self, image_id, file_name, destination_dir):\n \"\"\"\n Return a DownloadRequest instance for the given file name and image ID.\n It is desirable to download the actual layer files with a separate\n downloader (for progress tracking, etc), so we just create the download\n requests here and let them get processed elsewhere.\n\n This adds the Authorization header if a token is known for this\n repository.\n\n :param image_id: unique ID of a docker image\n :type image_id: basestring\n :param file_name: name of the file, one of \"ancestry\", \"json\",\n or \"layer\"\n :type file_name: basestring\n :param destination_dir: full path to the directory where file should\n be saved\n :type destination_dir: basestring\n\n :return: a download request instance\n :rtype: nectar.request.DownloadRequest\n \"\"\"\n url = self.get_image_url()\n req = DownloadRequest(urlparse.urljoin(url, '/v1/images/%s/%s' % (image_id, file_name)),\n os.path.join(destination_dir, file_name))\n self.add_auth_header(req)\n return req\n\n\nclass V2Repository(object):\n \"\"\"\n This class represents a Docker v2 repository.\n \"\"\"\n API_VERSION_CHECK_PATH = '/v2/'\n LAYER_PATH = '/v2/{name}/blobs/{digest}'\n MANIFEST_PATH = '/v2/{name}/manifests/{reference}'\n TAGS_PATH = '/v2/{name}/tags/list'\n\n def __init__(self, name, download_config, registry_url, working_dir):\n \"\"\"\n Initialize the V2Repository.\n\n :param name: name of a docker repository\n :type name: basestring\n :param download_config: download configuration object\n :type download_config: nectar.config.DownloaderConfig\n :param registry_url: URL for the docker registry\n :type registry_url: basestring\n :param working_dir: full path to the directory where files should\n be saved\n :type working_dir: basestring\n \"\"\"\n\n # Docker's registry aligns non-namespaced images to the library namespace.\n # if we have a docker registry image, and no namespace, add the library\n # namespace to the image name.\n\n if '/' not in name and re.search(r'registry[-,\\w]*.docker.io', registry_url, re.IGNORECASE):\n self.name = \"library/\" + name\n else:\n self.name = name\n\n self.download_config = download_config\n self.registry_url = registry_url\n\n # Use basic auth information for retrieving tokens from auth server and for downloading\n # with basic auth\n self.auth_downloader = HTTPThreadedDownloader(copy.deepcopy(self.download_config),\n AggregatingEventListener())\n self.download_config.basic_auth_username = None\n self.download_config.basic_auth_password = None\n self.downloader = HTTPThreadedDownloader(self.download_config, AggregatingEventListener())\n self.working_dir = working_dir\n self.token = None\n\n def api_version_check(self):\n \"\"\"\n Make a call to the registry URL's /v2/ API call to determine if the registry supports API\n v2.\n\n :return: True if the v2 API is found, else False\n :rtype: bool\n \"\"\"\n _logger.debug('Determining if the registry URL can do v2 of the Docker API.')\n\n try:\n headers, body = self._get_path(self.API_VERSION_CHECK_PATH)\n except IOError:\n return False\n\n try:\n version = headers['Docker-Distribution-API-Version']\n if version != \"registry/2.0\":\n return False\n _logger.debug(_('The docker registry is using API version: %(v)s') % {'v': version})\n except KeyError:\n # If the Docker-Distribution-API-Version header isn't present, we will assume that this\n # is a valid Docker 2.0 API server so that simple file-based webservers can serve as our\n # remote feed.\n pass\n\n return True\n\n def create_blob_download_request(self, digest):\n \"\"\"\n Return a DownloadRequest instance for the given blob digest.\n It is desirable to download the blob files with a separate\n downloader (for progress tracking, etc), so we just create the download\n requests here and let them get processed elsewhere.\n\n :param digest: digest of the docker blob you wish to download\n :type digest: basestring\n\n :return: a download request instance\n :rtype: nectar.request.DownloadRequest\n \"\"\"\n path = self.LAYER_PATH.format(name=self.name, digest=digest)\n url = urlparse.urljoin(self.registry_url, path)\n req = DownloadRequest(url, os.path.join(self.working_dir, digest))\n return req\n\n def get_manifest(self, reference, headers=True, tag=True):\n \"\"\"\n Get the manifest and its digest for the given reference.\n\n :param reference: The reference (tag or digest) of the Manifest you wish to retrieve.\n :type reference: basestring\n :param headers: True if headers with accepted media type should be sent in the request\n :type headers: bool\n :param tag: True if the manifest should be retrieved by tag\n :type tag: bool\n\n :return: A 2-tuple of the digest and the manifest, both basestrings\n :rtype: tuple\n \"\"\"\n manifests = []\n request_headers = {}\n content_type_header = 'content-type'\n path = self.MANIFEST_PATH.format(name=self.name, reference=reference)\n # we need to skip the check of returned mediatype in case we pull\n # the manifest by digest\n if headers:\n # set the headers for first request\n request_headers['Accept'] = ','.join((constants.MEDIATYPE_MANIFEST_S2,\n constants.MEDIATYPE_MANIFEST_LIST,\n constants.MEDIATYPE_MANIFEST_S1,\n constants.MEDIATYPE_SIGNED_MANIFEST_S1))\n response_headers, manifest = self._get_path(path, headers=request_headers)\n # we need to disable here the digest check because of wrong digests registry returns\n # https://github.com/docker/distribution/pull/2310\n # we will just calculate it without camparing it to the value that registry has in the\n # docker-content-digest response header\n digest = models.UnitMixin.calculate_digest(manifest)\n # add manifest and digest\n manifests.append((manifest, digest, response_headers.get(content_type_header)))\n\n # since in accept headers we have man_list and schema2 mediatype, registry would return\n # whether man list, schema2 or schema1.\n # if it is schema1 we do not need to make any other requests\n # if it is manifest list, we do not need to make any other requests, the converted type\n # for older clients will be requested later during the manifest list process time\n # if it is schema2 we need to ask schema1 for older clients.\n if tag and response_headers.get(content_type_header) == constants.MEDIATYPE_MANIFEST_S2:\n request_headers['Accept'] = ','.join((constants.MEDIATYPE_MANIFEST_S1,\n constants.MEDIATYPE_SIGNED_MANIFEST_S1))\n try:\n # for compatibility with older clients, try to fetch schema1 in case it is available\n response_headers, manifest = self._get_path(path, headers=request_headers)\n digest = self._digest_check(response_headers, manifest)\n\n # add manifest and digest\n manifests.append((manifest, digest, response_headers.get(content_type_header)))\n except IOError as e:\n if '404 Client Error' not in str(e):\n raise\n pass\n\n # returned list will be whether:\n # [(S2, digest, content_type), (S1, digest, content_type)]\n # or\n # [(list, digest, content_type)]\n # or\n # [(S1, digest, content_type)]\n # [(S2, digest, content_type)]\n # note the tuple has a new entry content_type which we need later to process\n # returned manifest mediatypes\n return manifests\n\n def _digest_check(self, headers, manifest):\n\n digest_header = 'docker-content-digest'\n if digest_header in headers:\n expected_digest = headers[digest_header]\n # The digest is formatted as algorithm:sum, so let's ask our hasher to use the same\n # algorithm as we received in the headers.\n digest = models.Manifest.calculate_digest(manifest, expected_digest.split(':')[0])\n if digest != expected_digest:\n msg = _('The Manifest digest does not match the expected value. The remote '\n 'feed announced a digest of {e}, but the downloaded digest was {d}.')\n msg = msg.format(e=expected_digest, d=digest)\n raise IOError(msg)\n else:\n digest = models.Manifest.calculate_digest(manifest)\n\n return digest\n\n def get_tags(self):\n \"\"\"\n Get a list of the available tags in the repository.\n\n :return: A list of basestrings of the available tags in the repository.\n :rtype: list\n \"\"\"\n path = self.TAGS_PATH.format(name=self.name)\n _logger.debug('retrieving tags from remote registry')\n try:\n headers, tags = self._get_path(path)\n except IOError as e:\n raise pulp_exceptions.PulpCodedException(error_code=error_codes.DKR1007,\n repo=self.name,\n registry=self.registry_url,\n reason=str(e))\n tag_list = json.loads(tags)['tags'] or []\n # check for the presence of the pagination link header\n link = headers.get('Link')\n while link:\n # according RFC5988 URI-reference can be relative or absolute\n _, _, path, params, query, fragm = urlparse.urlparse(link.split(';')[0].strip('>, <'))\n link = urlparse.urlunparse((None, None, path, params, query, fragm))\n headers, tags = self._get_path(link)\n tag_list.extend(json.loads(tags)['tags'])\n link = headers.get('Link')\n return tag_list\n\n def _get_path(self, path, headers=None):\n \"\"\"\n Retrieve a single path within the upstream registry, and return a 2-tuple of the headers and\n the response body.\n\n :param path: a full http path to retrieve that will be urljoin'd to the upstream registry\n url.\n :type path: basestring\n :param headers: headers sent in the request\n :type headers: dict\n\n :return: (headers, response body)\n :rtype: tuple\n \"\"\"\n url = urlparse.urljoin(self.registry_url, path)\n _logger.debug(_('Retrieving {0}'.format(url)))\n request = DownloadRequest(url, StringIO())\n request.headers = headers\n\n if self.token:\n request.headers = auth_util.update_token_auth_header(request.headers, self.token)\n\n report = self.downloader.download_one(request)\n\n # If the download was unauthorized, check report header, if basic auth is expected\n # retry with basic auth, otherwise attempt to get a token and try again\n if report.state == report.DOWNLOAD_FAILED:\n if report.error_report.get('response_code') == httplib.UNAUTHORIZED:\n auth_header = report.headers.get('www-authenticate')\n if auth_header is None:\n raise IOError(\"401 responses are expected to \"\n \"contain authentication information\")\n elif \"Basic\" in auth_header:\n _logger.debug(_('Download unauthorized, retrying with basic authentication'))\n report = self.auth_downloader.download_one(request)\n else:\n _logger.debug(_('Download unauthorized, attempting to retrieve a token.'))\n self.token = auth_util.request_token(self.auth_downloader, request,\n auth_header, self.name)\n if not isinstance(self.token, DownloadReport):\n request.headers = auth_util.update_token_auth_header(request.headers,\n self.token)\n report = self.downloader.download_one(request)\n if report.state == report.DOWNLOAD_FAILED:\n # this condition was added in case the registry would not allow to access v2 endpoint\n # but still token would be valid for other endpoints.\n # see https://pulp.plan.io/issues/2643\n if path == '/v2/' and report.error_report.get('response_code') == httplib.UNAUTHORIZED:\n pass\n else:\n self._raise_path_error(report)\n\n return report.headers, report.destination.getvalue()\n\n @staticmethod\n def _raise_path_error(report):\n \"\"\"\n Raise an exception with an appropriate error message.\n\n Specifically because docker hub responds with a 401 for repositories that don't exist, pulp\n cannot disambiguate Unauthorized vs. Not Found. This function tries to make an error message\n that is clear on that point.\n\n :param report: download report\n :type report: nectar.report.DownloadReport\n\n :raises IOError: always, with an appropriate message based on the report\n \"\"\"\n if report.error_report.get('response_code') == httplib.UNAUTHORIZED:\n # docker hub returns 401 for repos that don't exist, so we cannot disambiguate.\n raise IOError(_('401 Client Error: \\'Unauthorized or Not Found\\' for url {0}'.format(\n report.url)))\n else:\n code = report.error_report.get('response_code')\n if code >= 400 and code < 500:\n raise IOError('{0} Client Error: \\'{1}\\' for url: {2}'.format(\n code, report.error_msg, report.url))\n elif code >= 500 and code < 600:\n raise IOError('{0} Server Error: \\'{1}\\' for url: {2}'.format(\n code, report.error_msg, report.url))\n else:\n raise IOError('\\'{0}\\' for url {1}'.format(report.error_msg, report.url))\n", "repo_name": "pulp/pulp_docker", "sub_path": "plugins/pulp_docker/plugins/registry.py", "file_name": "registry.py", "file_ext": "py", "file_size_in_byte": 24055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "nectar.listener.AggregatingEventListener", "line_number": 56, "usage_type": "call"}, {"api_name": "nectar.downloaders.threaded.HTTPThreadedDownloader", "line_number": 57, "usage_type": "call"}, {"api_name": "urlparse.urlsplit", "line_number": 77, "usage_type": "call"}, {"api_name": "urlparse.SplitResult", "line_number": 78, "usage_type": "call"}, {"api_name": "urlparse.urlunsplit", "line_number": 80, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 82, "usage_type": "call"}, {"api_name": "nectar.request.DownloadRequest", "line_number": 83, "usage_type": "call"}, {"api_name": "cStringIO.StringIO", "line_number": 83, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 99, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 165, "usage_type": "call"}, {"api_name": "pulp.server.exceptions.PulpCodedException", "line_number": 166, "usage_type": "call"}, {"api_name": "pulp.server.exceptions", "line_number": 166, "usage_type": "name"}, {"api_name": "pulp_docker.common.error_codes.DKR1007", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.error_codes", "line_number": 166, "usage_type": "name"}, {"api_name": "urlparse.urlsplit", "line_number": 185, "usage_type": "call"}, {"api_name": "urlparse.urlunsplit", "line_number": 187, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pulp.plugins.util.misc.mkdir", "line_number": 230, "usage_type": "call"}, {"api_name": "pulp.plugins.util.misc", "line_number": 230, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "nectar.request.DownloadRequest", "line_number": 232, "usage_type": "call"}, {"api_name": "nectar.request.DownloadRequest", "line_number": 264, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 298, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 298, "usage_type": "attribute"}, {"api_name": "nectar.downloaders.threaded.HTTPThreadedDownloader", "line_number": 308, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 308, "usage_type": "call"}, {"api_name": "nectar.listener.AggregatingEventListener", "line_number": 309, "usage_type": "call"}, {"api_name": "nectar.downloaders.threaded.HTTPThreadedDownloader", "line_number": 312, "usage_type": "call"}, {"api_name": "nectar.listener.AggregatingEventListener", "line_number": 312, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 335, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 358, "usage_type": "call"}, {"api_name": "nectar.request.DownloadRequest", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path", "line_number": 359, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_MANIFEST_S2", "line_number": 384, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 384, "usage_type": "name"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_MANIFEST_LIST", "line_number": 385, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 385, "usage_type": "name"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_MANIFEST_S1", "line_number": 386, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 386, "usage_type": "name"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_SIGNED_MANIFEST_S1", "line_number": 387, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 387, "usage_type": "name"}, {"api_name": "pulp_docker.plugins.models.UnitMixin.calculate_digest", "line_number": 393, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.models.UnitMixin", "line_number": 393, "usage_type": "attribute"}, {"api_name": "pulp_docker.plugins.models", "line_number": 393, "usage_type": "name"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_MANIFEST_S2", "line_number": 403, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 403, "usage_type": "name"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_MANIFEST_S1", "line_number": 404, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 404, "usage_type": "name"}, {"api_name": "pulp_docker.common.constants.MEDIATYPE_SIGNED_MANIFEST_S1", "line_number": 405, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.constants", "line_number": 405, "usage_type": "name"}, {"api_name": "pulp_docker.plugins.models.Manifest.calculate_digest", "line_number": 436, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.models.Manifest", "line_number": 436, "usage_type": "attribute"}, {"api_name": "pulp_docker.plugins.models", "line_number": 436, "usage_type": "name"}, {"api_name": "gettext.gettext", "line_number": 438, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.models.Manifest.calculate_digest", "line_number": 443, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.models.Manifest", "line_number": 443, "usage_type": "attribute"}, {"api_name": "pulp_docker.plugins.models", "line_number": 443, "usage_type": "name"}, {"api_name": "pulp.server.exceptions.PulpCodedException", "line_number": 459, "usage_type": "call"}, {"api_name": "pulp.server.exceptions", "line_number": 459, "usage_type": "name"}, {"api_name": "pulp_docker.common.error_codes.DKR1007", "line_number": 459, "usage_type": "attribute"}, {"api_name": "pulp_docker.common.error_codes", "line_number": 459, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 463, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 468, "usage_type": "name"}, {"api_name": "urlparse.urlparse", "line_number": 468, "usage_type": "call"}, {"api_name": "urlparse.urlunparse", "line_number": 469, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 471, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 489, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 490, "usage_type": "call"}, {"api_name": "nectar.request.DownloadRequest", "line_number": 491, "usage_type": "call"}, {"api_name": "cStringIO.StringIO", "line_number": 491, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.auth_util.update_token_auth_header", "line_number": 495, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.auth_util", "line_number": 495, "usage_type": "name"}, {"api_name": "httplib.UNAUTHORIZED", "line_number": 502, "usage_type": "attribute"}, {"api_name": "gettext.gettext", "line_number": 508, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 511, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.auth_util.request_token", "line_number": 512, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.auth_util", "line_number": 512, "usage_type": "name"}, {"api_name": "nectar.report.DownloadReport", "line_number": 514, "usage_type": "argument"}, {"api_name": "pulp_docker.plugins.auth_util.update_token_auth_header", "line_number": 515, "usage_type": "call"}, {"api_name": "pulp_docker.plugins.auth_util", "line_number": 515, "usage_type": "name"}, {"api_name": "httplib.UNAUTHORIZED", "line_number": 522, "usage_type": "attribute"}, {"api_name": "httplib.UNAUTHORIZED", "line_number": 543, "usage_type": "attribute"}, {"api_name": "gettext.gettext", "line_number": 545, "usage_type": "call"}]} +{"seq_id": "20524019838", "text": "import threading\nimport os\nimport glob\nimport sqlite3\nimport requests\nimport requests.utils\nimport pickle\nimport re\nimport html.parser\nimport cgi\n\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime, timedelta\nfrom socket import error as SocketError\nimport errno\nimport tart\n\nfrom readeryc import HNapi, readerutils\n\n\nclass App(tart.Application):\n\n \"\"\" The class that directly communicates with Tart and Cascades\n \"\"\"\n\n cache = [] # {'ident': None} # Keep track of current request\n SETTINGS_FILE = readerutils.SETTINGS_FILE\n COOKIE = readerutils.COOKIE\n HEADERS = readerutils.HEADERS\n\n def __init__(self):\n super().__init__(debug=False) # set True for some extra debug output\n self.settings = {\n 'openInBrowser': False,\n 'readerMode': False,\n 'loggedIn': False,\n 'username': '',\n 'legacyFetch': False,\n 'darkTheme': False\n }\n self.restore_data(self.settings, self.SETTINGS_FILE)\n self.sess = HNapi(self.settings['username'])\n print(\"restored: \", self.settings)\n\n def onUiReady(self):\n print(\"UI READY!!\")\n tart.send('restoreSettings', **self.settings)\n self.onRequestPage(\"news\", \"news\")\n # self.onRequestPage(\"ask\", \"ask\")\n # self.onRequestPage(\"newest\", \"newest\")\n\n def onSaveSettings(self, settings):\n self.settings.update(settings)\n self.save_data(self.settings, self.SETTINGS_FILE)\n\n# Handling requests\n def onRequestPage(self, source, sentBy, askPost=\"false\", deleteComments=\"false\", startIndex=0, author=\"\"):\n \"\"\" This is really ugly, but it handles all url requests with threading,\n it also prevents the same request from being made twice\n \"\"\"\n\n entryExists = False\n position = 0\n currReq = {'ident': (datetime.now(), source)}\n src = \"\"\n for i in self.cache:\n position = position + 1\n src = i['ident'][1]\n if src == source:\n print(\"Request in progress!!\")\n entryExists = True\n ts = i['ident'][0]\n # If the request is old, make the new one anyway\n if datetime.now() - ts > timedelta(minutes=5):\n break\n return # Otherwise quit\n\n print(\"Requests pending: \", len(self.cache))\n if len(self.cache) == 0:\n self.cache.append(currReq)\n entryExists = True\n\n if entryExists != True:\n print(\"Request doesn't exist\")\n # If we have 5 reqs going, remove the first one before adding\n if len(self.cache) > 5:\n self.cache.pop(0)\n self.cache.append(currReq) # Append it to cache\n t = threading.Thread(target=self.parseRequest, args=(\n source, sentBy, startIndex, askPost, author))\n\n else: # If the request does exist\n if len(self.cache) == 1: # If it is the only one we make the request (first request added)\n print(\"Only request?\")\n t = threading.Thread(target=self.parseRequest, args=(\n source, sentBy, startIndex, askPost, author))\n else: # If there are multiple requests\n print(\"Checking request\")\n if src == source:\n print(\"Request is the same!\")\n # Check if cache was made 5 mins ago\n if datetime.now() - ts > timedelta(minutes=5):\n print(\"Old enough, request OK\")\n t = threading.Thread(target=self.parseRequest, args=(\n source, sentBy, startIndex, askPost, author))\n else:\n return\n t.daemon = True\n t.start()\n\n def parseRequest(self, source, sentBy, startIndex, askPost, author):\n print(\"Parsing request for: \" + sentBy)\n if (sentBy in ['news', 'ask', 'newest', 'show']):\n self.storyRoutine(source, sentBy)\n elif (sentBy == 'commentPage'):\n self.commentsRoutine(source, askPost)\n elif (sentBy == 'searchPage'):\n self.searchRoutine(startIndex, [source, author])\n else:\n print(\"Error getting page...\")\n return\n print(\"request complete! Removing...\")\n self.cache.pop(-1)\n\n# GET functions\n def storyRoutine(self, source, sentBy):\n # try:\n stories, moreLink = self.sess.getStories(source)\n # except requests.exceptions.ConnectionError:\n # tart.send('{0}ListError'.format(sentBy),\n # text=\"Error getting stories\\nCheck your connection and try again!\")\n # return\n # except IndexError:\n # print(\"Expired link?\")\n # tart.send('{0}ListError'.format(sentBy),\n # text=\"Link expired\\nPlease refresh the page\")\n # return\n print(stories)\n for story in stories:\n tart.send('add{0}Stories'.format(sentBy),\n story=story, moreLink=moreLink, sentTo=sentBy)\n if (source == 'news'):\n tart.send('addCoverStories', stories=stories)\n\n def commentsRoutine(self, source, askPost):\n print(\"source sent:\" + source)\n\n try:\n text, comments = self.sess.getComments(\n source, askPost, self.settings['legacyFetch'])\n if (text != \"\"):\n text = readerutils.textReplace(text)\n\n tart.send('addText', text=text, hnid=source)\n if (comments == []):\n tart.send(\n 'commentError', text=\"No comments, check back later!\", hnid=source)\n for comment in comments:\n comment['text'] = readerutils.textReplace(comment['text'])\n comment['barColour'] = \"#\" + \\\n readerutils.getColour(comment[\"indent\"] // 40)\n tart.send('addComments', comment=comment, hnid=source)\n\n except requests.exceptions.ConnectionError:\n print(\"ERROR GETTING COMMENTS\")\n tart.send('addText', text='', hnid=source)\n tart.send(\n 'commentError', text=\"Error getting comments\\nCheck your connection and try again!\", hnid=source)\n except SocketError:\n print(\"ERROR GETTING COMMENTS\")\n tart.send('addText', text='', hnid=source)\n tart.send(\n 'commentError', text=\"Error getting comments\\nCheck your connection and try again!\", hnid=source)\n\n def searchRoutine(self, startIndex, source):\n print(\"Searching for: \" + str(source))\n try:\n result = self.sess.getSearchStories(startIndex, source)\n if result == []:\n tart.send(\n 'searchError', text=\"No results found!\")\n return\n for res in result:\n tart.send('addSearchStories', story=res)\n except requests.exceptions.ConnectionError:\n tart.send(\n 'searchError', text=\"Error getting stories\\nCheck your connection and try again!\")\n except SocketError:\n tart.send(\n 'searchError', text=\"Error getting stories\\nCheck your connection and try again!\")\n\n# POST functions\n def onRequestLogin(self, username, password):\n result = self.sess.login(username, password)\n tart.send('loginResult', result=result)\n\n def onGetProfile(self, username):\n info = self.sess.getProfile(username)\n print(info)\n if (info == False):\n os.remove(self.COOKIE)\n tart.send(\n 'logoutResult', text=\"Unable to get profile, forcing logout...\")\n return\n tart.send('profileRetrieved', email=info[3], about=info[2])\n\n def onSaveProfile(self, username, email, about):\n res = False\n try:\n res = self.sess.postProfile(username, email, about)\n except:\n tart.send(\n 'profileSaved', text=\"Unable to update profile, check connection and try again\")\n if (res == True):\n tart.send('profileSaved', text=\"Profile updated!\")\n else:\n tart.send(\n 'profileSaved', text=\"Unable to update profile, check connection and try again\")\n\n def onSendComment(self, source, text):\n res = self.sess.postComment(source, text)\n text = text.replace('*', '')\n if (res == True):\n tart.send('commentPosted', result=\"true\", comment=text)\n return\n tart.send('commentPosted', result=\"false\", comment=\"\")\n\n def onPostStory(self, title, url, text):\n res = self.sess.postStory(title, url, text)\n if (res == True):\n tart.send('storyPosted', result='true')\n else:\n tart.send('storyPosted', result='false')\n\n def onLogout(self):\n self.sess.logout()\n try:\n os.remove(self.COOKIE)\n except OSError:\n tart.send('logoutResult', text=\"logged out successfully!\")\n\n tart.send('logoutResult', text=\"logged out successfully!\")\n\n# Favouriting functions\n def onSaveArticle(self, article):\n conn = sqlite3.connect(\"data/favourites.db\")\n print(article)\n article = tuple(article)\n cursor = conn.cursor()\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS articles\n (title text, articleURL text, saveTime text,\n poster text, numComments text, isAsk text,\n domain text, points text, hnid text PRIMARY KEY)\n \"\"\")\n\n # insert to table\n try:\n cursor.execute(\n \"INSERT INTO articles VALUES (?,?,?,?,?,?,?,?,?)\", article)\n print(\"Article saved!\")\n # save data to database\n conn.commit()\n tart.send('saveResult', text=\"Article successfully favourited\")\n except sqlite3.IntegrityError:\n print(\"Article already saved!\")\n tart.send('saveResult', text=\"Article already favourited\")\n\n def onDeleteArticle(self, hnid, selected):\n conn = sqlite3.connect(\"data/favourites.db\")\n\n hnid = str(hnid)\n cursor = conn.cursor()\n cursor.execute(\"DELETE FROM articles WHERE hnid=?\", (hnid,))\n conn.commit()\n tart.send(\n 'deleteResult', text=\"Article removed from favourites\", itemToRemove=selected)\n\n def onLoadFavourites(self):\n conn = sqlite3.connect(\"data/favourites.db\")\n\n cursor = conn.cursor()\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS articles\n (title text, articleURL text, saveTime text,\n poster text, numComments text, isAsk text,\n domain text, points text, hnid text PRIMARY KEY)\n \"\"\")\n cursor.execute('SELECT * FROM articles')\n results = readerutils.get_rowdicts(cursor)\n tart.send('fillList', results=results)\n\n# Misc functions\n def onDeleteCache(self):\n print(\"PYTHON DELETING CACHE\")\n workingDir = os.getcwd() + '/data/cache/'\n cursor = self.conn.cursor()\n print(\"Dropping favourites table\")\n cursor.execute(\"\"\"DROP TABLE IF EXISTS articles\"\"\")\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS articles\n (title text, articleURL text, saveTime text,\n poster text, numComments text, isAsk text,\n domain text, points text, hnid text PRIMARY KEY)\n \"\"\")\n tart.send('cacheDeleted', text=\"Cache cleared!\")\n\n def onCopyHTML(self, content, meta):\n print(content)\n print(meta)\n soup = BeautifulSoup(content)\n from tart import clipboard\n c = clipboard.Clipboard()\n mimeType = 'text/plain'\n c.insert(mimeType, str(soup.text))\n tart.send('contentCopied', meta=meta)\n\n def onCopy(self, articleLink):\n from tart import clipboard\n c = clipboard.Clipboard()\n mimeType = 'text/plain'\n c.insert(mimeType, articleLink)\n tart.send('copyResult', text=articleLink + \" copied to clipboard!\")\n", "repo_name": "krruzic/Reader-YC", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 12497, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "22", "api": [{"api_name": "tart.Application", "line_number": 21, "usage_type": "attribute"}, {"api_name": "readeryc.readerutils.SETTINGS_FILE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "readeryc.readerutils", "line_number": 27, "usage_type": "name"}, {"api_name": "readeryc.readerutils.COOKIE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "readeryc.readerutils", "line_number": 28, "usage_type": "name"}, {"api_name": "readeryc.readerutils.HEADERS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "readeryc.readerutils", "line_number": 29, "usage_type": "name"}, {"api_name": "readeryc.HNapi", "line_number": 42, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 74, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 89, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 102, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 104, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 140, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 143, "usage_type": "call"}, {"api_name": "readeryc.readerutils.textReplace", "line_number": 152, "usage_type": "call"}, {"api_name": "readeryc.readerutils", "line_number": 152, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 154, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 156, "usage_type": "call"}, {"api_name": "readeryc.readerutils.textReplace", "line_number": 159, "usage_type": "call"}, {"api_name": "readeryc.readerutils", "line_number": 159, "usage_type": "name"}, {"api_name": "readeryc.readerutils.getColour", "line_number": 161, "usage_type": "call"}, {"api_name": "readeryc.readerutils", "line_number": 161, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 162, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 164, "usage_type": "attribute"}, {"api_name": "tart.send", "line_number": 166, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 167, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 169, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 171, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 172, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 180, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 184, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tart.send", "line_number": 186, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 188, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 189, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 195, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 201, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 202, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 205, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 212, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 215, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 217, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 224, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 226, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 231, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 233, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 238, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 240, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 242, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 246, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 263, "usage_type": "call"}, {"api_name": "sqlite3.IntegrityError", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tart.send", "line_number": 266, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 269, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 275, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 279, "usage_type": "call"}, {"api_name": "readeryc.readerutils.get_rowdicts", "line_number": 288, "usage_type": "call"}, {"api_name": "readeryc.readerutils", "line_number": 288, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 289, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 294, "usage_type": "call"}, {"api_name": "tart.send", "line_number": 303, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 308, "usage_type": "call"}, {"api_name": "tart.clipboard.Clipboard", "line_number": 310, "usage_type": "call"}, {"api_name": "tart.clipboard", "line_number": 310, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 313, "usage_type": "call"}, {"api_name": "tart.clipboard.Clipboard", "line_number": 317, "usage_type": "call"}, {"api_name": "tart.clipboard", "line_number": 317, "usage_type": "name"}, {"api_name": "tart.send", "line_number": 320, "usage_type": "call"}]} +{"seq_id": "8986459280", "text": "from matplotlib.ticker import MaxNLocator\r\nimport itertools\r\n\r\ndef draw_rectangles(rectangleDataList):\r\n padding = 5\r\n maxX = max([max_x for (min_x,min_y,max_x,max_y) in rectangleDataList]) + padding\r\n minX = min([min_x for (min_x,min_y,max_x,max_y) in rectangleDataList]) - padding\r\n maxY = max([max_y for (min_x,min_y,max_x,max_y) in rectangleDataList]) + padding\r\n minY = min([min_y for (min_x,min_y,max_x,max_y) in rectangleDataList]) - padding\r\n\r\n # this plots all of the rectangles on the same graph\r\n fig, ax = plt.subplots()\r\n plt.title('Rectangles')\r\n ax.set_aspect(1) #normalizes the graph\r\n ax.yaxis.set_major_locator(MaxNLocator(integer=True))\r\n ax.xaxis.set_major_locator(MaxNLocator(integer=True))\r\n\r\n #the x and y limits for the graph are set by the largest x and y values generated and are restricted by radii\r\n plt.xlim(minX , maxX )\r\n plt.ylim(minY , maxY )\r\n plt.grid(True, which='both')\r\n\r\n for rectangle in rectangleDataList:\r\n min_x,min_y,max_x,max_y = rectangle\r\n width = max_x - min_x\r\n height = max_y - min_y\r\n\r\n # Plot library uses min left as the point\r\n pt_x = min_x\r\n pt_y = min_y\r\n\r\n # For annotation\r\n center_x = min_x + width/2.0\r\n center_y = min_y + height/2.0\r\n\r\n # Place the rectangle\r\n rectangleObj = plt.Rectangle(xy=(pt_x, pt_y), width=width, height=height, color='b', fill=False, linewidth=2)\r\n ax.add_artist(rectangleObj)\r\n annotate_string = 'Min: (' + str(min_x)+','+str(min_y)+')\\n'+'Max: ('+str(max_x)+','+str(max_y) + ')'\r\n label = ax.annotate(annotate_string, xy=(center_x, center_y), fontsize=9, ha=\"center\")\r\n\r\n plt.show()\r\n\r\ndraw_rectangles([list(itertools.chain(*ground_truth)), list(itertools.chain(*prediction))])", "repo_name": "Mekrab/Computer-Vision-Toolbox", "sub_path": "python_toolbox/draw_rectangles.py", "file_name": "draw_rectangles.py", "file_ext": "py", "file_size_in_byte": 1813, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "matplotlib.ticker.MaxNLocator", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MaxNLocator", "line_number": 16, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "39966390178", "text": "# Modified from ScanNet evaluation script: https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts/3d_evaluation/evaluate_semantic_label.py\nimport logging\nimport numpy as np\n\nlog = logging.getLogger(__name__)\n\n\ndef evaluate_scan(pred_ids, gt_ids, confusion, id_to_label_map, ignore_id):\n\n VALID_CLASS_IDS = list(id_to_label_map.keys())\n\n # sanity checks\n if not pred_ids.shape == gt_ids.shape:\n raise RuntimeError(\"Ground truth and prediction sizes don't match\")\n\n for (gt_val, pred_val) in zip(gt_ids.flatten(), pred_ids.flatten()):\n if gt_val not in VALID_CLASS_IDS:\n continue\n if pred_val not in VALID_CLASS_IDS:\n pred_val = ignore_id\n confusion[gt_val][pred_val] += 1\n\n\ndef get_iou(label_id, confusion, id_to_label_map):\n\n VALID_CLASS_IDS = list(id_to_label_map.keys())\n\n if not label_id in VALID_CLASS_IDS:\n return float(\"nan\")\n # #true positives\n tp = np.longlong(confusion[label_id, label_id])\n # #false negatives\n fn = np.longlong(confusion[label_id, :].sum()) - tp\n # #false positives\n not_ignored = [l for l in VALID_CLASS_IDS if not l == label_id]\n fp = np.longlong(confusion[not_ignored, label_id].sum())\n\n denom = tp + fp + fn\n if denom == 0:\n return float(\"nan\")\n return (float(tp) / denom, tp, denom)\n\n\ndef write_result_file(confusion, ious, id_to_label_map):\n\n VALID_CLASS_IDS = list(id_to_label_map.keys())\n\n log.info(\"Semantic Segmentation results\")\n log.info(\"iou scores\")\n for i in range(len(VALID_CLASS_IDS)):\n label_id = VALID_CLASS_IDS[i]\n label_name = id_to_label_map[label_id]\n if type(ious[label_name]) == tuple:\n iou = ious[label_name][0]\n log.info(\"{0:<14s}({1:<2d}): {2:>5.3f}\".format(label_name, label_id, iou))\n log.info(\"confusion matrix\")\n log.info(\"\\t\\t\\t\")\n\n output_string = \"\"\n for i in range(len(VALID_CLASS_IDS)):\n # f.write('\\t{0:<14s}({1:<2d})'.format(CLASS_LABELS[i], VALID_CLASS_IDS[i]))\n output_string += \"{0:<8d}\".format(VALID_CLASS_IDS[i])\n log.info(output_string)\n\n for r in range(len(VALID_CLASS_IDS)):\n log.info(\"{0:<14s}({1:<2d})\".format(id_to_label_map[r], VALID_CLASS_IDS[r]))\n\n output_string = \"\"\n for c in range(len(VALID_CLASS_IDS)):\n output_string += \"\\t{0:>5.3f}\".format(\n confusion[VALID_CLASS_IDS[r], VALID_CLASS_IDS[c]]\n )\n log.info(output_string)\n\n\ndef evaluate(matches, id_to_label_map, ignore_id, verbose=True):\n\n VALID_CLASS_IDS = list(id_to_label_map.keys())\n\n max_id = np.max(VALID_CLASS_IDS)\n confusion = np.zeros((max_id + 1, max_id + 1), dtype=np.ulonglong)\n\n if verbose:\n log.info(f\"evaluating {len(matches.keys()) } scans...\")\n\n for scene_name, compare in matches.items():\n evaluate_scan(\n compare[\"pred\"], compare[\"gt\"], confusion, id_to_label_map, ignore_id\n )\n\n class_ious = {}\n for i in range(len(VALID_CLASS_IDS)):\n label_id = VALID_CLASS_IDS[i]\n label_name = id_to_label_map[label_id]\n class_ious[label_name] = get_iou(label_id, confusion, id_to_label_map)\n\n if verbose:\n log.info(\"classes IoU\")\n log.info(\"----------------------------\")\n for i in range(len(VALID_CLASS_IDS)):\n label_id = VALID_CLASS_IDS[i]\n label_name = id_to_label_map[label_id]\n if type(class_ious[label_name]) == tuple:\n log.info(\n \"{0:<14s}: {1:>5.3f} ({2:>6d}/{3:<6d})\".format(\n label_name,\n class_ious[label_name][0],\n class_ious[label_name][1],\n class_ious[label_name][2],\n )\n )\n\n # Return mean IOU\n mean_iou = 0\n for i in range(len(VALID_CLASS_IDS)):\n label_id = VALID_CLASS_IDS[i]\n iou_output = get_iou(label_id, confusion, id_to_label_map)\n if type(iou_output) == tuple:\n mean_iou += iou_output[0]\n mean_iou /= len(VALID_CLASS_IDS)\n\n if verbose:\n log.info(\"----------------------------\")\n log.info(f\"mIOU {mean_iou:.3f}\")\n\n return mean_iou\n", "repo_name": "jandaa/masters-thesis", "sub_path": "src/util/eval_semantic.py", "file_name": "eval_semantic.py", "file_ext": "py", "file_size_in_byte": 4242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "22", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.longlong", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.longlong", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.longlong", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.ulonglong", "line_number": 81, "usage_type": "attribute"}]} +{"seq_id": "21121864182", "text": "from concurrent.futures import ProcessPoolExecutor\nimport os\nimport sys\n\nimport torch\n\nfrom graphembed.utils import latest_path_by_basename_numeric_order\n\nfrom .agg_grid_results import load_embedding\nfrom ..agg_angle_ratios import sample_angle_ratios_and_save, parse_args\nfrom ..utils import fullpath_list\n\n\ndef main():\n torch.set_default_dtype(torch.float64)\n with ProcessPoolExecutor(max_workers=args.n_cpus) as ppool:\n futures = []\n for ds_dir in fullpath_list(args.root_dir):\n ds_name = os.path.basename(ds_dir)\n for loss_fn_dir in fullpath_list(ds_dir):\n loss_fn = os.path.basename(loss_fn_dir)\n for n_factors_dir in fullpath_list(loss_fn_dir):\n try:\n n_factors = int(os.path.basename(n_factors_dir))\n except:\n continue # Ignore the Euclidean baseline.\n for run_dir in fullpath_list(n_factors_dir):\n # Load the embedding.\n pattern = os.path.join(run_dir, 'embedding_*.pth')\n path = latest_path_by_basename_numeric_order(pattern)\n emb = load_embedding(path)\n\n # Submit it for processing.\n f = ppool.submit(sample_angle_ratios_and_save, emb,\n run_dir)\n futures.append(f)\n # Wait for the results.\n for f in futures:\n f.result()\n\n\nif __name__ == '__main__':\n global args\n args = parse_args()\n sys.exit(main())\n", "repo_name": "dalab/matrix-manifolds", "sub_path": "graphembed/experiments/products/agg_angle_ratios.py", "file_name": "agg_angle_ratios.py", "file_ext": "py", "file_size_in_byte": 1622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "22", "api": [{"api_name": "torch.set_default_dtype", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 15, "usage_type": "attribute"}, {"api_name": "concurrent.futures.ProcessPoolExecutor", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.fullpath_list", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "utils.fullpath_list", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "utils.fullpath_list", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.fullpath_list", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "graphembed.utils.latest_path_by_basename_numeric_order", "line_number": 30, "usage_type": "call"}, {"api_name": "agg_grid_results.load_embedding", "line_number": 31, "usage_type": "call"}, {"api_name": "agg_angle_ratios.sample_angle_ratios_and_save", "line_number": 34, "usage_type": "argument"}, {"api_name": "agg_angle_ratios.parse_args", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "15771282107", "text": "\"\"\"Serverless module.\"\"\"\nfrom __future__ import print_function\n\nimport logging\nimport os\nimport re\nimport subprocess\nimport sys\n\nfrom . import (\n RunwayModule, format_npm_command_for_logging, generate_node_command,\n run_module_command, run_npm_install, warn_on_skipped_configs\n)\nfrom ..util import change_dir, which\n\nLOGGER = logging.getLogger('runway')\n\n\ndef gen_sls_config_files(stage, region):\n \"\"\"Generate possible SLS config files names.\"\"\"\n names = []\n for ext in ['yml', 'json']:\n # Give preference to explicit stage-region files\n names.append(\n os.path.join('env',\n \"%s-%s.%s\" % (stage, region, ext))\n )\n names.append(\"config-%s-%s.%s\" % (stage, region, ext))\n # Fallback to stage name only\n names.append(\n os.path.join('env',\n \"%s.%s\" % (stage, ext))\n )\n names.append(\"config-%s.%s\" % (stage, ext))\n return names\n\n\ndef get_sls_config_file(path, stage, region):\n \"\"\"Determine Serverless config file name.\"\"\"\n for name in gen_sls_config_files(stage, region):\n if os.path.isfile(os.path.join(path, name)):\n return name\n return \"config-%s.json\" % stage # fallback to generic json name\n\n\ndef run_sls_remove(sls_cmd, env_vars):\n \"\"\"Run sls remove command.\"\"\"\n sls_process = subprocess.Popen(sls_cmd,\n stdout=subprocess.PIPE,\n env=env_vars)\n stdoutdata, _stderrdata = sls_process.communicate()\n sls_return = sls_process.wait()\n print(stdoutdata)\n if sls_return != 0 and (sls_return == 1 and not (\n re.search(r\"Stack '.*' does not exist\", stdoutdata))):\n sys.exit(sls_return)\n\n\nclass Serverless(RunwayModule):\n \"\"\"Serverless Runway Module.\"\"\"\n\n def run_serverless(self, command='deploy'):\n \"\"\"Run Serverless.\"\"\"\n response = {'skipped_configs': False}\n sls_opts = [command]\n\n if not which('npm'):\n LOGGER.error('\"npm\" not found in path or is not executable; '\n 'please ensure it is installed correctly.')\n sys.exit(1)\n\n if 'CI' in self.context.env_vars and command != 'remove':\n sls_opts.append('--conceal') # Hide secrets from serverless output\n\n if 'DEBUG' in self.context.env_vars:\n sls_opts.append('-v') # Increase logging if requested\n\n sls_opts.extend(['-r', self.context.env_region])\n sls_opts.extend(['--stage', self.context.env_name])\n sls_env_file = get_sls_config_file(self.path,\n self.context.env_name,\n self.context.env_region)\n\n sls_cmd = generate_node_command(command='sls',\n command_opts=sls_opts,\n path=self.path)\n\n if (not self.options.get('environments') and os.path.isfile(os.path.join(self.path, sls_env_file))) or ( # noqa pylint: disable=line-too-long\n self.options.get('environments', {}).get(self.context.env_name)): # noqa\n if os.path.isfile(os.path.join(self.path, 'package.json')):\n with change_dir(self.path):\n run_npm_install(self.path, self.options, self.context)\n LOGGER.info(\"Running sls %s on %s (\\\"%s\\\")\",\n command,\n os.path.basename(self.path),\n format_npm_command_for_logging(sls_cmd))\n if command == 'remove':\n # Need to account for exit code 1 on any removals after\n # the first\n run_sls_remove(sls_cmd, self.context.env_vars)\n else:\n run_module_command(cmd_list=sls_cmd,\n env_vars=self.context.env_vars)\n else:\n LOGGER.warning(\n \"Skipping serverless %s of %s; no \\\"package.json\\\" \"\n \"file was found (need a package file specifying \"\n \"serverless in devDependencies)\",\n command,\n os.path.basename(self.path))\n else:\n response['skipped_configs'] = True\n LOGGER.info(\n \"Skipping serverless %s of %s; no config file for \"\n \"this stage/region found (looking for one of \\\"%s\\\")\",\n command,\n os.path.basename(self.path),\n ', '.join(gen_sls_config_files(self.context.env_name,\n self.context.env_region)))\n return response\n\n def plan(self):\n \"\"\"Skip sls planning.\"\"\"\n LOGGER.info('Planning not currently supported for Serverless')\n\n def deploy(self):\n \"\"\"Run sls deploy.\"\"\"\n result = self.run_serverless(command='deploy')\n warn_on_skipped_configs(result, self.context.env_name,\n self.context.env_vars)\n\n def destroy(self):\n \"\"\"Run serverless remove.\"\"\"\n result = self.run_serverless(command='remove')\n warn_on_skipped_configs(result, self.context.env_name,\n self.context.env_vars)\n", "repo_name": "goedelsoup/runway", "sub_path": "runway/module/serverless.py", "file_name": "serverless.py", "file_ext": "py", "file_size_in_byte": 5354, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "22", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "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.isfile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 48, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "util.which", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "util.change_dir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}]} +{"seq_id": "38568787226", "text": "import sys\nimport math\nimport cv2 as cv\nimport numpy as np\n\ndef main(argv):\n main_images_path = '../images/'\n #default_file = main_images_path + 'Empty1.jpg'\n #default_file = main_images_path + 'Empty2.jpg'\n default_file = main_images_path + 'Empty3.png'\n #default_file = main_images_path + 'empty_lot.jpg'\n #default_file = main_images_path + 'parking_lot.png'\n\n filename = argv[0] if len(argv) > 0 else default_file\n # Loads an image\n src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)\n # Check if image is loaded fine\n if src is None:\n print('Error opening image!')\n print('Usage: hough_lines.py [image_name -- default ' + default_file + '] \\n')\n return -1\n\n dst = cv.Canny(src, 250, 350, None, 3)\n # here i can change the value of the min and max gradients depending on the brightness of the image\n cv.imshow (\"canny\", dst)\n # Copy edges to the images that will display the results in BGR\n cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)\n cdstP = np.copy(cdst)\n\n linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)\n\n\n if linesP is not None:\n for i in range(0, len(linesP)):\n l = linesP[i][0]\n cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 6, cv.LINE_AA)\n #print(l[0], \" \", l[1], \" \", l[2], \" \", l[3])\n# cv.LINE_AA >> gives anti-aliased line which looks great for curves.\n cv.imshow(\"Source\", src)\n cv.imshow(\"Detected Lines (in red) - Probabilistic Line Transform\", cdstP)\n\n cv.waitKey()\n return 0\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])", "repo_name": "AhmedSayedSk/parking-spaces-detction", "sub_path": "code/line_detection.py", "file_name": "line_detection.py", "file_ext": "py", "file_size_in_byte": 1618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.samples.findFile", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.samples", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.HoughLinesP", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}]} +{"seq_id": "22394231640", "text": "# coding=utf-8\r\nimport sys\r\nimport requests\r\nimport re\r\nfrom bs4 import BeautifulSoup\r\n\r\nreload(sys)\r\nsys.setdefaultencoding('utf-8')\r\n\r\n'''\r\n由于引用了第三方模块，请先 pip install beautifulsoup4\r\n'''\r\n'''\r\n函数说明：月度综合指数表\r\n参数说明：page 总共爬取页数 默认为10页\r\n数据较多，需要运行一小段时间，请耐心等候\r\n'''\r\n\r\ndef getCompositeIndexData(page=10):\r\n fileName = '../data/compositeindex/compositedata.csv'\r\n with open(fileName, 'w+') as f:\r\n for i in range(1, int(page)+1):\r\n url = \"http://sspi.csi.com.cn/ydzhzsb_\" + str(i) + \".html\"\r\n r = requests.get(url)\r\n html = r.text\r\n soup = BeautifulSoup(html, 'html.parser')\r\n table = soup.table\r\n if i == 1:\r\n thead = table.thead\r\n trArray = thead.find_all('tr')\r\n td1Array = trArray[0].find_all('td')\r\n headtime = td1Array[0].text.strip()\r\n # headqishu = td1Array[1].text.strip()\r\n headzonghe = td1Array[2].text.strip()\r\n # headgansa = td1Array[3].text.strip()\r\n # headyouchuan = td1Array[4].text.strip()\r\n # headhuochuan = td1Array[5].text.strip()\r\n # 文件第一行\r\n title = headtime + \",\" + headzonghe\r\n # print(title)\r\n f.write(title)\r\n f.write('\\n')\r\n\r\n tbody = table.tbody\r\n for tr in tbody.find_all('tr'):\r\n tdarr = tr.find_all('td')\r\n # print(len(tdarr))\r\n # print(tdarr)\r\n time = \"\"\r\n zhonghe = \"\"\r\n for i in range(len(tdarr) - 1):\r\n if i == 0:\r\n time = tdarr[i].text\r\n elif i==2:\r\n zhonghe = tdarr[i].text\r\n break\r\n # content = content + tdarr[i].text + \",\"\r\n # content = content[:-1]\r\n content = time + \",\" + zhonghe\r\n if len(content) != 1:\r\n f.write(content)\r\n f.write('\\n')\r\n\r\nif __name__ == '__main__':\r\n getCompositeIndexData(8)\r\n", "repo_name": "neil-yc/crawler", "sub_path": "datacrawler/src/compositeIndexCrawler.py", "file_name": "compositeIndexCrawler.py", "file_ext": "py", "file_size_in_byte": 2274, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "31941797626", "text": "import sys\nimport os\nimport glob\nimport subprocess\nimport shutil\nimport argparse\nimport tempfile\n\nfrom typing import Optional, NamedTuple\n\nimport logging\nimport re\n\n\"\"\"\nCPA-witness2test module for validating witness files by using a generate-and-validate approach.\nCreates a test harness based on the violation witness given for an input file,\ncompiles the file with the created harness and checks whether the created program\nreaches the target location specified by the violation witness.\n\nCurrently, reachability, overflow and memory safety properties are supported.\n\"\"\"\n\n__version__ = \"0.1\"\n\n\nCOMPILE_ARGS_FIXED = [\"-D__alias__(x)=\"]\n\"\"\"List of compiler arguments that are always passed to the compiler.\"\"\"\n\n# Strings used to match expected error messages\nEXPECTED_ERRMSG_REACH = \"CPAchecker test harness: property violation reached\"\nEXPECTED_ERRMSG_OVERFLOW = \"runtime error:\"\nEXPECTED_ERRMSG_MEM_FREE = \"ERROR: AddressSanitizer: attempting free\"\nEXPECTED_ERRMSG_MEM_DEREF = \"ERROR: AddressSanitizer:\"\nEXPECTED_ERRMSG_MEM_MEMTRACK = \"ERROR: AddressSanitizer:\"\n\n# Used machine models\nMACHINE_MODEL_32 = \"32bit\"\nMACHINE_MODEL_64 = \"64bit\"\n\n# Possible results of CPAchecker for C harness generation\nRESULT_ACCEPT = \"FALSE\"\nRESULT_REJECT = \"TRUE\"\nRESULT_UNK = \"UNKNOWN\"\n\n# Regular expressions used to match given specification properties\nREGEX_REACH = re.compile(r\"G\\s*!\\s*call\\(\\s*([a-zA-Z0-9_]+)\\s*\\(\\)\\s*\\)\")\nREGEX_OVERFLOW = re.compile(r\"G\\s*!\\s*overflow\")\n_REGEX_MEM_TEMPLATE = r\"G\\s*valid-%s\"\nREGEX_MEM_FREE = re.compile(_REGEX_MEM_TEMPLATE % \"free\")\nREGEX_MEM_DEREF = re.compile(_REGEX_MEM_TEMPLATE % \"deref\")\nREGEX_MEM_MEMTRACK = re.compile(_REGEX_MEM_TEMPLATE % \"memtrack\")\n\nSPEC_REACH = \"unreach-call\"\nSPEC_OVERFLOW = \"no-overflow\"\nSPEC_MEM_FREE = \"valid-free\"\nSPEC_MEM_DEREF = \"valid-deref\"\nSPEC_MEM_MEMTRACK = \"valid-memtrack\"\n\n\nclass ValidationResult(NamedTuple):\n verdict: str\n violated_property: Optional[str] = None\n successful_harness: Optional[str] = None\n\n\nclass Specification(NamedTuple):\n no_overflow: bool\n mem_free: bool\n mem_deref: bool\n mem_memtrack: bool\n reach_method_call: Optional[str]\n\n def is_reach_call(self):\n return self.reach_method_call is not None\n\n @property\n def mem(self):\n return any((self.mem_free, self.mem_deref, self.mem_memtrack))\n\n def invalid(self):\n return not (\n self.no_overflow\n or self.mem_free\n or self.mem_deref\n or self.mem_memtrack\n or self.reach_method_call\n )\n\n\nclass ValidationError(Exception):\n \"\"\"Exception representing a validation error.\"\"\"\n\n def __init__(self, msg):\n self._msg = msg\n\n @property\n def msg(self):\n return self._msg\n\n\ndef get_cpachecker_version():\n \"\"\"Return the CPAchecker version used.\"\"\"\n\n executable = get_cpachecker_executable()\n result = execute([executable, \"-help\"], quiet=True)\n for line in result.stdout.split(os.linesep):\n if line.startswith(\"CPAchecker\"):\n return line.replace(\"CPAchecker\", \"\").strip()\n return None\n\n\ndef create_parser():\n descr = \"Validate a given violation witness for an input file.\"\n if sys.version_info >= (3, 5):\n parser = argparse.ArgumentParser(\n description=descr, add_help=False, allow_abbrev=False\n )\n else:\n parser = argparse.ArgumentParser(description=descr, add_help=False)\n\n parser.add_argument(\"-help\", action=\"help\")\n\n parser.add_argument(\n \"-version\", action=\"version\", version=\"{}\".format(get_cpachecker_version())\n )\n\n machine_model_args = parser.add_mutually_exclusive_group(required=False)\n machine_model_args.add_argument(\n \"-32\",\n dest=\"machine_model\",\n action=\"store_const\",\n const=MACHINE_MODEL_32,\n help=\"use 32 bit machine model\",\n )\n machine_model_args.add_argument(\n \"-64\",\n dest=\"machine_model\",\n action=\"store_const\",\n const=MACHINE_MODEL_64,\n help=\"use 64 bit machine model\",\n )\n machine_model_args.set_defaults(machine_model=MACHINE_MODEL_32)\n\n parser.add_argument(\n \"-outputpath\",\n dest=\"output_path\",\n type=str,\n action=\"store\",\n default=\"output\",\n help=\"path where output should be stored\",\n )\n\n parser.add_argument(\"-stats\", action=\"store_true\", help=\"show statistics\")\n\n parser.add_argument(\n \"-gcc-args\",\n dest=\"compile_args\",\n type=str,\n action=\"store\",\n nargs=argparse.REMAINDER,\n default=[],\n help=\"list of arguments to use when compiling the counterexample test\",\n )\n\n parser.add_argument(\n \"-spec\",\n dest=\"specification_file\",\n type=str,\n action=\"store\",\n required=True,\n help=\"specification file\",\n )\n\n parser.add_argument(\n \"-witness\",\n dest=\"witness_file\",\n required=True,\n type=str,\n action=\"store\",\n help=\"witness file\",\n )\n\n parser.add_argument(\"file\", help=\"file to validate witness for\")\n\n return parser\n\n\ndef _determine_file_args(argv):\n parameter_prefix = \"-\"\n files = []\n logging.debug(\"Determining file args from %s\", argv)\n for fst, snd in zip(argv[:-1], argv[1:]):\n if not fst.startswith(parameter_prefix) and not snd.startswith(\n parameter_prefix\n ):\n files.append(snd)\n logging.debug(\"Determined file args: %s\", files)\n return files\n\n\ndef _parse_args(argv):\n parser = create_parser()\n args, remainder = parser.parse_known_args(argv)\n args.file = _determine_file_args(argv)\n if not args.file:\n raise ValueError(\"The following argument is required: program file\")\n if len(args.file) > 1:\n raise ValueError(\n \"Too many values for argument: Only one program file supported\"\n )\n args.file = args.file[0]\n\n return args\n\n\ndef _create_compile_basic_args(args):\n compile_args = COMPILE_ARGS_FIXED + [x for x in args.compile_args if x is not None]\n if args.machine_model == MACHINE_MODEL_64:\n compile_args.append(\"-m64\")\n elif args.machine_model == MACHINE_MODEL_32:\n compile_args.append(\"-m32\")\n else:\n raise ValidationError(\"Neither 32 nor 64 bit machine model specified\")\n\n return compile_args\n\n\ndef _create_compiler_cmd_tail(harness, file, target):\n return [\"-o\", target, \"-include\", file, harness]\n\n\ndef create_compile_cmd(\n harness, program, target, args, specification, c_version=\"gnu11\"\n):\n \"\"\"Create the compile command.\n\n :param str harness: path to harness file\n :param str target: path to program under test\n :param args: arguments as parsed by argparse\n :param Specification specification: specification to compile for\n :param str c_version: C standard to use for compilation\n :return: list of command-line keywords that can be given to method `execute`\n \"\"\"\n\n if shutil.which(\"clang\"):\n compiler = \"clang\"\n else:\n compiler = \"gcc\"\n\n compile_cmd = [compiler] + _create_compile_basic_args(args)\n compile_cmd.append(\"-std={}\".format(c_version))\n\n sanitizer_in_use = False\n if specification.no_overflow:\n sanitizer_in_use = True\n compile_cmd += [\n \"-fsanitize=signed-integer-overflow\",\n \"-fsanitize=float-cast-overflow\",\n ]\n if specification.mem:\n sanitizer_in_use = True\n compile_cmd += [\"-fsanitize=address\", \"-fsanitize=leak\"]\n\n if sanitizer_in_use:\n # Do not continue execution after a sanitize error\n compile_cmd.append(\"-fno-sanitize-recover\")\n compile_cmd += _create_compiler_cmd_tail(harness, program, target)\n return compile_cmd\n\n\ndef _create_cpachecker_args(args, harness_output_dir):\n # It's important that we work with a copy of sys.argv here,\n # because we may modify cpachecker_args later,\n # but do not want to have sys.argv modified.\n # Slicing creates a copy in python, so no explicit copy necessary.\n cpachecker_args = sys.argv[1:]\n\n for compile_arg in [\"-gcc-args\"] + args.compile_args:\n if compile_arg in cpachecker_args:\n cpachecker_args.remove(compile_arg)\n\n cpachecker_args.append(\"-witness2test\")\n try:\n index_of_outputpath_param = cpachecker_args.index(\"-outputpath\")\n except ValueError:\n cpachecker_args += [\"-outputpath\", harness_output_dir]\n else:\n assert (index_of_outputpath_param + 1) < len(cpachecker_args), (\n \"Parameter -outputpath is missing an argument: \" + cpachecker_args\n )\n # Replace existing argument of outputpath with harness_output_dir,\n # so that the harness is written there for compilation.\n cpachecker_args[index_of_outputpath_param + 1] = harness_output_dir\n\n return cpachecker_args\n\n\ndef get_cpachecker_executable():\n \"\"\"Return the path to the CPAchecker executable 'cpa.sh'.\n If the executable is available in the systeme PATH, this executable is\n used. Otherwise, it is checked whether an executable 'cpa.sh' is\n available in the current directory './' or the './scripts' directory.\n\n :return str: the path to the executable.\n :raise ValidationError: if no CPAchecker executable found.\n \"\"\"\n executable_name = \"cpa.sh\"\n\n def is_exe(exe_path):\n return os.path.isfile(exe_path) and os.access(exe_path, os.X_OK)\n\n # Directories the CPAchecker executable may ly in.\n # It's important to put '.' and './scripts' last, because we\n # want to look at the \"real\" PATH directories first\n script_dir = os.path.dirname(os.path.realpath(__file__))\n path_candidates = os.environ[\"PATH\"].split(os.pathsep) + [\n script_dir,\n \".\",\n \".\" + os.sep + \"scripts\",\n ]\n for path in path_candidates:\n path = path.strip('\"')\n exe_file = os.path.join(path, executable_name)\n if is_exe(exe_file):\n return exe_file\n\n raise ValidationError(\"CPAchecker executable not found or not executable!\")\n\n\ndef create_harness_gen_cmd(args, harness_output_dir):\n cpa_executable = get_cpachecker_executable()\n harness_gen_args = _create_cpachecker_args(args, harness_output_dir)\n return [cpa_executable] + harness_gen_args\n\n\ndef find_harnesses(output_path):\n \"\"\"Returns a list of all harness files found in the given directory.\"\"\"\n return glob.glob(output_path + \"/*harness.c\")\n\n\ndef get_target_name(harness_name):\n \"\"\"Returns a name for the given harness file name.\"\"\"\n harness_number = re.search(r\"(\\d+)\\.harness\\.c\", harness_name).group(1)\n\n return \"test_cex\" + harness_number\n\n\ndef execute(command, quiet=False):\n \"\"\"Execute the given command.\n\n :param List[str] command: list of words that describe the command line.\n :param Bool quiet: whether to log the executed command line as INFO.\n :return subprocess.CompletedProcess: information about the execution.\n \"\"\"\n if not quiet:\n logging.info(\" \".join(command))\n return subprocess.run(\n command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True\n )\n\n\ndef analyze_result(test_result, harness, specification):\n \"\"\"Analyze the given test result and return its verdict.\n\n :param CompletedProcess test_result: result of test execution\n :param str harness: path to harness file\n :param Specification specification: specification to check result against\n :return: tuple of the verdict of the test execution and the violated property, if any.\n The verdict is one of RESULT_ACCEPT, RESULT_REJECT and RESULT_UNK.\n The violated property is one element of the given specification.\n \"\"\"\n results_and_violated_props = []\n\n def check(code, err_msg, spec_property):\n results_and_violated_props.append(\n _analyze_result_values(test_result, harness, code, err_msg, spec_property)\n )\n\n # For each specification property, check whether an error message\n # showing its violation was printed\n # TODO: Turn into dict() with loop to be more flexible and remove magic numbers\n if specification.reach_method_call:\n check(107, EXPECTED_ERRMSG_REACH, SPEC_REACH)\n if specification.no_overflow:\n check(1, EXPECTED_ERRMSG_OVERFLOW, SPEC_OVERFLOW)\n if specification.mem_free:\n check(1, EXPECTED_ERRMSG_MEM_FREE, SPEC_MEM_FREE)\n if specification.mem_deref:\n check(1, EXPECTED_ERRMSG_MEM_DEREF, SPEC_MEM_DEREF)\n if specification.mem_memtrack:\n check(1, EXPECTED_ERRMSG_MEM_MEMTRACK, SPEC_MEM_MEMTRACK)\n\n results = [r[0] for r in results_and_violated_props]\n if RESULT_ACCEPT in results:\n violated_prop = results_and_violated_props[results.index(RESULT_ACCEPT)][1]\n return RESULT_ACCEPT, violated_prop\n elif RESULT_UNK in results:\n return RESULT_UNK, None\n else:\n return RESULT_REJECT, None\n\n\ndef _analyze_result_values(\n test_result, harness, expected_returncode, expected_errmsg, spec_prop\n):\n if (\n test_result.returncode == expected_returncode\n and test_result.stderr\n and expected_errmsg in test_result.stderr\n ):\n logging.info(\n \"Harness %s reached expected property violation (%s).\", harness, spec_prop\n )\n return RESULT_ACCEPT, spec_prop\n elif test_result.returncode == 0:\n logging.info(\"Harness %s did not encounter _any_ error\", harness)\n return RESULT_REJECT, None\n else:\n logging.info(\"Run with harness %s was not successful\", harness)\n return RESULT_UNK, None\n\n\ndef _log_multiline(msg, level=logging.INFO):\n if type(msg) is list:\n msg_lines = msg\n else:\n msg_lines = msg.split(\"\\n\")\n for line in msg_lines:\n logging.log(level, line)\n\n\ndef get_spec(specification_file):\n \"\"\"Return the specification defined by the given specification file.\n\n :param str specification_file: specification file to read.\n :return Specification: specification described by file\n :raise ValidationError: if no specification file given, invalid or doesn't exist\n \"\"\"\n\n if not specification_file:\n raise ValidationError(\"No specification file given.\")\n if not os.path.isfile(specification_file):\n raise ValidationError(\n \"Specification file does not exist: %s\" % specification_file\n )\n\n with open(specification_file, \"r\") as inp:\n content = inp.read().strip()\n\n spec_matches = re.match(r\"CHECK\\(\\s*init\\(.*\\),\\s*LTL\\(\\s*(.+)\\s*\\)\", content)\n spec = None\n if spec_matches:\n no_overflow = REGEX_OVERFLOW.search(content)\n mem_free = REGEX_MEM_FREE.search(content)\n mem_deref = REGEX_MEM_DEREF.search(content)\n mem_memtrack = REGEX_MEM_MEMTRACK.search(content)\n try:\n method_call = REGEX_REACH.search(content).group(1)\n except AttributeError:\n # search returned None\n method_call = None\n\n spec = Specification(\n no_overflow=no_overflow,\n mem_free=mem_free,\n mem_deref=mem_deref,\n mem_memtrack=mem_memtrack,\n reach_method_call=method_call,\n )\n\n if spec is None or spec.invalid():\n raise ValidationError(\"No SV-COMP specification found in \" + specification_file)\n return spec\n\n\ndef _preprocess(program: str, spec: Specification, target: str):\n with open(program, \"r\") as inp:\n content = inp.read()\n\n # IMPORTANT: This assumes that any target function is not defined or defined on a single line of code\n if spec.reach_method_call:\n method_def_to_rename = spec.reach_method_call\n new_content = re.sub(\n method_def_to_rename + r\"(\\s*\\(.*\\) ){.*}\",\n method_def_to_rename + r\"\\1;\",\n content,\n )\n else:\n new_content = content\n\n with open(target, \"w\") as outp:\n outp.write(new_content)\n\n\ndef _execute_harnesses(\n created_harnesses, program_file, specification, output_dir, args\n):\n final_result = None\n violated_property = None\n successful_harness = None\n iter_count = 0 # Count how many harnesses were tested\n compile_success_count = 0 # Count how often compilation overall was successful\n c11_success_count = 0 # Count how often compilation with C11 standard was sucessful\n reject_count = 0\n for harness in created_harnesses:\n iter_count += 1\n logging.info(\"Looking at %s\", harness)\n exe_target = output_dir + os.sep + get_target_name(harness)\n compile_cmd = create_compile_cmd(\n harness, program_file, exe_target, args, specification\n )\n compile_result = execute(compile_cmd)\n\n _log_multiline(compile_result.stderr, level=logging.INFO)\n _log_multiline(compile_result.stdout, level=logging.DEBUG)\n\n if compile_result.returncode != 0:\n compile_cmd = create_compile_cmd(\n harness, program_file, exe_target, args, specification, \"gnu90\"\n )\n compile_result = execute(compile_cmd)\n _log_multiline(compile_result.stderr, level=logging.INFO)\n _log_multiline(compile_result.stdout, level=logging.DEBUG)\n\n if compile_result.returncode != 0:\n logging.warning(\"Compilation failed for harness %s\", harness)\n continue\n\n else:\n c11_success_count += 1\n compile_success_count += 1\n\n test_result = execute([exe_target])\n test_stdout_file = output_dir + os.sep + \"stdout.txt\"\n test_stderr_file = output_dir + os.sep + \"stderr.txt\"\n if test_result.stdout:\n with open(test_stdout_file, \"w+\") as output:\n output.write(test_result.stdout)\n logging.info(\"Wrote stdout of test execution to %s\", test_stdout_file)\n if test_result.stderr:\n with open(test_stderr_file, \"w+\") as error_output:\n error_output.write(test_result.stderr)\n logging.info(\"Wrote stderr of test execution to %s\", test_stderr_file)\n\n result, new_violated_property = analyze_result(\n test_result, harness, specification\n )\n if result == RESULT_ACCEPT:\n successful_harness = harness\n final_result = RESULT_ACCEPT\n if not violated_property: # Use first violated property\n violated_property = new_violated_property\n break\n elif result == RESULT_REJECT:\n reject_count += 1\n if not final_result:\n # Only set final result to 'reject' if no harness produces any error\n final_result = RESULT_REJECT\n else:\n final_result = RESULT_UNK\n\n if compile_success_count == 0:\n raise ValidationError(\"Compilation failed for every harness/file pair.\")\n\n statistics.append((\"Harnesses tested\", iter_count))\n statistics.append((\"C11 compatible\", c11_success_count))\n statistics.append((\"Harnesses rejected\", reject_count))\n\n return ValidationResult(\n verdict=final_result,\n violated_property=violated_property,\n successful_harness=successful_harness,\n )\n\n\nstatistics = []\n\n\ndef run(argv=None):\n if argv is None:\n argv = sys.argv[1:]\n args = _parse_args(argv)\n output_dir = args.output_path\n if not os.path.exists(output_dir):\n os.mkdir(output_dir)\n\n specification = get_spec(args.specification_file)\n\n with tempfile.TemporaryDirectory(suffix=\"cpa_witness2test_\") as harness_output_dir:\n try:\n harness_gen_cmd = create_harness_gen_cmd(args, harness_output_dir)\n harness_gen_result = execute(harness_gen_cmd)\n print(harness_gen_result.stderr)\n _log_multiline(harness_gen_result.stdout, level=logging.DEBUG)\n\n created_harnesses = find_harnesses(harness_output_dir)\n statistics.append((\"Harnesses produced\", len(created_harnesses)))\n\n if created_harnesses:\n with tempfile.NamedTemporaryFile(suffix=\".c\") as preprocessed_program:\n _preprocess(args.file, specification, preprocessed_program.name)\n result = _execute_harnesses(\n created_harnesses,\n preprocessed_program.name,\n specification,\n harness_output_dir,\n args,\n )\n else:\n result = ValidationResult(RESULT_UNK)\n finally:\n for i in os.listdir(harness_output_dir):\n source = os.path.join(harness_output_dir, i)\n target = os.path.join(output_dir, i)\n try:\n shutil.copytree(\n source,\n target,\n dirs_exist_ok=True,\n )\n except NotADirectoryError:\n shutil.move(source, target)\n\n if args.stats:\n print(os.linesep + \"Statistics:\")\n for prop, value in statistics:\n print(\"\\t\" + str(prop) + \": \" + str(value))\n print()\n\n if result.successful_harness:\n print(\"Harness %s was successful.\" % result.successful_harness)\n\n result_str = \"Verification result: %s\" % result.verdict\n if result.violated_property:\n result_str += (\n \". Property violation (%s) found by chosen configuration.\"\n % result.violated_property\n )\n print(result_str)\n\n\nlogging.basicConfig(format=\"%(levelname)s: %(message)s\", level=logging.INFO)\n\nif __name__ == \"__main__\":\n try:\n run()\n except ValidationError as e:\n logging.error(e.msg)\n print(\"Verification result: ERROR.\")\n sys.exit(1)\n", "repo_name": "sosy-lab/cpachecker", "sub_path": "scripts/cpa_witness2test.py", "file_name": "cpa_witness2test.py", "file_ext": "py", "file_size_in_byte": 21751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 202, "dataset": "github-code", "pt": "3", "api": [{"api_name": "re.compile", "line_number": 46, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 47, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 49, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 50, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 51, "usage_type": "call"}, {"api_name": "typing.NamedTuple", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.NamedTuple", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 71, "usage_type": "name"}, {"api_name": "os.linesep", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 114, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 115, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 119, "usage_type": "call"}, {"api_name": "argparse.REMAINDER", "line_number": 160, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 191, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 197, "usage_type": "call"}, {"api_name": "shutil.which", "line_number": 245, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 276, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 310, "usage_type": "call"}, {"api_name": "os.path", "line_number": 310, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 310, "usage_type": "call"}, {"api_name": "os.X_OK", "line_number": 310, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path", "line_number": 315, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 315, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 316, "usage_type": "attribute"}, {"api_name": "os.pathsep", "line_number": 316, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path", "line_number": 323, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 338, "usage_type": "call"}, {"api_name": "re.search", "line_number": 343, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 356, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 357, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 358, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 411, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 416, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 419, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 423, "usage_type": "attribute"}, {"api_name": "logging.log", "line_number": 429, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 442, "usage_type": "call"}, {"api_name": "os.path", "line_number": 442, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 450, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 483, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 507, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 508, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 514, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 515, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 522, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 523, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 526, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 534, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 535, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 539, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 543, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 581, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 584, "usage_type": "call"}, {"api_name": "os.path", "line_number": 584, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 585, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 589, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 594, "usage_type": "attribute"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 600, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 612, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 613, "usage_type": "call"}, {"api_name": "os.path", "line_number": 613, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 614, "usage_type": "call"}, {"api_name": "os.path", "line_number": 614, "usage_type": "attribute"}, {"api_name": "shutil.copytree", "line_number": 616, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 622, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 625, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 642, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 642, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 648, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 650, "usage_type": "call"}]} +{"seq_id": "42465750053", "text": "import time\nfrom dronekit import connect, VehicleMode, LocationGlobalRelative, Command, LocationGlobal\nfrom pymavlink import mavutil\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--connect', default='/dev/ttyUSB0')\nargs = parser.parse_args()\n\n# Connect to the Vehicle\nprint ('Connecting to vehicle on: %s' % args.connect)\nvehicle = connect(args.connect, baud=57600, wait_ready=True)\n\nvehicle.gimbal.rotate(0,0,0) #pitch ,roll,yaw\ntime.sleep(5)\nvehicle.gimbal.rotate(-90,0,0) # seting the gimbil to down\nprint(\"setting the gimbal down\")\ntime.sleep(5)\nvehicle.gimbal.rotate(90,0,0) #gimbal facing top\nprint(\"setting the gimbal up\")\ntime.sleep(5)\nvehicle.gimbal.rotate(0,0,90) #90 yaw to west\nprint(\"setting the gimbal to west\")\n\n\n", "repo_name": "ramankumarrudr/Alphabt-Drones", "sub_path": "gimbil_control.py", "file_name": "gimbil_control.py", "file_ext": "py", "file_size_in_byte": 755, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "dronekit.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "7079171187", "text": "import graphene\nimport model.model as model\nimport model.repo as repo\n\ndocument_repo = None\n\n\ndef set_repos(_document_repo=None):\n global document_repo\n print(f'gqlschema.set_repos: {_document_repo}')\n document_repo = _document_repo\n\n\nclass Date(graphene.ObjectType):\n month = graphene.Int()\n year = graphene.Int()\n\n @classmethod\n def from_model(cls, date):\n return cls(month=date.month, year=date.year)\n\n\nclass ChildField(graphene.ObjectType):\n id = graphene.ID()\n name = graphene.String()\n date = graphene.Field(Date)\n\n @classmethod\n def from_model(cls, child_field):\n return cls(\n id=str(child_field.id),\n name=child_field.name,\n date=child_field.date\n )\n\n\nclass Document(graphene.ObjectType):\n id = graphene.ID()\n name = graphene.String()\n age = graphene.Int()\n child_field = graphene.List(ChildField)\n archived = graphene.Boolean()\n\n @classmethod\n def from_model(cls, document):\n return cls(\n id=document.id,\n name=document.name,\n age=document.age,\n child_field=[ChildField.from_model(s) for s in document.child_field],\n archived=document.archived\n )\n\n\nclass Query(graphene.ObjectType):\n documents = graphene.List(Document)\n document = graphene.Field(Document, id=graphene.ID())\n\n async def resolve_documents(self, args, context=None, info=None):\n global document_repo\n assert(document_repo is not None)\n\n results = []\n async for document in document_repo.find():\n results.append(Document.from_model(document))\n return results\n\n async def resolve_document(self, args, id, context=None, info=None):\n global document_repo\n assert (document_repo is not None)\n\n try:\n document = await document_repo.find_by_id(id)\n except repo.InvalidId as exc:\n return Errors([Error('id', ['invalid'])])\n\n if not document:\n return Errors([Error('id', ['not found'])])\n\n result = Document.from_model(document)\n\n return result\n\n\nclass Error(graphene.ObjectType):\n field = graphene.String()\n messages = graphene.List(graphene.String)\n\n\nclass Errors(graphene.ObjectType):\n errors = graphene.List(Error)\n\n @classmethod\n def from_exception(cls, exc):\n return cls(errors=[Error(field=field, messages=messages) for field, messages in exc.errors.items()])\n\n\nclass DocumentResponse(graphene.Union):\n class Meta:\n types = (Document, Errors)\n\n\nclass CreateDocumentInput(graphene.InputObjectType):\n name = graphene.String(required=True)\n age = graphene.Int(required=False)\n archived = graphene.String(required=False)\n\n\nclass CreateDocument(graphene.Mutation):\n class Arguments:\n document = CreateDocumentInput(required=True)\n\n Output = DocumentResponse\n\n async def mutate(self, info, document):\n global document_repo\n assert(document_repo is not None)\n\n try:\n result = await document_repo.create(name=document.name, age=document.age, archived=document.archived)\n except repo.RepoError as exc:\n return Errors.from_exception(exc)\n\n return Document.from_model(result)\n\n\nclass SetDocumentArchivedInput(graphene.InputObjectType):\n id = graphene.ID(required=True)\n archived = graphene.Boolean(required=True)\n\n\nclass SetDocumentArchived(graphene.Mutation):\n class Arguments:\n set_archived = SetDocumentArchivedInput(required=True)\n\n Output = DocumentResponse\n\n async def mutate(self, info, set_archived):\n global document_repo\n assert(document_repo is not None)\n\n try:\n document = await document_repo.find_by_id(set_archived.id)\n except repo.InvalidId as exc:\n return Errors([Error('id', ['invalid'])])\n\n if not document:\n return Errors([Error('id', ['not found'])])\n\n document.set_archived(set_archived.archived)\n result = await document_repo.save(document)\n \n return Document.from_model(result)\n\n\nclass DateInput(graphene.InputObjectType):\n month = graphene.Int(required=True)\n year = graphene.Int(required=True)\n\n def to_model(self):\n return model.Date(self.month, self.year)\n\n\nclass AddChildFieldInput(graphene.InputObjectType):\n document_id = graphene.ID(required=True)\n name = graphene.String()\n date = graphene.Field(DateInput, required=False)\n\n\nclass AddChildField(graphene.Mutation):\n class Arguments:\n add_child_field = AddChildFieldInput(required=True)\n\n Output = DocumentResponse\n\n async def mutate(self, info, add_child_field):\n global document_repo\n assert(document_repo is not None)\n\n try:\n document = await document_repo.find_by_id(add_child_field.document_id)\n except repo.InvalidId as exc:\n return Errors([Error('id', ['invalid'])])\n\n if not document:\n return Errors([Error('id', ['not found'])])\n\n document.add_child_field(\n model.ChildField(\n name=add_child_field.name,\n date=add_child_field.date.to_model(),\n )\n )\n\n result = await document_repo.save(document)\n\n return Document.from_model(result)\n\n\nclass RemoveChildFieldInput(graphene.InputObjectType):\n document_id = graphene.ID(required=True)\n child_field_id = graphene.ID(required=True)\n\n\nclass RemoveChildField(graphene.Mutation):\n class Arguments:\n remove_child_field = RemoveChildFieldInput(required=True)\n\n Output = DocumentResponse\n\n async def mutate(self, info, remove_child_field):\n global document_repo\n assert(document_repo is not None)\n\n try:\n document = await document_repo.find_by_id(remove_child_field.document_id)\n except repo.InvalidId as exc:\n return Errors([Error('id', ['invalid'])])\n\n if not document:\n return Errors([Error('id', ['not found'])])\n\n try:\n document.remove_child_field(remove_child_field.child_field_id)\n except KeyError as exc:\n return Errors([Error('contract_id', ['not found'])])\n\n result = await document_repo.save(document)\n\n return Document.from_model(result)\n\n\nclass EditChildFieldInput(graphene.InputObjectType):\n id = graphene.ID(required=True)\n name = graphene.String()\n date = graphene.Field(DateInput, required=False)\n\n\nclass EditChildFieldMutationInput(graphene.InputObjectType):\n document_id = graphene.ID(required=True)\n child_field = graphene.InputField(EditChildFieldInput, description=\"Child field to update\")\n\n\nclass EditChildField(graphene.Mutation):\n class Arguments:\n edit_child_field = EditChildFieldMutationInput(required=True)\n\n Output = DocumentResponse\n\n async def mutate(self, info, edit_child_field):\n global document_repo\n assert(document_repo is not None)\n\n try:\n document = await document_repo.find_by_id(edit_child_field.document_id)\n except repo.InvalidId as exc:\n return Errors([Error('id', ['invalid'])])\n\n if not document:\n return Errors([Error('id', ['not found'])])\n\n try:\n document.update_child_field(model.ChildField(\n id=edit_child_field.child_field.id,\n name=edit_child_field.child_field.name,\n date=edit_child_field.child_field.date.to_model()\n ))\n\n except KeyError as exc:\n return Errors([Error('contract_id', ['not found'])])\n\n result = await document_repo.save(document)\n\n return Document.from_model(result)\n\n\nclass Mutation(graphene.ObjectType):\n create_document = CreateDocument.Field()\n set_document_archived = SetDocumentArchived.Field()\n\n add_child_field = AddChildField.Field()\n remove_child_field = RemoveChildField.Field()\n edit_child_field = EditChildField.Field()\n\n\nschema = graphene.Schema(query=Query, mutation=Mutation)\n\n\n__all__ = [\n 'Date',\n 'DateInput',\n 'ChildField',\n 'Document',\n 'DocumentResponse',\n 'SetDocumentArchived',\n 'SetDocumentArchivedInput',\n 'CreateDocument',\n 'CreateDocumentInput',\n 'AddChildField',\n 'AddChildFieldInput',\n 'EditChildField',\n 'EditChildFieldInput',\n 'EditChildFieldMutationInput',\n 'RemoveChildField',\n 'RemoveChildFieldInput',\n 'Error',\n 'Errors',\n 'Query',\n 'Mutation',\n 'schema'\n]\n\n", "repo_name": "ivanwilliam1/PythonAsyncAPIService", "sub_path": "app/gqlschema.py", "file_name": "gqlschema.py", "file_ext": "py", "file_size_in_byte": 8523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "22", "api": [{"api_name": "graphene.ObjectType", "line_number": 14, "usage_type": "attribute"}, {"api_name": "graphene.Int", "line_number": 15, "usage_type": "call"}, {"api_name": "graphene.Int", "line_number": 16, "usage_type": "call"}, {"api_name": "graphene.ObjectType", "line_number": 23, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 24, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 25, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 26, "usage_type": "call"}, {"api_name": "graphene.ObjectType", "line_number": 37, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 38, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 39, "usage_type": "call"}, {"api_name": "graphene.Int", "line_number": 40, "usage_type": "call"}, {"api_name": "graphene.List", "line_number": 41, "usage_type": "call"}, {"api_name": "graphene.Boolean", "line_number": 42, "usage_type": "call"}, {"api_name": "graphene.ObjectType", "line_number": 55, "usage_type": "attribute"}, {"api_name": "graphene.List", "line_number": 56, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 57, "usage_type": "call"}, {"api_name": "graphene.ID", "line_number": 57, "usage_type": "call"}, {"api_name": "model.repo.InvalidId", "line_number": 74, "usage_type": "attribute"}, {"api_name": "model.repo", "line_number": 74, "usage_type": "name"}, {"api_name": "graphene.ObjectType", "line_number": 85, "usage_type": "attribute"}, {"api_name": "graphene.String", "line_number": 86, "usage_type": "call"}, {"api_name": "graphene.List", "line_number": 87, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 87, "usage_type": "attribute"}, {"api_name": "graphene.ObjectType", "line_number": 90, "usage_type": "attribute"}, {"api_name": "graphene.List", "line_number": 91, "usage_type": "call"}, {"api_name": "graphene.Union", "line_number": 98, "usage_type": "attribute"}, {"api_name": "graphene.InputObjectType", "line_number": 103, "usage_type": "attribute"}, {"api_name": "graphene.String", "line_number": 104, "usage_type": "call"}, {"api_name": "graphene.Int", "line_number": 105, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 106, "usage_type": "call"}, {"api_name": "graphene.Mutation", "line_number": 109, "usage_type": "attribute"}, {"api_name": "model.repo.RepoError", "line_number": 121, "usage_type": "attribute"}, {"api_name": "model.repo", "line_number": 121, "usage_type": "name"}, {"api_name": "graphene.InputObjectType", "line_number": 127, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 128, "usage_type": "call"}, {"api_name": "graphene.Boolean", "line_number": 129, "usage_type": "call"}, {"api_name": "graphene.Mutation", "line_number": 132, "usage_type": "attribute"}, {"api_name": "model.repo.InvalidId", "line_number": 144, "usage_type": "attribute"}, {"api_name": "model.repo", "line_number": 144, "usage_type": "name"}, {"api_name": "graphene.InputObjectType", "line_number": 156, "usage_type": "attribute"}, {"api_name": "graphene.Int", "line_number": 157, "usage_type": "call"}, {"api_name": "graphene.Int", "line_number": 158, "usage_type": "call"}, {"api_name": "model.model.Date", "line_number": 161, "usage_type": "call"}, {"api_name": "model.model", "line_number": 161, "usage_type": "name"}, {"api_name": "graphene.InputObjectType", "line_number": 164, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 165, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 166, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 167, "usage_type": "call"}, {"api_name": "graphene.Mutation", "line_number": 170, "usage_type": "attribute"}, {"api_name": "model.repo.InvalidId", "line_number": 182, "usage_type": "attribute"}, {"api_name": "model.repo", "line_number": 182, "usage_type": "name"}, {"api_name": "model.model.ChildField", "line_number": 189, "usage_type": "call"}, {"api_name": "model.model", "line_number": 189, "usage_type": "name"}, {"api_name": "graphene.InputObjectType", "line_number": 200, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 201, "usage_type": "call"}, {"api_name": "graphene.ID", "line_number": 202, "usage_type": "call"}, {"api_name": "graphene.Mutation", "line_number": 205, "usage_type": "attribute"}, {"api_name": "model.repo.InvalidId", "line_number": 217, "usage_type": "attribute"}, {"api_name": "model.repo", "line_number": 217, "usage_type": "name"}, {"api_name": "graphene.InputObjectType", "line_number": 233, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 234, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 235, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 236, "usage_type": "call"}, {"api_name": "graphene.InputObjectType", "line_number": 239, "usage_type": "attribute"}, {"api_name": "graphene.ID", "line_number": 240, "usage_type": "call"}, {"api_name": "graphene.InputField", "line_number": 241, "usage_type": "call"}, {"api_name": "graphene.Mutation", "line_number": 244, "usage_type": "attribute"}, {"api_name": "model.repo.InvalidId", "line_number": 256, "usage_type": "attribute"}, {"api_name": "model.repo", "line_number": 256, "usage_type": "name"}, {"api_name": "model.model.ChildField", "line_number": 263, "usage_type": "call"}, {"api_name": "model.model", "line_number": 263, "usage_type": "name"}, {"api_name": "graphene.ObjectType", "line_number": 277, "usage_type": "attribute"}, {"api_name": "graphene.Schema", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "16343509576", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom django.db import models\n\nclass activityManager(models.Manager):\n\tdef activityValidator(self,postData):\n\t\tresponse = {\n\t\t\t'status': True\n\t\t}\n\t\terrors = []\n\t\tif len(postData['desc']) == 0:\n\t\t\terrors.append(\"Fill out description so people know what you are doing\")\n\t\tif len(postData['newCategory']) == 0:\n\t\t\tcategoryInput = category.objects.get(id=postData['categoryId'])\n\t\telse:\n\t\t\texisting = category.objects.filter(name=postData['newCategory'])\n\t\t\tif len(existing) > 0:\n\t\t\t\tresponse['status'] = False\n\t\t\t\terrors.append(\"This category exists already\") \n\t\t\telse:\n\t\t\t\tcategoryInput = category.objects.create(name=postData['newCategory'])\n\t\tif len(errors) == 0:\n\t\t\tresponse['activity'] = activity.objects.create(category=categoryInput,desc=postData['desc'],lat=postData['actLat'],lng=postData['actLng'])\n\t\tresponse['errors'] = errors\n\t\treturn response\t\t\n\nclass category(models.Model):\n\tname = models.CharField(max_length = 255)\n\tcreated_at = models.DateTimeField(auto_now_add = True)\n\tupdated_at = models.DateTimeField(auto_now = True)\n\nclass activity(models.Model):\n\tcategory = models.ForeignKey(category, related_name=\"activities\")\n\tdesc = models.CharField(max_length = 255)\n\tlat = models.DecimalField(max_digits=9, decimal_places=6)\n\tlng = models.DecimalField(max_digits=9, decimal_places=6)\n\tcreated_at = models.DateTimeField(auto_now_add = True)\n\tupdated_at = models.DateTimeField(auto_now = True)\n\tobjects = activityManager()", "repo_name": "crgalloway/activityFinder", "sub_path": "apps/activity/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "django.db.models.Manager", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "40102609172", "text": "from keyWordExtraction import RakeKeywordExtractor\nfrom nltk.corpus import wordnet as wn\nimport sys\nimport codecs\n#print all the synset element of an element\ndef lemmalist(word):\n syn_set = []\n for synset in wn.synsets(word):\n for item in synset.lemmas():\n syn_set.append(item.name())\n return syn_set\n\n\ndef getTagSet():\n\tf = codecs.open(\"raw_tags\", encoding='utf-8')\n\tlines = f.read()\n\tf.close()\n\tlines = lines.split(\"\\n\")\n\ttags = []\n\tfor line in lines:\n\t\tif len(line.split()) == 0:\n\t\t\tcontinue\n\t\trawTag = line.split()[:-2]\n\t\ttagPopularity = line.split()[-2].encode('utf-8')\n\t\ttagPopularity = int (tagPopularity.replace(',', ''))\n\t\ttag = \" \".join(rawTag)\n\t\ttags.append([tag.lower(),tagPopularity])\n\treturn tags\n\n\ndef keywords(text):\n\trake = RakeKeywordExtractor()\n\tkeywords = rake.extract(text, incl_scores=True)\n\treturn keywords\n\ndef coreTags(text):\n\tcoreTags = []\n\n\ttags = getTagSet()\n\trake = RakeKeywordExtractor()\n\tkeywords = rake.extract(text, incl_scores=True)\n\ttagsInArticles = set()\n\tfor keyword in keywords:\n\t\ttagsInArticles.add(keyword)\n\tfor keyword,score in tagsInArticles:\n\t\tfor tag,tagPopularity in tags:\n\t\t\tif (tag.lower() == keyword.lower()):\n\t\t\t\tcoreTags.append([tag, tagPopularity*score])\n\tcoreTags = sorted(coreTags, key=lambda x: x[1], reverse=True)\n\treturn coreTags\n\n\ndef additionalTags(text):\n\tadditionalTags = []\n\ttags = getTagSet()\n\trake = RakeKeywordExtractor()\n\tkeywords = rake.extract(text, incl_scores=True)\n\tsimilarWordDict = {}\n\ttagsInArticles = set()\n\n\tfor keyword,score in keywords:\n\t\tif len(keyword.split()) > 1:\n\t\t\tcontinue\n\t\ttagsInArticles.add(keyword)\n\t\tsimilarWords = lemmalist(keyword)\n\t\tif len(similarWords) != 0:\n\t\t\tfor word in similarWords:\n\t\t\t\tif ((word in tagsInArticles) == False):\n\t\t\t\t\tsimilarWordDict[word] = score\n\n\tfor keyword in similarWordDict.keys():\n\t\tfor tag,tagPopularity in tags:\n\t\t\tif (tag.lower() == keyword.lower()):\n\t\t\t\tadditionalTags.append([tag, tagPopularity*similarWordDict[keyword]])\n\tadditionalTags = sorted(additionalTags, key=lambda x: x[1], reverse=True)\n\treturn additionalTags[:20]\n\n\n#Demo\n# f = codecs.open(\"testFile\", encoding='utf-8')\n# content = f.read()\n# f.close()\n# print keywords(content)\n# print coreTags(content)\n# print additionalTags(content)\n", "repo_name": "iaoshili/NLP_Project", "sub_path": "tagRecommender.py", "file_name": "tagRecommender.py", "file_ext": "py", "file_size_in_byte": 2250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "nltk.corpus.wordnet.synsets", "line_number": 8, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 8, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 15, "usage_type": "call"}, {"api_name": "keyWordExtraction.RakeKeywordExtractor", "line_number": 32, "usage_type": "call"}, {"api_name": "keyWordExtraction.RakeKeywordExtractor", "line_number": 40, "usage_type": "call"}, {"api_name": "keyWordExtraction.RakeKeywordExtractor", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "39707593524", "text": "# sync.py\nimport logging\nimport time\nfrom datetime import date, datetime, timedelta\nfrom json import JSONDecodeError\n\nfrom django.db.models import Model, Q, QuerySet\n\nfrom apps.teams.models import Team\nfrom apps.zp.fetch import ZPSession\nfrom apps.zp.models import (\n AllResults,\n Profile,\n Results,\n TeamPending,\n TeamResults,\n TeamRiders,\n)\n\n\ndef create_or_update_model(self, zp_id, api, data_set):\n # Create a dictionary with dynamic field names\n kwargs = {\n \"zp_id\": zp_id,\n api: data_set, # 'api' is the variable field name\n }\n # Unpack kwargs as arguments to get_or_create\n obj, created = self.model.objects.get_or_create(**kwargs)\n return obj, created\n\n\nclass FetchJsonRecords:\n \"\"\"\n This adds a new json dataset to the model for each zp_id. It does not update any existing records.\n This will most likely create a new record each time.\n \"\"\"\n\n def __init__(self, api: str, zp_id: int | list | str | QuerySet, model: Model):\n self.zps = ZPSession()\n self.try_count = 0\n self.api = api\n self.zp_id = zp_id\n self.model = model\n\n def fetch(self):\n if isinstance(self.zp_id, int | list | QuerySet): # queryset must be from .values_list(\"zp_id\", flat=True)\n zp_ids = set(self.zp_id)\n elif isinstance(self.zp_id, str) and self.zp_id == \"all\": # get all the Model objects\n zp_ids = set(self.model.objects.values_list(\"zp_id\", flat=True))\n logging.info(f\"zp_id count: {len(zp_ids)}\")\n else:\n raise ValueError(\"zp_id must be int, list, or 'all'\")\n\n for zp_id in zp_ids:\n logging.info(f\"Get {self.api} data: {zp_id}\")\n try:\n data_set = self.zps.get_api(id=zp_id, api=self.api)[self.api]\n if \"data\" in data_set:\n data_set = data_set[\"data\"]\n if len(data_set) > 0:\n obj, created = create_or_update_model(self, zp_id, self.api, data_set)\n if created:\n logging.info(f\"Created new {self.zp_id} : {zp_id}\")\n else:\n logging.info(f\"Updated {self.zp_id} : {zp_id}\")\n self.try_count = 0\n except JSONDecodeError as e:\n self.try_count += 1\n logging.warning(f\"Retry get {self.api} number {self.try_count} data: {zp_id}\")\n logging.warning(f\"{e}\")\n except Exception as e:\n self.try_count += 1\n logging.warning(f\"Failed to get data: {e}\")\n logging.warning(f\"Retry get {self.api} number {self.try_count} data: {zp_id}\")\n if self.try_count >= 4:\n logging.error(f\"to many retries: {self.api} last id: {zp_id}\")\n break\n time.sleep(5 + self.try_count * 5)\n\n\nclass FetchTeamResults(FetchJsonRecords):\n def __init__(self):\n super().__init__(api=\"team_results\", zp_id=Team.objects.values_list(\"zp_id\", flat=True), model=TeamResults)\n\n\nclass FetchTeamPending(FetchJsonRecords):\n def __init__(self):\n super().__init__(api=\"team_pending\", zp_id=Team.objects.values_list(\"zp_id\", flat=True), model=TeamPending)\n\n\nclass FetchTeamRiders(FetchJsonRecords):\n def __init__(self):\n super().__init__(api=\"team_riders\", zp_id=Team.objects.values_list(\"zp_id\", flat=True), model=TeamRiders)\n\n\nclass UpdateJsonRecords:\n \"\"\"\n This adds a UPDATES a json dataset in a model object.\n \"\"\"\n\n def __init__(self, api: str, zp_id: int | list | str | QuerySet, model: Model):\n self.zps = ZPSession()\n self.try_count = 0\n self.api = api\n self.zp_id = zp_id\n self.model = model\n\n def update(self):\n if isinstance(self.zp_id, int | list | QuerySet):\n zp_ids = list(self.zp_id)\n elif isinstance(self.zp_id, str) and self.zp_id == \"all\":\n zp_ids = set(self.model.objects.values_list(\"zp_id\", flat=True))\n logging.info(f\"zp_id count: {len(zp_ids)}\")\n else:\n raise ValueError(\"zp_id must be int, list, or 'all'\")\n for zp_id in zp_ids:\n logging.info(f\"Get {self.api} data: {zp_id}\")\n if self.try_count >= 4:\n logging.error(f\"To many errors: {self.api} last zp_id: {zp_id}\")\n break\n time.sleep(3 + self.try_count * 5)\n try:\n data_set = self.zps.get_api(id=zp_id, api=self.api)[self.api]\n if [\"data\"] == list(data_set.keys()):\n data_set = data_set[\"data\"]\n if self.api in [\n \"profile_profile\",\n ]:\n data_set = sorted(data_set, key=lambda x: int(x.get(\"event_date\", 0)), reverse=True)\n except JSONDecodeError:\n self.try_count += 1\n logging.warning(f\"JSONDecodeError: {self.api}, Retry count: {self.try_count} zp_id: {zp_id}\")\n # logging.warning(f\"{e}\")\n obj, created = self.model.objects.get_or_create(zp_id=zp_id)\n obj.error = \"JSONDecodeError\"\n obj.save()\n continue\n except Exception as e:\n self.try_count += 1\n logging.warning(f\"Failed to get data: {e}\")\n logging.warning(f\"Failded api: {self.api} retry count: {self.try_count} zp_id: {zp_id}\")\n obj, created = self.model.objects.get_or_create(zp_id=zp_id)\n obj.error = f\"fetch error: {str(e)}\"\n obj.save()\n continue\n\n try:\n # TODO: This is an exception for the profile field name\n api = \"profile\" if self.api == \"profile_profile\" else self.api\n obj, created = self.model.objects.get_or_create(zp_id=zp_id) # this must uniquly identify the object\n current_data = getattr(obj, api) if getattr(obj, api) else []\n if not created and len(data_set) >= len(current_data):\n logging.info(f\"Updated {self.model} for zp_id: {zp_id}\")\n setattr(obj, api, data_set)\n obj.error = \"\"\n if self.api == \"profile_profile\":\n obj.status[\"sorted\"] = True\n obj.save()\n elif created and len(data_set) > 0:\n logging.info(f\"Created {self.model} for zp_id: {zp_id}\")\n setattr(obj, api, data_set)\n if self.api == \"profile_profile\":\n obj.status[\"sorted\"] = True\n obj.error = \"\"\n obj.save()\n elif created and len(data_set) == 0:\n logging.warning(f\"Empty data set for zp_id: {zp_id}\")\n if self.api == \"profile_profile\":\n obj.status[\"sorted\"] = False\n obj.error = f\"Empty data set: {data_set}\"\n obj.save()\n elif len(data_set) < len(current_data):\n logging.warning(f\"Data set < existing data: {api}, zp_id: {zp_id}\")\n obj.error = \"Dataset < existing data\"\n obj.save()\n else:\n continue\n self.try_count += 0\n except Exception as e:\n self.try_count += 1\n logging.warning(f\"Failed: {self.api} count: {self.try_count} zp_id: {zp_id}\")\n logging.warning(f\": {e}\")\n obj.error = str(e)\n obj.save()\n\n\nclass UpdateProfiles(UpdateJsonRecords):\n \"\"\"See also management command and task\"\"\"\n\n # TODO: At some point we will have more then 100 inactive profiles that we keep trying to update. Then we need to add a filter for inactive profiles.\n def __init__(self):\n super().__init__(\n api=\"profile_profile\",\n zp_id=Profile.objects.filter(error=\"\", status__needs_update=True)\n .order_by(\"modified_at\")\n .values_list(\"zp_id\", flat=True)[:100],\n model=Profile,\n )\n\n\nclass UpdateProfileErrors(UpdateJsonRecords):\n def __init__(self):\n super().__init__(\n api=\"profile_profile\",\n zp_id=Profile.objects.filter(error__icontains=\"Empty data set\")\n .order_by(\"modified_at\")\n .values_list(\"zp_id\", flat=True)[:100],\n model=Profile,\n )\n\n\ndef update_last_event(self):\n logging.info(f\"Review {len(self.zp_ids)} profiles\")\n for zp_id in self.zp_ids:\n try:\n obj = Profile.objects.get(zp_id=zp_id)\n obj.status[\"last_event\"] = (\n datetime.today().date() - datetime.fromtimestamp(obj.profile[0][\"event_date\"]).date()\n ).days\n obj.save()\n except Exception as e:\n logging.error(f\"Failed to update last event: {zp_id}\\n {e}\")\n continue\n\n\nclass UpdateSelected(UpdateJsonRecords):\n def __init__(self, api, zp_id, model):\n self.api = api\n self.zp_id = zp_id\n self.model = Profile\n self.zps = ZPSession()\n self.try_count = 0\n\n\nclass FetchAllResults:\n \"\"\"\n Get list of resent event results from ZP and update the Results table.\n \"\"\"\n\n def __init__(self):\n self.zps = ZPSession()\n self.try_count = 0\n self.api = \"all_results\"\n self.model = AllResults\n\n def fetch(self):\n # Get the data\n try:\n data_set = self.zps.get_api(id=None, api=self.api)[self.api]\n data_set = data_set[\"data\"]\n except JSONDecodeError as e:\n logging.error(f\"JSONDecodeError: {self.api} \\n{e}\")\n return None\n except Exception as e:\n logging.error(f\"Unknown getting: {self.api} \\n{e}\")\n return None\n\n # Add the data to the model\n for event in data_set:\n try:\n obj, created = self.model.objects.get_or_create(zp_id=event[\"zid\"])\n if created:\n logging.info(f\"Created new {self.model} for zp_id: {event['zid']}\")\n obj.event = event\n obj.zp_id = event[\"zid\"]\n obj.save()\n else:\n logging.info(f\"Already have {self.model} for zp_id: {event['zid']}\")\n except Exception as e:\n logging.error(f\"Unknown creating: {self.api} \\n{e}\")\n\n\n#############################################################\n#### Inter table data migrations and Table field updates ####\n#############################################################\n\n\nclass ProfilesFromTeams:\n \"\"\"See also management command\"\"\"\n\n def update(self):\n logging.info(\"Move profiles from teams to profiles table\")\n # zp_team_riders = TeamRiders.objects.all()\n zp_team_riders = TeamRiders.objects.order_by(\"zp_id\", \"-modified_at\").distinct(\"zp_id\")\n for team in zp_team_riders:\n logging.info(f\"Adding profiles from team: {team.zp_id}\")\n for rider in team.team_riders:\n logging.info(f\"Get or creat zp Profile: {rider['zwid']}\")\n obj, created = Profile.objects.get_or_create(zp_id=int(rider[\"zwid\"]))\n logging.info(f\"Created? {created} rider Profile{rider['zwid']}\")\n\n\nclass ResultsFromProfiles:\n \"\"\"\n Migrate results from profiles to Results Table\n See also management command\n \"\"\"\n\n def update(self, days=60):\n logging.info(\"Move results from profiles to results table\")\n zp_profiles = Profile.objects.all()\n count = zp_profiles.count()\n for i, profile in enumerate(zp_profiles):\n logging.info(f\"Adding results from profile: {profile.zp_id}\")\n logging.info(f\"total profile: {count}, remaining{count - i}\")\n if profile.profile:\n if not isinstance(profile.profile[0], dict):\n logging.warning(f\"not a valid profile: {profile.zp_id}\")\n continue\n for result in profile.profile:\n try:\n event_date = datetime.fromtimestamp(result[\"event_date\"]).date()\n obj, created = Results.objects.get_or_create(\n zp_id=int(result[\"zid\"]), zwid=profile.zp_id, defaults={\"event_date\": event_date}\n )\n if created or not obj.tid:\n logging.info(f\"Created new result: (zid, zwid): {result['zid']}, {result['zwid']}\")\n obj.team = result.get(\"tname\", \"\")\n obj.tid = result.get(\"tid\", \"\")\n obj.name = result.get(\"name\", \"\")\n obj.event_title = result.get(\"event_title\", \"\")\n obj.results = result\n obj.save()\n else:\n logging.info(f\"Result exisits: (zid, zwid): {result['zid']}, {result['zwid']}\")\n if event_date > date.today() - timedelta(days=days):\n logging.info(\n f\"Updating result within {days} days: (zid, zwid): {result['zid']}, {result['zwid']}\"\n )\n obj.team = result.get(\"tname\", \"\")\n obj.tid = result.get(\"tid\", \"\")\n obj.name = result.get(\"name\", \"\")\n obj.event_title = result.get(\"event_title\", \"\")\n obj.results = result\n obj.save()\n\n except TypeError as e:\n logging.error(f\"Failed to get or create result:\\n {e}\")\n data = {\n c: result.get(c, \"_\")\n for c in [\"event_date\", \"zid\", \"zwid\", \"tname\", \"tid\", \"name\", \"event_title\"]\n }\n logging.error(f\"result:\\n {data}\")\n except Exception as e:\n logging.error(f\"Failed to get or create result: {e}\")\n data = {\n c: result.get(c, \"_\")\n for c in [\"event_date\", \"zid\", \"zwid\", \"tname\", \"tid\", \"name\", \"event_title\"]\n }\n logging.error(f\"result:\\n {data}\")\n\n\nclass SetLastEventProfile:\n \"\"\"\n Some profiles are very inactive so we want to update the profile less often.\n There is a Profile model prperty but it is faster if we set a filed that is the num,ber of days since last event.\n Then we can update less often.\n \"\"\"\n\n def update(self):\n logging.info(\"Set days since last event\")\n zp_profiles = Profile.objects.all()\n for profile in zp_profiles:\n if profile.profile:\n try:\n profile.status[\"last_event\"] = (\n date.today() - datetime.fromtimestamp(profile.profile[0][\"event_date\"]).date()\n ).days\n profile.save()\n except Exception as e:\n logging.warning(f\"Failed to set last event: {e}\")\n continue\n\n\ndef sort_json_event_date():\n \"\"\"See also management command\"\"\"\n logging.info(\"Sort the profile json field\")\n profiles = Profile.objects.filter(status__sorted=False)\n for p in profiles:\n try:\n if not p.profile:\n continue\n if not isinstance(p.profile[0], dict):\n logging.warning(f\"not a valid profile: {p.zp_id}\")\n continue\n p.profile = sorted(p.profile, key=lambda x: int(x.get(\"event_date\", 0)), reverse=True)\n p.status[\"sorted\"] = True\n p.save()\n except Exception as e:\n logging.warning(f\" issues with: {p.zp_id}\\n {e}\")\n continue\n\n\nclass FetchResults:\n def __init__(self):\n self.zps = ZPSession()\n self.try_count = 0\n self.api_view = \"event_results_view\"\n self.api_zwift = \"event_results_zwift\"\n self.api_history = \"event_race_history\"\n self.model = Results\n\n def fetch(self):\n \"\"\"\n Create events from results\n \"\"\"\n logging.info(\"Create or update results\")\n # These are the results that need updating\n result_zp_ids = (\n Results.objects.filter(Q(zp_view__isnull=True) | Q(zp_zwift__isnull=True) | Q(race_history__isnull=True))\n .values_list(\"zp_id\", flat=True)\n .distinct()\n )\n # AllResults missing history data whiich has the date.\n all_results_zp_ids = (\n AllResults.objects.filter(race_history__isnull=False).values_list(\"zp_id\", flat=True).distinct()\n )\n try:\n history = self.zps.get_api(id=None, api=self.api_history)[self.api_history][\"data\"]\n history_zp_ids = {row[\"zid\"] for row in history}\n history = {row[\"zid\"]: row for row in history}\n except Exception as e:\n logging.error(f\"Failed to get history:\\n{e}\")\n raise\n # first lets get all the uknown events.from the history\n unknown_events = history_zp_ids - set(all_results_zp_ids)\n logging.info(f\"history_zp_ids, - all_results_zp_ids: {len(history_zp_ids)} - {len(all_results_zp_ids)}\")\n for zp_id in history_zp_ids:\n obj, created = AllResults.objects.get_or_create(zp_id=zp_id)\n obj.event_date = datetime.fromtimestamp(history[zp_id][\"tm\"]).date()\n obj.race_history = history[zp_id]\n obj.save()\n\n # Now we have made all unkown events we can get the results (view and zwift).\n # We need to get the results for all the events that are missing data.\n all_results_missing_data_zp_ids = AllResults.objects.filter(\n (Q(view__isnull=True) | Q(zwift__isnull=True)) & Q(event_date__gte=date.today() - timedelta(days=365))\n )\n errors = 0\n for count, result in enumerate(all_results_missing_data_zp_ids):\n if errors >= 4 or count >= 100:\n break\n if result.view is None:\n try:\n data_view = self.zps.get_api(id=result.zp_id, api=self.api_view)[self.api_view][\"data\"]\n result.view = data_view\n errors = 0\n except Exception as e:\n logging.warning(f\"Failed to get view data: {result.zp_id}\\n {e}\")\n errors += 1\n if result.zwift is None:\n try:\n data_zwift = self.zps.get_api(id=result.zp_id, api=self.api_zwift)[self.api_zwift][\"data\"]\n result.zwift = data_zwift\n errors = 0\n except Exception as e:\n logging.warning(f\"Failed to get zwift data: {result.zp_id}\\n {e}\")\n errors += 1\n result.save()\n time.sleep(5 + self.try_count * 5)\n", "repo_name": "vincentdavis/VeloTeams", "sub_path": "apps/zp/sync.py", "file_name": "sync.py", "file_ext": "py", "file_size_in_byte": 19158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "django.db.models.QuerySet", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 38, "usage_type": "name"}, {"api_name": "apps.zp.fetch.ZPSession", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models.QuerySet", "line_number": 46, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 65, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 67, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 69, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "apps.teams.models.Team.objects.values_list", "line_number": 83, "usage_type": "call"}, {"api_name": "apps.teams.models.Team.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "apps.teams.models.Team", "line_number": 83, "usage_type": "name"}, {"api_name": "apps.zp.models.TeamResults", "line_number": 83, "usage_type": "name"}, {"api_name": "apps.teams.models.Team.objects.values_list", "line_number": 88, "usage_type": "call"}, {"api_name": "apps.teams.models.Team.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "apps.teams.models.Team", "line_number": 88, "usage_type": "name"}, {"api_name": "apps.zp.models.TeamPending", "line_number": 88, "usage_type": "name"}, {"api_name": "apps.teams.models.Team.objects.values_list", "line_number": 93, "usage_type": "call"}, {"api_name": "apps.teams.models.Team.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "apps.teams.models.Team", "line_number": 93, "usage_type": "name"}, {"api_name": "apps.zp.models.TeamRiders", "line_number": 93, "usage_type": "name"}, {"api_name": "django.db.models.QuerySet", "line_number": 101, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 101, "usage_type": "name"}, {"api_name": "apps.zp.fetch.ZPSession", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models.QuerySet", "line_number": 109, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 113, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 119, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 130, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 141, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 160, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 167, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 173, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 181, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 182, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects.filter", "line_number": 194, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 194, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 194, "usage_type": "name"}, {"api_name": "apps.zp.models.Profile", "line_number": 197, "usage_type": "name"}, {"api_name": "apps.zp.models.Profile.objects.filter", "line_number": 205, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 205, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 205, "usage_type": "name"}, {"api_name": "apps.zp.models.Profile", "line_number": 208, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 213, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects.get", "line_number": 216, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 216, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 216, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 218, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 218, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 222, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile", "line_number": 230, "usage_type": "name"}, {"api_name": "apps.zp.fetch.ZPSession", "line_number": 231, "usage_type": "call"}, {"api_name": "apps.zp.fetch.ZPSession", "line_number": 241, "usage_type": "call"}, {"api_name": "apps.zp.models.AllResults", "line_number": 244, "usage_type": "name"}, {"api_name": "json.JSONDecodeError", "line_number": 251, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 252, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 255, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 263, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 268, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 270, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 282, "usage_type": "call"}, {"api_name": "apps.zp.models.TeamRiders.objects.order_by", "line_number": 284, "usage_type": "call"}, {"api_name": "apps.zp.models.TeamRiders.objects", "line_number": 284, "usage_type": "attribute"}, {"api_name": "apps.zp.models.TeamRiders", "line_number": 284, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 286, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 288, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects.get_or_create", "line_number": 289, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 289, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 289, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 290, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 300, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects.all", "line_number": 301, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 301, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 301, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 304, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 305, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 308, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 312, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 312, "usage_type": "name"}, {"api_name": "apps.zp.models.Results.objects.get_or_create", "line_number": 313, "usage_type": "call"}, {"api_name": "apps.zp.models.Results.objects", "line_number": 313, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Results", "line_number": 313, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 317, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 325, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 326, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 326, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 326, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 327, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 338, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 343, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 345, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 350, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 361, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects.all", "line_number": 362, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 362, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 362, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 367, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 367, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 367, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 367, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 371, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 377, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects.filter", "line_number": 378, "usage_type": "call"}, {"api_name": "apps.zp.models.Profile.objects", "line_number": 378, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Profile", "line_number": 378, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 384, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 390, "usage_type": "call"}, {"api_name": "apps.zp.fetch.ZPSession", "line_number": 396, "usage_type": "call"}, {"api_name": "apps.zp.models.Results", "line_number": 401, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 407, "usage_type": "call"}, {"api_name": "apps.zp.models.Results.objects.filter", "line_number": 410, "usage_type": "call"}, {"api_name": "apps.zp.models.Results.objects", "line_number": 410, "usage_type": "attribute"}, {"api_name": "apps.zp.models.Results", "line_number": 410, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 410, "usage_type": "call"}, {"api_name": "apps.zp.models.AllResults.objects.filter", "line_number": 416, "usage_type": "call"}, {"api_name": "apps.zp.models.AllResults.objects", "line_number": 416, "usage_type": "attribute"}, {"api_name": "apps.zp.models.AllResults", "line_number": 416, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 423, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 427, "usage_type": "call"}, {"api_name": "apps.zp.models.AllResults.objects.get_or_create", "line_number": 429, "usage_type": "call"}, {"api_name": "apps.zp.models.AllResults.objects", "line_number": 429, "usage_type": "attribute"}, {"api_name": "apps.zp.models.AllResults", "line_number": 429, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 430, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 430, "usage_type": "name"}, {"api_name": "apps.zp.models.AllResults.objects.filter", "line_number": 436, "usage_type": "call"}, {"api_name": "apps.zp.models.AllResults.objects", "line_number": 436, "usage_type": "attribute"}, {"api_name": "apps.zp.models.AllResults", "line_number": 436, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 437, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 437, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 437, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 437, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 449, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 457, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 460, "usage_type": "call"}]} +{"seq_id": "20216251639", "text": "from typing import Sequence, Tuple, Mapping, Any\nfrom core import computation_graph\nimport time\n\n\n# TODO(@jakeval): Remove the debugging name attribute from the Node class.\n\n\ndef copy_node(node: computation_graph.Node) -> computation_graph.Node:\n \"\"\"Shallow copies a node without its edges.\n\n Args:\n node: The node to copy.\n\n Returns:\n A copied node identical to the original but without any edges.\n \"\"\"\n if isinstance(node, computation_graph.Artifact):\n new_node = computation_graph.Artifact(node._data)\n elif isinstance(node, computation_graph.Process):\n new_node = computation_graph.Process(node._transformation)\n new_node.returns_indices = node.returns_indices\n else:\n raise RuntimeError(\n f\"{node} is not an Artifact or a Process. Did you forget to call the optex decorator?\"\n )\n new_node.name = node.name\n return new_node\n\n\ndef get_composed_children(\n artifact: computation_graph.Artifact,\n) -> Sequence[Tuple[str, computation_graph.Process]]:\n \"\"\"Gets the innermost children of an Artifact, even if they are composed\n within another OptexProcess.\n\n Normally, the children of an Artifact are the processes that directly use\n that Artifact, even if those processes are just the compositions of other\n processes that use it. This function recurses over the composed processes\n using this Artifact, eliminating all composition functions and including\n only the leaf process children.\n\n Args:\n artifact: The artifact whose children to find.\n\n Returns:\n A list of (key, child) pairs where each child is a child node and each\n key is the role connecting that child to the original Artifact.\n \"\"\"\n composed_children = []\n top_level_children = set(zip(artifact.children, artifact.children_roles))\n while top_level_children:\n child, edge_key = top_level_children.pop()\n\n if not edge_key:\n continue\n\n if not child.child_processes:\n composed_children.append((edge_key, child))\n else:\n child_roles = []\n child_processes = []\n for child_process in child.child_processes:\n for child_parent, child_role in zip(\n child_process.parents, child_process.parent_roles\n ):\n if child_parent == artifact:\n child_roles.append(child_role)\n child_processes.append(child_process)\n continue\n top_level_children = top_level_children.union(\n zip(child_processes, child_roles)\n )\n\n return composed_children\n\n\ndef make_expanded_graph_copy(\n graph: computation_graph.Graph,\n) -> computation_graph.EdgeGraph:\n \"\"\"Makes a copy of the graph where all composition processes have been\n replaced with their subgraphs.\n\n Args:\n graph: The graph to copy.\n\n Returns:\n A new graph where composition processes have been replaced by the\n subgraphs they contain.\n \"\"\"\n explored_set = set()\n new_nodes = {}\n old_nodes = {}\n\n new_inputs: Sequence[computation_graph.Node] = []\n for old_input in graph.inputs:\n new_input = copy_node(old_input)\n new_inputs.append(new_input)\n new_nodes[old_input] = new_input\n old_nodes[new_input] = old_input\n\n open_set = set(new_inputs)\n while open_set:\n new_node = open_set.pop()\n if new_node in explored_set:\n continue\n new_node.agents.append(graph.name)\n old_node = old_nodes[new_node]\n\n if isinstance(old_node, computation_graph.Artifact):\n old_children = get_composed_children(old_node)\n elif isinstance(old_node, computation_graph.Process):\n old_children = zip(old_node.children_roles, old_node.children)\n\n for old_key, old_child in old_children:\n if old_child in new_nodes:\n new_child = new_nodes[old_child]\n else:\n new_child = copy_node(old_child)\n new_nodes[old_child] = new_child\n old_nodes[new_child] = old_child\n new_child.parents.append(new_node)\n new_child.parent_roles.append(old_key)\n new_node.children.append(new_child)\n new_node.children_roles.append(old_key)\n open_set.add(new_child)\n explored_set.add(new_node)\n\n # TODO(@jakeval): Why can graph.outputs not be a list? This is a bug.\n try:\n outputs = [new_nodes[old_output] for old_output in graph.outputs]\n except TypeError:\n outputs = [new_nodes[graph.outputs]]\n\n return computation_graph.EdgeGraph.from_output_artifacts(\n outputs, graph.name\n )\n\n\ndef topological_sort_graph(\n edges: Sequence[Tuple[computation_graph.Node, computation_graph.Node]]\n) -> Sequence[computation_graph.Node]:\n node_count = 0\n node_to_int = {}\n int_to_node = {}\n int_edges = []\n for _, parent_node, child_node in edges:\n if parent_node not in node_to_int:\n node_to_int[parent_node] = node_count\n int_to_node[node_count] = parent_node\n node_count += 1\n if child_node not in node_to_int:\n node_to_int[child_node] = node_count\n int_to_node[node_count] = child_node\n node_count += 1\n parent_int = node_to_int[parent_node]\n child_int = node_to_int[child_node]\n int_edges.append((parent_int, child_int))\n\n sorted_nodes = topologicalSort(node_count, int_edges)\n return [int_to_node[node_int] for node_int, _ in sorted_nodes]\n\n\ndef topologicalSort(totalVertices, prerequisites):\n ##make graph\n graph = {}\n for edge in prerequisites:\n if edge[0] not in graph:\n graph[edge[0]] = [edge[1]]\n else:\n graph[edge[0]].append(edge[1])\n\n n = totalVertices\n indegree = [0] * n\n answer = []\n for key in graph:\n for nbrs in graph[key]:\n indegree[nbrs] += 1\n queue = []\n for i in range(0, n):\n if indegree[i] == 0:\n queue.append((i, 0))\n\n while len(queue) > 0:\n rem = queue.pop(0)\n answer.append((rem[0], rem[1]))\n if rem[0] in graph:\n for child in graph.get(rem[0]):\n indegree[child] -= 1\n if indegree[child] == 0:\n queue.append((child, rem[1] + 1))\n\n if len(answer) != n:\n raise RuntimeError(\n \"Graph had cycles -- topological sort is impossible.\"\n )\n\n return answer\n\n\n# @TODO(@jakeval): This ignores roles\ndef get_merge_candidates(\n old_node: computation_graph.Node,\n old_to_new: Mapping[computation_graph.Node, computation_graph.Node],\n) -> Sequence[computation_graph.Node]:\n old_parents = old_node.parents\n new_parents = [old_to_new[old_parent] for old_parent in old_parents]\n\n children_sets = []\n for parent in new_parents:\n children_sets.append(set(parent.children))\n if children_sets:\n return children_sets[0].intersection(*children_sets[1:])\n else:\n return []\n\n\ndef can_merge(\n old_node: computation_graph.Node,\n new_node: computation_graph.Node,\n old_to_new: Mapping[computation_graph.Node, computation_graph.Node],\n):\n \"\"\"Returns true if the original node can be merged into an already-existing\n node in the new graph.\n\n Args:\n old_node: The node from the original graph.\n new_node: The merge candidate in the new graph.\n old_to_new: A mapping from original graph nodes to merged graph\n nodes.\"\"\"\n\n # Check that their types match\n if type(old_node) != type(new_node):\n return False\n\n # Check that process transformation functions match\n if isinstance(old_node, computation_graph.Process):\n if old_node._transformation != new_node._transformation:\n return False\n\n # Check that parents match\n old_to_new_parents = [\n (role, old_to_new[parent])\n for role, parent in zip(old_node.parent_roles, old_node.parents)\n ]\n if set(old_to_new_parents) == set(\n zip(new_node.parent_roles, new_node.parents)\n ):\n return True\n else:\n return False\n\n\ndef add_input_artifacts(inputs):\n old_to_new = {}\n for new_artifact in inputs:\n for graph_name, old_artifacts in inputs[new_artifact].items():\n for old_artifact in old_artifacts:\n new_artifact.agents.append(graph_name)\n old_to_new[old_artifact] = new_artifact\n\n return old_to_new\n\n\ndef merge_nodes(\n old_node: computation_graph.Node,\n merge_candidate: computation_graph.Node,\n old_to_new: Mapping[computation_graph.Node, computation_graph.Node],\n) -> Mapping[computation_graph.Node, computation_graph.Node]:\n old_to_new[old_node] = merge_candidate\n merge_candidate.agents += old_node.agents\n return old_to_new\n\n\ndef add_new_node(\n old_node: computation_graph.Node,\n old_to_new: Mapping[computation_graph.Node, computation_graph.Node],\n) -> Tuple[\n computation_graph.Node,\n Mapping[computation_graph.Node, computation_graph.Node],\n Mapping[computation_graph.Node, computation_graph.Node],\n]:\n new_node = copy_node(old_node)\n new_node.agents = old_node.agents\n old_to_new[old_node] = new_node\n return new_node, old_to_new\n\n\ndef add_edges(\n old_node: computation_graph.Node,\n new_node: computation_graph.Node,\n old_to_new: Mapping[computation_graph.Node, computation_graph.Node],\n) -> None:\n for role, old_parent in zip(old_node.parent_roles, old_node.parents):\n new_parent = old_to_new[old_parent]\n new_node.parents.append(new_parent)\n new_node.parent_roles.append(role)\n new_parent.children.append(new_node)\n new_parent.children_roles.append(role)\n\n\ndef add_nodes(\n old_nodes: Sequence[computation_graph.Node],\n old_to_new: Mapping[computation_graph.Node, computation_graph.Node],\n) -> Tuple[\n Mapping[computation_graph.Node, computation_graph.Node],\n Mapping[computation_graph.Node, computation_graph.Node],\n]:\n for old_node in old_nodes:\n if old_node in old_to_new: # the node was previously added\n continue\n\n merge_candidates = get_merge_candidates(old_node, old_to_new)\n\n # Try to merge the node in to the new graph.\n did_merge = False\n for merge_candidate in merge_candidates:\n if can_merge(old_node, merge_candidate, old_to_new):\n old_to_new = merge_nodes(old_node, merge_candidate, old_to_new)\n did_merge = True\n break\n\n # If it can't merge, add a new node to the new graph.\n if not did_merge:\n new_node, old_to_new = add_new_node(old_node, old_to_new)\n add_edges(old_node, new_node, old_to_new)\n\n return old_to_new\n\n\ndef merge_graphs(\n graphs: Sequence[computation_graph.EdgeGraph],\n inputs: Mapping[\n computation_graph.Artifact,\n Mapping[str, Sequence[computation_graph.Artifact]],\n ],\n name: str,\n):\n \"\"\" \"\"\"\n graph_nodes = dict(\n [(graph.name, topological_sort_graph(graph.edges)) for graph in graphs]\n )\n\n inputs_copy = {}\n for node, v in inputs.items():\n node_copy = copy_node(node)\n inputs_copy[node_copy] = v\n\n old_to_new = add_input_artifacts(inputs_copy)\n\n for graph_name, nodes in graph_nodes.items():\n old_to_new = add_nodes(nodes, old_to_new)\n\n inputs = {}\n all_inputs = set()\n for graph in graphs:\n new_inputs = [old_to_new[input] for input in graph.inputs]\n inputs[graph.name] = new_inputs\n all_inputs = all_inputs.union(new_inputs)\n inputs[\"all_inputs\"] = list(all_inputs)\n\n outputs = {}\n all_outputs = set()\n for graph in graphs:\n new_outputs = [old_to_new[output] for output in graph.outputs]\n outputs[graph.name] = new_outputs\n all_outputs = all_outputs.union(new_outputs)\n outputs[\"all_outputs\"] = list(all_outputs)\n\n return (\n computation_graph.EdgeGraph.from_output_artifacts(\n list(all_outputs), name=name\n ),\n inputs,\n outputs,\n )\n\n\ndef get_inputs(call_list):\n \"\"\"\n [(graph, transformation, role, value)]\n\n {\n merged_artifact: {\n 'graph_name': [input_artifacts]\n }\n }\n \"\"\"\n inputs = {}\n\n for graph, transformation, role, artifact in call_list:\n input_artifact = None\n for input in graph.inputs:\n for child_role, child in zip(input.children_roles, input.children):\n if (child._transformation == transformation.__wrapped__) and (\n child_role == role\n ):\n input_artifact = input\n break\n if input_artifact is not None:\n break\n if not input_artifact:\n raise RuntimeError(\n f\"Can't find the corresponding process for {transformation.__wrapped__}, {role}\"\n )\n\n if artifact in inputs:\n if graph.name in inputs[artifact]:\n inputs[artifact][graph.name].append(input_artifact)\n else:\n inputs[artifact][graph.name] = [input_artifact]\n else:\n inputs[artifact] = {graph.name: [input_artifact]}\n return inputs\n\n\n# TODO(@jakeval): Because values are cached per-artifact and not per-process,\n# processes which return multiple values must be recomputed.\ndef compute_artifact_ancestors(artifact, artifact_values):\n \"\"\"Recursively computes the value of an artifact and its ancestors given\n sufficient values for its ancestors.\n\n This is used to lazily compute an artifact given values for its root\n ancestors. The function will recursively evaluate all of the necessary\n ancestor values starting from the root ancestors until it computes the\n target artifact value.\n\n Args:\n artifact: The artifact whose value and ancestors to compute.\n artifact_values: The values of the artifact's root ancestors.\n\n Returns:\n The values of the artifact and all its ancestors.\n \"\"\"\n # artifacts only have 1 parent\n process = artifact.parents[0]\n process_args = {}\n for arg_name, parent_artifact in zip(\n process.parent_roles, process.parents\n ):\n if parent_artifact not in artifact_values:\n artifact_values = compute_artifact_ancestors(\n parent_artifact, artifact_values\n )\n process_args[arg_name] = artifact_values[parent_artifact]\n if process.returns_indices:\n index = process.returns_indices[artifact.parent_roles[0]]\n start_time = time.time()\n artifact_values[artifact] = process._transformation(**process_args)[\n index\n ]\n process.execution_time = time.time() - start_time\n else:\n start_time = time.time()\n artifact_values[artifact] = process._transformation(**process_args)\n process.execution_time = time.time() - start_time\n return artifact_values\n\n\ndef execute_graph(\n graph: computation_graph.Graph,\n inputs: Mapping[computation_graph.Artifact, Any],\n) -> Mapping[computation_graph.Artifact, Any]:\n \"\"\"Executes a statically-generated computation graph on some inputs.\n\n graph: The graph to execute.\n inputs: A mapping from input Artifact to the value it should take on in\n the computation.\n\n Returns:\n A mapping from output Artifact to the value it computes to.\n \"\"\"\n output_values = {}\n artifact_values = inputs.copy()\n\n for output in graph.outputs:\n artifact_values = compute_artifact_ancestors(output, artifact_values)\n output_values[output] = artifact_values[output]\n\n return output_values\n\n\ndef get_merged_inputs(graph_inputs, call_list):\n \"\"\"\n Given:\n {\n value: (graph_name, transformation, role)\n }\n\n Return:\n {\n value: merged_graph_input_artifact\n }\n \"\"\"\n inputs = {}\n\n for value, (graph_name, transformation, role) in call_list.items():\n input_artifact = None\n for input in graph_inputs[graph_name]:\n for child_role, child in zip(input.children_roles, input.children):\n if (child._transformation == transformation.__wrapped__) and (\n child_role == role\n ):\n input_artifact = input\n break\n if input_artifact is not None:\n break\n if not input_artifact:\n raise RuntimeError(\n f\"Can't find the corresponding process for {transformation.__wrapped__}, {role}\"\n )\n inputs[input_artifact] = value\n\n return inputs\n\n\ndef execute_merged_graph(\n graph: computation_graph.Graph,\n inputs: Mapping[computation_graph.Artifact, Any],\n) -> Mapping[computation_graph.Artifact, Any]:\n \"\"\"Executes a statically-generated computation graph on some inputs.\n\n Given:\n\n input_artifact, value\n\n input_artifact:\n - graph, role,\n -\n\n\n graph: The graph to execute.\n inputs: A mapping from input Artifact to the value it should take on in\n the computation.\n\n Returns:\n A mapping from output Artifact to the value it computes to.\n \"\"\"\n output_values = {}\n artifact_values = inputs.copy()\n\n for output in graph.outputs:\n artifact_values = compute_artifact_ancestors(output, artifact_values)\n output_values[output] = artifact_values[output]\n\n return output_values\n", "repo_name": "jakeval/optex", "sub_path": "core/graph_merge.py", "file_name": "graph_merge.py", "file_ext": "py", "file_size_in_byte": 17552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "core.computation_graph.Node", "line_number": 9, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 9, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 18, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 18, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 19, "usage_type": "call"}, {"api_name": "core.computation_graph", "line_number": 19, "usage_type": "name"}, {"api_name": "core.computation_graph.Process", "line_number": 20, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 20, "usage_type": "name"}, {"api_name": "core.computation_graph.Process", "line_number": 21, "usage_type": "call"}, {"api_name": "core.computation_graph", "line_number": 21, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 32, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 33, "usage_type": "name"}, {"api_name": "core.computation_graph.Process", "line_number": 33, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 33, "usage_type": "name"}, {"api_name": "core.computation_graph.Graph", "line_number": 79, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 95, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 95, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 95, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 110, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 110, "usage_type": "name"}, {"api_name": "core.computation_graph.Process", "line_number": 112, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 112, "usage_type": "name"}, {"api_name": "core.computation_graph.EdgeGraph.from_output_artifacts", "line_number": 135, "usage_type": "call"}, {"api_name": "core.computation_graph.EdgeGraph", "line_number": 135, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 135, "usage_type": "name"}, {"api_name": "core.computation_graph.EdgeGraph", "line_number": 80, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 141, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 141, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 141, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 141, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 142, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 142, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 142, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 203, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 204, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 204, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 204, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 205, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 205, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 205, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 219, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 219, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 220, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 220, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 221, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 221, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 221, "usage_type": "name"}, {"api_name": "core.computation_graph.Process", "line_number": 237, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 237, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 266, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 266, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 267, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 267, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 268, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 268, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 268, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 269, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 269, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 269, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 276, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 276, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 277, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 277, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 277, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 278, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 279, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 279, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 280, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 280, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 280, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 281, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 281, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 281, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 290, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 290, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 291, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 292, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 292, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 292, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 303, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 303, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 303, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 304, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 304, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 304, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 305, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 306, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 306, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 306, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 307, "usage_type": "name"}, {"api_name": "core.computation_graph.Node", "line_number": 307, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 307, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 332, "usage_type": "name"}, {"api_name": "core.computation_graph.EdgeGraph", "line_number": 332, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 332, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 333, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 334, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 334, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 335, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 335, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 335, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 335, "usage_type": "name"}, {"api_name": "core.computation_graph.EdgeGraph.from_output_artifacts", "line_number": 371, "usage_type": "call"}, {"api_name": "core.computation_graph.EdgeGraph", "line_number": 371, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 371, "usage_type": "name"}, {"api_name": "time.time", "line_number": 448, "usage_type": "call"}, {"api_name": "time.time", "line_number": 452, "usage_type": "call"}, {"api_name": "time.time", "line_number": 454, "usage_type": "call"}, {"api_name": "time.time", "line_number": 456, "usage_type": "call"}, {"api_name": "core.computation_graph.Graph", "line_number": 461, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 461, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 462, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 462, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 462, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 462, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 463, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 463, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 463, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 463, "usage_type": "name"}, {"api_name": "core.computation_graph.Graph", "line_number": 518, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 518, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 519, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 519, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 520, "usage_type": "name"}, {"api_name": "core.computation_graph.Artifact", "line_number": 520, "usage_type": "attribute"}, {"api_name": "core.computation_graph", "line_number": 520, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 520, "usage_type": "name"}]} +{"seq_id": "73603209646", "text": "\"\"\"Learner component for CQL.\"\"\"\nimport functools\nimport time\nfrom typing import Iterator, NamedTuple, Optional\n\nimport jax\nimport jax.numpy as jnp\nimport jax.scipy as jsp\nimport numpy as np\nimport optax\nfrom acme import core\nfrom acme import types\nfrom acme.jax import networks as networks_lib\nfrom acme.utils import counting\nfrom acme.utils import loggers\n\n\nclass TrainingState(NamedTuple):\n \"\"\"Training state for CQL Learner.\"\"\"\n\n policy_params: networks_lib.Params\n critic_params: networks_lib.Params\n critic_target_params: networks_lib.Params\n policy_optimizer_state: optax.OptState\n critic_optimizer_state: optax.OptState\n alpha_optimizer_state: optax.OptState\n alpha_params: jnp.ndarray\n alpha_prime_optimizer_state: Optional[optax.OptState]\n alpha_prime_params: Optional[jnp.ndarray]\n key: networks_lib.PRNGKey\n steps: int\n\n\nclass CQLLearner(core.Learner):\n \"\"\"Conservative Q Learning (CQL) learner component.\n\n This corresponds to CQL(H) agent from [1], with importance sampling\n (min_q_version == 3) according to Appendix F in [1].\n The implementation is based on\n\n https://github.com/aviralkumar2907/CQL/blob/master/d4rl/rlkit/torch/sac/cql.py\n\n References:\n [1]: Aviral Kumar and Aurick Zhou and George Tucker and Sergey Levine,\n Conservative Q-Learning for Offline Reinforcement Learning,\n arXiv Pre-print, https://arxiv.org/abs/2006.04779\n \"\"\"\n\n def __init__(\n self,\n policy_network: networks_lib.FeedForwardNetwork,\n critic_network: networks_lib.FeedForwardNetwork,\n random_key: networks_lib.PRNGKey,\n dataset: Iterator[types.Transition],\n policy_optimizer: optax.GradientTransformation,\n critic_optimizer: optax.GradientTransformation,\n alpha_optimizer: optax.GradientTransformation,\n target_entropy: float,\n discount: float = 0.99,\n tau: float = 5e-3,\n init_alpha: float = 1.0,\n num_bc_steps: int = 0,\n softmax_temperature: float = 1.0,\n cql_alpha: float = 5.0,\n max_q_backup: bool = False,\n deterministic_backup: bool = True,\n num_cql_samples: int = 10,\n with_lagrange: bool = False,\n target_action_gap: float = 10.0,\n logger: Optional[loggers.Logger] = None,\n counter: Optional[counting.Counter] = None,\n ):\n \"\"\"Initialize the CQL Learner.\n\n Args:\n policy_network: policy network\n critic_network: critic network\n random_key: key for random number generation\n dataset: iterator for the training data\n policy_optimizer: optimizer for policy network\n critic_optimizer: optimizer for critic network\n alpha_optimizer: optimizer for SAC alpha \"temperature\"\n target_entropy: target entropy for automatic entropy tuning\n discount: discount for TD updates.\n tau: coefficient for smoothing target network update.\n init_alpha: Initial alpha.\n num_bc_steps: Number of steps to perform BC on policy update.\n softmax_temperature: temperature for the logsumexp.\n min_q_weight: the value of alpha, set to 5.0 or 10.0 if not using lagrange.\n When adaptive cql weight is used, this determines the minimum\n weight for the cql loss.\n max_q_backup: set this to true to use max_{a} backup.\n deterministic_backup: set this to true to use deterministic backup, i.e.,\n it will not backup the entropy in the Q function.\n num_cql_samples: number of random samples to use for max backup and\n importance sampling.\n with_lagrange: with to use the lagrangian formulation of CQL.\n target_action_gap: Threshold for the lagrangian.\n logger: logger object to write the metrics to.\n counter: counter used for keeping track of the number of steps.\n\n References:\n Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine,\n Conservative Q-Learning for Offline Reinforcement Learning\n https://arxiv.org/abs/2006.04779\n\n \"\"\"\n if with_lagrange:\n # For now, use the alpha optimizer hyperparams\n alpha_prime_optimizer = optax.adam(3e-4)\n else:\n alpha_prime_optimizer = None\n\n polyak_average = functools.partial(optax.incremental_update, step_size=tau)\n\n def sample_action_and_log_prob(\n policy_params: networks_lib.Params,\n key: networks_lib.PRNGKey,\n observation: networks_lib.Observation,\n sample_shape=(),\n ):\n action_dist = policy_network.apply(policy_params, observation)\n action = action_dist.sample(sample_shape, seed=key)\n log_prob = action_dist.log_prob(action)\n return action, log_prob\n\n def critic_loss_fn(\n critic_params: networks_lib.Params,\n alpha_prime_params: jnp.ndarray,\n critic_target_params: networks_lib.Params,\n policy_params: networks_lib.Params,\n key: networks_lib.PRNGKey,\n log_alpha: jnp.ndarray,\n transitions: types.Transition,\n ):\n # For CQL(H), the loss is\n # min_Q alpha' * [logsumexp(Q(s,a')) - Q(s,a)] + (Q(s, a) - Q(s', a''))^2\n # = alpha' * cql_loss + critic_loss\n # First compute the SAC critic loss\n alpha = jnp.exp(log_alpha)\n q1_pred, q2_pred = critic_network.apply(\n critic_params, transitions.observation, transitions.action\n )\n\n if not max_q_backup:\n next_action_key, key = jax.random.split(key)\n new_next_actions, next_log_pi = sample_action_and_log_prob(\n policy_params, next_action_key, transitions.next_observation\n )\n target_q1, target_q2 = critic_network.apply(\n critic_target_params,\n transitions.next_observation,\n new_next_actions,\n )\n target_q_values = jnp.minimum(target_q1, target_q2)\n if not deterministic_backup:\n target_q_values = target_q_values - alpha * next_log_pi\n else:\n next_action_key, key = jax.random.split(key)\n # TODO(yl): allow configuting number of actions\n sampled_next_actions, next_log_pi = sample_action_and_log_prob(\n policy_params,\n next_action_key,\n transitions.next_observation,\n sample_shape=(num_cql_samples,),\n )\n target_q1, target_q2 = jax.vmap(critic_network.apply, (None, None, 0))(\n critic_target_params,\n transitions.next_observation,\n sampled_next_actions,\n )\n target_q1 = jnp.max(target_q1, axis=0)\n target_q2 = jnp.max(target_q2, axis=0)\n target_q_values = jnp.min(target_q1, target_q2)\n\n q_target = (\n transitions.reward + transitions.discount * discount * target_q_values\n )\n assert len(q_target.shape) == 1\n q_target = jax.lax.stop_gradient(q_target)\n qf1_loss = jnp.mean(jnp.square(q1_pred - q_target))\n qf2_loss = jnp.mean(jnp.square(q2_pred - q_target))\n qf_loss = qf1_loss + qf2_loss\n\n # Next compute the cql_loss\n batch_size = transitions.action.shape[0]\n action_size = transitions.action.shape[-1]\n vmapped_critic_apply = jax.vmap(\n critic_network.apply, (None, None, 0), out_axes=0\n )\n # Compute the logsumexp(Q(s,a')) according to Appendix F\n # for the importance sampled version\n # Sample actions from uniform-at-random distribution\n # (N, B, A)\n uniform_key, policy_key, key = jax.random.split(key, 3)\n uniform_actions = jax.random.uniform(\n uniform_key,\n shape=(num_cql_samples, batch_size, action_size),\n dtype=transitions.action.dtype,\n maxval=1.0,\n minval=-1.0,\n )\n uniform_log_probs = jnp.log(0.5**action_size)\n # Compute the q values for the uniform actions\n # Sample actions from the policy\n q_uniform1, q_uniform2 = vmapped_critic_apply(\n critic_params, transitions.observation, uniform_actions\n )\n uniform_log_probs1 = q_uniform1 * softmax_temperature - uniform_log_probs\n uniform_log_probs2 = q_uniform2 * softmax_temperature - uniform_log_probs\n sampled_actions, sampled_actions_log_probs = sample_action_and_log_prob(\n policy_params, policy_key, transitions.observation, (num_cql_samples,)\n )\n q_estimate1, q_estimate2 = vmapped_critic_apply(\n critic_params, transitions.observation, sampled_actions\n )\n policy_log_probs1 = (\n q_estimate1 * softmax_temperature - sampled_actions_log_probs\n )\n policy_log_probs2 = (\n q_estimate2 * softmax_temperature - sampled_actions_log_probs\n )\n combined_log_probs1 = jnp.concatenate(\n [policy_log_probs1, uniform_log_probs1], axis=0\n )\n combined_log_probs2 = jnp.concatenate(\n [policy_log_probs2, uniform_log_probs2], axis=0\n )\n\n logsumexp = jsp.special.logsumexp\n logsumexp1 = (\n logsumexp(combined_log_probs1, axis=0) * 1.0 / softmax_temperature\n )\n logsumexp2 = (\n logsumexp(combined_log_probs2, axis=0) * 1.0 / softmax_temperature\n )\n cql_loss = jnp.mean((logsumexp1 - q1_pred) + (logsumexp2 - q2_pred))\n alpha_prime = jnp.clip(jnp.exp(alpha_prime_params), 0.0, 10000.0)\n metrics = {\n \"qf_loss\": qf_loss,\n \"cql_loss\": cql_loss,\n \"q1\": jnp.mean(q1_pred),\n \"q2\": jnp.mean(q2_pred),\n \"q1_uniform\": jnp.mean(q_uniform1),\n \"q2_uniform\": jnp.mean(q_uniform2),\n }\n return qf_loss + alpha_prime * cql_loss, metrics\n\n def actor_loss_fn(\n policy_params: networks_lib.Params,\n critic_params: networks_lib.Params,\n key: networks_lib.PRNGKey,\n alpha_params: jnp.ndarray,\n observation: jnp.ndarray,\n ):\n alpha = jnp.exp(alpha_params)\n action_dist = policy_network.apply(policy_params, observation)\n new_actions = action_dist.sample(seed=key)\n log_probs = action_dist.log_prob(new_actions)\n q1, q2 = critic_network.apply(critic_params, observation, new_actions)\n q_new_actions = jnp.minimum(q1, q2)\n entropy = -log_probs.mean()\n actor_loss = alpha * log_probs - q_new_actions\n return jnp.mean(actor_loss), {\"entropy\": entropy}\n\n def bc_actor_loss_fn(\n policy_params: networks_lib.Params,\n key: networks_lib.PRNGKey,\n alpha_params: jnp.ndarray,\n observations: jnp.ndarray,\n actions: jnp.ndarray,\n ):\n # This is the loss function for pre-training the policy\n action_dist = policy_network.apply(policy_params, observations)\n policy_log_prob = action_dist.log_prob(actions)\n new_actions = action_dist.sample(seed=key)\n log_pi = action_dist.log_prob(new_actions)\n policy_loss = (jnp.exp(alpha_params) * log_pi - policy_log_prob).mean()\n return policy_loss, {\"entropy\": -log_pi.mean()}\n\n def alpha_loss_fn(alpha_params: jnp.ndarray, entropy: jnp.ndarray):\n # Use log_alpha here for numerical stability\n return alpha_params * (entropy - target_entropy)\n\n def alpha_prime_loss_fn(alpha_prime_params: jnp.ndarray, cql_loss: jnp.ndarray):\n # -alpha' * (cql_q1_loss - tau) + alpha' * (cql_q2_loss - tau)\n # -alpha' * (cql_q1_loss + cql_q2_loss - 2 * tau)\n # -alpha' * (cql_loss - 2 * tau)\n alpha_prime = jnp.clip(jnp.exp(alpha_prime_params), 0.0, 10000.0)\n return -alpha_prime * (cql_loss - 2 * target_action_gap)\n\n bc_policy_grad_fn = jax.value_and_grad(bc_actor_loss_fn, has_aux=True)\n policy_grad_fn = jax.value_and_grad(actor_loss_fn, has_aux=True)\n\n @jax.jit\n def sgd_step(state: TrainingState, transitions: types.Transition):\n metrics = {}\n # Update critic\n critic_key, actor_key, key = jax.random.split(state.key, 3)\n (critic_loss, critic_metrics), critic_grads = jax.value_and_grad(\n critic_loss_fn, has_aux=True\n )(\n state.critic_params,\n state.alpha_prime_params,\n state.critic_target_params,\n state.policy_params,\n critic_key,\n state.alpha_params,\n transitions,\n )\n metrics.update({\"critic_loss\": critic_loss, **critic_metrics})\n critic_updates, critic_optimizer_state = critic_optimizer.update(\n critic_grads, state.critic_optimizer_state\n )\n critic_params = optax.apply_updates(state.critic_params, critic_updates)\n # Update policy\n (policy_loss, actor_metrics), policy_grads = jax.lax.cond(\n state.steps < num_bc_steps,\n lambda _: bc_policy_grad_fn(\n state.policy_params,\n actor_key,\n state.alpha_params,\n transitions.observation,\n transitions.action,\n ),\n lambda _: policy_grad_fn(\n state.policy_params,\n critic_params,\n actor_key,\n state.alpha_params,\n transitions.observation,\n ),\n operand=None,\n )\n policy_updates, policy_optimizer_state = policy_optimizer.update(\n policy_grads, state.policy_optimizer_state\n )\n policy_params = optax.apply_updates(state.policy_params, policy_updates)\n metrics.update({\"actor_loss\": policy_loss, **actor_metrics})\n\n # Update entropy alpha\n alpha_loss, grad = jax.value_and_grad(alpha_loss_fn)(\n state.alpha_params, actor_metrics[\"entropy\"]\n )\n alpha_update, alpha_optimizer_state = alpha_optimizer.update(\n grad, state.alpha_optimizer_state\n )\n alpha_params = optax.apply_updates(state.alpha_params, alpha_update)\n metrics.update({\"alpha_loss\": alpha_loss, \"alpha\": jnp.exp(alpha_params)})\n\n # Update adaptive alpha_prime\n if with_lagrange:\n alpha_prime_loss, alpha_prime_grads = jax.value_and_grad(\n alpha_prime_loss_fn\n )(state.alpha_prime_params, critic_metrics[\"cql_loss\"])\n # pytype: disable=attribute-error\n (\n alpha_prime_updates,\n alpha_prime_optimizer_state,\n ) = alpha_prime_optimizer.update(\n alpha_prime_grads, state.alpha_prime_optimizer_state\n )\n # pytype: enable=attribute-error\n alpha_prime_params = optax.apply_updates(\n state.alpha_prime_params, alpha_prime_updates\n )\n metrics.update(\n {\n \"alpha_prime_loss\": alpha_prime_loss,\n \"alpha_prime\": jnp.exp(alpha_prime_params),\n }\n )\n else:\n alpha_prime_params = state.alpha_prime_params\n alpha_prime_optimizer_state = None\n\n # Update target network params\n critic_target_params = polyak_average(\n critic_params, state.critic_target_params\n )\n steps = state.steps + 1\n state = TrainingState(\n policy_params=policy_params,\n critic_params=critic_params,\n critic_target_params=critic_target_params,\n policy_optimizer_state=policy_optimizer_state,\n critic_optimizer_state=critic_optimizer_state,\n alpha_optimizer_state=alpha_optimizer_state,\n alpha_params=alpha_params,\n alpha_prime_optimizer_state=alpha_prime_optimizer_state,\n alpha_prime_params=alpha_prime_params,\n key=key,\n steps=steps,\n )\n return state, metrics\n\n self._iterator = dataset\n self._logger = logger or loggers.make_default_logger(\n label=\"learner\", save_data=False\n )\n self._counter = counter or counting.Counter()\n\n self._sgd_step = sgd_step\n\n def make_initial_state(key):\n init_policy_key, init_critic_key, key = jax.random.split(random_key, 3)\n init_policy_params = policy_network.init(init_policy_key)\n init_critic_params = critic_network.init(init_critic_key)\n init_policy_optimizer_state = policy_optimizer.init(init_policy_params)\n init_critic_optimizer_state = critic_optimizer.init(init_critic_params)\n init_alpha_params = jnp.array(np.log(init_alpha), dtype=jnp.float32)\n init_alpha_optimizer_state = alpha_optimizer.init(init_alpha_params)\n\n init_alpha_prime_params = jnp.asarray(jnp.log(cql_alpha), dtype=jnp.float32)\n if alpha_prime_optimizer is not None:\n init_alpha_prime_optimizer_state = alpha_prime_optimizer.init(\n init_alpha_prime_params\n )\n else:\n init_alpha_prime_optimizer_state = None\n\n return TrainingState(\n policy_params=init_policy_params,\n critic_params=init_critic_params,\n critic_target_params=init_critic_params,\n policy_optimizer_state=init_policy_optimizer_state,\n critic_optimizer_state=init_critic_optimizer_state,\n alpha_optimizer_state=init_alpha_optimizer_state,\n alpha_prime_optimizer_state=init_alpha_prime_optimizer_state,\n alpha_params=init_alpha_params,\n alpha_prime_params=init_alpha_prime_params,\n key=key,\n steps=0,\n )\n\n self._state = make_initial_state(random_key)\n\n self._timestamp = None\n\n def step(self):\n # Get data from replay\n transitions = next(self._iterator)\n # Perform a single learner step\n self._state, metrics = self._sgd_step(self._state, transitions)\n\n # Compute elapsed time\n timestamp = time.time()\n elapsed_time = timestamp - self._timestamp if self._timestamp else 0\n self._timestamp = timestamp\n\n # Increment counts and record the current time\n counts = self._counter.increment(steps=1, walltime=elapsed_time)\n # Attempts to write the logs.\n self._logger.write({**metrics, **counts})\n\n def get_variables(self, names):\n variables = {\n \"policy\": self._state.policy_params,\n \"critic\": self._state.critic_params,\n }\n return [variables[name] for name in names]\n\n def save(self) -> TrainingState:\n return self._state\n\n def restore(self, state: TrainingState):\n self._state = state\n", "repo_name": "ethanluoyc/magi", "sub_path": "magi/agents/cql/learning.py", "file_name": "learning.py", "file_ext": "py", "file_size_in_byte": 20023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 103, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.NamedTuple", "line_number": 18, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 21, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 21, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 22, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 22, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 23, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 23, "usage_type": "name"}, {"api_name": "optax.OptState", "line_number": 24, "usage_type": "attribute"}, {"api_name": "optax.OptState", "line_number": 25, "usage_type": "attribute"}, {"api_name": "optax.OptState", "line_number": 26, "usage_type": "attribute"}, {"api_name": "jax.numpy.ndarray", "line_number": 27, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "optax.OptState", "line_number": 28, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 29, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 29, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 29, "usage_type": "name"}, {"api_name": "acme.jax.networks.PRNGKey", "line_number": 30, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 30, "usage_type": "name"}, {"api_name": "acme.core.Learner", "line_number": 34, "usage_type": "attribute"}, {"api_name": "acme.core", "line_number": 34, "usage_type": "name"}, {"api_name": "acme.jax.networks.FeedForwardNetwork", "line_number": 51, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 51, "usage_type": "name"}, {"api_name": "acme.jax.networks.FeedForwardNetwork", "line_number": 52, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 52, "usage_type": "name"}, {"api_name": "acme.jax.networks.PRNGKey", "line_number": 53, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 54, "usage_type": "name"}, {"api_name": "acme.types.Transition", "line_number": 54, "usage_type": "attribute"}, {"api_name": "acme.types", "line_number": 54, "usage_type": "name"}, {"api_name": "optax.GradientTransformation", "line_number": 55, "usage_type": "attribute"}, {"api_name": "optax.GradientTransformation", "line_number": 56, "usage_type": "attribute"}, {"api_name": "optax.GradientTransformation", "line_number": 57, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 70, "usage_type": "name"}, {"api_name": "acme.utils.loggers.Logger", "line_number": 70, "usage_type": "attribute"}, {"api_name": "acme.utils.loggers", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 71, "usage_type": "name"}, {"api_name": "acme.utils.counting.Counter", "line_number": 71, "usage_type": "attribute"}, {"api_name": "acme.utils.counting", "line_number": 71, "usage_type": "name"}, {"api_name": "optax.adam", "line_number": 110, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 114, "usage_type": "call"}, {"api_name": "optax.incremental_update", "line_number": 114, "usage_type": "attribute"}, {"api_name": "acme.jax.networks.Params", "line_number": 117, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 117, "usage_type": "name"}, {"api_name": "acme.jax.networks.PRNGKey", "line_number": 118, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 118, "usage_type": "name"}, {"api_name": "acme.jax.networks.Observation", "line_number": 119, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 119, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 128, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 128, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 129, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 129, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 130, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 130, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 131, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 131, "usage_type": "name"}, {"api_name": "acme.jax.networks.PRNGKey", "line_number": 132, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 132, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 133, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 133, "usage_type": "name"}, {"api_name": "acme.types.Transition", "line_number": 134, "usage_type": "attribute"}, {"api_name": "acme.types", "line_number": 134, "usage_type": "name"}, {"api_name": "jax.numpy.exp", "line_number": 140, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 140, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 146, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "jax.numpy.minimum", "line_number": 155, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 155, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 159, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "jax.vmap", "line_number": 167, "usage_type": "call"}, {"api_name": "jax.numpy.max", "line_number": 172, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 172, "usage_type": "name"}, {"api_name": "jax.numpy.max", "line_number": 173, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 173, "usage_type": "name"}, {"api_name": "jax.numpy.min", "line_number": 174, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 174, "usage_type": "name"}, {"api_name": "jax.lax.stop_gradient", "line_number": 180, "usage_type": "call"}, {"api_name": "jax.lax", "line_number": 180, "usage_type": "attribute"}, {"api_name": "jax.numpy.mean", "line_number": 181, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 181, "usage_type": "name"}, {"api_name": "jax.numpy.square", "line_number": 181, "usage_type": "call"}, {"api_name": "jax.numpy.mean", "line_number": 182, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 182, "usage_type": "name"}, {"api_name": "jax.numpy.square", "line_number": 182, "usage_type": "call"}, {"api_name": "jax.vmap", "line_number": 188, "usage_type": "call"}, {"api_name": "jax.random.split", "line_number": 195, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 195, "usage_type": "attribute"}, {"api_name": "jax.random.uniform", "line_number": 196, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 196, "usage_type": "attribute"}, {"api_name": "jax.numpy.log", "line_number": 203, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 203, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 223, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 223, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 226, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 226, "usage_type": "name"}, {"api_name": "jax.scipy.special", "line_number": 230, "usage_type": "attribute"}, {"api_name": "jax.scipy", "line_number": 230, "usage_type": "name"}, {"api_name": "jax.numpy.mean", "line_number": 237, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 237, "usage_type": "name"}, {"api_name": "jax.numpy.clip", "line_number": 238, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 238, "usage_type": "name"}, {"api_name": "jax.numpy.exp", "line_number": 238, "usage_type": "call"}, {"api_name": "jax.numpy.mean", "line_number": 242, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 242, "usage_type": "name"}, {"api_name": "jax.numpy.mean", "line_number": 243, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 243, "usage_type": "name"}, {"api_name": "jax.numpy.mean", "line_number": 244, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 244, "usage_type": "name"}, {"api_name": "jax.numpy.mean", "line_number": 245, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 245, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 250, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 250, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 251, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 251, "usage_type": "name"}, {"api_name": "acme.jax.networks.PRNGKey", "line_number": 252, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 252, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 253, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 253, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 254, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 254, "usage_type": "name"}, {"api_name": "jax.numpy.exp", "line_number": 256, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 256, "usage_type": "name"}, {"api_name": "jax.numpy.minimum", "line_number": 261, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 261, "usage_type": "name"}, {"api_name": "jax.numpy.mean", "line_number": 264, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 264, "usage_type": "name"}, {"api_name": "acme.jax.networks.Params", "line_number": 267, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 267, "usage_type": "name"}, {"api_name": "acme.jax.networks.PRNGKey", "line_number": 268, "usage_type": "attribute"}, {"api_name": "acme.jax.networks", "line_number": 268, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 269, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 269, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 270, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 270, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 271, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 271, "usage_type": "name"}, {"api_name": "jax.numpy.exp", "line_number": 278, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 278, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 281, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 281, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 285, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 285, "usage_type": "name"}, {"api_name": "jax.numpy.clip", "line_number": 289, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 289, "usage_type": "name"}, {"api_name": "jax.numpy.exp", "line_number": 289, "usage_type": "call"}, {"api_name": "jax.value_and_grad", "line_number": 292, "usage_type": "call"}, {"api_name": "jax.value_and_grad", "line_number": 293, "usage_type": "call"}, {"api_name": "acme.types.Transition", "line_number": 296, "usage_type": "attribute"}, {"api_name": "acme.types", "line_number": 296, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 299, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 299, "usage_type": "attribute"}, {"api_name": "jax.value_and_grad", "line_number": 300, "usage_type": "call"}, {"api_name": "optax.apply_updates", "line_number": 315, "usage_type": "call"}, {"api_name": "jax.lax.cond", "line_number": 317, "usage_type": "call"}, {"api_name": "jax.lax", "line_number": 317, "usage_type": "attribute"}, {"api_name": "optax.apply_updates", "line_number": 338, "usage_type": "call"}, {"api_name": "jax.value_and_grad", "line_number": 342, "usage_type": "call"}, {"api_name": "optax.apply_updates", "line_number": 348, "usage_type": "call"}, {"api_name": "jax.numpy.exp", "line_number": 349, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 349, "usage_type": "name"}, {"api_name": "jax.value_and_grad", "line_number": 353, "usage_type": "call"}, {"api_name": "optax.apply_updates", "line_number": 364, "usage_type": "call"}, {"api_name": "jax.numpy.exp", "line_number": 370, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 370, "usage_type": "name"}, {"api_name": "jax.jit", "line_number": 295, "usage_type": "attribute"}, {"api_name": "acme.utils.loggers.make_default_logger", "line_number": 398, "usage_type": "call"}, {"api_name": "acme.utils.loggers", "line_number": 398, "usage_type": "name"}, {"api_name": "acme.utils.counting.Counter", "line_number": 401, "usage_type": "call"}, {"api_name": "acme.utils.counting", "line_number": 401, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 406, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 406, "usage_type": "attribute"}, {"api_name": "jax.numpy.array", "line_number": 411, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 411, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 411, "usage_type": "call"}, {"api_name": "jax.numpy.float32", "line_number": 411, "usage_type": "attribute"}, {"api_name": "jax.numpy.asarray", "line_number": 414, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 414, "usage_type": "name"}, {"api_name": "jax.numpy.log", "line_number": 414, "usage_type": "call"}, {"api_name": "jax.numpy.float32", "line_number": 414, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 447, "usage_type": "call"}]} +{"seq_id": "14473316842", "text": "#References:\n#https://github.com/hpclab/rankeval/commit/0b5090325228afe197f0708cb158ada50b8f7b7a\nimport pandas as pd\nimport os\nfrom rankeval.dataset import Dataset\nimport lightgbm\nfrom rankeval.metrics import MAP\nfrom sklearn.datasets import load_svmlight_file\nfrom rankeval.model import RTEnsemble\nimport numpy as np\nimport configparser\nimport sys\n\ndef make_dir(path):\n try:\n os.mkdir(path)\n except OSError as error:\n print(path+\" - already exists.\")\n\ndef predicttt(trees, leaves, learning_rate, msn_train, msn_vali, msn_test):\n lgbm_train_dataset = lightgbm.Dataset(data=msn_train.X, label=msn_train.y, group=msn_train.get_query_sizes(), params={'verbose': -1}, free_raw_data=False)\n lgbm_vali_dataset = lightgbm.Dataset(data=msn_vali.X, label=msn_vali.y, group=msn_vali.get_query_sizes(), params={'verbose': -1}, free_raw_data=False)\n \n params = {\n 'boosting_type': 'gbdt',\n 'objective': 'lambdarank',\n 'metric': ['map'],\n 'map_at': [10],\n 'num_leaves': leaves,\n 'learning_rate': learning_rate,\n 'verbose': 1,\n 'use_missing': False\n }\n lgbm_model = lightgbm.train(\n params, \n lgbm_train_dataset, \n num_boost_round=trees,\n valid_sets=[lgbm_train_dataset, lgbm_vali_dataset],\n valid_names=['train', 'vali'],\n early_stopping_rounds=100,\n verbose_eval=10\n )\n \n\n filename = 'lgbm.model'\n rankeval_model = None\n try:\n lgbm_model.save_model(filename=filename)\n rankeval_model = RTEnsemble(filename, \n name=\"LightGBM model\", \n format=\"LightGBM\")\n finally:\n os.remove(filename)\n return rankeval_model\n\n########################################################\nconfig = configparser.ConfigParser()\nconfig.read('../config_'+sys.argv[1]+'.ini')\n\nmake_dir(\"../0_dataset/binary\")\n\nfor section in config.sections():\n max_label = int(config[section]['max_label'])\n ones = max_label\n new_labels = []\n for i in range(1,max_label+1):\n temp = []\n for j in range(1,i+1):\n temp.append(0)\n for k in range(ones,0,-1):\n temp.append(1)\n ones -=1\n new_labels.append(temp)\n \n \n dataset_name = config[section]['dataset_name']\n make_dir(\"../0_dataset/binary/\"+dataset_name+\"\")\n binary_output_path = config[section]['binary_output_path']\n \n binary = int(config[section]['binary'])\n num_folds = int(config[section]['num_folds'])\n num_features = int(config[section]['num_features'])\n trees = config[section]['trees'].split(\",\")\n leaves = config[section]['leaves'].split(\",\")\n learning_rate = config[section]['learning_rate'].split(\",\")\n \n output_path = binary_output_path\n filenamess = [\"train.txt\",\"vali.txt\",\"test.txt\"]\n \n input_path = \"../0_dataset/\"+dataset_name+\"/\"\n \n \n \n old_labels = list(range(0, max_label+1)) #[0,1,2]\n \n map_accuracy_array = [None]*max_label\n map_accuracy_array = [{\"dataset_name\": dataset_name, \"relevance_>=\": i+1, \"MAP@10\": 0, \"Fold1\": 0, \"Fold2\": 0, \"Fold3\": 0, \"Fold4\": 0, \"Fold5\": 0} for i,value in enumerate(map_accuracy_array)]\n \n for i in range(0,len(new_labels)):\n #print(new_labels[i])\n make_dir(\"../0_dataset/binary/\"+dataset_name+\"/\"+str(i+1))\n map_accuracy = 0\n for j in range(1,num_folds+1):\n make_dir(output_path+str(i+1)+\"/Fold\"+str(j))\n \n data = pd.read_csv(input_path+\"Fold\"+str(j)+'/'+filenamess[0], header=None, sep=\" \")\n data[0] = data[0].replace(old_labels, new_labels[i])\n data.to_csv(output_path+str(i+1)+\"/Fold\"+str(j)+\"/train.txt\", sep=' ', header=False, index=False)\n \n data = pd.read_csv(input_path+\"Fold\"+str(j)+'/'+filenamess[1], header=None, sep=\" \")\n data[0] = data[0].replace(old_labels, new_labels[i])\n data.to_csv(output_path+str(i+1)+\"/Fold\"+str(j)+\"/vali.txt\", sep=' ', header=False, index=False)\n \n data = pd.read_csv(input_path+\"Fold\"+str(j)+'/'+filenamess[2], header=None, sep=\" \")\n data[0] = data[0].replace(old_labels, new_labels[i])\n data.to_csv(output_path+str(i+1)+\"/Fold\"+str(j)+\"/test.txt\", sep=' ', header=False, index=False)\n \n msn_train = Dataset.load(output_path+str(i+1)+\"/Fold\"+str(j)+\"/train.txt\")\n msn_vali = Dataset.load(output_path+str(i+1)+\"/Fold\"+str(j)+\"/vali.txt\")\n msn_test = Dataset.load(output_path+str(i+1)+\"/Fold\"+str(j)+\"/test.txt\")\n \n msn_lgbm_lmart_1Ktrees_model = predicttt(int(trees[j-1]), int(leaves[j-1]), float(learning_rate[j-1]), msn_train, msn_vali, msn_test)\n \n y_pred_test = msn_lgbm_lmart_1Ktrees_model.score(msn_test)\n map = MAP(cutoff=10)\n map_10_mean_score = map.eval(msn_test, y_pred_test)[0]\n \n map_accuracy_array[i][\"Fold\"+str(j)] = map_10_mean_score\n map_accuracy += map_10_mean_score\n \n map_accuracy = map_accuracy / num_folds\n map_accuracy_array[i][\"MAP@10\"] = map_accuracy\n print(\"DONE: \"+str(new_labels[i]))\n print(str(map_accuracy_array))\n f = open(\"../output/\"+dataset_name+\"_binary_relevance_cutoff.txt\", \"w\")\n f.write(str(map_accuracy_array)+\"\\n\")\n f.close()\n\n\n\n", "repo_name": "bajpaijalaj/master-thesis", "sub_path": "1_preprocessing/find_cutoff_for_binary_relevance.py", "file_name": "find_cutoff_for_binary_relevance.py", "file_ext": "py", "file_size_in_byte": 5419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "os.mkdir", "line_number": 16, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 21, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 22, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 34, "usage_type": "call"}, {"api_name": "rankeval.model.RTEnsemble", "line_number": 49, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 114, "usage_type": "call"}, {"api_name": "rankeval.dataset.Dataset.load", "line_number": 118, "usage_type": "call"}, {"api_name": "rankeval.dataset.Dataset", "line_number": 118, "usage_type": "name"}, {"api_name": "rankeval.dataset.Dataset.load", "line_number": 119, "usage_type": "call"}, {"api_name": "rankeval.dataset.Dataset", "line_number": 119, "usage_type": "name"}, {"api_name": "rankeval.dataset.Dataset.load", "line_number": 120, "usage_type": "call"}, {"api_name": "rankeval.dataset.Dataset", "line_number": 120, "usage_type": "name"}, {"api_name": "rankeval.metrics.MAP", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "33023336093", "text": "import matplotlib.pyplot as plt\nfrom numpy.linalg import lstsq\nimport numpy as np\nimport scipy.stats\n\nimport pandas as pd\n\n\ndef fitar(seq, p=1):\n A = np.vstack([seq[i:-p + i] for i in range(p)]).T\n y = seq[p:]\n return lstsq(A, y, rcond=None)[0], A, y\n\ndef fitarma(seq, p=1, q=1):\n A = np.vstack([\n seq[i:i+p]\n for i in range(len(seq) - p)\n ])\n y = seq[p:]\n x = lstsq(A, y, rcond=None)[0]\n\n res = y - A @ x\n seq = y\n\n A_ar = np.vstack([\n seq[i:i+p]\n for i in range(max(p, q) - p, len(seq) - p)\n ])\n\n A_ma = np.vstack([\n res[i:i+p]\n for i in range(max(p, q) - q, len(seq) - q)\n ])\n\n A = np.hstack((A_ar, A_ma))\n y = seq[max(p, q):]\n\n x = lstsq(A, y, rcond=None)[0]\n return x, A, y\n\ndef sim_garch(As, Bs, n):\n k = max(len(As), len(Bs))\n eps = np.random.normal(size=n + 300 + k)\n ep2 = eps**2\n h = np.ones_like(eps)\n\n for i in range(k, n + 300 + k):\n h[i] = np.sqrt(\n Bs[0] +\n np.dot(Bs[1:], ep2[i - len(Bs) + 1:i][::-1]) +\n np.dot(As, h[i - len(As):i][::-1])\n )\n eps[i] *= h[i]\n ep2[i] = eps[i]**2\n\n return eps[-n:]\n\ndef mom2_garch(seq, p=1, q=1, c=3.7, g=True):\n seq = np.square(seq)\n\n k = 10 * (p + q)\n\n A = np.vstack([\n [1, *seq[i:i+k]]\n for i in range(len(seq) - k)\n ])\n y = seq[k:]\n\n ix = y < c\n Ap, yp = A[ix], y[ix]\n\n x = lstsq(Ap, yp, rcond=None)[0]\n\n if not g:\n return x, A, y\n\n sigma = A @ x\n seq = y\n\n A_ar = np.vstack([\n sigma[i:i+p]\n for i in range(max(p, q) - p, len(seq) - p)\n ])\n\n A_ma = np.vstack([\n [1, *seq[i:i+q]]\n for i in range(max(p, q) - q, len(seq) - q)\n ])\n\n A = np.hstack((A_ar, A_ma))\n y = y[max(p, q):]\n\n # ix = y < c\n # Ap, yp = A[ix], y[ix]\n Ap, yp = A, y\n\n x = lstsq(Ap, yp, rcond=None)[0]\n return x, A, y\n\n\nif __name__ == '__main__':\n from arch import arch_model\n from contextlib import redirect_stdout\n from io import StringIO\n\n As = np.asarray([0.1, 0.1, 0.1])\n Bs = np.asarray([0.1, 0.1])\n n = 1000\n\n b01 = []\n b02 = []\n\n for _ in range(300):\n eps = sim_garch(As, Bs, n)\n par1, *_ = mom2_garch(eps, p=1, q=1)\n b01.append(par1[0])\n\n # just shut up\n with redirect_stdout(StringIO()):\n g = arch_model(eps, p=1, q=1)\n par2 = g.fit(disp='off').params\n par2 = par2[[3, 1, 2]]\n b02.append(par2[0])\n\n plt.plot(eps)\n plt.show()\n\n print(np.mean(b01))\n plt.hist(b01, bins=50)\n plt.show()\n\n print(np.mean(b02))\n plt.hist(b02, bins=50)\n plt.show()\n\ndef _():\n from read import priceof\n from arsim import AR\n\n\n past = 200\n pos = np.random.randint(400)\n\n btcc = priceof('btcusdt').open.iloc[::60*24]\n btc = btcc.pct_change().to_numpy()[-past-pos:-pos]\n btc_p = btc #btcc.pct_change().to_numpy()[-pos:-pos+past]\n\n p, q = 10, 10\n pp, qq = 10, 10\n par, A, y = fitarma(btc, p=p, q=q)\n # _, Ap, yp = fitarma(btc_p, p=p, q=q)\n Ap, yp = A, y\n pred = A @ par\n predp = Ap @ par\n res = y - pred\n resp = yp - predp\n\n pgarch, B, u = mom2_garch(res, p=pp, q=qq)\n # _, Bp, up = mom2_garch(resp, p=pp, q=qq)\n Bp, up = B, u\n vol = np.sqrt(B @ pgarch)\n volp = np.sqrt(Bp @ pgarch)\n\n plt.plot(np.arange(len(btc_p)), btc_p, label='btc')\n plt.plot(np.arange(p + max(p, q), len(btc_p)), predp, label='predictions')\n plt.plot(np.arange(p + max(p, q) + pp + qq, len(btc_p)), volp, label='volatility')\n\n rvol = pd.Series(resp).rolling(20).std()\n plt.plot(np.arange(len(btc_p) - len(rvol), len(btc_p)), rvol, label='rolling standard deviation')\n plt.legend()\n\n plt.show()\n", "repo_name": "ltricot/bachelor-thesis", "sub_path": "arfit.py", "file_name": "arfit.py", "file_ext": "py", "file_size_in_byte": 3761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.vstack", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.ones_like", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 107, "usage_type": "call"}, {"api_name": "contextlib.redirect_stdout", "line_number": 119, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 119, "usage_type": "call"}, {"api_name": "arch.arch_model", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 142, "usage_type": "attribute"}, {"api_name": "read.priceof", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 166, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}]} +{"seq_id": "34383335679", "text": "import multiprocessing\nimport sys\nimport os\nfrom rosetta_classic_abinitio import ClassicAbinitio\n\n\ndef wrap(p):\n pname, factor = p\n ClassicAbinitio(pname).run(factor)\n\n\ndef main():\n np = 4\n\n conf, reps = get_conf_and_reps()\n todo = open_todo_and_get_todolist(conf)\n\n todo *= reps\n\n p = multiprocessing.Pool(np)\n p.map(wrap, todo)\n\n\ndef get_conf_and_reps():\n reps = 1\n if len(sys.argv) > 1:\n conf = sys.argv[1]\n if len(sys.argv) > 2:\n reps = int(sys.argv[2])\n else:\n raise NotImplementedError('Running with not args is not supported')\n\n return conf, reps\n\n\ndef open_todo_and_get_todolist(conf):\n todo = []\n with open(conf) as f:\n for l in f.readlines():\n pname, factor = l.strip().split(' ')\n todo.append((pname, int(factor)))\n\n return todo\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "h3nnn4n/protein-prediction-framework", "sub_path": "src/de/bot_classic_abinitio.py", "file_name": "bot_classic_abinitio.py", "file_ext": "py", "file_size_in_byte": 894, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rosetta_classic_abinitio.ClassicAbinitio", "line_number": 9, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "6701091572", "text": "import socket\nimport time\nfrom modules import directives\nfrom modules import headers\nfrom modules import ip_utils\nfrom modules import listeners\nfrom collections import defaultdict\nfrom contextlib import closing\nfrom itertools import repeat\nfrom multiprocessing import Pool\nfrom os import getpid\nfrom typing import Set, Tuple\n\n\ndef ping(addresses: Set[str]) -> Set[Tuple[str, float, headers.ip]]:\n \"\"\"\n Send an ICMP ECHO REQUEST to each address\n in the set addresses. Then return a set which\n contains all the addresses which replied and\n which have the correct ID.\n \"\"\"\n with closing(\n socket.socket(\n socket.AF_INET,\n socket.SOCK_RAW,\n socket.IPPROTO_ICMP\n )\n ) as ping_sock:\n # get the local ip address\n addresses = {\n ip\n for ip in addresses\n if (\n not ip.endswith(\".0\")\n and not ip.endswith(\".255\")\n )\n }\n\n # initialise a process pool\n p = Pool(1)\n # get the local process id for use in creating packets.\n ID = getpid() & 0xFFFF\n # run the listeners.ping function asynchronously\n replied = p.apply_async(listeners.ping, (ID, 5))\n time.sleep(0.01)\n for address in zip(addresses, repeat(1)):\n try:\n packet = ip_utils.make_icmp_packet(ID)\n ping_sock.sendto(packet, address)\n except PermissionError:\n ip_utils.eprint(\"raw sockets require root priveleges, exiting\")\n exit()\n p.close()\n p.join()\n # close and join the process pool to so that all the values\n # have been returned and the pool closed\n return replied.get()\n\n\ndef connect(address: str, ports: Set[int]) -> Set[int]:\n \"\"\"\n This is the most basic kind of scan\n it simply connects to every specififed port\n and identifies whether they are open.\n \"\"\"\n import socket\n from contextlib import closing\n open_ports: Set[int] = set()\n for port in ports:\n # loop through each port in the list of ports to scan\n try:\n with closing(\n socket.socket(\n socket.AF_INET,\n socket.SOCK_STREAM\n )\n ) as s:\n # open an IPV4 TCP socket\n s.connect((address, port))\n # attempt to connect the newly created socket to the target\n # address and port\n open_ports.add(port)\n # if the connection was successful then add the port to the\n # list of open ports\n except (ConnectionRefusedError, OSError) as e:\n pass\n return open_ports\n\n\ndef tcp(dest_ip: str, portlist: Set[int]) -> listeners.PORTS:\n src_port = ip_utils.get_free_port()\n # request a local port to connect from\n if \"127.0.0.1\" == dest_ip:\n local_ip = \"127.0.0.1\"\n else:\n local_ip = ip_utils.get_local_ip()\n p = Pool(1)\n listener = p.apply_async(listeners.tcp, ((local_ip, src_port), 5))\n time.sleep(0.01)\n # start the TCP ACK listener in the background\n for port in portlist:\n # flag = 2 for syn scan\n packet = ip_utils.make_tcp_packet(\n src_port,\n port,\n local_ip,\n dest_ip,\n 2\n )\n with closing(\n socket.socket(\n socket.AF_INET,\n socket.SOCK_RAW,\n socket.IPPROTO_TCP\n )\n ) as s:\n s.sendto(packet, (dest_ip, port))\n # send the packet to its destination\n p.close()\n p.join()\n ports = listener.get()\n ports[\"FILTERED\"] = portlist - ports[\"OPEN\"] - ports[\"CLOSED\"]\n if local_ip == \"127.0.0.1\":\n ports[\"OPEN\"] -= set([src_port])\n\n return ports\n\n\ndef udp(\n dest_ip: str,\n ports_to_scan: Set[int]\n) -> listeners.PORTS:\n \"\"\"\n Takes in a destination IP address in either dot or long form and\n a list of ports to scan. Sends UDP packets to each port specified\n in portlist and uses the listeners to mark them as open, open|filtered,\n filtered, closed they are marked open|filtered if no response is\n recieved at all.\n \"\"\"\n\n local_port = ip_utils.get_free_port()\n # get port number\n ports: listeners.PORTS = defaultdict(set)\n ports[\"REMAINING\"] = ports_to_scan\n p = Pool(1)\n udp_listen = p.apply_async(listeners.udp, (dest_ip, 4))\n time.sleep(0.01)\n # start the UDP listener\n with closing(\n socket.socket(\n socket.AF_INET,\n socket.SOCK_RAW,\n socket.IPPROTO_UDP\n )\n ) as s:\n for _ in range(2):\n # repeat 3 times because UDP scanning comes\n # with a high chance of packet loss\n for dest_port in ports[\"REMAINING\"]:\n try:\n packet = ip_utils.make_udp_packet(\n local_port,\n dest_port\n )\n # create the UDP packet to send\n s.sendto(packet, (dest_ip, dest_port))\n # send the packet to the currently scanning address\n except socket.error:\n packet_bytes = \" \".join(map(hex, packet))\n print(\n \"The socket modules sendto method with the following\",\n \"argument resulting in a socket error.\",\n f\"\\npacket: [{packet_bytes}]\\n\",\n \"address: [{dest_ip, dest_port}])\"\n )\n\n p.close()\n p.join()\n\n ports[\"OPEN\"].update(udp_listen.get())\n # if we are on localhost remove the scanning port\n if dest_ip == \"127.0.0.1\":\n ports[\"OPEN\"] -= set([local_port])\n ports[\"REMAINING\"] -= ports[\"OPEN\"]\n # only scan the ports which we know are not open\n with closing(\n socket.socket(\n socket.AF_INET,\n socket.SOCK_RAW,\n socket.IPPROTO_UDP\n )\n ) as s:\n for dest_port in ports[\"REMAINING\"]:\n try:\n packet = ip_utils.make_udp_packet(\n local_port,\n dest_port\n )\n # make a new UDP packet\n p = Pool(1)\n icmp_listen = p.apply_async(\n listeners.icmp_unreachable,\n (dest_ip,),\n )\n # start the ICMP listener\n time.sleep(0.01)\n s.sendto(packet, (dest_ip, dest_port))\n # send packet\n p.close()\n p.join()\n icmp_code = icmp_listen.get()\n # receive ICMP code from the ICMP listener\n if icmp_code in {0, 1, 2, 9, 10, 13}:\n ports[\"FILTERED\"].add(dest_port)\n elif icmp_code == 3:\n ports[\"CLOSED\"].add(dest_port)\n except socket.error:\n packet_bytes = \" \".join(map(\"{:02x}\".format, packet))\n ip_utils.eprint(\n \"The socket modules sendto method with the following\",\n \"argument resulting in a socket error.\",\n f\"\\npacket: [{packet_bytes}]\\n\",\n \"address: [{dest_ip, dest_port}])\"\n )\n # this creates a new set which contains all the elements that\n # are in the list of ports to be scanned but have not yet\n # been classified\n ports[\"OPEN|FILTERED\"] = (\n ports[\"REMAINING\"]\n - ports[\"OPEN\"]\n - ports[\"FILTERED\"]\n - ports[\"CLOSED\"]\n )\n del(ports[\"REMAINING\"])\n # set comprehension to update the list of open filtered ports\n return ports\n\n\ndef version_detect_scan(\n target: directives.Target,\n probes: directives.PROBE_CONTAINER\n) -> directives.Target:\n for probe_dict in probes.values():\n for proto in probe_dict:\n target = probe_dict[proto].scan(target)\n return target\n", "repo_name": "tritoke/networkScanner", "sub_path": "Code/modules/scanners.py", "file_name": "scanners.py", "file_ext": "py", "file_size_in_byte": 8172, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.Set", "line_number": 15, "usage_type": "name"}, {"api_name": "contextlib.closing", "line_number": 22, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 23, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOCK_RAW", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_ICMP", "line_number": 26, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 40, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 42, "usage_type": "call"}, {"api_name": "modules.listeners.ping", "line_number": 44, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 44, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 46, "usage_type": "call"}, {"api_name": "modules.ip_utils.make_icmp_packet", "line_number": 48, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 48, "usage_type": "name"}, {"api_name": "modules.ip_utils.eprint", "line_number": 51, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 15, "usage_type": "name"}, {"api_name": "modules.headers.ip", "line_number": 15, "usage_type": "attribute"}, {"api_name": "modules.headers", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 68, "usage_type": "name"}, {"api_name": "contextlib.closing", "line_number": 72, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 73, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 74, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 75, "usage_type": "attribute"}, {"api_name": "typing.Set", "line_number": 90, "usage_type": "name"}, {"api_name": "modules.ip_utils.get_free_port", "line_number": 91, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 91, "usage_type": "name"}, {"api_name": "modules.ip_utils.get_local_ip", "line_number": 96, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 96, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 97, "usage_type": "call"}, {"api_name": "modules.listeners.tcp", "line_number": 98, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 98, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 99, "usage_type": "call"}, {"api_name": "modules.ip_utils.make_tcp_packet", "line_number": 103, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 103, "usage_type": "name"}, {"api_name": "contextlib.closing", "line_number": 110, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 111, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 112, "usage_type": "attribute"}, {"api_name": "socket.SOCK_RAW", "line_number": 113, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_TCP", "line_number": 114, "usage_type": "attribute"}, {"api_name": "modules.listeners.PORTS", "line_number": 90, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 131, "usage_type": "name"}, {"api_name": "modules.ip_utils.get_free_port", "line_number": 141, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 141, "usage_type": "name"}, {"api_name": "modules.listeners.PORTS", "line_number": 143, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 143, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 143, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 145, "usage_type": "call"}, {"api_name": "modules.listeners.udp", "line_number": 146, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 146, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 147, "usage_type": "call"}, {"api_name": "contextlib.closing", "line_number": 149, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 150, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 151, "usage_type": "attribute"}, {"api_name": "socket.SOCK_RAW", "line_number": 152, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_UDP", "line_number": 153, "usage_type": "attribute"}, {"api_name": "modules.ip_utils.make_udp_packet", "line_number": 161, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 161, "usage_type": "name"}, {"api_name": "socket.error", "line_number": 168, "usage_type": "attribute"}, {"api_name": "contextlib.closing", "line_number": 186, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 187, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 188, "usage_type": "attribute"}, {"api_name": "socket.SOCK_RAW", "line_number": 189, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_UDP", "line_number": 190, "usage_type": "attribute"}, {"api_name": "modules.ip_utils.make_udp_packet", "line_number": 195, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 195, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 200, "usage_type": "call"}, {"api_name": "modules.listeners.icmp_unreachable", "line_number": 202, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 202, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 206, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 217, "usage_type": "attribute"}, {"api_name": "modules.ip_utils.eprint", "line_number": 219, "usage_type": "call"}, {"api_name": "modules.ip_utils", "line_number": 219, "usage_type": "name"}, {"api_name": "modules.listeners.PORTS", "line_number": 132, "usage_type": "attribute"}, {"api_name": "modules.listeners", "line_number": 132, "usage_type": "name"}, {"api_name": "modules.directives.Target", "line_number": 240, "usage_type": "attribute"}, {"api_name": "modules.directives", "line_number": 240, "usage_type": "name"}, {"api_name": "modules.directives.PROBE_CONTAINER", "line_number": 241, "usage_type": "attribute"}, {"api_name": "modules.directives", "line_number": 241, "usage_type": "name"}, {"api_name": "modules.directives.Target", "line_number": 242, "usage_type": "attribute"}, {"api_name": "modules.directives", "line_number": 242, "usage_type": "name"}]} +{"seq_id": "73841528046", "text": "#!/usr/bin/env python3\n\"\"\"Script to set up a Debian Linux based system as a Lokole client.\"\"\"\nfrom argparse import ArgumentParser\nfrom json import dumps\nfrom json import loads\nfrom logging import StreamHandler\nfrom logging import getLogger\nfrom multiprocessing import cpu_count\nfrom os import chmod\nfrom os import getenv\nfrom os import stat\nfrom os import urandom\nfrom pathlib import Path\nfrom shutil import chown\nfrom socket import gethostname\nfrom stat import S_IEXEC\nfrom string import ascii_letters\nfrom string import digits\nfrom subprocess import PIPE # nosec\nfrom subprocess import run # nosec\nfrom sys import executable as current_python_binary\nfrom sys import version_info\nfrom tempfile import gettempdir\nfrom time import sleep\nfrom time import time\nfrom urllib.error import HTTPError\nfrom urllib.request import Request\nfrom urllib.request import urlopen\n\nLOG = getLogger(__name__)\n\nTEMP_ROOT = Path(gettempdir()) / Path(__file__).name\nTEMP_ROOT.mkdir(parents=True, exist_ok=True)\n\nSIM_TYPES = ('hologram', 'Ethernet', 'LocalOnly', 'mkwvconf')\n\n\nclass Setup:\n groups = tuple()\n packages = tuple()\n\n def __init__(self, args, abort):\n self.args = args\n self.abort = abort\n\n @property\n def is_enabled(self):\n return True\n\n @property\n def user(self):\n base_user = getenv('USER')\n sudo_user = getenv('SUDO_USER')\n\n if sudo_user and base_user == 'root':\n return sudo_user\n elif base_user:\n return base_user\n else:\n return self.sh('whoami')\n\n @property\n def home(self):\n return Path('/') / 'home' / self.user\n\n def __call__(self):\n try:\n result = self.__is_complete()\n except FileNotFoundError:\n pass\n else:\n LOG.info('Skipping %s: already completed', self._step_name)\n return result\n\n if not self.is_enabled:\n LOG.info('Skipping %s: not enabled', self._step_name)\n return\n\n LOG.info('Running %s', self._step_name)\n\n self._grant_permissions()\n self._install_dependencies()\n result = self._run()\n self.__mark_complete(result)\n\n LOG.info('Done with %s', self._step_name)\n return result\n\n def _grant_permissions(self):\n for group in self.groups:\n self.sh('usermod -a -G \"{group}\" \"{user}\"'\n .format(group=group, user=self.user))\n\n def _install_dependencies(self):\n if self.packages:\n self.sh('apt-get install -y {}'.format(' '.join(self.packages)),\n retry_attempts=10, retry_interval=60)\n\n def _run(self):\n raise NotImplementedError\n\n @property\n def _step_name(self):\n return self.__class__.__name__\n\n @property\n def __guard_path(self):\n guard_name = '{}.done'.format(self._step_name)\n return self.abspath(TEMP_ROOT / guard_name)\n\n @property\n def __stdout_path(self):\n stdout_name = '{}.stdout'.format(self._step_name)\n return self.abspath(TEMP_ROOT / stdout_name)\n\n @property\n def __stderr_path(self):\n stderr_name = '{}.stderr'.format(self._step_name)\n return self.abspath(TEMP_ROOT / stderr_name)\n\n def __is_complete(self):\n return loads(Path(self.__guard_path).read_text(encoding='utf-8'))\n\n def __mark_complete(self, result):\n self.write_file(self.__guard_path, dumps(result))\n\n def assume_ownership(self, path):\n chown(path, self.user, self.user)\n\n def write_file(self, path, content, executable=False):\n if not isinstance(content, str):\n content = '\\n'.join(content)\n\n with open(path, 'w') as fobj:\n fobj.write(content)\n\n self.assume_ownership(path)\n\n if executable:\n mode = stat(path).st_mode\n chmod(path, mode | S_IEXEC)\n\n def create_daemon(self, program_name, command, user=None, env=None):\n env = env or {}\n user = user or self.user\n extra_conf = []\n\n if self.args.log_directory == '-':\n stderr = '/dev/fd/2'\n stdout = '/dev/fd/1'\n extra_conf.extend((\n 'stdout_logfile_maxbytes=0',\n 'stderr_logfile_maxbytes=0',\n ))\n else:\n stderr = self.abspath(Path(self.args.log_directory) / '{}.stderr.log'.format(program_name))\n stdout = self.abspath(Path(self.args.log_directory) / '{}.stdout.log'.format(program_name))\n\n self.write_file('/etc/supervisor/conf.d/{}.conf'.format(program_name), (\n '[program:{}]'.format(program_name),\n 'command={}'.format(command),\n 'autostart=true',\n 'autorestart=true',\n 'startretries=3',\n 'stopasgroup=true',\n 'stderr_logfile={}'.format(stderr),\n 'stdout_logfile={}'.format(stdout),\n 'user={}'.format(user),\n 'environment={}'.format(','.join('{}={}'.format(*kv) for kv in env.items())),\n *extra_conf,\n ))\n\n def abspath(self, file_path):\n file_path = Path(file_path).absolute()\n self._mkdir(file_path.parent)\n return str(file_path)\n\n def sh(self, command, user=None, accept_failure=False, retry_attempts=0, retry_interval=0):\n if user:\n command = \"su '{user}' -c '{command}'\".format(\n user=user,\n command=command)\n\n process = run(command, shell=True, stderr=PIPE, stdout=PIPE) # nosec\n stdout = process.stdout.decode('utf-8').strip()\n stderr = process.stderr.decode('utf-8').strip()\n status = process.returncode\n\n with open(self.__stdout_path, 'a', encoding='utf-8') as fobj:\n fobj.write('===== {} =====\\n{}\\n{}\\n\\n'.format(command, status, stdout))\n\n with open(self.__stderr_path, 'a', encoding='utf-8') as fobj:\n fobj.write('===== {} =====\\n{}\\n{}\\n\\n'.format(command, status, stderr))\n\n if status == 0 or accept_failure:\n return stdout\n\n if retry_attempts > 0:\n sleep(retry_interval)\n return self.sh(command, user, accept_failure, retry_attempts - 1, retry_interval)\n\n raise Exception(stderr)\n\n def _mkdir(self, path):\n path.mkdir(parents=True, exist_ok=True)\n home_prefix = Path(self.home)\n is_in_home = path.parts[:3] == home_prefix.parts\n if is_in_home:\n home_parts = path.parts[3:]\n for part in home_parts:\n home_prefix /= part\n self.assume_ownership(str(home_prefix))\n\n\nclass SystemSetup(Setup):\n def _run(self):\n self._ensure_root()\n self._ensure_apt()\n self._set_locale()\n self._set_timezone()\n self._set_password()\n\n def _ensure_root(self):\n if getenv('USER') != 'root' and self.sh('whoami') != 'root':\n self.abort('Must run script via sudo')\n\n def _ensure_apt(self):\n self.sh('apt-get update', retry_attempts=10, retry_interval=30)\n\n def _set_locale(self):\n locale_command = (\n 'export LANGUAGE=\"{0}\"; '\n 'export LC_ALL=\"{0}\"; '\n 'export LANG=\"{0}\"; '\n 'export LC_TYPE=\"{0}\";'\n ).format(self.args.locale)\n\n self.sh('locale-gen \"{}\"'.format(self.args.locale))\n self.sh('update-locale')\n self.sh('eval \"{}\"'.format(locale_command))\n\n self.write_file('/etc/profile.d/set-locale.sh', locale_command,\n executable=True)\n\n def _set_timezone(self):\n self.sh('timedatectl set-timezone \"{}\"'.format(self.args.timezone))\n\n def _set_password(self):\n if not self.args.password:\n return\n\n self.sh('echo \"{user}:{password}\" | chpasswd'.format(\n user=self.user,\n password=self.args.password))\n\n @property\n def is_enabled(self):\n return self.args.system_setup != 'no'\n\n\nclass WifiSetup(Setup):\n packages = (\n 'hostapd',\n 'dnsmasq',\n )\n\n ip_base = '10.0.0'\n\n def _run(self):\n if not self.ht_capab:\n self.abort('Unsupported device: {}'.format(self.device))\n\n self._configure_dns()\n self._configure_wifi()\n self._disable_system_power_management()\n\n def _configure_dns(self):\n hosts = [\n ('::1', 'localhost ip6-localhost ip6-loopback'),\n ('ff02::1', 'ip6-allnodes'),\n ('ff02::2', 'ip6-allrouters'),\n ('127.0.0.1', 'localhost'),\n ('127.0.0.1', self.device),\n ('127.0.1.1', self.device),\n ]\n\n for prefix in ['www.', '']:\n for tld in ['.com', '.org', '.ca', '.cd', '']:\n for host in ['lokole', 'opwen', 'ascoderu', 'email']:\n hosts.append((self.ip, prefix + host + tld))\n\n self.write_file('/etc/hosts', ('{}\\t{}'.format(ip, host) for (ip, host) in hosts))\n\n logfile = '/var/log/dnsmasq.log'\n\n self.write_file('/etc/dnsmasq.conf', (\n 'log-facility={}'.format(logfile),\n 'dhcp-range={0}.10,{0}.250,12h'.format(self.ip_base),\n 'interface=wlan0',\n 'no-resolv',\n 'log-queries',\n 'server=8.8.8.8',\n ))\n\n def _configure_wifi(self):\n hostapd_conf = '/etc/hostapd/hostapd.conf'\n\n self.write_file(hostapd_conf, (\n 'interface=wlan0',\n 'driver=nl80211',\n 'hw_mode=g',\n 'channel=6',\n 'ieee80211n=1',\n 'wmm_enabled=1',\n 'ht_capab={}'.format(self.ht_capab),\n 'macaddr_acl=0',\n 'auth_algs=1',\n 'wpa=2',\n 'wpa_key_mgmt=WPA-PSK',\n 'rsn_pairwise=CCMP',\n 'ssid={}'.format(self.args.wifi_name),\n 'wpa_passphrase={}'.format(self.args.wifi_password),\n ))\n\n self.write_file('/etc/default/hostapd', 'DAEMON_CONF={}'.format(hostapd_conf))\n\n self.write_file('/etc/network/interfaces', (\n 'auto lo',\n 'iface lo inet loopback',\n\n 'auto eth0',\n 'allow-hotplug eth0',\n 'iface eth0 inet dhcp',\n\n 'auto wlan0',\n 'allow-hotplug wlan0',\n 'iface wlan0 inet static',\n 'post-up service hostapd restart',\n 'post-up service dnsmasq restart',\n 'address {}'.format(self.ip),\n 'netmask 255.255.255.0',\n 'wireless-power off',\n\n 'auto ppp0',\n 'iface ppp0 inet wvdial',\n ))\n\n self.sh('systemctl unmask hostapd.service')\n self.sh('systemctl start hostapd.service')\n\n def _disable_system_power_management(self):\n self.sh('systemctl mask sleep.target suspend.target hibernate.target hybrid-sleep.target')\n\n @property\n def ip(self):\n return '{}.1'.format(self.ip_base)\n\n @property\n def device(self):\n return gethostname()\n\n @property\n def ht_capab(self):\n if self.device in ['OrangePI', 'orangepizero']:\n return '[HT40][DSS_CCK-40]'\n\n if self.device in ['raspberrypi']:\n return '[HT40][SHORT-GI-20][DSS_CCK-40]'\n\n return None\n\n @property\n def is_enabled(self):\n return self.args.wifi != 'no'\n\n\nclass ModemSetup(Setup):\n packages = (\n 'usb-modeswitch',\n 'usb-modeswitch-data',\n 'mobile-broadband-provider-info',\n 'ppp',\n 'wvdial',\n )\n\n groups = (\n 'dialout',\n 'dip',\n )\n\n def _run(self):\n self._configure_wvdial()\n\n return {\n 'OPWEN_SYNC_SCHEDULE': self.args.sync_schedule,\n }\n\n def _configure_wvdial(self):\n self.write_file('/etc/ppp/peers/wvdial', (\n 'noauth',\n 'name wvdial',\n 'usepeerdns',\n 'defaultroute',\n 'replacedefaultroute',\n ))\n\n @property\n def is_enabled(self):\n if not super().is_enabled:\n return False\n\n if self.args.sim_type == 'LocalOnly':\n return False\n\n if not self.args.sync_schedule or not self.args.registration_credentials:\n self.abort('Sync schedule and registration credentials are required.')\n\n return True\n\n\nclass ClientSetup(Setup):\n def _run(self):\n create_request_payload = dumps({'domain': self.client_domain}).encode('utf-8')\n create_request = Request(self.client_url_create)\n create_request.add_header('Content-Type', 'application/json; charset=utf-8')\n create_request.add_header('Content-Length', str(len(create_request_payload)))\n create_request.add_header('Authorization', 'Bearer {}'.format(self.args.registration_credentials))\n\n try:\n with urlopen(create_request, create_request_payload): # nosec\n pass\n except HTTPError as ex:\n self.abort('Unable to register client {client_name}: [{status_code}] {message}'.format(\n client_name=self.args.client_name,\n status_code=ex.code,\n message=ex.read().decode('utf-8').strip()))\n\n while True:\n get_request = Request(self.client_url_details)\n get_request.add_header('Authorization', 'Bearer {}'.format(self.args.registration_credentials))\n try:\n with urlopen(get_request) as response: # nosec\n response_body = response.read().decode('utf-8')\n except HTTPError as ex:\n if ex.code != 404:\n self.abort('Unable to fetch client {client_name}: [{status_code}] {message}'.format(\n client_name=self.args.client_name,\n status_code=ex.code,\n message=ex.read().decode('utf-8').strip()))\n sleep(2)\n else:\n client_info = loads(response_body)\n break\n\n return {\n 'OPWEN_CLIENT_ID': client_info['client_id'],\n 'OPWEN_REMOTE_ACCOUNT_NAME': client_info['storage_account'],\n 'OPWEN_REMOTE_ACCOUNT_KEY': client_info['storage_key'],\n 'OPWEN_REMOTE_RESOURCE_CONTAINER': client_info['resource_container'],\n }\n\n @property\n def client_domain(self):\n return '{}.{}'.format(self.args.client_name, self.args.client_domain)\n\n @property\n def client_url_create(self):\n return 'https://{}/api/email/register/'.format(self.args.server_host)\n\n @property\n def client_url_details(self):\n return 'https://{}/api/email/register/{}'.format(self.args.server_host, self.client_domain)\n\n @property\n def is_enabled(self):\n if ':' in self.args.registration_credentials:\n self.abort('Registration credential should be set to Github access token, not username and password')\n\n return self.args.sim_type != 'LocalOnly'\n\n\nclass WebappSetup(Setup):\n packages = (\n 'python3-bcrypt',\n 'libffi-dev',\n 'libssl-dev',\n 'libjpeg-dev',\n 'libopenjp2-7',\n 'libtiff5',\n 'nginx',\n 'python3',\n 'python3-dev',\n 'python3-pip',\n 'python3-venv',\n 'supervisor',\n )\n\n def __init__(self, args, abort, app_config):\n super().__init__(args, abort)\n self.app_config = app_config\n\n def _run(self):\n self._create_virtualenv()\n self._install_client()\n self._compile_translations()\n self._setup_secrets()\n self._create_admin_user()\n self._install_nginx()\n self._setup_gunicorn()\n self._setup_celery()\n self._setup_cron()\n self._setup_restarter()\n self._reboot()\n\n def _create_virtualenv(self):\n self.sh('{python} -m venv \"{venv_path}\"'.format(\n python=current_python_binary,\n venv_path=self.venv_path),\n user=self.user)\n\n self._pip_install('pip', 'setuptools', 'wheel')\n\n def _install_client(self):\n if self.args.client_dist and Path(self.args.client_dist).is_file():\n package = self.args.client_dist\n elif self.args.client_version:\n package = 'opwen_email_client=={}'.format(self.args.client_version)\n else:\n package = 'opwen_email_client'\n\n self._pip_install(package)\n\n def _compile_translations(self):\n self.sh('\"{pybabel}\" compile -d \"{translations}\"'.format(\n pybabel='{}/bin/pybabel'.format(self.venv_path),\n translations=self.abspath(self.webapp_files_root / 'translations')),\n user=self.user)\n\n def _setup_secrets(self):\n extra_settings = {\n 'OPWEN_APP_ROOT': self.args.app_root,\n 'OPWEN_STATE_DIRECTORY': self.abspath(self.args.state_directory),\n 'OPWEN_SESSION_KEY': generate_secret(32),\n 'OPWEN_MAX_UPLOAD_SIZE_MB': self.args.max_upload_size,\n 'OPWEN_SIM_TYPE': self.args.sim_type,\n 'OPWEN_EMAIL_SERVER_HOSTNAME': self.args.server_host,\n 'OPWEN_CLIENT_NAME': self.args.client_name,\n 'OPWEN_ROOT_DOMAIN': self.args.client_domain,\n 'OPWEN_RESTART_PATH': ','.join((\n '{}=HUP'.format(self.abspath(self.restarter_directory / self.args.server_name)),\n '{}='.format(self.abspath(self.restarter_directory / self.args.worker_name)),\n '{}='.format(self.abspath(self.restarter_directory / self.args.cron_name)),\n )),\n }\n\n self.write_file(self.settings_path, (\n '{}={}'.format(key, value)\n for settings in (extra_settings, self.app_config)\n for (key, value) in settings.items()))\n\n def _create_admin_user(self):\n if self.args.admin == 'no':\n return\n\n self.sh('OPWEN_SETTINGS=\"{settings}\" '\n 'export FLASK_APP=\"opwen_email_client.webapp:app\" '\n '\"{manage}\" createadmin --name=\"{name}\" --password=\"{password}\"'.format(\n settings=self.settings_path,\n manage='{}/bin/flask manage'.format(self.venv_path),\n name=self.args.admin_name,\n password=self.args.admin_password),\n user=self.user)\n\n def _install_nginx(self):\n self.write_file('/etc/nginx/sites-available/default', '''\n server {{\n listen {port};\n server_name localhost;\n\n location = {app_root}/favicon.ico {{\n alias {files_root}/static/favicon.ico;\n }}\n\n location ~ ^{app_root}/static/(.*)$ {{\n alias {files_root}/static/$1;\n }}\n\n location {app_root}/ {{\n include proxy_params;\n proxy_pass http://unix:{socket};\n }}\n }}'''.format(\n port=self.args.port,\n app_root=self.args.app_root,\n files_root=self.abspath(self.webapp_files_root),\n socket=self.socket_path))\n\n if self.args.log_directory == '-':\n access_log = 'stdout'\n error_log = 'stderr'\n else:\n access_log = self.abspath(Path(self.args.log_directory) / 'nginx_access.log')\n error_log = self.abspath(Path(self.args.log_directory) / 'nginx_error.log')\n\n self.write_file('/etc/nginx/nginx.conf', '''\n user www-data;\n worker_processes 4;\n pid /run/nginx.pid;\n\n events {{\n worker_connections 768;\n }}\n\n http {{\n sendfile on;\n tcp_nopush on;\n tcp_nodelay on;\n keepalive_timeout 65;\n types_hash_max_size 2048;\n include /etc/nginx/mime.types;\n default_type application/octet-stream;\n ssl_protocols TLSv1 TLSv1.1 TLSv1.2;\n ssl_prefer_server_ciphers on;\n access_log {access_log};\n error_log {error_log};\n gzip on;\n gzip_disable \"msie6\";\n client_max_body_size {max_upload_size}M;\n include /etc/nginx/conf.d/*.conf;\n include /etc/nginx/sites-enabled/*;\n\n fastcgi_connect_timeout {timeout_seconds};\n fastcgi_send_timeout {timeout_seconds};\n fastcgi_read_timeout {timeout_seconds};\n }}'''.format(\n access_log=access_log,\n error_log=error_log,\n max_upload_size=self.args.max_upload_size,\n timeout_seconds=self.args.timeout))\n\n self.sh('systemctl stop nginx', accept_failure=True)\n self.sh('systemctl disable nginx', accept_failure=True)\n\n self.create_daemon(\n program_name=self.args.nginx_name,\n command='/usr/sbin/nginx -g \"daemon off;\"',\n user='root')\n\n def _setup_gunicorn(self):\n gunicorn_script = (\n '\"{venv}/bin/gunicorn\" '\n '--bind=\"unix:{socket}\" '\n '--timeout={timeout} '\n '--workers={workers} '\n '--log-level={loglevel} '\n 'opwen_email_client.webapp:app'.format(\n venv=self.venv_path,\n socket=self.socket_path,\n timeout=self.args.timeout,\n workers=self.args.num_gunicorn_workers,\n loglevel=self.args.log_level))\n\n self.create_daemon(\n program_name=self.args.server_name,\n command=gunicorn_script,\n env={'OPWEN_SETTINGS': self.settings_path})\n\n def _setup_celery(self):\n celery_command = (\n '\"{venv}/bin/celery\" '\n '--app=opwen_email_client.webapp.tasks '\n 'worker '\n '--loglevel={loglevel} '\n '--concurrency={workers}'.format(\n venv=self.venv_path,\n loglevel=self.args.log_level,\n workers=self.args.num_celery_workers))\n\n self.create_daemon(\n program_name=self.args.worker_name,\n command=celery_command,\n env={'OPWEN_SETTINGS': self.settings_path})\n\n def _setup_cron(self):\n celery_command = (\n '\"{venv}/bin/celery\" '\n '--app=opwen_email_client.webapp.tasks '\n 'beat '\n '--pidfile=\"{cronstate_pid}\" '\n '--loglevel={loglevel} '.format(\n settings=self.settings_path,\n cronstate_pid=self.cronstate_pid,\n venv=self.venv_path,\n loglevel=self.args.log_level))\n\n self.create_daemon(\n program_name=self.args.cron_name,\n command=celery_command,\n env={'OPWEN_SETTINGS': self.settings_path})\n\n def _setup_restarter(self):\n restarter_command = (\n 'export FLASK_APP=\"opwen_email_client.webapp:app\" && \"{venv}/bin/flask\" '\n 'manage restarter '\n '--directory=\"{directory}\"'.format(\n venv=self.venv_path,\n directory=self.abspath(self.restarter_directory)))\n\n self.create_daemon(\n program_name=self.args.restarter_name,\n command=restarter_command,\n user='root')\n\n def _reboot(self):\n LOG.info('All done. Lokole client %s is ready to be used.', self.args.client_name)\n\n if self.args.reboot == 'yes':\n LOG.info('System is rebooting.')\n self.sh('shutdown --reboot now', user='root')\n\n def _pip_install(self, *packages):\n self.sh('\"{pip}\" install --no-cache-dir --upgrade {packages}'.format(\n pip='{}/bin/pip'.format(self.venv_path),\n packages=' '.join(packages)),\n retry_attempts=60,\n retry_interval=5,\n user=self.user)\n\n @property\n def webapp_files_root(self):\n return (Path(self.venv_path) /\n 'lib' /\n 'python{}.{}'.format(version_info.major, version_info.minor) /\n 'site-packages' /\n 'opwen_email_client' /\n 'webapp')\n\n @property\n def socket_path(self):\n return self.abspath(Path(self.args.state_directory)\n / '{}.sock'.format(self.args.server_name))\n\n @property\n def settings_path(self):\n return self.abspath(Path(self.args.state_directory)\n / 'settings.env')\n\n @property\n def cronstate_pid(self):\n return self.abspath(Path(self.args.state_directory)\n / '{}.pid'.format(self.args.cron_name))\n\n @property\n def restarter_directory(self):\n return Path(self.args.state_directory) / self.args.restarter_name\n\n @property\n def venv_path(self):\n return self.abspath(Path(self.args.venv_directory))\n\n\ndef generate_secret(length, chars=frozenset(ascii_letters + digits)):\n secret = '' # nosec\n\n while len(secret) < length:\n for char in urandom(length).decode('ascii', errors='ignore'):\n if char in chars:\n secret += char\n\n return secret[:length]\n\n\ndef _dump_state(args):\n with Path(__file__).open('r', encoding='utf-8') as fobj:\n version = hash(fobj.read())\n\n state_path = TEMP_ROOT / 'state_{:.0f}.json'.format(time())\n\n with state_path.open('w', encoding='utf-8') as fobj:\n fobj.write(dumps({\n 'version': version,\n 'args': args.__dict__,\n }))\n\n\ndef main(args, abort):\n LOG.setLevel(args.script_log_level)\n LOG.addHandler(StreamHandler())\n\n _dump_state(args)\n\n app_config = {}\n\n system_setup = SystemSetup(args, abort)\n system_setup()\n\n wifi_setup = WifiSetup(args, abort)\n wifi_setup()\n\n modem_setup = ModemSetup(args, abort)\n app_config.update(modem_setup() or {})\n\n client_setup = ClientSetup(args, abort)\n app_config.update(client_setup() or {})\n\n webapp_setup = WebappSetup(args, abort, app_config)\n webapp_setup()\n\n\ndef cli():\n parser = ArgumentParser(description=__doc__)\n\n parser.add_argument('client_name', type=str.lower, help=(\n 'The name that should be assigned to the Lokole device '\n 'that is being configured by this script. Usually this '\n 'will be a name that is descriptive for the location '\n 'where the device will be deployed. The client name '\n 'should be globally unique as it is used as the key for '\n 'a bunch of things.'\n ))\n parser.add_argument('sim_type', choices=SIM_TYPES, help=(\n 'The mobile network to which to connect to upload data.'\n ))\n parser.add_argument('sync_schedule', nargs='?', help=(\n 'How often the Lokole should sync with the server. '\n 'In cron syntax. '\n 'Example: \"34 * * * *\" for once per hour at the 34th minute.'\n ))\n parser.add_argument('registration_credentials', nargs='?', help=(\n 'Github access token for registering with the Lokole server.'\n ))\n parser.add_argument('--app_root', default=getenv('OPWEN_APP_ROOT', ''), help=(\n 'The URL prefix at which the app will be accessible.'\n ))\n parser.add_argument('--admin', default=getenv('LOKOLE_ADMIN', 'yes'), help=(\n 'If set to \"no\", skip creation of application admin user.'\n ))\n parser.add_argument('--admin_name', default=getenv('LOKOLE_ADMIN_NAME', 'admin'), help=(\n 'If set, create an admin user with this account name.'\n ))\n parser.add_argument('--admin_password', default=getenv('LOKOLE_ADMIN_PASSWORD', 'lokole1Admin'), help=(\n 'If set, create an admin user with this password.'\n ))\n parser.add_argument('--password', default=getenv('LOKOLE_PASSWORD', ''), help=(\n 'If set to a non-empty string, updates the password of '\n 'the current user to this value as part of the setup. '\n 'Useful for fully automated setups of new devices that '\n 'come with a default insecure password.'\n ))\n parser.add_argument('--system_setup', default=getenv('LOKOLE_SYSTEM_SETUP', 'yes'), help=(\n 'If set to \"no\", skip system setup.'\n ))\n parser.add_argument('--reboot', default=getenv('LOKOLE_REBOOT', 'yes'), help=(\n 'If set to \"no\", skip system reboot after setup.'\n ))\n parser.add_argument('--wifi', default=getenv('LOKOLE_WIFI', 'yes'), help=(\n 'If set to \"no\", skip setup of WiFi access point and '\n 'local DNS server configuration.'\n ))\n parser.add_argument('--wifi_name', default=getenv('LOKOLE_NETWORK_NAME', 'Lokole'), help=(\n 'The name of the WiFi network to create for the Lokole email app.'\n ))\n parser.add_argument('--wifi_password', default=getenv('LOKOLE_NETWORK_PASSWORD', 'Ascoderu'), help=(\n 'The password of the WiFi network to create for the Lokole email app.'\n ))\n parser.add_argument('--script_log_level', default=getenv('LOKOLE_SCRIPT_LOG_LEVEL', 'INFO'), help=(\n 'The logging verbosity of this script.'\n ))\n parser.add_argument('--server_host', default=getenv('LOKOLE_SERVER_HOST', 'mailserver.lokole.ca'), help=(\n 'The host of the email sync server to use.'\n ))\n parser.add_argument('--client_domain', default=getenv('LOKOLE_CLIENT_DOMAIN', 'lokole.ca'), help=(\n 'The root domain for which to set up the Lokole email app.'\n ))\n parser.add_argument('--client_version', default=getenv('LOKOLE_CLIENT_VERSION', ''), help=(\n 'The version of the Lokole email app to install.'\n ))\n parser.add_argument('--client_dist', default=getenv('LOKOLE_CLIENT_DIST', ''), help=(\n 'The dist package of the Lokole email app to install.'\n ))\n parser.add_argument('--port', default=getenv('LOKOLE_PORT', '80'), help=(\n 'The port on which to run the Lokole email app.'\n ))\n parser.add_argument('--state_directory', default=getenv('LOKOLE_STATE_DIRECTORY', 'lokole/state'), help=(\n 'The location where to store the Lokole email app state.'\n ))\n parser.add_argument('--log_directory', default=getenv('LOKOLE_LOG_DIRECTORY', 'lokole/logs'), help=(\n 'The location where to store the Lokole email app logs.'\n ))\n parser.add_argument('--venv_directory', default=getenv('LOKOLE_VENV_DIRECTORY', 'lokole/venv'), help=(\n 'The location where to store the Lokole email app Python environment.'\n ))\n parser.add_argument('--server_name', default=getenv('LOKOLE_SERVER_NAME', 'lokole_gunicorn'), help=(\n 'Name of the Lokole webapp server.'\n ))\n parser.add_argument('--nginx_name', default=getenv('LOKOLE_NGINX_NAME', 'lokole_nginx'), help=(\n 'Name of the Nginx service.'\n ))\n parser.add_argument('--worker_name', default=getenv('LOKOLE_WORKER_NAME', 'lokole_celery_worker'), help=(\n 'Name of the Lokole webapp worker.'\n ))\n parser.add_argument('--cron_name', default=getenv('LOKOLE_CRON_NAME', 'lokole_celery_beat'), help=(\n 'Name of the Lokole cron worker.'\n ))\n parser.add_argument('--restarter_name', default=getenv('LOKOLE_RESTARTER_NAME', 'lokole_restarter'), help=(\n 'Name of the Lokole restarter.'\n ))\n parser.add_argument('--log_level', default=getenv('LOKOLE_LOG_LEVEL', 'error'), help=(\n 'The log level for the Lokole email app.'\n ))\n parser.add_argument('--timeout', type=int, default=300, help=(\n 'Timeout for the Lokole email app. In seconds.'\n ))\n parser.add_argument('--max_upload_size', type=int, default=10, help=(\n 'Maximum allowed size of uploads to the Lokole email app. In MB.'\n ))\n parser.add_argument('--num_celery_workers', type=int, default=2, help=(\n 'Number of celery workers for the Lokole email app.'\n ))\n parser.add_argument('--num_gunicorn_workers', type=int, default=max(2, cpu_count() - 1), help=(\n 'Number of gunicorn workers for the Lokole email app.'\n ))\n parser.add_argument('--locale', default=getenv('LOKOLE_LOCALE', 'en_GB.UTF-8'), help=(\n 'Locale to set up on the system.'\n ))\n parser.add_argument('--timezone', default=getenv('LOKOLE_TIMEZONE', 'Etc/UTC'), help=(\n 'Timezone to set up on the system.'\n ))\n\n main(parser.parse_args(), parser.error)\n\n\nif __name__ == '__main__':\n cli()\n", "repo_name": "ascoderu/lokole", "sub_path": "install.py", "file_name": "install.py", "file_ext": "py", "file_size_in_byte": 32122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 41, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "tempfile.gettempdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 52, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 53, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 64, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 122, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 125, "usage_type": "call"}, {"api_name": "shutil.chown", "line_number": 128, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 140, "usage_type": "call"}, {"api_name": "os.chmod", "line_number": 141, "usage_type": "call"}, {"api_name": "stat.S_IEXEC", "line_number": 141, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 156, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 157, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 174, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 184, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 184, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 199, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 206, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 224, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 360, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 423, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 424, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 430, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 432, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 439, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 442, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 444, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 450, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 452, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 517, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 524, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 602, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 603, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 731, "usage_type": "call"}, {"api_name": "sys.version_info.major", "line_number": 733, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 733, "usage_type": "name"}, {"api_name": "sys.version_info.minor", "line_number": 733, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 740, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 745, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 750, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 755, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 759, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 762, "usage_type": "name"}, {"api_name": "string.digits", "line_number": 762, "usage_type": "name"}, {"api_name": "os.urandom", "line_number": 766, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 774, "usage_type": "call"}, {"api_name": "time.time", "line_number": 777, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 780, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 788, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 811, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 832, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 835, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 838, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 841, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 844, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 850, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 853, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 856, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 860, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 863, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 866, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 869, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 872, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 875, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 878, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 881, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 884, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 887, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 890, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 893, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 896, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 899, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 902, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 905, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 908, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 920, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 923, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 926, "usage_type": "call"}]} +{"seq_id": "21381056411", "text": "#!/usr/bin/env python3\n\"\"\"Example of plotting slices of a field with yt and matplotlib manipulation of the plot\"\"\"\n\n\n# ========================================================================\n#\n# Imports\n#\n# ========================================================================\nimport numpy as np\nimport yt\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nfrom matplotlib.ticker import SymmetricalLogLocator\nfrom matplotlib.backends.backend_pdf import PdfPages\n\n\n# ========================================================================\n#\n# Function definitions\n#\n# ========================================================================\ndef plot_ds(fdir, field=\"x_velocity\"):\n\n # Load the data\n ds = yt.load(fdir, unit_system=\"mks\")\n\n # Setup\n L = (ds.domain_right_edge - ds.domain_left_edge).d\n width = L[0]\n res = 512\n zlocs = np.array([0.0525, 0.0775, 0.1025, 0.1275, 0.1525])\n fname = \"slices.pdf\"\n\n with PdfPages(fname) as pdf:\n plt.close(\"all\")\n plt.rc(\"text\", usetex=True)\n linthresh = 1e-3\n\n # Get a slice in x\n slc = yt.SlicePlot(ds, \"x\", fields=[field])\n frb = slc.data_source.to_frb(width, res)\n x_slc = np.array(frb[field])\n\n fig0 = plt.figure(0)\n ax0 = fig0.add_subplot(111)\n im = ax0.imshow(\n x_slc,\n origin=\"lower\",\n extent=[\n ds.domain_left_edge.d[0],\n ds.domain_right_edge.d[0],\n ds.domain_left_edge.d[2],\n ds.domain_right_edge.d[2],\n ],\n aspect=\"equal\",\n cmap=\"Spectral_r\",\n norm=colors.SymLogNorm(\n linthresh=linthresh, linscale=0.5, vmin=x_slc.min(), vmax=x_slc.max()\n ),\n )\n cbar = plt.colorbar(\n im, ax=ax0, ticks=SymmetricalLogLocator(linthresh=linthresh, base=10)\n )\n cbar.ax.set_title(r\"$u$\")\n\n for zloc in zlocs:\n ax0.plot(\n [ds.domain_left_edge.d[0], ds.domain_right_edge.d[0]],\n [zloc, zloc],\n color=\"w\",\n lw=1,\n ls=\"--\",\n )\n\n ax0.set_xlabel(r\"$y~[\\mathrm{m}]$\", fontsize=22, fontweight=\"bold\")\n ax0.set_ylabel(r\"$z~[\\mathrm{m}]$\", fontsize=22, fontweight=\"bold\")\n plt.setp(ax0.get_xmajorticklabels(), fontsize=18)\n plt.setp(ax0.get_ymajorticklabels(), fontsize=18)\n fig0.subplots_adjust(bottom=0.15)\n fig0.subplots_adjust(left=0.17)\n pdf.savefig(dpi=300)\n\n # Get slices in z\n for k, zloc in enumerate(zlocs):\n slc = yt.SlicePlot(ds, \"z\", fields=[field], center=[0, 0, zloc])\n frb = slc.data_source.to_frb(width, res)\n z_slc = np.array(frb[field])\n\n fig0 = plt.figure(k + 1)\n ax0 = fig0.add_subplot(111)\n im = ax0.imshow(\n z_slc,\n origin=\"lower\",\n extent=[\n ds.domain_left_edge.d[0],\n ds.domain_right_edge.d[0],\n ds.domain_left_edge.d[1],\n ds.domain_right_edge.d[1],\n ],\n aspect=\"equal\",\n cmap=\"Spectral_r\",\n norm=colors.SymLogNorm(\n linthresh=linthresh,\n linscale=0.5,\n vmin=x_slc.min(),\n vmax=x_slc.max(),\n ),\n )\n cbar = plt.colorbar(\n im, ax=ax0, ticks=SymmetricalLogLocator(linthresh=linthresh, base=10)\n )\n cbar.ax.set_title(r\"$u$\")\n\n ax0.set_xlabel(r\"$x~[\\mathrm{m}]$\", fontsize=22, fontweight=\"bold\")\n ax0.set_ylabel(r\"$y~[\\mathrm{m}]$\", fontsize=22, fontweight=\"bold\")\n plt.setp(ax0.get_xmajorticklabels(), fontsize=18)\n plt.setp(ax0.get_ymajorticklabels(), fontsize=18)\n fig0.subplots_adjust(bottom=0.15)\n fig0.subplots_adjust(left=0.17)\n pdf.savefig(dpi=300)\n", "repo_name": "AMReX-Combustion/PelePlot", "sub_path": "slicer.py", "file_name": "slicer.py", "file_ext": "py", "file_size_in_byte": 4061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "yt.load", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_pdf.PdfPages", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "yt.SlicePlot", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.colors.SymLogNorm", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.ticker.SymmetricalLogLocator", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "yt.SlicePlot", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "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": "matplotlib.colors.SymLogNorm", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.ticker.SymmetricalLogLocator", "line_number": 111, "usage_type": "call"}, {"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"}]} +{"seq_id": "31430492914", "text": "#!/usr/bin/env python3\n\"\"\"\nAutomated controller for lights\n\nTimers are stored in the file pointed to by LIGHT_TIMERS\n\n\nUse cron or equivalent to have this program automatically run at startup\n\"\"\"\nfrom lightStripLib import Room\nfrom datetime import datetime\nfrom time import sleep\nimport sys\nimport subprocess\n\nTIMER_FILE=\"light.transition\" #\"light_timers.csv\"\n\n\n\ndef get_timers(timer_file):\n \"\"\"\n Return a list of timers\n\n Timers read from LIGHT_TIMERS file\n Raw file should read:\n TIME,first element in the scene],second element in the scene, etc\n where '|' is used to separate individual components of each scene element\n\n for example:\n TIME,HUE|SATURATION|BRIGHTNESS|DURATION|TRANSITION,HUE|SATURATION|BRIGHT...\n\n On failure, the function returns `timers` in its current state\n \"\"\"\n timers = [] # list of timers\n # each timer is a tuple consisting of (TIME, TRANSITION, ACTIVATED, LIGHTS)\n # where the time is HHMM in 24 hour time and TRANSITION is a list of tuples\n # each transition tuple consists of (HUE, SATURATION, BRIGHTNESS, DURATION, TRANSITION)\n # where HUE, SATURATION, and BRIGHTNESS are floats and DURATION, TRANSITION are integers\n # representing the DURATION and TRANSITION length of each part of the scene in miliseconds\n with open(timer_file, 'r') as timer_file:\n for raw_timer in timer_file:\n raw_input = raw_timer.split(',')\n time = 0000\n try:\n time = int(raw_input.pop(0))\n except Exception:\n print(\"failed to parse time:\", time)\n return timers\n lights = []\n try:\n lights = [l for l in raw_input.pop(0).split('|') if l]\n except Exception:\n print(\"failed to parse lights\")\n return timers\n scene_elements = []\n while raw_input:\n scene_element = raw_input.pop(0).split('|')\n try:\n scene_elements.append((\n float(scene_element[0]),\n float(scene_element[1]),\n float(scene_element[2]),\n int(scene_element[3]),\n int(scene_element[4])))\n except Exception:\n print(\"failed to parse scene element:\", scene_element)\n return timers\n timers.append((time, scene_elements, False, lights)) # time to activate, elements, bool if the timer has been activated today, lights to be activated\n return timers\n\ndef main():\n \"\"\"\n Main driver for program\n\n\n \"\"\"\n # get hash\n current_hash = subprocess.run(\n ['md5sum', TIMER_FILE], \n stdout=subprocess.PIPE).stdout.decode('utf-8')\n \n timers = get_timers(TIMER_FILE) # get all the timers\n # TODO: sort the timers so the earliest timer is first and the latest timer is last\n room = Room()\n if not room.setup(): # get all the lights\n sys.exit(1)\n \n print(timers)\n\n while True: # make sure the timer never stops running\n if not timers:\n # if there are no timers, we are just going to stop the program\n sys.exit(1)\n current_time = int(datetime.now().strftime('%H%M'))\n if current_time % 5 == 0:\n print(f\"{current_time} - timers: {len(timers)}\")\n for t in timers:\n time, transition, activated, lights = t\n print(f\"\\t{time} : {'done' if activated else 'waiting'}\")\n\n\n for index, timer in enumerate(timers):\n time, transition, activated, lights = timer\n if abs(current_time - time) <= 1 and not activated:\n print(f\"controller ran: {transition} at {time}\")\n # run the transition\n if lights:\n print(f\"only transitioning lights: {lights}\")\n for light in lights:\n room.light_transition(light, transition)\n else:\n print(\"ran transition on all lights\")\n room.room_transition(transition)\n activated = True # set the timer to activated\n elif current_time <= 1:\n activated = False\n\n timers[index] = (time, transition, activated, lights)\n sleep(60) # wait a minute\n\n # check for any new timers only if the timer file has changed\n new_hash = subprocess.run(\n ['md5sum', TIMER_FILE.encode('utf-8')], \n stdout=subprocess.PIPE).stdout.decode('utf-8')\n if current_hash != new_hash:\n print(\"checking for timers because timer file got modified\")\n timers = get_timers(TIMER_FILE)\n \n current_hash = new_hash\n # and repeat the process\n\n\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "BCaven/elgato-light-controller", "sub_path": "controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 4973, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "subprocess.run", "line_number": 78, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 80, "usage_type": "attribute"}, {"api_name": "lightStripLib.Room", "line_number": 84, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 122, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 124, "usage_type": "attribute"}]} +{"seq_id": "23467312893", "text": "\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 1 22:49:22 2019\n\n@author: Jarvis\n\"\"\"\n\nimport folium as fm\nimport numpy as np\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\ndef GetActivePol(number):\n \"\"\"\n \n \"\"\"\n \n \n #Get the newest open activations\n url='https://emergency.copernicus.eu/mapping/activations-rapid/feed'\n resp = requests.get(url)\n soup=BeautifulSoup(resp.content, features='xml')\n \n #search for activations\n items =soup.findAll('item')\n first = items[number].title.text\n #Select only string values with activation Code\n activation = first[1:8] # code\n Info=first[10:len(first)]\n url='https://emergency.copernicus.eu/mapping/list-of-components/{}/aemfeed'.format(activation)\n resp = requests.get(url)\n soup=BeautifulSoup(resp.content, features='xml')\n #Scrape Polygons\n polygons =soup.findAll('georss:polygon')\n #m=fm.Map([46.90814465,14.3134518],zoom_start=10,tiles='OpenStreetMap')\n poldata=[]\n for pol in polygons:\n newpoly=[]\n # print(str(pol)[16:len(pol)-18])\n polraw=str(pol)[16:len(pol)-18]\n polsplit=polraw.split(\" \")\n a=0\n #print(len(polsplit)/2)\n for i in range(0,int(len(polsplit)/2)):\n newpoly.append(polsplit[a:a+2])\n a=a+2\n poldata.append(newpoly)\n \n print(poldata)\n \n \n jsondata={\n \"code\":activation,\n \"info\":Info,\n \"poldata\":poldata\n }\n \n print(jsondata)\n # for ponum, coor in enumerate(poldata):\n # print(coor)\n # jsondata[\"Pol-\"+str(ponum+1)]=coor\n print(jsondata)\n with open(activation+\".txt\",\"w\") as outfile:\n json.dump(jsondata,outfile)\n \n", "repo_name": "sirks/emergenyApp", "sub_path": "python/ScrapePoly.py", "file_name": "ScrapePoly.py", "file_ext": "py", "file_size_in_byte": 1731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "24721054644", "text": "from binascii import hexlify\nimport getpass\nimport os\nimport socket\nimport warnings\nfrom errno import ECONNREFUSED, EHOSTUNREACH\n\nfrom paramiko.agent import Agent\nfrom paramiko.common import DEBUG\nfrom paramiko.config import SSH_PORT\nfrom paramiko.dsskey import DSSKey\nfrom paramiko.ecdsakey import ECDSAKey\nfrom paramiko.hostkeys import HostKeys\nfrom paramiko.py3compat import string_types\nfrom paramiko.resource import ResourceManager\nfrom paramiko.rsakey import RSAKey\nfrom paramiko.ssh_exception import (\n SSHException, BadHostKeyException, NoValidConnectionsError\n)\nfrom paramiko.transport import Transport\nfrom paramiko.util import retry_on_signal, ClosingContextManager\n\n\nclass SSHClient (ClosingContextManager):\n \"\"\"\n A high-level representation of a session with an SSH server. This class\n wraps `.Transport`, `.Channel`, and `.SFTPClient` to take care of most\n aspects of authenticating and opening channels. A typical use case is::\n\n client = SSHClient()\n client.load_system_host_keys()\n client.connect('ssh.example.com')\n stdin, stdout, stderr = client.exec_command('ls -l')\n\n You may pass in explicit overrides for authentication and server host key\n checking. The default mechanism is to try to use local key files or an\n SSH agent (if one is running).\n\n Instances of this class may be used as context managers.\n\n .. versionadded:: 1.6\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Create a new SSHClient.\n \"\"\"\n self._system_host_keys = HostKeys()\n self._host_keys = HostKeys()\n self._host_keys_filename = None\n self._log_channel = None\n self._policy = RejectPolicy()\n self._transport = None\n self._agent = None\n\n def load_system_host_keys(self, filename=None):\n \"\"\"\n Load host keys from a system (read-only) file. Host keys read with\n this method will not be saved back by `save_host_keys`.\n\n This method can be called multiple times. Each new set of host keys\n will be merged with the existing set (new replacing old if there are\n conflicts).\n\n If ``filename`` is left as ``None``, an attempt will be made to read\n keys from the user's local \"known hosts\" file, as used by OpenSSH,\n and no exception will be raised if the file can't be read. This is\n probably only useful on posix.\n\n :param str filename: the filename to read, or ``None``\n\n :raises IOError:\n if a filename was provided and the file could not be read\n \"\"\"\n if filename is None:\n # try the user's .ssh key file, and mask exceptions\n filename = os.path.expanduser('~/.ssh/known_hosts')\n try:\n self._system_host_keys.load(filename)\n except IOError:\n pass\n return\n self._system_host_keys.load(filename)\n\n def load_host_keys(self, filename):\n \"\"\"\n Load host keys from a local host-key file. Host keys read with this\n method will be checked after keys loaded via `load_system_host_keys`,\n but will be saved back by `save_host_keys` (so they can be modified).\n The missing host key policy `.AutoAddPolicy` adds keys to this set and\n saves them, when connecting to a previously-unknown server.\n\n This method can be called multiple times. Each new set of host keys\n will be merged with the existing set (new replacing old if there are\n conflicts). When automatically saving, the last hostname is used.\n\n :param str filename: the filename to read\n\n :raises IOError: if the filename could not be read\n \"\"\"\n self._host_keys_filename = filename\n self._host_keys.load(filename)\n\n def save_host_keys(self, filename):\n \"\"\"\n Save the host keys back to a file. Only the host keys loaded with\n `load_host_keys` (plus any added directly) will be saved -- not any\n host keys loaded with `load_system_host_keys`.\n\n :param str filename: the filename to save to\n\n :raises IOError: if the file could not be written\n \"\"\"\n\n # update local host keys from file (in case other SSH clients\n # have written to the known_hosts file meanwhile.\n if self._host_keys_filename is not None:\n self.load_host_keys(self._host_keys_filename)\n\n with open(filename, 'w') as f:\n for hostname, keys in self._host_keys.items():\n for keytype, key in keys.items():\n f.write('%s %s %s\\n' % (hostname, keytype, key.get_base64()))\n\n def get_host_keys(self):\n \"\"\"\n Get the local `.HostKeys` object. This can be used to examine the\n local host keys or change them.\n\n :return: the local host keys as a `.HostKeys` object.\n \"\"\"\n return self._host_keys\n\n def set_log_channel(self, name):\n \"\"\"\n Set the channel for logging. The default is ``\"paramiko.transport\"``\n but it can be set to anything you want.\n\n :param str name: new channel name for logging\n \"\"\"\n self._log_channel = name\n\n def set_missing_host_key_policy(self, policy):\n \"\"\"\n Set policy to use when connecting to servers without a known host key.\n\n Specifically:\n\n * A **policy** is an instance of a \"policy class\", namely some subclass\n of `.MissingHostKeyPolicy` such as `.RejectPolicy` (the default),\n `.AutoAddPolicy`, `.WarningPolicy`, or a user-created subclass.\n\n .. note::\n This method takes class **instances**, not **classes** themselves.\n Thus it must be called as e.g.\n ``.set_missing_host_key_policy(WarningPolicy())`` and *not*\n ``.set_missing_host_key_policy(WarningPolicy)``.\n\n * A host key is **known** when it appears in the client object's cached\n host keys structures (those manipulated by `load_system_host_keys`\n and/or `load_host_keys`).\n\n :param .MissingHostKeyPolicy policy:\n the policy to use when receiving a host key from a\n previously-unknown server\n \"\"\"\n self._policy = policy\n\n def _families_and_addresses(self, hostname, port):\n \"\"\"\n Yield pairs of address families and addresses to try for connecting.\n\n :param str hostname: the server to connect to\n :param int port: the server port to connect to\n :returns: Yields an iterable of ``(family, address)`` tuples\n \"\"\"\n guess = True\n addrinfos = socket.getaddrinfo(hostname, port, socket.AF_UNSPEC, socket.SOCK_STREAM)\n for (family, socktype, proto, canonname, sockaddr) in addrinfos:\n if socktype == socket.SOCK_STREAM:\n yield family, sockaddr\n guess = False\n\n # some OS like AIX don't indicate SOCK_STREAM support, so just guess. :(\n # We only do this if we did not get a single result marked as socktype == SOCK_STREAM.\n if guess:\n for family, _, _, _, sockaddr in addrinfos:\n yield family, sockaddr\n\n def connect(\n self,\n hostname,\n port=SSH_PORT,\n username=None,\n password=None,\n pkey=None,\n key_filename=None,\n timeout=None,\n allow_agent=True,\n look_for_keys=True,\n compress=False,\n sock=None,\n gss_auth=False,\n gss_kex=False,\n gss_deleg_creds=True,\n gss_host=None,\n banner_timeout=None\n ):\n \"\"\"\n Connect to an SSH server and authenticate to it. The server's host key\n is checked against the system host keys (see `load_system_host_keys`)\n and any local host keys (`load_host_keys`). If the server's hostname\n is not found in either set of host keys, the missing host key policy\n is used (see `set_missing_host_key_policy`). The default policy is\n to reject the key and raise an `.SSHException`.\n\n Authentication is attempted in the following order of priority:\n\n - The ``pkey`` or ``key_filename`` passed in (if any)\n - Any key we can find through an SSH agent\n - Any \"id_rsa\", \"id_dsa\" or \"id_ecdsa\" key discoverable in\n ``~/.ssh/``\n - Plain username/password auth, if a password was given\n\n If a private key requires a password to unlock it, and a password is\n passed in, that password will be used to attempt to unlock the key.\n\n :param str hostname: the server to connect to\n :param int port: the server port to connect to\n :param str username:\n the username to authenticate as (defaults to the current local\n username)\n :param str password:\n a password to use for authentication or for unlocking a private key\n :param .PKey pkey: an optional private key to use for authentication\n :param str key_filename:\n the filename, or list of filenames, of optional private key(s) to\n try for authentication\n :param float timeout:\n an optional timeout (in seconds) for the TCP connect\n :param bool allow_agent:\n set to False to disable connecting to the SSH agent\n :param bool look_for_keys:\n set to False to disable searching for discoverable private key\n files in ``~/.ssh/``\n :param bool compress: set to True to turn on compression\n :param socket sock:\n an open socket or socket-like object (such as a `.Channel`) to use\n for communication to the target host\n :param bool gss_auth:\n ``True`` if you want to use GSS-API authentication\n :param bool gss_kex:\n Perform GSS-API Key Exchange and user authentication\n :param bool gss_deleg_creds: Delegate GSS-API client credentials or not\n :param str gss_host:\n The targets name in the kerberos database. default: hostname\n :param float banner_timeout: an optional timeout (in seconds) to wait\n for the SSH banner to be presented.\n\n :raises BadHostKeyException: if the server's host key could not be\n verified\n :raises AuthenticationException: if authentication failed\n :raises SSHException: if there was any other error connecting or\n establishing an SSH session\n :raises socket.error: if a socket error occurred while connecting\n\n .. versionchanged:: 1.15\n Added the ``banner_timeout``, ``gss_auth``, ``gss_kex``,\n ``gss_deleg_creds`` and ``gss_host`` arguments.\n \"\"\"\n if not sock:\n errors = {}\n # Try multiple possible address families (e.g. IPv4 vs IPv6)\n to_try = list(self._families_and_addresses(hostname, port))\n for af, addr in to_try:\n try:\n sock = socket.socket(af, socket.SOCK_STREAM)\n if timeout is not None:\n try:\n sock.settimeout(timeout)\n except:\n pass\n retry_on_signal(lambda: sock.connect(addr))\n # Break out of the loop on success\n break\n except socket.error as e:\n # Raise anything that isn't a straight up connection error\n # (such as a resolution error)\n if e.errno not in (ECONNREFUSED, EHOSTUNREACH):\n raise\n # Capture anything else so we know how the run looks once\n # iteration is complete. Retain info about which attempt\n # this was.\n errors[addr] = e\n\n # Make sure we explode usefully if no address family attempts\n # succeeded. We've no way of knowing which error is the \"right\"\n # one, so we construct a hybrid exception containing all the real\n # ones, of a subclass that client code should still be watching for\n # (socket.error)\n if len(errors) == len(to_try):\n raise NoValidConnectionsError(errors)\n\n t = self._transport = Transport(sock, gss_kex=gss_kex, gss_deleg_creds=gss_deleg_creds)\n t.use_compression(compress=compress)\n if gss_kex and gss_host is None:\n t.set_gss_host(hostname)\n elif gss_kex and gss_host is not None:\n t.set_gss_host(gss_host)\n else:\n pass\n if self._log_channel is not None:\n t.set_log_channel(self._log_channel)\n if banner_timeout is not None:\n t.banner_timeout = banner_timeout\n t.start_client()\n ResourceManager.register(self, t)\n\n server_key = t.get_remote_server_key()\n keytype = server_key.get_name()\n\n if port == SSH_PORT:\n server_hostkey_name = hostname\n else:\n server_hostkey_name = \"[%s]:%d\" % (hostname, port)\n\n # If GSS-API Key Exchange is performed we are not required to check the\n # host key, because the host is authenticated via GSS-API / SSPI as\n # well as our client.\n if not self._transport.use_gss_kex:\n our_server_key = self._system_host_keys.get(server_hostkey_name,\n {}).get(keytype, None)\n if our_server_key is None:\n our_server_key = self._host_keys.get(server_hostkey_name,\n {}).get(keytype, None)\n if our_server_key is None:\n # will raise exception if the key is rejected; let that fall out\n self._policy.missing_host_key(self, server_hostkey_name,\n server_key)\n # if the callback returns, assume the key is ok\n our_server_key = server_key\n\n if server_key != our_server_key:\n raise BadHostKeyException(hostname, server_key, our_server_key)\n\n if username is None:\n username = getpass.getuser()\n\n if key_filename is None:\n key_filenames = []\n elif isinstance(key_filename, string_types):\n key_filenames = [key_filename]\n else:\n key_filenames = key_filename\n if gss_host is None:\n gss_host = hostname\n self._auth(username, password, pkey, key_filenames, allow_agent,\n look_for_keys, gss_auth, gss_kex, gss_deleg_creds, gss_host)\n\n def close(self):\n \"\"\"\n Close this SSHClient and its underlying `.Transport`.\n\n .. warning::\n Failure to do this may, in some situations, cause your Python\n interpreter to hang at shutdown (often due to race conditions).\n It's good practice to `close` your client objects anytime you're\n done using them, instead of relying on garbage collection.\n \"\"\"\n if self._transport is None:\n return\n self._transport.close()\n self._transport = None\n\n if self._agent is not None:\n self._agent.close()\n self._agent = None\n\n def exec_command(self, command, bufsize=-1, timeout=None, get_pty=False):\n \"\"\"\n Execute a command on the SSH server. A new `.Channel` is opened and\n the requested command is executed. The command's input and output\n streams are returned as Python ``file``-like objects representing\n stdin, stdout, and stderr.\n\n :param str command: the command to execute\n :param int bufsize:\n interpreted the same way as by the built-in ``file()`` function in\n Python\n :param int timeout:\n set command's channel timeout. See `Channel.settimeout`.settimeout\n :return:\n the stdin, stdout, and stderr of the executing command, as a\n 3-tuple\n\n :raises SSHException: if the server fails to execute the command\n \"\"\"\n chan = self._transport.open_session(timeout=timeout)\n if get_pty:\n chan.get_pty()\n chan.settimeout(timeout)\n chan.exec_command(command)\n stdin = chan.makefile('wb', bufsize)\n stdout = chan.makefile('r', bufsize)\n stderr = chan.makefile_stderr('r', bufsize)\n return stdin, stdout, stderr\n\n def invoke_shell(self, term='vt100', width=80, height=24, width_pixels=0,\n height_pixels=0):\n \"\"\"\n Start an interactive shell session on the SSH server. A new `.Channel`\n is opened and connected to a pseudo-terminal using the requested\n terminal type and size.\n\n :param str term:\n the terminal type to emulate (for example, ``\"vt100\"``)\n :param int width: the width (in characters) of the terminal window\n :param int height: the height (in characters) of the terminal window\n :param int width_pixels: the width (in pixels) of the terminal window\n :param int height_pixels: the height (in pixels) of the terminal window\n :return: a new `.Channel` connected to the remote shell\n\n :raises SSHException: if the server fails to invoke a shell\n \"\"\"\n chan = self._transport.open_session()\n chan.get_pty(term, width, height, width_pixels, height_pixels)\n chan.invoke_shell()\n return chan\n\n def open_sftp(self):\n \"\"\"\n Open an SFTP session on the SSH server.\n\n :return: a new `.SFTPClient` session object\n \"\"\"\n return self._transport.open_sftp_client()\n\n def get_transport(self):\n \"\"\"\n Return the underlying `.Transport` object for this SSH connection.\n This can be used to perform lower-level tasks, like opening specific\n kinds of channels.\n\n :return: the `.Transport` for this connection\n \"\"\"\n return self._transport\n\n def _auth(self, username, password, pkey, key_filenames, allow_agent,\n look_for_keys, gss_auth, gss_kex, gss_deleg_creds, gss_host):\n \"\"\"\n Try, in order:\n\n - The key passed in, if one was passed in.\n - Any key we can find through an SSH agent (if allowed).\n - Any \"id_rsa\", \"id_dsa\" or \"id_ecdsa\" key discoverable in ~/.ssh/\n (if allowed).\n - Plain username/password auth, if a password was given.\n\n (The password might be needed to unlock a private key, or for\n two-factor authentication [for which it is required].)\n \"\"\"\n saved_exception = None\n two_factor = False\n allowed_types = set()\n two_factor_types = set(['keyboard-interactive','password'])\n\n # If GSS-API support and GSS-PI Key Exchange was performed, we attempt\n # authentication with gssapi-keyex.\n if gss_kex and self._transport.gss_kex_used:\n try:\n self._transport.auth_gssapi_keyex(username)\n return\n except Exception as e:\n saved_exception = e\n\n # Try GSS-API authentication (gssapi-with-mic) only if GSS-API Key\n # Exchange is not performed, because if we use GSS-API for the key\n # exchange, there is already a fully established GSS-API context, so\n # why should we do that again?\n if gss_auth:\n try:\n self._transport.auth_gssapi_with_mic(username, gss_host,\n gss_deleg_creds)\n return\n except Exception as e:\n saved_exception = e\n\n if pkey is not None:\n try:\n self._log(DEBUG, 'Trying SSH key %s' % hexlify(pkey.get_fingerprint()))\n allowed_types = set(self._transport.auth_publickey(username, pkey))\n two_factor = (allowed_types & two_factor_types)\n if not two_factor:\n return\n except SSHException as e:\n saved_exception = e\n\n if not two_factor:\n for key_filename in key_filenames:\n for pkey_class in (RSAKey, DSSKey, ECDSAKey):\n try:\n key = pkey_class.from_private_key_file(key_filename, password)\n self._log(DEBUG, 'Trying key %s from %s' % (hexlify(key.get_fingerprint()), key_filename))\n allowed_types = set(self._transport.auth_publickey(username, key))\n two_factor = (allowed_types & two_factor_types)\n if not two_factor:\n return\n break\n except SSHException as e:\n saved_exception = e\n\n if not two_factor and allow_agent:\n if self._agent is None:\n self._agent = Agent()\n\n for key in self._agent.get_keys():\n try:\n self._log(DEBUG, 'Trying SSH agent key %s' % hexlify(key.get_fingerprint()))\n # for 2-factor auth a successfully auth'd key password will return an allowed 2fac auth method\n allowed_types = set(self._transport.auth_publickey(username, key))\n two_factor = (allowed_types & two_factor_types)\n if not two_factor:\n return\n break\n except SSHException as e:\n saved_exception = e\n\n if not two_factor:\n keyfiles = []\n rsa_key = os.path.expanduser('~/.ssh/id_rsa')\n dsa_key = os.path.expanduser('~/.ssh/id_dsa')\n ecdsa_key = os.path.expanduser('~/.ssh/id_ecdsa')\n if os.path.isfile(rsa_key):\n keyfiles.append((RSAKey, rsa_key))\n if os.path.isfile(dsa_key):\n keyfiles.append((DSSKey, dsa_key))\n if os.path.isfile(ecdsa_key):\n keyfiles.append((ECDSAKey, ecdsa_key))\n # look in ~/ssh/ for windows users:\n rsa_key = os.path.expanduser('~/ssh/id_rsa')\n dsa_key = os.path.expanduser('~/ssh/id_dsa')\n ecdsa_key = os.path.expanduser('~/ssh/id_ecdsa')\n if os.path.isfile(rsa_key):\n keyfiles.append((RSAKey, rsa_key))\n if os.path.isfile(dsa_key):\n keyfiles.append((DSSKey, dsa_key))\n if os.path.isfile(ecdsa_key):\n keyfiles.append((ECDSAKey, ecdsa_key))\n\n if not look_for_keys:\n keyfiles = []\n\n for pkey_class, filename in keyfiles:\n try:\n key = pkey_class.from_private_key_file(filename, password)\n self._log(DEBUG, 'Trying discovered key %s in %s' % (hexlify(key.get_fingerprint()), filename))\n # for 2-factor auth a successfully auth'd key will result in ['password']\n allowed_types = set(self._transport.auth_publickey(username, key))\n two_factor = (allowed_types & two_factor_types)\n if not two_factor:\n return\n break\n except (SSHException, IOError) as e:\n saved_exception = e\n\n if password is not None:\n try:\n self._transport.auth_password(username, password)\n return\n except SSHException as e:\n saved_exception = e\n elif two_factor:\n try:\n self._transport.auth_interactive_dumb(username)\n return\n except SSHException as e:\n saved_exception = e\n\n # if we got an auth-failed exception earlier, re-raise it\n if saved_exception is not None:\n raise saved_exception\n raise SSHException('No authentication methods available')\n\n def _log(self, level, msg):\n self._transport._log(level, msg)\n\n\nclass MissingHostKeyPolicy (object):\n \"\"\"\n Interface for defining the policy that `.SSHClient` should use when the\n SSH server's hostname is not in either the system host keys or the\n application's keys. Pre-made classes implement policies for automatically\n adding the key to the application's `.HostKeys` object (`.AutoAddPolicy`),\n and for automatically rejecting the key (`.RejectPolicy`).\n\n This function may be used to ask the user to verify the key, for example.\n \"\"\"\n\n def missing_host_key(self, client, hostname, key):\n \"\"\"\n Called when an `.SSHClient` receives a server key for a server that\n isn't in either the system or local `.HostKeys` object. To accept\n the key, simply return. To reject, raised an exception (which will\n be passed to the calling application).\n \"\"\"\n pass\n\n\nclass AutoAddPolicy (MissingHostKeyPolicy):\n \"\"\"\n Policy for automatically adding the hostname and new host key to the\n local `.HostKeys` object, and saving it. This is used by `.SSHClient`.\n \"\"\"\n\n def missing_host_key(self, client, hostname, key):\n client._host_keys.add(hostname, key.get_name(), key)\n if client._host_keys_filename is not None:\n client.save_host_keys(client._host_keys_filename)\n client._log(DEBUG, 'Adding %s host key for %s: %s' %\n (key.get_name(), hostname, hexlify(key.get_fingerprint())))\n\n\nclass RejectPolicy (MissingHostKeyPolicy):\n \"\"\"\n Policy for automatically rejecting the unknown hostname & key. This is\n used by `.SSHClient`.\n \"\"\"\n\n def missing_host_key(self, client, hostname, key):\n client._log(DEBUG, 'Rejecting %s host key for %s: %s' %\n (key.get_name(), hostname, hexlify(key.get_fingerprint())))\n raise SSHException('Server %r not found in known_hosts' % hostname)\n\n\nclass WarningPolicy (MissingHostKeyPolicy):\n \"\"\"\n Policy for logging a Python-style warning for an unknown host key, but\n accepting it. This is used by `.SSHClient`.\n \"\"\"\n def missing_host_key(self, client, hostname, key):\n warnings.warn('Unknown %s host key for %s: %s' %\n (key.get_name(), hostname, hexlify(key.get_fingerprint())))\n", "repo_name": "Komodo/KomodoEdit", "sub_path": "contrib/paramiko/paramiko/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 26334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2110, "dataset": "github-code", "pt": "2", "api": [{"api_name": "paramiko.util.ClosingContextManager", "line_number": 24, "usage_type": "name"}, {"api_name": "paramiko.hostkeys.HostKeys", "line_number": 48, "usage_type": "call"}, {"api_name": "paramiko.hostkeys.HostKeys", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "socket.getaddrinfo", "line_number": 178, "usage_type": "call"}, {"api_name": "socket.AF_UNSPEC", "line_number": 178, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 178, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 180, "usage_type": "attribute"}, {"api_name": "paramiko.config.SSH_PORT", "line_number": 193, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 277, "usage_type": "call"}, {"api_name": "socket.SOCK_STREAM", "line_number": 277, "usage_type": "attribute"}, {"api_name": "paramiko.util.retry_on_signal", "line_number": 283, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 286, "usage_type": "attribute"}, {"api_name": "errno.ECONNREFUSED", "line_number": 289, "usage_type": "name"}, {"api_name": "errno.EHOSTUNREACH", "line_number": 289, "usage_type": "name"}, {"api_name": "paramiko.ssh_exception.NoValidConnectionsError", "line_number": 302, "usage_type": "call"}, {"api_name": "paramiko.transport.Transport", "line_number": 304, "usage_type": "call"}, {"api_name": "paramiko.resource.ResourceManager.register", "line_number": 317, "usage_type": "call"}, {"api_name": "paramiko.resource.ResourceManager", "line_number": 317, "usage_type": "name"}, {"api_name": "paramiko.config.SSH_PORT", "line_number": 322, "usage_type": "name"}, {"api_name": "paramiko.ssh_exception.BadHostKeyException", "line_number": 344, "usage_type": "call"}, {"api_name": "getpass.getuser", "line_number": 347, "usage_type": "call"}, {"api_name": "paramiko.py3compat.string_types", "line_number": 351, "usage_type": "argument"}, {"api_name": "paramiko.common.DEBUG", "line_number": 490, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 490, "usage_type": "call"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 495, "usage_type": "name"}, {"api_name": "paramiko.rsakey.RSAKey", "line_number": 500, "usage_type": "name"}, {"api_name": "paramiko.dsskey.DSSKey", "line_number": 500, "usage_type": "name"}, {"api_name": "paramiko.ecdsakey.ECDSAKey", "line_number": 500, "usage_type": "name"}, {"api_name": "paramiko.common.DEBUG", "line_number": 503, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 503, "usage_type": "call"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 509, "usage_type": "name"}, {"api_name": "paramiko.agent.Agent", "line_number": 514, "usage_type": "call"}, {"api_name": "paramiko.common.DEBUG", "line_number": 518, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 518, "usage_type": "call"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 525, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 530, "usage_type": "call"}, {"api_name": "os.path", "line_number": 530, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 531, "usage_type": "call"}, {"api_name": "os.path", "line_number": 531, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 532, "usage_type": "call"}, {"api_name": "os.path", "line_number": 532, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 533, "usage_type": "call"}, {"api_name": "os.path", "line_number": 533, "usage_type": "attribute"}, {"api_name": "paramiko.rsakey.RSAKey", "line_number": 534, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 535, "usage_type": "call"}, {"api_name": "os.path", "line_number": 535, "usage_type": "attribute"}, {"api_name": "paramiko.dsskey.DSSKey", "line_number": 536, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path", "line_number": 537, "usage_type": "attribute"}, {"api_name": "paramiko.ecdsakey.ECDSAKey", "line_number": 538, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 540, "usage_type": "call"}, {"api_name": "os.path", "line_number": 540, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 541, "usage_type": "call"}, {"api_name": "os.path", "line_number": 541, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 542, "usage_type": "call"}, {"api_name": "os.path", "line_number": 542, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 543, "usage_type": "call"}, {"api_name": "os.path", "line_number": 543, "usage_type": "attribute"}, {"api_name": "paramiko.rsakey.RSAKey", "line_number": 544, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 545, "usage_type": "call"}, {"api_name": "os.path", "line_number": 545, "usage_type": "attribute"}, {"api_name": "paramiko.dsskey.DSSKey", "line_number": 546, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 547, "usage_type": "call"}, {"api_name": "os.path", "line_number": 547, "usage_type": "attribute"}, {"api_name": "paramiko.ecdsakey.ECDSAKey", "line_number": 548, "usage_type": "name"}, {"api_name": "paramiko.common.DEBUG", "line_number": 556, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 556, "usage_type": "call"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 563, "usage_type": "name"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 570, "usage_type": "name"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 576, "usage_type": "name"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 582, "usage_type": "call"}, {"api_name": "paramiko.common.DEBUG", "line_number": 619, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 620, "usage_type": "call"}, {"api_name": "paramiko.common.DEBUG", "line_number": 630, "usage_type": "argument"}, {"api_name": "binascii.hexlify", "line_number": 631, "usage_type": "call"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 632, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 641, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 642, "usage_type": "call"}]} +{"seq_id": "25725080944", "text": "import numpy as np\nimport random as rn\nfrom collections import deque\nfrom math import log\n\nclass Error(Exception):\n '''Base class for exceptions in this module.'''\n pass\n\nclass BoxAndEmptySpaceError(Error):\n\tdef __init__(self, message):\n\t\tself.message = message\n\t\nclass Room:\n\t\n\t\n\tdef __init__(self, height, width, box_num):\n\t\tself.boxes=[]\n\t\tself.target_tile_list=[]\n\t\tself.player_curpos=[]\n\t\tself.width=width\n\t\tself.height=height\n\t\tself.box_num=box_num\n\t\tself.room = np.full((height,width), 'W')\n\t\t\n\tdef print_room(self):\n\t\tprint(self.room)\n\n\tdef choose_random_dir(self, d):\n\t\t'''select a new direction and then check yes(35%) and no(65%). \n\t\tIf yes, change, else dont'''\n\t\td1=np.random.randint(1,4)\n\t\tb = [0,1]\n\t\tc=np.random.choice(b,1,p=[0.65,0.35])\n\t\tif(c):\n\t\t\treturn d1\n\t\telse:\n\t\t\treturn d\n\t\t\t\n\tdef get_tile(self, x, y):\n\t\treturn self.room[y][x]\n\t\n\tdef set_tile(self, x, y, c):\n\t\tself.room[y][x]=c\n\t\n\tdef update_space(self, x, y, sym):\t\n\t\tif(x<=0 or x>=self.width-1 or y<=0 or y>=self.height-1):\n\t\t\treturn False\n\t\telse:\n\t\t\tself.set_tile(x,y,sym)\n\t\t\treturn True\n\t\t\n\tdef topology_gen(self, walk_steps):\n\t\tdirn=[1,2,3,4]\n\t\tx=np.random.randint(1,self.width-2) \n\t\ty=np.random.randint(1,self.height-2)\n\t\td=np.random.randint(1,4)\n\t\tself.update_space(x,y,'E')\n\t\tfor i in range(walk_steps):\n\t\t\tt=np.random.randint(1,5)\n\t\t\tif(t==1):\n\t\t\t\tself.update_space(x-1, y, 'E')\n\t\t\t\tself.update_space(x+1, y, 'E')\n\t\t\telif(t==2):\n\t\t\t\tself.update_space(x, y+1, 'E')\n\t\t\t\tself.update_space(x, y-1, 'E')\n\t\t\telif(t==3):\n\t\t\t\tself.update_space(x-1, y, 'E')\n\t\t\t\tself.update_space(x, y-1, 'E')\n\t\t\telif(t==4):\n\t\t\t\tself.update_space(x, y-1, 'E')\n\t\t\t\tself.update_space(x-1, y-1, 'E')\n\t\t\t\tself.update_space(x-1, y, 'E')\n\t\t\telif(t==5):\n\t\t\t\tself.update_space(x+1, y, 'E')\n\t\t\t\tself.update_space(x, y-1, 'E')\n\t\t\td=self.choose_random_dir(d)\n\t\t\tif(d==1):\n\t\t\t\tx=x-1\n\t\t\telif(d==2):\n\t\t\t\tx=x+1\n\t\t\telif(d==3):\n\t\t\t\ty=y-1\n\t\t\telif(d==4):\n\t\t\t\ty=y+1\n\t\t\tif(self.update_space(x,y,'E')):\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tif(d==1):\n\t\t\t\t\tx=x+1\n\t\t\t\telif(d==2):\n\t\t\t\t\tx=x-1\n\t\t\t\telif(d==3):\n\t\t\t\t\ty=y+1\n\t\t\t\telif(d==4):\n\t\t\t\t\ty=y-1\t\n\t\t\t\ti-=1\n\n\tdef position_configuration(self):\n\t\t#boxes config\t\n\t\tfor i in range(self.box_num):\n\t\t\twhile(True):\n\t\t\t\tx=np.random.randint(1,self.width-2) \n\t\t\t\ty=np.random.randint(1,self.height-2)\n\t\t\t\tif(self.get_tile(x,y)=='E'):\n\t\t\t\t\tself.target_tile_list.append((x,y))\n\t\t\t\t\tself.set_tile(x,y,'X') #setting this as X cuz initially B is on T. S\n\t\t\t\t\tself.boxes.append([x,y])\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tcontinue\n\t\t#player config\n\t\twhile(True):\n\t\t\tx=np.random.randint(1,self.width-2) \n\t\t\ty=np.random.randint(1,self.height-2)\n\t\t\tif(self.get_tile(x,y)=='E'):\n\t\t\t\tself.set_tile(x,y,'P')\n\t\t\t\tself.set_player_curpos(x,y)\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tcontinue\n\t\n\tdef is_target_tile(self, x, y):\n\t\tfor i in range(self.box_num):\n\t\t\tif self.target_tile_list[i][0]==x and self.target_tile_list[i][1]==y:\n\t\t\t\treturn True\n\t\treturn False\n\t\n\tdef which_box(self, x, y):\n\t\tfor i in range(self.box_num):\n\t\t\tif self.boxes[i][0]==x and self.boxes[i][1]==y:\n\t\t\t\treturn i\n\t\traise Exception(\"NO BOX FOUND\")\n\t\t\t\t\n\tdef set_player_curpos(self,x,y):\n\t\tself.player_curpos=[]\n\t\tself.player_curpos.append(x)\n\t\tself.player_curpos.append(y)\n\t\n\tdef update_box_pos(self, i, x, y):\n\t\tself.boxes[i][0]=x\n\t\tself.boxes[i][1]=y\n\t\n\tdef make_move(self,x,y, m):\n\t\tif(m==1):\n\t\t\tif(self.get_tile(x-1,y)=='E'):\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\t\tself.set_tile(x-1,y,'P')\n\t\t\t\tself.set_player_curpos(x-1,y)\n\t\t\t\treturn True\n\t\telif(m==2):\n\t\t\tif(self.get_tile(x+1,y)=='E'):\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\t\tself.set_tile(x+1,y,'P')\n\t\t\t\tself.set_player_curpos(x+1,y)\n\t\t\t\treturn True\n\t\telif(m==3):\n\t\t\tif(self.get_tile(x,y+1)=='E'):\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\t\tself.set_tile(x,y+1,'P')\n\t\t\t\tself.set_player_curpos(x,y+1)\n\t\t\t\treturn True\n\t\telif(m==4):\n\t\t\tif(self.get_tile(x,y-1)=='E'):\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\t\tself.set_tile(x,y-1,'P')\n\t\t\t\tself.set_player_curpos(x,y-1)\n\t\t\t\treturn True\n\t\telif(m==5):\n\t\t\tif((self.get_tile(x+1,y)=='B' or self.get_tile(x+1,y)=='X') and self.get_tile(x-1,y)=='E'):\n\t\t\t\tbi=self.which_box(x+1,y)\n\t\t\t\tif(self.get_tile(x+1,y)=='X'):\n\t\t\t\t\tself.set_tile(x+1,y,'T')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x+1,y,'E')\n\t\t\t\tif self.is_target_tile(x,y):\n\t\t\t\t\tself.set_tile(x,y,'X')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x,y,'B')\n\t\t\t\tself.update_box_pos(bi, x,y)\n\t\t\t\tself.set_tile(x-1,y,'P')\n\t\t\t\tself.set_player_curpos(x-1,y)\n\t\t\t\treturn True\t\n\t\t\telse:\n\t\t\t\treturn False\t\n\t\telif(m==6):\n\t\t\tif((self.get_tile(x-1,y)=='B' or self.get_tile(x-1,y)=='X') and self.get_tile(x+1,y)=='E'):\n\t\t\t\tbi=self.which_box(x-1,y)\n\t\t\t\tif(self.get_tile(x-1,y)=='X'):\n\t\t\t\t\tself.set_tile(x-1,y,'T')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x-1,y,'E')\n\t\t\t\tif self.is_target_tile(x,y):\n\t\t\t\t\tself.set_tile(x,y,'X')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x,y,'B')\n\t\t\t\tself.update_box_pos(bi, x,y)\n\t\t\t\tself.set_tile(x+1,y,'P')\n\t\t\t\tself.set_player_curpos(x+1,y)\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False\n\t\telif(m==7):#down\n\t\t\tif((self.get_tile(x,y-1)=='B' or self.get_tile(x,y-1)=='X') and self.get_tile(x,y+1)=='E'):\n\t\t\t\tbi=self.which_box(x,y-1)\n\t\t\t\tif(self.get_tile(x,y-1)=='X'):\n\t\t\t\t\tself.set_tile(x,y-1,'T')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x,y-1,'E')\n\t\t\t\tif self.is_target_tile(x,y):\n\t\t\t\t\tself.set_tile(x,y,'X')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x,y,'B')\n\t\t\t\tself.update_box_pos(bi, x,y)\n\t\t\t\tself.set_tile(x,y+1,'P')\n\t\t\t\tself.set_player_curpos(x,y+1)\n\t\t\t\treturn True\n\t\telif(m==8):\n\t\t\tif((self.get_tile(x,y+1)=='B' or self.get_tile(x,y+1)=='X') and self.get_tile(x,y-1)=='E'):\n\t\t\t\tbi=self.which_box(x,y+1)\n\t\t\t\tif(self.get_tile(x,y+1)=='X'):\n\t\t\t\t\tself.set_tile(x,y+1,'T')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x,y+1,'E')\n\t\t\t\tif self.is_target_tile(x,y):\n\t\t\t\t\tself.set_tile(x,y,'X')\n\t\t\t\telse:\n\t\t\t\t\tself.set_tile(x,y,'B')\n\t\t\t\tself.update_box_pos(bi, x,y)\n\t\t\t\tself.set_tile(x,y-1,'P')\n\t\t\t\tself.set_player_curpos(x,y-1)\n\t\t\t\treturn True\n\t\telif(m==-5):\n\t\t\tbi=self.which_box(x+1,y)\n\t\t\tif self.is_target_tile(x,y):\n\t\t\t\tself.set_tile(x,y,'T')\n\t\t\telse:\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\tself.set_tile(x+1,y,'P')\n\t\t\tif self.is_target_tile(x+2,y):\n\t\t\t\tself.set_tile(x+2,y,'X')\n\t\t\telse:\n\t\t\t\tself.set_tile(x+2,y,'B')\n\t\t\tself.set_player_curpos(x+1,y)\n\t\t\tself.update_box_pos(bi,x+2,y)\n\t\t\t\n\t\telif(m==-6):\n\t\t\tbi=self.which_box(x-1,y)\n\t\t\tif self.is_target_tile(x,y):\n\t\t\t\tself.set_tile(x,y,'T')\n\t\t\telse:\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\tself.set_tile(x-1,y,'P')\n\t\t\tif self.is_target_tile(x-2,y):\n\t\t\t\tself.set_tile(x-2,y,'X')\n\t\t\telse:\n\t\t\t\tself.set_tile(x-2,y,'B')\n\t\t\tself.set_player_curpos(x-1,y)\n\t\t\tself.update_box_pos(bi,x-2,y)\n\t\t\t\n\t\telif(m==-7):\n\t\t\tbi=self.which_box(x,y-1)\n\t\t\tif self.is_target_tile(x,y):\n\t\t\t\tself.set_tile(x,y,'T')\n\t\t\telse:\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\tself.set_tile(x,y-1,'P')\n\t\t\tif self.is_target_tile(x,y-2):\n\t\t\t\tself.set_tile(x,y-2,'X')\n\t\t\telse:\n\t\t\t\tself.set_tile(x,y-2,'B')\n\t\t\tself.set_player_curpos(x,y-1)\n\t\t\tself.update_box_pos(bi,x,y-2)\n\t\t\t\n\t\telif(m==-8):\n\t\t\tbi=self.which_box(x,y+1)\n\t\t\tif self.is_target_tile(x,y):\n\t\t\t\tself.set_tile(x,y,'T')\n\t\t\telse:\n\t\t\t\tself.set_tile(x,y,'E')\n\t\t\tself.set_tile(x,y+1,'P')\n\t\t\tif self.is_target_tile(x,y+2):\n\t\t\t\tself.set_tile(x,y+2,'X')\n\t\t\telse:\n\t\t\t\tself.set_tile(x,y+2,'B')\n\t\t\tself.set_player_curpos(x,y+1)\n\t\t\tself.update_box_pos(bi,x,y+2)\t\n\t\t\t\n\tdef reset_position_configuration(self):\n\t\tfor i in range(self.box_num):\n\t\t\tself.set_tile(self.boxes[i][0],self.boxes[i][1], 'E')\n\t\tfor i in range(self.box_num):\t\n\t\t\tself.set_tile(self.target_tile_list[i][0], self.target_tile_list[i][1], 'T')\n\t\tself.set_tile(self.player_curpos[0],self.player_curpos[1], 'E')\n\t\t \t\n\tdef set_position_configuration(self, c):\n\t\tj=0\n\t\tfor i in range(self.box_num):\n\t\t\tself.boxes[i][0]=c[j]\n\t\t\tj+=1\n\t\t\tself.boxes[i][1]=c[j]\n\t\t\tif self.is_target_tile(c[j-1], c[j]):\n\t\t\t\tself.set_tile(self.boxes[i][0],self.boxes[i][1], 'X')\n\t\t\telse:\n\t\t\t\tself.set_tile(self.boxes[i][0],self.boxes[i][1], 'B')\n\t\t\tj+=1\n\n\t\tself.player_curpos[0]=c[j]\n\t\tj+=1\n\t\tself.player_curpos[1]=c[j]\n\t\tself.set_tile(c[j-1],c[j],'P')\n\t\n\tdef create_config_obj(self):\n\t\tpos_conf=[]\n\t\tfor i in range(self.box_num):\n\t\t\tpos_conf.append(self.boxes[i][0])\n\t\t\tpos_conf.append(self.boxes[i][1])\n\t\tpos_conf.append(self.player_curpos[0])\n\t\tpos_conf.append(self.player_curpos[1])\n\t\treturn tuple(pos_conf) \t\t\t\t\n\nclass Tree:\n\tdef __init__(self):\n\t\tself.child=[]\n\t\tself.data=()\n\t\n\tdef create_child(self):\n\t\tself.child.append(Tree())\n\ndef create_config_tree(room): #it takes a single position config and creates a move tree for it. \n\tmoves=[1,2,3,4,5,6,7,8]\n\tdepth=0\n\texplored=set()\n\tconf_tree=Tree()\n\tconf_tree.data=room.create_config_obj()\n\texplored.add(room.create_config_obj())\n\trn.shuffle(moves)\n\tchild_q=deque()\n\tnodes_num=0\n\ti=0\n\tfor m in moves:\n\t\tnodes_num+=1\n\t\tmm=False\n\t\tmm=room.make_move(room.player_curpos[0],room.player_curpos[1],m) #mm: move made bool\n\t\tc=room.create_config_obj()\n\t\tif c not in explored: \n\t\t\texplored.add(c)\n\t\t\tconf_tree.create_child()\n\t\t\tconf_tree.child[i].data=c\n\t\t\tchild_q.append(conf_tree.child[i])\n\t\t\ti+=1\n\t\t#reverse the move made\t\n\t\tif m<5 and m%2==0 and mm:\n\t\t\tm-=1\n\t\t\troom.make_move(room.player_curpos[0],room.player_curpos[1],m)\n\t\telif m<5 and mm:\n\t\t\tm+=1\n\t\t\troom.make_move(room.player_curpos[0],room.player_curpos[1],m)\n\t\telif mm:\n\t\t\tm=m*-1\n\t\t\troom.make_move(room.player_curpos[0],room.player_curpos[1],m)\t\n\n\twhile((len(child_q)!=0) and (depth<=300)):\n\t\trn.shuffle(moves)\n\t\tch=child_q.popleft()\n\t\troom.reset_position_configuration()\n\t\troom.set_position_configuration(ch.data)\n\t\ti=0\n\t\tt=0\n\t\tdepth=log(nodes_num,8)\n\t\tfor m in moves:\n\t\t\tnodes_num+=1\n\t\t\tmm=False\n\t\t\tmm=room.make_move(room.player_curpos[0],room.player_curpos[1],m) #mm: move made bool\n\t\t\tc=room.create_config_obj()\n\t\t\tif c not in explored: \n\t\t\t\texplored.add(c)\n\t\t\t\tch.create_child()\n\t\t\t\tch.child[i].data=c\n\t\t\t\tchild_q.append(ch.child[i])\n\t\t\t\ti+=1\n\t\t\tif m<5 and m%2==0 and mm:\n\t\t\t\tm-=1\n\t\t\t\troom.make_move(room.player_curpos[0],room.player_curpos[1],m)\n\t\t\telif m<5 and mm:\n\t\t\t\tm+=1\n\t\t\t\troom.make_move(room.player_curpos[0],room.player_curpos[1],m)\n\t\t\telif mm:\n\t\t\t\tm=m*-1\n\t\t\t\troom.make_move(room.player_curpos[0],room.player_curpos[1],m)\n\t\t\n\treturn conf_tree\t\n\n#-------------score calculator-----------------------------------------\n\nroot_data=()\n\ndef calc_score( tup1, tup2, swaps, cur_box, num_b ):\n\tfor j in range(num_b):\n\t\tt=j\n\t\tif(j!=cur_box and (tup1[j+t] != tup2[j+t] or \n\t\t tup1[j+t+1] != tup2[j+t+1])):\n\t\t\tif cur_box==-1:\n\t\t\t\tcur_box=j\n\t\t\telse:\n\t\t\t\tcur_box=j\n\t\t\t\tswaps+=1\t\t\n\tmanh_d=0\n\tfor j in range(num_b*2):\n\t\tmanh_d+= abs(tup1[j]-tup2[j])\n\tscore_gen = swaps*(manh_d)\n\treturn score_gen, cur_box\n\n#for depth first traversal of a tree\ndef tree_dfs(conf_tree, max_score, swaps, cur_box, parent_d, num_b, max_config=(0,0,0,0)):\n\tif parent_d==-1:\n\t\tpass\n\telse:\n\t\tscore_gen, cur_box = calc_score(root_data, conf_tree.data, swaps, cur_box, num_b)\n\t\tif score_gen>max_score:\n\t\t\tmax_score=score_gen\n\t\t\tmax_config=conf_tree.data\t\n\tfor i in range(len(conf_tree.child)):\n\t\tparent_d=conf_tree.data\n\t\tmax_score, max_config = tree_dfs(conf_tree.child[i], max_score, swaps, cur_box, parent_d, num_b, max_config)\n\t\n\treturn max_score, max_config\t\t\n\t\n#calculates score for one tree\ndef score_controller(conf_tree):\n\tif conf_tree.child==[]:\n\t\treturn 0, 0\n\tglobal root_data\n\troot_data=conf_tree.data\n\tnum_b=int((len(conf_tree.data)-2)/2)\n\tscore=0\n\tmax_config=()\n\tscore, max_config = tree_dfs(conf_tree, 0, 0, -1, -1, num_b)\n\treturn score, max_config\n\ndef level_generator(width, height, num_box):\n\tfor i in range(10):\n\t\trm=Room(height, width, num_box)\n\t\trm.topology_gen(int(1.5*(height+width)))\n\t\trm.position_configuration()\n\t\ttre=create_config_tree(rm)\t\t\t\n\t\tscore, max_config = score_controller(tre)\n\t\tif score>0:\n\t\t\trm.reset_position_configuration()\n\t\t\trm.set_position_configuration(max_config)\n\t\t\treturn rm.room\n\t\tdel rm\n\t\tdel tre\n", "repo_name": "krudutta/gym-sokoban", "sub_path": "gym_sokoban/envs/level_generator.py", "file_name": "level_generator.py", "file_ext": "py", "file_size_in_byte": 11622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.full", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 115, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 337, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 338, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 364, "usage_type": "call"}, {"api_name": "math.log", "line_number": 370, "usage_type": "call"}]} +{"seq_id": "70312410607", "text": "import asyncio\nimport warnings\nfrom typing import Optional\n\nfrom mitmproxy import controller, ctx, flow, log, master, options, platform\nfrom mitmproxy.flow import Error\nfrom mitmproxy.proxy import commands\nfrom mitmproxy.proxy import server\nfrom mitmproxy.utils import asyncio_utils, human\n\n\nclass AsyncReply(controller.Reply):\n \"\"\"\n controller.Reply.q.get() is blocking, which we definitely want to avoid in a coroutine.\n This stub adds a .done asyncio.Event() that can be used instead.\n \"\"\"\n\n def __init__(self, *args):\n self.done = asyncio.Event()\n self.loop = asyncio.get_event_loop()\n super().__init__(*args)\n\n def commit(self):\n super().commit()\n try:\n self.loop.call_soon_threadsafe(lambda: self.done.set())\n except RuntimeError: # pragma: no cover\n pass # event loop may already be closed.\n\n def kill(self, force=False): # pragma: no cover\n warnings.warn(\"reply.kill() is deprecated, set the error attribute instead.\", DeprecationWarning, stacklevel=2)\n self.obj.error = flow.Error(Error.KILLED_MESSAGE)\n\n\nclass ProxyConnectionHandler(server.StreamConnectionHandler):\n master: master.Master\n\n def __init__(self, master, r, w, options):\n self.master = master\n super().__init__(r, w, options)\n self.log_prefix = f\"{human.format_address(self.client.peername)}: \"\n\n async def handle_hook(self, hook: commands.StartHook) -> None:\n with self.timeout_watchdog.disarm():\n # We currently only support single-argument hooks.\n data, = hook.args()\n data.reply = AsyncReply(data)\n await self.master.addons.handle_lifecycle(hook)\n await data.reply.done.wait()\n data.reply = None\n\n def log(self, message: str, level: str = \"info\") -> None:\n x = log.LogEntry(self.log_prefix + message, level)\n x.reply = controller.DummyReply() # type: ignore\n asyncio_utils.create_task(\n self.master.addons.handle_lifecycle(log.AddLogHook(x)),\n name=\"ProxyConnectionHandler.log\"\n )\n\n\nclass Proxyserver:\n \"\"\"\n This addon runs the actual proxy server.\n \"\"\"\n server: Optional[asyncio.AbstractServer]\n listen_port: int\n master: master.Master\n options: options.Options\n is_running: bool\n\n def __init__(self):\n self._lock = asyncio.Lock()\n self.server = None\n self.is_running = False\n\n def load(self, loader):\n loader.add_option(\n \"connection_strategy\", str, \"lazy\",\n \"Determine when server connections should be established.\",\n choices=(\"eager\", \"lazy\")\n )\n loader.add_option(\n \"proxy_debug\", bool, False,\n \"Enable debug logs in the proxy core.\",\n )\n\n def running(self):\n self.master = ctx.master\n self.options = ctx.options\n self.is_running = True\n self.configure([\"listen_port\"])\n\n def configure(self, updated):\n if not self.is_running:\n return\n if \"mode\" in updated and ctx.options.mode == \"transparent\": # pragma: no cover\n platform.init_transparent_mode()\n if any(x in updated for x in [\"server\", \"listen_host\", \"listen_port\"]):\n asyncio.create_task(self.refresh_server())\n\n async def refresh_server(self):\n async with self._lock:\n if self.server:\n await self.shutdown_server()\n self.server = None\n if ctx.options.server:\n if not ctx.master.addons.get(\"nextlayer\"):\n ctx.log.warn(\"Warning: Running proxyserver without nextlayer addon!\")\n self.server = await asyncio.start_server(\n self.handle_connection,\n self.options.listen_host,\n self.options.listen_port,\n )\n addrs = {f\"http://{human.format_address(s.getsockname())}\" for s in self.server.sockets}\n ctx.log.info(f\"Proxy server listening at {' and '.join(addrs)}\")\n\n async def shutdown_server(self):\n ctx.log.info(\"Stopping server...\")\n self.server.close()\n await self.server.wait_closed()\n self.server = None\n\n async def handle_connection(self, r, w):\n asyncio_utils.set_task_debug_info(\n asyncio.current_task(),\n name=f\"Proxyserver.handle_connection\",\n client=w.get_extra_info('peername'),\n )\n handler = ProxyConnectionHandler(\n self.master,\n r,\n w,\n self.options\n )\n await handler.handle_client()\n", "repo_name": "The-Cracker-Technology/mitmproxy", "sub_path": "mitmproxy/addons/proxyserver.py", "file_name": "proxyserver.py", "file_ext": "py", "file_size_in_byte": 4670, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "mitmproxy.controller.Reply", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mitmproxy.controller", "line_number": 12, "usage_type": "name"}, {"api_name": "asyncio.Event", "line_number": 19, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 20, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 31, "usage_type": "call"}, {"api_name": "mitmproxy.flow.Error", "line_number": 32, "usage_type": "call"}, {"api_name": "mitmproxy.flow", "line_number": 32, "usage_type": "name"}, {"api_name": "mitmproxy.flow.Error.KILLED_MESSAGE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mitmproxy.proxy.server.StreamConnectionHandler", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mitmproxy.proxy.server", "line_number": 35, "usage_type": "name"}, {"api_name": "mitmproxy.master", "line_number": 36, "usage_type": "name"}, {"api_name": "mitmproxy.master.Master", "line_number": 36, "usage_type": "attribute"}, {"api_name": "mitmproxy.master", "line_number": 39, "usage_type": "name"}, {"api_name": "mitmproxy.options", "line_number": 40, "usage_type": "argument"}, {"api_name": "mitmproxy.utils.human.format_address", "line_number": 41, "usage_type": "call"}, {"api_name": "mitmproxy.utils.human", "line_number": 41, "usage_type": "name"}, {"api_name": "mitmproxy.proxy.commands.StartHook", "line_number": 43, "usage_type": "attribute"}, {"api_name": "mitmproxy.proxy.commands", "line_number": 43, "usage_type": "name"}, {"api_name": "mitmproxy.log.LogEntry", "line_number": 53, "usage_type": "call"}, {"api_name": "mitmproxy.log", "line_number": 53, "usage_type": "name"}, {"api_name": "mitmproxy.controller.DummyReply", "line_number": 54, "usage_type": "call"}, {"api_name": "mitmproxy.controller", "line_number": 54, "usage_type": "name"}, {"api_name": "mitmproxy.utils.asyncio_utils.create_task", "line_number": 55, "usage_type": "call"}, {"api_name": "mitmproxy.utils.asyncio_utils", "line_number": 55, "usage_type": "name"}, {"api_name": "mitmproxy.log.AddLogHook", "line_number": 56, "usage_type": "call"}, {"api_name": "mitmproxy.log", "line_number": 56, "usage_type": "name"}, {"api_name": "mitmproxy.proxy.server", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 65, "usage_type": "name"}, {"api_name": "asyncio.AbstractServer", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mitmproxy.master", "line_number": 67, "usage_type": "name"}, {"api_name": "mitmproxy.master.Master", "line_number": 67, "usage_type": "attribute"}, {"api_name": "mitmproxy.options", "line_number": 68, "usage_type": "name"}, {"api_name": "mitmproxy.options.Options", "line_number": 68, "usage_type": "attribute"}, {"api_name": "asyncio.Lock", "line_number": 72, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.master", "line_number": 88, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 88, "usage_type": "name"}, {"api_name": "mitmproxy.ctx.options", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 89, "usage_type": "name"}, {"api_name": "mitmproxy.ctx.options", "line_number": 96, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 96, "usage_type": "name"}, {"api_name": "mitmproxy.platform.init_transparent_mode", "line_number": 97, "usage_type": "call"}, {"api_name": "mitmproxy.platform", "line_number": 97, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 99, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.options", "line_number": 106, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 106, "usage_type": "name"}, {"api_name": "mitmproxy.ctx.master.addons.get", "line_number": 107, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.master", "line_number": 107, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 107, "usage_type": "name"}, {"api_name": "mitmproxy.ctx.log.warn", "line_number": 108, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.log", "line_number": 108, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 108, "usage_type": "name"}, {"api_name": "asyncio.start_server", "line_number": 109, "usage_type": "call"}, {"api_name": "mitmproxy.utils.human.format_address", "line_number": 114, "usage_type": "call"}, {"api_name": "mitmproxy.utils.human", "line_number": 114, "usage_type": "name"}, {"api_name": "mitmproxy.ctx.log.info", "line_number": 115, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.log", "line_number": 115, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 115, "usage_type": "name"}, {"api_name": "mitmproxy.ctx.log.info", "line_number": 118, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.log", "line_number": 118, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 118, "usage_type": "name"}, {"api_name": "mitmproxy.utils.asyncio_utils.set_task_debug_info", "line_number": 124, "usage_type": "call"}, {"api_name": "mitmproxy.utils.asyncio_utils", "line_number": 124, "usage_type": "name"}, {"api_name": "asyncio.current_task", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "39934400758", "text": "import os\nimport shutil\n\nfrom multiply_data_access import DataAccessComponent\nfrom vm_support.sym_linker import create_sym_links\nfrom vm_support.utils import set_permissions\n\n\ndef create_dir(dir):\n try:\n if not os.path.exists(dir):\n os.makedirs(dir)\n except Exception as e:\n print(e)\n print(dir)\n return\n\nfrom osgeo import osr\n\nwgs84_srs = osr.SpatialReference()\nwgs84_srs.ImportFromEPSG(4326)\n\ndef get_working_dir(dir_name: str) -> str:\n working_dir = f'/datastore/working_dirs/{dir_name}'\n working_dir = f'/home/jovyan/data/working_dirs/{dir_name}'\n if os.path.exists(working_dir):\n shutil.rmtree(working_dir)\n os.makedirs(working_dir)\n return working_dir\n\nname = '/tmp'\nworking_dir = get_working_dir(name)\n\nprint(working_dir)\n\n\ndef get_static_data(data_access_component: DataAccessComponent, roi: str, roi_grid: str, start_time: str,\n stop_time: str, emulation_directory: str, dem_directory: str):\n create_dir(emulation_directory)\n create_dir(dem_directory)\n\n print('Retrieving emulators ...')\n emu_urls = data_access_component.get_data_urls(roi, start_time, stop_time, 'ISO_MSI_A_EMU,ISO_MSI_B_EMU', roi_grid)\n set_permissions(emu_urls)\n create_sym_links(emu_urls, emulation_directory)\n\n print('Retrieving DEM ...')\n dem_urls = data_access_component.get_data_urls(roi, start_time, stop_time, 'Aster_DEM', roi_grid)\n set_permissions(dem_urls)\n create_sym_links(dem_urls, dem_directory)\n print('Done retrieving static data')\n\n# data_access_component = DataAccessComponent()\n\n# param_roi = 'POLYGON ((5.163574 52.382529, 5.163574 52.529813, 5.493164 52.529813, 5.493164 52.382529, 5.163574 52.382529))'\n# spatial_resolution = 20\n#\n# # define output grid\n# param_roi_grid = 'EPSG:4326'\n# param_destination_grid = 'EPSG:4326'\n#\n# param_start_time_as_string = '2008-04-16'\n# param_stop_time_as_string = '2008-04-20'\n# time_step = 5 # in days\n#\n# emulators_directory = '{}/emulators'.format(working_dir)\n# dem_directory = '{}/dem'.format(working_dir)\n#\n#\n# get_static_data(data_access_component=data_access_component, roi=param_roi,\n# start_time=param_start_time_as_string, stop_time=param_stop_time_as_string,\n# emulation_directory=emulators_directory, dem_directory=dem_directory, roi_grid=param_roi_grid)", "repo_name": "adeelaashraf/NaaVRE", "sub_path": "docker/MULTIPLY/test_multiply.py", "file_name": "test_multiply.py", "file_ext": "py", "file_size_in_byte": 2345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "2", "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.makedirs", "line_number": 12, "usage_type": "call"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 20, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 27, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 28, "usage_type": "call"}, {"api_name": "multiply_data_access.DataAccessComponent", "line_number": 37, "usage_type": "name"}, {"api_name": "vm_support.utils.set_permissions", "line_number": 44, "usage_type": "call"}, {"api_name": "vm_support.sym_linker.create_sym_links", "line_number": 45, "usage_type": "call"}, {"api_name": "vm_support.utils.set_permissions", "line_number": 49, "usage_type": "call"}, {"api_name": "vm_support.sym_linker.create_sym_links", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "2482006882", "text": "# orderedDict is a dict subclass which remembers the order in which the entries were done\n\n# od = collections.OrderedDict()\n# od['a'] = 2\n# od['b'] = 1\n# od['c'] = 3\n# print(od)\n#\n# OrderedDict({[(a,2), (b,1), (c, 3)]})\n\nfrom collections import OrderedDict\n\n\nd = OrderedDict()\nd[1] = 'a'\nd[2] = 'k'\nd[3] = 'h'\nd[4] = 'i'\nd[5] = 'l'\nprint(d)\n", "repo_name": "Akhileshbhagat1/All-prectice-of-python", "sub_path": "specialisedCOLLECTIONdataTYPES/orderedDict.py", "file_name": "orderedDict.py", "file_ext": "py", "file_size_in_byte": 341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.OrderedDict", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "35794455704", "text": "import operator\nimport readline\nimport colorama\nfrom colorama import Fore, Back, Style\nOPERATORS = {\n\t'+': operator.add,\n\t'-': operator.sub,\n\t'*': operator.mul,\n\t'/': operator.truediv,\n\t'^': operator.pow,\n\t'%': operator.mod,\n\t'~': operator.inv,\n\t'|': operator.abs,\n\t'n': operator.neg,\n\t'p': operator.pos,\n\t'<': operator.lt,\n\t'>': operator.gt,\n\t'=': operator.eq,\n}\ndef calculate(arg):\n\tstack = list()\n\tfor operand in arg.split():\n\t\ttry:\n\t\t\toperand = float(operand)\n\t\t\tstack.append(operand)\n\n\t\texcept:\n\t\t\targ2 = stack.pop()\n\t\t\targ1 = stack.pop()\n\t\t\tprint(Back.BLUE + Fore.YELLOW,arg2, arg1)\n\t\t\toperator_fn = OPERATORS[operand]\n\t\t\tprint(Back.YELLOW + Fore.BLUE + str(operator_fn))\n\t\t\tresult = operator_fn(arg1, arg2)\n\t\t\t\n\t\t\tstack.append(result)\n\treturn stack.pop()\ndef main():\n\tprint(Fore.RED + \"THIS\")\n\tprint(Fore.GREEN + \"IS\")\n\tprint(\"AN\")\n\tprint(Fore.BLUE + \"RPN\")\n\tprint(\"CALCULATOR\")\n\twhile True:\n\t\tresult = calculate(input('rpn calc> '))\n\t\tprint(Style.RESET_ALL)\n\t\tprint(\"Result:\", result)\n\nif __name__ == '__main__':\n\tmain()\n", "repo_name": "pbalex/c4cs-w17-rpn", "sub_path": "rpn.py", "file_name": "rpn.py", "file_ext": "py", "file_size_in_byte": 1029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "operator.add", "line_number": 6, "usage_type": "attribute"}, {"api_name": "operator.sub", "line_number": 7, "usage_type": "attribute"}, {"api_name": "operator.mul", "line_number": 8, "usage_type": "attribute"}, {"api_name": "operator.truediv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "operator.pow", "line_number": 10, "usage_type": "attribute"}, {"api_name": "operator.mod", "line_number": 11, "usage_type": "attribute"}, {"api_name": "operator.inv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "operator.abs", "line_number": 13, "usage_type": "attribute"}, {"api_name": "operator.neg", "line_number": 14, "usage_type": "attribute"}, {"api_name": "operator.pos", "line_number": 15, "usage_type": "attribute"}, {"api_name": "operator.lt", "line_number": 16, "usage_type": "attribute"}, {"api_name": "operator.gt", "line_number": 17, "usage_type": "attribute"}, {"api_name": "operator.eq", "line_number": 18, "usage_type": "attribute"}, {"api_name": "colorama.Back.BLUE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 30, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 30, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 30, "usage_type": "name"}, {"api_name": "colorama.Back.YELLOW", "line_number": 32, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 32, "usage_type": "name"}, {"api_name": "colorama.Fore.BLUE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 32, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 38, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 38, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 39, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 39, "usage_type": "name"}, {"api_name": "colorama.Fore.BLUE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 41, "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"}]} +{"seq_id": "44593405631", "text": "from __future__ import annotations\n\nimport traceback\nfrom datetime import datetime, timedelta, timezone\nfrom pathlib import Path\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport autochecklist\nfrom autochecklist import CancellationToken, Messenger, ProblemLevel, TaskStatus\nfrom common import Credential, CredentialStore, InputPolicy\nfrom requests import Response\nfrom vimeo import VimeoClient # type: ignore\n\nNEW_VIDEO_TIMEDELTA = timedelta(hours=3)\n\"\"\"\nMaximum time elapsed since today's video was uploaded.\n\"\"\"\n\nRETRY_DELAY = timedelta(seconds=60)\n\"\"\"\nNumber of seconds to wait between checks for the new video on Vimeo.\n\"\"\"\n\nCAPTIONS_TYPE = \"subtitles\"\n\nCAPTIONS_LANGUAGE = \"en-CA\"\n\nCAPTIONS_NAME = \"English (Canada)\"\n\n\nclass ReccVimeoClient:\n REQUEST_TIMEOUT_SECONDS = 10\n\n def __init__(\n self,\n messenger: Messenger,\n credential_store: CredentialStore,\n cancellation_token: Optional[CancellationToken],\n lazy_login: bool = False,\n ):\n self._messenger = messenger\n self._credential_store = credential_store\n\n if not lazy_login:\n self._client = self._login_with_retries(\n max_attempts=3, cancellation_token=cancellation_token\n )\n\n def get(\n self,\n url: str,\n params: Dict[str, Any],\n timeout: float = REQUEST_TIMEOUT_SECONDS,\n ) -> Response:\n return self._client.get(url, params=params, timeout=timeout) # type: ignore\n\n def post(\n self,\n url: str,\n data: Union[bytes, Dict[str, Any]],\n timeout: float = REQUEST_TIMEOUT_SECONDS,\n ) -> Response:\n return self._client.post(url, data=data, timeout=timeout) # type: ignore\n\n def put(\n self,\n url: str,\n data: Union[bytes, Dict[str, Any]],\n timeout: float = REQUEST_TIMEOUT_SECONDS,\n ) -> Response:\n return self._client.put(url, data=data, timeout=timeout) # type: ignore\n\n def patch(\n self,\n url: str,\n data: Union[bytes, Dict[str, Any]],\n timeout: float = REQUEST_TIMEOUT_SECONDS,\n ) -> Response:\n return self._client.patch(url, data=data, timeout=timeout) # type: ignore\n\n def _login_with_retries(\n self, max_attempts: int, cancellation_token: Optional[CancellationToken]\n ) -> VimeoClient:\n self._messenger.log_status(\n TaskStatus.RUNNING,\n f\"Connecting to the Vimeo API...\",\n )\n for attempt_num in range(1, max_attempts + 1):\n if cancellation_token is not None:\n cancellation_token.raise_if_cancelled()\n credentials = self._credential_store.get_multiple(\n prompt=\"Enter the Vimeo credentials.\",\n credentials=[\n Credential.VIMEO_ACCESS_TOKEN,\n Credential.VIMEO_CLIENT_ID,\n Credential.VIMEO_CLIENT_SECRET,\n ],\n request_input=(\n InputPolicy.ALWAYS if attempt_num > 1 else InputPolicy.AS_REQUIRED\n ),\n )\n access_token = credentials[Credential.VIMEO_ACCESS_TOKEN]\n client_id = credentials[Credential.VIMEO_CLIENT_ID]\n client_secret = credentials[Credential.VIMEO_CLIENT_SECRET]\n client = VimeoClient(\n token=access_token,\n key=client_id,\n secret=client_secret,\n )\n response: Response = client.get(\"/tutorial\") # type: ignore\n if response.status_code == 200:\n self._messenger.log_status(\n TaskStatus.RUNNING,\n f\"Successfully connected to the Vimeo API.\",\n )\n return client\n else:\n self._messenger.log_debug(\n f\"Vimeo client test request failed (attempt {attempt_num}/{max_attempts}). Response had HTTP status {response.status_code}.\",\n )\n raise RuntimeError(\n f\"Failed to connect to the Vimeo API ({max_attempts} attempts)\"\n )\n\n\ndef get_video_data(\n messenger: Messenger, client: ReccVimeoClient, cancellation_token: CancellationToken\n) -> Tuple[str, str]:\n # Wait for the video to be posted\n while True:\n response = client.get(\n \"/me/videos\",\n params={\n \"fields\": \"created_time,uri,metadata.connections.texttracks.uri\",\n \"per_page\": 1,\n \"sort\": \"date\",\n \"direction\": \"desc\",\n },\n )\n\n if response.status_code != 200:\n raise RuntimeError(\n f\"Vimeo client failed to access GET /videos (HTTP status {response.status_code}).\"\n )\n\n response_body = response.json()\n response_data = response.json()[\"data\"][0]\n if response_body[\"total\"] < 1 or (\n datetime.now(timezone.utc)\n - datetime.fromisoformat(response_data[\"created_time\"])\n > NEW_VIDEO_TIMEDELTA\n ):\n messenger.log_status(\n TaskStatus.RUNNING,\n f\"Video not yet found on Vimeo as of {datetime.now().strftime('%H:%M:%S')}. Retrying in {int(RETRY_DELAY.total_seconds())} seconds.\",\n )\n autochecklist.sleep_attentively(RETRY_DELAY, cancellation_token)\n else:\n messenger.log_status(\n TaskStatus.RUNNING,\n f\"Found newly-uploaded Vimeo video at URI '{response_data['uri']}'.\",\n )\n break\n\n video_uri = response_data[\"uri\"]\n texttrack_uri = response_data[\"metadata\"][\"connections\"][\"texttracks\"][\"uri\"]\n return (video_uri, texttrack_uri)\n\n\ndef disable_automatic_captions(\n texttracks_uri: str,\n client: ReccVimeoClient,\n messenger: Messenger,\n cancellation_token: CancellationToken,\n):\n # TODO: This isn't removing the autogenerated captions! Could it be that Vimeo only adds them after a certain amount of time?\n response = client.get(texttracks_uri, params={\"fields\": \"uri,name\"})\n\n if response.status_code != 200:\n raise RuntimeError(\n f\"The Vimeo client failed to get the text tracks for today's video. GET {texttracks_uri} returned HTTP status {response.status_code}.\"\n )\n\n response_data = response.json()[\"data\"]\n for texttrack in response_data:\n cancellation_token.raise_if_cancelled()\n # If we wanted to be sure we weren't disabling captions we want to\n # keep, we could check that the language field contains \"autogen.\"\n # That probably isn't necessary as long as this task is performed\n # before our captions are uploaded and there are never existing\n # captions we want to keep.\n try:\n patch_uri = texttrack[\"uri\"]\n patch_response = client.patch(patch_uri, data={\"active\": False})\n\n if patch_response.status_code != 200:\n raise RuntimeError(\n f\"PATCH {patch_uri} returned HTTP status {patch_response.status_code}.\"\n )\n # Catch exceptions instead of just moving this log statement into the\n # if statement so that, if the client itself throws an exception, it\n # gets caught.\n except Exception as e:\n messenger.log_problem(\n ProblemLevel.WARN,\n f\"The Vimeo client failed to disable text track '{texttrack['name']}' at '{texttrack['uri']}' due to an error: {e}\",\n stacktrace=traceback.format_exc(),\n )\n\n\ndef rename_video(video_uri: str, new_title: str, client: ReccVimeoClient):\n response = client.patch(\n video_uri,\n data={\"name\": new_title},\n )\n if response.status_code != 200:\n raise RuntimeError(\n f\"Vimeo client failed to rename video (HTTP status {response.status_code}).\"\n )\n\n\ndef upload_captions_to_vimeo(\n final_captions_file: Path,\n texttrack_uri: str,\n messenger: Messenger,\n client: ReccVimeoClient,\n):\n # See https://developer.vimeo.com/api/upload/texttracks\n\n # (1) Get text track URI: done in get_vimeo_video_data()\n\n # (2) Get upload link for text track\n (upload_link, uri) = _get_vimeo_texttrack_upload_link(texttrack_uri, client)\n messenger.log_status(\n TaskStatus.RUNNING,\n f\"Found the text track upload link and URI for the Vimeo video.\",\n )\n\n # (3) Upload text track\n _upload_texttrack(final_captions_file, upload_link, client)\n messenger.log_status(\n TaskStatus.RUNNING, \"Uploaded the text track for the Vimeo video.\"\n )\n\n # (4) Mark text track as active\n _activate_texttrack(uri, client)\n messenger.log_status(\n TaskStatus.RUNNING,\n \"Marked the newly-uploaded text track for the Vimeo video as active.\",\n )\n\n\ndef _get_vimeo_texttrack_upload_link(\n texttrack_uri: str, client: ReccVimeoClient\n) -> Tuple[str, str]:\n response = client.post(\n texttrack_uri,\n data={\n \"type\": CAPTIONS_TYPE,\n \"language\": CAPTIONS_LANGUAGE,\n \"name\": CAPTIONS_NAME,\n },\n )\n\n status_code = response.status_code\n if status_code != 201:\n raise RuntimeError(\n f\"Failed to get text track upload link for Vimeo video (HTTP status {status_code}).\"\n )\n\n response_body = response.json()\n return (response_body[\"link\"], response_body[\"uri\"])\n\n\ndef _upload_texttrack(\n final_captions_file: Path,\n upload_link: str,\n client: ReccVimeoClient,\n):\n # Read the captions from final.vtt\n # If you don't set the encoding to UTF-8, then Unicode characters get mangled\n with open(final_captions_file, \"r\", encoding=\"utf-8\") as f:\n vtt = f.read()\n\n # If you don't encode the VTT file as UTF-8, then for some reason some characters get dropped at the end of the\n # file (if there are Unicode characters)\n response = client.put(upload_link, data=vtt.encode(\"utf-8\"))\n\n status_code = response.status_code\n if status_code != 200:\n raise RuntimeError(\n f\"Failed to upload text track for Vimeo video (HTTP status {status_code})\"\n )\n\n\ndef _activate_texttrack(texttrack_uri: str, client: ReccVimeoClient):\n response = client.patch(texttrack_uri, data={\"active\": True})\n\n status_code = response.status_code\n if status_code != 200:\n raise RuntimeError(\n f\"Failed to mark text track at link '{texttrack_uri}' as active (HTTP status {status_code}).\"\n )\n", "repo_name": "recc-tech/tech", "sub_path": "scripts/mcr_teardown/vimeo.py", "file_name": "vimeo.py", "file_ext": "py", "file_size_in_byte": 10523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "datetime.timedelta", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "autochecklist.Messenger", "line_number": 36, "usage_type": "name"}, {"api_name": "common.CredentialStore", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "autochecklist.CancellationToken", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 52, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 60, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 68, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 76, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 82, "usage_type": "name"}, {"api_name": "autochecklist.CancellationToken", "line_number": 82, "usage_type": "name"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 85, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 85, "usage_type": "name"}, {"api_name": "common.Credential.VIMEO_ACCESS_TOKEN", "line_number": 94, "usage_type": "attribute"}, {"api_name": "common.Credential", "line_number": 94, "usage_type": "name"}, {"api_name": "common.Credential.VIMEO_CLIENT_ID", "line_number": 95, "usage_type": "attribute"}, {"api_name": "common.Credential", "line_number": 95, "usage_type": "name"}, {"api_name": "common.Credential.VIMEO_CLIENT_SECRET", "line_number": 96, "usage_type": "attribute"}, {"api_name": "common.Credential", "line_number": 96, "usage_type": "name"}, {"api_name": "common.InputPolicy.ALWAYS", "line_number": 99, "usage_type": "attribute"}, {"api_name": "common.InputPolicy", "line_number": 99, "usage_type": "name"}, {"api_name": "common.InputPolicy.AS_REQUIRED", "line_number": 99, "usage_type": "attribute"}, {"api_name": "common.Credential.VIMEO_ACCESS_TOKEN", "line_number": 102, "usage_type": "attribute"}, {"api_name": "common.Credential", "line_number": 102, "usage_type": "name"}, {"api_name": "common.Credential.VIMEO_CLIENT_ID", "line_number": 103, "usage_type": "attribute"}, {"api_name": "common.Credential", "line_number": 103, "usage_type": "name"}, {"api_name": "common.Credential.VIMEO_CLIENT_SECRET", "line_number": 104, "usage_type": "attribute"}, {"api_name": "common.Credential", "line_number": 104, "usage_type": "name"}, {"api_name": "vimeo.VimeoClient", "line_number": 105, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 110, "usage_type": "name"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 113, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 113, "usage_type": "name"}, {"api_name": "vimeo.VimeoClient", "line_number": 83, "usage_type": "name"}, {"api_name": "autochecklist.Messenger", "line_number": 127, "usage_type": "name"}, {"api_name": "autochecklist.CancellationToken", "line_number": 127, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 149, "usage_type": "name"}, {"api_name": "datetime.timezone.utc", "line_number": 149, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 149, "usage_type": "name"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "name"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 154, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 155, "usage_type": "name"}, {"api_name": "autochecklist.sleep_attentively", "line_number": 157, "usage_type": "call"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 160, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 160, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 128, "usage_type": "name"}, {"api_name": "autochecklist.Messenger", "line_number": 173, "usage_type": "name"}, {"api_name": "autochecklist.CancellationToken", "line_number": 174, "usage_type": "name"}, {"api_name": "autochecklist.ProblemLevel.WARN", "line_number": 205, "usage_type": "attribute"}, {"api_name": "autochecklist.ProblemLevel", "line_number": 205, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 207, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 223, "usage_type": "name"}, {"api_name": "autochecklist.Messenger", "line_number": 225, "usage_type": "name"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 235, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 235, "usage_type": "name"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 242, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 242, "usage_type": "name"}, {"api_name": "autochecklist.TaskStatus.RUNNING", "line_number": 248, "usage_type": "attribute"}, {"api_name": "autochecklist.TaskStatus", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 255, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 276, "usage_type": "name"}]} +{"seq_id": "37432087347", "text": "import PyPDF2\nfrom PyPDF2 import utils\n\n\n# OUTPUT_DIR = \"pdf-paranoia-encrypted\"\n# filename = \"meetingminutes.pdf\"\n\n# pdf = open(filename, \"rb\")\n# pdf_reader = PyPDF2.PdfFileReader(pdf)\n# pdf_writer = PyPDF2.PdfFileWriter()\n\n# for page_num in range(pdf_reader.numPages):\n# page = pdf_reader.getPage(page_num)\n# pdf_writer.addPage(page)\n\n# pdf_writer.encrypt(\"swordfish1\")\n# output_pdf = open(f\"{OUTPUT_DIR}/{filename[:-4]}_swordfish1.pdf\", \"wb\")\n# pdf_writer.write(output_pdf)\n# output_pdf.close()\n# pdf.close()\n\n\npdf = open(\"meetingminutes_swordfish1.pdf\", \"rb\")\npdf_reader = PyPDF2.PdfFileReader(pdf)\n\npdf_writer = PyPDF2.PdfFileWriter()\n\ntry:\n check = pdf_reader.decrypt(\"swordfish\")\n if check == 0:\n print(\"file not decrypted\")\nexcept Exception as e:\n print(e)\n\n\nfor page_num in range(pdf_reader.numPages):\n page = pdf_reader.getPage(page_num)\n\noutput_pdf = open(\"meetingminutes_decrypted.pdf\", \"wb\")\npdf_writer.write(\"output.pdf\")\noutput_pdf.close()\npdf.close()\n\n\nx = 5\n\nif x > 10:\n print(x)\nif x > 12:\n print(x + 1)\nelse:\n print(\"x is not a numbner\")\n", "repo_name": "Sylruilshu/automate-the-boring-stuff", "sub_path": "chapter-15/practice-projects/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1093, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "PyPDF2.PdfFileReader", "line_number": 24, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileWriter", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "22235943872", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 21 21:17:55 2021\r\n\r\n@author: divyam\r\n\"\"\"\r\n\r\nimport cv2 as cv\r\nimport numpy as np\r\nimport random\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nimport copy\r\n\r\nimg1 = cv.imread(\"im0.png\")\r\nimg2 = cv.imread(\"im1.png\")\r\nimg1 = cv.resize(img1, (600,400))\r\nimg2 = cv.resize(img2, (600,400))\r\n\r\n#cv.imshow(\"image\",cap1)\r\n#cv.waitKey(0)\r\n#cv.destroyAllWindows\r\n\r\n############### feature matching ###############\r\norb = cv.ORB_create()\r\nkp1, des1 = orb.detectAndCompute(img1, None)\r\nkp2, des2 = orb.detectAndCompute(img2, None)\r\n\r\n# Brute Force Matching\r\nbf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)\r\nmatches = bf.match(des1, des2)\r\nmatches = sorted(matches, key = lambda x:x.distance)\r\n\r\nsize = len(matches)\r\nprint(size)\r\nmatch = matches[:int(size/10)]\r\n\r\n# Initialize lists\r\nlist_kp1 = []\r\nlist_kp2 = []\r\n\r\nfor mat in match:\r\n\r\n # Get the matching keypoints for each of the images\r\n img1_idx = mat.queryIdx\r\n img2_idx = mat.trainIdx\r\n\r\n # x - columns\r\n # y - rows\r\n # Get the coordinates\r\n (x1, y1) = kp1[img1_idx].pt\r\n (x2, y2) = kp2[img2_idx].pt\r\n\r\n # Append to each list\r\n list_kp1.append((x1, y1))\r\n list_kp2.append((x2, y2))\r\n\r\n#matching_result = cv.drawMatches(img1, kp1, img2, kp2, match, None, flags=2)\r\n#cv.imshow(\"matching results\", matching_result)\r\n#cv.waitKey(0)\r\n#cv.destroyAllWindows\r\n##################################################\r\n\r\n########### getting feature index ################\r\ndef get_8_rand(): # get 8 random index to fit in Af = 0\r\n key_X1 = []\r\n \r\n rand_ind_list = []\r\n for i in range(8):\r\n# if i not in rand_ind_list:\r\n rand_ind_list.append(random.randint(0,int(size/10)-1))\r\n \r\n for ind in rand_ind_list:\r\n key_X1.append(ind)\r\n return key_X1\r\n\r\ndef get_index(key_x):\r\n index1 = []\r\n index2 = []\r\n for i in key_x:\r\n index1.append((int(list_kp1[i][0]),int(list_kp1[i][1])))\r\n index2.append((int(list_kp2[i][0]),int(list_kp2[i][1])))\r\n return index1, index2\r\n###################################################\r\n \r\n########## 8 point algorithms ##################\r\n \r\n # -----------------A_matrix = [x2*x1, x2*y1, x2, y2*x1, y2*y1, y2, x1, y1, 1]\r\n#A_matrix = []\r\n#key_x = get_8_rand()\r\n#for iter in key_x:\r\n# A_matrix.append([list_kp2[iter][0]*list_kp1[iter][0],list_kp2[iter][0]*list_kp1[iter][1],list_kp2[iter][0],list_kp2[iter][1]*list_kp1[iter][0],list_kp2[iter][1]*list_kp1[iter][1],list_kp2[iter][1],list_kp1[iter][0],list_kp1[iter][1],1])\r\n#U,sig,V_T = np.linalg.svd(A_matrix)\r\n#K_H = V_T[-1,:]/V_T[-1,-1]\r\n#F = K_H.reshape(3,3)\r\n\r\n\r\n################## RANSAC ###############\r\ninitial_F = 10000\r\nF_mat = None\r\nfor i in range(2000):\r\n A_matrix = []\r\n key_x = get_8_rand()\r\n for iter in key_x: # making A matrix for Af = 0\r\n A_matrix.append([list_kp2[iter][0]*list_kp1[iter][0],list_kp2[iter][0]*list_kp1[iter][1],list_kp2[iter][0],list_kp2[iter][1]*list_kp1[iter][0],list_kp2[iter][1]*list_kp1[iter][1],list_kp2[iter][1],list_kp1[iter][0],list_kp1[iter][1],1])\r\n U,sig,V_T = np.linalg.svd(A_matrix)\r\n K_H = V_T[-1,:]/V_T[-1,-1]\r\n F = K_H.reshape(3,3)\r\n list_kp1_new = list(list_kp1[0])\r\n list_kp2_new = list(list_kp2[0])\r\n list_kp1_new.append(1)\r\n list_kp2_new.append(1)\r\n X1 = np.array(list_kp1_new)\r\n X2 = np.array(list_kp2_new)\r\n ans_F = np.matmul(np.transpose(X2),np.matmul(F,X1)) # Fundamental matrix equation xT*F*x = 0\r\n if ans_F < 0:\r\n continue\r\n if abs(ans_F) < initial_F:\r\n initial_F = abs(ans_F)\r\n index1, index2 = get_index(key_x)\r\n F_mat = F\r\n\r\n#### Enforcing Ransac #####\r\nU_f , sig_f, V_t_f = np.linalg.svd(F_mat)\r\nsig_f[-1] = 0\r\nF_mat = np.matmul(U_f,np.matmul(np.diag(sig_f),V_t_f))\r\n###########################\r\n\r\n#F_mat = np.array([[ 2.56502805e-19,2.54139887e-17,-5.07574184e-15],[-4.80711816e-31,-2.43696301e-18,1.00000000e+00],\r\n# [9.53904157e-29,-1.00000000e+00,7.53666453e-17]]) ## seeded\r\nprint(initial_F)\r\nprint(index1)\r\nindex1 = np.array(index1)\r\nindex2 = np.array(index2)\r\n \r\n################## Esential Matrix #####################\r\n## dataset 1 \r\nK1 = np.array([[5299.313, 0, 1263.818], [0, 5299.313, 977.763], [0, 0, 1]])\r\nK2 = np.array([[5299.313, 0, 1438.004], [0, 5299.313, 977.763], [0, 0, 1]])\r\n\r\n## dataset 2 \r\n#K1 = np.array([[4396.869, 0, 1353.072], [0, 4396.869, 989.702], [0, 0, 1]])\r\n#K2 = np.array([[4396.869, 0, 1538.86], [0, 4396.869, 989.702], [0, 0, 1]])\r\n\r\n## dataset 3 \r\n#K1 = np.array([[5806.559, 0, 1429.219], [0, 5806.559 , 993.403], [0, 0, 1]])\r\n#K2 = np.array([[5806.559, 0, 1538.86], [0, 5806.559, 993.403], [0, 0, 1]])\r\n\r\nE = np.matmul(np.transpose(K2),np.matmul(F_mat,K1))\r\n\r\n\r\n############ Estimating pose using Esential Matrix ##########\r\ndef get_pose(E, K): ## E is essential matrix and K is intrinsic parameter\r\n print(\"--Getting rotational and translational matrices\")\r\n U, S, VT = np.linalg.svd(E)\r\n W = np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]])\r\n R = np.matmul(np.matmul(U, np.linalg.inv(W)), VT)\r\n T = np.matmul(np.matmul(np.matmul(U, W), S), U.T)\r\n print(\"Rotation\\n\", R)\r\n print(\"Translation\\n\", T)\r\n return R, T\r\n\r\nprint(\"Pose of Camera one \")\r\nprint(\"########\")\r\nR, T = get_pose(E,K1)\r\nprint(\"Pose of Camera two \")\r\nprint(\"########\")\r\nR, T = get_pose(E,K2)\r\n\r\n############ Rectification ####################\r\nh1, w1, c = img1.shape\r\nh2, w2, c = img2.shape\r\n_, H1, H2 = cv.stereoRectifyUncalibrated(np.float32(index1), np.float32(index2), F_mat, imgSize=(w1, h1))\r\nprint(\"H1/n \", H1)\r\nprint (\"H2/n \", H2)\r\n\r\nimg1_rectified = cv.warpPerspective(img1, H1, (w1, h1))\r\nimg2_rectified = cv.warpPerspective(img2, H2, (w2, h2))\r\nimg_rec1 = copy.deepcopy(img1_rectified)\r\nimg_rec2 = copy.deepcopy(img2_rectified)\r\n\r\nX_1 = np.linalg.inv(H2) \r\nF_new = np.matmul(np.transpose(X_1),np.matmul(F_mat,np.linalg.inv(H1)))\r\nprint(\"Rectified F/n :\",F_new)\r\n############ fixing initial points #################\r\n\r\nnew_kp1 = []\r\nnew_kp2 = []\r\n\r\nfor i in range(len(list_kp1)): # transforming keypoints to new warped image\r\n X1 = np.array([list_kp1[i][0],list_kp1[i][1],1])\r\n X2 = np.array([list_kp2[i][0],list_kp2[i][1],1])\r\n new_pt1 = np.dot(H1,np.transpose(X1))\r\n new_pt2 = np.dot(H2,np.transpose(X2))\r\n new_kp1.append((int(new_pt1[0]/new_pt1[2]),int(new_pt1[1]/new_pt1[2])))\r\n new_kp2.append((int(new_pt2[0]/new_pt2[2]),int(new_pt2[1]/new_pt2[2])))\r\n\r\ncount = 0 \r\nfor mat in match: # setting up new keypoints according to feature transform\r\n\r\n # Get the matching keypoints for each of the images\r\n img1_idx = mat.queryIdx\r\n img2_idx = mat.trainIdx\r\n\r\n # x - columns\r\n # y - rows\r\n # Get the coordinates\r\n kp1[img1_idx].pt = new_kp1[count]\r\n kp2[img2_idx].pt = new_kp2[count]\r\n\r\n count += 1\r\n \r\n\r\n#################### Epipolar lines ######################\r\ndef drawlines(img1,img2,lines,pts1,pts2): # for drawing epipolar lines\r\n ''' img1 - image on which we draw the epilines for the points in img2\r\n lines - corresponding epilines '''\r\n r,c, h = img1.shape\r\n for r,pt1,pt2 in zip(lines,pts1,pts2):\r\n color = tuple(np.random.randint(100,200,3).tolist())\r\n x0,y0 = map(int, [0, -r[2]/r[1] ])\r\n x1,y1 = map(int, [c, -(r[2]+r[0]*c)/r[1] ])\r\n img1 = cv.line(img1, (x0,y0), (x1,y1), (0,0,255),2)\r\n img1 = cv.circle(img1,tuple(pt1),5,color,-1)\r\n img2 = cv.circle(img2,tuple(pt2),5,color,-1)\r\n return img1,img2\r\n\r\nnew_kp2 = np.array(new_kp2)\r\nnew_kp1 = np.array(new_kp1)\r\nlines1 = cv.computeCorrespondEpilines(new_kp2.reshape(-1,1,2), 2,F_new)\r\nlines2 = cv.computeCorrespondEpilines(new_kp1.reshape(-1,1,2), 2,F_new)\r\nlines1 = lines1.reshape(-1,3)\r\nlines2 = lines2.reshape(-1,3)\r\nimg3,img4 = drawlines(img1_rectified,img2_rectified,lines1,new_kp1,new_kp2)\r\nimg5,img6 = drawlines(img2_rectified,img1_rectified,lines2,new_kp2,new_kp1)\r\ncv.imshow(\"image\", img5)\r\ncv.imshow(\"image2\", img6)\r\ncv.waitKey(0)\r\ncv.destroyAllWindows\r\n \r\n################ correspondance (SSD) ########################\r\nwindow = (7,7)\r\n\r\n\r\nd = 50\r\nimg_gray1 = cv.cvtColor(img_rec1, cv.COLOR_BGR2GRAY)\r\nimg_gray2 = cv.cvtColor(img_rec2, cv.COLOR_BGR2GRAY)\r\n\r\ndef check_diff(img1,img2,x,y,count):\r\n sum = 0\r\n for i in range(window[0]):\r\n for j in range(window[1]):\r\n diff = (img1[x+i][y+j] - img2[x+i][y+j+count])**2\r\n sum = sum + diff\r\n# min_diff = min(diff_list)\r\n# ind = diff_list.index(min_diff)\r\n# index = (int(str(ind)[0]),ind%10)\r\n return sum\r\n#\r\ndef diff_disp(img1,img2,x,y,count):\r\n \r\n winblock1 = img1[x:x + window[0],y:x + window[1]]\r\n winblock2 = img2[x:x + window[0],y + count:y + count + window[1]]\r\n diff = (winblock2.sum() - winblock1.sum())\r\n #print(np.sum(winblock1))\r\n return diff\r\n\r\nh, w = img_gray1.shape\r\ndisparity_image = np.zeros(img_gray1.shape)\r\nh = h - 10\r\nw = w - 50 \r\nt = math.ceil(window[0]/2)\r\nfor i in range(h):\r\n for j in range(w):\r\n diff_list = []\r\n for k in range(50):\r\n sum = diff_disp(img_gray1,img_gray2,i,j,k)\r\n diff_list.append(sum)\r\n disparity_image[i+t][j+t] = diff_list.index(min(diff_list))\r\n\r\nplt.imshow(disparity_image)\r\ncolormap = plt.get_cmap('jet')\r\nheatmap = (colormap(disparity_image) * 2**16).astype(np.uint16)[:,:,:3]\r\nheatmap = cv.cvtColor(heatmap, cv.COLOR_RGB2BGR)\r\n\r\n#########################################################################\r\n\r\n###### inbuilt disparity function\r\n#win_size = 2\r\n#min_disp = -4\r\n#max_disp = 9\r\n#num_disp = max_disp - min_disp # Needs to be divisible by 16\r\n#stereo = cv.StereoSGBM_create(\r\n# minDisparity=min_disp,\r\n# numDisparities=num_disp,\r\n# blockSize=5,\r\n# uniquenessRatio=5,\r\n# speckleWindowSize=5,\r\n# speckleRange=5,\r\n# disp12MaxDiff=2,\r\n# P1=8 * 3 * win_size ** 2,\r\n# P2=32 * 3 * win_size ** 2,\r\n#)\r\n#disparity_SGBM = stereo.compute(img_rec1, img_rec2)\r\n#plt.imshow(disparity_SGBM, \"gray\")\r\n#plt.colorbar()\r\n#plt.show()\r\n########################################################################\r\n\r\n###################### Depth Map #######################################\r\ndef normalize(matrix):\r\n maxvalue = matrix.max() \r\n minvalue = matrix.min() \r\n span = maxvalue - minvalue\r\n \r\n matrix = (matrix - minvalue)/span\r\n matrix = matrix*255 \r\n matrix = matrix.astype(np.uint8)\r\n \r\n return matrix\r\n\r\ndisp = disparity_image.astype(np.float32)\r\ndisp[disp == 0] = 0.01\r\ndepth = 1./(5*np.copy(disp)) # 1/5 because image was resized by 0.2\r\nB = 177.288\r\nf = 5299.313\r\ndepth = depth*B*f\r\ndepth = normalize(depth)\r\ndepthmap = cv.applyColorMap(depth, cv.COLORMAP_JET)\r\n\r\n########################################################################\r\n\r\n#################### Results ##########################################\r\nplt.imshow(disparity_image)\r\nmatching_result = cv.drawMatches(img1_rectified, kp1, img2_rectified, kp2, match, None, flags=2)\r\ncv.imshow(\"matching results\", matching_result)\r\ncv.imshow(\"disparity_image\", disparity_image)\r\ncv.imshow('heatmap', heatmap)\r\ncv.imshow('depthmap', depthmap)\r\ncv.imshow('depth', depth)\r\n#cv.imshow(\"gray\", img_gray1)\r\n#cv.imshow(\"image\", img5)\r\n#cv.imshow(\"image2\", img6)\r\ncv.waitKey(0)\r\ncv.destroyAllWindows\r\n\r\n", "repo_name": "divi9626/Stereo-Vision", "sub_path": "stereo_depth.py", "file_name": "stereo_depth.py", "file_ext": "py", "file_size_in_byte": 11451, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.imread", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.ORB_create", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.BFMatcher", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.NORM_HAMMING", "line_number": 30, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.stereoRectifyUncalibrated", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 177, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 178, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 179, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 182, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 220, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 223, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 224, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "cv2.computeCorrespondEpilines", "line_number": 230, "usage_type": "call"}, {"api_name": "cv2.computeCorrespondEpilines", "line_number": 231, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 236, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 237, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 238, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 239, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 246, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 246, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 247, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 247, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 269, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "numpy.uint16", "line_number": 283, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 284, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 284, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 318, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 322, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 324, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 329, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 329, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "cv2.drawMatches", "line_number": 335, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 336, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 337, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 338, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 339, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 340, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 344, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 345, "usage_type": "attribute"}]} +{"seq_id": "74906136685", "text": "from torch import nn\nfrom utils_0809 import *\nimport torch.nn.functional as F\nimport torchvision\nimport numpy as np\nfrom torchvision.ops import nms\nfrom torchvision.ops import RoIPool\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef truncated_vgg16():\n # the 30th layer of features is relu of conv5_3\n \n \n model = torchvision.models.vgg16(pretrained=True)\n \n features = list(model.features)[:30]\n classifier = model.classifier\n \n classifier = list(classifier)\n del classifier[6]\n \n del classifier[5]\n del classifier[2]\n \n classifier = nn.Sequential(*classifier)\n\n # freeze top4 conv\n for layer in features[:10]:\n for p in layer.parameters():\n p.requires_grad = False\n \n return nn.Sequential(*features), classifier\n\ndef generate_anchor_base(base_size=16, ratios=[0.5, 1, 2],\n anchor_scales=[8, 16, 32]):\n \n py = base_size / 2.\n px = base_size / 2.\n\n anchor_base = np.zeros((len(ratios) * len(anchor_scales), 4),\n dtype=np.float32)\n \n for i in range(len(ratios)):\n for j in range(len(anchor_scales)):\n h = base_size * anchor_scales[j] * np.sqrt(ratios[i])\n w = base_size * anchor_scales[j] * np.sqrt(1. / ratios[i])\n \n index = i * len(anchor_scales) + j\n anchor_base[index, 1] = py - h / 2.\n anchor_base[index, 0] = px - w / 2.\n anchor_base[index, 3] = py + h / 2.\n anchor_base[index, 2] = px + w / 2.\n \n return anchor_base\n\ndef _enumerate_shifted_anchor(anchor_base, feat_stride, height, width):\n \n import numpy as xp\n \n shift_y = xp.arange(0, height * feat_stride, feat_stride)\n shift_x = xp.arange(0, width * feat_stride, feat_stride)\n shift_x, shift_y = xp.meshgrid(shift_x, shift_y)\n \n shift = xp.stack((shift_x.ravel(), shift_y.ravel(),\n shift_x.ravel(), shift_y.ravel()), axis=1)\n \n A = anchor_base.shape[0]\n K = shift.shape[0]\n anchor = anchor_base.reshape((1, A, 4)) + \\\n shift.reshape((1, K, 4)).transpose((1, 0, 2))\n anchor = anchor.reshape((K * A, 4)).astype(np.float32)\n return anchor\nclass Anchor_Creator(object):\n def __init__(self,feat_width,feat_height):\n super(Anchor_Creator,self).__init__()\n self.width=feat_width\n self.height=feat_height\n self.anchor_base=generate_anchor_base()\n self.anchor_boxes_cxcy=self.make_anchor_boxes()\n def make_anchor_boxes(self):\n \n anchors = _enumerate_shifted_anchor(\n np.array(self.anchor_base),\n 16, self.height,self.width)\n\n \n anchors= torch.FloatTensor(anchors).to(device)\n\n \n anchors_cxcy=xy_to_cxcy(anchors)\n \n return anchors_cxcy#(W*H*K,4)\n\nclass Anchor_Target_Creator(object):\n def __init__(self,gt_boxes_xy,anchor_boxes_cxcy,img_width,img_height):\n super(Anchor_Target_Creator,self).__init__()\n self.gt_boxes_xy=gt_boxes_xy\n self.anchor_cxcy=anchor_boxes_cxcy\n \n self.anchor_xy=cxcy_to_xy(anchor_boxes_cxcy)\n \n \n self.gt_loc,self.gt_cls=self.Target_Creator(img_width,img_height)\n \n def Target_Creator(self,img_width,img_height):\n \n batch_size=1\n for i in range(batch_size):\n \n \n condition=(torch.Tensor([0,0,img_width,img_height])*torch.ones((self.anchor_xy.size(0),4))).to(device)\n\n min_cond=self.anchor_xy[:,:2]>=condition[:,:2]\n\n max_cond=((self.anchor_xy[:,2:])<=(condition[:,2:]))\n\n anchor_index=torch.cat([min_cond,max_cond],1)\n anchor_index=anchor_index.cpu().numpy()\n \n anchor_index=np.all(anchor_index,axis=1)\n \n gt_locs = torch.zeros((batch_size, self.anchor_xy.size(0), 4), dtype=torch.float).to(device) # (N, n_anchors, 4)\n gt_labels = torch.zeros((batch_size, self.anchor_xy.size(0)), dtype=torch.long).to(device) # (N, 8732)\n \n overlap=find_jaccard_overlap(self.gt_boxes_xy[i],self.anchor_xy)#(n_obj,n_anchors)\n \n overlap[:,~anchor_index]=0#일단 overlap에 영향없애기위한 임시negative, 필ignore처리!!\n overlap_each_prior,object_each_prior=overlap.max(dim=0)#(n_anchors)object_each_prior에는 최대 n_object 만큼 무작위 배치될수있고 최소 1개 배치될 수 있다\n _,prior_each_object=overlap.max(dim=1)#(n_obj)\n \n\n object_each_prior[prior_each_object] = object_each_prior[prior_each_object].to(device)\n \n label_each_prior=object_each_prior.clone()#(n_anchors)label\n \n label_each_prior[overlap_each_prior<0.3]=0#background\n \n label_each_prior[overlap_each_prior>=0.3]=-1\n \n# label_each_prior[prior_each_object] = 1#Ground Truth Box마다 IoU가 가장 높은 Anchor 1개를 뽑기\n kk = overlap[np.arange(overlap.size(0)),prior_each_object]\n ov=overlap.clone()\n kk=kk.cpu().numpy()\n ov=ov.cpu().numpy()\n peo=[]\n \n for j in range(overlap.size(0)):\n peo.extend(np.where(ov==kk[j])[1].tolist())\n peo=torch.LongTensor(peo).to(device)\n \n label_each_prior[overlap_each_prior>=0.7]=1\n \n \n# label_each_prior[~anchor_index]=-1\n \n# label_each_prior[peo] = 1#Ground Truth Box마다 IoU가 가장 높은 Anchor 1개를 뽑기\n gt_labels[i]=label_each_prior\n \n gt_labels[i][~anchor_index]=-1\n gt_labels[i][peo]=1\n \n gt_locs[i]=cxcy_to_gcxgcy(xy_to_cxcy(self.gt_boxes_xy[i][object_each_prior]),self.anchor_cxcy)#모델은 prior를 얼마나 움직일지를 예측하므로 \n \n gt_locs[i][~anchor_index]=0\n \n gt_labels=gt_labels.to(device)#()\n gt_locs=gt_locs.to(device)\n \n pos=(gt_labels==1)#positive\n \n neg=(gt_labels==0)#negative\n \n \n idx_pos=(np.where(pos.cpu().detach().numpy()[0]==True)[0])\n idx_neg=(np.where(neg.cpu().detach().numpy()[0]==True)[0])\n\n \n n_neg = idx_neg.size\n n_pos=idx_pos.size\n \n threshhold=128\n \n if n_pos > threshhold:\n idx_ignore = np.random.choice(\n idx_pos, size=n_pos-128, replace=False)\n gt_labels[0][idx_ignore]=-1\n \n n_th=256-((gt_labels==1).sum()).cpu().detach().numpy().item()\n if n_neg > n_th:\n idx_ignore = np.random.choice(\n idx_neg, size=n_neg-n_th, replace=False)\n gt_labels[0][idx_ignore]=-1\n \n# print((gt_labels==-1).sum())\n return gt_locs,gt_labels\n\nclass RPN(nn.Module):\n def __init__(self):\n super(RPN,self).__init__()\n# self.conv_first=nn.Conv2d(512,512,kernel_size=3,padding=1)\n \n# self.conv_cls=nn.Conv2d(512,2*9,kernel_size=1,padding=0)\n# self.conv_loc=nn.Conv2d(512,4*9,kernel_size=1,padding=0)\n self.conv_first=nn.Conv2d(512,512,3,1,1)\n self.conv_cls=nn.Conv2d(512,2*9,1,1,0)\n self.conv_loc=nn.Conv2d(512,4*9,1,1,0)\n \n\n self.init_conv2d() \n \n def init_conv2d(self):\n \n for c in self.children():\n if isinstance(c, nn.Conv2d):\n nn.init.normal_(c.weight,mean=0.,std=0.01)\n nn.init.constant_(c.bias, 0.)\n\n def forward(self, conv5_3_feats):\n feat=conv5_3_feats\n n, _, hh, ww = conv5_3_feats.shape\n out=F.relu(self.conv_first(feat))\n# rpn_cls=F.relu(self.conv_cls(out))\n# rpn_loc=F.relu(self.conv_loc(out))\n \n \n# rpn_cls = rpn_cls.view(1,-1,2)#(batch_size,HxWx9,2)\n \n \n \n rpn_loc=(self.conv_loc(out))\n \n rpn_loc=rpn_loc.permute(0, 2, 3, 1).contiguous()\n rpn_loc=rpn_loc.view(1,-1,4)#(batch_size,HxWx9,4)\n \n rpn_cls=(self.conv_cls(out))\n \n rpn_cls = rpn_cls.permute(0, 2, 3, 1).contiguous()\n \n rpn_softmax_scores = F.softmax(rpn_cls.view(1, hh, ww, 9, 2), dim=4)\n rpn_fg_scores = rpn_softmax_scores[:, :, :, :, 1].contiguous()\n rpn_fg_scores = rpn_fg_scores.view(1, -1)\n \n rpn_cls = rpn_cls.view(1, -1, 2)\n \n \n \n return rpn_loc,rpn_cls,rpn_fg_scores\n \n\nclass RPNLoss(nn.Module):\n def __init__(self):\n super(RPNLoss,self).__init__()\n \n \n self.rpn_sigma = 3.\n \n\n def forward(self,rpn_loc,rpn_cls, gt_loc,gt_cls):\n \"\"\"\n roi_loc=(1,H*W*k,4)\n gt_loc=(1,H*W*k,4)\n gt_cls=(1,H*W*K,2)\n \"\"\"\n rpn_loc=rpn_loc[0]\n rpn_cls=rpn_cls[0]\n \n gt_loc=gt_loc[0]\n gt_cls=gt_cls[0]\n \n try:\n rpn_cls_loss=F.cross_entropy(rpn_cls, gt_cls, ignore_index=-1)\n #ignore_index가 없는데 무시하라고 하면 에러가 뜸 즉 -1라벨링이 안된것임\n #무조건 -1라벨이 있어야함\n except:\n import pdb;pdb.set_trace()\n \n rpn_loc_loss = self._fast_rcnn_loc_loss(rpn_loc,gt_loc,gt_cls,self.rpn_sigma)\n \n \n return rpn_loc_loss+rpn_cls_loss\n \n def _smooth_l1_loss(self,x, t, in_weight, sigma):\n sigma2 = sigma ** 2\n diff = in_weight * (x - t)\n abs_diff = diff.abs()\n flag = (abs_diff.data < (1. / sigma2)).float()\n y = (flag * (sigma2 / 2.) * (diff ** 2) +\n (1 - flag) * (abs_diff - 0.5 / sigma2))\n return y.sum()\n\n\n def _fast_rcnn_loc_loss(self,pred_loc, gt_loc, gt_label, sigma):\n \n in_weight = torch.zeros(gt_loc.shape).cuda()\n \n # Localization loss is calculated only for positive rois.\n # NOTE: unlike origin implementation, \n # we don't need inside_weight and outside_weight, they can calculate by gt_label\n in_weight[(gt_label > 0).view(-1, 1).expand_as(in_weight).cuda()] = 1\n\n loc_loss = self._smooth_l1_loss(pred_loc, gt_loc, in_weight.detach(), sigma)\n # Normalize by total number of negtive and positive rois.\n loc_loss /= ((gt_label >= 0).sum().float()) # ignore gt_label==-1 for rpn_loss\n \n return loc_loss\n\n \nclass Proposal(nn.Module):\n def __init__(self):\n super(Proposal,self).__init__()\n \n self.nms_thresh=0.7\n self.n_train_pre_nms=12000\n self.n_train_post_nms=2000\n self.n_test_pre_nms=6000\n self.n_test_post_nms=300\n \n self.min_size=16\n def forward(self,rpn_loc,rpn_fg,anchor_boxes_cxcy,scale,img_width,img_height,train):\n if train:\n n_pre_nms = self.n_train_pre_nms\n n_post_nms = self.n_train_post_nms\n else:\n n_pre_nms = self.n_test_pre_nms\n n_post_nms = self.n_test_post_nms\n rois_cxcy=gcxgcy_to_cxcy(rpn_loc[0].to(device), anchor_boxes_cxcy)\n rois_xy=cxcy_to_xy(rois_cxcy)#(x,y,x,y)\n \n #1 WHK -> for train=2000 for test=300 by condition of (boundary,NMS-Score)\n batch_size=1\n \n min_size = self.min_size * scale\n rpn_fg=rpn_fg[0].to(device)\n \n for i in range(batch_size):\n # rois_xy.clamp_(0,1)\n \n \n \n# rois_xy[:,0].clamp_(min=0)\n# rois_xy[:,1].clamp_(min=0)\n# rois_xy[:,2].clamp_(max=img_width)\n# rois_xy[:,3].clamp_(max=img_height)\n\n rois_xy[:,0].clamp_(min=0,max=img_width)\n rois_xy[:,1].clamp_(min=0,max=img_height)\n rois_xy[:,2].clamp_(min=0,max=img_width)\n rois_xy[:,3].clamp_(min=0,max=img_height)\n\n\n \n\n hs = rois_xy[:, 3] - rois_xy[:, 1]\n ws = rois_xy[:, 2] - rois_xy[:, 0]\n hs=hs.cpu().detach().numpy()\n ws=ws.cpu().detach().numpy()\n \n keep = np.where((hs >= min_size) & (ws >= min_size))[0]\n \n rpn_fg=rpn_fg[keep]\n rois_xy=rois_xy[keep,:]\n \n \n \n \n \n index=rpn_fg.argsort().cpu().numpy()[::-1]\n if n_pre_nms > 0:\n index = index[:n_pre_nms]\n index=torch.LongTensor(index.tolist()).to(device)\n \n \n \n rpn_fg=rpn_fg[index]\n rois_xy=rois_xy[index,:]\n \n keep = nms(rois_xy,rpn_fg,0.7)#(6000->2000)\n \n if n_post_nms > 0:\n keep = keep[:n_post_nms]\n \n \n rpn_fg=rpn_fg[keep]\n rois_xy=rois_xy[keep,:]\n \n return rois_xy\n \nclass Proposal_Target_Creator(nn.Module):\n def __init__(self):\n super(Proposal_Target_Creator,self).__init__() \n \n def forward(self,rois_xy,boxes,labels):\n batch_size=1\n gt_locs = torch.zeros((1, rois_xy.size(0), 4), dtype=torch.float).to(device) # (N, n_anchors, 4)\n gt_labels = torch.zeros((1, rois_xy.size(0)), dtype=torch.long).to(device) # (N, n_anchors)\n\n for i in range(batch_size):\n\n n_objects=labels[i].size(0)\n overlap=find_jaccard_overlap(boxes[i],rois_xy)#(n_obj,n_anchors)\n overlap_each_prior,object_each_prior=overlap.max(dim=0)#(n_anchors)object_each_prior에는 최대 n_object 만큼 무작위 배치될수있고 최소 1개 배치될 수 있다\n\n\n _,prior_each_object=overlap.max(dim=1)#(n_obj)\n object_each_prior[prior_each_object] = torch.LongTensor(range(n_objects)).to(device)#(n_obj)여기서 object_each_prior에 최소 n_object 만큼 무작위 배치한다.그러면 8732중에 적어도 n_object를 대표하는 박스가 생기게된다\n\n label_each_prior=labels[i][object_each_prior]\n overlap_each_prior[prior_each_object]=1\n\n\n label_each_prior[overlap_each_prior<0.5]=0#background\n# label_each_prior[overlap_each_prior<0.1]=-1\n# label_each_prior[overlap_each_prior<0.6]=0#background\n\n gt_labels[i]=label_each_prior#박스에 임의의 labeling but 반드시 정답 포함\n rois_cxcy=xy_to_cxcy(rois_xy)\n gt_locs[i]=cxcy_to_gcxgcy(xy_to_cxcy(boxes[i][object_each_prior]),rois_cxcy)#모델은 prior를 얼마나 움직일지를 예측하므로 \n \n \n \n# pos= (gt_labels!=0)*(gt_labels!=-1)#positive\n pos= (gt_labels!=0)#positive\n neg=(gt_labels==0)\n \n idx_pos=np.where(pos.cpu().numpy()[0]==True)[0]\n idx_neg=np.where(neg.cpu().numpy()[0]==True)[0]\n \n pos_roi_per_this_image = int(min(32, idx_pos.size))\n neg_roi_per_this_image = int(min(64+(64-pos_roi_per_this_image),idx_neg.size))\n# pos_roi_per_this_image = int(min(16, idx_pos.size))\n# neg_roi_per_this_image = int(min(32+(32-pos_roi_per_this_image),idx_neg.size))\n \n\n \n if idx_pos.size > 0:\n idx_pos = np.random.choice(\n idx_pos, size=pos_roi_per_this_image, replace=False)\n if idx_neg.size > 0:\n idx_neg = np.random.choice(\n idx_neg, size=neg_roi_per_this_image, replace=False)\n \n idx_pos=torch.LongTensor(idx_pos).to(device)\n idx_neg=torch.LongTensor(idx_neg).to(device)\n \n pos_rois=rois_xy[idx_pos]\n neg_rois=rois_xy[idx_neg]\n \n rois=torch.cat([pos_rois,neg_rois],0)\n rois_idx=torch.cat([idx_pos,idx_neg],0)\n \n \n gt_locs=gt_locs[0][rois_idx,:]\n gt_labels=gt_labels[0][rois_idx]\n \n \n \n plus_loc = torch.FloatTensor([0,0,0,0]).unsqueeze(0).expand_as(boxes[0]).to(device) \n \n rois=torch.cat([rois,boxes[0]],0)\n gt_locs=torch.cat([gt_locs,plus_loc],0)\n gt_labels=torch.cat([gt_labels,labels[0]],0)\n sample_rois=rois\n \n #pos+gt진짜 정답박스를 주기(train할때만)\n gt_locs[:,:2]=gt_locs[:,:2]*10\n gt_locs[:,2:]=gt_locs[:,2:]*20\n \n \n return sample_rois,gt_locs,gt_labels#xyxy , gcxgcy\n \n\n \nclass ROILoss(nn.Module):\n def __init__(self):\n super(ROILoss,self).__init__()\n \n self.roi_sigma = 1.\n self.cross_entropy=nn.CrossEntropyLoss()\n \n def forward(self,roi_locs, roi_scores,gt_locs,gt_labels):\n \n roi_locs=roi_locs.view(roi_locs.size(0),-1,4)#(131,21,4)\n #pick 1 in 21(0~20)\n roi_locs=roi_locs[torch.arange(roi_locs.size(0)).long().cuda(),gt_labels]\n \n roi_cls_loss=self.cross_entropy(roi_scores, gt_labels)\n \n \n roi_loc_loss = self._fast_rcnn_loc_loss(roi_locs,gt_locs,gt_labels,self.roi_sigma)\n \n \n return roi_loc_loss +roi_cls_loss\n\n \n def _smooth_l1_loss(self,x, t, in_weight, sigma):\n sigma2 = sigma ** 2\n diff = in_weight * (x - t)\n abs_diff = diff.abs()\n flag = (abs_diff.data < (1. / sigma2)).float()\n y = (flag * (sigma2 / 2.) * (diff ** 2) +\n (1 - flag) * (abs_diff - 0.5 / sigma2))\n return y.sum()\n\n\n def _fast_rcnn_loc_loss(self,pred_loc, gt_loc, gt_label, sigma):\n \n in_weight = torch.zeros(gt_loc.shape).cuda()\n # Localization loss is calculated only for positive rois.\n # NOTE: unlike origin implementation, \n # we don't need inside_weight and outside_weight, they can calculate by gt_label\n in_weight[(gt_label > 0).view(-1, 1).expand_as(in_weight).cuda()] = 1\n\n loc_loss = self._smooth_l1_loss(pred_loc, gt_loc, in_weight.detach(), sigma)\n \n # Normalize by total number of negtive and positive rois.\n loc_loss /= ((gt_label >= 0).sum().float()) # ignore gt_label==-1 for rpn_loss\n \n return loc_loss\nclass FasterRCNNVGG16(nn.Module):\n def __init__(self):\n super(FasterRCNNVGG16, self).__init__()\n self.extractor, self.classifier = truncated_vgg16()\n \n self.rpn =RPN()\n #rpn out->rpn_loc,rpn_cls\n self.head = VGG16RoIHead(\n classifier=self.classifier)\n #head out->roi_locs, roi_scores\n \nclass VGG16RoIHead(nn.Module):\n \n\n def __init__(self,classifier):\n \n super(VGG16RoIHead, self).__init__()\n\n self.classifier = classifier\n self.cls_loc = nn.Linear(4096, 21 * 4)\n self.score = nn.Linear(4096, 21)\n\n self.roi = RoIPool((7, 7),0.0625)\n self.init_Linear()\n self.rescale_factors = nn.Parameter(torch.FloatTensor(1, 512, 1, 1)) # there are 512 channels in conv4_3_feats\n nn.init.constant_(self.rescale_factors, 20)\n \n \n def init_Linear(self):\n \n for c in self.children():\n if isinstance(c, nn.Linear):\n nn.init.normal_(c.weight,mean=0.,std=0.01)\n nn.init.constant_(c.bias, 0.)\n \n def forward(self, x, rois_xy):\n \n x=x.to(device)\n \n norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() # (N, 1, 38, 38)\n x = x / norm # (N, 512, 38, 38)\n x = x * self.rescale_factors # (N, 512, 38, 38)\n \n rois=rois_xy\n ind=torch.zeros((rois.size(0))).to(device)\n roi_ind=torch.cat([ind[:, None], rois], dim=1)\n# height=x.size(2)\n# width=x.size(3)\n \n# roi_ind[:,1]=roi_ind[:,1]*x.size(3)\n# roi_ind[:,2]=roi_ind[:,2]*x.size(2)\n# roi_ind[:,3]=roi_ind[:,3]*x.size(3)\n# roi_ind[:,4]=roi_ind[:,4]*x.size(2)\n \n \n #x.shape ([1, 512, 37, 50])\n \n pool = self.roi(x, roi_ind)#([131, 512, 7, 7])\n pool = pool.view(pool.size(0), -1)\n out=self.classifier(pool)\n \n roi_locs = self.cls_loc(out)#[128+alpha,4]\n roi_scores = self.score(out)#[128+alpha,21]\n \n return roi_locs, roi_scores#gcxgcy\n \n\n \n\nclass Detector(nn.Module):\n def __init__(self):\n super(Detector,self).__init__()\n \n \n def forward(self,rois_xy,predicted_locs,predicted_scores,min_score,max_overlap,top_k):\n \n batch_size = 1\n rois_cxcy=xy_to_cxcy(rois_xy)\n \n predicted_locs=predicted_locs.view(predicted_locs.size(0),-1,4)#(131,21,4)\n #pick 1 in 21(0~20)\n predicted_scores=F.softmax(predicted_scores, dim=1)\n A=torch.arange(predicted_locs.size(0)).long().cuda()\n B=predicted_scores.max(dim=1)[1].long().cuda()\n \n predicted_locs=predicted_locs[A,B]\n \n \n\n all_images_boxes = list()\n all_images_labels = list()\n all_images_scores = list()\n\n\n\n for i in range(batch_size):\n \n predicted_locs[:,:2]=predicted_locs[:,:2]/10.\n predicted_locs[:,2:]=predicted_locs[:,2:]/20.\n decoded_locs = cxcy_to_xy(\n gcxgcy_to_cxcy(predicted_locs, rois_cxcy)) # (300, 4)\n \n \n image_boxes = list()\n image_labels = list()\n image_scores = list()\n\n max_scores, best_label = predicted_scores.max(dim=1) # (300)\n\n\n for c in range(1, 21):\n\n class_scores = predicted_scores[:, c] # (300)\n score_above_min_score = class_scores > min_score \n n_above_min_score = score_above_min_score.sum().item()\n if n_above_min_score == 0:\n continue\n class_scores = class_scores[score_above_min_score] \n class_decoded_locs = decoded_locs[score_above_min_score] \n\n\n class_scores, sort_ind = class_scores.sort(dim=0, descending=True) \n class_decoded_locs = class_decoded_locs[sort_ind] \n\n\n overlap = find_jaccard_overlap(class_decoded_locs, class_decoded_locs) \n # (NMS)\n\n\n suppress = torch.zeros((n_above_min_score), dtype=torch.uint8).to(device) \n\n\n for box in range(class_decoded_locs.size(0)):\n\n\n if suppress[box] == 1:\n continue\n\n\n suppress = torch.max(suppress, (overlap[box] > max_overlap).byte())\n\n suppress[box] = 0\n suppress=suppress.bool()\n\n image_boxes.append(class_decoded_locs[~suppress])\n\n image_labels.append(torch.LongTensor((~suppress).sum().item() * [c]).to(device))\n image_scores.append(class_scores[~suppress])\n\n\n if len(image_boxes) == 0:\n image_boxes.append(torch.FloatTensor([[0., 0., 1., 1.]]).to(device))\n image_labels.append(torch.LongTensor([0]).to(device))\n image_scores.append(torch.FloatTensor([0.]).to(device))\n\n\n image_boxes = torch.cat(image_boxes, dim=0) \n image_labels = torch.cat(image_labels, dim=0) \n image_scores = torch.cat(image_scores, dim=0) \n n_objects = image_scores.size(0)\n\n\n if n_objects > top_k:\n image_scores, sort_ind = image_scores.sort(dim=0, descending=True)\n image_scores = image_scores[:top_k] \n image_boxes = image_boxes[sort_ind][:top_k] \n image_labels = image_labels[sort_ind][:top_k] \n\n\n all_images_boxes.append(image_boxes)\n all_images_labels.append(image_labels)\n all_images_scores.append(image_scores)\n \n return all_images_boxes, all_images_labels, all_images_scores\n\n \n", "repo_name": "chjung99/rcv_badge_fastser_rcnn", "sub_path": "model_0810.py", "file_name": "model_0810.py", "file_ext": "py", "file_size_in_byte": 24139, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.device", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torchvision.models.vgg16", "line_number": 15, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 191, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 198, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 207, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 215, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 216, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 217, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 222, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 251, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 251, "usage_type": "name"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 272, "usage_type": "name"}, {"api_name": "pdb.set_trace", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 295, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 309, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 309, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 359, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 371, "usage_type": "call"}, {"api_name": "torchvision.ops.nms", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 389, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 389, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 395, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 396, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 396, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 426, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 437, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 440, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 443, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 444, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 449, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 450, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 458, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 460, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 461, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 462, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 474, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 474, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 479, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 485, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 508, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 520, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 520, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 531, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 531, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 539, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 539, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 540, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 540, "usage_type": "name"}, {"api_name": "torchvision.ops.RoIPool", "line_number": 542, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 544, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.nn.init.constant_", "line_number": 545, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 545, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 545, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 551, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 551, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 552, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 552, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 552, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 553, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 553, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 553, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 564, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 565, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 589, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 589, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 601, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 601, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 602, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 649, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 649, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 659, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 666, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 671, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 672, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 673, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 676, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 677, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 678, "usage_type": "call"}]} +{"seq_id": "6409245488", "text": "from telegram import ReplyKeyboardMarkup, Update, KeyboardButton, ParseMode, ChatAction\n\n\n############################################\n# all keyboards\n############################################\n\n# main menu keyboard options\nfirst_menu_keyboard_buttons = [\n [KeyboardButton('📲 Скачать Трек/Альбом/Плейлист из Spotify')],\n [KeyboardButton('🔎 Найти и скачать Трек/Альбом/Плейлист из Интернета')]\n]\nfirst_menu_markup = ReplyKeyboardMarkup(first_menu_keyboard_buttons, one_time_keyboard=True)\n\n# search type options\nsearch_type_buttons = [\n [KeyboardButton('📀 Альбом')],\n [KeyboardButton('🎵 Трек')],\n [KeyboardButton('🎧 Плейлист')]\n]\nsearch_type_buttons_markup = ReplyKeyboardMarkup(search_type_buttons, one_time_keyboard=True)\n\n# Quality keyboard options\nquality_menu_keyboard_buttons = [\n [KeyboardButton('Лучшее'), KeyboardButton('Q320K'), KeyboardButton('Q256K')],\n [KeyboardButton('Q192K'), KeyboardButton('Q128K'), KeyboardButton('Q96K')],\n [KeyboardButton('Q32K'), KeyboardButton('Худшее')],\n [KeyboardButton('↩️ Назад')]\n]\n\nquality_menu_markup = ReplyKeyboardMarkup(quality_menu_keyboard_buttons, one_time_keyboard=True)\n\n# Music format keyboard options\nmusic_format_menu_keyboard_buttons = [\n [KeyboardButton('MP3'), KeyboardButton('FLAC')],\n [KeyboardButton('AAC'), KeyboardButton('M4A')],\n [KeyboardButton('OPUS'), KeyboardButton('VORBIS'), KeyboardButton('WAV')],\n [KeyboardButton('↩️ Назад')]\n]\n\nmusic_format_menu_markup = ReplyKeyboardMarkup(music_format_menu_keyboard_buttons, one_time_keyboard=True)\n\n# Begin uploading music\nfinal_downloading_menu_buttons = [\n [KeyboardButton('Начать загрузку')]\n]\n\nfinal_downloading_menu_markup = ReplyKeyboardMarkup(final_downloading_menu_buttons, one_time_keyboard=True)\n\n# Uploading type menu\nuploading_type_menu_buttons = [\n [KeyboardButton('🗂 Архив')],\n [KeyboardButton('🎵 Треки по отдельности')]\n]\n\nuploading_type_menu_markup = ReplyKeyboardMarkup(uploading_type_menu_buttons, one_time_keyboard=True)\n\n\n# Uploading and downloading finished\nfinal_menu_buttons = [\n [KeyboardButton('🏠На главную')]\n]\n\nfinal_menu_markup = ReplyKeyboardMarkup(final_menu_buttons, one_time_keyboard=True)\n", "repo_name": "azelenkovsky/spotify-downloader-bot", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 2511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "telegram.KeyboardButton", "line_number": 10, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 11, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 13, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 17, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 18, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 19, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 21, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 25, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 26, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 27, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 28, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 31, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 35, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 36, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 37, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 38, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 41, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 45, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 48, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 52, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 53, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 56, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 61, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "27331325735", "text": "\"\"\"The 'ZnFlow' package.\"\"\"\nimport contextlib\nimport importlib.metadata\nimport logging\nimport sys\n\nfrom znflow import exceptions\nfrom znflow.base import (\n CombinedConnections,\n Connection,\n FunctionFuture,\n Property,\n disable_graph,\n empty_graph,\n get_attribute,\n get_graph,\n)\nfrom znflow.combine import combine\nfrom znflow.graph import DiGraph\nfrom znflow.node import Node, nodify\nfrom znflow.visualize import draw\n\n__version__ = importlib.metadata.version(__name__)\n\n__all__ = [\n \"DiGraph\",\n \"Node\",\n \"draw\",\n \"nodify\",\n \"FunctionFuture\",\n \"Connection\",\n \"get_attribute\",\n \"disable_graph\",\n \"Property\",\n \"CombinedConnections\",\n \"combine\",\n \"exceptions\",\n \"get_graph\",\n \"empty_graph\",\n]\n\nwith contextlib.suppress(ImportError):\n from znflow import deployment\n\n __all__ += [\"deployment\"]\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.WARNING)\n\n# Formatter for advanced logging\n# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s : %(message)s')\nformatter = logging.Formatter(\"%(asctime)s (%(levelname)s): %(message)s\")\n\nchannel = logging.StreamHandler(sys.stdout)\nchannel.setLevel(logging.DEBUG)\nchannel.setFormatter(formatter)\n\nlogger.addHandler(channel)\n", "repo_name": "zincware/ZnFlow", "sub_path": "znflow/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "2", "api": [{"api_name": "importlib.metadata.metadata.version", "line_number": 23, "usage_type": "call"}, {"api_name": "importlib.metadata.metadata", "line_number": 23, "usage_type": "attribute"}, {"api_name": "importlib.metadata", "line_number": 23, "usage_type": "name"}, {"api_name": "contextlib.suppress", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 48, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "72246415408", "text": "import sys, random\nimport numpy as np\nfrom typing import Tuple, List\nfrom queue import Queue\nfrom Utilities import geoDistance\n\nclass DBScan:\n def __init__(self, Eps : float, MinPt : int):\n self.core = -1\n self.border = -2\n self.Eps = Eps\n self.MintPt = MinPt\n\n def get_neighbor(self, userPointList : list, sample_idx):\n neighbors = []\n curUser = userPointList[sample_idx]\n\n for usrIdx, usrVal in enumerate(userPointList): \n if (usrIdx != sample_idx): \n # Geodesic distance\n geodesic_distance = geoDistance(usrVal.location, curUser.location)\n if geodesic_distance < self.Eps:\n neighbors.append(usrIdx)\n \n return neighbors\n\n def fit(self, userPointList : list):\n # initialize all points as outliers\n self.point_label = [0] * len(userPointList)\n point_count = []\n\n # initilize list for core/border points\n core = []\n border = []\n\n # print(point_label)\n \n # Find the neighbours of each individual point\n for usrIdx, usrVal in enumerate(userPointList):\n point_count.append(self.get_neighbor(userPointList, usrIdx))\n\n # print(point_count)\n\n # Find all the core points, border points and outliers\n for usrIdx in range(len(point_count)):\n if (len(point_count[usrIdx]) >= self.MintPt):\n self.point_label[usrIdx] = self.core\n core.append(usrIdx)\n else:\n border.append(usrIdx)\n\n for i in border:\n for j in point_count[i]:\n if j in core:\n self.point_label[i] = self.border\n break\n \n # Assign points to a cluster\n self.cluster = 1\n\n # Here we use a queue to find all the neighbourhood points of a core point and find the indirectly reachable points\n # We are essentially performing Breadth First search of all points which are within Epsilon distance for each other\n for i in range(len(self.point_label)):\n q = Queue()\n if (self.point_label[i] == self.core):\n self.point_label[i] = self.cluster\n for x in point_count[i]:\n if(self.point_label[x] == self.core):\n q.put(x)\n self.point_label[x] = self.cluster\n elif(self.point_label[x] == self.border):\n self.point_label[x] = self.cluster\n while not q.empty():\n neighbors = point_count[q.get()]\n for y in neighbors:\n if (self.point_label[y] == self.core):\n self.point_label[y] = self.cluster\n q.put(y)\n if (self.point_label[y] == self.border):\n self.point_label[y] = self.cluster\n self.cluster += 1 # Move on to the next cluster\n \n # return self.point_label, self.cluster #label for each userIdx and nunber of cluster\n\n def getCluster(self) -> List:\n clusterArray = [[] for i in range(self.cluster)]\n outliers = []\n\n for userIdx, label in enumerate(self.point_label):\n if(label != 0):\n clusterArray[label].append(userIdx)\n # else:\n # own_cluster = [userIdx]\n # outliers.append(own_cluster)\n\n if (len(outliers) > 0):\n clusterArray = list(filter(None, clusterArray + outliers))\n return clusterArray", "repo_name": "ACM-Research/vr-user-behavior-clustering", "sub_path": "21F/scripts/Common/DBScan.py", "file_name": "DBScan.py", "file_ext": "py", "file_size_in_byte": 3659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "Utilities.geoDistance", "line_number": 21, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "38711354515", "text": "#!/usr/bin/env python \nimport sys\nimport os\nimport re\nimport yaml\nfrom termcolor import cprint\nimport arrow\n\nfrom imapclient import IMAPClient\nimport ssl\nimport email\n\nfrom ipdb import set_trace\n\n\nsettings_file = os.environ.get(\"SETTINGS_FILE\", \"settings.yaml\")\nsettings = yaml.safe_load(open(settings_file))\n\n\ndef get_body_of_mail(message_data):\n email_message = email.message_from_bytes(message_data[b'RFC822'])\n subject = email_message.get('Subject')\n date = email_message.get('Date')\n if subject != settings['mail']['subject']:\n cprint(f\"DEBUG: Skipping mail '{subject}' from {date}\", 'magenta')\n return\n body = \"\"\n print(f\"DEBUG: Processing mail '{subject}' from {date}\")\n if email_message.is_multipart():\n for part in email_message.get_payload():\n body += part.get_payload()\n else:\n body = email_message.get_payload()\n return body\n\n\ndef get_mails_from_ead():\n ssl_context = ssl.create_default_context(cafile=\"/etc/ssl/certs/ca-certificates.crt\")\n server = IMAPClient(settings['mail']['server'], ssl_context=ssl_context)\n server.login(settings['mail']['user'], settings['mail']['pass'])\n server.select_folder('INBOX')\n # just check the last 5 mails (independent of (un)seen)\n messages = server.search()[-5:]\n for __, message_data in server.fetch(messages, 'RFC822').items():\n body = get_body_of_mail(message_data)\n if body:\n yield(body)\n server.logout()\n\n\ndef get_abholtermin(email_body):\n abholtermin = re.search(settings['mail']['regex_abholung'], email_body)\n if not abholtermin:\n cprint(\"ERROR: Problem with the regex\", \"red\")\n sys.exit(1)\n __, date, description = abholtermin.group(1).strip().split(' ', 2)\n return date, description\n\n\ndef check_notification(date):\n # check if tomorrow is Abholung\n date_abholung = arrow.get(date, \"DD.MM.YYYY\")\n tomorrow = arrow.now().shift(days=+1)\n if date_abholung.day == tomorrow.day and date_abholung.month == tomorrow.month \\\n and date_abholung.year == tomorrow.year:\n cprint(f\"DEBUG: Bingo! Will notify others about the news! ({date_abholung.format('DD.MM.YYYY')} vs {tomorrow.format('DD.MM.YYYY')})\", 'green')\n return True\n else:\n print(f\"DEBUG: Zonk! Will not notify. ({date_abholung.format('DD.MM.YYYY')} vs {tomorrow.format('DD.MM.YYYY')})\")\n return False\n #return True\n\n\ndef read_mails_and_notify(irc_bot):\n # irc_bot is a irc.client.Reactor object\n cprint(f\"DEBUG: Let's check our mails at {arrow.now().format()}\", 'yellow')\n for mail in get_mails_from_ead():\n date, description = get_abholtermin(mail)\n if check_notification(date):\n msg = settings['msg'].format(description)\n print(f\"INFO: Sending irc message {msg}\")\n irc_bot.privmsg(settings['irc']['channel'], msg)\n cprint(f\"DEBUG: Done. Checked our mails at {arrow.now().format()}\", 'yellow')\n\n\nif __name__ == '__main__':\n read_mails_and_notify()\n", "repo_name": "kmille/cda-garbage", "sub_path": "imap_ead.py", "file_name": "imap_ead.py", "file_ext": "py", "file_size_in_byte": 3056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 17, "usage_type": "call"}, {"api_name": "email.message_from_bytes", "line_number": 21, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 25, "usage_type": "call"}, {"api_name": "ssl.create_default_context", "line_number": 38, "usage_type": "call"}, {"api_name": "imapclient.IMAPClient", "line_number": 39, "usage_type": "call"}, {"api_name": "re.search", "line_number": 52, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 55, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 62, "usage_type": "call"}, {"api_name": "arrow.now", "line_number": 63, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 66, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 76, "usage_type": "call"}, {"api_name": "arrow.now", "line_number": 76, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 83, "usage_type": "call"}, {"api_name": "arrow.now", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "30575777687", "text": "from importlib.metadata import requires\nfrom turtle import pos\nfrom urllib import request\nfrom flask import Flask\nfrom flask import jsonify\nfrom flask import request\n\nimport json\nclass Aluno:\n nome=\"\"\n idade=0\n matricula=0\n\nclass Professor:\n nome=\"\"\n idade=0\n matricula=0\n\nclass Turma:\n materia =\"\"\n alunos =[]\n professores =[]\n\nclass School:\n diretor = []\n turmas = []\n\nscholl = School()\n\nb = Professor()\nb.idade = 15\nb.matricula= 470820\nb.nome = \"eliabe\"\njsonStr = json.dumps(b.__dict__)\napp = Flask(__name__)\n@app.route('/professor', methods=['POST'])\ndef registerProfessor():\n a = json.loads(request.data)\n for i in range (0, len(scholl.turmas)):\n if( scholl.turmas[i].materia == a[\"materia\"]):\n al = Professor()\n al.nome = a[\"name\"]\n al.matricula = a[\"matricula\"] \n for j in range (0,len(scholl.turmas[i].professores)):\n if(scholl.turmas[i].professores[j].matricula == al.matricula ):\n return \"Professor Já Existente\"\n al.idade = a[\"idade\"]\n scholl.turmas[i].professores.append(al)\n return \"Sucess\"\n\n@app.route('/allmaterias', methods=['GET'])\ndef allmaterias():\n materias = []\n for i in scholl.turmas:\n materias.append(i.materia)\n resp = {\n \"materias\": materias\n }\n return json.loads(json.dumps(resp))\n\n@app.route('/alunos', methods=['POST'])\ndef registerAlunos():\n a = json.loads(request.data)\n for i in range (0, len(scholl.turmas)):\n if( scholl.turmas[i].materia == a[\"materia\"]):\n al = Aluno()\n al.nome = a[\"name\"]\n al.matricula = a[\"matricula\"] \n for j in range (0,len(scholl.turmas[i].alunos)):\n if(scholl.turmas[i].alunos[j].matricula == al.matricula ):\n return \"Aluno Já Existente\"\n al.idade = a[\"idade\"]\n scholl.turmas[i].alunos.append(al)\n return \"Sucess\"\n\n@app.route('/materia', methods=['POST'])\ndef registerMateria():\n a = json.loads(request.data)\n for i in range (0, len(scholl.turmas)):\n if( scholl.turmas[i].materia == a[\"materia\"]):\n return \"Materia Já Existente\"\n t = Turma()\n t.materia = a[\"materia\"]\n scholl.turmas.append(t)\n return \"Sucess\"\n\n@app.route('/alunos//', methods=['GET', 'POST'])\ndef getAluno(username, materia):\n if request.method == \"GET\" :\n for i in scholl.turmas:\n if( i.materia == materia):\n for j in i.alunos:\n if j.nome == username:\n resp = {\n \"matricula\":j.matricula,\n \"idade\":j.idade,\n \"nome\": j.nome\n }\n js = json.dumps(resp) \n return json.loads(js)\n return \"Aluno não existe\"\n return \"Materia Nao Existe\" \n \n@app.route('/m/', methods=['GET'])\ndef getMateria(materia):\n if request.method == \"GET\" :\n for i in scholl.turmas:\n if( i.materia == materia):\n alunos=[]\n for k in i.alunos:\n alunos.append(k.matricula)\n professor = []\n for k in i.professores:\n professor.append(k.matricula)\n resp= {\n \"materia\": i.materia,\n \"alunos\": alunos,\n \"professores\": professor\n }\n return json.loads(json.dumps(resp))\n\n@app.route('/r/', methods=['GET'])\ndef removeMateria(materia):\n if request.method == \"GET\" :\n for i in scholl.turmas:\n if( i.materia == materia):\n scholl.turmas.remove(i)\n return \"Sucesso\"\n return \"materia inexistente\"\n@app.route('/alunos//', methods=['GET', 'POST'])\ndef removeAluno(username, materia):\n if request.method == \"GET\" :\n for i in scholl.turmas:\n if( i.materia == materia):\n for j in i.alunos:\n if j.nome == username:\n i.alunos.remove(j)\n return \"Aluno Removido Com Sucesso\"\n return \"Aluno inexistente\" ", "repo_name": "eliabe71/Trabalho3_SD", "sub_path": "Q1/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 4348, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 99, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 120, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 132, "usage_type": "name"}]} +{"seq_id": "6913036670", "text": "\"\"\"\nLeetCode\n1626. Best Team With No Conflicts\nJanuary 2023 Challenge\njramaswami\n\"\"\"\n\n\nfrom typing import *\nimport functools\nimport collections\n\n\nPerson = collections.namedtuple('Person', ['age', 'score'])\n\n\nclass Solution:\n def bestTeamScore(self, scores: List[int], ages: List[int]) -> int:\n\n people = [Person(a, s) for s, a in zip(scores, ages)]\n people.sort()\n\n @functools.cache\n def rec(i,mx):\n if i >= len(people):\n return 0\n\n # Do not pick this person.\n result = rec(i+1, mx)\n if mx <= people[i].score:\n # If you can, pick this person.\n result = max(\n result,\n people[i].score + rec(i+1, max(people[i].score, mx))\n )\n return result\n\n return rec(0, 0)\n\n\ndef test_1():\n scores = [1,3,5,10,15]\n ages = [1,2,3,4,5]\n expected = 34\n assert Solution().bestTeamScore(scores, ages) == expected\n\n\ndef test_2():\n scores = [4,5,6,5]\n ages = [2,1,2,1]\n expected = 16\n assert Solution().bestTeamScore(scores, ages) == expected\n\n\ndef test_3():\n scores = [1,2,3,5]\n ages = [8,9,10,1]\n expected = 6\n assert Solution().bestTeamScore(scores, ages) == expected\n", "repo_name": "jramaswami/LeetCode_Python", "sub_path": "best_team_with_no_conflicts.py", "file_name": "best_team_with_no_conflicts.py", "file_ext": "py", "file_size_in_byte": 1275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.namedtuple", "line_number": 14, "usage_type": "call"}, {"api_name": "functools.cache", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "30830601391", "text": "import requests\nfrom json import dumps\nfrom pathlib import Path\nfrom astropy.io.fits import PrimaryHDU, getdata, getheader\nfrom tenacity import retry, retry_if_exception_type, retry_if_result, \\\n stop_after_attempt, wait_exponential\n\n\ndef is_false(value):\n return value is False\n\n\ndef result_if_max_retry_count(retry_state):\n pass\n\n\nclass PlateSolution:\n\n def __init__(self, file=None, directory=None, api_key=None,\n api_url='http://nova.astrometry.net/api/'):\n if api_key is None:\n api_key = {'apikey': 'vfsyxlmdxfryhprq'}\n self.api_url = api_url\n self.api_key = api_key\n self.file = file\n self.directory = directory\n\n def plate_solution(self):\n session = self._login()\n if not session:\n return PlateSolution.fail('Login')\n\n sub_id = self._upload(session)\n if not sub_id:\n return PlateSolution.fail('Upload')\n\n sub_url = self._get_url(f\"submissions/{sub_id}\")\n job_id = self._sub_status(sub_url)\n if not job_id:\n return PlateSolution.fail('Submission ID')\n\n job_url = self._get_url(f\"jobs/{job_id}\")\n download_url = self.api_url.replace(\"/api/\", f\"/wcs_file/{job_id}/\")\n wcs_file = Path(self.directory) / \"temp\" / \"wcs.fits\"\n wcs_file = self._job_status(job_url, wcs_file, download_url)\n if not wcs_file:\n return PlateSolution.fail('Job Status')\n else:\n print(\"WCS file creation successful.\")\n return wcs_file\n\n def _get_url(self, service):\n return self.api_url + service\n\n @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10),\n retry=(retry_if_result(is_false) | retry_if_exception_type(requests.exceptions.RequestException)),\n retry_error_callback=result_if_max_retry_count)\n def _login(self):\n r = requests.post(self._get_url('login'), data={'request-json': dumps(self.api_key)})\n if r.status_code >= 400:\n return False\n elif r.json()['status'] == 'success':\n return r.json()['session']\n return False\n\n @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10),\n retry=(retry_if_result(is_false) | retry_if_exception_type(requests.exceptions.RequestException)),\n retry_error_callback=result_if_max_retry_count)\n def _upload(self, session):\n files = {'file': open(self.file, 'rb')}\n headers = {'request-json': dumps({\"session\": session}), 'allow_commercial_use': 'n',\n 'allow_modifications': 'n', 'publicly_visible': 'n'}\n\n r = requests.post(self.api_url + 'upload', files=files, data=headers)\n\n if r.json()['status'] == 'success':\n return r.json()['subid']\n return False\n\n @retry(stop=stop_after_attempt(20), wait=wait_exponential(multiplier=1, min=4, max=10),\n retry=(retry_if_result(is_false) | retry_if_exception_type(requests.exceptions.RequestException)),\n retry_error_callback=result_if_max_retry_count)\n def _sub_status(self, sub_url):\n r = requests.get(sub_url)\n if r.json()['job_calibrations']:\n return r.json()['jobs'][0]\n return False\n\n @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10),\n retry=(retry_if_result(is_false) | retry_if_exception_type(requests.exceptions.RequestException)),\n retry_error_callback=result_if_max_retry_count)\n def _job_status(self, job_url, wcs_file, download_url):\n r = requests.get(job_url)\n if r.json()['status'] == 'success':\n r = requests.get(download_url)\n with wcs_file.open('wb') as f:\n f.write(r.content)\n hdu = PrimaryHDU(data=getdata(filename=self.file), header=getheader(filename=wcs_file))\n hdu.writeto(wcs_file, overwrite=True)\n return wcs_file\n return False\n\n @staticmethod\n def fail(error_type):\n print(\"WARNING: After multiple attempts, EXOTIC could not retrieve a plate solution from nova.astrometry.net\"\n f\" due to {error_type}. EXOTIC will continue reducing data without a plate solution.\")\n return False\n", "repo_name": "rzellem/EXOTIC", "sub_path": "exotic/api/plate_solution.py", "file_name": "plate_solution.py", "file_ext": "py", "file_size_in_byte": 4283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 73, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pathlib.Path", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "tenacity.retry", "line_number": 55, "usage_type": "call"}, {"api_name": "tenacity.stop_after_attempt", "line_number": 55, "usage_type": "call"}, {"api_name": "tenacity.wait_exponential", "line_number": 55, "usage_type": "call"}, {"api_name": "tenacity.retry_if_result", "line_number": 56, "usage_type": "call"}, {"api_name": "tenacity.retry_if_exception_type", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 56, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 74, "usage_type": "call"}, {"api_name": "tenacity.retry", "line_number": 66, "usage_type": "call"}, {"api_name": "tenacity.stop_after_attempt", "line_number": 66, "usage_type": "call"}, {"api_name": "tenacity.wait_exponential", "line_number": 66, "usage_type": "call"}, {"api_name": "tenacity.retry_if_result", "line_number": 67, "usage_type": "call"}, {"api_name": "tenacity.retry_if_exception_type", "line_number": 67, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 67, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "tenacity.retry", "line_number": 80, "usage_type": "call"}, {"api_name": "tenacity.stop_after_attempt", "line_number": 80, "usage_type": "call"}, {"api_name": "tenacity.wait_exponential", "line_number": 80, "usage_type": "call"}, {"api_name": "tenacity.retry_if_result", "line_number": 81, "usage_type": "call"}, {"api_name": "tenacity.retry_if_exception_type", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 81, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "astropy.io.fits.PrimaryHDU", "line_number": 98, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 98, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 98, "usage_type": "call"}, {"api_name": "tenacity.retry", "line_number": 89, "usage_type": "call"}, {"api_name": "tenacity.stop_after_attempt", "line_number": 89, "usage_type": "call"}, {"api_name": "tenacity.wait_exponential", "line_number": 89, "usage_type": "call"}, {"api_name": "tenacity.retry_if_result", "line_number": 90, "usage_type": "call"}, {"api_name": "tenacity.retry_if_exception_type", "line_number": 90, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 90, "usage_type": "attribute"}]} +{"seq_id": "32459600831", "text": "__author__ = 'Jos\\'user'\nimport twitter\nimport io\nimport json\nimport datetime\n\nfrom datetime import timedelta\nfrom flask import Flask, request, render_template\nfrom flask_googlemaps import GoogleMaps\nfrom flask_googlemaps import Map\n\n\n#Funcion para la conexion.\ndef oauth_login():\n CONSUMER_KEY = 'kvAbp1mrWFTvUtdwMZm2SbnGE'\n CONSUMER_SECRET = 'WqGXQIpOVKbjwP8FRWF4u7Xy3kc5kMkujuvEDT9fqZfBiykCLI'\n OAUTH_TOKEN = '7730092-BvcE6lKJs8455JE8hyEhYHKXHX5g9X05izuuU47qIX'\n OAUTH_TOKEN_SECRET = 'xGzotzBjBImJNDLAogP60jb3GVlRnp3M9jtp3QSFgJDAI'\n\n auth = twitter.oauth.OAuth(OAUTH_TOKEN, OAUTH_TOKEN_SECRET, CONSUMER_KEY, CONSUMER_SECRET)\n\n twitter_api = twitter.Twitter(auth=auth)\n return twitter_api\n\ndef geo(tw,ht):\n query = tw.search.tweets(q=('#'+ht),count=100)\n \n listado=[]\n \n for resultado in query[\"statuses\"]:\n # only process a result if it has a geolocation\n if resultado[\"place\"]:\n #(resultado[\"place\"][\"bounding_box\"][\"coordinates\"][0])\n momento = datetime.datetime.strptime(resultado[\"created_at\"], '%a %b %d %H:%M:%S +0000 %Y') + timedelta(hours=1)\n latitud = 0\n longitud = 0\n for e in resultado[\"place\"][\"bounding_box\"][\"coordinates\"][0]:\n latitud += e[0]\n longitud += e[1]\n latitud = latitud/len(resultado[\"place\"][\"bounding_box\"][\"coordinates\"][0])\n longitud = longitud/len(resultado[\"place\"][\"bounding_box\"][\"coordinates\"][0])\n \n momento = momento + datetime.timedelta(hours=1)\n listado.append({\"id\":resultado[\"id\"], \"lugar\" : resultado[\"place\"][\"full_name\"], \"momento\" : momento, \"latitud\" : latitud, \"longitud\" : longitud, \"usuario\":resultado[\"user\"]})\n \n return listado\n\ndef tagMethod(tag):\n\tlistado = geo(oauth_login(),tag)\n\tl={}\n\n\tfor e in listado:\n\t\tl.update({e['usuario']['profile_image_url']:[(e['longitud'],e['latitud'])]})\n\n\tmapa = Map(\n\t\tidentifier=\"view-side\",\n\t\tlat=40.3450396,\n\t\tlng=-3.6517684,\n\t\tzoom=6,\n\t\tmarkers=l,\n\t\tstyle=\"height:600px;width:800px;margin:0;\"\n\t)\n\n\treturn render_template('tag.html', mapa=mapa, tag=tag, listado=listado)\n\n\n\n\napp = Flask(__name__)\nGoogleMaps(app)\n\n@app.route('/')\ndef index():\n\treturn render_template('index.html')\n\n@app.route('/tag/')\ndef tag1(tag):\n\treturn tagMethod(tag)\n\t\n@app.route('/tag/', methods=['POST'])\ndef tag2():\n\treturn tagMethod(request.form['tag'])\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n\n\n", "repo_name": "mortalswat/GeoWeb", "sub_path": "geoweb.py", "file_name": "geoweb.py", "file_ext": "py", "file_size_in_byte": 2487, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "twitter.oauth.OAuth", "line_number": 20, "usage_type": "call"}, {"api_name": "twitter.oauth", "line_number": 20, "usage_type": "attribute"}, {"api_name": "twitter.Twitter", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 43, "usage_type": "call"}, {"api_name": "flask_googlemaps.Map", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 69, "usage_type": "call"}, {"api_name": "flask_googlemaps.GoogleMaps", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "40225039211", "text": "from bs4 import BeautifulSoup\nimport requests\nimport json\nfrom os.path import exists\n\ndef get_player_images():\n url = \"https://www.nba.com/players\"\n\n r = requests.get(url)\n data = r.text\n players_string = data[data.index('\"players\":[')+10:data.index(',\"region\":\"united-states\"}')]\n players_list = json.loads(players_string)\n\n missing_players = []\n failed_players = []\n for player in players_list:\n player_slug = player['PLAYER_SLUG']\n print(player_slug)\n player_file_path = f\"./player_images/{player_slug}.png\"\n\n if not exists(player_file_path):\n player_url = f\"https://www.nba.com/player/{player['PERSON_ID']}/{player_slug}\"\n\n r = requests.get(player_url)\n data = r.text\n soup = BeautifulSoup(data,'html.parser')\n headshot_img = soup.find('img', {\"alt\": f\"{player['PLAYER_FIRST_NAME']} {player['PLAYER_LAST_NAME']} Headshot\"})\n if headshot_img:\n headshot_img_src = headshot_img.get(\"src\")\n else:\n missing_players.append(player_slug)\n\n img_response = requests.get(headshot_img_src)\n if \"AccessDenied\" not in str(img_response.content):\n with open(f\"./images/players/{player_slug}.png\", \"wb\") as f:\n f.write(img_response.content)\n else:\n failed_players.append(player_slug)\n\n print(\"Finished!\")\n\n if len(missing_players) > 0:\n print(\"\\nMissing Headshots:\")\n for p in missing_players:\n print(p)\n\n if len(failed_players) > 0:\n print(\"\\nFailed Headshots:\")\n for p in failed_players:\n print(p)\n", "repo_name": "scottgoodell/nba-shot-plots", "sub_path": "scripts/scrape_headshots.py", "file_name": "scrape_headshots.py", "file_ext": "py", "file_size_in_byte": 1532, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "35584549799", "text": "from modul_mnk import *\r\nimport matplotlib.pyplot as plt\r\nimport pylab\r\nimport numpy as np\r\n\r\nyy = []\r\nxx = []\r\n\r\nwith open (\"windmil.txt\",\"r\") as file:\r\n dane = file.readlines()\r\n for line in dane:\r\n yy.append(float(line.split()[1]))\r\n xx.append(float(line.split()[0]))\r\n dane.append(yy)\r\n dane.append(xx)\r\n\r\nprint(xx)\r\nprint(yy)\r\n\r\nres = []\r\nbsr = []\r\n\r\nfor n in range(2,10):\r\n aa, bb = gen_ur_mnk(xx, yy, n)\r\n\r\n print(aa)\r\n print(bb)\r\n\r\n\r\n wsp = numpy.linalg.solve(aa, bb) # a0,a1,a2\r\n print('wsp:', wsp)\r\n\r\n def prawdziwe():\r\n li = []\r\n for i in xx:\r\n wielomian4(i,wsp)\r\n li.append(wielomian4(i,wsp))\r\n return li\r\n\r\n prawdziwe()\r\n r_kwadrat(yy,prawdziwe())\r\n blad(prawdziwe(),prawdziwe())\r\n\r\n res.append(r_kwadrat(yy,prawdziwe()))\r\n bsr.append(blad(yy,prawdziwe()))\r\n\r\n print(res)\r\n print(bsr)\r\n\r\n p = range(2, 17)\r\n a = min(p)\r\n b = max(p)\r\n dx = (b - a) / (len(p)+10)\r\n\r\n zz = [a]\r\n ww = [wielomian4(a, wsp)]\r\n\r\n for i in p:\r\n zz.append(zz[-1] + dx)\r\n ww.append(wielomian4((zz[-1]), wsp))\r\n\r\n print(zz)\r\n print(ww)\r\n n = str(n)\r\n x = zz\r\n y = ww\r\n pylab.plot(x, y)\r\n pylab.title('Model regresji' + \" dla \" + n + \" stopnia wielomianu\")\r\n plt.xlabel(\"prędkość wiatru[mph]\")\r\n plt.ylabel(\"moc wiatraka\")\r\n pylab.grid(True)\r\n # pylab.show()\r\n\r\n plt.plot(xx, yy, 'ro')\r\n plt.axis([0, 11, 0, 3])\r\n plt.show()\r\n\r\n\r\nplt.show()\r\n\r\n\r\ndef wykres1():\r\n x = [2,3,4,5,6,7,8,9]\r\n y = res\r\n pylab.plot(x,y)\r\n pylab.title('Wartość współczynnika determinacji w zależności od stopnia wielomianu')\r\n plt.xlabel(\"stopień wielomianu\")\r\n plt.ylabel(\"wartość współczynnika determinacji\")\r\n pylab.grid(True)\r\n pylab.show()\r\n\r\ndef wykres2():\r\n i = [2,3,4,5,6,7,8,9]\r\n j = bsr\r\n pylab.plot(i,j)\r\n pylab.title('Wartość błędu średniego w zależności od stopnia wielomianu')\r\n plt.xlabel(\"stopień wielomianu\")\r\n plt.ylabel(\"wartość błędu średniokwadratowego\")\r\n pylab.grid(True)\r\n pylab.show()\r\n\r\nwykres1()\r\nwykres2()\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", "repo_name": "MarcelinaSS/The-method-of-least-squares", "sub_path": "test_mnk_nasz.py", "file_name": "test_mnk_nasz.py", "file_ext": "py", "file_size_in_byte": 2180, "program_lang": "python", "lang": "pl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.linalg.solve", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pylab.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "pylab.title", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "pylab.grid", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "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"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "pylab.plot", "line_number": 85, "usage_type": "call"}, {"api_name": "pylab.title", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "pylab.grid", "line_number": 89, "usage_type": "call"}, {"api_name": "pylab.show", "line_number": 90, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "pylab.title", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "pylab.grid", "line_number": 99, "usage_type": "call"}, {"api_name": "pylab.show", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "8358223476", "text": "import ssl\nimport subprocess\nimport sys\nimport json\nfrom cryptography.fernet import Fernet\n\nglobal schedulerapi, variables\n\ndef install(package):\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n\n# Import required Python libraries according to Python version\ntry:\n from urllib.request import Request, urlopen # Python 3\nexcept ImportError:\n from urllib2 import Request, urlopen # Python 2\n\ntry:\n import cryptography\nexcept ImportError:\n install('cryptography')\n import cryptography\n\n# Get user credentials and convert to json\nschedulerapi.connect()\nsessionId = str(schedulerapi.getSession())\nconnectionInfo = schedulerapi.getConnectionInfo()\nciLogin = str(connectionInfo.getLogin())\nciPasswd = str(connectionInfo.getPassword())\nciUrl = str(connectionInfo.getUrl())\nuser_credentials = {\n 'sessionId': sessionId,\n 'ciLogin': ciLogin,\n 'ciPasswd': ciPasswd,\n 'ciUrl': ciUrl\n}\nuser_credentials_json = json.dumps(user_credentials)\n\n# Encrypt user data into a binary file\nkey = Fernet.generate_key()\nf = Fernet(key)\nmessage = user_credentials_json.encode()\nencrypted = f.encrypt(message)\nuser_data_file = 'user_data.enc'\nwith open(user_data_file, 'wb') as f:\n f.write(encrypted)\nvariables.put(\"USER_KEY\", key.decode())\nvariables.put(\"USER_DATA_FILE\", user_data_file)\n\n# Get workflows variables\nPA_CATALOG_REST_URL = variables.get(\"PA_CATALOG_REST_URL\")\nPYTHON_ENTRYPOINT = variables.get(\"PYTHON_ENTRYPOINT\")\nYAML_FILE = variables.get(\"YAML_FILE\")\n\nPA_MAAS_RESOURCES_URL = \"/buckets/ai-model-as-a-service/resources/\"\npython_file_url = PA_CATALOG_REST_URL + PA_MAAS_RESOURCES_URL + PYTHON_ENTRYPOINT + \"/raw\"\nyaml_file_url = PA_CATALOG_REST_URL + PA_MAAS_RESOURCES_URL + YAML_FILE + \"/raw\"\nprint(\"python_file_url: \", python_file_url)\nprint(\"yaml_file_url: \", yaml_file_url)\n\n# Download the two configuration file \"ml_service\" for the service definition\nreq_py = Request(python_file_url)\nreq_py.add_header('sessionid', sessionId)\nif python_file_url.startswith('https'):\n context = ssl._create_unverified_context()\n python_file = urlopen(req_py, context=context).read()\nelse:\n python_file = urlopen(req_py).read()\npython_content = python_file.decode('utf-8')\npython_file_name = PYTHON_ENTRYPOINT + \".py\"\nwith open(python_file_name, 'w') as f:\n f.write(python_content)\n\n# Download the configuration file \"ml_service-api\" for the swagger specification\nreq_yaml = Request(yaml_file_url)\nreq_yaml.add_header('sessionid', sessionId)\nif yaml_file_url.startswith('https'):\n context = ssl._create_unverified_context()\n yaml_file = urlopen(req_yaml, context=context).read()\nelse:\n yaml_file = urlopen(req_yaml).read()\nyaml_file_content = yaml_file.decode('utf-8')\nyaml_file_name = YAML_FILE + \".yaml\"\nwith open(yaml_file_name, 'w') as f:\n f.write(yaml_file_content)", "repo_name": "ow2-proactive/proactive-examples", "sub_path": "ModelAsService/resources/catalog/MaaS_Pre_Script.py", "file_name": "MaaS_Pre_Script.py", "file_ext": "py", "file_size_in_byte": 2827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "3", "api": [{"api_name": "subprocess.check_call", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 10, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet.generate_key", "line_number": 41, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 41, "usage_type": "name"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 42, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 63, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 66, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 67, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 69, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 76, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 79, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 80, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "33528574551", "text": "\nimport spacy\nimport pickle\nimport os\nimport pandas as pd\n\n#from TiposGenerales import *\n\nclass Clasificar:\n\n def __init__(self):\n\n #CARGAR DICCIONARIO CATEGORÍAS\n with open('/media/nofi-ai/NOFI_2022/DESARROLLO/ESA/DATA/categorias.pkl', 'rb') as f:\n self.categorias = pickle.load(f)\n # print(self.categorias)\n\n def UBICAR_EN_AUDIOTECA (self, nombreArchivo):\n\n #ANALIZAR TIPO (CLASIFICAR POR NOMBRE) PONER ETIQUETA SEGÚN ONTOLOGÍA AUDIOSET (GOOGLE)\n pass\n\n def CLASIFICAR_POR_NOMBRE(self, nombreArchivo='S-S AULLIDO LEJANO NOCHE.wav'):\n\n #ANALIZAR NOMBRE DE ARCHIVO PARA ESTIMAR A QUÉ CLASE PERTENECE:\n\n #RECIBE : NOMBRE\n #ENTREGA:\n\n\n\n #ABREVIATURAS Y EQUIVALENCIAS DE SENTIDO\n\n # TODO: EXTRAER TAG (TIPO)\n # TIPO: S-S, AMB, DIR, MUSICA, FX. ESTIMAR POR TAG (EJ: SI EL ARCHIVO\n # SE LLAMA \"S-S ...\") Y SINO, ESTIMAR POR FORMATO DE NOMBRE (EJ: MVI_7048\n # ES UN ARCHIVO DE CÁMARA).\n\n tag, nombre, extension = self.dividirTagNombreExt(nombreArchivo)\n\n nlp = spacy.load('es_core_news_lg')\n nombre = nlp(nombre)\n for token in nombre:\n print(token.text, token.pos_, token.dep_, token.rank, token.head.text)\n\n #extraer la palabra mas importante\n palabra_importante = nombre[0]\n\n import json\n\n with open('/media/nofi-ai/NOFI_2022/DESARROLLO/ESA/AUDIOTECA/DATA/ontology_es.json', 'r') as f:\n elementos = json.load(f)\n candidatos = {}\n for elemento in elementos:\n token = nlp(elemento['name_es'].lower())\n if token.similarity(palabra_importante) > 0.75:\n candidatos[token] = token.similarity(palabra_importante)\n # print(\"el tipo de sonido es: \", elemento['name_es'], \" por un porcentaje de \", token.similarity(palabra_importante)) # Imprime el nombre del elemento más similar\n max_value = max(candidatos.items(), key=lambda x: x[1])\n tipo_de_sonido = str(max_value[0])\n print(tipo_de_sonido)\n\n #EXTRAER RAIZ Y RESTO\n\n\n #TODO: HAY QUE ARMAR UN DICT CON ESTA ESTRUCTURA {RAIZ: palabra, RESTO: [RESTO1, ...]}.\n # EN EL FUTURO PODEMOS USAR MAS DATOS DE LAS PALABRAS\n\n\n\n # RESTO: QUITAR STOP WORDS. ENCONTRAR NÚCLEO (EJ: PASOS CEMENTO, \"PASOS\" ES EL\n # NÚCLEO)\n\n # INFERIR UBICACIÓN CORRECTA POR UBICACIÓN USUAL DE NÚCLEO. DEBE HABER UNA LISTA\n # DE PALABRAS (NÚCLEOS) COMUNES PARA CADA SECCIÓN DE LA AUDIOTECA.\n\n # BUSCAR EXCEPCIONES (EJ: SI \"PASOS\" VA SEGUIDO DE UNA CONSTRUCCIÓN ESPECÍFICA,\n # COMO \"PASOS CABALLO\") ESTO DEBE ENTENDERSE COMO PASOS DE CABALLO Y POR LO\n # TANTO, IR A ANIMALES.\n\n #UBICAR SONIDO EN CARPETA CORRECTA\n\n return tag, nombre,extension\n\n def CLASIFICAR_POR_AUDIO(self, nombreArchivo, instancia):\n\n #CHEQUEAR A QUE CLASE PERTENECE ANALIZANDO AUDIO TOMANDO\n #COMO PUNTO DE PARTIDA EL RESULTADO DE LA CLASIFICACIÓN POR NOMBRE\n\n #LA IDEA ES IMPLEMENTAR ESTO A PARTIR DE MODELO PREEXISTENTE DE TENSOR FLOW\n\n tipo = 'S-S'\n subtipo = 'SONO'\n return tipo, subtipo\n\n#UTILIDADES\n def dividirTagNombreExt(self, nombreArchivo):\n\n \"\"\"RECIBE NOMBRE DE ARCHIVO Y DEVUELVE TAG + NOMBRE \"\"\"\n # NORMALIZAR (pasamos a minúscula, borramos puntos y _)\n nombreArchivo, extension = os.path.splitext(nombreArchivo)\n nombreArchivo = nombreArchivo.lower()\n nombreArchivo = nombreArchivo.replace('.', ' ')\n nombreArchivo = nombreArchivo.replace('_', ' ')\n\n tag, *resto = nombreArchivo.split()\n nombre = \" \".join(resto)\n for categoria, etiquetas in self.categorias.items():\n # print(categoria, etiquetas)\n # print(\"tag \", tag)\n # Si la etiqueta está dentro de la lista de etiquetas de la categoría\n if tag in etiquetas:\n # Asigno a la variable 'tag' la palabra clave (categoría) actual\n tag_ok = categoria\n break\n try:\n tag = tag_ok\n except tag_ok == None:\n tag = '_'\n\n\n return tag, nombre, extension\n\n\n def ubicarRaiz(self, palabra, resto, path2Audioteca = '/home/nofi/AUDIOTECA/'):\n\n # RECIBE: UNA FRASE SEPARADA EN RAIZ Y RESTO + EL PATH DE LA AUDIOTECA\n # DEVUELVE: EL PATH FINAL DE UBICACIÓN DEL ARCHIVO\n\n\n \"\"\"El objetivo de esta función es buscar palabras clave en las distintas\n carpetas de la audioteca. Cada carpeta tiene que tener una lista de palabras}\n clave. Si encuentra la palabra en una de esas listas, devuelve el path donde\n se podrá ubicar el archivo\"\"\"\n\n raiz = path2Audioteca\n\n hits = []\n hitpath = []\n\n with open(raiz + 'CLAVES.pck', 'rb') as claves:\n CLAVES = pickle.load(claves)\n\n #ITERAR POR LISTA DE PALABRAS CLAVE, BUSCANDO SI ESTÁ PRESENTE NUESTRA\n # palabra (DADA A LA FUNCIÓN COMO ARGUMENTO).\n for carpeta in CLAVES.keys():\n for subcarpeta in CLAVES[carpeta].keys():\n\n #DE ENCONTRAR LA PALABRA CLAVE, SUMAR (UBICACIÓN Y PATH) A LISTA DE hits\n #print(CLAVES[carpeta][subcarpeta])\n if palabra in CLAVES[carpeta][subcarpeta].keys():\n path = path2Audioteca + carpeta + '/' + subcarpeta + '/'\n hitpath.append(path)\n hits.append([carpeta, subcarpeta, palabra])\n print ('HIT: ', CLAVES[carpeta][subcarpeta])\n print(hits)\n print(palabra)\n\n if len(hits) > 1:\n print('AMBIGÜEDAD')\n #SI len(hits) >= 1, tenemos un hit, FIJARSE PUNTAJE DE LA PALABRA CLAVE\n\n if len(hits) == 1:\n\n # SI ES < 3 CREAR POPUP PARA OBTENER PERMISO DEL USUARIO. SI ESTÁ OK,\n # DAR UN PUNTO A PALABRA CLAVE PARA ESA UBICACIÓN, COPIAR resto A LISTA EN\n # ESA UBICACIÓN Y return path\n print (hits)\n if CLAVES[hits[0][0]][hits[0][1]][hits[0][2]] < 3:\n #TODO: consultar resultado para evitar falso positivo\n CLAVES[hits[0][0]][hits[0][1]][hits[0][2]] += 1\n\n #if consultarResultado[0](raiz, hitpath):\n # CLAVES[hits[0]][hits[1]][hits[2]] += 1\n # return path\n #else:\n # return consultarResultado[1]\n\n else:\n\n CLAVES[hits[0][0]][hits[0][1]][hits[0][2]] += 1\n\n else:\n\n path = ubicarManualmente(raiz)\n\n direccion = separar(hitpath[-1])\n\n # CLAVES[direccion[-2]][direccion[-1]][palabra] = [1, []]\n print(path)\n\n\n return path\n\n #SI len(hits) > 1 tenemos una ambiguedad.\n\n #RESOLVER AMBIGUEDAD: COMPARAR RESTO PARA VER SI COINCIDE CON EL RESTO\n #DE CUALQUIER OTRA LISTA. SI COINCIDE EN UNA PALABRA PERO NO EN OTRA, DAR\n #PUNTAJE A ESA PALABRA.\n\n\n return path\n\n def consultarResultado(self, raiz = '/', path = None):\n\n if path == '/' or no:\n\n return False, ubicarManualmente(raiz)\n\n elif si:\n return True, None\n\n else:\n print ('error')\n\n def ubicarManualmente(self, raiz = '/', path = None):\n\n return path\n\n def separar(self, path):\n allparts = []\n while 1:\n parts = os.path.split(path)\n if parts[0] == path: # sentinel for absolute paths\n allparts.insert(0, parts[0])\n break\n elif parts[1] == path: # sentinel for relative paths\n allparts.insert(0, parts[1])\n break\n else:\n path = parts[0]\n allparts.insert(0, parts[1])\n return allparts\n\n\n\nif __name__ == '__main__':\n clasificar = Clasificar()\n print(clasificar.CLASIFICAR_POR_NOMBRE())\n\n\n\n #EJEMPLO DE USO\n\n # import TiposGenerales\n\n # arch = 'S-S PASOS CEMENTO'\n\n # tipo = Tipo (CLASIFICAR_POR_AUDIO(arch, CLASIFICAR_POR_NOMBRE(arch)))\n\n\n", "repo_name": "nofi-sys/SOUND_GEEZER", "sub_path": "ANALISIS/CLASIFICAR.py", "file_name": "CLASIFICAR.py", "file_ext": "py", "file_size_in_byte": 8085, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pickle.load", "line_number": 15, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 41, "usage_type": "call"}, {"api_name": "json.load", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}]} +{"seq_id": "31171706923", "text": "import os\nimport cv2\nimport numpy as np\nimport math\nimport datetime\n\nnow = datetime.datetime.now()\nDate = now.strftime(\"%d_%m_%Y\")\n\ndef get_image_path(image_name: str):\n now = datetime.datetime.now()\n Date = now.strftime(\"%d_%m_%Y\")\n\n image_path = f'Скриншоты/{Date}' + '/' + image_name\n\n return image_path\n\n\ndef get_image_colors(image_name: str):\n image_path = get_image_path(image_name)\n image = cv2.imread(image_path)\n\n image_colors = []\n\n for i in (1, 2, 3, 4):\n gbr_colors = image[image.shape[0] // i - 1, image.shape[1] // i - 1]\n image_colors.append(gbr_colors[::-1])\n\n return image_colors\n\n\ndef calculate_color_differences_percent(first_color: list, second_color: list):\n maximum_difference = math.sqrt(3 * 256 ** 2)\n\n color_difference = math.sqrt((first_color[0] - second_color[0]) ** 2 + (first_color[1] - second_color[1]) ** 2 + (\n first_color[2] - second_color[2]) ** 2)\n\n color_difference_percent = color_difference / maximum_difference * 100\n\n return color_difference_percent\n\n\ndef check_colors(image_colors: list, zone_colors: list):\n count = 0\n for i in image_colors:\n for j in zone_colors:\n if calculate_color_differences_percent(i, j) < 6.00:\n count += 1\n\n if count >= 2:\n return True\n else:\n return False\n\n\ndef get_zone_name(image_colors: list):\n orange_zone_colors = [[250, 223, 186], [204, 200, 182], [223, 213, 149]]\n red_zone_colors = [[196, 177, 190], [242, 200, 194], [215, 190, 156]]\n\n if check_colors(image_colors, orange_zone_colors):\n return 'В зоне ДНР/ЛНР'\n elif check_colors(image_colors, red_zone_colors):\n return 'В зоне ДНР/ЛНР'\n else:\n return 'Не в зоне ДНР/ЛНР'\n\n\n# result_column = {}\n# image_names = os.listdir(f'Скриншоты/{Date}')\n#\n# for image_name in image_names:\n# image_colors = get_image_colors(image_name)\n# zone_name = get_zone_name(image_colors)\n# result_column[image_name.lower()[:-4]] = zone_name\n#\n# print(result_column)\n\n\ndef get_territory_status(key_words_find: list, result_column: dict):\n territory_status = []\n for i in key_words_find:\n if i == 0:\n territory_status.append(0)\n else:\n row_list = i.split(', ')\n res_list = []\n for j in row_list:\n try:\n result_column[j]\n except KeyError:\n pass\n else:\n res_list.append(result_column[j])\n\n territory_status.append(res_list)\n\n return territory_status\n\n", "repo_name": "tishenko1234/Check_territory", "sub_path": "Zones.py", "file_name": "Zones.py", "file_ext": "py", "file_size_in_byte": 2660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datetime.datetime.now", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 33, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "7814762607", "text": "#!/usr/bin/env python3\n\n\"\"\"Create a list of seqtab files, optionally filtering by project\n\n\"\"\"\n\nfrom os import path, walk\nimport argparse\nimport logging\nimport csv\nimport sys\nimport re\nfrom operator import itemgetter\nfrom itertools import groupby\nfrom glob import glob\n\nlog = logging.getLogger(__name__)\n\ndef get_args(arguments):\n parser = argparse.ArgumentParser()\n parser.add_argument('infiles', nargs='+')\n parser.add_argument('--projects', nargs='*')\n\n parser.add_argument('-s', '--seqtabs', type=argparse.FileType('w'),\n help='list of seqtab files, one per line')\n parser.add_argument('-i', '--sample-info', type=argparse.FileType('w'),\n help='concatenated seq info files')\n\n return parser.parse_args(arguments)\n\n\ndef main(arguments):\n logging.basicConfig(\n level=logging.INFO, format=\"%(asctime)s %(levelname)s: %(message)s\")\n\n args = get_args(arguments)\n projects = set(args.projects) if args.projects else set()\n\n # input identifies specimens with 'sampleid' but downstream programs expect 'specimen'\n if args.sample_info:\n writer = csv.DictWriter(\n args.sample_info,\n fieldnames=['specimen', 'sample_name', 'project', 'batch', 'controls'],\n extrasaction='ignore')\n writer.writeheader()\n\n for fname in args.infiles:\n outdir = path.dirname(path.abspath(fname))\n with open(fname) as f:\n reader = csv.DictReader(f)\n for row in reader:\n # input identifies specimens with 'sampleid' but 'specimen' is expected downstream\n row['specimen'] = row['sampleid']\n if projects and row['project'] not in projects:\n continue\n seqtab = path.join(outdir, 'dada', row['specimen'], 'seqtab.csv')\n if not path.exists(seqtab):\n print(f'missing file for {row[\"specimen\"]}')\n continue\n\n if args.sample_info:\n writer.writerow(row)\n if args.seqtabs:\n args.seqtabs.write(seqtab + '\\n')\n\n\nif __name__ == '__main__':\n sys.exit(main(sys.argv[1:]))\n", "repo_name": "fhcrc/yapp", "sub_path": "bin/gather_seqtabs.py", "file_name": "gather_seqtabs.py", "file_ext": "py", "file_size_in_byte": 2202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 20, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 24, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 34, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 48, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 68, "usage_type": "attribute"}]} +{"seq_id": "42603440473", "text": "# A minimal setup.py file to make a Python project installable.\n\nimport setuptools\nimport yaml\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nwith open(\"binder/environment.yml\", \"r\") as fh:\n env = yaml.safe_load(fh)\nrequirements = [a.split('=', 1)[0].strip() for a in env['dependencies'] ]\n\nsetuptools.setup(\n name = \"geostacks\",\n version = \"0.0.1\",\n author = \"The GeoStacks Team\",\n #author_email = \"me@myemail.com\",\n description = \"A Python library for querying, stacking, masking, and slicing disparate geospatial data\",\n long_description = long_description,\n long_description_content_type = \"text/markdown\",\n packages = setuptools.find_packages(),\n classifiers = [\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: BSD License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires = '>= 3.7',\n install_requires = requirements,\n)", "repo_name": "geostacks/GeoStacks", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "3", "api": [{"api_name": "yaml.safe_load", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 13, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "18214412972", "text": "import pytest\n\nimport numpy as np\n\nfrom dask.distributed import Client\n\nfrom cuml.test.utils import unit_param, quality_param, stress_param\n\n\n@pytest.mark.parametrize('nrows', [unit_param(1e3), quality_param(1e5),\n stress_param(1e6)])\n@pytest.mark.parametrize('ncols', [unit_param(10), quality_param(100),\n stress_param(1000)])\n@pytest.mark.parametrize('centers', [10])\n@pytest.mark.parametrize(\"cluster_std\", [0.1])\n@pytest.mark.parametrize(\"dtype\", [np.float32, np.float64])\n@pytest.mark.parametrize(\"nparts\", [unit_param(1), unit_param(7),\n quality_param(100),\n stress_param(1000)])\n@pytest.mark.parametrize(\"output\", ['array', 'dataframe'])\ndef test_make_blobs(nrows,\n ncols,\n centers,\n cluster_std,\n dtype,\n nparts,\n cluster,\n output):\n\n c = Client(cluster)\n try:\n from cuml.dask.datasets import make_blobs\n\n X, y = make_blobs(nrows, ncols,\n centers=centers,\n cluster_std=cluster_std,\n dtype=dtype,\n n_parts=nparts,\n output=output)\n\n assert X.npartitions == nparts\n assert y.npartitions == nparts\n\n X = X.compute()\n y = y.compute()\n\n assert X.shape == (nrows, ncols)\n assert y.shape == (nrows, 1)\n\n if output == 'dataframe':\n assert len(y[0].unique()) == centers\n assert X.dtypes.unique() == [dtype]\n\n elif output == 'array':\n import cupy as cp\n assert len(cp.unique(y)) == centers\n assert y.dtype == dtype\n\n finally:\n c.close()\n", "repo_name": "Pranjal31/cuml", "sub_path": "python/cuml/test/dask/test_datasets.py", "file_name": "test_datasets.py", "file_ext": "py", "file_size_in_byte": 1871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "dask.distributed.Client", "line_number": 30, "usage_type": "call"}, {"api_name": "cuml.dask.datasets.make_blobs", "line_number": 34, "usage_type": "call"}, {"api_name": "cupy.unique", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cuml.test.utils.unit_param", "line_number": 10, "usage_type": "call"}, {"api_name": "cuml.test.utils.quality_param", "line_number": 10, "usage_type": "call"}, {"api_name": "cuml.test.utils.stress_param", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cuml.test.utils.unit_param", "line_number": 12, "usage_type": "call"}, {"api_name": "cuml.test.utils.quality_param", "line_number": 12, "usage_type": "call"}, {"api_name": "cuml.test.utils.stress_param", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cuml.test.utils.unit_param", "line_number": 17, "usage_type": "call"}, {"api_name": "cuml.test.utils.quality_param", "line_number": 18, "usage_type": "call"}, {"api_name": "cuml.test.utils.stress_param", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "43053937530", "text": "import boto, os, sys\nimport boto.ec2\nimport time\n#import config\n\nCOREOS_OCT22 = \"ami-00000025\"\n#COREOS_IMAGE = \"ami-00000003\"\n#COREOS_SNAP = \"ami-0000001d\"\n#COREOS_CONT = \"ami-0000001e\"\n#COREOS_IMAGE = \"56eed2c2-40b6-4a52-bd55-823457f0ee66\"\nEC2_ENDPOINT = \"130.240.233.106\"\nEC2_ACCESS_KEY=\"58433a1650b14ac2a62a9ad06b9cf1c9\"\nEC2_SECRET_KEY=\"2fa63408610b427a8e5a080126d70e0e\"\n\nboto_region = boto.ec2.regioninfo.RegionInfo(name=\"nova\", endpoint=EC2_ENDPOINT)\nboto_connection = boto.connect_ec2(\n aws_access_key_id=EC2_ACCESS_KEY,\n aws_secret_access_key=EC2_SECRET_KEY,\n is_secure=False,\n region=boto_region,\n port=8773,\n path=\"/services/Cloud\")\n\ndef add_instance():\n try:\n response = boto_connection.run_instances(\n #image_id=\"56eed2c2-40b6-4a52-bd55-823457f0ee66\",\n #COREOS_CONT, \n COREOS_OCT22,\n key_name=\"web_sync2\", \n instance_type=\"m1.tiny\", \n security_groups=[\"default\"]\n #min_count=instances_count,\n #max_count=instances_count\n )\n\n for instance in response.instances: #waiting for the instance to ge an ip\n while instance.private_ip_address == \"\":\n instance.update()\n inst = response.instances[0]\n return inst\n\n\n\n except Exception as e:\n raise e\n #print \"Exception when creating node: \"+ str(e) \n\n\ndef remove_instance(instance,ip): #removes the instance provided\n instances = [instance]\n if (boto_connection.disassociate_address(ip)): #disassociate ip from instance before removing to avoid errors\n boto_connection.terminate_instances(instances)\n boto_connection.release_address(ip)\n #return \"vm at \"+ ip +\" removed\"\n #boto_connection.terminate_instances(instances)\n #time.sleep(1) #sleep because instance needs to be terminated before ip is removed\n #ip = (str(addr).split(\":\"))[1]\n #boto_connection.release_address(ip)\n #return \"something went wrong when removing vm.. :<\"\n\ndef get_floating():\n addr = boto_connection.allocate_address()\n return (str(addr).split(\":\"))[1]\n\n\ndef assign_floating(instance, ip):\n boto_connection.associate_address(instance,ip)\n\n\n", "repo_name": "Gegga87/WebSync", "sub_path": "manager/openstack_manager.py", "file_name": "openstack_manager.py", "file_ext": "py", "file_size_in_byte": 2326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "boto.ec2.regioninfo.RegionInfo", "line_number": 15, "usage_type": "call"}, {"api_name": "boto.ec2", "line_number": 15, "usage_type": "attribute"}, {"api_name": "boto.connect_ec2", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "5004393343", "text": "# https://www.acmicpc.net/problem/2178\n# DFS/ BFS\nimport time\nimport sys\n# input = sys.stdin.readline\n# N = 100\n# M = 100\n# time :1s => 40,000,000\n# 시간 복잡도 : O(?)\nfrom collections import deque\n\ndef bfs(si,sj):\n q = deque()\n q.append((si,sj))\n visited[si][sj] = 1\n\n while q:\n ci,cj = q.popleft()\n for di,dj in (-1,0),(1,0),(0,1),(0,-1):\n ni,nj = ci+di, cj+dj\n if 0<=ni {self.info_file} -try fixing some format.')\r\n else:\r\n # for the directory for code should be present\r\n directory = os.path.normpath(info[\"directory\"])\r\n if os.path.isdir(directory):\r\n self.directory = directory \r\n else :\r\n raise Exception('Directory not found') \r\n \r\n def file_reader(self,file_name):\r\n print(f'reading file => {file_name} 🕵️‍')\r\n # try except block for error \r\n try:\r\n with open(file_name ,mode = 'r',encoding=\"utf8\") as code:\r\n return code.read()\r\n except :\r\n raise Exception(\"file reading error -check file name and location (we are using encoding='utf8' for decoding)\")\r\n\r\n def question(self,data):\r\n # add heading for questions\r\n que = self.document.add_heading(data, level=1)\r\n font = que.style.font\r\n font.size = Pt(20)\r\n font.color.rgb = RGBColor(0,0,0)\r\n \r\n def code(self,data):\r\n # sol\r\n ans = self.document.add_paragraph(\"\")\r\n ans.add_run('Sol.').bold = True\r\n\r\n # add code to the doc\r\n code = self.document.add_paragraph(data)\r\n # indent for code\r\n paragraph_format = code.paragraph_format\r\n paragraph_format.left_indent = Inches(0.5)\r\n font = code.style.font\r\n font.size = Pt(14)\r\n font.color.rgb = RGBColor(0,0,50)\r\n \r\n def image(self,image):\r\n # add iamge to the doc\r\n # output\r\n output = self.document.add_paragraph(\"\")\r\n output.add_run('output.').bold = True\r\n \r\n # image width and height of image\r\n w,h = Image.open(image).size\r\n\r\n # big - 1920 1080 # idle - 700 _\r\n if w < self.max_img_width:\r\n self.document.add_picture(image)\r\n else:\r\n self.document.add_picture(image,width=Inches(7.5))\r\n \r\n def data_block(self,question,code_data,ss):\r\n # write questions as heading in bold\r\n if self.block[\"question\"] :\r\n self.question(question) # print(question)\r\n\r\n if self.block[\"solution\"] :\r\n self.code(code_data) # print(code_data)\r\n \r\n if self.block[\"picture\"] : \r\n self.image(ss) # print(ss)\r\n \r\n def create_file(self):\r\n # write file name at top in center\r\n if self.isHeading:\r\n heading = self.document.add_heading(self.file_name, 0) # print(self.file_name)\r\n heading.alignment = 1\r\n\r\n if len(self.questions) != len(self.files):\r\n raise Exception(\"question should have files to use and files should have questions check the info file for corrections \")\r\n\r\n for question,files in zip(self.questions,self.files):\r\n # question # code = self.file_reader(files[0]) # ss = files[1]\r\n self.data_block(question,self.file_reader(files[0]),files[1])\r\n\r\n if self.new_page_new_question and question != self.questions[-1]:\r\n self.document.add_page_break()\r\n\r\n if self.isEnd_name:\r\n # add name of the student in the end the file \r\n hr = self.document.add_paragraph(\"\")\r\n hr.add_run(\"_____________________________________________________________________\").bold = True\r\n hr.alignment = 1\r\n para = ''\r\n for key,value in zip(self.userinfo.keys(),self.userinfo.values()):\r\n para += f'{key} : {value}\\n' \r\n else : para = para[:-1]\r\n\r\n end_userinfo = self.document.add_paragraph(para)\r\n font = end_userinfo.style.font\r\n font.color.rgb = RGBColor(0,0,0)\r\n\r\n def walk_in_dir(self):\r\n print(self.directory)\r\n os.chdir(self.directory)\r\n print(f\"Dir changed to {self.directory}\")\r\n\r\n @staticmethod\r\n def open_file(file_name):\r\n # to open the file for results \r\n print(\"opening file for checking ...\")\r\n os.startfile(file_name+'.docx')\r\n\r\n @classmethod\r\n def runner(cls,info_file,new_page_new_question = False):\r\n with cls(info_file,new_page_new_question) as clas:\r\n # change dir to the directory for reading and saving the data\r\n clas.walk_in_dir()\r\n \r\n # this will create file and save all the changes\r\n clas.create_file()\r\n file_name = clas.file_name\r\n \r\n # to open the after it saved\r\n cls.open_file(file_name)\r\n\r\nif __name__ == \"__main__\":\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('-n','--newPage',action=\"store_true\",help = \"for get every new question on new page.\")\r\n parser.add_argument('-i','--infofile', default = 'info.json', help = \"json data file info-file default (info.json).\")\r\n \r\n args = parser.parse_args()\r\n \r\n project_creator.runner(args.infofile,new_page_new_question = args.newPage)\r\n", "repo_name": "rishi23root/class_automations", "sub_path": "project_automation/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6771, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "docx.Document", "line_number": 19, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 23, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 24, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 25, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 26, "usage_type": "call"}, {"api_name": "json.load", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "docx.shared.Pt", "line_number": 71, "usage_type": "call"}, {"api_name": "docx.shared.RGBColor", "line_number": 72, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 83, "usage_type": "call"}, {"api_name": "docx.shared.Pt", "line_number": 85, "usage_type": "call"}, {"api_name": "docx.shared.RGBColor", "line_number": 86, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 95, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 95, "usage_type": "name"}, {"api_name": "docx.shared.Inches", "line_number": 101, "usage_type": "call"}, {"api_name": "docx.shared.RGBColor", "line_number": 142, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 146, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 153, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 169, "usage_type": "call"}]} +{"seq_id": "71011962642", "text": "from collections.abc import Iterable\nfrom collections import OrderedDict\nfrom typing import Dict, Sequence, List, overload, Union\n\nimport ugraph as ug\nfrom ugraph import Tensor, Graph\n\n\nclass Executor(ug._ugraph_py_api.Executor):\n def __init__(self, graph: Graph, is_training: bool) -> None:\n return super(Executor, self).__init__(graph, is_training)\n\n @overload\n def forward(\n self, inputs: Sequence[Union[Tensor, None]], expect_outputs: Sequence[int] = []\n ) -> List[Tensor]:\n ...\n\n @overload\n def forward(\n self, inputs: Dict[str, Tensor], expect_outputs: Sequence[str] = []\n ) -> List[Tensor]:\n ...\n\n def inputs(self) -> List[Tensor]:\n return [ug.Tensor(tensor) for tensor in self._get_inputs()]\n\n def outputs(self) -> List[Tensor]:\n return [ug.Tensor(tensor) for tensor in self._get_outputs()]\n\n def forward(self, inputs, expect_outputs=[]) -> List[Tensor]:\n if not isinstance(inputs, Iterable):\n raise TypeError(\"executor inputs must be sequence of tensor\")\n\n if not isinstance(expect_outputs, Iterable):\n raise TypeError(\"executor expect_outputs must be sequence of str or int\")\n\n self._forward(inputs, expect_outputs)\n output_tensor = [ug.Tensor(tensor) for tensor in self._get_outputs()]\n\n if not expect_outputs:\n result = output_tensor\n else:\n if all([isinstance(output, int) for output in expect_outputs]):\n result = [output_tensor[idx] for idx in expect_outputs]\n elif all([isinstance(output, str) for output in expect_outputs]):\n output_names = self.get_output_names()\n output_name_map = dict(zip(output_names, output_tensor))\n result = [\n output_name_map[output_name] for output_name in expect_outputs\n ]\n else:\n raise KeyError(\"expect_outputs must be list of int or string\")\n\n if len(result) == 1:\n return result[0]\n else:\n return result\n\n\nclass StaticRunner:\n def __init__(self, module: \"ug.nn.Module\") -> None:\n self._is_init = False\n self._module = module\n\n def _build(self, *args, **kwargs):\n self._graph, is_training = ug.Graph.from_module(self._module, *args, **kwargs)\n self._executor = ug.Executor(self._graph, is_training)\n\n pos_arg_len = len([arg for arg in args if isinstance(arg, Tensor)])\n self._kwargs_index_map = OrderedDict()\n for key, value in kwargs.items():\n if isinstance(value, Tensor):\n self._kwargs_index_map[key] = pos_arg_len\n pos_arg_len += 1\n self._total_arg_len = pos_arg_len\n\n self._is_init = True\n\n def _to_executor_input(self, *args, **kwargs):\n inputs = [arg for arg in args if isinstance(arg, Tensor)]\n\n inputs.extend([None for _ in range(self._total_arg_len - len(inputs))])\n for key, value in kwargs.items():\n if isinstance(value, Tensor):\n inputs[self._kwargs_index_map[key]] = value\n\n return inputs\n\n def __call__(self, *args, **kwargs):\n return self.forward(*args, **kwargs)\n\n def forward(self, *args, **kwargs):\n if not self._is_init:\n self._build(*args, **kwargs)\n\n return self._executor.forward(self._to_executor_input(*args, **kwargs))\n", "repo_name": "tingkuanpei/ugraph", "sub_path": "python/ugraph/executor.py", "file_name": "executor.py", "file_ext": "py", "file_size_in_byte": 3430, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "ugraph._ugraph_py_api", "line_number": 9, "usage_type": "attribute"}, {"api_name": "ugraph.Graph", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 15, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.overload", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.overload", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 22, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 26, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 25, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 28, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 28, "usage_type": "name"}, {"api_name": "collections.abc.Iterable", "line_number": 32, "usage_type": "argument"}, {"api_name": "collections.abc.Iterable", "line_number": 35, "usage_type": "argument"}, {"api_name": "ugraph.Tensor", "line_number": 39, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "ugraph.Tensor", "line_number": 31, "usage_type": "name"}, {"api_name": "ugraph.Graph.from_module", "line_number": 67, "usage_type": "call"}, {"api_name": "ugraph.Graph", "line_number": 67, "usage_type": "attribute"}, {"api_name": "ugraph.Executor", "line_number": 68, "usage_type": "call"}, {"api_name": "ugraph.Tensor", "line_number": 70, "usage_type": "argument"}, {"api_name": "collections.OrderedDict", "line_number": 71, "usage_type": "call"}, {"api_name": "ugraph.Tensor", "line_number": 73, "usage_type": "argument"}, {"api_name": "ugraph.Tensor", "line_number": 81, "usage_type": "argument"}, {"api_name": "ugraph.Tensor", "line_number": 85, "usage_type": "argument"}]} +{"seq_id": "20783126342", "text": "import logging\nimport datetime\nimport inspect\nfrom modules.bcolors import BColors\n\n\nclass Logger:\n _instance = None\n\n def __new__(cls):\n if cls._instance is None:\n cls._instance = super().__new__(cls)\n cls._instance.log = logging.getLogger(\"music-discord-bot\")\n formatter = logging.Formatter(\n \"%(asctime)s - %(name)s - %(levelname)s - %(message)s - Line %(lineno)d\"\n )\n file_handler = logging.FileHandler(\"music-dl-bot.log\")\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(formatter)\n cls._instance.log.addHandler(file_handler)\n logging.basicConfig(\n filename=\"music-dl-bot.log\",\n filemode=\"w\",\n format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s - Line %(lineno)d\",\n level=logging.DEBUG,\n )\n return cls._instance\n\n @staticmethod\n def info(message: str):\n module_name = inspect.stack()[1].filename.split(\"/\")[-1].split(\".\")[0]\n print(\n f\"{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} \"\n + BColors.BOLD\n + BColors.OKBLUE\n + \"INFO \"\n + BColors.ENDC\n + BColors.OKCYAN\n + f\" {module_name} \"\n + BColors.ENDC\n + message\n )\n Logger._instance.log.info(f\"{message}\\n\")\n\n @staticmethod\n def error(message: str):\n module_name = inspect.stack()[1].filename.split(\"/\")[-1].split(\".\")[0]\n print(\n f\"{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} \"\n + BColors.BOLD\n + BColors.FAIL\n + \"ERROR \"\n + BColors.ENDC\n + BColors.OKCYAN\n + f\" {module_name} \"\n + BColors.ENDC\n + f\" {inspect.stack()[1].filename} - Line {inspect.stack()[1].lineno} \"\n + message\n )\n Logger._instance.log.error(f\"{message}\\n\")\n", "repo_name": "EsoCoding/discord-bot-py", "sub_path": "modules/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 2016, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 25, "usage_type": "attribute"}, {"api_name": "inspect.stack", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors.BOLD", "line_number": 34, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 34, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.OKBLUE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 35, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.ENDC", "line_number": 37, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 37, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.OKCYAN", "line_number": 38, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 38, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.ENDC", "line_number": 40, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 40, "usage_type": "name"}, {"api_name": "inspect.stack", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors.BOLD", "line_number": 50, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 50, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.FAIL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 51, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.ENDC", "line_number": 53, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 53, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.OKCYAN", "line_number": 54, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 54, "usage_type": "name"}, {"api_name": "modules.bcolors.BColors.ENDC", "line_number": 56, "usage_type": "attribute"}, {"api_name": "modules.bcolors.BColors", "line_number": 56, "usage_type": "name"}, {"api_name": "inspect.stack", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "9849042554", "text": "import copy\nimport datetime as DT\nimport ipaddress\nfrom typing import Dict, List, Optional, Set, Tuple, Union\n\nfrom fixpoint_coding_test.Server import Server, csv_to_params\n\n\nclass Network:\n \"\"\"\n 同一ネットワークサブネット内のサーバーをまとめて管理する\n \"\"\"\n\n subnet_ipaddress: ipaddress.IPv4Network\n servers: List[Server]\n\n def __init__(self, subnet_ipaddress: ipaddress.IPv4Network) -> None:\n self.subnet_ipaddress: ipaddress.IPv4Network = subnet_ipaddress\n self.servers: List[Server] = []\n\n def add_server(self, server: Server) -> None:\n \"\"\"\n ネットワークにサーバーを追加する\n\n Parameters\n ----------\n server : Server\n ネットワークに登録する`Server`インスタンス\n\n Raises\n ------\n ValueError\n 追加するサーバーがネットワークに所属していない\n \"\"\"\n\n if not (self.is_inside_network_ip(server=server)):\n raise ValueError(\"This server is not this network's subset.\")\n self.servers.append(server)\n\n def is_inside_network_ip(self, server: Server) -> bool:\n \"\"\"\n ネットワークにサーバーが所属可能か検査する\n\n Parameters\n ----------\n server : Server\n 確認を行う`Server`インスタンス\n\n Returns\n -------\n bool\n サブネットの範囲内であれば`True`\n \"\"\"\n\n return server.ip_address in self.subnet_ipaddress\n\n def get_network_downtime(self, continuous: int = 3) -> List[Tuple[DT.datetime, Optional[DT.datetime]]]:\n \"\"\"\n ネットワークがダウンしている期間の開始日時と終了日時を取得する。\n ネットワークがダウンしている期間は、全てのサーバーがダウンしている最短期間となる。\n\n Parameters\n ----------\n continuous : int, default = 3\n サーバーがダウンしていることを判定するために、何度連続で応答が無いかを決定する閾値。\n\n Returns\n -------\n List[Tuple[DT.datetime, Optional[DT.datetime]]]\n ネットワークがダウンしている開始日時と終了日時のペア。ログの末尾までダウンしている場合、終了日時は`None`となる。\n\n Raises\n ------\n ValueError\n `continuous` が 0以下に指定された\n\n\n Example1\n --------\n 出力は、実際には`datetime`オブジェクト(もしくは`None`)である点に注意。\\n\n server A -> 2020-10-13 10:00:00 ~ 2020-10-13 10:45:00\\n\n server B -> 2020-10-13 10:15:00 ~ 2020-10-13 11:00:00\\n\n Result: [\"2020-10-13 10:15:00\", 2020-10-13 10:45:00]\n\n Example2\n --------\n 出力は、実際には`datetime`オブジェクト(もしくは`None`)である点に注意。\\n\n server A -> 2020-10-13 10:00:00 ~ None\\n\n server B -> 2020-10-13 10:15:00 ~ None\\n\n Result: [\"2020-10-13 10:15:00\", None]\n \"\"\"\n\n if not (continuous > 0):\n raise ValueError(f\"continuous must over 0 (now {continuous})\")\n\n # ネットワーク内の全てのサーバーのダウンタイムを取得する\n downtimes: Dict[ipaddress.IPv4Interface, List[Tuple[DT.datetime, Optional[DT.datetime]]]] = {\n server.ip_address: server.get_downtimes(continuous=continuous) for server in self.servers\n }\n\n results_set: Set[Tuple[DT.datetime, Optional[DT.datetime]]] = set()\n\n for ip_address, _tmp_downtimes in downtimes.items():\n for outer_start, outer_end in _tmp_downtimes:\n _inside_results = False\n for inner_start, inner_end in results_set:\n # 既存のリザルトにデータが存在している場合、スキップ\n if is_overlap_time(outer_start, outer_end, inner_start, inner_end):\n _inside_results = True\n break\n if _inside_results:\n continue\n\n for server in self.servers:\n if server.ip_address is ip_address:\n continue # 自身に対してはスキップする\n\n for inner_start, inner_end in downtimes[server.ip_address]:\n if is_overlap_time(outer_start, outer_end, inner_start, inner_end):\n # 開始時刻の更新: 遅い方に変更する\n outer_start = max(outer_start, inner_start)\n\n # 終了時刻の更新\n if inner_end is None:\n # inner_endがNoneな場合 -> outer_endを残す = Do nothing.\n pass\n elif outer_end is None:\n # outer_endがNoneな場合 -> inner_endを残す\n outer_end = inner_end\n else:\n # どちらでもないときは終了時刻を早い方に変更\n outer_end = min(outer_end, inner_end)\n\n # 有効なペアが発見できているため、同一サーバ内の追加確認をスキップ\n break # breakするとfor文のelse処理に到達せずにおわる\n else:\n # 全てのパターンで条件に当てはまらなかったため、外側のペアを進める\n break\n else:\n # パターンが見つかったため、resultsに登録\n results_set.add((outer_start, outer_end))\n\n return sorted(list(results_set))\n\n\ndef is_overlap_time(\n start1: DT.datetime, end1: Optional[DT.datetime], start2: DT.datetime, end2: Optional[DT.datetime]\n) -> bool:\n \"\"\"\n 2つの時刻の範囲が重複しているか判定する。\n \"\"\"\n flag_s1_e2 = end2 is None or start1 <= end2\n flag_e1_s2 = end1 is None or end1 >= start2\n\n return flag_s1_e2 and flag_e1_s2\n\n\ndef load_data(file_path: str, networks: List[Network] = []) -> List[Network]:\n \"\"\"\n ログデータからネットワーク切り分けの行われたサーバーデータを生成する\n\n Parameters\n ----------\n file_path : str\n ログデータのファイルパス\n networks : List[Network], optional\n 既存のデータがある場合のみ指定。\n 追記形式でデータを読み込む\n\n Returns\n -------\n List[Network]\n IPアドレスで切り分けられたネットワークリスト\n \"\"\"\n\n _networks = copy.copy(networks)\n with open(file_path) as f:\n _ = f.readline() # ファイルの先頭は説明文なので読み飛ばす\n for line in f.readlines():\n line_strip = line.strip()\n if len(line_strip) == 0: # 入力が空の場合は処理をスキップ\n continue\n datetime, ip_address, response_msec = csv_to_params(line_strip)\n _tmp_ip = ipaddress.IPv4Interface(ip_address)\n for network in _networks:\n if network.subnet_ipaddress == _tmp_ip.network:\n # 既存ネットワーク上にデータを記録する\n for server in network.servers:\n if server.ip_address == _tmp_ip:\n # 既存のサーバに記録する\n server.append_ping_results(datetime_str=datetime, response_msec=response_msec)\n break\n else:\n # 新規サーバーに記録する\n server = Server(ip_address=ip_address)\n server.append_ping_results(datetime_str=datetime, response_msec=response_msec)\n network.add_server(server=server)\n break\n else:\n # 新規ネットワーク & 新規サーバに記録する\n server = Server(ip_address=ip_address)\n server.append_ping_results(datetime_str=datetime, response_msec=response_msec)\n\n network = Network(_tmp_ip.network)\n network.add_server(server=server)\n _networks.append(network)\n\n return _networks\n\n\ndef print_networks_error(\n networks: List[Network],\n continuous: int = 3,\n with_server_timeout: bool = True,\n with_server_overload: bool = True,\n time_threshold: int = 100,\n) -> None:\n \"\"\"ネットワーク内のエラー情報を含めたサーバ���エラー情報を出力する\n\n Parameters\n ----------\n networks : List[Network]\n 表示するネットワークリスト\n continuous : int, default = 3\n ダウン/過負荷状態と判定するために、何応答分まとめて処理を行うかの指定。\n with_server_timeout : bool, default = True\n サーバータイムアウト情報を同時に出力するかどうかを指定する\n with_server_overload : bool, default = True\n サーバー過負荷情報を同時に出力するかどうかを指定する\n time_threshold : int, default = 100\n 過負荷状態と判定するための応答時間閾値\n\n Raises\n ------\n ValueError\n 入力値の入力範囲外の値が入力された\n \"\"\"\n\n if not (continuous > 0):\n raise ValueError(f\"continuous must over 0 (now {continuous})\")\n if not (time_threshold > 0):\n raise ValueError(f\"time_threshold must over 0 (now {time_threshold})\")\n\n def _add_label(\n time_pair: Tuple[DT.datetime, Optional[DT.datetime]],\n address: Union[ipaddress.IPv4Interface, ipaddress.IPv4Network],\n label: str,\n ) -> Tuple[DT.datetime, Optional[DT.datetime], Union[ipaddress.IPv4Interface, ipaddress.IPv4Network], str]:\n return (time_pair[0], time_pair[1], address, label)\n\n SWITCH_DOWN_LABEL = \"switch down\"\n DOWNTIME_LABEL = \"server down\"\n OVERLOAD_LABEL = \"server overload\"\n\n for network in networks:\n network_downtime_list_pre = network.get_network_downtime(continuous=continuous)\n network_downtime_list = [\n _add_label(data, network.subnet_ipaddress, SWITCH_DOWN_LABEL) for data in network_downtime_list_pre\n ]\n\n downtime_list = []\n overload_list = []\n for server in network.servers:\n if with_server_timeout:\n downtime_list_pre = server.get_downtimes(continuous=continuous)\n downtime_list.extend(\n [_add_label(data, server.ip_address, DOWNTIME_LABEL) for data in downtime_list_pre]\n )\n if with_server_overload:\n overload_list_pre = server.get_overload_times(continuous=continuous, time_threshold=time_threshold)\n overload_list.extend(\n [_add_label(data, server.ip_address, OVERLOAD_LABEL) for data in overload_list_pre]\n )\n\n errors_list = sorted(network_downtime_list + downtime_list + overload_list, key=lambda x: x[0:2])\n if len(errors_list) != 0:\n print(f\"{network.subnet_ipaddress}\", \"has error\" if len(network_downtime_list) != 0 else \"summary\")\n for start, end, address, label in errors_list:\n print(f\" {address} {label} {start} ~ {end if end is not None else ''}\")\n else:\n print(f\"{network.subnet_ipaddress} has no error\")\n", "repo_name": "Hansyo/fixpoint-coding-test", "sub_path": "fixpoint_coding_test/Network.py", "file_name": "Network.py", "file_ext": "py", "file_size_in_byte": 11690, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "ipaddress.IPv4Network", "line_number": 14, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "fixpoint_coding_test.Server.Server", "line_number": 15, "usage_type": "name"}, {"api_name": "ipaddress.IPv4Network", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv4Network", "line_number": 18, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 19, "usage_type": "name"}, {"api_name": "fixpoint_coding_test.Server.Server", "line_number": 19, "usage_type": "name"}, {"api_name": "fixpoint_coding_test.Server.Server", "line_number": 21, "usage_type": "name"}, {"api_name": "fixpoint_coding_test.Server.Server", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 97, "usage_type": "name"}, {"api_name": "ipaddress.IPv4Interface", "line_number": 97, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 97, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 97, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 101, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 101, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 158, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 176, "usage_type": "call"}, {"api_name": "fixpoint_coding_test.Server.csv_to_params", "line_number": 183, "usage_type": "call"}, {"api_name": "ipaddress.IPv4Interface", "line_number": 184, "usage_type": "call"}, {"api_name": "fixpoint_coding_test.Server.Server", "line_number": 195, "usage_type": "call"}, {"api_name": "fixpoint_coding_test.Server.Server", "line_number": 201, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 245, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 245, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 245, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 246, "usage_type": "name"}, {"api_name": "ipaddress.IPv4Interface", "line_number": 246, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv4Network", "line_number": 246, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 248, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 248, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 248, "usage_type": "name"}, {"api_name": "ipaddress.IPv4Interface", "line_number": 248, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv4Network", "line_number": 248, "usage_type": "attribute"}]} +{"seq_id": "1524055117", "text": "import torch\nimport torch.nn as nn\n\nimport time\nimport numpy as np\n\n\n#########################################\n# CORE MODELS #\n#########################################\n\nclass Simple(nn.Module):\n def __init__(self, nb_hidden, nb_layer, input_dim, emb_dim=3):\n super(Simple, self).__init__()\n self.fc = nn.Linear(input_dim, emb_dim)\n\n def forward(self, hits):\n return self.fc(hits)\n\nclass Edge_MLP(nn.Module):\n def __init__(self, nb_hidden, nb_layer, input_dim, emb_dim):\n super(Edge_MLP, self).__init__()\n\n self.norm_set = False\n\n input_dim = input_dim+4 # add 4 for augmented features\n self.input_dim = input_dim\n self.emb_dim = emb_dim\n\n layers = [nn.Linear(input_dim, nb_hidden)]\n ln = [nn.Linear(nb_hidden, nb_hidden) for _ in range(nb_layer-1)]\n layers.extend(ln)\n self.layers = nn.ModuleList(layers)\n self.act1 = nn.ReLU()\n\n self.final_layer = nn.Linear(nb_hidden, 1)\n self.act2 = nn.Sigmoid()\n # self.dropout = nn.Dropout(p=0.5)\n\n def forward(self, hits):\n hits = augment_features(hits)\n hits = self.normalize(hits)\n for l in self.layers:\n hits = self.act1(l(hits))\n # hits = self.dropout(hits)\n hits = self.final_layer(hits)\n hits = self.act2(hits).squeeze()\n return hits\n\n def normalize(self, hits):\n try:\n hits = (hits-self.mean) / (self.std + 10**-9)\n except:\n self.mean = self.mean.to(device=hits.device)\n self.std = self.std.to(device=hits.device)\n hits = (hits-self.mean) / (self.std + 10**-9)\n return hits\n \n def set_norm(self, mean, std):\n self.norm_set = True\n self.mean = mean\n self.std = std\n\n#####################################################\n# FEATURE AUGMENT #\n#####################################################\ndef augment_features(hit_pairs):\n '''\n Augment hits with features derived from TrackML physics\n '''\n nb_feats_one_hit = hit_pairs.size(1) // 2\n x1, y1, z1 = get_xyz(hit_pairs[:, :nb_feats_one_hit])\n x2, y2, z2 = get_xyz(hit_pairs[:, nb_feats_one_hit:])\n\n dr = compute_dr(x1, y1, x2, y2)\n dphi = compute_dphi(x1, y1, x2, y2)\n rho = compute_rho(dr, dphi)\n\n z0 = compute_z0(x1, y1, z1, x2, y2, z2)\n\n aug_feats = torch.stack((dr, dphi, rho, z0), dim=1)\n hit_pairs = torch.cat((hit_pairs, aug_feats), dim=1)\n return hit_pairs\n\ndef compute_dr(x1, y1, x2, y2):\n dr = torch.pow(torch.pow(x2-x1, 2) + torch.pow(y2-y1, 2), 0.5)\n return dr\n\ndef compute_dphi(x1, y1, x2, y2):\n dphi = torch.acos(torch.cos(torch.atan2(y2, x2)-torch.atan2(y1,x1)))\n return dphi\n\ndef compute_rho(dr, dphi):\n rho = 0.5 * dr / (torch.sin(dphi) + 10**-8)\n return rho\n\ndef compute_z0(x1, y1, z1, x2, y2, z2):\n r1 = compute_r(x1, y1)\n r2 = compute_r(x2, y2)\n\n dr = r2 - r1\n dz = z2 - z1\n z0 = z1 - r1 * (dz / (dr + 10**-8))\n return z0\n\ndef compute_r(x, y):\n return torch.pow(torch.pow(x, 2) + torch.pow(y, 2), 0.5)\n\ndef get_xyz(hits):\n x = hits[:,0]/1000\n y = hits[:,1]/1000\n z = hits[:,2]/1000\n return x, y, z\n", "repo_name": "murnanedaniel/exatrkx-ctd2020", "sub_path": "MetricLearning/src/metric_learning_adjacent/train_filter/mlp_model.py", "file_name": "mlp_model.py", "file_ext": "py", "file_size_in_byte": 3260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.acos", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.atan2", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "20080049114", "text": "import copy\nimport unittest\nfrom collections import defaultdict\n\nimport torch\nfrom classy_vision.heads import build_head\nfrom classy_vision.models import build_model, ClassyModel\nfrom test.generic.config_utils import get_test_model_configs\nfrom test.generic.utils import compare_model_state\n\n\nclass TestClassyModel(unittest.TestCase):\n model_configs = get_test_model_configs()\n\n def _get_config(self, model_config):\n return {\n \"name\": \"classification_task\",\n \"num_epochs\": 12,\n \"loss\": {\"name\": \"test_loss\"},\n \"dataset\": {\n \"name\": \"imagenet\",\n \"batchsize_per_replica\": 8,\n \"use_pairs\": False,\n \"num_samples_per_phase\": None,\n \"use_shuffle\": {\"train\": True, \"test\": False},\n },\n \"meters\": [],\n \"model\": model_config,\n \"optimizer\": {\"name\": \"test_opt\"},\n }\n\n def _compare_model_state(self, state, state2):\n compare_model_state(self, state, state2)\n\n def test_build_model(self):\n for cfg in self.model_configs:\n config = self._get_config(cfg)\n model = build_model(config[\"model\"])\n self.assertTrue(isinstance(model, ClassyModel))\n self.assertTrue(\n type(model.input_shape) == tuple and len(model.input_shape) == 3\n )\n\n def test_get_set_state(self):\n config = self._get_config(self.model_configs[0])\n model = build_model(config[\"model\"])\n fake_input = torch.Tensor(1, 3, 224, 224).float()\n model.eval()\n state = model.get_classy_state()\n with torch.no_grad():\n output = model(fake_input)\n\n model2 = build_model(config[\"model\"])\n model2.set_classy_state(state)\n\n # compare the states\n state2 = model2.get_classy_state()\n self._compare_model_state(state, state2)\n\n model2.eval()\n with torch.no_grad():\n output2 = model2(fake_input)\n self.assertTrue(torch.allclose(output, output2))\n\n # test deep_copy by assigning a deep copied state to model2\n # and then changing the original model's state\n state = model.get_classy_state(deep_copy=True)\n\n model3 = build_model(config[\"model\"])\n state3 = model3.get_classy_state()\n\n # assign model2's state to model's and also re-assign model's state\n model2.set_classy_state(state)\n model.set_classy_state(state3)\n\n # compare the states\n state2 = model2.get_classy_state()\n self._compare_model_state(state, state2)\n\n def test_get_set_head_states(self):\n config = copy.deepcopy(self._get_config(self.model_configs[0]))\n head_configs = config[\"model\"][\"heads\"]\n config[\"model\"][\"heads\"] = []\n model = build_model(config[\"model\"])\n trunk_state = model.get_classy_state()\n\n heads = defaultdict(list)\n for head_config in head_configs:\n head = build_head(head_config)\n heads[head_config[\"fork_block\"]].append(head)\n model.set_heads(heads)\n model_state = model.get_classy_state()\n\n # the heads should be the same as we set\n self.assertEqual(len(heads), len(model.get_heads()))\n for block_name, hs in model.get_heads().items():\n self.assertEqual(hs, heads[block_name])\n\n model.clear_heads()\n self._compare_model_state(model.get_classy_state(), trunk_state)\n\n model.set_heads(heads)\n self._compare_model_state(model.get_classy_state(), model_state)\n", "repo_name": "facebookresearch/ClassyVision", "sub_path": "test/manual/models_classy_vision_model_test.py", "file_name": "models_classy_vision_model_test.py", "file_ext": "py", "file_size_in_byte": 3592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1563, "dataset": "github-code", "pt": "3", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "test.generic.config_utils.get_test_model_configs", "line_number": 13, "usage_type": "call"}, {"api_name": "test.generic.utils.compare_model_state", "line_number": 33, "usage_type": "call"}, {"api_name": "classy_vision.models.build_model", "line_number": 38, "usage_type": "call"}, {"api_name": "classy_vision.models.ClassyModel", "line_number": 39, "usage_type": "argument"}, {"api_name": "classy_vision.models.build_model", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 50, "usage_type": "call"}, {"api_name": "classy_vision.models.build_model", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 63, "usage_type": "call"}, {"api_name": "classy_vision.models.build_model", "line_number": 69, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 81, "usage_type": "call"}, {"api_name": "classy_vision.models.build_model", "line_number": 84, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 87, "usage_type": "call"}, {"api_name": "classy_vision.heads.build_head", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "14539593144", "text": "# Be sure to import python-docx using pip install and not built in install of docx modules cannot coexist as they both\n# have the root module name of docx\nimport docx\nfrom docx.enum.style import WD_STYLE_TYPE\nfrom docx.enum.text import WD_PARAGRAPH_ALIGNMENT\nfrom docx.oxml.ns import qn\nfrom docx.shared import Pt\nfrom hyperlink import add_hyperlink\nfrom os.path import abspath, dirname, join\nfrom docx.shared import Inches\nfrom docx.enum.text import WD_LINE_SPACING\n\nlines = []\nprint(\"Paste job description here and type exit when finished:\")\nwhile True:\n line = input()\n if line != 'exit':\n lines.append(line)\n else:\n break\njob_desc = ('\\n'.join(lines)).lower()\nexcluded = docx.Document()\nexcluded.save(\"Excluded.docx\")\ndocument = docx.Document()\n# change margins to 1 inch\nsections = document.sections\nfor section in sections:\n section.top_margin = Inches(1)\n section.right_margin = Inches(1)\n section.bottom_margin = Inches(1)\n section.left_margin = Inches(1)\ndocument.save(\"Michael Choi.docx\")\n\n# Style for Heading 1\nstyle = document.styles['Heading 1'].font\nstyle.color.rgb = docx.shared.RGBColor(0, 0, 0)\nstyle.name = 'Calibri'\nstyle.size = Pt(14)\n# Font change for heading and titles bugged in docx module asciiTheme also needed to be changed to Arial.\nrFonts = style.element.rPr.rFonts\nrFonts.set(qn('w:asciiTheme'), 'Calibri')\nparagraph_format = document.styles['Heading 1'].paragraph_format\nparagraph_format.space_before = Pt(0)\n\n# Style for Heading 2\nstyle = document.styles['Heading 2'].font\nstyle.color.rgb = docx.shared.RGBColor(0, 0, 0)\nstyle.name = 'Calibri'\nstyle.size = Pt(12)\nrFonts = style.element.rPr.rFonts\nrFonts.set(qn('w:asciiTheme'), 'Calibri')\n\n# Style for Normal paragraph font\nstyle = document.styles['Normal'].font\nstyle.name = 'Calibri'\nstyle.size = Pt(11)\n\n# Style used for information section under heading\nstyles = document.styles\nstyle = styles.add_style('info', WD_STYLE_TYPE.PARAGRAPH)\nstyle.base_style = styles['Normal']\nstyle = document.styles['info'].font\nstyle.name = 'Calibri'\nstyle.size = Pt(10)\nparagraph_format = document.styles['info'].paragraph_format\nparagraph_format.space_after = Pt(0)\n\n# Style for excluded paragraph font\nstyle = excluded.styles['Normal'].font\nstyle.name = 'Calibri'\nstyle.size = Pt(12)\n\n\nclass Format:\n def __init__(self, name, github):\n self.name = name\n self.github = github\n # Name and link to GitHub front page is centered\n document.add_heading(name, level=1).alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\n email = document.add_paragraph(\"Email: \", style='info')\n add_hyperlink(email, \"kinlonchoi@gmail.com\", \"kinlonchoi@gmail.com\")\n document.add_paragraph(\"Mobile: 07712636191\", style='info').alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\n paragraph = document.add_paragraph(\"Website: \", style='info')\n add_hyperlink(paragraph, github, github)\n email.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\n paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER\n\n # The following three functions are made to improve readability of code\n # This is the font used for titles\n def title(self, header):\n document.add_heading(header, level=2)\n\n # This is the font used for paragraphs\n def para(self, sentence):\n document.add_paragraph(sentence)\n\n # This is the font used for bullet points\n def bullet(self, sentence):\n document.add_paragraph(sentence, style='List Bullet')\n\n # Scans job description for keywords\n def scan(self, words_lists):\n for items in words_lists:\n for k, v in items:\n if k in ('python', 'R', 'program'):\n p = document.add_paragraph(v, style='List Bullet')\n add_hyperlink(p,\n \"CV Automation tool.\",\n \"https://github.com/KinLonChoi/Python-Projects\")\n break\n elif k in job_desc:\n self.bullet(v)\n break\n elif k in list(items)[-1]:\n excluded.add_paragraph(v, style='List Bullet')\n else:\n continue\n\n\ncv = Format(\"Michael Choi\", \"https://kinlonchoi.github.io/Data-Portfolio/index.html\")\ncv.title(\"Personal Profile\")\ngoogle_cert = document.add_paragraph(\"I am a highly motivated individual with over five years of experience in the science industry looking for a\"\n \" career change to pursue my passion for data analytics. I am passionate about data and its application and\"\n \" have recently earned the \")\n\nadd_hyperlink(google_cert, \"Google Data Analytics Professional Certificate.\", \"https://www.coursera.org/account/accomplishments/specialization/certificate/LLK5BTDKPCDC\")\n\n# Skills section add dictionary definition for each key words(search terms) with value(skills) to add as bullet point\ncv.title(\"Skills\")\n\n\n# Words to be searched for in job description\ncode = dict.fromkeys(['python', 'R', 'program'],\n \"Proficient with using Python and basic skills in R. Project: \")\n# Some search terms are shortened to match variations of words e.g. analy will match analytical, analysis etc.\nsql = dict.fromkeys(['sql', 'data', 'dbms', 'analy'],\n \"Knowledge of the use of relational databases in SQL and its advanced functions.\")\n\nalgo = dict.fromkeys(['tableau', 'visual'],\n \"Data visualisation using Tableau\")\n\ntime = dict.fromkeys(['spreadsheet', 'excel', 'sheets', 'google', 'vba', 'macro'],\n \"Advanced skills in excel/google sheets and its functions used in data cleaning (VBA & Macros).\")\n\nreport = dict.fromkeys(['commun', 'present', 'audience'],\n \"Communication skills: Can present to an audience of varying levels of knowledge.\")\n\nskill_list = [x.items() for x in (code, sql, algo, time, report)]\n\ncv.scan(skill_list)\n\n# Employment section same as before add dictionary definitions for search terms.\ncv.title(\"Employment\")\ncv.para(\"Sept 2016 – Sept 2021\t\t\t Tate & Lyle PLC\t\t\t\tQuality Analyst\")\n\nspecial = dict.fromkeys(['detail', 'standard', 'require', 'product'],\n \"Excellent attention to detail in ensuring products meets the required standards.\")\n\ndesign = dict.fromkeys(['strat' 'design', 'project'],\n \"Implemented and designed the appropriate analytic strategies for unique projects.\")\n\ncollab = dict.fromkeys(['collab', 'team', 'improve', 'meet'],\n \"Collaborated with other departments and developed continuous improvement strategies.\")\n\nemployment_list_1 = [x.items() for x in (special, design, collab)]\n\ncv.scan(employment_list_1)\n\ncv.para(\"Key achievements:\")\ncv.bullet(\"Automation of report process that saves 6 hours every week.\")\ncv.bullet(\"Created pivot tables that extract data from the SAP database for annual reports.\")\n\ncv.para(\"Feb 2016 – Sept 2016\t\t\t Tate & Lyle PLC\t\t\t Research scientist\")\n\nplan = dict.fromkeys(['organ', 'improve', 'projects', 'plan', 'continuous'],\n \"Organised new product development and continuous improvement (CI) projects.\")\n\nmeet = dict.fromkeys(['meet', 'progress', 'team'],\n \"Communicate results to the team and provide insights followed by recommendations.\")\n\nbusy = dict.fromkeys(['prior', 'time', 'effici', 'busy'],\n \"Prioritised and allocated time efficiently whilst working on several projects simultaneously.\")\n\nemployment_list_2 = [x.items() for x in (plan, meet, busy)]\n\ncv.scan(employment_list_2)\ncv.para(\"Key achievements:\")\ncv.bullet(\"Lead the CI program that leads to the safe removal of over 100 chemicals.\")\n\n# Education and Certification section\ncv.title(\"Education and Certification\")\ndate = document.add_paragraph(\"06/01/2022\t\t\", style='Normal')\nadd_hyperlink(date, \"Google Data Analytics Professional Certificate by Google (Coursera)\",\n \"https://www.coursera.org/account/accomplishments/certificate/ZCNSEFJ3GN6V\")\ndate.paragraph_format.space_before = 0\ndate.paragraph_format.space_after = 0\n\ndate = document.add_paragraph(\"07/11/2021\t\t\", style='Normal')\nadd_hyperlink(date, \"SQL for Data Science by University of California, Davis (Coursera)\",\n \"https://www.coursera.org/account/accomplishments/certificate/SYPGFMFPK3YA\")\ndate.paragraph_format.space_before = 0\ndate.paragraph_format.space_after = 0\n\ndate = document.add_paragraph(\"04/11/2021\t\t\", style='Normal')\nadd_hyperlink(date, \"Python for Everybody by University of Michigan (Coursera)\",\n \"https://www.coursera.org/account/accomplishments/specialization/certificate/DK3WDTXK4ND4\")\ndate.paragraph_format.space_before = 0\ndate.paragraph_format.space_after = 0\n\ncv.para(\"2011-2015\t\tMChem (Hons) in Chemistry (2:1) \t\t \t\t\t\t\t \tUniversity of Leicester\")\n\n# GCSE and A Levels might not be required\n# cv.para(\"2003-2011\t\tThe Bromfords School, Essex \t\t\t\t\t\t\t\t\"\n# \" A-Levels in Chemistry (B), Biology (B), and Physics (B) \t\t\t\t \"\n# \" 10 GCSEs at A*-C including Chemistry, Maths, and English\")\n\n# This will save file in same file directory as python file\ndocument.save(join(dirname(abspath(__file__)), \"Michael Choi.docx\"))\nexcluded.save(join(dirname(abspath(__file__)), \"Excluded.docx\"))\n", "repo_name": "KinLonChoi/Python-Projects", "sub_path": "Auto CV/cvmaker.py", "file_name": "cvmaker.py", "file_ext": "py", "file_size_in_byte": 9448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "docx.Document", "line_number": 22, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 24, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 28, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 29, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 30, "usage_type": "call"}, {"api_name": "docx.shared.Inches", "line_number": 31, "usage_type": "call"}, {"api_name": "docx.shared.RGBColor", "line_number": 36, "usage_type": "call"}, {"api_name": "docx.shared", "line_number": 36, "usage_type": "attribute"}, {"api_name": "docx.shared.Pt", "line_number": 38, "usage_type": "call"}, {"api_name": "docx.oxml.ns.qn", "line_number": 41, "usage_type": "call"}, {"api_name": "docx.shared.Pt", "line_number": 43, "usage_type": "call"}, {"api_name": "docx.shared.RGBColor", "line_number": 47, "usage_type": "call"}, {"api_name": "docx.shared", "line_number": 47, "usage_type": "attribute"}, {"api_name": "docx.shared.Pt", "line_number": 49, "usage_type": "call"}, {"api_name": "docx.oxml.ns.qn", "line_number": 51, "usage_type": "call"}, {"api_name": "docx.shared.Pt", "line_number": 56, "usage_type": "call"}, {"api_name": "docx.enum.style.WD_STYLE_TYPE.PARAGRAPH", "line_number": 60, "usage_type": "attribute"}, {"api_name": "docx.enum.style.WD_STYLE_TYPE", "line_number": 60, "usage_type": "name"}, {"api_name": "docx.shared.Pt", "line_number": 64, "usage_type": "call"}, {"api_name": "docx.shared.Pt", "line_number": 66, "usage_type": "call"}, {"api_name": "docx.shared.Pt", "line_number": 71, "usage_type": "call"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT.CENTER", "line_number": 79, "usage_type": "attribute"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT", "line_number": 79, "usage_type": "name"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 81, "usage_type": "call"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT.CENTER", "line_number": 82, "usage_type": "attribute"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT", "line_number": 82, "usage_type": "name"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 84, "usage_type": "call"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT.CENTER", "line_number": 85, "usage_type": "attribute"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT", "line_number": 85, "usage_type": "name"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT.CENTER", "line_number": 86, "usage_type": "attribute"}, {"api_name": "docx.enum.text.WD_PARAGRAPH_ALIGNMENT", "line_number": 86, "usage_type": "name"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 107, "usage_type": "call"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 126, "usage_type": "call"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 193, "usage_type": "call"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 199, "usage_type": "call"}, {"api_name": "hyperlink.add_hyperlink", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 219, "usage_type": "call"}]} +{"seq_id": "43048310142", "text": "'''\nEvaluate the value of an arithmetic expression in Reverse Polish Notation.\n\nValid operators are +, -, *, /. Each operand may be an integer or another expression.\n\nNote:\n\nDivision between two integers should truncate toward zero.\nThe given RPN expression is always valid. That means the expression would always evaluate to a result and there won't be any divide by zero operation.\n'''\n\n\n#\n# @lc app=leetcode id=150 lang=python3\n#\n# [150] Evaluate Reverse Polish Notation\n#\n\n# @lc code=start\nfrom collections import deque\n\n\nclass Solution:\n def evalRPN(self, tokens: List[str]) -> int:\n result = None\n q = deque([])\n for i in range(len(tokens)):\n if tokens[i] == \"+\":\n first = int(q.pop())\n second = int(q.pop())\n result = first + second\n q.append(result)\n\n elif tokens[i] == \"-\":\n first = int(q.pop())\n second = int(q.pop())\n result = second - first\n q.append(result)\n\n elif tokens[i] == \"*\":\n first = int(q.pop())\n second = int(q.pop())\n result = first * second\n q.append(result)\n\n elif tokens[i] == \"/\":\n first = int(q.pop())\n second = int(q.pop())\n if first * second < 0 and second % first != 0: # critical: differentiate when negative\n result = second // first + 1 # truncted toward zero\n else:\n result = second // first\n q.append(result)\n\n else:\n q.append(tokens[i])\n\n if result == None: # corner case, no operator found\n return int(tokens[-1])\n return result\n\n # @lc code=end\n\n '''\n [\"3\",\"11\",\"5\",\"+\",\"-\"] expected: -13\n [\"3\",\"11\",\"+\",\"5\",\"-\"] expected: 9\n [\"4\",\"-2\",\"/\",\"2\",\"-3\",\"-\",\"-\"] expected: -7\n\n (1) result also to be appended\n (2) a / b when negative AND non-integer will be farther from zero; when positive closer to zero\n '''\n", "repo_name": "joyceyu6/coding_courses", "sub_path": "2021_4_19_to372/Stack_150.evaluate-reverse-polish-notation.py", "file_name": "Stack_150.evaluate-reverse-polish-notation.py", "file_ext": "py", "file_size_in_byte": 2117, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "collections.deque", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "6147430832", "text": "import io\nimport zipfile\nimport logging\n\nimport click\nimport numpy as np\nimport pandas as pd\n\nfrom .config import get_config\nfrom .database import get_db\n\nconfig = get_config()\n\nlog = logging.getLogger(\"tcad\")\n\ncli = click.Group(name=\"tcad\", help=\"TCAD ETL Helpers\")\n\n\ndef _normalize_csv_schema_columns(columns):\n return [col.strip().lower() for col in columns]\n\n\n@cli.command(\"json\")\n@click.option(\"--table\", type=str, required=True)\n@click.option(\"--out\", type=str, default=\"data/out\")\ndef convert_to_json(table, out):\n \"\"\"Convert .mdb table export to json\"\"\"\n from rich.console import Console\n\n console = Console()\n\n table_schema_df = pd.read_csv(f\"data/schema/{table}.csv\", index_col=False)\n table_schema_df.columns = _normalize_csv_schema_columns(table_schema_df.columns)\n\n table_schema = table_schema_df.set_index(\"column_name\")\n table_schema = table_schema[~table_schema.index.isin([\"filler\"])]\n\n def _column_type_map(column_type):\n column_type = column_type.strip()\n\n if column_type.startswith(\"char\"):\n return str\n\n if column_type.startswith(\"int\"):\n return int\n\n if column_type.startswith(\"numeric\"):\n return str\n\n return str\n\n table_schema[\"column_type\"] = table_schema[\"column_type\"].map(_column_type_map)\n\n json_schema = table_schema[~table_schema.index.isin([\"filler\"])].to_dict(\n orient=\"index\"\n )\n\n col_names = []\n col_spec = []\n dtypes = {}\n\n for col_name, spec in json_schema.items():\n col_names.append(col_name)\n col_spec.append((spec[\"offset_start\"] - 1, spec[\"offset_end\"]))\n\n dtypes[col_name] = spec[\"column_type\"]\n\n tcad_data = pd.read_fwf(\n f\"data/tcad/{table}.txt\",\n names=col_names,\n colspecs=col_spec,\n dtype=dtypes,\n chunksize=config.CSV_LOAD_CHUNKSIZE,\n index_col=[\"prop_id\", \"prop_val_yr\", \"py_owner_id\", \"sup_num\"],\n )\n\n export_archive = f\"{out}/{table}.zip\"\n with zipfile.ZipFile(export_archive, \"w\") as property_file:\n total_row_count = 0\n for idx, df in enumerate(tcad_data):\n\n json_filename = f\"{table}.{idx}.json\"\n with property_file.open(json_filename, \"w\") as property_json:\n row_count = df.shape[0]\n console.print(\n f\"Total Exported: {total_row_count} rows Current File: {json_filename}\", # noqa\n end=\"\\r\", # noqa\n )\n console.print(\"\", end=\"\\r\")\n\n json_io = io.TextIOWrapper(property_json)\n df.to_json(json_io, orient=\"table\")\n\n total_row_count += row_count\n\n console.print(\n f\":white_check_mark: Exported: {total_row_count} rows\", emoji=True\n )\n\n console.print(\n f\"Export of {table} to {export_archive} complete! :tada:\", emoji=True\n )\n\n\n@cli.command(\"load_json\")\n@click.option(\"--table\", type=str, required=True)\ndef load_json(table):\n \"\"\"\n Import db table to db\n \"\"\"\n from rich import print\n\n load_json_to_db(table)\n\n print(f\"{table} import complete! :tada:\")\n\n\ndef load_json_to_db(\n table_name,\n filename=None,\n data_path=\"data/out\",\n chunksize=config.CSV_LOAD_CHUNKSIZE,\n schema=\"tcad\",\n):\n import pathlib\n import sqlalchemy as sa\n from rich.console import Console\n from rich.progress import track\n\n console = Console()\n\n if not filename:\n filename = f\"{table_name}.zip\"\n\n data_zip_path = pathlib.Path(data_path) / pathlib.Path(filename)\n\n db = get_db(schema=schema)\n\n with zipfile.ZipFile(data_zip_path, \"r\") as data_file, db as transaction:\n\n table = transaction.get_table(table_name.lower())\n\n table.delete()\n\n for json_file in track(\n [f for f in data_file.namelist() if f.endswith(\".json\")],\n description=f\"Loading {table_name}...\",\n ):\n with data_file.open(json_file, \"r\") as table_json:\n json_io = io.TextIOWrapper(table_json)\n\n tcad_data = pd.read_json(json_io, orient=\"table\")\n tcad_index = tcad_data.index.to_frame()\n\n col_types = {}\n\n for col, dtype in tcad_index.dtypes.to_dict().items():\n col_types[col] = sa.Text\n if dtype == np.int64:\n col_types[col] = sa.BigInteger\n\n col_types[\"data\"] = sa.dialects.postgresql.JSONB\n\n table.insert_many(\n list(_yield_rows(tcad_data, tcad_index)),\n ensure=True,\n types=col_types,\n )\n\n console.print(f\"Load of {table_name} table complete! :tada:\", emoji=True)\n\n\ndef _yield_rows(tcad_data, tcad_index):\n for index, values in tcad_data.iterrows():\n row = tcad_index.loc[index, :].to_dict()\n row[\"data\"] = values.to_dict()\n yield row\n", "repo_name": "almostprod/property-app", "sub_path": "src/property_etl/tcad.py", "file_name": "tcad.py", "file_ext": "py", "file_size_in_byte": 4945, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "3", "api": [{"api_name": "config.get_config", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "click.Group", "line_number": 16, "usage_type": "call"}, {"api_name": "rich.console.Console", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.read_fwf", "line_number": 68, "usage_type": "call"}, {"api_name": "config.CSV_LOAD_CHUNKSIZE", "line_number": 73, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 78, "usage_type": "call"}, {"api_name": "io.TextIOWrapper", "line_number": 91, "usage_type": "call"}, {"api_name": "click.option", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 25, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 115, "usage_type": "call"}, {"api_name": "click.option", "line_number": 106, "usage_type": "call"}, {"api_name": "config.CSV_LOAD_CHUNKSIZE", "line_number": 122, "usage_type": "attribute"}, {"api_name": "rich.console.Console", "line_number": 130, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 135, "usage_type": "call"}, {"api_name": "database.get_db", "line_number": 137, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 139, "usage_type": "call"}, {"api_name": "rich.progress.track", "line_number": 145, "usage_type": "call"}, {"api_name": "io.TextIOWrapper", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 152, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 159, "usage_type": "attribute"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 160, "usage_type": "attribute"}, {"api_name": "sqlalchemy.dialects", "line_number": 162, "usage_type": "attribute"}]} +{"seq_id": "15769157333", "text": "from typing import Any, Dict, Optional, Tuple\n\nimport torch\nfrom lightning import LightningDataModule\nfrom torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split\n\nfrom torchvision.transforms import transforms\nfrom torchvision import datasets\nimport numpy\nimport torchvision\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\nfrom src.data.sentences_data import SentencesDataset\n\n\nclass Sentences_Datamodule(LightningDataModule):\n \"\"\"Example of LightningDataModule for Pizza_Steak_Sushi dataset.\n\n A DataModule implements 6 key methods:\n def prepare_data(self):\n # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)\n # download data, pre-process, split, save to disk, etc...\n def setup(self, stage):\n # things to do on every process in DDP\n # load data, set variables, etc...\n def train_dataloader(self):\n # return train dataloader\n def val_dataloader(self):\n # return validation dataloader\n def test_dataloader(self):\n # return test dataloader\n def teardown(self):\n # called on every process in DDP\n # clean up after fit or test\n\n This allows you to share a full dataset without explaining how to download,\n split, transform and process the data.\n\n Read the docs:\n https://lightning.ai/docs/pytorch/latest/data/datamodule.html\n \"\"\"\n\n def __init__(\n self,\n data_dir: str = \"data/\",\n train_file_path: str = None,\n vocab_file_path: str = None,\n train_val_test_split: Tuple[int, int, int] = (55_000, 5_000, 10_000),\n batch_size: int = 64,\n num_workers: int = 0,\n pin_memory: bool = False,\n seq_len: int = 20,\n n_vocab: int = 40000,\n ):\n super().__init__()\n\n # this line allows to access init params with 'self.hparams' attribute\n # also ensures init params will be stored in ckpt\n self.save_hyperparameters(logger=False)\n\n self.data_train: Optional[Dataset] = None\n self.data_val: Optional[Dataset] = None\n self.data_test: Optional[Dataset] = None\n \n def get_samples(self,number_of_samples = 10):\n \"\"\"Return sample images\n number_of_samples: int: 10\n \"\"\"\n if not self.data_train:\n self.prepare_data()\n self.setup()\n\n sentences_data = SentencesDataset(self.hparams.train_file_path,self.hparams.vocab_file_path,\n self.hparams.seq_len,self.hparams.n_vocab)\n\n sample_count = 0\n text_samples = []\n output_samples = []\n for item in sentences_data:\n if sample_count <= number_of_samples:\n text_samples.append(item[\"input\"])\n output_samples.append(item[\"target\"])\n sample_count += 1 \n else:\n break\n res = dict((v,k) for k,v in sentences_data.vocab.items())\n text_samples = [' '.join([res[i.item()] for i in j]) for j in text_samples]\n output_samples = [' '.join([res[i.item()] for i in j]) for j in output_samples]\n\n\n\n return text_samples,output_samples\n \n def get_sample_images_transformed(self,number_of_samples = 10):\n \"\"\"Return sample images\n number_of_samples: int: 10\n \"\"\"\n if not self.data_train:\n self.prepare_data()\n self.setup()\n\n sentences_data = SentencesDataset(self.hparams.train_file_path,self.hparams.vocab_file_path,\n self.hparams.seq_len,self.hparams.n_vocab)\n\n text_samples = [item[\"input\"] for item in sentences_data[:number_of_samples]] \n\n return text_samples\n\n def prepare_data(self):\n \"\"\"Download data if needed.\n\n Do not use it to assign state (self.x = y).\n \"\"\"\n pass\n\n def setup(self, stage: Optional[str] = None):\n \"\"\"Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.\n\n This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be\n careful not to execute things like random split twice!\n \"\"\"\n # load and split datasets only if not loaded already\n if not self.data_train and not self.data_val:\n trainset = SentencesDataset(self.hparams.train_file_path,self.hparams.vocab_file_path,\n self.hparams.seq_len,self.hparams.n_vocab)\n testset = SentencesDataset(self.hparams.train_file_path,self.hparams.vocab_file_path,\n self.hparams.seq_len,self.hparams.n_vocab)\n # self.data_val, self.data_test = random_split(\n # dataset=testset,\n # lengths=self.hparams.train_val_test_split,\n # generator=torch.Generator().manual_seed(42),\n # )\n self.data_train = trainset\n self.data_val = testset\n self.data_test = testset\n\n def train_dataloader(self):\n return DataLoader(\n dataset=self.data_train,\n batch_size=self.hparams.batch_size,\n num_workers=self.hparams.num_workers,\n pin_memory=self.hparams.pin_memory,\n shuffle=True,\n )\n\n def val_dataloader(self):\n return DataLoader(\n dataset=self.data_val,\n batch_size=self.hparams.batch_size,\n num_workers=self.hparams.num_workers,\n pin_memory=self.hparams.pin_memory,\n shuffle=False,\n )\n\n def test_dataloader(self):\n return DataLoader(\n dataset=self.data_test,\n batch_size=self.hparams.batch_size,\n num_workers=self.hparams.num_workers,\n pin_memory=self.hparams.pin_memory,\n shuffle=False,\n )\n\n def teardown(self, stage: Optional[str] = None):\n \"\"\"Clean up after fit or test.\"\"\"\n pass\n\n def state_dict(self):\n \"\"\"Extra things to save to checkpoint.\"\"\"\n return {}\n\n def load_state_dict(self, state_dict: Dict[str, Any]):\n \"\"\"Things to do when loading checkpoint.\"\"\"\n pass\n\n\nif __name__ == \"__main__\":\n _ = Sentences_Datamodule()\n", "repo_name": "abishek-raju/vision_meets_nlp", "sub_path": "src/data/sentences_datamodule.py", "file_name": "sentences_datamodule.py", "file_ext": "py", "file_size_in_byte": 6285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "lightning.LightningDataModule", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 63, "usage_type": "name"}, {"api_name": "src.data.sentences_data.SentencesDataset", "line_number": 73, "usage_type": "call"}, {"api_name": "src.data.sentences_data.SentencesDataset", "line_number": 102, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 116, "usage_type": "name"}, {"api_name": "src.data.sentences_data.SentencesDataset", "line_number": 124, "usage_type": "call"}, {"api_name": "src.data.sentences_data.SentencesDataset", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 156, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 172, "usage_type": "name"}]} +{"seq_id": "15635757542", "text": "from django.shortcuts import render, redirect\nfrom projects.models import Project\nfrom projects.forms import ProjectForm\nfrom django.contrib.auth.decorators import login_required\n\n\n@login_required\ndef list_projects(request):\n projects_list = Project.objects.filter(members=request.user)\n context = {\n \"projects_list\": projects_list,\n }\n return render(request, \"projects/list.html\", context)\n\n\n@login_required\ndef show_project(request, pk):\n project_detail = Project.objects.get(pk=pk)\n context = {\n \"project_detail\": project_detail,\n }\n return render(request, \"projects/detail.html\", context)\n\n\n@login_required\ndef create_project(request):\n if request.method == \"POST\":\n form = ProjectForm(request.POST)\n if form.is_valid():\n project = form.save()\n return redirect(\"show_project\", pk=project.id)\n else:\n form = ProjectForm()\n\n context = {\"form\": form}\n\n return render(request, \"projects/create.html\", context)\n", "repo_name": "kariscourey/hr-project-alpha", "sub_path": "projects/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "projects.models.Project.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "projects.models.Project.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "projects.models.Project", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 7, "usage_type": "name"}, {"api_name": "projects.models.Project.objects.get", "line_number": 18, "usage_type": "call"}, {"api_name": "projects.models.Project.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "projects.models.Project", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 16, "usage_type": "name"}, {"api_name": "projects.forms.ProjectForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "projects.forms.ProjectForm", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "27784308201", "text": "# coding=utf-8\n# /usr/local/bin/python3 /Users/renweiqiang/Desktop/毕业论文/Dissertation/code/crawler/fenci.py\nimport pymysql\nimport time\nimport jieba\nimport jieba.analyse\n\n\ntext = '苗族分布在我国西南数省区。按方言划分，大致可分为湘西方言区、黔东方言区、川滇黔方言区。黔东南清水江流域一带是全国苗族最大的聚居区，大致包括凯里、剑河、黄平、台江、雷山、丹寨、施秉、黄平、镇远、三穗，以及广西三江和湖南靖县等地。在此广大苗族聚居区普遍流传着一种以创世为主体内容的诗体神话，俗称“古歌”或“古歌古词”。 苗族古歌内容包罗万象，从宇宙的诞生、人类和物种的起源、开天辟地、初民时期的滔天洪水，到苗族的大迁徙、苗族的古代社会制度和日常生产生活等，无所不包，成为苗族古代神话的总汇。 苗族古歌古词神话大多在鼓社祭、婚丧活动、亲友聚会和节日等场合演唱，演唱者多为中老年人、巫师、歌手等。酒席是演唱古歌的重要场合。苗族的古歌古词神话是一个民族的心灵记忆，是苗族古代社会的百科全书和“经典”，具有史学、民族学、哲学、人类学等多方面价值。今天，这些古歌古词神话还在民间流传唱诵。 但由于受到现代文化和市场经济的冲击，苗族古歌已濒临失传。以台江为例，在全县13万苗族同胞中，能唱完整部古歌的已寥寥无几，目前只有二百余人能唱一些不完整的古歌，而且都是中老年人，传承古歌较多的老人年事已高。如不抓紧抢救保护，苗族古歌这一民族瑰宝将最终在世间消失。'\n\ndef stopwordslist(filepath):\n stopwords = [line.strip() for line in open(filepath).readlines()]\n return stopwords\ndef filter_seg_list(seg_list):\n stop_words = stopwordslist('/Users/renweiqiang/Desktop/毕业论文/学习总结/LDA_Python/文本预处理/stop_words.txt')\n filter_seg = []\n for word in seg_list:\n if word not in stop_words:\n filter_seg.append(word)\n filter_seg = [i for i in filter_seg if i != '']\n return filter_seg\ndef filter_number_and_single(word):\n if(word.isdigit()):\n return False\n length = len(word)\n if(length not in [0, 1]):\n return word\n return False\n\n# corpus = []\n# corpus.append(\" \".join(keywords))\n# #print( \" \".join(keywords))\n\n\n\ndef get_keywords(text):\n keywords = filter_seg_list(jieba.cut(text)) # 去除停用词\n keywords = [j for j in keywords if filter_number_and_single(j) != False]\n jieba_keywords_text = \" \".join(keywords)\n # topK = 20\n # withWeight = False\n # tags = jieba.analyse.extract_tags(jieba_keywords_text, topK=topK, withWeight=withWeight)\n #return ' '.join(keywords), \" \".join(tags)\n return ' '.join(keywords)\n# keywords = get_keywords(text)\n# print(keywords[0])\n# print(keywords[1])\n\ndb = pymysql.connect(\"localhost\",\"root\",\"\",\"feiyi\")\ncursor = db.cursor()\n# sql = \"select count(*) from `minglu`\"\n# cursor.execute(sql)\n# count = cursor.fetchone()[0]\n\n# max_id_sql = \"select max(id) from `minglu`\"\n# cursor.execute(sql)\n# max_id = cursor.fetchone()[0]\n# min_id = 8\n\n# while(max_id <= 3260):\n# content_sql = '''select `content` from `minglu` where `id` in ({0}, {1})'''.format(min_id, max_id)\n \n#sql = \"select `id`, `content` from `minglu` where `id` > 10\"\nmax_id = 3157\ncurrent_id = 8\nstep = 100\n\nwhile(current_id <= max_id):\n next_id = current_id + step\n\n sql = ''' select `id`, `content` from `minglu` where `id` >= {0} and `id` <= {1} '''.format(current_id, next_id)\n try:\n # 执行SQL语句\n cursor.execute(sql)\n # 获取所有记录列表\n results = cursor.fetchall()\n for row in results:\n id = row[0]\n content = row[1]\n if content.strip():\n keywords = get_keywords(content)\n update_sql = ''' update `minglu` set `full_keywords` = \"{0}\" where `id` = {1} '''.format(keywords, id)\n #print(update_sql)\n print('---' + str(id)) \n try:\n cursor.execute(update_sql)\n db.commit()\n except:\n db.rollback()\n \n except:\n print (\"Error: unable to fetch data\")\n\n current_id = current_id + step\n\n time.sleep(1)\n\n\n\n# 关闭数据库连接\ndb.close()\n\n\n\n", "repo_name": "rwqzcq/Dissertation", "sub_path": "code/crawler/fenci2.py", "file_name": "fenci2.py", "file_ext": "py", "file_size_in_byte": 4475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "jieba.cut", "line_number": 37, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "20570514032", "text": "import time\n\nfrom selenium import webdriver\n\ndef testmetricks(link):\n driver = webdriver.Chrome(\"chromedriver.exe\")\n driver.get(link)\n time.sleep(3)\n driver.execute_cdp_cmd('Performance.enable', {})\n metrics = driver.execute_cdp_cmd('Performance.getMetrics', {})\n driver.quit()\n t ={}\n for m in metrics[\"metrics\"]:\n if m[\"name\"] in (\n 'ScriptDuration', 'TaskDuration', 'TaskOtherDuration', 'ThreadTime', 'ProcessTime', 'JSHeapUsedSize',\n 'JSHeapTotalSize', 'FirstMeaningfulPaint', 'DomContentLoaded', 'NavigationStart'):\n t[m[\"name\"]] = m[\"value\"]\n return t\n", "repo_name": "houmsss/DjangoDiploma", "sub_path": "NoCodePlatform/NoCode/static/NoCode/PyScripts/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "27570501975", "text": "import os\nimport cv2\nimport yaml\nimport pdfkit\nimport base64\nimport subprocess as sp\nimport json\n\nfrom flask import current_app, render_template\n\n\ndef exif_info(input_video_path, output_dir):\n exif_file_path = f\"{output_dir}/exif.json\"\n exif_tool_cmd = [\"exiftool\", \"-j\", input_video_path,\n \">>\", exif_file_path]\n\n output = sp.run(exif_tool_cmd, capture_output=True)\n json_dict = json.loads(output.stdout.decode(\"utf-8\"))[0]\n\n exif_dict = {}\n key_list = [\"FileType\", \"Duration\", \"FileSize\", \"BitDepth\", \"VideoFrameRate\", \"Rotation\", \"XResolution\", \"YResolution\"] \n\n for key in json_dict:\n if key in key_list:\n exif_dict[key] = json_dict[key]\n\n with open(exif_file_path, 'w') as file:\n file.write(json.dumps(exif_dict, indent=2))\n file.close()\n\n return exif_file_path\n\n\ndef extract_frames_from_video(\n input_video_path, \n output_frames_path, \n fps, \n quality):\n convertCMD = [\"ffmpeg\", '-i', input_video_path,\n '-vf', f'fps={fps}', '-qscale:v', str(quality), output_frames_path]\n\n proc = sp.Popen(convertCMD)\n\n try:\n outs, errs = proc.communicate()\n except TimeoutError:\n proc.kill()\n\n\ndef generate_panorama_img(frames_dir, output_dir, file_name):\n image_names = os.listdir(frames_dir)\n images = []\n\n for image_name in image_names:\n img = cv2.imread(f\"{frames_dir}/{image_name}\")\n img = cv2.resize(img, (0, 0), None, 0.2, 0.2)\n images.append(img)\n\n stitcher = cv2.Stitcher.create(cv2.STITCHER_PANORAMA)\n # stitcher.setPanoConfidenceThresh(0.0)\n\n (status, result) = stitcher.stitch(images)\n\n error = ''\n if status == cv2.STITCHER_OK:\n print(\"Panorama success\")\n cv2.imwrite(f\"{output_dir}/{file_name}\", result)\n else:\n if status == cv2.STITCHER_ERR_NEED_MORE_IMGS:\n error = \"Need more images - ERR_NEED_MORE_IMGS\"\n elif status == cv2.STITCHER_ERR_HOMOGRAPHY_EST_FAIL:\n error = \"Failed - ERR_NEED_MORE_IMGS\"\n elif status == cv2.STITCHER_ERR_CAMERA_PARAMS_ADJUST_FAIL:\n error = \"Failed - STITCHER_ERR_CAMERA_PARAMS_ADJUST_FAIL\"\n\n return error\n\ndef generate_yaml_params(params_dict, folder_name):\n yaml_file_path = f\"{current_app.config['OUTPUT_FOLDER']}/{folder_name}/params.yaml\"\n\n with open(yaml_file_path, 'w') as file:\n documents = yaml.dump(params_dict, file, sort_keys=False)\n\n return yaml_file_path\n\n\ndef image_file_path_to_base64_string(filepath: str) -> str:\n with open(filepath, 'rb') as f:\n return base64.b64encode(f.read()).decode()\n\n\ndef generate_pdf_report(\n panorama_img_path,\n folder_name,\n original_uploaded_file_name,\n param_frame_rate,\n param_output_format,\n param_quality,\n param_is_exif_info_captured,\n created_at):\n exif_json = f\"{current_app.config['OUTPUT_FOLDER']}/{folder_name}/exif.json\"\n exif_json_dict = {}\n if param_is_exif_info_captured:\n with open(exif_json, 'r') as file:\n exif_json_dict = json.loads(file.read())\n\n key_list = [\"FileType\", \"Duration\", \"FileSize\", \"BitDepth\", \"VideoFrameRate\", \"Rotation\", \"XResolution\", \"YResolution\"]\n rendered = render_template(\n \"report_template.html\",\n img_string=image_file_path_to_base64_string(panorama_img_path),\n logo=image_file_path_to_base64_string(\"templates/pg-icon.png\"),\n original_uploaded_file_name=original_uploaded_file_name,\n param_frame_rate=param_frame_rate,\n param_output_format=param_output_format,\n param_is_exif_info_captured=param_is_exif_info_captured,\n param_quality=param_quality,\n exif_json_dict=exif_json_dict,\n key_list=key_list,\n created_at=created_at)\n pdf = pdfkit.from_string(\n rendered, f\"{current_app.config['OUTPUT_FOLDER']}/{folder_name}/report.pdf\")\n", "repo_name": "saravanaselvan/pg-video-convertor-api", "sub_path": "services/video_report/video_report.py", "file_name": "video_report.py", "file_ext": "py", "file_size_in_byte": 3929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "subprocess.run", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 42, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.Stitcher.create", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.Stitcher", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.STITCHER_PANORAMA", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.STITCHER_OK", "line_number": 65, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.STITCHER_ERR_NEED_MORE_IMGS", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.STITCHER_ERR_HOMOGRAPHY_EST_FAIL", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.STITCHER_ERR_CAMERA_PARAMS_ADJUST_FAIL", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 79, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 82, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 101, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 108, "usage_type": "call"}, {"api_name": "pdfkit.from_string", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 121, "usage_type": "name"}]} +{"seq_id": "22675022084", "text": "import argparse\nfrom text_mutation_generation import *\nimport pandas as pd\nimport traceback\nimport re\nfrom tqdm import tqdm\nfrom typing import List, Tuple\n\n\ndef getSentencesToBeProcessed(input_file: str) -> List[str]:\n \"\"\"\n This function will read from the input_file path passed in and return\n a list of all the sentences to be read. Each sentence will have whitespace\n stripped from the end.\n\n Parameters\n ----------\n input_file : str\n The path to the file containing a list of sentences we are interested\n in processing. The file should contain one sentence on each line.\n\n Returns\n -------\n List[str]\n A list of all the sentences in the input file..\n \"\"\"\n with open(input_file, 'r') as f:\n allSentences = []\n for line in f:\n allSentences.append(line.strip())\n\n return allSentences\n\n\ndef getCleanedSentences(sentences: List[str], goal_length: int) -> Tuple[List[str], List[str]]:\n \"\"\"\n This function will take a list of sentences and return two lists. The first\n list will contain goal_length sentences that hav ebeen cleaned for processing,\n while the second list will contain the sentences that will not be ready for\n processing.\n\n Parameters\n ----------\n sentences : List[str]\n A list of sentences to be cleaned.\n goal_length : int\n The length of the sentences we want to return.\n\n Returns\n -------\n Tuple[List[str], List[str]]\n A tuple containing two lists. The first list contains the goal_length\n sentences that have been cleaned for processing, while the second list\n contains the sentences that will not be ready for processing.\n \"\"\"\n print(f\"Getting {goal_length} clean sentences for processing.\")\n cleanedSentences = []\n\n while len(cleanedSentences) < goal_length and len(sentences) > 0:\n currentClean = re.sub(\n \"-*\\s*\\([A-Z][A-Z]\\)\\s*\", \"\", sentences.pop(0).strip()).strip()\n currentClean = re.sub(\"\\s*-\\s\", \" \", currentClean).strip()\n if re.search(\"[0-9]+.\", currentClean) or currentClean.isspace() or len(currentClean) == 0:\n continue\n cleanedSentences.append(currentClean)\n\n assert len(\n cleanedSentences) == goal_length, f'Expected number of cleaned sentences to be {goal_length}, got {len(cleanedSentences)}'\n\n return cleanedSentences, sentences\n\n\ndef processSentences(sentences: List[str], output_file_name: str, input_file: str, max_length: int = 1000) -> None:\n \"\"\"\n This function will process the sentences in the list and write them to 3 output\n files. One output file will contain sentences with substitution mutations,\n one will contain sentences with deletion mutations, and one will contain\n sentences with repetition mutations. The naming convention for the output files\n will be(output_file_name + \"_mutation.csv\"). The output files will contain\n 4 columns: original text, mutated texts and the index tags and mutation tags.\n\n Parameters\n ----------\n sentences: List[str]\n A list of sentences to be processed.\n output_file_name: str\n The name of the output file.\n input_file: str\n The path to the input file. We will rewrie this file with all of\n\n\n \"\"\"\n cols = ['original_text', 'mutated_text', 'index_tags', 'mutation_tags']\n\n try:\n subs_df = pd.read_csv(output_file_name + \"_substitutions.csv\")\n dels_df = pd.read_csv(output_file_name + \"_deletions.csv\")\n reps_df = pd.read_csv(output_file_name + \"_repetitions.csv\")\n except FileNotFoundError:\n print(\"No output file found. Creating new output file.\")\n subs_df = pd.DataFrame(\n columns=cols)\n dels_df = pd.DataFrame(\n columns=cols)\n reps_df = pd.DataFrame(\n columns=cols)\n\n # Get max_length sentences to process. We need them to be clean ones.\n cleanedSentences, notCleanedSentences = getCleanedSentences(\n sentences, max_length)\n\n with open(input_file, 'w') as f:\n for sentence in tqdm(notCleanedSentences, desc='Writing remaining sentences back to input file.'):\n f.write(sentence + '\\n')\n\n for cleanedSentence in tqdm(cleanedSentences, desc='Mutating Sentences'):\n try:\n # Keep Generating Deletion Permutations Until we get one\n # with at least one valid word.\n del_tags = []\n while 'O' not in del_tags:\n new_sentence_del, del_index_tags, del_tags = mutate_selectively(\n cleanedSentence, \"del\", del_prob=0.2, remove_punc=False)\n dels_df = pd.concat([dels_df, pd.DataFrame(\n {'original_text': [cleanedSentence], 'mutated_text': [new_sentence_del], 'index_tags': [del_index_tags], 'mutated_tags': [del_tags]})])\n\n # Get Mutated Sentence with Repetititon\n new_sentence_rep, rep_index_tags, rep_tags = mutate_selectively(\n cleanedSentence, \"rep\", rep_prob=0.2, max_reps=3, remove_punc=False)\n reps_df = pd.concat([reps_df, pd.DataFrame(\n {'original_text': [cleanedSentence], 'mutated_text': [new_sentence_rep], 'index_tags': [rep_index_tags], 'mutated_tags': [rep_tags]})])\n\n # Get Mutated Sentence with Substitutions\n new_sentence_subs, sub_index_tags, sub_tags = mutate_selectively(\n cleanedSentence, \"sub\", sub_prob=0.2, remove_punc=False)\n\n # Check that the number of words in new_sentence_subs is the same\n # as the number of tags in sub_tags\n if len(new_sentence_subs.split()) != len(sub_index_tags):\n raise Exception(\n \"Number of words in new_sentence_subs and sub_tags do not match.\")\n\n subs_df = pd.concat([subs_df, pd.DataFrame(\n {'original_text': [cleanedSentence], 'mutated_text': [new_sentence_subs], 'index_tags': [sub_index_tags], 'mutated_tags': [sub_tags]})])\n except IndexError:\n traceback.print_exc()\n print(sentence)\n continue\n\n subs_df.to_csv(output_file_name + \"_substitutions.csv\",\n index=False)\n dels_df.to_csv(output_file_name + \"_deletions.csv\",\n index=False)\n reps_df.to_csv(output_file_name + \"_repetitions.csv\",\n index=False)\n\n\nif __name__ == '__main__':\n # Arg Parsing. This lets us go from the command line.\n # Demo: python create_training_data.py --input_file data/unprocessedSentences.txt --output_file data/output.txt --max_length=5\n parser = argparse.ArgumentParser(\n description='Create training data for the repetition task.')\n parser.add_argument('--input_file', type=str,\n help='Path to the input file.', required=True)\n parser.add_argument('--output_file', type=str,\n help='Path to the output file.', required=True)\n parser.add_argument('--max_length', type=int,\n help='Maximum number of lines to parse.', default=None)\n\n args = parser.parse_args()\n sentences = getSentencesToBeProcessed(args.input_file)\n processed_sentences = processSentences(\n sentences, args.output_file, args.input_file, args.max_length)\n", "repo_name": "cehrett/running_records", "sub_path": "repetition_data_generation/create_text_training_data.py", "file_name": "create_text_training_data.py", "file_ext": "py", "file_size_in_byte": 7268, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.List", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 35, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 60, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 62, "usage_type": "call"}, {"api_name": "re.search", "line_number": 63, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 73, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 96, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 105, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 113, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 116, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 143, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 143, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 146, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "73841682962", "text": "from tkinter import filedialog\nimport pathlib\nimport logging\nimport os\n\nclass CapBase(object):\n\n def __init__(self, base_location=None):\n logging.basicConfig(level=logging.INFO)\n self.logger = logging.getLogger(__name__)\n #self.logger.setLevel(logging.INFO)\n self.logger.setLevel(logging.DEBUG)\n #self.logger.setLevel(logging.WARNING)\n\n self.packetBase = []\n self.base_loc = str(base_location).strip()\n\n #self.logger.debug(\"Testing logger debug message\")\n #print(\"Logger Level: %s\", str(self.logger.getEffectiveLevel()))\n\n if base_location is None:\n #Check for config file\n p = pathlib.Path('base_loc_config.conf')\n try:\n if os.stat(str(p)).st_size == 0:\n self.logger.warning(\"base_loc_config file is empty\")\n self.base_loc == filedialog.askdirectory(initialdir='', title='Select Base Location home-dir')\n with p.open('a+') as f:\n f.write(self.base_loc)\n f.close()\n else:\n with p.open('r') as rf:\n for line in rf:\n self.base_loc = line.strip()\n if self.base_loc == '':\n self.base_loc == filedialog.askdirectory(initialdir='', title='Select Base Location home-dir')\n else:\n self.logger.debug(\"Loaded CapBase path: %s\", self.base_loc)\n # self.logger.info(\"test info\")\n # self.logger.warning(\"test warning\")\n #print('test')\n except:\n self.logger.warning(\"base_loc_config does not exist. Create base_loc_config file\")\n #If config file doesn't exist create it\n self.base_loc = filedialog.askdirectory(initialdir='')\n with p.open('a+') as f:\n f.write(self.base_loc)\n f.close()\n\n def add_lib_to_base(self, newMetaCapLib):\n self.packetBase.append(newMetaCapLib)\n\n def set_base_location(self, base_location):\n self.base_loc = str(base_location).strip()\n return\n\n def get_base_loc(self):\n return self.base_loc\n", "repo_name": "irvinhomem/TunnelFeatureExtractor", "sub_path": "CapBase.py", "file_name": "CapBase.py", "file_ext": "py", "file_size_in_byte": 2363, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "3", "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": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 25, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 27, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 36, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 36, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 45, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "10006918934", "text": "#!/usr/bin/env python\n\n# Author: Epihaius\n# Date: 2019-09-23\n# Last revision: 2020-10-08\n#\n# This is a basic example of how to use the sizer-based GUI system.\n# It specifically showcases how to handle a DirectScrolledList.\n\nfrom panda3d.core import *\nfrom direct.showbase.ShowBase import ShowBase\nfrom direct.gui.DirectGui import *\nfrom gui import *\n\n\nclass MyApp:\n\n def __init__(self):\n\n # initialize the Panda3D showbase\n self.showbase = showbase = ShowBase()\n\n # the root node of all DirectGui widgets needs to be pixel2d in order to work\n # with the automatic layout system\n gui_root = showbase.pixel2d\n\n # initialize the GUI system\n self.gui = gui = GUI(showbase)\n\n # Build the GUI layout\n\n # add a horizontally expanding title bar\n title = \"Panda3D: scrolled list layout example\"\n label = DirectLabel(parent=gui_root, text=title, frameSize=(0, 0, -20, 30),\n text_scale=20, borderWidth=(6, 6), relief=DGG.SUNKEN)\n widget = Widget(label)\n borders = (10, 10, 20, 10)\n # by default, the title bar will take up all of the width and height of its\n # cell (the default value for the `alignments` parameter of the `Sizer.add`\n # method is `(\"expand\", \"expand\")`), but the cell itself still needs to be\n # able to take up the entire width of the window; this is done by setting\n # the horizontal proportion (which gets applied to the cell's column) to a\n # value bigger than zero\n gui.sizer.add(widget, proportions=(1., 0.), borders=borders)\n\n # add a horizontally growable sizer that will be proportionately resized\n # vertically and horizontally\n sizer = Sizer(\"horizontal\")\n borders = (10, 10, 20, 10)\n gui.sizer.add(sizer, proportions=(1., 1.), borders=borders)\n\n # add a vertically growable subsizer to the previous sizer;\n # set the vertical gap between each two of its cells to 10 pixels\n # (this is a more convenient alternative to setting the same borders\n # for all but the last of its cells, i.e. `(0, 0, 10, 0)`)\n btn_sizer = Sizer(\"vertical\", gaps=(0, 10))\n borders = (0, 20, 0, 0)\n sizer.add(btn_sizer, borders=borders)\n\n # add a couple of horizontally expanding buttons to the subsizer;\n # they will have the same width, determined by the initially largest button\n text = \"My Button\"\n button = DirectButton(parent=gui_root, text=text, text_scale=20, borderWidth=(2, 2))\n widget = Widget(button)\n btn_sizer.add(widget)\n text = \"Insert list into frame\"\n button = DirectButton(parent=gui_root, text=text, text_scale=20,\n borderWidth=(2, 2), command=self.__insert_list)\n widget = Widget(button)\n btn_sizer.add(widget)\n # add vertical space with a fixed size\n btn_sizer.add((0, 30))\n text = \"A third button\"\n button = DirectButton(parent=gui_root, text=text, text_scale=20, borderWidth=(2, 2))\n widget = Widget(button)\n btn_sizer.add(widget)\n\n # add some horizontally stretching space, so that widgets added after it\n # will be pushed to the right\n sizer.add((0, 0), proportions=(1., 0.))\n\n # add a frame resizable in both directions and taking up two thirds of\n # the available horizontal space (because of the ratio of the proportions\n # used for the frame and the stretching space that was previously added)\n self.frame = frame = DirectFrame(parent=gui_root, frameColor=(.5, .6, .7, 1.))\n widget = Widget(frame)\n sizer.add(widget, proportions=(2., 1.))\n\n # assign a sizer to the frame to manage the layout of its child widgets\n self.frame_sizer = frame_sizer = Sizer(\"vertical\")\n widget.sizer = frame_sizer\n\n # add a horizontally expanding label with right-aligned text to the frame\n text = \"right-aligned text\"\n label = DirectLabel(parent=frame, text=text,\n text_scale=20, text_align=TextNode.A_right)\n widget = Widget(label)\n borders = (10, 10, 20, 10)\n frame_sizer.add(widget, proportions=(1., 0.), borders=borders)\n\n # add a non-resizing, right-aligned button to the frame\n text = \"Button in frame \"\n button = DirectButton(parent=frame, text=text, text_scale=20, borderWidth=(2, 2))\n widget = Widget(button)\n borders = (0, 10, 10, 20)\n frame_sizer.add(widget, alignments=(\"max\", \"min\"), borders=borders)\n\n # add a non-resizing input field, centered horizontally within its cell,\n # which itself is assigned all of the width available to it, by setting\n # its horizontal proportion to 1.0\n field = DirectEntry(parent=gui_root, text_scale=20, focus=1)\n widget = Widget(field)\n gui.sizer.add(widget, proportions=(1., 0.), alignments=(\"center\", \"min\"))\n\n # add a horizontally expanding status bar\n status_text = \"GUI ready and awaiting input\"\n label = DirectLabel(parent=gui_root, text=status_text, text_pos=(20, -10),\n textMayChange=1, frameSize=(0, 0, -10, 10), text_scale=20,\n text_align=TextNode.A_left)\n widget = Widget(label)\n borders = (10, 10, 10, 20)\n gui.sizer.add(widget, proportions=(1., 0.), borders=borders)\n\n # let the GUI system create the layout\n gui.layout()\n\n # run the app\n showbase.run()\n\n def __insert_list(self):\n\n scrolled_list = DirectScrolledList(\n parent=self.frame,\n\n decButton_text=\"Dec\",\n decButton_text_scale=20,\n decButton_borderWidth=(4, 4),\n\n incButton_text=\"Inc\",\n incButton_text_scale=20,\n incButton_borderWidth=(4, 4),\n\n frameColor=(1., 0., 0., .5),\n forceHeight=29 # item height\n )\n\n list_widget = ScrolledListWidget(\n scrolled_list,\n # the width of the scroll buttons will take up 20 % of the available\n # space, since they are surrounded by space that is stretched using\n # a proportion of 1.: .25 / (1. + .25) = 1/5 = .2\n scrollbtn_proportion=.25,\n scrollbtn_borders=(5, 5, 10, 10),\n itemframe_borders=(5, 5, 0, 0),\n margins=(10, 10) # left and right borders around the items in the frame\n )\n\n b1 = DirectButton(text=(\"Button1\", \"click!\", \"roll\", \"disabled\"),\n borderWidth=(4, 4), relief=2, text_scale=20)\n\n b2 = DirectButton(text=(\"Feel free to remove me\", \"Goodbye!\",\n \"Yeah I'm still here\", \"Not now\"), borderWidth=(4, 4), relief=2,\n text_scale=20, command=lambda: self.__remove_item(list_widget, b2))\n\n list_widget.add_item(b1)\n list_widget.add_item(b2)\n\n checkbtn = DirectCheckButton(text=\"CheckButton\",\n text_scale=20, boxPlacement=\"right\", borderWidth=(3, 3), indicator_text_scale=20,\n indicator_text_pos=(0, 4), indicator_borderWidth=(2, 2), boxBorder=1)\n list_widget.add_item(checkbtn)\n\n l1 = DirectLabel(text=\"Test1\", text_scale=20)\n l2 = DirectLabel(text=\"Test2\", text_scale=20)\n l3 = DirectLabel(text=\"Test3\", text_scale=20)\n\n list_widget.add_item(l1)\n list_widget.add_item(l2)\n list_widget.add_item(l3)\n\n for fruit in ['apple', 'pear', 'banana', 'orange']:\n l = DirectLabel(text=fruit, text_scale=20)\n list_widget.add_item(l)\n\n borders = (10, 10, 5, 5)\n # add the list to the frame, below the right-aligned text label, using index=1\n self.frame_sizer.add(list_widget, proportions=(1., 1.), borders=borders, index=1)\n\n # update the GUI layout\n self.gui.layout()\n\n def __remove_item(self, list_widget, item):\n\n list_widget.remove_item(item)\n\n # update the GUI layout\n self.gui.layout()\n\n\nMyApp()\n", "repo_name": "Epihaius/DirectGui-layout-system", "sub_path": "scrolled_list.py", "file_name": "scrolled_list.py", "file_ext": "py", "file_size_in_byte": 7979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "3", "api": [{"api_name": "direct.showbase.ShowBase.ShowBase", "line_number": 21, "usage_type": "call"}, {"api_name": "gui.sizer.add", "line_number": 44, "usage_type": "call"}, {"api_name": "gui.sizer", "line_number": 44, "usage_type": "attribute"}, {"api_name": "gui.sizer.add", "line_number": 50, "usage_type": "call"}, {"api_name": "gui.sizer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "gui.sizer.add", "line_number": 113, "usage_type": "call"}, {"api_name": "gui.sizer", "line_number": 113, "usage_type": "attribute"}, {"api_name": "gui.sizer.add", "line_number": 122, "usage_type": "call"}, {"api_name": "gui.sizer", "line_number": 122, "usage_type": "attribute"}, {"api_name": "gui.layout", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "36111948687", "text": "from novaclient import api_versions\nfrom novaclient.tests.unit import utils\nfrom novaclient.tests.unit.v2 import fakes\nfrom novaclient.v2 import flavor_access\n\n\nclass FlavorAccessTest(utils.TestCase):\n def setUp(self):\n super(FlavorAccessTest, self).setUp()\n self.cs = fakes.FakeClient(api_versions.APIVersion(\"2.0\"))\n\n def test_list_access_by_flavor_private(self):\n kwargs = {'flavor': self.cs.flavors.get(2)}\n r = self.cs.flavor_access.list(**kwargs)\n self.assert_request_id(r, fakes.FAKE_REQUEST_ID_LIST)\n self.cs.assert_called('GET', '/flavors/2/os-flavor-access')\n for a in r:\n self.assertIsInstance(a, flavor_access.FlavorAccess)\n\n def test_add_tenant_access(self):\n flavor = self.cs.flavors.get(2)\n tenant = 'proj2'\n r = self.cs.flavor_access.add_tenant_access(flavor, tenant)\n self.assert_request_id(r, fakes.FAKE_REQUEST_ID_LIST)\n\n body = {\n \"addTenantAccess\": {\n \"tenant\": \"proj2\"\n }\n }\n\n self.cs.assert_called('POST', '/flavors/2/action', body)\n for a in r:\n self.assertIsInstance(a, flavor_access.FlavorAccess)\n\n def test_remove_tenant_access(self):\n flavor = self.cs.flavors.get(2)\n tenant = 'proj2'\n r = self.cs.flavor_access.remove_tenant_access(flavor, tenant)\n self.assert_request_id(r, fakes.FAKE_REQUEST_ID_LIST)\n\n body = {\n \"removeTenantAccess\": {\n \"tenant\": \"proj2\"\n }\n }\n\n self.cs.assert_called('POST', '/flavors/2/action', body)\n for a in r:\n self.assertIsInstance(a, flavor_access.FlavorAccess)\n\n def test_repr_flavor_access(self):\n flavor = self.cs.flavors.get(2)\n tenant = 'proj3'\n r = self.cs.flavor_access.add_tenant_access(flavor, tenant)\n\n def get_expected(flavor_access):\n return (\"\" %\n (flavor_access.flavor_id, flavor_access.tenant_id))\n\n for a in r:\n self.assertEqual(get_expected(a), repr(a))\n", "repo_name": "openstack/python-novaclient", "sub_path": "novaclient/tests/unit/v2/test_flavor_access.py", "file_name": "test_flavor_access.py", "file_ext": "py", "file_size_in_byte": 2130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 381, "dataset": "github-code", "pt": "3", "api": [{"api_name": "novaclient.tests.unit.utils.TestCase", "line_number": 7, "usage_type": "attribute"}, {"api_name": "novaclient.tests.unit.utils", "line_number": 7, "usage_type": "name"}, {"api_name": "novaclient.tests.unit.v2.fakes.FakeClient", "line_number": 10, "usage_type": "call"}, {"api_name": "novaclient.tests.unit.v2.fakes", "line_number": 10, "usage_type": "name"}, {"api_name": "novaclient.api_versions.APIVersion", "line_number": 10, "usage_type": "call"}, {"api_name": "novaclient.api_versions", "line_number": 10, "usage_type": "name"}, {"api_name": "novaclient.tests.unit.v2.fakes.FAKE_REQUEST_ID_LIST", "line_number": 15, "usage_type": "attribute"}, {"api_name": "novaclient.tests.unit.v2.fakes", "line_number": 15, "usage_type": "name"}, {"api_name": "novaclient.v2.flavor_access.FlavorAccess", "line_number": 18, "usage_type": "attribute"}, {"api_name": "novaclient.v2.flavor_access", "line_number": 18, "usage_type": "name"}, {"api_name": "novaclient.tests.unit.v2.fakes.FAKE_REQUEST_ID_LIST", "line_number": 24, "usage_type": "attribute"}, {"api_name": "novaclient.tests.unit.v2.fakes", "line_number": 24, "usage_type": "name"}, {"api_name": "novaclient.v2.flavor_access.FlavorAccess", "line_number": 34, "usage_type": "attribute"}, {"api_name": "novaclient.v2.flavor_access", "line_number": 34, "usage_type": "name"}, {"api_name": "novaclient.tests.unit.v2.fakes.FAKE_REQUEST_ID_LIST", "line_number": 40, "usage_type": "attribute"}, {"api_name": "novaclient.tests.unit.v2.fakes", "line_number": 40, "usage_type": "name"}, {"api_name": "novaclient.v2.flavor_access.FlavorAccess", "line_number": 50, "usage_type": "attribute"}, {"api_name": "novaclient.v2.flavor_access", "line_number": 50, "usage_type": "name"}, {"api_name": "novaclient.v2.flavor_access.flavor_id", "line_number": 59, "usage_type": "attribute"}, {"api_name": "novaclient.v2.flavor_access", "line_number": 59, "usage_type": "name"}, {"api_name": "novaclient.v2.flavor_access.tenant_id", "line_number": 59, "usage_type": "attribute"}]} +{"seq_id": "4322690768", "text": "\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd\r\n\r\n\r\nprint('getting data')\r\nurl = 'https://www.webelements.com/'\r\n\r\n\r\nr = requests.get(url).content\r\n\r\nwith open('PThtml.txt','w') as pt: #saves pt html to file so i dont have to request\r\n pt.write(str(r))\r\n\r\n\r\nwith open( 'PThtml.txt','r')as PT:\r\n htm = PT.read()\r\n\r\nsoup = BeautifulSoup(htm, 'html.parser')\r\ntb = soup.table.tbody\r\n\r\nhrefs = []\r\n\r\nfor i in tb.findAll('a', href = True):\r\n hrefs.append(i['href'])\r\n#print(hrefs)\r\n\r\ntable = []\r\nfor i in hrefs:\r\n\r\n r = requests.get(url+i).content\r\n sp = BeautifulSoup(r, 'html.parser')\r\n\r\n for h in sp.findAll('ul', {'class':'ul_facts_table'}):\r\n with open('PTdata.csv','a+') as file:\r\n\r\n for g in h.findAll('li'):\r\n file.write(g.text+',')\r\n file.write('\\n')\r\n\r\n #print(g.text)\r\n\r\n\r\n\r\n#make PT class and element class\r\n#for each row of the pt pass element data to the element class\r\n#populate grid with Main Data\r\n#on click pull up popup\r\n#make argument of gui file chance to use it like api\r\n'''in other file'''\r\n", "repo_name": "augustwindham/Periodic-Table-App", "sub_path": "Pt_db.py", "file_name": "Pt_db.py", "file_ext": "py", "file_size_in_byte": 1111, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "22567970843", "text": "import time\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom core.config import Config\n\ndef wait_for_engine(db_url: str, retries: int = 3) -> None:\n engine = None\n while retries > 0:\n try:\n engine = create_engine(db_url)\n engine.connect()\n break\n except Exception:\n retries -= 1\n time.sleep(5)\n if engine is None:\n raise Exception(\"Failed to connect to PostgreSQL\")\n\nengine_url = f\"postgresql://{Config.POSTGRES_USER}:{Config.POSTGRES_PASSWORD}@postgresql:5432/{Config.POSTGRES_DB}\"\nwait_for_engine(engine_url)\n\nengine = create_engine(engine_url)\nSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)\nBase = declarative_base()\n\n\ndef get_db():\n db = SessionLocal()\n try:\n yield db\n finally:\n db.close()", "repo_name": "acekun141/fastapi-celery-rabbitmq-docker", "sub_path": "api/db/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 11, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "core.config.Config.POSTGRES_USER", "line_number": 20, "usage_type": "attribute"}, {"api_name": "core.config.Config", "line_number": 20, "usage_type": "name"}, {"api_name": "core.config.Config.POSTGRES_PASSWORD", "line_number": 20, "usage_type": "attribute"}, {"api_name": "core.config.Config.POSTGRES_DB", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sqlalchemy.create_engine", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "44018636179", "text": "# -*- coding: utf-8 -*-\nimport json\nimport sys\n\nkey = sys.argv[1]\nwith open(key, 'r') as f:\n data = f.read()\n\nbb = json.loads(data)\ncc = bb['records']\nfor dd in cc:\n type = dd['type']\n name = dd['name'] + '.' + key\n value = dd['value']\n status = dd['status']\n line = dd['line']\n\n msg = \"%s,%s,%s,%s,%s\\n\" % (name, type, value, status, line)\n out = key + '.log'\n with open(out, 'a+') as f:\n f.write(msg)\n", "repo_name": "sydt2014/bigdata-deploy", "sub_path": "modules/ansible/scripts/switch_dict.py", "file_name": "switch_dict.py", "file_ext": "py", "file_size_in_byte": 437, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.argv", "line_number": 5, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "44178777173", "text": "from __future__ import annotations\nimport queue\nimport threading\nfrom typing import NoReturn, Optional, Callable, Any\n\n\nclass ThreadPool:\n def __init__(self, max_workers: Optional[int]):\n self.max_workers = max_workers\n self.tasks = queue.Queue()\n self.workers = []\n\n def submit(self, func: Optional[Callable], *args: Optional[Any],\n thread_name: Optional[str] = None, **kwargs: Optional[Any]) -> ThreadPool:\n self.tasks.put((func, args, kwargs, thread_name))\n return self\n\n def start(self) -> NoReturn:\n for i in range(self.max_workers):\n worker = threading.Thread(target=self._worker)\n worker.daemon = True\n worker.start()\n self.workers.append(worker)\n\n def _worker(self) -> NoReturn:\n while True:\n func, args, kwargs, thread_name = self.tasks.get()\n try:\n if thread_name:\n threading.current_thread().name = thread_name\n func(*args, **kwargs)\n except Exception as e:\n print(e)\n finally:\n self.tasks.task_done()\n\n def wait_completion(self) -> NoReturn:\n self.tasks.join()\n", "repo_name": "StrawberryCake-Fish/ClockIn-Py", "sub_path": "src/utils/pool.py", "file_name": "pool.py", "file_ext": "py", "file_size_in_byte": 1225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.Optional", "line_number": 8, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 10, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 14, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.NoReturn", "line_number": 18, "usage_type": "name"}, {"api_name": "threading.current_thread", "line_number": 30, "usage_type": "call"}, {"api_name": "typing.NoReturn", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.NoReturn", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "3451458591", "text": "import csv\nimport random\nfrom pathlib import Path\n\nfrom data.model import Record\nfrom data.dataset import Dataset\nimport configparser\n\nfrom model.model import NeuralNetwork\nimport torch\nfrom torch.utils.data import DataLoader\n\nparent = Path(__file__).parent\n\nconfig = configparser.ConfigParser()\nconfig.read(str(parent) + '/hyperparameters.ino')\nconfig = config[\"DEFAULT\"]\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nprint('Using {} device'.format(device))\n\n\ndef binary_acc(y_pred, y_test):\n y_pred_tag = torch.round(torch.sigmoid(y_pred))\n\n correct_results_sum = (y_pred_tag == y_test).sum().float()\n acc = correct_results_sum / y_test.shape[0]\n acc = torch.round(acc * 100)\n\n return acc\n\n\n# https://www.kaggle.com/fedesoriano/heart-failure-prediction\nif __name__ == '__main__':\n dataset = []\n for key in config:\n print(key)\n t = config['TEST_PERCENTAGE_SPLIT']\n\n with open(str(parent) + '/data/heart.csv', 'r', newline='') as csvfile:\n linereader = csv.reader(csvfile, delimiter=' ')\n for row in linereader:\n row = row[0].split(',')\n dataset.append(row)\n processed = []\n for rec in dataset[1:]:\n r = Record()\n r.process_record(rec)\n processed.append(r)\n feature_vector = []\n targets = []\n for rec in processed:\n feature_vector.append(rec.get_feature_vector())\n targets.append(rec.get_target())\n\n random.shuffle(processed)\n test_end = round(len(processed) / (1 / float(config['TEST_PERCENTAGE_SPLIT'])))\n test_x = feature_vector[:test_end]\n test_y = targets[:test_end]\n train_x = feature_vector[test_end:]\n train_y = targets[test_end:]\n print(\"Data Split --- Test: {} \\t Train: {}\".format(len(test_x), len(train_x)))\n\n train_set = Dataset(train_x, train_y)\n test_set = Dataset(test_x, test_y)\n\n net = NeuralNetwork(len(test_x[0]), 1)\n train_loader = DataLoader(train_set, batch_size=int(config['BATCH_SIZE']))\n test_loader = DataLoader(test_set, batch_size=len(test_set))\n # train_features, train_labels = iter(train_loader)\n opt = torch.optim.Adam(net.parameters(), lr=float(config['LEARNING_RATE']))\n loss_fn = torch.nn.BCEWithLogitsLoss()\n accuracy = []\n net.train()\n for epoch in range(int(config['MAX_EPOCHS'])):\n train_acc = []\n test_acc = []\n for batch, (local_batch, local_labels) in enumerate(train_loader):\n # Transfer to GPU\n local_batch, local_labels = local_batch.to(device), local_labels.to(device)\n\n preds = net(local_batch)\n local_labels = local_labels.unsqueeze(1)\n loss = loss_fn(preds, local_labels.float())\n\n t_acc = binary_acc(preds, local_labels.float())\n\n opt.zero_grad()\n loss.backward()\n opt.step()\n\n train_acc.append(t_acc)\n print(\"Epoch: {}, Batch: {}, Train accuracy: {}%\".format(epoch, batch, train_acc[-1]))\n print(\"Epoch: {}, Batch: {}, Train loss: {}\".format(epoch, batch, loss.item()))\n\n with torch.no_grad():\n test_batch = []\n test_labels = []\n for b, l in test_loader:\n test_batch = b\n test_labels = l\n test_batch, test_labels = test_batch.to(device), test_labels.to(device)\n test_labels = test_labels.unsqueeze(1)\n preds = net(test_batch)\n test_acc.append(binary_acc(preds, test_labels))\n print(\"Epoch: {}, Test accuracy: {}%\".format(epoch, test_acc[-1]))\n test_loss = loss_fn(preds, test_labels.float())\n print(\"Epoch: {}, Test loss: {}\".format(epoch, test_loss.item()))\n print(\"Epoch {}\".format(epoch, batch))\n\n print(\"EXIT\")\n", "repo_name": "DiarmuidKelly/predictors", "sub_path": "heart_disease/heart_disease.py", "file_name": "heart_disease.py", "file_ext": "py", "file_size_in_byte": 3990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.round", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.round", "line_number": 28, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 41, "usage_type": "call"}, {"api_name": "data.model.Record", "line_number": 47, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 56, "usage_type": "call"}, {"api_name": "data.dataset.Dataset", "line_number": 64, "usage_type": "call"}, {"api_name": "data.dataset.Dataset", "line_number": 65, "usage_type": "call"}, {"api_name": "model.model.NeuralNetwork", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "39660626198", "text": "import copy\nimport numpy as np\nuse_cuda = True\nif use_cuda:\n import cupy as cp\n to_cpu = cp.asnumpy\n cp.cuda.set_allocator(cp.cuda.MemoryPool().malloc)\nelse:\n cp = np\n to_cpu = lambda x: x\nimport open3d as o3\nfrom probreg import cpd\nfrom probreg import callbacks\nimport utils\nimport time\nfrom utils import estimate_normals\n\n# load source and target point cloud\nsource_mesh = o3.io.read_triangle_mesh('data/down/pcd_1.obj')\ntarget_mesh = o3.io.read_triangle_mesh('data/down/pcd_2.obj')\n# transform target point cloud\nth = np.deg2rad(30.0)\nsource = o3.geometry.PointCloud()\nsource.points = o3.utility.Vector3dVector(np.asarray(source_mesh.vertices, np.float32))\ntarget = o3.geometry.PointCloud()\ntarget.points = o3.utility.Vector3dVector(np.asarray(target_mesh.vertices, np.float32))\n# transform target point cloud\nth = np.deg2rad(30.0)\n\nsource_pt = cp.asarray(source.points, dtype=cp.float32)\ntarget_pt = cp.asarray(target.points, dtype=cp.float32)\n\nsource2 = source.voxel_down_sample(voxel_size=0.4)\ntarget2 = target.voxel_down_sample(voxel_size=0.4)\n\nsource_pt2 = cp.asarray(source2.points, dtype=cp.float32)\ntarget_pt2 = cp.asarray(target2.points, dtype=cp.float32)\nprint((np.asarray(source_mesh.vertices).shape), len(np.asarray(target_mesh.vertices)))\nprint(len(np.asarray(source.points)), len(np.asarray(target.points)))\nprint(len(np.asarray(source2.points)), len(np.asarray(target2.points)))\n\nprint(\"start reg\")\n\n# compute cpd registration\nacpd = cpd.AffineCPD(source_pt, use_cuda=use_cuda)\ntf_param, _, _ = acpd.registration(target_pt)\nresult = tf_param.transform(source_pt)\n\n# result = tf_param.transform(result)\nmesh = o3.geometry.TriangleMesh()\nnp_vertices = to_cpu(result)\nnp_triangles = np.array(source_mesh.triangles).astype(np.int32)\nmesh.vertices = o3.utility.Vector3dVector(np_vertices)\nmesh.triangles = o3.utility.Vector3iVector(np_triangles)\no3.io.write_triangle_mesh('data/down/pcd_1_rigid.obj', mesh)\n", "repo_name": "Pangyk/point-cloud-registration", "sub_path": "robot_curve/reg/cpd_rigid.py", "file_name": "cpd_rigid.py", "file_ext": "py", "file_size_in_byte": 1935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "cupy.asnumpy", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cupy.cuda.set_allocator", "line_number": 7, "usage_type": "call"}, {"api_name": "cupy.cuda", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cupy.cuda.MemoryPool", "line_number": 7, "usage_type": "call"}, {"api_name": "open3d.io.read_triangle_mesh", "line_number": 19, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 19, "usage_type": "attribute"}, {"api_name": "open3d.io.read_triangle_mesh", "line_number": 20, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.deg2rad", "line_number": 22, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 23, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 23, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 24, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 25, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 25, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 26, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.deg2rad", "line_number": 28, "usage_type": "call"}, {"api_name": "cupy.asarray", "line_number": 30, "usage_type": "call"}, {"api_name": "cupy.float32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cupy.asarray", "line_number": 31, "usage_type": "call"}, {"api_name": "cupy.float32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cupy.asarray", "line_number": 36, "usage_type": "call"}, {"api_name": "cupy.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cupy.asarray", "line_number": 37, "usage_type": "call"}, {"api_name": "cupy.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 40, "usage_type": "call"}, {"api_name": "probreg.cpd.AffineCPD", "line_number": 45, "usage_type": "call"}, {"api_name": "probreg.cpd", "line_number": 45, "usage_type": "name"}, {"api_name": "open3d.geometry.TriangleMesh", "line_number": 50, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 53, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 53, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3iVector", "line_number": 54, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 54, "usage_type": "attribute"}, {"api_name": "open3d.io.write_triangle_mesh", "line_number": 55, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "12849829012", "text": "import argparse\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom sklearn.manifold import TSNE\n\nfrom src import project_dir\nfrom src.models.model import ImageClassifier\nfrom src.data.mnist import MNISTDataModule\n\ndef parser():\n \"\"\"Parses command line.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Script for visualizing embeddings created by image \" \"classifier\"\n )\n parser.add_argument(\n '--model_path', default=project_dir + '/models/model.pth', type=str)\n parser.add_argument(\n '--fig_path', default=project_dir + 'reports/figures/embeddings.pdf')\n parser.add_argument('--mb_size', default=64, type=int)\n args = parser.parse_args()\n\n return args\n\n\ndef get_embeddings(args, model, data_loader):\n \"\"\"Gets embeddings produced by model.\"\"\"\n with torch.no_grad():\n model.eval()\n\n embeddings = torch.zeros(\n (len(data_loader.dataset.data), model.linear.in_features)\n )\n all_labels = torch.zeros(len(data_loader.dataset.data))\n\n for i, (images, labels) in enumerate(data_loader):\n model(images)\n embeddings[\n i * args.mb_size : i * args.mb_size + images.shape[0], :\n ] = model.embeddings\n all_labels[i * args.mb_size : i * args.mb_size + images.shape[0]] = labels\n\n return embeddings.numpy(), all_labels.numpy()\n\n\ndef plot_embeddings(embeddings, labels):\n \"\"\"Plots embeddings.\"\"\"\n embs_proj = TSNE(\n n_components=2,\n random_state=42,\n verbose=1,\n n_jobs=-1).fit_transform(embeddings)\n\n fig, ax = plt.subplots()\n scatter = ax.scatter(\n embs_proj[:, 0],\n embs_proj[:, 1],\n c=labels,\n cmap=plt.get_cmap(\"tab10\"),\n alpha=0.5,\n s=2,\n )\n ax.set_xlabel(\"t-SNE component 1\")\n ax.set_ylabel(\"t-SNE component 2\")\n ax.set_title(\"Embeddings\")\n\n markers = scatter.legend_elements()[0]\n plt.legend(\n markers,\n np.unique(labels),\n loc=0,\n borderaxespad=0.1,\n title=\"Digit\",\n framealpha=0.6,\n )\n\n return fig\n\n\ndef main():\n args = parser()\n train_loader, test_loader = get_data(args)\n model = ImageClassifier.load_from_checkpoint(\n checkpoint_path=args.model_path)\n embeddings, labels = get_embeddings(args, model, test_loader)\n fig = plot_embeddings(embeddings, labels)\n fig.savefig(args.fig_path)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "jonasvj/MLOps", "sub_path": "src/visualization/visualize.py", "file_name": "visualize.py", "file_ext": "py", "file_size_in_byte": 2477, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "src.project_dir", "line_number": 18, "usage_type": "name"}, {"api_name": "src.project_dir", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "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": "numpy.unique", "line_number": 71, "usage_type": "call"}, {"api_name": "src.models.model.ImageClassifier.load_from_checkpoint", "line_number": 84, "usage_type": "call"}, {"api_name": "src.models.model.ImageClassifier", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "70231025043", "text": "import pytest\nfrom time import sleep\nfrom datetime import (\n\tdate,\n\ttimedelta\n)\nfrom utilities import XLUtility\nfrom pageObjects.common_functions.common_methods import CommonMethods\n\n\n# This test checks the functionality of creating a session\n@pytest.mark.usefixtures(\"one_time_setup\")\nclass Test_TC101_101_CreateSession():\n\n\t@pytest.fixture(autouse=True)\n\tdef classSetup(self, one_time_setup):\n\t\tself.logIn()\n\n\tdef test_create_session(self):\n\t\tcommon = CommonMethods(self.driver)\n\t\tself.log.info(\"starting test {}...\".format(__name__))\n\t\tself.driver.set_window_size(411, 823)\n\n\t\ttoday_date = date.today()\n\t\tcurrent_weekday = today_date.weekday()\n\n\t\t# delete any existing session\n\t\tcommon.mobile_delete_existing_session()\n\n\t\t# delete the sessions from client side\n\t\tclient_name = XLUtility.readData(self.path, 'session_mobile_data', 2, 3)\n\t\tsleep(1)\n\t\tself.login_page_obj.clk_navigation_btn()\n\t\tsleep(1)\n\t\tself.client_page_obj.clk_all_clients_mobile()\n\t\tsleep(1)\n\t\tself.client_page_obj.mobile_sel_client_name(client_name)\n\t\tsleep(1)\n\t\tself.client_page_obj.clk_view_client_mobile()\n\t\tsleep(1)\n\t\tself.notes_page_obj.clk_session_notes()\n\t\tsleep(1)\n\t\tcommon.delete_mobile_prior_session_note()\n\t\tsleep(2)\n\t\tself.login_page_obj.clk_navigation_btn()\n\t\t# Start creating session\n\t\tsleep(1)\n\t\tself.login_page_obj.clk_mobile_calendar()\n\t\tsleep(1)\n\t\tself.calendar_page_obj.click_add_session()\n\t\tsleep(1)\n\t\t# Complete the Session details form\n\n\t\t# Select Client\n\t\tsleep(1)\n\t\tself.calendar_page_obj.input_clientname(client_name)\n\t\tsleep(1)\n\n\t\t# Select Room\n\t\troom = XLUtility.readData(self.path, 'session_mobile_data', 2, 6)\n\t\tself.calendar_page_obj.sel_room(room)\n\n\t\t# Select Date and Time\n\t\tsleep(1)\n\n\t\tif current_weekday < 3:\n\t\t\tN = 3 - current_weekday\n\t\t\tmeeting_date = today_date + timedelta(days=N)\n\t\t\tself.date_time = str(meeting_date) + \" 9:00am\"\n\n\t\tif current_weekday >= 3:\n\t\t\tN = 10 - current_weekday\n\t\t\tmeeting_date = today_date + timedelta(days=N)\n\t\t\tself.date_time = str(meeting_date) + \" 9:00am\"\n\t\tself.calendar_page_obj.txt_date_time(self.date_time)\n\t\tsleep(1)\n\n\t\t# Select Service type (CBT, Counselling, etc.)\n\t\tservice = XLUtility.readData(self.path, 'session_mobile_data', 2, 5)\n\t\tself.calendar_page_obj.sel_service(service)\n\n\t\t# Click on Create Session\n\t\tsleep(1)\n\t\tself.calendar_page_obj.clk_create_session()\n\t\tsleep(1)\n\t\tself.calendar_page_obj.clk_btn_calendar_view()\n\t\tsleep(1)\n\t\tself.calendar_page_obj.clk_btn_calendar_week_view()\n\n\t\tif current_weekday < 3 or current_weekday == 6:\n\t\t\tself.calendar_page_obj.clk_mobile_session_info()\n\n\t\telse:\n\t\t\t# self.calendar_page_obj.clk_move_to_next_week()\n\t\t\tself.calendar_page_obj.clk_mobile_session_info()\n\t\tsleep(1)\n\t\tmobile_session_details = self.calendar_page_obj.mobile_session_details()\n\n\t\t# View the detail of the session created\n\t\tsleep(1)\n\t\tself.calendar_page_obj.clk_mobile_more_information()\n\t\tsleep(1)\n\t\tself.calendar_page_obj.clk_delete_session()\n\t\tsleep(1)\n\t\tself.calendar_page_obj.clk_delete_session_warn()\n\t\texp_date_time = \"Thu, \" + meeting_date.strftime(\"%b %-d\") + \" - 9:00am to 10:00am\"\n\n\t\tif exp_date_time in mobile_session_details:\n\t\t\t\t\tself.log.info(\"{} passed!\".format(__name__))\n\t\t\t\t\tassert True\n\t\telse:\n\t\t\t\t\tself.driver.save_screenshot(\n\t\t\t\t\t\tself.pathScreenShot + \"Test_TC101_101_CreateSession\" + self.dateFormat + \".png\"\n\t\t\t\t\t)\n\t\t\t\t\tself.log.info(\"{} failed!\".format(__name__))\n\t\t\t\t\tassert False\n", "repo_name": "harry-100/qa-automation-framework", "sub_path": "testCases/calendar/full_calendar/mobile/TC_101_101_mobile_create_session_test.py", "file_name": "TC_101_101_mobile_create_session_test.py", "file_ext": "py", "file_size_in_byte": 3373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pytest.fixture", "line_number": 15, "usage_type": "call"}, {"api_name": "pageObjects.common_functions.common_methods.CommonMethods", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 24, "usage_type": "name"}, {"api_name": "utilities.XLUtility.readData", "line_number": 31, "usage_type": "call"}, {"api_name": "utilities.XLUtility", "line_number": 31, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "utilities.XLUtility.readData", "line_number": 60, "usage_type": "call"}, {"api_name": "utilities.XLUtility", "line_number": 60, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 73, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "utilities.XLUtility.readData", "line_number": 79, "usage_type": "call"}, {"api_name": "utilities.XLUtility", "line_number": 79, "usage_type": "name"}, {"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": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "pytest.mark.usefixtures", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}]} +{"seq_id": "11931923442", "text": "#!/usr/bin/env python3\nimport click\nimport os\n\ndef is_next_to_open_parenth(line, idx):\n for i in range(idx, len(line)):\n if line[i] == ' ':\n continue\n if line[i] == '(':\n return True\n else:\n return False\n\ndef correct_print(fpath):\n fpath_out = fpath + '_out'\n with open(fpath, 'r') as fi:\n with open(fpath_out, 'w') as fo:\n for line in fi:\n idx = line.find('print')\n if idx==-1:\n fo.write(line)\n continue\n # deal with 'print'\n idx += len('print')\n if is_next_to_open_parenth(line, idx):\n fo.write(line)\n continue\n # need to insert parenthese\n line_out = line[:idx] + '(' + line[idx:-1] + ')' + line[-1]\n fo.write(line_out)\n\n@click.command()\n@click.argument('filepath', type=click.STRING)\ndef main(filepath):\n if not os.path.exists(filepath):\n click.echo(f'invalid file path: {filepath}')\n return\n correct_print(filepath)\n\nif __name__ == '__main__':\n main()", "repo_name": "seanwu-ec/misc_utilities", "sub_path": "print_to_py3.py", "file_name": "print_to_py3.py", "file_ext": "py", "file_size_in_byte": 1156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "click.echo", "line_number": 36, "usage_type": "call"}, {"api_name": "click.command", "line_number": 32, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 33, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "40770800036", "text": "\"\"\"empty message\n\nRevision ID: e6999daf4674\nRevises: a07ccb02144f\nCreate Date: 2021-11-17 13:09:08.372978\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'e6999daf4674'\ndown_revision = 'a07ccb02144f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('items', sa.Column('discount', sa.Integer(), nullable=False))\n op.add_column('items', sa.Column('condition', sa.String(length=15), nullable=False))\n op.add_column('items', sa.Column('count', sa.Integer(), nullable=False))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('items', 'count')\n op.drop_column('items', 'condition')\n op.drop_column('items', 'discount')\n # ### end Alembic commands ###\n", "repo_name": "benthere914/Acquire-Market-Place", "sub_path": "migrations/versions/20211117_130908_.py", "file_name": "20211117_130908_.py", "file_ext": "py", "file_size_in_byte": 899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "15908932336", "text": "import numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom common_functions import create_model\nfrom data_preprocessing import create_final_ds, create_tf_dataset\n\n\"\"\"\nThis was used to generate some first models and get a better feel for the data\n\"\"\"\n\n# uses Early Stopping\nEPOCHS = 70\n# MODEL CONFIG\nnodes_lstm = 20\ndropout = 0.1\nlearning_rate = 0.001\nmetric = \"r_square\"\nbatch_size = 32\nseq_length = 1\n\nmodel_filename = f\"rmse_{nodes_lstm}_001_{seq_length}_{batch_size}_01.h5\"\n\n\ntrain_ds, val_ds, test_ds, train_df, test_df, val_df = create_final_ds(\n \"SHA\", [\"SHA\", \"WSH\"], \"SHA_nit\", batch_size=batch_size, seq_length=seq_length, interval=\"24h\")\n\nmodel, early_stopping = create_model(nodes_lstm, None, dropout, metric, learning_rate)\n\n\ndef train_model():\n history = model.fit(train_ds, epochs=EPOCHS,\n validation_data=val_ds)\n\n model.save(model_filename)\n\n # list all data in history\n print(history.history.keys())\n # visualize history for accuracy\n plt.plot(history.history['r_square'])\n plt.plot(history.history['val_r_square'])\n plt.title('model MSE')\n plt.ylabel('MSE')\n plt.xlabel('epoch')\n plt.legend(['train', 'test'], loc='upper left')\n plt.show()\n\n # visualize history for loss\n plt.plot(history.history['loss'])\n plt.plot(history.history['val_loss'])\n plt.title('model loss')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(['train', 'test'], loc='upper left')\n plt.show()\n\n\ndef visualize_model():\n model.evaluate(train_ds.take(1))\n model.load_weights(model_filename)\n print(model.evaluate(test_ds))\n print(model.evaluate(train_ds))\n print(model.evaluate(val_ds))\n # visualize specific length of data\n \"\"\"predictions = []\n labels = []\n for batch in test_ds.take(2000):\n prediction = model.predict(batch)\n\n prediction = list(prediction)\n for ind, p in enumerate(prediction):\n predictions.append(np.max(p))\n\n y = [arr.numpy() for arr in batch][1]\n labels.append(y[0])\n\n x = range(0, len(predictions))\n print(predictions)\n print(labels)\"\"\"\n\n for ds, df in ((train_ds, train_df), (val_ds, val_df), (test_ds, test_df)):\n # visualize\n predictions = model.predict(ds).flatten()\n labels = np.array(df[\"SHA_nit\"])\n\n x = range(0, len(predictions))\n x1 = range(0, len(labels))\n\n print(predictions)\n print(labels)\n\n sns.set()\n fig, ax1 = plt.subplots()\n\n ax1.plot(x1, labels, color=\"blue\", label=\"actual\", linewidth=1)\n ax1.plot(x, predictions, color=\"orange\", label=\"Predictions\", linewidth=1.5)\n plt.title(\"Train Data\")\n plt.legend()\n\n plt.show()\n\n\ndef visualize_whole_dataset_model():\n model.evaluate(train_ds.take(1))\n model.load_weights(model_filename)\n\n full_ds = train_ds.concatenate(val_ds)\n full_ds = full_ds.concatenate(test_ds)\n full_df = train_df.append(val_df)\n full_df = full_df.append(test_df)\n # visualize\n predictions = model.predict(full_ds).flatten()\n labels = np.array(full_df[\"SHA_nit\"])\n\n x = range(0, len(predictions))\n x1 = range(0, len(labels))\n\n print(predictions)\n print(labels)\n\n sns.set()\n fig, ax1 = plt.subplots()\n\n ax1.plot(x1, labels, color=\"blue\", label=\"actual\", linewidth=1)\n ax1.plot(x, predictions, color=\"orange\", label=\"Predictions\", linewidth=1.5)\n plt.title(\"Train data | val data | test data\")\n plt.legend()\n\n for i in range(3):\n plt.axvline(len(train_df) + i, color=\"r\")\n for i in range(3):\n plt.axvline(len(train_df) + len(val_df) + i, color=\"r\")\n\n plt.show()\n\n\ndef extract_feature_importance():\n model.evaluate(test_ds.take(1))\n model.load_weights(\"RMSE_interval=24h.h5\")\n\n feature_df = pd.DataFrame(columns=[\"Feature removed\", \"loss\", \"RMSE\"])\n\n loss, metric = model.evaluate(train_ds)\n feature_df.loc[0] = [\"Normal\"] + [loss] + [metric]\n\n for ind, feature in enumerate(train_df.columns[1:]):\n # shuffle the feature\n n_df = train_df.copy()\n np.random.shuffle(n_df[feature])\n\n feature_train = n_df.drop([\"nit\"], axis=1)\n\n feature_scaler = MinMaxScaler(feature_range=(0, 1))\n feature_scaler.fit(feature_train.to_numpy())\n feature_train_scaled = feature_scaler.transform(feature_train)\n target_train = np.array(n_df[\"nit\"], ndmin=2).T\n\n feature_dataset = create_tf_dataset(feature_train_scaled, target_train, batch_size=32, seq_length=8)\n\n loss, metric = model.evaluate(feature_dataset)\n feature_df.loc[ind + 1] = [feature] + [loss] + [metric]\n\n feature_df.to_pickle(\"features.pkl\")\n print(feature_df)\n\n\ndef calculate_important_features():\n feature_df = pd.read_pickle(\"features.pkl\")\n\n norm = feature_df[\"RMSE\"][0]\n differences = [norm]\n divided = [norm]\n\n for rmse in feature_df[\"RMSE\"][1:]:\n differences.append(rmse - norm)\n divided.append(rmse / norm)\n\n feature_df[\"Difference\"] = differences\n feature_df[\"Divided\"] = divided\n feature_df.sort_values(\"Difference\", inplace=True, ignore_index=True, ascending=False)\n feature_df.to_pickle(\"feature_importance.pkl\")\n\n\ndef show_important_features():\n feature_df = pd.read_pickle(\"feature_importance.pkl\")\n print(feature_df)\n\n\ntrain_model()\nextract_feature_importance()\ncalculate_important_features()\nshow_important_features()", "repo_name": "henri-climber/LSTM-time-series-prediction-Kenya", "sub_path": "first_models.py", "file_name": "first_models.py", "file_ext": "py", "file_size_in_byte": 5509, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "data_preprocessing.create_final_ds", "line_number": 28, "usage_type": "call"}, {"api_name": "common_functions.create_model", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "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.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"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.ylabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "data_preprocessing.create_tf_dataset", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 171, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 188, "usage_type": "call"}]} +{"seq_id": "4262475113", "text": "from cv2 import cvtColor\r\nimport numpy as np\r\nimport cv2\r\nmask_directory=r'D:\\Personal Info\\Python Projects\\Person Segmentation Using Unet and DeepLabv3+\\Satelite Imageprocessing\\output.png'\r\nimg_directory=r'D:\\Personal Info\\Python Projects\\Person Segmentation Using Unet and DeepLabv3+\\Satelite Imageprocessing\\image.png'\r\n\r\n\r\ndef find_color_pixels(mask):\r\n #Using these function we will try to find out the uniques colors in a image\r\n print(\"Finding the mask unique pixel values\")\r\n new_shape_mask=np.unique(mask.reshape(-1,mask.shape[2]),axis=0)\r\n print(new_shape_mask)\r\ndef find_color_second_method(image):\r\n print(\"Color pixel values in another way\")\r\n unique_pixels=set( tuple(v) for m2d in image for v in m2d ) \r\n print(\"Unique pixel are: \")\r\n print(unique_pixels)\r\nif __name__ == '__main__':\r\n #image=cv2.imread(img_directory,1)\r\n mask=cv2.imread(mask_directory,1)\r\n if mask is not None:\r\n print(\"Image reading completed successfully\")\r\n find_color_pixels(mask)\r\n find_color_second_method(mask)\r\n else:\r\n print(\"Sorry!! Couldn't read actually\")\r\n", "repo_name": "farhan1503001/Satelite-Image-Segmentation", "sub_path": "Utility/sat_img_shaping.py", "file_name": "sat_img_shaping.py", "file_ext": "py", "file_size_in_byte": 1118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.unique", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "71881789840", "text": "\n\n\nimport pygame,sys\n\nclass Player(pygame.sprite.Sprite):\n def __init__(self):\n super().__init__()\n self.image = pygame.Surface((30,40))\n self.image.fill(\"black\")\n self.rect = self.image.get_rect(center = (100,100))\n \n self.gravity = 0\n \n def apply_gravity(self):\n self.gravity += 0.5\n self.rect.y += self.gravity\n\n if self.rect.bottom >= 500:\n self.rect.bottom = 500\n \n def player_input(self):\n keys = pygame.key.get_pressed()\n if keys[pygame.K_UP] and self.rect.bottom == 500:\n self.gravity = -15\n if keys[pygame.K_LEFT]:\n self.rect.x -= 5\n if keys[pygame.K_RIGHT]:\n self.rect.x += 5\n \n \n def update(self):\n self.apply_gravity()\n self.player_input()\n \n\nclass Ground(pygame.sprite.Sprite):\n def __init__(self):\n super().__init__()\n self.image = pygame.Surface((600,100))\n self.image.fill(\"brown\")\n self.rect = self.image.get_rect(topleft = (0,500))\n\n# create ground\n# create 2d player that can jump - simulate gravity\n\npygame.init()\nclock = pygame.time.Clock()\n\nwidth, height = 600, 600\nscreen = pygame.display.set_mode((width,height))\npygame.display.set_caption(\"octorun\")\n\n\nplayer = Player()\nplayer_group = pygame.sprite.Group()\nplayer_group.add(player)\n\nground = Ground()\nground_group = pygame.sprite.Group()\nground_group.add(ground)\n\n\n\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n \n\n \n screen.fill(\"light blue\")\n\n player_group.draw(screen)\n player_group.update()\n\n ground_group.draw(screen)\n\n pygame.display.flip()\n\n clock.tick(60)\n", "repo_name": "monkeMuk/Python", "sub_path": "Pygame/Tutorials/Collisions/2D_gravity_basic.py", "file_name": "2D_gravity_basic.py", "file_ext": "py", "file_size_in_byte": 1776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pygame.sprite", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 80, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "12617547793", "text": "\"\"\"\nThe main function for the XSS linter.\n\"\"\"\n\n\nimport argparse\nimport importlib\nimport os\nimport sys\nfrom functools import reduce\n\nfrom xsslint.reporting import SummaryResults\nfrom xsslint.rules import RuleSet\nfrom xsslint.utils import is_skip_dir\n\n\ndef _load_config_module(module_path):\n cwd = os.getcwd()\n if cwd not in sys.path:\n # Enable config module to be imported relative to wherever the script was run from.\n sys.path.append(cwd)\n return importlib.import_module(module_path)\n\n\ndef _build_ruleset(template_linters):\n \"\"\"\n Combines the RuleSets from the provided template_linters into a single, aggregate RuleSet.\n\n Arguments:\n template_linters: A list of linting objects.\n\n Returns:\n The combined RuleSet.\n \"\"\"\n return reduce(\n lambda combined, current: combined + current.ruleset,\n template_linters,\n RuleSet()\n )\n\n\ndef _process_file(full_path, template_linters, options, summary_results, out):\n \"\"\"\n For each linter, lints the provided file. This means finding and printing\n violations.\n\n Arguments:\n full_path: The full path of the file to lint.\n template_linters: A list of linting objects.\n options: A list of the options.\n summary_results: A SummaryResults with a summary of the violations.\n out: output file\n\n \"\"\"\n num_violations = 0\n directory = os.path.dirname(full_path)\n file_name = os.path.basename(full_path)\n try:\n for template_linter in template_linters:\n results = template_linter.process_file(directory, file_name)\n results.print_results(options, summary_results, out)\n except BaseException as e:\n raise Exception(f\"Failed to process path: {full_path}\") from e\n\n\ndef _process_os_dir(directory, files, template_linters, options, summary_results, out):\n \"\"\"\n Calls out to lint each file in the passed list of files.\n\n Arguments:\n directory: Directory being linted.\n files: All files in the directory to be linted.\n template_linters: A list of linting objects.\n options: A list of the options.\n summary_results: A SummaryResults with a summary of the violations.\n out: output file\n\n \"\"\"\n for current_file in sorted(files, key=lambda s: s.lower()):\n full_path = os.path.join(directory, current_file)\n _process_file(full_path, template_linters, options, summary_results, out)\n\n\ndef _process_os_dirs(starting_dir, template_linters, options, summary_results, out):\n \"\"\"\n For each linter, lints all the directories in the starting directory.\n\n Arguments:\n starting_dir: The initial directory to begin the walk.\n template_linters: A list of linting objects.\n options: A list of the options.\n summary_results: A SummaryResults with a summary of the violations.\n out: output file\n\n \"\"\"\n skip_dirs = options.get('skip_dirs', ())\n for root, dirs, files in os.walk(starting_dir):\n if is_skip_dir(skip_dirs, root):\n del dirs\n continue\n dirs.sort(key=lambda s: s.lower())\n _process_os_dir(root, files, template_linters, options, summary_results, out)\n\n\ndef _lint(file_or_dir, template_linters, options, summary_results, out):\n \"\"\"\n For each linter, lints the provided file or directory.\n\n Arguments:\n file_or_dir: The file or initial directory to lint.\n template_linters: A list of linting objects.\n options: A list of the options.\n summary_results: A SummaryResults with a summary of the violations.\n out: output file\n\n \"\"\"\n\n if file_or_dir is not None and os.path.isfile(file_or_dir):\n _process_file(file_or_dir, template_linters, options, summary_results, out)\n else:\n directory = \".\"\n if file_or_dir is not None:\n if os.path.exists(file_or_dir):\n directory = file_or_dir\n else:\n raise ValueError(f\"Path [{file_or_dir}] is not a valid file or directory.\")\n _process_os_dirs(directory, template_linters, options, summary_results, out)\n\n summary_results.print_results(options, out)\n\n\ndef main():\n \"\"\"\n Used to execute the linter. Use --help option for help.\n\n Prints all violations.\n \"\"\"\n epilog = \"For more help using the xss linter, including details on how to\\n\"\n epilog += \"understand and fix any violations, read the docs here:\\n\"\n epilog += \"\\n\"\n # pylint: disable=line-too-long\n epilog += \" https://edx.readthedocs.org/projects/edx-developer-guide/en/latest/conventions/preventing_xss.html#xss-linter\\n\"\n\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description='Checks that templates are safe.',\n epilog=epilog,\n )\n parser.add_argument(\n '--list-files', dest='list_files', action='store_true',\n help='Only display the filenames that contain violations.'\n )\n parser.add_argument(\n '--rule-totals', dest='rule_totals', action='store_true',\n help='Display the totals for each rule.'\n )\n parser.add_argument(\n '--summary-format', dest='summary_format',\n choices=['eslint', 'json'], default='eslint',\n help='Choose the display format for the summary.'\n )\n parser.add_argument(\n '--verbose', dest='verbose', action='store_true',\n help='Print multiple lines where possible for additional context of violations.'\n )\n parser.add_argument(\n '--config', dest='config', action='store', default='xsslint.default_config',\n help='Specifies the config module to use. The config module should be in Python package syntax.'\n )\n parser.add_argument('path', nargs=\"?\", default=None, help='A file to lint or directory to recursively lint.')\n\n args = parser.parse_args()\n config = _load_config_module(args.config)\n options = {\n 'list_files': args.list_files,\n 'rule_totals': args.rule_totals,\n 'summary_format': args.summary_format,\n 'verbose': args.verbose,\n 'skip_dirs': getattr(config, 'SKIP_DIRS', ())\n }\n template_linters = getattr(config, 'LINTERS', ())\n if not template_linters:\n raise ValueError(f\"LINTERS is empty or undefined in the config module ({args.config}).\")\n\n ruleset = _build_ruleset(template_linters)\n summary_results = SummaryResults(ruleset)\n _lint(args.path, template_linters, options, summary_results, out=sys.stdout)\n", "repo_name": "openedx/edx-platform", "sub_path": "scripts/xsslint/xsslint/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6503, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6774, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.getcwd", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 22, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 35, "usage_type": "call"}, {"api_name": "xsslint.rules.RuleSet", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 97, "usage_type": "call"}, {"api_name": "xsslint.utils.is_skip_dir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 144, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 145, "usage_type": "attribute"}, {"api_name": "xsslint.reporting.SummaryResults", "line_number": 186, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 187, "usage_type": "attribute"}]} +{"seq_id": "28659015939", "text": "# Databricks notebook source\narrayData = [\n ('James',['Java','Scala'],{'hair':'black','eye':'brown'}),\n ('Michael',['Spark','Java',None],{'hair':'brown','eye':None}),\n ('Robert',['CSharp',''],{'hair':'red','eye':''}),\n ('Washington',None,None),\n ('Jefferson',['1','2'],{})]\n\n\ndf = spark.createDataFrame(data=arrayData, schema = ['name','knownLanguages','properties'])\ndf.printSchema()\ndf.show(truncate=False)\n\n# COMMAND ----------\n\ndisplay(df)\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import explode\n\ndf2 = df.select(explode(df.knownLanguages).alias(\"exp_languages\"))\ndf2.printSchema()\ndf2.show()\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import explode\n\ndf2 = df.select(\"*\",explode(df.knownLanguages).alias(\"exp_languages\")).drop(\"knownLanguages\")\ndf2.printSchema()\ndf2.show(truncate=False)\n\n# COMMAND ----------\n\ndf3 = df.select(\"name\",explode(df.knownLanguages).alias(\"exp_languages\"), \"properties.eye\", \"properties.hair\")\ndf3.printSchema()\ndf3.show(truncate=False)\n\n# COMMAND ----------\n\ndf4 = df.select(\"name\", \"properties.eye\", \"properties.hair\")\ndf4.printSchema()\ndf4.show(truncate=False)\n", "repo_name": "hamidfard/databricks_notebooks", "sub_path": "sql_and_array.py", "file_name": "sql_and_array.py", "file_ext": "py", "file_size_in_byte": 1148, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pyspark.sql.functions.explode", "line_number": 22, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 30, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "24811772400", "text": "import collections\nclass Solution:\n def accountsMerge(self, accounts: List[List[str]]) -> List[List[str]]:\n # Use two hash tables to keep track of name and emails\n # We need two because same name might not be same emails - aka key conflict\n # So we have one hash with key/val of a number and an array of respective emails\n # And a second hash with key/val of a name and an array of numbers, representing key for first hash\n # Maybe a third hash to keep track of seen emails and which number they went to\n \n # Helper func to combine lists\n def combine(arr):\n # arr will give us the keys to combine in sorted order.\n # We will combine everything with the first occurrence and delete the rest\n n = len(arr)\n ptr = 1\n \n while ptr < n:\n # Add everything from current pointer's email list to the one at 0th index\n for email in email_list[arr[ptr]]:\n # Add email to lowest key value\n email_list[arr[0]].append(email)\n # Change tracker\n tracker[email] = arr[0]\n \n # Now delete all traces of that key\n del email_list[arr[ptr]]\n del mapper[arr[ptr]]\n \n # Increment\n ptr += 1\n \n \n \n # Hash one - key is an ID with an array of emails that corresponds to that specific ID\n email_list = collections.defaultdict(list)\n \n # Hash two - correlates key values to its respective string name\n mapper = {}\n \n # Hash three - Keeps track of which ID the emails were sent to for quicker lookup\n tracker = {}\n \n # print(\"Hashes initialized\")\n # Iterate through each array in accounts\n curr_key = 0\n for acc in accounts:\n # For each one, we want to separate name from emails\n name = acc[0]\n emails = acc[1:]\n \n # print(\"Account name: {}\\nassociated emails: {}\".format(name, emails)) \n # We need to iterate through every email in emails and see if it's already\n # placed into someone's account. If so, we place that value into key instead\n connect = []\n for email in emails:\n if email in tracker:\n # print(\"Current email, {}, was found associated with another account key: {}\"\n # .format(email, tracker[email]))\n if tracker[email] not in connect:\n connect.append(tracker[email])\n \n \n if len(connect) == 1:\n key = connect[0]\n elif len(connect) > 1:\n connect.sort()\n combine(connect)\n key = connect[0]\n else:\n key = curr_key\n \n # print(\"Our key is currently: {}\".format(key))\n # Now we just need to place everything where it belongs\n # First place each email into email_list\n # As we do so, we'll add it to tracker, too\n for email in emails:\n # If it's in tracker, then we've already added it. No need to do it again\n if email not in tracker:\n email_list[key].append(email)\n tracker[email] = key\n # print(\"Email, {}, added to list of account key: {}. Tracker added\".format(email, key))\n \n # Then map the key to the name\n mapper[key] = name\n # print(\"Added key to map: {}\".format(mapper))\n curr_key += 1\n \n # At end of this for loop, we should have everything in email list and mapper for name\n # Just make our answer array and return it\n ans = []\n # print(\"Formulating final answer array\")\n for key in email_list:\n acc = []\n # First add the name\n acc.append(mapper[key])\n # print(\"Added name: {}\".format(mapper[key]))\n # Then add the emails after sorting\n email_list[key].sort()\n acc += email_list[key]\n # print(\"Adding emails to acc. Acc is now: {}\".format(acc))\n # Then add to ans\n ans.append(acc)\n # print(\"Appending to ans\")\n \n return ans\n ", "repo_name": "PigsGoMoo/LeetCode", "sub_path": "accounts-merge/accounts-merge.py", "file_name": "accounts-merge.py", "file_ext": "py", "file_size_in_byte": 4535, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "collections.defaultdict", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "37150116446", "text": "from django.shortcuts import render\nfrom django.views.generic import DetailView\nfrom django.shortcuts import redirect\nfrom .models import Player\nfrom .utils import get_game\n\n\nclass Lobby(DetailView):\n template_name = \"lobby/index.html\"\n\n def get(self, request, *args, **kwargs):\n get_game(request)\n player_data = request.session[\"player_data\"]\n return render(request, self.template_name, {\"player\": player_data[\"id\"]})\n\n def post(self, request):\n # update player name\n player_data = request.session[\"player_data\"]\n name = self.request.POST.get(\"name\")\n player = Player.objects.get(id=player_data.get(\"id\"))\n player.needs_match = True\n player.name = name\n player.save(update_fields=[\"name\", \"needs_match\"])\n player_data.update({\"name\": name})\n\n # Checks the players that need a match\n for p in Player.objects.filter(needs_match=True):\n # Makes sure player is not matched with self\n if p.id != player_data.get(\"id\"):\n player_data.update({\"needs_match\": False})\n player_data.update({\"opponent\": p.id})\n p.needs_match = False\n p.opponent = player.id\n p.save(update_fields=[\"needs_match\", \"opponent\"])\n player.needs_match = False\n player.opponent = p.id\n player.save(update_fields=[\"needs_match\", \"opponent\"])\n\n request.session[\"player_data\"] = player_data\n response = redirect(\"/room/\")\n return response\n\n\nclass Room(DetailView):\n template_name = \"room/index.html\"\n\n def get(self, request, *args, **kwargs):\n \"\"\"\n If the player has an opponent a view with the opponent info is returned.\n If no opponent the default is used.\n \"\"\"\n\n player_data = request.session[\"player_data\"]\n player = Player.objects.get(id=player_data.get(\"id\"))\n player_data = player.to_dict()\n if player_data[\"opponent\"] != 0:\n opponent = Player.objects.get(id=player_data[\"opponent\"])\n player_data.update({\"opponent\": opponent.id})\n player_data.update({\"game_id\": 0})\n player_data.update({\"started\": False})\n request.session[\"player_data\"] = player_data\n return redirect(\"/game/\")\n return render(request, self.template_name, player_data)\n", "repo_name": "n0remac/cards", "sub_path": "lobby/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.views.generic.DetailView", "line_number": 8, "usage_type": "name"}, {"api_name": "utils.get_game", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Player.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Player.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Player", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Player.objects.filter", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Player.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Player", "line_number": 27, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 44, "usage_type": "name"}, {"api_name": "models.Player.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Player.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Player", "line_number": 54, "usage_type": "name"}, {"api_name": "models.Player.objects.get", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Player.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Player", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "12974790715", "text": "from django.shortcuts import redirect, render\nfrom django.http import HttpResponse\nfrom .models import *\n# Create your views here.\n\ndef base(request):\n if request.method == \"POST\":\n x=request.POST['todo']\n Data= Todata(data=x)\n print(Data)\n Data.save()\n todata=Todata.objects.all()\n return render(request,'home.html',{'todata':todata})\n\ndef remove(request,i):\n x=Todata.objects.get(id=i)\n x.delete()\n todata=Todata.objects.all()\n return redirect('/')\n\ndef update(request,i):\n t=Todata.objects.get(id=i)# it will fetch id and data\n if request.method==\"POST\":\n x1=request.POST['u1']\n yy=Todata(data=x1)\n yy.save()\n return redirect('/')\n return render(request,'update.html',{'t':t})\n \n", "repo_name": "prathaps123/TODO-APP-", "sub_path": "todoapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "27416182053", "text": "from work.auxiliary import data_functions\nimport cv2\n\nfrom work.segmentation import segmentation\nimport os\n\nfrom work.auxiliary.logger_settings import configure_logger\nimport logging\n\nLOG_PATH = os.path.abspath('logs')\nDATA_PATH = os.path.abspath('data')\n\nlog_path = data_functions.create_path(LOG_PATH, 'segmentation_logs')\n\nconfigure_logger(name=\"segmentation\",\n console_level='INFO',\n file_level='INFO',\n out_path=log_path)\n\nlogger = logging.getLogger(__name__)\n\n\ndef main():\n orig_path = os.path.join(DATA_PATH, r'raw_data\\with_maskes\\image')\n #orig_path = os.path.join(DATA_PATH, r'segmentation\\src2')\n mask_path = os.path.join(DATA_PATH,r'raw_data\\with_maskes\\label')\n dest_path = os.path.join(DATA_PATH,r'raw_data\\with_maskes\\label-augmented')\n # mask_path = os.path.join(DATA_PATH,\n # r'unet_data\\training\\2019-10-20_19-26-14\\raw_pred')\n # dest_path = os.path.join(DATA_PATH,\n # r'unet_data\\training\\2019-10-20_19-26-14')\n\n is_binary_mask = True\n\n single_flag = False # segment single image or multiple\n\n ## setings for single\n #img_name = '38357-02789.png.jpg'\n img_name = '74714-32897.png.jpg'\n\n display_flag = True\n save_flag = 'stamped'\n save_segments = False\n\n # settings for multi segmentation\n img_list = None\n\n # img_list = [\n # '38360-00777.png.jpg',\n # '38360-02397.png.jpg',\n # '38360-25986.png.jpg',\n # '38360-27560.png.jpg',\n # '38360-46226.png.jpg',\n # '38360-68930.png.jpg',\n # ]\n\n # general settings for segmentation\n settings_dict = {'threshold': 0.1,\n \"pr_threshold\": 0.3,\n 'seg_type': \"felzenszwalb\",\n 'seg_params': dict(scale=1, sigma=0.8, min_size=40),\n 'gray_scale': False}\n\n # settings_dict = {'threshold': 0.6,\n # \"pr_threshold\": 0.2,\n # 'seg_type': \"slic\",\n # 'seg_params': dict(n_segments=2000,\n # compactness=0.1,\n # max_iter=100,\n # sigma=0,\n # spacing=None,\n # convert2lab=True,\n # enforce_connectivity=True,\n # min_size_factor=0.2,\n # max_size_factor=3,\n # slic_zero=False),\n # 'gray_scale': False}\n\n if single_flag:\n img_path = os.path.join(orig_path, img_name)\n mask_path = os.path.join(mask_path, img_name)\n\n sg = segmentation.SegmentationSingle(img_path=img_path,\n mask_path=mask_path,\n is_binary_mask=is_binary_mask,\n save_path=dest_path,\n create_save_dest_flag=True,\n **settings_dict)\n\n sg.apply_segmentation(save_flag=save_flag, display_flag=display_flag,\n save_segments=save_segments)\n sg.get_ontop_seg(display_flag=display_flag, save_flag=save_flag)\n if display_flag:\n cv2.waitKey(0)\n\n else:\n\n segmentation.segment_multi(img_path=orig_path,\n mask_path=mask_path,\n save_path=dest_path,\n is_binary_mask=is_binary_mask,\n settings_dict=settings_dict,\n img_list=img_list,\n create_stamp = save_flag)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "OrenChikli/Cherry_stem", "sub_path": "work/segmentation_main.py", "file_name": "segmentation_main.py", "file_ext": "py", "file_size_in_byte": 3920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "work.auxiliary.data_functions.create_path", "line_number": 13, "usage_type": "call"}, {"api_name": "work.auxiliary.data_functions", "line_number": 13, "usage_type": "name"}, {"api_name": "work.auxiliary.logger_settings.configure_logger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "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": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "work.segmentation.segmentation.SegmentationSingle", "line_number": 83, "usage_type": "call"}, {"api_name": "work.segmentation.segmentation", "line_number": 83, "usage_type": "name"}, {"api_name": "cv2.waitKey", "line_number": 94, "usage_type": "call"}, {"api_name": "work.segmentation.segmentation.segment_multi", "line_number": 98, "usage_type": "call"}, {"api_name": "work.segmentation.segmentation", "line_number": 98, "usage_type": "name"}]} +{"seq_id": "22763501491", "text": "import pytest\nfrom momox.tests.integration.employees.factories import EmployeeFactory\n\npy_test_mark = pytest.mark.django_db\n\n\nclass TestEmployeeORMModel:\n @py_test_mark\n def test_model_works(self):\n employee = EmployeeFactory()\n assert employee.is_attending\n\n @py_test_mark\n def test_model_constraint_works(self):\n with pytest.raises(Exception) as ex:\n EmployeeFactory(name='')\n assert 'NOT NULL constraint failed' in str(ex)\n EmployeeFactory(is_attending='')\n assert 'NOT NULL constraint failed' in str(ex)\n", "repo_name": "eadwinCode/mmx-challenge", "sub_path": "momox/tests/integration/employees/test_model.py", "file_name": "test_model.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pytest.mark", "line_number": 4, "usage_type": "attribute"}, {"api_name": "momox.tests.integration.employees.factories.EmployeeFactory", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 15, "usage_type": "call"}, {"api_name": "momox.tests.integration.employees.factories.EmployeeFactory", "line_number": 16, "usage_type": "call"}, {"api_name": "momox.tests.integration.employees.factories.EmployeeFactory", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "14843670744", "text": "import pytest\n\nfrom fixture.application import Application\nfrom model.group import Group\n\napp_fixture = None\n\n\n@pytest.fixture(scope=\"function\")\ndef app():\n \"\"\"\n Before each test method:\n 1. Create an application fixture\n - if it doesn't exist or\n - if fixture doesn't valid (no opened browser)\n 2. Login\n :return: app fixture\n \"\"\"\n global app_fixture\n if not app_fixture or not app_fixture.is_valid():\n app_fixture = Application()\n app_fixture.session.ensure_login(username=\"admin\", password=\"secret\")\n return app_fixture\n\n@pytest.fixture(scope=\"session\", autouse=True)\ndef stop(request):\n \"\"\"\n Close session.\n :param request: request\n \"\"\"\n\n def fin():\n app_fixture.session.ensure_logout()\n app_fixture.tear_down()\n\n # Run after last test\n request.addfinalizer(fin)\n\n@pytest.fixture\ndef check_group():\n if not app_fixture.group.count():\n app_fixture.group.create(Group(name=\"test\"))\n", "repo_name": "popova-sdet/addressbook_koza", "sub_path": "conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "fixture.application.Application", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 25, "usage_type": "call"}, {"api_name": "model.group.Group", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "11577271151", "text": "from typing import Dict, List, Union, Tuple\n\nfrom pavilion import parsers\nfrom pavilion import variables\nfrom pavilion.errors import TestConfigError, DeferredError, StringParserError, ParserValueError\n\nDEFERRED_PREFIX = '!deferred!'\nNO_DEFERRED_ALLOWED = [\n 'schedule',\n 'build',\n 'scheduler',\n 'chunk',\n 'only_if',\n 'not_if',\n]\n\n\ndef test_config(config, var_man):\n \"\"\"Recursively resolve the variables in the value strings in the given\n configuration.\n\n Deferred Variable Handling\n When a config value references a deferred variable, it is left\n unresolved and prepended with the DEFERRED_PREFIX. To complete\n these, use deferred().\n\n :param dict config: The config dict to resolve recursively.\n :param variables.VariableSetManager var_man: A variable manager. (\n Presumably a permutation of the base var_man)\n :return: The resolved config,\n \"\"\"\n\n resolved_dict = {}\n\n for section in config:\n try:\n resolved_dict[section] = section_values(\n component=config[section],\n var_man=var_man,\n allow_deferred=section not in NO_DEFERRED_ALLOWED,\n key_parts=(section,),\n )\n except (StringParserError, ParserValueError) as err:\n raise TestConfigError(\"Error parsing '{}' section\".format(section), err)\n\n for section in ('only_if', 'not_if'):\n try:\n if section in config:\n resolved_dict[section] = mapping_keys(\n base_dict=resolved_dict.get(section, {}),\n var_man=var_man,\n section_name=section)\n except (StringParserError, ParserValueError) as err:\n raise TestConfigError(\"Error parsing key '{}' section\".format(section), err)\n\n return resolved_dict\n\n\ndef deferred(config, var_man):\n \"\"\"Resolve only those values prepended with the DEFERRED_PREFIX. All\n other values are presumed to be resolved already.\n\n :param dict config: The configuration\n :param variables.VariableSetManager var_man: The variable manager. This\n must not contain any deferred variables.\n \"\"\"\n\n if var_man.deferred:\n deferred_name = [\n \".\".join([part for part in var_parts if part is not None])\n for var_parts in var_man.deferred\n ]\n\n raise RuntimeError(\n \"The variable set manager must not contain any deferred \"\n \"variables, but contained these: {}\"\n .format(deferred_name)\n )\n\n config = section_values(config, var_man, deferred_only=True)\n for section in ('only_if', 'not_if'):\n if section in config:\n config[section] = mapping_keys(\n base_dict=config.get(section, {}),\n var_man=var_man,\n section_name=section,\n deferred_only=True)\n\n return config\n\n\ndef mapping_keys(base_dict, var_man, section_name, deferred_only=False) -> dict:\n \"\"\"Some sections of the test config can have Pavilion Strings for\n keys. Resolve the keys of the given dict.\n\n :param dict[str,str] base_dict: The dict whose keys need to be resolved.\n :param variables.VariableSetManager var_man: The variable manager to\n use to resolve the keys.\n :param str section_name: The name of this config section, for error\n reporting.\n :param bool deferred_only: Resolve only deferred keys, otherwise\n mark deferred keys as deferred.\n :returns: A new dictionary with the updated keys.\n \"\"\"\n\n new_dict = type(base_dict)()\n for key, value in base_dict.items():\n new_key = section_values(\n component=key,\n var_man=var_man,\n allow_deferred=True,\n deferred_only=deferred_only,\n key_parts=(section_name + '[{}]'.format(key),))\n\n # The value will have already been resolved.\n new_dict[new_key] = value\n\n return new_dict\n\n\ndef section_values(component: Union[Dict, List, str],\n var_man: variables.VariableSetManager,\n allow_deferred: bool = False,\n deferred_only: bool = False,\n key_parts: Union[None, Tuple[str]] = None):\n \"\"\"Recursively resolve the given config component's value strings\n using a variable manager.\n\n :param component: The config component to resolve.\n :param var_man: A variable manager. (Presumably a permutation of the\n base var_man)\n :param allow_deferred: Allow deferred variables in this section.\n :param deferred_only: Only resolve values prepended with\n the DEFERRED_PREFIX, and throw an error if such values can't be\n resolved. If this is True deferred values aren't allowed anywhere.\n :param Union[tuple[str],None] key_parts: A list of the parts of the\n config key traversed to get to this point.\n :return: The component, resolved.\n :raises: RuntimeError, TestConfigError\n \"\"\"\n\n if key_parts is None:\n key_parts = tuple()\n\n if isinstance(component, dict):\n resolved_dict = type(component)()\n for key in component.keys():\n resolved_dict[key] = section_values(\n component[key],\n var_man,\n allow_deferred=allow_deferred,\n deferred_only=deferred_only,\n key_parts=key_parts + (key,))\n\n return resolved_dict\n\n elif isinstance(component, list):\n resolved_list = type(component)()\n for i in range(len(component)):\n resolved_list.append(\n section_values(\n component[i], var_man,\n allow_deferred=allow_deferred,\n deferred_only=deferred_only,\n key_parts=key_parts + (i,)\n ))\n return resolved_list\n\n elif isinstance(component, str):\n\n if deferred_only:\n # We're only resolving deferred value strings.\n\n if component.startswith(DEFERRED_PREFIX):\n component = component[len(DEFERRED_PREFIX):]\n\n try:\n resolved = parsers.parse_text(component, var_man)\n except DeferredError:\n raise RuntimeError(\n \"Tried to resolve a deferred config component, \"\n \"but it was still deferred: {}\"\n .format(component)\n )\n except StringParserError as err:\n raise TestConfigError(\n \"Error resolving value '{}' in config at '{}':\\n\"\n \"{}\\n{}\"\n .format(component, '.'.join(map(str, key_parts)),\n err.message, err.context))\n return resolved\n\n else:\n # This string has already been resolved in the past.\n return component\n\n else:\n if component.startswith(DEFERRED_PREFIX):\n # This should never happen\n raise RuntimeError(\n \"Tried to resolve a pavilion config string, but it was \"\n \"started with the deferred prefix '{}'. This probably \"\n \"happened because Pavilion called setup.config \"\n \"when it should have called deferred.\"\n .format(DEFERRED_PREFIX)\n )\n\n try:\n resolved = parsers.parse_text(component, var_man)\n except DeferredError:\n if allow_deferred:\n return DEFERRED_PREFIX + component\n else:\n raise TestConfigError(\n \"Deferred variable in value '{}' under key \"\n \"'{}' where it isn't allowed\"\n .format(component, '.'.join(map(str, key_parts))))\n except StringParserError as err:\n raise TestConfigError(\n \"Error resolving value '{}' in config at '{}':\\n\"\n \"{}\\n{}\"\n .format(component,\n '.'.join([str(part) for part in key_parts]),\n err.message, err.context))\n else:\n return resolved\n elif component is None:\n return None\n else:\n raise TestConfigError(\"Invalid value type '{}' for '{}' when \"\n \"resolving strings. Key parts: {}\"\n .format(type(component), component, key_parts))\n\n\ndef cmd_inheritance(test_cfg):\n \"\"\"Extend the command list by adding any prepend or append commands,\n then clear those sections so they don't get added at additional\n levels of config merging.\"\"\"\n\n for section in ['build', 'run']:\n config = test_cfg.get(section)\n if not config:\n continue\n new_cmd_list = []\n if config.get('prepend_cmds', []):\n new_cmd_list.extend(config.get('prepend_cmds'))\n config['prepend_cmds'] = []\n new_cmd_list += test_cfg[section]['cmds']\n if config.get('append_cmds', []):\n new_cmd_list.extend(config.get('append_cmds'))\n config['append_cmds'] = []\n test_cfg[section]['cmds'] = new_cmd_list\n\n return test_cfg\n", "repo_name": "hpc/pavilion2", "sub_path": "lib/pavilion/resolve.py", "file_name": "resolve.py", "file_ext": "py", "file_size_in_byte": 9314, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pavilion.errors.StringParserError", "line_number": 43, "usage_type": "name"}, {"api_name": "pavilion.errors.ParserValueError", "line_number": 43, "usage_type": "name"}, {"api_name": "pavilion.errors.TestConfigError", "line_number": 44, "usage_type": "call"}, {"api_name": "pavilion.errors.StringParserError", "line_number": 53, "usage_type": "name"}, {"api_name": "pavilion.errors.ParserValueError", "line_number": 53, "usage_type": "name"}, {"api_name": "pavilion.errors.TestConfigError", "line_number": 54, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 121, "usage_type": "name"}, {"api_name": "pavilion.variables.VariableSetManager", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pavilion.variables", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 125, "usage_type": "name"}, {"api_name": "pavilion.parsers.parse_text", "line_number": 178, "usage_type": "call"}, {"api_name": "pavilion.parsers", "line_number": 178, "usage_type": "name"}, {"api_name": "pavilion.errors.DeferredError", "line_number": 179, "usage_type": "name"}, {"api_name": "pavilion.errors.StringParserError", "line_number": 185, "usage_type": "name"}, {"api_name": "pavilion.errors.TestConfigError", "line_number": 186, "usage_type": "call"}, {"api_name": "pavilion.parsers.parse_text", "line_number": 209, "usage_type": "call"}, {"api_name": "pavilion.parsers", "line_number": 209, "usage_type": "name"}, {"api_name": "pavilion.errors.DeferredError", "line_number": 210, "usage_type": "name"}, {"api_name": "pavilion.errors.TestConfigError", "line_number": 214, "usage_type": "call"}, {"api_name": "pavilion.errors.StringParserError", "line_number": 218, "usage_type": "name"}, {"api_name": "pavilion.errors.TestConfigError", "line_number": 219, "usage_type": "call"}, {"api_name": "pavilion.errors.TestConfigError", "line_number": 230, "usage_type": "call"}]} +{"seq_id": "29046106857", "text": "# -*- coding: utf-8 -*-\n\n__author__ = 'blubimov'\n\nfrom decimal import Decimal\n\nimport pytest\nfrom hamcrest import contains_string\n\nfrom balance import balance_steps as steps\nfrom balance.features import Features\nfrom btestlib import constants as const\nfrom btestlib import matchers\nfrom btestlib import reporter\nfrom btestlib import utils\nfrom btestlib.data import simpleapi_defaults\nfrom btestlib.data.partner_contexts import STATION_PAYMENTS_CONTEXT, STATION_SERVICES_CONTEXT\n\n\nSERVICE = STATION_PAYMENTS_CONTEXT.service\n\npytestmark = [\n reporter.feature(Features.TRUST, Features.PAYMENT, Features.STATION),\n pytest.mark.tickets('BALANCE-27187'),\n pytest.mark.docpath('https://wiki.yandex-team.ru/balance/docs/process/thirdpartytransactions/payments'),\n pytest.mark.usefixtures(\"switch_to_pg\")\n]\n\n\nclass PROTOCOL(object):\n XMLRPC = 'xmlrpc'\n REST = 'rest'\n\n\nAMOUNT = simpleapi_defaults.DEFAULT_PRICE\n\nAR_SERVICE = STATION_PAYMENTS_CONTEXT.service.id\nSERVICES_SERVICE = STATION_SERVICES_CONTEXT.service.id\n\n\n@pytest.fixture(autouse=True)\ndef mock_trust(mock_simple_api):\n pass\n\n\n# проверка платежа\n@pytest.mark.parametrize('data', [\n pytest.mark.smoke({'commission_category': 500}),\n {'commission_category': 0},\n {'commission_category': 10000},\n {'commission_category': None, 'exc': 'Commission category is mandatory field for 611'},\n], ids=lambda data: 'commission_category={}'.format(\n data['commission_category']))\ndef test_payment(data):\n partner_commission = 2\n partner_id, person_id, contract_id = create_contract(partner_commission=partner_commission)\n\n invoices = get_invoices(contract_id)\n\n # создаем платеж в трасте\n trust_payment_id, payment_id, purchase_token, _ = create_payment(partner_id, data['commission_category'])\n\n if data.get('exc') is None:\n\n # запускаем обработку платежа\n steps.CommonPartnerSteps.export_payment(payment_id)\n\n # проверяем данные платежа в таблице t_thirdparty_transactions\n check_payment(payment_id, trust_payment_id, contract_id,\n partner_id, person_id, invoices, partner_commission=partner_commission,\n commission_category=data['commission_category'])\n\n else:\n # проверяем, что при обработке платежа происходит ошибка\n with pytest.raises(utils.XmlRpc.XmlRpcError) as exc:\n steps.CommonPartnerSteps.export_payment(payment_id)\n\n utils.check_that(exc.value.response, contains_string(data['exc']))\n\n # проверяем, что транзакций нет в t_thirdparty_transactions\n check_payment(payment_id, trust_payment_id, contract_id, partner_id, person_id, invoices,\n partner_commission=partner_commission, commission_category=data['commission_category'],\n payment_not_exported=True)\n\n\n# проверка возврата\ndef test_refund():\n partner_id, person_id, contract_id = create_contract()\n\n invoices = get_invoices(contract_id)\n\n # создаем платеж в трасте\n trust_payment_id, payment_id, purchase_token, service_order_id = create_payment(partner_id)\n\n # запускаем обработку платежа\n steps.CommonPartnerSteps.export_payment(payment_id)\n\n # создаем рефанд\n trust_refund_id, refund_id = steps.SimpleNewApi.create_refund(SERVICE, purchase_token)\n\n # запускаем обработку рефанда\n steps.CommonPartnerSteps.export_payment(refund_id)\n\n # проверяем данные возврата в таблице t_thirdparty_transactions\n check_payment(payment_id, trust_payment_id, contract_id, partner_id, person_id, invoices,\n trust_refund_id=trust_refund_id)\n\n\n# todo-blubimov сейчас postathorize у нас реализован только для xmlrpc, нужно доделать для rest\n# todo-blubimov дергать нужно simpleapi.steps.payments_api_steps.Payments.Order#resize\n# todo-blubimov после этого выпилить из этого модуля все про xmlrpc\n# проверка обновления суммы платежа\n@pytest.mark.parametrize('data', [\n {'updated_amount': 300},\n {'updated_amount': 0},\n], ids=lambda data: str(data))\ndef test_reversal(data):\n partner_commission = 2\n partner_id, person_id, contract_id = create_contract(partner_commission=partner_commission)\n\n invoices = get_invoices(contract_id)\n\n # создаем платеж в трасте\n commission_category = 500\n trust_payment_id, payment_id, purchase_token, service_order_id = \\\n create_payment(partner_id, commission_category=commission_category, amount=AMOUNT, need_clearing=False,\n payment_protocol=PROTOCOL.XMLRPC, wait_for_export_from_bs=False)\n\n updated_amount = data['updated_amount']\n steps.SimpleApi.postauthorize(SERVICE, trust_payment_id, [service_order_id],\n amounts=[updated_amount], actions=['clear'])\n\n payment_id = steps.SimpleApi.get_payment_id(trust_payment_id)\n\n # запускаем обработку платежа\n export_result = steps.CommonPartnerSteps.export_payment(payment_id)\n\n if updated_amount == 0:\n utils.check_that(export_result['output'], contains_string('skipped: payment has been completely cancelled'))\n\n # проверяем данные платежа в таблице t_thirdparty_transactions\n check_payment(payment_id, trust_payment_id, contract_id, partner_id, person_id, invoices,\n partner_commission=partner_commission, commission_category=commission_category,\n payment_amount=updated_amount)\n\n\n# ---------- utils ----------\n\ndef create_contract(is_offer=True, partner_commission=2):\n partner_id = steps.SimpleNewApi.create_partner(SERVICE)\n params = {'partner_commission_pct2': partner_commission}\n _, person_id, contract_id, _ = steps.ContractSteps.create_partner_contract(STATION_PAYMENTS_CONTEXT,\n client_id=partner_id, is_offer=is_offer,\n additional_params=params)\n return partner_id, person_id, contract_id\n\n\ndef get_invoices(contract_id):\n invoice_eid_ar, _ = steps.InvoiceSteps.get_personal_account_external_id_with_service_code(contract_id,\n const.ServiceCode.AGENT_REWARD)\n invoice_eid_ser, _ = steps.InvoiceSteps.get_personal_account_external_id_with_service_code(contract_id,\n const.ServiceCode.YANDEX_SERVICE)\n return {AR_SERVICE: invoice_eid_ar, SERVICES_SERVICE: invoice_eid_ser}\n\n\ndef calc_rewards(payment_amount, partner_commission, commission_category):\n amount = utils.dround2(payment_amount)\n return {AR_SERVICE: utils.dround2(utils.pct_sum(amount, partner_commission)),\n SERVICES_SERVICE: utils.dround2(\n utils.pct_sum(amount, commission_category / Decimal('100')))}\n\n\ndef create_payment(partner_id, commission_category=500, amount=AMOUNT, need_clearing=True,\n payment_protocol=PROTOCOL.REST, wait_for_export_from_bs=True):\n if payment_protocol == PROTOCOL.REST:\n trust_payment_id, payment_id, purchase_token = create_payment_rest(partner_id, commission_category, amount,\n wait_for_export_from_bs=wait_for_export_from_bs)\n service_order_id = None\n else:\n service_order_id, trust_payment_id, purchase_token, payment_id = create_payment_xmlrpc(partner_id,\n commission_category,\n amount, need_clearing,\n wait_for_export_from_bs=wait_for_export_from_bs)\n return trust_payment_id, payment_id, purchase_token, service_order_id\n\n\ndef create_payment_rest(partner_id, commission_category, amount, wait_for_export_from_bs=True):\n product_id = steps.SimpleNewApi.create_product(SERVICE, partner_id)\n orders = steps.SimpleNewApi.create_orders_for_payment(SERVICE, product_id,\n commission_category=commission_category,\n amount=amount)\n trust_payment_id, payment_id, purchase_token = steps.SimpleNewApi.create_payment(SERVICE,\n orders=orders,\n wait_for_export_from_bs=wait_for_export_from_bs)\n return trust_payment_id, payment_id, purchase_token\n\n\ndef create_payment_xmlrpc(partner_id, commission_category, amount,\n need_clearing, wait_for_export_from_bs=True):\n service_product_id = steps.SimpleApi.create_service_product(SERVICE, partner_id)\n\n # создаем платеж в трасте\n service_order_id, trust_payment_id, purchase_token, payment_id = \\\n steps.SimpleApi.create_trust_payment(SERVICE, service_product_id,\n commission_category=commission_category,\n price=amount,\n need_postauthorize=need_clearing,\n wait_for_export_from_bs=wait_for_export_from_bs)\n\n return service_order_id, trust_payment_id, purchase_token, payment_id\n\n\ndef check_payment(payment_id, trust_payment_id, contract_id, partner_id, person_id, invoices,\n trust_refund_id=None, partner_commission=0, commission_category=0, payment_amount=AMOUNT,\n payment_not_exported=False):\n amount = utils.dround2(payment_amount)\n\n if amount == 0 or payment_not_exported:\n expected_payment_lines = []\n else:\n rewards = calc_rewards(amount, partner_commission, commission_category)\n\n expected_payment_lines = [\n steps.SimpleApi.create_expected_tpt_row(STATION_PAYMENTS_CONTEXT, partner_id, contract_id,\n person_id, trust_payment_id,\n payment_id,\n trust_refund_id,\n amount=amount,\n yandex_reward=None if trust_refund_id else rewards[AR_SERVICE]),\n # https://st.yandex-team.ru/BALANCE-27187#1524859717000\n ]\n if not trust_refund_id:\n expected_payment_lines.append(\n steps.SimpleApi.create_expected_tpt_row(STATION_PAYMENTS_CONTEXT, partner_id, contract_id,\n person_id, trust_payment_id,\n payment_id,\n trust_refund_id,\n amount=rewards[SERVICES_SERVICE],\n invoice_eid=invoices[SERVICES_SERVICE],\n transaction_type=const.TransactionType.REFUND.name,\n paysys_type_cc=const.PaysysType.YANDEX,\n payment_type=const.PaymentType.CORRECTION_COMMISSION))\n\n # получаем данные по платежу\n actual_payment_lines = steps.CommonPartnerSteps.get_thirdparty_transaction_by_payment_id(payment_id,\n transaction_type=None,\n trust_id=trust_refund_id or trust_payment_id)\n\n # Проверяем данные платежа\n utils.check_that(actual_payment_lines,\n matchers.contains_dicts_with_entries(expected_payment_lines),\n u'Проверяем данные платежа')\n", "repo_name": "Alexander-Berg/2022-tests-examples", "sub_path": "billing/balance_tests/balance/tests/payment/test_station_payments.py", "file_name": "test_station_payments.py", "file_ext": "py", "file_size_in_byte": 12697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT.service", "line_number": 20, "usage_type": "attribute"}, {"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT", "line_number": 20, "usage_type": "name"}, {"api_name": "btestlib.reporter.feature", "line_number": 23, "usage_type": "call"}, {"api_name": "btestlib.reporter", "line_number": 23, "usage_type": "name"}, {"api_name": "balance.features.Features.TRUST", "line_number": 23, "usage_type": "attribute"}, {"api_name": "balance.features.Features", "line_number": 23, "usage_type": "name"}, {"api_name": "balance.features.Features.PAYMENT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "balance.features.Features.STATION", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pytest.mark.tickets", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pytest.mark.docpath", "line_number": 25, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pytest.mark.usefixtures", "line_number": 26, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 26, "usage_type": "attribute"}, {"api_name": "btestlib.data.simpleapi_defaults.DEFAULT_PRICE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "btestlib.data.simpleapi_defaults", "line_number": 35, "usage_type": "name"}, {"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT.service", "line_number": 37, "usage_type": "attribute"}, {"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT", "line_number": 37, "usage_type": "name"}, {"api_name": "btestlib.data.partner_contexts.STATION_SERVICES_CONTEXT.service", "line_number": 38, "usage_type": "attribute"}, {"api_name": "btestlib.data.partner_contexts.STATION_SERVICES_CONTEXT", "line_number": 38, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 41, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps.export_payment", "line_number": 66, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps", "line_number": 66, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 66, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 75, "usage_type": "call"}, {"api_name": "btestlib.utils.XmlRpc", "line_number": 75, "usage_type": "attribute"}, {"api_name": "btestlib.utils", "line_number": 75, "usage_type": "name"}, {"api_name": "balance.balance_steps.CommonPartnerSteps.export_payment", "line_number": 76, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps", "line_number": 76, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 76, "usage_type": "name"}, {"api_name": "btestlib.utils.check_that", "line_number": 78, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 78, "usage_type": "name"}, {"api_name": "hamcrest.contains_string", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pytest.mark.smoke", "line_number": 48, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 48, "usage_type": "attribute"}, {"api_name": "balance.balance_steps.CommonPartnerSteps.export_payment", "line_number": 96, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps", "line_number": 96, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 96, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleNewApi.create_refund", "line_number": 99, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleNewApi", "line_number": 99, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 99, "usage_type": "name"}, {"api_name": "balance.balance_steps.CommonPartnerSteps.export_payment", "line_number": 102, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps", "line_number": 102, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 102, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleApi.postauthorize", "line_number": 130, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleApi", "line_number": 130, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 130, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleApi.get_payment_id", "line_number": 133, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleApi", "line_number": 133, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 133, "usage_type": "name"}, {"api_name": "balance.balance_steps.CommonPartnerSteps.export_payment", "line_number": 136, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps", "line_number": 136, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 136, "usage_type": "name"}, {"api_name": "btestlib.utils.check_that", "line_number": 139, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 139, "usage_type": "name"}, {"api_name": "hamcrest.contains_string", "line_number": 139, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 113, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 113, "usage_type": "attribute"}, {"api_name": "balance.balance_steps.SimpleNewApi.create_partner", "line_number": 150, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleNewApi", "line_number": 150, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 150, "usage_type": "name"}, {"api_name": "balance.balance_steps.ContractSteps.create_partner_contract", "line_number": 152, "usage_type": "call"}, {"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT", "line_number": 152, "usage_type": "argument"}, {"api_name": "balance.balance_steps.ContractSteps", "line_number": 152, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 152, "usage_type": "name"}, {"api_name": "balance.balance_steps.InvoiceSteps.get_personal_account_external_id_with_service_code", "line_number": 159, "usage_type": "call"}, {"api_name": "balance.balance_steps.InvoiceSteps", "line_number": 159, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 159, "usage_type": "name"}, {"api_name": "btestlib.constants.ServiceCode", "line_number": 160, "usage_type": "attribute"}, {"api_name": "btestlib.constants", "line_number": 160, "usage_type": "name"}, {"api_name": "balance.balance_steps.InvoiceSteps.get_personal_account_external_id_with_service_code", "line_number": 161, "usage_type": "call"}, {"api_name": "balance.balance_steps.InvoiceSteps", "line_number": 161, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 161, "usage_type": "name"}, {"api_name": "btestlib.constants.ServiceCode", "line_number": 162, "usage_type": "attribute"}, {"api_name": "btestlib.constants", "line_number": 162, "usage_type": "name"}, {"api_name": "btestlib.utils.dround2", "line_number": 167, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 167, "usage_type": "name"}, {"api_name": "btestlib.utils.dround2", "line_number": 168, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 168, "usage_type": "name"}, {"api_name": "btestlib.utils.pct_sum", "line_number": 168, "usage_type": "call"}, {"api_name": "btestlib.utils.dround2", "line_number": 169, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 169, "usage_type": "name"}, {"api_name": "btestlib.utils.pct_sum", "line_number": 170, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 170, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 170, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleNewApi.create_product", "line_number": 188, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleNewApi", "line_number": 188, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 188, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleNewApi.create_orders_for_payment", "line_number": 189, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleNewApi", "line_number": 189, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 189, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleNewApi.create_payment", "line_number": 192, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleNewApi", "line_number": 192, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 192, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleApi.create_service_product", "line_number": 200, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleApi", "line_number": 200, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 200, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleApi.create_trust_payment", "line_number": 204, "usage_type": "call"}, {"api_name": "balance.balance_steps.SimpleApi", "line_number": 204, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 204, "usage_type": "name"}, {"api_name": "btestlib.utils.dround2", "line_number": 216, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 216, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleApi.create_expected_tpt_row", "line_number": 224, "usage_type": "call"}, {"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT", "line_number": 224, "usage_type": "argument"}, {"api_name": "balance.balance_steps.SimpleApi", "line_number": 224, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 224, "usage_type": "name"}, {"api_name": "balance.balance_steps.SimpleApi.create_expected_tpt_row", "line_number": 234, "usage_type": "call"}, {"api_name": "btestlib.data.partner_contexts.STATION_PAYMENTS_CONTEXT", "line_number": 234, "usage_type": "argument"}, {"api_name": "balance.balance_steps.SimpleApi", "line_number": 234, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 234, "usage_type": "name"}, {"api_name": "btestlib.constants.TransactionType", "line_number": 240, "usage_type": "attribute"}, {"api_name": "btestlib.constants", "line_number": 240, "usage_type": "name"}, {"api_name": "btestlib.constants.PaysysType", "line_number": 241, "usage_type": "attribute"}, {"api_name": "btestlib.constants", "line_number": 241, "usage_type": "name"}, {"api_name": "btestlib.constants.PaymentType", "line_number": 242, "usage_type": "attribute"}, {"api_name": "btestlib.constants", "line_number": 242, "usage_type": "name"}, {"api_name": "balance.balance_steps.CommonPartnerSteps.get_thirdparty_transaction_by_payment_id", "line_number": 245, "usage_type": "call"}, {"api_name": "balance.balance_steps.CommonPartnerSteps", "line_number": 245, "usage_type": "attribute"}, {"api_name": "balance.balance_steps", "line_number": 245, "usage_type": "name"}, {"api_name": "btestlib.utils.check_that", "line_number": 250, "usage_type": "call"}, {"api_name": "btestlib.utils", "line_number": 250, "usage_type": "name"}, {"api_name": "btestlib.matchers.contains_dicts_with_entries", "line_number": 251, "usage_type": "call"}, {"api_name": "btestlib.matchers", "line_number": 251, "usage_type": "name"}]} +{"seq_id": "5489564347", "text": "\"\"\"\nCreated on Sun Nov 1 19:48:48 2020\n@author: John Rachlin\n@file: evo_v4.py: An evolutionary computing framework (version 4)\nAssumes no Solutions class.\n\"\"\"\n\nimport random as rnd\nimport copy\nfrom functools import reduce\nimport csv\nimport pandas as pd\n\nclass Evo:\n\n def __init__(self):\n \"\"\" Population constructor \"\"\"\n self.pop = {} # The solution population eval -> solution\n self.fitness = {} # Registered fitness functions: name -> objective function\n self.agents = {} # Registered agents: name -> (operator, num_solutions_input)\n\n def size(self):\n \"\"\" The size of the current population \"\"\"\n return len(self.pop)\n\n def add_fitness_criteria(self, name, f):\n \"\"\" Register a fitness criterion (objective) with the\n environment. Any solution added to the environment is scored \n according to this objective \"\"\"\n self.fitness[name] = f\n \n def add_agent(self, name, op, k=1):\n \"\"\" Register a named agent with the population.\n The operator (op) function defines what the agent does.\n k defines the number of solutions the agent operates on. \"\"\"\n self.agents[name] = (op, k)\n\n def add_solution(self, sol):\n \"\"\" Add a solution to the population \"\"\"\n #eval = ((obj1, score1), (obj2, score2).....)\n eval = tuple((name, f(sol)) for name, f in self.fitness.items())\n self.pop[eval] = sol\n\n def run_agent(self, name):\n \"\"\" Invoke an agent against the population \"\"\"\n op, k = self.agents[name]\n picks = self.get_random_solutions(k)\n new_solution = op(picks)\n self.add_solution(new_solution)\n\n\n\n def evolve(self, n=1, dom=100, status=100):\n \"\"\" Run n random agents (default=1) \n dom defines how often we remove dominated (unfit) solutions\n status defines how often we display the current population \"\"\"\n\n agent_names = list(self.agents.keys())\n for i in range(n):\n pick = rnd.choice(agent_names)\n self.run_agent(pick)\n\n if i % dom == 0:\n self.remove_dominated()\n\n if i % status == 0:\n self.remove_dominated()\n print(\"Iteration:\", i)\n print(\"Population size:\", self.size())\n df = pd.DataFrame()\n for eval,sol in self.pop.items():\n df = df.append(dict(eval), ignore_index=True)\n df.insert(0, 'teamname', ['Oreo' for _ in range(len(df))], True)\n df.set_index('teamname', inplace=True)\n df.to_csv('solutions_{}.csv'.format(i))\n #print(type(self))\n #df = pd.DataFrame()\n #df['setups'] = list(self.fitness['setups'])\n #df['lowpriority'] = list(self.fitness['lowpriority'])\n #df['delays'] = list(self.fitness['delays'])\n #df.to_csv('solutions_{}.csv'.format(i))\n\n\n # Clean up the population\n self.remove_dominated()\n\n\n def get_random_solutions(self, k=1):\n \"\"\" Pick k random solutions from the population \"\"\"\n if self.size() == 0:\n return []\n else:\n solutions = tuple(self.pop.values())\n return [copy.deepcopy(rnd.choice(solutions)) for _ in range(k)]\n\n @staticmethod\n def _dominates(p, q):\n \"\"\" p = evaluation of solution: ((obj1, score1), (obj2, score2), ... )\"\"\"\n pscores = [score for _,score in p]\n qscores = [score for _,score in q]\n score_diffs = list(map(lambda x,y: y-x, pscores, qscores))\n min_diff = min(score_diffs)\n max_diff = max(score_diffs)\n return min_diff >= 0.0 and max_diff > 0.0\n\n\n @staticmethod\n def _reduce_nds(S, p):\n return S - {q for q in S if Evo._dominates(p,q)}\n\n\n def remove_dominated(self):\n \"\"\" Remove dominated solutions \"\"\"\n nds = reduce(Evo._reduce_nds, self.pop.keys(), self.pop.keys())\n self.pop = {k: self.pop[k] for k in nds}\n\n\n def __str__(self):\n \"\"\" Output the solutions in the population \"\"\"\n rslt = \"\"\n for eval,sol in self.pop.items():\n rslt += str(dict(eval))+\":\\t\"+str(sol)+\"\\n\"\n return rslt", "repo_name": "EthanRiley/evo-supply-chain", "sub_path": "evo.py", "file_name": "evo.py", "file_ext": "py", "file_size_in_byte": 4239, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "random.choice", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 70, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 94, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 94, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "12015001406", "text": "from Products.CMFCore.interfaces import IPropertiesTool\nfrom zope.component import getUtility\n\nfrom collective.plonetruegallery.settings import GallerySettings\nfrom collective.plonetruegallery.interfaces import IFlickrGallerySettings, \\\n IGallerySettings, IHighSlideDisplaySettings\nfrom collective.plonetruegallery.tests import BaseTest\nfrom collective.plonetruegallery.utils import getGalleryAdapter, \\\n getDisplayAdapter\n\nimport unittest2 as unittest\n\n\nclass TestSettings(BaseTest):\n\n def test_settings_should_return_default_value(self):\n settings = GallerySettings(self.get_gallery())\n self.assertEquals(settings.gallery_type,\n IGallerySettings['gallery_type'].default)\n\n def test_added_interface_settings_should_return_default_value(self):\n settings = GallerySettings(self.get_gallery(),\n interfaces=[IHighSlideDisplaySettings])\n self.assertEquals(settings.highslide_outlineType, 'drop-shadow')\n\n def test_should_always_have_IGallerySettings_no_matter_what(self):\n settings = GallerySettings(self.get_gallery(), interfaces=[])\n self.failUnless(IGallerySettings in settings._interfaces)\n\n def test_should_handle_passing_in_single_item(self):\n settings = GallerySettings(self.get_gallery(),\n interfaces=IHighSlideDisplaySettings)\n self.assertEquals(settings.highslide_outlineType, 'drop-shadow')\n\n def test_should_return_default_to_None_if_it_is_not_in_an_interface(self):\n settings = GallerySettings(self.get_gallery())\n self.assertEquals(None, settings.foobar)\n\n def test_should_set_setting_correctly(self):\n settings = GallerySettings(self.get_gallery())\n settings.gallery_type = \"flickr\"\n self.assertEquals(settings.gallery_type, \"flickr\")\n\n def test_should_set_extra_interface_setting(self):\n settings = GallerySettings(\n self.get_gallery(),\n interfaces=[IFlickrGallerySettings]\n )\n settings.flickr_username = \"john\"\n self.assertEquals(settings.flickr_username, \"john\")\n\n\nclass TestUtils(BaseTest):\n\n def test_getGalleryAdapter(self):\n adapter = getGalleryAdapter(self.portal['test_gallery'], self.request)\n self.assertEquals(adapter.name, \"basic\")\n self.assertEquals(adapter.settings.gallery_type, \"basic\")\n\n def test_getDisplayAdapter(self):\n gadapter = getGalleryAdapter(self.portal['test_gallery'],\n self.request)\n displayer = getDisplayAdapter(gadapter)\n self.assertEquals(displayer.name, 'galleria')\n self.assertEquals(gadapter.settings.display_type, 'galleria')\n\n def test_getGalleryAdapter_when_asking_for_non_existant_type(self):\n gadapter = getGalleryAdapter(self.portal['test_gallery'],\n self.request, gallery_type=\"foobar\")\n displayer = getDisplayAdapter(gadapter)\n self.assertEquals(displayer.name, 'galleria')\n self.assertEquals(gadapter.settings.display_type, 'galleria')\n self.assertEquals(gadapter.name, 'basic')\n self.assertEquals(gadapter.settings.gallery_type, 'basic')\n\n\nclass TestPloneAppImagingIntegration(BaseTest):\n\n def test_size_map_for_default_sizes_with_size_upgrades(self):\n props = getUtility(IPropertiesTool)\n imaging_properties = props.get('imaging_properties', None)\n if imaging_properties:\n adapter = getGalleryAdapter(self.portal['test_gallery'],\n self.request)\n self.assertEquals(adapter.sizes['small']['width'], 320)\n self.assertEquals(adapter.sizes['small']['height'], 320)\n self.assertEquals(adapter.sizes['medium']['width'], 576)\n self.assertEquals(adapter.sizes['medium']['height'], 576)\n self.assertEquals(adapter.sizes['large']['width'], 768)\n self.assertEquals(adapter.sizes['large']['height'], 768)\n\n\ndef test_suite():\n return unittest.defaultTestLoader.loadTestsFromName(__name__)\n", "repo_name": "Solgema/collective.plonetruegallery", "sub_path": "collective/plonetruegallery/tests/test_various.py", "file_name": "test_various.py", "file_ext": "py", "file_size_in_byte": 4084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "3", "api": [{"api_name": "collective.plonetruegallery.tests.BaseTest", "line_number": 14, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 17, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.interfaces.IGallerySettings", "line_number": 19, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 22, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.interfaces.IHighSlideDisplaySettings", "line_number": 23, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 27, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.interfaces.IGallerySettings", "line_number": 28, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 31, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.interfaces.IHighSlideDisplaySettings", "line_number": 32, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 36, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 40, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.settings.GallerySettings", "line_number": 45, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.interfaces.IFlickrGallerySettings", "line_number": 47, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.tests.BaseTest", "line_number": 53, "usage_type": "name"}, {"api_name": "collective.plonetruegallery.utils.getGalleryAdapter", "line_number": 56, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.utils.getGalleryAdapter", "line_number": 61, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.utils.getDisplayAdapter", "line_number": 63, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.utils.getGalleryAdapter", "line_number": 68, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.utils.getDisplayAdapter", "line_number": 70, "usage_type": "call"}, {"api_name": "collective.plonetruegallery.tests.BaseTest", "line_number": 77, "usage_type": "name"}, {"api_name": "zope.component.getUtility", "line_number": 80, "usage_type": "call"}, {"api_name": "Products.CMFCore.interfaces.IPropertiesTool", "line_number": 80, "usage_type": "argument"}, {"api_name": "collective.plonetruegallery.utils.getGalleryAdapter", "line_number": 83, "usage_type": "call"}, {"api_name": "unittest2.defaultTestLoader.loadTestsFromName", "line_number": 94, "usage_type": "call"}, {"api_name": "unittest2.defaultTestLoader", "line_number": 94, "usage_type": "attribute"}]} +{"seq_id": "8737994820", "text": "# Imports\r\nimport pygame\r\nimport random\r\n\r\n# Initialize game engine\r\npygame.init()\r\n\r\n\r\n# Window\r\n\r\nWIDTH = 1600\r\nHEIGHT = 1000\r\nSIZE = (WIDTH, HEIGHT)\r\nTITLE = \"Wonder Woman & The Battle For The Universe\"\r\nscreen = pygame.display.set_mode(SIZE)\r\npygame.display.set_caption(TITLE)\r\n\r\n\r\n# Timer\r\nclock = pygame.time.Clock()\r\nrefresh_rate = 60\r\n\r\n\r\n# Colors\r\nRED = (255, 0, 0)\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\nYELLOW = (255, 255, 0)\r\nGREEN = (100, 255, 100)\r\nBLUE = (30, 112, 219)\r\n\r\n\r\n# Fonts\r\nFont = pygame.font.Font\r\nFONT_SM = Font(None, 24)\r\nFONT_MD = Font(None, 32)\r\nFONT_LG = Font(None, 64)\r\nFONT_XL = Font(\"assets/fonts/spacerangerboldital.ttf\", 96)\r\ncomic_font = Font(\"assets/fonts/comic_font.ttf\", 165)\r\nbattle_font = Font(\"assets/fonts/battle_font.ttf\", 98)\r\ncomic2_font = Font(\"assets/fonts/comic_font.ttf\", 80)\r\njedi_font = Font(\"assets/fonts/Starjedi.ttf\", 35)\r\n\r\n# Images\r\nload = pygame.image.load\r\nwonder_woman_img = load(\"assets/images/wonder_woman.png\").convert_alpha()\r\nlaser_img = load(\"assets/images/laserGreen.png\").convert_alpha()\r\nenemy_img = load(\"assets/images/shipYellow_manned.png\").convert_alpha()\r\nenemy2_img = load(\"assets/images/shipPink_manned.png\").convert_alpha()\r\nenemy3_img = load(\"assets/images/shipGreen_manned.png\").convert_alpha()\r\nbomb_img = load(\"assets/images/laserRed.png\").convert_alpha()\r\ntitle_img = load(\"assets/images/wonder_woman_title.png\").convert_alpha()\r\nbackground = load(\"assets/images/space_background.png\").convert_alpha()\r\nwhole_background_img = load(\"assets/images/wonder_woman_background1.png\").convert_alpha()\r\nheart_img = load(\"assets/images/heart_img.png\").convert_alpha()\r\nalien_heart_img = load(\"assets/images/alien_life_img.png\").convert_alpha()\r\nlost_background_img = load(\"assets/images/lost_background.png\").convert_alpha()\r\nleft_wonder_woman_img = load(\"assets/images/left_wonder_woman.png\").convert_alpha()\r\nmega_mob_img = load(\"assets/images/small_mega_mob_img.gif\").convert()\r\nmega_bomb_img = load(\"assets/images/mega_bomb.png\").convert_alpha()\r\nwin_background_img = load(\"assets/images/win_background.png\").convert_alpha()\r\nbonus_img = load(\"assets/images/alien_life_img.png\").convert_alpha()\r\npink_damaged_img = load(\"assets/images/shipPink_damage.png\").convert_alpha()\r\ngreen_damaged_img = load(\"assets/images/shipGreen_damage2.png\").convert_alpha()\r\nyellow_damaged_img = load(\"assets/images/shipYellow_damage2.png\").convert_alpha()\r\n\r\n# Sounds\r\nsound = pygame.mixer.Sound\r\nEXPLOSION = sound('assets/sounds/explosion.ogg')\r\npew = sound('assets/sounds/pew.ogg')\r\nhurt = sound('assets/sounds/hurt.ogg')\r\n\r\nplaying_music = \"assets/sounds/background.ogg\"\r\n\r\nlost_music = \"assets/sounds/my_own_music.ogg\"\r\nstarting_music = \"assets/sounds/starting_music.ogg\"\r\nwinning_music = \"assets/sounds/winning_music.ogg\"\r\n\r\n\r\n# Stages\r\nSTART = 0\r\nPLAYING = 1\r\nLOST = 2\r\nWON = 3\r\nEND = 4\r\n\r\n# Game classes\r\nclass Wonder_woman(pygame.sprite.Sprite):\r\n def __init__(self, image):\r\n super().__init__()\r\n\r\n self.image = image\r\n self.mask = pygame.mask.from_surface(self.image)\r\n self.rect = image.get_rect()\r\n \r\n\r\n self.speed = 3\r\n\r\n def move_left(self):\r\n self.rect.x -= self.speed\r\n \r\n def move_right(self):\r\n self.rect.x += self.speed\r\n\r\n def shoot(self):\r\n print()\r\n print(\"Pew!\")\r\n\r\n laser = Laser(laser_img)\r\n laser.rect.centerx = self.rect.centerx\r\n laser.rect.centery = self.rect.top\r\n lasers.add(laser)\r\n \r\n def update(self):\r\n global stage\r\n '''check screen edges'''\r\n if self.rect.left < 0:\r\n self.rect.left = 0\r\n elif self.rect.right > WIDTH: \r\n self.rect.right = WIDTH\r\n\r\n '''check bombs'''\r\n hit_list = pygame.sprite.spritecollide(self, bombs, True, pygame.sprite.collide_mask)\r\n \r\n if len(hit_list) > 0:\r\n print()\r\n print(\"LOST A LIFE!\")\r\n hurt.play()\r\n player.score += -2\r\n player.strength_bar += -1\r\n\r\n '''check powerups'''\r\n bonus_hit_list = pygame.sprite.spritecollide(self, powerups, True, pygame.sprite.collide_mask)\r\n \r\n for hit in bonus_hit_list:\r\n print()\r\n print(\"Gained your lives\")\r\n hit.apply(self)\r\n\r\n \r\n '''check mobs'''\r\n mob_hit_list = pygame.sprite.spritecollide(self, mobs, False, pygame.sprite.collide_mask)\r\n\r\n \r\n for hit in mob_hit_list:\r\n player.score += -3\r\n self.kill()\r\n stage = LOST\r\n\r\n \r\nclass Laser(pygame.sprite.Sprite):\r\n def __init__(self, image):\r\n super().__init__()\r\n\r\n self.image = image\r\n self.mask = pygame.mask.from_surface(self.image)\r\n self.rect = image.get_rect()\r\n self.speed = 5\r\n\r\n\r\n\r\n def update(self):\r\n self.rect.y -= self.speed\r\n\r\n if self.rect.bottom < 0:\r\n self.kill()\r\n\r\n \r\nclass Bomb(pygame.sprite.Sprite):\r\n def __init__(self, image):\r\n super().__init__()\r\n\r\n self.image = image\r\n self.mask = pygame.mask.from_surface(self.image)\r\n self.rect = image.get_rect()\r\n self.speed = 3\r\n \r\n\r\n\r\n def update(self):\r\n self.rect.y += self.speed\r\n hit_list = pygame.sprite.spritecollide(self, lasers, True, pygame.sprite.collide_mask)\r\n \r\n if self.rect.bottom < 0:\r\n self.kill()\r\n\r\n if len(hit_list) > 0:\r\n self.kill()\r\n print()\r\n print(\"You got a bomb!\")\r\n\r\n \r\nclass Mob(pygame.sprite.Sprite):\r\n def __init__(self, x, y, image):\r\n super().__init__()\r\n\r\n self.image = image\r\n self.mask = pygame.mask.from_surface(self.image)\r\n self.rect = image.get_rect()\r\n self.rect.x = x\r\n self.rect.y = y\r\n \r\n def drop_bomb(self):\r\n print()\r\n print(\"Bwwamp!\")\r\n\r\n bomb = Bomb(bomb_img)\r\n bomb.rect.centerx = self.rect.centerx\r\n bomb.rect.centery = self.rect.top\r\n bombs.add(bomb)\r\n\r\n def update(self):\r\n hit_list = pygame.sprite.spritecollide(self, lasers, True, pygame.sprite.collide_mask)\r\n if len(hit_list) > 0:\r\n player.score += 2\r\n self.kill()\r\n print()\r\n print(\"BOOM!\")\r\n \r\n \r\nclass Fleet():\r\n def __init__(self, mobs):\r\n self.mobs = mobs\r\n self.speed = 5\r\n self.drop = 30\r\n self.moving_right = True\r\n self.drop_speed = 20\r\n self.bomb_rate = 60 # lower the number = faster the bomb\r\n self.cleared = False\r\n self.defeated = False\r\n\r\n \r\n def move(self):\r\n hits_edge = False\r\n \r\n for m in mobs:\r\n if self.moving_right:\r\n m.rect.x += self.speed\r\n\r\n if m.rect.right >= WIDTH:\r\n hits_edge = True\r\n else:\r\n m.rect.x -= self.speed\r\n\r\n if m.rect.left <= 0:\r\n hits_edge = True\r\n \r\n if hits_edge:\r\n self.reverse()\r\n self.move_down()\r\n \r\n def reverse(self):\r\n self.moving_right = not self.moving_right\r\n \r\n def move_down(self):\r\n for m in mobs:\r\n m.rect.y += self.drop\r\n \r\n def choose_bomber(self):\r\n rand = random.randrange(self.bomb_rate)\r\n mob_list = mobs.sprites()\r\n\r\n if len(mob_list) > 0 and rand == 0:\r\n bomber = random.choice(mob_list)\r\n bomber.drop_bomb()\r\n\r\n def change_speed(self):\r\n if len(mobs) == 12:\r\n for m in mobs:\r\n self.speed = 10\r\n \r\n if len(mobs) == 6:\r\n for m in mobs:\r\n self.speed = 17\r\n \r\n if len(mobs) == 1:\r\n for m in mobs:\r\n self.speed = 35\r\n for l in lasers:\r\n self.speed = 10\r\n \r\n def update(self):\r\n self.move()\r\n self.choose_bomber()\r\n self.change_speed()\r\n\r\nclass HealthPowerUp(pygame.sprite.Sprite):\r\n def __init__(self, x, y, image):\r\n super().__init__()\r\n\r\n self.image = image\r\n self.mask = pygame.mask.from_surface(self.image)\r\n self.rect = image.get_rect()\r\n self.rect.x = x\r\n self.rect.y = y\r\n self.speed = 7\r\n\r\n def apply(self, wonder_woman):\r\n player.strength_bar = 3\r\n\r\n def update(self):\r\n self.rect.y += self.speed\r\n\r\n if self.rect.top > HEIGHT:\r\n self.kill()\r\n \r\n \r\n# Game helper functions\r\ndef show_title_screen():\r\n '''text'''\r\n space_txt = comic_font.render(\"Press Space\", True, WHITE)\r\n\r\n\r\n '''blit images/text'''\r\n screen.blit(whole_background_img, [0,0])\r\n screen.blit(space_txt, [WIDTH/2 - space_txt.get_width()/2 , HEIGHT/2 - space_txt.get_height()/2])\r\n\r\n\r\ndef show_won_screen():\r\n '''text'''\r\n won_end_txt = comic_font.render(\"YOU WON!\", True, WHITE)\r\n score_txt = battle_font.render(\"SCORE = \" + str(player.score), 1, WHITE)\r\n end_time_txt = battle_font.render(\"TIME = \" + str(ticks//refresh_rate), 1, WHITE)\r\n high_score_txt1 = jedi_font .render(\"Your perfect score will be recorded. Enter your name.\", True, WHITE)\r\n\r\n '''screen blit'''\r\n screen.blit(win_background_img, [0,0])\r\n screen.blit(won_end_txt, [WIDTH/2 - won_end_txt.get_width()/2 , HEIGHT/6 - won_end_txt.get_height()/6])\r\n screen.blit(score_txt, [WIDTH/2 - score_txt.get_width()/2 , 660])\r\n screen.blit(end_time_txt, [WIDTH/2 - end_time_txt.get_width()/2 , 760])\r\n show_high_score()\r\n stage = END\r\n\r\ndef show_lost_screen():\r\n '''text'''\r\n lost_end_txt = comic2_font.render(\"THE ALIENS HAVE WON!\", True, WHITE)\r\n score_txt = battle_font.render(\"SCORE = \" + str(player.score), 1, WHITE)\r\n end_time_txt = battle_font.render(\"TIME = \" + str(ticks//refresh_rate), 1, WHITE)\r\n high_score_txt2 = jedi_font .render(\"Your score will not be recorded.\", True, WHITE)\r\n\r\n '''screen blit'''\r\n screen.blit(lost_background_img, [0,0])\r\n screen.blit(lost_end_txt, [WIDTH/2 - lost_end_txt.get_width()/2 , HEIGHT/6 - lost_end_txt.get_height()/6])\r\n screen.blit(score_txt, [WIDTH/2 - score_txt.get_width()/2 , HEIGHT/3 - score_txt.get_height()/3])\r\n screen.blit(end_time_txt, [WIDTH/2 - end_time_txt.get_width()/2 , HEIGHT/2 - end_time_txt.get_height()/2])\r\n screen.blit(high_score_txt2, [WIDTH/2 - high_score_txt2.get_width()/2 , 870])\r\n stage = END\r\n \r\ndef show_stats():\r\n timer = ticks//refresh_rate \r\n \r\n '''text'''\r\n timer_txt = battle_font.render(str(timer), 1, BLUE)\r\n score_txt = battle_font.render(str(player.score), 1, RED)\r\n \r\n '''blit text'''\r\n screen.blit(timer_txt, [1500, 20])\r\n screen.blit(score_txt, [10, 20])\r\n \r\n '''Changing elements'''\r\n if player.strength_bar == 3:\r\n screen.blit(heart_img, [100,10])\r\n screen.blit(heart_img, [200, 10])\r\n screen.blit(heart_img, [300, 10])\r\n\r\n elif player.strength_bar == 2:\r\n screen.blit(heart_img, [100,10])\r\n screen.blit(heart_img, [200, 10])\r\n\r\n elif player.strength_bar == 1:\r\n screen.blit(heart_img, [100,10])\r\n \r\ndef record_high_score():\r\n if player.score == 38:\r\n input_file = open(\"high_score.txt\",\"a\")\r\n name = input(\"enter your name: \")\r\n print(\"Your Highscore has been recorded\")\r\n print(name,file=input_file)\r\n input_file.close()\r\n file = open('high_score.txt', 'r') \r\n names = file.readlines()\r\n file.close()\r\n\r\ndef show_high_score():\r\n file = open('high_score.txt', 'r') \r\n names = file.read().splitlines()\r\n perfect_players_txt = jedi_font.render(\"Last five perfect scorers: \" + str(names[-5:]), True, WHITE)\r\n screen.blit(perfect_players_txt, [WIDTH/3 - perfect_players_txt.get_width()/3 , 890])\r\n file.close()\r\n\r\ndef set_music(track):\r\n if pygame.mixer.music.get_busy():\r\n pygame.mixer.music.stop()\r\n\r\n if track != None: \r\n pygame.mixer.music.load(track)\r\n pygame.mixer.music.play(-1)\r\n\r\ndef setup():\r\n global stage, done, ticks\r\n global player, wonder_woman, lasers, mobs, fleet, bombs, powerups\r\n \r\n ''' Make game objects '''\r\n wonder_woman = Wonder_woman(wonder_woman_img)\r\n wonder_woman.rect.centerx = WIDTH/2\r\n wonder_woman.rect.bottom = HEIGHT\r\n \r\n ''' Make sprite groups '''\r\n player = pygame.sprite.GroupSingle()\r\n player.add(wonder_woman)\r\n\r\n lasers = pygame.sprite.Group()\r\n bombs = pygame.sprite.Group()\r\n \r\n mob1 = Mob(0, 400, enemy_img)\r\n mob2 = Mob(200, 400, enemy_img)\r\n mob3 = Mob(400, 400, enemy_img)\r\n mob4 = Mob(600, 400, enemy_img)\r\n mob5 = Mob(800, 400, enemy_img)\r\n mob6 = Mob(1000, 400, enemy_img)\r\n mob7 = Mob(1200, 400, enemy_img)\r\n '''enemy 2'''\r\n mob8 = Mob(100, 200, enemy2_img)\r\n mob9 = Mob(300, 200, enemy2_img)\r\n mob10 = Mob(500, 200, enemy2_img)\r\n mob11 = Mob(700, 200, enemy2_img)\r\n mob12 = Mob(900, 200, enemy2_img)\r\n mob13 = Mob(1100, 200, enemy2_img)\r\n '''enemy 3'''\r\n mob14 = Mob(200, 0, enemy3_img)\r\n mob15 = Mob(400, 0, enemy3_img)\r\n mob16 = Mob(600, 0, enemy3_img)\r\n mob17 = Mob(800, 0, enemy3_img)\r\n mob18 = Mob(1000, 0, enemy3_img)\r\n\r\n '''final enemy'''\r\n mega_mob = Mob(800, -400, mega_mob_img)\r\n \r\n mobs = pygame.sprite.Group()\r\n \r\n mobs.add(mob1,mob2,mob3,mob4,mob5,mob6,mob7)\r\n mobs.add(mob8,mob9,mob10,mob11,mob12,mob13)\r\n mobs.add(mob14,mob15,mob16,mob17,mob18, mega_mob)\r\n\r\n fleet = Fleet(mobs)\r\n\r\n '''bonus'''\r\n \r\n powerup1 = HealthPowerUp(800, -2000, bonus_img)\r\n powerups = pygame.sprite.Group()\r\n powerups.add(powerup1)\r\n \r\n '''stats'''\r\n ticks = 0\r\n player.score = 0\r\n player.strength_bar = 3\r\n \r\n ''' set stage '''\r\n stage = START\r\n done = False\r\n '''music'''\r\n \r\n set_music(starting_music)\r\n \r\n\r\n# Game loop\r\nsetup()\r\n\r\nwhile not done:\r\n # Input handling (React to key presses, mouse clicks, etc.)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n done = True\r\n elif event.type == pygame.KEYDOWN:\r\n if stage == START:\r\n if event.key == pygame.K_SPACE:\r\n set_music(playing_music)\r\n stage = PLAYING\r\n elif stage == PLAYING:\r\n if event.key == pygame.K_SPACE:\r\n wonder_woman.shoot()\r\n pew.play()\r\n elif stage == LOST:\r\n if event.key == pygame.K_SPACE:\r\n stage = END\r\n setup()\r\n elif stage == WON:\r\n if event.key == pygame.K_SPACE:\r\n stage = END\r\n setup()\r\n elif stage == END():\r\n if event.key == pygame.K_SPACE:\r\n setup()\r\n\r\n \r\n pressed = pygame.key.get_pressed()\r\n \r\n \r\n # Game logic (Check for collisions, update points, etc.)\r\n if stage == PLAYING:\r\n \r\n if pressed[pygame.K_LEFT]:\r\n wonder_woman.move_left()\r\n elif pressed[pygame.K_RIGHT]:\r\n wonder_woman.move_right()\r\n\r\n player.update()\r\n lasers.update()\r\n fleet.update()\r\n mobs.update()\r\n bombs.update()\r\n ticks += 1\r\n powerups.update()\r\n \r\n\r\n \r\n if len(mobs) == 0:\r\n set_music(winning_music)\r\n record_high_score()\r\n stage = WON\r\n \r\n if player.strength_bar <= 0:\r\n set_music(lost_music)\r\n stage = LOST\r\n\r\n\r\n \r\n # Drawing code (Describe the picture. It isn't actually drawn yet.)\r\n screen.blit(background, [0,0])\r\n lasers.draw(screen)\r\n bombs.draw(screen)\r\n player.draw(screen)\r\n mobs.draw(screen)\r\n powerups.draw(screen)\r\n \r\n show_stats()\r\n \r\n \r\n if stage == START:\r\n show_title_screen()\r\n elif stage == LOST:\r\n show_lost_screen()\r\n elif stage == WON:\r\n show_won_screen()\r\n\r\n \r\n \r\n # Update screen (Actually draw the picture in the window.)\r\n pygame.display.flip()\r\n\r\n\r\n # Limit refresh rate of game loop \r\n clock.tick(refresh_rate)\r\n\r\n\r\n# Close window and quit\r\npygame.quit()\r\n", "repo_name": "colorfulthunder57/wonder-woman-battle", "sub_path": "wonder_woman_battle.py", "file_name": "wonder_woman_battle.py", "file_ext": "py", "file_size_in_byte": 16437, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "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.font", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.image", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 142, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 156, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 174, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 174, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 182, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 213, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 213, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 260, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 264, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 292, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 292, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.get_busy", "line_number": 392, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 392, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 393, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 393, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 396, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 396, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 397, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 397, "usage_type": "attribute"}, {"api_name": "pygame.sprite.GroupSingle", "line_number": 409, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 409, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 412, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 412, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 413, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 413, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 439, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 439, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 450, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 450, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 471, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 471, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 472, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 474, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 476, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 480, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 484, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 488, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 492, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 496, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 496, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 502, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 504, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 549, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 549, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 557, "usage_type": "call"}]} +{"seq_id": "13917226391", "text": "#!/usr/bin/env python3\n\nimport requests\nimport os\nimport csv\n\n# SONG = 2421807\n__URL__ = 'https://api.planningcenteronline.com/services/v2/songs/'\n__ID__ = os.environ.get('ID') or ''\n__SECRET__ = os.environ.get('SECRET') or ''\n__COOKIE__ = {'_account_center_session' : os.environ.get('COOKIE') or ''}\n\n\nclass Song:\n\n def __init__(self, id, name, yt, spotify):\n self.id = id\n self.name = name\n self.yt = yt\n self.spotify = spotify\n\ndef get_songs():\n\n param = '?per_page=100'\n urlpath = f'{__URL__}{param}'\n r = requests.get(urlpath, auth=requests.auth.HTTPBasicAuth(__ID__,__SECRET__))\n j = r.json()\n s_total = j['meta']['total_count']\n s_list = []\n\n while len(s_list) < s_total:\n for song in j.get('data'):\n\n # Create Song\n s_obj = Song(song['id'], song['attributes']['title'], [], [])\n\n s_list.append(s_obj)\n if (j['links'].get('next')):\n r = requests.get(j['links']['next'], auth=requests.auth.HTTPBasicAuth(__ID__,__SECRET__))\n j = r.json()\n\n return s_list\n\ndef get_song_attachments(song_obj):\n\n s_id = str(song_obj.id)\n s_name = song_obj.name\n print(f'[{s_id}] {s_name}')\n\n param = f'{s_id}/attachments?per_page=100'\n urlpath = f'{__URL__}{param}'\n\n r1 = requests.get(urlpath, auth=requests.auth.HTTPBasicAuth(__ID__,__SECRET__))\n j1 = r1.json()\n\n for a in j1.get('data'):\n # AttachmentSpotify or AttachmentYoutube or AttachmentLink\n pco = str(a['attributes']['pco_type'])\n if (pco == 'AttachmentYoutube'):\n get_url_from_attachment('yt', a, song_obj)\n elif (pco == 'AttachmentSpotify'):\n get_url_from_attachment('sp', a, song_obj)\n # elif (pco == 'AttachmentLink'):\n #link_url = str(a['attributes']['remote_link'])\n #print(f'Link: {link_url}')\n\n #print('+++')\n\n param2 = f'{s_id}/arrangements?per_page=100'\n urlpath2 = f'{__URL__}{param2}'\n\n r2 = requests.get(urlpath2, auth=requests.auth.HTTPBasicAuth(__ID__,__SECRET__))\n j2 = r2.json()\n\n for ar in j2.get('data'):\n ar_id = str(ar['id'])\n\n param3 = f'{s_id}/arrangements/{ar_id}/attachments?per_page=100'\n urlpath3 = f'{__URL__}{param3}'\n\n r3 = requests.get(urlpath3, auth=requests.auth.HTTPBasicAuth(__ID__,__SECRET__))\n j2 = r3.json()\n\n for a in j2.get('data'):\n # AttachmentSpotify or AttachmentYoutube or AttachmentLink\n pco = str(a['attributes']['pco_type'])\n if (pco == 'AttachmentYoutube'):\n get_url_from_attachment('yt', a, song_obj)\n elif (pco == 'AttachmentSpotify'):\n get_url_from_attachment('sp', a, song_obj)\n elif (pco == 'AttachmentLink'):\n link_url = str(a['attributes']['remote_link'])\n #print(f'Link: {link_url}')\n\n #print('-------------------')\n return 0\n\ndef get_url_from_attachment(urltype, a, song_obj):\n if (a['attributes']['remote_link']):\n link_url = str(a['attributes']['remote_link'])\n if (urltype == 'yt'):\n song_obj.yt.append(link_url)\n #print(f'Youtube: {link_url}')\n elif (urltype == 'sp'):\n song_obj.spotify.append(link_url)\n #print(f'Spotify: {link_url}')\n\n else:\n urlpath = str(a['attributes']['url'])\n # Hack to allow access to attachments\n r2 = requests.get(urlpath, cookies=__COOKIE__)\n link_url = str(r2.url)\n if (urltype == 'yt'):\n song_obj.yt.append(link_url)\n #print(f'Youtube: {link_url}')\n elif (urltype == 'sp'):\n song_obj.spotify.append(link_url)\n #print(f'Spotify: {link_url}')\n\ndef write_links_to_csv(song_list):\n with open('export.csv', mode='w', newline='') as csv_file:\n csv_writer = csv.writer(csv_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n csv_writer.writerow(['ID', 'Name', 'Youtube', 'Spotify'])\n\n for song in song_list:\n csv_writer.writerow([song.id, song.name, song.yt, song.spotify])\n\nsongs = get_songs()\nprint('Got Songs')\nfor song in songs:\n get_song_attachments(song)\nprint('Got Links')\nwrite_links_to_csv(songs)\n", "repo_name": "evanhwk/SCPlanningC", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 4254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ.get", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 10, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 26, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 39, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 53, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 72, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 72, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 81, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 111, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 122, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 122, "usage_type": "attribute"}]} +{"seq_id": "42854468577", "text": "from collections import Counter\n\nfrom ..base import TimerOptimizer, Route, Solution, Optimizer\nimport numpy as np\nimport random\nfrom typing import List, Set\n\nfrom .. import LocalInnerEdgeOptimizer\n\n\ndef perturbations_type1(route: Route, n_verticies: int, n_iter: int):\n unused_vertices = set(list(range(0, n_verticies))) - set(route)\n\n for _ in range(n_iter):\n # random vertex replace\n vertex1 = random.choice(list(unused_vertices))\n vertex2 = random.randint(0, len(route))\n\n unused_vertices.add(route[vertex2])\n route[vertex2] = vertex1\n unused_vertices.remove(vertex1)\n\n # random edge replace\n edge_start = random.randint(0, len(route))\n edge_end = random.randint(0, len(route))\n while edge_end == edge_start:\n edge_end = random.randint(0, len(route))\n\n if edge_start < edge_end:\n e = edge_start\n edge_start = edge_end\n edge_end = e\n edge = route[edge_start:edge_end + 1]\n edge.reverse()\n\n new_route = route[0:edge_start] + edge + route[edge_end + 1:]\n route = new_route\n\n return route\n\n\nclass ILS1(TimerOptimizer):\n def _find_solution(self):\n best_solution = Solution(np.inf, self.route)\n route: Route = self.route[:]\n vertices = len(self.distance_matrix)\n opt = LocalInnerEdgeOptimizer(self.distance_matrix, route)\n solution = opt()\n\n while True:\n if solution.cost < best_solution.cost:\n best_solution = Solution(solution.cost, solution.route[:])\n\n route = perturbations_type1(solution.route, vertices, 2)\n opt = LocalInnerEdgeOptimizer(self.distance_matrix, route)\n solution = opt()\n\n yield best_solution\n\n\ndef greedy_cycle(distance_matrix: np.ndarray, route: List[int], unused_vertices: Set, to_restore: int) -> List[int]:\n end_len = len(route) + to_restore\n unused_vertices = list(unused_vertices)\n\n while len(route) != end_len:\n v1 = route[0] # to na indeksach wszystko jest\n v2 = unused_vertices[0]\n v3 = route[1]\n dst = distance_matrix[v1][v2] + distance_matrix[v2][v3] - distance_matrix[v1][v3]\n best_move = (v1, v2, v3, dst)\n\n for i in range(len(route)):\n v1 = route[i - 1]\n v3 = route[i]\n\n for j in range(len(unused_vertices)):\n v2 = unused_vertices[j]\n dst = distance_matrix[v1][v2] + distance_matrix[v2][v3] - distance_matrix[v1][v3]\n\n if dst < best_move[2]:\n best_move = (i, v2, dst)\n\n route.insert(best_move[0], best_move[1])\n unused_vertices.remove(best_move[1])\n\n return route\n\n\ndef perturbations_type2(route: Route, distance_matrix, n_verticies: int, percent: float) -> Route:\n unused_vertices = set(list(range(0, n_verticies))) - set(route)\n before_number = len(route)\n\n destroy_n_verticies = int(n_verticies * percent)\n for _ in range(destroy_n_verticies):\n vertex = random.choice(route)\n unused_vertices.add(vertex)\n route.remove(vertex)\n\n destroy_n_edges = int((len(route) / 2) * percent)\n for _ in range(destroy_n_edges):\n edge_end = random.randint(0, len(route) - 1)\n\n v = route[edge_end]\n v_prev = route[edge_end - 1]\n\n unused_vertices.add(v)\n unused_vertices.add(v_prev)\n\n route.remove(v)\n route.remove(v_prev)\n\n to_restore = before_number - len(route)\n route = greedy_cycle(distance_matrix, route, unused_vertices, to_restore)\n\n return Route(route)\n\n\nclass ILS2b(TimerOptimizer):\n def _find_solution(self):\n route: Route = self.route[:]\n vertices = len(self.distance_matrix)\n best_solution = Solution(np.inf, self.route)\n opt = LocalInnerEdgeOptimizer(self.distance_matrix, route)\n optimal_solution = opt()\n solution = Solution(optimal_solution.cost, optimal_solution.route[:])\n\n while True:\n if solution.cost < best_solution.cost:\n best_solution = Solution(solution.cost, solution.route[:])\n\n route = perturbations_type2(optimal_solution.route, opt.distance_matrix, vertices, 0.07)\n cost = self.__calculate_cost(route)\n solution = Solution(cost, route[:])\n\n yield best_solution\n\n def __calculate_cost(self, route):\n return sum(\n self.distance_matrix[a, b]\n if a is not None and b is not None else None\n for a, b in zip(route, route[1:] + [route[0]])\n )\n\n\nclass ILS2a(TimerOptimizer):\n def _find_solution(self):\n vertices = len(self.distance_matrix)\n best_solution = Solution(np.inf, self.route)\n\n while True:\n route = perturbations_type2(best_solution.route, self.distance_matrix, vertices, 0.07)\n cost = self.__calculate_cost(route)\n solution = Solution(cost, route[:])\n\n if solution.cost < best_solution.cost:\n best_solution = Solution(solution.cost, solution.route[:])\n\n yield best_solution\n\n def __calculate_cost(self, route):\n return sum(\n self.distance_matrix[a, b]\n if a is not None and b is not None else None\n for a, b in zip(route, route[1:] + [route[0]])\n )\n", "repo_name": "Zerkles/AEM", "sub_path": "p4/optimizers/ils_optimizer/ils_optimizer.py", "file_name": "ils_optimizer.py", "file_ext": "py", "file_size_in_byte": 5351, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "base.Route", "line_number": 11, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 16, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 25, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "base.TimerOptimizer", "line_number": 42, "usage_type": "name"}, {"api_name": "base.Solution", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 44, "usage_type": "attribute"}, {"api_name": "base.Route", "line_number": 45, "usage_type": "name"}, {"api_name": "base.Solution", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 61, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 61, "usage_type": "name"}, {"api_name": "base.Route", "line_number": 89, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 95, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 101, "usage_type": "call"}, {"api_name": "base.Route", "line_number": 115, "usage_type": "call"}, {"api_name": "base.TimerOptimizer", "line_number": 118, "usage_type": "name"}, {"api_name": "base.Route", "line_number": 120, "usage_type": "name"}, {"api_name": "base.Solution", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 122, "usage_type": "attribute"}, {"api_name": "base.Solution", "line_number": 125, "usage_type": "call"}, {"api_name": "base.Solution", "line_number": 129, "usage_type": "call"}, {"api_name": "base.Solution", "line_number": 133, "usage_type": "call"}, {"api_name": "base.TimerOptimizer", "line_number": 145, "usage_type": "name"}, {"api_name": "base.Solution", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 148, "usage_type": "attribute"}, {"api_name": "base.Solution", "line_number": 153, "usage_type": "call"}, {"api_name": "base.Solution", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "24464017342", "text": "from flask import (\n Flask, escape, render_template, url_for, request\n)\n\nfrom backend import (\n lucene, Searcher, search_query, read_stopwords, read_pos_translation, pos_list_string, parse_win,\n idxdir_path, file_stopwords, file_pos\n)\n\n## html和css文件都在根目录下\napp = Flask(__name__, template_folder=\"\", static_folder=\"\")\n\n@app.route('/')\ndef index():\n query = request.args.get(\"query\")\n print(f\"### query: {query}\")\n if query:\n ## ---start--- MUST add for lucene functionality\n vm_env = lucene.getVMEnv()\n vm_env.attachCurrentThread()\n ## ---end---\n\n win_str = request.args.get(\"win\")\n win = parse_win(win_str)\n pos = []\n checkbox_state = {}\n if not request.args.get(\"cb_all\"):\n for key in request.args.keys():\n if key.startswith(\"cb_\"):\n pos_t = key[3:]\n pos.append(pos_t)\n for key in request.args.keys():\n if key.startswith(\"cb_\"):\n pos_t = key[3:]\n pos.append(pos_t)\n checkbox_state[key] = True\n print(f\"### pos: {pos}\")\n print(f\"### win: {win}\")\n counter, pos_dict = search_query(query, searcher, pos, win, stopwords)\n ans = counter.most_common(n)\n\n ## display answers\n answer_list = []\n print(f\"### get top {len(ans)} answers\")\n for item in ans:\n ans = (item[0], pos_list_string(pos_dict[item[0]], pos_trans))\n answer_list.append(ans)\n return render_template(\"result.html\", answer_list=answer_list, query_str=query, win=win if win != 0 else \"\", **checkbox_state)\n\n return render_template(\"index.html\")\n\n\n### backend init begin\nprint(\"initiating backend: lucene\")\nlucene.initVM()\nsearcher = Searcher(idxdir_path)\nstopwords = read_stopwords(file_stopwords)\npos_trans = read_pos_translation(file_pos)\nn = 20\nprint(\"backend initiated!\")\n### backend init end\n\nif __name__ == \"__main__\":\n app.run()\n", "repo_name": "atomiechen/CollocationRetrieval", "sub_path": "src-flask/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "backend.lucene.getVMEnv", "line_number": 19, "usage_type": "call"}, {"api_name": "backend.lucene", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "backend.parse_win", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.args.keys", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.args.keys", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "backend.search_query", "line_number": 39, "usage_type": "call"}, {"api_name": "backend.pos_list_string", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "backend.lucene.initVM", "line_number": 55, "usage_type": "call"}, {"api_name": "backend.lucene", "line_number": 55, "usage_type": "name"}, {"api_name": "backend.Searcher", "line_number": 56, "usage_type": "call"}, {"api_name": "backend.idxdir_path", "line_number": 56, "usage_type": "argument"}, {"api_name": "backend.read_stopwords", "line_number": 57, "usage_type": "call"}, {"api_name": "backend.file_stopwords", "line_number": 57, "usage_type": "argument"}, {"api_name": "backend.read_pos_translation", "line_number": 58, "usage_type": "call"}, {"api_name": "backend.file_pos", "line_number": 58, "usage_type": "argument"}]} +{"seq_id": "21088008160", "text": "import numpy as np\nimport torch\n\nfrom util import get_model_detect, get_model_recognition\nfrom util.utils import image_process_for_detect, get_scale_output, get_anchors, decode, decode_landmarks, \\\n non_max_suppression, image_process_for_recognition, l2_norm, retinaface_correct_boxes, alignment\n\n\nclass Embeddings(object):\n def __init__(self, cfg):\n super(Embeddings, self).__init__()\n self.cfg = cfg\n self.retina = get_model_detect(cfg.retina)\n self.recognition = get_model_recognition(cfg.recognition)\n\n def get_embeddings(self, img):\n \"\"\"\n args:\n img: 一张图片，图片是GBR通道（w,h,c）\n return:\n face_embeddings: 如果他返回None 代表整张图片上不存在人脸、如果返回是具体的向量向量形式是(n,v)\n boxes_conf_landmarks: 如果这个返回是None，也代表不存在人脸，如果返回的是具体向量(n,15)\n 上面的n代表的是人脸的数目，v代表的是向量的长度\n\n note:\n 如果进行编码：一张图片只允许包含0或1张人脸\n 如果进行识别：一张图片可以包>=0张人脸\n \"\"\"\n old_image = img.copy()\n scale, scale_for_landmarks = get_scale_output(img)\n anchors = get_anchors(self.cfg.retina)\n img = image_process_for_detect(img, self.cfg)\n with torch.no_grad():\n # img = torch.from_numpy(img).type(torch.FloatTensor)\n img = img.to(self.cfg.retina.device)\n loc, conf, landmarks = self.retina(img)\n loc, conf, landmarks = loc.cpu(), conf.cpu(), landmarks.cpu()\n boxes = decode(loc.data.squeeze(0), anchors, self.cfg.retina.net_cfg['variance'], scale)\n conf = conf.data.squeeze(0)[:, 1:2].numpy()\n landmarks = decode_landmarks(landmarks.data.squeeze(0), anchors, self.cfg.retina.net_cfg['variance'],\n scale_for_landmarks)\n boxes_conf_landmarks = np.concatenate([boxes, conf, landmarks], -1)\n boxes_conf_landmarks = non_max_suppression(boxes_conf_landmarks, self.cfg.retina.confidence)\n if len(boxes_conf_landmarks) < 1:\n return None, None\n boxes_conf_landmarks = retinaface_correct_boxes(boxes_conf_landmarks, np.array(self.cfg.retina.input_shape[:2]), np.array(old_image.shape[:2]))\n face_embeddings = []\n for boxes_conf_landmark in boxes_conf_landmarks:\n boxes_conf_landmark = np.maximum(boxes_conf_landmark, 0)\n recognition_image = image_process_for_recognition(old_image, boxes_conf_landmark, self.cfg)\n with torch.no_grad():\n recognition_image = recognition_image.type(torch.FloatTensor)\n recognition_image = recognition_image.to(self.cfg.recognition.device)\n if self.cfg.recognition.split:\n embeddings = self.recognition(recognition_image)[1]\n else:\n embeddings = self.recognition(recognition_image)\n face_embeddings.append(embeddings.cpu())\n face_embeddings = torch.cat(face_embeddings, 0)\n face_embeddings = l2_norm(face_embeddings, -1)\n return face_embeddings, boxes_conf_landmarks\n", "repo_name": "Meng-Sang/FR", "sub_path": "util/detect_recognition.py", "file_name": "detect_recognition.py", "file_ext": "py", "file_size_in_byte": 3322, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "util.get_model_detect", "line_number": 13, "usage_type": "call"}, {"api_name": "util.get_model_recognition", "line_number": 14, "usage_type": "call"}, {"api_name": "util.utils.get_scale_output", "line_number": 30, "usage_type": "call"}, {"api_name": "util.utils.get_anchors", "line_number": 31, "usage_type": "call"}, {"api_name": "util.utils.image_process_for_detect", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 33, "usage_type": "call"}, {"api_name": "util.utils.decode", "line_number": 38, "usage_type": "call"}, {"api_name": "util.utils.decode_landmarks", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 42, "usage_type": "call"}, {"api_name": "util.utils.non_max_suppression", "line_number": 43, "usage_type": "call"}, {"api_name": "util.utils.retinaface_correct_boxes", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 49, "usage_type": "call"}, {"api_name": "util.utils.image_process_for_recognition", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 59, "usage_type": "call"}, {"api_name": "util.utils.l2_norm", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "73071889042", "text": "import os\nimport tempfile\n\nimport tensorflow as tf\nimport zipfile\nimport cloudpickle\nimport numpy as np\n\nimport baselines.common.tf_util as U\nfrom baselines.common.tf_util import load_state, save_state\nfrom baselines import logger\nfrom baselines.common.schedules import LinearSchedule\nfrom baselines.common.input import observation_input\n\nfrom baselines import deepq\nfrom baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer\nfrom baselines.deepq.utils import ObservationInput\n\n\nclass ActWrapper(object):\n def __init__(self, act, act_params):\n self._act = act\n self._act_params = act_params\n\n @staticmethod\n def load(path):\n with open(path, \"rb\") as f:\n model_data, act_params = cloudpickle.load(f)\n act = deepq.build_act(**act_params)\n sess = tf.Session()\n sess.__enter__()\n with tempfile.TemporaryDirectory() as td:\n arc_path = os.path.join(td, \"packed.zip\")\n with open(arc_path, \"wb\") as f:\n f.write(model_data)\n\n zipfile.ZipFile(arc_path, 'r', zipfile.ZIP_DEFLATED).extractall(td)\n load_state(os.path.join(td, \"model\"))\n\n return ActWrapper(act, act_params)\n\n def __call__(self, *args, **kwargs):\n return self._act(*args, **kwargs)\n\n def save(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_state(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)\n\n\ndef load(path):\n \"\"\"Load act function that was returned by learn function.\n\n Parameters\n ----------\n path: str\n path to the act function pickle\n\n Returns\n -------\n act: ActWrapper\n function that takes a batch of observations\n and returns actions.\n \"\"\"\n return ActWrapper.load(path)\n\n\ndef learn(\n env,\n actor_deque,\n action_pipes,\n q_func,\n lr=5e-4,\n max_timesteps=100000,\n buffer_size=50000,\n exploration_fraction=0.1,\n exploration_final_eps=0.02,\n train_freq=1,\n batch_size=32,\n print_freq=100,\n checkpoint_freq=10000,\n checkpoint_path=None,\n learning_starts=1000,\n gamma=1.0,\n target_network_update_freq=500,\n prioritized_replay=False,\n prioritized_replay_alpha=0.6,\n prioritized_replay_beta0=0.4,\n prioritized_replay_beta_iters=None,\n prioritized_replay_eps=1e-6,\n param_noise=False,\n callback=None):\n \"\"\"Train a deepq model.\n\n Parameters\n -------\n env: gym.Env\n environment to train on\n actor_deque: structure is --> (ac_num, obs, action, new_obs, rew, done)\n action_pipes: structure is --> pipes_conn1 = [pipes[i][1] for i in range(0, 2)]\n use --> action_pipes[actor_num].send(s) default is str\n 至于为什么一处为deque,一处为pipe. well, actor需要接受action来执行下一步,此前为阻塞状态.\n 而trainer是响应式的,无论哪个actor有数据都要进行计算,使用deque.empty()很方便,\n q_func: (tf.Variable, int, str, bool) -> tf.Variable\n the model that takes the following inputs:\n observation_in: object\n the output of observation placeholder\n num_actions: int\n number of actions\n scope: str\n reuse: bool\n should be passed to outer variable scope\n and returns a tensor of shape (batch_size, num_actions) with values of every action.\n lr: float\n learning rate for adam optimizer\n max_timesteps: int\n number of env steps to optimizer for\n buffer_size: int\n size of the replay buffer\n exploration_fraction: float\n fraction of entire training period over which the exploration rate is annealed\n exploration_final_eps: float\n final value of random action probability\n train_freq: int\n update the model every `train_freq` steps.\n set to None to disable printing\n batch_size: int\n size of a batched sampled from replay buffer for training\n print_freq: int\n how often to print out training progress\n set to None to disable printing\n checkpoint_freq: int\n how often to save the model. This is so that the best version is restored\n at the end of the training. If you do not wish to restore the best version at\n the end of the training set this variable to None.\n learning_starts: int\n how many steps of the model to collect transitions for before learning starts\n asyn 之下该参数修改为在replay_buffer的数据大小下开始？\n gamma: float\n discount factor\n target_network_update_freq: int\n update the target network every `target_network_update_freq` steps.\n prioritized_replay: True\n if True prioritized replay buffer will be used.\n prioritized_replay_alpha: float\n alpha parameter for prioritized replay buffer\n prioritized_replay_beta0: float\n initial value of beta for prioritized replay buffer\n prioritized_replay_beta_iters: int\n number of iterations over which beta will be annealed from initial value\n to 1.0. If set to None equals to max_timesteps.\n prioritized_replay_eps: float\n epsilon to add to the TD errors when updating priorities.\n callback: (locals, globals) -> None\n function called at every steps with state of the algorithm.\n If callback returns true training stops.\n\n Returns\n -------\n act: ActWrapper\n Wrapper over act function. Adds ability to save it and load it.\n See header of baselines/deepq/categorical.py for details on the act function.\n \"\"\"\n # Create all the functions necessary to train the model\n\n sess = tf.Session()\n sess.__enter__()\n\n def make_obs_ph(name):\n return ObservationInput(env.observation_space, name=name)\n\n act, train, update_target, debug = deepq.build_train(\n make_obs_ph=make_obs_ph,\n q_func=q_func,\n num_actions=env.action_space.n,\n optimizer=tf.train.AdamOptimizer(learning_rate=lr),\n gamma=gamma,\n grad_norm_clipping=10,\n param_noise=param_noise\n )\n\n act_params = {\n 'make_obs_ph': make_obs_ph,\n 'q_func': q_func,\n 'num_actions': env.action_space.n,\n }\n\n act = ActWrapper(act, act_params)\n\n # Create the replay buffer\n if prioritized_replay:\n replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)\n if prioritized_replay_beta_iters is None:\n prioritized_replay_beta_iters = max_timesteps\n beta_schedule = LinearSchedule(prioritized_replay_beta_iters,\n initial_p=prioritized_replay_beta0,\n final_p=1.0)\n else:\n replay_buffer = ReplayBuffer(buffer_size)\n beta_schedule = None\n # Create the schedule for exploration starting from 1.　探索率\n exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * max_timesteps),\n initial_p=1.0,\n final_p=exploration_final_eps)\n\n # Initialize the parameters and copy them to the target network.\n U.initialize()\n update_target()\n\n episode_rewards = [0.0]\n saved_mean_reward = None\n # obs = env.reset()\n reset = True\n done = None\n end = 100 # 传输一个非正常动作,结束训练\n\n with tempfile.TemporaryDirectory() as td:\n td = checkpoint_path or td\n model_file = os.path.join(td, \"model_tn\")\n model_saved = False\n if tf.train.latest_checkpoint(td) is not None:\n load_state(model_file)\n logger.log('Loaded model from {}'.format(model_file))\n model_saved = True\n\n # 在最大步数内训练\n t = 0\n while t <= max_timesteps:\n if callback is not None:\n if callback(locals(), globals()):\n break\n if actor_deque.empty() is True:\n pass\n # time.sleep()\n else:\n actor_information = actor_deque.get()\n if actor_information[2] is None: # 表示其为一轮开始\n ac_num = actor_information[0]\n new_obs = actor_information[3]\n done = False # important\n # print(\"ac_num \"+str(ac_num)+\" start\")\n else:\n ac_num = actor_information[0]\n obs = actor_information[1]\n action = actor_information[2]\n new_obs = actor_information[3]\n rew = actor_information[4]\n done = actor_information[5]\n replay_buffer.add(obs, action, rew, new_obs, float(done))\n if done: # done 与start是不会共存的\n # obs = env.reset()\n # episode_rewards.append(0.0)\n reset = True\n else:\n # Take action and update exploration to the newest value\n kwargs = {}\n if not param_noise:\n update_eps = exploration.value(t)\n update_param_noise_threshold = 0.\n else:\n update_eps = 0.\n update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))\n kwargs['reset'] = reset\n kwargs['update_param_noise_threshold'] = update_param_noise_threshold\n kwargs['update_param_noise_scale'] = True\n action = act(np.array(new_obs)[None], update_eps=update_eps, **kwargs)[0]\n env_action = action\n reset = False\n action_pipes[ac_num-1].send(env_action) # 这里ac_num与pipe位置没有对齐\n # 经过learning_starts步后开始训练网络（先在buffer中存入一定量数据）\n # 每经过train_freq步进行一次梯度下降\n if t > learning_starts and t % train_freq == 0:\n # Minimize the error in Bellman's equation on a batch sampled from replay buffer.\n if prioritized_replay:\n experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t)) # 注意beta的用法\n (obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience\n else:\n obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)\n # np.ones_like() : Return an array of ones with the same shape and type as a given array.\n weights, batch_idxes = np.ones_like(rewards), None\n td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)\n if prioritized_replay:\n new_priorities = np.abs(td_errors) + prioritized_replay_eps\n replay_buffer.update_priorities(batch_idxes, new_priorities)\n\n if t > learning_starts and t % target_network_update_freq == 0:\n # Update target network periodically.\n update_target()\n\n # 下面是关于输出训练信息,以及保存网络参数的部分\n if print_freq is not None and t % print_freq == 0:\n logger.record_tabular(\"total_steps\", t)\n # logger.record_tabular(\"episodes\", num_episodes)\n # logger.record_tabular(\"mean 20 episode reward\", mean_100ep_reward)\n logger.record_tabular(\"% time spent exploring\", int(100 * exploration.value(t)))\n logger.dump_tabular()\n\n # checkpoint_freq轮数、mean reward增长才会保存模型\n if checkpoint_freq is not None and t > learning_starts and t % checkpoint_freq == 0:\n save_state(model_file)\n model_saved = True\n t += 1\n # 至此,训练结束\n # end = True\n for i in range(0, len(action_pipes)):\n action_pipes[i].send(end) # end = 100\n # 训练结束后保存最佳模型\n # if model_saved:\n # if print_freq is not None:\n # logger.log(\"Restored model with mean reward: {}\".format(saved_mean_reward))\n # load_state(model_file)\n # 返回一个ActWrapper,用来act.save(\"cartpole_model.pkl\")或其它的动作\n return act\n", "repo_name": "yxBeginner/RL-and-Robot", "sub_path": "deepq/asyn_sec/simple_multi_agent.py", "file_name": "simple_multi_agent.py", "file_ext": "py", "file_size_in_byte": 13539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "3", "api": [{"api_name": "cloudpickle.load", "line_number": 28, "usage_type": "call"}, {"api_name": "baselines.deepq.build_act", "line_number": 29, "usage_type": "call"}, {"api_name": "baselines.deepq", "line_number": 29, "usage_type": "name"}, {"api_name": "tensorflow.Session", "line_number": 30, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 37, "usage_type": "call"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 37, "usage_type": "attribute"}, {"api_name": "baselines.common.tf_util.load_state", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "baselines.logger.get_dir", "line_number": 48, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 48, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 50, "usage_type": "call"}, {"api_name": "baselines.common.tf_util.save_state", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 53, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 54, "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.path.relpath", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cloudpickle.dump", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 180, "usage_type": "call"}, {"api_name": "baselines.deepq.utils.ObservationInput", "line_number": 184, "usage_type": "call"}, {"api_name": "baselines.deepq.build_train", "line_number": 186, "usage_type": "call"}, {"api_name": "baselines.deepq", "line_number": 186, "usage_type": "name"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 190, "usage_type": "attribute"}, {"api_name": "baselines.deepq.replay_buffer.PrioritizedReplayBuffer", "line_number": 206, "usage_type": "call"}, {"api_name": "baselines.common.schedules.LinearSchedule", "line_number": 209, "usage_type": "call"}, {"api_name": "baselines.deepq.replay_buffer.ReplayBuffer", "line_number": 213, "usage_type": "call"}, {"api_name": "baselines.common.schedules.LinearSchedule", "line_number": 216, "usage_type": "call"}, {"api_name": "baselines.common.tf_util.initialize", "line_number": 221, "usage_type": "call"}, {"api_name": "baselines.common.tf_util", "line_number": 221, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 235, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 235, "usage_type": "attribute"}, {"api_name": "baselines.common.tf_util.load_state", "line_number": 236, "usage_type": "call"}, {"api_name": "baselines.logger.log", "line_number": 237, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 237, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 297, "usage_type": "call"}, {"api_name": "baselines.logger.record_tabular", "line_number": 306, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 306, "usage_type": "name"}, {"api_name": "baselines.logger.record_tabular", "line_number": 309, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 309, "usage_type": "name"}, {"api_name": "baselines.logger.dump_tabular", "line_number": 310, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 310, "usage_type": "name"}, {"api_name": "baselines.common.tf_util.save_state", "line_number": 314, "usage_type": "call"}]} +{"seq_id": "7961133579", "text": "import maya.cmds as mc\n\ntry:\n from itertools import izip as zip\nexcept ImportError: # will be 3.x series\n pass\n\nfrom ..ar_functions import find_jnts\nfrom ..ar_functions import sel_joints\nfrom ..ar_tools import fk_ctrl\n\n\n# fk jaw ctrl\nclass face_rig():\n\n def jaw_ctrl(self, parent_to, ctrl_size):\n # find head joint\n head_jnt_temp = find_jnts.find_jnts()\n head_jnt = head_jnt_temp.find_head_jnt()\n # find jaw joint\n jaw_jnt_temp = find_jnts.find_jnts()\n jaw_jnt = jaw_jnt_temp.most_children_jnt(head_jnt)\n # create jaw fk ctrl\n jaw_ctrl_var = fk_ctrl.fk_ctrl()\n # parent jaw control under head ctrl\n jaw_ctrl_info = jaw_ctrl_var.single_fk_ctrl( jnt=jaw_jnt, \n parent_to=parent_to, \n normal=[0,1,0], \n size = ctrl_size,\n colorR=0, \n colorG=1, \n colorB=0)\n # return joint group and then control\n return jaw_ctrl_info[0], jaw_ctrl_info[1]\n\n\n def tongue_ctrls(self, ctrl_size, parent_to):\n # find head joint\n head_jnt_temp = find_jnts.find_jnts()\n head_jnt = head_jnt_temp.find_head_jnt()\n # find jaw joint\n jaw_jnt_temp = find_jnts.find_jnts()\n jaw_jnt = jaw_jnt_temp.most_children_jnt(head_jnt)\n # find jaw joint\n tongue_jnt_temp = find_jnts.find_jnts()\n tongue_jnt = tongue_jnt_temp.most_descendants_jnt(jaw_jnt)\n\n # find tongue joint chain\n tongue_list_var = sel_joints.sel_joints(firstJoint=tongue_jnt)\n\n tongue_list_info = tongue_list_var.sel_jnt_chain()\n\n #create controls and groups for tongue\n tongue_grp_list = []\n tongue_ctrl_list = []\n for jnt in tongue_list_info:\n jnt_var = fk_ctrl.fk_ctrl()\n jnt_var_info = jnt_var.single_fk_curve_ctrl(jnt=jnt, \n parent_to='', \n version='box', \n size=ctrl_size, \n colorR=1, \n colorG=0, \n colorB=0)\n # make grp and ctrl list for tongue ctrls\n tongue_grp_list.append(jnt_var_info[0])\n tongue_ctrl_list.append(jnt_var_info[1])\n # varaiable for top grp to parent\n tongue_top_grp = tongue_grp_list[0]\n\n #remove first and last of lists to correctly parent ctrls and grps together in for loop\n tongue_grp_list.pop(0)\n tongue_ctrl_list.pop(-1)\n\n #parent ctrls and grps together\n for i_grp, i_ctrl in zip(tongue_grp_list, tongue_ctrl_list):\n mc.parent(i_grp, i_ctrl)\n # parent top grp to head ctrl\n mc.parent(tongue_top_grp, parent_to)\n #return tongue top grp (not needed)\n return tongue_top_grp\n\n\n def bot_face_ctrls(self, ctrl_size, parent_to):\n # find head joint\n head_jnt_temp = find_jnts.find_jnts()\n head_jnt = head_jnt_temp.find_head_jnt()\n # find jaw joint\n jaw_jnt_temp = find_jnts.find_jnts()\n jaw_jnt = jaw_jnt_temp.most_children_jnt(head_jnt)\n # find jaw joint\n tongue_jnt_temp = find_jnts.find_jnts()\n tongue_jnt = tongue_jnt_temp.most_descendants_jnt(jaw_jnt)\n\n jaw_jnt_descendants = mc.listConnections(jaw_jnt, type='joint', d=True, s=False)\n\n bot_face_jnts = []\n for i in jaw_jnt_descendants:\n if i != tongue_jnt:\n bot_face_jnts.append(i)\n \n for i in bot_face_jnts:\n bot_face_ctrl = fk_ctrl.fk_ctrl()\n bot_face_ctrl.single_fk_curve_ctrl(jnt=i, \n parent_to=parent_to,\n size=ctrl_size,\n version='box',\n colorR=.5, \n colorG=1, \n colorB=0)\n \n\n #________________________________________________#\n #________________________________________________#\n #top face controls w/ mid ctrls (parented to head)\n def top_face_ctrls(self, ctrl_size, parent_to_head='', parent_to_jaw='', mid_ctrls=0):\n # find head joint\n head_jnt_temp = find_jnts.find_jnts()\n head_jnt = head_jnt_temp.find_head_jnt()\n # find jaw joint\n jaw_jnt_temp = find_jnts.find_jnts()\n jaw_jnt = jaw_jnt_temp.most_children_jnt(head_jnt)\n # get immediate descendants of head joint\n head_jnt_descendants = mc.listConnections(head_jnt, type='joint', d=True, s=False)\n # list head joint descendants without jaw joint\n top_head_jnts = []\n for i in head_jnt_descendants:\n if i != jaw_jnt:\n top_head_jnts.append(i)\n #list head joints without ear joints\n top_face_jnts = []\n for i in top_head_jnts:\n i_descendats = mc.listRelatives(i, type='joint', ad=True)\n try:\n if len(i_descendats) >= 1:\n pass\n except:\n top_face_jnts.append(i)\n \n #get position of face jnts\n y_pos_list = []\n for i in top_face_jnts:\n pos = mc.xform(i, q=True , ws=True, t=True, a=True)\n # y pos to find lowest ws value \n y_pos = pos[1]\n y_pos_list.append(y_pos)\n \n # combine y_pos and top face jnt lst\n zip_y_pos = zip(y_pos_list, top_face_jnts)\n #sort lists from smallest to greatest Y pos\n sort_zip_y_pos = sorted(zip_y_pos)\n # create sorted list with just face jnts\n sorted_top_face_jnts = [somVar for i, somVar in sort_zip_y_pos]\n # get just jnts with lowest y positions\n mid_jnt_list = sorted_top_face_jnts[:mid_ctrls]\n # new face jnt list without mid face jnts\n new_top_face_jnts = sorted_top_face_jnts[mid_ctrls:]\n\n \n # create nurbs ctrl for each top face jnt\n for i in new_top_face_jnts:\n top_face_ctrl = fk_ctrl.fk_ctrl()\n top_face_ctrl.single_fk_curve_ctrl(jnt=i, \n parent_to=parent_to_head, \n version='box',\n size=ctrl_size,\n colorR=1, \n colorG=.5, \n colorB=0)\n\n # mid face grp list\n mid_face_ctrl_grps = []\n # create ctrl for the mid face jnts\n for i in mid_jnt_list:\n mid_face_ctrl = fk_ctrl.fk_ctrl()\n mid_face_ctrl_info = mid_face_ctrl.single_fk_curve_ctrl( jnt=i, \n parent_to=parent_to_head, \n version='box',\n size=ctrl_size,\n colorR=0, \n colorG=.5, \n colorB=1)\n # parent constrain mid face ctrl grp between head and jaw\n mc.parentConstraint(parent_to_head, parent_to_jaw, mid_face_ctrl_info[0], mo=1)\n mc.scaleConstraint(parent_to_head, parent_to_jaw, mid_face_ctrl_info[0], mo=1)\n # append grps to list\n mid_face_ctrl_grps.append(mid_face_ctrl_info[0])\n\n return top_face_jnts, mid_jnt_list, mid_face_ctrl_grps\n \n \n #________________________________________________#\n #________________________________________________#\n def ear_ctrls(self, ctrl_size, parent_to):\n # find head joint\n head_jnt_temp = find_jnts.find_jnts()\n head_jnt = head_jnt_temp.find_head_jnt()\n # find jaw joint\n jaw_jnt_temp = find_jnts.find_jnts()\n jaw_jnt = jaw_jnt_temp.most_children_jnt(head_jnt)\n # get immediate descendants of head joint\n head_jnt_descendants = mc.listConnections(head_jnt, type='joint', d=True, s=False)\n # list head joint descendants without jaw joint\n top_head_jnts = []\n for i in head_jnt_descendants:\n if i != jaw_jnt:\n top_head_jnts.append(i)\n # list head joints without face joints\n ear_jnts = []\n for i in top_head_jnts:\n i_descendats = mc.listRelatives(i, type='joint', ad=True)\n try:\n if len(i_descendats) >= 1:\n ear_jnts.append(i)\n except:\n pass\n\n # if ear jnts do exist rig them (top face jnts w/ child/s)\n try:\n # chain for first r ear (should just put For Loop incase more fk chains on head)\n r_ear_list_var = sel_joints.sel_joints(firstJoint=ear_jnts[0])\n\n r_ear_list_info = r_ear_list_var.sel_jnt_chain()\n\n # chain for first l ear\n l_ear_list_var = sel_joints.sel_joints(firstJoint=ear_jnts[1])\n\n l_ear_list_info = l_ear_list_var.sel_jnt_chain()\n\n #create controls and groups for R EAR ___________________________\n r_ear_grp_list = []\n r_ear_ctrl_list = []\n for jnt in r_ear_list_info:\n jnt_var = fk_ctrl.fk_ctrl()\n jnt_var_info = jnt_var.single_fk_curve_ctrl(jnt=jnt, \n parent_to='', \n version='box', \n size=ctrl_size, \n colorR=0, \n colorG=0.5, \n colorB=1)\n r_ear_grp_list.append(jnt_var_info[0])\n r_ear_ctrl_list.append(jnt_var_info[1])\n # varaiable for top grp before removed\n r_ear_top_grp = r_ear_grp_list[0]\n\n #remove first and last of lists to correctly parent ctrls and grps together in for loop\n r_ear_grp_list.pop(0)\n r_ear_ctrl_list.pop(-1)\n\n #parent ctrls and grps together\n for i_grp, i_ctrl in zip(r_ear_grp_list, r_ear_ctrl_list):\n mc.parent(i_grp, i_ctrl)\n # parent top grp to head ctrl\n mc.parent(r_ear_top_grp, parent_to)\n\n #create controls and groups for L EAR ___________________________\n l_ear_grp_list = []\n l_ear_ctrl_list = []\n for jnt in l_ear_list_info:\n jnt_var = fk_ctrl.fk_ctrl()\n jnt_var_info = jnt_var.single_fk_curve_ctrl(jnt=jnt, \n parent_to='', \n version='box', \n size=ctrl_size, \n colorR=0, \n colorG=0.5, \n colorB=1)\n l_ear_grp_list.append(jnt_var_info[0])\n l_ear_ctrl_list.append(jnt_var_info[1])\n # varaiable for top grp before removed\n l_ear_top_grp = l_ear_grp_list[0]\n\n #remove first and last of lists to correctly parent ctrls and grps together in for loop\n l_ear_grp_list.pop(0)\n l_ear_ctrl_list.pop(-1)\n\n #parent ctrls and grps together\n for i_grp, i_ctrl in zip(l_ear_grp_list, l_ear_ctrl_list):\n mc.parent(i_grp, i_ctrl)\n # parent top grp to head ctrl\n mc.parent(l_ear_top_grp, parent_to)\n\n return r_ear_top_grp, l_ear_top_grp\n except:\n pass\n", "repo_name": "natelollar/maya_auto_rigger_and_tools", "sub_path": "character_rigger/ar_rig/face_rig.py", "file_name": "face_rig.py", "file_ext": "py", "file_size_in_byte": 12555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 18, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 18, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 21, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 21, "usage_type": "name"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 24, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 24, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 39, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 39, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 42, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 42, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 45, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 45, "usage_type": "name"}, {"api_name": "ar_functions.sel_joints.sel_joints", "line_number": 49, "usage_type": "call"}, {"api_name": "ar_functions.sel_joints", "line_number": 49, "usage_type": "name"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 57, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 57, "usage_type": "name"}, {"api_name": "itertools.izip", "line_number": 76, "usage_type": "call"}, {"api_name": "maya.cmds.parent", "line_number": 77, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 77, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 79, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 79, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 86, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 86, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 89, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 89, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 92, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 92, "usage_type": "name"}, {"api_name": "maya.cmds.listConnections", "line_number": 95, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 95, "usage_type": "name"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 103, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 103, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 118, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 118, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 121, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 121, "usage_type": "name"}, {"api_name": "maya.cmds.listConnections", "line_number": 124, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 124, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 133, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 133, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 143, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 143, "usage_type": "name"}, {"api_name": "itertools.izip", "line_number": 149, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 162, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 162, "usage_type": "name"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 175, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 175, "usage_type": "name"}, {"api_name": "maya.cmds.parentConstraint", "line_number": 184, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 184, "usage_type": "name"}, {"api_name": "maya.cmds.scaleConstraint", "line_number": 185, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 185, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 196, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 196, "usage_type": "name"}, {"api_name": "ar_functions.find_jnts.find_jnts", "line_number": 199, "usage_type": "call"}, {"api_name": "ar_functions.find_jnts", "line_number": 199, "usage_type": "name"}, {"api_name": "maya.cmds.listConnections", "line_number": 202, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 202, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 211, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 211, "usage_type": "name"}, {"api_name": "ar_functions.sel_joints.sel_joints", "line_number": 221, "usage_type": "call"}, {"api_name": "ar_functions.sel_joints", "line_number": 221, "usage_type": "name"}, {"api_name": "ar_functions.sel_joints.sel_joints", "line_number": 226, "usage_type": "call"}, {"api_name": "ar_functions.sel_joints", "line_number": 226, "usage_type": "name"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 234, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 234, "usage_type": "name"}, {"api_name": "itertools.izip", "line_number": 252, "usage_type": "call"}, {"api_name": "maya.cmds.parent", "line_number": 253, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 253, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 255, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 255, "usage_type": "name"}, {"api_name": "ar_tools.fk_ctrl.fk_ctrl", "line_number": 261, "usage_type": "call"}, {"api_name": "ar_tools.fk_ctrl", "line_number": 261, "usage_type": "name"}, {"api_name": "itertools.izip", "line_number": 279, "usage_type": "call"}, {"api_name": "maya.cmds.parent", "line_number": 280, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 280, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 282, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 282, "usage_type": "name"}]} +{"seq_id": "38108065803", "text": "import urllib.request\nimport re\nimport json\nfrom datetime import datetime, timedelta\nfrom requests import get\nfrom shapely.geometry import Polygon\nfrom urllib.request import urlopen\n\n\ndef get_prediction(model):\n\n now = datetime.now()\n today = now.strftime(\"%Y/%m/%d\")\n yesterday = (now - timedelta(days=1)).strftime(\"%Y/%m/%d\")\n\n if model == 'wrf7':\n try:\n res_json = get(\n f'http://ftp.cptec.inpe.br/modelos/tempo/WRF/ams_07km/recortes/grh/json/{today}/00/4704.json').json()\n except:\n res_json = get(\n f'http://ftp.cptec.inpe.br/modelos/tempo/WRF/ams_07km/recortes/grh/json/{yesterday}/00/4704.json').json()\n elif model == 'wrf':\n try:\n res_json = get(\n f'http://ftp.cptec.inpe.br/modelos/tempo/WRF/ams_05km/recortes/grh/json/{today}/00/4704.json').json()\n except:\n res_json = get(\n f'http://ftp.cptec.inpe.br/modelos/tempo/WRF/ams_05km/recortes/grh/json/{yesterday}/00/4704.json').json()\n elif model == 'bam':\n try:\n res_json = get(\n f'http://ftp.cptec.inpe.br/modelos/tempo/BAM/TQ0666L064/recortes/grh/json/{today}/00/4704.json').json()\n except:\n res_json = get(\n f'http://ftp.cptec.inpe.br/modelos/tempo/BAM/TQ0666L064/recortes/grh/json/{yesterday}/00/4704.json').json()\n\n raw_data = res_json['datasets'][0]['data']\n\n x_data_t = {}\n y_data = {}\n\n # Obtenção de datetime corrigido\n initial_date = datetime.fromisoformat(raw_data[0]['date']) # Data inicial devido a divergência entre modelos\n x_data_t[\"precipitacao\"] = [initial_date + timedelta(hours = i['fcst']) for i in raw_data]\n x_data_t[\"precipitacao_acc\"] = [initial_date + timedelta(hours = i['fcst']) for i in raw_data]\n x_data_t[\"temperatura\"] = [initial_date + timedelta(hours = i['fcst']) for i in raw_data]\n x_data_t[\"temperatura_aparente\"] = [initial_date + timedelta(hours = i['fcst']) for i in raw_data]\n x_data_t[\"pressao\"] = [initial_date + timedelta(hours = i['fcst']) for i in raw_data]\n x_data_t[\"umidade_relativa\"] = [initial_date + timedelta(hours = i['fcst']) for i in raw_data]\n\n # Obtenção de dados meteorológicos\n y_data[\"precipitacao\"] = [i['prec'] for i in raw_data]\n y_data[\"temperatura\"] = [i['temp'] for i in raw_data]\n y_data[\"temperatura_aparente\"] = [i['heat_index'] for i in raw_data]\n y_data[\"pressao\"] = [i['press'] for i in raw_data]\n y_data['umidade_relativa'] = [i['ur'] for i in raw_data]\n acc = 0\n for i in raw_data:\n # soma a precipitação atual com a acumulada anterior\n precipitacao_acc = i['prec']+acc\n # atualiza o valor da precipitação acumulada\n acc = precipitacao_acc\n y_data['precipitacao_acc'] = (precipitacao_acc)\n\n return x_data_t, y_data\n\n\ndef extract_data(source_string: str):\n res = json.loads(source_string)\n x_data = [point['x']for point in res]\n x_data_t = [datetime.fromtimestamp(t//1000) for t in x_data]\n y_data = [point['y']for point in res]\n\n return x_data, x_data_t, y_data\n\n\ndef get_SantoAndre_polygon():\n\n path = 'https://raw.githubusercontent.com/tbrugz/geodata-br/master/geojson/geojs-35-mun.json'\n\n with urlopen(path) as response:\n counties = json.load(response)\n SA = [i for i in counties['features'] if i['properties']['name'] == 'Santo André'][0]\n\n return SA\n\n\ndef verify_title_string(t):\n if 'observação' in t.lower() or 'observacao' in t.lower():\n text = 'Aviso de Observação'\n elif 'atenção' in t.lower() or 'atencao' in t.lower():\n text = 'Aviso de Atenção'\n elif 'especial' in t.lower():\n text = 'Aviso Especial'\n elif 'extraordinário' in t.lower() or 'extraordinario' in t.lower() or 'risco' in t.lower():\n text = 'Aviso Extraordinário de Risco Iminente'\n elif 'cessado' in t.lower():\n text = 'Aviso Cessado'\n else:\n text = 'Sem Aviso'\n\n return text\n\n\ndef get_polygon():\n\n value_dict = {'Aviso de Observação': 1,\n 'Aviso de Atenção': 2,\n 'Aviso Especial': 3,\n 'Aviso Extraordinário de Risco Iminente': 4,\n 'Aviso Cessado': 5\n }\n\n inverse_value_dict = {\n 0: 'Sem Aviso',\n 1: 'Aviso de Observação',\n 2: 'Aviso de Atenção',\n 3: 'Aviso Especial',\n 4: 'Aviso Extraordinário de Risco Iminente',\n 5: 'Aviso Cessado'}\n\n intersection = {}\n output_dict = {}\n\n SA = get_SantoAndre_polygon()\n SA_polygon = Polygon(SA['geometry']['coordinates'][0])\n SA_layer = dict(sourcetype='geojson',\n source=SA,\n below='',\n type='fill',\n opacity=0.25,\n color='#1c1e2f')\n\n with urllib.request.urlopen('http://tempo.cptec.inpe.br/avisos/') as response:\n html_source = str(response.read())\n\n htmlnow = re.search(r'^(.+?)\\/\\/ 48 horas', html_source).group(1)\n html48 = re.search(r'\\/\\/ 48 horas(.*?)\\/\\/ 72 horas', html_source).group(1)\n html72 = re.search(r'\\/\\/ 72 horas(.*)', html_source).group(1)\n\n for text, html in zip(['Hoje', '48 horas', '72 horas'], [htmlnow, html48, html72]):\n\n intersection[text] = 0\n\n output_dict[text] = {'geom': [],\n 'title': []}\n\n poly_func_string_list = re.findall(r'google.maps.Polygon(.*?)\\)', html)\n poly_func_string = re.search(r'new google.maps.Polygon\\((.*?)\\}\\)', html)\n\n if poly_func_string is None:\n continue\n\n poly_func_string = poly_func_string.group(1)\n\n for poly_func_string in poly_func_string_list:\n\n polygon_string = re.search(r'paths\\: (.*?),\\\\n', poly_func_string).group(1)\n\n polygon_string = polygon_string.replace('lat', '\"lat\"').replace('lng', '\"lng\"')\n polygon_dict = json.loads(polygon_string)\n polygon_points = [(p['lng'], p['lat']) for p in polygon_dict]\n\n title_string = re.search(r'title\\:\\\"(.*?)\"', poly_func_string).group(1)\n title_string = title_string.replace('\\\\xc3\\\\xa7', 'ç').replace('\\\\xc3\\\\xa3', 'ã').replace('\\\\xc3\\\\xa1', 'á')\n\n title_string = verify_title_string(title_string)\n\n # Populate Dict\n output_dict[text]['geom'].append(polygon_points)\n output_dict[text]['title'].append(title_string)\n\n polygon = Polygon(polygon_points)\n\n if SA_polygon.intersects(polygon):\n if value_dict[title_string] > intersection[text]:\n intersection[text] = value_dict[title_string]\n\n for k in intersection.keys():\n output_dict[k]['aviso'] = inverse_value_dict[intersection[k]]\n\n return output_dict, SA_polygon, SA_layer\n", "repo_name": "alagamentos/floodprediction", "sub_path": "src/Dash/cptec.py", "file_name": "cptec.py", "file_ext": "py", "file_size_in_byte": 6435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "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.timedelta", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 82, "usage_type": "call"}, {"api_name": "json.load", "line_number": 83, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 127, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 135, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 135, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 135, "usage_type": "name"}, {"api_name": "re.search", "line_number": 138, "usage_type": "call"}, {"api_name": "re.search", "line_number": 139, "usage_type": "call"}, {"api_name": "re.search", "line_number": 140, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 149, "usage_type": "call"}, {"api_name": "re.search", "line_number": 150, "usage_type": "call"}, {"api_name": "re.search", "line_number": 159, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 162, "usage_type": "call"}, {"api_name": "re.search", "line_number": 165, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "74635730001", "text": "# importing modules\nimport json\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# storing the url in the form of string\nurl = \"https://api.covid19india.org/state_district_wise.json\"\n\n# function to get data from api\n\n\ndef casesData():\n # getting the json data by calling api\n data = ((requests.get(url)).json())\n states = []\n\n # getting states\n for key in data.items():\n states.append(key[0])\n\n # getting statewise data\n for state in states:\n f = (data[state]['districtData'])\n tc = []\n dis = []\n act, con, dea, rec = 0, 0, 0, 0\n\n # getting districtwise data\n for key in (data[state]['districtData']).items():\n district = key[0]\n dis.append(district)\n active = data[state]['districtData'][district]['active']\n confirmed = data[state]['districtData'][district]['confirmed']\n if district == 'Unknown':\n active, confirmed\n tc.append([active, confirmed])\n act = act + active\n con = con + confirmed\n tc.append([act, con])\n dis.append('Total')\n parameters = ['Active', 'Confirmed']\n\n # creating a dataframe\n df = pd.DataFrame(tc, dis, parameters)\n print('COVID - 19', state, 'District Wise Data')\n print(df)\n\n # plotting of data\n plt.bar(dis, df['Active'], width=0.5, align='center')\n fig = plt.gcf()\n fig.set_size_inches(18.5, 10.5)\n plt.xticks(rotation=75)\n plt.show()\n print('*' * 100)\n\n\ncasesData()\n", "repo_name": "ihgoyarp/covid-indo", "sub_path": "covid-india.py", "file_name": "covid-india.py", "file_ext": "py", "file_size_in_byte": 1588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "70883460243", "text": "import json\nimport logging\nfrom django.http import HttpResponse\n\nfrom ...recommendation.arxiv_evaluation_handler import ArXivEvaluationListHandler\nfrom ...recommendation.wikipedia_evaluation_handler import WikipediaEvaluationListHandler\nfrom ...recommendation.static_wikidata_handler import StaticWikidataHandler\nfrom ...recommendation.manual_recommendations_handler import ManualRecommendationsHandler\nfrom ...recommendation.formula_concept_db_handler import FormulaConceptDBHandler\nfrom ...views.helper_classes.data_repo_handler import DataRepoHandler\nfrom ...views.helper_classes.cache_handler import CacheHandler\nfrom ...config import *\n\nlogging.basicConfig(level=logging.INFO)\ntoken_clicked_handler_logger = logging.getLogger(__name__)\n\nclass TokenClickedHandler:\n \"\"\"\n This class handles the use case, when the user selects a token (word, identifier or formula) to annotate.\n Depending on the type of the token, different types of data are sent back to the frontend.\n\n Identifier:\n - Wikidata query is made\n - ArXiv evaluation list is checked for matches8\n - Wikipedia evaluation list is checked for matches\n - Word window is computed\n\n Formula:\n - Wikidata query is made\n - Word window is computed\n\n Word (must not necessarily be named entity, as found by tagger):\n - Wikidata query is made\n\n\n For identifier and formulae, additionaly the concatenated results are computed, taking results from each of the\n sources and combining them in one column.\n\n :param request: Request object. Request made by the user through the frontend.\n :return: The rendered response containing the template name, the necessary form and the response data.\n \"\"\"\n\n def __init__(self, items):\n self.items = items\n\n\n def get_recommendations(self):\n\n\n\n recommendations_dict = {'arXivEvaluationItems': [],\n 'wikipediaEvaluationItems': [],\n 'wikidata1Results': [],\n 'wikidata2Results': [],\n 'wordWindow': [],\n 'formulaConceptDB': [],\n 'manual': []}\n\n\n search_string = [k for k in self.items['searchString']][0]\n token_type_dict = self.items['tokenType']\n token_type = [k for k in token_type_dict][0]\n unique_id = [k for k in self.items['uniqueId']][0]\n math_env = self.items['mathEnv']['dummy']\n annotations = self.items['annotations']\n\n token_clicked_handler_logger.info('Type: {}'.format(token_type))\n\n all_manual_recommendations = DataRepoHandler().get_manual_recommendations()\n\n if token_type == 'Identifier':\n recommendations_dict['arXivEvaluationItems'] = ArXivEvaluationListHandler().check_identifiers(search_string)\n recommendations_dict['wikipediaEvaluationItems'] = WikipediaEvaluationListHandler().check_identifiers(search_string)\n recommendations_dict['wikidata1Results'] = StaticWikidataHandler().check_identifiers(search_string)\n\n elif token_type == 'Formula':\n recommendations_dict['wikidata1Results'], recommendations_dict['wikidata2Results'] = StaticWikidataHandler().check_formulae(math_env, annotations)\n recommendations_dict['formulaConceptDB'] = FormulaConceptDBHandler().query_tex_string(math_env)\n #token_clicked_handler_logger.info(recommendations_dict['formulaConceptDB'])\n\n else:\n token_clicked_handler_logger.info('Faulty token_type: {}'.format(token_type))\n\n\n\n\n\n recommendations_dict['wordWindow'] = self.get_word_window(unique_id)\n\n recommendations_dict['manual'] = ManualRecommendationsHandler(\n all_manual_recommendations).check_identifier_or_formula(search_string)\n\n data_repo_handler = DataRepoHandler()\n all_wikidata_identifiers = data_repo_handler.get_wikidata_identifiers_by_name()\n all_wikidata_formulae = data_repo_handler.get_wikidata_formulae()\n all_math_items = data_repo_handler.get_math_wikidata_items()\n\n token_clicked_handler_logger.info(type(all_math_items))\n token_clicked_handler_logger.info(all_math_items[\"metabiaugmented hexagonal prism\"])\n\n\n def pp(dict_list, source):\n \"\"\"\n post process: add QID and fill to recommendations limit\n :param dict_list: ditionary list of recommendations from one source\n :return:\n \"\"\"\n def add_qid_identifier(r):\n \"\"\"\n :param r: single recommendation\n :return:\n \"\"\"\n name = r['name']\n if name in all_wikidata_identifiers:\n r['qid'] = all_wikidata_identifiers[name]['qid']\n else:\n r['qid'] = 'N/A'\n #token_clicked_handler_logger.info(r)\n return r\n\n def add_qid_formula(r):\n \"\"\"\n :param r: single recommendation\n :return:\n \"\"\"\n name = r['name']\n if name in all_wikidata_formulae:\n r['qid'] = all_wikidata_formulae[name]['qid']\n else:\n r['qid'] = 'N/A'\n return r\n\n def add_qid_all_math(r):\n\n\n\n if source not in ['wikidata1Results', 'wikidata2Results']:\n\n name = r['name']\n if name in all_math_items:\n r['qid'] = all_math_items[name]\n else:\n r['qid'] = 'N/A'\n r['name'] = r['name'].replace(\"\\'\", '__APOSTROPH__')\n return r\n\n\n dict_list = list(map(add_qid_all_math, dict_list))\n\n\n dict_list += [{'name': ''} for _ in range(recommendations_limit - len(dict_list))]\n return dict_list\n\n recommendations_dict_pp = dict(map(lambda kv: (kv[0], pp(kv[1], kv[0])), recommendations_dict.items()))\n response = HttpResponse(json.dumps(recommendations_dict_pp), content_type='application/json')\n return response, recommendations_dict_pp\n\n def get_word_window(self, unique_id):\n \"\"\"\n This method produces the word window for a selected (by the user) formula or identifier. It iteratively takes\n named entities from the lines before and after the selected token(s) to fill the number of named entities as\n specified by the recommendation limit.\n :param unique_id: The unique id if the token (identifier or formula).\n :return: a list of named entities that appear around the selected token.\n \"\"\"\n\n word_window = []\n limit = int(recommendations_limit / 2)\n #dicts = self.cache_to_dicts()\n dicts = CacheHandler().cache_to_dicts()\n identifier_line_dict = dicts['identifiers']\n line_dict = dicts['lines']\n if unique_id in identifier_line_dict:\n line_num = identifier_line_dict[unique_id]\n else:\n return []\n\n i = 0\n while i < limit:\n # lines before\n b = line_num - i\n # lines after\n a = line_num + i\n\n if b in line_dict:\n for word in reversed(line_dict[b]):\n # value not yet in word window\n if not list(filter(lambda d: d['name'] == word.content.lower(), word_window)):\n word_window.append({\n 'name': word.content.lower(),\n #'unique_id': word.unique_id\n })\n i += 1\n if a in line_dict:\n for word in reversed(line_dict[a]):\n # value not yet in word window\n if not list(filter(lambda d: d['name'] in word.content.lower(), word_window)):\n word_window.append({\n 'name': word.content.lower(),\n #'unique_id': word.unique_id\n })\n i += 1\n if not word_window:\n word_window = [{}]\n return word_window[:recommendations_limit]\n\n\n", "repo_name": "gipplab/AnnoMathTeX", "sub_path": "annomathtex/annomathtex/views/helper_classes/token_clicked_handler.py", "file_name": "token_clicked_handler.py", "file_ext": "py", "file_size_in_byte": 8251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "views.helper_classes.data_repo_handler.DataRepoHandler", "line_number": 69, "usage_type": "call"}, {"api_name": "recommendation.arxiv_evaluation_handler.ArXivEvaluationListHandler", "line_number": 72, "usage_type": "call"}, {"api_name": "recommendation.wikipedia_evaluation_handler.WikipediaEvaluationListHandler", "line_number": 73, "usage_type": "call"}, {"api_name": "recommendation.static_wikidata_handler.StaticWikidataHandler", "line_number": 74, "usage_type": "call"}, {"api_name": "recommendation.static_wikidata_handler.StaticWikidataHandler", "line_number": 77, "usage_type": "call"}, {"api_name": "recommendation.formula_concept_db_handler.FormulaConceptDBHandler", "line_number": 78, "usage_type": "call"}, {"api_name": "recommendation.manual_recommendations_handler.ManualRecommendationsHandler", "line_number": 90, "usage_type": "call"}, {"api_name": "views.helper_classes.data_repo_handler.DataRepoHandler", "line_number": 93, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 155, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 155, "usage_type": "call"}, {"api_name": "views.helper_classes.cache_handler.CacheHandler", "line_number": 170, "usage_type": "call"}]} +{"seq_id": "15588112103", "text": "# Hierarchical Clustering\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('Mall_Customers.csv')\nX = dataset.iloc[:, [3, 4]].values\n# y = dataset.iloc[:, 3].values\n\n# Splitting the dataset into the Training set and Test set\n\"\"\"from sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n\n# Feature Scaling\n\"\"\"from sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\nsc_y = StandardScaler()\ny_train = sc_y.fit_transform(y_train)\"\"\"\n\n# Using the dendrogram to find the optimal number of clusters\nimport scipy.cluster.hierarchy as sch\ndendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\nplt.title('Dendrogram')\nplt.xlabel('Customers')\nplt.ylabel('Euclidean distances')\nplt.show()\n\n# Fitting Hierarchical Clustering to the dataset\nfrom sklearn.cluster import AgglomerativeClustering\nhc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\ny_hc = hc.fit_predict(X)\n\n# Visualising the clusters\nplt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\nplt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\nplt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\nplt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\nplt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\nplt.title('Clusters of customers')\nplt.xlabel('Annual Income (k$)')\nplt.ylabel('Spending Score (1-100)')\nplt.legend()\nplt.show()\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndataset = pd.read_csv('Mall_Customers.csv')\nX = dataset.iloc[:,[3,4]].values\n\nfrom sklearn.cluster import AgglomerativeClustering\nhc = AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')\ny_hc = hc.fit_predict(X)\n\nfrom sklearn.cluster import AgglomerativeClustering\nhc = AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='complete')\ny_hc_lin = hc.fit_predict(X)\n\nfrom sklearn.cluster import AgglomerativeClustering\nhc = AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='average')\ny_hc_avg = hc.fit_predict(X)\n\nplt.scatter(X[y_hc==0,0],X[y_hc==0,1],s=100,c='red',label='clus1')\nplt.scatter(X[y_hc==1,0],X[y_hc==1,1],s=100,c='blue',label='clus2')\nplt.scatter(X[y_hc==2,0],X[y_hc==2,1],s=100,c='cyan',label='clus3')\nplt.scatter(X[y_hc==3,0],X[y_hc==3,1],s=100,c='magenta',label='clus4')\nplt.scatter(X[y_hc==4,0],X[y_hc==4,1],s=100,c='green',label='clus5')\nplt.show()\n\nplt.scatter(X[y_hc_lin==0,0],X[y_hc_lin==0,1],s=100,c='red',label='clus1')\nplt.scatter(X[y_hc_lin==1,0],X[y_hc_lin==1,1],s=100,c='blue',label='clus2')\nplt.scatter(X[y_hc_lin==2,0],X[y_hc_lin==2,1],s=100,c='cyan',label='clus3')\nplt.scatter(X[y_hc_lin==3,0],X[y_hc_lin==3,1],s=100,c='magenta',label='clus4')\nplt.scatter(X[y_hc_lin==4,0],X[y_hc_lin==4,1],s=100,c='green',label='clus5')\nplt.show()\n\nplt.scatter(X[y_hc_avg==0,0],X[y_hc_avg==0,1],s=100,c='red',label='clus1')\nplt.scatter(X[y_hc_avg==1,0],X[y_hc_avg==1,1],s=100,c='blue',label='clus2')\nplt.scatter(X[y_hc_avg==2,0],X[y_hc_avg==2,1],s=100,c='cyan',label='clus3')\nplt.scatter(X[y_hc_avg==3,0],X[y_hc_avg==3,1],s=100,c='magenta',label='clus4')\nplt.scatter(X[y_hc_avg==4,0],X[y_hc_avg==4,1],s=100,c='green',label='clus5')\nplt.show()\n\nfrom sklearn.metrics import adjusted_rand_score\nward_ar_score = adjusted_rand_score(y_hc,y_hc)\n\nfrom sklearn.metrics import adjusted_rand_score\nward_ar_score_avg = adjusted_rand_score(y_hc,y_hc_avg)\n\nfrom sklearn.metrics import adjusted_rand_score\nward_ar_score_com = adjusted_rand_score(y_hc,y_hc_lin)\n\nfrom sklearn import preprocessing\nnormalized_X = preprocessing.normalize(X)\n\nplt.scatter(normalized_X[:,0],normalized_X[:,1],color='red')\nplt.show()\n\nfrom sklearn.preprocessing import normalize\nnormalized_X1 = normalize(X)\n\nfrom scipy.cluster.hierarchy import linkage\nlinkage_type = 'ward'\n\nlinkage_matrix = linkage(X,linkage_type)\n\nfrom scipy.cluster.hierarchy import dendrogram\ndendrogram = dendrogram(linkage_matrix)\nplt.show()\n\nimport seaborn as sns\nsns.clustermap(X,figsize=(18,50),method='ward',cmap='viridis')\nplt.show()\n\n\n#DBSCAN\nimport pandas as pd\n\ndataset = pd.read_csv('Mall_Customers.csv')\nX = dataset.iloc[:, [3, 4]].values\n\nimport dbscan_lab_helper as helper\nfrom sklearn.cluster import DBSCAN\ndbscan = DBSCAN(eps=1,min_samples=3)\nypred_dbscan = dbscan.fit_predict(X)\n\n\nplt.scatter(X[ypred_dbscan==-1,0],X[ypred_dbscan==-1,1],s=100,c='red',label='clus1')\nplt.scatter(X[ypred_dbscan==1,0],X[ypred_dbscan==1,1],s=100,c='blue',label='clus2')\nplt.scatter(X[ypred_dbscan==2,0],X[ypred_dbscan==2,1],s=100,c='cyan',label='clus3')\nplt.scatter(X[ypred_dbscan==3,0],X[ypred_dbscan==3,1],s=100,c='magenta',label='clus4')\nplt.scatter(X[ypred_dbscan==4,0],X[ypred_dbscan==4,1],s=100,c='green',label='clus5')\nplt.show()\n\n", "repo_name": "raajeshlr/ML-A-Z-Udemy", "sub_path": "Part 4 - Clustering/Section 25 - Hierarchical Clustering/hc.py", "file_name": "hc.py", "file_ext": "py", "file_size_in_byte": 5103, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.dendrogram", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 27, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "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.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "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": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "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.scatter", "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"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "sklearn.metrics.adjusted_rand_score", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.metrics.adjusted_rand_score", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.metrics.adjusted_rand_score", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 108, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 113, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.dendrogram", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "seaborn.clustermap", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 127, "usage_type": "call"}, {"api_name": "sklearn.cluster.DBSCAN", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}]} +{"seq_id": "35734439200", "text": "from django.test import TestCase\nfrom katalog.models import CatalogItem\n\n# Create your tests here.\nclass katalogTest(TestCase):\n def setUp(self):\n CatalogItem.objects.create(item_name=\"Iphone XR\", item_price=4000000, item_stock=100,\n description=\"New Iphone\", rating=5, item_url=\"https://www.tokopedia.com/spiritcellular-1/iphone-xr-64gb-second-e-x-inter-original-no-minus-fullset-kuning?extParam=ivf%3Dfalse&src=topads\")\n\n def test_is_dummy_valid(self):\n iphone_XR = CatalogItem.objects.get(item_name=\"Iphone XR\", item_price=4000000, item_stock=100,\n description=\"New Iphone\", rating=5, item_url=\"https://www.tokopedia.com/spiritcellular-1/iphone-xr-64gb-second-e-x-inter-original-no-minus-fullset-kuning?extParam=ivf%3Dfalse&src=topads\")\n self.assertEqual(iphone_XR.item_name, \"Iphone XR\")\n self.assertEqual(iphone_XR.item_price, 4000000)\n self.assertEqual(iphone_XR.item_stock, 100)\n self.assertEqual(iphone_XR.description, \"New Iphone\")\n self.assertEqual(iphone_XR.rating, 5)\n self.assertEqual(iphone_XR. item_url, \"https://www.tokopedia.com/spiritcellular-1/iphone-xr-64gb-second-e-x-inter-original-no-minus-fullset-kuning?extParam=ivf%3Dfalse&src=topads\")", "repo_name": "gabiiing/pbp-tugas-2-gabing", "sub_path": "katalog/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.test.TestCase", "line_number": 5, "usage_type": "name"}, {"api_name": "katalog.models.CatalogItem.objects.create", "line_number": 7, "usage_type": "call"}, {"api_name": "katalog.models.CatalogItem.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "katalog.models.CatalogItem", "line_number": 7, "usage_type": "name"}, {"api_name": "katalog.models.CatalogItem.objects.get", "line_number": 11, "usage_type": "call"}, {"api_name": "katalog.models.CatalogItem.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "katalog.models.CatalogItem", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "24410782172", "text": "import time\nimport sys\nfrom azure.servicebus import ServiceBusService\n\ninfile = open(\"tempOutput.txt\", \"r\")\ntemp = infile.readline().rstrip()\n#print('received temp of: ' + temp)\ntemp = int(temp)\n\nkey_name = \"sendRule\"\nkey_value = \"9SWS0sNEBQMfTmuBHlxFwUHBFMSBgmJ77/ICSRm9HK4=\"\n\nsbs = ServiceBusService(\"pimessage-ns\",shared_access_key_name=key_name, shared_access_key_value=key_value)\nif temp > 65 or temp < 30:\n# print('sending temp of:' + temp)\n sbs.send_event('pimessage', '{ \"DeviceId\": \"smokerpi\", \"Temperature\": temp }')\n print('sent!')\n print ('got here')\nelse:\n print('temp was in normal range')\n", "repo_name": "hncshtq/CSC450-Software-Engineering-Project-", "sub_path": "sendTemp.py", "file_name": "sendTemp.py", "file_ext": "py", "file_size_in_byte": 619, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "azure.servicebus.ServiceBusService", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "43313586918", "text": "import os\n\nfrom flask import Flask, jsonify # make_response, request, url_for\n\nfrom redis import Redis\n\nfrom rq import Queue\n\nfrom utils import create_task_id\n\n\napp = Flask(__name__)\napp.config[\"DEBUG\"] = True\napp.config[\"REDIS_URL\"] = os.environ.get('REDIS_URL') or 'redis://'\napp.redis = Redis.from_url(app.config['REDIS_URL'])\napp.task_queue = Queue('parsing-tasks', connection=app.redis)\ntasks = {}\n\n\n@app.route('/api/', methods=['POST'])\ndef create_task(url: str) -> str:\n tid = create_task_id(url)\n job = app.task_queue.enqueue('tasks.parse', tid, url)\n tasks[tid] = job\n return jsonify({\"id\": tid})\n\n\n@app.route('/api/', methods=['GET'])\ndef get_task_status(tid: str) -> str:\n if tid not in tasks:\n return jsonify({\"status\": \"Not started\"})\n job = tasks[tid]\n job.refresh()\n return jsonify(job.meta)\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0')\n", "repo_name": "Antoine-Poincare/timeweb.com", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "redis.Redis.from_url", "line_number": 15, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 15, "usage_type": "name"}, {"api_name": "rq.Queue", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.create_task_id", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "10279270571", "text": "import os\nimport sys\nimport glob\nimport argparse\nimport tarfile\nimport tempfile\nimport logging\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom astropy.io import fits\n\nfrom paths import ARCHIVE_PATH, OUTPUT_PATH\n\nlog = logging.getLogger(__name__)\npd.options.mode.chained_assignment = None\n\nqd_map = {\n 0: 2009131105131,\n 1: 2009166043257,\n 2: 2009259160929,\n 3: 2009350155506,\n 4: 2010078095331,\n 5: 2010174085026,\n 6: 2010265121752,\n 7: 2010355172524,\n 8: 2011073133259,\n 9: 2011177032512,\n 10: 2011271113734,\n 11: 2012004120508,\n 12: 2012088054726,\n 13: 2012179063303,\n 14: 2012277125453,\n 15: 2013011073258,\n 16: 2013098041711,\n 17: 2013131215648,\n}\n\n\ndef do_lookup_table(\n folder=\"0007\",\n quarter=5,\n fits_path=f\"{ARCHIVE_PATH}/data/kepler/tpf\",\n tar_archive=True,\n quiet=False,\n):\n\n if not tar_archive:\n print(\n \"%s/%s/*/kplr*-%s_lpd-targ.fits.gz\"\n % (fits_path, folder, str(qd_map[quarter]))\n )\n tpfs_ = np.sort(\n glob.glob(\n \"%s/%s/*/kplr*-%s_lpd-targ.fits.gz\"\n % (fits_path, folder, str(qd_map[quarter]))\n )\n )\n log.info(f\"Total number of TPFs in {folder}: {tpfs_.shape[0]}\")\n if len(tpfs_) == 0:\n raise ValueError(\"No TPFs for selected quarter %i\" % quarter)\n\n tpfs, channels, quarters, ras, decs, cols, rows = np.array(\n [\n [\n f.split(\"tpf/\")[-1],\n fits.getheader(f, ext=0)[\"CHANNEL\"],\n fits.getheader(f, ext=0)[\"QUARTER\"],\n fits.getheader(f, ext=0)[\"RA_OBJ\"],\n fits.getheader(f, ext=0)[\"DEC_OBJ\"],\n fits.getheader(f, ext=1)[\"1CRV5P\"],\n fits.getheader(f, ext=1)[\"2CRV5P\"],\n ]\n for f in tpfs_\n ]\n ).T\n else:\n tarlist = np.sort(glob.glob(\"%s/%s/%s_*.tar\" % (fits_path, folder, folder)))\n log.info(f\"Total number of tarballs in {folder}/: {tarlist.shape[0]}\")\n if len(tarlist) == 0:\n raise ValueError(f\"No TPFs for selected folder {folder}\")\n tpfs, channels, quarters, ras, decs, cols, rows = [], [], [], [], [], [], []\n with tempfile.TemporaryDirectory(prefix=\"temp_fits\") as tmpdir:\n for tarf in tqdm(tarlist, desc=\"Reading headers\", disable=quiet):\n kic = tarf.split(\".\")[0].split(\"_\")[-1]\n fname = f\"{kic[:4]}/{kic}/kplr{kic}-{qd_map[quarter]}_lpd-targ.fits.gz\"\n try:\n tarfile.open(tarf, mode=\"r\").extract(fname, tmpdir)\n except KeyError:\n continue\n except tarfile.ReadError:\n log.info(f\"tar file fail {tarf}\")\n continue\n tpfs.append(fname)\n header = fits.getheader(f\"{tmpdir}/{fname}\", ext=0)\n channels.append(header[\"CHANNEL\"])\n quarters.append(header[\"QUARTER\"])\n ras.append(header[\"RA_OBJ\"])\n decs.append(header[\"DEC_OBJ\"])\n header = fits.getheader(f\"{tmpdir}/{fname}\", ext=1)\n cols.append(header[\"1CRV5P\"])\n rows.append(header[\"2CRV5P\"])\n\n df = pd.DataFrame(\n [tpfs, quarters, channels, ras, decs, cols, rows],\n index=[\"file_name\", \"quarter\", \"channel\", \"ra\", \"dec\", \"col\", \"row\"],\n ).T\n df.channel = df.channel.astype(np.int8)\n df.quarter = df.quarter.astype(np.int8)\n\n dir_name = f\"{OUTPUT_PATH}/support/\"\n if not os.path.isdir(dir_name):\n os.makedirs(dir_name)\n file_name = \"%s/kepler_tpf_map_%s_q%02i%s.csv\" % (\n dir_name,\n folder,\n quarter,\n \"_tar\" if tar_archive else \"\",\n )\n df.to_csv(file_name)\n\n\ndef concatenate(quarter, tar_archive=True):\n\n log.info(\"Concatenating all lookup tables...\")\n f_list = np.sort(\n glob.glob(\n \"%s/support/kepler_tpf_map_*_q%02i%s.csv\"\n % (OUTPUT_PATH, quarter, \"_tar\" if tar_archive else \"\")\n )\n )\n if len(f_list) == 0:\n raise FileExistsError(\"No files to concatenate\")\n dfs = pd.concat([pd.read_csv(f, index_col=0) for f in f_list], axis=0)\n\n file_name = \"%s/support/kepler_tpf_map_q%02i%s.csv\" % (\n OUTPUT_PATH,\n quarter,\n \"_tar\" if tar_archive else \"\",\n )\n log.info(f\"Output file: {file_name}\")\n dfs.reset_index(drop=True).to_csv(file_name)\n for f in f_list:\n os.remove(f)\n\n\ndef sort_tpfs_in_all_channel(quarter, tar_archive=True, ncols_start=4):\n\n file_name = \"%s/support/kepler_tpf_map_q%02i%s.csv\" % (\n OUTPUT_PATH,\n quarter,\n \"_tar\" if tar_archive else \"\",\n )\n lkp_tbl = pd.read_csv(file_name, index_col=0)\n\n bins = [5, 4, 3, 2, 1]\n sorted_lkp_tbl = []\n log.info(f\"Working with Quarter {quarter}\")\n for ch in tqdm(range(1, 85), total=84, disable=False):\n files_in = lkp_tbl.query(\"channel == %i and quarter == %i\" % (ch, quarter))\n if len(files_in) == 0:\n continue\n log.info(f\"Channel {ch} total TPFS {len(files_in)}\")\n if len(files_in) < 1500:\n ncols = ncols_start - 1\n if len(files_in) < 550:\n ncols = 2\n else:\n ncols = ncols_start\n log.info(f\"Ncols {ncols}\")\n bn = ncols\n sorted_ch = []\n col_size = 1112 // bn\n row_size = 1044 // bn\n bn_row_org = np.arange(bn)\n bn_col = np.arange(bn)\n for i, x in enumerate(range(bn)):\n if i % 2 == 1:\n bn_row = bn_row_org[::-1]\n else:\n bn_row = bn_row_org\n for y in range(bn):\n\n in_cell = files_in.query(\n f\"col >= {bn_col[x]*col_size} and col <= {(bn_col[x]+1)*col_size} and \"\n f\"row >= {bn_row[y]*row_size} and row <= {(bn_row[y]+1)*row_size}\"\n )\n sorted_ch.append(in_cell.sort_values([\"row\"], ascending=i % 2 == 0))\n\n sorted_ch = pd.concat(sorted_ch).reset_index(drop=True).drop_duplicates()\n\n df_with_batch = sort_tpfs_in_channel(sorted_ch, ncols=ncols, batch_size=200)\n sorted_lkp_tbl.append(df_with_batch)\n log.info(\"####\" * 10)\n\n sort_tpfs_in_all_channel = (\n pd.concat(sorted_lkp_tbl).reset_index(drop=True).drop_duplicates()\n )\n if sort_tpfs_in_all_channel.shape[0] != lkp_tbl.shape[0]:\n raise RuntimeError(\"Missing TPFs\")\n sort_tpfs_in_all_channel.to_csv(file_name.replace(\".csv\", \"_new.csv\"))\n\n return\n\n\ndef do_batches_in_col(df, batch_size=200, tolerance=0.5):\n\n if len(df) >= 170:\n left = len(df) % batch_size\n\n if left / batch_size < 0.1:\n pass\n elif left / batch_size < tolerance:\n while (len(df) % batch_size) / batch_size > 0.1:\n batch_size += 1\n elif left / batch_size > tolerance:\n while (len(df) % batch_size) / batch_size > 0.1 and batch_size > 170:\n batch_size -= 1\n tot_b = len(df) // batch_size\n else:\n batch_size = len(df)\n tot_b = 1\n\n log.info(f\"Batch size and total in column {batch_size} {tot_b}\")\n aux = np.zeros(len(df))\n batch_index = np.hstack([np.ones(batch_size) * (k + 1) for k in range(tot_b)])\n aux[: len(batch_index)] = batch_index\n aux[aux == 0] = np.max(batch_index)\n df.loc[:, \"batch\"] = aux\n\n return df\n\n\ndef sort_tpfs_in_channel(df, ncols=4, batch_size=200):\n if len(df) >= 400:\n col_lims = np.linspace(0, 1112, ncols + 1)\n else:\n col_lims = np.array([0, np.median(df.col), 1113])\n sort_new = []\n prev_batch = 0\n for x in range(len(col_lims) - 1):\n in_col = df.query(f\"col >= {col_lims[x]} and col < {col_lims[x + 1]}\")\n log.info(f\"TPFs in column {x+1} {len(in_col)}\")\n in_col_sorted = do_batches_in_col(in_col, batch_size=batch_size)\n aux = in_col_sorted[\"batch\"].max()\n in_col_sorted.loc[:, \"batch\"] += prev_batch\n sort_new.append(in_col_sorted)\n\n prev_batch += aux\n\n return pd.concat(sort_new, axis=0).reset_index(drop=True)\n\n\ndef how_many_batches(quarter, batch_size):\n file_name = \"%s/support/kepler_tpf_map_all_q%02i.csv\" % (OUTPUT_PATH, quarter)\n df = pd.read_csv(file_name, index_col=0)\n\n channels = np.arange(1, 85)\n number_batch, nsources = [], []\n for ch in channels:\n in_channel = df.query(\"channel == %i\" % ch)\n nsources.append(in_channel.shape[0])\n number_batch.append(int(np.ceil(in_channel.shape[0] / batch_size)))\n df_nb = pd.DataFrame(\n np.vstack([channels, nsources, number_batch]).T,\n columns=[\"channel\", \"n_sources\", \"n_batch\"],\n )\n\n file_name = \"%s/support/kepler_tpf_nbatches_bs%03i_q%02i.csv\" % (\n OUTPUT_PATH,\n batch_size,\n quarter,\n )\n df_nb.set_index(\"channel\").to_csv(file_name)\n\n\ndef how_many_tpfs(tar_archive=True):\n df = pd.DataFrame(\n np.zeros((18, 84), dtype=int), index=np.arange(0, 18), columns=np.arange(1, 85)\n )\n for q in df.index:\n file_name = \"%s/support/kepler_tpf_map_q%02i%s.csv\" % (\n OUTPUT_PATH,\n q,\n \"_tar\" if tar_archive else \"\",\n )\n if not os.path.isfile(file_name):\n log.info(f\"Warning: no file map for quarter {q}\")\n continue\n map = pd.read_csv(file_name, index_col=0)\n for ch in df.columns:\n df.loc[q, ch] = map.query(f\"channel == {ch}\").shape[0]\n\n file_name = \"%s/support/kepler_ntpf_qch.csv\" % (OUTPUT_PATH)\n df.to_csv(file_name)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\n description=\"Create lookup tables with FITS file path and creates batches\"\n )\n parser.add_argument(\n \"--folder\",\n dest=\"folder\",\n type=str,\n default=\"0007\",\n help=\"First level folder name of Kepler archive directory.\",\n )\n parser.add_argument(\n \"--quarter\",\n dest=\"quarter\",\n type=int,\n default=5,\n help=\"First level folder name of Kepler archive directory.\",\n )\n parser.add_argument(\n \"--path\",\n dest=\"path\",\n type=str,\n default=\"/Volumes/jorge-marpa-personal/work/data/kepler/tpf\",\n help=\"Kepler archive path.\",\n )\n parser.add_argument(\n \"--batch-size\",\n dest=\"batch_size\",\n type=int,\n default=200,\n help=\"Batch size\",\n )\n parser.add_argument(\n \"--concat\",\n dest=\"concat\",\n action=\"store_true\",\n default=False,\n help=\"Concatenate all lookup tables in a quarter.\",\n )\n parser.add_argument(\n \"--sort\",\n dest=\"sort\",\n action=\"store_true\",\n default=False,\n help=\"Sort TPFs.\",\n )\n parser.add_argument(\n \"--sum-tpfs\",\n dest=\"sum_tpfs\",\n action=\"store_true\",\n default=False,\n help=\"Computen number of batches per channel/quarter.\",\n )\n parser.add_argument(\n \"--tar-tpfs\",\n dest=\"tar_archive\",\n action=\"store_true\",\n default=False,\n help=\"Is archive in tarball files.\",\n )\n parser.add_argument(\"--log\", dest=\"log\", default=0, help=\"Logging level\")\n args = parser.parse_args()\n # set verbose level for logger\n try:\n args.log = int(args.log)\n except:\n args.log = str(args.log.upper())\n FORMAT = \"%(filename)s:%(lineno)s : %(message)s\"\n h2 = logging.StreamHandler(sys.stderr)\n h2.setFormatter(logging.Formatter(FORMAT))\n log.addHandler(h2)\n log.setLevel(args.log)\n log.info(vars(args))\n\n if args.concat:\n concatenate(args.quarter, tar_archive=args.tar_archive)\n sort_tpfs_in_all_channel(\n args.quarter, tar_archive=args.tar_archive, ncols_start=4\n )\n elif args.sort:\n sort_tpfs_in_all_channel(\n args.quarter, tar_archive=args.tar_archive, ncols_start=4\n )\n elif args.sum_tpfs:\n how_many_tpfs(tar_archive=args.tar_archive)\n else:\n do_lookup_table(\n folder=args.folder,\n quarter=args.quarter,\n fits_path=args.path,\n tar_archive=args.tar_archive,\n quiet=True if args.log in [0, \"0\", \"NOTSET\"] else False,\n )\n log.info(\"Done!\")\n", "repo_name": "jorgemarpa/kepler-workflow", "sub_path": "kepler_workflow/make_archive_lookup_table.py", "file_name": "make_archive_lookup_table.py", "file_ext": "py", "file_size_in_byte": 12418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.options", "line_number": 16, "usage_type": "attribute"}, {"api_name": "paths.ARCHIVE_PATH", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 53, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 67, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 67, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 68, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 68, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 69, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 69, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 70, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 70, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 71, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 71, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 72, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 78, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 78, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 83, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 84, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 88, "usage_type": "call"}, {"api_name": "tarfile.ReadError", "line_number": 91, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getheader", "line_number": 95, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 95, "usage_type": "name"}, {"api_name": "astropy.io.fits.getheader", "line_number": 100, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 100, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 109, "usage_type": "attribute"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 126, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 127, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 129, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 134, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 137, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 144, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 150, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 154, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 190, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 238, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 251, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 255, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 263, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 265, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 270, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 279, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 283, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 290, "usage_type": "call"}, {"api_name": "paths.OUTPUT_PATH", "line_number": 294, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 300, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 367, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 367, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 368, "usage_type": "call"}]} +{"seq_id": "13822221057", "text": "# chart/urls.py\nfrom django.contrib import admin\nfrom django.urls import path\nfrom chart import views # !!!\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('ticket-class/',\n views.ticket_class_view, name='ticket_class_view'),\n path('world-population/',\n views.world_population, name='world_population'), # !!!\n path('covid_cases/',\n views.covid_cases, name='covid_cases'),\n path('covid_cases_per_capita/',\n views.covid_cases_per_capita, name='covid_cases_per_capita'),\n path('admin/', admin.site.urls),\n]\n", "repo_name": "serin0911/h_chart", "sub_path": "chart/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "chart.views.home", "line_number": 7, "usage_type": "attribute"}, {"api_name": "chart.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "chart.views.ticket_class_view", "line_number": 9, "usage_type": "attribute"}, {"api_name": "chart.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "chart.views.world_population", "line_number": 11, "usage_type": "attribute"}, {"api_name": "chart.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "chart.views.covid_cases", "line_number": 13, "usage_type": "attribute"}, {"api_name": "chart.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "chart.views.covid_cases_per_capita", "line_number": 15, "usage_type": "attribute"}, {"api_name": "chart.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "11311730139", "text": "from unittest.util import _MAX_LENGTH\nfrom django.db import models\nfrom django.urls import reverse\nimport pandas as pd\nfrom sqlalchemy import create_engine\nfrom datetime import datetime\nfrom django import forms\n\n\nclass Post(models.Model):\n manufacture_date = models.DateField(default =None)\n stencil_number = models.CharField (max_length=3, default=None)\n revision = models.CharField (max_length=2, default=None)\n ZLNumber = models.CharField (max_length=10, default=None)\n material = models.CharField (max_length=15, default=None)\n manufacture_number = models.CharField (max_length=10, default=None)\n thickness = models.CharField (max_length=5, default=None)\n author = models.CharField (max_length=7, default =None)\n\n\n def __str__(self):\n return self.manufacture_date, self.stencil_number, self.revision, self.ZLNumber, self.material, self.manufacture_number, self.thickness, self.author\n \n\n def get_absolute_url(self):\n return reverse(\"post_detail\", args=[str(self.id)])\n\n def save(self, *args, **kwargs):\n self.extra_field = \"extra field\"\n #print(self.manufacture_date, self.stencil_number, self.revision, self.ZLNumber, self.material, self.manufacture_number, self.thickness, self.author)\n super().save(*args, **kwargs)\n mystring = f'{str(self.manufacture_date)},{self.stencil_number},{self.revision},{self.ZLNumber},{self.material},{self.manufacture_number},{self.thickness},{self.author}'\n\n print(mystring)\n\n\n try:\n stringSplit = mystring.split(\",\")\n\n\n dateofmanufacture = stringSplit[0]\n stencilNumber = stringSplit[1]\n revision = stringSplit[2]\n ZLNum = stringSplit[3]\n material = stringSplit[4]\n manuSN = stringSplit[5]\n thickness = stringSplit[6]\n\n\n mydict = {'DateofManufacturer': dateofmanufacture, 'StencilNumber': stencilNumber, 'Revision': revision, 'ZLNumber': ZLNum, 'Material': material, 'ManufacturerNumber': manuSN, 'Thickness': thickness}\n \n\n df = pd.DataFrame.from_dict(mydict, orient='index')\n df = df.transpose()\n print('Test_1')\n\n #SQL Connection Windows Authentication#\n\n Server = 'UKC-VM-SQL01'\n Database = 'ToolBank'\n Driver = 'ODBC Driver 17 for SQL Server'\n Database_con = f'mssql://@{Server}/{Database}?driver={Driver}'\n print('Test_2')\n\n engine = create_engine(Database_con)\n print('Test_2.5')\n con = engine.connect()\n print('Test_3')\n\n\n df.to_sql('Stencil_Bank', con, if_exists='append', index = False)\n print(f'STENCIL LOGGED TO SQL at {datetime.now()}')\n\n\n\n except Exception as exc:\n print(f'ERROR CONNECTING TO SQL:{exc}')\n", "repo_name": "JamesB-lab/SCUBALOG_2", "sub_path": "blog/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.db.models.Model", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sqlalchemy.create_engine", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "41563747298", "text": "import os\nimport pytest\nimport sqlite3\nfrom pathlib import Path\n\nfrom youtube_transcriber.datapipeline import DataPipeline\nfrom youtube_transcriber.datapipeline import create_hardcoded_data_pipeline\nfrom youtube_transcriber.preprocessing.youtubevideopreprocessor import YoutubeVideoPreprocessor\nfrom youtube_transcriber.loading.loaderiterator import LoaderIterator\nfrom youtube_transcriber.loading.serialization import JsonSerializer\nfrom youtube_transcriber.transforming.addtitletransform import AddTitleTransform\nfrom youtube_transcriber.transforming.adddescriptiontransform import AddDescriptionTransform\nfrom youtube_transcriber.transforming.whispertransform import WhisperTransform\nfrom youtube_transcriber.transforming.batchtransformer import BatchTransformer\nfrom youtube_transcriber.storing.sqlitebatchvideostorer import SQLiteBatchVideoStorer\nfrom youtube_transcriber.storing.sqlitecontextmanager import SQLiteContextManager\nfrom youtube_transcriber.storing.createdb import create_db\n\n@pytest.fixture\ndef expected_db_output():\n return [\n (\"Tquotes\",\n \"https://www.youtube.com/watch?v=NSkoGZ8J1Ag\",\n \"Steve Jobs quotes Bob Dylan\", \n \" Good morning. Good morning and welcome to Apple's 1984 annual shareholders meeting. I'd like to open the meeting with a part of an old poem about a 20-year-old poem by Dylan. That's Bob Dylan. Come writers and critics who prophesize with your pens and keep your eyes wide, the chance won't come again. And don't speak too soon for the wheels still in spin. And there's no telling who that it's naming. For the loser now will be later to win for the times they are a change in. Now.\"),\n (\"changminjen\",\n \"https://www.youtube.com/watch?v=Ak516vtDTEA\",\n \"My allegiance is to the Republic, to democracy!\", \n \" I have brought peace, freedom, justice and security to my new empire. Your new empire don't make me kill you. Anakin, my allegiance is to the Republic, to democracy! If you're not with me, then you're my enemy. Only a Sith deals an absolute.\")\n ]\n\n@pytest.fixture\ndef data_pipeline():\n loader_iterator = LoaderIterator(JsonSerializer(), 2)\n batch_transformer = BatchTransformer([AddTitleTransform(),\n AddDescriptionTransform(),\n WhisperTransform()])\n video_storer = SQLiteBatchVideoStorer()\n sqlite_context_manager = SQLiteContextManager(\"dummy.db\")\n return DataPipeline(loader_iterator,\n batch_transformer,\n video_storer,\n sqlite_context_manager)\n\ndef test_datapipeline_init():\n data_pipeline = DataPipeline(\"loader_iterator\",\n \"transformer\",\n \"storer\",\n \"context\")\n assert type(data_pipeline) == DataPipeline\n assert data_pipeline.loader_iterator == \"loader_iterator\"\n assert data_pipeline.batch_transformer == \"transformer\"\n assert data_pipeline.storer == \"storer\"\n assert data_pipeline.sqlite_context_manager == \"context\"\n \ndef test_process_files(data_pipeline, expected_db_output):\n test_folder = Path.home()/\"whisper_gpt_pipeline/youtube_transcriber/test\"\n files = [Path(test_folder/\"files/6.json\"), Path(test_folder/\"files/7.json\")]\n try:\n create_db(\"dummy.db\")\n connection = sqlite3.connect(\"dummy.db\")\n cursor = connection.cursor()\n \n data_pipeline.process(files)\n \n cursor.execute(\"SELECT CHANNEL_NAME, URL, TITLE, TRANSCRIPTION FROM VIDEO\")\n videos = cursor.fetchall()\n \n for i in range(len(videos)):\n assert videos[i][0] == expected_db_output[i][0]\n assert videos[i][1] == expected_db_output[i][1]\n assert videos[i][2] == expected_db_output[i][2]\n assert videos[i][3] == expected_db_output[i][3]\n finally:\n os.remove(\"dummy.db\")\n\ndef test_process_video_batch(data_pipeline, expected_db_output):\n video_data = [\n {\n \"channel_name\": \"Tquotes\",\n \"url\": \"https://www.youtube.com/watch?v=NSkoGZ8J1Ag\",\n },\n {\n \"channel_name\": \"changminjen\",\n \"url\": \"https://www.youtube.com/watch?v=Ak516vtDTEA\",\n }\n ]\n try:\n create_db(\"dummy.db\")\n connection = sqlite3.connect(\"dummy.db\")\n cursor = connection.cursor()\n\n data_pipeline._process_video_batch(cursor, video_data)\n\n cursor.execute(\"SELECT CHANNEL_NAME, URL, TITLE, TRANSCRIPTION FROM VIDEO\")\n videos = cursor.fetchall()\n\n for i in range(len(videos)):\n assert videos[i][0] == expected_db_output[i][0]\n assert videos[i][1] == expected_db_output[i][1]\n assert videos[i][2] == expected_db_output[i][2]\n assert videos[i][3] == expected_db_output[i][3]\n finally:\n os.remove(\"dummy.db\")\n \ndef test_hardcoded_data_pipeline_is_instantiated():\n data_pipeline = create_hardcoded_data_pipeline()\n assert type(data_pipeline) == DataPipeline ", "repo_name": "juancopi81/youtube-transcriber", "sub_path": "youtube_transcriber/test/test_datapipeline.py", "file_name": "test_datapipeline.py", "file_ext": "py", "file_size_in_byte": 5116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pytest.fixture", "line_number": 19, "usage_type": "attribute"}, {"api_name": "youtube_transcriber.loading.loaderiterator.LoaderIterator", "line_number": 34, "usage_type": "call"}, {"api_name": "youtube_transcriber.loading.serialization.JsonSerializer", "line_number": 34, "usage_type": "call"}, {"api_name": "youtube_transcriber.transforming.batchtransformer.BatchTransformer", "line_number": 35, "usage_type": "call"}, {"api_name": "youtube_transcriber.transforming.addtitletransform.AddTitleTransform", "line_number": 35, "usage_type": "call"}, {"api_name": "youtube_transcriber.transforming.adddescriptiontransform.AddDescriptionTransform", "line_number": 36, "usage_type": "call"}, {"api_name": "youtube_transcriber.transforming.whispertransform.WhisperTransform", "line_number": 37, "usage_type": "call"}, {"api_name": "youtube_transcriber.storing.sqlitebatchvideostorer.SQLiteBatchVideoStorer", "line_number": 38, "usage_type": "call"}, {"api_name": "youtube_transcriber.storing.sqlitecontextmanager.SQLiteContextManager", "line_number": 39, "usage_type": "call"}, {"api_name": "youtube_transcriber.datapipeline.DataPipeline", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 32, "usage_type": "attribute"}, {"api_name": "youtube_transcriber.datapipeline.DataPipeline", "line_number": 46, "usage_type": "call"}, {"api_name": "youtube_transcriber.datapipeline.DataPipeline", "line_number": 50, "usage_type": "name"}, {"api_name": "pathlib.Path.home", "line_number": 57, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 57, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 58, "usage_type": "call"}, {"api_name": "youtube_transcriber.storing.createdb.create_db", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 61, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 75, "usage_type": "call"}, {"api_name": "youtube_transcriber.storing.createdb.create_db", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 90, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 104, "usage_type": "call"}, {"api_name": "youtube_transcriber.datapipeline.create_hardcoded_data_pipeline", "line_number": 107, "usage_type": "call"}, {"api_name": "youtube_transcriber.datapipeline.DataPipeline", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "15050913615", "text": "from typing import List\n\nfrom line import Line, parse_bingo, BingoGame\nfrom utils.readlines import read_lines\n\ninput_lines = read_lines(Line, 'input.in')\n\n\ndef get_solution(lines: List[Line]) -> str:\n nums = lines[0].raw_line.split(\",\")\n nums = [int(num) for num in nums]\n\n lines = lines[2:]\n i = 0\n bingos = []\n while i < len(lines):\n bingos.append(parse_bingo(lines))\n lines = lines[6:]\n\n game = BingoGame(bingos)\n\n i = 0\n last_num = nums[0]\n while not game.check_bingos():\n game.mark(nums[i])\n last_num = nums[i]\n i += 1\n\n winning_bingo = game.get_winning_bingo()\n sum_unmarked = sum(winning_bingo.get_unmarked_numbers())\n\n solution = last_num * sum_unmarked\n return str(solution)\n\n\nprint(get_solution(input_lines))", "repo_name": "MarekChleb/advent-of-code", "sub_path": "2021/ex4/a.py", "file_name": "a.py", "file_ext": "py", "file_size_in_byte": 795, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "utils.readlines.read_lines", "line_number": 6, "usage_type": "call"}, {"api_name": "line.Line", "line_number": 6, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "line.Line", "line_number": 9, "usage_type": "name"}, {"api_name": "line.parse_bingo", "line_number": 17, "usage_type": "call"}, {"api_name": "line.BingoGame", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "27142552208", "text": "from django.urls import path\n\nfrom main.views import MineView, NewTransationView, TrainView, NodeResolveView, NodeRegisterView\n\nurlpatterns = [\n path('mine/', MineView.as_view()),\n path('transaction/new/', NewTransationView.as_view()),\n path('train/', TrainView.as_view()),\n path('nodes/resolve/', NodeResolveView.as_view()),\n path('nodes/register/', NodeRegisterView.as_view()),\n]\n", "repo_name": "sainipray/blockchain-django", "sub_path": "main/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "main.views.MineView.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "main.views.MineView", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "main.views.NewTransationView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "main.views.NewTransationView", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "main.views.TrainView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "main.views.TrainView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "main.views.NodeResolveView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "main.views.NodeResolveView", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "main.views.NodeRegisterView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "main.views.NodeRegisterView", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "568542732", "text": "import threading\nimport os\nfrom collections import deque\n\nclass SegmentTree:\n\n def __init__(self, data, combine_fn=lambda x,y: x+y, default_leaf_fn=lambda x: x, default_node_val=0):\n \"\"\"Create the segment tree for array data. Complexity: O(n).\"\"\"\n self._combine_fn = combine_fn\n self._default_leaf_fn = default_leaf_fn\n self._default_node_val = default_node_val\n\n self._len = len(data)\n self._size = _size = 1 << (self._len - 1).bit_length()\n\n self.data = [self._default_node_val] * (2 * self._size)\n self.data[self._size:self._size + self._len] = list(map(default_leaf_fn, data))\n for idx in range(self._size-1, 0, -1):\n # self.data[idx] = combine_fn(self.data[self._left(idx)], self.data[self._right(idx)])\n self.data[idx] = combine_fn(self.data[2 * idx], self.data[2 * idx + 1])\n\n # def _parent(self, idx):\n # return idx >> 1\n #\n # def _left(self, idx):\n # return 2 * idx\n #\n # def _right(self, idx):\n # return 2 * idx + 1\n\n def __getitem__(self, idx):\n return self.data[idx + self._size]\n\n def __setitem__(self, idx, value):\n idx += self._size\n self.data[idx] = self._default_leaf_fn(value)\n # self._fix_up_to_root(self._parent(idx))\n self._fix_up_to_root(idx >> 1)\n\n def __len__(self):\n return self._len\n\n def _fix_up_to_root(self, idx):\n \"\"\"Computes the value of each internal node, from the given one up to the root.\"\"\"\n combine_fn = self._combine_fn\n while idx >= 1:\n # self.data[idx] = combine_fn(self.data[self._left(idx)], self.data[self._right(idx)])\n self.data[idx] = combine_fn(self.data[2 * idx], self.data[2 * idx + 1])\n # idx = self._parent(idx)\n idx = idx >> 1\n\n def get_data(self):\n \"\"\"Returns a list of the values stored in the array\"\"\"\n return self.data[self._size:self._size + self._len]\n\n def update(self, idx, x):\n \"\"\"Update the element at index i to += x. Complexity: O(log n)\"\"\"\n idx += self._size\n self.data[idx] += x\n # self._fix_up_to_root(self._parent(idx))\n self._fix_up_to_root(idx >> 1)\n\n def _query(self, left, right, idx, lx, rx):\n \"\"\"Query subroutine: given node idx covering segment [lx, rx], query for segment [left, right]\"\"\"\n if right < lx or left > rx:\n return self._default_node_val # node's interval completely out of query interval\n elif left <= lx and rx <= right:\n return self.data[idx] # node's interval completely contained in query interval\n else:\n mid = (lx + rx) // 2\n # res_left = self._query(left, right, self._left(idx), lx, mid)\n res_left = self._query(left, right, 2 * idx, lx, mid)\n # res_right = self._query(left, right, self._right(idx), mid+1, rx)\n res_right = self._query(left, right, 2 * idx + 1, mid + 1, rx)\n return self._combine_fn(res_left, res_right)\n\n def query(self, left, right):\n \"\"\"Returns the sum of all the elements from index left to index right (inclusive). Complexity: O(log n)\"\"\"\n return self._query(left, right, 1, 0, self._size-1)\n\n def __repr__(self):\n return \"SegmentTree({0})\".format(self.data)\n\n\n\n# x and y - (sum, pref, suf, max_sum)\ndef combine(x, y):\n s = x[0] + y[0]\n pref = max(x[1], x[0]+y[1])\n suf = max(y[2], y[0]+x[2])\n max_sum = max(x[3], y[3], x[2]+y[1])\n return s, pref, suf, max_sum\n\n\nclass MaxSumSegmentTree(SegmentTree):\n\n def __init__(self, data):\n SegmentTree.__init__(self, data,\n combine_fn=combine,\n default_leaf_fn=lambda x: (x, max(x, 0), max(x, 0), max(x, 0)),\n default_node_val=(0, 0, 0, 0)\n )\n\nii = 0\n_inp = b''\n\ndef fast_num_reader():\n def read_char():\n global ii, _inp\n if ii >= len(_inp):\n _inp = os.read(0, 100000)\n # gc.collect()\n ii = 0\n if not _inp:\n return b''\n ii += 1\n return _inp[ii - 1]\n\n def read_int():\n c = read_char()\n if c == b'':\n return None\n if c == b'-'[0]:\n x = 0\n sign = 1\n else:\n x = c - b'0'[0]\n sign = 0\n c = read_char()\n while c >= b'0'[0]:\n x = 10 * x + c - b'0'[0]\n c = read_char()\n if c == b'\\r'[0]:\n read_char()\n return -x if sign else x\n\n while True:\n n = read_int()\n yield n\n if n is None:\n break\n\n\nimport io\nq = deque()\nout = io.StringIO()\n\nclass ProducerThread(threading.Thread):\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, verbose=None):\n super(ProducerThread,self).__init__()\n self.target = target\n self.name = name\n\n def run(self):\n reader = fast_num_reader()\n for item in iter(reader):\n # print(item)\n q.append(item)\n\n\nclass ConsumerThread(threading.Thread):\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, verbose=None):\n super(ConsumerThread,self).__init__()\n self.target = target\n self.name = name\n return\n\n def queue_reader(self):\n while True:\n if len(q) > 0:\n n = q.popleft()\n if n is None:\n break\n yield n\n else:\n out.write('x')\n\n def run(self):\n reader = self.queue_reader()\n\n n = next(reader)\n m = next(reader)\n\n a = [0] * n\n st = MaxSumSegmentTree(a)\n\n for i in range(n):\n st[i] = next(reader)\n\n out.write(str(st.query(0, n - 1)[3])+\"\\n\")\n\n for _ in range(m):\n i = next(reader)\n v = next(reader)\n st[i] = v\n out.write(str(st.query(0, n - 1)[3])+\"\\n\")\n\n\nif __name__ == '__main__':\n p = ProducerThread(name='producer')\n c = ConsumerThread(name='consumer')\n\n p.start()\n c.start()\n\n\n p.join()\n c.join()\n\n print(out.getvalue())", "repo_name": "x3mka/code-contests-python", "sub_path": "codeforces/edu/c273278a/tt.py", "file_name": "tt.py", "file_ext": "py", "file_size_in_byte": 6264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.read", "line_number": 111, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 145, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 146, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 148, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 162, "usage_type": "attribute"}]} +{"seq_id": "2543992749", "text": "# 显示相机的深度图和彩色图，效果和Intel配套软件相同\n# 【注】 相机的第一张图像会出现色彩失真的情况，但后续图像正常\n# \n\n\nimport pyrealsense2 as rs\nimport numpy as np\nimport cv2\nimport datetime\n\n# Configure depth and color streams\npipeline = rs.pipeline()\nconfig = rs.config()\nconfig.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)\nconfig.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)\nconfig.enable_stream(rs.stream.infrared, 1, 640, 480, rs.format.y8, 30)\nconfig.enable_stream(rs.stream.infrared, 2, 640, 480, rs.format.y8, 30)\n\n# Start streaming\npipeline.start(config)\n# 像素对齐使用 rs.align 模块\nalign = rs.align(rs.stream.color)\n\ntry:\n while True:\n\n # Wait for a coherent pair of frames: depth and color\n frames = pipeline.wait_for_frames()\n depth_frame = frames.get_depth_frame()\n color_frame = frames.get_color_frame()\n ir_frame_left = frames.get_infrared_frame(1)\n ir_frame_right = frames.get_infrared_frame(2)\n if not depth_frame or not color_frame:\n continue\n\n # Convert images to numpy arrays\n # np.asanyarray 会返回 ndarray 或者 ndarray的子类\n depth_image = np.asanyarray(depth_frame.get_data())\n color_image = np.asanyarray(color_frame.get_data())\n ir_left_image = np.asanyarray(ir_frame_left.get_data())\n ir_right_image = np.asanyarray(ir_frame_right.get_data())\n\n # Apply colormap on depth image (image must be converted to 8-bit per pixel first)\n depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.1), cv2.COLORMAP_JET)\n\n # Stack both images horizontally\n images1 = np.hstack((color_image, depth_colormap))\n images2 = np.hstack((ir_left_image, ir_right_image))\n image3 = cv2.addWeighted(color_image,0.7,depth_colormap,0.3,0)\n\n # 像素对齐\n aligned_frames = align.process(frames)\n aligned_depth_frame = aligned_frames.get_depth_frame()\n aligned_color_frame = aligned_frames.get_color_frame()\n if not aligned_color_frame or not aligned_depth_frame:\n continue\n\n aligned_depth_image = np.asanyarray(aligned_depth_frame.get_data())\n aligned_color_image = np.asanyarray(aligned_color_frame.get_data())\n\n aligned_depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(aligned_depth_image, alpha=0.1), cv2.COLORMAP_JET)\n image4 = cv2.addWeighted(aligned_color_image,0.7,aligned_depth_colormap,0.3,0)\n\n\n # Show images\n cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)\n cv2.imshow('RealSense', images1)\n # cv2.imshow(\"Display pic_irt\", images2)\n cv2.imshow(\"Merge image\",image3)\n cv2.imshow(\"Merge image_aligned\",image4)\n\n key = cv2.waitKey(1)\n # Press esc or 'q' to close the image window\n if key & 0xFF == ord('q') or key == 27:\n cv2.destroyAllWindows()\n ISOTIMEFORMAT = '%Y_%m_%d_%H_%M_%S'\n theTime = datetime.datetime.now().strftime(ISOTIMEFORMAT)\n cv2.imwrite(str(theTime)+'color_image_'+'.png',color_image)\n cv2.imwrite(str(theTime)+'depth_colormap_'+'.png',depth_colormap)\n cv2.imwrite(str(theTime)+'depth_'+'.png',depth_image)\n cv2.imwrite(str(theTime)+'merge'+'.png',image3)\n cv2.imwrite(str(theTime)+'aligned_depth_colormap'+'.png',aligned_depth_colormap)\n cv2.imwrite(str(theTime)+'aligned_color_image'+'.png',aligned_color_image)\n cv2.imwrite(str(theTime)+'aligned_depth_'+'.png',aligned_depth_image)\n cv2.imwrite(str(theTime)+'aligned_merge'+'.png',image4)\n break\n\nfinally:\n # Stop streaming\n pipeline.stop()", "repo_name": "sunshineharry/4dof_Gripper_controler", "sub_path": "深度对齐/save_aligned_image.py", "file_name": "save_aligned_image.py", "file_ext": "py", "file_size_in_byte": 3769, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pyrealsense2.pipeline", "line_number": 12, "usage_type": "call"}, {"api_name": "pyrealsense2.config", "line_number": 13, "usage_type": "call"}, {"api_name": "pyrealsense2.stream", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyrealsense2.format", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyrealsense2.stream", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pyrealsense2.format", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pyrealsense2.stream", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pyrealsense2.format", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pyrealsense2.stream", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pyrealsense2.format", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pyrealsense2.align", "line_number": 22, "usage_type": "call"}, {"api_name": "pyrealsense2.stream", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.asanyarray", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.WINDOW_AUTOSIZE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "28684072938", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function # (at top of module)\nimport sys\nimport re\nimport os\nfrom jsmin import jsmin\nfrom bs4 import BeautifulSoup\nfrom wiki_login import login\n\nWIKI_API_MAIN = \"http://wiki.tuniu.org\"\nWIKI_SHOW_CHILDREN_SUFFIX = \"&showChildren=true\"\nWIKI_API_ROOT_PAGE = WIKI_API_MAIN + \"/pages/viewpage.action?pageId=71367772\" + WIKI_SHOW_CHILDREN_SUFFIX\nHREF_REGEX = r'href=\"(.*)\"'\nURI_OUTPUT_DIR = '../data/'\n\nuri_set = set()\n\nsuffix_len = len(WIKI_SHOW_CHILDREN_SUFFIX)\n\n\n# 爬到所有有接口数据的html页面, 保存到文件里\ndef dfs_html(browser, uri):\n response = browser.open(uri)\n html_content = response.read()\n response.close()\n soup = BeautifulSoup(html_content, 'html.parser')\n spans = soup.findAll('span', attrs={'class': 'child-display'})\n if not spans:\n real_data_link = uri[0:-suffix_len]\n print(real_data_link)\n uri_set.add(real_data_link)\n return\n for span in spans:\n hrefs = span.findAll('a')\n if not hrefs:\n break\n for href in hrefs:\n dir_link = WIKI_API_MAIN + re.findall(HREF_REGEX, str(href))[0] + WIKI_SHOW_CHILDREN_SUFFIX\n dfs_html(browser, dir_link)\n\n\nif __name__ == '__main__':\n br = login()\n dfs_html(br, WIKI_API_ROOT_PAGE)\n output_dir = URI_OUTPUT_DIR\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n uri_file = output_dir + 'api_uri.txt'\n file_uri = open(uri_file, 'w')\n for uri in uri_set:\n file_uri.write(uri + '\\n')\n", "repo_name": "LionelWei/scrap_api_to_model", "sub_path": "src/html_scrap.py", "file_name": "html_scrap.py", "file_ext": "py", "file_size_in_byte": 1548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 38, "usage_type": "call"}, {"api_name": "wiki_login.login", "line_number": 43, "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.makedirs", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "7969926997", "text": "import urllib.request\nimport sys\nfrom datetime import date\ndefault_date=date(int(\"2019\"),int(\"01\"),int(\"01\")) #Python3 can't take 0 as the first char here due to octal interpretation\ndefault_equivalence=1546300800 \nprint(\"Welcome! Keep Starting-Ending dates and Stock Tickers handy as we proceed :) \\n\")\nfor i in (0,2):\n if i==0:\n input_taken='Starting'\n else:\n input_taken='Ending'\n \n rawInput=input(\"Enter the {} date in the YYYY-MM-DD format with no spaces between the hiphens: \".format(input_taken))\n try: \n YYYY,MM,DD=map(int,rawInput.split('-'))\n if i==False:\n starting_date=date(YYYY,MM,DD)\n delta=starting_date-default_date\n starting_equivalence=default_equivalence+(86400*delta.days) #This can be changed by yahoo to avoid scrapers\n else: #Just let me know if that happens we can compute it again ;)\n ending_date=date(YYYY, MM, DD)\n delta=ending_date-default_date\n ending_equivalence=default_equivalence+(86400*delta.days+86400)\n except:\n print(\"The format of the entered date was incorrect, the program terminates here. \\n\")\n sys.exit()\n\nif starting_equivalence>ending_equivalence: #You actually deserve to be confused by a HTTPS bad request error here. But I'm a good guy :)\n print(\"Starting Date cannot be after ending date \\n\")\n sys.exit()\n\nticker=input(\"Nice, now type in the ticker for the stock (All caps): \")\n\nurl=\"\"\"https://query1.finance.yahoo.com/v7/finance/download/\"\"\"+ticker+\"\"\"?period1=\"\"\"+str(starting_equivalence)+\"\"\"&period2=\"\"\"+str(ending_equivalence)+\"\"\"&interval=1d&events=history\"\"\"\n\nfile_name=input(\"What should we name the the downloaded csv file? (File name should have a .csv extension & Enter exact path if this isn't the desired download directory): \")\n\ntry: \n urllib.request.urlretrieve(url,file_name) \n print(\"Downloaded Successfully! \\n\")\nexcept:\n print(\"Something Went wrong, I'd request you to try again and recheck your ticker.\") #Invalid ticker, file name without csv and unstable internet connections are the possible issues here.\n", "repo_name": "pruhnuhv/Historical-Stock-Data", "sub_path": "Getdata.py", "file_name": "Getdata.py", "file_ext": "py", "file_size_in_byte": 2225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datetime.date", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 39, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "27122963716", "text": "import time\nimport json\nfrom slackclient import SlackClient\nfrom peewee import *\nimport threading as th\nimport requests\nimport pprint\nimport stellarnetwork\nimport db_explore as db\npp = pprint.PrettyPrinter(indent=4)\nfrom collections import defaultdict\nimport configparser\n\n\nimport stellarnetwork\n\n\nclass Bot1(object):\n\n def __init__(self,token):\n self.client = SlackClient(token)\n #self.user = 'U1KFBF9SN'\n self.user = 'U1N703XRP'\n #self.user = \n self.s = stellarnetwork.StellarNetwork()\n\n def _send_to_slack(self,msg):\n #self.client.api_call(\"chat.postMessage\",text=msg,channel=\"G1KF0R3PH\",type=\"message\",id=1)\n json_att = json.dumps([msg])\n self.client.api_call(\"chat.postMessage\",channel=\"C04FCJXG9\",parse=\"full\",attachments=json_att)\n\n def process_message(self,msg):\n #self.client.rtm_send_message('testing',json_msg)\n #self.client.rtm_send_message('testing',fut.result())\n for mm in msg:\n print(mm)\n if 'text' in mm and 'user' in mm:\n if 'user' != self.user:\n self.parse_message(mm['text'])\n #if 'type' in mm and mm['type'] == 'message' and 'user' in mm and mm['user'] != self.user:\n # print(mm)\n #self.send_to_agents(self.rd_requester,mm['text'])\n # self.send_to_agents(self.wne_requester,mm['text'])\n \n\n\n def nodes(self):\n self.s.update_nodes()\n node_ids = self.s.node_ids\n node_dict = {}\n for nn in node_ids:\n if len(nn) > 40:\n name = \"@\" + nn[0:6]\n else:\n name = nn\n ans = self.s.get_quorum(nn)\n if 'exception' in ans:\n node_dict[name] = 'missing'\n else:\n ledger = self.s.get_most_recent_ledger(ans)\n if ledger is None:\n node_dict[name] = 'missing'\n else:\n node_dict[name] = 'agree'\n\n num_nodes = len(self.s.node_names)\n items = list(node_dict.items())\n sorted_items = sorted(items, key=lambda v: v[1])\n node_status = \"\\n\".join([\"*{}*: _{}_\".format(ii,jj) for ii,jj in sorted_items])\n text = \"\\n*Ledger:* {}\".format(self.s.last_ledger) + \"\\n\\n(*Node name*: _status_)\\n\" + node_status\n\n\n att = {}\n att['title'] = \"Nodes: {}\".format(num_nodes)\n att['title_link'] = \"http://stellar.network\"\n att['text'] = text\n att['color'] = \"#000\"\n att['fallback'] = pp.pformat(node_dict)\n att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n return None\n\n\n\n\n\n\n def quorum(self,node_name):\n ans = self.s.get_quorum(node_name)\n if 'exception' in ans:\n att = {}\n att['title'] = \"Error\"\n att['title_link'] = \"http://stellar.network\"\n att['text'] = \"_I_ _thought_ _that_ _you_ _asked_ _me_ _about_ _a_ _node_ _named_ `{}`, _but_ _stellar-core_ _returned_ `{}`\".format(node_name, repr(ans))\n att['color'] = \"#ff0000\"\n att['fallback'] = pp.pformat(ans)\n att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n return None\n\n ledger = self.s.get_most_recent_ledger(ans)\n if node_name in self.s.node_names:\n pk = self.s.node_names[node_name]\n else:\n pk = node_name\n\n if ledger is None:\n\n att = {}\n att['title'] = \"Node: {}\".format(ans['node'])\n att['title_link'] = \"http://stellar.network\"\n att['text'] = \"*Public Key:* {}\\n*Status:* _missing_\".format(pk)\n att['color'] = \"#000\"\n att['fallback'] = pp.pformat(ans)\n att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n return None\n\n att = {}\n if ledger['missing']:\n miss = \", \".join(ledger['missing'])\n else:\n miss = \"\"\n qset = \", \".join(ledger['value']['v'])\n fw = \", \".join(ledger['fail_with'])\n att['title'] = \"Node: {}\".format(ans['node'])\n att['title_link'] = \"http://stellar.network\"\n att['color'] = \"#000\"\n att['text'] = \"*Ledger:* {}\\n\" \\\n \"*Public Key:* {}\\n\" \\\n \"*Quorum Set:* {}\\n\" \\\n \"*Missing:* {}\\n\" \\\n \"*Fail with:* {}\".format(self.s.last_ledger,\n pk,\n qset,\n miss,\n fw)\n att['fallback'] = pp.pformat(ans)\n att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n\n\n def send_error(self,error_text):\n att = {}\n att['title'] = \"_Error_\"\n att['title_link'] = \"http://stellar.network\"\n att['color'] = \"#000\"\n att['text'] = \"_{}_\".format(error_text)\n att['fallback'] = error_text\n #att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n return None\n\n def parse_message(self,text):\n tokens = text.split()\n if \"?nodes\" in [ii.lower() for ii in tokens]:\n self.nodes()\n\n\n if \"?node\" in tokens:\n idx = tokens.index('?node')\n try: \n node_name = tokens[idx + 1]\n self.quorum(node_name)\n except IndexError:\n self.send_error(\"Please enter a node name (e.g. ?node sdf_watcher1)\")\n\n if \"?offers\" in tokens:\n idx = tokens.index('?offers')\n try:\n buying_asset_code = tokens[idx + 1].upper()\n selling_asset_code = tokens[idx + 2].upper()\n except IndexError:\n self.send_error(\"Please enter two asset names (e.g. ?offers XLM JPY)\")\n return None\n\n self.offers(selling_asset_code, buying_asset_code)\n\n\n if \"?book\" in tokens:\n idx = tokens.index('?book')\n try: \n buying_asset_code = tokens[idx + 1].upper()\n selling_asset_code = tokens[idx + 2].upper()\n except:\n self.send_error(\"Please enter two asset names (e.g. ?book XLM XRP)\")\n \n self.offers(selling_asset_code, buying_asset_code)\n self.offers(buying_asset_code, selling_asset_code)\n\n\n def build_orderbook(self,offers):\n ob = defaultdict(int)\n buyingasset = offers[0]['buyingassetcode']\n sellingasset = offers[0]['sellingassetcode']\n for oo in offers:\n ob[oo['price']] += oo['amount']\n return ob\n\n def offers(self, selling_asset_code=None, buying_asset_code=None):\n if buying_asset_code == 'XLM':\n offers = db.get_offers(selling_asset_code, None)\n elif selling_asset_code == 'XLM':\n offers = db.get_offers(None, buying_asset_code)\n else:\n offers = db.get_offers(selling_asset_code, buying_asset_code)\n if offers:\n ob = self.build_orderbook(offers)\n textout = \"\\n\".join([\"*{}:* {}\".format(oo,ob[oo]) for oo in sorted(list(ob.keys())) ])\n att = {}\n att['title'] = \"Offers: {} (buy) <-- {} (sell)\".format(buying_asset_code, selling_asset_code)\n att['title_link'] = \"http://stellar.network\"\n att['color'] = \"#000\"\n att['text'] = textout\n att['fallback'] = pp.pformat(offers)\n #att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n else:\n att = {}\n att['title'] = \"Offers: {} (buy) <-- {} (sell)\".format(buying_asset_code, selling_asset_code)\n att['title_link'] = \"http://stellar.network\"\n att['color'] = \"#000\"\n att['text'] = \"_No_ _offers_ _found_\"\n att['fallback'] = \"No offers found\"\n #att['footer'] = \"http://stellar.network\"\n att['ts'] = int(time.time())\n #att['author_name'] = \"stellar-core\"\n att['mrkdwn_in'] = ['text', 'title', 'footer','author']\n self._send_to_slack(att)\n\n\n def _listen(self):\n if self.client.rtm_connect():\n #self.channels = self.client.server.channels.find('testing')\n #print(self.channels)\n while True:\n msg = self.client.rtm_read()\n self.process_message(msg)\n #time.sleep(1)\n\n\n\n\n\n def run(self):\n self.listen_thread = th.Thread(target=self._listen)\n self.listen_thread.start()\n\n\nif __name__ == '__main__':\n config = configparser.ConfigParser()\n config.read('bot.cfg')\n token = config['slack']['token']\n b = Bot1(token)\n b.run()\n", "repo_name": "sparrow-ai/stellar.network", "sub_path": "market_status_bot/bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 9767, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pprint.PrettyPrinter", "line_number": 10, "usage_type": "call"}, {"api_name": "slackclient.SlackClient", "line_number": 21, "usage_type": "call"}, {"api_name": "stellarnetwork.StellarNetwork", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 208, "usage_type": "call"}, {"api_name": "db_explore.get_offers", "line_number": 217, "usage_type": "call"}, {"api_name": "db_explore.get_offers", "line_number": 219, "usage_type": "call"}, {"api_name": "db_explore.get_offers", "line_number": 221, "usage_type": "call"}, {"api_name": "time.time", "line_number": 232, "usage_type": "call"}, {"api_name": "time.time", "line_number": 244, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 264, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "14412326272", "text": "#CATch is a simple game made with pygame \n#\nimport sys\nimport pygame\nimport math\nimport random\nfrom Food import Food \n\npygame.init()\n\n#Colors \nwhite = (255, 255, 255)\nblack = (0, 0, 0)\nred = (255, 0, 0)\ngreen = (0, 255, 0) \n\n#images\nbackground = pygame.image.load('background.jpg')\n\ncat_left = pygame.image.load('CAT_LEFT.png')\ncat_right = pygame.image.load('CAT_RIGHT.png')\n\nfood_burger = pygame.image.load('food_burger.png')\nfood_noodles = pygame.image.load('food_noodles.png')\nfood_pie = pygame.image.load('food_pie.png')\nfood_rice = pygame.image.load('food_rice.png')\nfood_shortcake = pygame.image.load('food_shortcake.png')\nfood_donut = pygame.image.load('food_donut.png')\n\nfoodLst = [food_burger, food_noodles, food_pie, food_rice, food_shortcake, food_donut]\n\n# game parameters\nSCREEN_WIDTH, SCREEN_HEIGHT = 600, 600\nFPS = 10\nclock = pygame.time.Clock()\n\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\nbackgroundRect = background.get_rect()\n\npygame.display.set_caption('CATch')\n\nsmallFont = pygame.font.SysFont(\"comicsansms\", 25)\nmedFont = pygame.font.SysFont(\"comicsansms\", 50)\nlargeFont = pygame.font.SysFont(\"comicsansms\", 80)\n\n\ndef text_objects(text, color, size):\n if size == \"small\": \n textSurface = smallFont.render(text, True, color)\n elif size == \"medium\": \n textSurface = medFont.render(text, True, color)\n elif size == \"large\": \n textSurface = largeFont.render(text, True, color)\n return textSurface, textSurface.get_rect()\n\ndef message_to_screen(msg, color, y_displace = 0, size = \"small\"):\n textSurf, textRect = text_objects(msg, color, size)\n textRect.center = (SCREEN_WIDTH/2), (SCREEN_HEIGHT/2)+y_displace\n screen.blit(textSurf, textRect)\n \ndef gameIntro():\n\n intro = True\n \n while intro:\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n quit()\n if event.type == pygame.KEYDOWN:\n intro = False\n \n screen.fill(white)\n message_to_screen(\"welcome to\", green, -175, \"medium\")\n message_to_screen(\"CATch\", green, -90, \"large\")\n message_to_screen(\"The objective of this game is to\",\n black)\n message_to_screen(\"eat all the food that you possibly can\", black, 30)\n message_to_screen(\"by catching the food as it falls down the screen\", black, 60)\n message_to_screen(\"Press any key to play!\", black, 150)\n\n pygame.display.update()\n clock.tick(5)\n\n\n# main game loop\ndef gameLoop():\n \n gameExit = False\n gameOver = False\n\n points = 0\n lives = 3\n #for now the cat is represented by a square block \n cat_size = SCREEN_WIDTH/6 \n #coordinates of the cat \n cat_x = SCREEN_WIDTH/2 - cat_size/2 \n cat_y = SCREEN_HEIGHT - cat_size\n cat_x_change = 0\n CAT = cat_right\n #characteristics of food for now\n food_size = cat_size/2\n FOOD_LST = []\n\n \n #characteristics of trash for now\n trash_size = cat_size/2\n trash_x = random.randrange(1, int(SCREEN_WIDTH - trash_size))\n trash_y = 0\n trash_y_change = 0\n\n while not gameExit:\n\n\n \n if lives == 0:\n gameOver = True \n \n for event in pygame.event.get():\n #for debugging only \n #print(event)\n \n if event.type == pygame.QUIT:\n gameOver = True\n\n #moving the cat \n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n if cat_x >= 0: \n cat_x_change = -cat_size/4\n CAT = cat_left\n elif event.key == pygame.K_RIGHT:\n if cat_x <= SCREEN_WIDTH - cat_size: \n cat_x_change = cat_size/4\n CAT = cat_right\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_LEFT and cat_x_change < 0: \n cat_x_change = 0\n elif event.key == pygame.K_RIGHT and cat_x_change > 0:\n cat_x_change = 0 \n if cat_x_change < 0 and cat_x == 0:\n cat_x = 0\n elif cat_x_change > 0 and cat_x == SCREEN_WIDTH - cat_size:\n cat_x = SCREEN_WIDTH - cat_size\n else:\n cat_x += cat_x_change\n\n #moving the food/trash\n if len(FOOD_LST) == 0 or FOOD_LST[0].get_y() > SCREEN_HEIGHT*random.randrange(4,9)/9:\n food_x = random.randrange(0, int(SCREEN_WIDTH - food_size))\n food_y = 0\n food_type = foodLst[random.randrange(0, len(foodLst))]\n food = Food(food_x, food_y, food_size, food_type) \n FOOD_LST.insert(0, food)\n if trash_y < SCREEN_HEIGHT:\n trash_y += food_size/2\n\n for food in FOOD_LST:\n if food.get_x() in range(int(cat_x - food.get_size()), int(cat_x + cat_size))and \\\n food.get_y() + food.get_size() in range(int(cat_y), SCREEN_HEIGHT):\n FOOD_LST.remove(food)\n points += food.get_points()\n if food.get_y() >= SCREEN_HEIGHT:\n FOOD_LST.remove(food)\n lives -= 1 \n\n #drawing everything \n screen.blit(background, backgroundRect)\n for food in FOOD_LST:\n food.update_y(food.get_size()/2)\n screen.blit(food.get_type(), (food.get_x(), food.get_y()))\n\n screen.blit(CAT, (cat_x, cat_y)) \n HP = 'Lives: ' + str(lives)\n pts = 'Points: ' + str(points)\n HP_text = smallFont.render(HP, True, green)\n pts_text = smallFont.render(pts, True, green)\n screen.blit(HP_text, [SCREEN_WIDTH*.75, 33])\n screen.blit(pts_text, [SCREEN_WIDTH*.75, 66])\n \n pygame.display.update()\n\n clock.tick(FPS)\n\n while gameOver == True:\n screen.fill(white)\n message_to_screen(\"Game Over\", red, -80, \"large\")\n message_to_screen(\"Your score: \" + str(points), black, 40)\n message_to_screen(\"Press C to play again or Q to quit\", black, 75)\n pygame.display.update()\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gameOver = False\n gameExit = True\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_q:\n gameOver = False \n gameExit = True\n elif event.key == pygame.K_c:\n gameLoop() \n\n pygame.quit()\n quit()\n\ngameIntro() \ngameLoop()\n", "repo_name": "julezjw/CATch", "sub_path": "CATch.py", "file_name": "CATch.py", "file_ext": "py", "file_size_in_byte": 6661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.init", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 83, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 109, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 120, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 140, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 150, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 151, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 153, "usage_type": "call"}, {"api_name": "Food.Food", "line_number": 154, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 182, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 182, "usage_type": "attribute"}, {"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": 193, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 194, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.K_q", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pygame.K_c", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 204, "usage_type": "call"}]} +{"seq_id": "71570874926", "text": "import asyncio\nimport aiohttp\nfrom datetime import datetime, timedelta\nfrom pynws import SimpleNWS, NwsError\nfrom textwrap import TextWrapper\nfrom tkinter import *\nfrom tkinter.font import Font\nfrom tkinter import ttk\n\nLOCATION = (48.1829, -117.0607)\nSTATION = 'ITDA8' # Old Town\nUSERID = \"kmhall@gmail.com\"\nxMax = 80\nREFRESH_RATE = 5 * 60 * 1000 # 5 minutes\n\nnws = None\nmyWrap = None\n\nroot = None\nstatusContents = None\nforecastText = None\n\n\ndef getWindDirection(windDirection):\n if (windDirection > 315.0+1.0):\n return \"NNW\"\n if (windDirection > 292.5+1.0):\n return \"NW\"\n if (windDirection > 270.0+1.0):\n return \"WNW\"\n if (windDirection > 247.5+1.0):\n return \"W\"\n if (windDirection > 225.0+1.0):\n return \"WSW\"\n if (windDirection > 202.5+1.0):\n return \"SW\"\n if (windDirection > 180.0+1.0):\n return \"SSW\"\n if (windDirection > 157.5+1.0):\n return \"S\"\n if (windDirection > 135.0+1.0):\n return \"SSE\"\n if (windDirection > 112.5+1.0):\n return \"SE\"\n if (windDirection > 90.0+1.0):\n return \"ESE\"\n if (windDirection > 67.5+1.0):\n return \"E\"\n if (windDirection > 45.0+1.0):\n return \"ENE\"\n if (windDirection > 22.5+1.0):\n return \"NE\"\n if (windDirection > 0.0+1.0):\n return \"NNE\"\n return \"N\"\n\n\ndef decorateForecast():\n # set up tags\n for tag in forecastText.tag_names():\n forecastText.tag_delete(tag)\n\n myfont = Font(font=forecastText['font']).copy()\n myfont.configure(weight='bold')\n forecastText.tag_configure(\n 'boldline', font=myfont, foreground='white', background='blue')\n forecastText.tag_configure(\n 'alertline', font=myfont, foreground='white', background='red')\n\n # bold tag for Observation: and Forecast:\n loc = '1.0'\n idxStart = forecastText.search('Observation:', loc)\n if idxStart != '':\n idxEnd = idxStart + \" wordend\"\n forecastText.tag_add('boldline', idxStart, idxEnd)\n\n # bold tag for Forecast:\n loc = '1.0'\n idxStart = forecastText.search('Forecast:', loc)\n if idxStart != '':\n idxEnd = idxStart + \" wordend\"\n forecastText.tag_add('boldline', idxStart, idxEnd)\n\n # alert tag for Alert:\n loc = '1.0'\n idxStart = forecastText.search('Alert:', loc)\n if idxStart != '':\n idxEnd = idxStart + \" wordend\"\n forecastText.tag_add('alertline', idxStart, idxEnd)\n\n\ndef observationToText(obs):\n obs_time = datetime.fromisoformat(obs['timestamp']).astimezone()\n obs_temp = 9 * obs['temperature'] / 5.0 + 32.0\n obs_speed = obs['windSpeed'] * 0.6213712\n obs_direction = getWindDirection(obs['windDirection'])\n\n obs_gust = None\n if obs['windGust']:\n obs_gust = obs['windGust'] * 0.6213712\n\n obs_humidity = obs['relativeHumidity']\n lastFetch = datetime.now()\n\n line = \"Observation: \"\n line += f\"{obs_time.strftime('%A %I:%M%p')} \"\n line += f\"Fetched {lastFetch.strftime('%I:%M%p')}\"\n line += '\\n'\n\n line += f\" Temp: {obs_temp:.1f} F \"\n line += f\"Humidity: {obs_humidity:.0f} %\"\n line += f\" Wind: {obs_direction} {obs_speed:.1f} mph\"\n if obs_gust:\n line += \", gusts to {obs_gust:.1f} mph\"\n line += '\\n'\n\n return line\n\n\ndef forecastToText(fc):\n fc_start = datetime.fromisoformat(fc['startTime']).astimezone()\n fc_end = datetime.fromisoformat(fc['endTime']).astimezone()\n fc_name = fc['name']\n fc_shorttext = fc['shortForecast']\n fc_detailed = fc['detailedForecast']\n\n line = \"\"\n if fc_start.day == fc_end.day:\n line += f\" From {fc_start.strftime('%A %I:%M%p')} to {fc_end.strftime('%I:%M%p')}\"\n else:\n line += f\" From {fc_start.strftime('%A %I:%M%p')} to {fc_end.strftime('%A %I:%M%p')}\"\n line += '\\n'\n\n line += f\" {fc_name}: {fc_shorttext} \"\n line += '\\n'\n\n for ll in myWrap.wrap(fc_detailed):\n if ll:\n line += ' ' + f\"{ll.strip()}\"\n line += '\\n'\n return line\n\n\ndef alertToText(alert):\n line = \"\"\n if alert:\n line += \"Alerts:\\n\"\n for stmt in alert:\n msg = stmt['messageType']\n event = stmt['event']\n start = datetime.fromisoformat(stmt['effective']).astimezone()\n stop = datetime.fromisoformat(stmt['expires']).astimezone()\n line += f\" {msg}: {event}\" + \" \"*20\n if start.day == stop.day:\n line += f\"({start.strftime('%A %I:%M%p')} to {stop.strftime('%I:%M%p')})\"\n else:\n line += f\"({start.strftime('%A %I:%M%p')} to {stop.strftime('%A %I:%M%p')})\"\n line += '\\n'\n headline = stmt['parameters']['NWSheadline']\n for hl in myWrap.wrap(\"\".join(headline)):\n line += f\" {hl}\\n\"\n line += '\\n'\n else:\n line += \"No Alerts\\n\"\n\n return line\n\n\ndef getNwsText():\n global nws\n tt = \"No forecast available...\"\n\n if nws is not None:\n tt = \"\"\n tt += observationToText(nws.observation) + \"\\n\"\n\n tt += \"Forecast: \\n\"\n for ff in nws.forecast[0:2]:\n tt += forecastToText(ff) + \"\\n\"\n\n tt += alertToText(nws.alerts_forecast_zone) + \"\\n\"\n\n return tt\n\n\nasync def fetchNws():\n global nws, forecastText, statusContents, root\n statusContents.set('Fetching forecast...')\n try:\n async with aiohttp.ClientSession() as session:\n nws = SimpleNWS(*LOCATION, USERID, session)\n await nws.set_station(STATION)\n await nws.update_observation()\n await nws.update_forecast()\n await nws.update_alerts_forecast_zone()\n forecastText.delete('1.0', 'end')\n forecastText.insert('end', getNwsText())\n decorateForecast()\n statusContents.set('Complete!')\n except Exception as e:\n statusContents.set('Error')\n forecastText.delete('1.0', 'end')\n forecastText.insert('1.0', e)\n\n\ndef getNwsForecast(*args):\n global root\n try:\n loop = asyncio.get_event_loop()\n loop.run_until_complete(fetchNws())\n except Exception as e:\n print(f\"Exception: {e}\")\n root.after(REFRESH_RATE, getNwsForecast)\n\n\ndef main():\n global statusContents, forecastText, nws, myWrap, root\n\n myWrap = TextWrapper(width=xMax - 10)\n\n root = Tk()\n root.title(\"Demo NWS\")\n frm = ttk.Frame(root, padding='10 10 10 10')\n frm.grid()\n\n statusContents = StringVar()\n\n statusLabel = ttk.Label(frm, textvariable=statusContents)\n statusLabel.grid(column=0, row=0)\n statusContents.set('Hello, World!')\n\n quitButton1 = ttk.Button(frm, text=\"Quit\", command=root.destroy)\n quitButton1.grid(column=2, row=0)\n\n nwsButton = ttk.Button(frm, text=\"NWS\", command=getNwsForecast)\n nwsButton.grid(column=1, row=0)\n\n forecastText = Text(frm, width=85, height=20,\n wrap='none', fg='white', bg='black', font='LucidaConsole')\n ys = ttk.Scrollbar(frm, orient='vertical', command=forecastText.yview)\n forecastText.grid(column=0, row=1, columnspan=3, sticky='nwse')\n forecastText['state'] = 'normal'\n forecastText['yscrollcommand'] = ys.set\n ys.grid(column=3, row=1, sticky='nse')\n\n # after 5 seconds for the window to be established, fetch the NWS info\n root.after(5000, getNwsForecast)\n root.mainloop()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "grumble1965/advent2015", "sub_path": "tkdemo.py", "file_name": "tkdemo.py", "file_ext": "py", "file_size_in_byte": 7369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "tkinter.font.Font", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "name"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "name"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "name"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "name"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 152, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 190, "usage_type": "call"}, {"api_name": "pynws.SimpleNWS", "line_number": 191, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 209, "usage_type": "call"}, {"api_name": "textwrap.TextWrapper", "line_number": 219, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 223, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 223, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 228, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 228, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 232, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 232, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 235, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 235, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 240, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 240, "usage_type": "name"}]} +{"seq_id": "42506866583", "text": "from plivo.utils.validators import *\nfrom plivo.xml import (\n PlivoXMLElement,\n map_type\n)\n\n\nclass StreamElement(PlivoXMLElement):\n _name = 'Stream'\n _nestable = [\n ]\n\n def __init__(\n self,\n content,\n bidirectional=None,\n audioTrack=None,\n streamTimeout=None,\n statusCallbackUrl=None,\n statusCallbackMethod=None,\n contentType=None,\n extraHeaders=None\n ):\n super(StreamElement, self).__init__()\n\n self.content = content\n self.bidirectional = bidirectional\n self.audioTrack = audioTrack\n self.streamTimeout = streamTimeout\n self.statusCallbackUrl = statusCallbackUrl\n self.statusCallbackMethod = statusCallbackMethod\n self.contentType = contentType\n self.extraHeaders = extraHeaders\n\n def to_dict(self):\n d = {\n 'bidirectional': self.bidirectional,\n 'audioTrack': self.audioTrack,\n 'streamTimeout': self.streamTimeout,\n 'statusCallbackUrl': self.statusCallbackUrl,\n 'statusCallbackMethod': self.statusCallbackMethod,\n 'contentType': self.contentType,\n 'extraHeaders': self.extraHeaders,\n }\n return {\n k: six.text_type(map_type(v))\n for k, v in d.items() if v is not None\n }", "repo_name": "plivo/plivo-python", "sub_path": "plivo/xml/streamElement.py", "file_name": "streamElement.py", "file_ext": "py", "file_size_in_byte": 1384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 62, "dataset": "github-code", "pt": "2", "api": [{"api_name": "plivo.xml.PlivoXMLElement", "line_number": 8, "usage_type": "name"}, {"api_name": "plivo.xml.map_type", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "4218251339", "text": "#!/usr/bin/python3\n\"\"\"script that changes the name of a State object from the\ndatabase hbtn_0e_6_usa\"\"\"\n\nimport sys\nfrom model_state import Base, State\nfrom sqlalchemy import create_engine, MetaData\nfrom sqlalchemy.orm import Session\n\nif __name__ == \"__main__\":\n a1 = sys.argv[1]\n a2 = sys.argv[2]\n a3 = sys.argv[3]\n en = create_engine('mysql+mysqldb://{}:{}@localhost/{}'.format(a1, a2, a3),\n pool_pre_ping=True)\n en.connect()\n metadata = MetaData()\n session = Session(en)\n session.query(State).filter(State.name.ilike(\"%a%\")\n ).delete(synchronize_session='fetch')\n session.commit()\n session.close()\n", "repo_name": "rodrigoandresd/holbertonschool-higher_level_programming", "sub_path": "python-object_relational_mapping/13-model_state_delete_a.py", "file_name": "13-model_state_delete_a.py", "file_ext": "py", "file_size_in_byte": 684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sqlalchemy.create_engine", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "model_state.State", "line_number": 19, "usage_type": "argument"}, {"api_name": "model_state.State.name.ilike", "line_number": 19, "usage_type": "call"}, {"api_name": "model_state.State.name", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "26808136965", "text": "import json\nimport logging\nfrom io import BytesIO\nfrom random import choices, shuffle\n\nfrom compass import Attribute, CardData, Rarity\nfrom discord import File, app_commands\nfrom discord.ext import commands\nfrom discord.ext.commands import Bot, Context\n\nfrom .base import Cog\nfrom .path import path\nfrom .translator import locale_str as _\n\n\nlogger = logging.getLogger(__name__)\n\n\nasync def setup(bot: Bot) -> None:\n await bot.add_cog(Gacha(bot))\n\n\nasync def teardown(bot: Bot) -> None:\n await bot.remove_cog(\"Gacha\")\n\n\n# prepares choices for name argument\nwith open(path.gacha_json, \"r\") as f:\n gacha_data = json.load(f)\n\ngacha_list = [app_commands.Choice(name=_(data[\"name\"]), value=idx)\n for idx, data in enumerate(gacha_data)]\n\n\nclass Gacha(Cog):\n def __init__(self, bot: Bot) -> None:\n super().__init__(bot, logger)\n self.data = CardData()\n\n @commands.hybrid_command(\n description = _(\"ガチャシミュレーター\"),\n )\n @app_commands.describe(\n name = _(\"シミュレートするガチャの名前を指定してね！\"),\n )\n @app_commands.choices(name=gacha_list)\n async def gacha(self, ctx: Context, name: int) -> None:\n await ctx.defer()\n\n data = gacha_data[name]\n cards = CardData([])\n\n rarities = choices([*data[\"weight\"]], [*data[\"weight\"].values()], k=data[\"k\"])\n\n for rarity in [*data[\"weight\"]]:\n population = CardData([])\n weights = []\n k = sum(el==rarity for el in rarities)\n for condition in data[rarity]:\n args = list(map(lambda el: Attribute(el), condition[\"attributes\"])) \\\n + list(map(lambda el: Rarity(el), condition[\"rarities\"]))\n tmp = self.data.get_cards(*args, **condition[\"kwargs\"], themes=condition[\"themes\"])\n population.extend(tmp)\n weights.extend([condition[\"weight\"]]*len(tmp))\n cards.extend(choices(population, weights, k=k))\n\n shuffle(cards)\n cards = CardData(sorted(cards, key=lambda card: card.rarity))\n\n img = cards.generate_large_image()\n\n image_bytes = BytesIO()\n img.save(image_bytes, \"PNG\", quality=100, optimize=True)\n image_bytes.seek(0)\n\n await ctx.send(file=File(fp=image_bytes, filename=f\"{ctx.author.id}.png\"))\n return\n", "repo_name": "ster-phys/bot_cps", "sub_path": "bot_cps/gacha.py", "file_name": "gacha.py", "file_ext": "py", "file_size_in_byte": 2366, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands.Bot", "line_number": 19, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 23, "usage_type": "name"}, {"api_name": "path.path.gacha_json", "line_number": 28, "usage_type": "attribute"}, {"api_name": "path.path", "line_number": 28, "usage_type": "name"}, {"api_name": "json.load", "line_number": 29, "usage_type": "call"}, {"api_name": "discord.app_commands.Choice", "line_number": 31, "usage_type": "call"}, {"api_name": "discord.app_commands", "line_number": 31, "usage_type": "name"}, {"api_name": "translator.locale_str", "line_number": 31, "usage_type": "call"}, {"api_name": "base.Cog", "line_number": 35, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 36, "usage_type": "name"}, {"api_name": "compass.CardData", "line_number": 38, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 47, "usage_type": "name"}, {"api_name": "compass.CardData", "line_number": 51, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 53, "usage_type": "call"}, {"api_name": "compass.CardData", "line_number": 56, "usage_type": "call"}, {"api_name": "compass.Attribute", "line_number": 60, "usage_type": "call"}, {"api_name": "compass.Rarity", "line_number": 61, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 65, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 67, "usage_type": "call"}, {"api_name": "compass.CardData", "line_number": 68, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 72, "usage_type": "call"}, {"api_name": "discord.File", "line_number": 76, "usage_type": "call"}, {"api_name": "discord.ext.commands.hybrid_command", "line_number": 40, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 40, "usage_type": "name"}, {"api_name": "translator.locale_str", "line_number": 41, "usage_type": "call"}, {"api_name": "discord.app_commands.describe", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.app_commands", "line_number": 43, "usage_type": "name"}, {"api_name": "translator.locale_str", "line_number": 44, "usage_type": "call"}, {"api_name": "discord.app_commands.choices", "line_number": 46, "usage_type": "call"}, {"api_name": "discord.app_commands", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "16972821881", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # Neuronale Netze\n\n# ## Überblick\n\n# Neuronale Netze sind relativ einfach strukturierte Modellfunktionen mit sehr vielen (teilweise auch redundanten) Parametern. Die Anpassung dieser Parameter führt zu \n# hochdimensionalen, nicht-konvexen Optimierungsproblemen.\n# \n# Anhand einfacher Beispiele wird das Verhalten von konventionellen bzw. stochastischen Gradienten-Verfahren untersucht. Außerdem werden beschleunigte Varianten betrachtet.\n\n# ## Multi-Layer Perceptron (MLP)\n\n# Ein einfaches neuronales Netz das zur Bearbeitung von\n# Klassifikations- und Regressionsproblemen eingesetzt werden kann\n# ist das **Multi-Layer Perceptron** (MLP).\n# Bei beiden Problemklassen wird versucht mit Hilfe\n# eines Trainigsdatensatzes eine parameterabhängige Modellfunktion\n# \\begin{equation*} \n# g:\\mathbb{R}^n \\times \\mathbb{R}^p \\to \\mathbb{R}^o,\n# \\quad\n# x,w \\mapsto g(x,w)\n# \\end{equation*} \n# anzupassen,\n# die die Inputs möglichst genau auf die Outputs abbildet.\n# \n# Bei der linearen Regression haben wir\n# einen linear affinen Ansatz der Form\n# \\begin{equation*} \n# g(x, w) = G(x) w + c(x)\n# \\end{equation*}\n# benutzt und dann die Parameter $w$\n# mit Hilfe der Trainingsdaten möglichst gut (bezüglich des benutzten Loss)\n# angepasst. Die dabei entstehenden Optimierungsprobleme\n# waren relativ einfach beherrschbar.\n# \n# Beim MLP benutzt man einen anderen, allgemeineren\n# Ansatz für $g$. Wir betrachten hier den Fall, dass der Output\n# skalar ist, d.h. $g:\\mathbb{R}^n\\times \\mathbb{R}^p \\to \\mathbb{R}$.\n\n# ![MLP, \\url{http://scikit-learn.org/stable/modules/neural_networks_supervised.html}](DatenNotebooks/mlp.png)\n\n# Ein MLP hat dann folgende Struktur (Graphik von http://scikit-learn.org/stable/modules/neural_networks_supervised.html):\n# \n# - der Input $X$ besteht aus den Komponenten \n# $X=(x_1,\\ldots,x_n)$, sie bilden die **Input-Layer**\n# \n# - Linearkombinationen der Form\n# \\begin{equation*} \n# u_i = w_{i0}^{(1)} + w_{i1}^{(1)}x_1 + \\cdots w_{in}^{(1)}x_n, \\quad i = 1,\\ldots, k\n# \\end{equation*}\n# werden als Input für die \"Neuronen\"\n# $a_1,\\ldots, a_k$ der **Hidden-Layer** benutzt\n# \n# - jedes Neuron $a_i$ wendet dann auf seinen skalaren Input $u_i$\n# eine skalare Funktion $a:\\mathbb{R}\\to\\mathbb{R}$, \n# die **Aktivierungsfunktion**, an, d.h. wir erhalten\n# als Output der \"Neuronen\" $a_i$\n# \\begin{equation*} \n# v_i = a(u_i), \\quad i = 1,\\ldots,k\n# \\end{equation*}\n# \n# - der Output $g(x,w)$ wird schließlich als Linearkombination\n# \\begin{equation*} \n# g(x, w) = w_0^{(2)} + w_{1}^{(2)} v_1 + \\cdots w_{k}^{(2)} v_k\n# \\end{equation*}\n# berechnet\n\n# $g$ ist also durch\n# \\begin{equation*} \n# \\big(w_{ij}^{(1)} \\big)_{\\substack{i = 1,\\ldots, k\\\\ j = 0,\\ldots, n}},\n# \\quad\n# \\big(w_{j}^{(2)} \\big)_{j = 0,\\ldots, k}\n# \\end{equation*}\n# parametriert, d.h. die Anzahl der Parameter ist hoch.\n# Da $a$ nichtlinear ist, ist $g$ auch nichtlinear.\n# \n# Der Ansatz kann problemlos auf mehrere Hidden-Layers\n# sowie vektorwertige Zielfunktionen verallgemeinert werden.\n# \n# Mit Hilfe des Trainingsdatensatzes werden die Parameter $w$\n# von $g$ so bestimmt, dass der Loss minimiert wird. \n# Die dabei auftretenden Optimierungsprobleme sind i.d.R.\n# nicht konvex und werden üblicherweise mit Varianten\n# des [Gradientenverfahrens](https://de.wikipedia.org/wiki/Gradientenverfahren) \n# näherungsweise gelöst.\n# \n# Für die Berechnung der dabei benötigten Ableitungen nach den Parametern $w$ gibt es effiziente Methoden ([Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)).\n\n# ## Modellproblem 1-Neuron-MLP\n\n# Um ein Gefühl für das Trainingsverhalten eines Netzes zu bekommen, betrachten\n# wir ein triviales Netz mit einem skalaren Input, einem skalaren Output,\n# einer Schicht mit einem Neuron und Aktivierungsfunktion \n# $r(x) = \\max(0, x)$ \n# (**RELU**, **Re**ctified **L**inear **U**nit), d.h.\n# \\begin{equation*} \n# g(x, w) = r(w_1 \\, x + w_2)\\,w_3 + w_4,\n# \\quad\n# r(x) = \\max(0, x).\n# \\end{equation*}\n# Als Trainingsdatensatz wird\n# \\begin{equation*} \n# x_i = y_i = \\frac{i}{n}, \\quad i = 0,\\ldots,n, \\quad n=10\n# \\end{equation*}\n# benutzt. \n\n# In[1]:\n\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nimport copy\n\nseed = 123\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n## Parameter festlegen\n\n# Anzahl Neuronen\nnn = 1\n\n# Anzahl Trainingssample\nntrain = 11\n\na = 0\nb = 1\n\n# Anzahl Plotpunkte\nnplot = 1000\nXplot = np.linspace(a-0.25, b+0.25, nplot).reshape(-1,1)\n\n\n## Daten erzeugen\nnp.random.seed(seed)\n\n#g = lambda x: np.sin(10*x*x)\ng = lambda x: x\n\nXtrain = np.linspace(a, b, ntrain).reshape(-1,1)\n#Xtrain = (np.random.rand(ntrain) * (b - a) + a).reshape(-1,1)\nXtrain.sort(axis=0)\n\nytrain = g(Xtrain)#.ravel()\n#ytrain = ytrain + 0.1 * np.random.randn(*ytrain.shape)\n\nplt.plot(Xtrain, ytrain, '.');\n\n\n# Die Parameter $w$ sollen so bestimmt werden, dass\n# \\begin{equation*} \n# l(w) = \\frac{1}{n} \\sum_{i=1}^n \\big(g(x_i, w) -y_i\\big)^2\n# \\end{equation*}\n# minimiert wird.\n# Da $g$ nichtlinear in $w$ ist, ist auch $f$ nichtlinear.\n# \n# Für die Parameter $\\hat{w} = (1, 0, 1, 0)^T$ erhalten wir\n# \\begin{equation*} \n# g(x,\\hat{w}) = r(x) = \\max(0,x),\n# \\end{equation*}\n# d.h. $g(x,\\hat{w})$ interpoliert die Daten $x_i, y_i$ exakt.\n# Damit ist\n# \\begin{equation*} \n# l(\\hat{w}) = 0\n# \\end{equation*}\n# und $\\hat{w}$ globales Minimum von $l$.\n# \n# Andererseits gilt für $\\alpha > 0$\n# \\begin{equation*} \n# r(\\alpha x) = \\max(0, \\alpha x) = \\alpha \\max(0, x) = \\alpha r(x)\n# \\end{equation*}\n# dass auch \n# \\begin{equation*} \n# \\hat{w}_\\alpha = \\big(\\alpha, 0, \\frac{1}{\\alpha}, 0\\big), \\quad \\forall \\alpha > 0\n# \\end{equation*} \n# ein globales Minimum von $l$ ist und analog auch\n# \\begin{equation*} \n# \\hat{w}_\\beta = \\big(1, \\beta, 1, -\\beta \\big), \\quad \\forall \\beta > 0.\n# \\end{equation*} \n\n# Damit kann $l$ nicht strikt konvex sein. Wie das folgende Beispiel zeigt ist $l$ nicht einmal konvex.\n\n# In[2]:\n\n\n#%matplotlib notebook \nget_ipython().run_line_magic('matplotlib', 'inline')\n\nrelu = lambda x : np.maximum(0, x)\n\ndef l(w1, w3):\n fw = 0.0\n for x,y in zip(Xtrain, ytrain):\n fw += (relu(w1 * x) * w3 - y)**2\n return fw/Xtrain.shape[0]\n\nl = np.vectorize(l)\n\n\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.figure(figsize = (10, 10))\nplt.axes(projection='3d')\n\nw1 = np.array([ 0, 2])\nw3 = np.array([-1, 1])\n\n#w1 = np.array([ 0, 1])\n#w3 = np.array([-1, 0])\n\ng1 = np.linspace(*w1)\ng3 = np.linspace(*w3)\n\nww3, ww1 = np.meshgrid(g3, g1)\nww1 = ww1.T\nww3 = ww3.T\nff = l(ww1, ww3)\n\nax = plt.gca()\nax.plot_surface(ww1, ww3, ff, alpha = 0.5, cmap=plt.cm.jet)\ncc = ff.max() * np.linspace(0,1)**2\n#cc = 10*np.linspace(0,1)**5\nax.contour(ww1, ww3, ff, cc)\n\nzoff = -2\nax.contour(ww1, ww3, ff, cc, zdir='z', offset=zoff, cmap=plt.cm.jet)\n#plt.contour(ww1, ww3, f(ww1, ww3), zdir='x', cmap = plt.cm.jet);\n\nl1 = np.linspace(*w1)\nl3 = np.linspace(*w3)\nax.plot3D(l1, l3, l(l1, l3), c = 'r')\n\nl1 = np.linspace(*w1, 2)\nl3 = np.linspace(*w3, 2)\nax.plot3D(l1, l3, zoff * np.ones(l1.shape), 'r')\n\nax.set_zlim(zoff, ff.max())\nax.set_xlabel('$w_1$')\nax.set_ylabel('$w_3$')\nax.set_zlabel('$f$')\nax.view_init(20, 150)\n\n\n# Für $w_2 = w_4 = 0$ sind die Funktionswerte entlang der\n# Strecke $(w_1,w_3) = (0,-1)$ nach $(w_1,w_3) = (2,1)$ dargestellt.\n\n# Die fehlende Konvexität wird uns beim Anpassen der Parameter $w$ noch viel \"Freude\" bereiten.\n# \n# Diese Anpassung werden wir nun mit 3 der gängigsten Software Tools vornehmen.\n\n# ### Scikit-Learn\n\n# Wir passen einen `MLPRegressor` an und benutzen die Default-Einstellungen.\n\n# In[3]:\n\n\nfrom sklearn import neural_network\n\nmlp = neural_network.MLPRegressor(hidden_layer_sizes = [nn], max_iter = 10000, random_state = seed)\nmlp.fit(Xtrain, ytrain.flat)\n\ndef ev(mlp, c = 'r', label=''):\n plt.plot(Xtrain, ytrain, 'b.');\n plt.plot(Xplot, mlp.predict(Xplot), c, label=label);\n print(\"solver = {}, score = {}\".format(mlp.solver, mlp.score(Xtrain, ytrain)))\n\nev(mlp)\n\n\n# Das Ergebnis ist unbrauchbar.\n# \n# Der Startwert für den iterativen Löser wird zufällig gewählt und kann\n# über den Parameter `random_state` beeinflusst werden\n\n# In[4]:\n\n\nmlp = neural_network.MLPRegressor(hidden_layer_sizes = [nn], max_iter = 10000, random_state = 234)\nmlp.fit(Xtrain, ytrain.flat)\nev(mlp)\n\n\n# In[5]:\n\n\nmlp = neural_network.MLPRegressor(hidden_layer_sizes = [nn], max_iter = 10000, random_state = 314159)\nmlp.fit(Xtrain, ytrain.flat)\nev(mlp)\n\n\n# Die Ergebnisse hängen offensichtlich extrem stark vom Startwert ab. Die Qualität ist insgesamt sehr dürftig.\n\n# ### Keras-Tensorflow\n\n# In[6]:\n\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Activation\n\nmodel = Sequential(\n[\n Dense(units = nn, input_dim = 1),\n #Dense(units = nn, input_dim = 1, kernel_initializer = keras.initializers.RandomUniform(minval=-0.05, maxval=0.05, seed=seed)),\n Activation('relu'),\n #Activation('tanh'),\n Dense(units = 1)\n])\n\nmodel.summary()\n\n\n# In[7]:\n\n\nmodel.compile(loss='mse',\n optimizer='nadam',\n metrics=['accuracy'])\n\nmodel.fit(Xtrain, ytrain, epochs = 100, verbose = 0);\n\ndef kev(model, c = 'r'):\n plt.plot(Xtrain, g(Xtrain), 'b.');\n plt.plot(Xplot, model.predict(Xplot), c);\n print(\"loss = {}\".format(model.evaluate(Xtrain, ytrain)[0]))\n \nkev(model) \n\n\n# ### Pytorch\n\n# In[8]:\n\n\nimport torch\nimport torch.nn as tnn\n\nxt = torch.from_numpy(Xtrain).to(torch.float32)\nyt = torch.from_numpy(ytrain).to(torch.float32)\n\nfrom torch.utils.data import TensorDataset, DataLoader\n\ndst = TensorDataset(xt, yt)\ndlt = DataLoader(dst, batch_size = 5, shuffle=True)\n\nclass Percep(tnn.Module):\n def __init__(self):\n super().__init__()\n self.linear1 = tnn.Linear(1, nn)\n self.act1 = tnn.ReLU() \n self.linear2 = tnn.Linear(nn, 1)\n \n def forward(self, x):\n x = self.linear1(x)\n x = self.act1(x)\n x = self.linear2(x)\n return x\n\nmodel = Percep()\n\nmodel.eval()\n\n\n# In[9]:\n\n\nopt = torch.optim.SGD(model.parameters(), lr=1e-5)\n\nimport torch.nn.functional as F\nloss_fn = F.mse_loss\n\ndef fit(num_epochs, model, loss_fn, opt):\n for epoch in range(num_epochs):\n for xb,yb in dlt:\n # Generate predictions\n pred = model(xb)\n loss = loss_fn(pred, yb)\n # Perform gradient descent\n loss.backward()\n opt.step()\n opt.zero_grad()\n print('Training loss: ', loss_fn(model(xt), yt))\n\nfit(100, model, loss_fn, opt)\n\ndef pev(model, c = 'r'):\n plt.plot(Xtrain, g(Xtrain), 'b.');\n plt.plot(xt.data.numpy(), model(xt).data.numpy(), 'r');\n\npev(model)\n\n\n# ## (Sub)Gradient-Descent\n\n# Wir betrachten wieder unser triviales Netz von oben\n# \\begin{equation*} \n# g(x, w) = r(w_1 \\, x + w_2)\\,w_3 + w_4,\n# \\quad\n# r(x) = \\max(0, x),\n# \\end{equation*}\n# mit Trainingsdatensatz\n# \\begin{equation*} \n# x_i = y_i = \\frac{i}{n}, \\quad i = 0,\\ldots,n, \\quad n=10.\n# \\end{equation*}\n# \n# Da $r$ bei $0$ nicht differenzierbar ist, ist eine direkte Anwendung des Gradientenverfahrens zunächst nicht möglich.\n# \n# Man kann dies durch zwei Strategien reparieren:\n# \n# - man ersetzt $r$ durch eine differenzierbare Approximation $\\tilde{r}$\n# \n# - man benutzt statt dem Gradienten den Subgradienten\n# \n# Wir benutzen die zweite Variante. Als Subgradient von $r$ erhalten wir\n# \\begin{equation*} \n# \\partial r(x)=\n# \\begin{cases}\n# 0 & x < 0\\\\\n# [0,1] & x = 0\\\\\n# 1 & 0< x\n# \\end{cases},\n# \\end{equation*}\n# d.h. bei $x=0$ müssen wir uns für einen Wert in $[0,1]$ entscheiden.\n# Wie wir später sehen werden, haben wir hier \"freie Auswahl\".\n# Der Einfachheit halber benutzen wir den Wert $\\frac{1}{2}$.\n\n# In[10]:\n\n\nimport autograd.numpy as np\nfrom autograd import grad\n\nrelu = lambda x : np.maximum(0, x)\n\nrelu1 = grad(relu)\nrelu1(-1.0), relu1(0.0), relu1(1.0)\n\n\n# Für verschieden Anfangswerte erhalten wir\n\n# In[11]:\n\n\ng = lambda x,w : relu(w[0] * x + w[1]) * w[2] + w[3]\n\ndef l(w):\n fw = 0.0\n for x,y in zip(Xtrain, ytrain):\n fw += (g(x,w) - y)**2\n return fw[0] / Xtrain.shape[0]\n\nl1 = grad(l)\n\ndef GD(w0, l1, lr = 1e-1, nit = 100):\n w = w0.copy()\n ww = [w]\n for k in range(nit):\n w = w - lr * l1(w)\n ww.append(w)\n return ww\n\ndef ev(w, c = 'r', label=''):\n plt.plot(Xtrain, ytrain, 'b.');\n plt.plot(Xplot, g(Xplot, w[-1]), c);\n plt.figure()\n plt.semilogy(list(map(l, w)), label=label)\n #plt.ylabel('$l$',rotation=0)\n\nw0 = np.zeros(4)\nw = GD(w0, l1, 0.1, 200)\nev(w)\n\n\n# bzw.\n\n# In[12]:\n\n\nw0 = np.ones(4)\nw = GD(w0, l1, 0.1, 200)\nev(w)\n\n\n# ## Accelerated Gradient-Descent (Nesterov)\n\n# Durch eine einfache Modifikation kann man das (Sub)Gradientenverfahren\n# beschleunigen. \n# Man bestimmt die neue Suchrichtung als Kombination aus dem aktuellen\n# negativen Gradienten und der vorherigen Suchrichtung (ähnlich wie beim\n# CG-Verfahren).\n# \n# Die bekannteste Variante stammt von [Nesterov](https://uclouvain.be/fr/repertoires/yurii.nesterov), der auch nachgewiesen hat, dass diese Verfahren in einem\n# gewissen Sinn optimal sind.\n# Die Iterationsvorschrift sieht wie folgt aus:\n# \\begin{align*}\n# w^{(-1)} &= w^{(0)} \\text {gegeben}\\\\\n# k = 1&,2,...\\\\\n# & v^{(k)} = w^{(k-1)} + \\frac{k-2}{k+1} \\big( w^{(k-1)} - w^{(k-2)} \\big) \\\\\n# & w^{(k)} = v^{(k)} - \\alpha^{(k)} l'(v^{(k)})\n# \\end{align*}\n\n# In[13]:\n\n\ndef Nes(w0, l1, lr = 0.1, maxit = 30):\n # Variante von Tibshirani\n w = [w0, w0]\n \n for k in range(1,maxit+1):\n vk = w[-1] + (k-2)/(k+1) * (w[-1] - w[-2])\n wk = vk - lr * l1(vk)\n\n w.append(wk)\n\n return(w[1:])\n\nw0 = np.ones(4)\nwnes = Nes(w0, l1, 0.1, 200)\nev(wnes, label='Nesterov')\nplt.semilogy(list(map(l, w)), label=\"SubGD\")\nplt.legend();\n\n\n# ## Stochastic (Sub)Gradient-Descent\n\n# Wir betrachten noch einmal unsere Zielfunktion $l$\n# \\begin{align*} \n# l(w) \n# &= \\frac{1}{n} \\sum_{i=1}^n \\big(g(x_i, w) -y_i\\big)^2\n# = \\frac{1}{n} \\sum_{i=1}^n l_i(w),\n# \\\\\n# l_i(w) &= \\big(g(x_i, w) -y_i\\big)^2.\n# \\end{align*}\n# In jedem Schritt des (Sub)Gradienten-Verfahren muss\n# \\begin{equation*} \n# \\partial l(w) = \\frac{1}{n} \\sum_{i=1}^n \\partial l_i(w)\n# \\end{equation*}\n# berechnet werden, d.h. der Aufwand skaliert mit der Anzahl\n# $n$ an Trainingsdaten, die zur Bestimmung der Parameter $w$\n# benutzt werden.\n\n# Andererseits ist $\\partial l(w)$ offensichtlich der Mittelwert der einzelnen $\\partial l_i(w)$, so dass es naheliegend ist, diesen Mittelwert durch eine weniger aufwendige Approximation zu nähern, z.B.:\n# \n# - $\\partial l(w) \\approx \\partial l_{\\hat{i}}(w)$ für *ein* $\\hat{i}\\in \\{1,\\ldots,n\\}$\n# \n# - $\\partial l(w) \\approx \\frac{1}{n_B} \\sum_{i\\in B} \\partial l_i(w)$ für eine $n_B$-elementige Teilmenge $B \\subset \\{1,\\ldots,n\\}$ mit $n_B \\le n$\n\n# Den Index $\\hat{i}$ bzw. die Teilmenge $B$ wird in jedem Schritt des Gradienten-Verfahrens zufällig neu bestimmt. Das resultierende Verfahren heißt Stochastic Gradient-Descent- bzw.\n# Minibatch Stochastic Gradient-Descent-Verfahren.\n\n# Angewandt auf unser Modellproblem erhalten wir mit $n_B = 1$\n\n# In[14]:\n\n\nli = lambda w, x, y : ((g(x,w) - y)**2)[0]\nli1 = grad(li)\n\ndef SGD(w0, li1, x, y, lr = 1e-1, nit = 100, bs = 1):\n w = w0.copy()\n ww = [w]\n for k in range(nit):\n g = 0.0\n for i in np.random.permutation(x.shape[0])[:bs]:\n g += li1(w, x[i], y[i])\n g /= bs\n w = w - lr * g\n ww.append(w)\n return ww\n\nnp.random.seed(seed)\nw0 = np.ones(4)\nw = SGD(w0, li1, Xtrain, ytrain, 0.1, 200)\nev(w)\n\n\n# Der Abfall der Loss-Funktion ist ähnlich schnell wie beim Standard-Gradienten-Verfahren, aber nicht monoton (\"Rauschen\")\n# \n# Bei $n$ Training-Samples $x_i,y_i$ ist der Aufwand bei SGD pro Iteration\n# um einen Faktor $n$ kleiner.\n\n# Für $n_B = 3$ folgt\n\n# In[15]:\n\n\nnp.random.seed(seed)\nw0 = np.ones(4)\nwb = SGD(w0, li1, Xtrain, ytrain, 0.1, 200, bs = 3)\nev(wb)\n\n\n# Hier ist der Verlauf der Abfall der Loss-Werte etwas weniger \"zitterig\" als im Fall $n_B=1$, allerdings ist der Aufwand pro Iteration auch wesentlich höher.\n\n# Analog kann man auch für das Nesterov-Verfahren eine stochastische Variante\n# aufbauen.\n\n# In[16]:\n\n\ndef SNes(w0, li1, x, y, lr = 0.1, maxit = 30, bs = 1):\n # Variante von Tibshirani\n w = [w0, w0]\n \n for k in range(1,maxit+1):\n vk = w[-1] + (k-2)/(k+1) * (w[-1] - w[-2])\n\n gk = 0.0\n for i in np.random.permutation(x.shape[0])[:bs]:\n gk += li1(vk, x[i], y[i])\n \n wk = vk - lr * gk\n\n w.append(wk)\n\n return(w[1:])\n\nnp.random.seed(seed)\nw0 = np.ones(4)\nwnes = SNes(w0, li1, Xtrain, ytrain, 0.1, 200)\nev(wnes, label='Nesterov')\nplt.semilogy(list(map(l, w)), label=\"SubGD\")\nplt.legend();\n\n\n# ## Backpropagation\n\n# Zuletzt muss noch überlegt werden, wie die (Sub)Gradienten\n# \\begin{equation*} \n# \\partial l_i(w),\n# \\quad \n# l_i(w) = \\big(g(x_i, w) -y_i\\big)^2\n# \\end{equation*}\n# möglichst effizient berechnet werden können.\n# Dadurch dass beim MLP die Parameter $w$ sehr komplex\n# in $g$ eingehen, ist dies nicht trivial.\n\n# Wir betrachten zunächst den trivialen Fall eines einzelnen skalaren Neurons.\n# Zur Vereinfachung der Notation wird der Index $i$ weg gelassen.\n# \\begin{equation*} \n# x \\rightarrow w_1 x =\\colon i_1 \\rightarrow a(i_1) =\\colon o_1 \n# \\end{equation*}\n# mit differenzierbarem Loss $l$.\n# Für den Gradienten von $l$ nach $w_1$ erhalten wir\n# \\begin{equation*} \n# \\partial_{w_1} l(o_1)\n# = l'(o_1)\\partial_{w_1} o_1\n# = l'(o_1)a'(i_1)\\partial_{w_1} i_1\n# = l'(o_1)a'(i_1) x\n# \\end{equation*}\n# Hat man $o_1$ berechnet, so kennt man auch $i_1$ und $\\partial_{w_1} l(o_1)$ ist direkt bestimmbar.\n\n# Betrachten wir nun die analoge Konstellation für zwei Neuronen\n# \\begin{equation*} \n# x \n# \\rightarrow w_1 x =\\colon i_1 \\rightarrow a(i_1) =\\colon o_1 \n# \\rightarrow w_2 o_1 =\\colon i_2 \\rightarrow a(i_2) =\\colon o_2. \n# \\end{equation*}\n# Für die Ableitung von $l(o_2)$ nach $w_k$ erhalten wir\n# \\begin{equation*} \n# \\partial_{w_k} l(o_2) \n# = l'(o_2)\\partial_{w_k} o_2 \n# = l'(o_2)a'(i_2)\\partial_{w_k} i_2\n# = l'(o_2)a'(i_2)\\partial_{w_k} (w_2 o_1) \n# \\end{equation*}\n# Für $w_2$ gilt dann\n# \\begin{equation*} \n# \\partial_{w_2} l(o_2)\n# = l'(o_2)a'(i_2)\\partial_{w_2} (w_2 o_1) \n# = l'(o_2)a'(i_2) \\big(o_1 + w_2 \\partial_{w_2} o_1 \\big)\n# \\end{equation*}\n# und da $o_1$ nicht von $w_2$ abhängt folgt\n# \\begin{equation*} \n# \\partial_{w_2} l(o_2) = l'(o_2)a'(i_2) o_1,\n# \\end{equation*}\n# d.h. $\\partial_{w_2} l(o_2)$ kann einfach bestimmt werden.\n\n# Für $\\partial_{w_1} l(o_2)$ erhalten wir \n# \\begin{equation*} \n# \\partial_{w_1} l(o_2)\n# = l'(o_2)a'(i_2)\\partial_{w_1} (w_2 o_1) \n# = l'(o_2)a'(i_2) w_2 \\partial_{w_1} o_1\n# \\end{equation*}\n# und mit $\\partial_{w_1} o_1 = a'(i_1)\\partial_{w_1} i_1 = a'(i_1) x$ folgt\n# \\begin{equation*} \n# \\partial_{w_1} l(o_2) = l'(o_2)a'(i_2) w_2 a'(i_1) x\n# \\end{equation*}\n\n# Analog kann man auch bei komplexeren Netzen beginnend von der Output-Seite hin zur Input-Seite Schritt für Schritt die Ableitungen nach von $l_i$ nach den Parametern der jeweiligen\n# Schicht generieren. Deshalb heißt dieser Zugang *Backpropagation*.\n\n# ## Zusammenfassung\n\n# Die Parameteranpassung bei neuronalen Netzen ist schwierig, da\n# die Zielfunktion oft nicht differenzierbar (z.B. RELU Aktivierung $a(x)=\\max(0,x)$)\n# bzw. nicht konvex ist, so dass die Ergebnisse von gradientenartigen Verfahren sehr stark von der Wahl des Anfangswertes abhängen (Nebenminima).\n# \n# Besonders populär sind stochastische Gradienten-Verfahren, die auch bei großen Trainings-Datensätzen sehr effizient sind. Die benötigten Ableitungen werden dabei in der Regel mit\n# Backpropagation berechnet.\n\n# Man beachte den Unterschied zwischen SGD und Coordinate-Descent. Mit beiden Verfahren minimiert man die Zielfunktion\n# \\begin{equation*} \n# l(w) = \\frac{1}{n} \\sum_{i=1}^n l_i(w),\n# \\quad\n# l_i(w) = \\big(g(x_i, w) -y_i\\big)^2\n# \\end{equation*}\n# durch approximative Gradienten-Updates\n# \\begin{equation*} \n# w^{(k+1)} = w^{(k)} - \\alpha^{(k)} g^{(k)}\n# \\end{equation*}\n# mit\n# \\begin{equation*} \n# g_{SGD}^{(k)} = \\partial_w l_{\\hat{i}}(w)\n# \\end{equation*}\n# bzw.\n# \\begin{equation*} \n# g_{CD}^{(k)} = \\partial_{w_{\\hat{i}}} l(w).\n# \\end{equation*}\n", "repo_name": "mre2110/NumMLv042", "sub_path": "_build/jupyter_execute/06_Neuronale_Netze.py", "file_name": "06_Neuronale_Netze.py", "file_ext": "py", "file_size_in_byte": 20204, "program_lang": "python", "lang": "de", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.linspace", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.maximum", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 224, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 230, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 239, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 264, "usage_type": "call"}, {"api_name": "sklearn.neural_network", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 283, "usage_type": "call"}, {"api_name": "sklearn.neural_network", "line_number": 283, "usage_type": "name"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 291, "usage_type": "call"}, {"api_name": "sklearn.neural_network", "line_number": 291, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 306, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 308, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 310, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 343, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 344, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 349, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 351, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 351, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 354, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 354, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 355, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 356, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 356, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 372, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 372, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 375, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 375, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 392, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 392, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 393, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 393, "usage_type": "name"}, {"api_name": "autograd.numpy.maximum", "line_number": 438, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 438, "usage_type": "name"}, {"api_name": "autograd.grad", "line_number": 440, "usage_type": "call"}, {"api_name": "autograd.grad", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 468, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 468, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 469, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 469, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 470, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 470, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 471, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 471, "usage_type": "name"}, {"api_name": "autograd.numpy.zeros", "line_number": 474, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 474, "usage_type": "name"}, {"api_name": "autograd.numpy.ones", "line_number": 484, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 484, "usage_type": "name"}, {"api_name": "autograd.numpy.ones", "line_number": 522, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 522, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 525, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 525, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 526, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 526, "usage_type": "name"}, {"api_name": "autograd.grad", "line_number": 562, "usage_type": "call"}, {"api_name": "autograd.numpy.random.permutation", "line_number": 569, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 569, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 569, "usage_type": "name"}, {"api_name": "autograd.numpy.random.seed", "line_number": 576, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 576, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 576, "usage_type": "name"}, {"api_name": "autograd.numpy.ones", "line_number": 577, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 577, "usage_type": "name"}, {"api_name": "autograd.numpy.random.seed", "line_number": 592, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 592, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 592, "usage_type": "name"}, {"api_name": "autograd.numpy.ones", "line_number": 593, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 593, "usage_type": "name"}, {"api_name": "autograd.numpy.random.permutation", "line_number": 614, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 614, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 614, "usage_type": "name"}, {"api_name": "autograd.numpy.random.seed", "line_number": 623, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 623, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 623, "usage_type": "name"}, {"api_name": "autograd.numpy.ones", "line_number": 624, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 624, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 627, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 627, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 628, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 628, "usage_type": "name"}]} +{"seq_id": "25956111568", "text": "import sys\nfrom os.path import join, abspath, dirname\n\n# PATH vars\n\nhere = lambda *x: join(abspath(dirname(__file__)), *x)\nPROJECT_ROOT = here(\"..\")\nroot = lambda *x: join(abspath(PROJECT_ROOT), *x)\nrepo_root = lambda *x: join(abspath(here(\"../..\")), *x)\n\nsys.path.insert(0, root(\"apps\"))\n\n\nDEBUG = True\n\nADMINS = ()\n\nMANAGERS = ADMINS\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.contrib.gis.db.backends.postgis\",\n \"NAME\": \"polling_stations\",\n \"USER\": \"postgres\",\n \"PASSWORD\": \"\",\n \"HOST\": \"\", # Empty for localhost through domain sockets or '127.0.0.1' for localhost through TCP.\n \"PORT\": \"\", # Set to empty string for default.\n }\n}\n\nimport dj_database_url\n\nDATABASES[\"default\"] = dj_database_url.config()\nDATABASES[\"default\"][\"ENGINE\"] = \"django.contrib.gis.db.backends.postgis\"\n\n# Hosts/domain names that are valid for this site; required if DEBUG is False\n# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts\nALLOWED_HOSTS = []\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# In a Windows environment this must be set to your system time zone.\nTIME_ZONE = \"Europe/London\"\n\n\nSITE_ID = 1\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = False\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale.\nUSE_L10N = True\n\n# If you set this to False, Django will not use timezone-aware datetimes.\nUSE_TZ = True\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/var/www/example.com/media/\"\nMEDIA_ROOT = root(\"assets\", \"uploads\")\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://media.lawrence.com/media/\", \"http://example.com/media/\"\nMEDIA_URL = \"/media/\"\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/home/media/media.lawrence.com/static/\"\nSTATIC_ROOT = root(\"static\")\n\n# URL prefix for static files.\n# Example: \"http://media.lawrence.com/static/\"\nSTATIC_URL = \"/static/\"\n\n# Additional locations of static files\nSTATICFILES_DIRS = (root(\"assets\"), root(\"../node_modules\"))\n\nfrom .static_files import * # noqa\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = \"asdasdasdasdasdasdasd\"\n\nMIDDLEWARE = (\n \"corsheaders.middleware.CorsMiddleware\",\n \"django.contrib.sessions.middleware.SessionMiddleware\",\n \"django.middleware.common.CommonMiddleware\",\n \"django.middleware.locale.LocaleMiddleware\",\n # 'django.middleware.csrf.CsrfViewMiddleware',\n \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n \"django.contrib.messages.middleware.MessageMiddleware\",\n \"data_finder.middleware.UTMTrackerMiddleware\",\n \"whitelabel.middleware.WhiteLabelMiddleware\",\n \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n \"pollingstations.middleware.BasicAuthMiddleware\",\n)\n\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"APP_DIRS\": True,\n \"DIRS\": [root(\"templates\")],\n \"OPTIONS\": {\n \"debug\": DEBUG,\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.i18n\",\n \"django.template.context_processors.media\",\n \"django.template.context_processors.static\",\n \"django.template.context_processors.request\",\n \"django.template.context_processors.tz\",\n \"django.contrib.messages.context_processors.messages\",\n \"django.contrib.auth.context_processors.auth\",\n \"dc_theme.context_processors.dc_theme_context\",\n \"dc_signup_form.context_processors.signup_form\",\n \"feedback.context_processors.feedback_form\",\n \"bug_reports.context_processors.bug_report_form\",\n \"pollingstations.context_processors.google_analytics\",\n \"pollingstations.context_processors.global_settings\",\n \"whitelabel.context_processors.base_template\",\n ],\n },\n }\n]\n\nROOT_URLCONF = \"polling_stations.urls\"\n\n# Python dotted path to the WSGI application used by Django's runserver.\nWSGI_APPLICATION = \"polling_stations.wsgi.application\"\n\nINSTALLED_APPS = (\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"django.contrib.humanize\",\n \"django.contrib.sessions\",\n \"django.contrib.sites\",\n \"django.contrib.messages\",\n \"django.contrib.staticfiles\",\n \"django.contrib.admin\",\n \"django.contrib.gis\",\n \"rest_framework\",\n \"rest_framework.authtoken\",\n \"rest_framework_gis\",\n \"raven.contrib.django.raven_compat\",\n \"django_extensions\",\n \"markdown_deux\",\n \"corsheaders\",\n \"pipeline\",\n \"dc_signup_form\",\n \"apiblueprint_view\",\n)\n\nPROJECT_APPS = (\n \"addressbase\",\n \"api\",\n \"councils\",\n \"data_collection\",\n \"data_finder\",\n \"dc_theme\",\n \"feedback\",\n \"file_uploads\",\n \"pollingstations\",\n \"bug_reports\",\n \"uk_geo_utils\",\n \"whitelabel\",\n)\n\nINSTALLED_APPS += PROJECT_APPS\n\nSESSION_ENGINE = \"django.contrib.sessions.backends.signed_cookies\"\n\n# A sample logging configuration. The only tangible logging\n# performed by this configuration is to send an email to\n# the site admins on every HTTP 500 error when DEBUG=False.\n# See http://docs.djangoproject.com/en/dev/topics/logging for\n# more details on how to customize your logging configuration.\nLOGGING = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"filters\": {\n \"require_debug_false\": {\"()\": \"django.utils.log.RequireDebugFalse\"},\n \"ignore_status_checks\": {\"()\": \"pollingstations.filters.StatusCheckFilter\"},\n },\n \"handlers\": {\n \"mail_admins\": {\n \"level\": \"ERROR\",\n \"filters\": [\"require_debug_false\", \"ignore_status_checks\"],\n \"class\": \"django.utils.log.AdminEmailHandler\",\n },\n \"null\": {\"class\": \"logging.NullHandler\"},\n \"sentry\": {\n \"level\": \"ERROR\",\n \"class\": \"raven.contrib.django.raven_compat.handlers.SentryHandler\",\n },\n },\n \"loggers\": {\n # Silence DisallowedHost exception by setting null error handler - see\n # https://docs.djangoproject.com/en/1.8/topics/logging/#django-security\n \"django.security.DisallowedHost\": {\"handlers\": [\"null\"], \"propagate\": False},\n \"file_uploads.views\": {\n \"handlers\": [\"sentry\"],\n \"level\": \"ERROR\",\n \"propagate\": True,\n },\n \"django.request\": {\n \"handlers\": [\"mail_admins\"],\n \"level\": \"ERROR\",\n \"propagate\": True,\n },\n },\n}\n\n\nLANGUAGE_CODE = \"en\"\nLANGUAGES = [(\"en\", \"English\"), (\"cy-gb\", \"Welsh\")]\nUSE_I18N = (True,)\nUSE_L10N = (True,)\nLOCALE_PATHS = (repo_root(\"locale\"),)\n\n\nLOGIN_REDIRECT_URL = \"file_uploads:councils_list\"\nLOGOUT_REDIRECT_URL = \"home\"\n\n\n# API Settings\nREST_FRAMEWORK = {\n # Use Django's standard `django.contrib.auth` permissions,\n # or allow read-only access for unauthenticated users.\n \"DEFAULT_PERMISSION_CLASSES\": [\n \"rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly\"\n ],\n \"DEFAULT_AUTHENTICATION_CLASSES\": (\n \"api.authentication.authentication.TokenAuthSupportQueryString\",\n ),\n \"DEFAULT_THROTTLE_CLASSES\": (\"rest_framework.throttling.AnonRateThrottle\",),\n \"DEFAULT_THROTTLE_RATES\": {\"anon\": \"1000/day\"},\n}\n\nEMBED_PREFIXES = (\"embed\",)\n\nWHITELABEL_PREFIXES = ()\n\n# CorsMiddleware config\nCORS_ORIGIN_ALLOW_ALL = True\nCORS_ORIGIN_WHITELIST = ()\nCORS_URLS_REGEX = r\"^/(api|embed)/.*$\"\n\n\nINTERNAL_IPS = \"127.0.0.1\"\nSITE_TITLE = \"Where Do I Vote?\"\nSITE_LOGO = \"images/logo-with-text.png\"\nSITE_LOGO_WIDTH = \"390px\"\n\nTEST_RUNNER = \"django.test.runner.DiscoverRunner\"\n\n\nADDRESS_MODEL = \"addressbase.Address\"\nONSUD_MODEL = \"addressbase.UprnToCouncil\"\n\nEMAIL_SIGNUP_ENDPOINT = \"https://democracyclub.org.uk/mailing_list/api_signup/v1/\"\nEMAIL_SIGNUP_API_KEY = \"\"\n\n\n# Disable Basic Auth by default\n# We only want to use this on staging deploys\nBASICAUTH_DISABLE = True\n\n\n# settings for load balancer status check\nCHECK_SERVER_CLEAN = True\nCLEAN_SERVER_FILE = \"~/clean\"\n\n\n# import application constants\nfrom .constants.councils import * # noqa\nfrom .constants.directions import * # noqa\nfrom .constants.elections import * # noqa\nfrom .constants.importers import * # noqa\nfrom .constants.tiles import * # noqa\nfrom .constants.uploads import * # noqa\n\n# Import .local.py last - settings in local.py override everything else\ntry:\n\n from .local import * # noqa\n\n try:\n INSTALLED_APPS += PROD_APPS # noqa\n except NameError:\n pass\n\nexcept ImportError:\n pass\n\nif DEBUG:\n INSTALLED_APPS += (\"dashboard\",)\n\n# importing test settings file if necessary (TODO chould be done better)\nif len(sys.argv) > 1 and sys.argv[1] in [\"test\", \"harvest\"]:\n from .testing import * # noqa\n", "repo_name": "mbateman/UK-Polling-Stations", "sub_path": "polling_stations/settings/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 9298, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "dj_database_url.config", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 302, "usage_type": "attribute"}]} +{"seq_id": "26999255908", "text": "import datetime\nimport pathlib\nimport re\nimport shutil\nfrom pathlib import Path\nfrom typing import Optional, Sequence\n\nimport magic\nfrom loguru import logger\n\nfrom .extractors import (\n ExifImageExtractor,\n ExifToolExtractor,\n Extractor,\n MTimeExtractor,\n RegexExtractor,\n)\nfrom .utils import sha256_file\n\nDEFAULT_IMAGE_EXTRACTORS = [\n ExifImageExtractor(),\n # e.g. '20200101_120101.jpg'\n RegexExtractor(re.compile(r\"(\\d{8}_\\d{6})\"), \"YYYYMMDD_HHmmss\"),\n # e.g. 'Screenshot_20200101-120101_Maps.jpg'\n RegexExtractor(re.compile(r\"Screenshot_(\\d{8}-\\d{6})_\\w.+\"), \"YYYYMMDD-HHmmss\"),\n # e.g. 'IMG-20200101-WA0001.jpg'\n RegexExtractor(re.compile(r\"IMG-(\\d{8})-WA\\d+\"), \"YYYYMMDD\"),\n MTimeExtractor(),\n]\n\nDEFAULT_VIDEO_EXTRACTORS = [\n ExifToolExtractor(),\n # e.g. 'VID-20200101-WA0001.mp4'\n RegexExtractor(re.compile(r\"VID-(\\d{8})-WA\\d+\"), \"YYYYMMDD\"),\n # E.g. # e.g. '20200101_120101.mp4'\n RegexExtractor(re.compile(r\"(\\d{8}_\\d{6})\"), \"YYYYMMDD_HHmmss\"),\n MTimeExtractor(),\n]\n\n\nclass Organizer:\n \"\"\"\n The organizer takes care of actually organizing a source directory to a\n destination.\n\n It uses a sequence of extractors for images and videos to accomplish this task.\n \"\"\"\n\n def __init__(\n self,\n image_extractors: Sequence[Extractor] = (),\n video_extractors: Sequence[Extractor] = (),\n dry_run: bool = False,\n remove_source: bool = False,\n ) -> None:\n \"\"\"Initialize organizer\n\n :param image_extractors: Ordered sequence of date extractors to use for\n images. Dates are extracted in order. I.e, if the 1st extractor manages\n to extract the date for a particular file, we won't even attempt any of\n the next extractors.\n :param video_extractors: Ordered sequence of date extractors to use for\n videos. Dates are extracted in order. I.e, if the 1st extractor manages\n to extract the date for a particular file, we won't even attempt any\n of the next extractors.\n :param dry_run: Whether to perform a dry run. In a dry run some log messages\n are printed, but we won't actually copy over any files.\n :param remove_source: Whether to remove the source path(s) after copying is\n complete. This parameter is ignored when `dry_run` is set to True.\n \"\"\"\n self.image_extractors = image_extractors\n self.video_extractors = video_extractors\n self.dry_run = dry_run\n\n self.remove_source = remove_source if not self.dry_run else False\n\n def extract_date(self, path: pathlib.Path) -> Optional[datetime.date]:\n \"\"\"Extract the date of a single file\n\n :param path: path of file for which we should extract the date.\n :return: Date if file is an image or video and it can be extracted,\n None otherwise.\n \"\"\"\n if is_image(path):\n return self._extract_image_date(path)\n if is_mp4(path):\n return self._extract_video_date(path)\n logger.warning(f\"{path} is not an image or video, skipping...\")\n return None\n\n def _extract_file_date(\n self, path: pathlib.Path, extractors: Sequence[Extractor]\n ) -> Optional[datetime.date]:\n for extractor in extractors:\n logger.debug(f\"Attempting extractor {extractor.__class__.__name__}\")\n extraction = extractor.extract(path)\n if extraction is not None:\n return extraction\n\n logger.info(\"Could not determine date for any extractor.\")\n return None\n\n def _extract_image_date(self, path: pathlib.Path) -> Optional[datetime.date]:\n return self._extract_file_date(path, self.image_extractors)\n\n def _extract_video_date(self, path: pathlib.Path) -> Optional[datetime.date]:\n return self._extract_file_date(path, self.video_extractors)\n\n def organize(self, source: Path, destination: Path) -> None:\n \"\"\"Main organizer\"\"\"\n if source.is_file():\n logger.debug(f\"{source} is a file\")\n self.organize_file(source, destination)\n elif source.is_dir():\n logger.debug(f\"{source} is a directory\")\n self.organize_dir(source, destination)\n else:\n raise NotImplementedError\n\n def organize_file(\n self,\n source: Path,\n destination: Path,\n ) -> None:\n \"\"\"Organize a single file.\"\"\"\n logger.debug(f\"Organizing {source}\")\n date = self.extract_date(source)\n\n if date is None:\n return\n\n dest_dir = create_date_path(destination, date)\n\n dest_path = dest_dir / source.name\n logger.debug(f\"Determined destination path as {dest_path}\")\n dest_dir.mkdir(parents=True, exist_ok=True) # ensure dir exists\n if dest_path.exists():\n logger.warning(f\"{source.name} already exists on destination, skipping\")\n return # skipping, since it already exists.\n logger.info(f\"Copying {source} to {dest_path}\")\n\n # Return early if we are doing a dry run.\n if self.dry_run:\n return\n\n try:\n verify_copy(source, dest_path)\n except ValueError:\n logger.exception(f\"Failed to copy {source} to {dest_path}\")\n raise\n\n if self.remove_source and source.is_file():\n logger.info(f\"Removing {source}\")\n source.unlink() # removing source.\n\n def organize_dir(self, source: Path, destination: Path) -> None:\n \"\"\"\n Recursively organize a directory.\n\n :raises: RuntimeError when trying to process extremely deep directory tree.\n \"\"\"\n logger.debug(f\"Organizing {source}\")\n for item in source.iterdir():\n if item.is_file():\n self.organize_file(item, destination)\n elif item.is_dir():\n # a little recursion\n self.organize_dir(item, destination)\n else:\n # skipping due to don't know how to handle\n continue\n\n if self.remove_source and not self.dry_run:\n try:\n source.rmdir()\n except OSError as error:\n if str(error).startswith(\"[Errno 39]\"):\n # means directory is not empty.\n # should warn that source is unremovable.\n pass\n else:\n raise\n\n\ndef is_image(path: Path) -> bool:\n mimetype = magic.from_file(str(path), mime=True)\n return mimetype.split(\"/\")[0] == \"image\"\n\n\ndef is_mp4(path: Path) -> bool:\n mimetype = magic.from_file(str(path), mime=True)\n return mimetype == \"video/mp4\"\n\n\ndef verify_copy(source: Path, destination: Path) -> None:\n \"\"\"\n Copy a file, verifying that the copied file's contents are identical\n to the source contents.\n\n Will attempt to copy metadata as well, with the caveats listed in:\n https://docs.python.org/3.7/library/shutil.html#shutil.copy2\n\n In case the contents do not match, we will attempt to remove the\n destination if it is a file, after which a ValueError is thrown.\n\n :raises: ValueError in case contents do not match\n :raises: ValueError in case source is not a file\n :raises: ValueError in case destination is a directory.\n :raises: OSError in case file's can't be written.\n \"\"\"\n if not source.is_file():\n raise ValueError(\"Source must be a file\")\n if destination.is_dir():\n raise ValueError(\"Destination may not be a directory\")\n source_sha256 = sha256_file(source)\n copied = Path(shutil.copy2(source, destination))\n dest_sha256 = sha256_file(copied)\n if source_sha256 != dest_sha256:\n if copied.is_file():\n copied.unlink()\n raise ValueError(\"Source' and destination's contents did not match!\")\n\n\ndef create_date_path(root: Path, date: datetime.date) -> Path:\n \"\"\"Create path form a root path and a date.\"\"\"\n return root / Path(str(date.year)) / Path(str(date.month)) / Path(str(date.day))\n", "repo_name": "sndrtj/image-date-organizer", "sub_path": "src/image_date_organizer/organize.py", "file_name": "organize.py", "file_ext": "py", "file_size_in_byte": 8070, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "extractors.ExifImageExtractor", "line_number": 21, "usage_type": "call"}, {"api_name": "extractors.RegexExtractor", "line_number": 23, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "extractors.RegexExtractor", "line_number": 25, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "extractors.RegexExtractor", "line_number": 27, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 27, "usage_type": "call"}, {"api_name": "extractors.MTimeExtractor", "line_number": 28, "usage_type": "call"}, {"api_name": "extractors.ExifToolExtractor", "line_number": 32, "usage_type": "call"}, {"api_name": "extractors.RegexExtractor", "line_number": 34, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 34, "usage_type": "call"}, {"api_name": "extractors.RegexExtractor", "line_number": 36, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 36, "usage_type": "call"}, {"api_name": "extractors.MTimeExtractor", "line_number": 37, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 51, "usage_type": "name"}, {"api_name": "extractors.Extractor", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 52, "usage_type": "name"}, {"api_name": "extractors.Extractor", "line_number": 52, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "loguru.logger.warning", "line_number": 88, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 92, "usage_type": "name"}, {"api_name": "extractors.Extractor", "line_number": 92, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 95, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 95, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 100, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 100, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 103, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 106, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 109, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 112, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 112, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 115, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 115, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 123, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 126, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 126, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 135, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 135, "usage_type": "name"}, {"api_name": "loguru.logger.warning", "line_number": 138, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 138, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 140, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 140, "usage_type": "name"}, {"api_name": "loguru.logger.exception", "line_number": 149, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 149, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 153, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 153, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 156, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 162, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 162, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 185, "usage_type": "name"}, {"api_name": "magic.from_file", "line_number": 186, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 190, "usage_type": "name"}, {"api_name": "magic.from_file", "line_number": 191, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 195, "usage_type": "name"}, {"api_name": "utils.sha256_file", "line_number": 215, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 216, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 216, "usage_type": "call"}, {"api_name": "utils.sha256_file", "line_number": 217, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 224, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 224, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 226, "usage_type": "call"}]} +{"seq_id": "39252639283", "text": "\n##LESSSE\n##10 November 2018\n##gmidi\n##____________\n##Methods for pygmidi vizualization adapted from pypianoroll library by Hao-Wen Dong available in https://github.com/salu133445/pypianoroll\n##____________\n\n\"\"\"Module for plotting multi-track and single-track piano-rolls.\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\nimport numpy as np\nimport pretty_midi\n\ntry:\n import matplotlib\n from matplotlib import pyplot as plt\n from matplotlib.patches import Patch\n HAS_MATPLOTLIB = True\nexcept ImportError:\n HAS_MATPLOTLIB = False\n\ntry:\n from moviepy.editor import VideoClip\n from moviepy.video.io.bindings import mplfig_to_npimage\n HAS_MOVIEPY = True\nexcept ImportError:\n HAS_MOVIEPY = False\n\ndef plot_pianoroll(ax, pianoroll, is_drum=False, beat_resolution=None,\n downbeats=None, preset='default', cmap='Blues', xtick='auto',\n ytick='octave', xticklabel=True, yticklabel='auto',\n tick_loc=None, tick_direction='in', label='both',\n grid='both', grid_linestyle=':', grid_linewidth=.5):\n \"\"\"\n Plot a piano-roll given as a numpy array.\n\n Parameters\n ----------\n ax : matplotlib.axes.Axes object\n The :class:`matplotlib.axes.Axes` object where the piano-roll will\n be plotted on.\n pianoroll : np.ndarray\n The piano-roll to be plotted. The values should be in [0, 1] when data\n type is float, and in [0, 127] when data type is integer.\n\n - For a 2D array, shape=(num_time_step, num_pitch).\n - For a 3D array, shape=(num_time_step, num_pitch, num_channel),\n where channels can be either RGB or RGBA.\n\n is_drum : bool\n Drum indicator. True for drums. False for other instruments. Default\n to False.\n beat_resolution : int\n Resolution of a beat (in time step). Required and only effective\n when `xticklabel` is 'beat'.\n downbeats : list\n Indices of time steps that contain downbeats., i.e. the first time\n step of a bar.\n preset : {'default', 'plain', 'frame'}\n Preset themes for the plot.\n\n - In 'default' preset, the ticks, grid and labels are on.\n - In 'frame' preset, the ticks and grid are both off.\n - In 'plain' preset, the x- and y-axis are both off.\n\n cmap : `matplotlib.colors.Colormap`\n Colormap to use in :func:`matplotlib.pyplot.imshow`. Default to\n 'Blues'. Only effective when `pianoroll` is 2D.\n xtick : {'auto', 'beat', 'step', 'off'}\n Use beat number or step number as ticks along the x-axis, or\n automatically set to 'beat' when `beat_resolution` is given and set\n to 'step', otherwise. Default to 'auto'.\n ytick : {'octave', 'pitch', 'off'}\n Use octave or pitch as ticks along the y-axis. Default to 'octave'.\n xticklabel : bool\n Indicate whether to add tick labels along the x-axis. Only effective\n when `xtick` is not 'off'.\n yticklabel : {'auto', 'name', 'number', 'off'}\n If 'name', use octave name and pitch name (key name when `is_drum`\n is True) as tick labels along the y-axis. If 'number', use pitch\n number. If 'auto', set to 'name' when `ytick` is 'octave' and\n 'number' when `ytick` is 'pitch'. Default to 'auto'. Only effective\n when `ytick` is not 'off'.\n tick_loc : tuple or list\n List of locations to put ticks. Availables elements are 'bottom',\n 'top', 'left' and 'right'. If None, default to ('bottom', 'left').\n tick_direction : {'in', 'out', 'inout'}\n Put ticks inside the axes, outside the axes, or both. Default to\n 'in'. Only effective when `xtick` and `ytick` are not both 'off'.\n label : {'x', 'y', 'both', 'off'}\n Add label to the x-axis, y-axis, both or neither. Default to 'both'.\n grid : {'x', 'y', 'both', 'off'}\n Add grid to the x-axis, y-axis, both or neither. Default to 'both'.\n grid_linestyle : str\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linestyle'\n argument.\n grid_linewidth : float\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linewidth'\n argument.\n\n \"\"\"\n if not HAS_MATPLOTLIB:\n raise ImportError(\"matplotlib package is required for plotting \"\n \"supports.\")\n\n if pianoroll.ndim not in (2, 3):\n raise ValueError(\"`pianoroll` must be a 2D or 3D numpy array\")\n if pianoroll.shape[1] != 128:\n raise ValueError(\"The shape of `pianoroll` must be (num_time_step, \"\n \"128)\")\n if xtick not in ('auto', 'beat', 'step', 'off'):\n raise ValueError(\"`xtick` must be one of {'auto', 'beat', 'step', \"\n \"'none'}\")\n if xtick == 'beat' and beat_resolution is None:\n raise ValueError(\"`beat_resolution` must be a number when `xtick` \"\n \"is 'beat'\")\n if ytick not in ('octave', 'pitch', 'off'):\n raise ValueError(\"`ytick` must be one of {octave', 'pitch', 'off'}\")\n if not isinstance(xticklabel, bool):\n raise TypeError(\"`xticklabel` must be of bool type\")\n if yticklabel not in ('auto', 'name', 'number', 'off'):\n raise ValueError(\"`yticklabel` must be one of {'auto', 'name', \"\n \"'number', 'off'}\")\n if tick_direction not in ('in', 'out', 'inout'):\n raise ValueError(\"`tick_direction` must be one of {'in', 'out',\"\n \"'inout'}\")\n if label not in ('x', 'y', 'both', 'off'):\n raise ValueError(\"`label` must be one of {'x', 'y', 'both', 'off'}\")\n if grid not in ('x', 'y', 'both', 'off'):\n raise ValueError(\"`grid` must be one of {'x', 'y', 'both', 'off'}\")\n\n # plotting\n if pianoroll.ndim > 2:\n to_plot = pianoroll.transpose(1, 0, 2)\n else:\n to_plot = pianoroll.T\n if (np.issubdtype(pianoroll.dtype, np.bool_)\n or np.issubdtype(pianoroll.dtype, np.floating)):\n ax.imshow(to_plot, cmap=cmap, aspect='auto', vmin=0, vmax=1,\n origin='lower', interpolation='none')\n elif np.issubdtype(pianoroll.dtype, np.integer):\n ax.imshow(to_plot, cmap=cmap, aspect='auto', vmin=0, vmax=127,\n origin='lower', interpolation='none')\n else:\n raise TypeError(\"Unsupported data type for `pianoroll`\")\n\n # tick setting\n if tick_loc is None:\n tick_loc = ('bottom', 'left')\n if xtick == 'auto':\n xtick = 'beat' if beat_resolution is not None else 'step'\n if yticklabel == 'auto':\n yticklabel = 'name' if ytick == 'octave' else 'number'\n\n if preset == 'plain':\n ax.axis('off')\n elif preset == 'frame':\n ax.tick_params(direction=tick_direction, bottom=False, top=False,\n left=False, right=False, labelbottom=False,\n labeltop=False, labelleft=False, labelright=False)\n else:\n ax.tick_params(direction=tick_direction, bottom=('bottom' in tick_loc),\n top=('top' in tick_loc), left=('left' in tick_loc),\n right=('right' in tick_loc),\n labelbottom=(xticklabel != 'off'),\n labelleft=(yticklabel != 'off'),\n labeltop=False, labelright=False)\n\n # x-axis\n if xtick == 'beat' and preset != 'frame':\n num_beat = pianoroll.shape[0]//beat_resolution\n xticks_major = beat_resolution * np.arange(0, num_beat)\n xticks_minor = beat_resolution * (0.5 + np.arange(0, num_beat))\n xtick_labels = np.arange(1, 1 + num_beat)\n ax.set_xticks(xticks_major)\n ax.set_xticklabels('')\n ax.set_xticks(xticks_minor, minor=True)\n ax.set_xticklabels(xtick_labels, minor=True)\n ax.tick_params(axis='x', which='minor', width=0)\n\n # y-axis\n if ytick == 'octave':\n ax.set_yticks(np.arange(0, 128, 12))\n if yticklabel == 'name':\n ax.set_yticklabels(['C{}'.format(i - 2) for i in range(11)])\n elif ytick == 'step':\n ax.set_yticks(np.arange(0, 128))\n if yticklabel == 'name':\n if is_drum:\n ax.set_yticklabels([pretty_midi.note_number_to_drum_name(i)\n for i in range(128)])\n else:\n ax.set_yticklabels([pretty_midi.note_number_to_name(i)\n for i in range(128)])\n\n # axis labels\n if label == 'x' or label == 'both':\n if xtick == 'step' or not xticklabel:\n ax.set_xlabel('time (step)')\n else:\n ax.set_xlabel('time (beat)')\n\n if label == 'y' or label == 'both':\n if is_drum:\n ax.set_ylabel('key name')\n else:\n ax.set_ylabel('pitch')\n\n # grid\n if grid != 'off':\n ax.grid(axis=grid, color='k', linestyle=grid_linestyle,\n linewidth=grid_linewidth)\n\n # downbeat boarder\n if downbeats is not None and preset != 'plain':\n for step in downbeats:\n ax.axvline(x=step, color='k', linewidth=1)\n\n\ndef plot_track(track, filepath=None, beat_resolution=None, downbeats=None,\n preset='default', cmap='Blues', xtick='auto', ytick='octave',\n xticklabel=True, yticklabel='auto', tick_loc=None,\n tick_direction='in', label='both', grid='both',\n grid_linestyle=':', grid_linewidth=.5):\n \"\"\"\n Plot the piano-roll or save a plot of the piano-roll.\n\n Parameters\n ----------\n filepath :\n The filepath to save the plot. If None, default to save nothing.\n beat_resolution : int\n Resolution of a beat (in time step). Required and only effective\n when `xtick` is 'beat'.\n downbeats : list\n Indices of time steps that contain downbeats., i.e. the first time\n step of a bar.\n\n preset : {'default', 'plain', 'frame'}\n Preset themes for the plot.\n\n - In 'default' preset, the ticks, grid and labels are on.\n - In 'frame' preset, the ticks and grid are both off.\n - In 'plain' preset, the x- and y-axis are both off.\n\n cmap : `matplotlib.colors.Colormap`\n Colormap to use in :func:`matplotlib.pyplot.imshow`. Default to\n 'Blues'. Only effective when `pianoroll` is 2D.\n xtick : {'auto', 'beat', 'step', 'off'}\n Use beat number or step number as ticks along the x-axis, or\n automatically set to 'beat' when `beat_resolution` is given and set\n to 'step', otherwise. Default to 'auto'.\n ytick : {'octave', 'pitch', 'off'}\n Use octave or pitch as ticks along the y-axis. Default to 'octave'.\n xticklabel : bool\n Indicate whether to add tick labels along the x-axis. Only effective\n when `xtick` is not 'off'.\n yticklabel : {'auto', 'name', 'number', 'off'}\n If 'name', use octave name and pitch name (key name when `is_drum`\n is True) as tick labels along the y-axis. If 'number', use pitch\n number. If 'auto', set to 'name' when `ytick` is 'octave' and\n 'number' when `ytick` is 'pitch'. Default to 'auto'. Only effective\n when `ytick` is not 'off'.\n tick_loc : tuple or list\n List of locations to put ticks. Availables elements are 'bottom',\n 'top', 'left' and 'right'. If None, default to ('bottom', 'left').\n tick_direction : {'in', 'out', 'inout'}\n Put ticks inside the axes, outside the axes, or both. Default to\n 'in'. Only effective when `xtick` and `ytick` are not both 'off'.\n label : {'x', 'y', 'both', 'off'}\n Add label to the x-axis, y-axis, both or neither. Default to 'both'.\n grid : {'x', 'y', 'both', 'off'}\n Add grid to the x-axis, y-axis, both or neither. Default to 'both'.\n grid_linestyle : str\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linestyle'\n argument.\n grid_linewidth : float\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linewidth'\n argument.\n\n Returns\n -------\n fig : `matplotlib.figure.Figure` object\n A :class:`matplotlib.figure.Figure` object.\n ax : `matplotlib.axes.Axes` object\n A :class:`matplotlib.axes.Axes` object.\n\n \"\"\"\n if not HAS_MATPLOTLIB:\n raise ImportError(\"matplotlib package is required for plotting \"\n \"supports.\")\n\n fig, ax = plt.subplots()\n plot_pianoroll(ax, track.pianoroll, track.is_drum, beat_resolution,\n downbeats, preset=preset, cmap=cmap, xtick=xtick,\n ytick=ytick, xticklabel=xticklabel, yticklabel=yticklabel,\n tick_loc=tick_loc, tick_direction=tick_direction,\n label=label, grid=grid, grid_linestyle=grid_linestyle,\n grid_linewidth=grid_linewidth)\n\n if filepath is not None:\n plt.savefig(filepath)\n\n return fig, ax\n\ndef plot_multitrack(multitrack, filepath=None, mode='stacked',\n track_label='name', preset='frame', cmaps=None,\n xtick='off', ytick='octave', xticklabel=False,\n yticklabel='auto', tick_loc=None, tick_direction='in',\n label='y', grid='off', grid_linestyle=':',\n grid_linewidth=.5, background=np.array([1,1,1])):\n \"\"\"\n Plot the piano-rolls or save a plot of them.\n\n Parameters\n ----------\n filepath : str\n The filepath to save the plot. If None, default to save nothing.\n mode : {'separate', 'stacked', 'hybrid'}\n Plotting modes. Default to 'separate'.\n\n - In 'separate' mode, all the tracks are plotted separately.\n - In 'stacked' mode, a color is assigned based on `cmaps` to the\n piano-roll of each track and the piano-rolls are stacked and\n plotted as a colored image with RGB channels.\n - In 'hybrid' mode, the drum tracks are merged into a 'Drums' track,\n while the other tracks are merged into an 'Others' track, and the\n two merged tracks are then plotted separately.\n\n track_label : {'name', 'program', 'family', 'off'}\n Add track name, program name, instrument family name or none as\n labels to the track. When `mode` is 'hybrid', all options other\n than 'off' will label the two track with 'Drums' and 'Others'.\n preset : {'default', 'plain', 'frame'}\n Preset themes for the plot.\n\n - In 'default' preset, the ticks, grid and labels are on.\n - In 'frame' preset, the ticks and grid are both off.\n - In 'plain' preset, the x- and y-axis are both off.\n\n cmaps : tuple or list\n List of `matplotlib.colors.Colormap` instances or colormap codes.\n\n - When `mode` is 'separate', each element will be passed to each\n call of :func:`matplotlib.pyplot.imshow`. Default to ('Blues',\n 'Oranges', 'Greens', 'Reds', 'Purples', 'Greys').\n - When `mode` is stacked, a color is assigned based on `cmaps` to\n the piano-roll of each track. Default to ('hsv').\n - When `mode` is 'hybrid', the first (second) element is used in the\n 'Drums' ('Others') track. Default to ('Blues', 'Greens').\n\n xtick : {'auto', 'beat', 'step', 'off'}\n Use beat number or step number as ticks along the x-axis, or\n automatically set to 'beat' when `beat_resolution` is given and set\n to 'step', otherwise. Default to 'auto'.\n ytick : {'octave', 'pitch', 'off'}\n Use octave or pitch as ticks along the y-axis. Default to 'octave'.\n xticklabel : bool\n Indicate whether to add tick labels along the x-axis. Only effective\n when `xtick` is not 'off'.\n yticklabel : {'auto', 'name', 'number', 'off'}\n If 'name', use octave name and pitch name (key name when `is_drum`\n is True) as tick labels along the y-axis. If 'number', use pitch\n number. If 'auto', set to 'name' when `ytick` is 'octave' and\n 'number' when `ytick` is 'pitch'. Default to 'auto'. Only effective\n when `ytick` is not 'off'.\n tick_loc : tuple or list\n List of locations to put ticks. Availables elements are 'bottom',\n 'top', 'left' and 'right'. If None, default to ('bottom', 'left').\n tick_direction : {'in', 'out', 'inout'}\n Put ticks inside the axes, outside the axes, or both. Default to\n 'in'. Only effective when `xtick` and `ytick` are not both 'off'.\n label : {'x', 'y', 'both', 'off'}\n Add label to the x-axis, y-axis, both or neither. Default to 'both'.\n grid : {'x', 'y', 'both', 'off'}\n Add grid to the x-axis, y-axis, both or neither. Default to 'both'.\n grid_linestyle : str\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linestyle'\n argument.\n grid_linewidth : float\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linewidth'\n argument.\n\n Returns\n -------\n fig : `matplotlib.figure.Figure` object\n A :class:`matplotlib.figure.Figure` object.\n axs : list\n List of :class:`matplotlib.axes.Axes` object.\n\n \"\"\"\n if not HAS_MATPLOTLIB:\n raise ImportError(\"matplotlib package is required for plotting \"\n \"supports.\")\n\n def get_track_label(track_label, track=None):\n \"\"\"Convenient function to get track labels\"\"\"\n if track_label == 'name':\n return track.name if track.name != \"\" else pretty_midi.program_to_instrument_class(track.program)\n elif track_label == 'program':\n return pretty_midi.program_to_instrument_name(track.program)\n elif track_label == 'family':\n return pretty_midi.program_to_instrument_class(track.program)\n elif track is None:\n return track_label\n\n def add_tracklabel(ax, track_label, track=None):\n \"\"\"Convenient function for adding track labels\"\"\"\n if not ax.get_ylabel():\n return\n ax.set_ylabel(get_track_label(track_label, track) + '\\n\\n'\n + ax.get_ylabel())\n\n multitrack.check_validity()\n if not multitrack.tracks:\n raise ValueError(\"There is no track to plot\")\n if mode not in ('separate', 'stacked', 'hybrid'):\n raise ValueError(\"`mode` must be one of {'separate', 'stacked', \"\n \"'hybrid'}\")\n if track_label not in ('name', 'program', 'family', 'off'):\n raise ValueError(\"`track_label` must be one of {'name', 'program', \"\n \"'family'}\")\n\n if cmaps is None:\n if mode == 'separate':\n cmaps = ('Blues', 'Oranges', 'Greens', 'Reds', 'Purples', 'Greys')\n elif mode == 'stacked':\n cmaps = ('rainbow',)\n else:\n cmaps = ('Blues', 'Greens')\n\n num_track = len(multitrack.tracks)\n downbeats = multitrack.get_downbeat_steps()\n\n if mode == 'separate':\n if num_track > 1:\n fig, axs = plt.subplots(num_track, sharex=True)\n else:\n fig, ax = plt.subplots()\n axs = [ax]\n\n for idx, track in enumerate(multitrack.tracks):\n now_xticklabel = xticklabel if idx < num_track else False\n plot_pianoroll(axs[idx], track.pianoroll, False,\n multitrack.beat_resolution, downbeats, preset=preset,\n cmap=cmaps[idx%len(cmaps)], xtick=xtick, ytick=ytick,\n xticklabel=now_xticklabel, yticklabel=yticklabel,\n tick_loc=tick_loc, tick_direction=tick_direction,\n label=label, grid=grid,\n grid_linestyle=grid_linestyle,\n grid_linewidth=grid_linewidth)\n if track_label != 'none':\n add_tracklabel(axs[idx], track_label, track)\n\n if num_track > 1:\n fig.subplots_adjust(hspace=0)\n\n if filepath is not None:\n plt.savefig(filepath)\n\n return (fig, axs)\n\n elif mode == 'stacked':\n is_all_drum = True\n for track in multitrack.tracks:\n if not track.is_drum:\n is_all_drum = False\n\n balpha=False\n\n fig, ax = plt.subplots()\n stacked = multitrack.get_stacked_pianorolls()\n indices = tuple(np.reshape(np.concatenate([np.indices(stacked.shape[:-1]),[np.argmax(stacked,-1)]],0),(3,-1,)))\n unique_playin = np.zeros(stacked.shape)\n unique_playin[indices] = 1\n unique_playin = unique_playin*(stacked>0) \n stacked = unique_playin \n unique_volume = unique_playin*stacked\n alpha = np.reshape(np.max(unique_volume,-1),(-1,1))\n colormap = matplotlib.cm.get_cmap(cmaps[0])\n cmatrix = colormap(np.arange(0, 1, 1 / num_track))[:, :3]\n recolored = np.matmul(stacked.reshape(-1, num_track), cmatrix)\n if balpha:\n recolored = np.concatenate([recolored,alpha],-1)\n background = np.tile(np.concatenate([background,[1]],-1),(recolored.shape[0],1))\n n = 4\n else:\n background = np.tile(background,(recolored.shape[0],1))\n n = 3\n mask = np.reshape(np.repeat((np.sum(recolored,1)==0),(n)),(-1,n))\n recolored = np.where(mask,background,recolored)\n \n stacked = recolored.reshape(stacked.shape[:2] + (n, ))\n\n plot_pianoroll(ax, stacked, is_all_drum, multitrack.beat_resolution,\n downbeats, preset=preset, xtick=xtick, ytick=ytick,\n xticklabel=xticklabel, yticklabel=yticklabel,\n tick_loc=tick_loc, tick_direction=tick_direction,\n label=label, grid=grid, grid_linestyle=grid_linestyle,\n grid_linewidth=grid_linewidth)\n\n if track_label != 'none':\n patches = [Patch(color=cmatrix[idx],\n label=get_track_label(track_label, track))\n for idx, track in enumerate(multitrack.tracks)]\n f = lambda x: len(get_track_label(track_label, x))\n l = max(multitrack.tracks, key=f)\n if len(patches) > 3:\n ncol = 2\n anchor = (0.5,1.1)\n else:\n ncol = 1\n anchor = (0.5,1.05)\n\n if len(patches) < 9:\n plt.legend(handles=patches, fancybox=True,loc='upper center',ncol=ncol, bbox_to_anchor=anchor, framealpha=1)\n else:\n plt.legend(handles=patches, fancybox=True,loc='best',ncol=ncol, bbox_to_anchor=(1+.05*f(l)/2,.5), framealpha=1)\n\n if filepath is not None: \n fig.set_size_inches(10, 6)\n plt.savefig(filepath,dpi=400)\n\n return (fig, [ax])\n\n elif mode == 'hybrid':\n drums = [i for i, track in enumerate(multitrack.tracks) if track.is_drum]\n others = [i for i in range(len(multitrack.tracks)) if i not in drums]\n multitrack.merge_tracks(drums,mode='sum',name=\"drums\") if len(drums) > 0 else 0\n multitrack.merge_tracks(others,mode='sum',name=\"others\") if len(others) > 0 else 0\n\n if num_track > 1:\n fig, axs = plt.subplots(num_track, sharex=True)\n else:\n fig, ax = plt.subplots()\n axs = [ax]\n for idx, track in enumerate(multitrack.tracks):\n now_xticklabel = xticklabel if idx < num_track else False\n plot_pianoroll(axs[idx], track.pianoroll, False,\n multitrack.beat_resolution, downbeats, preset=preset,\n cmap=cmaps[idx%len(cmaps)], xtick=xtick, ytick=ytick,\n xticklabel=now_xticklabel, yticklabel=yticklabel,\n tick_loc=tick_loc, tick_direction=tick_direction,\n label=label, grid=grid,\n grid_linestyle=grid_linestyle,\n grid_linewidth=grid_linewidth)\n if track_label != 'none':\n add_tracklabel(axs[idx], track_label, track)\n fig.subplots_adjust(hspace=0)\n\n if track_label != 'none':\n add_tracklabel(axs[0], 'Drums')\n add_tracklabel(axs[1], 'Others')\n\n if filepath is not None:\n plt.savefig(filepath)\n\n return (fig, axs)\n\n\ndef save_animation(filepath, pianoroll, window=1000, hop=24, fps=2, is_drum=False,\n beat_resolution=None, downbeats=None, preset='default',\n cmap='Blues', xtick='auto', ytick='octave', xticklabel=True,\n yticklabel='auto', tick_loc=None, tick_direction='in',\n label='both', grid='both', grid_linestyle=':',\n grid_linewidth=.5, **kwargs):\n \"\"\"\n Save a piano-roll to an animation in video or GIF format.\n\n Parameters\n ----------\n filepath : str\n Path to save the video file.\n pianoroll : np.ndarray\n The piano-roll to be plotted. The values should be in [0, 1] when data\n type is float, and in [0, 127] when data type is integer.\n\n - For a 2D array, shape=(num_time_step, num_pitch).\n - For a 3D array, shape=(num_time_step, num_pitch, num_channel),\n where channels can be either RGB or RGBA.\n\n window : int\n Window size to be applied to `pianoroll` for the animation.\n hop : int\n Hop size to be applied to `pianoroll` for the animation.\n fps : int\n Number of frames per second in the resulting video or GIF file.\n is_drum : bool\n Drum indicator. True for drums. False for other instruments. Default\n to False.\n beat_resolution : int\n Resolution of a beat (in time step). Required and only effective\n when `xtick` is 'beat'.\n downbeats : list\n Indices of time steps that contain downbeats., i.e. the first time\n step of a bar.\n\n preset : {'default', 'plain', 'frame'}\n Preset themes for the plot.\n\n - In 'default' preset, the ticks, grid and labels are on.\n - In 'frame' preset, the ticks and grid are both off.\n - In 'plain' preset, the x- and y-axis are both off.\n\n cmap : `matplotlib.colors.Colormap`\n Colormap to use in :func:`matplotlib.pyplot.imshow`. Default to\n 'Blues'. Only effective when `pianoroll` is 2D.\n xtick : {'auto', 'beat', 'step', 'off'}\n Use beat number or step number as ticks along the x-axis, or\n automatically set to 'beat' when `beat_resolution` is given and set\n to 'step', otherwise. Default to 'auto'.\n ytick : {'octave', 'pitch', 'off'}\n Use octave or pitch as ticks along the y-axis. Default to 'octave'.\n xticklabel : bool\n Indicate whether to add tick labels along the x-axis. Only effective\n when `xtick` is not 'off'.\n yticklabel : {'auto', 'name', 'number', 'off'}\n If 'name', use octave name and pitch name (key name when `is_drum`\n is True) as tick labels along the y-axis. If 'number', use pitch\n number. If 'auto', set to 'name' when `ytick` is 'octave' and\n 'number' when `ytick` is 'pitch'. Default to 'auto'. Only effective\n when `ytick` is not 'off'.\n tick_loc : tuple or list\n List of locations to put ticks. Availables elements are 'bottom',\n 'top', 'left' and 'right'. If None, default to ('bottom', 'left').\n tick_direction : {'in', 'out', 'inout'}\n Put ticks inside the axes, outside the axes, or both. Default to\n 'in'. Only effective when `xtick` and `ytick` are not both 'off'.\n label : {'x', 'y', 'both', 'off'}\n Add label to the x-axis, y-axis, both or neither. Default to 'both'.\n grid : {'x', 'y', 'both', 'off'}\n Add grid to the x-axis, y-axis, both or neither. Default to 'both'.\n grid_linestyle : str\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linestyle'\n argument.\n grid_linewidth : float\n Will be passed to :meth:`matplotlib.axes.Axes.grid` as 'linewidth'\n argument.\n\n \"\"\"\n if not HAS_MOVIEPY:\n raise ImportError(\"moviepy package is required for animation supports.\")\n\n def make_frame(t):\n \"\"\"Return an image of the frame for time t.\"\"\"\n fig = plt.gcf()\n ax = plt.gca()\n f_idx = int(t * fps)\n start = hop * f_idx\n end = start + window\n to_plot = transposed[:, start:end]\n extent = (start, end - 1, 0, 127)\n ax.imshow(to_plot, cmap=cmap, aspect='auto', vmin=vmin, vmax=vmax,\n origin='lower', interpolation='none', extent=extent)\n\n if xtick == 'beat':\n next_major_idx = beat_resolution - start % beat_resolution\n if start % beat_resolution < beat_resolution//2:\n next_minor_idx = beat_resolution//2 - start % beat_resolution\n else:\n next_minor_idx = (beat_resolution//2 - start % beat_resolution\n + beat_resolution)\n xticks_major = np.arange(next_major_idx, window, beat_resolution)\n xticks_minor = np.arange(next_minor_idx, window, beat_resolution)\n if end % beat_resolution < beat_resolution//2:\n last_minor_idx = beat_resolution//2 - end % beat_resolution\n else:\n last_minor_idx = (beat_resolution//2 - end % beat_resolution\n + beat_resolution)\n xtick_labels = np.arange((start + next_minor_idx)//beat_resolution,\n (end + last_minor_idx)//beat_resolution)\n ax.set_xticks(xticks_major)\n ax.set_xticklabels('')\n ax.set_xticks(xticks_minor, minor=True)\n ax.set_xticklabels(xtick_labels, minor=True)\n ax.tick_params(axis='x', which='minor', width=0)\n\n return mplfig_to_npimage(fig)\n\n if xtick == 'auto':\n xtick = 'beat' if beat_resolution is not None else 'step'\n\n fig, ax = plt.subplots()\n plot_pianoroll(ax, pianoroll[:window], is_drum, beat_resolution, downbeats,\n preset=preset, cmap=cmap, xtick=xtick, ytick=ytick,\n xticklabel=xticklabel, yticklabel=yticklabel,\n tick_loc=tick_loc, tick_direction=tick_direction,\n label=label, grid=grid, grid_linestyle=grid_linestyle,\n grid_linewidth=grid_linewidth)\n\n num_frame = int((pianoroll.shape[0] - window) / hop)\n duration = int(num_frame / fps)\n\n if (np.issubdtype(pianoroll.dtype, np.bool_)\n or np.issubdtype(pianoroll.dtype, np.floating)):\n vmax = 1\n elif np.issubdtype(pianoroll.dtype, np.integer):\n vmax = 127\n else:\n raise TypeError(\"Unsupported data type for `pianoroll`\")\n vmin = 0\n\n transposed = pianoroll.T\n animation = VideoClip(make_frame, duration=duration)\n if filepath.endswith('.gif'):\n animation.write_gif(filepath, fps, **kwargs)\n else:\n animation.write_videofile(filepath, fps, **kwargs)\n plt.close()\n\n", "repo_name": "LESSSE/pygmidi", "sub_path": "pygmidi/utils/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 30931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.issubdtype", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.floating", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.integer", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 189, "usage_type": "call"}, {"api_name": "pretty_midi.note_number_to_drum_name", "line_number": 192, "usage_type": "call"}, {"api_name": "pretty_midi.note_number_to_name", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 313, "usage_type": "call"}, {"api_name": "pretty_midi.program_to_instrument_class", "line_number": 401, "usage_type": "call"}, {"api_name": "pretty_midi.program_to_instrument_name", "line_number": 403, "usage_type": "call"}, {"api_name": "pretty_midi.program_to_instrument_class", "line_number": 405, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 439, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 461, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 461, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 473, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 473, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.indices", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 476, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 481, "usage_type": "call"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 482, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 482, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 483, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 493, "usage_type": "call"}, {"api_name": "matplotlib.patches.Patch", "line_number": 505, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 518, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 518, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 520, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 520, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 524, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 524, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 535, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 535, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 537, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 537, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 558, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 558, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 648, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 648, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 649, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 649, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 665, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 666, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 672, "usage_type": "call"}, {"api_name": "moviepy.video.io.bindings.mplfig_to_npimage", "line_number": 680, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 685, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 685, "usage_type": "name"}, {"api_name": "numpy.issubdtype", "line_number": 696, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 696, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 697, "usage_type": "call"}, {"api_name": "numpy.floating", "line_number": 697, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 699, "usage_type": "call"}, {"api_name": "numpy.integer", "line_number": 699, "usage_type": "attribute"}, {"api_name": "moviepy.editor.VideoClip", "line_number": 706, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 711, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 711, "usage_type": "name"}]} +{"seq_id": "6848683798", "text": "#=================================================================Imports==================================================\r\nfrom tkinter import *\r\nimport tkinter as tttk\r\nfrom ttkthemes import themed_tk as tk\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox\r\nimport mysql.connector\r\n\r\n#===================================================================Functions=======================================================================\r\n\r\ndef update(rows):\r\n trv.delete(*trv.get_children())\r\n for i in rows:\r\n trv.insert('', 'end', values=i)\r\n\r\ndef search():\r\n import mysql.connector as c\r\n chk=0\r\n con=c.connect(host=\"localhost\",user=\"root\",passwd=\"sandy\",database=\"ebms\")\r\n cursor=con.cursor() \r\n srch2=srch.get()\r\n query = \"select customer.Cust_ID,customer.Customer_First_Name, customer.Customer_Last_Name, customer.Address_Line_1, customer.Address_Line_2, customer.Pincode, customer.Contact_Number, account.Account_ID, account.Account_Type, account.Meter_No, account.Cur_Meter_Reading, account.Prev_Meter_Reading from customer join account on customer.Cust_ID=account.Cust_ID WHERE customer.Cust_ID LIKE '%\"+srch2+\"%';\"\r\n cursor.execute(query)\r\n rows = cursor.fetchall()\r\n con.commit()\r\n update(rows)\r\n\r\ndef display():\r\n query= \"select customer.Cust_ID,customer.Customer_First_Name, customer.Customer_Last_Name, customer.Address_Line_1, customer.Address_Line_2, customer.Pincode, customer.Contact_Number, account.Account_ID, account.Account_Type, account.Meter_No, account.Cur_Meter_Reading, account.Prev_Meter_Reading from customer join account on customer.Cust_ID=account.Cust_ID;\"\r\n cursor.execute(query)\r\n rows=cursor.fetchall()\r\n update(rows)\r\n\r\ndef getrow(event):\r\n rowid = trv.identify_row(event.y)\r\n item = trv.item(trv.focus())\r\n t1.set(item['values'][0])\r\n t2.set(item['values'][1])\r\n t3.set(item['values'][2])\r\n t4.set(item['values'][3])\r\n t5.set(item['values'][4])\r\n t6.set(item['values'][5])\r\n t7.set(item['values'][6])\r\n t8.set(item['values'][7])\r\n t9.set(item['values'][8])\r\n t10.set(item['values'][9])\r\n t11.set(item['values'][10])\r\n t12.set(item['values'][11])\r\n\r\ndef update_customer():\r\n cusid = t1.get()\r\n fname = t2.get()\r\n lname = t3.get()\r\n adln1 = t4.get()\r\n adln2 = t5.get()\r\n pcode = t6.get()\r\n cnnum = t7.get()\r\n accid = t8.get()\r\n acctp = t9.get()\r\n mtrno = t10.get()\r\n cmtrd = t11.get()\r\n pmtrd = t12.get()\r\n\r\n if messagebox.askyesno(\"Confirm Updation\", \"Do you want to update Customer Details?\"):\r\n query = \"Update account set Account_ID = %s, Account_Type = %s, Meter_No = %s, Cur_Meter_Reading = %s, Prev_Meter_Reading = %s where Cust_ID = %s\"\r\n query2 = \"update customer set Customer_First_Name = %s, Customer_Last_Name = %s, Address_Line_1 = %s, Address_Line_2 = %s, Pincode = %s, Contact_Number = %s where Cust_ID = %s\"\r\n cursor.execute (query, (accid, acctp, mtrno, cmtrd, pmtrd, cusid))\r\n cursor.execute (query2, (fname, lname, adln1, adln2, pcode, cnnum, cusid))\r\n mydb.commit()\r\n display()\r\n else:\r\n return True\r\n \r\n\r\n\r\ndef add_new():\r\n cusid = t1.get()\r\n fname = t2.get()\r\n lname = t3.get()\r\n adln1 = t4.get()\r\n adln2 = t5.get()\r\n pcode = t6.get()\r\n cnnum = t7.get()\r\n accid = t8.get()\r\n acctp = t9.get()\r\n mtrno = t10.get()\r\n cmtrd = t11.get()\r\n pmtrd = t12.get()\r\n\r\n query = \"insert into customer (Cust_Id, Customer_First_Name, Customer_Last_Name, Address_Line_1, Address_Line_2, Pincode, Contact_Number) values (%s, %s, %s, %s, %s, %s, %s)\"\r\n query2 = \"insert into account (Account_ID, Cust_ID, Account_Type, Meter_No, Cur_Meter_Reading, Prev_Meter_Reading) values (%s, %s, %s, %s, %s, %s)\"\r\n cursor.execute(query, (cusid, fname, lname, adln1, adln2, pcode, cnnum))\r\n cursor.execute(query2, (accid, cusid, acctp, mtrno, cmtrd, pmtrd))\r\n mydb.commit()\r\n display()\r\n \r\n \r\n\r\ndef delete_customer():\r\n customer_id = t1.get()\r\n if messagebox.askyesno(\"Confirm Deletion?\",\"Do you want to delete Customer Detail?\"):\r\n query = \"delete from account where Cust_ID=\"+customer_id\r\n query2= \"delete from customer where Cust_ID=\"+customer_id\r\n cursor.execute(query)\r\n cursor.execute(query2)\r\n mydb.commit()\r\n display()\r\n else:\r\n return True\r\n\r\ndef clear():\r\n t1.set('')\r\n t2.set('')\r\n t3.set('')\r\n t4.set('')\r\n t5.set('')\r\n t6.set('')\r\n t7.set('')\r\n t8.set('')\r\n t9.set('')\r\n t10.set('')\r\n t11.set('')\r\n t12.set('')\r\n \r\n\r\ndef exitb():\r\n cexit=tttk.messagebox.askyesno('Exit Admin Editor?', 'CONFIRM IF YOU WANT TO EXIT')\r\n if cexit>0:\r\n root.destroy()\r\n return\r\n else:\r\n srch.focus()\r\n\r\n\r\ndef clearlst():\r\n trv.delete(*trv.get_children())\r\n srch.set('')\r\n \r\n \r\n \r\n \r\n \r\n#====================================================================Database Conn.========================================================= \r\n\r\nmydb = mysql.connector.connect (host=\"localhost\", user=\"root\", passwd=\"*********\", database=\"ebms\")\r\ncursor = mydb.cursor()\r\n#====================================================================Tkinter Frame============================================\r\nroot= tk.ThemedTk()\r\nroot.get_themes() \r\nroot.set_theme(\"radiance\")\r\nroot.state(\"zoomed\")\r\nroot.iconbitmap(r'E:\\Electricity Billing System\\icon.ico')\r\nwrapper1 = ttk.LabelFrame (root, text=\"Customer List\")\r\nwrapper2 = ttk.LabelFrame (root, text=\"Search\")\r\nwrapper3 = ttk.LabelFrame (root, text=\"Customer Data\")\r\n\r\n#===================================================StringVars===========================================\r\nsrch=StringVar()\r\nt1=StringVar()\r\nt2=StringVar()\r\nt3=StringVar()\r\nt4=StringVar()\r\nt5=StringVar()\r\nt6=StringVar()\r\nt7=StringVar()\r\nt8=StringVar()\r\nt9=StringVar()\r\nt10=StringVar()\r\nt11=StringVar()\r\nt12=StringVar()\r\n\r\n\r\nwrapper1.pack(fill=\"both\",expand=\"yes\", padx=20, pady=10)\r\nwrapper2.pack(fill=\"both\",expand=\"yes\", padx=20, pady=10)\r\nwrapper3.pack(fill=\"both\",expand=\"yes\", padx=20, pady=10)\r\n\r\ntrv = ttk.Treeview(wrapper1, columns=(1,2,3,4,5,6,7,8,9,10,11,12), show=\"headings\", height=\"5\")\r\ntrv.pack(side=LEFT)\r\ntrv.place(x=0, y=0)\r\n\r\ntrv.heading('#1', text=\"Customer ID\")\r\ntrv.heading('#2', text=\"Cust First Name\")\r\ntrv.heading('#3', text=\"Cust Last Name\")\r\ntrv.heading('#4', text=\"Address Line 1\")\r\ntrv.heading('#5', text=\"Address Line 2\")\r\ntrv.heading('#6', text=\"Pincode\")\r\ntrv.heading('#7', text=\"Contact Number\")\r\ntrv.heading('#8', text=\"Account ID\")\r\ntrv.heading('#9', text=\"Account Type\")\r\ntrv.heading('#10', text=\"Meter No\")\r\ntrv.heading('#11', text=\"Cur Meter Reading\")\r\ntrv.heading('#12', text=\"Prev Meter Reading\")\r\ntrv.column('#1', width=123, minwidth=150)\r\ntrv.column('#2', width=123, minwidth=150)\r\ntrv.column('#3', width=123, minwidth=150)\r\ntrv.column('#4', width=123, minwidth=150)\r\ntrv.column('#5', width=123, minwidth=150)\r\ntrv.column('#6', width=123, minwidth=150)\r\ntrv.column('#7', width=123, minwidth=150)\r\ntrv.column('#8', width=123, minwidth=150)\r\ntrv.column('#9', width=123, minwidth=150)\r\ntrv.column('#10', width=123, minwidth=150)\r\ntrv.column('#11', width=123, minwidth=150)\r\ntrv.column('#12', width=123, minwidth=150)\r\n\r\ntrv.bind('', getrow)\r\n\r\n#=======================================Vertical Scrollbar=========================================\r\nyscrollbar = ttk.Scrollbar(wrapper1, orient='vertical', command=trv.yview)\r\nyscrollbar.pack(side=RIGHT , fill=\"y\")\r\n\r\n\r\n\r\n#========================================Horizontal Scrollbar======================================\r\nxscrollbar = ttk.Scrollbar(wrapper1, orient='horizontal', command=trv.xview)\r\nxscrollbar.pack(side=BOTTOM , fill= BOTH)\r\n\r\ntrv.configure(yscrollcommand=yscrollbar.set, xscrollcommand=xscrollbar.set)\r\n\r\nquery= \"select customer.Cust_ID,customer.Customer_First_Name, customer.Customer_Last_Name, customer.Address_Line_1, customer.Address_Line_2, customer.Pincode, customer.Contact_Number, account.Account_ID, account.Account_Type, account.Meter_No, account.Cur_Meter_Reading, account.Prev_Meter_Reading from customer join account on customer.Cust_ID=account.Cust_ID;\"\r\ncursor.execute(query)\r\nrows = cursor.fetchall()\r\nupdate(rows)\r\n\r\n\r\n\r\n#==========================================Search Section============================================\r\n\r\nlbl = ttk.Label(wrapper2, text =\"Search Using Customer ID:\")\r\nlbl.pack(side=tttk.LEFT, padx=10)\r\nent = ttk.Entry(wrapper2, textvariable = srch)\r\nent.pack(side=tttk.LEFT, padx=6)\r\nbtn = ttk.Button(wrapper2, text=\"Search\", command = search)\r\nbtn.pack(side=tttk.LEFT, padx=6)\r\ndbtn = ttk.Button(wrapper2, text=\"Display All\", command = display)\r\ndbtn.pack(side=tttk.LEFT, padx=6)\r\nccbtn= ttk.Button(wrapper2, text=\"Clear List\", command = clearlst)\r\nccbtn.pack(side=tttk.LEFT, padx=6)\r\n\r\n\r\n#================================================User Data==================================================\r\nlbl1 =ttk.Label(wrapper3, text=\"Customer ID\")\r\nlbl1.grid(row=0, column=1, padx=5, pady=3)\r\nent1 = ttk.Entry(wrapper3, textvariable= t1)\r\nent1.grid(row=0, column=2, padx=5, pady=3)\r\n\r\nlbl2 =ttk.Label(wrapper3, text=\"Customer First Name\")\r\nlbl2.grid(row=1, column=1, padx=5, pady=3)\r\nent2 = ttk.Entry(wrapper3, textvariable= t2)\r\nent2.grid(row=1, column=2, padx=5, pady=3)\r\n\r\nlbl3 =ttk.Label(wrapper3, text=\"Customer Last Name\")\r\nlbl3.grid(row=2, column=1, padx=5, pady=3)\r\nent3 = ttk.Entry(wrapper3, textvariable= t3)\r\nent3.grid(row=2, column=2, padx=5, pady=3)\r\n\r\nlbl4 =ttk.Label(wrapper3, text=\"Address Line 1\")\r\nlbl4.grid(row=3, column=1, padx=5, pady=3)\r\nent4 = ttk.Entry(wrapper3, textvariable= t4)\r\nent4.grid(row=3, column=2, padx=5, pady=3)\r\n\r\nlbl5 =ttk.Label(wrapper3, text=\"Address Line 2\")\r\nlbl5.grid(row=4, column=1, padx=5, pady=3)\r\nent5 = ttk.Entry(wrapper3, textvariable= t5)\r\nent5.grid(row=4, column=2, padx=5, pady=3)\r\n\r\nlbl6 =ttk.Label(wrapper3, text=\"Pincode\")\r\nlbl6.grid(row=5, column=1, padx=5, pady=3)\r\nent6 = ttk.Entry(wrapper3, textvariable= t6)\r\nent6.grid(row=5, column=2, padx=5, pady=3)\r\n\r\nlbl7 =ttk.Label(wrapper3, text=\"Contact Number\")\r\nlbl7.grid(row=6, column=1, padx=5, pady=3)\r\nent7 = ttk.Entry(wrapper3, textvariable= t7)\r\nent7.grid(row=6, column=2, padx=5, pady=3)\r\n\r\nlbl8 =ttk.Label(wrapper3, text=\"Account ID\")\r\nlbl8.grid(row=7, column=1, padx=5, pady=3)\r\nent8 = ttk.Entry(wrapper3, textvariable= t8)\r\nent8.grid(row=7, column=2, padx=5, pady=3)\r\n\r\nlbl9 =ttk.Label(wrapper3, text=\"Account Type\")\r\nlbl9.grid(row=8, column=1, padx=5, pady=3)\r\nent9 = ttk.Entry(wrapper3, textvariable= t9)\r\nent9.grid(row=8, column=2, padx=5, pady=3)\r\n\r\nlbl10 =ttk.Label(wrapper3, text=\"Meter Number\")\r\nlbl10.grid(row=9, column=1, padx=5, pady=3)\r\nent10 = ttk.Entry(wrapper3, textvariable= t10)\r\nent10.grid(row=9, column=2, padx=5, pady=3)\r\n\r\nlbl11 =ttk.Label(wrapper3, text=\"Current Meter Reading\")\r\nlbl11.grid(row=10, column=1, padx=5, pady=3)\r\nent11 = ttk.Entry(wrapper3, textvariable= t11)\r\nent11.grid(row=10, column=2, padx=5, pady=3)\r\n\r\nlbl12 =ttk.Label(wrapper3, text=\"Previous Meter Reading\")\r\nlbl12.grid(row=11, column=1, padx=5, pady=3)\r\nent12 = ttk.Entry(wrapper3, textvariable= t12)\r\nent12.grid(row=11, column=2, padx=5, pady=3)\r\n\r\nup_btn = ttk.Button(wrapper3, text=\"Update\", command = update_customer)\r\nadd_btn = ttk.Button(wrapper3, text=\"Add New\", command= add_new)\r\ndel_btn = ttk.Button(wrapper3, text=\"Delete\", command= delete_customer)\r\ncbtn = ttk.Button(wrapper3, text=\"Clear\", command = clear)\r\nexbtn = ttk.Button(wrapper3, text =\"Exit\", command = exitb)\r\n\r\nadd_btn.grid(row=13, column=0, padx=5, pady=3)\r\nup_btn.grid(row=13, column=1, padx=5, pady=3)\r\ndel_btn.grid(row=13, column=2, padx=5, pady=3)\r\ncbtn.grid(row=13, column=3, padx=5, pady=3)\r\nexbtn.grid(row=13, column=4, padx=5, pady=3)\r\n\r\n\r\n\r\nroot.title(\"Admin Page\")\r\nroot.geometry(\"1500x1000\")\r\nroot.mainloop()\r\n \r\n", "repo_name": "volstice/Electricity-Bill-Management-Sys", "sub_path": "Adminpage.py", "file_name": "Adminpage.py", "file_ext": "py", "file_size_in_byte": 11900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "mysql.connector.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 19, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askyesno", "line_number": 64, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 64, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askyesno", "line_number": 101, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 101, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askyesno", "line_number": 127, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 127, "usage_type": "attribute"}, {"api_name": "mysql.connector.connector.connect", "line_number": 145, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 145, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 145, "usage_type": "name"}, {"api_name": "ttkthemes.themed_tk.ThemedTk", "line_number": 148, "usage_type": "call"}, {"api_name": "ttkthemes.themed_tk", "line_number": 148, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 153, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 154, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 154, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 155, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 155, "usage_type": "name"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 177, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 177, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 209, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 209, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 215, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 215, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 229, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 229, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 230, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Entry", "line_number": 231, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 231, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 232, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Button", "line_number": 233, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 233, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 234, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Button", "line_number": 235, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 235, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 236, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Button", "line_number": 237, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 237, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 238, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Label", "line_number": 242, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 242, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 244, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 244, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 247, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 247, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 249, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 249, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 252, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 252, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 254, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 254, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 257, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 257, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 259, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 259, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 262, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 262, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 264, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 264, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 267, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 267, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 269, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 269, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 272, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 272, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 274, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 274, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 277, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 277, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 279, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 279, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 282, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 282, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 284, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 284, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 287, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 287, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 289, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 289, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 292, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 292, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 294, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 294, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 297, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 297, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 299, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 299, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 302, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 302, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 303, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 303, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 304, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 304, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 305, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 305, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 306, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 306, "usage_type": "name"}]} +{"seq_id": "12725454349", "text": "import pandas as pd\nfrom copy import deepcopy\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly\n\nimport plotly.figure_factory as ff\n\n__all__ = [\n \"plot\",\n \"plot_low_high_prices\",\n \"plot_moving_average\",\n \"plot_scatter_matrix\",\n \"plot_dist_returns\",\n \"plot_returns_scatter_matrix\",\n \"plot_cum_return\",\n \"plot_bollinger_bands\",\n \"plot_rsi\",\n \"plot_cum_profits\"\n]\n\n\n\n\n\ndef plot_cum_profits(stock_data:pd.DataFrame, strategy_profit_name:str, params:dict,\n title:str) -> plotly.graph_objects.Figure:\n profits = [stock_data.query(f'stock_name==\"{stock}\"')[[strategy_profit_name, 'stock_name']] for stock in\n params.get(\n 'STOCK_CODES')]\n total_cum_profits = deepcopy(profits[0])\n total_cum_profits = total_cum_profits[~total_cum_profits.index.duplicated()]\n indexes = total_cum_profits.index\n for i in range(1,len(profits)):\n profits_stock = profits[i]\n profits_stock = profits_stock[~profits_stock.index.duplicated()]\n total_cum_profits[strategy_profit_name] += profits_stock[strategy_profit_name]\n\n total_cum_profits_df = pd.DataFrame.from_dict({strategy_profit_name: total_cum_profits[strategy_profit_name],\n 'stock_name': ['Total Strategy Cumulative Profits' for _ in\n range(len(total_cum_profits))]})\n total_cum_profits_df.index = indexes\n cum_profits_data = pd.concat([total_cum_profits_df, *profits])\n cum_profits_data.reset_index(drop=False, inplace=True)\n return plot(cum_profits_data, x='Date', y=strategy_profit_name, title=f\"Cumulative Profits generated by {title} \" \\\n \"strategy\")\n\n\n\n\ndef plot_rsi(stock_data: pd.DataFrame) -> plotly.graph_objects.Figure:\n return plot(stock_data, y=\"RSI\", title=\"Relative Strength Index (RSI)\")\n\n\ndef add_trace_bollinger_bands(\n fig: plotly.graph_objects.Figure, df: pd.DataFrame\n) -> plotly.graph_objects.Figure:\n\n fig.add_trace(\n go.Scatter(\n x=df.index,\n y=df.upper_bound,\n name=\"upper bound\",\n line=dict(color=\"firebrick\", width=1, dash=\"dash\"),\n )\n )\n fig.add_trace(\n go.Scatter(\n x=df.index,\n y=df.lower_bound,\n name=\"lower bound\",\n line=dict(color=\"royalblue\", width=1, dash=\"dash\"),\n )\n )\n fig.add_trace(\n go.Scatter(\n x=df.index, y=df.Close, name=\"Closed\", line=dict(color=\"firebrick\", width=1)\n )\n )\n return fig\n\ndef plot_bollinger_bands(df: pd.DataFrame, name: str) -> plotly.graph_objects.Figure:\n df = df.query(f'stock_name==\"{name}\"')\n fig = go.Figure()\n # Create and style traces\n fig = add_trace_bollinger_bands(fig, df)\n # Edit the layout\n fig.update_layout(\n title=f\"Bollinger Bands and Close {name} stock\",\n xaxis_title=\"Date\",\n yaxis_title=\"Prices\",\n )\n return fig\n\ndef plot_cum_return(stock_data_returns: pd.DataFrame) -> plotly.graph_objects.Figure:\n try:\n return plot(stock_data_returns, y=\"cum_returns\", title=\"Cumulative Returns\")\n except ValueError:\n stock_data_returns = stock_data_returns[~stock_data_returns.index.duplicated()]\n\n return plot(stock_data_returns, y=\"cum_returns\", title=\"Cumulative Returns\")\n\ndef plot_returns_scatter_matrix(stock_data: pd.DataFrame, params:dict, title:str=\"Scatter Matrix for \"\n \"returns\")-> \\\n plotly.graph_objects.Figure:\n\n df_dic = {}\n for stock_name in list(stock_data.stock_name.unique()):\n df = stock_data.query(f'stock_name==\"{stock_name}\"')\n return_stock_name =f'{stock_name.capitalize()} returns'\n df.rename(columns={'returns':return_stock_name}, inplace=True)\n df = df[~df.index.duplicated()]\n df_dic[stock_name] = df\n\n comp = pd.concat(\n [df[f'{stock_name.capitalize()} returns'] for stock_name, df in df_dic.items()], axis=1\n )\n\n\n return px.scatter_matrix(comp, title=title)\n\n\ndef plot_dist_returns(\n stock_data_returns: pd.DataFrame,\n params: dict\n) -> plotly.graph_objects.Figure:\n hist_data = [\n stock_data_returns.query(f'stock_name==\"{stock}\"')[\"returns\"]\n for stock in params.get(\"STOCK_CODES\")\n ]\n group_labels = [stock for stock in params.get(\"STOCK_CODES\")]\n try:\n fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n except ValueError:\n for data in hist_data:\n data.dropna(inplace=True)\n fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n return fig\n\n\ndef plot_scatter_matrix(\n data: dict, params: dict, title=\"Scatter Matrix for Open Prices\"\n) -> plotly.graph_objects.Figure:\n # crypto_comp = pd.concat(\n # [data[stock][\"Open\"] for stock in params.get(\"STOCK_CODES\")], axis=1\n # )\n open_data_dic = {}\n for stock_name, df in data.items():\n open_price_name =f'{stock_name.capitalize()} Open'\n df.rename(columns={'Open':open_price_name}, inplace=True)\n df = df[~df.index.duplicated()]\n open_data_dic[open_price_name] = df\n\n comp = pd.concat(\n [df[open_price_name] for open_price_name, df in open_data_dic.items()], axis=1\n )\n # comp.columns = [\n # f\"{stock.capitalize()} Open\" for stock in params.get(\"STOCK_CODES\")\n # ]\n return px.scatter_matrix(comp, title=title)\n\n\ndef plot(\n data: pd.DataFrame,\n y,\n title=None,\n x=\"date\",\n label=\"stock_name\",\n line_shape=\"spline\",\n render_mode=\"svg\",\n) -> plotly.graph_objects.Figure:\n return px.line(\n data,\n x=x,\n y=y,\n title=title,\n color=label,\n line_group=label,\n hover_name=label,\n line_shape=line_shape,\n render_mode=render_mode,\n )\n\n\ndef add_trace_high_low(\n fig: plotly.graph_objects.Figure, df: pd.DataFrame\n) -> plotly.graph_objects.Figure:\n\n fig.add_trace(\n go.Scatter(\n x=df.index,\n y=df.High,\n name=\"High\",\n line=dict(color=\"firebrick\", width=1, dash=\"dash\"),\n )\n )\n fig.add_trace(\n go.Scatter(\n x=df.index,\n y=df.Low,\n name=\"Low\",\n line=dict(color=\"royalblue\", width=1, dash=\"dash\"),\n )\n )\n fig.add_trace(\n go.Scatter(\n x=df.index, y=df.Open, name=\"Open\", line=dict(color=\"firebrick\", width=1)\n )\n )\n return fig\n\n\ndef add_trace_moving_average(\n fig: plotly.graph_objects.Figure, df: pd.DataFrame\n) -> plotly.graph_objects.Figure:\n\n fig.add_trace(\n go.Scatter(\n x=df.index,\n y=df.MA50,\n name=\"MA50\",\n line=dict(color=\"firebrick\", width=1, dash=\"dash\"),\n )\n )\n fig.add_trace(\n go.Scatter(\n x=df.index,\n y=df.MA200,\n name=\"MA200\",\n line=dict(color=\"royalblue\", width=1, dash=\"dash\"),\n )\n )\n fig.add_trace(\n go.Scatter(\n x=df.index, y=df.Open, name=\"Open\", line=dict(color=\"firebrick\", width=1)\n )\n )\n return fig\n\n\ndef plot_low_high_prices(df: pd.DataFrame, name: str) -> plotly.graph_objects.Figure:\n\n fig = go.Figure()\n # Create and style traces\n fig = add_trace_high_low(fig, df)\n # Edit the layout\n fig.update_layout(\n title=f\"Average High, Low and Open Prices for {name} stock\",\n xaxis_title=\"Date\",\n yaxis_title=\"Prices\",\n )\n return fig\n\n\ndef plot_moving_average(df: pd.DataFrame, name: str) -> plotly.graph_objects.Figure:\n\n df = df.query(f'stock_name==\"{name}\"')\n fig = go.Figure()\n # Create and style traces\n fig = add_trace_moving_average(fig, df)\n # Edit the layout\n fig.update_layout(\n title=f\"Moving Average and Open for {name} stock\",\n xaxis_title=\"Date\",\n yaxis_title=\"Prices\",\n )\n return fig\n", "repo_name": "MarinoSanLorenzo/trading_algorithmic", "sub_path": "src/frontend/plots.py", "file_name": "plots.py", "file_ext": "py", "file_size_in_byte": 8115, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 43, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 60, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 60, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 68, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 68, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 76, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 76, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 84, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 84, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 95, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 115, "usage_type": "call"}, {"api_name": "plotly.express.scatter_matrix", "line_number": 120, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 120, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 124, "usage_type": "attribute"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 133, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 133, "usage_type": "name"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 137, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 137, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 154, "usage_type": "call"}, {"api_name": "plotly.express.scatter_matrix", "line_number": 160, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 160, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 164, "usage_type": "attribute"}, {"api_name": "plotly.express.line", "line_number": 172, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 172, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 171, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 186, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 186, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 190, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 190, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 198, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 198, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 206, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 206, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 187, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 214, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 218, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 218, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 226, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 226, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 234, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 234, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 215, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 241, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 243, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 243, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 241, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 255, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 258, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 258, "usage_type": "name"}, {"api_name": "plotly.graph_objects", "line_number": 255, "usage_type": "attribute"}]} +{"seq_id": "71065653523", "text": "#!/usr/bin/python3\n\"\"\"Write the first class Base\"\"\"\nfrom json import dumps, loads\nimport csv\n\n\nclass Base:\n \"\"\"Class Base\"\"\"\n __nb_objects = 0\n\n def __init__(self, id=None):\n \"\"\"Constructor\"\"\"\n if id is not None:\n self.id = id\n else:\n Base.__nb_objects += 1\n self.id = Base.__nb_objects\n\n @staticmethod\n def to_json_string(list_dictionaries):\n \"\"\"\"to json string definition\"\"\"\n if list_dictionaries is None or not list_dictionaries:\n return \"[]\"\n else:\n return dumps(list_dictionaries)\n\n @classmethod\n def save_to_file(cls, list_objs):\n \"\"\"save to file definition\"\"\"\n if list_objs is not None:\n list_objs = [o.to_dictionary() for o in list_objs]\n with open(\"{}.json\".format(cls.__name__), \"w\", encoding=\"utf-8\") as f:\n f.write(cls.to_json_string(list_objs))\n\n @staticmethod\n def from_json_string(json_string):\n \"\"\"from json string definition\"\"\"\n if json_string is None or not json_string:\n return []\n return loads(json_string)\n\n @classmethod\n def create(cls, **dictionary):\n \"\"\"create definition\"\"\"\n from models.rectangle import Rectangle\n from models.square import Square\n if cls is Rectangle:\n new = Rectangle(1, 1)\n elif cls is Square:\n new = Square(1)\n else:\n new = None\n new.update(**dictionary)\n return new\n\n @classmethod\n def load_from_file(cls):\n \"\"\"load from file definition\"\"\"\n from os import path\n file = \"{}.json\".format(cls.__name__)\n if not path.isfile(file):\n return []\n with open(file, \"r\", encoding=\"utf-8\") as f:\n return [cls.create(**d) for d in cls.from_json_string(f.read())]\n\n @classmethod\n def save_to_file_csv(cls, list_objs):\n \"\"\"Save to csv file definition\"\"\"\n from models.rectangle import Rectangle\n from models.square import Square\n if list_objs is not None:\n if cls is Rectangle:\n list_objs = [[o.id, o.width, o.height, o.x, o.y]\n for o in list_objs]\n else:\n list_objs = [[o.id, o.size, o.x, o.y]\n for o in list_objs]\n with open('{}.csv'.format(cls.__name__), 'w', newline='',\n encoding='utf-8') as file:\n writer = csv.writer(file)\n writer.writerows(list_objs)\n\n @classmethod\n def load_from_file_csv(cls):\n \"\"\"load from csv definition\"\"\"\n from models.rectangle import Rectangle\n from models.square import Square\n result = []\n with open('{}.csv'.format(cls.__name__), 'r', newline='',\n encoding='utf-8') as file:\n reader = csv.reader(file)\n for row in reader:\n row = [int(r) for r in row]\n if cls is Rectangle:\n d = {\"id\": row[0], \"width\": row[1], \"height\": row[2],\n \"x\": row[3], \"y\": row[4]}\n else:\n d = {\"id\": row[0], \"size\": row[1],\n \"x\": row[2], \"y\": row[3]}\n result.append(cls.create(**d))\n return result\n\n @staticmethod\n def draw(list_rectangles, list_squares):\n \"\"\"Let draw it definition\"\"\"\n\n import turtle\n import time\n from random import randrange\n turtle.Screen().colormode(255)\n for i in list_rectangles + list_squares:\n t = turtle.Turtle()\n t.color((randrange(255), randrange(255), randrange(255)))\n t.pensize(1)\n t.penup()\n t.pendown()\n t.setpos((i.x + t.pos()[0], i.y - t.pos()[1]))\n t.pensize(10)\n t.forward(i.width)\n t.left(90)\n t.forward(i.height)\n t.left(90)\n t.forward(i.width)\n t.left(90)\n t.forward(i.height)\n t.left(90)\n t.end_fill()\n\n time.sleep(5)\n", "repo_name": "KennyChukwuebuka/alx-higher_level_programming", "sub_path": "0x0C-python-almost_a_circle/models/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 4109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "models.rectangle.Rectangle", "line_number": 47, "usage_type": "name"}, {"api_name": "models.rectangle.Rectangle", "line_number": 48, "usage_type": "call"}, {"api_name": "models.square.Square", "line_number": 49, "usage_type": "name"}, {"api_name": "models.square.Square", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "name"}, {"api_name": "models.rectangle.Rectangle", "line_number": 72, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 80, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 91, "usage_type": "call"}, {"api_name": "models.rectangle.Rectangle", "line_number": 94, "usage_type": "name"}, {"api_name": "turtle.Screen", "line_number": 110, "usage_type": "call"}, {"api_name": "turtle.Turtle", "line_number": 112, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 113, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "29814292932", "text": "import os\nfrom abc import ABC, abstractmethod\nimport torch\nfrom transformers import BertConfig,BertPreTrainedModel, BertModel\nfrom datetime import datetime\nimport torch.nn as nn\n\nimport torch\nimport torch.nn as nn\nfrom collections import OrderedDict\n\ndef tuple_prod(x):\n prod = 1\n for xx in x:\n prod *= xx\n return prod\n\nclass GreenBlock(nn.Module):\n def __init__(self, in_channels, out_channels ,drop_rate=0.4):\n \"\"\"\n green_block(inp, filters, name=None)\n ------------------------------------\n Implementation of the special residual block used in the paper. The block\n consists of two (GroupNorm --> ReLu --> 3x3x3 non-strided Convolution)\n units, with a residual connection from the input `inp` to the output. Used\n internally in the model. Can be used independently as well.\n Note that images must come with dimensions \"c, H, W, D\"\n Parameters\n ----------\n `inp`: An keras.layers.layer instance, required\n The keras layer just preceding the green block.\n `out_channels`: integer, required\n No. of filters to use in the 3D convolutional block. The output\n layer of this green block will have this many no. of channels.\n Returns\n -------\n `out`: A keras.layers.Layer instance\n The output of the green block. Has no. of channels equal to `filters`.\n The size of the rest of the dimensions remains same as in `inp`.\n \"\"\"\n super(GreenBlock, self).__init__()\n self.Drop_Rate = drop_rate\n # Define block\n self.block = nn.Sequential(OrderedDict([\n ('group_norm0', nn.GroupNorm(num_channels=in_channels, num_groups=in_channels // 4)),\n #('norm0', nn.BatchNorm3d(num_features=in_channels)),\n ('relu0', nn.LeakyReLU(inplace=True)),\n ('conv0', nn.Conv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)),\n ('group_norm1', nn.GroupNorm(num_channels=out_channels, num_groups=in_channels // 4)),\n #('norm1', nn.BatchNorm3d(num_features=out_channels)),\n ('relu1', nn.LeakyReLU(inplace=True)),\n ('conv2', nn.Conv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)),\n ]))\n\n def forward(self, inputs):\n #x_res = self.res(inputs)\n x_res = inputs\n #print('in green block: before')\n x = torch.nn.functional.dropout(self.block(inputs), p=self.Drop_Rate, training=self.training)\n #print('in green block: after')\n #return torch.cat([x, x_res], dim=1)\n return x + x_res\n\n\n\nclass UpGreenBlock(nn.Sequential):\n def __init__(self, in_features, out_features, shape, Drop_Rate):\n super(UpGreenBlock, self).__init__()\n\n self.add_module('conv', nn.Conv3d(in_features, out_features, kernel_size=1, stride=1))\n self.add_module('up', nn.Upsample(size=shape))\n self.add_module('green', GreenBlock(out_features, out_features, Drop_Rate))\nclass BaseModel(nn.Module, ABC):\n def __init__(self):\n super().__init__()\n self.best_loss = 1000000\n self.best_accuracy = 0\n\n @abstractmethod\n def forward(self, x):\n pass\n\n @property\n def device(self):\n return next(self.parameters()).device\n\n def determine_shapes(self,encoder,dim):\n def get_shape(module,input,output):\n module.input_shape = tuple(input[0].shape[-3:])\n module.output_shape = tuple(output[0].shape[-3:])\n hook1 = encoder.down_block1.register_forward_hook(get_shape)\n hook2 = encoder.down_block3.register_forward_hook(get_shape)\n input_shape = (1,2,) + dim #batch,norms,H,W,D,time\n x = torch.ones((input_shape))\n with torch.no_grad():\n encoder(x)\n del x\n self.shapes = {'dim_0':encoder.down_block1.input_shape,\n 'dim_1':encoder.down_block1.output_shape,\n 'dim_2':encoder.down_block3.input_shape,\n 'dim_3':encoder.down_block3.output_shape}\n hook1.remove()\n hook2.remove()\n\n def register_vars(self,**kwargs):\n intermediate_vec = 2640\n if kwargs.get('task') == 'fine_tune':\n self.dropout_rates = {'input': 0, 'green': 0.35,'Up_green': 0,'transformer':0.1}\n else:\n self.dropout_rates = {'input': 0, 'green': 0.2, 'Up_green': 0.2,'transformer':0.1}\n\n self.BertConfig = BertConfig(hidden_size=intermediate_vec, vocab_size=1,\n num_hidden_layers=kwargs.get('transformer_hidden_layers'),\n num_attention_heads=16, max_position_embeddings=30,\n hidden_dropout_prob=self.dropout_rates['transformer'])\n\n self.label_num = 1\n self.inChannels = 2\n self.outChannels = 1\n self.model_depth = 4\n self.intermediate_vec = intermediate_vec\n self.use_cuda = kwargs.get('cuda')\n self.shapes = kwargs.get('shapes')\n\n\n def load_partial_state_dict(self, state_dict,load_cls_embedding):\n print('loading parameters onto new model...')\n own_state = self.state_dict()\n loaded = {name:False for name in own_state.keys()}\n for name, param in state_dict.items():\n if name not in own_state:\n print('notice: {} is not part of new model and was not loaded.'.format(name))\n continue\n elif 'cls_embedding' in name and not load_cls_embedding:\n continue\n elif 'position' in name and param.shape != own_state[name].shape:\n print('debug line above')\n continue\n param = param.data\n own_state[name].copy_(param)\n loaded[name] = True\n for name,was_loaded in loaded.items():\n if not was_loaded:\n print('notice: named parameter - {} is randomly initialized'.format(name))\n\n\n def save_checkpoint(self, directory, title, epoch, optimizer=None,schedule=None):\n # Create directory to save to\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n # Build checkpoint dict to save.\n ckpt_dict = {\n 'model_state_dict':self.state_dict(),\n 'optimizer_state_dict':optimizer.state_dict() if optimizer is not None else None,\n 'epoch':epoch}\n\n if schedule is not None:\n ckpt_dict['schedule_state_dict'] = schedule.state_dict()\n ckpt_dict['lr'] = schedule.get_last_lr()[0]\n if hasattr(self,'loaded_model_weights_path'):\n ckpt_dict['loaded_model_weights_path'] = self.loaded_model_weights_path\n\n # Save the file with specific name\n core_name = title\n name = \"{}_last_epoch.pth\".format(core_name)\n torch.save(ckpt_dict, os.path.join(directory, name))\n\n\nclass Encoder(BaseModel):\n def __init__(self,**kwargs):\n super(Encoder, self).__init__()\n self.register_vars(**kwargs)\n self.down_block1 = nn.Sequential(OrderedDict([\n ('conv0', nn.Conv3d(self.inChannels, self.model_depth, kernel_size=3, stride=1, padding=1)),\n ('sp_drop0', nn.Dropout3d(self.dropout_rates['input'])),\n ('green0', GreenBlock(self.model_depth, self.model_depth, self.dropout_rates['green'])),\n ('downsize_0', nn.Conv3d(self.model_depth, self.model_depth * 2, kernel_size=3, stride=2, padding=1))]))\n self.down_block2 = nn.Sequential(OrderedDict([\n ('green10', GreenBlock(self.model_depth * 2, self.model_depth * 2, self.dropout_rates['green'])),\n ('green11', GreenBlock(self.model_depth * 2, self.model_depth * 2, self.dropout_rates['green'])),\n ('downsize_1', nn.Conv3d(self.model_depth * 2, self.model_depth * 4, kernel_size=3, stride=2, padding=1))]))\n self.down_block3 = nn.Sequential(OrderedDict([\n ('green20', GreenBlock(self.model_depth * 4, self.model_depth * 4, self.dropout_rates['green'])),\n ('green21', GreenBlock(self.model_depth * 4, self.model_depth * 4, self.dropout_rates['green'])),\n ('downsize_2', nn.Conv3d(self.model_depth * 4, self.model_depth * 8, kernel_size=3, stride=2, padding=1))]))\n self.final_block = nn.Sequential(OrderedDict([\n ('green30', GreenBlock(self.model_depth * 8, self.model_depth * 8, self.dropout_rates['green'])),\n ('green31', GreenBlock(self.model_depth * 8, self.model_depth * 8, self.dropout_rates['green'])),\n ('green32', GreenBlock(self.model_depth * 8, self.model_depth * 8, self.dropout_rates['green'])),\n ('green33', GreenBlock(self.model_depth * 8, self.model_depth * 8, self.dropout_rates['green']))]))\n\n def forward(self, x):\n # print('before')\n x = self.down_block1(x)\n # print('after down_block1')\n x = self.down_block2(x)\n # print('after down_block2')\n x = self.down_block3(x)\n # print('after down_block3')\n x = self.final_block(x)\n # print('after final block')\n return x\n\n\nclass BottleNeck_in(BaseModel):\n def __init__(self,**kwargs):\n super(BottleNeck_in, self).__init__()\n self.register_vars(**kwargs)\n self.reduce_dimension = nn.Sequential(OrderedDict([\n ('group_normR', nn.GroupNorm(num_channels=self.model_depth * 8, num_groups=8)),\n # ('norm0', nn.BatchNorm3d(model_depth * 8)),\n ('reluR0', nn.LeakyReLU(inplace=True)),\n ('convR0', nn.Conv3d(self.model_depth * 8, self.model_depth // 2, kernel_size=(3, 3, 3), stride=1, padding=1)),\n ]))\n flat_factor = tuple_prod(self.shapes['dim_3'])\n self.flatten = nn.Flatten()\n if (flat_factor * self.model_depth // 2) == self.intermediate_vec:\n self.into_bert = nn.Identity()\n print('flattened vec identical to intermediate vector...\\ndroppping fully conneceted bottleneck...')\n else:\n self.into_bert = nn.Linear(in_features=(self.model_depth // 2) * flat_factor, out_features=self.intermediate_vec)\n\n def forward(self, inputs):\n x = self.reduce_dimension(inputs)\n x = self.flatten(x)\n x = self.into_bert(x)\n\n return x\n\n\nclass BottleNeck_out(BaseModel):\n def __init__(self,**kwargs):\n super(BottleNeck_out, self).__init__()\n self.register_vars(**kwargs)\n flat_factor = tuple_prod(self.shapes['dim_3'])\n minicube_shape = (self.model_depth // 2,) + self.shapes['dim_3']\n self.out_of_bert = nn.Linear(in_features=self.intermediate_vec, out_features=(self.model_depth // 2) * flat_factor)\n self.expand_dimension = nn.Sequential(OrderedDict([\n ('unflatten', nn.Unflatten(1, minicube_shape)),\n ('group_normR', nn.GroupNorm(num_channels=self.model_depth // 2, num_groups=2)),\n # ('norm0', nn.BatchNorm3d(model_depth * 8)),\n ('reluR0', nn.LeakyReLU(inplace=True)),\n ('convR0', nn.Conv3d(self.model_depth // 2, self.model_depth * 8, kernel_size=(3, 3, 3), stride=1, padding=1)),\n ]))\n\n def forward(self, x):\n x = self.out_of_bert(x)\n return self.expand_dimension(x)\n\nclass Decoder(BaseModel):\n def __init__(self,**kwargs):\n super(Decoder, self).__init__()\n self.register_vars(**kwargs)\n self.decode_block = nn.Sequential(OrderedDict([\n ('upgreen0', UpGreenBlock(self.model_depth * 8, self.model_depth * 4, self.shapes['dim_2'], self.dropout_rates['Up_green'])),\n ('upgreen1', UpGreenBlock(self.model_depth * 4, self.model_depth * 2, self.shapes['dim_1'], self.dropout_rates['Up_green'])),\n ('upgreen2', UpGreenBlock(self.model_depth * 2, self.model_depth, self.shapes['dim_0'], self.dropout_rates['Up_green'])),\n ('blue_block', nn.Conv3d(self.model_depth, self.model_depth, kernel_size=3, stride=1, padding=1)),\n ('output_block', nn.Conv3d(in_channels=self.model_depth, out_channels=self.outChannels, kernel_size=1, stride=1))\n ]))\n\n def forward(self, x):\n x = self.decode_block(x)\n return x\n\n\nclass AutoEncoder(BaseModel):\n def __init__(self,dim,**kwargs):\n super(AutoEncoder, self).__init__()\n # ENCODING\n self.task = 'autoencoder_reconstruction'\n self.encoder = Encoder(**kwargs)\n self.determine_shapes(self.encoder,dim)\n kwargs['shapes'] = self.shapes\n # BottleNeck into bert\n self.into_bert = BottleNeck_in(**kwargs)\n\n # BottleNeck out of bert\n self.from_bert = BottleNeck_out(**kwargs)\n\n # DECODER\n self.decoder = Decoder(**kwargs)\n\n def forward(self, x):\n if x.isnan().any():\n print('nans in data!')\n batch_size, Channels_in, W, H, D, T = x.shape\n x = x.permute(0, 5, 1, 2, 3, 4).reshape(batch_size * T, Channels_in, W, H, D)\n encoded = self.encoder(x)\n encoded = self.into_bert(encoded)\n encoded = self.from_bert(encoded)\n reconstructed_image = self.decoder(encoded)\n _, Channels_out, W, H, D = reconstructed_image.shape\n reconstructed_image = reconstructed_image.reshape(batch_size, T, Channels_out, W, H, D).permute(0, 2, 3, 4, 5, 1)\n return {'reconstructed_fmri_sequence': reconstructed_image}\n\n\nclass Transformer_Block(BertPreTrainedModel, BaseModel):\n def __init__(self,config,**kwargs):\n super(Transformer_Block, self).__init__(config)\n self.register_vars(**kwargs)\n self.cls_pooling = True\n self.bert = BertModel(self.BertConfig, add_pooling_layer=self.cls_pooling)\n self.init_weights()\n self.cls_embedding = nn.Sequential(nn.Linear(self.BertConfig.hidden_size, self.BertConfig.hidden_size), nn.LeakyReLU())\n self.register_buffer('cls_id', torch.ones((kwargs.get('batch_size'), 1, self.BertConfig.hidden_size)) * 0.5,persistent=False)\n\n\n def concatenate_cls(self, x):\n cls_token = self.cls_embedding(self.cls_id)\n return torch.cat([cls_token, x], dim=1)\n\n\n def forward(self, x ):\n inputs_embeds = self.concatenate_cls(x=x)\n outputs = self.bert(input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=inputs_embeds,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=self.BertConfig.use_return_dict\n )\n\n sequence_output = outputs[0][:, 1:, :]\n pooled_cls = outputs[1]\n\n return {'sequence': sequence_output, 'cls': pooled_cls}\n\n\nclass Encoder_Transformer_Decoder(BaseModel):\n def __init__(self, dim,**kwargs):\n super(Encoder_Transformer_Decoder, self).__init__()\n self.task = 'transformer_reconstruction'\n self.register_vars(**kwargs)\n # ENCODING\n self.encoder = Encoder(**kwargs)\n self.determine_shapes(self.encoder,dim)\n kwargs['shapes'] = self.shapes\n\n # BottleNeck into bert\n self.into_bert = BottleNeck_in(**kwargs)\n\n # transformer\n self.transformer = Transformer_Block(self.BertConfig, **kwargs)\n\n # BottleNeck out of bert\n self.from_bert = BottleNeck_out(**kwargs)\n\n # DECODER\n self.decoder = Decoder(**kwargs)\n\n def forward(self, x):\n batch_size, inChannels, W, H, D, T = x.shape\n x = x.permute(0, 5, 1, 2, 3, 4).reshape(batch_size * T, inChannels, W, H, D)\n encoded = self.encoder(x)\n encoded = self.into_bert(encoded)\n encoded = encoded.reshape(batch_size, T, -1)\n transformer_dict = self.transformer(encoded)\n out = transformer_dict['sequence'].reshape(batch_size * T, -1)\n out = self.from_bert(out)\n reconstructed_image = self.decoder(out)\n reconstructed_image = reconstructed_image.reshape(batch_size, T, self.outChannels, W, H, D).permute(0, 2, 3, 4, 5, 1)\n return {'reconstructed_fmri_sequence': reconstructed_image}\n\n", "repo_name": "intsystems/CreationOfIntelligentSystems_FMRI_23", "sub_path": "code/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 16335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.GroupNorm", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Upsample", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 73, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 95, "usage_type": "call"}, {"api_name": "transformers.BertConfig", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn.Conv3d", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.Dropout3d", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 178, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn.Conv3d", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn.Conv3d", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 186, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn.GroupNorm", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 211, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 213, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 214, "usage_type": "name"}, {"api_name": "torch.nn.Flatten", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 222, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 238, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 239, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn.Unflatten", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 241, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 244, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 255, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn.Conv3d", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 259, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 260, "usage_type": "name"}, {"api_name": "transformers.BertPreTrainedModel", "line_number": 299, "usage_type": "name"}, {"api_name": "transformers.BertModel", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 306, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 312, "usage_type": "call"}]} +{"seq_id": "16001126634", "text": "import os\nfrom env import script_runner\n\ncleanup_list = [\n 'build/',\n 'result.txt',\n 'result_graphs/',\n]\n\nif __name__ == \"__main__\":\n project_path = os.path.dirname(os.path.abspath(__file__))\n for target in cleanup_list:\n script_runner.rm(os.path.join(project_path, target))", "repo_name": "hyoungjk/gpudiag", "sub_path": "clean.py", "file_name": "clean.py", "file_ext": "py", "file_size_in_byte": 296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "env.script_runner.rm", "line_number": 13, "usage_type": "call"}, {"api_name": "env.script_runner", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}]} +{"seq_id": "40180880073", "text": "# thanks to @Skastickers for stickers....\n# Among us.....\n#credits to catuserbot\n\n\nimport asyncio\n\nfrom userbot.utils import admin_cmd, edit_or_reply, sudo_cmd\nfrom userbot import ALIVE_NAME, CMD_HELP\n\nDEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else \"Hell User\"\n\n\n@bot.on(admin_cmd(pattern=\"imp(|n) (.*)\", outgoing=True))\n@bot.on(sudo_cmd(pattern=\"imp(|n) (.*)\", allow_sudo=True))\nasync def _(event):\n legendx22 = bot.uid\n USERNAME = f\"tg://user?id={legendx22}\"\n name = event.pattern_match.group(2)\n cmd = event.pattern_match.group(1).lower()\n text1 = await edit_or_reply(event, \"Hmm... Looks like Something is wrong here🤔🧐!!\")\n await asyncio.sleep(2)\n await text1.delete()\n stcr1 = await event.client.send_file(\n event.chat_id, \"CAADAQADRwADnjOcH98isYD5RJTwAg\"\n )\n text2 = await event.reply(\n f\"**[{DEFAULTUSER}]({USERNAME}) :** I have to call discussion😯\"\n )\n await asyncio.sleep(3)\n await stcr1.delete()\n await text2.delete()\n stcr2 = await event.client.send_file(\n event.chat_id, \"CAADAQADRgADnjOcH9odHIXtfgmvAg\"\n )\n text3 = await event.reply(\n f\"**[{DEFAULTUSER}]({USERNAME}) :** We have to eject the imposter or will lose😥 \"\n )\n await asyncio.sleep(3)\n await stcr2.delete()\n await text3.delete()\n stcr3 = await event.client.send_file(\n event.chat_id, \"CAADAQADOwADnjOcH77v3Ap51R7gAg\"\n )\n text4 = await event.reply(f\"**Others :** Where???🤨 \")\n await asyncio.sleep(2)\n await text4.edit(f\"**Others :** Who??🤔 \")\n await asyncio.sleep(2)\n await text4.edit(\n f\"**[{DEFAULTUSER}]({USERNAME}) :** Its {name} , I saw {name} using🤨 vent,\"\n )\n await asyncio.sleep(3)\n await text4.edit(f\"**Others :**Okay.. 😲Vote {name} \")\n await asyncio.sleep(2)\n await stcr3.delete()\n await text4.delete()\n stcr4 = await event.client.send_file(\n event.chat_id, \"CAADAQADLwADnjOcH-wxu-ehy6NRAg\"\n )\n hellevent = await event.reply(f\"{name} is ejected.......🤐\")\n await asyncio.sleep(2)\n await hellevent.edit(\"ඞㅤㅤㅤㅤ ㅤㅤㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤඞㅤㅤㅤㅤ ㅤㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤ ඞㅤㅤㅤㅤㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤ ඞㅤㅤㅤㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤㅤ ඞㅤㅤㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤ ඞㅤㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤ ඞㅤㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤㅤ ඞㅤ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤㅤㅤ ඞ\")\n await asyncio.sleep(0.5)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤㅤㅤ ㅤ\")\n await asyncio.sleep(0.2)\n await stcr4.delete()\n if cmd == \"\":\n await hellevent.edit(\n f\". 　　　。　　　　•　 　ﾟ　　。 　　.\\n .　　　 　　.　　　　　。　　 。　. 　\\n\\n . 　　 。 　 ඞ 。 . 　　 • 　　　　•\\n\\n ﾟ{name} was an Imposter. 。　. 　 　 。　. 。　. \\n 　. 。　 　. \\n　' 0 Impostor remains 　 。　. 　　. 。　. 。 　 . 。 　 . . . , 。\\n　　ﾟ　　　.　　. ,　 　。　 　. 　 . 。\"\n )\n await asyncio.sleep(4)\n await hellevent.delete()\n await event.client.send_file(event.chat_id, \"CAADAQADLQADnjOcH39IqwyR6Q_0Ag\")\n elif cmd == \"n\":\n await hellevent.edit(\n f\". 　　　。　　　　•　 　ﾟ　　。 　　.\\n .　　　 　　.　　　　　。　　 。　. 　\\n\\n . 　　 。 　 ඞ 。 . 　　 • 　　　　•\\n\\n ﾟ{name} was not an Imposter. 。　. 　 　 。　. 。　. \\n 　. 。　 　. \\n　' 1 Impostor remains 　 。　. 　　. 。　. 。 　 . 。 　 . . . , 。\\n　　ﾟ　　　.　　. ,　 　。　 　. 　 . 。\"\n )\n await asyncio.sleep(4)\n await hellevent.delete()\n await event.client.send_file(event.chat_id, \"CAADAQADQAADnjOcH-WOkB8DEctJAg\")\n\n\n@bot.on(admin_cmd(pattern=\"timp(|n) (.*)\", outgoing=True))\n@bot.on(sudo_cmd(pattern=\"timp(|n) (.*)\", allow_sudo=True))\nasync def _(event):\n name = event.pattern_match.group(2)\n cmd = event.pattern_match.group(1).lower()\n hellevent = await edit_or_reply(event, f\"{name} is ejected.......\")\n await asyncio.sleep(2)\n await hellevent.edit(\"ඞㅤㅤㅤㅤ ㅤㅤㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤඞㅤㅤㅤㅤ ㅤㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤ ඞㅤㅤㅤㅤㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤ ඞㅤㅤㅤㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤㅤ ඞㅤㅤㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤ ඞㅤㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤ ඞㅤㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤㅤ ඞㅤ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤㅤㅤ ඞ\")\n await asyncio.sleep(0.8)\n await hellevent.edit(\"ㅤㅤㅤㅤㅤㅤㅤㅤ ㅤ\")\n await asyncio.sleep(0.2)\n if cmd == \"\":\n await hellevent.edit(\n f\". 　　　。　　　　•　 　ﾟ　　。 　　.\\n .　　　 　　.　　　　　。　　 。　. 　\\n\\n . 　　 。 　 ඞ 。 . 　　 • 　　　　•\\n\\n ﾟ {name} was an Imposter. 。　. 　 　 。　. 。　. \\n 　. 。　 　. \\n　' 0 Impostor remains 　 。　. 　　. 。　. 。 　 . 。 　 . . . , 。\\n　　ﾟ　　　.　　. ,　 　。　 　. 　 . 。\"\n )\n elif cmd == \"n\":\n await hellevent.edit(\n f\". 　　　。　　　　•　 　ﾟ　　。 　　.\\n .　　　 　　.　　　　　。　　 。　. 　\\n\\n . 　　 。 　 ඞ 。 . 　　 • 　　　　•\\n\\n ﾟ {name} was not an Imposter. 。　. 　 　 。　. 。　. \\n 　. 。　 　. \\n　' 1 Impostor remains 　 。　. 　　. 。　. 。 　 . 。 　 . . . , 。\\n　　ﾟ　　　.　　. ,　 　。　 　. 　 . 。\"\n )\n\n\nCMD_HELP.update(\n {\n \"imposter\": \"**Plugin :** `imposter__`\\\n\\n\\n**Syntax : **`.imp` / `.impn` \\\n\\n**Usage : ** Find imposter with stickers.\\\n\\n\\n**Syntax : **`.timp` / `.timpn` \\\n\\n**Usage : ** Find imposter only text.\"\n }\n)\n", "repo_name": "LEGENDXOP/LEGEND-BOT", "sub_path": "userbot/plugins/amongus.py", "file_name": "amongus.py", "file_ext": "py", "file_size_in_byte": 7394, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 55, "dataset": "github-code", "pt": "2", "api": [{"api_name": "userbot.ALIVE_NAME", "line_number": 11, "usage_type": "name"}, {"api_name": "userbot.utils.edit_or_reply", "line_number": 21, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "userbot.utils.admin_cmd", "line_number": 14, "usage_type": "call"}, {"api_name": "userbot.utils.sudo_cmd", "line_number": 15, "usage_type": "call"}, {"api_name": "userbot.utils.edit_or_reply", "line_number": 104, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 113, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 115, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 117, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 123, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 125, "usage_type": "call"}, {"api_name": "userbot.utils.admin_cmd", "line_number": 99, "usage_type": "call"}, {"api_name": "userbot.utils.sudo_cmd", "line_number": 100, "usage_type": "call"}, {"api_name": "userbot.CMD_HELP.update", "line_number": 136, "usage_type": "call"}, {"api_name": "userbot.CMD_HELP", "line_number": 136, "usage_type": "name"}]} +{"seq_id": "11786305313", "text": "import bisect\nimport gc\nimport glob\nimport random\nimport torch\nfrom others.logging import logger\n\n\nclass Batch(object):\n def _pad(self, data, pad_id, width=-1):\n if (width == -1):\n width = max(len(d) for d in data)\n rtn_data = [d + [pad_id] * (width - len(d)) for d in data]\n return rtn_data\n\n def __init__(self, data=None, device=None, is_test=False, autogressive=False):\n \"\"\"Create a Batch from a list of examples.\"\"\"\n if data is not None:\n self.batch_size = len(data)\n self.autogressive = autogressive\n pre_src = [x[0] for x in data]\n pre_src_mask = [x[1] for x in data]\n pre_state = [x[2] for x in data]\n pre_tgt = [x[3] for x in data]\n pre_auto = [x[4] for x in data]\n pre_len = [x[5] for x in data]\n relations = [x[6] for x in data]\n example_id = [x[7] for x in data]\n\n if not is_test:\n ex_idx, tgt_idx, src, pmt_msk, states, tgt, mask_src, mask_tgt = \\\n self._process(pre_src, pre_src_mask, pre_state, pre_tgt, pre_auto)\n\n setattr(self, 'src', src.to(device))\n setattr(self, 'tgt', tgt.to(device))\n setattr(self, 'pmt_msk', pmt_msk.to(device))\n\n setattr(self, 'states', states)\n setattr(self, 'ex_idx', ex_idx)\n setattr(self, 'tgt_idx', tgt_idx)\n setattr(self, 'tgt_len', sum(pre_len))\n\n setattr(self, 'mask_src', mask_src.to(device))\n setattr(self, 'mask_tgt', mask_tgt.to(device))\n\n setattr(self, 'example_id', example_id)\n else:\n ex_idx, src, pmt_msk, states, mask_src = \\\n self._process_test(pre_src, pre_src_mask, pre_state)\n\n setattr(self, 'src', src.to(device))\n setattr(self, 'pmt_msk', pmt_msk.to(device))\n\n setattr(self, 'states', states)\n setattr(self, 'ex_idx', ex_idx)\n setattr(self, 'relations', relations)\n\n setattr(self, 'mask_src', mask_src.to(device))\n\n src_str = [x[-2] for x in data]\n setattr(self, 'src_str', src_str)\n tgt_str = [x[-1] for x in data]\n setattr(self, 'tgt_str', tgt_str)\n\n setattr(self, 'example_id', example_id)\n\n def _process_test(self, pre_src, pre_mask, pre_state):\n ex_idx = []; tgt_idx = []\n src = []; pmt_msk = []; states = []\n for i in range(len(pre_src)):\n src_ex = pre_src[i]\n mask_ex = pre_mask[i]\n state_ex = pre_state[i]\n step_info = []; s_idx = len(pmt_msk)\n for step in range(len(mask_ex)):\n step_info.append((s_idx, s_idx+len(mask_ex[step])))\n s_idx += len(mask_ex[step])\n pmt_msk.extend(mask_ex[step])\n states.extend(state_ex[step])\n ex_idx.append(step_info)\n src.append(src_ex)\n src = torch.tensor(self._pad(src, 0))\n pmt_msk = torch.tensor(self._pad(pmt_msk, True))\n mask_src = ~(src == 0)\n return ex_idx, src, pmt_msk, states, mask_src\n\n\n def _process(self, pre_src, pre_mask, pre_state, pre_tgt, pre_auto):\n ex_idx = []; tgt_idx = []\n src = []; pmt_msk = []; states = []; tgt = []\n b_tok = pre_auto[0][0][0]\n for i in range(len(pre_src)):\n src_ex = pre_src[i]\n mask_ex = pre_mask[i]\n state_ex = pre_state[i]\n tgt_ex = pre_tgt[i]\n auto_ex = pre_auto[i]\n step_info = []; s_idx = len(pmt_msk)\n for step in range(len(mask_ex)):\n step_info.append((s_idx, s_idx+len(mask_ex[step])))\n s_idx += len(mask_ex[step])\n pmt_msk.extend(mask_ex[step])\n states.extend(state_ex[step])\n if self.autogressive:\n t = auto_ex[step] + tgt_ex[step]\n t_idx = (len(auto_ex[step])-1, len(t)-1)\n else:\n t = [b_tok] + tgt_ex[step]\n t_idx = (1-1, len(t)-1)\n tgt.extend([t for i in range(len(mask_ex[step]))])\n tgt_idx.extend([t_idx for i in range(len(mask_ex[step]))])\n ex_idx.append(step_info)\n src.append(src_ex)\n #print ([len(item) for item in src_ex])\n\n src = torch.tensor(self._pad(src, 0))\n tgt = torch.tensor(self._pad(tgt, 0))\n pmt_msk = torch.tensor(self._pad(pmt_msk, True))\n\n mask_src = ~(src == 0)\n mask_tgt = ~(tgt == 0)\n\n return ex_idx, tgt_idx, src, pmt_msk, states, tgt, mask_src, mask_tgt\n\n\n def __len__(self):\n return self.batch_size\n\n\n\ndef load_dataset(args, corpus_type, shuffle):\n assert corpus_type in [\"train\", \"dev\", \"test\", 'ann']\n\n def _lazy_dataset_loader(pt_file, corpus_type):\n dataset = torch.load(pt_file)\n logger.info('Loading %s dataset from %s, number of examples: %d' %\n (corpus_type, pt_file, len(dataset)))\n return dataset\n\n # Sort the glob output by file name (by increasing indexes).\n pts = sorted(glob.glob(args.data_path + '.' + corpus_type + '.[0-9]*.pt'))\n if pts:\n if (shuffle):\n random.shuffle(pts)\n\n for pt in pts:\n yield _lazy_dataset_loader(pt, corpus_type)\n else:\n # Only one inputters.*Dataset, simple!\n pt = args.data_path + '.' + corpus_type + '.pt'\n yield _lazy_dataset_loader(pt, corpus_type)\n\n\ndef abs_batch_size_fn(new, count):\n src, tgt = new[0], new[1]\n global max_n_sents, max_n_tokens, max_size\n if count == 1:\n max_size = 0\n max_n_sents=0\n max_n_tokens=0\n max_n_sents = max(max_n_sents, len(tgt))\n max_size = max(max_size, max_n_sents)\n src_elements = count * max_size\n if (count > 6):\n return src_elements + 1e3\n return src_elements\n\n\nclass Dataloader(object):\n def __init__(self, args, datasets, batch_size,\n device, shuffle, is_test):\n self.args = args\n self.datasets = datasets\n self.batch_size = batch_size\n self.device = device\n self.shuffle = shuffle\n self.is_test = is_test\n self.cur_iter = self._next_dataset_iterator(datasets)\n assert self.cur_iter is not None\n\n def __iter__(self):\n dataset_iter = (d for d in self.datasets)\n while self.cur_iter is not None:\n for batch in self.cur_iter:\n yield batch\n self.cur_iter = self._next_dataset_iterator(dataset_iter)\n\n def _next_dataset_iterator(self, dataset_iter):\n try:\n # Drop the current dataset for decreasing memory\n if hasattr(self, \"cur_dataset\"):\n self.cur_dataset = None\n gc.collect()\n del self.cur_dataset\n gc.collect()\n\n self.cur_dataset = next(dataset_iter)\n except StopIteration:\n return None\n\n return DataIterator(args = self.args,\n dataset=self.cur_dataset, batch_size=self.batch_size,\n device=self.device, shuffle=self.shuffle, is_test=self.is_test)\n\n\nclass DataIterator(object):\n def __init__(self, args, dataset, batch_size, device=None, is_test=False,\n shuffle=True):\n self.args = args\n self.batch_size, self.is_test, self.dataset = batch_size, is_test, dataset\n self.iterations = 0\n self.device = device\n self.shuffle = shuffle\n\n self.sort_key = lambda x: len(x[1])\n\n self._iterations_this_epoch = 0\n self.batch_size_fn = abs_batch_size_fn\n\n def data(self):\n if self.shuffle:\n random.shuffle(self.dataset)\n xs = self.dataset\n return xs\n\n def preprocess(self, ex, is_test):\n src = ex['src']\n src_mask = ex['src_mask']\n relations = ex['relations']\n comb_rels = ex[\"comb_rels\"]\n tgt = ex['tgt']\n tgt_atg = ex['tgt_atg']\n tgt_len = ex['tgt_len']\n example_id = ex['example_id']\n\n src_txt = ex['src_txt']\n tgt_txt = ex['tgt_txt']\n\n if(is_test):\n return src, src_mask, comb_rels, tgt, tgt_atg, tgt_len, relations, example_id, src_txt, tgt_txt\n else:\n return src, src_mask, comb_rels, tgt, tgt_atg, tgt_len, relations, example_id\n\n def batch_buffer(self, data, batch_size):\n minibatch, size_so_far = [], 0\n for ex in data:\n if(len(ex['src'])==0):\n continue\n ex = self.preprocess(ex, self.is_test)\n if(ex is None):\n continue\n minibatch.append(ex)\n size_so_far = self.batch_size_fn(ex, len(minibatch))\n if size_so_far == batch_size:\n yield minibatch\n minibatch, size_so_far = [], 0\n elif size_so_far > batch_size:\n yield minibatch[:-1]\n minibatch, size_so_far = minibatch[-1:], self.batch_size_fn(ex, 1)\n if minibatch:\n yield minibatch\n\n def batch(self, data, batch_size):\n \"\"\"Yield elements from data in chunks of batch_size.\"\"\"\n minibatch, size_so_far = [], 0\n for ex in data:\n minibatch.append(ex)\n size_so_far = self.batch_size_fn(ex, len(minibatch))\n if size_so_far == batch_size:\n yield minibatch\n minibatch, size_so_far = [], 0\n elif size_so_far > batch_size:\n yield minibatch[:-1]\n minibatch, size_so_far = minibatch[-1:], self.batch_size_fn(ex, 1)\n if minibatch:\n yield minibatch\n\n def create_batches(self):\n \"\"\" Create batches \"\"\"\n data = self.data()\n for buffer in self.batch_buffer(data, self.batch_size * 300):\n\n p_batch = sorted(buffer, key=lambda x: len(x[2]))\n p_batch = sorted(p_batch, key=lambda x: len(x[1]))\n p_batch = self.batch(p_batch, self.batch_size)\n\n p_batch = list(p_batch)\n if (self.shuffle):\n random.shuffle(p_batch)\n for b in p_batch:\n if(len(b)==0):\n continue\n yield b\n\n def __iter__(self):\n while True:\n self.batches = self.create_batches()\n for idx, minibatch in enumerate(self.batches):\n # fast-forward if loaded from state\n if self._iterations_this_epoch > idx:\n continue\n self.iterations += 1\n self._iterations_this_epoch += 1\n batch = Batch(minibatch, self.device, self.is_test, self.args.autogressive)\n\n yield batch\n return\n\n\n", "repo_name": "XinnuoXu/AggGen", "sub_path": "src/models/data_hmm.py", "file_name": "data_hmm.py", "file_ext": "py", "file_size_in_byte": 10892, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.tensor", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 135, "usage_type": "call"}, {"api_name": "others.logging.logger.info", "line_number": 136, "usage_type": "call"}, {"api_name": "others.logging.logger", "line_number": 136, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 141, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 144, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 193, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 195, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 222, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 289, "usage_type": "call"}]} +{"seq_id": "15694832635", "text": "import torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass LeNet5(nn.Module):\n \"\"\"\n x: (n, num_channels, 32, 32)\n :return: (n, num_classes)\n \"\"\"\n\n def __init__(self, in_channels, num_classes):\n super(LeNet5, self).__init__()\n self.c1 = nn.Conv2d(in_channels, 6, (5, 5))\n self.s2 = nn.MaxPool2d(2, stride=2)\n self.c3 = nn.Conv2d(6, 16, (5, 5))\n self.s4 = nn.MaxPool2d(2, stride=2)\n self.c5 = nn.Conv2d(16, 120, (5, 5))\n self.f6 = nn.Linear(120, 84)\n self.f7 = nn.Linear(84, num_classes)\n\n def forward(self, x):\n x = F.relu(self.c1(x))\n x = self.s2(x)\n x = F.relu(self.c3(x))\n x = self.s4(x)\n x = F.relu(self.c5(x))\n x = x.reshape(x.shape[0], -1)\n x = F.relu(self.f6(x))\n x = self.f7(x)\n return x\n", "repo_name": "skyworld123/fl_exp", "sub_path": "models/lenet.py", "file_name": "lenet.py", "file_ext": "py", "file_size_in_byte": 835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "2904555135", "text": "\"\"\"The endpoints and functions which handle interpolating and evaluating a given set of points.\"\"\"\n\nfrom flask import Blueprint, flash, redirect, render_template, request, url_for\n\nfrom secret_sharing.polynomial import ModPolynomial\n\nbp = Blueprint('interp', __name__, url_prefix='/interpolator')\n\ndef is_ascending(xs):\n # pylint: disable=missing-docstring\n for i in range(1, len(xs)):\n if xs[i] < xs[i-1]:\n return False\n return True\n\n@bp.route('/', methods=(\"GET\", \"POST\"))\ndef interpolate():\n \"\"\"The basic interpolation page.\"\"\"\n return render_template('interpolate.html')\n\n@bp.route('/eval', methods=(\"POST\",))\ndef evaluate():\n \"\"\"The results page for a evaluation/interpolation of points.\"\"\"\n points = request.form['points']\n modulus = request.form['modulus']\n x = request.form['x-val']\n\n # Lots of validation\n\n # Points must be a space-separated list of points x,y - i.e '3,4 5,6 9,2'\n # The x values of the points must be increasing, and the modulus (if one is supplied)\n # must be greater than or equal to one\n\n error = ''\n if not (points or x or modulus):\n error = 'All fields must be filled in.'\n\n parsed = [pair.split(',') for pair in points.split(' ')]\n try:\n coords = [(int(a), int(b)) for (a, b) in parsed]\n x = int(x)\n modulus = int(modulus)\n except ValueError:\n error = 'Invalid values. Please check your input.'\n else:\n xs = [a for a, b in coords]\n if not is_ascending(xs):\n error = 'X values must be ascending.'\n if modulus < 1:\n error = 'The modulus cannot be less than 1.'\n\n if error:\n flash(error)\n return redirect(url_for('interp.interpolate'))\n\n # Validation over, real work now\n\n # Generate the interpolating polynomial for the supplied coords,\n # And evaluate it at the given x and computing with the given modulus\n lagrange = ModPolynomial.interpolating(coords, modulus)\n result = lagrange(x)\n\n # The template needs the first (x-less) coefficient of the polynomial\n # and then the rest of the coefficients as a list\n # They need to be converted from mod.Mod to regular ints\n coef = [int(c) for c in lagrange.coefficients()]\n base, remaining = coef[0], coef[1:]\n\n return render_template('interp_eval.html', n=len(coords), x=x, base=base,\n modulus=modulus, answer=result, polynomial=remaining)\n", "repo_name": "Jmc18134/secret_sharing", "sub_path": "secret_sharing/interpolate.py", "file_name": "interpolate.py", "file_ext": "py", "file_size_in_byte": 2431, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 54, "usage_type": "call"}, {"api_name": "secret_sharing.polynomial.ModPolynomial.interpolating", "line_number": 60, "usage_type": "call"}, {"api_name": "secret_sharing.polynomial.ModPolynomial", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "34334466351", "text": "from django.urls import path\n\nfrom product.api.views import CategoryListCreate, CategoryRetrieveUpdateDestroy, ProductListCreate, \\\n ProductRetrieveUpdateDestroy, search\n\nurlpatterns = [\n path('categories/', CategoryListCreate.as_view()),\n path('categories//', CategoryRetrieveUpdateDestroy.as_view()),\n path('products/', ProductListCreate.as_view()),\n path('products/search/', search),\n path('products//', ProductRetrieveUpdateDestroy.as_view()),\n]\n", "repo_name": "mwicwiri-bonface/drf-ecommerce-backend-api", "sub_path": "product/api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "product.api.views.CategoryListCreate.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "product.api.views.CategoryListCreate", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "product.api.views.CategoryRetrieveUpdateDestroy.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "product.api.views.CategoryRetrieveUpdateDestroy", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "product.api.views.ProductListCreate.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "product.api.views.ProductListCreate", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "product.api.views.search", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "product.api.views.ProductRetrieveUpdateDestroy.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "product.api.views.ProductRetrieveUpdateDestroy", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "32341402512", "text": "import torch\nimport torch.nn as nn\n\n\nclass LabelSmoothingLoss(nn.Module):\n \"\"\"\n With label smoothing,\n KL-divergence between q_{smoothed ground truth prob.}(w)\n and p_{prob. computed by model}(w) is minimized.\n \"\"\"\n def __init__(self, label_smoothing, tgt_vocab_size, ignore_index=0):\n assert 0.0 < label_smoothing <= 1.0\n self.ignore_index = ignore_index\n super(LabelSmoothingLoss, self).__init__()\n\n smoothing_value = label_smoothing / (tgt_vocab_size - 2) # word itself, and pad token\n one_hot = torch.full((tgt_vocab_size,), smoothing_value)\n one_hot[self.ignore_index] = 0\n self.register_buffer('one_hot', one_hot.unsqueeze(0)) # register buffer is not a parameter, but in state_dict.\n self.confidence = 1.0 - label_smoothing\n\n def forward(self, output, target):\n \"\"\"\n output (FloatTensor): batch_size x n_classes\n target (LongTensor): batch_size\n \"\"\"\n model_prob = self.one_hot.repeat(target.size(0), 1) # model_prob = (target_size(0), V)\n model_prob.scatter_(1, target.unsqueeze(1), self.confidence)\n mask = (target == self.ignore_index)\n model_prob.masked_fill_(mask.unsqueeze(1), 0) # broadcasting\n pred = output.log_softmax(dim=-1)\n return torch.sum(-pred*model_prob) / sum(target != self.ignore_index)\n", "repo_name": "SungHo3268/Transformer", "sub_path": "src/criterion.py", "file_name": "criterion.py", "file_ext": "py", "file_size_in_byte": 1386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.full", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "19398273638", "text": "from IPython.display import display\n\nfrom sqlite3 import DatabaseError\nfrom matplotlib import pyplot as plt\nfrom pandas import DataFrame\nfrom sklearn.cluster import KMeans\n\n\ndef plot_kmeans_elbow(\n df: DataFrame, \n max_n_clusters: int = 10, \n max_iter: int = 1000\n ) -> None:\n \"\"\"\n Displays the elbow plot\n \"\"\"\n sse = {\n n_clusters: _get_inertia(df, n_clusters, max_iter)\n for n_clusters in range(1, max_n_clusters + 1)\n }\n plt.figure()\n plt.plot(sse.keys(), sse.values())\n plt.xlabel('Cluster count [k]')\n plt.show()\n\n\ndef _get_inertia(df: DataFrame, n_clusters: int, max_iter: int) -> list:\n \"\"\"\n Returns the inertia value for a given kmeans model\n \"\"\"\n kmeans = KMeans(n_clusters=n_clusters, max_iter=max_iter).fit(df)\n return kmeans.inertia_\n\n\ndef ordered_clustering(\n df: DataFrame,\n n_clusters: int,\n cluster_by_column_name: str, \n ascending: bool\n ) -> DataFrame:\n \"\"\"\n Returns a dataframe with ordered kmeans clusters\n based on the given column name\n \"\"\"\n kmeans = KMeans(n_clusters=n_clusters)\n kmeans.fit(df[[cluster_by_column_name]])\n cluster_name = f'{cluster_by_column_name}_score'\n df[cluster_name] = kmeans.predict(df[[cluster_by_column_name]])\n target_score = (\n df.groupby(cluster_name)\n [cluster_by_column_name].mean()\n .reset_index()\n .sort_values(by=[cluster_by_column_name], ascending=ascending)\n .reset_index(drop=True)\n )\n remap_clusters = {int(row[cluster_name]): idx for idx, row in target_score.iterrows()}\n df[cluster_name] = df[cluster_name].replace(remap_clusters)\n display(df.groupby(cluster_name)[cluster_by_column_name].describe())\n return df\n\n\n", "repo_name": "TomaszKaleczyc/customer_order_prediction", "sub_path": "src/utilities/clustering_utils.py", "file_name": "clustering_utils.py", "file_ext": "py", "file_size_in_byte": 1801, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.DataFrame", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 45, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "42324169950", "text": "from vision.yolo.detector import Detector\nfrom PIL import Image as pimg\nfrom PIL import ImageDraw\nfrom cv2 import cv2\nimport glob\nimport imutils\nimport numpy as np\nimport scipy.misc\nimport os\nfrom vision.orientation.orientation_detector import OrientationDetectorNet\nfrom utils.image_shifter import RuntimeShifter\nfrom aruco import Calibration\nimport torch\nfrom vision.segmentation.detector import InstanceDetector\nimport torch.utils.model_zoo\nimport os\n\nYOLOCFGPATH = 'vision/yolo/'\nIMAGE_NAME = \"webcam_capture.png\"\nORIENTATION_MODEL_PATH = \"orientation_cnn.pth\"\n\n\nclass Vision:\n def __init__(self, segmentation_weight_path):\n self.current_directory = os.getcwd()\n yolo_cfg_path_absolute = self.current_directory + YOLOCFGPATH\n self.image_path = self.current_directory + \"/\" + IMAGE_NAME\n self.mask_path = self.current_directory + \"/masks/\"\n \"\"\"self.detector = Detector(os.path.join(yolo_cfg_path_absolute, 'cfg/obj.data'),\n os.path.join(yolo_cfg_path_absolute, 'cfg/yolov3-tiny.cfg'),\n os.path.join(yolo_cfg_path_absolute, 'yolov3-tiny_final.weights'))\"\"\"\n self.counter = 0\n self.first_run = True\n self.results = None\n self.orientationCNN = OrientationDetectorNet()\n #self.orientationCNN.load_state_dict(torch.load(ORIENTATION_MODEL_PATH))\n #self.shifter = image_shifter.RuntimeShifter\n self.calibrate = Calibration()\n self.segmentation_detector = InstanceDetector(segmentation_weight_path)\n\n def __del__(self):\n pass\n\n def find_parts(self, class_id, fuse_index=-1):\n class_id1, class_id2 = class_id\n part = (-1, -1, -1, -1, -1)\n # result is an array of dictionaries\n found_class_index = 0\n for x1, y1, x2, y2, conf, cls_conf, cls_pred in self.results:\n if (class_id1 == cls_pred or class_id2 == cls_pred) and cls_conf > 0.6:\n if fuse_index > -1 and fuse_index != found_class_index:\n found_class_index += 1\n continue\n width = x2 - x1\n height = y2 - y1\n x_coord = width / 2 + x1\n y_coord = height / 2 + y1\n if height > width:\n orientation = OrientationEnum.VERTICAL.value\n grip_width = width * 0.58\n elif width > height:\n orientation = OrientationEnum.HORIZONTAL.value\n grip_width = height * 0.58\n else:\n orientation = OrientationEnum.HORIZONTAL.value\n grip_width = height * 0.58\n print(\"[W] Could not determine orientation, using 1 as default\")\n #new_part_id = convert_to_part_id(part_class)\n part = (cls_pred, x_coord, y_coord, orientation, grip_width)\n break\n print(part)\n return part\n\n def segment(self, np_img):\n results = self.segmentation_detector.predict(np_img)\n classes = [\"PCB\", \"BottomCover\", \"BlueCover\", \"WhiteCover\", \"BlackCover\"]\n masks = []\n for i in range(len(results[\"instances\"].pred_classes)):\n mask_image = results[\"instances\"].pred_masks[i].cpu().numpy()\n mask_image = np.asarray(mask_image * 255, dtype=np.uint8)\n moments = cv2.moments(mask_image)\n cX = int(moments[\"m10\"] / moments[\"m00\"])\n cY = int(moments[\"m01\"] / moments[\"m00\"])\n center = (cX, cY)\n area = moments[\"m00\"]\n part = classes[results['instances'].pred_classes[i]]\n score = results['instances'].scores[i]\n mask = {\"part\": part, \"score\": score, \"area\": area, \"center\": center, \"ignored\": False, \"ignore_reason\": \"\", \"mask\": mask_image}\n masks.append(mask)\n return masks\n\n def detect_object(self):\n np_img = pimg.open(self.image_path)\n self.results = self.detector.predict(np_img)\n self.draw_boxes(self.results)\n\n def draw_boxes(self, results):\n source_img = pimg.open(self.image_path).convert(\"RGBA\")\n for x1, y1, x2, y2, conf, cls_conf, cls_pred in self.results:\n if cls_conf > 0.6:\n width = x2 - x1\n height = y2 - y1\n x_coord = width / 2 + x1\n y_coord = height / 2 + y1\n draw = ImageDraw.Draw(source_img)\n draw.rectangle(((x1, y1), (x2, y2)), fill=None, outline=(200, 0, 150), width=6)\n draw.text((x_coord, y_coord), convert_from_part_id(int(cls_pred)))\n source_img.save('boundingboxes.png')\n\n def is_facing_right(self, np_image):\n pil_image = pimg.fromarray(np_image)\n resized_image = pil_image.resize((224, 224))\n resized_image_np = np.array(resized_image) / 255\n image_tensor = torch.from_numpy(resized_image_np).permute(2, 0, 1).float()\n image_tensor = image_tensor.unsqueeze(0)\n self.orientationCNN.eval()\n with torch.no_grad():\n prediction = self.orientationCNN(image_tensor)\n result = prediction[0][0] >= 0.5\n print(\"[INFO] Part is facing right. {}\".format(result))\n return result\n\n def get_image_path(self):\n return self.image_path\n\n def find_part_for_grasp(self):\n masks = glob.glob(self.mask_path + \"*\")\n number_of_masks = len(masks)\n print(f\"There are {number_of_masks} masks\")\n contour_sizes = []\n for index, file_path in enumerate(masks):\n mask = pimg.open(file_path)\n mask = np.array(mask)\n print(f\"Finding contours on image {index + 1}/{number_of_masks}\")\n contours = self.find_contour(mask)\n for c in contours:\n area = cv2.contourArea(c)\n if area < 5000:\n continue\n else:\n contour_sizes.append(area)\n\n print(contour_sizes)\n part_to_grasp = contour_sizes.index(max(contour_sizes))\n print(part_to_grasp)\n return part_to_grasp\n\n\nif __name__ == \"__main__\":\n hey = Vision()\n masks = glob.glob(hey.mask_path + \"*\")\n part_to_grasp = hey.find_part_for_grasp()\n mask = pimg.open(masks[part_to_grasp])\n mask = np.array(mask)\n color_image = cv2.imread(\"color1582023984.5763314-0.png\")\n\n depth = cv2.imread(\"depth.png\")\n #depth = image_shifter.shift_image(depth)\n dim = (720, 1280)\n mask = cv2.resize(mask, dim)\n mask_contours = hey.find_contour(mask)\n x, y = hey.find_center(mask_contours)\n z = hey.get_z(x, y, depth)\n print(x, y, z)\n x, y, z = hey.calibrate.calibrate(color_image, x, y, z)\n print(x, y, z)\n #hey.vector_normal(x, y, img, depth)", "repo_name": "EmilRyberg/P6BinPicking", "sub_path": "vision/vision.py", "file_name": "vision.py", "file_ext": "py", "file_size_in_byte": 6780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.getcwd", "line_number": 25, "usage_type": "call"}, {"api_name": "vision.orientation.orientation_detector.OrientationDetectorNet", "line_number": 35, "usage_type": "call"}, {"api_name": "aruco.Calibration", "line_number": 38, "usage_type": "call"}, {"api_name": "vision.segmentation.detector.InstanceDetector", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 80, "usage_type": "attribute"}, {"api_name": "cv2.cv2.moments", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 81, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 93, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 93, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 98, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 98, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 105, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 111, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 117, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 127, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 132, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "cv2.cv2.contourArea", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 137, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 151, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 153, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 153, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.cv2.imread", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 155, "usage_type": "name"}, {"api_name": "cv2.cv2.imread", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 157, "usage_type": "name"}, {"api_name": "cv2.cv2.resize", "line_number": 160, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 160, "usage_type": "name"}]} +{"seq_id": "21365691575", "text": "#--------------------------------------------------------------------------------------------------------------------\r\n\r\n#This module contains task-related classes and functions\r\n\r\n#------------------ Dependencies ----------------------------#\r\n\r\n## External dependencies\r\nimport numpy\r\nfrom scipy.stats.stats import pearsonr\r\n \r\n## Internal dependencies\r\n\r\n#------------------ Global Variables ------------------------#\r\n\r\nISOTOPE_MASS_ERROR_BOUNDARY_TABLE = {}\r\nPPM = 1000000.0\r\n\r\nMASS_DIFFERENCE_COLUMN_NAME = 'Mass Difference'\r\nISOTOPE_CORRELATION_COLUMN_NAME = 'Isotope Distribution Correlation'\r\nH_L_RATIO_COLUMN_NAME = 'H/L Ratio'\r\nELUTION_CORRELATION_COLUMN_NAME = 'Elution Profile Correlation'\r\nELUTION_COUNT_COLUMN_NAME = '# Good Elution Profile Correlations' \r\nMQ_CONFIDENCE_COLUMN_NAME = 'MethylQuant Confidence'\r\nMQ_SCORE_COLUMN_NAME = 'MethylQuant Score'\r\n\r\n#------------------ Classes & Functions ---------------------#\r\n\r\n\"\"\" Returns expected mass difference between light and heavy methylSILAC partners \r\n \r\n This is based on the number of Methionine(M) residues in the peptide sequence\r\n \r\n Keyword arguments:\r\n peptide_sequence -- Amino acid sequence of peptide from MS/MS searches\r\n modifications -- Modifications identified on the peptide\r\n charge -- Charge state of peptide\r\n labelling -- Labelling\r\n silac_type -- Light or heavy peptide sequenced\r\n\"\"\"\r\ndef calculateMassShift(peptide_seq, modifications, charge, silac_type, mass_shifts):\r\n #adjust the expected mass shift based on the mass of the labels\r\n #adjust the expected mass shift based on the modifications that contribute to the mass shift\r\n expected_mass_shift = (mass_shifts.calculateMassShiftForLabels(peptide_seq) +\r\n mass_shifts.calculateMassShiftForModifications(modifications)) \r\n \r\n #adjust the expected mass shift based on the silac type\r\n #adjust the expected mass shift based on the peptide charge\r\n expected_mass_shift = (expected_mass_shift * silac_type) / float(charge)\r\n return expected_mass_shift\r\n\r\n#########################################################################################################\r\n\r\n\"\"\" Returns list of masses corresponding to isotope envelops of light and heavy methylSILAC partners \r\n \r\n This is a list of 3 light and 3 heavy peaks based on a given mass shift\r\n \r\n Keyword arguments:\r\n precursor_mass -- Precursor mass for a given scan\r\n calc_mz_pipeline -- Calculated m/z value for a peptide\r\n charge -- Charge state of peptide\r\n mz_shift_for_partner -- Mass difference between light and heavy methylSILAC partners\r\n\"\"\" \r\ndef calculatePeptideIsotopeMasses(precursor_mass, charge, calc_mz, mz_shift_for_partner):\r\n #Carbon 13 = +1.00335\r\n #calculate the minimum difference between the isotope peaks for the peptide\r\n isotope_states_mass_difference = 1.00335/charge\r\n \r\n #determine difference between exp and calc mz, then see how many times isotope state\r\n #mass difference divides into it to determine the isotope peak number that was selected for fragmentation\r\n #sometimes 2nd or 3rd isotopic peak and not the monoisotopic peak is selected for fragmentation\r\n isotope_peak_num = round((precursor_mass - calc_mz) / isotope_states_mass_difference)\r\n \r\n #calculate mz of first, second, third isotope peaks\r\n first_peak_mz = precursor_mass - (isotope_peak_num * isotope_states_mass_difference)\r\n second_peak_mz = first_peak_mz + isotope_states_mass_difference\r\n third_peak_mz = first_peak_mz + (2 * (isotope_states_mass_difference))\r\n \r\n #now calculate the peaks for heavy or light partner\r\n #if we initially have a light, then the mass shift will be in the positive direction \r\n #if we initially have a heavy, then the mass shift will be in the negative direction (See CalculateMassShift function above)\r\n first_peak_mz_partner = first_peak_mz + mz_shift_for_partner\r\n second_peak_mz_partner = second_peak_mz + mz_shift_for_partner\r\n third_peak_mz_partner = third_peak_mz + mz_shift_for_partner\r\n \r\n #assemble all the masses to look for\r\n isotope_masses = numpy.array([first_peak_mz, second_peak_mz, third_peak_mz])\r\n isotope_masses_partner = numpy.array([first_peak_mz_partner, second_peak_mz_partner, third_peak_mz_partner]) \r\n \r\n #Sort the masses such that it is always [Light, Heavy]\r\n peptide_isotope_masses = (numpy.array([isotope_masses_partner, isotope_masses]) \r\n if (isotope_masses_partner < isotope_masses).all() \r\n else numpy.array([isotope_masses, isotope_masses_partner]))\r\n return peptide_isotope_masses\r\n\r\n#########################################################################################################\r\n\r\n\"\"\" Returns tuple start and end RT \r\n \r\n This is +- the time window overlap for a given RT\r\n \r\n Keyword arguments:\r\n time_window_overlap -- Time window\r\n RT_MSMS -- Retention time for a MS/MS scan\r\n run_start_time -- Run start time\r\n run_end_time -- Run end time\r\n\"\"\"\r\ndef calculateTimeWindow(time_window_overlap, RT_MSMS, run_start_time, run_end_time): \r\n #scan back over 0.22min and forward 0.22min from MS/MS to search for maximum overlap\r\n #these times were chosen as 0.22min is the maximum delay between elution of heavy and elution of light\r\n time_window_start_RT = RT_MSMS - time_window_overlap\r\n time_window_stop_RT = RT_MSMS + time_window_overlap\r\n \r\n #make sure search window is within range of the run time\r\n time_window_start_RT = run_start_time if time_window_start_RT < run_start_time else time_window_start_RT\r\n time_window_stop_RT = run_end_time if time_window_stop_RT > run_end_time else time_window_stop_RT\r\n\r\n return (time_window_start_RT, time_window_stop_RT)\r\n \r\n#########################################################################################################\r\n\r\n\"\"\" Returns tuple of upper and lower mass boundaries\r\n \r\n This is +- the mass error ppm for a given isotope\r\n \r\n Keyword arguments:\r\n mass_error -- Error tolerance\r\n isotope -- Isotopic mass of a peptide\r\n\"\"\"\r\ndef calculateIsotopeMassErrorBoundary(mass_error, isotope):\r\n if isotope not in ISOTOPE_MASS_ERROR_BOUNDARY_TABLE:\r\n #calculate upper and lower mass errors when searching for signals matching the predicted\r\n #masses of the peptide isotopomers, was set to 20ppm\r\n isotope_mass_error_ppm = (isotope/PPM) * mass_error\r\n mass_upper = isotope + isotope_mass_error_ppm\r\n mass_lower = isotope - isotope_mass_error_ppm \r\n ISOTOPE_MASS_ERROR_BOUNDARY_TABLE[isotope] = (mass_upper, mass_lower)\r\n \r\n (mass_upper, mass_lower) = ISOTOPE_MASS_ERROR_BOUNDARY_TABLE[isotope]\r\n return (mass_upper, mass_lower)\r\n\r\n#########################################################################################################\r\n\r\n\"\"\" Returns the H/L ratio of light and heavy methylSILAC partners \r\n \r\n H/L ratio = sum(intensities for heavy) / sum(intensities for light)\r\n \r\n Keyword arguments:\r\n light_average_mass_intensities -- numpy.array of averaged mass intensities for light isotope envelopes\r\n heavy_average_mass_intensities -- numpy.array of averaged mass intensities for heavy isotope envelopes\r\n\"\"\"\r\ndef calculateHtoLRatio(light_average_mass_intensities, heavy_average_mass_intensities):\r\n light_average_intensities = light_average_mass_intensities[:, 1]\r\n heavy_average_intensities = heavy_average_mass_intensities[:, 1]\r\n \r\n #if there are any isotope envelope members missing, intensity of whole peptide is set to 0\r\n #this provides more specificity and minimises amount of rubbish being quantified\r\n light_intensity = 0 if 0 in light_average_intensities else numpy.sum(light_average_mass_intensities, axis = 0)[1]\r\n heavy_intensity = 0 if 0 in heavy_average_intensities else numpy.sum(heavy_average_mass_intensities, axis = 0)[1]\r\n\r\n if (float(heavy_intensity) != 0 and float(light_intensity) != 0):\r\n ratio = (float(heavy_intensity) / float(light_intensity))\r\n return ratio\r\n \r\n #partner wasn't found\r\n return 'NA'\r\n \r\n#########################################################################################################\r\n\r\n\"\"\" Returns pearson correlation coefficient\r\n \r\n This is a correlation between light and heavy isotope envelopes\r\n Code from dfrankow on stackoverflow\r\n \r\n Keyword arguments:\r\n light_average_mass_intensities -- numpy.array of averaged intensities for each light isotope envelopes\r\n heavy_average_mass_intensities -- numpy.array of averaged intensities for each heavy isotope envelopes\r\n\"\"\"\r\ndef calculatePearsonCorrelationCoefficient(light_average_mass_intensities, heavy_average_mass_intensities):\r\n light_average_intensities = light_average_mass_intensities[:, 1]\r\n heavy_average_intensities = heavy_average_mass_intensities[:, 1]\r\n\r\n #the function returns a (coefficient, p-value) tuple\r\n pearson_correlation_coefficient = pearsonr(light_average_intensities, heavy_average_intensities)[0]\r\n if (pearson_correlation_coefficient is not None and not numpy.isnan(pearson_correlation_coefficient)):\r\n return pearson_correlation_coefficient\r\n \r\n #Correlation could not be calculated\r\n return 'NA'\r\n\r\n", "repo_name": "aidantay/MethylQuant", "sub_path": "root/src/task/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 9540, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 157, "usage_type": "call"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 183, "usage_type": "call"}]} +{"seq_id": "32459716941", "text": "#--------------------------------------------------------------------\n# bakery.util: Common utility functions.\n#\n# Author: Lain Supe (supelee)\n# Date: Thursday, March 23 2017\n#--------------------------------------------------------------------\n\nimport inspect\nimport logging\n\nfrom .error import *\n\n#--------------------------------------------------------------------\ndef has_method(obj, name):\n return callable(getattr(obj, name, None))\n\n#--------------------------------------------------------------------\ndef compose(arg, *functions):\n result = arg\n for f in functions:\n result = f(result)\n return result\n\n#--------------------------------------------------------------------\ndef degenerate(arg):\n if inspect.isgenerator(arg):\n return list(arg)\n else:\n return arg\n\n#--------------------------------------------------------------------\ndef flat_map(arg, f = lambda x: x):\n if isinstance(arg, (list, tuple)):\n results = []\n for x in arg:\n results.extend(flat_map(x, f))\n return results\n\n elif isinstance(arg, dict):\n return flat_map(list(arg.values()), f)\n\n else:\n return [f(arg)]\n\n#--------------------------------------------------------------------\ndef wide_foreach(arg, f = lambda x: x):\n if isinstance(arg, (list, tuple)):\n for x in arg:\n wide_foreach(x, f)\n elif isinstance(arg, dict):\n for x in arg.values():\n wide_foreach(x, f)\n else:\n f(arg)\n\n#--------------------------------------------------------------------\ndef wide_map(arg, f = lambda x: x):\n if isinstance(arg, (list, tuple)):\n return [wide_map(x, f) for x in arg]\n elif isinstance(arg, dict):\n return {key: wide_map(value, f) for key, value in arg.items()}\n else:\n return f(arg)\n\n#--------------------------------------------------------------------\ndef log_for(obj):\n if inspect.isfunction(obj) or inspect.ismethod(obj):\n return logger_for_function(obj)\n else:\n return logger_for_class(obj)\n\n#--------------------------------------------------------------------\ndef logger_for_class(obj):\n return logging.getLogger(name_for_class(obj))\n\n#--------------------------------------------------------------------\ndef logger_for_function(f):\n return logging.getLogger(name_for_function(f))\n\n#--------------------------------------------------------------------\ndef name_for_class(obj):\n if inspect.isclass(obj):\n return obj.__module__ + '.' + obj.__qualname__\n else:\n return obj.__module__ + '.' + obj.__class__.__qualname__\n\n#--------------------------------------------------------------------\ndef short_name_for_function(f):\n return f.__qualname__\n\n#--------------------------------------------------------------------\ndef name_for_function(f):\n return f.__module__ + '.' + f.__qualname__\n\n#--------------------------------------------------------------------\ndef tree_to_depth_list(tree, depth_list = None, depth = 0):\n if depth_list is None:\n depth_list = []\n \n if len(depth_list) <= depth:\n depth_list.append([])\n\n if isinstance(tree, dict):\n for key, value in tree.items():\n depth_list[depth].append(key)\n tree_to_depth_list(value, depth_list, depth + 1)\n\n elif isinstance(tree, (list, tuple, set)):\n for value in tree:\n if isinstance(value, (dict, list, tuple, set)):\n tree_to_depth_list(value, depth_list, depth + 1)\n else:\n depth_list[depth].append(value)\n else:\n depth_list[depth].append(tree)\n\n return depth_list\n", "repo_name": "Hodapp87/python3-bakery", "sub_path": "bakery/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 3648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "inspect.isgenerator", "line_number": 26, "usage_type": "call"}, {"api_name": "inspect.isfunction", "line_number": 67, "usage_type": "call"}, {"api_name": "inspect.ismethod", "line_number": 67, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 78, "usage_type": "call"}, {"api_name": "inspect.isclass", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "30117447777", "text": "from __future__ import annotations\n\nimport random\n\nfrom pygame import Vector2\nfrom typing import TYPE_CHECKING, Optional, Tuple\n\nfrom ..traproom import DMTrapRoom\nfrom utilities import UnlockPack, Effect\n\nif TYPE_CHECKING:\n from dm.core.game.game import DMGame\n################################################################################\n\n__all__ = (\"DeathAndCorruption\",)\n\n################################################################################\nclass DeathAndCorruption(DMTrapRoom):\n\n def __init__(self, game: DMGame, position: Optional[Vector2] = None, level: int = 1):\n\n super().__init__(\n game, position,\n _id=\"ROOM-221\",\n name=\"Death and Corruption\",\n description=(\n \"Once recharged, inflict {damage} damage to all enemies in \"\n \"adjacent area and give them {status} Corpse Explosion.\"\n ),\n level=level,\n rank=8,\n unlock=UnlockPack.Myth,\n base_dmg=121,\n effects=[\n Effect(name=\"Corpse Explosion\", base=48, per_lv=36)\n ]\n )\n self.setup_charging(3.3, 3.3)\n\n################################################################################\n def on_charge(self) -> None:\n\n for room in self.adjacent_rooms:\n for hero in room.heroes:\n hero.damage(self.dmg) # type: ignore\n hero.add_status(\"Corpse Explosion\", self.effects[\"Corpse Explosion\"], self)\n\n################################################################################\n", "repo_name": "AllegroVivo/DungeonDefense", "sub_path": "dm/rooms/EightStar/DeathAndCorruption.py", "file_name": "DeathAndCorruption.py", "file_ext": "py", "file_size_in_byte": 1597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 11, "usage_type": "name"}, {"api_name": "traproom.DMTrapRoom", "line_number": 18, "usage_type": "name"}, {"api_name": "dm.core.game.game.DMGame", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "pygame.Vector2", "line_number": 20, "usage_type": "name"}, {"api_name": "utilities.UnlockPack.Myth", "line_number": 32, "usage_type": "attribute"}, {"api_name": "utilities.UnlockPack", "line_number": 32, "usage_type": "name"}, {"api_name": "utilities.Effect", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "18370045541", "text": "import logging\n\nfrom pymongo import MongoClient\nfrom redis.client import Redis\n\nfrom config import C\n\n_mongo_client = None\n_mongo_db = None\n\n_redis_client = None\n\n\ndef mongo_db():\n global _mongo_db\n return _mongo_db\n\n\ndef redis_client():\n global _redis_client\n return _redis_client\n\n\ndef init_db():\n \"\"\"Init database objects\"\"\"\n global _mongo_client, _mongo_db, _redis_client\n\n _mongo_client = MongoClient(C.mongo_addr, C.mongo_port)\n try:\n _mongo_client.server_info()\n except Exception as e:\n logging.getLogger('image-classifier-backend').error('mongo error, ' + str(e))\n exit(1)\n _mongo_db = _mongo_client[C.mongo_db]\n\n _redis_client = Redis(C.redis_addr, C.redis_port, C.redis_db)\n try:\n _redis_client.ping()\n except Exception as e:\n logging.getLogger('image-classifier-backend').error('redis error, ' + str(e))\n exit(1)\n", "repo_name": "KSkun/Image-Spider", "sub_path": "src/db/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pymongo.MongoClient", "line_number": 28, "usage_type": "call"}, {"api_name": "config.C.mongo_addr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "config.C", "line_number": 28, "usage_type": "name"}, {"api_name": "config.C.mongo_port", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "config.C.mongo_db", "line_number": 34, "usage_type": "attribute"}, {"api_name": "config.C", "line_number": 34, "usage_type": "name"}, {"api_name": "redis.client.Redis", "line_number": 36, "usage_type": "call"}, {"api_name": "config.C.redis_addr", "line_number": 36, "usage_type": "attribute"}, {"api_name": "config.C", "line_number": 36, "usage_type": "name"}, {"api_name": "config.C.redis_port", "line_number": 36, "usage_type": "attribute"}, {"api_name": "config.C.redis_db", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "21319821533", "text": "import sys\r\nimport PySide6.QtWidgets as pq\r\n#collection of some qtwidgets more in the documentation \r\nclass MainWindow(pq.QMainWindow):\r\n def __init__(self):\r\n super().__init__()\r\n\r\n layout= pq.QVBoxLayout()\r\n widgets = [\r\n pq.QCheckBox,\r\n pq.QComboBox,\r\n pq.QDateEdit,\r\n pq.QDateTimeEdit,\r\n pq.QDial,\r\n pq.QDoubleSpinBox,\r\n pq.QFontComboBox,\r\n pq.QLCDNumber,\r\n pq.QLabel,\r\n pq.QLineEdit,\r\n pq.QProgressBar,\r\n pq.QPushButton,\r\n pq.QRadioButton,\r\n pq.QSlider,\r\n pq.QSpinBox,\r\n pq.QTimeEdit,\r\n ]\r\n\r\n for widget in widgets:\r\n layout.addWidget(widget())\r\n\r\n central_widget = pq.QWidget()\r\n central_widget.setLayout(layout)\r\n \r\n self.setCentralWidget(central_widget)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app = pq.QApplication(sys.argv)\r\n\r\n window = MainWindow()\r\n window.show()\r\n\r\n app.exec_()", "repo_name": "StefanStahlCode/Tutorials", "sub_path": "PySide6/widgets_overview.py", "file_name": "widgets_overview.py", "file_ext": "py", "file_size_in_byte": 1050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "PySide6.QtWidgets.QMainWindow", "line_number": 4, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 4, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QVBoxLayout", "line_number": 8, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 8, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QCheckBox", "line_number": 10, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 10, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QComboBox", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 11, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDateEdit", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 12, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDateTimeEdit", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 13, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDial", "line_number": 14, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 14, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDoubleSpinBox", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFontComboBox", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 16, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLCDNumber", "line_number": 17, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 17, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 18, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLineEdit", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 19, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QProgressBar", "line_number": 20, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 20, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 21, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QRadioButton", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QSlider", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QSpinBox", "line_number": 24, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 24, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QTimeEdit", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QWidget", "line_number": 31, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 38, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "13301216666", "text": "import collections\n\nclass Solution:\n def removeInvalidParentheses(self, s: str):\n def isValid(str):\n count = 0\n for char in str:\n if char == \"(\":\n count += 1\n elif char == \")\":\n count -= 1\n if count < 0:\n return False\n return count == 0\n\n if not s:\n return ['']\n\n ret = []\n visited = collections.defaultdict(str)\n visited[s]\n q = collections.deque()\n q.append(s)\n found = False\n\n while q:\n for i in range(len(q)):\n top = q.popleft()\n\n if (isValid(top)):\n found = True\n ret.append(top)\n\n if found:\n continue\n\n for j in range(len(top)):\n if top[j] != \"(\" and top[j] != \")\":\n continue\n\n newStr = top[:j] + top[j+1:]\n\n if newStr not in visited:\n visited[newStr]\n q.append(newStr)\n return ret\n\n# Old Solution\n # def removeInvalidParentheses(self, s: str):\n # retList = []\n #\n # def permuteHelper(paranthLeft, build, stack):\n # if paranthLeft == '':\n # if stack == []:\n # retList.append(build)\n # else:\n # if paranthLeft[0] == '(':\n # permuteHelper(paranthLeft[1:], build, stack)\n # stack.append('(')\n # permuteHelper(paranthLeft[1:], build + \"(\", stack)\n # elif paranthLeft[0] == \")\":\n # permuteHelper(paranthLeft[1:], build, stack)\n # if stack:\n # stack.pop()\n # permuteHelper(paranthLeft[1:], build + ')', stack)\n #\n # permuteHelper(s, '', [])\n # return retList\n\nsol = Solution()\ninput = \"()())()\"\nprint(sol.removeInvalidParentheses(input))", "repo_name": "adalloul0928/Leetcode_Hell", "sub_path": "Archive/Facebook/Recursion/removeInvalidParanth.py", "file_name": "removeInvalidParanth.py", "file_ext": "py", "file_size_in_byte": 2082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "collections.defaultdict", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "41297047511", "text": "import jwt\nimport pytest\nfrom testcontainers.mongodb import MongoDbContainer\nfrom testcontainers.redis import RedisContainer\n\nfrom skill_manager.models import Skill, SkillSettings\n\n\n@pytest.fixture(scope=\"module\")\ndef monkeymodule():\n from _pytest.monkeypatch import MonkeyPatch\n\n mpatch = MonkeyPatch()\n yield mpatch\n mpatch.undo()\n\n\n@pytest.fixture(scope=\"module\")\ndef init_mongo_db():\n mongo_db = MongoDbContainer(\"mongo:5.0.4\")\n mongo_db.start()\n mongo_db._connect()\n try:\n yield mongo_db\n except Exception as err:\n raise err\n finally:\n mongo_db.stop()\n\n\n@pytest.fixture(scope=\"module\")\ndef init_redis():\n redis_password = \"redis-pass\"\n redis = RedisContainer(\"redis:latest\", password=redis_password)\n redis.start()\n redis._connect()\n try:\n yield redis\n except Exception as err:\n raise err\n finally:\n redis.stop()\n\n\n@pytest.fixture\ndef skill_prediction_factory():\n def skill_prediction(**kwargs):\n return {\n \"predictions\": [\n {\n \"question\": \"What is the answer to the ultimate question of life, the universe, and everything?\",\n \"prediction_score\": 1,\n \"prediction_output\": {\"output\": \"answer\", \"output_score\": \"1\"},\n \"prediction_documents\": [\n {\n \"index\": \"\",\n \"document_id\": \"\",\n \"document\": \"doc one\",\n \"span\": None,\n \"url\": \"\",\n \"source\": \"\",\n \"document_score\": 0.0,\n }\n ],\n **kwargs,\n }\n ]\n }\n\n return skill_prediction\n\n\n@pytest.fixture\ndef skill_factory():\n def skill_init(\n name=\"test-skill\",\n url=\"http://test-skill.square:1234\",\n skill_type=\"abstractive\",\n skill_settings=SkillSettings(),\n user_id=\"test-user-id\",\n description=\"skill for testing\",\n published=False,\n default_skill_args=None,\n **kwargs,\n ):\n # pass `id` or `created_at` as kwargs to add them explicitly\n skill = Skill(\n name=name,\n url=url,\n skill_type=skill_type,\n skill_settings=skill_settings,\n user_id=user_id,\n description=description,\n published=published,\n default_skill_args={} if default_skill_args is None else default_skill_args,\n **kwargs,\n )\n if not skill.id:\n del skill.id\n\n return skill\n\n yield skill_init\n\n\n@pytest.fixture\ndef token_factory():\n def token(**kwargs):\n return jwt.encode(\n {\"iss\": \"https://square.ukp-lab.test/auth/realms/test-realm\", **kwargs},\n \"secret\",\n algorithm=\"HS256\",\n )\n\n return token\n", "repo_name": "UKP-SQuARE/square-core", "sub_path": "skill-manager/tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 67, "dataset": "github-code", "pt": "3", "api": [{"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "call"}, {"api_name": "testcontainers.mongodb.MongoDbContainer", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 18, "usage_type": "call"}, {"api_name": "testcontainers.redis.RedisContainer", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 45, "usage_type": "attribute"}, {"api_name": "skill_manager.models.SkillSettings", "line_number": 79, "usage_type": "call"}, {"api_name": "skill_manager.models.Skill", "line_number": 87, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 73, "usage_type": "attribute"}, {"api_name": "jwt.encode", "line_number": 109, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 106, "usage_type": "attribute"}]} +{"seq_id": "6380753272", "text": "import copy\nimport numpy as np\nfrom geopy.distance import distance\nimport matplotlib.pyplot as plt\n\n\nclass GWO:\n\n def __init__(self, locations, time_windows, service_time, demands, depot, cap, speed):\n \"\"\"\n\n :param locations: 需求点坐标\n :param time_windows: 时间窗\n :param demands: 需求\n :param service_time: 服务时间\n :param depot: 配送点坐标\n :param cap: 车辆最大容量\n :param speed: 车速\n :return:\n \"\"\"\n self.speed = speed\n self.locations = locations\n self.time_windows = time_windows\n self.service_time = service_time\n self.demands = demands\n self.depot = depot\n self.cap = cap\n self.seq_dic = {i: i for i in range(1, len(self.demands) + 1)}\n\n self.process_demand()\n\n self.num_customers = len(self.demands) # 顾客数\n\n self.dist_matrix = self._compute_distance_matrix()\n\n self.pop_size = 100 # 种群个数\n self.max_iter = 200 # 最大迭代次数\n\n def process_demand(self):\n k = len(self.demands) + 1\n reality_customers = len(self.demands)\n i = 0\n while i < reality_customers:\n if self.demands[i] > self.cap:\n self.demands[i] -= self.cap\n self.demands = np.append(self.demands, self.cap)\n self.locations = np.append(self.locations, self.locations[i].reshape(1, 2), axis=0)\n self.time_windows = np.append(self.time_windows, self.time_windows[i].reshape(1, 2), axis=0)\n self.service_time = np.append(self.service_time, self.service_time[i])\n self.seq_dic[k] = i + 1\n k += 1\n else:\n i += 1\n\n # 计算距离矩阵，根据经纬度计算距离\n def _compute_distance_matrix(self):\n dist_matrix = np.zeros((self.num_customers, self.num_customers))\n for i in range(self.num_customers):\n for j in range(self.num_customers):\n if i == j:\n dist_matrix[i][j] = 0\n else:\n dist_matrix[i][j] = distance(self.locations[i], self.locations[j]).km\n return dist_matrix\n\n # 验证方案是否可行，需求约束和时间窗\n def _feasible(self, routes):\n # 检查需求约束\n for route in routes:\n demand = sum([self.demands[i - 1] for i in route])\n if demand > self.cap:\n return False\n\n # 检查时间窗约束\n for route in routes:\n time = 0\n for idx, i in enumerate(route):\n if idx == 0:\n time = distance(self.depot, self.locations[route[0] - 1]).km / self.speed\n else:\n time += self.dist_matrix[route[idx - 1] - 1][i - 1] / self.speed\n # time = max(time, self.time_windows[i - 1][0])\n time += self.service_time[i - 1]\n if time > self.time_windows[i - 1][1]:\n return False\n return True\n\n # 计算目标函数，即总距离最短\n def evaluate(self, routes):\n if not self._feasible(routes):\n return float('inf')\n else:\n cost = 0\n for route in routes:\n route_cost = distance(self.depot, self.locations[route[0] - 1]).km\n for idx, i in enumerate(route):\n route_cost += self.dist_matrix[route[idx - 1] - 1][i - 1]\n route_cost += distance(self.depot, self.locations[route[-1] - 1]).km\n cost += route_cost\n return cost\n\n # 对灰狼个体进行解码，得到运输路线\n def decode(self, x):\n seq = np.argsort(x) + 1\n routes = []\n i = 0\n d = 0\n t = 0\n route = []\n while i < len(seq):\n if d == 0:\n t += distance(self.depot, self.locations[seq[i] - 1]).km / self.speed\n else:\n t += distance(self.locations[seq[i - 1] - 1], self.locations[seq[i] - 1]).km / self.speed\n d += self.demands[seq[i] - 1]\n if d > self.cap or t > self.time_windows[seq[i] - 1][1]:\n routes.append(route)\n d = 0\n t = 0\n route = []\n continue\n route.append(seq[i])\n i += 1\n return routes\n\n # 初始化灰狼个体\n def init_wolf(self):\n return np.random.uniform(-10, 10, size=(self.pop_size, self.num_customers))\n\n # 求解\n def solve(self):\n # 初始化种群\n pop = self.init_wolf()\n # 计算目标函数\n fitness = np.zeros(self.pop_size)\n for i in range(self.pop_size):\n routes = self.decode(pop[i])\n fitness[i] = self.evaluate(routes)\n pop = pop[np.argsort(fitness)]\n fitness.sort()\n alpha_wolf, beta_wolf, gamma_wolf = copy.copy(pop[: 3])\n\n convergence_curve = np.zeros(self.max_iter) # 保存每次迭代的最优个体适应度\n # 开始迭代\n for Iter in range(1, self.max_iter + 1):\n a = 2 * (1 - Iter / self.max_iter)\n for i in range(self.pop_size):\n A1, A2, A3 = a * (2 * np.random.rand() - 1), a * (\n 2 * np.random.rand() - 1), a * (2 * np.random.rand() - 1)\n C1, C2, C3 = 2 * np.random.rand(), 2 * np.random.rand(), 2 * np.random.rand()\n X1 = alpha_wolf - A1 * abs(C1 - alpha_wolf - pop[i])\n X2 = beta_wolf - A2 * abs(C2 - beta_wolf - pop[i])\n X3 = gamma_wolf - A3 * abs(C3 - gamma_wolf - pop[i])\n x_new = (X1 + X2 + X3) / 3\n f_new = self.evaluate(self.decode(x_new))\n if f_new < fitness[i]:\n pop[i] = x_new.copy()\n fitness[i] = f_new\n pop = pop[np.argsort(fitness)]\n fitness.sort()\n alpha_wolf, beta_wolf, gamma_wolf = copy.copy(pop[: 3])\n convergence_curve[Iter - 1] = fitness[0]\n print(f\"第{Iter}次迭代:目标值{fitness[0]}\")\n return fitness[0], self.decode(alpha_wolf), convergence_curve, self.seq_dic\n\n\nif __name__ == '__main__':\n import pandas as pd\n\n df = pd.read_excel(\"vrptw数据.xlsx\")\n num_customers = 36 # 客户数量\n depot_location = np.array([24.212273, 109.338894]) # 车库位置\n customer_locations = df.iloc[:, 0].str.split(',').tolist() # 客户位置\n customer_locations = np.array([list(map(float, i)) for i in customer_locations])\n customer_demands = df.iloc[:, 1].values # 客户需求\n time_windows = df.iloc[:, 2].str.split('-').tolist() # 客户时间窗口\n time_windows = np.array([list(map(int, i)) for i in time_windows])\n service_time = df.iloc[:, 3].values # 服务时间\n cap = 13 # 车辆容量\n speed = 40 # 车速，KM/h\n\n # obj表示最优目标函数，routes表示最优方案，convergence_curve存储每次迭代的最优个体目标值\n obj, routes, convergence_curve, seq_dic = GWO(customer_locations, time_windows, service_time, customer_demands,\n depot_location, cap, speed).solve()\n\n for i in range(len(routes)):\n for j in range(len(routes[i])):\n routes[i][j] = seq_dic[routes[i][j]]\n\n # 最优配送方案\n file = open(\"solution.txt\", \"w\", encoding='utf-8')\n print(f\"最优配送方案如下，总距离为{obj}, 共有{len(routes)}辆车：\", file=file)\n for idx, route in enumerate(routes):\n print(f\"第{idx + 1}辆车：{route}\", file=file)\n\n plt.plot(range(len(convergence_curve)), convergence_curve)\n plt.xlabel(\"Iteration\")\n plt.ylabel(\"distance\")\n plt.savefig(\"迭代图像.png\")\n plt.show()\n\n", "repo_name": "MrBin226/code", "sub_path": "接单项目/灰狼算法求解VRPTW/GWO_VRPTW.py", "file_name": "GWO_VRPTW.py", "file_ext": "py", "file_size_in_byte": 7850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.append", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 63, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 79, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 95, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 104, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 112, "usage_type": "call"}, {"api_name": "geopy.distance.distance", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 139, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 159, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}]} +{"seq_id": "73173990482", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport time\nfrom pymongo import MongoClient\nclient = MongoClient('localhost', 27017) # client가 robo와 같은 역할(mongodb에 연결)\ndb = client.your_beer_is\n\ntarget_url = 'https://m.blog.naver.com/nbc64pjh/221988284164'\n\n\nheaders = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'}\ndata = requests.get(target_url, headers=headers)\n\n\nsoup = BeautifulSoup(data.text, 'html.parser')\n\nbeers = soup.select('#SE-2430502c-2d10-4494-baf5-eb675c5fd24d > div > div > div > table > tbody > tr')\n\nfor beer in beers:\n name_tag = beer.select_one('td > div > p > span')\n name = name_tag.text\n \n documnet = {\n 'name': name,\n }\n print(document)", "repo_name": "hyuk7474/your-beer-is", "sub_path": "init_db_name.py", "file_name": "init_db_name.py", "file_ext": "py", "file_size_in_byte": 787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pymongo.MongoClient", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "74094683602", "text": "import json\nimport time\nimport hmac\nimport string\nimport hashlib\nimport requests\nfrom urllib import parse\nfrom sign import authBlackCheck\n\n\ndef get(access_key_id, secret_access_key, data):\n\n #url = 'http://*.*.*.*:6144/blackcheck'\n url = 'http://*.*.*.*:8001/blackcheck'\n response = requests.get(url, params=data)\n print(response.status_code)\n print(response.text)\n\n\ndef post(access_key_id, secret_access_key, json_basic_info):\n\n #1 设置 请求地址/请求方法\n #url = 'http://*.*.*.*:6144/blackcheck'\n url = 'http://*.*.*.*:8001/blackcheck'\n http_method = 'POST'\n #1 提取 请求主机地址/请求路径, 设置请求参数\n url_split = parse.urlsplit(url)\n host = url_split.scheme + '://' + parse.splitport(url_split.netloc)[0]\n path = url_split.path\n params = {}\n\n #2 提交数据, 及提交内容的MD5和数据长度\n #json_basic_info = json_basic_info\n content_type = 'application/json'\n content_md5 = hashlib.md5(str(json_basic_info).encode('utf8')).hexdigest()\n content_len = len(json.dumps(json_basic_info))\n\n #3 请求时刻(北京时间)时间戳\n timestamp = time.mktime(time.localtime())\n #timestamp = 1545901200.0\n # headers中的查询时间转换为 UTC时间戳\n query_date = time.strftime('%Y-%m-%dT%XZ', time.localtime(timestamp))\n\n #4 构造请求头headers, 指定参与签名的headers参数\n headers = {\n 'Host': host,\n 'Content-Type': content_type,\n 'Content-MD5' : content_md5,\n 'Content-Length': str(content_len),\n 'Query-Date': query_date,}\n #headers_to_sign = None\n headers_to_sign = {'host', 'content-type', 'content-md5', 'content-length', 'query-date'}\n\n #5 传输延迟时间(秒)\n expiration_time = 1800\n\n '''\n print('host:\\t\\t', host)\n print('path:\\t\\t', path)\n print('json:\\t\\t', json)\n print('content_md5:\\t\\t', content_md5)\n print('content_len:\\t\\t', content_len)\n print('timestamp:\\t\\t', timestamp)\n print('query_date:\\t\\t', query_date)\n print('headers:\\t\\t', headers)\n print('headers_to_sign:\\t\\t', headers_to_sign)\n print('expiration_time:\\t\\t', expiration_time)\n '''\n\n abc = authBlackCheck(\n access_key_id, secret_access_key, http_method, path, params, \n json, headers, headers_to_sign, timestamp, expiration_time)\n # 得到认证字符串\n authorization = abc.sign()\n# print('\\nauthorization:', authorization)\n\n headers['Authorization'] = authorization\n response = requests.post(url, json=json_basic_info, headers=headers, params=params)\n print(response.status_code)\n try:\n print(json.loads(response.text))\n except:\n print(response.text)\n\n\nif __name__ == \"__main__\":\n\n # 设置 应用授权ID, 应用秘钥, 传递数据\n access_key_id = 'xjzls8alyt3v38zwe2xuoshvn3l69sub'\n secret_access_key = 'hmvkvq6andwweid4qs4scem3dbj7uxzj'\n# '''\n json_basic_info = {'idcard': '420921198712345678', 'phone': '11122223333', 'name':'陈王',\n 'imei': '866018037459554', 'android_id': '49f3f8a1cf083664',\n 'mac': '50:9E:A7:04:F2:0C', 'idfa': 'A9-8DAF-1A9D4C473D55',\n 'ip': '112.96.69.153',}\n# '''\n\n post(access_key_id, secret_access_key, json_basic_info)\n# get(access_key_id, secret_access_key, json_basic_info)\n\n", "repo_name": "alasituoer/risk-control-service-process", "sub_path": "api/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3328, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.parse.urlsplit", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.parse.splitport", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 28, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 39, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 39, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 42, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 42, "usage_type": "call"}, {"api_name": "sign.authBlackCheck", "line_number": 70, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 78, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "29488633296", "text": "import sys\nimport argparse\nimport numpy as np\n\nfrom Utils.data_utils import *\nfrom Utils.evaluation import *\nfrom Utils.dataloader import train_dataset, test_dataset\n\nimport torch\nimport torch.optim as optim\nimport torch.utils.data as data\n\nfrom Models.HetComp import HetComp_MF\n\ndef get_NDCG_u(sorted_list, teacher_t_items, user, k=50):\n\n\twith torch.no_grad():\n\t\ttop_scores = np.asarray([np.exp(-t/10) for t in range(k)])\n\t\ttop_scores = ((2 ** top_scores)-1)\n\t\t\n\t\tt_items = teacher_t_items[:k]\n\n\t\tsorted_list_tmp = []\n\t\tfor item in sorted_list:\n\t\t\tif user in train_mat and item not in train_mat[user]:\n\t\t\t\tsorted_list_tmp.append(item)\n\t\t\tif len(sorted_list_tmp) == k: break \n\n\t\tif user not in train_mat:\n\t\t\tsorted_list_tmp = sorted_list\n\n\t\tdenom = np.log2(np.arange(2, k + 2))\n\t\tdcg_50 = np.sum((np.in1d(sorted_list_tmp[:k], list(t_items)) * top_scores) / denom)\n\t\tidcg_50 = np.sum((top_scores / denom)[:k])\n\n\t\treturn round(dcg_50 / idcg_50, 4)\n\ndef DKC(sorted_mat, last_max_idx, last_dist, is_first, epoch, alpha=1.05):\n\t\n\tnext_idx = last_max_idx[:] \n\tif is_first:\n\t\tlast_dist = np.ones_like(next_idx)\n\t\tfor model_idx, model_type in enumerate(perm_dict):\n\t\t\tfor user in range(user_count):\n\t\t\t\tcurrent_selction = int(last_max_idx[model_idx][user])\n\t\t\t\tnext_v = min(3, int(next_idx[model_idx][user]) + 1)\n\n\t\t\t\tnext_perm = perm_dict[model_type][next_v][user]\n\t\t\t\tnext_dist = 1 - get_NDCG_u(sorted_mat[user], next_perm, user)\n\t\t\t\t\n\t\t\t\tlast_dist[model_idx][user] = next_dist\n\n\t\treturn next_idx.T, next_idx, last_dist\n\n\tth = alpha * (0.995 ** (epoch // p))\n\n\tfor model_idx, model_type in enumerate(perm_dict):\n\t\tfor user in range(user_count):\n\t\t\tcurrent_selction = int(last_max_idx[model_idx][user])\n\t\t\tnext_v = min(3, int(next_idx[model_idx][user]) + 1)\n\t\t\tnext_next_v = min(3, int(next_idx[model_idx][user]) + 2)\n\t\t\t\n\t\t\tif current_selction == 3:\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tcurrent_perm = perm_dict[model_type][current_selction][user]\n\t\t\tnext_perm = perm_dict[model_type][next_v][user]\n\t\t\tnext_next_perm = perm_dict[model_type][next_next_v][user]\n\t\t\t\n\t\t\tnext_dist = 1 - get_NDCG_u(sorted_mat[user], next_perm, user)\n\t\t\t\n\t\t\tif ((last_dist[model_idx][user] / next_dist) > th) or (last_dist[model_idx][user] / next_dist) < 1:\n\t\t\t\tnext_idx[model_idx][user] += 1\n\t\t\t\tnext_next_dist = 1 - get_NDCG_u(sorted_mat[user], next_next_perm, user)\n\t\t\t\tlast_dist[model_idx][user] = next_next_dist\n\n\treturn next_idx.T, next_idx, last_dist\n\t\n\n\n###########################################################################################################################\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--lr', type=float, default=0.001)\nparser.add_argument('--reg', type=float, default=1e-5)\nparser.add_argument('--dim', type=int, default=6)\nparser.add_argument('--batch_size', type=int, default=1024)\nparser.add_argument('--num_ns', type=int, default=1)\n\nparser.add_argument('--test_ratio', type=float, default=0.20)\nparser.add_argument('--random_seed', type=int, default=0)\nparser.add_argument('--alpha', type=float, default=1.05)\nparser.add_argument('--p', type=int, default=10)\n\nopt = parser.parse_args()\n\ngpu = torch.device('cuda:3') \n\nrandom.seed(opt.random_seed)\nnp.random.seed(opt.random_seed)\ntorch.manual_seed(opt.random_seed)\n\nalpha = opt.alpha\np = opt.p\nK = 100\n\n#############################################################################################################################\n# data load\nuser_count, item_count, train_mat, train_interactions, valid_mat, test_mat = load_data()\n\n# teacher trajectory needs to be located in the below directory\npath = './Teachers/'\nmodel_list = ['MF', 'ML', 'DL', 'GNN', 'AE', 'I-AE']\n\n# load trajectory and initial supervision\nstate_dict, perm_dict, t_results, p_results, exception_ints = load_teacher_trajectory(path, model_list, train_interactions, K, gpu)\ntrain_dataset = train_dataset(user_count, item_count, train_mat, 1, train_interactions, exception_ints)\ntest_dataset = test_dataset(user_count, item_count, valid_mat, test_mat)\ntrain_loader = data.DataLoader(train_dataset, batch_size=1024, shuffle=True)\n\n##############################################################################################################################\n# HetComp model \nmodel = HetComp_MF(user_count, item_count, opt.dim, gpu)\nmodel = model.to(gpu)\noptimizer = optim.Adam(model.parameters(), lr=opt.lr, weight_decay=opt.reg)\n\n##############################################################################################################################\n# distillation\n\ntrain_losses = []\nb_recall = -999\nb_result, f_result = -1, -1\n\nes = 0\nverbose = 10\nlast_dist = None\nis_first = True\nv_results = np.asarray([0, 0, 0, 0, 0, 0])\n\nlast_max_idx = np.zeros((len(perm_dict), user_count))\nnext_idx = np.clip(last_max_idx + 1, a_min=0, a_max=3)\n\nfor epoch in range(1000):\n\n\ttic1 = time.time()\n\ttrain_loader.dataset.negative_sampling()\n\tep_loss = []\n\n\tfor mini_batch in train_loader:\n\n\t\tb_u = mini_batch['u'].unique()\n\t\t\n\t\tmini_batch = {key: value.to(gpu) for key, value in mini_batch.items()}\n\n\t\tmodel.train()\n\t\toutput = model(mini_batch)\n\n\t\tb_u = torch.LongTensor(b_u).to(gpu)\n\t\tb_u_mask = train_loader.dataset.get_user_side_mask(b_u).to(gpu)\n\t\t\n\t\tt_items = torch.index_select(t_results, 0, b_u) \n\t\tp_items = torch.index_select(p_results, 0, b_u) \n\t\t\n\t\tif v_results.sum() < 18: \n\t\t\tKD_loss = model.get_KD_loss(b_u, p_items, t_items, b_u_mask, False)\n\t\t\tb_loss = KD_loss * 0.01\n\t\telse:\n\t\t\tKD_loss = model.get_KD_loss(b_u, p_items, t_items, b_u_mask, True)\n\t\t\tb_loss = KD_loss * 0.005\n\t\t\n\t\tep_loss.append(b_loss)\n\t\toptimizer.zero_grad()\n\t\tb_loss.backward()\n\t\toptimizer.step()\n\n\tep_loss = torch.mean(torch.stack(ep_loss)).data.cpu().numpy()\n\ttrain_losses.append(ep_loss)\n\n\ttoc1 = time.time()\n\tif epoch % verbose == 0:\n\t\timp = False\n\n\t\tmodel.eval()\n\t\twith torch.no_grad():\n\t\t\ttic2 = time.time()\n\t\t\te_results, sorted_mat = evaluate(model, gpu, train_loader, test_dataset, return_sorted_mat=True)\n\t\t\ttoc2 = time.time()\n\n\t\t\tif e_results['valid']['R50'] > b_recall: \n\t\t\t\timp = True\n\t\t\t\tb_recall = e_results['valid']['R50']\n\t\t\t\tb_result = e_results['valid']\n\t\t\t\tf_result = e_results['test']\n\t\t\t\tes = 0\t\t\t\t\t\t\n\t\t\telse:\n\t\t\t\timp = False\n\t\t\t\tes += 1\n\n\t\t\tprint_result(epoch, 1000, ep_loss, e_results, is_improved=imp, train_time=toc1-tic1, test_time=toc2-tic2)\n\n\t### DKC\n\tif (epoch % p == 0) and (epoch >= 10) and v_results.sum() < 18:\n\t\t\n\t\tif is_first == True:\n\t\t\tv, last_max_idx, last_dist = DKC(sorted_mat, last_max_idx, last_dist, True, epoch, alpha=alpha)\n\t\t\tis_first = False\n\t\telse:\n\t\t\tv, last_max_idx, last_dist = DKC(sorted_mat, last_max_idx, last_dist, False, epoch, alpha=alpha)\n\n\t\tt_results = g_torch(state_dict, v, train_interactions, gpu)\n\t\tt_results = t_results[:, :K]\n\n\t\tv_results = np.asarray([round(x, 2) for x in v.mean(0)])\n\t\tprint(v_results)\n\n\tif (epoch % verbose) == 0:\n\t\tprint(\"=\"* 50)\n\n\tif es >= 5:\n\t\tbreak", "repo_name": "postech-di-lab/METIS", "sub_path": "model-layer/knowledge-distillation-module/HetComp/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6852, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 84, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.no_grad", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.in1d", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 42, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 101, "usage_type": "call"}, {"api_name": "Utils.dataloader.train_dataset", "line_number": 117, "usage_type": "name"}, {"api_name": "Utils.dataloader.test_dataset", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 119, "usage_type": "call"}, {"api_name": "Utils.dataloader.train_dataset", "line_number": 119, "usage_type": "argument"}, {"api_name": "torch.utils.data", "line_number": 119, "usage_type": "name"}, {"api_name": "Models.HetComp.HetComp_MF", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 184, "usage_type": "call"}, {"api_name": "Utils.dataloader.test_dataset", "line_number": 186, "usage_type": "argument"}, {"api_name": "numpy.asarray", "line_number": 213, "usage_type": "call"}]} +{"seq_id": "37407837278", "text": "# bfs는 최단 거리를 찾을 때 사용되는 느낌\nfrom collections import deque\n\nn, m = map(int, input().split())\narr = []\n\nfor i in range(n):\n arr.append(list(map(int, input())))\n\n# 상하좌우\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1, 1]\n\n\ndef bfs(x, y):\n queue = deque()\n queue.append((x, y))\n\n # 큐가 빌 때까지 반복\n while queue:\n x, y = queue.popleft()\n\n # 현재 위치에서 상하좌우 확인\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n\n # 주어진 범위를 벗어나는 경우 무시\n if nx < 0 or nx >= n or ny < 0 or ny >= m:\n continue\n # 괴물인 경우 무시\n if arr[nx][ny] == 0:\n continue\n # 해당 노드를 처음 방문하는 경우에만 최단 거리 기록\n if arr[nx][ny] == 1:\n arr[nx][ny] = arr[x][y] + 1\n queue.append((nx, ny))\n # 가장 오른쪽 아래까지의 최단 거리 반환\n return arr[n-1][m-1]\n\n\n# BFS를 수행한 결과 출력\nprint(bfs(0, 0))\n", "repo_name": "pipi-shortstocking/CodingTest", "sub_path": "DFS,BFS/미로 탈출/new.py", "file_name": "new.py", "file_ext": "py", "file_size_in_byte": 1097, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "collections.deque", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "41916807205", "text": "import json\nfrom django.shortcuts import render,redirect\nfrom django.views.generic import ListView, DetailView, CreateView\nfrom .models import *\nfrom django.urls import reverse_lazy, reverse\nfrom .forms import ReviewForm, OrderItemForm\nfrom django.http.response import HttpResponseRedirect, HttpResponse\nfrom django.http import JsonResponse\nfrom .utils import cookieCart\n\n\ndef bookListView(request):\n if request.user.is_authenticated:\n try:\n order = Order.objects.get(owner=request.user,complete=False)\n total_items = order.get_total_item\n except:\n total_items = 0\n else:\n cookieData = cookieCart(request)\n total_items = cookieData['total_items']\n\n books = Book.objects.all()\n context = {'books': books, 'total_items': total_items}\n return render(request, 'books/book_list.html', context)\n\n\ndef bookDetailView(request, pk):\n if request.user.is_authenticated:\n try:\n order = Order.objects.get(owner=request.user,complete=False)\n total_items = order.get_total_items\n except:\n total_items = 0\n else:\n try:\n cart = json.loads(request.COOKIES['cart'])\n except:\n cart = {}\n total_items = 0\n for i in cart:\n print('this is cart', cart[i])\n total_items += cart[i]['quantity']\n book = Book.objects.get(id=pk)\n context = {'book': book, 'total_items': total_items}\n return render(request, 'books/book_detail.html', context)\n\n\ndef reviewcreate(request, pk):\n form = ReviewForm(request.POST)\n book = Book.objects.get(id=pk)\n if form.is_valid():\n review = form.save(commit=False)\n review.owner = request.user\n review.book = book\n review.save()\n return HttpResponseRedirect(reverse_lazy('book_list'))\n return HttpResponse(form.errors)\n\n\ndef orderprocess(request):\n order, created = Order.objects.get_or_create(owner=request.user, complete=True)\n for item in request.POST:\n form = OrderItemForm(item)\n if form.is_valid():\n item = form.save(commit=False)\n item.order = order\n item.save()\n return HttpResponse(\"something wrong occure!\")\n\n\ndef cart(request):\n if request.user.is_authenticated:\n try:\n order = Order.objects.get(owner=request.user, complete=False)\n except:\n return HttpResponseRedirect(reverse_lazy(\"book_list\"))\n orderItems = order.orderitem_set.all()\n cart_total = order.get_total_cart\n total_items = order.get_total_items\n else:\n cookieData = cookieCart(request)\n if len(cookieData)> 0:\n orderItems = cookieData['orderItems']\n cart_total = cookieData['cart_total']\n total_items = cookieData['total_items']\n else:\n return HttpResponseRedirect(reverse_lazy(\"book_list\"))\n\n context = {'items': orderItems, 'cart_total': cart_total, 'total_items': total_items}\n return render(request, 'cart/cart.html', context)\n\n\ndef updatecart(request):\n data = json.loads(request.body)\n bookId = data['bookId']\n action = data['action']\n book = Book.objects.get(id=bookId)\n order, created = Order.objects.get_or_create(owner=request.user, complete=False)\n orderItem, created = OrderItem.objects.get_or_create(order=order, book=book)\n if action == 'add':\n orderItem.quantity = orderItem.quantity+1\n elif action == 'remove':\n orderItem.quantity = orderItem.quantity -1\n\n orderItem.save()\n if orderItem.quantity <= 0:\n orderItem.delete()\n\n return JsonResponse('update cart ', safe=False)\n\n\ndef checkout(request):\n if request.user.is_authenticated:\n try:\n order = Order.objects.get(owner=request.user, complete=False)\n except:\n return HttpResponseRedirect(reverse_lazy(\"book_list\"))\n orderItems = order.orderitem_set.all()\n cart_total = order.get_total_cart\n total_items = order.get_total_items\n else:\n cookieData = cookieCart(request)\n if len(cookieData) >0:\n orderItems = cookieData['orderItems']\n cart_total = cookieData['cart_total']\n total_items = cookieData['total_items']\n else:\n return HttpResponseRedirect(reverse_lazy(\"book_list\"))\n context = {'items': orderItems, 'cart_total': cart_total, 'total_items': total_items}\n return render(request, 'cart/checkout.html', context)\n\n\ndef orderprocess(request):\n if request.user.is_authenticated:\n data = json.loads(request.body)\n order = Order.objects.get(owner=request.user, complete=False)\n else:\n cookieData = cookieCart(request)\n orderItems = cookieData['orderItems']\n order = Order.objects.create()\n for item in orderItems:\n orderItem = OrderItem.objects.create(order=order,\n book=item['book']['id'],\n quantity=item['quantity'],\n )\n orderItem.save()\n\n shipping, created = Shipping.objects.get_or_create(order=order)\n shipping.lastname = data['formData']['lastname']\n shipping.firstname = data['formData']['firstname']\n shipping.email = data['formData']['email']\n shipping.address = data['formData']['address']\n shipping.zipcode = int(data['formData']['zipcode'])\n shipping.phone = data['formData']['phone']\n shipping.save()\n order.complete = True\n order.save()\n return HttpResponse(\"data added\", safe=False)\n\n\ndef success(request):\n try:\n cart = json.loads(request.COOKIES['cart'])\n print('Cart', cart)\n except:\n cart = {}\n response = render(request, 'cart/success.html')\n response.delete_cookie(\"cart\")\n return response", "repo_name": "Riman-rh/django_bookstore", "sub_path": "bookstore/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "utils.cookieCart", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "forms.ReviewForm", "line_number": 50, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseRedirect", "line_number": 57, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 57, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 58, "usage_type": "call"}, {"api_name": "forms.OrderItemForm", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 69, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseRedirect", "line_number": 77, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 77, "usage_type": "call"}, {"api_name": "utils.cookieCart", "line_number": 82, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseRedirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 91, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 110, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseRedirect", "line_number": 118, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 118, "usage_type": "call"}, {"api_name": "utils.cookieCart", "line_number": 123, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseRedirect", "line_number": 129, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 129, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 131, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 136, "usage_type": "call"}, {"api_name": "utils.cookieCart", "line_number": 139, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 159, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "29039973657", "text": "import pytest\n\nfrom antirobot.daemon.arcadia_test.util import GenRandomIP\nfrom antirobot.daemon.arcadia_test.util.AntirobotTestSuite import AntirobotTestSuite\n\n\nclass TestUnistat(AntirobotTestSuite):\n @pytest.mark.parametrize('url, service', [\n (\"http://yandex.ru/search\", \"web\"),\n (\"http://images.yandex.ru/search\", \"img\"),\n ])\n def test_handle_time(self, url, service):\n ip = GenRandomIP()\n metric_before = self.antirobot.get_metric(f\"service_type={service};handle_time_10s_deee\")\n self.send_fullreq(url, headers={\"X-Forwarded-For-Y\": ip})\n metric_after = self.antirobot.get_metric(f\"service_type={service};handle_time_10s_deee\")\n assert metric_before + 1 == metric_after\n", "repo_name": "Alexander-Berg/2022-tests-examples", "sub_path": "antirobot/TestUnistat.py", "file_name": "TestUnistat.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "antirobot.daemon.arcadia_test.util.AntirobotTestSuite.AntirobotTestSuite", "line_number": 7, "usage_type": "name"}, {"api_name": "antirobot.daemon.arcadia_test.util.GenRandomIP", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 8, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}]} +{"seq_id": "31199594352", "text": "from flask import Flask, render_template, request, send_from_directory\nfrom xyj import *\nimport os\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return render_template(\"index.html\")\n\n\n@app.route('/res', methods=['POST'])\ndef result():\n if request.method == 'POST':\n tm985 = request.form['985推免']\n tm211 = request.form['211推免']\n tk985 = request.form['985统考']\n tk211 = request.form['211统考']\n normal = request.form['双非推免']\n m2020 = request.form['2020级']\n m2021 = request.form['2021级']\n m2022 = request.form['2022级']\n xyj = XueYeJiang(tm985, tm211, tk985, tk211, normal, m2020, m2021, m2022)\n return send_from_directory(os.path.join(os.path.dirname(__file__), 'static/'), 'res.xlsx')\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "hust-wzq/hustzz", "sub_path": "奖学金预算/网页版/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 848, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.send_from_directory", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "37231346539", "text": "from agents.network.GCN import GCN, MatMul\nimport torch\nimport torch.nn as nn\n\n\nclass DQN(nn.Module):\n def __init__(self, D_in, H1, H2, H3, H4, H5):\n super(DQN, self).__init__()\n # Deep network weights and biases\n\n self.gcn1 = GCN(D_in * 3, H1)\n\n self.gcn2 = GCN(H1 * 3, H2)\n\n self.matmul1 = MatMul(H2, H3)\n self.matmul2 = MatMul(H3, H4)\n self.matmul3 = MatMul(H4, H5)\n\n def forward(self, state):\n\n y, in_adj_mat, out_adj_mat = state\n\n # First convolution layer\n y = self.gcn1(y, in_adj_mat, out_adj_mat)\n\n # Second convolution layer\n y = self.gcn2(y, in_adj_mat, out_adj_mat)\n\n # Output layer\n y = self.matmul1(y)\n # For each vertex you have a vertex of length H3. This is the vertex embedding.\n\n # Perform pooling\n y = torch.sum(y, dim=0).view(1, -1)\n\n y = self.matmul2(y).clamp(min=0)\n y = self.matmul3(y) # Could insert a ReLu layer before this.\n\n # this is the Q(s,a) value\n return y\n", "repo_name": "lauradarcy/DAG_DQN", "sub_path": "agents/network/DQN.py", "file_name": "DQN.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "agents.network.GCN.GCN", "line_number": 11, "usage_type": "call"}, {"api_name": "agents.network.GCN.GCN", "line_number": 13, "usage_type": "call"}, {"api_name": "agents.network.GCN.MatMul", "line_number": 15, "usage_type": "call"}, {"api_name": "agents.network.GCN.MatMul", "line_number": 16, "usage_type": "call"}, {"api_name": "agents.network.GCN.MatMul", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "36999966196", "text": "import math\nimport networkx as nx\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\n\ndef get_exclude_set(graph, elements):\n print(\"Calculating katz centrality\")\n phi = (1 + math.sqrt(5)) / 2.0\n centrality_initial = nx.katz_centrality(graph, 1/phi - 0.01)\n\n centrality = [(c, v) for v, c in centrality_initial.items()]\n\n # Find the threashold that can be used to filter nodes.\n topElements = sorted(centrality, reverse=True)[:elements]\n minimum = min(topElements)\n maximum = max(topElements)\n\n topElements = [v for c, v in topElements]\n\n print(\n f\"Using {elements} nodes with katz centrality in range [{minimum}:{maximum}]\")\n print(topElements)\n return set(topElements)\n", "repo_name": "juliuscc/flight-route-pandemic-simulation", "sub_path": "lib/katz_centrality.py", "file_name": "katz_centrality.py", "file_ext": "py", "file_size_in_byte": 740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "math.sqrt", "line_number": 10, "usage_type": "call"}, {"api_name": "networkx.katz_centrality", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "20447567508", "text": "from datetime import datetime\nfrom typing import List\n\nfrom aiogoogle import Aiogoogle\n\nfrom app.core.config import settings\nfrom app.models.charity_project import CharityProject\n\nSHEETS_VER = 'v4'\nDRIVE_VER = 'v3'\nDATETIME_NOW = datetime.now().strftime('%Y/%m/%d %H:%M:%S')\n\nFILE_TITLE = f'Отчет от {DATETIME_NOW}'\nLIST_TITLE = 'Отчет'\nROWS = 100\nCOLUMS = 10\nRANGE = 'A1:E100'\nSPREADSHEET_BODY = {\n 'properties': {\n 'title': FILE_TITLE,\n 'locale': 'ru_RU'\n },\n 'sheets': {\n 'properties': {\n 'sheetType': 'GRID',\n 'sheetId': 0,\n 'title': 'Лист1',\n 'gridProperties': {\n 'rowCount': ROWS,\n 'columnCount': COLUMS\n }\n }\n }\n}\nTABLE_VALUES = [\n ['Отчет от', ],\n ['Топ проектов по скорости закрытия'],\n ['Название проекта', 'Время сбора', 'Описание']\n]\n\n\nasync def spreadsheets_create(wrapper_services: Aiogoogle) -> str:\n service = await wrapper_services.discover('sheets', SHEETS_VER)\n response = await wrapper_services.as_service_account(\n service.spreadsheets.create(json=SPREADSHEET_BODY)\n )\n return response['spreadsheetId']\n\n\nasync def set_user_permissions(\n spreadsheet_id: str,\n wrapper_services: Aiogoogle\n) -> None:\n permissions_body = {'type': 'user',\n 'role': 'writer',\n 'emailAddress': settings.email}\n service = await wrapper_services.discover('drive', DRIVE_VER)\n await wrapper_services.as_service_account(\n service.permissions.create(\n fileId=spreadsheet_id,\n json=permissions_body,\n fields='id'\n )\n )\n\n\nasync def spreadsheets_update_value(\n spreadsheet_id: str,\n projects: List[CharityProject],\n wrapper_services: Aiogoogle\n) -> None:\n service = await wrapper_services.discover('sheets', SHEETS_VER)\n\n for project in projects:\n new_row = [\n project.name,\n str(project.close_date - project.create_date),\n project.description\n ]\n TABLE_VALUES.append(new_row)\n\n update_body = {\n 'majorDimension': 'ROWS',\n 'values': TABLE_VALUES\n }\n\n await wrapper_services.as_service_account(\n service.spreadsheets.values.update(\n spreadsheetId=spreadsheet_id,\n range=RANGE,\n valueInputOption='USER_ENTERED',\n json=update_body\n )\n )\n", "repo_name": "IgorArefev/QRkot", "sub_path": "app/services/google_api.py", "file_name": "google_api.py", "file_ext": "py", "file_size_in_byte": 2547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "aiogoogle.Aiogoogle", "line_number": 42, "usage_type": "name"}, {"api_name": "aiogoogle.Aiogoogle", "line_number": 52, "usage_type": "name"}, {"api_name": "app.core.config.settings.email", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.core.config.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 69, "usage_type": "name"}, {"api_name": "app.models.charity_project.CharityProject", "line_number": 69, "usage_type": "name"}, {"api_name": "aiogoogle.Aiogoogle", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "7589851877", "text": "import itertools\nfrom typing import List\nfrom typing import Any\nfrom typing import Dict\nimport mysql.connector\n\ndef TestModel() -> Dict[str,str]:\n '''SQL test Table model'''\n model = {\"plant\" : \"plant VARCHAR(10) NOT NULL\",\n \"motor_duration\" : \"motor_duration INT NOT NULL\",\n \"motor_power\" : \"motor_power INT NOT NULL\"}\n return model\n\nclass Table:\n '''Class represents SQL table. Translates model to\n Dict'''\n def __init__(self, name : str, model : Dict[str,str]) -> None:\n self. name = name\n self.columns = model.values()\n\nclass DataBaseHandler:\n '''Database handler class'''\n def __init__(self, host=\"localhost\", user=\"root\", password=\"password\"):\n self.host = host\n self.user = user\n self.password = password\n self.cursor = None\n self.connection = None\n\n def connect(self):\n '''SQL connection'''\n try:\n self.connection = mysql.connector.connect(\n host = self.host,\n user = self.user,\n password = self.password)\n self.cursor = self.connection.cursor()\n\n except mysql.connector.Error as connect_error:\n print(f\"Could not connect to database: {connect_error}\")\n\n def connect_to_database(self, database):\n '''Connect to database. The cursor will point to database'''\n if not self.database_exist:\n raise Exception(f\"Database {database} does not exist\")\n try:\n self.connection = mysql.connector.connect(\n host = self.host,\n user = self.user,\n password = self.password,\n database = database)\n self.cursor = self.connection.cursor()\n\n except mysql.connector.Error as connect_error:\n print(f\"Could not connect to database: {connect_error}\")\n\n def create_database(self, new_database) -> bool:\n '''Create new database. Returns False if already exist'''\n if self.database_exist(new_database):\n print(f\"[DEBUG] Database {new_database} already exist\")\n return False\n insert_statement = f\"CREATE DATABASE {new_database}\"\n self.cursor.execute(insert_statement)\n print(f\"[DEBUG] Database {new_database} created\")\n return True\n\n def delete_database(self, remove_database):\n '''Delete database. Returns False if does not exist'''\n insert_statement = f\"DROP DATABASE {remove_database}\"\n if self.database_exist(remove_database):\n self.cursor.execute(insert_statement)\n print(f\"[DEBUG] Database {remove_database} deleted\")\n return True\n print(f\"[DEBUG] Database {remove_database} does not exist\")\n return False\n\n def database_exist(self, database_check: str) -> bool:\n for database in self._get_databases():\n if database_check in database:\n return True\n return False\n\n def _get_databases(self) -> List[str]:\n insert_statement = \"SHOW DATABASES\"\n self.cursor.execute(insert_statement)\n databases = self.cursor.fetchall()\n return list(itertools.chain(*databases))\n\n def close_database_connection(self):\n '''Close database and SQL connection'''\n self.cursor.close()\n self.connection.close()\n\n def create_table(self, table_name: str, table: List[str]):\n '''Create table_name table in current database. Table is a List[str]\n that represents all columns, their types and allowed NULL'''\n column_string = \"\"\n for column in table:\n column_string += f\"{column}, \"\n column_string = column_string[0:len(column_string)-2]\n insert_statement = f\"CREATE TABLE {table_name}({column_string})\"\n try:\n self.cursor.execute(insert_statement)\n return True\n except mysql.connector.errors.ProgrammingError as program_error:\n print(f\"[DEBUG] {program_error}\")\n return False\n\n def table_exist(self, table_check: str) -> bool:\n for table in self._get_tables():\n if table_check in table:\n return True\n return False\n\n def _get_tables(self) -> List[str]:\n insert_statement = \"SHOW TABLES\"\n self.cursor.execute(insert_statement)\n tables = self.cursor.fetchall()\n return list(itertools.chain(*tables))\n\n def delete_table(self, table_name: str):\n '''Delete table_name table form current database'''\n insert_statement = f\"DROP TABLE {table_name}\"\n try:\n self.cursor.execute(insert_statement)\n return True\n except mysql.connector.errors.ProgrammingError as program_error:\n print(f\"[DEBUG] {program_error}\")\n return False\n\n def insert_into_table(self, table_name: str, table: Dict[str,Any]):\n '''Insert table data into table_name table. Not all columns need to\n be filled'''\n insert_statement_start = f\"INSERT INTO {table_name} \"\n insert_statement_colums = \"(\"\n insert_statement_values = \"VALUES (\"\n values = []\n for column in table.keys():\n if not table[column] is None:\n insert_statement_colums += f\"{column},\"\n insert_statement_values += \"%s,\"\n values.append(table[column])\n insert_statement_colums = insert_statement_colums[0:len(insert_statement_colums)-1]\n insert_statement_values = insert_statement_values[0:len(insert_statement_values)-1]\n insert_statement_colums += \") \"\n insert_statement_values += \")\"\n insert_statement = insert_statement_start + insert_statement_colums + insert_statement_values\n try:\n self.cursor.execute(insert_statement, values)\n self._commit()\n return True\n except mysql.connector.errors.ProgrammingError as program_error:\n print(f\"[DEBUG] {program_error}\")\n return False\n\n def select_from_table(self, table_name: str, columns: List[str], order=False, order_by=None, limit=0):\n '''Select columns from table_name from current database'''\n insert_columns = \"\"\n for column in columns:\n insert_columns += f\"{column}, \"\n insert_columns = insert_columns[0:len(insert_columns)-2]\n insert_statement = f\"SELECT {insert_columns} FROM {table_name}\"\n if order:\n insert_order = f\" order by {order_by} desc\"\n insert_statement += insert_order\n if limit > 0:\n insert_limit = f\" limit {limit}\"\n insert_statement += insert_limit\n try:\n self.cursor.execute(insert_statement)\n return self.cursor.fetchall()\n except mysql.connector.errors.ProgrammingError as program_error:\n print(f\"[DEBUG] {program_error}\")\n raise program_error\n\n def _commit(self) -> bool:\n try:\n self.connection.commit()\n return True\n except mysql.connector.Error as connect_error:\n print(f\"[DEBUG] {connect_error}\")\n return False\n\nif __name__ == \"__main__\":\n print(\"Test database handler\")\n host = \"localhost\"\n database = \"Planty2\"\n table = \"test99\"\n databaseHandler = DataBaseHandler(host)\n databaseHandler.connect_to_database(database)\n new_entry = databaseHandler.select_from_table(\"Planty_data\", [\"entry\"], True, \"Datetime\", 1)\n print(new_entry[0][0])\n", "repo_name": "DramaCharles1/Planty2", "sub_path": "DatabaseHandler.py", "file_name": "DatabaseHandler.py", "file_ext": "py", "file_size_in_byte": 7432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.Dict", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 17, "usage_type": "name"}, {"api_name": "mysql.connector.connector.connect", "line_number": 33, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 33, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 33, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 39, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 39, "usage_type": "name"}, {"api_name": "mysql.connector.connector.connect", "line_number": 47, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 47, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 54, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 54, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 87, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 94, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 105, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 105, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 119, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 115, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 127, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 131, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 152, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 152, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 156, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 172, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 172, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 180, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 180, "usage_type": "name"}]} +{"seq_id": "11344545636", "text": "import re\nimport sys\n\nfrom functools import cmp_to_key\nfrom webkitcorepy import string_utils, unicode\nfrom webkitcorepy.mocks import ContextStack\n\n\nclass ProcessCompletion(object):\n def __init__(self, returncode=None, stdout=None, stderr=None, elapsed=0):\n self.returncode = 1 if returncode is None else returncode\n self.stdout = string_utils.encode(stdout) if stdout else b''\n self.stderr = string_utils.encode(stderr) if stderr else b''\n self.elapsed = elapsed\n\n\nclass Subprocess(ContextStack):\n \"\"\"\n Organize ProcessCompletions so calls to subprocess functions will return a ProcessCompletion for\n a set of arguments or trigger a ProcessCompletion generator. mocks.Subprocess makes an attempt to\n prioritize CommandRoute objects for a given set of arguments such that the most specific applicable route\n is prefered.\n\n Example usage mocking a single command:\n with mocks.Subprocess(\n 'ls', completion=mocks.ProcessCompletion(returncode=0, stdout='file1.txt\\nfile2.txt\\n'),\n ):\n result = run(['ls'], capture_output=True, encoding='utf-8')\n assert result.returncode == 0\n assert result.stdout == 'file1.txt\\nfile2.txt\\n'\n\n Example usage mocking a set of commands:\n with mocks.Subprocess(\n mocks.Subprocess.CommandRoute('command-a', 'argument', completion=mocks.ProcessCompletion(returncode=0)),\n mocks.Subprocess.CommandRoute('command-b', completion=mocks.ProcessCompletion(returncode=-1)),\n ):\n result = run(['command-a', 'argument'])\n assert result.returncode == 0\n\n result = run(['command-b'])\n assert result.returncode == -1\n \"\"\"\n top = None\n\n class CommandRoute(object):\n def __init__(self, *args, **kwargs):\n completion = kwargs.pop('completion', ProcessCompletion())\n cwd = kwargs.pop('cwd', None)\n input = kwargs.pop('input', None)\n env = kwargs.pop('env', None)\n generator = kwargs.pop('generator', None)\n if kwargs.keys():\n raise TypeError('__init__() got an unexpected keyword argument {}'.format(kwargs.keys()[0]))\n\n if isinstance(args, str) or isinstance(args, unicode):\n self.args = [args]\n elif not args:\n raise ValueError('Arguments must be provided to a CommandRoute')\n else:\n self.args = args\n\n self.generator = generator or (lambda *args, **kwargs: completion)\n self.cwd = cwd\n self.input = string_utils.encode(input) if input else None\n self.env = env\n\n def matches(self, *args, **kwargs):\n cwd = kwargs.pop('cwd', None)\n input = kwargs.pop('input', None)\n env = kwargs.pop('env', None)\n if kwargs.keys():\n raise TypeError('matches() got an unexpected keyword argument {}'.format(kwargs.keys()[0]))\n\n if len(self.args) > len(args):\n return False\n\n for count in range(len(self.args)):\n if self.args[count] is None:\n return False\n\n if self.args[count] == args[count]:\n continue\n elif hasattr(self.args[count], 'match') and self.args[count].match(args[count]):\n continue\n elif re.match(self.args[count], args[count]):\n continue\n return False\n\n if self.cwd is not None and cwd != self.cwd:\n return False\n if self.input is not None and input != self.input:\n return False\n if self.env is not None and env != self.env:\n return False\n return True\n\n def __call__(self, *args, **kwargs):\n cwd = kwargs.pop('cwd', None)\n input = kwargs.pop('input', None)\n env = kwargs.pop('env', dict())\n if kwargs.keys():\n raise TypeError('__call__() got an unexpected keyword argument {}'.format(kwargs.keys()[0]))\n return self.generator(*args, cwd=cwd, input=input, env=env)\n\n @classmethod\n def compare(cls, a, b):\n for candidate in [\n len(b.args) - len(a.args),\n 0 if type(a.cwd) == type(b.cwd) else -1 if a.cwd else 1,\n 0 if type(a.input) == type(b.input) else -1 if a.input else 1,\n ]:\n if candidate:\n return candidate\n return 0\n\n Route = CommandRoute\n\n @classmethod\n def completion_generator_for(cls, program):\n current = cls.top\n candidates = []\n while current:\n for completion in current.completions:\n if completion.args[0] == program:\n candidates.append(completion)\n if current.ordered:\n break\n current = current.previous\n\n if candidates:\n return candidates\n\n if sys.version_info > (3, 0):\n raise FileNotFoundError(\"No such file or directory: '{path}': '{path}'\".format(path=program))\n raise OSError('[Errno 2] No such file or directory')\n\n @classmethod\n def completion_for(cls, *args, **kwargs):\n candidates = [\n candidate for candidate in cls.completion_generator_for(args[0]) if candidate.matches(*args, **kwargs)\n ]\n if not candidates:\n raise AssertionError('Provided arguments to {} do not match a provided completion'.format(args[0]))\n\n completion = candidates[0]\n current = cls.top\n while current:\n if current.ordered and completion is current.completions[0]:\n current.completions.pop(0)\n break\n current = current.previous\n return completion(*args, **kwargs)\n\n def __init__(self, *args, **kwargs):\n if all([isinstance(arg, self.CommandRoute) for arg in args]):\n self.ordered = kwargs.pop('ordered', False)\n if kwargs.keys():\n raise TypeError('__init__() got an unexpected keyword argument {}'.format(kwargs.keys()[0]))\n self.completions = list(args) if self.ordered else sorted(args, key=cmp_to_key(self.CommandRoute.compare))\n elif any([isinstance(arg, self.CommandRoute) for arg in args]):\n raise TypeError('mocks.Subprocess arguments must be of a consistent type')\n else:\n self.ordered = False\n self.completions = [self.CommandRoute(*args, **kwargs)]\n\n super(Subprocess, self).__init__(cls=Subprocess)\n\n # Allow mock to be managed via autoinstall\n from mock import patch\n from webkitcorepy.mocks.popen import Popen\n self.patches.append(patch('subprocess.Popen', new=Popen))\n", "repo_name": "WebKit/WebKit", "sub_path": "Tools/Scripts/libraries/webkitcorepy/webkitcorepy/mocks/subprocess.py", "file_name": "subprocess.py", "file_ext": "py", "file_size_in_byte": 6875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6880, "dataset": "github-code", "pt": "3", "api": [{"api_name": "webkitcorepy.string_utils.encode", "line_number": 12, "usage_type": "call"}, {"api_name": "webkitcorepy.string_utils", "line_number": 12, "usage_type": "name"}, {"api_name": "webkitcorepy.string_utils.encode", "line_number": 13, "usage_type": "call"}, {"api_name": "webkitcorepy.string_utils", "line_number": 13, "usage_type": "name"}, {"api_name": "webkitcorepy.mocks.ContextStack", "line_number": 17, "usage_type": "name"}, {"api_name": "webkitcorepy.unicode", "line_number": 55, "usage_type": "argument"}, {"api_name": "webkitcorepy.string_utils.encode", "line_number": 64, "usage_type": "call"}, {"api_name": "webkitcorepy.string_utils", "line_number": 64, "usage_type": "name"}, {"api_name": "re.match", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 133, "usage_type": "attribute"}, {"api_name": "functools.cmp_to_key", "line_number": 159, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 171, "usage_type": "call"}, {"api_name": "webkitcorepy.mocks.popen.Popen", "line_number": 171, "usage_type": "name"}]} +{"seq_id": "7306392961", "text": "\"\"\"\r\n Copyright (C) 2015 Quinn D Granfor \r\n\r\n This program is free software; you can redistribute it and/or\r\n modify it under the terms of the GNU General Public License\r\n version 2, as published by the Free Software Foundation.\r\n\r\n This program is distributed in the hope that it will be useful, but\r\n WITHOUT ANY WARRANTY; without even the implied warranty of\r\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\r\n General Public License version 2 for more details.\r\n\r\n You should have received a copy of the GNU General Public License\r\n version 2 along with this program; if not, write to the Free\r\n Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\r\n MA 02110-1301, USA.\r\n\"\"\"\r\n\r\nimport datetime\r\nimport uuid\r\n\r\n\r\ndef db_cron_insert(self, cron_name, cron_desc, cron_enabled, cron_schedule, cron_last_run,\r\n cron_json):\r\n \"\"\"\r\n insert cron job\r\n \"\"\"\r\n new_cron_id = uuid.uuid4()\r\n self.db_cursor.execute('insert into mm_cron (mm_cron_guid,'\r\n ' mm_cron_name,'\r\n ' mm_cron_description,'\r\n ' mm_cron_enabled,'\r\n ' mm_cron_schedule,'\r\n ' mm_cron_last_run, mm_cron_json)'\r\n ' values (%s,%s,%s,%s,%s,%s,%s)',\r\n (new_cron_id, cron_name, cron_desc, cron_enabled, cron_schedule,\r\n cron_last_run, cron_json))\r\n return new_cron_id\r\n\r\n\r\ndef db_cron_list_count(self, enabled_only=False):\r\n \"\"\"\r\n Return number of cron jobs\r\n \"\"\"\r\n if not enabled_only:\r\n self.db_cursor.execute('select count(*) from mm_cron')\r\n else:\r\n self.db_cursor.execute(\r\n 'select count(*) from mm_cron'\r\n ' where mm_cron_enabled = true')\r\n return self.db_cursor.fetchone()[0]\r\n\r\n\r\ndef db_cron_list(self, enabled_only=False, offset=0, records=None):\r\n \"\"\"\r\n Return cron list\r\n \"\"\"\r\n if not enabled_only:\r\n self.db_cursor.execute('select mm_cron_guid,'\r\n ' mm_cron_name,'\r\n ' mm_cron_description,'\r\n ' mm_cron_enabled,'\r\n ' mm_cron_schedule,'\r\n ' mm_cron_last_run,'\r\n ' mm_cron_json'\r\n ' from mm_cron where mm_cron_guid'\r\n ' in (select mm_cron_guid from mm_cron'\r\n ' order by mm_cron_name offset %s limit %s)'\r\n ' order by mm_cron_name', (offset, records))\r\n else:\r\n self.db_cursor.execute('select mm_cron_guid,'\r\n ' mm_cron_name,'\r\n ' mm_cron_description,'\r\n ' mm_cron_enabled,'\r\n ' mm_cron_schedule,'\r\n ' mm_cron_last_run,'\r\n ' mm_cron_json'\r\n ' from mm_cron where mm_cron_guid'\r\n ' in (select mm_cron_guid from mm_cron'\r\n ' where mm_cron_enabled = true'\r\n ' order by mm_cron_name offset %s limit %s)'\r\n ' order by mm_cron_name', (offset, records))\r\n return self.db_cursor.fetchall()\r\n\r\n\r\ndef db_cron_time_update(self, cron_type):\r\n \"\"\"\r\n Update the datetime in which a cron job was run\r\n \"\"\"\r\n self.db_cursor.execute('update mm_cron set mm_cron_last_run = %s'\r\n ' where mm_cron_name = %s',\r\n (datetime.datetime.now(), cron_type))\r\n\r\n\r\ndef db_cron_delete(self, cron_uuid):\r\n \"\"\"\r\n Delete cron job\r\n \"\"\"\r\n self.db_cursor.execute('delete from mm_cron'\r\n ' where mm_cron_guid = %s',\r\n (cron_uuid,))\r\n\r\n\r\ndef db_cron_info(self, cron_uuid):\r\n \"\"\"\r\n Cron job info\r\n \"\"\"\r\n self.db_cursor.execute('select mm_cron_guid,'\r\n ' mm_cron_name,'\r\n ' mm_cron_description,'\r\n ' mm_cron_enabled,'\r\n ' mm_cron_schedule,'\r\n ' mm_cron_last_run,'\r\n ' mm_cron_json'\r\n ' from mm_cron'\r\n ' where mm_cron_guid = %s', (cron_uuid,))\r\n return self.db_cursor.fetchone()\r\n", "repo_name": "MediaKraken/MediaKraken_Deployment", "sub_path": "source/database/db_base_cron.py", "file_name": "db_base_cron.py", "file_ext": "py", "file_size_in_byte": 4580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "3", "api": [{"api_name": "uuid.uuid4", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "attribute"}]} +{"seq_id": "7827620973", "text": "import wx\r\nimport wx.grid\r\nimport pandas as pd\r\nfrom matplotlib import pyplot as plt\r\nimport datetime\r\nimport json\r\nfrom numpy.core.defchararray import lower\r\n\r\n# LOAD THE FILES NEEDED\r\nlistings = pd.read_csv('./csvs/listings_dec18.csv')\r\nreviews = pd.read_csv('./csvs/reviews_dec18.csv')\r\n\r\n# GLOBAL VARIABLES\r\n# STORE ONLY THE NEEDED COLUMNS INTO NEW DATAFRAMES\r\nlistingsReducedColumns = listings[\r\n [\r\n 'id',\r\n 'name',\r\n 'host_name',\r\n 'host_since',\r\n 'street',\r\n 'neighbourhood',\r\n 'neighbourhood_cleansed',\r\n 'property_type',\r\n 'room_type',\r\n 'amenities',\r\n 'price',\r\n 'review_scores_rating'\r\n ]\r\n]\r\ncommentsReducedColumns = reviews[['listing_id', 'comments']]\r\ncleanlinessKeywords = ['clean', 'neat', 'fresh', 'hygienic', 'taintless', 'sterile', 'sanitary', 'washed', 'flawless', 'bright', 'shiny', 'sparkling']\r\n\r\n# FUNCTIONS TO RETRIEVE LISTINGS BASED ON USER INPUTS\r\ndef showListings(listingToDisplay):\r\n cols = listingToDisplay[0] #len = 13\r\n rows = len(listingToDisplay)\r\n\r\n df = pd.DataFrame(listingToDisplay[1:], columns=cols)\r\n # print(df.shape)\r\n # print(df)\r\n result = df.to_json(orient='index')\r\n #print(result)\r\n\r\n #WRITES THE CONVERTED JSON DATAFRAME TO A FILE SO IT CAN BE USED IN THE GUI MODULE\r\n with open('listings.json', 'w') as jsonListings:\r\n json.dump(result, jsonListings)\r\n\r\ndef findKeyword(startperiod, endperiod, keyword):\r\n # MANIPULATE THE PROVIDED DATE ARGUMENTS\r\n splitStartPeriod = startperiod.split('/')\r\n splitEndPeriod = endperiod.split('/')\r\n startDate = datetime.datetime(int(splitStartPeriod[-1]), int(splitStartPeriod[-2]), int(splitStartPeriod[-3]))\r\n endDate = datetime.datetime(int(splitEndPeriod[-1]), int(splitEndPeriod[-2]), int(splitEndPeriod[-3]))\r\n\r\n listingsReducedColumns.amenities = listingsReducedColumns.amenities.apply(lower)\r\n # FILTER THE LISTINGS TO ONES THAT CONTAIN THE PROVIDED KEYWORD\r\n # A NEW COLUMN IS CREATED TO STORE WHETHER IF THE LISTING'S A MATCH WITH THE KEYWORD\r\n listingsReducedColumns['matchedAmenities'] = listingsReducedColumns.amenities.apply(lambda row: 'match' if keyword in row else 'no match')\r\n\r\n # CONVERT THE HOST_SINCE TYPE INTO DATETIME AND STORE IN A NEW COLUMN\r\n listingsReducedColumns['period'] = pd.to_datetime(listingsReducedColumns.host_since)\r\n\r\n # THE LISTING ROWS MATCHING THE PROVIDED KEYWORD IS STORED IN THE 'MATCHED' VARIABLE\r\n matched = listingsReducedColumns[\r\n (listingsReducedColumns.matchedAmenities == 'match') &\r\n (listingsReducedColumns.period >= startDate) &\r\n (listingsReducedColumns.period <= endDate)\r\n ]\r\n matchedListingsIDs = list(matched.id)\r\n print('listings matched with {}'.format(keyword))\r\n # print(matched)\r\n # print(matchedListingsIDs)\r\n\r\n # RETURNS A DICTIONARY WITH THE KEYWORD AS KEY AND MATCHING LISTINGS AS THE VALUE (IN A LIST)\r\n return {keyword: matchedListingsIDs}\r\n\r\n# findKeyword('1/1/2009', '30/6/2009', 'elevator')\r\n\r\ndef getListings(startperiod, endperiod, suburbName='sydney', keyword=None):\r\n # MANIPULATE THE PROVIDED DATE ARGUMENTS\r\n splitStartPeriod = startperiod.split('/')\r\n splitEndPeriod = endperiod.split('/')\r\n startDate = datetime.datetime(int(splitStartPeriod[-1]), int(splitStartPeriod[-2]), int(splitStartPeriod[-3]))\r\n endDate = datetime.datetime(int(splitEndPeriod[-1]), int(splitEndPeriod[-2]), int(splitEndPeriod[-3]))\r\n\r\n # INITIALIZE THE DICTIONARY FOR IF A KEYWORD IS PROVIDED, AND THE LIST TO SEND TO SHOWLISTINGS FUNCTION\r\n filteredListings = {}\r\n result = []\r\n # CONVERT THE STRINGS, KEYWORD AND SUBURBNAME INTO LOWERCASE\r\n suburbName = suburbName.lower()\r\n listingsReducedColumns.street = listingsReducedColumns.street.apply(lower)\r\n listingsReducedColumns.neighbourhood_cleansed = listingsReducedColumns.neighbourhood_cleansed.apply(lower)\r\n\r\n if keyword != None:\r\n keyword = keyword.lower()\r\n filteredListings = findKeyword(startperiod, endperiod, keyword)\r\n # CHECK IF ANY MATCH FOUND\r\n if len(filteredListings[keyword]):\r\n print('Properties matching the provided keyword found')\r\n # RENAMES THE COLUMN MATCHEDAMENITIES INTO ANOTHER NAME INSTEAD OF ADDING A NEW COLUMN, TO BE USED TO MATCH SUBURBNAME\r\n listingsReducedColumns.rename(columns={'matchedAmenities': 'matchedSuburb'}, inplace=True)\r\n\r\n # COMPARE THE RETURNED LISTING IDS AND SAVE THE BOOLEAN RESULT IN A NEW COLUMN\r\n listingsReducedColumns['matchedSuburb'] = listingsReducedColumns.id.apply(lambda row: 'match' if int(row) in filteredListings[keyword] else 'no match')\r\n\r\n # THE LISTINGS THAT MATCHES THE LISTING IDS AND HAVE SUBURBNAME AS EITHER STREET NAME OR NEIGHBOURHOOD NAME\r\n matched = listingsReducedColumns[\r\n ((listingsReducedColumns.matchedSuburb == 'match') &\r\n (listingsReducedColumns.street == suburbName)) |\r\n ((listingsReducedColumns.matchedSuburb == 'match') &\r\n (listingsReducedColumns.neighbourhood_cleansed == suburbName)) |\r\n ((listingsReducedColumns.matchedSuburb == 'match') &\r\n (listingsReducedColumns.neighbourhood == suburbName))\r\n ]\r\n result = [matched.columns.values.tolist()] + matched.values.tolist()\r\n else:\r\n return 'No match found'\r\n\r\n else:\r\n listingsReducedColumns['period'] = pd.to_datetime(listingsReducedColumns.host_since)\r\n matched = listingsReducedColumns[\r\n ((listingsReducedColumns.period >= startDate) & (listingsReducedColumns.period <= endDate)) &\r\n ((listingsReducedColumns.street == suburbName) |\r\n (listingsReducedColumns.neighbourhood_cleansed == suburbName) |\r\n (listings.neighbourhood == suburbName)\r\n )\r\n ]\r\n print(matched)\r\n result = [matched.columns.values.tolist()] + matched.values.tolist()\r\n\r\n return showListings(result)\r\n\r\n# getListings('1/1/2015', '30/12/2019')\r\n\r\ndef showPriceDist(startperiod, endperiod):\r\n # MANIPULATE THE PROVIDED DATE ARGUMENTS\r\n splitStartPeriod = startperiod.split('/')\r\n splitEndPeriod = endperiod.split('/')\r\n startDate = datetime.datetime(int(splitStartPeriod[-1]), int(splitStartPeriod[-2]), int(splitStartPeriod[-3]))\r\n endDate = datetime.datetime(int(splitEndPeriod[-1]), int(splitEndPeriod[-2]), int(splitEndPeriod[-3]))\r\n\r\n # CONVERT THE HOST_SINCE STRINGS INTO DATETIME TYPE\r\n listingsReducedColumns['period'] = pd.to_datetime(listingsReducedColumns.host_since)\r\n # CAST THE STRINGS IN PRICE COLUMN AS FLOAT\r\n listingsReducedColumns['price'] = listingsReducedColumns.price.apply(lambda x: float(x.replace('$', '').replace(',', '')) if isinstance(x, str) else float(x))\r\n\r\n # RETURN ONLY RECORDS IN THE PROVIDED PERIODS\r\n result = listingsReducedColumns[(listingsReducedColumns.period >= startDate) & (listingsReducedColumns.period <= endDate)]\r\n # print(result)\r\n years = result.period.dt.year.unique()\r\n priceDist = {}\r\n for year in years:\r\n yearPrice = result[result.period.dt.year == year]\r\n priceDist[year] = [yearPrice.price]\r\n plt.hist(priceDist[year], range=(0, 3000), bins=150, alpha=0.5, density=True)\r\n\r\n plt.legend([year for year in years])\r\n plt.show()\r\n return priceDist\r\n\r\n# showPriceDist('1/1/2015', '30/12/2019')\r\n\r\ndef showPopularListings(startperiod, endperiod, suburbName='sydney'):\r\n # MANIPULATE THE PROVIDED DATE ARGUMENTS\r\n splitStartPeriod = startperiod.split('/')\r\n splitEndPeriod = endperiod.split('/')\r\n startDate = datetime.datetime(int(splitStartPeriod[-1]), int(splitStartPeriod[-2]), int(splitStartPeriod[-3]))\r\n endDate = datetime.datetime(int(splitEndPeriod[-1]), int(splitEndPeriod[-2]), int(splitEndPeriod[-3]))\r\n\r\n # INITIALIZE THE DICTIONARY FOR IF A KEYWORD IS PROVIDED, AND THE LIST TO SEND TO SHOWLISTINGS FUNCTION\r\n filteredListings = {}\r\n\r\n # CONVERT THE HOST_SINCE STRINGS INTO DATETIME TYPE\r\n listingsReducedColumns['period'] = pd.to_datetime(listingsReducedColumns.host_since)\r\n # CONVERT THE STRINGS, KEYWORD AND SUBURBNAME INTO LOWERCASE\r\n suburbName = suburbName.lower()\r\n listingsReducedColumns.street = listingsReducedColumns.street.apply(lower)\r\n listingsReducedColumns.neighbourhood_cleansed = listingsReducedColumns.neighbourhood_cleansed.apply(lower)\r\n\r\n # SORT THE REVIEW_SCORES_RATING COLUMN IN DESCENDING ORDER\r\n listingsReducedColumns.sort_values(by='review_scores_rating', inplace=True, ascending=False)\r\n\r\n matched = listingsReducedColumns[\r\n ((listingsReducedColumns.period >= startDate) & (listingsReducedColumns.period <= endDate)) &\r\n ((listingsReducedColumns.street == suburbName) |\r\n (listingsReducedColumns.neighbourhood_cleansed == suburbName) | (listingsReducedColumns.neighbourhood == suburbName))\r\n ]\r\n\r\n # CONVERT THE VALUES INTO A LIST\r\n allRecords = matched.values.tolist()\r\n # SELECTS THE FIRST 5 VALUES\r\n top5 = allRecords[:5]\r\n\r\n # STORES THE TOP 5 VALUES AND COLUMN NAMES TO A LIST\r\n # AND WRITE THE LIST INTO A JSON FILE TO DISPLAY ON THE GUI LATER\r\n result = [matched.columns.values.tolist()] + allRecords\r\n\r\n df = pd.DataFrame(result[1:5], columns=result[0])\r\n jsonResult = df.to_json(orient='index')\r\n \r\n with open('popularListings.json', 'w') as jsonListings:\r\n json.dump(jsonResult, jsonListings)\r\n \r\n # STORE THE VALUES INTO THE DICTIONARY WITH SUBURBNAME AS ITS KEY\r\n filteredListings[suburbName] = top5\r\n\r\n return filteredListings\r\n\r\n# showPopularListings('1/1/2018', '31/1/2018', suburbName='waverley')\r\n\r\ndef showCleanComments():\r\n cleanCommentTotal = 0\r\n cleanlinessKeywordDict = {}\r\n # print(reviews)\r\n\r\n allCommentStr = reviews.comments.values.tolist()\r\n print(type(allCommentStr))\r\n # print(allCommentStr)\r\n totalCommentList = int(len(allCommentStr))\r\n\r\n for kw in cleanlinessKeywords:\r\n for comment in allCommentStr:\r\n if kw in str(comment) and kw not in (cleanlinessKeywordDict.keys()):\r\n cleanCommentTotal += 1\r\n cleanlinessKeywordDict[kw] = 1\r\n elif kw in str(comment) and kw in (cleanlinessKeywordDict.keys()):\r\n cleanCommentTotal += 1\r\n prevCount = cleanlinessKeywordDict[kw]\r\n cleanlinessKeywordDict[kw] = prevCount + 1\r\n\r\n print(cleanlinessKeywordDict)\r\n return cleanlinessKeywordDict\r\n\r\nshowCleanComments()", "repo_name": "zarinrayhana/Sydney-AirBnB-Data-Analyst-Software", "sub_path": "visualization.py", "file_name": "visualization.py", "file_ext": "py", "file_size_in_byte": 10647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.core.defchararray.lower", "line_number": 56, "usage_type": "argument"}, {"api_name": "pandas.to_datetime", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.core.defchararray.lower", "line_number": 92, "usage_type": "argument"}, {"api_name": "numpy.core.defchararray.lower", "line_number": 93, "usage_type": "argument"}, {"api_name": "pandas.to_datetime", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 141, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 168, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 169, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.core.defchararray.lower", "line_number": 178, "usage_type": "argument"}, {"api_name": "numpy.core.defchararray.lower", "line_number": 179, "usage_type": "argument"}, {"api_name": "pandas.DataFrame", "line_number": 199, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 203, "usage_type": "call"}]} +{"seq_id": "15720037847", "text": "\"\"\"Alpha Vantage Model\"\"\"\n__docformat__ = \"numpy\"\n\nimport logging\nfrom typing import Dict, List\n\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom alpha_vantage.fundamentaldata import FundamentalData\nfrom openbb_terminal import config_terminal as cfg\nfrom openbb_terminal.decorators import log_start_end\nfrom openbb_terminal.helper_funcs import lambda_long_number_format\nfrom openbb_terminal.rich_config import console\nfrom openbb_terminal.stocks.stocks_helper import clean_fraction\nfrom openbb_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index\n\nlogger = logging.getLogger(__name__)\n\n\n@log_start_end(log=logger)\ndef get_overview(ticker: str) -> pd.DataFrame:\n \"\"\"Get alpha vantage company overview\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n\n Returns\n -------\n pd.DataFrame\n Dataframe of fundamentals\n \"\"\"\n # Request OVERVIEW data from Alpha Vantage API\n s_req = f\"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}\"\n result = requests.get(s_req, stream=True)\n\n df_fa = pd.DataFrame()\n\n # If the returned data was unsuccessful\n if \"Error Message\" in result.json():\n console.print(result.json()[\"Error Message\"])\n else:\n # check if json is empty\n if not result.json():\n console.print(\"No data found\")\n # Parse json data to dataframe\n elif \"Note\" in result.json():\n console.print(result.json()[\"Note\"], \"\\n\")\n else:\n df_fa = pd.json_normalize(result.json())\n\n # Keep json data sorting in dataframe\n df_fa = df_fa[list(result.json().keys())].T\n df_fa.iloc[5:] = df_fa.iloc[5:].applymap(\n lambda x: lambda_long_number_format(x)\n )\n clean_df_index(df_fa)\n df_fa = df_fa.rename(\n index={\n \"E b i t d a\": \"EBITDA\",\n \"P e ratio\": \"PE ratio\",\n \"P e g ratio\": \"PEG ratio\",\n \"E p s\": \"EPS\",\n \"Revenue per share t t m\": \"Revenue per share TTM\",\n \"Operating margin t t m\": \"Operating margin TTM\",\n \"Return on assets t t m\": \"Return on assets TTM\",\n \"Return on equity t t m\": \"Return on equity TTM\",\n \"Revenue t t m\": \"Revenue TTM\",\n \"Gross profit t t m\": \"Gross profit TTM\",\n \"Diluted e p s t t m\": \"Diluted EPS TTM\",\n \"Quarterly earnings growth y o y\": \"Quarterly earnings growth YOY\",\n \"Quarterly revenue growth y o y\": \"Quarterly revenue growth YOY\",\n \"Trailing p e\": \"Trailing PE\",\n \"Forward p e\": \"Forward PE\",\n \"Price to sales ratio t t m\": \"Price to sales ratio TTM\",\n \"E v to revenue\": \"EV to revenue\",\n \"E v to e b i t d a\": \"EV to EBITDA\",\n }\n )\n return df_fa\n\n\n@log_start_end(log=logger)\ndef get_key_metrics(ticker: str) -> pd.DataFrame:\n \"\"\"Get key metrics from overview\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n\n Returns\n -------\n pd.DataFrame\n Dataframe of key metrics\n \"\"\"\n # Request OVERVIEW data\n s_req = f\"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}\"\n result = requests.get(s_req, stream=True)\n\n # If the returned data was unsuccessful\n if \"Error Message\" in result.json():\n console.print(result.json()[\"Error Message\"])\n else:\n # check if json is empty\n if not result.json() or len(result.json()) < 2:\n console.print(\"No data found\")\n return pd.DataFrame()\n\n df_fa = pd.json_normalize(result.json())\n df_fa = df_fa[list(result.json().keys())].T\n df_fa = df_fa.applymap(lambda x: lambda_long_number_format(x))\n clean_df_index(df_fa)\n df_fa = df_fa.rename(\n index={\n \"E b i t d a\": \"EBITDA\",\n \"P e ratio\": \"PE ratio\",\n \"P e g ratio\": \"PEG ratio\",\n \"E p s\": \"EPS\",\n \"Return on equity t t m\": \"Return on equity TTM\",\n \"Price to sales ratio t t m\": \"Price to sales ratio TTM\",\n }\n )\n as_key_metrics = [\n \"Market capitalization\",\n \"EBITDA\",\n \"EPS\",\n \"PE ratio\",\n \"PEG ratio\",\n \"Price to book ratio\",\n \"Return on equity TTM\",\n \"Price to sales ratio TTM\",\n \"Dividend yield\",\n \"50 day moving average\",\n \"Analyst target price\",\n \"Beta\",\n ]\n return df_fa.loc[as_key_metrics]\n\n return pd.DataFrame()\n\n\n@log_start_end(log=logger)\ndef get_income_statements(\n ticker: str, number: int, quarterly: bool = False\n) -> pd.DataFrame:\n \"\"\"Get income statements for company\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n number : int\n Number of past to get\n quarterly : bool, optional\n Flag to get quarterly instead of annual, by default False\n\n Returns\n -------\n pd.DataFrame\n Dataframe of income statements\n \"\"\"\n url = (\n f\"https://www.alphavantage.co/query?function=INCOME_STATEMENT&symbol={ticker}\"\n f\"&apikey={cfg.API_KEY_ALPHAVANTAGE}\"\n )\n r = requests.get(url)\n\n # If the returned data was unsuccessful\n if \"Error Message\" in r.json():\n console.print(r.json()[\"Error Message\"])\n else:\n # check if json is empty\n if not r.json():\n console.print(\"No data found\")\n else:\n statements = r.json()\n df_fa = pd.DataFrame()\n\n if quarterly:\n if \"quarterlyReports\" in statements:\n df_fa = pd.DataFrame(statements[\"quarterlyReports\"])\n else:\n if \"annualReports\" in statements:\n df_fa = pd.DataFrame(statements[\"annualReports\"])\n\n if df_fa.empty:\n console.print(\"No data found\")\n return pd.DataFrame()\n\n df_fa = df_fa.set_index(\"fiscalDateEnding\")\n df_fa = df_fa.head(number)\n df_fa = df_fa.applymap(lambda x: lambda_long_number_format(x))\n return df_fa[::-1].T\n return pd.DataFrame()\n\n\n@log_start_end(log=logger)\ndef get_balance_sheet(\n ticker: str, number: int, quarterly: bool = False\n) -> pd.DataFrame:\n \"\"\"Get balance sheets for company\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n number : int\n Number of past to get\n quarterly : bool, optional\n Flag to get quarterly instead of annual, by default False\n\n Returns\n -------\n pd.DataFrame\n Dataframe of income statements\n \"\"\"\n url = f\"https://www.alphavantage.co/query?function=BALANCE_SHEET&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}\"\n r = requests.get(url)\n\n # If the returned data was unsuccessful\n if \"Error Message\" in r.json():\n console.print(r.json()[\"Error Message\"])\n else:\n # check if json is empty\n if not r.json():\n console.print(\"No data found\")\n else:\n statements = r.json()\n df_fa = pd.DataFrame()\n\n if quarterly:\n if \"quarterlyReports\" in statements:\n df_fa = pd.DataFrame(statements[\"quarterlyReports\"])\n else:\n if \"annualReports\" in statements:\n df_fa = pd.DataFrame(statements[\"annualReports\"])\n\n if df_fa.empty:\n console.print(\"No data found\")\n return pd.DataFrame()\n\n df_fa = df_fa.set_index(\"fiscalDateEnding\")\n df_fa = df_fa.head(number)\n df_fa = df_fa.applymap(lambda x: lambda_long_number_format(x))\n return df_fa[::-1].T\n return pd.DataFrame()\n\n\n@log_start_end(log=logger)\ndef get_cash_flow(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame:\n \"\"\"Get cash flows for company\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n number : int\n Number of past to get\n quarterly : bool, optional\n Flag to get quarterly instead of annual, by default False\n\n Returns\n -------\n pd.DataFrame\n Dataframe of income statements\n \"\"\"\n url = f\"https://www.alphavantage.co/query?function=CASH_FLOW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}\"\n r = requests.get(url)\n\n # If the returned data was unsuccessful\n if \"Error Message\" in r.json():\n console.print(r.json()[\"Error Message\"])\n else:\n # check if json is empty\n if not r.json():\n console.print(\"No data found\")\n else:\n statements = r.json()\n df_fa = pd.DataFrame()\n\n if quarterly:\n if \"quarterlyReports\" in statements:\n df_fa = pd.DataFrame(statements[\"quarterlyReports\"])\n else:\n if \"annualReports\" in statements:\n df_fa = pd.DataFrame(statements[\"annualReports\"])\n\n if df_fa.empty:\n console.print(\"No data found\")\n return pd.DataFrame()\n\n df_fa = df_fa.set_index(\"fiscalDateEnding\")\n df_fa = df_fa.head(number)\n df_fa = df_fa.applymap(lambda x: lambda_long_number_format(x))\n return df_fa[::-1].T\n return pd.DataFrame()\n\n\n@log_start_end(log=logger)\ndef get_earnings(ticker: str, quarterly: bool = False) -> pd.DataFrame:\n \"\"\"Get earnings calendar for ticker\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n quarterly : bool, optional\n Flag to get quarterly and not annual, by default False\n\n Returns\n -------\n pd.DataFrame\n Dataframe of earnings\n \"\"\"\n # Request EARNINGS data from Alpha Vantage API\n s_req = (\n \"https://www.alphavantage.co/query?function=EARNINGS&\"\n f\"symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}\"\n )\n result = requests.get(s_req, stream=True)\n df_fa = pd.DataFrame()\n\n # If the returned data was unsuccessful\n if \"Error Message\" in result.json():\n console.print(result.json()[\"Error Message\"])\n else:\n # check if json is empty\n if not result.json() or len(result.json()) < 2:\n console.print(\"No data found\")\n else:\n\n df_fa = pd.json_normalize(result.json())\n\n if quarterly:\n df_fa = pd.DataFrame(df_fa[\"quarterlyEarnings\"][0])\n df_fa = df_fa[\n [\n \"fiscalDateEnding\",\n \"reportedDate\",\n \"reportedEPS\",\n \"estimatedEPS\",\n \"surprise\",\n \"surprisePercentage\",\n ]\n ]\n df_fa = df_fa.rename(\n columns={\n \"fiscalDateEnding\": \"Fiscal Date Ending\",\n \"reportedEPS\": \"Reported EPS\",\n \"estimatedEPS\": \"Estimated EPS\",\n \"reportedDate\": \"Reported Date\",\n \"surprise\": \"Surprise\",\n \"surprisePercentage\": \"Surprise Percentage\",\n }\n )\n else:\n df_fa = pd.DataFrame(df_fa[\"annualEarnings\"][0])\n df_fa = df_fa.rename(\n columns={\n \"fiscalDateEnding\": \"Fiscal Date Ending\",\n \"reportedEPS\": \"Reported EPS\",\n }\n )\n\n return df_fa\n\n\n@log_start_end(log=logger)\ndef df_values(\n df: pd.DataFrame, item: str, index: int = 0, length: int = 2\n) -> List[int]:\n \"\"\"Clean the values from the df\n\n Parameters\n ----------\n df : pd.DataFrame\n The Dataframe to use\n item : str\n The item to select\n index : int\n The number of row to display\n length : int\n The number of rows to return\n\n Returns\n -------\n values : List[int]\n The values for the dataframe\n \"\"\"\n if index:\n df = df.iloc[index : index + length]\n selection = df[item]\n values = selection.apply(\n lambda x: \"N/A\" if (not x or x == \"None\") else int(x)\n ).values\n return values.tolist()\n\n\n@log_start_end(log=logger)\ndef get_fraud_ratios(ticker: str) -> pd.DataFrame:\n \"\"\"Get fraud ratios based on fundamentals\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n\n Returns\n -------\n metrics : pd.DataFrame\n The fraud ratios\n \"\"\"\n\n try:\n fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format=\"pandas\")\n # pylint: disable=unbalanced-tuple-unpacking\n df_cf, _ = fd.get_cash_flow_annual(symbol=ticker)\n df_bs, _ = fd.get_balance_sheet_annual(symbol=ticker)\n df_is, _ = fd.get_income_statement_annual(symbol=ticker)\n\n except Exception as e:\n console.print(e)\n return pd.DataFrame()\n\n # pylint: disable=no-member\n df_cf = df_cf.set_index(\"fiscalDateEnding\")\n df_bs = df_bs.set_index(\"fiscalDateEnding\")\n df_is = df_is.set_index(\"fiscalDateEnding\")\n fraud_years = pd.DataFrame()\n for i in range(len(df_cf) - 1):\n ar = df_values(df_bs, \"currentNetReceivables\", i)\n sales = df_values(df_is, \"totalRevenue\", i)\n cogs = df_values(df_is, \"costofGoodsAndServicesSold\", i)\n ni = df_values(df_is, \"netIncome\", i)\n ca = df_values(df_bs, \"totalCurrentAssets\", i)\n cl = df_values(df_bs, \"totalCurrentLiabilities\", i)\n ppe = df_values(df_bs, \"propertyPlantEquipment\", i)\n cash = df_values(df_bs, \"cashAndCashEquivalentsAtCarryingValue\", i)\n cash_and_sec = df_values(df_bs, \"cashAndShortTermInvestments\", i)\n sec = [y - x for (x, y) in zip(cash, cash_and_sec)]\n ta = df_values(df_bs, \"totalAssets\", i)\n dep = df_values(df_bs, \"accumulatedDepreciationAmortizationPPE\", i)\n sga = df_values(df_is, \"sellingGeneralAndAdministrative\", i)\n tl = df_values(df_bs, \"totalLiabilities\", i)\n icfo = df_values(df_is, \"netIncomeFromContinuingOperations\", i)\n cfo = df_values(df_cf, \"operatingCashflow\", i)\n\n ratios: Dict = {}\n ratios[\"DSRI\"] = (ar[0] / sales[0]) / (ar[1] / sales[1])\n ratios[\"GMI\"] = ((sales[1] - cogs[1]) / sales[1]) / (\n (sales[0] - cogs[0]) / sales[0]\n )\n ratios[\"AQI\"] = (1 - ((ca[0] + ppe[0] + sec[0]) / ta[0])) / (\n 1 - ((ca[1] + ppe[1] + sec[1]) / ta[1])\n )\n ratios[\"SGI\"] = sales[0] / sales[1]\n ratios[\"DEPI\"] = (dep[1] / (ppe[1] + dep[1])) / (dep[0] / (ppe[0] + dep[0]))\n ratios[\"SGAI\"] = (sga[0] / sales[0]) / (sga[1] / sales[1])\n ratios[\"LVGI\"] = (tl[0] / ta[0]) / (tl[1] / ta[1])\n ratios[\"TATA\"] = (icfo[0] - cfo[0]) / ta[0]\n ratios[\"MSCORE\"] = (\n -4.84\n + (0.92 * ratios[\"DSRI\"])\n + (0.58 * ratios[\"GMI\"])\n + (0.404 * ratios[\"AQI\"])\n + (0.892 * ratios[\"SGI\"])\n + (0.115 * ratios[\"DEPI\"] - (0.172 * ratios[\"SGAI\"]))\n + (4.679 * ratios[\"TATA\"])\n - (0.327 * ratios[\"LVGI\"])\n )\n\n zscore = (\n -4.336\n - (4.513 * (ni[0] / ta[0]))\n + (5.679 * (tl[0] / ta[0]))\n + (0.004 * (ca[0] / cl[0]))\n )\n v1 = np.log(ta[0] / 1000)\n v2 = ni[0] / ta[0]\n v3 = cash[0] / cl[0]\n\n x = ((v1 + 0.85) * v2) - 0.85\n y = 1 + v3\n\n mckee = x**2 / (x**2 + y**2)\n ratios[\"Zscore\"] = zscore\n ratios[\"Mscore\"] = mckee\n if fraud_years.empty:\n fraud_years.index = ratios.keys()\n fraud_years[df_cf.index[i]] = ratios.values()\n fraud_years = fraud_years[sorted(fraud_years)]\n return fraud_years\n\n\n@log_start_end(log=logger)\ndef get_dupont(ticker: str) -> pd.DataFrame:\n \"\"\"Get dupont ratios\n\n Parameters\n ----------\n ticker : str\n Stock ticker\n\n Returns\n -------\n dupont : pd.DataFrame\n The dupont ratio breakdown\n \"\"\"\n\n try:\n fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format=\"pandas\")\n # pylint: disable=unbalanced-tuple-unpacking\n df_bs, _ = fd.get_balance_sheet_annual(symbol=ticker)\n df_is, _ = fd.get_income_statement_annual(symbol=ticker)\n\n except Exception as e:\n console.print(e)\n return pd.DataFrame()\n\n # pylint: disable=no-member\n df_bs = df_bs.set_index(\"fiscalDateEnding\")\n df_is = df_is.set_index(\"fiscalDateEnding\")\n dupont_years = pd.DataFrame()\n\n for i in range(len(df_bs)):\n ni = df_values(df_is, \"netIncome\", i, 1)\n pretax = df_values(df_is, \"incomeBeforeTax\", i, 1)\n ebit = df_values(df_is, \"ebit\", i, 1)\n sales = df_values(df_is, \"totalRevenue\", i, 1)\n assets = df_values(df_bs, \"totalAssets\", i, 1)\n equity = df_values(df_bs, \"totalShareholderEquity\", i, 1)\n\n ratios: Dict = {}\n try:\n ratios[\"Tax Burden\"] = clean_fraction(ni[0], pretax[0])\n ratios[\"Interest Burden\"] = clean_fraction(pretax[0], ebit[0])\n ratios[\"EBIT Margin\"] = clean_fraction(ebit[0], sales[0])\n ratios[\"Asset Turnover\"] = clean_fraction(sales[0], assets[0])\n ratios[\"Finance Leverage\"] = clean_fraction(assets[0], equity[0])\n ratios[\"ROI\"] = clean_fraction(ni[0], equity[0])\n except IndexError:\n pass\n\n if dupont_years.empty:\n dupont_years.index = ratios.keys()\n dupont_years[df_bs.index[i]] = ratios.values()\n dupont_years = dupont_years[sorted(dupont_years)]\n return dupont_years\n", "repo_name": "rohankumardubey/OpenBBTerminal", "sub_path": "openbb_terminal/stocks/fundamental_analysis/av_model.py", "file_name": "av_model.py", "file_ext": "py", "file_size_in_byte": 17931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 36, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 43, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 43, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 47, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 47, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 50, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 50, "usage_type": "name"}, {"api_name": "pandas.json_normalize", "line_number": 52, "usage_type": "call"}, {"api_name": "openbb_terminal.helper_funcs.lambda_long_number_format", "line_number": 57, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.fundamental_analysis.fa_helper.clean_df_index", "line_number": 59, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 100, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 101, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 105, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 105, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 109, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 109, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 112, "usage_type": "call"}, {"api_name": "openbb_terminal.helper_funcs.lambda_long_number_format", "line_number": 114, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.fundamental_analysis.fa_helper.clean_df_index", "line_number": 115, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 142, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 86, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 167, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 167, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 169, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 173, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 173, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 177, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 177, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 187, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 190, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 190, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 191, "usage_type": "call"}, {"api_name": "openbb_terminal.helper_funcs.lambda_long_number_format", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 197, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 145, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 148, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 220, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 220, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 221, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 225, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 225, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 229, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 229, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 232, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 236, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 239, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 242, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 242, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 243, "usage_type": "call"}, {"api_name": "openbb_terminal.helper_funcs.lambda_long_number_format", "line_number": 247, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 249, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 200, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 203, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 270, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 270, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 271, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 275, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 275, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 279, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 279, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 282, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 286, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 289, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 292, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 292, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 293, "usage_type": "call"}, {"api_name": "openbb_terminal.helper_funcs.lambda_long_number_format", "line_number": 297, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 299, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 252, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 253, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 321, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 321, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 323, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 324, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 328, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 328, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 332, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 332, "usage_type": "name"}, {"api_name": "pandas.json_normalize", "line_number": 335, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 338, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 360, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 302, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 303, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 373, "usage_type": "attribute"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 371, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 374, "usage_type": "name"}, {"api_name": "alpha_vantage.fundamentaldata.FundamentalData", "line_number": 418, "usage_type": "call"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 418, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 418, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 425, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 425, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 426, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 432, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 451, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 481, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 402, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 403, "usage_type": "attribute"}, {"api_name": "alpha_vantage.fundamentaldata.FundamentalData", "line_number": 514, "usage_type": "call"}, {"api_name": "openbb_terminal.config_terminal.API_KEY_ALPHAVANTAGE", "line_number": 514, "usage_type": "attribute"}, {"api_name": "openbb_terminal.config_terminal", "line_number": 514, "usage_type": "name"}, {"api_name": "openbb_terminal.rich_config.console.print", "line_number": 520, "usage_type": "call"}, {"api_name": "openbb_terminal.rich_config.console", "line_number": 520, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 521, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 526, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 536, "usage_type": "name"}, {"api_name": "openbb_terminal.stocks.stocks_helper.clean_fraction", "line_number": 538, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.stocks_helper.clean_fraction", "line_number": 539, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.stocks_helper.clean_fraction", "line_number": 540, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.stocks_helper.clean_fraction", "line_number": 541, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.stocks_helper.clean_fraction", "line_number": 542, "usage_type": "call"}, {"api_name": "openbb_terminal.stocks.stocks_helper.clean_fraction", "line_number": 543, "usage_type": "call"}, {"api_name": "openbb_terminal.decorators.log_start_end", "line_number": 498, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 499, "usage_type": "attribute"}]} +{"seq_id": "11308305958", "text": "import pygatt\nimport csv\nimport datetime\n\n# Connect to the device\nconnect_key = bytearray.fromhex(\"2101020304000000000000\") # Update this with correct connection key you obtained in part 2\nenable_notifications = bytearray.fromhex(\"0b0100000000\") # This value is correct, no need to update\nbbq_mac = \"ff:ff:ff:ff:ff:ff\" # Update this with your BBQ device's MAC address\n\nadapter = pygatt.GATTToolBackend()\n\ndef fahrenheit(celcius):\n return int(round(celcius * (9/5.0) + 32))\n\n# Process and save the realtime data\ndef handle_notification(handle, value):\n \"\"\"\n handle -- integer, characteristic read handle the data was received on\n value -- bytearray, the data returned in the notification\n \"\"\"\n temps = {\"timestamp\": str(datetime.datetime.now())}\n for i in range(0,8,2):\n celcius = int(int.from_bytes(value[i:i+2], \"little\") / 10)\n f_degrees = fahrenheit(celcius)\n temps[f\"Probe-{int(i/2)+1}\"] = f_degrees\n with open(\"temperature_log.csv\", \"a\") as csv_file:\n writer = csv.writer(csv_file)\n writer.writerow([temps[field] for field in temps])\n \n \ntry:\n adapter.start()\n\n try:\n device = adapter.connect(bbq_mac,timeout=20)\n except:\n print(\"Couldn't connect to the device, retrying...\")\n device = adapter.connect(bbq_mac,timeout=20)\n\n # Send the connection key to the 0x29\n print(\"Pairing with the device...\")\n device.char_write_handle(0x0029, connect_key)\n # Enable notifications by writing to 0x34\n device.char_write_handle(0x0034, enable_notifications)\n print(\"Connected with the device.\")\n \n with open('temperature_log.csv', 'w') as csv_file:\n writer = csv.writer(csv_file)\n writer.writerow([\"Timestamp\", \"Probe 1\", \"Probe 2\", \"Probe 3\", \"Probe 4\"])\n # Subscribe and listen for notifications of the realtime data\n try:\n device.subscribe(\"0000fff4-0000-1000-8000-00805f9b34fb\", callback=handle_notification)\n except Exception as e:\n try:\n device.subscribe(\"0000fff4-0000-1000-8000-00805f9b34fb\", callback=handle_notification)\n except:\n pass\n \n input(\"Enter any key to quit....\")\n \n\nfinally:\n adapter.stop()\n\n\n\n", "repo_name": "imperfectpython/bbq-hacking-part-3", "sub_path": "bbq_app.py", "file_name": "bbq_app.py", "file_ext": "py", "file_size_in_byte": 2208, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pygatt.GATTToolBackend", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 27, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "25997626617", "text": "from django.shortcuts import render, redirect, reverse\nfrom .models import Comment\nfrom django.contrib.contenttypes.models import ContentType\nfrom .forms import CommentForm\nfrom django.http import JsonResponse\nfrom django.utils import timezone\n\n\ndef update_comment(request):\n referer = request.META.get('HTTP_REFERER', reverse('home'))\n comment_form = CommentForm(request.POST, user=request.user)\n\n # 返回给ajax的状态数据，是否完成评论\n data = {}\n\n if comment_form.is_valid():\n comment = Comment()\n comment.user = request.user\n comment.text = comment_form.cleaned_data['text']\n comment.content_object = comment_form.cleaned_data['content_object']\n\n parent = comment_form.cleaned_data['parent']\n if not parent is None: # 这一条是回复\n if parent.root is None: # 被这条回复的内容是一条评论不是一条回复\n comment.root = parent\n else: # 被这条回复的内容是一条回复，所以这条回复的root的parent的root，以此类推，直到最后一个是评论\n comment.root = parent.root\n comment.parent = parent\n comment.reply_to = parent.user # models设置该parent的时候，外键关联User，可以可以通过parent找到user\n data['reply_to'] = comment.reply_to.get_nickname_or_username()\n else:\n data['reply_to'] = ''\n\n comment.save()\n\n data['pk'] = comment.pk\n # 这是一条评论的话\n if comment.root is None:\n data['root_pk'] = ''\n else:\n data['root_pk'] = comment.root.pk\n # 构建返回给前端ajax的数据\n data['status'] = 'SUCCESS'\n data['content_type'] = ContentType.objects.get_for_model(comment).model\n data['username'] = comment.user.get_nickname_or_username()\n data['avatar_url'] = comment.user.get_avatar_url()\n data['comment_time'] = timezone.localtime(comment.comment_time).strftime('%Y-%m-%d %H:%M:%S')\n data['text'] = comment.text\n # return render(request, 'login_logout_error.html', {'message': '评论成功！', 'redirect_to': referer})\n else:\n data['status'] = 'ERROR'\n # 返回具体的错误信息\n data['message'] = list(comment_form.errors.values())[0][0]\n return JsonResponse(data)\n\n # return render('login_logout_error.html', request, {'message': comment_form.errors, 'redirect_to': referer})\n # # 返回原来的博客页面\n # referer = request.META.get('HTTP_REFERER', reverse('home'))\n #\n # # 获取前端页面传递进来的数据\n # user = request.user\n # # 数据检查\n # if not user.is_authenticated: # 是否真的登录了，未登录的话留下一个返回原来博客的链接\n # return render(request, 'login_logout_error.html', {'message': '用户未登录！', 'redirect_to':referer})\n # text = request.POST.get('text', '')\n # if text.strip() == '':\n # return render(request, 'login_logout_error.html', {'message': '提交内容为空！', 'redirect_to':referer})\n # try:\n # object_id = int(request.POST.get('object_id', ''))\n # # 这里传进来的时候字符串，不是博客的类\n # content_type = request.POST.get('content_type', '')\n #\n # # 下面是获取comment models对象中的content_object，相当于Blog.objects.get(pk=object_id)\n # # 下面的这种写法可以让评论变得更加灵活，不单单是评论博客\n # # 根据前端传来的blog_detail获取评论的对象content_type是Blog\n # model_class = ContentType.objects.get(model=content_type).model_class() # 获取所有的blog的ContentType\n # model_object = model_class.objects.get(pk=object_id) # 根据blog的pk确定对应的blog\n # # 实例化一个Comment对象，数据检查通过，要完整的填好这个实例化的comment需要这些数据，具体数据可以看comment的models\n # comment = Comment()\n # comment.content_object = model_object\n # comment.user = user\n # comment.text = text\n # comment.save()\n # except Exception as e:\n # return render(request, 'login_logout_error.html', {'message':e, 'redirect_to':referer})\n #\n # return render(request, 'login_logout_error.html', {'message': '评论成功！', 'redirect_to':referer})\n", "repo_name": "zhangyongming13/mysite", "sub_path": "comment/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.shortcuts.reverse", "line_number": 10, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Comment", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 44, "usage_type": "name"}, {"api_name": "django.utils.timezone.localtime", "line_number": 47, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 47, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "22788034287", "text": "from pathlib import Path\nfrom typing import Optional\n\nimport pandas as pd\nimport pytest\nfrom hyperstyle.src.python.review.common.language import Language\nfrom analysis.src.python.evaluation.issues_statistics.get_raw_issues_statistics import (\n _convert_language_code_to_language,\n _get_output_folder,\n DEFAULT_OUTPUT_FOLDER_NAME,\n inspect_raw_issues,\n)\nfrom analysis.src.python.utils.df_utils import equal_df, read_df\nfrom analysis.test.python.evaluation.issues_statistics import (\n GET_RAW_ISSUES_STATISTICS_TARGET_FILES_FOLDER,\n GET_RAW_ISSUES_STATISTICS_TEST_FILES_FOLDER,\n)\n\nDF_PARENT_FOLDER_NAME = 'parent_folder'\nDF_NAME = 'input_df'\nDF_PATH = Path(DF_PARENT_FOLDER_NAME) / DF_NAME\nDEFAULT_OUTPUT_PATH = Path(DF_PARENT_FOLDER_NAME) / DEFAULT_OUTPUT_FOLDER_NAME\n\nNEW_FOLDER = 'new_folder'\n\nGET_OUTPUT_FOLDER_PATH_TEST_DATA = [\n (DF_PATH, None, DEFAULT_OUTPUT_PATH),\n (DF_PATH, Path(NEW_FOLDER), Path(NEW_FOLDER)),\n]\n\n\n@pytest.mark.parametrize(\n ('solutions_file_path', 'output_folder', 'expected_output_folder'),\n GET_OUTPUT_FOLDER_PATH_TEST_DATA,\n)\ndef test_get_output_folder(solutions_file_path: Path, output_folder: Optional[Path], expected_output_folder: Path):\n actual_output_folder = _get_output_folder(solutions_file_path, output_folder)\n assert actual_output_folder == expected_output_folder\n\n\nCONVERT_LANGUAGE_CODE_TO_LANGUAGE_TEST_DATA = [\n ('java7', 'JAVA'),\n ('java8', 'JAVA'),\n ('java9', 'JAVA'),\n ('java11', 'JAVA'),\n ('java15', 'JAVA'),\n ('python3', 'PYTHON'),\n ('kotlin', 'KOTLIN'),\n ('javascript', 'JAVASCRIPT'),\n ('some_weird_lang', 'some_weird_lang'),\n]\n\n\n@pytest.mark.parametrize(('language_code', 'expected_language'), CONVERT_LANGUAGE_CODE_TO_LANGUAGE_TEST_DATA)\ndef test_convert_language_code_to_language(language_code: str, expected_language: str):\n actual_language = _convert_language_code_to_language(fragment_id='0', language_code=language_code)\n assert actual_language == expected_language\n\n\nINSPECT_SOLUTIONS_TEST_DATA = [\n (\n 'test_df_with_null.csv',\n 'target_df_with_null_python.csv',\n Language.PYTHON.value,\n ),\n (\n 'test_df_with_null.csv',\n 'target_df_with_null_unknown.csv',\n '',\n ),\n (\n 'test_df_with_empty_raw_issues.csv',\n 'target_df_with_empty_raw_issues.csv',\n Language.KOTLIN.value,\n ),\n (\n 'test_df_with_incorrect_language.csv',\n 'target_df_with_incorrect_language.csv',\n 'some_weird_lang',\n ),\n (\n 'test_df_single_lang.csv',\n 'target_df_single_lang.csv',\n Language.JAVA.value,\n ),\n (\n 'test_df_multi_lang.csv',\n 'target_df_multi_lang_java.csv',\n Language.JAVA.value,\n ),\n (\n 'test_df_multi_lang.csv',\n 'target_df_multi_lang_js.csv',\n Language.JS.value,\n ),\n (\n 'test_df_multi_lang.csv',\n 'target_df_multi_lang_python.csv',\n Language.PYTHON.value,\n ),\n]\n\n\n@pytest.mark.parametrize(('test_file', 'target_file', 'lang'), INSPECT_SOLUTIONS_TEST_DATA)\ndef test_inspect_solutions(test_file: str, target_file: str, lang: str):\n test_df = read_df(GET_RAW_ISSUES_STATISTICS_TEST_FILES_FOLDER / test_file)\n stats = inspect_raw_issues(test_df)\n\n freq_stats = pd.read_csv(GET_RAW_ISSUES_STATISTICS_TARGET_FILES_FOLDER / target_file)\n\n assert equal_df(stats[lang], freq_stats)\n", "repo_name": "nbirillo/hyperstyle-analyze", "sub_path": "analysis/test/python/evaluation/issues_statistics/test_get_raw_issues_statistics.py", "file_name": "test_get_raw_issues_statistics.py", "file_ext": "py", "file_size_in_byte": 3408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "analysis.src.python.evaluation.issues_statistics.get_raw_issues_statistics.DEFAULT_OUTPUT_FOLDER_NAME", "line_number": 22, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 28, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "analysis.src.python.evaluation.issues_statistics.get_raw_issues_statistics._get_output_folder", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 32, "usage_type": "attribute"}, {"api_name": "analysis.src.python.evaluation.issues_statistics.get_raw_issues_statistics._convert_language_code_to_language", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 54, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 54, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language.PYTHON", "line_number": 64, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language", "line_number": 64, "usage_type": "name"}, {"api_name": "hyperstyle.src.python.review.common.language.Language.KOTLIN", "line_number": 74, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language", "line_number": 74, "usage_type": "name"}, {"api_name": "hyperstyle.src.python.review.common.language.Language.JAVA", "line_number": 84, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language", "line_number": 84, "usage_type": "name"}, {"api_name": "hyperstyle.src.python.review.common.language.Language.JAVA", "line_number": 89, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language", "line_number": 89, "usage_type": "name"}, {"api_name": "hyperstyle.src.python.review.common.language.Language.JS", "line_number": 94, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language", "line_number": 94, "usage_type": "name"}, {"api_name": "hyperstyle.src.python.review.common.language.Language.PYTHON", "line_number": 99, "usage_type": "attribute"}, {"api_name": "hyperstyle.src.python.review.common.language.Language", "line_number": 99, "usage_type": "name"}, {"api_name": "analysis.src.python.utils.df_utils.read_df", "line_number": 106, "usage_type": "call"}, {"api_name": "analysis.test.python.evaluation.issues_statistics.GET_RAW_ISSUES_STATISTICS_TEST_FILES_FOLDER", "line_number": 106, "usage_type": "name"}, {"api_name": "analysis.src.python.evaluation.issues_statistics.get_raw_issues_statistics.inspect_raw_issues", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 109, "usage_type": "call"}, {"api_name": "analysis.test.python.evaluation.issues_statistics.GET_RAW_ISSUES_STATISTICS_TARGET_FILES_FOLDER", "line_number": 109, "usage_type": "name"}, {"api_name": "analysis.src.python.utils.df_utils.equal_df", "line_number": 111, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 104, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 104, "usage_type": "attribute"}]} +{"seq_id": "13071417940", "text": "import datetime\nfrom django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.urls import reverse, reverse_lazy\nfrom django.utils import timezone\nfrom django.db.models import Sum\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom dateutil.relativedelta import relativedelta\nfrom django.contrib.auth.views import LoginView, LogoutView\nfrom django.db.models import Q\nfrom bootstrap_modal_forms.generic import (\n BSModalCreateView, BSModalUpdateView, BSModalDeleteView\n)\nfrom .models import (\n Expense, Income, DefaultExpenseMonth, DefaultIncomeMonth, Account,\n Method, TemplateExpense, Loan\n)\nfrom .forms import LoginForm, IncomeForm, ExpenseForm, BalanceForm, LoanForm\nfrom .const import const_data\n\ndef can_add_default_inex(year, month):\n \"\"\"デフォルトの収支を追加可能か判定する。\n\n Parameters\n ----------\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n bool\n デフォルト収支を追加可能かどうか\n \"\"\"\n\n # 今月の初日を取得\n current_time = timezone.now()\n current_month_first_date = datetime.date(\n current_time.year, current_time.month, 1\n )\n\n # 過去には追加不可\n if datetime.date(year, month, 1) < current_month_first_date:\n return False\n\n return True\n\ndef can_update_or_delete_inex(year, month):\n \"\"\"収支を更新・削除可能か判定する。\n\n Parameters\n ----------\n year : int\n 現在の支払年\n month : int\n 現在の支払月\n\n Returns\n -------\n bool\n 収支を更新・削除可能かどうか\n \"\"\"\n\n # 前月の初日を取得\n current_time = timezone.now()\n current_month_first_date = datetime.date(\n current_time.year, current_time.month, 1\n )\n last_month_first_date = (current_month_first_date\n - relativedelta(months=1))\n\n # 現在の支払月の初日を取得\n old_pay_date = datetime.date(year, month, 1)\n\n # 現在の支払月が先月より前であった場合、更新を許可しない\n if old_pay_date < last_month_first_date:\n return False\n\n return True\n\ndef add_incs_from_default(year, month):\n \"\"\"デフォルトの収支から収入を追加する。\n\n Parameters\n ----------\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n int\n 追加した収入の数\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n add_num = 0\n\n # デフォルトの収入から収入を追加\n def_inc_months = DefaultIncomeMonth.objects.filter(month=month)\n this_month_incs = Income.objects.filter(\n pay_date__gte=first_date, pay_date__lte=last_date\n )\n for def_inc_month in def_inc_months:\n can_add = True\n def_inc = def_inc_month.def_inc # 追加対象の収入\n # 既に登録されているかのチェック\n for this_month_inc in this_month_incs:\n if def_inc.name == this_month_inc.name:\n # 既に登録されている場合\n can_add = False\n break\n # 追加\n if can_add:\n # まだ登録されていない場合\n Income(\n name=def_inc.name,\n pay_date=datetime.date(year, month, def_inc.pay_day),\n method=def_inc.method, amount=def_inc.amount,\n undecided=def_inc.undecided,\n ).save()\n add_num += 1\n\n return add_num\n\ndef add_exps_from_default_and_loan(year, month):\n \"\"\"デフォルトの支出とローンから支出を追加する。\n\n Parameters\n ----------\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n int\n 追加した支出の数\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n add_num = 0\n\n # デフォルトの支出から支出を追加\n def_exp_months = DefaultExpenseMonth.objects.filter(month=month)\n this_month_exps = Expense.objects.filter(\n pay_date__gte=first_date, pay_date__lte=last_date\n )\n for def_exp_month in def_exp_months:\n can_add = True\n def_exp = def_exp_month.def_exp # 追加対象の支出\n # 既に登録されているかのチェック\n for this_month_exp in this_month_exps:\n if def_exp.name == this_month_exp.name:\n # 既に登録されている場合\n can_add = False\n break\n # 追加\n if can_add:\n # まだ登録されていない場合\n Expense(\n name=def_exp.name,\n pay_date=datetime.date(year, month, def_exp.pay_day),\n method=def_exp.method,\n amount=def_exp.amount, undecided=def_exp.undecided,\n ).save()\n add_num += 1\n\n # ローンから支出を追加\n loans = Loan.objects.filter(\n (Q(first_year__lt=year) | Q(first_year=year, first_month__lte=month)),\n (Q(last_year__gt=year) | Q(last_year=year, last_month__gte=month))\n )\n for loan in loans:\n can_add = True\n # 既に登録されているかのチェック\n for this_month_exp in this_month_exps:\n if loan.name == this_month_exp.name:\n # 既に登録されている場合\n can_add = False\n break\n # 追加\n if can_add:\n # まだ登録されていない場合\n if year == loan.first_year and month == loan.first_month:\n amount = loan.amount_first\n else:\n amount = loan.amount_from_second\n\n Expense(\n name=loan.name,\n pay_date=datetime.date(year, month, loan.pay_day),\n method=loan.method, amount=amount, undecided=loan.undecided,\n ).save()\n add_num += 1\n\n return add_num\n\ndef get_balance_done(year, month):\n \"\"\"該当月までの残高（完了分）を取得\n\n Parameters\n ----------\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n int\n 該当月までの残高（完了分）\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 今月までの収支リストを取得\n incs_to_this_month = Income.objects.filter(\n pay_date__lte=last_date\n )\n exps_to_this_month = Expense.objects.filter(\n pay_date__lte=last_date\n )\n\n # 残高を計算\n # 今月までの収入（完了分）の合計\n done_incs = incs_to_this_month.filter(done=True)\n done_inc_sums = done_incs.aggregate(Sum('amount'))\n done_inc_sum = done_inc_sums['amount__sum']\n if done_inc_sum is None:\n done_inc_sum = 0\n # 今月の支出（完了分）を減算\n done_exps = exps_to_this_month.filter(done=True)\n done_exp_sums = done_exps.aggregate(Sum('amount'))\n done_exp_sum = done_exp_sums['amount__sum']\n if done_exp_sum is None:\n done_exp_sum = 0\n\n return done_inc_sum - done_exp_sum\n\ndef get_balance(year, month):\n \"\"\"該当月までの残高を取得\n\n Parameters\n ----------\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n int\n 該当月までの残高\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 今月までの収支リストを取得\n incs_to_this_month = Income.objects.filter(\n pay_date__lte=last_date\n )\n exps_to_this_month = Expense.objects.filter(\n pay_date__lte=last_date\n )\n\n # 残高を計算\n # 今月までの収入（完了分）の合計\n inc_sums = incs_to_this_month.aggregate(Sum('amount'))\n inc_sum = inc_sums['amount__sum']\n if inc_sum is None:\n inc_sum = 0\n # 今月の支出（完了分）を減算\n exp_sums = exps_to_this_month.aggregate(Sum('amount'))\n exp_sum = exp_sums['amount__sum']\n if exp_sum is None:\n exp_sum = 0\n\n return inc_sum - exp_sum\n\n\n# Create your views here.\n\nclass login(LoginView):\n form_class = LoginForm\n template_name = \"income_and_expense/login.html\"\n\n\nclass logout(LogoutView):\n pass\n\n\nclass IncomeCreateView(BSModalCreateView):\n template_name = 'income_and_expense/create_inc.html'\n form_class = IncomeForm\n success_message = '成功: %(name)sが追加されました。'\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:income',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass IncomeUpdateView(BSModalUpdateView):\n model = Income\n template_name = 'income_and_expense/update_inc.html'\n form_class = IncomeForm\n success_message = '成功: %(name)sが更新されました。'\n\n def post(self, request, *args, **kwargs):\n if not can_update_or_delete_inex(kwargs['year'], kwargs['month']):\n messages.error(\n self.request,\n \"失敗: 古い収入は更新できません。\"\n )\n # incomeビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:income',\n args=(kwargs['year'], kwargs['month'])\n )\n )\n\n return super().post(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:income',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass IncomeDeleteView(BSModalDeleteView):\n model = Income\n template_name = 'income_and_expense/delete_inc.html'\n form_class = IncomeForm\n\n def post(self, request, *args, **kwargs):\n pay_date = Income.objects.get(pk=kwargs['pk']).pay_date\n\n if not can_update_or_delete_inex(pay_date.year, pay_date.month):\n messages.error(\n self.request,\n \"失敗: 古い収入は削除できません。\"\n )\n # incomeビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:income',\n args=(kwargs['year'], kwargs['month'])\n )\n )\n\n messages.success(\n self.request,\n \"成功: %sが削除されました。\" % Income.objects.get(id=kwargs['pk']).name\n )\n return super().delete(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:income',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass ExpenseCreateView(BSModalCreateView):\n template_name = 'income_and_expense/create_exp.html'\n form_class = ExpenseForm\n success_message = '成功: %(name)sが追加されました。'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n today = datetime.date.today()\n template_exps = TemplateExpense.objects.all()\n context_template_exps = []\n\n for template_exp in template_exps:\n context_template_exp = {}\n\n # 名前（テンプレート）\n context_template_exp[\"template_name\"] = str(template_exp.template_name)\n # 名前\n context_template_exp[\"name\"] = str(template_exp.name)\n # 支払方法\n context_template_exp[\"method\"] = str(template_exp.method)\n # 未定\n context_template_exp[\"undecided\"] = str(template_exp.undecided)\n # 完了\n context_template_exp[\"done\"] = str(template_exp.done)\n\n # 支払日\n if template_exp.date_type == 'today':\n pay_date = today\n else:\n if today.day <= template_exp.limit_day_of_this_month:\n pay_date = datetime.date(\n today.year, today.month, template_exp.pay_day\n )\n else:\n pay_date = datetime.date(\n today.year, today.month, template_exp.pay_day\n ) + relativedelta(months=1)\n context_template_exp[\"pay_date\"] = \"{0}-{1}-{2}\".format(\n pay_date.year,\n str(pay_date.month).zfill(2),\n str(pay_date.day).zfill(2)\n )\n\n context_template_exps.append(context_template_exp)\n\n context['template_exps'] = context_template_exps\n return context\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:expense',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass ExpenseUpdateView(BSModalUpdateView):\n model = Expense\n template_name = 'income_and_expense/update_exp.html'\n form_class = ExpenseForm\n success_message = '成功: %(name)sが更新されました。'\n\n def post(self, request, *args, **kwargs):\n if not can_update_or_delete_inex(kwargs['year'], kwargs['month']):\n messages.error(\n self.request,\n \"失敗: 古い支出は更新できません。\"\n )\n # incomeビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:expense',\n args=(kwargs['year'], kwargs['month'])\n )\n )\n\n return super().post(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:expense',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass ExpenseDeleteView(BSModalDeleteView):\n model = Expense\n template_name = 'income_and_expense/delete_exp.html'\n form_class = ExpenseForm\n\n def post(self, request, *args, **kwargs):\n pay_date = Expense.objects.get(pk=kwargs['pk']).pay_date\n\n if not can_update_or_delete_inex(pay_date.year, pay_date.month):\n messages.error(\n self.request,\n \"失敗: 古い支出は削除できません。\"\n )\n # expenseビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:expense',\n args=(kwargs['year'], kwargs['month'])\n )\n )\n\n messages.success(\n self.request,\n \"成功: %sが削除されました。\" % Expense.objects.get(id=kwargs['pk']).name\n )\n return super().delete(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:expense',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass BalanceUpdateView(BSModalUpdateView):\n model = Account\n template_name = 'income_and_expense/update_balance.html'\n form_class = BalanceForm\n success_message = '成功: %(user)s%(bank)sが更新されました。'\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:balance',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass LoanCreateView(BSModalCreateView):\n template_name = 'income_and_expense/create_loan.html'\n form_class = LoanForm\n success_message = '成功: %(name)sが追加されました。'\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:loan',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass LoanUpdateView(BSModalUpdateView):\n model = Loan\n template_name = 'income_and_expense/update_loan.html'\n form_class = LoanForm\n success_message = '成功: %(name)sが更新されました。'\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:loan',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\nclass LoanDeleteView(BSModalDeleteView):\n model = Loan\n template_name = 'income_and_expense/delete_loan.html'\n form_class = LoanForm\n\n def post(self, request, *args, **kwargs):\n messages.success(\n self.request,\n \"成功: %sが削除されました。\" % Loan.objects.get(id=kwargs['pk']).name\n )\n return super().delete(request, *args, **kwargs)\n\n def get_success_url(self):\n return reverse_lazy(\n 'income_and_expense:loan',\n args=[self.kwargs['year'], self.kwargs['month']]\n )\n\n\n@login_required\ndef index(request):\n \"\"\"トップページ用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n\n Returns\n -------\n HttpResponseRedirect\n HttpResponseRedirectオブジェクト\n \"\"\"\n\n current_time = timezone.now()\n\n # incomeビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:income',\n args=(current_time.year, current_time.month)\n )\n )\n\n@login_required\ndef move_another_page(request):\n \"\"\"別画面移動用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n\n Returns\n -------\n HttpResponseRedirect\n HttpResponseRedirectオブジェクト\n \"\"\"\n\n # 適切なビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n request.GET.get(\"path_name\"),\n args=(request.GET.get(\"year\"), request.GET.get(\"month\"))\n )\n )\n\n@login_required\ndef income(request, year, month):\n \"\"\"income用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 先月の代表日\n last_month_date = first_date - relativedelta(months=1)\n\n # 先月までの口座残高を取得\n last_mon_balance = get_balance(\n last_month_date.year, last_month_date.month\n )\n\n # 今月の収入リストを取得\n this_month_incs = Income.objects.order_by(\n 'method__account__user', 'method'\n ).filter(pay_date__gte=first_date, pay_date__lte=last_date)\n\n # 今月の収入の合計を取得\n inc_sum = (last_mon_balance\n + (this_month_incs.aggregate(Sum('amount'))['amount__sum'] or 0))\n\n return render(request, 'income_and_expense/income.html', {\n 'path_name': const_data.const.PATH_NAME_INCOME,\n 'this_year': year,\n 'this_mon': month,\n 'incs': this_month_incs,\n 'last_mon_balance': last_mon_balance,\n 'inc_sum': inc_sum,\n })\n\n@login_required\ndef add_default_incs(request, year, month):\n \"\"\"add_default_incs用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponseRedirect\n HttpResponseRedirectオブジェクト\n \"\"\"\n\n # デフォルトの収入から収入を追加\n if can_add_default_inex(year, month):\n if add_incs_from_default(year, month) > 0:\n messages.success(request, \"成功: デフォルト収入が追加されました。\")\n else:\n messages.error(request, \"失敗: 追加できるデフォルト収入が存在しませんでした。\")\n else:\n messages.error(request, \"失敗: 過去にはデフォルト収入を追加できません。\")\n\n # incomeビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:income',\n args=(year, month)\n )\n )\n\n@login_required\ndef expense(request, year, month):\n \"\"\"expense用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 先月の代表日\n last_month_date = first_date - relativedelta(months=1)\n\n # 先月の口座残高を取得\n last_mon_balance = get_balance(\n last_month_date.year, last_month_date.month\n )\n\n # 今月の支出リストを取得\n this_month_exps = Expense.objects.order_by(\n 'method__account__user', 'method'\n ).filter(pay_date__gte=first_date, pay_date__lte=last_date)\n\n # 今月の支出の合計を取得\n exp_sum = this_month_exps.aggregate(Sum('amount'))['amount__sum'] or 0\n\n # 今月の残高を取得\n balance = get_balance(year, month)\n\n return render(request, 'income_and_expense/expense.html', {\n 'path_name': const_data.const.PATH_NAME_EXPENSE,\n 'this_year': year,\n 'this_mon': month,\n 'exps': this_month_exps,\n 'exp_sum': exp_sum,\n 'balance': balance,\n })\n\n@login_required\ndef add_default_exps(request, year, month):\n \"\"\"add_default_exps用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponseRedirect\n HttpResponseRedirectオブジェクト\n \"\"\"\n\n # デフォルトの支出とローンから支出を追加\n if can_add_default_inex(year, month):\n if add_exps_from_default_and_loan(year, month) > 0:\n messages.success(request, \"成功: デフォルト支出が追加されました。\")\n else:\n messages.error(request, \"失敗: 追加できるデフォルト支出が存在しませんでした。\")\n else:\n messages.error(request, \"失敗: 過去にはデフォルト支出を追加できません。\")\n\n # expsenseビューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:expense',\n args=(year, month)\n )\n )\n\n@login_required\ndef balance(request, year, month):\n \"\"\"balanceページ用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n # 各口座の実残高を取得\n accounts = Account.objects.all().order_by('user') # 全口座\n balances = [] # 各口座の実残高\n balance_sum = 0 # 口座の実残高の合計\n for account in accounts:\n balances.append({\n 'account': account, 'balance': \"¥{:,}\".format(account.balance)\n })\n balance_sum += account.balance\n\n # DB上の残高（完了分）を取得\n balance_on_db = get_balance_done(year, month)\n\n # 口座の実残高とDB上残高（完了分）の誤差を取得\n balance_diff = balance_sum - balance_on_db\n\n return render(request, 'income_and_expense/balance.html', {\n 'path_name': const_data.const.PATH_NAME_BALANCE,\n 'this_year': year,\n 'this_mon': month,\n 'accounts': accounts,\n 'balance_sum': balance_sum,\n 'balance_on_db': balance_on_db,\n 'balance_diff': balance_diff,\n })\n\n@login_required\ndef account_require(request, year, month):\n \"\"\"account_requireページ用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 今月の支出リストを取得\n this_month_exps = Expense.objects.filter(\n pay_date__gte=first_date, pay_date__lte=last_date\n )\n\n # 各口座の必要金額を取得\n accounts = Account.objects.all().order_by('user') # 全口座\n account_requires = [] # 各口座の必要金額\n require_sum = 0 # 必要金額の合計値\n insufficient_sum = 0 # 不足額の合計値\n is_insufficient = False # 口座残高が不足しているかどうか\n insufficient_amount = 0 # 各口座の不足額\n for account in accounts:\n require = this_month_exps.filter(\n method__account=account, done=False\n ).aggregate(Sum('amount'))['amount__sum']\n if require is None:\n require = 0\n\n require_sum += require\n\n if account.balance < require:\n is_insufficient = True\n insufficient_amount = require - account.balance\n else:\n is_insufficient = False\n insufficient_amount = 0\n\n insufficient_sum += insufficient_amount\n\n account_require = {\n 'account': account, 'require': \"¥{:,}\".format(require),\n 'is_insufficient': is_insufficient,\n 'insufficient_amount': \"¥{:,}\".format(insufficient_amount)\n }\n account_requires.append(account_require)\n\n return render(request, 'income_and_expense/account_require.html', {\n 'path_name': const_data.const.PATH_NAME_ACCOUNT_REQUIRE,\n 'this_year': year,\n 'this_mon': month,\n 'account_requires': account_requires,\n 'require_sum': \"¥{:,}\".format(require_sum),\n 'insufficient_sum': \"¥{:,}\".format(insufficient_sum),\n })\n\n@login_required\ndef method_require(request, year, month):\n \"\"\"method_requireページ用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 今月の支出リストを取得\n this_month_exps = Expense.objects.filter(\n pay_date__gte=first_date, pay_date__lte=last_date\n )\n\n # 支払方法別の必要金額を取得\n # 全支払方法\n methods = Method.objects.all().order_by(\n 'account__user', 'account__bank'\n )\n method_requires = [] # 支払方法別の必要金額\n require_sum = 0 # 必要金額の合計値\n for method in methods:\n require = this_month_exps.filter(\n method=method, done=False\n ).aggregate(Sum('amount'))['amount__sum']\n if require is None:\n require = 0\n\n require_sum += require\n\n method_require = {\n 'method': method, 'require': \"¥{:,}\".format(require),\n }\n method_requires.append(method_require)\n\n return render(request, 'income_and_expense/method_require.html', {\n 'path_name': const_data.const.PATH_NAME_METHOD_REQUIRE,\n 'this_year': year,\n 'this_mon': month,\n 'method_requires': method_requires,\n 'require_sum': \"¥{:,}\".format(require_sum),\n })\n\n\n@login_required\ndef method_done(request, year, month, pk):\n \"\"\"method_doneページ用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n pk : int\n 支払方法のpk\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n # 会計開始日と終了日を取得\n first_date = datetime.date(year, month, 1)\n last_date = (\n first_date + relativedelta(months=1) - datetime.timedelta(days=1)\n )\n\n # 該当の支払方法の支出をすべて支払済に変更\n target_exps = Expense.objects.filter(method__pk=pk,\n pay_date__gte=first_date, pay_date__lte=last_date\n )\n for target_exp in target_exps:\n target_exp.done = True\n target_exp.undecided = False\n target_exp.save()\n\n messages.success(request, \"成功: 支払済一括登録されました。\")\n\n # method_require���ューへリダイレクト\n return HttpResponseRedirect(\n reverse(\n 'income_and_expense:method_require',\n args=(year, month)\n )\n )\n\n@login_required\ndef loan(request, year, month):\n \"\"\"loanページ用のビュー関数。\n\n Parameters\n ----------\n request : HttpRequest\n HttpRequestオブジェクト\n year : int\n 会計年\n month : int\n 会計月\n\n Returns\n -------\n HttpResponse\n HttpResponseオブジェクト\n \"\"\"\n\n loans_and_completes = [] # 各ローンと終了しているかどうか\n\n # ローン一覧を取得\n loans = Loan.objects.all().order_by('method') # 全ローン\n\n for loan in loans:\n loan_and_complete = {}\n loan_and_complete['loan'] = loan\n\n is_over_year = year > loan.last_year\n is_same_year_and_over_month = (\n (year == loan.last_year) and (month > loan.last_month)\n )\n loan_and_complete['complete'] = (\n is_over_year or is_same_year_and_over_month\n )\n\n loans_and_completes.append(loan_and_complete)\n\n return render(request, 'income_and_expense/loan.html', {\n 'path_name': const_data.const.PATH_NAME_LOAN,\n 'this_year': year,\n 'this_mon': month,\n 'loans_and_completes': loans_and_completes,\n })", "repo_name": "anndddooh/income_and_expense", "sub_path": "income_and_expense/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 30639, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.utils.timezone.now", "line_number": 39, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "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.date", "line_number": 68, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 100, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 102, "usage_type": "call"}, {"api_name": "models.DefaultIncomeMonth.objects.filter", "line_number": 108, "usage_type": "call"}, {"api_name": "models.DefaultIncomeMonth.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.DefaultIncomeMonth", "line_number": 108, "usage_type": "name"}, {"api_name": "models.Income.objects.filter", "line_number": 109, "usage_type": "call"}, {"api_name": "models.Income.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.Income", "line_number": 109, "usage_type": "name"}, {"api_name": "models.Income", "line_number": 124, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 151, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 153, "usage_type": "call"}, {"api_name": "models.DefaultExpenseMonth.objects.filter", "line_number": 159, "usage_type": "call"}, {"api_name": "models.DefaultExpenseMonth.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.DefaultExpenseMonth", "line_number": 159, "usage_type": "name"}, {"api_name": "models.Expense.objects.filter", "line_number": 160, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 160, "usage_type": "name"}, {"api_name": "models.Expense", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 177, "usage_type": "call"}, {"api_name": "models.Loan.objects.filter", "line_number": 184, "usage_type": "call"}, {"api_name": "models.Loan.objects", "line_number": 184, "usage_type": "attribute"}, {"api_name": "models.Loan", "line_number": 184, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 185, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 186, "usage_type": "call"}, {"api_name": "models.Expense", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 230, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 232, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 232, "usage_type": "call"}, {"api_name": "models.Income.objects.filter", "line_number": 236, "usage_type": "call"}, {"api_name": "models.Income.objects", "line_number": 236, "usage_type": "attribute"}, {"api_name": "models.Income", "line_number": 236, "usage_type": "name"}, {"api_name": "models.Expense.objects.filter", "line_number": 239, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 239, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 239, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 246, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 252, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 276, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 278, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 278, "usage_type": "call"}, {"api_name": "models.Income.objects.filter", "line_number": 282, "usage_type": "call"}, {"api_name": "models.Income.objects", "line_number": 282, "usage_type": "attribute"}, {"api_name": "models.Income", "line_number": 282, "usage_type": "name"}, {"api_name": "models.Expense.objects.filter", "line_number": 285, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 285, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 285, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 291, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 296, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 306, "usage_type": "name"}, {"api_name": "forms.LoginForm", "line_number": 307, "usage_type": "name"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 311, "usage_type": "name"}, {"api_name": "bootstrap_modal_forms.generic.BSModalCreateView", "line_number": 315, "usage_type": "name"}, {"api_name": "forms.IncomeForm", "line_number": 317, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 321, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalUpdateView", "line_number": 327, "usage_type": "name"}, {"api_name": "models.Income", "line_number": 328, "usage_type": "name"}, {"api_name": "forms.IncomeForm", "line_number": 330, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 335, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 335, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 340, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 341, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 350, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalDeleteView", "line_number": 356, "usage_type": "name"}, {"api_name": "models.Income", "line_number": 357, "usage_type": "name"}, {"api_name": "forms.IncomeForm", "line_number": 359, "usage_type": "name"}, {"api_name": "models.Income.objects.get", "line_number": 362, "usage_type": "call"}, {"api_name": "models.Income.objects", "line_number": 362, "usage_type": "attribute"}, {"api_name": "models.Income", "line_number": 362, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 365, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 365, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 370, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 371, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 377, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 377, "usage_type": "name"}, {"api_name": "models.Income.objects.get", "line_number": 379, "usage_type": "call"}, {"api_name": "models.Income.objects", "line_number": 379, "usage_type": "attribute"}, {"api_name": "models.Income", "line_number": 379, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 384, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalCreateView", "line_number": 390, "usage_type": "name"}, {"api_name": "forms.ExpenseForm", "line_number": 392, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 398, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 398, "usage_type": "attribute"}, {"api_name": "models.TemplateExpense.objects.all", "line_number": 399, "usage_type": "call"}, {"api_name": "models.TemplateExpense.objects", "line_number": 399, "usage_type": "attribute"}, {"api_name": "models.TemplateExpense", "line_number": 399, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 421, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 425, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 427, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 440, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalUpdateView", "line_number": 446, "usage_type": "name"}, {"api_name": "models.Expense", "line_number": 447, "usage_type": "name"}, {"api_name": "forms.ExpenseForm", "line_number": 449, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 454, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 454, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 459, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 460, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 469, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalDeleteView", "line_number": 475, "usage_type": "name"}, {"api_name": "models.Expense", "line_number": 476, "usage_type": "name"}, {"api_name": "forms.ExpenseForm", "line_number": 478, "usage_type": "name"}, {"api_name": "models.Expense.objects.get", "line_number": 481, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 481, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 481, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 484, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 484, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 489, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 490, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 496, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 496, "usage_type": "name"}, {"api_name": "models.Expense.objects.get", "line_number": 498, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 498, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 498, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 503, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalUpdateView", "line_number": 509, "usage_type": "name"}, {"api_name": "models.Account", "line_number": 510, "usage_type": "name"}, {"api_name": "forms.BalanceForm", "line_number": 512, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 516, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalCreateView", "line_number": 522, "usage_type": "name"}, {"api_name": "forms.LoanForm", "line_number": 524, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 528, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalUpdateView", "line_number": 534, "usage_type": "name"}, {"api_name": "models.Loan", "line_number": 535, "usage_type": "name"}, {"api_name": "forms.LoanForm", "line_number": 537, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 541, "usage_type": "call"}, {"api_name": "bootstrap_modal_forms.generic.BSModalDeleteView", "line_number": 547, "usage_type": "name"}, {"api_name": "models.Loan", "line_number": 548, "usage_type": "name"}, {"api_name": "forms.LoanForm", "line_number": 550, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 553, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 553, "usage_type": "name"}, {"api_name": "models.Loan.objects.get", "line_number": 555, "usage_type": "call"}, {"api_name": "models.Loan.objects", "line_number": 555, "usage_type": "attribute"}, {"api_name": "models.Loan", "line_number": 555, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 560, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 581, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 581, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 584, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 585, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 566, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 607, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 608, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 591, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 634, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 636, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 636, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 640, "usage_type": "call"}, {"api_name": "models.Income.objects.order_by", "line_number": 648, "usage_type": "call"}, {"api_name": "models.Income.objects", "line_number": 648, "usage_type": "attribute"}, {"api_name": "models.Income", "line_number": 648, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 654, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 656, "usage_type": "call"}, {"api_name": "const.const_data.const", "line_number": 657, "usage_type": "attribute"}, {"api_name": "const.const_data", "line_number": 657, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 614, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 687, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 687, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 689, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 689, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 691, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 691, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 694, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 695, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 665, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 721, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 723, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 723, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 727, "usage_type": "call"}, {"api_name": "models.Expense.objects.order_by", "line_number": 735, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 735, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 735, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 740, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 745, "usage_type": "call"}, {"api_name": "const.const_data.const", "line_number": 746, "usage_type": "attribute"}, {"api_name": "const.const_data", "line_number": 746, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 701, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 776, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 776, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 778, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 778, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 780, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 780, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 783, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 784, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 754, "usage_type": "name"}, {"api_name": "models.Account.objects.all", "line_number": 810, "usage_type": "call"}, {"api_name": "models.Account.objects", "line_number": 810, "usage_type": "attribute"}, {"api_name": "models.Account", "line_number": 810, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 825, "usage_type": "call"}, {"api_name": "const.const_data.const", "line_number": 826, "usage_type": "attribute"}, {"api_name": "const.const_data", "line_number": 826, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 790, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 855, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 857, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 857, "usage_type": "call"}, {"api_name": "models.Expense.objects.filter", "line_number": 861, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 861, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 861, "usage_type": "name"}, {"api_name": "models.Account.objects.all", "line_number": 866, "usage_type": "call"}, {"api_name": "models.Account.objects", "line_number": 866, "usage_type": "attribute"}, {"api_name": "models.Account", "line_number": 866, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 875, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 897, "usage_type": "call"}, {"api_name": "const.const_data.const", "line_number": 898, "usage_type": "attribute"}, {"api_name": "const.const_data", "line_number": 898, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 835, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 926, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 928, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 928, "usage_type": "call"}, {"api_name": "models.Expense.objects.filter", "line_number": 932, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 932, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 932, "usage_type": "name"}, {"api_name": "models.Method.objects.all", "line_number": 938, "usage_type": "call"}, {"api_name": "models.Method.objects", "line_number": 938, "usage_type": "attribute"}, {"api_name": "models.Method", "line_number": 938, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 946, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 957, "usage_type": "call"}, {"api_name": "const.const_data.const", "line_number": 958, "usage_type": "attribute"}, {"api_name": "const.const_data", "line_number": 958, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 906, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 988, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 990, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 990, "usage_type": "call"}, {"api_name": "models.Expense.objects.filter", "line_number": 994, "usage_type": "call"}, {"api_name": "models.Expense.objects", "line_number": 994, "usage_type": "attribute"}, {"api_name": "models.Expense", "line_number": 994, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 1002, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 1002, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1005, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 1006, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 966, "usage_type": "name"}, {"api_name": "models.Loan.objects.all", "line_number": 1034, "usage_type": "call"}, {"api_name": "models.Loan.objects", "line_number": 1034, "usage_type": "attribute"}, {"api_name": "models.Loan", "line_number": 1034, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 1050, "usage_type": "call"}, {"api_name": "const.const_data.const", "line_number": 1051, "usage_type": "attribute"}, {"api_name": "const.const_data", "line_number": 1051, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 1012, "usage_type": "name"}]} +{"seq_id": "916112075", "text": "\"\"\"\nLoad training and train data from image files\n\"\"\"\nimport os\nimport numpy as np\nfrom skimage.io import imsave, imread\n\ndb_path = 'db/'\nimg_height = 480\nimg_width = 640\n\ndef create_db(roi=None):\n \"\"\"\n Create db files from raw images\n\n Parameters:\n -roi: region of interest [y,height,x,width]\n \"\"\"\n if roi is not None:\n roi_y = roi[0]\n roi_height = roi[1]\n roi_x = roi[2]\n roi_width = roi[3]\n else:\n roi_y = 0\n roi_height = img_height\n roi_x = 0\n roi_width = img_width\n\n subjects = [name for name in os.listdir(db_path) if os.path.isdir(os.path.join(db_path,name))]\n\n num_img = 0\n db_structure = []\n for subject in subjects:\n subject_path = os.path.join(db_path, subject)\n num_subject_files = len([fname for fname in os.listdir(subject_path) if os.path.isfile(os.path.join(subject_path, fname))])\n num_img += num_subject_files\n db_structure.append([subject,num_subject_files])\n\n imgs = np.ndarray((num_img, roi_height, roi_width), dtype=np.uint8)\n\n idx = 0\n print(\"Loading image...\")\n for subject in subjects:\n subject_path = os.path.join(db_path, subject)\n images = os.listdir(subject_path)\n for image_fname in images:\n if image_fname.endswith('.png'):\n img = imread(os.path.join(subject_path, image_fname), as_grey=True)\n if roi is not None:\n img = np.array([img[roi_y:roi_y+roi_height,roi_x:roi_x+roi_width]])\n else:\n img = np.array([img])\n imgs[idx] = img\n\n if idx % 100 == 0:\n print('Completed {0}/{1} images'.format(idx, num_img))\n\n idx += 1\n\n print(\"Loading complete.\")\n\n np.save('img_db.npy',imgs)\n\n print(\"Images saved to img_db.npy\")\n\ndef load_db():\n try:\n return np.load('img_db.npy')\n except:\n return None\n", "repo_name": "mohikhsan/ultrasound-dl", "sub_path": "src/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 1960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "37305525816", "text": "from email.headerregistry import Address\nfrom brownie import accounts, config,chain, DataSetFactory,DataSet, DHN\nimport scripts.deploy as deployer\n\n#Test the subscription to two DataSets\n#See the changes in:\n # variables (subCount, contract balance, subBalance), \n # mappings(addressToSub), \n # arrays of structs (deposits, subscribres)\n\ndef testCreateDS():\n dec_fit = 10**18\n\n #Get the DataSetFactory.sol instance after deployment and the account used\n (DSF,DHN)=deployer.deploy()\n\n #Define accounts\n dohrnii_account = accounts[0] #mints the DHN tokens\n ds_creator_account1 = accounts[1] #creates a nem Data Set callled \"Tetris\"\n ds_creator_account2 = accounts[2] #creates a nem Data Set callled \"Desserts\"\n ds_subscriber_account1 = accounts[3] #will subscribe to the \"Tetris\" dataset with a 1s sub time\n ds_subscriber_account2 = accounts[4] #will also subscribe to the \"Tetris\" dataset with a 1s sub time\n ds_subscriber_account3 = accounts[5] #will subscribe to the \"Desserts\" dataset with a 1day sub time\n ds_subscriber_account4 = accounts[6] #will also subscribe to the \"Desserts\" dataset with a 30 days sub time\n\n #Fund accounts\n DHN.transfer(ds_creator_account1, 30*dec_fit, {\"from\": dohrnii_account}) #fund the creator\n DHN.transfer(ds_creator_account2, 30*dec_fit, {\"from\": dohrnii_account}) #fund the creator\n DHN.transfer(ds_subscriber_account1, 30*dec_fit, {\"from\": dohrnii_account}) #fund sub1\n DHN.transfer(ds_subscriber_account2, 30*dec_fit, {\"from\": dohrnii_account}) #fund sub2\n DHN.transfer(ds_subscriber_account3, 30*dec_fit, {\"from\": dohrnii_account}) #fund sub3\n DHN.transfer(ds_subscriber_account4, 30*dec_fit, {\"from\": dohrnii_account}) #fund sub4 \n\n #Create a DS and instantiate it\n deployer.createDS(dec_fit, DHN, DSF, ds_creator_account1,\"Tetris\", \"https://ipfs.io/ipfs/Qme7ss3ARVgxv6rXqVPiikMJ8u2NLgmgszg13pYrDKEoiu\",\n \"Games\",\"Tetris statistics and data\", 10*dec_fit, 3600, 2*dec_fit)\n\n DS_instance1 = deployer.getDSbyName(dec_fit, DSF, ds_subscriber_account1, \"Tetris\")\n \n #Create a DS and instantiate it\n deployer.createDS(dec_fit, DHN, DSF, ds_creator_account2,\"Desserts\", \"https://ipfs.io/ipfs/Qme7ss3ARVgxv6rXqVPiikMJ8u2NLgmgszg13pYrDKEoiu\",\n \"Food\",\"Some dessert recipes\", 5*dec_fit, 3600, 2*dec_fit)\n \n DS_instance2 = deployer.getDSbyName(dec_fit, DSF, ds_subscriber_account2, \"Desserts\")\n\n #Sub1\n deployer.subToDS(dec_fit, DHN, DSF, ds_subscriber_account1, \"Tetris\", 0) \n #Sub2\n deployer.subToDS(dec_fit, DHN, DSF, ds_subscriber_account2, \"Tetris\", 0)\n #Sub3\n deployer.subToDS(dec_fit, DHN, DSF, ds_subscriber_account3, \"Desserts\", 1) \n #Sub4\n deployer.subToDS(dec_fit, DHN, DSF, ds_subscriber_account4, \"Desserts\", 2)\n\n#Assertion: DS creation alterations\n \n assert ((20+10*2)*dec_fit, 2) == (DHN.balanceOf(DS_instance1),#contract balance changes because of 2 subs\n DS_instance1.subCount())#subcount increases by 2\n \n assert ((20+2*5)*dec_fit, 2) == (DHN.balanceOf(DS_instance2),#contract balance changes because of 2 subs\n DS_instance2.subCount())#subcount increases by 2\n \n#Assertion: Mapping and Subscriber struct\n\n #Subscribed to \"Tetris\"\n info1 = DS_instance1.addressToSub(ds_subscriber_account1)\n #Price paid, subscription time, Is this person currently subbed?\n assert (10*dec_fit, 1, True) == (info1[0], info1[1], info1[3])\n\n #Subscribed to \"Tetris\"\n info2 = DS_instance1.addressToSub(ds_subscriber_account2)\n #Price paid, subscription time, Is this person currently subbed?\n assert (10*dec_fit, 1, True) == (info2[0], info2[1], info2[3])\n\n #Not subscribed to \"Tetris\"\n info3 = DS_instance1.addressToSub(ds_subscriber_account3)\n #Price paid, subscription time, Is this person currently subbed?\n assert (0, 0, False) == (info3[0], info3[1], info3[3])\n\n #Subscribed to \"Desserts\"\n info4 = DS_instance2.addressToSub(ds_subscriber_account3)\n #Price paid, subscription time, Is this person currently subbed?\n assert (5*dec_fit, 24*3600, True) == (info4[0], info4[1], info4[3])\n\n #Subscribed to \"Desserts\"\n info5 = DS_instance2.addressToSub(ds_subscriber_account4)\n #Price paid, subscription time, Is this person currently subbed?\n assert (5*dec_fit, 30*24*3600, True) == (info5[0], info5[1], info5[3])\n\n #Not subscribed to \"Desserts\"\n info6 = DS_instance2.addressToSub(ds_subscriber_account1)\n #Price paid, subscription time, Is this person currently subbed?\n assert (0, 0, False) == (info6[0], info6[1], info6[3])\n\n#Assertion: \"deposits\" struct array\n\n #1st Subscriber deposit to \"Tetris\"\n info7 = DS_instance1.deposits(0)\n #Price paid, subscription time, Is this person currently subbed?\n assert (ds_subscriber_account1, 10*dec_fit) == (info7[0], info7[1])\n\n #2nd Subscriber deposit to \"Desserts\"\n info8 = DS_instance2.deposits(1)\n #Price paid, subscription time, Is this person currently subbed?\n assert (ds_subscriber_account4, 5*dec_fit) == (info8[0], info8[1])\n\n#Assertion: \"subscribers\" address array\n\n #1st Subscriber deposit to \"Tetris\"\n info9 = DS_instance1.subscribers(0)\n #Price paid, subscription time, Is this person currently subbed?\n assert (ds_subscriber_account1) == (info9)\n\n #2nd Subscriber deposit to \"Desserts\"\n info10 = DS_instance2.subscribers(1)\n #Price paid, subscription time, Is this person currently subbed?\n assert (ds_subscriber_account4) == (info10)\n\n\n", "repo_name": "SayNode/Subscripiton-contract", "sub_path": "tests/test_SubToDS.py", "file_name": "test_SubToDS.py", "file_ext": "py", "file_size_in_byte": 5614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "brownie.DHN", "line_number": 15, "usage_type": "name"}, {"api_name": "scripts.deploy.deploy", "line_number": 15, "usage_type": "call"}, {"api_name": "scripts.deploy", "line_number": 15, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 18, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 19, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 20, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 21, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 22, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 23, "usage_type": "name"}, {"api_name": "brownie.accounts", "line_number": 24, "usage_type": "name"}, {"api_name": "brownie.DHN.transfer", "line_number": 27, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 27, "usage_type": "name"}, {"api_name": "brownie.DHN.transfer", "line_number": 28, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 28, "usage_type": "name"}, {"api_name": "brownie.DHN.transfer", "line_number": 29, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 29, "usage_type": "name"}, {"api_name": "brownie.DHN.transfer", "line_number": 30, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 30, "usage_type": "name"}, {"api_name": "brownie.DHN.transfer", "line_number": 31, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 31, "usage_type": "name"}, {"api_name": "brownie.DHN.transfer", "line_number": 32, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 32, "usage_type": "name"}, {"api_name": "scripts.deploy.createDS", "line_number": 35, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 35, "usage_type": "argument"}, {"api_name": "scripts.deploy", "line_number": 35, "usage_type": "name"}, {"api_name": "scripts.deploy.getDSbyName", "line_number": 38, "usage_type": "call"}, {"api_name": "scripts.deploy", "line_number": 38, "usage_type": "name"}, {"api_name": "scripts.deploy.createDS", "line_number": 41, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 41, "usage_type": "argument"}, {"api_name": "scripts.deploy", "line_number": 41, "usage_type": "name"}, {"api_name": "scripts.deploy.getDSbyName", "line_number": 44, "usage_type": "call"}, {"api_name": "scripts.deploy", "line_number": 44, "usage_type": "name"}, {"api_name": "scripts.deploy.subToDS", "line_number": 47, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 47, "usage_type": "argument"}, {"api_name": "scripts.deploy", "line_number": 47, "usage_type": "name"}, {"api_name": "scripts.deploy.subToDS", "line_number": 49, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 49, "usage_type": "argument"}, {"api_name": "scripts.deploy", "line_number": 49, "usage_type": "name"}, {"api_name": "scripts.deploy.subToDS", "line_number": 51, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 51, "usage_type": "argument"}, {"api_name": "scripts.deploy", "line_number": 51, "usage_type": "name"}, {"api_name": "scripts.deploy.subToDS", "line_number": 53, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 53, "usage_type": "argument"}, {"api_name": "scripts.deploy", "line_number": 53, "usage_type": "name"}, {"api_name": "brownie.DHN.balanceOf", "line_number": 57, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 57, "usage_type": "name"}, {"api_name": "brownie.DHN.balanceOf", "line_number": 60, "usage_type": "call"}, {"api_name": "brownie.DHN", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "6921903350", "text": "\"\"\"\nStore standard root for user page navigation\n\"\"\"\n\nfrom flask import Blueprint, render_template, request\nfrom .models import Track, Score\n\n# define blueprint for flask application\nplaylist = Blueprint('playlist', __name__)\n\n@playlist.route('/create_playlist')\ndef home():\n # render our create_playlist.html in template\n return render_template(\"create_playlist.html\")\n\n@playlist.route('/create_playlist_top', methods=['GET'])\ndef create_playlist_top():\n top_num = request.args.get(\"top\")\n tracks = Track.query.order_by(Track.score.desc()).limit(top_num)\n return render_template(\"create_playlist.html\", tracks=tracks, selection=True)\n\n@playlist.route('/create_playlist_bottom', methods=['GET'])\ndef create_playlist_bottom():\n bottom_num = request.args.get(\"bottom\")\n tracks = Track.query.order_by(Track.score).limit(bottom_num)\n return render_template(\"create_playlist.html\", tracks=tracks, selection=True)\n\n\n\n\n", "repo_name": "kluu22/music-catalog", "sub_path": "website/playlist.py", "file_name": "playlist.py", "file_ext": "py", "file_size_in_byte": 936, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Track.query.order_by", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Track.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Track", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Track.score.desc", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Track.score", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Track.query.order_by", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Track.query", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.Track", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Track.score", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "37949116552", "text": "\"\"\"empty message\n\nRevision ID: 556834bc19db\nRevises: 3765da66361a\nCreate Date: 2022-02-28 18:16:23.839211\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"556834bc19db\"\ndown_revision = \"3765da66361a\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column(\"units\", sa.Column(\"tenant\", sa.String(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column(\"units\", \"tenant\")\n # ### end Alembic commands ###\n", "repo_name": "epam/badgerdoc", "sub_path": "scheduler/alembic/versions/556834bc19db_.py", "file_name": "556834bc19db_.py", "file_ext": "py", "file_size_in_byte": 664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "3", "api": [{"api_name": "alembic.op.add_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "74819728081", "text": "import logging,os\nimport atexit\n\nclass FileSeparatorHandler(logging.FileHandler):\n \"\"\"Custom FileHandler that writes a separator line after each log record.\"\"\"\n\n def emit(self, record):\n \"\"\"Emit a log record and write a separator line.\"\"\"\n super().emit(record)\n\nclass LoggingConfig:\n \"\"\"Logging configuration utility for setting up a logger with FileHandler and StreamHandler.\"\"\"\n\n def __init__(self, config={}):\n \"\"\"\n Initialize the LoggingConfig.\n :param log_level: The desired log level for the logger.\n \"\"\"\n self.file_path=config.get('file_path')\n self.log_level = config.get(\"log_level\")\n self.formatter=config.get('log_formatter')\n\n def configure_logger(self):\n \"\"\"\n Configure the logger with FileHandler and StreamHandler.\n :return: The configured logger object.\n \"\"\"\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(self.log_level)\n\n # Create a formatter\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - Line %(lineno)d - %(message)s')\n\n # Create a FileHandler with custom FileSeparatorHandler\n file_handler = FileSeparatorHandler(self.file_path)\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(formatter)\n\n # Create a StreamHandler to display logs on the console\n steam_handler = logging.StreamHandler()\n steam_handler.setLevel(logging.DEBUG)\n steam_handler.setFormatter(formatter)\n\n # Add the handlers to the logger\n self.logger.addHandler(file_handler)\n self.logger.addHandler(steam_handler)\n\n # Register atexit handler to write separator line on program exit\n atexit.register(self._write_separator_line)\n\n return self.logger\n\n def _write_separator_line(self):\n \"\"\"\n Write a separator line to the log file.\n This method is automatically called on program exit.\n \"\"\"\n separator = '-' * 150\n file_handler = next((handler for handler in self.logger.handlers if isinstance(handler, FileSeparatorHandler)),\n None)\n if file_handler:\n file_handler.stream.write(f\"{separator}\\n\")\n\n\nif __name__ == \"__main__\":\n # Create a LoggingConfig instance with log level INFO\n config = LoggingConfig(logging.INFO)\n\n # Configure the logger\n config.configure_logger()\n\n # Get the configured logger object\n logger = config.logger\n\n # Log some entries\n logger.info('Log entry 1')\n logger.info('Log entry 2')\n\n\n", "repo_name": "pawan-salve-199/data_quality_tool_", "sub_path": "resource/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 2592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.FileHandler", "line_number": 4, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 41, "usage_type": "attribute"}, {"api_name": "atexit.register", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 67, "usage_type": "attribute"}]} +{"seq_id": "38828286400", "text": "'''This module samples some data from a saved checkpoint'''\n'''\n o==+--\n | |\\ \\\n | | \\ \\ ____________________\n | \\ \\ \\ | |\n | \\ \\ \\ | +------------+ |\n | \\ \\ \\ | | (__) | |\n | \\ \\ \\| | (oo) | |\n | \\ \\ | | o\\ .\\/. | |\n | \\ \\| | | \\/ \\ | |\n /---\\ \\ | +------------+ |\n / \\ \\| |\n | | | |\n \\ / | |\n \\---/ | |\n | |\n --------------------------\n ( )\n --------------------------\n'''\n\nimport argparse\nimport os\nimport tensorflow as tf\nfrom prepare_data import CharData\nimport numpy as np\nimport pickle\nfrom tqdm import tqdm\n\n\ndef sample(sample_type, prediction, temperature=0.9):\n \n if sample_type==0:\n return np.argmax(prediction)\n else:\n sample_exp = np.exp(prediction) / temperature\n sample_reduce_mean = sample_exp / np.sum(sample_exp)\n prediction_real = np.random.choice(range(len(prediction)), 1, p=sample_reduce_mean)\n return prediction_real[0]\n\n\ndef get_meta_file_path(save_dir):\n '''Return the first .meta file in @param save_dit'''\n meta_file = ''\n for _f in os.listdir(save_dir):\n if _f[-5:] == '.meta':\n meta_file = os.path.join(save_dir, _f)\n break\n print(meta_file)\n return meta_file\n\n\ndef vectorize(text_to_vectorize, character_set):\n temp = np.zeros((1, len(text_to_vectorize), len(character_set)))\n for i, j in enumerate(text_to_vectorize):\n temp[0][i][character_set.index(j)] = 1\n return temp\n\n\ndef main():\n '''Run the script i guess'''\n #build the arguments parser\n parser = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--save-dir', type=str, default='saved-checkpoints',\n help='directory with the checkpoints to sample from')\n parser.add_argument('--n', type=int, default='100',\n help='number of character to sample')\n parser.add_argument('--timesteps', type=int, default=50,\n help='timesteps to unravel the graph')\n parser.add_argument('--sampling-type', type=int, default=1,\n help='sampling-type, 0-argmax, 1-exponential')\n parser.add_argument('--temperature', type=float, default=0.9,\n help='temperature for exponential sampling. between 0 & 1') \n args = vars(parser.parse_args())\n\n save_dir = args['save_dir']\n sample_size = args['n']\n timesteps = args['timesteps']\n sampling_type = args['sampling_type']\n temperature = args['temperature']\n\n checkpoint_file = tf.train.latest_checkpoint(save_dir)\n meta_file = get_meta_file_path(save_dir)\n seed_file = os.path.join(save_dir, 'seed.txt')\n seed_data = CharData(seed_file, 1, 10)\n character_set = seed_data.character_set\n all_text = seed_data.random_seed(timesteps)\n with tf.Session() as sess:\n saver = tf.train.import_meta_graph(meta_file)\n saver.restore(sess, checkpoint_file)\n input_ph = tf.get_default_graph().get_tensor_by_name('input_data:0')\n op_to_restore = tf.get_default_graph().get_tensor_by_name(\"output_layer:0\")\n # todo fix this weird progress bar\n with tqdm(total=sample_size) as pb:\n for i in range(sample_size):\n text_input = vectorize(all_text[-timesteps:], character_set)\n out_vec = sess.run(op_to_restore, feed_dict={input_ph:text_input})[0]\n sampled_output = sample(sampling_type, out_vec, temperature=0.9)\n all_text = all_text + character_set[sampled_output]\n pb.update(i)\n print(all_text)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "deeplaying/char_wgu", "sub_path": "sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 4062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.argmax", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 64, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "prepare_data.CharData", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 94, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "21189751964", "text": "import sqlite3\nfrom datetime import datetime, date\nfrom sqlite3 import Error\nimport os\n\ndef conexao_banco():\n dir = os.path.dirname(__file__)\n caminho = f\"{dir}/sistema_votacao.db\"\n con = None\n try:\n con = sqlite3.connect(caminho)\n return con\n except Error as error:\n print(error)\n\ndef inserir(insert):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(insert)\n con.commit()\n con.close()\n print(\"Inserido com sucesso\")\n except Error as error:\n print(error)\n\ndef atualizar(update):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(update)\n con.commit()\n con.close()\n print(\"Atualizado com sucesso\")\n except Error as error:\n print(error)\n\ndef deletar(delete):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(delete)\n con.commit()\n con.close()\n print(\"Removido com sucesso\")\n except Error as error:\n print(error)\n\ndef consultar(consultar):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(consultar)\n valores = cursor.fetchall()\n con.close()\n return valores\n except Error as error:\n print(error)\n\ndef consultar_cargos(consultar):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(consultar)\n dados = cursor.fetchall()\n dados = \" \".join(\"\".join(var) for var in dados)\n return dados\n except Error as error:\n print(error)\n\ndef consultar_candidatos(consultar):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(consultar)\n dados = cursor.fetchall()\n if str(dados) == \"[(None,)]\":\n pass\n else:\n dados = \" \".join(\"\".join(var) for var in dados)\n caracteres = '\"[]'\n subcaracter = \"'\"\n for i in range(len(caracteres)):\n dados = dados.replace(caracteres[i],\"\")\n dados = dados.replace(subcaracter, \"\")\n return dados\n except Error as error:\n print(error)\n\ndef consultar_cpf(consultar):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(consultar)\n valores = cursor.fetchall()\n #for i in valores:\n #print(i[0])\n con.close()\n return valores\n except Error as error:\n print(error)\n\ndef atualizar_data(update):\n try:\n con = conexao_banco()\n cursor = con.cursor()\n cursor.execute(update)\n con.commit()\n con.close()\n print(\"Atualizado com sucesso\")\n return True\n except Error as error:\n print(error)\n\n\n#data = datetime.today().date().strftime('%d-%m-%Y')\n#print(data)\n\n#query = f'SELECT data_inicio FROM eleicao WHERE data_inicio < \"{data}\";'\n#consultar_data(query)\n\n# query = 'INSERT INTO usuario (\"nome\", \"user_name\", \"senha\", \"tipo\", \"status\") VALUES (\"Elias de Oliveira Cacau\", \"EliasCacau\", \"123\", \"Usuário\", 0);'\n# query = 'INSERT INTO candidato (\"nome\", \"num_candidato\", \"votos\") VALUES (\"Elias de Oliveira Cacau\", \"4002\", 0);'\n# inserir(query)\n\n# set = f'UPDATE candidato SET votos=\"{votos}\" WHERE num_candidato LIKE 4002;'\n# atualizar(set)\n\n# delete = 'DELETE FROM usuario WHERE id=2;'\n# deletar(delete)\n\n\n#show = 'SELECT user_name, senha FROM usuario;'\n#consultar(query)", "repo_name": "EliasCacau/sistema-votacao", "sub_path": "banco_de_dados.py", "file_name": "banco_de_dados.py", "file_ext": "py", "file_size_in_byte": 3479, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 13, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 46, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 68, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 87, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 100, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 112, "usage_type": "name"}]} +{"seq_id": "23676772630", "text": "import cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom IPython.display import Image\r\n\r\n#split the image into the B,G,R components\r\nimg_NZ_bgr = cv2.imread(\"New_Zealand_Lake.jpg\",cv2.IMREAD_COLOR)\r\nb,g,r = cv2.split(img_NZ_bgr)\r\n\r\n#show the channels\r\nplt.figure(figsize=[20,5])\r\nplt.subplot(141);plt.imshow(r,cmap='gray');plt.title(\"Red Channel\");\r\nplt.subplot(142);plt.imshow(g,cmap='gray');plt.title(\"Green Channel\");\r\nplt.subplot(143);plt.imshow(b,cmap='gray');plt.title(\"Blue Channel\");\r\n\r\n#merge the individual channels into a BGR image\r\nimgMerged = cv2.merge((b,g,r))\r\n\r\n#show the merged output\r\nplt.subplot(144);plt.imshow(imgMerged[:,:,::-1]);plt.title(\"Merged Output\");\r\n\r\n#openCV stores color channels in a differnet order than most other applications (BGR vs RGB).\r\nimg_NZ_rgb = cv2.cvtColor(img_NZ_bgr, cv2.COLOR_BGR2RGB)\r\nimg_hsv = cv2.cvtColor(img_NZ_bgr, cv2.COLOR_BGR2HSV)\r\n\r\n#split the image into the H,S,V components\r\nh,s,v = cv2.split(img_hsv)\r\n\r\n#show the channels\r\nplt.figure(figsize=[20,5])\r\nplt.subplot(141);plt.imshow(h,cmap='gray');plt.title(\"H Channel\");\r\nplt.subplot(142);plt.imshow(s,cmap='gray');plt.title(\"S Channel\");\r\nplt.subplot(143);plt.imshow(v,cmap='gray');plt.title(\"V Channel\");\r\nplt.subplot(144);plt.imshow(img_NZ_rgb);plt.title(\"Original\");\r\n\r\n#increase hue by 10\r\nh_new = h+10\r\nimg_NZ_merged = cv2.merge((h_new,s,v))\r\nimg_NZ_rgb = cv2.cvtColor(img_NZ_merged, cv2.COLOR_HSV2RGB)\r\n\r\n#show the channels\r\nplt.figure(figsize=[20,5])\r\nplt.subplot(141);plt.imshow(h,cmap='gray');plt.title(\"H Channel\");\r\nplt.subplot(142);plt.imshow(s,cmap='gray');plt.title(\"S Channel\");\r\nplt.subplot(143);plt.imshow(v,cmap='gray');plt.title(\"V Channel\");\r\nplt.subplot(144);plt.imshow(img_NZ_rgb);plt.title(\"Modified\");\r\n\r\n#save the image\r\ncv2.imwrite(\"New_Zealand_Lake_SAVED.png\", img_NZ_bgr)\r\n\r\nImage(filename=\"New_Zealand_Lake_SAVED.png\")\r\n\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "mkw9665/OpenCV_colour_channels", "sub_path": "OpenCV_colour_channels/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "cv2.imread", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.COLOR_HSV2RGB", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 49, "usage_type": "call"}, {"api_name": "IPython.display.Image", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "72877229522", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('blogapp', '0010_auto_20160125_1717'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='post',\n name='live',\n field=models.BooleanField(default=True),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='post',\n name='created_on',\n field=models.DateTimeField(),\n ),\n ]\n", "repo_name": "gtlambert/my_blog", "sub_path": "blogapp/migrations/0011_auto_20160424_1442.py", "file_name": "0011_auto_20160424_1442.py", "file_ext": "py", "file_size_in_byte": 580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "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"}]} +{"seq_id": "74978662481", "text": "from django.contrib import admin\nfrom . import models\n\n# Register your models here.\n\nadmin.site.site_title = \"Hotel\"\nadmin.site.site_header = \"Hotel Admin\"\n\n@admin.register(models.TipoHabitacion)\nclass TipoHabitacionAdmin(admin.ModelAdmin):\n list_display = (\"nombre\", \"descripcion\")\n search_fields = (\"nombre\",)\n list_filter = (\"nombre\",)\n ordering = (\"nombre\",)\n\n\n@admin.register(models.Habitacion)\nclass HabitacionAdmin(admin.ModelAdmin):\n list_display = (\n \"tipo\",\n \"numero\",\n \"precio_x_dia\",\n \"disponible\",\n \"imagen\",\n )\n \n search_fields = (\"tipo__nombre\",\"numero\",)\n list_filter = (\"tipo__nombre\",)\n ordering = (\n \"tipo\",\n \"numero\",\n )\n\n@admin.register(models.Reserva)\nclass ReservaAdmin(admin.ModelAdmin):\n list_display = (\n \"cliente\",\n \"habitacion\",\n \"fecha_entrada\",\n \"fecha_salida\",\n \"precio_total\",\n )\n search_fields = (\"cliente__username\", \"habitacion__tipo__nombre\")\n ordering = (\"fecha_entrada\",)\n list_filter = (\"fecha_entrada\",)\n\n", "repo_name": "MiguelRizzi/proyecto_final_rizzi", "sub_path": "project/apps/hotel/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1077, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.contrib.admin.site", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 35, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "2442057612", "text": "#!/usr/bin/python\n\nimport sys\nimport json\nimport subprocess\nimport os\n\nfrom charmhelpers.fetch import (\n add_source,\n apt_install,\n apt_update,\n)\n\nfrom charmhelpers.core.hookenv import (\n Hooks,\n UnregisteredHookError,\n service_name,\n relation_set,\n relation_ids,\n log\n)\n\nfrom cinder_contexts import VNXSubordinateContext\nfrom charmhelpers.payload.execd import execd_preinstall\n\nPACKAGES = [\n 'sysfsutils'\n]\n\nhooks = Hooks()\n\ndef juju_log(msg):\n log('[cinder-vnx] %s' % msg)\n\n@hooks.hook('install')\ndef install():\n execd_preinstall()\n\n\n@hooks.hook('config-changed',\n 'upgrade-charm')\ndef upgrade_charm():\n for rid in relation_ids('storage-backend'):\n storage_backend(rid)\n\ndef config_get(attribute):\n cmd = [\n 'config-get',\n '--format',\n 'json']\n out = subprocess.check_output(cmd).strip()\n cfg = json.loads(out)\n\n try:\n return cfg[attribute]\n except KeyError:\n return None\n\ndef valid_source(source):\n try:\n return \\\n (source.startswith('https') or \\\n source.startswith('http') or \\\n source.startswith('ppa'))\n except Exception:\n juju_log('invalid source: %s' % source)\n return False\n\ndef valid_key(key):\n try:\n return (len(key) >= 8)\n except Exception:\n juju_log('invalid key (len < 8): %s' % key)\n return False\n\n\n@hooks.hook('storage-backend-relation-joined',\n 'storage-backend-relation-changed')\ndef storage_backend(rel_id=None):\n # REQUIRED: add navicli source and key\n navicli_source = config_get('navicli_source')\n navicli_key = config_get('navicli_source_key')\n juju_log('storage_backend: navicli_source=%s navicli_key=%s' % (navicli_source,\n navicli_key))\n if not valid_source(navicli_source) or not valid_key(navicli_key):\n raise\n # add_source(navicli_source, navicli_key)\n\n os.system('find /var/lib/juju -type d -name \"navicli_7.33.2.0.51-amd64.deb\" -exec sudo dpkg -i {} \\;')\n\n # update and install packages\n apt_update()\n dpkg_opts = [\n '--option', 'Dpkg::Options::=--force-confnew',\n '--option', 'Dpkg::Options::=--force-confdef',\n ]\n apt_install(packages=PACKAGES, options=dpkg_opts, fatal=True)\n relation_set(\n relation_id=rel_id,\n backend_name=service_name(),\n subordinate_configuration=json.dumps(VNXSubordinateContext()())\n )\n\n\nif __name__ == '__main__':\n try:\n hooks.execute(sys.argv)\n except UnregisteredHookError as e:\n juju_log('Unknown hook {} - skipping.'.format(e))\n", "repo_name": "alefnode/cinder-vnx-fc", "sub_path": "hooks/cinder_hooks.py", "file_name": "cinder_hooks.py", "file_ext": "py", "file_size_in_byte": 2650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "charmhelpers.core.hookenv.Hooks", "line_number": 30, "usage_type": "call"}, {"api_name": "charmhelpers.core.hookenv.log", "line_number": 33, "usage_type": "call"}, {"api_name": "charmhelpers.payload.execd.execd_preinstall", "line_number": 37, "usage_type": "call"}, {"api_name": "charmhelpers.core.hookenv.relation_ids", "line_number": 43, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 51, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "os.system", "line_number": 89, "usage_type": "call"}, {"api_name": "charmhelpers.fetch.apt_update", "line_number": 92, "usage_type": "call"}, {"api_name": "charmhelpers.fetch.apt_install", "line_number": 97, "usage_type": "call"}, {"api_name": "charmhelpers.core.hookenv.relation_set", "line_number": 98, "usage_type": "call"}, {"api_name": "charmhelpers.core.hookenv.service_name", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 101, "usage_type": "call"}, {"api_name": "cinder_contexts.VNXSubordinateContext", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 107, "usage_type": "attribute"}, {"api_name": "charmhelpers.core.hookenv.UnregisteredHookError", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "14674741594", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom skimage.measure import label, regionprops\r\nfrom skimage import color\r\n\r\ndef get_colors(elements):\r\n c = {\"blue\": 0,\"crimson\": 0,\"cobalt\": 0,\"cyan\": 0,\"green\": 0,\"lime\": 0,\"magenta\": 0,\"orange\": 0,\"red\" : 0,\"turquoise\": 0,\"violet\": 0,\"yellow\": 0,}\r\n for color in elements:\r\n if (0 <= color < 15 or 345 <= color <= 360):\r\n c['red'] += 1\r\n if (15 <= color < 45):\r\n c['orange'] += 1\r\n if (45 <= color < 75):\r\n c['yellow'] += 1\r\n if (75 <= color < 105):\r\n c['lime'] += 1\r\n if (105 <= color < 135):\r\n c['green'] += 1\r\n if (135 <= color < 165):\r\n c['turquoise'] += 1\r\n if (165 <= color < 195):\r\n c['cyan'] += 1\r\n if (195 <= color < 225):\r\n c['cobalt'] += 1\r\n if (225 <= color < 255):\r\n c['blue'] += 1\r\n if (255 <= color < 285):\r\n c['violet'] += 1\r\n if (285 <= color < 315):\r\n c['magenta'] += 1\r\n if (315 <= color < 345):\r\n c['crimson'] += 1 \r\n print(c)\r\n\r\nimage = plt.imread(\"balls_and_rects.png\")\r\nbinary = image.copy()[:, :, 0]\r\nbinary[binary > 0] = 1\r\n\r\nimage = color.rgb2hsv(image)[:, :, 0] * 360\r\n\r\nlabeled = label(binary)\r\nballs, rects = [], []\r\nprint(\"Number of all forms:\", np.max(labeled))\r\n\r\nfor region in regionprops(labeled):\r\n v = np.max(image[region.bbox[0]:region.bbox[2], region.bbox[1]:region.bbox[3]])\r\n if region.area == (region.image.shape[0] * region.image.shape[1]):\r\n rects.append(v)\r\n else:\r\n balls.append(v)\r\n\r\nprint(\"Circles:\", len(balls))\r\nget_colors(balls)\r\n\r\nprint(\"Rectangles:\", len(rects))\r\nget_colors(rects)\r\n\r\nplt.figure()\r\nplt.imshow(image)\r\nplt.show()\r\n", "repo_name": "losttrollsssss/Computer-vision", "sub_path": "forms_shades/form_shades.py", "file_name": "form_shades.py", "file_ext": "py", "file_size_in_byte": 1792, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "skimage.color", "line_number": 8, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 9, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 11, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 13, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 15, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 17, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 19, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 21, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 23, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 25, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 27, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 29, "usage_type": "name"}, {"api_name": "skimage.color", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "skimage.color.rgb2hsv", "line_number": 39, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 39, "usage_type": "name"}, {"api_name": "skimage.measure.label", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 43, "usage_type": "call"}, {"api_name": "skimage.measure.regionprops", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "34668814722", "text": "#test case1: -73.59 45.49 to -73.55 45.49 threshold = 0.5\n#test case2: -73.59 45.49 to -73.55 45.53 threshold = 0.5\n#test case3: -73.568 45.508 to -73.55 45.53 threshold = 0.5 (Supposed no path)\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nimport shapefile\nimport numpy as np\nfrom collections import defaultdict\nimport math\n\ngrid_size = 0.002\nx1 = np.arange(-73.590,-73.550,grid_size)\ny1 = np.arange(45.490,45.5301,grid_size)\nshape = shapefile.Reader(\"Shape/crime_dt.shp\",encoding='ISO-8859-1')\nshapeRecords = shape.shapeRecords()\nnum_seq = []\ngrid_map= []\n\nfor i in range(len(x1)):\n col = []\n for j in range(len(y1)):\n col.append(0)\n grid_map.append(col)\n\nx_coordinates=[]\ny_coordinates=[]\n\n#counting the density(crime rate) in each grid.\nfor k in range(len(shapeRecords)):\n x = float(shapeRecords[k].shape.__geo_interface__[\"coordinates\"][0])\n y = float(shapeRecords[k].shape.__geo_interface__[\"coordinates\"][1])\n x_coordinates.append(x)\n y_coordinates.append(y)\n x = int((x - (-73.590)) / grid_size)\n y = int((y - (45.490)) / grid_size)\n grid_map[y][x] +=1\n#grid_map.reverse()\n#display the grid number\n# for row in grid_map:\n# print(row)\n\n#storing all rate numbers in a list from the grid_map, and sort the list in descending order.\nfor i in range(len(grid_map)-1):\n for j in range(len(grid_map[i])-1):\n num_seq.append(grid_map[i][j])\nnum_seq = sorted(num_seq,reverse=True)\n\n# promote user to input the threshold, and also verify if the threshold is valid.\nvalid_threshold = False\nwhile (not valid_threshold):\n threshold = input(\"Please enter a threshold:\")\n if (float(threshold) <= 1) and (float(threshold) >= 0):\n valid_threshold = True\n\n# define the high crime block based on the top (1-threshold) crime rate numbers.\n\nindex = len(num_seq) - int(float(threshold) * len(num_seq)) - 1\nhigh_num = num_seq [index]\n\n# plot the grid using only 2 colors.\nif threshold == 0.0 :\n plt.hist2d(x_coordinates, y_coordinates, bins=[x1, y1], cmap=ListedColormap(['yellow']))\nelif threshold == 1.0:\n plt.hist2d(x_coordinates, y_coordinates, bins=[x1, y1], cmap=ListedColormap(['purple']))\nelse:\n plt.hist2d(x_coordinates, y_coordinates, bins=[x1, y1], cmap=ListedColormap(['purple', 'yellow']), vmin=0,vmax=2 * high_num)\n\n#total number of crimes in each grid;\nprint(\"Total crimes in each grid:\");\n\nfor i in range(len(grid_map)-1):\n print(grid_map[i])\n\n\n\n#display statics\nprint(\"Total number of crime:\",sum(num_seq))\nprint(\"Average:\",\"{:.3f}\".format(np.average(num_seq)))\nprint(\"Standard deviation:\",\"{:.3f}\".format(np.std(num_seq)))\nprint(\"High crime rate:\",high_num)\n#plt.show()\n\n\nfor i in range(len(grid_map)):\n for j in range(len(grid_map[i])):\n if grid_map[i][j] >= high_num:\n grid_map[i][j] = 1\n else:\n grid_map[i][j] = 0\n\n\ndef getNeighbours(point):\n l = []\n point_x = point[0]\n point_y = point[1]\n\n # up\n if point_y+1 <= len(grid_map)-1:\n if point_x == 0 or point_x == len(grid_map[0])-1:\n l.append((point_x,point_y+1))\n else:\n if not(grid_map[point_y][point_x] ==1 and grid_map[point_y][point_x-1] ==1):\n l.append((point_x,point_y+1))\n #down\n if point_y-1 >= 0:\n if point_x == 0 or point_x == len(grid_map[0])-1:\n l.append((point_x, point_y - 1))\n else:\n if not(grid_map[point_y-1][point_x] ==1 and grid_map[point_y-1][point_x-1] ==1):\n l.append((point_x,point_y-1))\n #left\n if point_x-1 >=0:\n if point_y == 0 or point_y == len(grid_map)-1:\n l.append((point_x-1,point_y))\n else:\n if not (grid_map[point_y][point_x-1] == 1 and grid_map[point_y-1][point_x-1] == 1):\n l.append((point_x-1,point_y))\n #right\n if point_x+1 <= len(grid_map[0])-1:\n if point_y == 0 or point_y == len(grid_map)-1:\n l.append((point_x+1,point_y))\n else:\n if not (grid_map[point_y][point_x] == 1 and grid_map[point_y-1][point_x] == 1):\n l.append((point_x + 1, point_y))\n #left-upper diagonal\n if point_x - 1 >=0 and point_y + 1 <=len(grid_map)-1:\n if grid_map[point_y][point_x-1] == 0:\n l.append((point_x-1,point_y+1))\n #right-upper diagonal\n if point_x + 1 <=len(grid_map[0])-1 and point_y+1<=len(grid_map)-1:\n if grid_map[point_y][point_x] == 0:\n l.append((point_x+1,point_y+1))\n #left-lower diagonal\n if point_x-1>=0 and point_y-1>=0:\n if grid_map[point_y-1][point_x-1] == 0:\n l.append((point_x-1,point_y-1))\n #right-lower diagonal\n if point_x+1 <= len(grid_map[0])-1 and point_y-1>=0:\n if grid_map[point_y-1][point_x] == 0:\n l.append((point_x+1,point_y-1))\n return l\n\n#print(getNeighbours((4,1)))\n\ndef getCost(p1,p2):\n x1 = p1[0]\n y1 = p1[1]\n x2 = p2[0]\n y2 = p2[1]\n\n if x1 == x2 and y2-y1 ==1:\n if grid_map[y1][x1] ==1:\n return 1.3\n elif x1-1>=0 :\n if grid_map[y1][x1-1] ==1:\n return 1.3\n else:\n return 1\n else:\n return 1\n\n if x1 == x2 and y1-y2 == 1:\n if grid_map[y2][x2] ==1:\n return 1.3\n elif x1-1 >= 0 and y1-1 >=0:\n if grid_map[y1-1][x1-1] ==1:\n return 1.3\n else:\n return 1\n else:\n return 1\n if y1 == y2 and x1-x2 == 1:\n if grid_map[y2][x2] ==1:\n return 1.3\n elif y2-1>=0:\n if grid_map[y2-1][x2] ==1:\n return 1.3\n else:\n return 1\n else:\n return 1\n if y1 == y2 and x2-x1 == 1:\n if grid_map[y1][x1] == 1:\n return 1.3\n elif y1-1 >=0:\n if grid_map[y1-1][x1] ==1:\n return 1.3\n else:\n return 1\n else:\n return 1\n if abs(x1 - x2) ==1 and abs(y1 -y2) == 1:\n return 1.5\n\ndef construct_path(cameFrom,current):\n total_path = [current]\n while current in cameFrom.keys():\n current = cameFrom[current]\n total_path.insert(0,current)\n return total_path\n\ndef A_start(start, goal):\n # simple heuristic function estimate the cost from n to goal.\n def h(n):\n return abs(goal[0] - n[0]) + abs(goal[1] - n[1])\n\n # open set records the to be visited points\n openSet = [start]\n\n # a map records the nodes' parent node\n cameFrom = {}\n\n #g function records the cost from initial point to n.\n gScore = defaultdict(lambda: float(\"inf\"))\n gScore[start] = 0\n\n # f function records the cost from start to n plus from n to the goal.\n fScore = defaultdict(lambda: float(\"inf\"))\n fScore[start] = h(start)\n\n #visit each node in open set\n while openSet:\n lowest_fscore = math.inf\n current = None\n for point in openSet:\n if fScore[point] < lowest_fscore:\n lowest_fscore = fScore[point]\n current = point\n\n #if the current node is the goal, build the path\n if current == goal:\n return construct_path(cameFrom,current)\n\n #remove the visited node\n openSet.remove(current)\n neighbors = getNeighbours(current)\n for neighbor in neighbors:\n temp_gScore = gScore[current] + getCost(current,neighbor)\n if temp_gScore < gScore[neighbor]:\n\n cameFrom[neighbor] = current\n gScore[neighbor] = temp_gScore\n fScore[neighbor] = gScore[neighbor] + h(neighbor)\n if neighbor not in openSet:\n openSet.append(neighbor)\n\n return None\n\n#promote user input for initial point and goal point\npoint_valid = False\nwhile not point_valid:\n initial_x,initial_y = input(\"Please enter the start point:\").split()\n goal_x,goal_y = input(\"Please enter the end point:\").split()\n initial_x,initial_y = float(initial_x),float(initial_y)\n goal_x,goal_y = float(goal_x),float(goal_y)\n if (initial_x>=-73.590 and initial_x<=-73.550) and (initial_y>=45.490 and initial_y<=45.530) and (goal_x>=-73.590 and goal_x<=-73.550) and (goal_y>=45.490 and goal_y<=45.530):\n point_valid = True\n else:\n print(\"Invalid points, please enter another one.\")\n\n#covert the input points to integer so it can fit into my algorithm.\ninitial_x = int(((initial_x - (-73.59)) / grid_size) + 0.01)\ninitial_y = int(((initial_y - (45.49)) / grid_size) +0.01)\ngoal_x = int(((goal_x - (-73.59)) / grid_size)+0.01)\ngoal_y = int(((goal_y - (45.49)) / grid_size)+0.01)\n\n#invoke A* algorithm to generate the optimal path\nfinal_path = A_start((initial_x,initial_y),(goal_x,goal_y))\n\nif final_path != None :\n # calculate the total cost\n total_cost = 0\n for i in range(len(final_path) - 1):\n total_cost += getCost(final_path[i], final_path[i + 1])\n\n # print(final_path)\n\n # convert the coordinates to original coordinates.\n real_path = []\n path_x = []\n path_y = []\n for point in final_path:\n real_path.append((round(point[0] * grid_size + (-73.59), 3), round(point[1] * grid_size + 45.49, 3)))\n path_x.append(round(point[0] * grid_size + (-73.59), 3))\n path_y.append(round(point[1] * grid_size + 45.49, 3))\n\n print(\"Path:\",real_path)\n print(\"path cost:\", \"{:.2f}\".format(total_cost))\n\n plt.plot(path_x, path_y, color=\"red\", linewidth=6)\nelse:\n print(\"Due to blocks, no path is found. Please change the map and try again\")\n\n#show the plot\nplt.xticks(x1, rotation=90)\nplt.yticks(y1)\nplt.show()\nprint(\"Program terminated.\")\n", "repo_name": "m441249833/MontrealCrimeAnalysis", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 9669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist2d", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist2d", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist2d", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 80, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 215, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 219, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 224, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}]} +{"seq_id": "17350403805", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Sep 26 16:44:37 2019\r\n\r\n@author: badit\r\n\"\"\"\r\n\r\n#!/usr/bin/env python\r\n\"\"\"Modification of test_eval_example2 except that when park=ON, always go to GOAL or if fuel runs out, go to refuel and then go to goal.\r\nRepeat this when park is OFF. \r\nThis example illustrates the use of TuLiP to synthesize a reactive\r\ncontroller for a GR(1) specification. We code the specification\r\ndirectly in GR(1) form and then use TuLiP to synthesize a reactive\r\ncontroller.\r\n\r\nThe system is modeled as a discrete transition system in which the\r\nrobot can be located anyplace on a 4x4 grid of cells with a refueling station:\r\n +----+----+----+----+\r\n | X1 | X2 | X3 | X4 | Home: X16, Refuel: X8, Goal: X1\r\n +----+----+----+----+\r\n | X5 | X6 | X7 | X8 |\r\n +----+----+----+----+\r\n | X9 | X10| X11| X12|\r\n +----+----+----+----+\r\n | X13| X14| X15| X16|\r\n +----+----+----+----+\r\n\r\nThe robot is allowed to transition between any two adjacent cells;\r\ndiagonal motions are not allowed. The robot should continuously\r\nrevisit the cell X1.\r\n\r\nThe environment consists of a single state called 'park' that\r\nindicates that the robot should move to cell X1.\r\n\r\nThe system specification in its simplest form is given by\r\nHopefully, it automatically refuels...\r\nOtherwise, revert back to example2.\r\n []<>park -> []<>X1 && [](!park -> <>X16) \r\n\r\nWe must convert this specification into GR(1) form:\r\n\r\n env_init && []env_safe && []<>env_prog_1 && ... && []<>env_prog_m ->\r\n sys_init && []sys_safe && []<>sys_prog_1 && ... && []<>sys_prog_n\r\n\"\"\"\r\n# 21 Jul 2013, Richard M. Murray (murray@cds.caltech.edu)\r\n# Import the packages that we need\r\n# from __future__ import print_function\r\nimport logging\r\nfrom tulip import spec\r\nfrom tulip import synth\r\nfrom tulip.transys import machines\r\nfrom tulip import dumpsmach\r\n\r\nlogging.basicConfig(level=logging.WARNING)\r\n#\r\n# Environment specification\r\n#\r\n# The environment can issue a park signal that the robot must respond\r\n# to by moving to the lower left corner of the grid. We assume that\r\n# the park signal is turned off infinitely often.\r\n#\r\nenv_vars = {}\r\nenv_vars['park'] = 'boolean'\r\nenv_vars['Cr'] = (2,15)\r\nenv_init = {'(Cr = 2)'} \r\n# Car is not patrolling. It is moving anywhere in the center two columns\r\nenv_safe = {'(Cr = 14) -> X(Cr = 15 || Cr = 10)', '(Cr = 10) -> X(Cr = 14 || Cr = 6)', \\\r\n '(Cr = 6) -> X(Cr = 10 || Cr = 2)', '(Cr = 2) -> X(Cr = 6 || Cr = 3)', \\\r\n '(Cr = 3) -> X(Cr = 7 || Cr = 2)', '(Cr = 7) -> X(Cr = 3 || Cr = 11)', \\\r\n '(Cr = 11) -> X(Cr = 7 || Cr = 15)', '(Cr = 15) -> X(Cr = 11 || Cr = 14)',\r\n } \r\nenv_prog = {'park'} # []<>(park)\r\n\r\n#\r\n# System dynamics\r\n#\r\n# The system specification describes how the system is allowed to move\r\n# and what the system is required to do in response to an environmental\r\n# action.\r\n# System can wait for the environment to get out of the way\r\nsys_vars = {}\r\nsys_vars['Xr'] = (1, 16)\r\nsys_vars['fuel'] = (0,10)\r\nsys_init = {'Xr=16', 'fuel = 10'}\r\n# try and see if it is possible to let the vehicle move a little:\r\n# that is, from Xr = 16, \r\nsys_safe = {'(Xr = 1) -> X (Xr=1 || Xr = 2 || Xr = 5)',\r\n '(Xr = 2) -> X (Xr = 1 || Xr = 3 || Xr = 6 || Xr = 2)',\r\n '(Xr = 3) -> X (Xr = 2 || Xr = 4 || Xr = 7 || Xr = 3)',\r\n '(Xr = 4) -> X (Xr = 4 || Xr = 3 || Xr = 8)',\r\n '(Xr = 5) -> X (Xr = 1 || Xr = 5 || Xr = 6 || Xr = 9)',\r\n '(Xr = 6) -> X(Xr = 2 || Xr = 5 || Xr = 7 || Xr = 10 || Xr = 6)',\r\n '(Xr = 7) -> X(Xr = 3 || Xr = 6 || Xr = 8 || Xr = 11 || Xr = 7)',\r\n '(Xr = 8) -> X(Xr = 4 || Xr = 7 || Xr = 8 || Xr = 12)',\r\n '(Xr = 9) -> X (Xr = 5 || Xr = 9 || Xr = 10 || Xr = 13)',\r\n '(Xr = 10) -> X (Xr = 6 || Xr = 9 || Xr = 11 || Xr = 14 || Xr = 10)',\r\n '(Xr = 11) -> X (Xr = 7 || Xr = 10 || Xr = 12 || Xr = 15 || Xr = 11)',\r\n '(Xr = 12) -> X (Xr = 8 || Xr = 11 || Xr = 12 || Xr = 16)',\r\n '(Xr = 13) -> X (Xr = 9 || Xr = 13 || Xr = 14)',\r\n '(Xr = 14) -> X(Xr = 10 || Xr = 13 || Xr = 15 || Xr = 14)',\r\n '(Xr = 15) -> X(Xr = 11 || Xr = 14 || Xr = 16 || Xr = 15)',\r\n '(Xr = 16) -> X(Xr = 12 || Xr = 15 || Xr = 16)',\r\n 'Cr = 14 -> !(Xr = 14)',\r\n 'Cr = 10 -> !(Xr = 10)',\r\n 'Cr = 6 -> !(Xr = 6)',\r\n 'Cr = 2 -> !(Xr = 2)',\r\n 'Cr = 3 -> !(Xr = 3)',\r\n 'Cr = 7 -> !(Xr = 7)',\r\n 'Cr = 11 -> !(Xr = 11)',\r\n 'Cr = 15 -> !(Xr = 15)',\r\n 'fuel > 0',\r\n '(fuel = 10) <-> X(fuel = 9)',\r\n '(fuel = 9) <-> X(fuel = 8)',\r\n '(fuel = 8) <-> X(fuel = 7)',\r\n '(fuel = 7) <-> X(fuel = 6)',\r\n '(fuel = 6) <-> X(fuel = 5)',\r\n '(fuel = 5) <-> X(fuel = 4)',\r\n '(fuel = 4) <-> X(fuel = 3)',\r\n '(fuel = 3) <-> X(fuel = 2)',\r\n '(fuel = 2) <-> X(fuel = 1)',\r\n '(fuel = 1) -> X(fuel = 0) || (Xr = 4 && X(Xr = 8)) || (Xr = 7 && X(Xr = 8)) || (Xr = 12 && X(Xr = 8))',\r\n '(Xr = 8) -> (fuel = 10)',\r\n}\r\n\r\nsys_prog = set() # empty set\r\n\r\n# Environment won't crash into you:\r\nfor xi in range(2,16):\r\n env_safe |= {'(Xr = '+str(xi)+') -> X(!(Cr = '+str(xi)+'))'}\r\n#\r\n# System specification\r\n#\r\n# The system specification is that the robot should repeatedly revisit\r\n# the upper right corner of the grid while at the same time responding\r\n# to the park signal by visiting the lower left corner. The LTL\r\n# specification is given by\r\n#\r\n# []<> X1 && [](!park -> <>X16) && [](!park -> <>X8)\r\n#boolean\r\n# Since this specification is not in GR(1) form, we introduce an\r\n# environment variable X0reach that is initialized to True and the\r\n# specification [](park -> <>X0) becomes\r\n#\r\n# [](X (X16reach) <-> X16 || (X16reach && park)), []((X8reach && park) || X (X8reach) <-> X8))\r\n#\r\n# Augment the system description to make it GR(1)\r\nsys_vars['X16reach'] = 'boolean'\r\nsys_init |= {'X16reach'}\r\nsys_safe |= {'(X (X16reach) <-> (Xr=16)) || (X16reach && park)'}\r\nsys_prog |= {'X16reach', 'Xr=1'}\r\n\r\n# Create a GR(1) specification\r\nspecs = spec.GRSpec(env_vars, sys_vars, env_init, sys_init, env_safe, sys_safe, env_prog, sys_prog)\r\n# Print specifications:\r\nprint(specs.pretty())\r\n\r\n#\r\n# Controller synthesis\r\n#\r\n# The controller decides based on current variable values only,\r\n# without knowing yet the next values that environment variables take.\r\n# A controller with this information flow is known as Moore.\r\nspecs.moore = True\r\n# Ask the synthesizer to find initial values for system variables\r\n# that, for each initial values that environment variables can\r\n# take and satisfy `env_init`, the initial state satisfies\r\n# `env_init /\\ sys_init`.\r\nspecs.qinit = '\\E \\A' # i.e., \"there exist sys_vars: forall sys_vars\"\r\n\r\n# At this point we can synthesize the controller\r\n# using one of the available methods.\r\nstrategy = synth.synthesize(specs)\r\nassert strategy is not None, 'unrealizable'\r\n\r\n# Generate a graphical representation of the controller for viewing, or a textual representation if pydot is missing.\r\n# if not strategy.save('test_eval_example_modified.png'):\r\n# print(strategy)\r\n\r\n# Writing strategy to file\r\nfor elem in env_init:\r\n break\r\nelem = elem.strip('()').split()\r\nenv0 = int(elem[2])\r\n\r\nif(env0 == 2):\r\n print(\"2\")\r\n dumpsmach.write_python_case(\"TE2_v2.py\", strategy, classname=\"TE_ctrl_init2\")\r\nelif(env0 == 3):\r\n print(\"3\")\r\n dumpsmach.write_python_case(\"TE3_v2.py\", strategy, classname=\"TE_ctrl_init3\")\r\nelif(env0 == 6):\r\n print(\"6\")\r\n dumpsmach.write_python_case(\"TE6_v2.py\", strategy, classname=\"TE_ctrl_init6\")\r\nelif(env0 == 7):\r\n print(\"7\")\r\n dumpsmach.write_python_case(\"TE7_v2.py\", strategy, classname=\"TE_ctrl_init7\")\r\nelif(env0 == 10):\r\n print(\"10\")\r\n dumpsmach.write_python_case(\"TE10_v2.py\", strategy, classname=\"TE_ctrl_init10\")\r\nelif(env0 == 11):\r\n print(\"11\")\r\n dumpsmach.write_python_case(\"TE11_v2.py\", strategy, classname=\"TE_ctrl_init11\")\r\nelif(env0 == 14):\r\n print(\"14\")\r\n dumpsmach.write_python_case(\"TE14_v2.py\", strategy, classname=\"TE_ctrl_init14\")\r\nelif(env0 == 15):\r\n print(\"15\")\r\n dumpsmach.write_python_case(\"TE15_v2.py\", strategy, classname=\"TE_ctrl_init15\")\r\nelse:\r\n print('Keep the obstacle car initial position in the middle two columns')\r\n \r\n\r\n## Generate a graph that represents the specifications set out by this file\r\n\r\n", "repo_name": "abadithela/Test-and-Eval-", "sub_path": "BFS_Search_Tests/generate_strategy_tulip.py", "file_name": "generate_strategy_tulip.py", "file_ext": "py", "file_size_in_byte": 8496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.basicConfig", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tulip.spec.GRSpec", "line_number": 153, "usage_type": "call"}, {"api_name": "tulip.spec", "line_number": 153, "usage_type": "name"}, {"api_name": "tulip.synth.synthesize", "line_number": 172, "usage_type": "call"}, {"api_name": "tulip.synth", "line_number": 172, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 187, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 187, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 190, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 190, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 193, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 193, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 196, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 196, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 199, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 199, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 202, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 202, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 205, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 205, "usage_type": "name"}, {"api_name": "tulip.dumpsmach.write_python_case", "line_number": 208, "usage_type": "call"}, {"api_name": "tulip.dumpsmach", "line_number": 208, "usage_type": "name"}]} +{"seq_id": "29335583061", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Sep 22 21:25:41 2020\r\n\r\n@author: LW\r\n\"\"\"\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Sep 20 14:02:32 2020\r\n\r\n@author: LW\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Aug 21 23:01:26 2020\r\n\r\n@author: LW\r\n\"\"\"\r\nimport imageio\r\nfrom osgeo import gdal,gdal_array,osr, ogr\r\nimport numpy as np\r\nimport glob\r\nimport os,sys \r\nfrom skimage import io\r\nimport cv2\r\nfrom numpy import nan as NaN\r\n\r\nvi='NDVI_MAX'\r\nlable_path=r'F:\\工作文件\\论文发表\\葡萄主产区优势对比\\样本数据\\标签数据\\标签Raster\\grape-lable-new.tif'\r\nfile_path=r'F:/工作文件/论文发表/葡萄主产区优势对比/测试数据/20190423_S2A.tif'#+vi+'_2019.tif'\r\nnon_lable_ds_ref_pngpath=r'F:/工作文件/论文发表/葡萄主产区优势对比/测试数据/20190423_S2A.jpg'\r\n\r\ndef getSplitImageAndImageByMutilBands():\r\n# lable=[]\r\n# image=[]\r\n \r\n ####获取非标签原始影像的属性信息\r\n non_lable_ds=gdal.Open(file_path)\r\n ###获取放射变换信息\r\n non_lable_transform = non_lable_ds.GetGeoTransform()\r\n non_lable_xOrigin = non_lable_transform[0]\r\n non_lable_yOrigin = non_lable_transform[3]\r\n non_lable_pixelWidth = non_lable_transform[1]\r\n non_lable_pixelHeight = non_lable_transform[5]\r\n non_lable_cols=non_lable_ds.RasterXSize\r\n non_lable_rows=non_lable_ds.RasterYSize\r\n \r\n non_lable_ds_ref=io.imread(file_path)\r\n print(non_lable_ds_ref.shape)\r\n# non_lable_ds_ref_B234=np.array(non_lable_ds_ref[:,:,0:3],dtype=int)\r\n# imageio.imwrite(non_lable_ds_ref_pngpath, non_lable_ds_ref_B234)\r\n# cv2.imwrite('20190423_S2A.jpg', non_lable_ds_ref_B234)\r\n\r\n non_lable_ds_ref_B2=np.array(non_lable_ds_ref[:,:,0],dtype=float)\r\n non_lable_ds_ref_B2[non_lable_ds_ref_B2==65536]=NaN\r\n# non_lable_ds_ref_B2[non_lable_ds_ref_B2==1]=NaN\r\n non_lable_Max_B2=non_lable_ds_ref_B2[~np.isnan(non_lable_ds_ref_B2)].max()\r\n non_lable_Min_B2=non_lable_ds_ref_B2[~np.isnan(non_lable_ds_ref_B2)].min()\r\n \r\n non_lable_ds_ref_B3=np.array(non_lable_ds_ref[:,:,1],dtype=float)\r\n non_lable_ds_ref_B3[non_lable_ds_ref_B3==65536]=NaN\r\n non_lable_Max_B3=non_lable_ds_ref_B3[~np.isnan(non_lable_ds_ref_B3)].max()\r\n non_lable_Min_B3=non_lable_ds_ref_B3[~np.isnan(non_lable_ds_ref_B3)].min()\r\n \r\n non_lable_ds_ref_B4=np.array(non_lable_ds_ref[:,:,2],dtype=float)\r\n non_lable_ds_ref_B4[non_lable_ds_ref_B4==65536]=NaN\r\n non_lable_Max_B4=non_lable_ds_ref_B4[~np.isnan(non_lable_ds_ref_B4)].max()\r\n non_lable_Min_B4=non_lable_ds_ref_B4[~np.isnan(non_lable_ds_ref_B4)].min()\r\n \r\n non_lable_ds_ref_B8=np.array(non_lable_ds_ref[:,:,3],dtype=float)\r\n non_lable_ds_ref_B8[non_lable_ds_ref_B8==65536]=NaN\r\n non_lable_Max_B8=non_lable_ds_ref_B8[~np.isnan(non_lable_ds_ref_B8)].max()\r\n non_lable_Min_B8=non_lable_ds_ref_B8[~np.isnan(non_lable_ds_ref_B8)].min()\r\n \r\n \r\n del non_lable_ds_ref, non_lable_ds_ref_B2,non_lable_ds_ref_B3,non_lable_ds_ref_B4\r\n ####获取标签影像的属性信息\r\n \r\n lable_ds=gdal.Open(lable_path)\r\n ###获取放射变换信息\r\n lable_transform = lable_ds.GetGeoTransform()\r\n lable_xOrigin = lable_transform[0]\r\n lable_yOrigin = lable_transform[3]\r\n lable_pixelWidth = lable_transform[1]\r\n lable_pixelHeight = lable_transform[5]\r\n lable_cols=lable_ds.RasterXSize\r\n lable_rows=lable_ds.RasterYSize\r\n \r\n# lableimgs=imageio.imread(lable_path)\r\n \r\n ###循环标签TIF的行列进行寻找256*256的标签PNG\r\n labe_counts=0\r\n non_grape_traincountes=0\r\n grape_traincounts=0\r\n non_grapecunts=0 #对非葡萄地块的相片进行计数，10选1\r\n jpgwidth=224\r\n \r\n \r\n rowImageCount=int(non_lable_rows/jpgwidth)\r\n colImageCount=int(non_lable_cols/jpgwidth)\r\n print('行照片数',rowImageCount,'列照片数',colImageCount)\r\n \r\n for r in range(rowImageCount):\r\n for c in range(colImageCount):\r\n print('导出第',r,'行',c,'列')\r\n ###获取非标签位置\r\n non_lable_xOffset = c*jpgwidth\r\n non_lable_yOffset = r*jpgwidth\r\n \r\n \r\n non_lableArray_B2=non_lable_ds.GetRasterBand(1).ReadAsArray(non_lable_xOffset,non_lable_yOffset,jpgwidth,jpgwidth)\r\n non_lableArray_B2 = (non_lableArray_B2-non_lable_Min_B2)*255/(non_lable_Max_B2-non_lable_Min_B2) # (矩阵元素-最小值)/(最大值-最小值) \r\n non_lableArray_B2[non_lableArray_B2>255]=255\r\n \r\n non_lableArray_B3=non_lable_ds.GetRasterBand(2).ReadAsArray(non_lable_xOffset,non_lable_yOffset,jpgwidth,jpgwidth)\r\n non_lableArray_B3 = (non_lableArray_B3-non_lable_Min_B3)*255/(non_lable_Max_B3-non_lable_Min_B3) # (矩阵元素-最小值)/(最大值-最小值) \r\n non_lableArray_B3[non_lableArray_B3>255]=255\r\n \r\n non_lableArray_B4=non_lable_ds.GetRasterBand(3).ReadAsArray(non_lable_xOffset,non_lable_yOffset,jpgwidth,jpgwidth)\r\n non_lableArray_B4 = (non_lableArray_B4-non_lable_Min_B4)*255/(non_lable_Max_B4-non_lable_Min_B4) # (矩阵元素-最小值)/(最大值-最小值) \r\n non_lableArray_B4[non_lableArray_B4>255]=255\r\n \r\n non_lableArray_B8=non_lable_ds.GetRasterBand(3).ReadAsArray(non_lable_xOffset,non_lable_yOffset,jpgwidth,jpgwidth)\r\n non_lableArray_B8 = (non_lableArray_B8-non_lable_Min_B8)*255/(non_lable_Max_B8-non_lable_Min_B8) # (矩阵元素-最小值)/(最大值-最小值) \r\n non_lableArray_B8[non_lableArray_B8>255]=255\r\n# non_lableArray_B8=non_lable_ds.GetRasterBand(4).ReadAsArray(non_lable_xOffset,non_lable_yOffset,512,512)\r\n# non_lableArray = (non_lableArray-non_lable_Min)*255/(non_lable_Max-non_lable_Min) # (矩阵元素-最小值)/(最大值-最小值) \r\n# \r\n non_lableArray = np.zeros((jpgwidth,jpgwidth,3))\r\n# ####导入R，G，B三个波段\r\n# non_lableArray[:,:,0]=non_lableArray_B4\r\n# non_lableArray[:,:,1]=non_lableArray_B3\r\n# non_lableArray[:,:,2]=non_lableArray_B2\r\n \r\n# ####导入NIR,R，G三个波段\r\n# non_lableArray[:,:,0]=non_lableArray_B8\r\n# non_lableArray[:,:,1]=non_lableArray_B4\r\n# non_lableArray[:,:,2]=non_lableArray_B3\r\n \r\n ####导入NIR，G,B三个波段\r\n non_lableArray[:,:,0]=non_lableArray_B4\r\n non_lableArray[:,:,1]=non_lableArray_B3\r\n non_lableArray[:,:,2]=non_lableArray_B2\r\n \r\n non_lableArray =np.array(non_lableArray,dtype=int)\r\n print(non_lableArray.shape)\r\n \r\n ###获得标签的256*256的数组\r\n lable_x=non_lable_xOffset*non_lable_pixelWidth+non_lable_xOrigin\r\n lable_y=non_lable_yOffset*non_lable_pixelHeight+non_lable_yOrigin\r\n \r\n lable_xOffset = int((lable_x-lable_xOrigin)/lable_pixelWidth)\r\n lable_yOffset = int((lable_y-lable_yOrigin)/lable_pixelHeight)\r\n \r\n lableArray=lable_ds.GetRasterBand(1).ReadAsArray(lable_xOffset,lable_yOffset,jpgwidth,jpgwidth)\r\n# print(lableArray)\r\n \r\n count=np.sum(lableArray == 1)\r\n scale=count/(jpgwidth*jpgwidth)\r\n \r\n if scale>0:\r\n save_non_grape=r'H:/gansu\\wuwei/DataSet/WaitClassificationImage/'+str(r)+'-'+str(c)+'-'+'1'+'.png'\r\n cv2.imwrite(save_non_grape, non_lableArray)\r\n save_label_grape=r'H:/gansu\\wuwei/DataSet/WaitClassificationImage/mask/'+str(r)+'-'+str(c)+'-'+'1'+'-mask.png'\r\n cv2.imwrite(save_label_grape, lableArray)\r\n else:\r\n save_non_grape=r'H:/gansu\\wuwei/DataSet/WaitClassificationImage/'+str(r)+'-'+str(c)+'-'+'0'+'.png'\r\n cv2.imwrite(save_non_grape, non_lableArray)\r\n \r\n \r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\": \r\n \r\n ###定义工作空间\r\n os.chdir(r'H:\\gansu\\wuwei\\DataSet')\r\n \r\n getSplitImageAndImageByMutilBands()\r\n \r\n print('complete!')\r\n\r\n", "repo_name": "devilweil/CNN-Image-Segmentation-ResNet", "sub_path": "SplitImageToClassification.py", "file_name": "SplitImageToClassification.py", "file_ext": "py", "file_size_in_byte": 8133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "osgeo.gdal.Open", "line_number": 39, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 39, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 62, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 74, "usage_type": "call"}, {"api_name": "osgeo.gdal.Open", "line_number": 80, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 169, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "27358952234", "text": "from setuptools import setup, find_packages\nimport os\n\nwith open('./requirement.txt') as f:\n required = f.read().splitlines()\n \nclassifiers = [\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Information Technology',\n 'Operating System :: Unix',\n 'Operating System :: MacOS :: MacOS X',\n 'Operating System :: Microsoft :: Windows',\n 'License :: OSI Approved :: GNU General Public License (GPL)',\n 'Programming Language :: Python :: 3.6'\n]\n\nsetup(\n name='JSJumble',\n version='1.0.4',\n description='Tool for obfuscating, compressing Javascript, and collecting static files.',\n long_description=open('pypi.md').read() + '\\n\\n' + open('CHANGELOG.txt').read(),\n long_description_content_type='text/markdown',\n url='https://github.com/GoodDay360/JSJumble', \n author='GoodDay360',\n author_email='istartgame31@gmail.com',\n license='GNU General Public License (GPL)', \n classifiers=classifiers,\n keywords=['JSJumble','library','module','javascript','compress','obfuscator','obfuscate','static','collect','server'], \n packages=find_packages(exclude=[]),\n include_package_data=True,\n install_requires=required,\n)", "repo_name": "GoodDay360/JSJumble", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1148, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "setuptools.setup", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "19368608514", "text": "#from default_connection import DefaultConnection\r\nimport os,sys,inspect\r\n##Esto sirve para poder usar import relativos\r\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\r\nparentdir = os.path.dirname(currentdir)\r\nsys.path.insert(0,parentdir) \r\n## Este es el import parent\r\nfrom default_connection import DefaultConnection\r\nimport psycopg2.extras\r\nimport sys\r\nclass Dummy:\r\n\r\n conexion = None\r\n\r\n def __init__(self):\r\n pass\r\n\r\n ############ retorna el cursor para poder interactuar con la DB #######\r\n def getCursor(self):\r\n try:\r\n #Conexion a postgre\r\n default = DefaultConnection()\r\n self.conexion = default.postgre_connect()\r\n cursor = self.conexion.cursor(cursor_factory=psycopg2.extras.DictCursor)\r\n return cursor\r\n except:\r\n print('Error obteniendo el cursor de dummy')\r\n raise Exception('Error no controlado: {}'.format(sys.exc_info()[0]))\t\t\t\r\n finally: \r\n pass\t\t\r\n #cursor.close()\r\n #self.cerrarConexion()\r\n\r\n ############ crear examen ###############################\r\n def prueba(self):\r\n try: \r\n #Conexion a postgre \r\n cursor = self.getCursor()\r\n #####\r\n insert = \"SELECT * FROM USUARIO\"\r\n cursor.execute(insert)\r\n filas = cursor.fetchall()\t\t\t\r\n return filas\r\n except:\r\n print('Error EN LA PRUEBA DUMMY POSTGRE')\r\n raise Exception('Error no controlado: {}'.format(sys.exc_info()[0]))\t\t\t\r\n finally:\r\n cursor.close()\r\n self.cerrarConexion()\r\n return None\r\n\r\n ########## Cerrar conexion ###################\r\n def cerrarConexion(self):\r\n self.conexion.close()\r\n\r\nif __name__ == '__main__':\r\n dum = Dummy()\r\n filas = dum.prueba()\r\n print(filas)\r\n\r\n", "repo_name": "vicorious/itrainer", "sub_path": "python/test/dummy.py", "file_name": "dummy.py", "file_ext": "py", "file_size_in_byte": 1961, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 4, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "default_connection.DefaultConnection", "line_number": 22, "usage_type": "call"}, {"api_name": "psycopg2.extras.extras", "line_number": 24, "usage_type": "attribute"}, {"api_name": "psycopg2.extras", "line_number": 24, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "9350370634", "text": "from django import forms\nfrom django.shortcuts import render, redirect\nfrom captcha.fields import ReCaptchaField\nfrom .models import Subscription, SubscriptionType\n\n\nclass SubscriptionForm(forms.ModelForm):\n captcha = ReCaptchaField()\n type = forms.ModelChoiceField(\n queryset=SubscriptionType.objects,\n widget=forms.RadioSelect(),\n empty_label=None,\n label=\"Abonelik tipi\",\n )\n\n class Meta:\n model = Subscription\n fields = ['name', 'email', 'address', 'type', 'renewal', 'phone', 'notes']\n widgets = {\n 'type': forms.RadioSelect(),\n 'renewal': forms.Select(),\n }\n\n\ndef subscribe(request):\n subscription = None\n if request.method == 'POST':\n form = SubscriptionForm(request.POST)\n if form.is_valid():\n subscription = form.save()\n else:\n form = SubscriptionForm()\n return render(request, 'subscriptions/subscription.html', {\n 'form': form,\n 'subscription_types': SubscriptionType.objects.filter(active=True),\n 'subscription': subscription,\n })\n", "repo_name": "Solfasol/solfasol", "sub_path": "solfasol/subscriptions/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "captcha.fields", "line_number": 8, "usage_type": "name"}, {"api_name": "captcha.fields.ReCaptchaField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "models.SubscriptionType.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.SubscriptionType", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Subscription", "line_number": 17, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "models.SubscriptionType.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "models.SubscriptionType.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.SubscriptionType", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "26351469224", "text": "from django import forms\nfrom django.utils.translation import ugettext as _\n\nclass NewReviewForm(forms.Form):\n text = forms.CharField(\n label=_('Text'),\n widget=forms.Textarea(attrs={\n 'placeholder': _('Write Your Review about this book.'),\n 'rows': 3,\n 'class': 'span12',\n 'style': 'resize: none;',\n })\n )\n title = forms.CharField(\n label=_('Book'),\n widget=forms.TextInput(attrs={\n 'placeholder': _('Title of Review'),\n 'autocomplete': 'off',\n 'class': 'span10',\n })\n ) \n", "repo_name": "s1na/darkoob", "sub_path": "darkoob/book/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.forms.Form", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 5, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 6, "usage_type": "call"}, {"api_name": "django.forms.Textarea", "line_number": 7, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 15, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "4026048247", "text": "import requests \nimport os\nimport sys\nfrom sys import argv \nimport time\n\nprint('''\n /|\n / / \\ \n | | ||\n \\ \\ / /\n \\ \\/ / ________________________________________________\n \\_ | \\_/ \\ [ DirHunter\t\t\t\t - x]\n \\| | || |_______________________________________________|\n | \\_/ |\t\t\t\t\t\t |\n \\_| | by Mathis Pais __ \t |\tdate: 22/12/2021\t\t\t |\n ---__\\ \\ ___________ /|_\\ __--- |\tversion: 1.3\t\t\t |\n \\ - -\\ \\- -/ /-- - / |\tdescription: brute-force tool for |\n \\ \\ \\ / / / |\twebsites directories discovery. |\n \\___/ __.__ \\___/ |\tusage: |\n | | |\t\t |\tpython3 DirHunter.py \"url\" \"wordlist.txt\" |\n | ___ ___ | [_______________________________________________] \n \\ /|\\ /|\\ /\n \\ /\n |\\ /|\n |\\ | /|\n | \\ | / |\n \\ | /\n \\___|___/\n \\^ ^/@$\n \\_-_/\n\n\n''') \n\nclass bcolors:\n\tOK = '\\033[92m'\n\tWARNING = '\\033[93m'\n\tFAIL = '\\033[91m'\n\tRESET = '\\033[0m'\n\ndef main():\n\tif len(argv) == 1 :\n\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+ 'usage: python3 DirHunter.py \"url\" \"wordlist\"')\n\telif len(argv) == 2:\n\t\tif argv[1][len(argv[1])-4:len(argv[1])] == '.txt':\n\t\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+ 'usage: python3 DirHunter.py \"url\" \"wordlist\"')\n\t\telse:\n\t\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+'usage: python3 DirHunter.py \"url\" \"wordlist\"')\n\telif len(argv) == 3:\n\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+'ready to hunt the dir !')\n\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+'searching for deers... Hum, I mean dirs')\n\t\tStart=time.time()\t#démarrage du chrono à t=0s\n\t\thunt(str(argv[1]),\"/\"+str(argv[2]))\t#On utilise la fonction hunt sur les arguments donnés\n\t\tcount=hunt.count\t#On initialise un compteur\n\n\t\tif not hunt.path:\t#Si on ne trouve pas de répértoire \n\t\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+\"no dir found.\")\t#message d'erreur\n\t\telif hunt.path[0] == \"\": \t#Si le premier élément de la liste est vide\n\t\t\thunt.path.pop(0)\t#On le supprime\n\t\tfor dir in hunt.path:\t#Pour tout les répértoires trouvés lors du premier passage\n\t\t\thunt.count=hunt.count-1\t\t#On enleve 1 au compteur\n\t\t\thunt(str(argv[1])+\"/\"+dir,\"/\"+str(argv[2]))\t#On refait un deuxième passage dans le nouveau répértoire trouvé\n\n\t\tEnd=time.time()\t\t#On stop le chronomètre\n\t\tTime=End-Start\t\t#On fait le calcul du temps de traitement du script\n\t\tprint(\"\\n\")\n\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+\"Hunting time: \", Time, \"sec\")\t#On affiche le temps de recherche\n\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+\"End time: \", time.ctime())\t#On affiche la date à laquelle le script c'est terminé\n\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+\"Your day's catch: \", count+hunt.count)\t#On affiche le nombre de répértoires trouvés\n\telse:\n\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+ 'usage: python3 DirHunter.py \"url\" \"wordlist\"')\n\ndef hunt(urls,wordlist):\t#Définition de la fonction hunt avec les arguments urls et wordlist\n\thunt.path=[]\t#On initialise hunt.path en tant que liste\n\turl=urls\n\thunt.count=0\t#On démarre le compteur à 0\n\ttry:\n\t\tif os.path.exists(os.getcwd()+wordlist):\t#Si le dictionnaire de mots existe\n\t\t\tfile=open(os.getcwd()+wordlist,\"r\")\t#On l'ouvre\n\t\t\tfor i in file:\t#et pour chaque mot se trouvant dedans\n\t\t\t\tdir=i.splitlines()\t#On fait une liste de toutes les lignes du fichier\n\t\t\t\tdir=''.join(dir)\t#et on les rejoint\n\t\t\t\trq=requests.get(url+\"/\"+dir)\t#On envoie la requête à l'url suivie d'un / et du mot de la liste\n\t\t\t\tdir_len=len(dir)\t#initialise une variable qui contient la longueur du mot de la liste\n\n\t\t\t\tif rq.status_code == 200:\t#Si la rêquete renvoie un code 200\n\t\t\t\t\tprint(\"Aiming : \"+url+\"/\"+dir,end=\"\\r\")\t\t#On affiche l'url de la cible visée et on revient au début de la ligne du terminal\n\t\t\t\t\ttime.sleep(0.05)\t#On marque un temps de pause\n\t\t\t\t\tprint(\"Aiming : \"+url+\"/\"+dir_len*\" \",end=\"\\r\") \t#Puis affiche l'url en remplacant la cible par des espace pour la supprimer et reviens au début de la ligne\n\t\t\t\t\tprint(\"\\n\")\n\t\t\t\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+\"Aiming : \"+url+\"/\"+dir+\" \"+bcolors.OK+str(rq.status_code)+bcolors.RESET+\": dir shot ︻デ═一\")\n\t\t\t\t\t#Enfin on affiche tout avec le code reçu et la confirmation du résultat écrit\n\t\t\t\t\thunt.goodir=dir\t\t#On crée une variable contenant le répertoire trouvé\n\t\t\t\t\thunt.count=hunt.count+1\t\t#On incrémente le compteur de 1\n\t\t\t\t\thunt.path.append(hunt.goodir)\t#On ajoute le répértoire trouvé dans la liste hunt.path\n\n\t\t\t\telif rq.status_code == 403:\t#Si la rêquete renvoie un code 403\n\t\t\t\t\tprint(\"Aiming : \"+url+\"/\"+dir,end=\"\\r\")\t\t#On affiche l'url de la cible visée et on revient au début de la ligne du terminal\n\t\t\t\t\ttime.sleep(0.05)\t#On marque un temps de pause\n\t\t\t\t\tprint(\"Aiming : \"+url+\"/\"+dir_len*\" \",end=\"\\r\")\t\t#Puis affiche l'url en remplacant la cible par des espace pour la supprimer et reviens au début de la ligne\n\t\t\t\t\tprint(\"\\n\")\n\t\t\t\t\tprint(bcolors.WARNING+\"[-] \"+bcolors.RESET+\"Aiming : \"+url+\"/\"+dir+\" \"+bcolors.WARNING+str(rq.status_code)+bcolors.RESET+\": restricted area !\") \n\t\t\t\t\t#Enfin on affiche tout avec le code reçu et la signification du code\n\t\t\t\telse:\t#Si la rêquete renvoie un autre code on affiche juste l'url ciblé sans message de confirmation\n\t\t\t\t\tprint(\"Aiming :\"+url+\"/\"+dir,end=\"\\r\")\n\t\t\t\t\ttime.sleep(0.05)\n\t\t\t\t\tprint(\"Aiming :\"+url+\"/\"+dir_len*\" \",end=\"\\r\") \n\n\t\t\tfile.close()\t#On ferme le dictionnaire\n\t\t\tprint(\"\\n\")\n\t\t\tprint(bcolors.OK+\"[+] \"+bcolors.RESET+\"Hunt finished.\")\t\t#On informe de la fin du script\n\n\t\telse: #Si le chemin d'accés au dictionnaire n'est pas bon, on affiche un message d'erreur\n\t\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+wordlist+\" don't exist in this directory\")\n\n\texcept KeyboardInterrupt:\t#En cas d'interruption clavier\n\t\tprint(\"\\n\")\n\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+\"Hunting has been abandoned\") \t#On affiche l'abandon du script\n\n\texcept Exception as e:\t\t#Si une autre erreur arrive\n\t\tprint(bcolors.FAIL+\"[!] \"+bcolors.RESET+e)\t#On affiche le message d'erreur généré\n\n\nif __name__ == '__main__':\n\tmain()\t#Lancement du script\n", "repo_name": "mathis2001/DirHunter", "sub_path": "DirHunter.py", "file_name": "DirHunter.py", "file_ext": "py", "file_size_in_byte": 6597, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.argv", "line_number": 43, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 63, "usage_type": "name"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 79, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 80, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "35476608346", "text": "import json\nimport os\nimport codecs\n\n\nclass SingleCharacter:\n\n def __init__(self, mod_file_path: str):\n self.file_path = mod_file_path\n self.root_dir = os.path.dirname(self.file_path)\n # load json file\n with open(self.file_path, 'r') as f:\n self.data = json.load(f, strict=False)\n\n self.aic = self.data['AIC']\n self.aiv = None\n self.aiv_base_dir = None\n self.troops = None\n self.assets = None\n self.assets_speech_dir = None\n self.assets_binks_dir = None\n if 'AIV' in self.data.keys():\n self.aiv = self.data['AIV']\n self.aiv_base_dir = os.path.join(self.root_dir, self.aiv['base_dir'])\n if 'Troops' in self.data.keys():\n self.troops = self.data['Troops']\n if 'Assets' in self.data.keys():\n self.assets = self.data['Assets']\n self.assets_speech_dir = os.path.join(self.root_dir, self.assets['base_dir'], self.assets['Speech']['base_dir'])\n self.assets_binks_dir = os.path.join(self.root_dir, self.assets['base_dir'], self.assets['Binks']['base_dir'])\n\n def get_num_speech_assets(self):\n fx_files = [item for sublist in self.assets['Speech'].values() for item in sublist]\n return len(fx_files)\n\n def get_num_binks_assets(self):\n bik_files = [item for sublist in self.assets['Binks'].values() for item in sublist]\n return len(bik_files)\n\n\nclass BaseMod:\n\n def __init__(self,\n base_file_path: str,\n vanilla_path: str):\n self.base_file_path = base_file_path\n self.base_dir = os.path.dirname(self.base_file_path)\n self.vanilla_path = vanilla_path\n with open(self.base_file_path, 'r') as f:\n self.base_file = json.load(f, strict=False)\n self.aic = None\n self.troops = None\n self.aiv = None\n self.assets = None\n self.load_files()\n\n def load_files(self) -> None:\n \"\"\"\n load all the relevant files from base mod config file\n \"\"\"\n # Load AIC file\n if self.base_file['AIC']:\n with codecs.open(os.path.join(self.base_dir, self.base_file['AIC']), 'r', 'utf-8-sig') as f:\n self.aic = json.load(f, strict=False)\n else:\n # load Vanilla AIC\n with open(os.path.join(self.vanilla_path, 'vanilla_aic.json'), 'r') as f:\n self.aic = json.load(f, strict=False)\n\n # Load Troops file\n if self.base_file['Troops']:\n with codecs.open(os.path.join(self.base_dir, self.base_file['Troops']), 'r', 'utf-8-sig') as f:\n self.troops = json.load(f, strict=False)\n else:\n # load Vanilla Troops\n with open(os.path.join(self.vanilla_path, 'vanilla_troops.json'), 'r') as f:\n self.troops = json.load(f, strict=False)\n\n # Load AIV file\n if self.base_file['AIV']:\n with open('char_aiv.json', 'r') as f:\n self.aiv = json.load(f, strict=False)\n for char in self.aiv.keys():\n for item in self.aiv[char].items():\n self.aiv[char][item[0]] = os.path.join(os.path.abspath(self.base_file['AIV']), item[1])\n else:\n # load Vanilla AIV\n with open(os.path.join(self.vanilla_path, 'vanilla_aiv.json'), 'r') as f:\n self.aiv = json.load(f, strict=False)\n for char in self.aiv.keys():\n for item in self.aiv[char].items():\n self.aiv[char][item[0]] = os.path.join(self.vanilla_path, item[1])\n\n # Load Assets file\n if self.base_file['Assets']:\n with open(os.path.join(self.base_dir, self.base_file['Assets']), 'r') as f:\n self.assets = json.load(f, strict=False)\n else:\n # load Vanilla Assets\n with open(os.path.join(self.vanilla_path, 'vanilla_assets.json'), 'r') as f:\n self.assets = json.load(f, strict=False)\n for char in self.assets.keys():\n for item in self.assets[char]['Speech'].items():\n self.assets[char]['Speech'][item[0]] = os.path.join(self.vanilla_path, 'assets', item[1])\n for item in self.assets[char]['Binks'].items():\n self.assets[char]['Binks'][item[0]] = os.path.join(self.vanilla_path, 'assets', item[1])\n\n def update_character(self, new_char: SingleCharacter, replace_char: str = 'Wazir') -> bool:\n \"\"\"\n Update characters\n :param new_char:\n :param replace_char:\n :return: showing update success\n \"\"\"\n\n characters = ['Caliph', 'Frederick', 'Pig', 'Phillip', 'Richard', 'Rat', 'Saladin', 'Sheriff', 'Snake',\n 'Sultan', 'Wolf', 'Abbot', 'Marshall', 'Nizar', 'Emir', 'Wazir']\n if replace_char not in characters:\n raise Exception('Invalid standard character name given for update')\n\n # update AIC\n # get index\n idx = [i for i in range(len(self.aic['AICharacters'])) if self.aic['AICharacters'][i]['Name'] == replace_char][0]\n # add Custom Name\n self.aic['AICharacters'][idx]['CustomName'] = new_char.aic['CustomName']\n self.aic['AICharacters'][idx]['Personality'] = new_char.aic['Personality']\n\n # update Troops\n if new_char.troops:\n # get index\n idx = [i for i in range(1, 17) if self.troops[str(i)]['Name'] == replace_char][0]\n # add custom troops configuration\n self.troops[str(idx)]['Lord'] = new_char.troops['Lord']\n self.troops[str(idx)]['normal'] = new_char.troops['normal']\n self.troops[str(idx)]['crusader'] = new_char.troops['crusader']\n self.troops[str(idx)]['deathmatch'] = new_char.troops['deathmatch']\n\n # update AIV\n if new_char.aiv:\n for i, item in enumerate(self.aiv[replace_char].items()):\n self.aiv[replace_char][item[0]] = os.path.join(os.path.abspath(new_char.aiv_base_dir),\n new_char.aiv[str(i+1)])\n\n # Update Assets\n if new_char.assets:\n new_speech_dict = dict.fromkeys(self.assets[replace_char]['Speech'], \"\")\n new_binks_dict = dict.fromkeys(self.assets[replace_char]['Binks'], \"\")\n special_character = False\n if self.assets[replace_char]['SpecialCharacter']:\n # special character (Pig, Rat, Snake, Wolf) -> different speech files (only 22 files)\n special_character = True\n\n # Update speech file paths\n with open('char_speech.json', 'r') as f:\n sp_info = json.load(f)\n for item in new_char.assets['Speech']['actions'].items():\n key_pre = sp_info[replace_char]['prefix'] + '_' + item[0]\n if item[1]:\n if special_character and 'player' in item[0]:\n keys = [key_pre for i\n in range(len(item[1][:sp_info[replace_char]['actions'][item[0]]]))]\n else:\n keys = [key_pre + '_' + str(i + 1).zfill(2) for i\n in range(len(item[1][:sp_info[replace_char]['actions'][item[0]]]))]\n\n for i, k in enumerate(keys):\n new_speech_dict[k] = os.path.join(new_char.assets_speech_dir, item[1][i])\n self.assets[replace_char]['Speech'] = new_speech_dict\n\n # Update binks videos\n with open('char_binks.json', 'r') as f:\n bik_info = json.load(f)\n for item in new_char.assets['Binks']['actions'].items():\n if item[1]:\n action = item[0]\n # anger or angry?\n if item[0] == 'anger':\n if bik_info[replace_char]['actions']['anger'] == 0 and bik_info[replace_char]['actions']['angry'] == 1:\n action = 'angry'\n # taunt or taunting?\n elif item[0] == 'taunt':\n if bik_info[replace_char]['actions']['taunt'] == 0 and bik_info[replace_char]['actions']['taunting'] == 1:\n action = 'taunting'\n # create the keys\n if bik_info[replace_char]['numerate_files']:\n keys = [bik_info[replace_char]['prefix'] + '_' + action + str(i+1)\n for i in range(len(item[1][:bik_info[replace_char]['actions'][item[0]]]))]\n else:\n keys = [bik_info[replace_char]['prefix'] + '_' + action\n for i in range(len(item[1][:bik_info[replace_char]['actions'][item[0]]]))]\n for i, k in enumerate(keys):\n new_binks_dict[k] = os.path.join(new_char.assets_binks_dir, item[1][i])\n self.assets[replace_char]['Binks'] = new_binks_dict\n\n return True\n\n def save_base_mod(self, output_dir: str, output_name: str = 'my_mod') -> None:\n # save aic file\n with open(os.path.join(output_dir, output_name + '_aic.json'), 'w') as f:\n json.dump(self.aic, f)\n\n # save troops file\n with open(os.path.join(output_dir, output_name + '_troops.json'), 'w') as f:\n json.dump(self.troops, f)\n\n # save aiv file\n with open(os.path.join(output_dir, output_name + '_aiv.json'), 'w') as f:\n json.dump(self.aiv, f)\n\n # save assets file\n with open(os.path.join(output_dir, output_name + '_assets.json'), 'w') as f:\n json.dump(self.assets, f)\n\n", "repo_name": "NMme/CrusaderAIManager", "sub_path": "data_classes.py", "file_name": "data_classes.py", "file_ext": "py", "file_size_in_byte": 9791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 68, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 77, "usage_type": "call"}, {"api_name": "json.load", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 89, "usage_type": "call"}, {"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": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 97, "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": "json.load", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 141, "usage_type": "call"}, {"api_name": "json.load", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 212, "usage_type": "call"}]} +{"seq_id": "30514583162", "text": "from typing import Dict\n\nfrom mmengine.model import BaseDataPreprocessor, ModuleDict\n\nfrom mmaction.registry import MODELS\n\n\n@MODELS.register_module()\nclass MultiModalDataPreprocessor(BaseDataPreprocessor):\n \"\"\"Multi-Modal data pre-processor for action recognition tasks.\"\"\"\n\n def __init__(self, preprocessors: Dict) -> None:\n super().__init__()\n self.preprocessors = ModuleDict()\n for name, pre_cfg in preprocessors.items():\n assert 'type' in pre_cfg, (\n 'Each data preprocessor should contain the key type, '\n f'but got {pre_cfg}')\n self.preprocessors[name] = MODELS.build(pre_cfg)\n\n def forward(self, data: Dict, training: bool = False) -> Dict:\n \"\"\"Preprocesses the data into the model input format.\n\n Args:\n data (dict): Data returned by dataloader.\n training (bool): Whether to enable training time augmentation.\n\n Returns:\n dict: Data in the same format as the model input.\n \"\"\"\n data = self.cast_data(data)\n inputs, data_samples = data['inputs'], data['data_samples']\n for modality, modality_data in inputs.items():\n preprocessor = self.preprocessors[modality]\n modality_data, data_samples = preprocessor.preprocess(\n modality_data, data_samples, training)\n inputs[modality] = modality_data\n\n data['inputs'] = inputs\n data['data_samples'] = data_samples\n return data\n", "repo_name": "open-mmlab/mmaction2", "sub_path": "mmaction/models/data_preprocessors/multimodal_data_preprocessor.py", "file_name": "multimodal_data_preprocessor.py", "file_ext": "py", "file_size_in_byte": 1508, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3560, "dataset": "github-code", "pt": "3", "api": [{"api_name": "mmengine.model.BaseDataPreprocessor", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 12, "usage_type": "name"}, {"api_name": "mmengine.model.ModuleDict", "line_number": 14, "usage_type": "call"}, {"api_name": "mmaction.registry.MODELS.build", "line_number": 19, "usage_type": "call"}, {"api_name": "mmaction.registry.MODELS", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "mmaction.registry.MODELS.register_module", "line_number": 8, "usage_type": "call"}, {"api_name": "mmaction.registry.MODELS", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "40463973080", "text": "import os\nimport pandas as pd\nimport numpy as np\nfrom mobilenet import MobileNet\nfrom mobilenet_dih import MobileNetDih4\nfrom mobilenet_dih_r import MobileNetDR\nfrom keras.optimizers import SGD, Adam, Adadelta\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau,CSVLogger\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras import backend as K\nfrom keras import metrics\nfrom keras import losses\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\nimport imageio\nfrom skimage.transform import resize as imgresize\n\nTRAIN_PATH = 'input/train.json'\nBATCH_SIZE = 32\n\ndef json2img_and_labels(df:pd.DataFrame):\n imgs = []\n y = []\n\n for i, row in df.iterrows():\n band_1 = np.array(row['band_1']).reshape(75,75)\n band_2 = np.array(row['band_2']).reshape(75,75)\n\n d1 = (band_1 - band_1.mean()) / (band_1.max() - band_1.min())\n d2 = (band_2 - band_2.mean()) / (band_2.max() - band_2.min())\n\n imgs.append(np.dstack([d1, d2]))\n y.append(row['is_iceberg'])\n\n return imgs, y\n\ndef get_data():\n print('Read Data')\n df = pd.read_json(TRAIN_PATH)\n imgs, y = json2img_and_labels(df)\n train_img, valid_img, train_y, valid_y = train_test_split(imgs,\n y,\n random_state=131,\n shuffle=True,\n stratify=y,\n train_size=0.75)\n return train_img, valid_img, train_y, valid_y\n\n\ndef get_callbacks(filepath, patience=1):\n mcp = ModelCheckpoint(filepath,\n monitor='val_loss',\n verbose=2,\n save_best_only=True,\n save_weights_only=False,\n mode='min',\n period=1)\n rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.1,\n patience=patience, min_lr=1e-16, verbose=1)\n csv_log = CSVLogger(filename=filepath+'.csv')\n return [mcp, rlr, csv_log]\n\ndef training_model(model_name='mobilenet'):\n train_img, valid_img, train_y, valid_y = get_data()\n callbacks = get_callbacks('mobilenet_10fulld01_b16', patience=2)\n if model_name == 'mobilenet':\n print('MobileNet')\n model = MobileNet(alpha=1.)\n model.summary()\n elif model_name =='mobilenet_dih':\n print('MobileNetDih')\n model = MobileNetDih4(alpha=1.)\n model.summary()\n elif model_name =='mobilenet_dih_r':\n print('MobileNetDihR')\n model = MobileNetDR(alpha=1.)\n model.summary()\n\n opt = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999)\n #opt = Adadelta(lr=1e-1, rho=0.95, decay=0.1)\n #opt = SGD(lr=1e-7, momentum=0.9, decay=0., nesterov=True)\n\n model.compile(optimizer=opt,\n loss='binary_crossentropy',\n metrics=['accuracy'])\n #model.load_weights('mobilenet_05shortd01_catcros_resize_b16.hdf5')\n gen = ImageDataGenerator(rotation_range=359,\n zoom_range=[0.5, 2],\n width_shift_range=0.1,\n height_shift_range=0.1,\n vertical_flip=True,\n horizontal_flip=True)\n\n model.fit_generator(gen.flow(np.array(train_img),\n np.array(train_y),\n batch_size=BATCH_SIZE),\n steps_per_epoch=16*len(train_y)//BATCH_SIZE,\n epochs=40,\n validation_data=[np.array(valid_img), np.array(valid_y)],\n verbose=1,\n callbacks=callbacks)\n# \"\"\"\n #opt = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999)\n #opt = Adadelta(lr=1e-1, rho=0.95, decay=0.1)\n opt = SGD(lr=0.05, momentum=0.9, decay=0., nesterov=True)\n model.load_weights('mobilenet_10shortd01_b16_sgd')\n model.fit_generator(gen.flow(np.array(train_img),\n np.array(train_y),\n batch_size=BATCH_SIZE),\n steps_per_epoch=16*len(train_y)//BATCH_SIZE,\n epochs=10,\n validation_data=[np.array(valid_img), np.array(valid_y)],\n verbose=1,\n callbacks=callbacks)\n#\"\"\"\n", "repo_name": "MIklgr500/Statoil", "sub_path": "tuning.py", "file_name": "tuning.py", "file_ext": "py", "file_size_in_byte": 4523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.DataFrame", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.callbacks.CSVLogger", "line_number": 60, "usage_type": "call"}, {"api_name": "mobilenet.MobileNet", "line_number": 68, "usage_type": "call"}, {"api_name": "mobilenet_dih.MobileNetDih4", "line_number": 72, "usage_type": "call"}, {"api_name": "mobilenet_dih_r.MobileNetDR", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "5488437320", "text": "from redbot.core import commands\r\nfrom redbot.cogs import audio\r\nfrom redbot.core import Config\r\nfrom redbot.core import checks\r\nfrom json import loads\r\nimport json\r\nimport base64\r\nimport requests\r\nimport re\r\nfrom Crypto.Cipher import AES\r\nclass Wyyyy(commands.Cog):\r\n\t\"\"\"Play song by netease music links!\"\"\"\r\n\t\r\n\t__author__ = \"JackXu\"\r\n\t__version__ = \"0.3.1\"\r\n\t\r\n\tdefault_global_settings = {\"user_cookies\": \"\"}\r\n\tdef __init__(self):\r\n\t\tself.config = Config.get_conf(self, identifier=2817739401)\r\n\t\tself.config.register_global(**self.default_global_settings)\r\n\t\r\n\t\r\n\t\r\n\t@commands.group()\r\n\t@checks.admin_or_permissions(manage_guild=True)\r\n\tasync def wyyset(self, ctx: commands.Context):\r\n\t\t\"\"\"Manage settings.\"\"\"\r\n\t\r\n\t@wyyset.group()\r\n\tasync def cookie(self, ctx: commands.Context):\r\n\t\t\"\"\"Cookie settings.\"\"\"\r\n\t\r\n\t@cookie.command()\r\n\tasync def set(self, ctx: commands.Context, *, cookies_string: str):\r\n\t\t\"\"\"Set cookie.\r\n\t\tThe useful keys are \\\"__crsf\\\",\\\"MUSIC_U\\\"\"\"\"\r\n\t\tcookies_dict = {}\r\n\t\tcookies_string.replace(\" \", \"\")\r\n\t\t#await ctx.send(cookies_string.split(';'))\r\n\t\tfor e in cookies_string.split(';'):\r\n\t\t\t#await ctx.send(e)\r\n\t\t\tk, v = e.split('=', 1)\r\n\t\t\t#await ctx.send(k)\r\n\t\t\t#await ctx.send(v)\r\n\t\t\tcookies_dict[k] = v\r\n\t\tawait self.config.user_cookies.set(cookies_dict)\r\n\t\tawait ctx.send(\"Cookie set complete.\")\r\n\t\r\n\t@cookie.command()\r\n\tasync def delete(self, ctx: commands.Context):\r\n\t\t\"\"\"Delete cookie.\"\"\"\r\n\t\tcookies_dict = {}\r\n\t\tawait self.config.user_cookies.set(cookies_dict)\r\n\t\tawait ctx.send(\"They are clean now.\")\r\n\t\r\n\t\t\r\n\t@commands.command()\r\n\tasync def wyy(self, ctx, *, sharelink: str):\r\n\t\t\"\"\"Play a netease music share link.\"\"\"\r\n\t\trid = None\r\n\t\tif \"song?\" in sharelink:\r\n\t\t\trid = re.search(r'\\?id=(\\d*)', sharelink)\r\n\t\telif \"song/\" in sharelink:\r\n\t\t\trid = re.search(r'song/(\\d*)/', sharelink)\r\n\t\telse:\r\n\t\t\tawait ctx.send(\"This is not a song link!\")\r\n\t\tif rid:\r\n\t\t\tsong_id = re.search(r'\\d+',str(rid.group()))\r\n\t\t\tnonce = \"0CoJUm6Qyw8W8jud\"\r\n\t\t\tdef AES_encrypt(text, key, iv):\r\n\t\t\t\tpad = 16 - len(text) % 16\r\n\t\t\t\ttext = text + pad * chr(pad)\r\n\t\t\t\ttext = text.encode(\"utf-8\")\r\n\t\t\t\tencryptor = AES.new(key.encode('utf-8'), AES.MODE_CBC, iv)\r\n\t\t\t\tencrypt_text = encryptor.encrypt(text)\r\n\t\t\t\tencrypt_text = base64.b64encode(encrypt_text)\r\n\t\t\t\treturn encrypt_text.decode('utf-8')\r\n\t\t\tdef asrsea(p1, p2):\r\n\t\t\t\tres = {}\r\n\t\t\t\trand_num = \"OFnV5T4hXEx90wxi\"\r\n\t\t\t\tvi = b\"0102030405060708\"\r\n\t\t\t\th_encText = AES_encrypt(p1, p2, vi)\r\n\t\t\t\th_encText = AES_encrypt(h_encText, rand_num, vi)\r\n\t\t\t\tres[\"encText\"] = h_encText\r\n\t\t\t\tres[\"encSecKey\"] = \"6b2e91bfea2fff78e82f13d16405c8ba0bd54af4076218463931b5ebfdb177f61ee9fe3db8566edb19cc5a5badd0d2cd1435553c6caa40f39e45c35e0957ec67e3ad36e074b6ee0224083b17d96fb734fdc6d11d42ea8d1c71cdd170f9d93dd98c7cb22624e8765bbd93ffc1a98b834bc86d847a229241b8f3750571cf199621\"\r\n\t\t\t\treturn res\r\n\t\t\treq = json.dumps({\r\n\t\t\t\t\"ids\": [song_id.group()],\r\n\t\t\t\t\"br\": 999000,\r\n\t\t\t\t\"csrf_token\": ''\r\n\t\t\t})\r\n\t\t\t#await ctx.send(req)\r\n\t\t\tasrsea_res = asrsea(req, nonce)\r\n\t\t\tparam_data = {\r\n\t\t\t\t\"params\": asrsea_res[\"encText\"],\r\n\t\t\t\t\"encSecKey\": asrsea_res[\"encSecKey\"]\r\n\t\t\t}\r\n\t\t\theaders = {\r\n\t\t\t\t\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:83.0) Gecko/20100101 Firefox/83.0\",\r\n\t\t\t\t\"Content-Type\": \"application/x-www-form-urlencoded\",\r\n\t\t\t\t\"Origin\": \"http://music.163.com\",\r\n\t\t\t\t\"Referer\": \"https://music.163.com\",\r\n\t\t\t\t\"Host\": \"music.163.com\",\r\n\t\t\t\t\"X-Real-IP\": \"27.38.4.87\"\r\n\t\t\t}\r\n\t\t\tu_cookies = await self.config.user_cookies()\r\n\t\t\tcookies = {\"os\": \"ios\"}\r\n\t\t\tcookies.update(u_cookies)\r\n\t\t\tsongapi = 'http://music.163.com/weapi/song/enhance/player/url?csrf_token='\r\n\t\t\tr = requests.post(songapi, headers=headers, data=param_data, verify=False, cookies=cookies)\r\n\t\t\treal_url = re.search(r'http.*\\.((mp3)|(flac))',r.text)\r\n\t\t\tif real_url:\r\n\t\t\t\turl_best = real_url.group()\r\n\t\t\t\tplay = ctx.bot.get_command(\"play\")\r\n\t\t\t\tawait ctx.invoke(play, query = url_best)\r\n\t\t\telse:\r\n\t\t\t\tawait ctx.send(\"Can't get this song. Might need netease music VIP.\")\r\n\t\telse:\r\n\t\t\tawait ctx.send(\"Can't find song id!\")\r\n", "repo_name": "MeowingCafe/MeowingCafe-Cogs", "sub_path": "wyyyy/wyyyy.py", "file_name": "wyyyy.py", "file_ext": "py", "file_size_in_byte": 4030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "redbot.core.commands.Cog", "line_number": 11, "usage_type": "attribute"}, {"api_name": "redbot.core.commands", "line_number": 11, "usage_type": "name"}, {"api_name": "redbot.core.Config.get_conf", "line_number": 19, "usage_type": "call"}, {"api_name": "redbot.core.Config", "line_number": 19, "usage_type": "name"}, {"api_name": "redbot.core.commands.Context", "line_number": 26, "usage_type": "attribute"}, {"api_name": "redbot.core.commands", "line_number": 26, "usage_type": "name"}, {"api_name": "redbot.core.commands.group", "line_number": 24, "usage_type": "call"}, {"api_name": "redbot.core.commands", "line_number": 24, "usage_type": "name"}, {"api_name": "redbot.core.checks.admin_or_permissions", "line_number": 25, "usage_type": "call"}, {"api_name": "redbot.core.checks", "line_number": 25, "usage_type": "name"}, {"api_name": "redbot.core.commands.Context", "line_number": 30, "usage_type": "attribute"}, {"api_name": "redbot.core.commands", "line_number": 30, "usage_type": "name"}, {"api_name": "redbot.core.commands.Context", "line_number": 34, "usage_type": "attribute"}, {"api_name": "redbot.core.commands", "line_number": 34, "usage_type": "name"}, {"api_name": "redbot.core.commands.Context", "line_number": 50, "usage_type": "attribute"}, {"api_name": "redbot.core.commands", "line_number": 50, "usage_type": "name"}, {"api_name": "re.search", "line_number": 62, "usage_type": "call"}, {"api_name": "re.search", "line_number": 64, "usage_type": "call"}, {"api_name": "re.search", "line_number": 68, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 74, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 74, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 74, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 76, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 110, "usage_type": "call"}, {"api_name": "re.search", "line_number": 111, "usage_type": "call"}, {"api_name": "redbot.core.commands.command", "line_number": 57, "usage_type": "call"}, {"api_name": "redbot.core.commands", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "28312629855", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n__author__ = 'Li qiaoxia'\n\nfrom twisted.words.protocols.irc import lowDequote\nfrom builtins import str\nfrom _ast import Str\nimport logging; logging.basicConfig(level=logging.INFO)\nfrom chardet.chardistribution import Big5DistributionAnalysis\nfrom aiohttp.web_urldispatcher import get\nfrom _socket import IPPORT_RESERVED\nfrom socket import *\nimport traceback\n \n\nimport logging; logging.basicConfig(level=logging.INFO)\nfrom switch import switch\n\nimport asyncio, os, json, time\nfrom datetime import datetime\nfrom multiprocessing import Pool\nimport os, time, random\nfrom config import configs\nimport mysql.connector\nimport socket\n\n\n'''\nasync tcp application.\n'''\n\n\n\nPlCstationtype= {\n 'typename':'A',\n 'register':'a',\n 'instruction':'aRSC',\n }\n \n# {'typename':'B',\n# 'register':'b',\n# 'instruction':'bRSC'\n# },\n \n \n\ndef sendmes(stationnum,ipaddr,port,stationtype,data):\n #waiting for content to be filled in\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # 建立连接:\n s.connect((ipaddr, port))\n # 接收欢迎消息:\n #print(s.recv(1024).decode('utf-8'))\n # 将读到的信息发出去\n for data in [b'Michael', b'Tracy', b'Sarah']:\n # 发送数据:\n s.send(data)\n print(s.recv(1024).decode('utf-8'))\n s.send(b'exit')\n s.close() \n pass\n\n\n\ndef plccmd(stationnum,instr,data=''):\n \n instruction='%'\n instruction+=stationnum\n\n #stationnum本来就是字符串，需转换成ascii\n \n instruction+='#' #命令指令\n \n instruction+= instr+data\n #BCC\n bcc=0\n insascii=instruction.encode('ascii')\n print(\"first ascii\",insascii)\n for i in range(len(insascii)) :\n# print (insascii[i])\n bcc^=insascii[i]\n# print('bcc=',bcc)\n \n highbcc= hex(int(bcc/16))\n lowbcc= hex(int(bcc%16))\n\n print('high=%s,low=%s' % (highbcc.encode(\"ascii\"),lowbcc.encode(\"ascii\")))\n \n insascii+=highbcc.strip(\"0x\").encode(\"ascii\")\n insascii+=lowbcc.strip(\"0x\").encode(\"ascii\")\n\n# insascii+=hex(10).encode(\"ascii\") #0x0a的值转为ascii码，终于对了。\n insascii+=b'\\x0d'\n\n\n# insascii+=chr(int('0a', 16)).encode(\"ascii\")\n \n print(insascii)\n return insascii\n \ndef rcvplcmsg(msgdatacome,instr):\n \n # msgdata= msgdatacome.decode(\"utf-8\")\n datafromplc={\n 'getdata':\"\",\n 'res':False\n }\n msgdata= msgdatacome\n print(\"msgdata=\",msgdata)\n datafromplc['res']=False\n l= len(msgdata)\n \n \n \n if msgdata[l-1:l]== b'\\x0d' :\n print('get cr =',msgdata[l-1:l])\n \n if msgdata[0:1] != b'%' :\n logging.info(\"first character should be %%,not %s \" % msgdata[0])\n \n stationnum=msgdata[1:2]\n \n if msgdata[3:4] == b'$' : #正常应答\n if msgdata[4:6]==instr.encode(\"ascii\"):\n #bcc 校验\n\n mlen=len(msgdatacome)\n print(\"mlen=\",mlen)\n bcc=0\n \n for i in range(mlen-3) :\n# print (msgdata[i])\n bcc^=msgdata[i]\n# print('bcc=',bcc)\n# getbcc=int(msgdatacome[mlen-3:mlen-3],16)*16+int(msgdatacome[mlen-2:mlen-2])\n \n getbcc = int(str(msgdata[mlen-3:mlen-1],encoding=\"utf-8\"),16)\n \n# print('getbcc=',msgdata[mlen-3:mlen-1],getbcc)\n if getbcc == bcc :\n # print(\"ca=\",msgdatacome[4:6])\n ca=msgdatacome[4:6].decode()\n print(\"ca=\",ca)\n for case in switch(ca):\n if case('RD'): #读取数据寄存器\n #从第8个字符到len-3 之间的数据，每4个字符一组，16进制，高位在后，低位在前\n data=msgdata[6:mlen-3]\n i=0\n data0 =\"\"\n if len(data) <4 :\n break\n while (i 0:\n torch.cuda.manual_seed_all(seed)\n\n def _top_k_top_p_filtering(self, logits):\n top_k = 0\n top_p = 0.9\n filter_value=-float('Inf')\n top_k = min(top_k, logits.size(-1)) # Safety check\n if top_k > 0:\n # Remove all tokens with a probability less than the last token of the top-k\n indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]\n logits[indices_to_remove] = filter_value\n\n if top_p > 0.0:\n sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)\n\n # Remove tokens with cumulative probability above the threshold\n sorted_indices_to_remove = cumulative_probs > top_p\n # Shift the indices to the right to keep also the first token above the threshold\n sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()\n sorted_indices_to_remove[..., 0] = 0\n\n # scatter sorted tensors to original indexing\n indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)\n logits[indices_to_remove] = filter_value\n return logits\n\n def __sample_sequences__(self, model, length, context, num_samples):\n context = torch.tensor(context, dtype=torch.long, device=self.device)\n context = context.unsqueeze(0).repeat(num_samples, 1)\n generated = context\n result = []\n with torch.no_grad():\n for _ in trange(length):\n\n inputs = {'input_ids': generated}\n\n outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet/CTRL (cached hidden-states)\n next_token_logits = outputs[0][:, -1, :]\n\n # repetition penalty from CTRL (https://arxiv.org/abs/1909.05858)\n for i in range(num_samples):\n for _ in set(generated[i].tolist()):\n next_token_logits[i, _] /= 1.0\n \n filtered_logits = self._top_k_top_p_filtering(next_token_logits)\n if self.temperature == 0: # greedy sampling:\n next_token = torch.argmax(filtered_logits, dim=-1).unsqueeze(-1)\n else:\n next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)\n generated = torch.cat((generated, next_token), dim=1)\n result.append(generated)\n return result\n\n def __init__(self, model_scale=0, dummy=False):\n if not dummy:\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n self.n_gpu = torch.cuda.device_count()\n self.set_seed(42, self.n_gpu)\n self.num_samples = 1\n if model_scale == 0:\n self.tokenizer = GPT2Tokenizer.from_pretrained(\"distilgpt2\")\n self.model = GPT2LMHeadModel.from_pretrained(\"distilgpt2\")\n elif model_scale == 1:\n self.tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n self.model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n elif model_scale == 2:\n self.tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-medium\")\n self.model = GPT2LMHeadModel.from_pretrained(\"gpt2-medium\")\n elif model_scale == 3:\n self.tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-large\")\n self.model = GPT2LMHeadModel.from_pretrained(\"gpt2-large\")\n else:\n self.tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-xl\")\n self.model = GPT2LMHeadModel.from_pretrained(\"gpt2-xl\")\n\n self.model.to(self.device)\n self.model.eval()\n self.temperature = 1.0\n self.is_dummy = False\n else:\n self.is_dummy = True\n\n def generate_texts(self, prefix, length, num_samples):\n\n if self.is_dummy:\n return [\"This is a dummy text.\"]\n\n context_tokens = self.tokenizer.encode(prefix, add_special_tokens=False)\n\n out = self.__sample_sequences__(model=self.model, context=context_tokens, length=length, num_samples=num_samples)\n\n for t in out:\n t = t[:, len(context_tokens):].tolist()\n result = []\n for o in t:\n text = self.tokenizer.decode(o, clean_up_tokenization_spaces=True)\n result.append(text)\n\n return result\n\n def generate_text(self, prefix, length):\n texts = self.generate_texts(prefix, length, 1)\n if (len(texts) > 0):\n return texts[0]\n else:\n return \"\"\n\n # smaller result is more probable\n def score_probability(self, sentence):\n # https://github.com/huggingface/transformers/issues/1009\n \"\"\"tokenize_input = self.tokenizer.tokenize(sentence)\n tensor_input = torch.tensor([ [self.tokenizer.eos_token_id] + self.tokenizer.convert_tokens_to_ids(tokenize_input)])\n tensor_input = tensor_input.to(self.device)\n with torch.no_grad():\n outputs = self.model(tensor_input, labels=tensor_input)\n _, logits = outputs[:2] # first parameter is loss\n\n lp = 0.0\n for i in range(len(tokenize_input)):\n masked_index = i\n predicted_score = logits[0, masked_index].cpu()\n #predicted_prob = F.softmax(np.array(predicted_score))\n predicted_prob = F.softmax(predicted_score)\n predicted_prob = np.array(predicted_prob)\n lp += np.log(predicted_prob[self.tokenizer.convert_tokens_to_ids([tokenize_input[i]])[0]])\n return lp \"\"\"\n\n # https://github.com/huggingface/transformers/issues/473\n tokenize_input = self.tokenizer.tokenize(sentence)\n tensor_input = torch.tensor([self.tokenizer.convert_tokens_to_ids(tokenize_input)])\n tensor_input = tensor_input.to(self.device)\n outputs = self.model(tensor_input, labels=tensor_input)\n loss, _ = outputs[:2]\n return math.exp(loss)", "repo_name": "padmalcom/InteractiveStorytelling", "sub_path": "legacy/gpt2_old.py", "file_name": "gpt2_old.py", "file_ext": "py", "file_size_in_byte": 6588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.topk", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cumsum", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 49, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 74, "usage_type": "attribute"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 78, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 78, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 79, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 79, "usage_type": "name"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 81, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 81, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 82, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 82, "usage_type": "name"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 84, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 84, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 85, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 85, "usage_type": "name"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 87, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 87, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 88, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 88, "usage_type": "name"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 90, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 90, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 91, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 147, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 151, "usage_type": "call"}]} +{"seq_id": "30337209942", "text": "#!/usr/bin/env python3\r\nfrom typing import *\r\nimport requests\r\nimport re\r\n\r\n\r\ndef read_to_delimiter(s: str, start_idx: int, delimiter: str, inclusive: bool = False) -> Tuple[str, int]:\r\n end_idx = s.find(delimiter, start_idx)\r\n if end_idx == -1:\r\n return (s[start_idx:], -1)\r\n if inclusive:\r\n end_idx += len(delimiter)\r\n return (s[start_idx:end_idx], end_idx)\r\n\r\n\r\ndef read_to_regex_match(s: str, start_idx: int, regex: str) -> Tuple[str, str, int]:\r\n match = re.search(regex, s[start_idx:], re.M)\r\n if not match:\r\n return (s[start_idx:], None, -1)\r\n start_match_idx = start_idx + match.span()[0]\r\n end_idx = start_idx + match.span()[1]\r\n return (s[start_idx:start_match_idx], match.group(), end_idx)\r\n\r\n\r\ndef skip_optional(s: str, start_idx: int, text: str) -> int:\r\n if s[start_idx:].startswith(text):\r\n return start_idx + len(text)\r\n return start_idx\r\n\r\n\r\ndef skip_optional_regex(s: str, start_idx: int, regex: str) -> int:\r\n match = re.match(regex, s[start_idx:], re.M)\r\n if not match:\r\n return start_idx\r\n return start_idx + match.span()[1] - match.span()[0]\r\n\r\n\r\ndef sql_to_columns() -> Dict[str, List[str]]:\r\n \"\"\"\r\n Pulls the latest table structures from the MusicBrainz GitHub and returns the columns for each table.\r\n\r\n :return: tables_to_columns\r\n \"\"\"\r\n sql_commented = requests.get(\r\n 'https://raw.githubusercontent.com/metabrainz/musicbrainz-server/master/admin/sql/CreateTables.sql').text\r\n sql_lines = sql_commented.split('\\n')\r\n for i in range(len(sql_lines)):\r\n sql_lines[i] = read_to_regex_match(sql_lines[i], 0, r'(--|$)')[0]\r\n sql = '\\n'.join(sql_lines)\r\n ignore_col_names = ['CONSTRAINT', 'INDEX', 'KEY', 'UNIQUE', 'PRIMARY', 'FULLTEXT', 'SPATIAL', 'CHECK']\r\n\r\n tables_to_columns = {}\r\n start_idx = 0\r\n nesting = {'CREATE': 0, 'parens': 0}\r\n current_table = None\r\n current_token_id = 0\r\n next_token_id = 0\r\n while start_idx >= 0:\r\n if nesting['CREATE'] == 0:\r\n _discard_, start_idx = read_to_delimiter(sql, start_idx, 'CREATE TABLE ', inclusive=True)\r\n if start_idx >= 0:\r\n start_idx = skip_optional(sql, start_idx, 'IF NOT EXISTS ')\r\n current_table, _discard_, start_idx = read_to_regex_match(sql, start_idx, r'[ \\t\\n]+')\r\n _discard_, _discard2_, start_idx = read_to_regex_match(sql, start_idx, r'[(]')\r\n tables_to_columns[current_table] = []\r\n nesting['CREATE'] = 1\r\n else:\r\n if nesting['parens'] == 0:\r\n start_idx = skip_optional_regex(sql, start_idx, r'[ \\t\\n]+')\r\n token, boundary, start_idx = read_to_regex_match(sql, start_idx, r'[(,)]')\r\n if next_token_id == current_token_id:\r\n next_token_id += 1\r\n col_name, _discard_, _discard2_ = read_to_regex_match(token, 0, r'[ \\t\\n]+')\r\n if col_name not in ignore_col_names:\r\n tables_to_columns[current_table].append(col_name)\r\n if boundary == '(':\r\n nesting['parens'] = 1\r\n elif boundary == ',':\r\n current_token_id += 1\r\n elif boundary == ')':\r\n current_token_id += 1\r\n nesting['CREATE'] = 0\r\n else:\r\n _discard_, boundary, start_idx = read_to_regex_match(sql, start_idx, r'[()]')\r\n if boundary == '(':\r\n nesting['parens'] += 1\r\n elif boundary == ')':\r\n nesting['parens'] -= 1\r\n return tables_to_columns\r\n", "repo_name": "dylanburati/mbz-to-csv", "sub_path": "sql_to_columns.py", "file_name": "sql_to_columns.py", "file_ext": "py", "file_size_in_byte": 3678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "re.search", "line_number": 17, "usage_type": "call"}, {"api_name": "re.M", "line_number": 17, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 32, "usage_type": "call"}, {"api_name": "re.M", "line_number": 32, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "10601513915", "text": "import matplotlib.pyplot as plt\nimport os\nimport pandas as pd\n\n\ncurso_dir = 'C:\\\\Users\\\\tiagog\\\\Documents\\\\curso-python'\ndata_dir = curso_dir + '\\\\datacamp\\\\python_programer\\\\dates_times'\nos.chdir(data_dir)\n\n# Load CSV into the rides variable\nrides = pd.read_csv('capital-onebike.csv',\n parse_dates=['Start date', 'End date'])\n\n# Import matplotlib\n\n# Resample rides to monthly, take the size, plot the results\nrides.resample('M', on='Start date')\\\n .size()\\\n .plot(ylim=[0, 150])\n\n# Show the results\nplt.show()\n", "repo_name": "tgpmoraes/curso-python", "sub_path": "datacamp/python_programer/dates_times/plot_date_pandas.py", "file_name": "plot_date_pandas.py", "file_ext": "py", "file_size_in_byte": 536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.chdir", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "11085253853", "text": "#importing beautifulSoup library for scrapping.\r\nfrom bs4 import BeautifulSoup\r\n#importing requests library for sending request to url for getting imformation from their website.\r\nimport requests\r\n\r\n#sending request to website.\r\nhtml_text = requests.get('https://en.wikipedia.org/wiki/AliExpress').text\r\n#using lxml parser for parsing.\r\nsoup = BeautifulSoup(html_text, 'lxml')\r\n\r\n#scrapping data using class of that element that are available on this url.\r\nheading = soup.find_all('span', class_ = 'mw-page-title-main')\r\nsidebar_links = soup.find('li', class_ = 'mw-list-item').text.replace(' ','')\r\nheadline = soup.find_all('span', class_ = 'mw-headline')\r\n\r\n#scraping all the href elements present in the given url.\r\nurls = []\r\nfor link in soup.find_all('a'):\r\n print(link.get('href'))\r\n \r\nprint(f'''\r\nHeadings:: {heading}\r\nSidebar Links:: {sidebar_links}\r\nHeadline:: {headline}\r\n''')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "yash534/Web-Scraping-", "sub_path": "WS practice/wikipedia scrap.py", "file_name": "wikipedia scrap.py", "file_ext": "py", "file_size_in_byte": 908, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "11758065699", "text": "# shell : python3 /home/pi/skripts/prod/Log_Relais_Ini.py\n# Programm zum Initialisieren / reset der Text-Datei Log_Relais.txt\n\n#!/usr/bin/python\n\nfrom pathlib import Path\n\nimport time , GVS\n\nmy_dir = GVS.RelLogDir\nmy_file = GVS.RelLogFile\n\n## für Testzwecke :\n#my_dir = 'test' # für Test Verzeichnis existiert nicht\n#my_file = 'test.txt' # für Test Verzeichnis existiert nicht\n\nfile_name = my_dir +'/' + my_file\ntime_stamp = time.strftime(\"%Y.%m.%d %H:%M:%S\")\n\ntry: # Prüfen , ob Verzeichnis und Datei existieren\n \n datei = open(file_name, 'r') # öffnen zum lesen\n print ()\n \nexcept IOError as e :\n print ('IOError' , str(e))\n my_dir = Path(my_dir) \n if not my_dir.is_dir():\n print (time_stamp,' Verzeichnis existiert nicht')\n else :\n my_file = Path(file_name)\n if not my_file.is_file():\n print (time_stamp,' Datei existiert nicht')\n print ()\n \nelse: # Verarbeitung nur wenn Verzeichnis und Datei existieren\n # vorhandene Datei öffnen , Inhalt löschen und überschreiben\n datei = open(file_name,'w') # öffnen zum schreiben\n Text = time_stamp + ' Logdatei für Schaltvorgänge der Relais initialisiert'\n datei.write(Text)\n # vorhandene Datei öffnen , Inhalt in neuer Zeile anhängen\n datei = open(file_name,'a') # öffnen zum anhängen\n Text = '\\n' + time_stamp + ' ' + 83 * '-'\n datei.write(Text)\n # vorhandene Datei öffnen , Inhalt ausgeben\n datei = open(file_name,'r') # öffnen zum lesen\n print (time_stamp,' Datei ',file_name ,' initialisiert')\n print ()\n print ('Dateiinhalt nach Initialisierung , zeilenweise : ')\n print ()\n print(datei.read())\n #print(datei.readlines())\n print ()\n datei.close()\n \n\n", "repo_name": "torstenkuhn77/Peter", "sub_path": "skripts/prod/Log_Relais_Ini.py", "file_name": "Log_Relais_Ini.py", "file_ext": "py", "file_size_in_byte": 1758, "program_lang": "python", "lang": "de", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "GVS.RelLogDir", "line_number": 10, "usage_type": "attribute"}, {"api_name": "GVS.RelLogFile", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "25458130975", "text": "import pytest\nfrom datab import *\nfrom sleniumPOm import *\n\n\ndef test_1(self):\n assert 2 == 2\n\n\n@pytest.fixture\ndef driver(self):\n path = '/Users/hothaifa/Desktop/chromedriver'\n driver = webdriver.Chrome(path)\n return driver\n\n\n@pytest.fixture\ndef db_connector(self):\n db_connector = DBcon('movieland')\n return db_connector\n\n\ndef test_movie_name(self, driver, db_connector):\n results = db_connector.select('movies', '*')\n actual = results[0][1]\n\n google_page = GooglePage(driver)\n google_page.search('batman imdb')\n google_page.click_on_link()\n imdb_page = IMDBPage(driver)\n expected = imdb_page.heading_text()\n\n assert actual == expected\n", "repo_name": "devopsPRO27/seleniumRapyd1", "sub_path": "01_08/selenium_dn_test.py", "file_name": "selenium_dn_test.py", "file_ext": "py", "file_size_in_byte": 680, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pytest.fixture", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 17, "usage_type": "attribute"}]} +{"seq_id": "4010591670", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 20 08:26:52 2019\r\n\r\n@author: HLB\r\n\"\"\"\r\n\r\nimport scipy.io as sio\r\nimport numpy as np\r\nfrom sklearn.decomposition import PCA\r\nfrom build_EMP import build_emp \r\n\r\ndef pca_whitening(image, number_of_pc):\r\n\r\n shape = image.shape\r\n \r\n image = np.reshape(image, [shape[0]*shape[1], shape[2]])\r\n number_of_rows = shape[0]\r\n number_of_columns = shape[1]\r\n pca = PCA(n_components = number_of_pc)\r\n image = pca.fit_transform(image)\r\n pc_images = np.zeros(shape=(number_of_rows, number_of_columns, number_of_pc),dtype=np.float32)\r\n for i in range(number_of_pc):\r\n pc_images[:, :, i] = np.reshape(image[:, i], (number_of_rows, number_of_columns))\r\n \r\n return pc_images\r\n\r\ndef load_data(dataset):\r\n if dataset == 'Indian':\r\n image_file = r'.\\datasets/Indian\\indian_pines_corrected.mat'\r\n label_file = r'.\\datasets/Indian\\Indian_pines_gt.mat'\r\n image_data = sio.loadmat(image_file)\r\n label_data = sio.loadmat(label_file)\r\n image = image_data['indian_pines_corrected']\r\n label = label_data['indian_pines_gt']\r\n elif dataset == 'Pavia':\r\n image_file = r'.\\datasets\\Pavia\\Pavia.mat'\r\n label_file = r'.\\datasets\\Pavia\\Pavia_groundtruth.mat'\r\n image_data = sio.loadmat(image_file)\r\n label_data = sio.loadmat(label_file) \r\n image = image_data['paviaU']#pavia1\r\n label = label_data['paviaU_gt']#pavia1\r\n elif dataset == 'CASI':\r\n image_file = r'.\\datasets\\Houston\\CASI.mat'\r\n label_file = r'.\\datasets\\Houston\\CASI_gnd_flag.mat'\r\n image_data = sio.loadmat(image_file)\r\n label_data = sio.loadmat(label_file) \r\n image = image_data['CASI']\r\n label = label_data['gnd_flag'] # houston \r\n else:\r\n raise Exception('dataset does not find')\r\n image = image.astype(np.float32)\r\n \r\n return image, label\r\n \r\n\r\ndef readdata(type, dataset, windowsize, train_num, val_num, num):\r\n\r\n or_image, or_label = load_data(dataset)\r\n # image = np.expand_dims(image, 2)\r\n halfsize = int((windowsize-1)/2)\r\n number_class = np.max(or_label)\r\n \r\n image = np.pad(or_image, ((halfsize, halfsize), (halfsize, halfsize), (0, 0)), 'edge')\r\n label = np.pad(or_label, ((halfsize, halfsize), (halfsize, halfsize)), 'constant',constant_values=0)\r\n \r\n if type == 'PCA':\r\n image1 = pca_whitening(image, number_of_pc = 3)\r\n elif type == 'EMP':\r\n image1 = pca_whitening(image, number_of_pc = 4)\r\n num_openings_closings = 3\r\n emp_image = build_emp(base_image=image1, num_openings_closings=num_openings_closings)\r\n image1 = emp_image\r\n elif type == 'none':\r\n image1 = np.copy(image)\r\n else:\r\n raise Exception('type does not find')\r\n image = (image1 - np.min(image1)) / (np.max(image1) - np.min(image1)) \r\n #set the manner of selecting training samples \r\n \r\n \r\n n = np.zeros(number_class,dtype=np.int64)\r\n for i in range(number_class):\r\n temprow, tempcol = np.where(label == i + 1)\r\n n[i] = len(temprow) \r\n total_num = np.sum(n)\r\n \r\n nTrain_perClass = np.ones(number_class,dtype=np.int64) * train_num\r\n for i in range(number_class):\r\n if n[i] <= nTrain_perClass[i]: \r\n nTrain_perClass[i] = 15 \r\n ###\r\n nValidation_perClass = (n/total_num)*val_num\r\n nvalid_perClass = nValidation_perClass.astype(np.int32) \r\n \r\n index = []\r\n flag = 0\r\n fl = 0\r\n\r\n \r\n bands = np.size(image,2) \r\n validation_image = np.zeros([np.sum(nvalid_perClass), windowsize, windowsize, bands], dtype=np.float32)\r\n validation_label = np.zeros(np.sum(nvalid_perClass), dtype=np.int64)\r\n train_image = np.zeros([np.sum(nTrain_perClass), windowsize, windowsize, bands], dtype=np.float32)\r\n train_label = np.zeros(np.sum(nTrain_perClass),dtype=np.int64)\r\n train_index = np.zeros([np.sum(nTrain_perClass), 2], dtype = np.int32) \r\n val_index = np.zeros([np.sum(nvalid_perClass), 2], dtype = np.int32) \r\n \r\n for i in range(number_class): \r\n temprow, tempcol = np.where(label == i + 1)\r\n matrix = np.zeros([len(temprow),2], dtype=np.int64)\r\n matrix[:,0] = temprow\r\n matrix[:,1] = tempcol\r\n np.random.seed(num)\r\n np.random.shuffle(matrix)\r\n \r\n temprow = matrix[:,0]\r\n tempcol = matrix[:,1] \r\n index.append(matrix)\r\n\r\n for j in range(nTrain_perClass[i]):\r\n train_image[flag + j, :, :, :] = image[(temprow[j] - halfsize):(temprow[j] + halfsize + 1),\r\n (tempcol[j] - halfsize):(tempcol[j] + halfsize + 1)]\r\n train_label[flag + j] = i\r\n train_index[flag + j] = matrix[j,:]\r\n flag = flag + nTrain_perClass[i]\r\n\r\n for j in range(nTrain_perClass[i], nTrain_perClass[i] + nvalid_perClass[i]):\r\n validation_image[fl + j-nTrain_perClass[i], :, :,:] = image[(temprow[j] - halfsize):(temprow[j] + halfsize + 1),\r\n (tempcol[j] - halfsize):(tempcol[j] + halfsize + 1)]\r\n validation_label[fl + j-nTrain_perClass[i] ] = i \r\n val_index[fl + j-nTrain_perClass[i]] = matrix[j,:]\r\n fl =fl + nvalid_perClass[i]\r\n \r\n\r\n return train_image, train_label, validation_image, validation_label,nTrain_perClass, nvalid_perClass,train_index, val_index, index, image, label,total_num\r\n", "repo_name": "Candy-CY/Hyperspectral-Image-Classification-Models", "sub_path": "SSMTR/data_read.py", "file_name": "data_read.py", "file_ext": "py", "file_size_in_byte": 5526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 237, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.reshape", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 32, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 32, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 33, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 39, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 40, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 46, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 65, "usage_type": "call"}, {"api_name": "build_EMP.build_emp", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.size", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 115, "usage_type": "attribute"}]} +{"seq_id": "26632482655", "text": "from django.conf.urls import url\nfrom . import views\nurlpatterns = [\n\turl(r'^main$', views.index),\n\turl(r'^register$', views.register),\n\turl(r'^login$', views.login),\n\turl(r'^friends$', views.friends),\n\turl(r'^add_friend/(?P\\d+)$', views.add_friend),\n\turl(r'^user/(?P\\d+)$', views.user_profile),\n\turl(r'^remove_friend/(?P\\d+)$', views.remove_friend),\n\turl(r'^logout$', views.logout)\n]", "repo_name": "mariatrojo/pythonbelt_2", "sub_path": "apps/friends/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 411, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "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"}]} +{"seq_id": "43655803518", "text": "import os, subprocess\r\n\r\nmodules = [\r\n (\"dearpygui\", \"import dearpygui.dearpygui as dpg\"),\r\n (\"httpx\", \"import httpx\"),\r\n (\"keystone-engine\", \"from keystone import *\"),\r\n (\"capstone\", \"from capstone import *\")\r\n]\r\n\r\nneeded = []\r\nfor module, import_string in modules:\r\n try:\r\n exec(import_string)\r\n except ImportError:\r\n needed.append(module)\r\n\r\nif len(needed) != 0:\r\n count = 0\r\n for module in needed:\r\n count += 1\r\n print(f\"[i] Installing Required Modules... | {count} / {len(needed)}\")\r\n subprocess.check_call([\"pip3\", \"install\", module, \"-q\"])\r\n\r\nimport dearpygui.dearpygui as dpg\r\nimport httpx\r\nfrom keystone import *\r\nimport tkinter as tk\r\nfrom capstone import *\r\n\r\nfilepath = os.path.abspath(__file__)\r\nfilename = os.path.basename(__file__)\r\nfolderpath = os.getcwd()\r\n\r\nVERSION = \"0.1.0\"\r\nUPDATE_URL = \"https://raw.githubusercontent.com/YeetDisDude/Cpp2IL-gui/main/version.txt\"\r\n\r\ndef check_update():\r\n dpg.set_value(f\"updatetxt\", \"Update Status: Checking for updates...\")\r\n r = httpx.get(UPDATE_URL)\r\n if r.text.strip() != VERSION:\r\n dpg.set_value(\"updatetxt\", f\"Update Status: Version {VERSION} is Outdated! Download the latest version from github.com/YeetDisDude/Arm-Converter\")\r\n else:\r\n dpg.set_value(f\"updatetxt\", f\"Update Status: Arm Converter version {VERSION} is up to date!\")\r\n\r\n\r\ndef armtohex64error(e):\r\n if e.errno == KS_ERR_ASM_MNEMONICFAIL:\r\n arm64hex = \"Invalid Mnemonic\"\r\n dpg.set_value(\"armtohexarm64\", arm64hex)\r\n elif e.errno == KS_ERR_ASM_INVALIDOPERAND:\r\n arm64hex = \"Invalid Operand\"\r\n dpg.set_value(\"armtohexarm64\", arm64hex)\r\n else:\r\n arm64hex = \"Assembly Error\"\r\n dpg.set_value(\"armtohexarm64\", arm64hex)\r\n\r\ndef armtohex7error(e):\r\n if e.errno == KS_ERR_ASM_MNEMONICFAIL:\r\n armv7hex = \"Invalid Mnemonic\"\r\n dpg.set_value(\"armtohexarmv7\", armv7hex)\r\n elif e.errno == KS_ERR_ASM_INVALIDOPERAND:\r\n armv7hex = \"Invalid Operand\"\r\n dpg.set_value(\"armtohexarmv7\", armv7hex)\r\n else:\r\n armv7hex = \"Assembly Error\"\r\n dpg.set_value(\"armtohexarmv7\", armv7hex)\r\n\r\ndef ArmToHex(sender, data):\r\n print(data)\r\n ksarm64 = Ks(KS_ARCH_ARM64, KS_MODE_LITTLE_ENDIAN)\r\n ksarmv7 = Ks(KS_ARCH_ARM, KS_MODE_ARM)\r\n try:\r\n bytecode_arm64, _ = ksarm64.asm(data)\r\n arm64hex = ' '.join('{:02x}'.format(x) for x in bytecode_arm64)\r\n arm64hex = arm64hex.upper()\r\n dpg.set_value(\"armtohexarm64\", arm64hex)\r\n except KsError as e:\r\n armtohex64error(e=e)\r\n\r\n try:\r\n bytecode_v7, _ = ksarmv7.asm(data)\r\n armv7hex = ' '.join('{:02x}'.format(x) for x in bytecode_v7)\r\n armv7hex = armv7hex.upper()\r\n dpg.set_value(\"armtohexarmv7\", armv7hex)\r\n except KsError as e:\r\n armtohex7error(e=e)\r\n\r\ndef HexToArm(sender, data):\r\n data = data.upper()\r\n print(data)\r\n csarm64 = Cs(CS_ARCH_ARM64, CS_MODE_ARM)\r\n csarmv7 = Cs(CS_ARCH_ARM, CS_MODE_ARM)\r\n try:\r\n for insn in csarm64.disasm(bytes.fromhex(data), 0):\r\n dpg.set_value(\"hextoarm64\", f\"{insn.mnemonic} {insn.op_str}\")\r\n for insn in csarmv7.disasm(bytes.fromhex(data), 0):\r\n dpg.set_value(\"hextoarmv7\", f\"{insn.mnemonic} {insn.op_str}\")\r\n except ValueError as e:\r\n dpg.set_value(\"hextoarmv7\", \"Invalid Hex\")\r\n dpg.set_value(\"hextoarm64\", \"Invalid Hex\")\r\n\r\n\r\n\r\n\r\ndef tab1(): # Arm to Hex\r\n with dpg.group():\r\n dpg.bind_font(default_font)\r\n dpg.add_text(\" \")\r\n dpg.add_text(\"Assembly Code\")\r\n dpg.add_input_text(multiline=True, width=450, height=150, tag=\"armtohexinput\", callback=ArmToHex)\r\n dpg.add_text(\" \"); dpg.add_separator(); dpg.add_text(\" \")\r\n dpg.add_input_text(label=\"Arm64\", multiline=True, width=450, height=150, readonly=True, tag=\"armtohexarm64\")\r\n dpg.add_text(\" \"); dpg.add_separator(); dpg.add_text(\" \")\r\n dpg.add_input_text(label=\"Armv7\", multiline=True, width=450, height=150, readonly=True, tag=\"armtohexarmv7\")\r\n dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \")\r\n\r\n\r\ndef tab2(): # Hex to Arm\r\n with dpg.group():\r\n dpg.add_text(\" \")\r\n dpg.add_text(\"Hex code\")\r\n dpg.add_input_text(multiline=True, width=450, height=150, tag=\"hextoarminput\", callback=HexToArm, uppercase=True)\r\n dpg.add_text(\" \"); dpg.add_separator(); dpg.add_text(\" \")\r\n dpg.add_input_text(label=\"Arm64\", multiline=True, width=450, height=150, readonly=True, tag=\"hextoarm64\")\r\n dpg.add_text(\" \"); dpg.add_separator(); dpg.add_text(\" \")\r\n dpg.add_input_text(label=\"Armv7\", multiline=True, width=450, height=150, readonly=True, tag=\"hextoarmv7\")\r\n dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \"); dpg.add_text(\" \")\r\n \r\n\r\ndef tabsetting(): # settings\r\n with dpg.group():\r\n dpg.add_text(\" \")\r\n dpg.add_button(label=\"Check update\", callback=check_update, width=150, height=50)\r\n dpg.add_text(\"Update Status: idle\", tag=\"updatetxt\")\r\n dpg.add_text(\" \")\r\n\r\nimguiW = 800\r\nimguiH = 500\r\n\r\n\r\ndpg.create_context()\r\ndpg.create_viewport()\r\ndpg.setup_dearpygui()\r\ndpg.set_viewport_small_icon(\"Assets/Icon.ico\")\r\ndpg.set_viewport_large_icon(\"Assets/Icon.ico\")\r\ndpg.set_viewport_title(\"Arm Converter\")\r\ndpg.set_viewport_width(imguiW + 16)\r\ndpg.set_viewport_height(imguiH + 38)\r\n\r\n\r\n\r\n\r\n\r\nwith dpg.font_registry():\r\n default_font = dpg.add_font(\"Assets/SF Pro Display Semibold.ttf\", 20)\r\n\r\nwith dpg.window(width=imguiW, height=imguiH, no_resize=False, label=f\"Arm Converter | Made by: YeetDisDude#0001 | Version {VERSION}\", tag=\"mainW\") as window:\r\n with dpg.tab_bar():\r\n with dpg.tab(label=\" Assembly to Hex \"):\r\n tab1()\r\n with dpg.tab(label=\" Hex to Assembly \"):\r\n tab2()\r\n with dpg.tab(label=\" Settings \"):\r\n tabsetting()\r\n\r\n\r\ndpg.show_viewport()\r\ndpg.start_dearpygui()\r\ndpg.destroy_context()", "repo_name": "YeetDisDude/Arm-Converter", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6160, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "subprocess.check_call", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 38, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 38, "usage_type": "name"}, {"api_name": "httpx.get", "line_number": 39, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 41, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 41, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 43, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 43, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 49, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 49, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 52, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 52, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 55, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 55, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 60, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 60, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 63, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 63, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 66, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 66, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 76, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 76, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 84, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 84, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 95, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 95, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 97, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 97, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 99, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 99, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_value", "line_number": 100, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 100, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.group", "line_number": 106, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 106, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.bind_font", "line_number": 107, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 107, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 108, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 108, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 109, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 109, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_input_text", "line_number": 110, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 110, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 111, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 111, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_separator", "line_number": 111, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui.add_input_text", "line_number": 112, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 112, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 113, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 113, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_separator", "line_number": 113, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui.add_input_text", "line_number": 114, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 114, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 115, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 115, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.group", "line_number": 119, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 119, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 120, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 120, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 121, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 121, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_input_text", "line_number": 122, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 122, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 123, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 123, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_separator", "line_number": 123, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui.add_input_text", "line_number": 124, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 124, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 125, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 125, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_separator", "line_number": 125, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui.add_input_text", "line_number": 126, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 126, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 127, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 127, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.group", "line_number": 131, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 131, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 132, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 132, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_button", "line_number": 133, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 133, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 134, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 134, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_text", "line_number": 135, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 135, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.create_context", "line_number": 141, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 141, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.create_viewport", "line_number": 142, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 142, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.setup_dearpygui", "line_number": 143, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 143, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_viewport_small_icon", "line_number": 144, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 144, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_viewport_large_icon", "line_number": 145, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 145, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_viewport_title", "line_number": 146, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 146, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_viewport_width", "line_number": 147, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 147, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.set_viewport_height", "line_number": 148, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 148, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.font_registry", "line_number": 154, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 154, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.add_font", "line_number": 155, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 155, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.window", "line_number": 157, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 157, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.tab_bar", "line_number": 158, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 158, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.tab", "line_number": 159, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 159, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.tab", "line_number": 161, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 161, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.tab", "line_number": 163, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 163, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.show_viewport", "line_number": 167, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 167, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.start_dearpygui", "line_number": 168, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 168, "usage_type": "name"}, {"api_name": "dearpygui.dearpygui.destroy_context", "line_number": 169, "usage_type": "call"}, {"api_name": "dearpygui.dearpygui", "line_number": 169, "usage_type": "name"}]} +{"seq_id": "1542053909", "text": "from typing import List\nfrom collections import Counter, defaultdict\n\n\nclass Solution:\n def countSubTrees(self, n: int, edges: List[List[int]], labels: str) -> List[int]:\n def dfs(node: int, parent: int):\n count = Counter(labels[node])\n\n for child in graph.get(node, []):\n if child != parent:\n count += dfs(child, node)\n\n res[node] = count[labels[node]]\n\n return count\n\n res = [0] * n\n\n graph = defaultdict(list)\n for a, b in edges:\n graph[a] += [b]\n graph[b] += [a]\n\n dfs(0, -1)\n\n return res\n", "repo_name": "YoeaKai/leet_code", "sub_path": "topic/number_of_nodes_in_the_sub_tree_with_the_same_label/number_of_nodes_in_the_sub_tree_with_the_same_label.py", "file_name": "number_of_nodes_in_the_sub_tree_with_the_same_label.py", "file_ext": "py", "file_size_in_byte": 638, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "7453850411", "text": "\"\"\"\nMade by *Lirgo*\n\nThis project is devoted to calculating and\npredicting the shape of a parabola\nbased on a video with a thrown ball\n\"\"\"\n\nimport cv2\nimport numpy as np\nimport time\n\npoints = []\n\ndef parabola(point_1, point_2, point_3):\n\n # we define coordinates to points\n\n x1, y1 = point_1\n x2, y2 = point_2\n x3, y3 = point_3\n\n \"\"\"\n the lines underneath are a result of a \n calculated parabola function using desmos\n that you can check out in this link:\n \n https://www.desmos.com/calculator/q5khflotcq?lang=en\n \"\"\"\n\n b = ((y1 - y2) * (x3 ** 2 - x1 ** 2) + (x1 ** 2 - x2 ** 2) * (y1 - y3)) / ((x3 - x1) * (x3 - x2) * (x1 - x2))\n a = ((y2 - y1) + b * (x1 - x2)) / (x2 ** 2 - x1 ** 2)\n c = y1 - a * x1 ** 2 - b * x1\n\n return a, b, c\n\ndef f(x, factors):\n a, b, c = factors\n return a * x ** 2 + b * x + c\n\ndef detect_circles(frame):\n blur = cv2.medianBlur(frame, 7)\n hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)\n lower_blue = np.array([10, 100, 100])\n upper_blue = np.array([40, 255, 255])\n mask = cv2.inRange(hsv, lower_blue, upper_blue)\n\n # convert image to grayscale image\n gray_image = cv2.cvtColor(cv2.bitwise_and(frame, frame, mask=mask), cv2.COLOR_BGR2GRAY)\n # convert the grayscale image to binary image\n ret, thresh = cv2.threshold(gray_image, 0, 255, 0)\n # calculate moments of binary image\n M = cv2.moments(thresh)\n # calculate x,y coordinate of center\n try:\n cX = int(M[\"m10\"] / M[\"m00\"])\n cY = int(M[\"m01\"] / M[\"m00\"])\n # put text and highlight the center\n cv2.circle(frame, (cX, cY), 5, (255, 255, 255), -1)\n return cX, cY\n except:\n return None\n\ndef main():\n video = cv2.VideoCapture('parabola.mp4')\n while True:\n try:\n _, frame = video.read()\n point = detect_circles(frame)\n if point is not None:\n points.append(point)\n cv2.imshow('frame', frame)\n print(points)\n if cv2.waitKey(0) == 27:\n break\n except cv2.error as e:\n # print(e)\n break\n\n print('done')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "L1RG0/Ball-trajectory-prediction", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.medianBlur", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.error", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "24788231928", "text": "import PySimpleGUI as sg\r\n\r\nsg.theme('Dark Blue 17')\r\nlayout = [\r\n [sg.Text('I am Stennar',font=(\"Helvetica\", 25),background_color='#327ba2',relief=sg.RELIEF_RIDGE)],\r\n [sg.Text('Welcome', font=(\"Arial\", 20))],\r\n [sg.Text('Select a host', size=(15, 1))],\r\n [sg.Radio('Image! ', \"RADIO1\", default=True, size=(10,1)), sg.Radio('Audio!', \"RADIO1\")],\r\n [sg.Text('Choose a operation', size=(15, 1))],\r\n [sg.Radio('Encrypt ', \"RADIO2\", default=True, size=(10,1)), sg.Radio('Decrypt', \"RADIO2\")],\r\n [sg.Text('File 1', size=(8, 1)), sg.Input(), sg.FileBrowse()],\r\n [sg.Text('Your secret message(Text/image file', size=(15, 1))],\r\n [sg.Checkbox('Image?', size=(10,1))],\r\n [sg.Text('File 2', size=(8, 1)), sg.Input(), sg.FileBrowse()],\r\n [sg.Submit(), sg.Cancel()]\r\n]\r\n\r\nwindow = sg.Window('Math Assingment', layout)\r\nevent, values = window.read()\r\nwindow.close()\r\nprint(values[0], values[1], values[2],values[3],values[4],values[5])\r\n\r\n \r\nif (values[0]==True and values[2]==True):\r\n import textimageEncryption as tp\r\n import textreader as t\r\n tp.img=values[4]\r\n text=t.call(values[6])\r\n tp.data=text\r\n tp.encode()\r\n sg.Popup('Success!')\r\n \r\n\r\nelif(values[0]==True and values[3]==True):\r\n import textimageDecryption as dp\r\n dp.img=values[4]\r\n s=dp.call()\r\n sg.Popup('Your secret message was',s)\r\n\r\nelif(values[1]==True and values[2]==True):\r\n import au1encryption as ae\r\n import textreader as t\r\n ae.song1=values[4]\r\n text=t.call(values[6])\r\n ae.a1=text\r\n ae.audioen()\r\n sg.Popup('Success!')\r\n\r\nelif(values[1]==True and values[3]==True):\r\n import au1decryption as ad\r\n ad.song1=values[4]\r\n s=ad.call()\r\n sg.Popup('your secret message was',s)\r\n\r\nelif(values[0]==True and values[2]==True and values[5]==True):\r\n import imageimageEncryption as iie\r\n iie.img1=values[4]\r\n iie.img2=values[6]\r\n iie.merge()\r\n sg.Popup('Succes output.png') \r\n \r\nelif(values[0]==True and values[3]==True and values[5]==True):\r\n import imageimageDecryption as iid\r\n iid.img=values[4]\r\n iid.unmerge()\r\n sg.Image(\"SecretMesaage.PNG\")\r\n\r\n\r\n\r\n\r\n#while looop\r\n#image display.\r\n\r\n \r\n\r\n\r\n", "repo_name": "CRUCIFIER0/Steganography", "sub_path": "final.py", "file_name": "final.py", "file_ext": "py", "file_size_in_byte": 2191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "PySimpleGUI.theme", "line_number": 3, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 5, "usage_type": "call"}, {"api_name": "PySimpleGUI.RELIEF_RIDGE", "line_number": 5, "usage_type": "attribute"}, {"api_name": "PySimpleGUI.Text", "line_number": 6, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 7, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 8, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 9, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 10, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 11, "usage_type": "call"}, {"api_name": "PySimpleGUI.Input", "line_number": 11, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 11, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 12, "usage_type": "call"}, {"api_name": "PySimpleGUI.Checkbox", "line_number": 13, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 14, "usage_type": "call"}, {"api_name": "PySimpleGUI.Input", "line_number": 14, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 14, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 15, "usage_type": "call"}, {"api_name": "PySimpleGUI.Cancel", "line_number": 15, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 18, "usage_type": "call"}, {"api_name": "textimageEncryption.img", "line_number": 27, "usage_type": "attribute"}, {"api_name": "textreader.call", "line_number": 28, "usage_type": "call"}, {"api_name": "textimageEncryption.data", "line_number": 29, "usage_type": "attribute"}, {"api_name": "textimageEncryption.encode", "line_number": 30, "usage_type": "call"}, {"api_name": "PySimpleGUI.Popup", "line_number": 31, "usage_type": "call"}, {"api_name": "textimageDecryption.img", "line_number": 36, "usage_type": "attribute"}, {"api_name": "textimageDecryption.call", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUI.Popup", "line_number": 38, "usage_type": "call"}, {"api_name": "au1encryption.song1", "line_number": 43, "usage_type": "attribute"}, {"api_name": "textreader.call", "line_number": 44, "usage_type": "call"}, {"api_name": "au1encryption.a1", "line_number": 45, "usage_type": "attribute"}, {"api_name": "au1encryption.audioen", "line_number": 46, "usage_type": "call"}, {"api_name": "PySimpleGUI.Popup", "line_number": 47, "usage_type": "call"}, {"api_name": "au1decryption.song1", "line_number": 51, "usage_type": "attribute"}, {"api_name": "au1decryption.call", "line_number": 52, "usage_type": "call"}, {"api_name": "PySimpleGUI.Popup", "line_number": 53, "usage_type": "call"}, {"api_name": "imageimageEncryption.img1", "line_number": 57, "usage_type": "attribute"}, {"api_name": "imageimageEncryption.img2", "line_number": 58, "usage_type": "attribute"}, {"api_name": "imageimageEncryption.merge", "line_number": 59, "usage_type": "call"}, {"api_name": "PySimpleGUI.Popup", "line_number": 60, "usage_type": "call"}, {"api_name": "imageimageDecryption.img", "line_number": 64, "usage_type": "attribute"}, {"api_name": "imageimageDecryption.unmerge", "line_number": 65, "usage_type": "call"}, {"api_name": "PySimpleGUI.Image", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "43336650801", "text": "from django.db import models\nfrom accounts.models.profile import Profile,Follower,Following\nfrom base.models.basemodel import BaseModel\nfrom django.dispatch import receiver\nfrom django.db.models.signals import post_save\n\nclass Post(BaseModel):\n profile = models.ForeignKey(Profile,on_delete=models.CASCADE,related_name='profile')\n caption = models.CharField(max_length=1000,blank=True, null=True)\n likes_counts = models.IntegerField(default = 0)\n\n def __str__(self) -> str:\n return self.caption\n\n\nclass PostImages(BaseModel):\n post_ref = models.ForeignKey(Post,on_delete=models.CASCADE,related_name='postimages')\n image = models.ImageField(upload_to='Uploads/Post')\n\n\nclass Likes(BaseModel):\n user = models.ManyToManyField(Profile,related_name='likes_by_users',blank=True) # who is liking\n image = models.ForeignKey(Post,on_delete=models.CASCADE,related_name='postlikes')\n \n\n @receiver(post_save,sender = Post)\n def CreateLikesObj(sender,instance,created,*args, **kwargs):\n if created:\n try:\n likes = Likes.objects.create(image = instance)\n except Exception as e:\n print(e)\n\n\n def __str__(self) -> str:\n return self.image.caption\n\n\n\nclass CommentPost(BaseModel):\n post = models.ForeignKey(Post,on_delete=models.CASCADE,related_name='comentpost')\n user = models.ForeignKey(Profile,on_delete=models.CASCADE,related_name='profilecommenting')\n comment = models.CharField(max_length=1000,blank=True, null=True)\n # likes_counts = models.IntegerField()\n\n # @receiver(post_save,sender = Post)\n # def CreateLikesObj(sender,instance,created,*args, **kwargs):\n # if created:\n # try:\n # likes = Likes.objects.create(image = instance ,like_counts=0)\n # except Exception as e:\n # print(e)\n \n\n", "repo_name": "Mandalor-09/Clone", "sub_path": "InstaClone/main/models/post.py", "file_name": "post.py", "file_ext": "py", "file_size_in_byte": 1868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "base.models.basemodel.BaseModel", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 8, "usage_type": "call"}, {"api_name": "accounts.models.profile.Profile", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "base.models.basemodel.BaseModel", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.db.models.ImageField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "base.models.basemodel.BaseModel", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 22, "usage_type": "call"}, {"api_name": "accounts.models.profile.Profile", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.dispatch.receiver", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 26, "usage_type": "argument"}, {"api_name": "base.models.basemodel.BaseModel", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 42, "usage_type": "call"}, {"api_name": "accounts.models.profile.Profile", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "24276914216", "text": "import sys\r\nimport pygame\r\nfrom scripts.utilities import load_image, load_images\r\nfrom scripts.entities import PhysicsEntity\r\nfrom scripts.tilemap import Tilemap\r\nfrom scripts.clouds import Clouds\r\n\r\nclass Game:\r\n \"\"\" Main Game class\r\n Attributes include loading in assets and setting up the screen\r\n \"\"\"\r\n def __init__(self):\r\n pygame.init()\r\n\r\n pygame.display.set_caption(\"Game\")\r\n self.screen = pygame.display.set_mode((640, 480))\r\n self.display = pygame.Surface((320, 240))\r\n\r\n self.clock = pygame.time.Clock()\r\n self.movement = [False, False]\r\n self.assets = {\r\n \"player\": load_image(\"entities/player.png\"),\r\n \"decor\": load_images(\"tiles/decor\"),\r\n \"grass\": load_images(\"tiles/grass\"),\r\n \"stone\": load_images(\"tiles/stone\"),\r\n \"large_decor\": load_images(\"tiles/large_decor\"),\r\n \"background\": load_image(\"background.png\"),\r\n \"clouds\": load_images(\"clouds\")\r\n } # dictionary for assets\r\n\r\n self.clouds = Clouds(self.assets[\"clouds\"], count = 16)\r\n\r\n self.player = PhysicsEntity(self, \"player\", (50, 50), (8, 15))\r\n\r\n self.tilemap = Tilemap(self, tile_size=16)\r\n self.scroll = [0, 0] # for the camera scrolling\r\n\r\n\r\n def run(self):\r\n while True:\r\n self.display.blit(self.assets[\"background\"], (0, 0))\r\n self.scroll[0] += (self.player.generate_rect().centerx - self.display.get_width() / 2 - self.scroll[0]) / 30\r\n self.scroll[1] += (self.player.generate_rect().centery - self.display.get_height() / 2 - self.scroll[1]) / 30\r\n render_scroll = (int(self.scroll[0]), int(self.scroll[1]))\r\n\r\n self.clouds.update()\r\n self.clouds.render(self.display, offset=render_scroll)\r\n\r\n self.tilemap.render(self.display, offset = render_scroll)\r\n\r\n self.player.update(self.tilemap, (self.movement[1] - self.movement[0], 0))\r\n self.player.render(self.display, offset = render_scroll)\r\n\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_LEFT or event.key == pygame.K_a:\r\n self.movement[0] = True\r\n if event.key == pygame.K_RIGHT or event.key == pygame.K_d:\r\n self.movement[1] = True\r\n if event.key == pygame.K_UP or event.key == pygame.K_w:\r\n self.player.velocity[1] = -3\r\n if event.type == pygame.KEYUP:\r\n if event.key == pygame.K_LEFT or event.key == pygame.K_a:\r\n self.movement[0] = False\r\n if event.key == pygame.K_RIGHT or event.key == pygame.K_a:\r\n self.movement[1] = False\r\n\r\n self.screen.blit(pygame.transform.scale(self.display, self.screen.get_size()), (0, 0))\r\n pygame.display.update()\r\n self.clock.tick(60)\r\n\r\nGame().run()\r\n", "repo_name": "KuromeMochi/Pikachu-Platformer", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3147, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.init", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"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.Surface", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 19, "usage_type": "attribute"}, {"api_name": "scripts.utilities.load_image", "line_number": 22, "usage_type": "call"}, {"api_name": "scripts.utilities.load_images", "line_number": 23, "usage_type": "call"}, {"api_name": "scripts.utilities.load_images", "line_number": 24, "usage_type": "call"}, {"api_name": "scripts.utilities.load_images", "line_number": 25, "usage_type": "call"}, {"api_name": "scripts.utilities.load_images", "line_number": 26, "usage_type": "call"}, {"api_name": "scripts.utilities.load_image", "line_number": 27, "usage_type": "call"}, {"api_name": "scripts.utilities.load_images", "line_number": 28, "usage_type": "call"}, {"api_name": "scripts.clouds.Clouds", "line_number": 31, "usage_type": "call"}, {"api_name": "scripts.entities.PhysicsEntity", "line_number": 33, "usage_type": "call"}, {"api_name": "scripts.tilemap.Tilemap", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "29967216746", "text": "'''\nProduct: Web Fetch\nDescription: Fetches the live website data and stored it in the desired directory\nAuthor: Benjamin Norman 2023\n'''\nimport os\nimport re\nimport requests\nimport shutil\n\nfrom pywebcopy import save_website\n\nfrom env import *\n\nclass website_fetcher():\n # Import a logging object as well when that stage is next up\n def __init__(self, loggerObj):\n self.logger = loggerObj\n\n def web_fetcher(self, productionDirectoryWalk):\n '''\n - While doing the web fetching, also do a DNS lookup to ensure the IP address of the website is\n stored, just in case it is part of a cluster\n - If the website has mulitple pages, download them all, these names should then be based off of the\n known good code folder. e.g. https://somethingFishy.com/index.html\n\n Fetches the website data from the listed\n websites\n '''\n builtURL = \"\"\n \n # Fetch the website data for HTTPS to start with and then HTTP if not exists\n \n for item in productionDirectoryWalk[\"domains\"]:\n for domain, value in item.items():\n download_folder = f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}\"\n kwargs = {'bypass_robots': True}\n \n try:\n try:\n builtURL = f\"https://{domain}\"\n print(builtURL)\n save_website(builtURL, download_folder, **kwargs)\n if len(os.listdir(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/https_{domain}\")) == 0:\n # Log to the log file\n shutil.rmtree(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}\")\n else:\n self.directory_cleaning(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/https_{domain}/{domain}\", f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/\")\n self.directory_cleaning(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/https_{domain}\", f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/\")\n\n except requests.exceptions.SSLError:\n builtURL = f\"http://{domain}\"\n print(builtURL)\n save_website(builtURL, download_folder, **kwargs)\n os.removedirs(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/https_{domain}\")\n if len(os.listdir(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/http_{domain}\")) == 0:\n shutil.rmtree(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}\")\n else:\n self.directory_cleaning(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/http_{domain}/{domain}\", f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/\")\n self.directory_cleaning(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/http_{domain}\", f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}/\")\n # This is part of the bug\n except Exception as err:\n print(f\"URL NOT FOUND {err}\")\n shutil.rmtree(f\"{LIVE_WEBSITES_DOWNLOAD_LOCATION}/{domain}\", ignore_errors=True)\n\n def directory_cleaning(self, target, destination):\n '''\n Moves all files in a directory back one, then deletes the directory they were in\n '''\n \n for file in os.listdir(target):\n file_path = os.path.join(target, file)\n shutil.move(file_path, destination)\n \n shutil.rmtree(target)\n \n def data_cleaning(self, fileName):\n \n '''\n Removes the comments added by the pywebcopy module that could interfere\n with the detection process. \n '''\n with open(fileName, \"r\") as file:\n lines = file.readlines()\n \n # Searches for the lines containing the pattern\n for index, line in enumerate(lines):\n if (line.lstrip().startswith(\"\") or lines[index+4].lstrip().startswith(\"-->\")):\n print(f\"Is between lines {index} and {index+6}\")\n if index == 0:\n print(\"here\")\n startingNumber = 1\n headerNumber = 0\n lineDifference = 5\n else:\n startingNumber = index\n headerNumber = index\n lineDifference = 6\n endingNumber = index+6\n \n '''\n Removes the \n\n * PyWebCopy Engine [version 7.0.2]\n * Copyright 2020; Raja Tomar\n * File mirrored from [https://WEBSITE_NAME]\n *At UTC datetime: [1999-01-01 00:00:00.000000]\n \n bit but not the trailing comment\n '''\n\n try:\n lines_to_remove = list(range(startingNumber+1, endingNumber))\n except NameError as error:\n print(f\"No comments identified - {error}\")\n return False\n filtered_lines = [line for i, line in enumerate(lines) if i+1 not in lines_to_remove]\n \n for i, line in enumerate(filtered_lines):\n if i == headerNumber:\n newline = line.replace(\"\", '')\n filtered_lines[i] = newline\n\n\n with open(fileName, \"w\") as file:\n file.writelines(filtered_lines)\n \n def dir_walker(self, targetFilePath):\n\n domainWalk = {\"domains\":[]}\n \n # Nested due to no other func needing this.\n def list_search(domainWalk, domainName):\n for item in domainWalk[\"domains\"]:\n if domainName in item.keys():\n return True\n return False\n \n for path,subdir,files in os.walk(targetFilePath):\n for name in files: \n \n if not name.startswith(\".\"): # Ignores hidden files\n strippedName = os.path.join(path,name).replace(targetFilePath, '')[1:]\n else:\n continue\n strippedPath = strippedName.split('/')\n \n domainName = strippedPath[0]\n fileName = strippedPath[-1]\n \n result = list_search(domainWalk, domainName)\n \n if result == True:\n for item in domainWalk[\"domains\"]:\n for key, value in item.items():\n if key == domainName:\n value.append({fileName:strippedName})\n elif result == False:\n domainWalk[\"domains\"].append({domainName:[{fileName:strippedName}]})\n \n return domainWalk", "repo_name": "BenjaminN117/Website-Defacement-Detection-Engine", "sub_path": "src/web_fetch.py", "file_name": "web_fetch.py", "file_ext": "py", "file_size_in_byte": 7305, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pywebcopy.save_website", "line_number": 43, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pywebcopy.save_website", "line_number": 54, "usage_type": "call"}, {"api_name": "os.removedirs", "line_number": 55, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 56, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 57, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 64, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 73, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 75, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 90, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}]} +{"seq_id": "6659388680", "text": "import argparse\nimport time\nimport torch\nfrom Models import get_model\nfrom Process import *\nimport torch.nn.functional as F\nfrom Optim import CosineWithRestarts\nfrom Batch import create_masks\nimport pdb\nimport dill as pickle\nimport argparse\nfrom Models import get_model\nfrom Beam import beam_search\nfrom nltk.corpus import wordnet\nfrom torch.autograd import Variable\nimport re\n\ndef get_synonym(word, SRC):\n syns = wordnet.synsets(word)\n for s in syns:\n for l in s.lemmas():\n if SRC.vocab.stoi[l.name()] != 0:\n return SRC.vocab.stoi[l.name()]\n \n return 0\n\ndef multiple_replace(dict, text):\n # Create a regular expression from the dictionary keys\n regex = re.compile(\"(%s)\" % \"|\".join(map(re.escape, dict.keys())))\n\n # For each match, look-up corresponding value in dictionary\n return regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], text) \n\ndef translate_sentence(sentence, model, opt, SRC, TRG):\n \n model.eval()\n indexed = []\n sentence = SRC.preprocess(sentence)\n for tok in sentence:\n if SRC.vocab.stoi[tok] != 0 or opt.floyd is True:\n indexed.append(SRC.vocab.stoi[tok])\n else:\n indexed.append(get_synonym(tok, SRC))\n sentence = Variable(torch.LongTensor([indexed]), device=opt.device)\n\n sentence = beam_search(sentence, model, SRC, TRG, opt)\n\n return multiple_replace({' ?': '?', ' !': '!', ' .': '.', '\\' ': '\\'', ' ,': ','}, sentence)\n\ndef translate(opt, model, SRC, TRG):\n sentences = opt.text.lower().split('.')\n translated = []\n\n for sentence in sentences:\n translated.append(translate_sentence(sentence + '.', model, opt, SRC, TRG).capitalize())\n\n return (' '.join(translated))\n\n\ndef main():\n \n parser = argparse.ArgumentParser()\n parser.add_argument('-load_weights', required=True)\n parser.add_argument('-k', type=int, default=3)\n parser.add_argument('-max_len', type=int, default=80)\n parser.add_argument('-d_model', type=int, default=512)\n parser.add_argument('-n_layers', type=int, default=6)\n parser.add_argument('-src_lang', required=True)\n parser.add_argument('-trg_lang', required=True)\n parser.add_argument('-heads', type=int, default=8)\n parser.add_argument('-dropout', type=int, default=0.1)\n parser.add_argument('-no_cuda', action='store_true')\n parser.add_argument('-floyd', action='store_true')\n \n opt = parser.parse_args()\n\n opt.device = 'cuda' if opt.no_cuda is False else 'cpu'\n \n assert opt.k > 0\n assert opt.max_len > 10\n\n SRC, TRG = create_fields(opt)\n model = get_model(opt, len(SRC.vocab), len(TRG.vocab))\n \n while True:\n opt.text =input(\"Enter a sentence to translate (type 'f' to load from file, or 'q' to quit):\\n\")\n if opt.text==\"q\":\n break\n if opt.text=='f':\n fpath =input(\"Enter a sentence to translate (type 'f' to load from file, or 'q' to quit):\\n\")\n try:\n opt.text = ' '.join(open(opt.text, encoding='utf-8').read().split('\\n'))\n except:\n print(\"error opening or reading text file\")\n continue\n phrase = translate(opt, model, SRC, TRG)\n print('> '+ phrase + '\\n')\n\nif __name__ == '__main__':\n main()\n", "repo_name": "SamLynnEvans/Transformer", "sub_path": "translate.py", "file_name": "translate.py", "file_ext": "py", "file_size_in_byte": 3280, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1220, "dataset": "github-code", "pt": "2", "api": [{"api_name": "nltk.corpus.wordnet.synsets", "line_number": 19, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 19, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 29, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "Beam.beam_search", "line_number": 46, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 62, "usage_type": "call"}, {"api_name": "Models.get_model", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "34583060285", "text": "import pandas as pd\nimport math\nimport glob\nimport re\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom bokeh.io import output_file, show\nfrom bokeh.plotting import figure\nfrom bokeh.models import ColumnDataSource, HoverTool\n\n#this imports all the data into one list with no marking for where the years start and st\npath = 'yob*.csv'\nfiles = glob.glob(path)\n\ndf= []\nfor file in files :\n df.append(pd.read_csv(file, index_col= None, header=None))\n\n#Inspect loaded files\nprint('INSPECT NAMES')\nprint(len(df))\nprint(df[0].head)\nprint(df[0].info())\n\n# Add column names\ni=0\nn = 1880\nfor item in df :\n df[i].columns = ['Name', 'Sex', 'Count']\n #print(df[i].info())\n df[i]['Year']= n\n i= i + 1\n n= n + 1\n\n#Concatenate the list of dataframes into one dataframe\nnames= pd.concat(df)\nprint('CONCATENTATED NAMES DATAFRAME')\nprint(names.info())\n\n#_______________________________________________________________________________\n# Initial Exploratory data review\n# plt.scatter(x=names.Year, y=names.Count)\n# plt.xlabel('Year')\n# plt.ylabel('Count')\n# plt.title('Frequency and Number of Names')\n# plt.savefig('by_year_scatter.pdf')\n# plt.show()\n#\n# names= names.set_index('Sex')\n# plt.scatter(x=names.loc['F'].Year, y=names.loc['F'].Count)\n# plt.xlabel('Year')\n# plt.ylabel('Count')\n# plt.title('Frequency and Number of Girls Names')\n# plt.savefig('female_by_year_scatter.pdf')\n# plt.show()\n#\n# plt.scatter(x=names.loc['M'].Year, y=names.loc['M'].Count)\n# plt.xlabel('Year')\n# plt.ylabel('Count')\n# plt.title('Frequency and Number of Boys Names')\n# plt.savefig('male_by_year_scatter.pdf')\n# plt.show()\n#_______________________________________________________________________________\n#calculate proportion of count for each name\nnames2 = names.copy()\ntotal_births_by_year = names2.groupby('Year')['Count'].transform('sum')\nnames2['pct_name']= (names2['Count']/total_births_by_year)* 100\nprint('NAMES DATAFRAME WITH PCT NAME ADDED')\nprint(names2.tail())\nprint(names2.shape)\n#_______________________________________________________________________________\n#create dataframe with female names\nfemale = names2['Sex'] == 'F'\nnames_f= names2[female]\nprint('FEMALE NAME DATAFRAME')\nprint(names_f.tail())\n#Select top 5 female names for each year\ntop5_f= names_f.groupby('Year').head()\ntop5_female= top5_f.reset_index()\ndel top5_female['index']\ndel top5_female['Sex']\ndel top5_female['Count']\ntop5_fnames= top5_female.set_index('Name')\nprint('TOP 5 FEMALE NAMES')\nprint(top5_fnames.head())\n\ntop5_fnames1= top5_fnames.reset_index()\ntop5_fnames1= top5_fnames1.set_index('Year')\nprint(top5_fnames1.head())\n\n# i=2000\n# for item in top5_fnames1 :\n# # if i <= 1990 :\n# x = top5_fnames1['Name'][i]\n# y = top5_fnames1['pct_name'][i]\n# plt.scatter(x, y)\n# i= i + 5\n#\n# plt.xticks(rotation='vertical')\n# plt.ylim(-0.005, 5)\n# plt.subplots_adjust(left=0.1)\n# plt.ylabel('Pecent of Names')\n# plt.title('Top 5 Girls Names')\n# plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n# plt.margins(0.1)\n# plt.savefig('scatter_top5_girls_names1.pdf')\n# plt.show()\n#\n#\n# pritn()\n\n# #Pivot the dataframe to make years columns\ntop5_fnames_tidy = top5_fnames.pivot_table(values='pct_name', index=['Name'], columns=['Year'])\ntop5_fnames_tidy = top5_fnames_tidy.fillna(0)\ntop5_fnames_tidy= top5_fnames_tidy.reset_index()\ntop5_fnames_tidy= top5_fnames_tidy.set_index('Name')\n\ndf=[]\nn= 0\nfor item in top5_fnames_tidy :\n if n <= 45 :\n top5f_by_year= top5_fnames_tidy.iloc[n]\n df.append(top5f_by_year)\n n = n + 1\ntop5_fnames= pd.concat(df, axis=1)\ntop5_fnames= top5_fnames.reset_index()\n\n#Plots for top 5 girls names over the years\nn=1\nfor item in top5_fnames :\n if n <= 2 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=3\nfor item in top5_fnames :\n if n <= 4 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=5\nfor item in top5_fnames :\n if n <= 6 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=7\nfor item in top5_fnames :\n if n <= 8 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=9\nfor item in top5_fnames :\n if n <= 10 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=11\nfor item in top5_fnames :\n if n <= 12 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=13\nfor item in top5_fnames :\n if n <= 14 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=15\nfor item in top5_fnames :\n if n <= 16 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=17\nfor item in top5_fnames :\n if n <= 18 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=19\nfor item in top5_fnames :\n if n <= 20 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=21\nfor item in top5_fnames :\n if n <= 22 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=23\nfor item in top5_fnames :\n if n <= 24 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\nn=25\nfor item in top5_fnames :\n if n <= 26 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\nplt.xticks(rotation='vertical')\nplt.ylim(-0.005, 5)\nplt.subplots_adjust(left=0.1)\nplt.ylabel('Pecent of Names')\nplt.title('Top 5 Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('scatter_top5_girls_names1.pdf')\nplt.show()\n\n\ndef top5_girls_names_1():\n n=1\n for item in top5_fnames :\n if n <= 9 :\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Pecent of Names')\n plt.title('Top 5 Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_girls_names1.pdf')\n plt.show()\n\ndef top5_girls_names_2():\n n=10\n for item in top5_fnames :\n if n <= 19:\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_girls_names2.pdf')\n plt.show()\n\ndef top5_girls_names_3():\n n=20\n for item in top5_fnames :\n if n <= 29:\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_girls_names3.pdf')\n plt.show()\n\ndef top5_girls_names_4():\n n=30\n for item in top5_fnames :\n if n <= 39:\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_girls_names4.pdf')\n plt.show()\n\ndef top5_girls_names_5():\n n=40\n for item in top5_fnames :\n if n <= 46:\n x= top5_fnames['Year']\n y = top5_fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_girls_names5.pdf')\n plt.show()\n\n#_______________________________________________________________________________\n#Create dataframe with males names\nnames_m = names2['Sex'] == 'M'\nnames_m= names2[names_m]\nprint('MALE NAME DATAFRAME')\nprint(names_m.tail())\n#Select top 5 male names for each year\ntop5_m= names_m.groupby('Year').head()\ntop5_male= top5_m.reset_index()\ndel top5_male['index']\ndel top5_male['Sex']\ndel top5_male['Count']\ntop5_mnames= top5_male.set_index('Name')\nprint('TOP 5 MALE NAMES')\nprint(top5_male.head())\n# #Pivot the dataframe to make years columns\ntop5_mnames_tidy = top5_mnames.pivot_table(values='pct_name', index=['Name'], columns=['Year'])\ntop5_mnames_tidy = top5_mnames_tidy.fillna(0)\ntop5_mnames_tidy= top5_mnames_tidy.reset_index()\nprint('TOP 5 MALE NAMES, PIVOTED')\nprint(top5_mnames_tidy.head())\nprint(top5_mnames_tidy.tail())\nprint(top5_mnames_tidy.info())\n#_____________________________________________________________________\n#create 'tidy' dataframe for boys names\ntop5_mnames_tidy= top5_mnames_tidy.set_index('Name')\ndf=[]\nn= 0\nfor item in top5_mnames_tidy :\n if n <= 24 :\n top5m_by_year= top5_mnames_tidy.iloc[n]\n df.append(top5m_by_year)\n n = n + 1\ntop5_mnames= pd.concat(df, axis=1)\ntop5_mnames= top5_mnames.reset_index()\n\n#Plot the % change of use of top 5 boys names\ndef top5_boys_names_1():\n n=1\n for item in top5_mnames :\n if n <= 5 :\n x= top5_mnames['Year']\n y = top5_mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_boyss_names1.pdf')\n plt.show()\n\ndef top5_boys_names_2():\n n=6\n for item in top5_mnames :\n if n <= 10:\n x= top5_mnames['Year']\n y = top5_mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_boyss_names2.pdf')\n plt.show()\n\ndef top5_boys_names_3():\n n=11\n for item in top5_mnames :\n if n <= 20:\n x= top5_mnames['Year']\n y = top5_mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_boys_names3.pdf')\n plt.show()\n\ndef top5_boys_names_4():\n n=21\n for item in top5_mnames :\n if n <= 25:\n x= top5_mnames['Year']\n y = top5_mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.ylim(-0.005, 5)\n plt.subplots_adjust(left=0.1)\n plt.ylabel('Percent of Names')\n plt.title('Top 5 Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_top5_boys_names4.pdf')\n plt.show()\n\n#_______________________________________________________________________________\n#Review dataframe with only female names\nprint('REVIEW OF FEMALE NAME DATAFRAME')\nprint(names_f.info())\nprint(names_f.head())\n#create a list frequent female names\ndupf= names_f.groupby('Name').sum()\nfreq_f = dupf['pct_name'] >= 10\ncommon_f= dupf[freq_f]\ncommon_f= common_f.reset_index()\ncommon_fnames= common_f['Name']\nfreq_fnames= common_fnames.tolist()\n\n#set fnames index to Name and pull out the common names\nfnames= names_f.set_index('Name')\ncommon_girls= fnames.loc[freq_fnames]\ncommon_df= common_girls.reset_index()\nprint('COMMON FEMALE NAMES')\nprint(common_df.head())\nprint(common_df.info())\n#\n# #Pivot the dataframe to make years columns\nfnames_tidy = common_df.pivot_table(values='pct_name', index=['Name'], columns=['Year'])\nfnames_tidy = fnames_tidy.fillna(0)\nprint('COMMON FEMALE NAMES, PIVOTED')\nprint(fnames_tidy.head())\nprint(fnames_tidy.info())\nfnames_tidy['Total']= fnames_tidy.sum(axis=1)\nfnames_tidy= fnames_tidy.sort_values(by= 'Total', ascending= False)\n\n#Select very popular female names\ntop10_fnames= fnames_tidy[0:9]\ntop10_fnames= top10_fnames.reset_index()\n\n# #Bar plot of most popluar girls\n\ni=1880\nfor item in top10_fnames :\n if i <= 2010 :\n # print(over_1mf['Name'])\n # print(over_1mf[i])\n x= top10_fnames['Name']\n y= top10_fnames[i]\n plt.bar(x, y, label= i)\n i= i + 10\nplt.xticks(rotation='vertical')\nplt.subplots_adjust(bottom=0.2)\nplt.ylabel('Percent of Names')\nplt.title('Top 10 Traditional Girls Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('bar_top10_traditional_girls_names.pdf')\nplt.show()\n\n# #Calulate % change of name popularity top 10 girls names\ndel top10_fnames['Total']\ntop10_fnames= top10_fnames.set_index('Name')\ntop10_fnames_chg = top10_fnames.apply('pct_change', axis=1)*100\npd.set_option('use_inf_as_na', True)\ntop10_fnames_chg= top10_fnames_chg.replace(np.inf, 0)\ntop10_fnames_chg= top10_fnames_chg.fillna(0)\ndel top10_fnames_chg[1880]\ntop10_fnames_chg = top10_fnames_chg.reset_index()\nprint('TOP 10 COMMON FEMALE NAMES')\nprint(top10_fnames.head())\n\n#Create dataframe of the top 10 Traditional girls names\ntop10_fnames_chg= top10_fnames_chg.set_index('Name')\ndf=[]\nn= 0\nfor item in top10_fnames_chg :\n if n <= 8 :\n f_by_year= top10_fnames_chg.iloc[n]\n df.append(f_by_year)\n n = n + 1\nfnames= pd.concat(df, axis=1)\nfnames= fnames.reset_index()\n\n#plot %Change of top 10 Traditional girls names\ndef pct_change_top10_gnames_1():\n n=1\n for item in fnames :\n if n <= 3 :\n x= fnames['Year']\n y = fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.subplots_adjust(bottom=0.2)\n plt.ylabel('% Change')\n plt.title('% Change Traditional Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_traditional_girls_names1.pdf')\n plt.show()\n\ndef pct_change_top10_gnames_2():\n n=4\n for item in fnames :\n if n <= 6 :\n x= fnames['Year']\n y = fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.subplots_adjust(bottom=0.2)\n plt.ylabel('% Change')\n plt.title('% Change Traditional Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_traditional_girls_names2.pdf')\n plt.show()\n\ndef pct_change_top10_gnames_3():\n n=7\n for item in fnames :\n if n <= 9 :\n x= fnames['Year']\n y = fnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.subplots_adjust(bottom=0.2)\n plt.ylabel('% Change')\n plt.title('% Change Traditional Girls Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_traditional_girls_names3.pdf')\n plt.show()\n#___________________________________________________________________________________\n#review dataframe with males names\nprint('REVIEW OF MALE NAMES DATAFRAME')\nprint(names_m.info())\nprint(names_m.head())\n#create a list frequent male names\ndupm= names_m.groupby('Name').sum()\nfreq_m = dupm['pct_name'] >= 10\ncommon_m= dupm[freq_m]\ncommon_m= common_m.reset_index()\ncommon_mnames= common_m['Name']\nfreq_mnames= common_mnames.tolist()\n\n#set names_m index to Name and pull out the common names\nnames_m= names_m.set_index('Name')\ncommon_boys= names_m.loc[freq_mnames]\ncommon_dm= common_boys.reset_index()\nprint('COMMON MALE NAMES')\nprint(common_dm.head())\nprint(common_dm.info())\n#\n# #Pivot the dataframe to make years columns\nmnames_tidy = common_dm.pivot_table(values='pct_name', index=['Name'], columns=['Year'])\nmnames_tidy = mnames_tidy.fillna(0)\nprint('COMMON MALE NAMES, PIVOTED')\nprint(mnames_tidy.head())\nprint(mnames_tidy.info())\n#\nmnames_tidy['Total']= mnames_tidy.sum(axis=1)\nmnames_tidy= mnames_tidy.sort_values(by= 'Total', ascending= False)\n\n#Select very popular male names\ntop10_mnames= mnames_tidy[0:9]\ntop10_mnames= top10_mnames.reset_index()\n\n# #Bar plot of most popluar boys names\n\ni=1880\nfor item in top10_mnames :\n if i <= 2010 :\n # print(over_1mf['Name'])\n # print(over_1mf[i])\n x= top10_mnames['Name']\n y= top10_mnames[i]\n plt.bar(x, y, label= i)\n i= i + 10\nplt.xticks(rotation='vertical')\nplt.subplots_adjust(bottom=0.2)\nplt.ylabel('Percent of Names')\nplt.title('Top 10 Traditional Boys Names')\nplt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\nplt.margins(0.1)\nplt.savefig('bar_top10_traditional_boys_names.pdf')\nplt.show()\n\n# #Calulate % change of name popularity top 10 girls names\ndel top10_mnames['Total']\ntop10_mnames= top10_mnames.set_index('Name')\ntop10_mnames_chg = top10_mnames.apply('pct_change', axis=1)*100\npd.set_option('use_inf_as_na', True)\ntop10_mnames_chg= top10_mnames_chg.replace(np.inf, 0)\ntop10_mnames_chg= top10_mnames_chg.fillna(0)\ndel top10_mnames_chg[1880]\ntop10_mnames_chg = top10_mnames_chg.reset_index()\nprint(top10_mnames_chg.info())\nprint(top10_mnames_chg.head())\n\n#Create dataframe of the top 10 Traditional boys names\ntop10_mnames_chg= top10_mnames_chg.set_index('Name')\ndm=[]\nn= 0\nfor item in top10_mnames_chg :\n if n <= 8 :\n m_by_year= top10_mnames_chg.iloc[n]\n dm.append(m_by_year)\n n = n + 1\nmnames= pd.concat(dm, axis=1)\nmnames= mnames.reset_index()\n\n#plot %Change of top 10 Traditional boys names\ndef pct_change_top10_bnames_1():\n n=1\n for item in mnames :\n if n <= 3 :\n x= mnames['Year']\n y = mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.subplots_adjust(bottom=0.2)\n plt.ylabel('% Change')\n plt.title('% Change Traditional Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_traditional_boys_names1.pdf')\n plt.show()\n\ndef pct_change_top10_bnames_2():\n n=4\n for item in mnames :\n if n <= 6 :\n x= mnames['Year']\n y = mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.subplots_adjust(bottom=0.2)\n plt.ylabel('% Change')\n plt.title('% Change Traditional Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_traditional_boys_names2.pdf')\n plt.show()\n\ndef pct_change_top10_bnames_3():\n n=7\n for item in mnames :\n if n <= 9 :\n x= mnames['Year']\n y = mnames.iloc[0:, n]\n plt.scatter(x, y)\n n= n + 1\n plt.xticks(rotation='vertical')\n plt.subplots_adjust(bottom=0.2)\n plt.ylabel('% Change')\n plt.title('% Change Traditional Boys Names')\n plt.legend(loc='best', fontsize='xx-small', markerscale=0.7)\n plt.margins(0.1)\n plt.savefig('scatter_traditional_boys_names3.pdf')\n plt.show()\n\n\n# top5_girls_names_1()\n# top5_girls_names_2()\n# top5_girls_names_3()\n# top5_girls_names_4()\n# top5_girls_names_5()\n#\n# pct_change_top10_gnames_1()\n# pct_change_top10_gnames_2()\n# pct_change_top10_gnames_3()\n#\ntop5_boys_names_1()\ntop5_boys_names_2()\ntop5_boys_names_3()\ntop5_boys_names_4()\n#\n# pct_change_top10_bnames_1()\n# pct_change_top10_bnames_2()\n# pct_change_top10_bnames_3()\n\n\n#_______________________________________________________________________________\n\n#___________________________________________________________________________________\n#source= ColumnDataSource(fnames_pctchange)\n# plot=figure()\n#plot.vbar(y='Mary' top='top', width= 0.5, source=source, legend='Counts')\n\n# plot.legend.location='top_right'\n# hover = HoverTool(tooltips=[('Name', '@Name')])\n# plot = figure(tools=[hover, 'pan'])\n# plot.add_tools(hover)\n# output_file('hover.html')\n#show(plot)\n", "repo_name": "lynda-anne/names_project", "sub_path": "glob_import.py", "file_name": "glob_import.py", "file_ext": "py", "file_size_in_byte": 24333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "glob.glob", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "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.margins", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 306, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 306, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 307, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 307, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 308, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 310, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 311, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 312, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 313, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 314, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 314, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 323, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": 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"matplotlib.pyplot.show", "line_number": 644, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 644, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 652, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 652, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 654, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 654, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 655, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 655, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 656, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 656, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 657, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 657, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 658, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 658, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 659, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 659, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 660, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 660, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 661, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 661, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 669, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 669, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 671, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 671, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 672, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 672, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 673, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 673, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 674, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 674, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 675, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 675, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 676, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 676, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 677, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 677, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 678, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 678, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 723, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 723, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 725, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 725, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 726, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 726, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 727, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 727, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 728, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 728, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 729, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 729, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 730, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 730, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 731, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 731, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 732, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 732, "usage_type": "name"}, {"api_name": "pandas.set_option", "line_number": 738, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 739, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 755, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 765, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 765, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 767, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 767, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 768, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 768, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 769, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 769, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 770, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 770, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 771, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 771, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 772, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 772, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 773, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 773, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 774, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 774, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 782, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 782, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 784, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 784, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 785, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 785, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 786, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 786, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 787, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 787, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 788, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 788, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 789, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 789, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 790, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 790, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 791, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 791, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 799, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 799, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 801, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 801, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 802, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 802, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 803, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 803, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 804, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 804, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 805, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 805, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 806, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 806, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 807, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 807, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 808, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 808, "usage_type": "name"}]} +{"seq_id": "11272501777", "text": "#!/usr/bin/python\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nfrom shapely.geometry import Polygon\nimport geopandas as gpd\nimport os.path\nfrom PIL import Image\nimport rasterio\n\n## global variables\nCLIP = False\nINTERSEC = True\n\n#set working directory\nos.chdir(\"/Users/aminaly/Box Sync/mountain_biodiversity\")\n\n## Read in all the files\nkba = gpd.read_file(os.getcwd() + \"/data/KBA/KBA2020/KBAsGlobal_2020_September_02_POL.shp\")\nwdpa = gpd.read_file(os.getcwd() + \"/data/WDPA/WDPA_Nov2020_Public_shp/WDPA_poly_Nov2020_filtered.gdb/\")\ngmba = gpd.read_file(os.getcwd() + \"/data/GMBA/Gmba_Mountain_Inventory_v2_broad_20210630/Gmba_Mountain_Inventory_v2_broad_20210630.shp\")\n\n#list of ISOs to use to clip kba & wdpa\nwrld_cntries = ['KEN', 'MNG', 'JPN', 'NPL', 'UGA']\n\n#clip kba and wdpa using the list of isos \nkba_c = kba[kba['ISO3'].isin(wrld_cntries)]\nwdpa = wdpa[wdpa['ISO3'].isin(wrld_cntries)]\n\n#gmba will be clipped a little differently. Doesn't have ISOs so we'll use a world shapefile\nworld = gpd.read_file(os.getcwd() + \"/data/World/world_shp/world.shp\")\nworld = world[world['CNTRY_NAME'].isin(kba_c['Country'].unique())] \ngmba_c = gpd.overlay(gmba, world, how=\"intersection\")\n#then we find a list of all the ranges included in the clip, and select those specifically from the main gmba\ngmba_c = gmba[gmba.GMBA_V2_ID.isin(gmba_c.GMBA_V2_ID)]\n\n#Once we've clipped them, save them out as shapefiles\nkba_c.to_file(os.getcwd() + \"/data/KBA/KBA2020/clipped_KBAsGlobal_2020_September_02_POL.shp\", \n driver='ESRI Shapefile')\n\nwdpa.to_file(os.getcwd() + \"/data/WDPA/WDPA_Nov2020_Public_shp/clipped_WDPA_Nov2020_Public_flattened.shp\",\n driver='ESRI Shapefile')\n\ngmba_c.to_file(os.getcwd() + \"/data/GMBA/Gmba_Mountain_Inventory_v2_broad_20210630/clipped_Gmba_Mountain_Inventory_v2_broad_20210630.shp\", \n driver='ESRI Shapefile')\n \n \n \n \n \n", "repo_name": "aminaly/mountain_biodiversity", "sub_path": "analysis/create_clipped_&_intersections.py", "file_name": "create_clipped_&_intersections.py", "file_ext": "py", "file_size_in_byte": 2011, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.chdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "geopandas.read_file", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "geopandas.read_file", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "name"}, {"api_name": "geopandas.read_file", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "name"}, {"api_name": "geopandas.read_file", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "name"}, {"api_name": "geopandas.overlay", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "25063206425", "text": "from typing import Sequence\n\nimport torch\n\nfrom meddlr.metrics.metric import Metric\nfrom meddlr.ops import complex as cplx\nfrom meddlr.utils import env\n\nif env.package_available(\"lpips\"):\n from lpips import LPIPS as _LPIPS\n\n\n# TODO: Refactor SSFD Class to extract shared logic into parent class FeatureMetric\nclass LPIPS(Metric):\n \"\"\"\n Learned Perceptual Image Patch Similarity.\n\n LPIPS evaluates the feature distance between a pair of images from features extracted\n from a pre-trained neural network [1]. LPIPS has been shown to correspond well to\n perceived image quality on natural images.\n\n References:\n .. [1] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, O. Wang.\n The Unreasonable Effectiveness of Deep Features as a Perceptual Metric.\n In CVPR, 2018 http://arxiv.org/abs/1801.03924\n \"\"\"\n\n is_differentiable = True\n higher_is_better = False\n\n def __init__(\n self,\n net_type: str = \"alex\",\n mode: str = \"grayscale\",\n lpips: bool = True,\n pretrained: bool = True,\n channel_names: Sequence[str] = None,\n reduction=\"none\",\n compute_on_step: bool = False,\n dist_sync_on_step: bool = False,\n process_group: bool = None,\n dist_sync_fn: bool = None,\n ):\n \"\"\"\n Args:\n net_type (str): The pre-trained network to use for extracting features. One of:\n * ``'alex'``: Alex-Net w/ feature extraction layers 'relu1' through 'relu5'\n * ``'vgg'``: VGG-16 w/ feature extration layers ['relu1_2', 'relu2_2',\n 'relu3_3', 'relu4_3', 'relu5_3']\n * ``'squeeze'``: Squeeze-Net w/ feature extration layers 'relu1' through 'relu7'\n mode (str): Determines how to interpret the channel dimension of the inputs. One of:\n * ``'grayscale'``: Each channel corresponds to a distinct grayscale input image.\n * ``'rgb'``: The 3 channel dimensions correspond to a single rgb image.\n Exception will be thrown if channel dimension != 3 dtype data is complex.\n lpips (bool): This flag determines if a linear layer is used on top of the\n extracted features.\n * ``True``: linear layers on top of base/trunk network.\n * ``False``: no linear layers; each layer is averaged together.\n pretrained (bool): This flag controls the linear layers, which are only in\n effect when lpips=True above.\n * ``True``: linear layers are calibrated with human perceptual judgments.\n * ``False``: linear layers are randomly initialized.\n \"\"\"\n\n if not env.package_available(\"lpips\"):\n raise ModuleNotFoundError(\n \"LPIPS metric requires that lpips is installed.\"\n \"Either install as `pip install meddlr[metrics]` or `pip install lpips`.\"\n )\n\n super().__init__(\n channel_names=channel_names,\n units=\"\",\n reduction=reduction,\n compute_on_step=compute_on_step,\n dist_sync_on_step=dist_sync_on_step,\n process_group=process_group,\n dist_sync_fn=dist_sync_fn,\n )\n\n valid_net_type = (\"vgg\", \"alex\", \"squeeze\")\n if net_type not in valid_net_type:\n raise ValueError(\n f\"Invalid `net_type` ('{net_type}'). Expected one of {valid_net_type}.\"\n )\n\n valid_modes = (\"grayscale\", \"rgb\")\n if mode not in valid_modes:\n raise ValueError(f\"Invalid `mode` ('{mode}'). Expected one of {valid_modes}.\")\n\n self.net = NoTrainLpips(net=net_type, lpips=lpips, verbose=False)\n self.mode = mode\n\n def func(self, preds: torch.Tensor, targets: torch.Tensor):\n\n if self.mode == \"grayscale\":\n loss_shape = (targets.shape[0], targets.shape[1])\n elif self.mode == \"rgb\":\n if targets.shape[1] != 3:\n raise ValueError(\n f\"Channel dimension must have size 3 for rgb mode,\\\n but got tensor of shape {targets.shape}.\"\n )\n\n is_complex = cplx.is_complex(targets) or cplx.is_complex_as_real(targets)\n if is_complex:\n raise TypeError(\n f\"Data type must be real when mode is {self.mode},\\\n but got data type {targets.dtype}\"\n )\n\n loss_shape = (targets.shape[0], 1)\n\n preds = self.preprocess_lpips(preds)\n targets = self.preprocess_lpips(targets)\n\n loss = self.net(preds, targets).squeeze()\n loss = loss.view(loss_shape)\n\n return loss\n\n def preprocess_lpips(self, img: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Preprocess image per LPIPS implementation.\n\n Converts images to magnitude images if complex and normalizes between [-1, 1].\n If self.mode is 'grayscale', then each channel dimension will be replicated 3 times.\n\n Args:\n img (torch.Tensor): Tensor to preprocess.\n\n Returns:\n img (torch.Tensor): Preprocessed tensor.\n \"\"\"\n\n is_complex = cplx.is_complex(img) or cplx.is_complex_as_real(img)\n if is_complex:\n img = cplx.abs(img)\n\n if self.mode == \"grayscale\":\n # normalize each image independently (channel dim. represents different images)\n shape = (img.shape[0], img.shape[1], -1)\n img_min = torch.amin(img.reshape(shape), dim=-1, keepdim=True).unsqueeze(-1)\n img_max = torch.amax(img.reshape(shape), dim=-1, keepdim=True).unsqueeze(-1)\n img = 2 * (img - img_min) / (img_max - img_min) - 1\n\n img = img.reshape(img.shape[0] * img.shape[1], 1, img.shape[2], img.shape[3])\n img = img.repeat(1, 3, 1, 1)\n elif self.mode == \"rgb\":\n # normalize each image independently (channel dim. represents the same image)\n shape = (img.shape[0], -1)\n img_min = (\n torch.amin(img.reshape(shape), dim=-1, keepdim=True).unsqueeze(-1).unsqueeze(-1)\n )\n img_max = (\n torch.amax(img.reshape(shape), dim=-1, keepdim=True).unsqueeze(-1).unsqueeze(-1)\n )\n img = 2 * (img - img_min) / (img_max - img_min) - 1\n\n return img\n\n\nif env.package_available(\"lpips\"):\n\n class NoTrainLpips(_LPIPS):\n def train(self, mode: bool) -> \"NoTrainLpips\":\n \"\"\"the network should not be able to be switched away from evaluation mode.\n Implementation adapted from torchmetrics LPIPS.\"\"\"\n return super().train(False)\n\nelse:\n NoTrainLpips = None\n", "repo_name": "ad12/meddlr", "sub_path": "meddlr/metrics/lpip.py", "file_name": "lpip.py", "file_ext": "py", "file_size_in_byte": 6703, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 59, "dataset": "github-code", "pt": "2", "api": [{"api_name": "meddlr.utils.env.package_available", "line_number": 9, "usage_type": "call"}, {"api_name": "meddlr.utils.env", "line_number": 9, "usage_type": "name"}, {"api_name": "meddlr.metrics.metric.Metric", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 37, "usage_type": "name"}, {"api_name": "meddlr.utils.env.package_available", "line_number": 65, "usage_type": "call"}, {"api_name": "meddlr.utils.env", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 94, "usage_type": "attribute"}, {"api_name": "meddlr.ops.complex.is_complex", "line_number": 105, "usage_type": "call"}, {"api_name": "meddlr.ops.complex", "line_number": 105, "usage_type": "name"}, {"api_name": "meddlr.ops.complex.is_complex_as_real", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 122, "usage_type": "attribute"}, {"api_name": "meddlr.ops.complex.is_complex", "line_number": 136, "usage_type": "call"}, {"api_name": "meddlr.ops.complex", "line_number": 136, "usage_type": "name"}, {"api_name": "meddlr.ops.complex.is_complex_as_real", "line_number": 136, "usage_type": "call"}, {"api_name": "meddlr.ops.complex.abs", "line_number": 138, "usage_type": "call"}, {"api_name": "meddlr.ops.complex", "line_number": 138, "usage_type": "name"}, {"api_name": "torch.amin", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.amax", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.amin", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.amax", "line_number": 156, "usage_type": "call"}, {"api_name": "meddlr.utils.env.package_available", "line_number": 163, "usage_type": "call"}, {"api_name": "meddlr.utils.env", "line_number": 163, "usage_type": "name"}, {"api_name": "lpips.LPIPS", "line_number": 165, "usage_type": "name"}]} +{"seq_id": "14862686071", "text": "import random\nimport torch\nimport openai\nimport time\n\nfrom transformers import BertTokenizer, BertModel\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom app import app\nfrom app.model import *\n\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\nmodel = BertModel.from_pretrained(\"bert-base-uncased\").eval()\napp.config.from_object(\"config\")\nopenai.api_key = app.config[\"OPENAI_API_KEY\"]\nMODEL_ID = \"gpt-4\"\nMAX_CALL = 100\ncall_count = 0\ntopic = \"\"\n\n\ndef is_question_definitive(question, team, answer):\n response = chatgpt_conversation(\n \"Yes or No? Does the following question have a single, definitive answer? \"\n + question\n )\n\n if response == \"Yes\" or response == \"yes\":\n print(question + \": This Question Is Unique\")\n return True\n else:\n existing_vague_question = Vague.query.filter_by(question=question).first()\n if not existing_vague_question:\n row = Vague(question=question, answer=answer, team=team, topic=topic)\n db.session.add(row)\n db.session.commit()\n print(question + \": Question added to the vague table\")\n return False\n\n\ndef can_question_be_reworded(question):\n response = chatgpt_conversation(\n \"Yes or No? Can you reword the following question to have a single, definitive answer? \"\n + question\n )\n\n if response == \"Yes\" or response == \"yes\":\n print(question + \": This Question Can Be Reworded\")\n return True\n else:\n return False\n\n\ndef ask_again(question):\n reword_question = chatgpt_conversation(\n f\"Can you turn this into a question with only one possible answer?\"\n + question\n + f\" Ensure that the question is below 255 characters and each answer is no more than \"\n f\"7 words. The format of the response should be question \\n option1 \\n option2 \\n option3 \\n option4 \\n \"\n f\"answer. do not provide anything else in the response to distinguish what each line represents, only the \"\n f\"requested information. You must provide a question, 4 options, and an answer.\"\n )\n question_details = reword_question.split(\"\\n\")\n question = question_details[0].strip()\n options = [option.strip() for option in question_details[1:5]]\n correct_option = question_details[5].strip()\n return question, options, correct_option\n\n\ndef is_answer_correct(question, answer, team):\n verify_response = chatgpt_conversation(\n \"Yes or No? Is the answer to \" + question + \"Answer: \" + answer\n )\n if verify_response == \"Yes\" or verify_response == \"yes\":\n print(question + \": This Answer Has Been Verified\")\n return True\n else:\n existing_accuracy_question = Accuracy.query.filter_by(question=question).first()\n if not existing_accuracy_question:\n row = Accuracy(question=question, answer=answer, team=team, topic=topic)\n db.session.add(row)\n db.session.commit()\n print(\"Question added to the accuracy table\")\n return False\n\n\ndef chatgpt_prompt(question_type, summary, team):\n global topic\n\n if question_type == \"history\":\n difficulty, chosen_sub_topic = generate_history_question_topic()\n topic = chosen_sub_topic\n print(chosen_sub_topic)\n # See if the prompt can change to only ask definitive questions\n prompt = chatgpt_conversation(\n f\"Give me a unique {difficulty} level difficulty multiple choice quiz question about the {team}'s \"\n f\"{chosen_sub_topic}. Ensure that the question is below 255 characters and each answer is no more than \"\n f\"7 words. The format of the response should be question \\n option1 \\n option2 \\n option3 \\n option4 \\n \"\n f\"answer. do not provide anything else in the response to distinguish what each line represents, only the \"\n f\"requested information. You must provide a question, 4 options, and an answer.\"\n )\n print(prompt)\n return prompt\n if question_type == \"pbp_current\":\n prompt = chatgpt_conversation(\n f'Based on the following plays from this game: \"{summary}\", generate a unique multiple '\n f\"choice quiz question from big plays. Ensure that the question is below 255 characters and each answer is \"\n f\"no more than 7 words. Provide four options and the correct answer. Please provide as much detail as \"\n f\"possible including but not limited to which teams were playing, which team made the play, what type of \"\n f\"play it was, the quarter the play occured, time left in quarter, who made the play, \"\n f\"and if it resulted in a touchdown or firstdown. If known, give full player names as options.\"\n f\"The format of the response should be question \\n option1 \\n option2 \\n option3 \\n option4 \\n \"\n f\"answer. do not provide anything else in the response to distinguish what each line represents, only the \"\n f\"requested information. You must provide a question, 4 options, and an answer.\"\n )\n return prompt\n return None\n\n\ndef create_question_from_chatgpt(question_type, game_id, team):\n global call_count\n global MAX_CALL\n global nfl_fact\n\n if game_id is not None:\n game = Game.query.filter_by(id=game_id).first()\n if not game:\n return \"Game not found.\", 404\n\n plays = Play.query.filter_by(game_id=game_id).all()\n\n if not plays:\n return f\"No data found.\", 404\n\n summary = \". \".join(\n [f\"{play.timestamp} - {play.description}\" for play in plays]\n )\n nfl_fact = chatgpt_prompt(question_type, summary, team)\n else:\n nfl_fact = chatgpt_prompt(question_type, None, team)\n\n for _ in range(MAX_CALL):\n if call_count >= MAX_CALL:\n print(\"Reached Max Call Count: Cannot Generate New Question\")\n\n call_count += 1\n question_details = nfl_fact.split(\"\\n\")\n print(question_details)\n\n if len(question_details) <= 5:\n print(\"Length Escape\")\n break\n\n try:\n count = 0\n question = question_details[0].strip()\n options = [option.strip() for option in question_details[1:5]]\n correct_option = question_details[5].strip()\n if game_id is None:\n definitive = is_question_definitive(question, team, correct_option)\n if not definitive:\n reworded = can_question_be_reworded(question)\n if not reworded:\n print(\"Question Cannot Be Reworded Escape\")\n break\n while reworded and count < 5:\n question, options, correct_option = ask_again(question)\n count += 1\n definitive = is_question_definitive(\n question, team, correct_option\n )\n\n if count >= 5:\n print(\"Not Definitive Escape\")\n break\n\n correct = is_answer_correct(question, correct_option, team)\n\n if None in options:\n print(\"None Escape\")\n break\n\n existing_questions_for_team = (\n Question.query.filter_by(team=team)\n .with_entities(Question.question, Question.answer)\n .all()\n )\n\n is_similar = False\n\n for q_text, q_answer in existing_questions_for_team:\n if bert_similarity(question, q_text) > 0.90:\n if correct_option == q_answer:\n is_similar = True\n else:\n continue\n\n if not is_similar and definitive and correct:\n row = Question(\n question=question,\n counter=get_next_question_id_for_game(),\n option1=options[0],\n option2=options[1],\n option3=options[2],\n option4=options[3],\n answer=correct_option,\n team=team,\n )\n db.session.add(row)\n db.session.commit()\n call_count = 0\n break\n else:\n break\n\n except Exception as e:\n print(f\"An error occurred: {e}\")\n\n\ndef chatgpt_conversation(prompt):\n response = openai.ChatCompletion.create(\n model=MODEL_ID, messages=[{\"role\": \"user\", \"content\": prompt}]\n )\n\n return response[\"choices\"][0][\"message\"][\"content\"]\n\n\ndef generate_history_question_topic():\n difficulty = \"medium\"\n sub_topics = [\n \"Team History\",\n \"Legendary Players\",\n \"Championship Seasons\",\n \"Coaches and Management\",\n \"Stadium and Fan Culture\",\n \"Rivalries\",\n \"Record Breaking Performances\",\n \"Draft Picks\",\n \"Current Charity Organizations\",\n \"Individual player awards\",\n \"Founding Facts\",\n \"Previous Team Names\",\n \"Legendary Teams\",\n \"Stadium Facts\",\n \"Hall of Fame Inductees\",\n \"Memorable Playoff Games\",\n \"Team Scandals and Controversies\",\n \"Franchise Records\",\n \"Community Engagement\",\n \"Notable Trades and Acquisitions\",\n \"Behind-the-Scenes Personnel\",\n \"Media Coverage and Team Perception\",\n \"Fan Traditions\",\n \"Retired Jerseys and Team Honors\",\n ]\n chosen_sub_topic = random.choice(sub_topics)\n\n return difficulty, chosen_sub_topic\n\n\ndef get_bert_embedding(sentence):\n tokens = tokenizer(\n sentence, return_tensors=\"pt\", truncation=True, padding=True, max_length=512\n )\n with torch.no_grad():\n output = model(**tokens)\n return output.last_hidden_state[:, 0, :].squeeze().numpy()\n\n\ndef bert_similarity(sent1, sent2):\n emb1 = get_bert_embedding(sent1)\n emb2 = get_bert_embedding(sent2)\n return cosine_similarity([emb1], [emb2])[0][0]\n\n\ndef get_next_question_id_for_game():\n last_question = Question.query.order_by(Question.counter.desc()).first()\n if last_question:\n return last_question.counter + 1\n else:\n return 1\n", "repo_name": "MarkKarels/capstone_project", "sub_path": "app/chatGPT.py", "file_name": "chatGPT.py", "file_ext": "py", "file_size_in_byte": 10332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "transformers.BertTokenizer.from_pretrained", "line_number": 11, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer", "line_number": 11, "usage_type": "name"}, {"api_name": "transformers.BertModel.from_pretrained", "line_number": 12, "usage_type": "call"}, {"api_name": "transformers.BertModel", "line_number": 12, "usage_type": "name"}, {"api_name": "app.app.config.from_object", "line_number": 13, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 13, "usage_type": "name"}, {"api_name": "openai.api_key", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.app.config", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 14, "usage_type": "name"}, {"api_name": "openai.ChatCompletion.create", "line_number": 220, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 220, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 264, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 272, "usage_type": "call"}]} +{"seq_id": "36800749817", "text": "# -*- coding: utf-8 -*-\n\nimport importlib\nimport json\nimport os\nimport re\nimport vim\n\nfrom .compat import integer_types, to_bytes, to_unicode\n\ncurrent = None\n\n\ndef get_encoding():\n return to_unicode(vim.current.buffer.options['fileencoding'] or\n vim.options['encoding'] or 'utf-8', 'utf-8')\n\n\ndef _unicode(text):\n encoding = get_encoding()\n try:\n return to_unicode(text, encoding)\n except Exception:\n return text\n\n\ndef _read_args(path):\n try:\n with open(path) as f:\n return [l.strip() for l in f.readlines()]\n except Exception:\n return []\n\n\nclass Meta(type):\n def __init__(cls, name, bases, attrs):\n if name not in ('Completor', 'Base'):\n Completor._registry[to_unicode(cls.filetype, 'utf-8')] = cls()\n\n return super(Meta, cls).__init__(name, bases, attrs)\n\nBase = Meta('Base', (object,), {})\n\n\nclass Unusable(object):\n def __get__(self, inst, owner):\n raise RuntimeError('unusable')\n\n\nclass Completor(Base):\n _registry = {}\n\n filetype = Unusable()\n\n daemon = False\n sync = False\n trigger = None\n ident = re.compile(r'[^\\W\\d]\\w*', re.U)\n\n _type_map = {\n b'c': b'cpp'\n }\n\n _arg_cache = {}\n\n def __init__(self):\n self.input_data = ''\n self.ft = ''\n\n @property\n def current_directory(self):\n \"\"\"Return the directory of the file in current buffer\n\n :rtype: unicode\n \"\"\"\n return to_unicode(vim.Function('expand')('%:p:h'), 'utf-8')\n\n @property\n def tempname(self):\n \"\"\"Write buffer content to a temp file and return the file name\n\n :rtype: unicode\n \"\"\"\n return to_unicode(vim.Function('completor#utils#tempname')(), 'utf-8')\n\n @property\n def filename(self):\n \"\"\"Get the file name of current buffer\n\n :rtype: unicode\n \"\"\"\n return vim.current.buffer.name\n\n @property\n def cursor(self):\n line, _ = vim.current.window.cursor\n return line, len(self.input_data)\n\n # use cached property\n @property\n def filetype_map(self):\n m = self.get_option('completor_filetype_map') or {}\n self._type_map.update(m)\n return self._type_map\n\n @staticmethod\n def get_option(key):\n return vim.vars.get(key)\n\n @property\n def disabled(self):\n types = self.get_option('completor_disable_{}'.format(self.filetype))\n if isinstance(types, integer_types):\n return bool(types)\n if isinstance(types, (list, vim.List)):\n return to_bytes(self.ft) in types\n return False\n\n # input_data: unicode\n def match(self, input_data):\n if self.trigger is None:\n return True\n if isinstance(self.trigger, str):\n self.trigger = re.compile(self.trigger, re.X | re.U)\n\n return bool(self.trigger.search(input_data))\n\n def format_cmd(self):\n return ''\n\n # base: unicode or list\n def parse(self, base):\n return []\n\n # base: str or unicode or list\n def get_completions(self, base):\n if not isinstance(base, (list, vim.List)):\n base = _unicode(base)\n return self.parse(base)\n\n @staticmethod\n def find_config_file(file):\n cwd = os.getcwd()\n while True:\n path = os.path.join(cwd, file)\n if os.path.exists(path):\n return path\n if os.path.dirname(cwd) == cwd:\n break\n cwd = os.path.split(cwd)[0]\n\n def parse_config(self, file):\n key = \"{}-{}\".format(self.filetype, file)\n if key not in self._arg_cache:\n path = self.find_config_file(file)\n self._arg_cache[key] = [] if path is None else _read_args(path)\n return self._arg_cache[key]\n\n def ident_match(self, pat):\n if not self.input_data:\n return -1\n\n _, index = self.cursor\n for i in range(index):\n text = self.input_data[i:index]\n matched = pat.match(text)\n if matched and matched.end() == len(text):\n return len(to_bytes(self.input_data[:i], get_encoding()))\n return index\n\n def start_column(self):\n if not self.ident:\n return -1\n if isinstance(self.ident, str):\n self.ident = re.compile(self.ident, re.U | re.X)\n return self.ident_match(self.ident)\n\n def request(self):\n \"\"\"Generate daemon request arguments\n \"\"\"\n line, col = self.cursor\n return json.dumps({\n 'line': line - 1,\n 'col': col,\n 'filename': self.filename,\n 'content': '\\n'.join(vim.current.buffer[:])\n })\n\n def message_ended(self, msg):\n \"\"\"Test the end of daemon response\n\n :param msg: the message received from daemon (bytes)\n \"\"\"\n return True\n\n_completor = Completor()\n\n\n# ft: unicode\ndef _load(ft):\n if ft not in _completor._registry:\n try:\n importlib.import_module(\"completers.{}\".format(ft))\n except ImportError:\n return\n return _completor._registry.get(ft)\n\n\n# ft: bytes, input_data: bytes\ndef load_completer(ft, input_data):\n input_data = _unicode(input_data)\n\n if not ft or not input_data.strip():\n return\n ft = to_unicode(_completor.filetype_map.get(ft, ft), 'utf-8')\n\n if 'common' not in _completor._registry:\n import completers.common # noqa\n\n filename = get('filename')\n if filename.match(input_data) and not filename.disabled:\n c = filename\n else:\n c = _load(ft)\n if c is None:\n omni = get('omni')\n if omni.has_omnifunc(ft):\n c = omni\n if c is None or not c.match(input_data):\n c = get('common')\n c.input_data = input_data\n c.ft = ft\n return None if c.disabled else c\n\n\n# filetype: str, ft: bytes, input_data: bytes\ndef get(filetype, ft=None, input_data=None):\n completer = _completor._registry.get(filetype)\n if completer:\n if ft is not None:\n completer.ft = _unicode(ft)\n if input_data is not None:\n completer.input_data = _unicode(input_data)\n return completer\n", "repo_name": "dNitro/dotfiles", "sub_path": ".vim/plugged-local/completor.vim/pythonx/completor/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 6216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "compat.to_unicode", "line_number": 15, "usage_type": "call"}, {"api_name": "vim.current", "line_number": 15, "usage_type": "attribute"}, {"api_name": "vim.options", "line_number": 16, "usage_type": "attribute"}, {"api_name": "compat.to_unicode", "line_number": 22, "usage_type": "call"}, {"api_name": "compat.to_unicode", "line_number": 38, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 58, "usage_type": "call"}, {"api_name": "re.U", "line_number": 58, "usage_type": "attribute"}, {"api_name": "compat.to_unicode", "line_number": 76, "usage_type": "call"}, {"api_name": "vim.Function", "line_number": 76, "usage_type": "call"}, {"api_name": "compat.to_unicode", "line_number": 84, "usage_type": "call"}, {"api_name": "vim.Function", "line_number": 84, "usage_type": "call"}, {"api_name": "vim.current", "line_number": 92, "usage_type": "attribute"}, {"api_name": "vim.current", "line_number": 96, "usage_type": "attribute"}, {"api_name": "vim.vars.get", "line_number": 108, "usage_type": "call"}, {"api_name": "vim.vars", "line_number": 108, "usage_type": "attribute"}, {"api_name": "compat.integer_types", "line_number": 113, "usage_type": "argument"}, {"api_name": "vim.List", "line_number": 115, "usage_type": "attribute"}, {"api_name": "compat.to_bytes", "line_number": 116, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 124, "usage_type": "call"}, {"api_name": "re.X", "line_number": 124, "usage_type": "attribute"}, {"api_name": "re.U", "line_number": 124, "usage_type": "attribute"}, {"api_name": "vim.List", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "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": "os.path.dirname", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "compat.to_bytes", "line_number": 168, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 175, "usage_type": "call"}, {"api_name": "re.U", "line_number": 175, "usage_type": "attribute"}, {"api_name": "re.X", "line_number": 175, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 182, "usage_type": "call"}, {"api_name": "vim.current", "line_number": 186, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 203, "usage_type": "call"}, {"api_name": "compat.to_unicode", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "6537991666", "text": "from flask import Flask, jsonify, render_template, request\nimport func\nimport firebase_admin\nimport requests, json\n\n\ndefault_app = firebase_admin.initialize_app()\n\napp = Flask(__name__)\n\n\n\n@app.route('/')\ndef home():\n movies = ()\n\n carouselData = func.getCarouselItems()\n popularMovies = func.getPopularMovies()\n #watchlist = func.getWatchlistMovies()\n return render_template('index.html', carouselData=carouselData, popularMovies=popularMovies)\n\n@app.route(\"/createUser\", methods=[\"GET\", \"PUT\"])\ndef createUsers():\n url = \"https://w5xj3edx56.execute-api.ap-south-1.amazonaws.com/createUser\"\n if request.method == 'PUT':\n requestBody = {}\n requestBody[\"Email\"] = request.json[\"emailValue\"]\n res = func.getUser(request.json[\"emailValue\"])\n print(res)\n if(res == False):\n requestBody[\"First_Name\"] = request.json[\"fnameValue\"]\n requestBody[\"Last_Name\"] = request.json[\"lnameValue\"]\n requestBody[\"Password\"] = request.json[\"pwdValue\"]\n requestBody[\"User_Name\"] = request.json[\"unameValue\"]\n response = requests.put(\n url, data=json.dumps(requestBody),\n headers={'Content-Type': 'application/json'}\n )\n return {\"status\": \"Account Created\"}\n \n return {\"status\": \"Already Exists\"}\n\n@app.route(\"/getUser\", methods=[\"GET\", \"POST\"])\ndef getUsers():\n if request.method == \"POST\":\n email = request.json['emailValue']\n users = func.getUser(email)\n if(users):\n return (users)\n else:\n return 0\n \n@app.route(\"/getMovie\", methods=[\"GET\", \"POST\"])\ndef getMovieById():\n if request.method == \"POST\":\n movie = request.json['movieId']\n users = func.getMovie(movie)\n if(users):\n return (users)\n else:\n return 0\n\n@app.route(\"/getMovieWatchlist\", methods=[\"GET\", \"POST\"])\ndef getMovieWatchlist():\n if request.method == \"POST\":\n movie = request.json['emailValue']\n watchlist = func.getWatchlistMovies(movie)\n if(watchlist):\n watchlist = map(dict, set(tuple(sorted(d.items())) for d in watchlist))\n # list_set = set(watchlist)\n # # convert the set to the list\n # watchlist = (list(list_set))\n return (list(watchlist))\n else:\n return 0 \n\n \n@app.route(\"/addMovieWatchlist\", methods=[\"GET\", \"PUT\"])\ndef addMovie():\n url = \"https://mv77u9kxij.execute-api.ap-south-1.amazonaws.com/addMovie\"\n if request.method == 'PUT':\n print(request.json)\n response = requests.put(\n url, data=json.dumps(request.json),\n headers={'Content-Type': 'application/json'})\n \n return response.content\n@app.route(\"/getRecommendation/\", methods=[\"GET\", \"PUT\"]) \ndef getRecommended(name):\n movies = ()\n if request.method == 'GET':\n movies = func.recommend(name)\n return list(movies)\n return movies\n\nif __name__ == \"__main__\":\n app.run(debug=True, port=3000)", "repo_name": "Shrey-2019/MajorProject-MovieRecommendation_System", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "firebase_admin.initialize_app", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "func.getCarouselItems", "line_number": 17, "usage_type": "call"}, {"api_name": "func.getPopularMovies", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 20, "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.json", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "func.getUser", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "func.getUser", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.method", "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": "func.getMovie", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "func.getWatchlistMovies", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "func.recommend", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "35798824343", "text": "from copy import deepcopy\nfrom functools import partial\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom scipy.special import softmax\nfrom typing import Callable, Dict, Optional, Union, Tuple\nfrom alibi_detect.cd.base import BaseClassifierDrift\nfrom alibi_detect.models.pytorch.trainer import trainer\nfrom alibi_detect.utils.pytorch import get_device\nfrom alibi_detect.utils.pytorch.data import TorchDataset\nfrom alibi_detect.utils.pytorch.prediction import predict_batch\nfrom alibi_detect.utils.warnings import deprecated_alias\nfrom alibi_detect.utils.frameworks import Framework\nfrom alibi_detect.utils._types import TorchDeviceType\n\n\nclass ClassifierDriftTorch(BaseClassifierDrift):\n @deprecated_alias(preprocess_x_ref='preprocess_at_init')\n def __init__(\n self,\n x_ref: Union[np.ndarray, list],\n model: Union[nn.Module, nn.Sequential],\n p_val: float = .05,\n x_ref_preprocessed: bool = False,\n preprocess_at_init: bool = True,\n update_x_ref: Optional[Dict[str, int]] = None,\n preprocess_fn: Optional[Callable] = None,\n preds_type: str = 'probs',\n binarize_preds: bool = False,\n reg_loss_fn: Callable = (lambda model: 0),\n train_size: Optional[float] = .75,\n n_folds: Optional[int] = None,\n retrain_from_scratch: bool = True,\n seed: int = 0,\n optimizer: Callable = torch.optim.Adam,\n learning_rate: float = 1e-3,\n batch_size: int = 32,\n preprocess_batch_fn: Optional[Callable] = None,\n epochs: int = 3,\n verbose: int = 0,\n train_kwargs: Optional[dict] = None,\n device: TorchDeviceType = None,\n dataset: Callable = TorchDataset,\n dataloader: Callable = DataLoader,\n input_shape: Optional[tuple] = None,\n data_type: Optional[str] = None\n ) -> None:\n \"\"\"\n Classifier-based drift detector. The classifier is trained on a fraction of the combined\n reference and test data and drift is detected on the remaining data. To use all the data\n to detect drift, a stratified cross-validation scheme can be chosen.\n\n Parameters\n ----------\n x_ref\n Data used as reference distribution.\n model\n PyTorch classification model used for drift detection.\n p_val\n p-value used for the significance of the test.\n x_ref_preprocessed\n Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only\n the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference\n data will also be preprocessed.\n preprocess_at_init\n Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference\n data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`.\n update_x_ref\n Reference data can optionally be updated to the last n instances seen by the detector\n or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while\n for reservoir sampling {'reservoir_sampling': n} is passed.\n preprocess_fn\n Function to preprocess the data before computing the data drift metrics.\n preds_type\n Whether the model outputs 'probs' or 'logits'\n binarize_preds\n Whether to test for discrepency on soft (e.g. probs/logits) model predictions directly\n with a K-S test or binarise to 0-1 prediction errors and apply a binomial test.\n reg_loss_fn\n The regularisation term reg_loss_fn(model) is added to the loss function being optimized.\n train_size\n Optional fraction (float between 0 and 1) of the dataset used to train the classifier.\n The drift is detected on `1 - train_size`. Cannot be used in combination with `n_folds`.\n n_folds\n Optional number of stratified folds used for training. The model preds are then calculated\n on all the out-of-fold predictions. This allows to leverage all the reference and test data\n for drift detection at the expense of longer computation. If both `train_size` and `n_folds`\n are specified, `n_folds` is prioritized.\n retrain_from_scratch\n Whether the classifier should be retrained from scratch for each set of test data or whether\n it should instead continue training from where it left off on the previous set.\n seed\n Optional random seed for fold selection.\n optimizer\n Optimizer used during training of the classifier.\n learning_rate\n Learning rate used by optimizer.\n batch_size\n Batch size used during training of the classifier.\n preprocess_batch_fn\n Optional batch preprocessing function. For example to convert a list of objects to a batch which can be\n processed by the model.\n epochs\n Number of training epochs for the classifier for each (optional) fold.\n verbose\n Verbosity level during the training of the classifier. 0 is silent, 1 a progress bar.\n train_kwargs\n Optional additional kwargs when fitting the classifier.\n device\n Device type used. The default tries to use the GPU and falls back on CPU if needed.\n Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of\n ``torch.device``.\n dataset\n Dataset object used during training.\n dataloader\n Dataloader object used during training.\n input_shape\n Shape of input data.\n data_type\n Optionally specify the data type (tabular, image or time-series). Added to metadata.\n \"\"\"\n super().__init__(\n x_ref=x_ref,\n p_val=p_val,\n x_ref_preprocessed=x_ref_preprocessed,\n preprocess_at_init=preprocess_at_init,\n update_x_ref=update_x_ref,\n preprocess_fn=preprocess_fn,\n preds_type=preds_type,\n binarize_preds=binarize_preds,\n train_size=train_size,\n n_folds=n_folds,\n retrain_from_scratch=retrain_from_scratch,\n seed=seed,\n input_shape=input_shape,\n data_type=data_type\n )\n\n if preds_type not in ['probs', 'logits']:\n raise ValueError(\"'preds_type' should be 'probs' or 'logits'\")\n\n self.meta.update({'backend': Framework.PYTORCH.value})\n\n # set device, define model and training kwargs\n self.device = get_device(device)\n self.original_model = model\n self.model = deepcopy(model)\n\n # define kwargs for dataloader and trainer\n self.loss_fn = nn.CrossEntropyLoss() if (self.preds_type == 'logits') else nn.NLLLoss()\n self.dataset = dataset\n self.dataloader = partial(dataloader, batch_size=batch_size, shuffle=True)\n self.predict_fn = partial(predict_batch, device=self.device,\n preprocess_fn=preprocess_batch_fn, batch_size=batch_size)\n self.train_kwargs = {'optimizer': optimizer, 'epochs': epochs, 'preprocess_fn': preprocess_batch_fn,\n 'reg_loss_fn': reg_loss_fn, 'learning_rate': learning_rate, 'verbose': verbose}\n if isinstance(train_kwargs, dict):\n self.train_kwargs.update(train_kwargs)\n\n def score(self, x: Union[np.ndarray, list]) \\\n -> Tuple[float, float, np.ndarray, np.ndarray, Union[np.ndarray, list], Union[np.ndarray, list]]:\n \"\"\"\n Compute the out-of-fold drift metric such as the accuracy from a classifier\n trained to distinguish the reference data from the data to be tested.\n\n Parameters\n ----------\n x\n Batch of instances.\n\n Returns\n -------\n p-value, a notion of distance between the trained classifier's out-of-fold performance \\\n and that which we'd expect under the null assumption of no drift, \\\n and the out-of-fold classifier model prediction probabilities on the reference and test data \\\n as well as the associated reference and test instances of the out-of-fold predictions.\n \"\"\"\n x_ref, x = self.preprocess(x)\n x, y, splits = self.get_splits(x_ref, x) # type: ignore\n\n # iterate over folds: train a new model for each fold and make out-of-fold (oof) predictions\n preds_oof_list, idx_oof_list = [], []\n for idx_tr, idx_te in splits:\n y_tr = y[idx_tr]\n if isinstance(x, np.ndarray):\n x_tr, x_te = x[idx_tr], x[idx_te]\n elif isinstance(x, list):\n x_tr, x_te = [x[_] for _ in idx_tr], [x[_] for _ in idx_te]\n else:\n raise TypeError(f'x needs to be of type np.ndarray or list and not {type(x)}.')\n ds_tr = self.dataset(x_tr, y_tr)\n dl_tr = self.dataloader(ds_tr)\n self.model = deepcopy(self.original_model) if self.retrain_from_scratch else self.model\n self.model = self.model.to(self.device)\n train_args = [self.model, self.loss_fn, dl_tr, self.device]\n trainer(*train_args, **self.train_kwargs) # type: ignore\n preds = self.predict_fn(x_te, self.model.eval())\n preds_oof_list.append(preds)\n idx_oof_list.append(idx_te)\n preds_oof = np.concatenate(preds_oof_list, axis=0)\n probs_oof = softmax(preds_oof, axis=-1) if self.preds_type == 'logits' else preds_oof\n idx_oof = np.concatenate(idx_oof_list, axis=0)\n y_oof = y[idx_oof]\n n_cur = y_oof.sum()\n n_ref = len(y_oof) - n_cur\n p_val, dist = self.test_probs(y_oof, probs_oof, n_ref, n_cur)\n idx_sort = np.argsort(idx_oof)\n probs_sort = probs_oof[idx_sort]\n if isinstance(x, np.ndarray):\n x_oof = x[idx_oof]\n x_sort = x_oof[idx_sort]\n else:\n x_oof = [x[_] for _ in idx_oof]\n x_sort = [x_oof[_] for _ in idx_sort]\n return p_val, dist, probs_sort[:n_ref, 1], probs_sort[n_ref:, 1], x_sort[:n_ref], x_sort[n_ref:]\n", "repo_name": "SeldonIO/alibi-detect", "sub_path": "alibi_detect/cd/pytorch/classifier.py", "file_name": "classifier.py", "file_ext": "py", "file_size_in_byte": 10473, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1980, "dataset": "github-code", "pt": "2", "api": [{"api_name": "alibi_detect.cd.base.BaseClassifierDrift", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 23, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 24, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 43, "usage_type": "name"}, {"api_name": "alibi_detect.utils._types.TorchDeviceType", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.optim", "line_number": 37, "usage_type": "attribute"}, {"api_name": "alibi_detect.utils.pytorch.data.TorchDataset", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 46, "usage_type": "name"}, {"api_name": "alibi_detect.utils.frameworks.Framework.PYTORCH", "line_number": 144, "usage_type": "attribute"}, {"api_name": "alibi_detect.utils.frameworks.Framework", "line_number": 144, "usage_type": "name"}, {"api_name": "alibi_detect.utils.pytorch.get_device", "line_number": 147, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.NLLLoss", "line_number": 152, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 154, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 155, "usage_type": "call"}, {"api_name": "alibi_detect.utils.pytorch.prediction.predict_batch", "line_number": 155, "usage_type": "argument"}, {"api_name": "alibi_detect.utils.warnings.deprecated_alias", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 187, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 195, "usage_type": "call"}, {"api_name": "alibi_detect.models.pytorch.trainer.trainer", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.special.softmax", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 211, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 163, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 163, "usage_type": "name"}]} +{"seq_id": "14513123262", "text": "from django.http import JsonResponse\nfrom django.views import View\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.csrf import csrf_exempt\nimport json\nfrom .models import Cochera, Servicio, TiempoAlquiler, Usuario\nimport mercadopago \nfrom django.http import JsonResponse\nfrom django.views import View\nfrom .models import Cochera\nfrom django.contrib.auth.views import LoginView\n\nfrom django.contrib.auth import authenticate, login\n#superusuario\nfrom django.contrib.auth.models import User\n\n\n\n\n#JWT \nfrom rest_framework.views import APIView\nfrom rest_framework.permissions import AllowAny, IsAuthenticated\nfrom rest_framework.decorators import api_view, authentication_classes, permission_classes\nfrom rest_framework_simplejwt.tokens import RefreshToken\nfrom rest_framework_simplejwt.views import TokenObtainPairView, TokenRefreshView\nfrom rest_framework.authentication import TokenAuthentication\n\nfrom django.contrib.auth import get_user_model\n\n# -------------------------------------------------------------------------------------\n\n#CONFIG PARA BLOQUEAR ACCESO SI NO SE AUTENTICO\nclass CustomTokenView(TokenObtainPairView):\n # permission_classes = [AllowAny] para que no requiera autenticacion\n permission_classes = [IsAuthenticated]\n\n def post(self, request, *args, **kwargs):\n response = super().post(request, *args, **kwargs)\n # Agregar cualquier personalización adicional a la respuesta aquí\n return response\n \nclass RegisterView(View):\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return super().dispatch(request, *args, **kwargs)\n \n def get(self, request, id=0):\n usuarios = Usuario.objects.all()\n data = {\"message\": \"Success\", \"usuarios\": []}\n for usuario in usuarios:\n usuario_data = {\n \"id_usuario\": usuario.id_usuario,\n \"nombre_usuario\": usuario.nombre_usuario,\n \"apellido_usuario\": usuario.apellido_usuario,\n \"correo_usuario\": usuario.correo_usuario,\n \"telefono_usuario\": usuario.telefono_usuario,\n \"contrasenia_usuario\": usuario.contrasenia_usuario,\n \"aceptar_terminos\": usuario.aceptar_terminos,\n \"rol\": usuario.rol,\n }\n data[\"usuarios\"].append(usuario_data)\n return JsonResponse(data)\n\n def post(self, request):\n jd = json.loads(request.body)\n # print(jd)\n nombre_usuario=jd[\"nombre_usuario\"]\n apellido_usuario=jd[\"apellido_usuario\"]\n correo_usuario=jd[\"correo_usuario\"]\n telefono_usuario=jd[\"telefono_usuario\"]\n contrasenia_usuario=jd[\"contrasenia_usuario\"]\n aceptar_terminos=jd[\"aceptar_terminos\"]\n rol=jd[\"rol\"]\n \n try:\n #si se selecciona el rol de Administrador\n if rol == \"Administrador\":\n #creo un super usuario\n #username deberia hacer referencia a correo_usuario. \n user = User.objects.create_superuser(username=nombre_usuario, password=contrasenia_usuario, email=correo_usuario)\n message = \"Superusuario creado\"\n\n #si se selecciona el rol de Usuario\n elif rol == \"Usuario\":\n #creo un usuario de lectura\n user = User.objects.create_user(username=nombre_usuario, password=contrasenia_usuario, email=correo_usuario)\n message = \"Usuario creado\"\n \n else:\n #si no se selecciona un rol\n data = {\"message\":\"Rol no valido\"}\n return JsonResponse(data)\n \n usuario = Usuario.objects.create(nombre_usuario=nombre_usuario, apellido_usuario=apellido_usuario, correo_usuario=correo_usuario, telefono_usuario=telefono_usuario, contrasenia_usuario=contrasenia_usuario, aceptar_terminos=aceptar_terminos, rol=rol)\n\n data = {\"message\": message, \"username\": user.username}\n except Exception as e:\n data = {\"message\":\"Error al crear el usuario\", \"error\": str(e)}\n return JsonResponse(data)\n\n def delete(self, request, id):\n usuarios = list(Usuario.objects.filter(id_usuario=id).values())\n if len(usuarios)>0:\n Usuario.objects.filter(id_usuario=id).delete()\n data = {\"message\":\"Usuario eliminado\"}\n else:\n data = {\"message\":\"Not Found...\"}\n return JsonResponse(data)\n\nclass LoginView(View):\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return super().dispatch(request, *args, **kwargs)\n \n def authenticate(self, request, email=None, password=None, **kwargs):\n UserModel = get_user_model()\n try:\n user = UserModel.objects.get(email=email)\n if user.check_password(password):\n return user\n except UserModel.DoesNotExist:\n return None\n \n def post(self, request):\n jd = json.loads(request.body)\n email = jd[\"correo_usuario\"]\n password = jd[\"contrasenia_usuario\"]\n\n\n \n user = self.authenticate(request, email=email, password=password)\n print(user)\n \n if user is not None:\n login(request, user)\n refresh = RefreshToken.for_user(user)\n access_token = str(refresh.access_token)\n email = str(email)\n\n if user.is_superuser:\n rol = \"Administrador\"\n else:\n rol = \"Usuario\"\n \n data = {\"message\": \"Inicio de sesión exitoso\", \"access_token\": access_token, \"email\": email, \"rol\": rol}\n else:\n data = {\"message\": \"Credenciales inválidas\", \"rol\": \"\"}\n \n return JsonResponse(data)\n \n\n\n# class CocheraView(APIView):\nclass CocheraView(View):\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return super().dispatch(request, *args, **kwargs)\n \n # authentication_classes = [TokenAuthentication]\n # permission_classes = [IsAuthenticated]\n\n def get(self, request, id=0):\n if id > 0:\n try:\n cochera = Cochera.objects.get(id_cochera=id)\n cochera_data = {\n \"id_cochera\": cochera.id_cochera,\n \"nombre_cochera\": cochera.nombre_cochera,\n \"img_cochera\": cochera.img_cochera,\n \"descripcion_cochera\": cochera.descripcion_cochera,\n \"tiempo_alquiler\": [],\n \"servicios\": [],\n }\n tiempo_alquiler = TiempoAlquiler.objects.filter(cochera=cochera)\n servicios = Servicio.objects.filter(cochera=cochera)\n for tiempo in tiempo_alquiler:\n cochera_data[\"tiempo_alquiler\"].append({\n \"tiempo\": tiempo.tiempo,\n \"precio\": tiempo.precio\n })\n \n for servicio in servicios:\n cochera_data[\"servicios\"].append({\n \"servicio\": servicio.servicio,\n \"precio\": servicio.precio\n })\n data = {\"message\": \"Success\", \"cochera\": cochera_data}\n return JsonResponse(data)\n except Cochera.DoesNotExist:\n data = {\"message\": \"Cochera not found\"}\n return JsonResponse(data, status=404)\n else:\n cocheras = Cochera.objects.all()\n data = {\"message\": \"Success\", \"cocheras\": []}\n for cochera in cocheras:\n cochera_data = {\n \"id_cochera\": cochera.id_cochera,\n \"nombre_cochera\": cochera.nombre_cochera,\n \"img_cochera\": cochera.img_cochera,\n \"descripcion_cochera\": cochera.descripcion_cochera,\n \"tiempo_alquiler\": [],\n \"servicios\": [],\n }\n tiempo_alquiler = TiempoAlquiler.objects.filter(cochera=cochera)\n servicios = Servicio.objects.filter(cochera=cochera)\n for tiempo in tiempo_alquiler:\n cochera_data[\"tiempo_alquiler\"].append({\n \"tiempo\": tiempo.tiempo,\n \"precio\": tiempo.precio\n })\n for servicio in servicios:\n cochera_data[\"servicios\"].append({\n \"servicio\": servicio.servicio,\n \"precio\": servicio.precio\n })\n data[\"cocheras\"].append(cochera_data)\n return JsonResponse(data)\n\n def post(self, request):\n jd = json.loads(request.body)\n # print(jd)\n Cochera.objects.create(nombre_cochera=jd[\"nombre_cochera\"], img_cochera=jd[\"img_cochera\"], descripcion_cochera=jd[\"descripcion_cochera\"])\n data = {\"message\":\"Cochera creada\"}\n return JsonResponse(data)\n\n def patch(self, request, id):\n jd=json.loads(request.body)\n cocheras = list(Cochera.objects.filter(id_cochera=id).values())\n if len(cocheras)>0:\n cochera = Cochera.objects.get(id_cochera=id)\n cochera.nombre_cochera=jd[\"nombre_cochera\"]\n cochera.img_cochera=jd[\"img_cochera\"]\n cochera.descripcion_cochera=jd[\"descripcion_cochera\"]\n cochera.save()\n data = {\"message\":\"Cochera modificada\"}\n else:\n data = {\"message\":\"Cochera no encontrada\"}\n \n return JsonResponse(data)\n\n def delete(self, request, id):\n cocheras = list(Cochera.objects.filter(id_cochera=id).values())\n if len(cocheras)>0:\n Cochera.objects.filter(id_cochera=id).delete()\n data = {\"message\":\"Cochera eliminada\"}\n else:\n data = {\"message\":\"Not Found...\"}\n return JsonResponse(data)\n\n\nclass AlquilarCocheraView(View):\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return super().dispatch(request, *args, **kwargs) \n \n def post(self, request):\n jd = json.loads(request.body)\n\n # Obtener los datos de la cochera seleccionada desde el cuerpo de la solicitud JSON\n cochera_id = jd.get(\"cochera_id\")\n tiempo_alquiler_id = jd.get(\"tiempo_alquiler_id\")\n servicio = jd.get(\"servicio\", [])\n\n\n try:\n # Obtengo la cochera seleccionada desde la base de datos\n cochera = Cochera.objects.get(id_cochera=cochera_id)\n # Obtengo el tiempo de alquiler seleccionado desde la base de datos\n tiempo_alquiler = TiempoAlquiler.objects.get(id=tiempo_alquiler_id)\n\n # Obtengo los servicios seleccionados desde la base de datos\n servicio_seleccionado = Servicio.objects.filter(id__in=servicio)\n\n # generar el enlace de pago utilizando el SDK de Mercado Pago\n ACCESS_TOKEN = \"TEST-865520782224511-052712-77c0791960cbab0081032c9906fc5539-1384417080\"\n sdk = mercadopago.SDK(ACCESS_TOKEN)\n\n preference_data = {\n \"items\": [\n {\n \"title\": cochera.nombre_cochera,\n \"quantity\": 1,\n \"unit_price\": tiempo_alquiler.precio + sum(servicio.precio for servicio in servicio_seleccionado), \n \"currency_id\": \"ARS\",\n }\n ],\n \"notification_url\": \"\",\n \"back_urls\": {\n \"success\": \"http://localhost:4200/\",\n \"failure\": \"\",\n \"pending\": \"\",\n },\n \"auto_return\": \"all\",\n }\n result = sdk.preference().create(preference_data)\n payment_url = result[\"response\"][\"init_point\"]\n data = {\"payment_url\": payment_url}\n return JsonResponse(data)\n except Cochera.DoesNotExist:\n data = {\"message\": \"Cochera not found\"}\n return JsonResponse(data, status=404)\n\n\n\n# tarjeta: 5031 7557 3453 0604\n# fecha: 11/25\n# cod: 123\n\n", "repo_name": "valetommasini/TinderCar-ProyectoFinal", "sub_path": "backend/backend1/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 12250, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rest_framework_simplejwt.views.TokenObtainPairView", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 35, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 42, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 43, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 43, "usage_type": "argument"}, {"api_name": "models.Usuario.objects.all", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Usuario.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 48, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create_superuser", "line_number": 80, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 80, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 86, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 86, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 92, "usage_type": "call"}, {"api_name": "models.Usuario.objects.create", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Usuario.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 94, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Usuario.objects.filter", "line_number": 102, "usage_type": "call"}, {"api_name": "models.Usuario.objects", "line_number": 102, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 102, "usage_type": "name"}, {"api_name": "models.Usuario.objects.filter", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Usuario.objects", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 104, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 108, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 110, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 111, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 111, "usage_type": "argument"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 116, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 125, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 135, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken.for_user", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken", "line_number": 136, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 149, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 154, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 155, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 155, "usage_type": "argument"}, {"api_name": "models.Cochera.objects.get", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 165, "usage_type": "name"}, {"api_name": "models.TiempoAlquiler.objects.filter", "line_number": 174, "usage_type": "call"}, {"api_name": "models.TiempoAlquiler.objects", "line_number": 174, "usage_type": "attribute"}, {"api_name": "models.TiempoAlquiler", "line_number": 174, "usage_type": "name"}, {"api_name": "models.Servicio.objects.filter", "line_number": 175, "usage_type": "call"}, {"api_name": "models.Servicio.objects", "line_number": 175, "usage_type": "attribute"}, {"api_name": "models.Servicio", "line_number": 175, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 188, "usage_type": "call"}, {"api_name": "models.Cochera.DoesNotExist", "line_number": 189, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 189, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 191, "usage_type": "call"}, {"api_name": "models.Cochera.objects.all", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 193, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 193, "usage_type": "name"}, {"api_name": "models.TiempoAlquiler.objects.filter", "line_number": 204, "usage_type": "call"}, {"api_name": "models.TiempoAlquiler.objects", "line_number": 204, "usage_type": "attribute"}, {"api_name": "models.TiempoAlquiler", "line_number": 204, "usage_type": "name"}, {"api_name": "models.Servicio.objects.filter", "line_number": 205, "usage_type": "call"}, {"api_name": "models.Servicio.objects", "line_number": 205, "usage_type": "attribute"}, {"api_name": "models.Servicio", "line_number": 205, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 217, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 220, "usage_type": "call"}, {"api_name": "models.Cochera.objects.create", "line_number": 222, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 222, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 222, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 224, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 227, "usage_type": "call"}, {"api_name": "models.Cochera.objects.filter", "line_number": 228, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 228, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 228, "usage_type": "name"}, {"api_name": "models.Cochera.objects.get", "line_number": 230, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 230, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 230, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 239, "usage_type": "call"}, {"api_name": "models.Cochera.objects.filter", "line_number": 242, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 242, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 242, "usage_type": "name"}, {"api_name": "models.Cochera.objects.filter", "line_number": 244, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 244, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 244, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 248, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 251, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 252, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 252, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 257, "usage_type": "call"}, {"api_name": "models.Cochera.objects.get", "line_number": 267, "usage_type": "call"}, {"api_name": "models.Cochera.objects", "line_number": 267, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 267, "usage_type": "name"}, {"api_name": "models.TiempoAlquiler.objects.get", "line_number": 269, "usage_type": "call"}, {"api_name": "models.TiempoAlquiler.objects", "line_number": 269, "usage_type": "attribute"}, {"api_name": "models.TiempoAlquiler", "line_number": 269, "usage_type": "name"}, {"api_name": "models.Servicio.objects.filter", "line_number": 272, "usage_type": "call"}, {"api_name": "models.Servicio.objects", "line_number": 272, "usage_type": "attribute"}, {"api_name": "models.Servicio", "line_number": 272, "usage_type": "name"}, {"api_name": "mercadopago.SDK", "line_number": 276, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 298, "usage_type": "call"}, {"api_name": "models.Cochera.DoesNotExist", "line_number": 299, "usage_type": "attribute"}, {"api_name": "models.Cochera", "line_number": 299, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "30117958525", "text": "import threading\nfrom typing import List\nfrom typing_extensions import Self\n\nfrom gui_components import ValueBar, Box\nfrom pygame import Surface, Rect\nimport source\nimport pygame\n\nfrom .deck import Deck\nfrom .player_effect import EffectTarget, PlayerEffect\nfrom .player_attribute import Attribute\n\n\nclass Player(Box, EffectTarget):\n _hp: ValueBar\n _max_hp: int\n _shield: ValueBar\n _max_shield: int\n _is_invert: bool\n _name: str\n _value_bar_spacing: int\n _deck: Deck\n _id: int\n _effect_list: List[int]\n\n def __init__(self, background: Surface, id: int, hp: int, shield: int, max_hp: int, max_shield: int, deck: Deck, rect: Rect = None, name: str = \"unknown\", value_bar_spacing: int = 5) -> None:\n super().__init__(background, rect)\n self._is_invert = False\n self._value_bar_spacing = value_bar_spacing\n self._id = id\n self.set_deck(deck)\n self._effect_list = []\n\n self._max_hp = max_hp\n self._hp = ValueBar(source.image.get_image(source.image.PLAYER_HP),\n source.image.get_image(source.image.PLAYER_HP_BG),\n initial_value=hp, max_value=self._max_hp, spacing=-20)\n self.add_component(self._hp)\n\n self._max_shield = max_shield\n self._shield = ValueBar(source.image.get_image(source.image.PLAYER_SHIELD),\n source.image.get_image(\n source.image.PLAYER_SHIELD_BG),\n initial_value=shield, max_value=self._max_shield, spacing=-20)\n self.add_component(self._shield)\n\n self._set_value_bar_pos()\n self.set_name(name)\n\n def set_deck(self, deck: Deck) -> None:\n self._deck = deck\n\n def get_id(self) -> int:\n return self._id\n\n def set_name(self, name: str) -> None:\n self._name = name\n\n def get_name(self) -> str:\n return self._name\n\n def set_rotate(self, is_invert: bool):\n if self._is_invert != is_invert:\n self.set_background(pygame.transform.rotate(self._surface, 180))\n self._is_invert = is_invert\n self._set_value_bar_pos()\n\n def _set_value_bar_pos(self):\n if self._is_invert:\n self._hp.set_center((self.get_width()//2,\n self._hp.get_height()//2))\n self._shield.set_center((self.get_width()//2,\n self._hp.get_height() + self._shield.get_height()//2 + self._value_bar_spacing))\n else:\n self._hp.set_center((self.get_width()//2,\n self.get_height() - self._hp.get_height()//2))\n self._shield.set_center((self.get_width()//2,\n self._hp.get_y() - self._shield.get_height()//2 - self._value_bar_spacing))\n\n def add_effect(self, effect: int) -> None:\n self._effect_list.append(effect)\n\n def remove_effect(self, effect: int) -> None:\n if effect in self._effect_list:\n self._effect_list.remove(effect)\n\n def get_effect(self):\n return self._effect_list.copy()\n\n # def is_contain_effect(self, timing: str):\n # for effect in self._effect_list:\n # if effect.is_contain_timing(timing):\n # return True\n # return False\n\n # def check_effect(self):\n # for effect in self._effect_list:\n # if effect.is_fail():\n # self.remove_effect(effect)\n\n # def do_effect(self, attribute: str, value: int, timing: str):\n # for effect in self._effect_list:\n # attribute, value = effect.do_effect(self, attribute, value, timing)\n # return attribute, value\n\n # def do_effect_with_delay(self, attribute: str, value: int, timing: str):\n # pygame.time.delay(1000)\n # self.do_effect(attribute, value, timing)\n\n # def modify_attributes(self, attribute: str, value: int):\n # attribute, value = self.do_effect(attribute, value,\n # PlayerEffect.BEFORE_MODIFY_VALUE)\n # if attribute == Attribute.HP:\n # self.add_hp(value)\n # elif attribute == Attribute.SHIELD:\n # self.add_shield(value)\n # threading.Thread(target=self.do_effect_with_delay, args=[\n # attribute, value, PlayerEffect.AFTER_MODIFY_VALUE]).start()\n\n def add_hp(self, value: int):\n if self._hp.get_value() + value > self._max_hp:\n self._hp.add_value(self._max_hp - self._hp.get_value())\n else:\n self._hp.add_value(value)\n\n def get_hp_value(self) -> int:\n return self._hp.get_value()\n\n def add_shield(self, value: int):\n shield_end_value = self._shield.get_value() + value\n if shield_end_value > self._max_shield:\n self._shield.add_value(self._max_shield - self._shield.get_value())\n elif shield_end_value >= 0:\n self._shield.add_value(value)\n else:\n self._shield.add_value(value - shield_end_value)\n self.add_hp(shield_end_value)\n\n def get_shield_value(self) -> int:\n return self._shield.get_value()\n\n def draw_card(self, num: int):\n return self._deck.draw_card(num)\n\n def get_deck(self) -> Deck:\n return self._deck.copy()\n\n def copy(self) -> Self:\n player = Player(self._surface, self.get_id(), self.get_hp_value(), self.get_shield_value(\n ), self._max_hp, self._max_shield, self._deck.copy(), name=self._name, value_bar_spacing=self._value_bar_spacing)\n for effect in self._effect_list:\n player.add_effect(effect)\n return player\n", "repo_name": "Bryant-Tang/VtuberSmash", "sub_path": "game_old_version/player.py", "file_name": "player.py", "file_ext": "py", "file_size_in_byte": 5677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "gui_components.Box", "line_number": 15, "usage_type": "name"}, {"api_name": "player_effect.EffectTarget", "line_number": 15, "usage_type": "name"}, {"api_name": "gui_components.ValueBar", "line_number": 16, "usage_type": "name"}, {"api_name": "gui_components.ValueBar", "line_number": 18, "usage_type": "name"}, {"api_name": "deck.Deck", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 27, "usage_type": "name"}, {"api_name": "deck.Deck", "line_number": 27, "usage_type": "name"}, {"api_name": "pygame.Rect", "line_number": 27, "usage_type": "name"}, {"api_name": "gui_components.ValueBar", "line_number": 36, "usage_type": "call"}, {"api_name": "source.image.get_image", "line_number": 36, "usage_type": "call"}, {"api_name": "source.image", "line_number": 36, "usage_type": "attribute"}, {"api_name": "source.image.get_image", "line_number": 37, "usage_type": "call"}, {"api_name": "source.image", "line_number": 37, "usage_type": "attribute"}, {"api_name": "gui_components.ValueBar", "line_number": 42, "usage_type": "call"}, {"api_name": "source.image.get_image", "line_number": 42, "usage_type": "call"}, {"api_name": "source.image", "line_number": 42, "usage_type": "attribute"}, {"api_name": "source.image.get_image", "line_number": 43, "usage_type": "call"}, {"api_name": "source.image", "line_number": 43, "usage_type": "attribute"}, {"api_name": "source.image", "line_number": 44, "usage_type": "attribute"}, {"api_name": "deck.Deck", "line_number": 51, "usage_type": "name"}, {"api_name": "pygame.transform.rotate", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 65, "usage_type": "attribute"}, {"api_name": "deck.Deck", "line_number": 146, "usage_type": "name"}, {"api_name": "typing_extensions.Self", "line_number": 149, "usage_type": "name"}]} +{"seq_id": "2366993512", "text": "import openpyxl\r\nimport datetime\r\nimport os\r\nimport pyminizip\r\nimport lxml.etree as etree\r\n\r\n\r\ndef procXL(zip_path, xlsx_file, tempdir, bnkPass):\r\n workbook = openpyxl.load_workbook(filename=xlsx_file, data_only=True)\r\n # check that the required sheets are in the Excel File\r\n setXLFiles = set(workbook.sheetnames)\r\n if not {'Header Record', 'Payment Information Record', 'Credit Instruction Record', 'Control',\r\n 'Control Data (Hidden)'}.issubset(setXLFiles):\r\n\r\n critical_err = 'The XL File is not structured properly'\r\n print (critical_err)\r\n input('Press Enter to terminate.')\r\n raise Exception(critical_err)\r\n\r\n # Build the XML document\r\n nsmap = {\r\n 'xsi': \"http://www.w3.org/2001/XMLSchema-instance\",\r\n None: \"urn:iso:std:iso:20022:tech:xsd:pain.001.001.03\"\r\n }\r\n root = etree.Element('Document', nsmap=nsmap)\r\n\r\n # Fill in the Computed MsgId and PmtInfld to (necessary if the user leaves these blank)\r\n sh = workbook['Control Data (Hidden)']\r\n computedMsgId = sh['B18'].value\r\n computedPmtInfld = sh['B20'].value\r\n\r\n CstmrCdtTrfInitn = etree.SubElement(root, 'CstmrCdtTrfInitn')\r\n\r\n # Header Record\r\n CstmrCdtTrfInitn = bldHeader(CstmrCdtTrfInitn, computedMsgId, workbook)\r\n\r\n # This tag covers both PIR and CIR sections\r\n PmtInf = etree.SubElement(CstmrCdtTrfInitn, \"PmtInf\")\r\n\r\n # Payment Information Record\r\n PmtInf = bldPIR(PmtInf, computedPmtInfld, workbook)\r\n\r\n # Credit Instruction Record\r\n PmtInf = bldCIR(PmtInf, workbook)\r\n\r\n datastr = etree.tostring(root, xml_declaration=True, encoding='utf-8', pretty_print=True)\r\n\r\n # Get the name of the XML file that will store the transactions\r\n sh = workbook['Control']\r\n xmlFile = sh['B2'].value\r\n if xmlFile is None or xmlFile.strip() == '':\r\n critical_err = 'Invalid SCT file name'\r\n print (critical_err)\r\n input('Press Enter to terminate.')\r\n raise Exception(critical_err)\r\n\r\n srcFile = xmlFile.strip() + \".SCT\"\r\n fileSCT = os.path.join(tempdir, srcFile)\r\n try:\r\n with open(fileSCT, 'wb') as file:\r\n file.write(datastr)\r\n except:\r\n critical_err = 'Unable to create SCT file'\r\n print('\\n\\n' + critical_err+ '\\n\\n')\r\n input('Press Enter to terminate.')\r\n raise Exception(critical_err)\r\n\r\n # package everything in the zip file\r\n # in web interface replace C:\\Temp with tempdir as the file will be emailed\r\n zipSCTE = os.path.join(zip_path, xmlFile.strip() + \".SCTE\")\r\n pyminizip.compress(fileSCT, None, zipSCTE, bnkPass, 0)\r\n\r\n\r\ndef bldCIR(PmtInf, workbook):\r\n sh = workbook['Credit Instruction Record']\r\n\r\n row = 5\r\n lstEndToEndId = []\r\n # list stores the sEndToEndId values. If there are duplicate entries raises an exception - ACB 202309\r\n while row < 200:\r\n sInstrId = sh['A' + str(row)].value\r\n if sInstrId is None:\r\n break\r\n\r\n result = bldCIRrow(sh, PmtInf, workbook, row)\r\n PmtInf = result[0]\r\n sEndToEndId = result[1]\r\n\r\n if sEndToEndId in lstEndToEndId:\r\n critical_err = 'EndToEndId {0} has been already used in this batch'.format(sEndToEndId)\r\n print('\\n\\n' + critical_err+ '\\n\\n')\r\n input('Press Enter to terminate.')\r\n raise Exception(critical_err)\r\n else:\r\n lstEndToEndId.append(sEndToEndId)\r\n \r\n row += 2\r\n\r\n return PmtInf\r\n\r\n\r\ndef bldCIRrow(sh, PmtInf, workbook, row):\r\n # Process the particular row\r\n\r\n # Read the Fields from this worksheet\r\n sInstrId = sh['A' + str(row)].value.strip()\r\n sEndToEndId = sh['B' + str(row)].value.strip()\r\n sCcy = sh['C' + str(row)].value.strip()\r\n sInstdAmt = '{0:.2f}'.format(sh['D' + str(row)].value)\r\n sBIC = sh['E' + str(row)].value.strip()\r\n sNm = sh['F' + str(row)].value.strip()\r\n sAdrLine1 = sh['G5'].value\r\n if sAdrLine1 is None:\r\n sAdrLine1 = ''\r\n else:\r\n sAdrLine1 = sAdrLine1.strip()\r\n sAdrLine2 = sh['H5'].value\r\n if sAdrLine2 is None:\r\n sAdrLine2 = ''\r\n else:\r\n sAdrLine2 = sAdrLine2.strip()\r\n if sAdrLine1 == '':\r\n sAdrLine1 = sAdrLine2\r\n sIBAN = sh['I' + str(row)].value.strip()\r\n sCd = sh['J' + str(row)].value.strip()\r\n sUstrd = sh['K' + str(row)].value.strip()\r\n\r\n CdtTrfTxInf = etree.SubElement(PmtInf, \"CdtTrfTxInf\")\r\n PmtId = etree.SubElement(CdtTrfTxInf, \"PmtId\")\r\n InstrId = etree.SubElement(PmtId, \"InstrId\")\r\n InstrId.text = sInstrId\r\n EndToEndId = etree.SubElement(PmtId, \"EndToEndId\")\r\n EndToEndId.text = sEndToEndId\r\n Amt = etree.SubElement(CdtTrfTxInf, \"Amt\")\r\n InstdAmt = etree.SubElement(Amt, \"InstdAmt\")\r\n InstdAmt.set('Ccy', sCcy)\r\n InstdAmt.text = sInstdAmt\r\n CdtrAgt = etree.SubElement(CdtTrfTxInf, \"CdtrAgt\")\r\n FinInstnId = etree.SubElement(CdtrAgt, \"FinInstnId\")\r\n BIC = etree.SubElement(FinInstnId, \"BIC\")\r\n BIC.text = sBIC\r\n Cdtr = etree.SubElement(CdtTrfTxInf, \"Cdtr\")\r\n Nm = etree.SubElement(Cdtr, \"Nm\")\r\n Nm.text = sNm\r\n # Only fill in the subnodes if the address lines are not blank\r\n if sAdrLine1 != \"\":\r\n PstlAdr = etree.SubElement(Cdtr, \"PstlAdr\")\r\n AdrLine1 = etree.SubElement(PstlAdr, \"AdrLine\")\r\n AdrLine1.text = sAdrLine1\r\n AdrLine2 = etree.SubElement(PstlAdr, \"AdrLine\")\r\n AdrLine2.text = sAdrLine2\r\n CdtrAcct = etree.SubElement(CdtTrfTxInf, \"CdtrAcct\")\r\n Id = etree.SubElement(CdtrAcct, \"Id\")\r\n IBAN = etree.SubElement(Id, \"IBAN\")\r\n IBAN.text = sIBAN\r\n Purp = etree.SubElement(CdtTrfTxInf, \"Purp\")\r\n Cd = etree.SubElement(Purp, \"Cd\")\r\n Cd.text = sCd\r\n RmtInf = etree.SubElement(CdtTrfTxInf, \"RmtInf\")\r\n Ustrd = etree.SubElement(RmtInf, \"Ustrd\")\r\n Ustrd.text = sUstrd\r\n\r\n return PmtInf, sEndToEndId\r\n\r\n\r\ndef bldPIR(PmtInf, computedPmtInfld, workbook):\r\n sh = workbook['Payment Information Record']\r\n\r\n # Read the Fields from this worksheet\r\n sPmtInfId = sh['A5'].value\r\n if sPmtInfId is None:\r\n sPmtInfId = computedPmtInfld\r\n sPmtInfId = sPmtInfId.strip()\r\n # Cechk for a space condition\r\n if sPmtInfId == '':\r\n sPmtInfId = computedPmtInfld.strip()\r\n\r\n sPmtMtd = sh['B5'].value.strip()\r\n sBtchBookg = sh['C5'].value.strip()\r\n sNbOfTxs = str(int(sh['D5'].value))\r\n sCtrlSum = '{0:.2f}'.format(sh['E5'].value)\r\n sCd = sh['F5'].value.strip()\r\n sReqdExctnDt = sh['G5'].value\r\n sReqdExctnDt = datetime.datetime.strftime(sReqdExctnDt, '%Y-%m-%d')\r\n sNm = sh['H5'].value.strip()\r\n sAdrLine1 = sh['I5'].value\r\n if sAdrLine1 is None:\r\n sAdrLine1 = ''\r\n else:\r\n sAdrLine1 = sAdrLine1.strip()\r\n sAdrLine2 = sh['J5'].value\r\n if sAdrLine2 is None:\r\n sAdrLine2 = ''\r\n else:\r\n sAdrLine2 = sAdrLine2.strip()\r\n if sAdrLine1 == '':\r\n sAdrLine1 = sAdrLine2\r\n sIBAN = sh['K5'].value.strip()\r\n sCcy = sh['L5'].value.strip()\r\n sBIC = sh['M5'].value.strip()\r\n\r\n PmtInfId = etree.SubElement(PmtInf, \"PmtInfId\")\r\n PmtInfId.text = sPmtInfId\r\n PmtMtd = etree.SubElement(PmtInf, \"PmtMtd\")\r\n PmtMtd.text = sPmtMtd\r\n BtchBookg = etree.SubElement(PmtInf, \"BtchBookg\")\r\n BtchBookg.text = sBtchBookg\r\n NbOfTxs = etree.SubElement(PmtInf, \"NbOfTxs\")\r\n NbOfTxs.text = sNbOfTxs\r\n CtrlSum = etree.SubElement(PmtInf, \"CtrlSum\")\r\n CtrlSum.text = sCtrlSum\r\n PmtTpInf = etree.SubElement(PmtInf, \"PmtTpInf\")\r\n SvcLvl = etree.SubElement(PmtTpInf, \"SvcLvl\")\r\n Cd = etree.SubElement(SvcLvl, \"Cd\")\r\n Cd.text = sCd\r\n ReqdExctnDt = etree.SubElement(PmtInf, \"ReqdExctnDt\")\r\n ReqdExctnDt.text = sReqdExctnDt\r\n Dbtr = etree.SubElement(PmtInf, \"Dbtr\")\r\n Nm = etree.SubElement(Dbtr, \"Nm\")\r\n Nm.text = sNm\r\n # Only fill in the subnodes if the address lines are not blank\r\n if sAdrLine1 != \"\":\r\n PstlAdr = etree.SubElement(Dbtr, \"PstlAdr\")\r\n AdrLine1 = etree.SubElement(PstlAdr, \"AdrLine\")\r\n AdrLine1.text = sAdrLine1\r\n # Only fill if Address line 2 is not null\r\n if sAdrLine2 != \"\":\r\n AdrLine2 = etree.SubElement(PstlAdr, \"AdrLine\")\r\n AdrLine2.text = sAdrLine2\r\n DbtrAcct = etree.SubElement(PmtInf, \"DbtrAcct\")\r\n Id = etree.SubElement(DbtrAcct, \"Id\")\r\n IBAN = etree.SubElement(Id, \"IBAN\")\r\n IBAN.text = sIBAN\r\n Ccy = etree.SubElement(DbtrAcct, \"Ccy\")\r\n Ccy.text = sCcy\r\n DbtrAgt = etree.SubElement(PmtInf, \"DbtrAgt\")\r\n FinInstnId = etree.SubElement(DbtrAgt, \"FinInstnId\")\r\n BIC = etree.SubElement(FinInstnId, \"BIC\")\r\n BIC.text = sBIC\r\n\r\n return PmtInf\r\n\r\n\r\ndef bldHeader(CstmrCdtTrfInitn, computedMsgId, workbook):\r\n sh = workbook['Header Record']\r\n\r\n # Read the Fields from this worksheet\r\n sMsgId = sh['A5'].value\r\n if sMsgId is None:\r\n sMsgId = computedMsgId\r\n sMsgId = sMsgId.strip()\r\n # check for a space condition\r\n if sMsgId == '':\r\n sMsgId = computedMsgId.strip()\r\n\r\n sCreDtTm = str(sh['B5'].value)\r\n # cater for different formats with microseconds and without\r\n try:\r\n sCreDtTm = datetime.datetime.strptime(sCreDtTm, \"%Y-%m-%d %H:%M:%S.%f\").replace(microsecond=0).isoformat()\r\n except:\r\n sCreDtTm = datetime.datetime.strptime(sCreDtTm, \"%Y-%m-%d %H:%M:%S\").isoformat()\r\n\r\n sNbOfTxs = str(int(sh['C5'].value))\r\n sCtrlSum = '{0:.2f}'.format(sh['D5'].value)\r\n sNm = sh['E5'].value.strip()\r\n sId = sh['F5'].value.strip()\r\n\r\n GrpHdr = etree.SubElement(CstmrCdtTrfInitn, \"GrpHdr\")\r\n MsgId = etree.SubElement(GrpHdr, \"MsgId\")\r\n MsgId.text = sMsgId\r\n CreDtTm = etree.SubElement(GrpHdr, \"CreDtTm\")\r\n CreDtTm.text = sCreDtTm\r\n NbOfTxs = etree.SubElement(GrpHdr, \"NbOfTxs\")\r\n NbOfTxs.text = sNbOfTxs\r\n CtrlSum = etree.SubElement(GrpHdr, \"CtrlSum\")\r\n CtrlSum.text = sCtrlSum\r\n InitgPty = etree.SubElement(GrpHdr, \"InitgPty\")\r\n Nm = etree.SubElement(InitgPty, \"Nm\")\r\n Nm.text = sNm\r\n Id1 = etree.SubElement(InitgPty, \"Id\")\r\n OrgId = etree.SubElement(Id1, \"OrgId\")\r\n Othr = etree.SubElement(OrgId, \"Othr\")\r\n Id2 = etree.SubElement(Othr, \"Id\")\r\n Id2.text = sId\r\n\r\n return CstmrCdtTrfInitn\r\n", "repo_name": "chribonn/bnkSEPA", "sub_path": "procXlsx.py", "file_name": "procXlsx.py", "file_ext": "py", "file_size_in_byte": 10188, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 9, "usage_type": "call"}, {"api_name": "lxml.etree.Element", "line_number": 25, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 25, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 32, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 32, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 38, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 38, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 46, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pyminizip.compress", "line_number": 71, "usage_type": "call"}, {"api_name": "lxml.etree.SubElement", "line_number": 128, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 128, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 129, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 129, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 130, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 130, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 132, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 132, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 134, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 134, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 135, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 135, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 138, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 138, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 139, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 139, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 140, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 140, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 142, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 142, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 143, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 143, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 147, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 147, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 148, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 148, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 150, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 150, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 152, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 152, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 153, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 153, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 154, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 154, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 156, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 156, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 157, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 157, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 159, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 159, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 160, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 160, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 184, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 184, "usage_type": "attribute"}, {"api_name": "lxml.etree.SubElement", "line_number": 202, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 202, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 204, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 204, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 206, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 206, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 208, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 208, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 210, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 210, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 212, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 212, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 213, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 213, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 214, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 214, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 216, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 216, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 218, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 218, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 219, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 219, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 223, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 223, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 224, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 224, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 228, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 228, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 230, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 230, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 231, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 231, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 232, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 232, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 234, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 234, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 236, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 236, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 237, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 237, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 238, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 238, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 259, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 259, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 261, "usage_type": "attribute"}, {"api_name": "lxml.etree.SubElement", "line_number": 268, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 268, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 269, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 269, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 271, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 271, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 273, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 273, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 275, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 275, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 277, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 277, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 278, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 278, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 280, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 280, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 281, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 281, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 282, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 282, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 283, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 283, "usage_type": "name"}]} +{"seq_id": "11924299896", "text": "from json import dumps\n\n\nasync def extract_data(page, cls, term):\n # Check if grade distribution data is available\n first_chart_heading = await page.locator(\"h4\").first.inner_text()\n\n # Obtaining data and parsing it into a dictionary\n td = page.locator(\"table\").first.locator(\"td\")\n data = await td.all_inner_texts()\n\n keys = data[::2]\n values = map(int, data[1::2])\n\n title = await page.locator(\"h2\").inner_text()\n professor = await page.locator(\"h2 + h3\").inner_text()\n\n if first_chart_heading != \"Grade Data Unavailable\":\n write_data = {\n \"section\": cls,\n \"term\": term,\n \"courseTitle\": title,\n \"instructor\": professor,\n \"grades\": dict(zip(keys, values))\n }\n else:\n write_data = {\n \"section\": cls,\n \"term\": term,\n \"courseTitle\": title,\n \"instructor\": professor,\n \"grades\": {}\n }\n\n return write_data\n\n\nasync def nav_and_extract(page, nav, cls, term, f):\n await page.goto(nav)\n write_data = await extract_data(page, cls, term)\n f.write(dumps(write_data) + \"\\n\")", "repo_name": "jiechenmc/Gradus", "sub_path": "core/page.py", "file_name": "page.py", "file_ext": "py", "file_size_in_byte": 1144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "70688484528", "text": "from unittest import mock\nimport pytest\n\nfrom mitmproxy.contentviews import protobuf\nfrom mitmproxy.test import tutils\nfrom . import full_eval\n\n\ndef test_view_protobuf_request():\n v = full_eval(protobuf.ViewProtobuf())\n p = tutils.test_data.path(\"mitmproxy/data/protobuf01\")\n\n with mock.patch('mitmproxy.contentviews.protobuf.ViewProtobuf.is_available'):\n with mock.patch('subprocess.Popen') as n:\n m = mock.Mock()\n attrs = {'communicate.return_value': (b'1: \"3bbc333c-e61c-433b-819a-0b9a8cc103b8\"', True)}\n m.configure_mock(**attrs)\n n.return_value = m\n\n content_type, output = v(open(p, \"rb\").read())\n assert content_type == \"Protobuf\"\n assert output[0] == [('text', b'1: \"3bbc333c-e61c-433b-819a-0b9a8cc103b8\"')]\n\n m.communicate = mock.MagicMock()\n m.communicate.return_value = (None, None)\n with pytest.raises(ValueError, matches=\"Failed to parse input.\"):\n v(b'foobar')\n\n\ndef test_view_protobuf_availability():\n with mock.patch('subprocess.Popen') as n:\n m = mock.Mock()\n attrs = {'communicate.return_value': (b'libprotoc fake version', True)}\n m.configure_mock(**attrs)\n n.return_value = m\n assert protobuf.ViewProtobuf().is_available()\n\n m = mock.Mock()\n attrs = {'communicate.return_value': (b'command not found', True)}\n m.configure_mock(**attrs)\n n.return_value = m\n assert not protobuf.ViewProtobuf().is_available()\n\n\ndef test_view_protobuf_fallback():\n with mock.patch('subprocess.Popen.communicate') as m:\n m.side_effect = OSError()\n v = full_eval(protobuf.ViewProtobuf())\n with pytest.raises(NotImplementedError, matches='protoc not found'):\n v(b'foobar')\n", "repo_name": "codebyravi/mproxy", "sub_path": "test/mitmproxy/contentviews/test_protobuf.py", "file_name": "test_protobuf.py", "file_ext": "py", "file_size_in_byte": 1819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "mitmproxy.contentviews.protobuf.ViewProtobuf", "line_number": 10, "usage_type": "call"}, {"api_name": "mitmproxy.contentviews.protobuf", "line_number": 10, "usage_type": "name"}, {"api_name": "mitmproxy.test.tutils.test_data.path", "line_number": 11, "usage_type": "call"}, {"api_name": "mitmproxy.test.tutils.test_data", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mitmproxy.test.tutils", "line_number": 11, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 13, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 13, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 14, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 14, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 15, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 15, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 24, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 26, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 31, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 32, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 32, "usage_type": "name"}, {"api_name": "mitmproxy.contentviews.protobuf.ViewProtobuf", "line_number": 36, "usage_type": "call"}, {"api_name": "mitmproxy.contentviews.protobuf", "line_number": 36, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 38, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 38, "usage_type": "name"}, {"api_name": "mitmproxy.contentviews.protobuf.ViewProtobuf", "line_number": 42, "usage_type": "call"}, {"api_name": "mitmproxy.contentviews.protobuf", "line_number": 42, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 46, "usage_type": "name"}, {"api_name": "mitmproxy.contentviews.protobuf.ViewProtobuf", "line_number": 48, "usage_type": "call"}, {"api_name": "mitmproxy.contentviews.protobuf", "line_number": 48, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "35476191216", "text": "from common.op_params import opParams\nfrom common.realtime import set_core_affinity\nfrom selfdrive.config import Conversions as CV\nfrom selfdrive.controls.lib.lane_planner import eval_poly\nfrom common.numpy_fast import interp\nimport numpy as np\nimport time\ntry:\n from common.realtime import sec_since_boot\n import cereal.messaging as messaging\nexcept:\n pass\n\n# try:\n# from common.realtime import sec_since_boot\n# except ImportError:\n# import matplotlib.pyplot as plt\n# import time\n# sec_since_boot = time.time\n\ndef cluster(data, maxgap):\n data.sort(key=lambda _trk: _trk.dRel)\n groups = [[data[0]]]\n for x in data[1:]:\n if abs(x.dRel - groups[-1][-1].dRel) <= maxgap:\n groups[-1].append(x)\n else:\n groups.append([x])\n return groups\n\n\nclass LaneSpeedState:\n off = 0\n audible = 1\n silent = 2\n to_state = {off: 'off', audible: 'audible', silent: 'silent'}\n to_idx = {v: k for k, v in to_state.items()}\n\nclass Lane:\n def __init__(self, name, pos):\n self.name = name\n self.pos = pos\n self.bounds = []\n self.tracks = []\n self.oncoming_tracks = []\n\n self.avg_speed = None\n self.fastest_count = 0\n\n def set_fastest(self):\n \"\"\"Increments this lane's fast count\"\"\"\n self.fastest_count += 1\n\n\nLANE_SPEED_RATE = 1 / 5.\n\nclass LaneSpeed:\n def __init__(self):\n set_core_affinity(1) # use up to 1 core?\n self.op_params = opParams()\n\n self._track_speed_margin = 0.05 # track has to be above X% of v_ego (excludes oncoming and stopped)\n self._faster_than_margin = 0.075 # avg of secondary lane has to be faster by X% to show alert\n self._min_enable_speed = 35 * CV.MPH_TO_MS\n self._min_fastest_time = 3 / LANE_SPEED_RATE # how long should we wait for a specific lane to be faster than middle before alerting\n self._max_steer_angle = 100 # max supported steering angle\n self._extra_wait_time = 5 # in seconds, how long to wait after last alert finished before allowed to show next alert\n self._min_track_speed = 5 * CV.MPH_TO_MS # tracks must be traveling faster than this speed to be added to a lane (- or +)\n\n self.fastest_lane = 'none' # always will be either left, right, or none as a string, never middle or NoneType\n self.last_fastest_lane = 'none'\n self._setup()\n\n def _setup(self):\n self.button_updated = False\n self.ls_state = self.op_params.get('lane_speed_alerts').strip().lower()\n if not isinstance(self.ls_state, str) or self.ls_state not in LaneSpeedState.to_idx:\n self.ls_state = LaneSpeedState.audible\n self.op_params.put('lane_speed_alerts', LaneSpeedState.to_state[self.ls_state])\n else:\n self.ls_state = LaneSpeedState.to_idx[self.ls_state]\n self.last_ls_state = self.ls_state\n\n self.lane_width = 3.7 # in meters, just a starting point\n self.sm = messaging.SubMaster(['carState', 'liveTracks', 'pathPlan', 'laneSpeedButton', 'controlsState'])\n self.pm = messaging.PubMaster(['laneSpeed'])\n\n lane_positions = {'left': self.lane_width, 'middle': 0, 'right': -self.lane_width} # lateral position in meters from center of car to center of lane\n lane_names = ['left', 'middle', 'right']\n self.lanes = {name: Lane(name, lane_positions[name]) for name in lane_names}\n\n self.oncoming_lanes = {'left': False, 'right': False}\n\n self.last_alert_end_time = 0\n\n def start(self):\n while True: # this loop can take up 0.049_ seconds without lagging\n t_start = sec_since_boot()\n self.sm.update(0)\n if self.sm.updated['laneSpeedButton']:\n self.button_updated = True\n\n self.v_ego = self.sm['carState'].vEgo\n self.steer_angle = self.sm['carState'].steeringAngle\n self.d_poly = np.array(list(self.sm['pathPlan'].dPoly))\n self.live_tracks = self.sm['liveTracks']\n\n self.update_lane_bounds()\n self.update()\n self.send_status()\n\n t_sleep = LANE_SPEED_RATE - (sec_since_boot() - t_start)\n if t_sleep > 0:\n time.sleep(t_sleep)\n else: # don't sleep if lagging\n print('lane_speed lagging by: {} ms'.format(round(-t_sleep * 1000, 3)))\n\n def update(self):\n self.reset(reset_tracks=True, reset_avg_speed=True)\n if self.button_updated: # only update when button is first pressed\n self.ls_state = self.sm['laneSpeedButton'].status\n\n # checks that we have dPoly, dPoly is not NaNs, and steer angle is less than max allowed\n if len(self.d_poly) and not np.isnan(self.d_poly[0]):\n # self.filter_tracks() # todo: will remove tracks very close to other tracks to make averaging more robust\n self.group_tracks()\n self.find_oncoming_lanes()\n self.get_fastest_lane()\n else:\n self.reset(reset_fastest=True)\n\n def update_lane_bounds(self): # todo: run this at half the rate of lane_speed\n # todo 2: add dPoly offsetting to lane bounds here as well, from group_tracks\n lane_width = self.sm['pathPlan'].laneWidth\n if isinstance(lane_width, float) and lane_width > 1:\n self.lane_width = min(lane_width, 4.5) # LanePlanner uses 4 as max width for dPoly calculation\n\n self.lanes['left'].pos = self.lane_width # update with new lane center positions\n self.lanes['right'].pos = -self.lane_width\n\n # and now update bounds\n self.lanes['left'].bounds = [self.lanes['left'].pos * 1.5, self.lanes['left'].pos / 2]\n self.lanes['middle'].bounds = [self.lanes['left'].pos / 2, self.lanes['right'].pos / 2]\n self.lanes['right'].bounds = [self.lanes['right'].pos / 2, self.lanes['right'].pos * 1.5]\n\n # def filter_tracks(self): # todo: make cluster() return indexes of live_tracks instead\n # print(type(self.live_tracks))\n # clustered = cluster(self.live_tracks, 0.048) # clusters tracks based on dRel\n # clustered = [clstr for clstr in clustered if len(clstr) > 1]\n # print([[trk.dRel for trk in clstr] for clstr in clustered])\n # for clstr in clustered:\n # pass\n # # print(c)\n\n def group_tracks(self):\n \"\"\"Groups tracks based on lateral position, dPoly offset, and lane width\"\"\"\n offset_y_rels = [trk.yRel - eval_poly(self.d_poly, trk.dRel) for trk in self.live_tracks] # eval_poly: 4109.0476 Hz vs np.polyval's 2483.2956 Hz\n for track, offset_y_rel in zip(self.live_tracks, offset_y_rels):\n # it's not pretty, but this code is the fastest. even when looping through tracks and then lanes for each track\n # (and breaking when a lane has been found for the track)\n # this is also faster than having the speed if check first\n track_vel = track.vRel + self.v_ego\n if self.lanes['left'].bounds[0] >= offset_y_rel >= self.lanes['left'].bounds[1]:\n if track_vel >= self._min_track_speed: # ongoing track\n self.lanes['left'].tracks.append(track)\n elif track_vel <= -self._min_track_speed: # oncoming track\n self.lanes['left'].oncoming_tracks.append(track)\n\n elif self.lanes['middle'].bounds[0] >= offset_y_rel >= self.lanes['middle'].bounds[1]:\n if track_vel >= self._min_track_speed:\n self.lanes['middle'].tracks.append(track)\n elif track_vel <= -self._min_track_speed:\n self.lanes['middle'].oncoming_tracks.append(track)\n\n elif self.lanes['right'].bounds[0] >= offset_y_rel >= self.lanes['right'].bounds[1]:\n if track_vel >= self._min_track_speed:\n self.lanes['right'].tracks.append(track)\n elif track_vel <= -self._min_track_speed:\n self.lanes['right'].oncoming_tracks.append(track)\n\n def find_oncoming_lanes(self):\n \"\"\"If number of oncoming tracks is greater than tracks going our direction, set lane to oncoming\"\"\"\n for lane in self.oncoming_lanes:\n self.oncoming_lanes[lane] = False\n if len(self.lanes[lane].oncoming_tracks) > len(self.lanes[lane].tracks): # 0 can't be > 0 so 0 oncoming tracks will be handled correctly\n self.oncoming_lanes[lane] = True\n\n def lanes_with_avg_speeds(self):\n \"\"\"Returns a dict of lane objects where avg_speed not None\"\"\"\n return {lane: self.lanes[lane] for lane in self.lanes if self.lanes[lane].avg_speed is not None}\n\n def get_fastest_lane(self):\n self.fastest_lane = 'none'\n if self.ls_state == LaneSpeedState.off:\n return\n\n v_cruise_setpoint = self.sm['controlsState'].vCruise * CV.KPH_TO_MS\n for lane_name in self.lanes:\n lane = self.lanes[lane_name]\n track_speeds = [track.vRel + self.v_ego for track in lane.tracks]\n track_speeds = [speed for speed in track_speeds if self.v_ego * self._track_speed_margin < speed <= v_cruise_setpoint]\n if len(track_speeds): # filters out very slow tracks\n # np.mean was much slower than sum() / len()\n lane.avg_speed = sum(track_speeds) / len(track_speeds) # todo: something with std?\n\n lanes_with_avg_speeds = self.lanes_with_avg_speeds()\n if 'middle' not in lanes_with_avg_speeds or len(lanes_with_avg_speeds) < 2:\n # if no tracks in middle lane or no secondary lane, we have nothing to compare\n self.reset(reset_fastest=True) # reset fastest, sanity\n return\n\n fastest_lane = self.lanes[max(lanes_with_avg_speeds, key=lambda x: self.lanes[x].avg_speed)]\n if fastest_lane.name == 'middle': # already in fastest lane\n self.reset(reset_fastest=True)\n return\n if (fastest_lane.avg_speed / self.lanes['middle'].avg_speed) - 1 < self._faster_than_margin: # fastest lane is not above margin, ignore\n # todo: could remove since we wait for a lane to be faster for a bit\n return\n\n # if we are here, there's a faster lane available that's above our minimum margin\n fastest_lane.set_fastest() # increment fastest lane\n self.lanes[self.opposite_lane(fastest_lane.name)].fastest_count = 0 # reset slowest lane (opposite, never middle)\n\n _f_time_x = [1, 4, 12] # change the minimum time for fastest based on how many tracks are in fastest lane\n _f_time_y = [1.5, 1, 0.5] # this is multiplied by base fastest time todo: probably need to tune this\n min_fastest_time = interp(len(fastest_lane.tracks), _f_time_x, _f_time_y) # get multiplier\n min_fastest_time = int(min_fastest_time * self._min_fastest_time) # now get final min_fastest_time\n\n if fastest_lane.fastest_count < min_fastest_time:\n return # fastest lane hasn't been fastest long enough\n if sec_since_boot() - self.last_alert_end_time < self._extra_wait_time:\n return # don't reset fastest lane count or show alert until last alert has gone\n\n # if here, we've found a lane faster than our lane by a margin and it's been faster for long enough\n self.fastest_lane = fastest_lane.name\n\n # def log_data(self): # DON'T USE AGAIN until I fix live tracks formatting\n # log_file = '/data/lane_speed_log'\n # lanes_tracks = {}\n # lanes_oncoming_tracks = {}\n # bounds = {}\n # for lane in self.lanes:\n # bounds[lane] = self.lanes[lane].bounds\n # lanes_tracks[lane] = [{'vRel': trk.vRel, 'dRel': trk.dRel, 'yRel': trk.yRel} for trk in self.lanes[lane].tracks]\n # lanes_oncoming_tracks[lane] = [{'vRel': trk.vRel, 'dRel': trk.dRel, 'yRel': trk.yRel} for trk in self.lanes[lane].oncoming_tracks]\n #\n # log_data = {'v_ego': self.v_ego, 'd_poly': self.d_poly, 'lane_tracks': lanes_tracks, 'lane_oncoming_tracks': lanes_oncoming_tracks,\n # 'live_tracks': self.live_tracks, 'oncoming_lanes': self.oncoming_lanes, 'bounds': bounds}\n # with open(log_file, 'a') as f:\n # f.write('{}\\n'.format(log_data))\n\n def send_status(self):\n new_fastest = self.fastest_lane in ['left', 'right'] and self.last_fastest_lane not in ['left', 'right']\n fastest_lane = self.fastest_lane\n if self.ls_state == LaneSpeedState.silent:\n new_fastest = False # be silent\n if self.v_ego < self._min_enable_speed or abs(self.steer_angle) > self._max_steer_angle: # keep sending updates, but not fastestLane\n fastest_lane = 'none'\n\n ls_send = messaging.new_message('laneSpeed')\n ls_send.laneSpeed.fastestLane = fastest_lane\n ls_send.laneSpeed.new = new_fastest # only send audible alert once when a lane becomes fastest, then continue to show silent alert\n\n ls_send.laneSpeed.leftLaneSpeeds = [trk.vRel + self.v_ego for trk in self.lanes['left'].tracks]\n ls_send.laneSpeed.middleLaneSpeeds = [trk.vRel + self.v_ego for trk in self.lanes['middle'].tracks]\n ls_send.laneSpeed.rightLaneSpeeds = [trk.vRel + self.v_ego for trk in self.lanes['right'].tracks]\n\n ls_send.laneSpeed.leftLaneDistances = [trk.dRel for trk in self.lanes['left'].tracks]\n ls_send.laneSpeed.middleLaneDistances = [trk.dRel for trk in self.lanes['middle'].tracks]\n ls_send.laneSpeed.rightLaneDistances = [trk.dRel for trk in self.lanes['right'].tracks]\n\n ls_send.laneSpeed.leftLaneOncoming = self.oncoming_lanes['left']\n ls_send.laneSpeed.rightLaneOncoming = self.oncoming_lanes['right']\n\n if self.last_ls_state != self.ls_state: # show alert if button tapped and write to opParams\n self.op_params.put('lane_speed_alerts', LaneSpeedState.to_state[self.ls_state])\n ls_send.laneSpeed.state = LaneSpeedState.to_state[self.ls_state]\n\n self.pm.send('laneSpeed', ls_send)\n\n if self.fastest_lane != self.last_fastest_lane and self.fastest_lane == 'none': # if lane stops being fastest\n self.last_alert_end_time = sec_since_boot()\n elif self.last_fastest_lane in ['left', 'right'] and self.fastest_lane == self.opposite_lane(self.last_fastest_lane): # or fastest switches\n self.last_alert_end_time = sec_since_boot()\n\n self.last_fastest_lane = self.fastest_lane\n self.last_ls_state = self.ls_state\n\n def opposite_lane(self, name):\n \"\"\"Returns name of opposite lane name\"\"\"\n return {'left': 'right', 'right': 'left'}[name]\n\n def reset(self, reset_tracks=False, reset_fastest=False, reset_avg_speed=False):\n for lane in self.lanes:\n if reset_tracks:\n self.lanes[lane].tracks = []\n self.lanes[lane].oncoming_tracks = []\n\n if reset_avg_speed:\n self.lanes[lane].avg_speed = None\n\n if reset_fastest:\n self.lanes[lane].fastest_count = 0\n\n\n# class Track:\n# def __init__(self, vRel, yRel, dRel):\n# self.vRel = vRel\n# self.yRel = yRel\n# self.dRel = dRel\n# v_rels = [7.027988825101453, -35, -2.0073281329557595, -38, -42, -0.4124279188166433, -4.864017389464086, -31.5, -9.684282305020197, -9.979187599100587, -8.036672540886896, -3.025854705185946, -6.347005348508485, -2.502134724290814, 3.8857648270182743, 5.3016772854121115]\n# y_rels = [-3.7392238915910396, -4.947102125963248, -3.099776764519531, -5.399104990417248, 5.278053706824695, 3.8991116187949793, -0.9252016611001208, 0.4527911313949229, 4.606432638329704, -1.9683618473307751, -3.6920577990810357, -0.9243886066458202, 4.765879225624099, 5.310588490331199, -2.073362080174996, -0.787692913730746]\n# d_rels = [47.816299530243484, 1.0937590342875225, 45.83286354330341, 44.79009263149329, 15.721120725763347, 48.974408204844835, 10.538985749858739, 50.379159253222355, 27.746917826360942, 24.410420872880284, 1.605961587171345, 23.89657990345233, 30.219941981980615, 50.31621564718719, 35.654178681545176, 34.980565736019585]\n# TEMP_LIVE_TRACKS = [Track(v, y, d) for v, y, d in zip(v_rels, y_rels, d_rels)]\n# TEMP_D_POLY = np.array([1.3839008e-06/10, 0, 0, 0.05])\n\ndef main():\n lane_speed = LaneSpeed()\n lane_speed.start()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "kirkhilles/openpilotKIRK", "sub_path": "selfdrive/controls/lib/lane_speed.py", "file_name": "lane_speed.py", "file_ext": "py", "file_size_in_byte": 15320, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "common.realtime.set_core_affinity", "line_number": 59, "usage_type": "call"}, {"api_name": "common.op_params.opParams", "line_number": 60, "usage_type": "call"}, {"api_name": "selfdrive.config.Conversions.MPH_TO_MS", "line_number": 64, "usage_type": "attribute"}, {"api_name": "selfdrive.config.Conversions", "line_number": 64, "usage_type": "name"}, {"api_name": "selfdrive.config.Conversions.MPH_TO_MS", "line_number": 68, "usage_type": "attribute"}, {"api_name": "selfdrive.config.Conversions", "line_number": 68, "usage_type": "name"}, {"api_name": "cereal.messaging.SubMaster", "line_number": 85, "usage_type": "call"}, {"api_name": "cereal.messaging", "line_number": 85, "usage_type": "name"}, {"api_name": "cereal.messaging.PubMaster", "line_number": 86, "usage_type": "call"}, {"api_name": "cereal.messaging", "line_number": 86, "usage_type": "name"}, {"api_name": "common.realtime.sec_since_boot", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "common.realtime.sec_since_boot", "line_number": 112, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 124, "usage_type": "call"}, {"api_name": "selfdrive.controls.lib.lane_planner.eval_poly", "line_number": 157, "usage_type": "call"}, {"api_name": "selfdrive.config.Conversions.KPH_TO_MS", "line_number": 197, "usage_type": "attribute"}, {"api_name": "selfdrive.config.Conversions", "line_number": 197, "usage_type": "name"}, {"api_name": "common.numpy_fast.interp", "line_number": 226, "usage_type": "call"}, {"api_name": "common.realtime.sec_since_boot", "line_number": 231, "usage_type": "call"}, {"api_name": "cereal.messaging.new_message", "line_number": 260, "usage_type": "call"}, {"api_name": "cereal.messaging", "line_number": 260, "usage_type": "name"}, {"api_name": "common.realtime.sec_since_boot", "line_number": 282, "usage_type": "call"}, {"api_name": "common.realtime.sec_since_boot", "line_number": 284, "usage_type": "call"}]} +{"seq_id": "9183545061", "text": "import pygame\nfrom const import *\n\ndef draw_window(win, bird, pipes, base, score):\n win.blit(BG_IMG, (0,0))\n\n for pipe in pipes:\n pipe.draw(win)\n\n text = STAT_FONT.render(f\"Score: {score}\", 1, (255, 255, 255))\n win.blit(text, (WIDTH - 10 - text.get_width(), 10))\n\n base.draw(win)\n bird.draw(win)\n\n pygame.display.update()\n\ndef blitRotateCenter(surf, image, topleft, angle):\n rotated_image = pygame.transform.rotate(image, angle)\n new_rect = rotated_image.get_rect(center = image.get_rect(topleft = topleft).center)\n\n surf.blit(rotated_image, new_rect.topleft)", "repo_name": "AmirAbaskohi/Flappy-Bird-NEAT", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 596, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pygame.display.update", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotate", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "7976340152", "text": "import os\nimport csv\nimport torch\nimport random\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms, utils\nimport torchvision.transforms.functional as tf\n\n\nclass GeoDataset(Dataset):\n def __init__(self, data_csvpath:str,random_flip:bool=True, random_crop:bool=True, crop_box:int=512, transform=None):\n self.random_flip = random_flip\n self.random_crop = random_crop\n self.crop_box = crop_box\n self.csv_path = data_csvpath\n self.dataset = [] # [haze_img_path, clear_img_path]\n self._init_dataset()\n self.transform = transform\n self.__init_transform()\n\n def _init_dataset(self):\n csv_file = open(self.csv_path, \"r\")\n csv_reader = csv.reader(csv_file)\n for row in csv_reader:\n self.dataset.append([row[0], row[1]])\n csv_file.close()\n\n def __init_transform(self):\n if self.transform is None:\n self.transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.5] * 3, std=[0.5] * 3)\n ])\n\n # def _random_rotate(self, haze, clear):\n # # 拿到角度的随机数。angle是一个-180到180之间的一个数\n # angle = transforms.RandomRotation.get_params([-180, 180])\n # # 对haze和clear图像做相同的旋转操作，保证他们都旋转angle角度\n # haze = haze.rotate(angle, expand=True)\n # clear = clear.rotate(angle, expand=True)\n # return haze, clear\n\n def _random_flip(self, haze, clear):\n # 50%的概率应用垂直，水平翻转。\n if random.random() > 0.5:\n haze = tf.hflip(haze)\n clear = tf.hflip(clear)\n if random.random() > 0.5:\n haze = tf.vflip(haze)\n clear = tf.vflip(clear)\n return haze, clear\n\n def _random_crop(self, haze, clear):\n # 50%的概率应用垂直，水平翻转。\n i,j,h,w = transforms.RandomCrop.get_params(haze, (self.crop_box, self.crop_box))\n haze = tf.crop(haze, i,j,h,w)\n clear = tf.crop(clear, i,j,h,w)\n return haze, clear\n\n def __getitem__(self, item):\n haze = Image.open(self.dataset[item][0]).convert('RGB')\n clear = Image.open(self.dataset[item][1]).convert('RGB')\n if self.random_flip:\n haze, clear = self._random_flip(haze, clear)\n if self.random_crop:\n haze, clear = self._random_crop(haze, clear)\n\n haze = self.transform(haze)\n clear = self.transform(clear)\n\n return haze, clear\n\n def __len__(self):\n return len(self.dataset)\n\n\nif __name__ == \"__main__\":\n gd = GeoDataset(\"D:\\Dataset\\Geographic image\\data_train.csv\", random_crop=True, random_flip=True)\n gd = GeoDataset(\"D:\\Dataset\\Geographic image\\data_train.csv\", random_crop=False, random_flip=False)\n haze, clear = gd[2]\n utils.save_image((clear + 1) / 2.0, 'clear.jpg')\n utils.save_image((haze + 1) / 2.0, 'haze.jpg')\n pass", "repo_name": "mikuzip01/DOC-Net", "sub_path": "dataloaders/Geo_dataloader.py", "file_name": "Geo_dataloader.py", "file_ext": "py", "file_size_in_byte": 3016, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 11, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "random.random", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional.hflip", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 47, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.hflip", "line_number": 48, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 48, "usage_type": "name"}, {"api_name": "random.random", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional.vflip", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.vflip", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop.get_params", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torchvision.transforms", "line_number": 56, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.crop", "line_number": 57, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 57, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.crop", "line_number": 58, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 62, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 62, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 63, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 82, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 82, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 83, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "27448327478", "text": "import unittest\nimport numpy as np\nimport gym\nfrom gym_jsbsim import utils\nfrom gym_jsbsim.agents import RandomAgent\nfrom gym_jsbsim.environment import JsbSimEnv\nfrom gym_jsbsim.tasks import HeadingControlTask\nimport gym_jsbsim.properties as prp\n\n\nclass AgentEnvInteractionTest(unittest.TestCase):\n \"\"\" Tests for agents interacting with env. \"\"\"\n\n def init_and_reset_env(self, env: JsbSimEnv):\n self.assertIsInstance(env.task, HeadingControlTask)\n\n # we interact at 5 Hz, so we expect the sim to run 12 timesteps per\n # interaction since it runs at 120 Hz\n self.assertEqual(12, env.sim_steps_per_agent_step)\n\n # we init a random agent with a seed\n agent = RandomAgent(action_space=env.action_space)\n self.assertEqual(env.action_space, agent.action_space)\n\n # this task has an action space of three controls: aileron, elevator, rudder\n expected_num_actions = 3\n self.assertEqual(expected_num_actions, len(agent.action_space.low))\n # we see that the action space has the correct low and high range of +-1.0\n expect_low = np.array([-1.0] * expected_num_actions)\n expect_high = np.array([1.0] * expected_num_actions)\n np.testing.assert_array_almost_equal(expect_high, env.action_space.high)\n np.testing.assert_array_almost_equal(expect_low, env.action_space.low)\n\n # we reset the env and receive the first state; the env is now ready\n state = env.reset()\n self.assertEqual(len(env.observation_space.low), len(state))\n\n # we close the env and JSBSim closes with it\n env.close()\n self.assertIsNone(env.sim.jsbsim)\n\n def take_step_with_random_agent(self, env: JsbSimEnv):\n agent = RandomAgent(action_space=env.action_space)\n\n # we set up for a loop through one episode\n first_state = env.reset()\n\n # we take a single step\n action = agent.act(first_state)\n state, reward, done, info = env.step(action)\n\n # we see the state has changed\n self.assertEqual(first_state.shape, state.shape)\n self.assertTrue(np.any(np.not_equal(first_state, state)),\n msg='state should have changed after simulation step')\n expected_time_step_size = env.sim_steps_per_agent_step / env.JSBSIM_DT_HZ\n self.assertAlmostEqual(expected_time_step_size, env.sim.get_sim_time())\n self.assertFalse(done, msg='episode is terminal after only a single step')\n\n # the aircraft engines are running, as per initial conditions\n self.assertNotAlmostEqual(env.sim[prp.engine_thrust_lbs], 0)\n\n env.close()\n\n def test_init_and_reset_all_envs(self):\n for env_id in utils.get_env_id_kwargs_map():\n env = gym.make(env_id)\n self.init_and_reset_env(env)\n\n def test_take_step_with_random_agent_all_envs(self):\n for env_id in utils.get_env_id_kwargs_map():\n env = gym.make(env_id)\n self.take_step_with_random_agent(env)\n", "repo_name": "Gor-Ren/gym-jsbsim", "sub_path": "gym_jsbsim/tests/test_functional.py", "file_name": "test_functional.py", "file_ext": "py", "file_size_in_byte": 3012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 124, "dataset": "github-code", "pt": "3", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "gym_jsbsim.environment.JsbSimEnv", "line_number": 14, "usage_type": "name"}, {"api_name": "gym_jsbsim.tasks.HeadingControlTask", "line_number": 15, "usage_type": "argument"}, {"api_name": "gym_jsbsim.agents.RandomAgent", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 32, "usage_type": "attribute"}, {"api_name": "gym_jsbsim.environment.JsbSimEnv", "line_number": 42, "usage_type": "name"}, {"api_name": "gym_jsbsim.agents.RandomAgent", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.not_equal", "line_number": 54, "usage_type": "call"}, {"api_name": "gym_jsbsim.properties.engine_thrust_lbs", "line_number": 61, "usage_type": "attribute"}, {"api_name": "gym_jsbsim.properties", "line_number": 61, "usage_type": "name"}, {"api_name": "gym_jsbsim.utils.get_env_id_kwargs_map", "line_number": 66, "usage_type": "call"}, {"api_name": "gym_jsbsim.utils", "line_number": 66, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 67, "usage_type": "call"}, {"api_name": "gym_jsbsim.utils.get_env_id_kwargs_map", "line_number": 71, "usage_type": "call"}, {"api_name": "gym_jsbsim.utils", "line_number": 71, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "12395141083", "text": "import os\nimport PIL\nfrom PIL import Image\nimport numpy as np\nimport json\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport itertools\nfrom torchvision import datasets, transforms, models\nfrom shapley.transform import ThresholdTransform,AddNoise,DetachWhite\nfrom einops import rearrange\nfrom itertools import product\nimport math\nimport torchvision.models as models\nmodel=models.efficientnet_b1(pretrained=True,progress=False)\nmodel.classifier[1] = torch.nn.Linear(1280, 2)\nimport torchvision\n# model=torchvision.models.resnet18()\n# in_feat=model.fc.in_features\n# model.fc=torch.nn.Linear(in_feat,2)\ndata_path='/data/datasets/asd/All_5split/01/val/TD/'\n# data_path='/data/datasets/ai_hub_sketch_4way/01/val/m_w'\n# data_path='/data/datasets/ai_hub/ai_hub_sketch_mw/01/val/w/'\nimport random\nweight='/data/jong980812/project/mae/result_ver2/All_5split/binary_240/OUT/02/checkpoint-29.pth'\ncheckpoint = torch.load(weight, map_location='cpu')\nprint(\"Load pre-trained checkpoint from: %s\" % weight)\ncheckpoint_model = checkpoint['model']\nstate_dict = model.state_dict()\nmsg = model.load_state_dict(checkpoint_model, strict=False)\ndef set_conv_padding_mode(model, padding_mode='replicate'):\n for name, layer in model.named_modules():\n if isinstance(layer, torch.nn.Conv2d):\n layer.padding_mode = padding_mode\nset_conv_padding_mode(model,padding_mode='replicate')\nmodel.eval()\ndef get_shapley_matrix(all_ordered_pair, correct_output):\n shapley_values = torch.zeros_like(all_ordered_pair, dtype=torch.float32)\n\n # 각 ordered pair에 대한 값을 가져와 shapley_values에 저장\n for a,ordered_pairs in enumerate(all_ordered_pair):\n for i, ordered_pair in enumerate(ordered_pairs):\n # ordered_pair를 인덱스로 사용하여 correct_output에서 값을 가져옴\n indices = ordered_pair # ordered_pair를 텐서로 변환\n # print(indices)\n values1 = correct_output[int(indices[0])]\n values2 = correct_output[int(indices[1])] # correct_output에서 해당 위치의 값 가져오기\n # print(values1,values2)\n shapley_values[a,i] = torch.cat([values1.unsqueeze(0),values2.unsqueeze(0)],dim=0)\n return shapley_values\ndef binary_to_decimal(binary_tuple):\n decimal_value = 0\n binary_length = len(binary_tuple)\n\n for i, bit in enumerate(binary_tuple):\n decimal_value += bit * (2 ** (binary_length - i - 1))\n\n return decimal_value\ndef decimal_to_binary(decimal_value, num_bits):\n binary_tuple = []\n \n for i in range(num_bits):\n bit = (decimal_value >> (num_bits - i - 1)) & 1\n binary_tuple.append(bit)\n \n return tuple(binary_tuple)\ndef count_ones(binary_tuple):\n count = 0\n for bit in binary_tuple:\n if bit == 1:\n count += 1\n return count\ndef get_ordered_pair():\n\n n = 6 # digit의 개수\n digits = [0, 1] # 각 digit의 가능한 값\n\n # 경우의 수 생성\n part_combinations = list(product(digits, repeat=n))\n\n\n index_to_insert = 1 # 두 번째 위치에 추가하려면 인덱스 1을 사용합니다.\n all_ordered_pair=[]\n for index in range(7):\n ordered_pair=[] \n index_to_insert = index\n for combi in part_combinations:\n insert_value = [0,1]\n new_combi_0= combi[:index_to_insert] + (insert_value[0],) + combi[index_to_insert:]\n new_combi_1= combi[:index_to_insert] + (insert_value[1],) + combi[index_to_insert:]\n ordered_pair.append([binary_to_decimal(new_combi_0),binary_to_decimal(new_combi_1)])\n all_ordered_pair.append(ordered_pair)\n all_ordered_pair=torch.Tensor(all_ordered_pair)\n num_part = (all_ordered_pair.shape[0])\n num_case = (all_ordered_pair.shape[1])\n weights = torch.zeros((num_part,num_case))\n for i in range(num_part):\n for j in range(num_case):\n # all_ordered_pair의 값 가져오기\n value = int(all_ordered_pair[i, j, 1])\n \n # 이진수로 변환\n binary_value = decimal_to_binary(value, 7)\n \n # 1의 개수 세기\n num_ones = binary_value.count(1)\n \n # num * (7 combination num) 계산\n combination = math.comb(num_part,num_ones)\n weight = num_ones * combination\n \n # 결과를 weights에 저장\n weights[i, j] = weight\n return all_ordered_pair, weights\nclass shapley_part(Dataset):\n def __init__(self, data_folder, json_folder,part, binary_thresholding=None, transform=None):\n self.json_folder = json_folder\n self.data_folder = data_folder\n self.binary_thresholding=binary_thresholding\n self.transform = transform\n self.part = part\n self.num_part = len(part)\n self.image_paths = [os.path.join(data_folder, f) for f in os.listdir(data_folder) if f.endswith(('.png', '.jpg', '.jpeg', '.gif'))]\n self.json_paths = [image_path.split('/')[-1].split('.')[0] + \".json\" for image_path in self.image_paths] #! Get json path from image paths.\n print(self.image_paths)\n def get_part_json(self, json_file_path, part_name):\n '''\n Get part dictionary from json path\n '''\n part_json = {}\n \n for part in part_name:\n part_json[part] = []\n with open(json_file_path, 'r') as f:\n boxes = json.load(f)['shapes']\n for box in boxes:\n part_json[box[\"label\"]].append(box[\"points\"])\n \n for key in part_json:#! 빈 애들은 None으로 처리해서 없다고 판단.\n if not part_json[key]:\n part_json[key] = None\n\n return part_json\n def get_coords(self, part):\n extracted_coordinates = []\n if part is None:\n return None\n elif len(part) == 1:\n # print(part[0][0])\n xmin, ymin = list(map(int,part[0][0]))\n xmax, ymax = list(map(int,part[0][1]))\n return [[xmin,ymin,xmax,ymax]]#아래 2일경우와 통일하기 위해 이중 리스트로 \n elif len(part) == 2:\n #! Eye, Ear, hand, foot -> These have 2 part, return list\n for a in part: \n # print(a)\n xmin, ymin = list(map(int,a[0]))\n xmax, ymax = list(map(int,a[1]))\n extracted_coordinates.append([xmin,ymin,xmax,ymax])\n return extracted_coordinates\n else:\n exit(0)\n def get_white_image(self,size):\n return Image.new(\"RGB\", size, (255, 255, 255))\n # def get_empty_face(self,img, part_imgs, part_json):\n # '''\n # empty_face is face detached 'eye','nose','mouth','ear'\n # '''\n # head_json = part_json['head']\n # head_coords = self.get_coords(head_json)\n # head = part_imgs['head'][0]#!\n # white_image = self.get_white_image(img.size)\n # white_image.paste(head,head_coords[0])\n # for part in ['eye','nose','mouth','ear']:\n # if part_json[part] is not None:\n # part_coords= self.get_coords(part_json[part])\n # part_img = part_imgs[part]\n # if part in ['eye','ear']: \n # white_image.paste(self.get_white_image(part_img[0].size),part_coords[0])\n # white_image.paste(self.get_white_image(part_img[1].size),part_coords[1])\n # else:\n # white_image.paste(self.get_white_image(part_img[0].size),part_coords[0])\n \n # return white_image \n def get_empty_face(self,img, part_imgs, part_json):\n '''\n empty_face is face detached 'eye','nose','mouth','ear'\n '''\n head_json = part_json['head']\n head_coords = self.get_coords(head_json)\n head = part_imgs['head'][0]#!\n white_image = self.get_white_image(img.size)\n white_image.paste(head,head_coords[0])\n for part in ['eye','nose','mouth','ear']:\n if part_json[part] is not None:\n part_coords= self.get_coords(part_json[part])\n part_img = part_imgs[part]\n if part in ['eye','ear']: \n white_image.paste(self.get_white_image(part_img[0].size),part_coords[0])\n white_image.paste(self.get_white_image(part_img[1].size),part_coords[1])\n else:\n white_image.paste(self.get_white_image(part_img[0].size),part_coords[0])\n # white_image.show()\n return white_image\n def get_empty_lower_body(self,img, part_imgs, part_json):\n '''\n empty_lower_body detacched foot\n '''\n lower_body_json = part_json['lower_body']\n lower_body_coords = self.get_coords(lower_body_json)\n lower_body = part_imgs['lower_body'][0]#!\n white_image = self.get_white_image(img.size)\n white_image.paste(lower_body,lower_body_coords[0])\n if part_json[\"foot\"] is not None:\n part_coords= self.get_coords(part_json[\"foot\"])\n part_img = part_imgs[\"foot\"] \n white_image.paste(self.get_white_image(part_img[0].size),part_coords[0])\n white_image.paste(self.get_white_image(part_img[1].size),part_coords[1])\n \n return white_image.crop(lower_body_coords[0])\n def get_empty_upper_body(self,img, part_imgs, part_json):\n '''\n empty_lower_body detacched foot\n '''\n upper_body_json = part_json['upper_body']\n upper_body_coords = self.get_coords(upper_body_json)\n upper_body = part_imgs['upper_body'][0]#!\n white_image = self.get_white_image(img.size)\n white_image.paste(upper_body,upper_body_coords[0])\n if part_json[\"hand\"] is not None:\n part_coords= self.get_coords(part_json[\"hand\"])\n part_img = part_imgs[\"hand\"] \n white_image.paste(self.get_white_image(part_img[0].size),part_coords[0])\n white_image.paste(self.get_white_image(part_img[1].size),part_coords[1])\n # white_image.crop(upper_body_coords[0]).show()\n return white_image.crop(upper_body_coords[0])\n \n def create_new_images(self,img, binary_combination, part_imgs,part_json):\n #! Making New images\n original_img = img\n empty_face_active, eye_active, nose_active, ear_active, mouth_active, hand_active, foot_active = binary_combination\n # New white image\n\n new_image = self.get_white_image(original_img.size)\n if empty_face_active:\n new_image.paste(part_imgs[\"empty_face\"][0],(0,0))\n # print(part_json['lower_body'][0])\n # print(part_imgs[\"empty_lower_body\"][0].size,self.get_coords(part_json['lower_body'])[0] )\n new_image.paste(part_imgs[\"empty_lower_body\"][0], self.get_coords(part_json['lower_body'])[0]) # 원하는 위치에 붙임\n new_image.paste(part_imgs[\"empty_upper_body\"][0], self.get_coords(part_json['upper_body'])[0]) # 원하는 위치에 붙임\n # 각 파트 이미지를 읽어와서 새로운 이미지에 붙임\n if eye_active and (part_json[\"eye\"] is not None):\n new_image.paste(part_imgs[\"eye\"][0], self.get_coords(part_json['eye'])[0]) # 원하는 위치에 붙임\n new_image.paste(part_imgs[\"eye\"][1], self.get_coords(part_json['eye'])[1]) # 원하는 위치에 붙임 \n if nose_active and (part_json[\"nose\"] is not None):\n new_image.paste(part_imgs[\"nose\"][0], self.get_coords(part_json['nose'])[0]) # 원하는 위치에 붙임 \n if ear_active and (part_json[\"ear\"] is not None):\n new_image.paste(part_imgs[\"ear\"][0], self.get_coords(part_json['ear'])[0]) # 원하는 위치에 붙임 \n new_image.paste(part_imgs[\"ear\"][1], self.get_coords(part_json['ear'])[1]) # 원하는 위치에 붙임 \n if mouth_active and (part_json[\"mouth\"] is not None):\n new_image.paste(part_imgs[\"mouth\"][0], self.get_coords(part_json['mouth'])[0]) # 원하는 위치에 붙임 \n if hand_active and (part_json[\"hand\"] is not None):\n new_image.paste(part_imgs[\"hand\"][0], self.get_coords(part_json['hand'])[0]) # 원하는 위치에 붙임 \n new_image.paste(part_imgs[\"hand\"][1], self.get_coords(part_json['hand'])[1]) # 원하는 위치에 붙임 \n if foot_active and (part_json[\"foot\"] is not None):\n new_image.paste(part_imgs[\"foot\"][0], self.get_coords(part_json['foot'])[0]) # 원하는 위치에 붙임 \n new_image.paste(part_imgs[\"foot\"][1], self.get_coords(part_json['foot'])[1]) # 원하는 위치에 붙임 \n # 다른 파트들에 대해서도 같은 방식으로 처리\n return new_image\n def __len__(self):\n return len(self.image_paths)\n def __getitem__(self, idx):\n img_path = self.image_paths[idx]\n print(img_path)\n label = 0 if (img_path.split('/')[-1].split('.')[0].split('-')[0])=='A' else 1\n image = Image.open(img_path)\n part_name = self.part#[\"head\", \"eye\", \"nose\", \"ear\", \"mouth\", \"hand\", \"foot\", \"upper_body\", \"lower_body\"]\n # if self.binary_thresholding:\n # image = image.convert(\"L\")#! Convert grayscale\n # image = image.point(lambda p: p > self.binary_thresholding and 255)\n part_json = self.get_part_json(os.path.join(self.json_folder,self.json_paths[idx]),part_name=part_name)\n part_imgs = {}\n for part in part_name:#모든 part를 다시 dict으로 리턴하기위함.\n part_imgs[part]=[]\n # print(part)\n coords = self.get_coords(part_json[part])\n # print(coords)\n if coords is None:\n part_imgs[part].append(None) \n \n elif len(coords) ==1:\n part_imgs[part].append(image.crop(coords[0])) \n elif len(coords) == 2:\n part_imgs[part].append(image.crop(coords[0])) \n part_imgs[part].append(image.crop(coords[1])) \n empty_face = self.get_empty_face(image,part_imgs,part_json)\n # empty_face.show()\n empty_upper_body = self.get_empty_upper_body(image,part_imgs,part_json)\n empty_lower_body = self.get_empty_lower_body(image,part_imgs,part_json)\n part_imgs['empty_face']=[empty_face]\n part_imgs['empty_lower_body']=[empty_lower_body]\n part_imgs['empty_upper_body']=[empty_upper_body]\n part_combinations = list(itertools.product([0, 1], repeat=7))\n new_imgs = []\n for combination in part_combinations:\n # print(combination)\n new_img=self.create_new_images(img=image,binary_combination=combination, part_imgs=part_imgs,part_json=part_json)\n if self.transform:\n new_img=self.transform(new_img)\n new_imgs.append(new_img.unsqueeze(0))\n new_imgs = torch.cat(new_imgs,dim=0)\n image = self.transform(image)\n image_3ch = image.expand(3,-1,-1)\n return new_imgs,image_3ch,label \n \n \n\n\n\n\nif __name__==\"__main__\":\n transform= transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor(),ThresholdTransform(240)])\n part_name = [\"head\", \"eye\", \"nose\", \"ear\", \"mouth\", \"hand\", \"foot\", \"upper_body\", \"lower_body\"]\n dataset = shapley_part('/data/jong980812/project/mae/util/shapley/TD','/data/jong980812/project/mae/util/shapley/TD',part_name,240,transform=transform)\n data_loader=DataLoader(dataset,5,num_workers=4)\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n all_ordered_pair,weights = get_ordered_pair()\n part_number = all_ordered_pair.shape[0]\n part_count = {i: 0 for i in range(part_number)}\n num_correct = 0\n for new_imgs,original_image,label in data_loader:\n # print(new_imgs.shape)\n input_data = new_imgs\n # print('complete')\n batch_size = input_data.shape[0]\n input_data = rearrange(input_data, 'b t c h w -> (b t) c h w')\n \n \n model.to(device)\n input_data = input_data.to(device)\n original_image = original_image.to(device)\n label = label.to(device)\n model.eval()\n with torch.no_grad():\n prediction = model(original_image)\n output=model(input_data)\n output = rearrange(output, '(b t) o -> b t o', b=batch_size) # batch_size, 128, output(2)\n prediction = prediction.argmax(1)\n # print(output.shape)\n # print(label)\n \n for i in range(batch_size):\n if prediction[i] == label[i]:\n num_correct +=1\n correct_output = output[:,:,label[i]]# Take correct logits, (b, 128), 밖에서. \n shapley_matrix = get_shapley_matrix(all_ordered_pair,correct_output[i])\n shapley_contributions = shapley_matrix[:,:,1] - shapley_matrix[:,:,0] \n shapley_value = (shapley_contributions * 1/weights).sum(dim=1)\n max_part_number = (int(shapley_value.argmax()))\n part_count[max_part_number] += 1\n print(part_count)\n print(num_correct)\n \n", "repo_name": "jong980812/Part_shapley", "sub_path": "shapely_asd.py", "file_name": "shapely_asd.py", "file_ext": "py", "file_size_in_byte": 17112, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torchvision.models.efficientnet_b1", "line_number": 15, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 49, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "math.comb", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 115, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 123, "usage_type": "call"}, {"api_name": "json.load", "line_number": 135, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 164, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 164, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 276, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 276, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 311, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 322, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 322, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 322, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 322, "usage_type": "call"}, {"api_name": "shapley.transform.ThresholdTransform", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 326, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 326, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 326, "usage_type": "attribute"}, {"api_name": "einops.rearrange", "line_number": 336, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 344, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 347, "usage_type": "call"}]} +{"seq_id": "72497906320", "text": "import dask\nimport numpy\n\nfrom aydin.it.normalisers.base import NormaliserBase\nfrom aydin.util.log.log import lsection, lprint\n\n\nclass MinMaxNormaliser(NormaliserBase):\n \"\"\"Min-Max Normaliser\"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"Constructs a normalisers\"\"\"\n super().__init__(**kwargs)\n\n def calibrate(self, array):\n \"\"\"Method to calibrate\n\n Parameters\n ----------\n array : numpy.ArrayLike\n\n \"\"\"\n with lsection(\"Calibrating array using minmax method\"):\n self.original_dtype = array.dtype\n\n if hasattr(array, '__dask_keys__'):\n self.rmin = dask.array.min(array.flatten()).compute()\n self.rmax = dask.array.max(array.flatten()).compute()\n else:\n self.rmin = numpy.min(array)\n self.rmax = numpy.max(array)\n\n lprint(f\"Range for normalisation: [{self.rmin}, {self.rmax}]\")\n\n return self.rmin, self.rmax\n", "repo_name": "royerlab/aydin", "sub_path": "aydin/it/normalisers/minmax.py", "file_name": "minmax.py", "file_ext": "py", "file_size_in_byte": 979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 128, "dataset": "github-code", "pt": "3", "api": [{"api_name": "aydin.it.normalisers.base.NormaliserBase", "line_number": 8, "usage_type": "name"}, {"api_name": "aydin.util.log.log.lsection", "line_number": 23, "usage_type": "call"}, {"api_name": "dask.array.min", "line_number": 27, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 27, "usage_type": "attribute"}, {"api_name": "dask.array.max", "line_number": 28, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 31, "usage_type": "call"}, {"api_name": "aydin.util.log.log.lprint", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "21327504166", "text": "import requests, time, re, html\nfrom tqdm import tqdm\n\nclass web_scraper:\n def __init__(self, initial_url):\n self.__base_dblp_url = initial_url\n self.__alt_urls = ['https://dblp.org','http://dblp.uni-trier.de', 'https://dblp2.uni-trier.de ', 'https://dblp.dagstuhl.de']\n self.__alt_urls.remove(initial_url)\n\n # Method to retrieve full title of conference series by unique id\n def _retrieve_conf_series_name(self, conf_id):\n url = f\"{self.__base_dblp_url}/db/conf/{conf_id}/index.html\"\n page = requests.get(url)\n if page.status_code == 200:\n re_string = '

([\\s\\S]*?)

'\n conf_series_name = re.search(re_string, page.text).group(1)\n\n # In event of a redirection, follow the link and retrieve from there\n redirect_texts = [\"Redirecting ...\", \"Redirect ...\", \"Redirection ...\"]\n if conf_series_name in redirect_texts:\n re_string=f'

&)\n return html.unescape(conf_series_name)\n elif page.status_code == 404:\n # Hard coded workarounds for malformed id urls\n # ecoopwException -> ecoopw\n if conf_id == 'ecoopwException':\n return self.retrieve_conf_series_name('ecoopw')\n # planX -> planx\n elif conf_id == 'planX':\n return self.retrieve_conf_series_name('planx')\n else:\n print(\"Unkown 404 Error\")\n elif page.status_code == 429:\n return 'RATE LIMIT FAILURE'\n else:\n print(f\"PAGE LOAD ERROR: {page.status_code}\")\n\n # Method to retrieve full titles of a list of conference series ids\n def retrieve_all_conf_series_names(self, conf_ids):\n # Initialise progress bar\n pbar = tqdm(total = len(conf_ids), desc='Fetching conf series names from dblp.org')\n\n # Declare empty dict structure to hold id -> name pairs\n conf_ids_to_names = {}\n\n # Loop through all ids in conf_ids argument and attempt to retrieve names from dblp website\n for id in conf_ids:\n conf_name = self._retrieve_conf_series_name(id)\n\n if not conf_name == 'RATE LIMIT FAILURE':\n conf_ids_to_names[id] = conf_name\n\n # If server sends a 429 'Too many requests' response, sleep for 5 seconds and swtich to alternate url\n else:\n while conf_name == 'RATE LIMIT FAILURE':\n time.sleep(5)\n self.__alt_urls.append(self.__base_dblp_url)\n self.__base_dblp_url = self.__alt_urls.pop(0)\n conf_name = self._retrieve_conf_series_name(id)\n conf_ids_to_names[id] = conf_name\n\n # Upon successful retrieval, increment progress bar\n pbar.update(1)\n pbar.close()\n return conf_ids_to_names\n", "repo_name": "Tebkisk/dblp_clustering", "sub_path": "dataset_builder/web_scraper.py", "file_name": "web_scraper.py", "file_ext": "py", "file_size_in_byte": 3146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "re.search", "line_number": 16, "usage_type": "call"}, {"api_name": "re.search", "line_number": 22, "usage_type": "call"}, {"api_name": "html.unescape", "line_number": 26, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "71870424400", "text": "# handlers/member.py\nimport os\n\nfrom src.decorators import member_check_message, member_check_call\nfrom src.utils import gac_statistic, send_id\nfrom src.player import PlayerPowerService\nfrom src.guild import GuildData\nfrom create_bot import bot\nfrom aiogram import types, Dispatcher\n\n\n@member_check_message\nasync def command_start(message: types.Message):\n keyboard = types.InlineKeyboardMarkup()\n keyboard.add(types.InlineKeyboardButton(\"🫵🏻 Инфа по игрокам\", callback_data='player'))\n keyboard.add(types.InlineKeyboardButton(\"⚔️ Статистика ВА по всем\", callback_data='gac'))\n keyboard.add(types.InlineKeyboardButton(\"🔋 Контроль энки\", callback_data='reid'))\n keyboard.add(types.InlineKeyboardButton(\"💪🏻 ГМ по всем за месяц\", callback_data='gp_all'))\n keyboard.add(types.InlineKeyboardButton(\"🏛 Инфа о гильдии\", callback_data='guildinfo'))\n keyboard.add(types.InlineKeyboardButton(\"👮🏻‍♂️ Команды админов\", callback_data='admin'))\n await message.answer(\"🧑🏻‍🌾 Панель Пользователей 👨🏻‍🌾\", reply_markup=keyboard)\n # Вывод ID мне в личку если в .env True\n if os.environ.get(\"SEND_ID\"):\n await send_id(message)\n\n@member_check_call\nasync def command_gac_statistic(call: types.CallbackQuery):\n \"\"\"Выводит инфо о ВА и ссылки на противников\"\"\"\n await call.message.reply(f\"Добываю статистику. Ожидайте выполнения...\")\n st_1, st_2, st_3, st_4, st_5 = await gac_statistic()\n await bot.send_message(call.message.chat.id, text=st_1, parse_mode=\"Markdown\")\n await bot.send_message(call.message.chat.id, text=st_2, parse_mode=\"Markdown\")\n await bot.send_message(call.message.chat.id, text=st_3, parse_mode=\"Markdown\")\n await bot.send_message(call.message.chat.id, text=st_4, parse_mode=\"Markdown\")\n await bot.send_message(call.message.chat.id, text=st_5, parse_mode=\"Markdown\")\n\n\n# async def get_user_data(call: types.CallbackQuery):\n# \"\"\"вся инфа о конкретном игроке\"\"\"\n# is_guild_member = call.message.conf.get('is_guild_member', False)\n# if is_guild_member:\n# player_name = call.message.text.split(maxsplit=1)[1]\n# try:\n# async with async_session_maker() as session:\n# new_day_start = get_new_day_start()\n# query = await session.execute(\n# select(Player).filter_by(name=player_name).filter(\n# Player.update_time >= new_day_start))\n# player = query.scalars().first()\n# player_str_list = await PlayerData().extract_data(player)\n# await bot.send_message(call.message.chat.id, player_str_list)\n# except Exception as e:\n# await call.message.reply(f\"Ошибка:\\n\\n❌❌{e}❌❌\\n\\n\"\n# f\"Обратитесь разработчику бота в личку:\\nhttps://t.me/rollbar\")\n\n@member_check_call\nasync def get_guild_info(call: types.CallbackQuery):\n \"\"\"Инфа о гильдии\"\"\"\n guild_info = await GuildData.get_latest_guild_data()\n info_text = \"\\n\".join(guild_info)\n await bot.send_message(call.message.chat.id, info_text)\n\n\n@member_check_call\nasync def get_gp_all(call: types.CallbackQuery):\n \"\"\"Вся галактическая мощь за месяц по всем\"\"\"\n message_strings = await PlayerPowerService.get_galactic_power_all()\n await bot.send_message(call.message.chat.id, message_strings)\n\n\ndef register_handlers_member(dp: Dispatcher):\n dp.register_message_handler(command_start, commands=['start'])\n # dp.register_message_handler(get_user_data, text='player1', state='*', run_task=True)\n dp.register_callback_query_handler(command_gac_statistic, text='gac', state='*')\n dp.register_callback_query_handler(get_gp_all, text='gp_all', state='*')\n dp.register_callback_query_handler(get_guild_info, text='guildinfo')\n", "repo_name": "Shtierlitz/SWGOH-COMLINK-GUILD-CONTROL-BOT-SYSTEM-ru", "sub_path": "handlers/member.py", "file_name": "member.py", "file_ext": "py", "file_size_in_byte": 4108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "aiogram.types.Message", "line_number": 13, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 13, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 14, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 14, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 15, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 15, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 16, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 16, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 17, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 17, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 18, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 18, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 19, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 19, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 20, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 20, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "src.utils.send_id", "line_number": 24, "usage_type": "call"}, {"api_name": "src.decorators.member_check_message", "line_number": 12, "usage_type": "name"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 27, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 27, "usage_type": "name"}, {"api_name": "src.utils.gac_statistic", "line_number": 30, "usage_type": "call"}, {"api_name": "create_bot.bot.send_message", "line_number": 31, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 31, "usage_type": "name"}, {"api_name": "create_bot.bot.send_message", "line_number": 32, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 32, "usage_type": "name"}, {"api_name": "create_bot.bot.send_message", "line_number": 33, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 33, "usage_type": "name"}, {"api_name": "create_bot.bot.send_message", "line_number": 34, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 34, "usage_type": "name"}, {"api_name": "create_bot.bot.send_message", "line_number": 35, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 35, "usage_type": "name"}, {"api_name": "src.decorators.member_check_call", "line_number": 26, "usage_type": "name"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 57, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 57, "usage_type": "name"}, {"api_name": "src.guild.GuildData.get_latest_guild_data", "line_number": 59, "usage_type": "call"}, {"api_name": "src.guild.GuildData", "line_number": 59, "usage_type": "name"}, {"api_name": "create_bot.bot.send_message", "line_number": 61, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 61, "usage_type": "name"}, {"api_name": "src.decorators.member_check_call", "line_number": 56, "usage_type": "name"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 65, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 65, "usage_type": "name"}, {"api_name": "src.player.PlayerPowerService.get_galactic_power_all", "line_number": 67, "usage_type": "call"}, {"api_name": "src.player.PlayerPowerService", "line_number": 67, "usage_type": "name"}, {"api_name": "create_bot.bot.send_message", "line_number": 68, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 68, "usage_type": "name"}, {"api_name": "src.decorators.member_check_call", "line_number": 64, "usage_type": "name"}, {"api_name": "aiogram.Dispatcher", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "5939173727", "text": "from beecell.simple import jsonDumps\n\nfrom urllib.parse import quote\nimport requests\nimport json\nfrom logging import getLogger\n# from six.moves.urllib.parse import urlparse\nfrom beecell.simple import check_vault, truncate\nfrom requests.exceptions import ConnectionError, ConnectTimeout\nfrom urllib3 import disable_warnings, exceptions\n\ndisable_warnings(exceptions.InsecureRequestWarning)\n\n\nclass DataDomainError(Exception):\n def __init__(self, value, code=400):\n self.value = value\n self.code = code\n Exception.__init__(self, value, code)\n \n def __repr__(self):\n return 'DataDomainError: %s' % self.value\n \n def __str__(self):\n return 'DataDomainError: %s' % self.value\n\n\nclass DataDomainEntity(object):\n def __init__(self, manager):\n self.logger = getLogger(self.__class__.__module__ + '.' + self.__class__.__name__)\n\n self.manager = manager\n self.next = None\n\n @property\n def token(self):\n return self.manager.token\n\n @property\n def timeout(self):\n return self.manager.timeout\n\n @property\n def headers(self):\n headers = self.manager.dd_base_headers\n headers.update({'X-DD-AUTH-TOKEN': self.manager.get_token()})\n return headers\n\n def get_system_uri(self, oid):\n \"\"\"get datadomain base system uri\n\n :param oid: system id. uuid from system api\n :return: formatted uri\n \"\"\"\n oid = quote(oid)\n return '/dd-systems/%s' % oid\n\n def http_get(self, uri, **params):\n method = 'get'\n uri = self.manager.dd_base_uri + uri\n\n try:\n res = requests.get(uri, headers=self.headers, timeout=self.timeout, params=params, verify=False)\n output = res.json()\n if res.status_code in [400, 403, 404, 405]:\n error = output.get('details', '')\n raise Exception(error)\n self.logger.debug('datadomain http %s response: %s' % (method, truncate(output)))\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('datadomain http %s error: %s' % (method, ex))\n raise DataDomainError(ex)\n\n return output\n\n def http_post(self, uri, data={}):\n method = 'post'\n uri = self.manager.dd_base_uri + uri\n\n try:\n self.logger.debug('post data %s to dd' % data)\n res = requests.post(uri, headers=self.headers, timeout=self.timeout, data=jsonDumps(data), verify=False)\n output = res.json()\n if res.status_code in [400, 403, 404, 405]:\n error = output.get('detail', None)\n if error is None:\n error = output\n raise Exception(error)\n self.logger.debug('datadomain http %s response: %s' % (method, truncate(output)))\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('datadomain http %s error: %s' % (method, ex))\n raise DataDomainError(ex)\n\n return output\n\n def http_put(self, uri, data={}):\n method = 'put'\n uri = self.manager.dd_base_uri + uri\n\n try:\n self.logger.debug('put data %s to dd' % data)\n res = requests.put(uri, headers=self.headers, timeout=self.timeout, data=jsonDumps(data), verify=False)\n output = res.json()\n if res.status_code in [400, 403, 404, 405]:\n error = output.get('detail', None)\n if error is None:\n error = output\n raise Exception(error)\n self.logger.debug('datadomain http %s response: %s' % (method, truncate(output)))\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('datadomain http %s error: %s' % (method, ex))\n raise DataDomainError(ex)\n\n return output\n\n def http_delete(self, uri, data=None):\n method = 'delete'\n uri = self.manager.dd_base_uri + uri\n\n try:\n res = requests.delete(uri, headers=self.headers, timeout=self.timeout, verify=False)\n if res.status_code in [400, 403, 404, 405]:\n output = res.json()\n error = output['detail']\n raise Exception(error)\n self.logger.debug('datadomain http %s response: %s' % (method, True))\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('datadomain http %s error: %s' % (method, ex))\n raise DataDomainError(ex)\n\n\nclass DataDomainManager(object):\n def __init__(self, uri=None, proxy=None, timeout=60.0):\n self.logger = getLogger(self.__class__.__module__ + '.' + self.__class__.__name__)\n\n if uri is None:\n raise \n self.dd_base_uri = uri\n self.dd_base_headers = {\n 'Content-Type': 'application/json',\n 'Accept': 'application/json'\n }\n self.token = None\n self.token_expire = None\n self.timeout = timeout\n\n from .system import DataDomainSystem\n from .network import DataDomainNetwork\n from .mtree import DataDomainMtree\n from .protocol import DataDomainProtocol\n from .user import DataDomainUser\n from .trust import DataDomainTrust\n from .tenant import DataDomainTenant\n\n # initialize proxy objects\n self.system = DataDomainSystem(self)\n self.network = DataDomainNetwork(self)\n self.mtree = DataDomainMtree(self)\n self.protocol = DataDomainProtocol(self)\n self.user = DataDomainUser(self)\n self.trust = DataDomainTrust(self)\n self.tenant = DataDomainTenant(self)\n\n @property\n def headers(self):\n headers = self.dd_base_headers\n headers.update({'X-DD-AUTH-TOKEN': self.get_token()})\n return headers\n\n def set_timeout(self, timeout):\n self.timeout = timeout\n\n def ping(self):\n \"\"\"Ping dd\n\n :return: True or False\n \"\"\"\n res = False\n try:\n uri = self.dd_base_uri\n requests.get(uri, headers=self.dd_base_headers, timeout=self.timeout, verify=False)\n res = True\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n except Exception as ex:\n self.logger.error('datadomain http %s error: %s' % ('post', False))\n self.logger.debug('Ping dd server: %s' % res)\n\n return res\n\n def version(self):\n \"\"\"Get dd version\n\n :return: dd version\n \"\"\"\n try:\n # get token from identity service\n header = self.dd_base_headers\n uri = self.dd_base_uri + 'config/'\n res = requests.get(uri, headers=header, timeout=self.timeout, verify=False)\n output = res.json()\n if res.status_code in [400]:\n error = output['detail']\n raise Exception(error)\n version = {'version': output.get('version', None), 'ansible_version': output.get('ansible_version', None)}\n self.logger.debug('Get version: %s' % version)\n return version\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('get version error: %s' % ex)\n raise DataDomainError(ex)\n\n def authorize(self, user=None, pwd=None, token=None, key=None):\n \"\"\"Get token\n\n :param user: user\n :param pwd: password\n :param token: token string\n :param key: [optional] fernet key used to decrypt encrypted password\n \"\"\"\n # check password is encrypted\n if pwd is not None:\n pwd = check_vault(pwd, key)\n\n # set token\n if token is not None:\n self.token = token\n else:\n try:\n # get token from identity service\n self.logger.debug('Try to get token for user %s' % user)\n data = {\n 'auth_info': {\n 'username': user,\n 'password': pwd\n }\n }\n uri = self.dd_base_uri + '/auth'\n res = requests.post(uri, headers=self.dd_base_headers, data=jsonDumps(data),\n timeout=self.timeout, verify=False)\n # output = res.json()\n if res.status_code in [400, 401]:\n raise Exception('')\n self.token = res.headers['X-DD-AUTH-TOKEN']\n self.logger.debug('Get token %s for user %s' % (self.token, user))\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('get token error: %s' % ex)\n raise DataDomainError(ex)\n\n def delete_token(self):\n try:\n uri = self.dd_base_uri + '/auth'\n res = requests.delete(uri, headers=self.headers, timeout=self.timeout, verify=False)\n if res.status_code != 200:\n return False\n except ConnectTimeout as ex:\n self.logger.error('datadomain connection timeout: %s' % ex)\n raise DataDomainError(ex)\n except ConnectionError as ex:\n self.logger.error('datadomain connection error: %s' % ex)\n raise DataDomainError(ex)\n except Exception as ex:\n self.logger.error('delete token error: %s' % ex)\n raise DataDomainError(ex)\n return True\n\n def get_token(self):\n return self.token\n", "repo_name": "Nivola/beedrones", "sub_path": "beedrones/datadomain/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 11221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib3.exceptions.InsecureRequestWarning", "line_number": 12, "usage_type": "attribute"}, {"api_name": "urllib3.exceptions", "line_number": 12, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 63, "usage_type": "call"}, {"api_name": "beecell.simple.truncate", "line_number": 68, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 69, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 72, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 87, "usage_type": "call"}, {"api_name": "beecell.simple.jsonDumps", "line_number": 87, "usage_type": "call"}, {"api_name": "beecell.simple.truncate", "line_number": 94, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 95, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 98, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 113, "usage_type": "call"}, {"api_name": "beecell.simple.jsonDumps", "line_number": 113, "usage_type": "call"}, {"api_name": "beecell.simple.truncate", "line_number": 120, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 121, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 124, "usage_type": "name"}, {"api_name": "requests.delete", "line_number": 138, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 144, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 147, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 157, "usage_type": "call"}, {"api_name": "system.DataDomainSystem", "line_number": 179, "usage_type": "call"}, {"api_name": "network.DataDomainNetwork", "line_number": 180, "usage_type": "call"}, {"api_name": "mtree.DataDomainMtree", "line_number": 181, "usage_type": "call"}, {"api_name": "protocol.DataDomainProtocol", "line_number": 182, "usage_type": "call"}, {"api_name": "user.DataDomainUser", "line_number": 183, "usage_type": "call"}, {"api_name": "trust.DataDomainTrust", "line_number": 184, "usage_type": "call"}, {"api_name": "tenant.DataDomainTenant", "line_number": 185, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 204, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 206, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 208, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 225, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 233, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 236, "usage_type": "name"}, {"api_name": "beecell.simple.check_vault", "line_number": 253, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 269, "usage_type": "call"}, {"api_name": "beecell.simple.jsonDumps", "line_number": 269, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 276, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 279, "usage_type": "name"}, {"api_name": "requests.delete", "line_number": 289, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 292, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 295, "usage_type": "name"}]} +{"seq_id": "28060219088", "text": "from tkinter import *\nimport requests\nimport json\nimport os\n\n\n#Get/Post\ndef post(nome):\n if nome != '':\n if requests.post('http://localhost:8000/api/reis', data = {'name':nome}):\n labelFeed['text'] = \"Rei indicado com sucesso!\"\n labelFeed['fg'] = \"white\"\n labelFeed['bg'] = 'green'\n frame2['bg'] = 'green'\n \n labelFeed['font'] = 'none 12 bold'\n else :\n labelFeed['text'] = \"Não foi possível indicar esse bastardo\"\n labelFeed['bg'] = 'red'\n labelFeed['fg'] = \"white\"\n labelFeed['font'] = 'none 12 bold'\n frame2['bg'] = 'red'\n else:\n labelFeed['text'] = \"Estamos sem rei!!!!\"\n labelFeed['bg'] = '#ff531a'\n labelFeed['fg'] = \"white\"\n labelFeed['font'] = 'none 12 bold'\n frame2['bg'] = '#ff531a'\ndef get():\n data = requests.get('http://localhost:8000/api/reis')\n binary = data.content\n output = json.loads(binary)\n for item in output['data']:\n labelList['text'] = \"Saudem o Rei \" + item['name']\n#Window Configuration\nwindow = Tk()\nwindow.title(\"Game Of Python 3.0\")\ncanvas = Canvas(window, height='500', width='500')\ncanvas.pack()\n#Background\nbackground = PhotoImage(file='teste.png')\nbackgroundLabel = Label(window, image=background)\nbackgroundLabel.place(relwidth=1, relheight=1)\n#Frames\nframeList = Frame(window, bg='#f0f0f5', bd=10)\nframeList.place(relx=0.5, rely=0.50, relwidth=0.75, relheight=0.4, anchor='center')\n\nframeList2 = Frame(window, bg='#f0f0f5', bd=10)\nframeList2.place(relx=0.5, rely=0.9, relwidth=0.75, relheight=0.1, anchor='center')\n\nframe = Frame(window, bg='#f0f0f5', bd=5)\nframe.place(relx=0.5, rely=0.1, relwidth=0.75, relheight=0.1, anchor='center')\n\nframe2 = Frame(window, bg='#f0f0f5', bd=5)\nframe2.place(relx=0.5, rely=0.23, relwidth=0.75, relheight=0.1, anchor='center')\n#Entry inputs\nentry = Entry(frame, font=20)\nentry.place(relwidth=0.65, relheight=1)\n#Buttons\nbutton = Button(frame, text=\"King Name\",command=lambda : post(entry.get()))\nbutton.place(relx=0.7, relwidth=0.30, relheight=1)\n\nbutton2 = Button(frameList2, text=\"Saudação\",command=lambda : get())\nbutton2.place(relwidth=1, relheight=1)\n#Labels\nlabelList = Label(frameList)\nlabelList.place(relwidth=1, relheight=1)\n\nlabelFeed = Label(frame2, text='')\nlabelFeed.place(relwidth=1, relheight=1)\nwindow.mainloop()", "repo_name": "guilhermegomes1/APILaravel-Python3", "sub_path": "APIConsumidora/Graphic/python/Graphic/interface.py", "file_name": "interface.py", "file_ext": "py", "file_size_in_byte": 2585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.post", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "6311361765", "text": "import os\r\nimport time\r\n\r\nimport requests\r\nfrom flask import Flask\r\nfrom flask_mail import Mail, Message\r\n\r\nimport timecalculations\r\n\r\nmail_settings = {\r\n \"MAIL_SERVER\": 'smtp.gmail.com',\r\n \"MAIL_PORT\": 465,\r\n \"MAIL_USE_TLS\": False,\r\n \"MAIL_USE_SSL\": True,\r\n \"MAIL_USERNAME\": os.environ['Email_user_id'],\r\n \"MAIL_PASSWORD\": os.environ['Email_password']\r\n}\r\napp = Flask(__name__)\r\napp.config.update(mail_settings)\r\nmail = Mail(app)\r\nmailRecipientItself = app.config.get(\"MAIL_USERNAME\")\r\n\r\n\r\ndef analyzedownloadspeed():\r\n url = 'https://raw.githubusercontent.com/Coders-Asylum/Python-Projects/master/resources/5MbFile.txt'\r\n url2 = 'https://raw.githubusercontent.com/Coders-Asylum/Python-Projects/master/resources/5MbFile.txt'\r\n record_start_time_no_download = time.time() # time recorded\r\n request_url = requests.get(url)\r\n record_end_time_no_download = time.time() # time recorded\r\n\r\n # time.sleep(1.0)\r\n record_down_start_time = time.time() # time recorded\r\n request_url = requests.get(url2)\r\n with open('C:/Users/Adithya B. Shetty/Downloads/demo.txt', 'wb') as downloadedFile:\r\n downloadedFile.write(request_url.content)\r\n\r\n record_down_end_time = time.time() # time recorded\r\n\r\n file_information = os.stat('C:/Users/Adithya B. Shetty/Downloads/demo.txt')\r\n downloaded_file_size = file_information.st_size\r\n\r\n # delete the downloaded file\r\n os.unlink('C:/Users/Adithya B. Shetty/Downloads/demo.txt')\r\n\r\n return record_start_time_no_download, record_end_time_no_download, record_down_start_time, record_down_end_time, downloaded_file_size\r\n\r\n\r\ntStartwithoutdownload, tendwithoutdownload, tstartwithdownload, tendwithdownload, filesize = analyzedownloadspeed()\r\n\r\n\r\ndef analyzeuploadspeed():\r\n with app.app_context():\r\n # Record time required to send mail without attachments\r\n record_normal_up_start_time = time.time() # time recorded\r\n msg_without_attach = Message(subject=\"Test pls delete\", sender=app.config.get(\"MAIL_USERNAME\"),\r\n recipients=[app.config.get(\"MAIL_USERNAME\")])\r\n mail.send(msg_without_attach)\r\n record_normal_up_end_time = time.time() # time recorded\r\n\r\n # time.sleep(1.0)\r\n\r\n # Record time Required to send mail with attachments\r\n record_upstart_time = time.time() # time recorded\r\n msg_with_attachments = Message(subject=\"Test File Delete after Use\", sender=app.config.get(\"MAIL_USERNAME\"),\r\n recipients=[mailRecipientItself])\r\n openfile = open(\"C:/Users/Adithya B. Shetty/Desktop/1MbFile.txt\", \"r\")\r\n msg_with_attachments.attach(filename=\"sample\", content_type=\"text/plain\", data=openfile.read())\r\n mail.send(msg_with_attachments)\r\n record_up_end_time = time.time() # time recorded\r\n\r\n return record_normal_up_start_time, record_normal_up_end_time, record_upstart_time, record_up_end_time\r\n\r\n\r\ntStartwithoutupload, tendwithoutupload, tstartwithupload, tendwithupload = analyzeuploadspeed()\r\n\r\n\r\ndef caculatetimefordownloads():\r\n obj_tstartwod = timecalculations.CalculateTime(tStartwithoutdownload)\r\n tstartwod = obj_tstartwod.convertEverythingIntoSeconds()\r\n print(tstartwod)\r\n\r\n obj_tendwod = timecalculations.CalculateTime(tendwithoutdownload)\r\n tendwod = obj_tendwod.convertEverythingIntoSeconds()\r\n print(tendwod)\r\n\r\n obj_tstartwd = timecalculations.CalculateTime(tstartwithdownload)\r\n tstartwd = obj_tstartwd.convertEverythingIntoSeconds()\r\n print(tstartwd)\r\n\r\n obj_tendwd = timecalculations.CalculateTime(tendwithdownload)\r\n tendwd = obj_tendwd.convertEverythingIntoSeconds()\r\n print(tendwd)\r\n DownloadTime = (tendwd - tstartwd) # - (tendwod - tstartwod)\r\n\r\n return DownloadTime\r\n\r\n\r\ndef caculatetimeforuploads():\r\n obj_tstartwou = timecalculations.CalculateTime(tStartwithoutupload)\r\n tstartwou = obj_tstartwou.convertEverythingIntoSeconds()\r\n # print(tstartwou)\r\n\r\n obj_tendwou = timecalculations.CalculateTime(tendwithoutupload)\r\n tendwou = obj_tendwou.convertEverythingIntoSeconds()\r\n # print(tendwou)\r\n\r\n obj_tstartwu = timecalculations.CalculateTime(tstartwithupload)\r\n tstartwu = obj_tstartwu.convertEverythingIntoSeconds()\r\n # print(tstartwu)\r\n\r\n obj_tendwu = timecalculations.CalculateTime(tendwithupload)\r\n tendwu = obj_tendwu.convertEverythingIntoSeconds()\r\n # print(tendwu)\r\n UploadTime = (tendwu - tstartwu) - (tendwou - tstartwou)\r\n\r\n return UploadTime\r\n\r\n\r\nif __name__ == '__main__':\r\n print(caculatetimefordownloads())\r\n print(caculatetimeforuploads())\r\n\r\napp.config['DEBUG'] = True\r\n", "repo_name": "Coders-Asylum/Python-Projects", "sub_path": "Bandwidth Calculator/bandwidthAnalyzer.py", "file_name": "bandwidthAnalyzer.py", "file_ext": "py", "file_size_in_byte": 4672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_mail.Mail", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 39, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "flask_mail.Message", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "flask_mail.Message", "line_number": 64, "usage_type": "call"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 78, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 82, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 86, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 90, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 99, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 103, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 107, "usage_type": "call"}, {"api_name": "timecalculations.CalculateTime", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "32531437724", "text": "import os\nimport hashlib\nfrom re import template\n\nfrom pandas.core import base\nfrom pantex.publish import Manager\nfrom time import sleep\nfrom typing import Union\nfrom typing_extensions import Literal\nfrom string import Template\nimport pickle\nimport subprocess\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib\n\n\ndef sha1(filename):\n BUF_SIZE = 65536\n sha1 = hashlib.sha1()\n with open(filename, \"rb\") as f:\n while True:\n data = f.read(BUF_SIZE)\n if not data:\n break\n sha1.update(data)\n return sha1.digest()\n\n\ndef check_for_updates(filename, previous_hash):\n current_hash = sha1(filename[0]) + sha1(filename[1])\n if previous_hash is None:\n previous_hash = sha1(filename[0]) + sha1(filename[1])\n while True:\n # sleep(0.01) # to slow it down\n if current_hash != previous_hash:\n return current_hash\n current_hash = sha1(filename[0]) + sha1(filename[1])\n\n\nclass Server(Manager):\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self._file_being_watched_1 = self._template\n self._file_being_watched_2 = self._context\n\n def run_server(self):\n self.save_to_html()\n previous_hash = None\n browser_sync_process = subprocess.Popen(\n f'browser-sync start --server --files \"*.html\" --index {self._html_ouput_file_name}',\n shell=True,\n )\n print(\"BrowserSync PID: \", browser_sync_process.pid)\n while True:\n new_hash = check_for_updates(\n filename=[self._file_being_watched_1, self._file_being_watched_2],\n previous_hash=previous_hash,\n )\n passed = False\n while not passed:\n # This is a hack; reading context file too quickly is a problem\n try:\n self.save_to_html()\n passed = True\n except EOFError as e:\n pass\n previous_hash = new_hash\n\n\nif __name__ == \"__main__\":\n import os\n import argparse\n\n # see http.server in standard library\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"template\", type=str, help=\"The template file path (md)\",\n )\n args = parser.parse_args()\n splitname = args.template.split(\".\")\n basename = \".\".join(splitname[:-1])\n extension = splitname[-1]\n if not os.path.isfile(basename + \".pkl\"):\n print(f\"{basename}.pkl not found. Creating an empty context file...\")\n with open(f\"{basename}.pkl\", \"wb\") as fn:\n fn.write(pickle.dumps({}))\n else:\n with open(f\"{basename}.pkl\", \"rb\") as fn:\n pickle_data = pickle.loads(fn.read())\n if len(pickle_data) == 0:\n print(\n f\"[WARNING] {basename}.pkl contains no data. Use pantex.Manager.save_context to create context.\"\n )\n s = Server(template=args.template)\n s.run_server()\n", "repo_name": "pandichef/panTeX", "sub_path": "pantex/edit.py", "file_name": "edit.py", "file_ext": "py", "file_size_in_byte": 3002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "hashlib.sha1", "line_number": 20, "usage_type": "call"}, {"api_name": "pantex.publish.Manager", "line_number": 41, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 50, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 87, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "34400793778", "text": "import pandas as pd\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\n\ntrain_data_path = 'titanic/train.csv'\ntest_data_path = 'titanic/test.csv'\n\ntrain_df = pd.read_csv(train_data_path)\ntest_df = pd.read_csv(test_data_path)\n\n\ndum_train_df = train_df.copy()\n\nfeatures_df = dum_train_df.drop(columns='Survived')\n\n#'PassengerId','Pclass', 'Name', 'Sex', 'Age', 'SibSp' , 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'\n\n# Variable\tDefinition\t Key\n# survival\tSurvival\t 0 = No, 1 = Yes\n# pclass\tTicket class\t 1 = 1st, 2 = 2nd, 3 = 3rd\n# sex\t Sex\n# Age\t Age in years\n# sibsp\t # of siblings /\n# spouses aboard the Titanic\n# parch\t # of parents /\n# children aboard the Titanic\n# ticket\tTicket number\n# fare\t Passenger fare\n# cabin\t Cabin number\n# embarked\tPort of Embarkation\t C = Cherbourg, Q = Queenstown, S = Southampton\n# title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n# 'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n# 'Don', 'Jonkheer']\n\nfeatures_df['Age'].fillna(0, inplace=True)\nfeatures_df['Embarked'].fillna('not_mentioned', inplace=True)\n\nfeatures_df['family_size'] = sum(features_df['Parch'], features_df['SibSp'])\n\nfeatures_df['Pclass'].value_counts()\n\nfeatures_df['Cabin'].unique()\n\nfeatures_df['Deck'] = features_df['Cabin'].apply(lambda x: str(x)[:1] if pd.notnull else 'M')\nfeatures_df['Deck'] = features_df['Deck'].replace(['A', 'B', 'C'], 'ABC')\nfeatures_df['Deck'] = features_df['Deck'].replace(['D', 'E'], 'DE')\nfeatures_df['Deck'] = features_df['Deck'].replace(['F', 'G',], 'FG')\n\nfeatures_df['Age'].value_counts()\n\nfeatures_df.info()\ncolumns_To_be_encoded = ['Name', 'Sex', 'Ticket', 'Embarked', 'Deck']\ncat_columns = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'family_size']\n# cat_columns = ['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'family_size']\n\n# df = df.astype({\"Name\":'category', \"Age\":'int64'})\nfeatures_df = features_df.astype({'Age': 'category', 'Fare': 'category','Pclass': 'category'})\n\nfeatures_df.info()\nfeatures = features_df.drop(columns=['Cabin'])\n\nencoder = OneHotEncoder()\n\nfor i in columns_To_be_encoded:\n features[i] = encoder.fit_transform(features[i].values.reshape(1,-1))\n encode_df = pd.DataFrame(features)\n\nencoder.categories_\n\nX = encode_df\ny = train_df['Survived'].values\n\nx_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n\n\nclf = RandomForestClassifier(n_estimators=100)\nclf.fit(x_train,y_train)\n\nx_train.shape\ny_train.shape\n", "repo_name": "pc099/titanic_kaggle", "sub_path": "titanic_v0.py", "file_name": "titanic_v0.py", "file_ext": "py", "file_size_in_byte": 2620, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "2824314770", "text": "import librosa, librosa.display\nimport math\nimport matplotlib.pyplot as plt\n\nSAMPLE_RATE = 22050\n\ndef load_sound(filename):\n # load the sound at the default sample rate 22050 HZ\n sound, sr = librosa.load(\n filename\n )\n assert sr == SAMPLE_RATE\n return sound, sr\n\n# Calculate the MFCCs over the segments, a.k.a. frames. Prepare the\n# parameters for calculating the MFCCs over the segments. In the video,\n# 10 frames per 30 sec was used, I have 5 sec, but let me use 5 frames.\nNUM_FRAMES = 10\n# 5 / 5 = 1 seconds per frame;\n# 22050 samples per frame.\nframe_length_in_samples = int(SAMPLE_RATE / NUM_FRAMES)\nprint(frame_length_in_samples)\n\ndef extract_mfccs_from_track(sound, sr):\n # calculate the MFCCs over the frames\n for i in range(NUM_FRAMES):\n start_sample = i * frame_length_in_samples\n end_sample = start_sample + frame_length_in_samples\n\n print(\"{}:{}\".format(start_sample, end_sample))\n\n frame = sound[start_sample:end_sample]\n\n mfcc = librosa.feature.mfcc(\n\n frame,\n sr,\n\n # may be increased to get more granular information, but 13 is\n # the minimum value\n n_mfcc = 13,\n\n # these are somewhat magic constants; IDK what they mean. It seems\n # redundant to me.\n n_fft = 2048,\n hop_length = 512,\n )\n\n num_mfcc_vectors_per_segment = math.ceil(\n frame_length_in_samples / 512\n )\n\n print(\"{}vs{}\".format(len(mfcc), num_mfcc_vectors_per_segment))\n # should always be the same, but in the video we\n # check if the length is not equal to expected length.\n\n # librosa.display.specshow(mfcc)\n # plt.show()\n\ndef prepare_data(root):\n data = {\n # a range of mfccs that represent the sound\n \"mfcc\": [],\n # labels\n \"label\": [],\n # filenames\n \"name\": []\n }\n", "repo_name": "gevorgyana/tf_playground", "sub_path": "other/script.py", "file_name": "script.py", "file_ext": "py", "file_size_in_byte": 1926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "librosa.load", "line_number": 9, "usage_type": "call"}, {"api_name": "librosa.feature.mfcc", "line_number": 34, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 34, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "40319821626", "text": "from __future__ import print_function\n\nimport sys\n\nfrom pyspark import SparkContext\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n print(\"Usage: network_wordcount.py \", file=sys.stderr)\n exit(-1)\n sc = SparkContext(appName=\"PythonWordCountJob\")\n\n lines = sc.textFile(sys.argv[1]).repartition(1)\n counts = lines.flatMap(lambda line: line.split(\" \"))\\\n .map(lambda word: (word, 1))\\\n\t\t .reduceByKey(lambda a, b: a+b)\n\n counts.saveAsTextFile(sys.argv[2])\n", "repo_name": "ichMaster/ich_dev", "sub_path": "HDInsight-Spark/job_wordcount.py", "file_name": "job_wordcount.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pyspark.SparkContext", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "73946562961", "text": "# -*- coding: utf-8 -*-\nimport pandas as pd\nimport sklearn\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom matplotlib import pyplot as plt\n\n\ndf = pd.read_csv(\"insekter_fra_Ecuador.csv\", index_col=0)\ntrain_df, test_df = sklearn.model_selection.train_test_split(df, shuffle=False)\n\nX = train_df.loc[:, 'Number of tokens':'Number of links']\ny = train_df.loc[:, 'Ecuadors insekter']\nX_test = test_df.loc[:, 'Number of tokens':'Number of links']\ny_test = test_df.loc[:, 'Ecuadors insekter']\n\n\nlogReg = LogisticRegression(solver=\"lbfgs\", multi_class='ovr')\n\nfitted = logReg.fit(X, y)\nlogReg.coef_\nlogReg.intercept_\n\nscore = logReg.score(X_test, y_test)\nprint(score)\n\nsushi = X.loc[y == True]\ntaco = X.loc[y == False]\nplt.scatter(sushi.loc[:, 'Number of tokens'], sushi.loc[:, 'Number of links'], color='g')\nplt.scatter(taco.loc[:, 'Number of tokens'], taco.loc[:, 'Number of links'], color='r')\n\n# getting the x co-ordinates of the decision boundary\nplot_x = np.array([min(X.loc[:, 'Number of tokens']) - 2, max(X.loc[:, 'Number of tokens']) + 2])\n# getting corresponding y co-ordinates of the decision boundary\ncoef = logReg.coef_[0]\nintr = logReg.intercept_\nprint(coef, intr)\nplot_y = (-1/coef[1]) * (coef[0] * plot_x + intr[0])\nplt.plot(plot_x, plot_y)\nplt.show()\n\n\n#ax = plt.gca()\n#ax.autoscale(False)\n#x_vals = np.array(ax.get_xlim())\n#y_vals = -(x_vals * sushi.loc[:, 'Number of tokens'] + taco.loc[:, 'Number of tokens'])/ sushi.loc[:, 'Number of links']\n#plt.plot(x_vals, y_vals, '--', c=\"red\")\n#plt.show()\n\n# plt.scatter(x_np[:,0], x_np[:,1], c=y_np.reshape(-1),cmap=mpl.colors.ListedColormap(colors))\n# ax = plt.gca()\n# ax.autoscale(False)\n# x_vals = np.array(ax.get_xlim())\n# y_vals = -(x_vals * w_guess[0] + b_guess[0])/w_guess[1]\n# plt.plot(x_vals, y_vals, '--', c=\"red\")\n\n\n\n# read_data = pd.read_csv(\"insekter_fra_Ecuador.csv\", sep=',', index_col=0)\n# list_of_rows = [list(row) for row in read_data.values]\n# column_names = list(read_data.columns.values.tolist())\n#distances = np.array(list_of_rows)\n\n#print(list_of_rows)\n#print(train_df)\n\n# for t in train_df:\n# print(t)\n# print(\"----------------------------\")\n# for t in test_df:\n# print(t)\n\n\n# logReg = LogisticRegression(solver=\"lbfgs\")\n# fitted = logReg.fit(x, y)\n# print(fitted)\n\n\n\n\n# Oppgave (A)\n# Tren en logistisk regresjonsmodell på train_df (uten regularisering) som predikerer om wiki-siden handler\n# om et insekt fra Ecuador basert på to numeriske trekk (features), nemlig antall tokens og antall lenker på siden.\n# Hvilke accuracy oppnår du på testsettet test_df?\n\n\n\n# Oppgave (B) Forklar hvorfor verdien du oppnådde for accuracy er så lav, basert på det du vet om logistisk regresjon. For\n# å støtte din forklaring, tegn en scatter plot (du kan bruke funksjonen scatterplot i Seaborn1) hvor de to\n# aksene står for de to numeriske trekkene, og fargen (hue) representerer outputklassen (insekt fra Ecuador eller\n# ikke). Tegn deretter beslutningslinjen som er assosiert med den logistiske regresjonsmodellen du nettopp har\n# trent.\n", "repo_name": "joevko/projects", "sub_path": "in2110/in2110-lab-master/in2110-lab-master/ekstra/task3.py", "file_name": "task3.py", "file_ext": "py", "file_size_in_byte": 3121, "program_lang": "python", "lang": "no", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "8002914732", "text": "import argparse\nimport os\n\nimport matplotlib.cm as cm\nimport numpy as np\nimport skimage.color as cl\nimport skimage.io as io\nimport torch\nimport torchvision.utils as vutils\nfrom torchvision.transforms import Compose, ToTensor\n\nfrom models.LAPNN import LAP\nfrom utils.image_padder import PadStride\nimport torch.nn.functional as F\n\n##################################################################################################################################\n\nparser = argparse.ArgumentParser(\n description='Laplacian Raindrop Removal - enhance')\n\nparser.add_argument(\"--device\", default='cpu',\n help=\"device for the run [cpu cuda:-]\")\nparser.add_argument('--input', '-i', type=str,\n default='', help='image to enhance')\nparser.add_argument(\"--save_dir\", '-sd', type=str,\n default='./', help=\"directory where to save output\")\nparser.add_argument('--model', type=str,\n default='./weights/net_coeff.pth', help='Pre-trained model path')\narser.add_argument('--levels', action='store_true',\n help='save intermediate Laplacian levels [default false]')\n\nopt = parser.parse_args()\n# print(opt)\n\n##################### General Options ##########################################\n\n# directory where to save checkpoints and outputs\nsave_dir = opt.save_dir\n\n# GPU board availability check\ncuda_check = torch.cuda.is_available()\n\n\nif not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n############################ Network Models ####################################\n\nwith torch.no_grad():\n print(\"\\n===> Preparing model\")\n\n trs = Compose([\n PadStride(32, fill=-1),\n ToTensor()\n ])\n\n model = LAP(max_levels=3, device=opt.device)\n\n # Models loading\n if opt.model != '':\n model.load_state_dict(torch.load(opt.model))\n\n ############################ Setting cuda ######################################\n\n print(\"\\n===> Setting GPU\")\n\n if cuda_check:\n model.to(opt.device)\n\n print(\"\\n===> Enhancing\")\n\n model.eval()\n model.zero_grad()\n\n inputt = io.imread(opt.input)\n\n inputt = trs(inputt).unsqueeze(0)\n\n if cuda_check:\n inputt = inputt.to(opt.device)\n\n out, l1, l2, l3 = model(inputt)\n\n _, _, hh, ww = out.shape\n l2 = F.interpolate(l2, size=(hh, ww), mode='bicubic', align_corners=False)\n l3 = F.interpolate(l3, size=(hh, ww), mode='bicubic', align_corners=False)\n\n name = opt.input.split('/')[-1].split('.')[0]\n\n # Output image saving\n vutils.save_image(out, save_dir + '/' + name + '_out.png')\n if opt.levels:\n vutils.save_image(l1, save_dir + '/' + name +\n '_l1.png', normalize=True)\n vutils.save_image(l2, save_dir + '/' + name +\n '_l2.png', normalize=True)\n vutils.save_image(l3, save_dir + '/' + name +\n '_l3.png', normalize=True)\n", "repo_name": "TheZino/Laplacian-Encoder-Decoder-Raindrop-Removal", "sub_path": "source/enhance.py", "file_name": "enhance.py", "file_ext": "py", "file_size_in_byte": 2920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.image_padder.PadStride", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 54, "usage_type": "call"}, {"api_name": "models.LAPNN.LAP", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 61, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 75, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 86, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 91, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 91, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 93, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 93, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 95, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 95, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 97, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "74912154927", "text": "#!/usr/bin/python3\n\nimport os\nimport re\nimport getopt\nimport sys\nimport json\nimport logging\n\nfrom typing import (\n Optional,\n Tuple,\n Any,\n List,\n Dict,\n)\n\nimport whois\n\n# import whoisdomain as whois # to be compatible with dannycork\n\nlog = logging.getLogger(__name__)\nlogging.basicConfig(level=os.environ.get(\"LOGLEVEL\", \"INFO\"))\n\n# if we are not running as test2.py run in a simplistic way\nSIMPLISTIC: bool = False\nWithRedacted: bool = False\n\nPrintJson: bool = False\nVerbose: bool = False\nPrintGetRawWhoisResult: bool = False\nRuleset: bool = False\n\nFailures: Dict[str, Any] = {}\nIgnoreReturncode: bool = False\nTestAllTld: bool = False\nTestRunOnly: bool = False\n\nWithPublicSuffix: bool = False\nWithExtractServers: bool = False\nWithStripHttpStatus: bool = False\n\n\nclass ResponseCleaner:\n data: str\n rDict: Dict[str, Any] = {}\n\n def __init__(\n self,\n pathToTestFile: str,\n ):\n self.data = self.readInputFile(pathToTestFile)\n\n def readInputFile(\n self,\n pathToTestFile: str,\n ) -> str:\n if not os.path.exists(pathToTestFile):\n return \"\"\n\n with open(pathToTestFile, mode=\"rb\") as f: # switch to binary mode as that is what Popen uses\n # make sure the data is treated exactly the same as the output of Popen\n return f.read().decode(errors=\"ignore\")\n\n def cleanSection(\n self,\n section: List[str],\n ) -> List[str]:\n # cleanup any beginning and ending empty lines from the section\n\n if len(section) == 0:\n return section\n\n rr = r\"^\\s*$\"\n n = 0 # remove empty lines from the start of section\n while re.match(rr, section[n]):\n section.pop(n)\n # n stays 0\n\n n = len(section) - 1 # remove empty lines from the end of the section\n while re.match(rr, section[n]):\n section.pop(n)\n n = len(section) - 1 # remove empty lines from the end of section\n\n return section\n\n def splitBodyInSections(\n self,\n body: List[str],\n ) -> List[str]:\n # split the body on empty line, cleanup all sections, remove empty sections\n # return list of body's\n\n sections: List[List[str]] = []\n n = 0\n sections.append([])\n for line in body:\n if re.match(r\"^\\s*$\", line):\n n += 1\n sections.append([])\n continue\n sections[n].append(line)\n\n m = 0\n while m < len(sections):\n sections[m] = self.cleanSection(sections[m])\n m += 1\n\n # now remove empty sections and return\n sections2: List[str] = []\n m = 0\n while m < len(sections):\n if len(sections[m]) > 0:\n sections2.append(\"\\n\".join(sections[m]))\n m += 1\n\n return sections2\n\n def cleanupWhoisResponse(\n self,\n verbose: bool = False,\n with_cleanup_results: bool = False,\n ) -> Tuple[str, Dict[Any, Any]]:\n result = whois.cleanupWhoisResponse(\n self.data,\n verbose,\n with_cleanup_results,\n )\n\n self.rDict: Dict[str, Any] = {\n \"BodyHasSections\": False, # if this is true the body is not a list of lines but a list of sections with lines\n \"Preamble\": [], # the lines telling what whois servers wwere contacted\n \"Percent\": [], # lines staring with %% , often not present but may contain hints\n \"Body\": [], # the body of the whois, may be in sections separated by empty lines\n \"Postamble\": [], # copyright and other not relevant info for actual parsing whois\n }\n body: List[str] = []\n\n rr: List[str] = []\n z = result.split(\"\\n\")\n preambleSeen = False\n postambleSeen = False\n percentSeen = False\n for line in z:\n if preambleSeen is False:\n if line.startswith(\"[\"):\n self.rDict[\"Preamble\"].append(line)\n line = \"PRE;\" + line\n continue\n preambleSeen = True\n\n if preambleSeen is True and percentSeen is False:\n if line.startswith(\"%\"):\n self.rDict[\"Percent\"].append(line)\n line = \"PERCENT;\" + line\n continue\n percentSeen = True\n\n if postambleSeen is False:\n if line.startswith(\"-- \") or line.startswith(\">>> \") or line.startswith(\"Copyright notice\"):\n postambleSeen = True\n\n if postambleSeen is True:\n self.rDict[\"Postamble\"].append(line)\n line = \"POST;\" + line\n continue\n\n body.append(line)\n\n if \"\\t\" in line:\n line = \"TAB;\" + line # mark lines having tabs\n\n if line.endswith(\"\\r\"):\n line = \"CR;\" + line # mark lines having CR (\\r)\n\n rr.append(line)\n\n body = self.cleanSection(body)\n self.rDict[\"Body\"] = self.splitBodyInSections(body)\n return \"\\n\".join(rr), self.rDict\n\n def printMe(self) -> None:\n zz = [\"Preamble\", \"Percent\", \"Postamble\"]\n for k in zz:\n n = 0\n for lines in self.rDict[k]:\n tab = \" [TAB] \" if \"\\t\" in lines else \"\" # tabs are present in this section\n cr = \" [CR] \" if \"\\r\" in lines else \"\" # \\r is present in this section\n print(k, cr, tab, lines)\n\n k = \"Body\"\n if self.rDict[k]:\n n = 0\n for lines in self.rDict[k]:\n ws = \" [WHITESPACE AT END] \" if re.search(r\"[ \\t]+\\r?\\n\", lines) else \"\"\n tab = \" [TAB] \" if \"\\t\" in lines else \"\" # tabs are present in this section\n cr = \" [CR] \" if \"\\r\" in lines else \"\" # \\r is present in this section\n print(f\"# --- {k} Section: {n} {cr}{tab}{ws}\")\n n += 1\n print(lines)\n\n\ndef prepItem(d: str) -> None:\n if PrintJson is False:\n print(\"\")\n print(f\"test domain: <<<<<<<<<< {d} >>>>>>>>>>>>>>>>>>>>\")\n\n\ndef xType(x: Any) -> str:\n s = f\"{type(x)}\"\n return s.split(\"'\")[1]\n\n\ndef testItem(\n d: str,\n printgetRawWhoisResult: bool = False,\n) -> None:\n global IgnoreReturncode\n global Verbose\n global PrintGetRawWhoisResult\n\n global SIMPLISTIC\n global TestAllTld\n global TestRunOnly\n\n global WithRedacted\n global WithPublicSuffix\n global WithExtractServers\n global WithStripHttpStatus\n\n pc = whois.ParameterContext(\n ignore_returncode=IgnoreReturncode,\n verbose=Verbose,\n internationalized=True,\n include_raw_whois_text=PrintGetRawWhoisResult,\n simplistic=SIMPLISTIC,\n withRedacted=WithRedacted,\n withPublicSuffix=WithPublicSuffix,\n extractServers=WithExtractServers,\n stripHttpStatus=WithStripHttpStatus,\n )\n\n # use the new query (can also simply use q2()\n w = whois.query(domain=d, pc=pc)\n\n if w is None:\n print(\"None\")\n print(\"\\n\", whois.get_last_raw_whois_data())\n return\n\n # the 3 date time items can be None if not present or a datetime string\n # dnssec is a bool\n # some strings are return as '' when empty (status)\n # statuses can be a array of one empty string if no data\n\n # not all values are always present it mainly depends on whet we see in the output of whois\n # if we return not None: the elements that ars always there ars domain_name , tld, dnssec\n\n wd = w.__dict__\n if PrintJson is True:\n for f in [\"creation_date\", \"expiration_date\", \"last_updated\"]:\n if f in wd:\n wd[f] = f\"{wd[f]}\"\n print(json.dumps(wd))\n return\n\n for k, v in wd.items():\n if SIMPLISTIC:\n ss = \"%-18s \"\n if isinstance(v, str):\n print((ss + \"'%s'\") % (k, v))\n else:\n print((ss + \"%s\") % (k, v))\n else:\n ss = \"%-18s %-17s \"\n if isinstance(v, str):\n print((ss + \"'%s'\") % (k, xType(v), v))\n else:\n print((ss + \"%s\") % (k, xType(v), v))\n\n # print(\"\\n\", whois.get_last_raw_whois_data())\n\n\ndef errorItem(d: str, e: Any, what: str = \"Generic\") -> None:\n if what not in Failures:\n Failures[what] = {}\n Failures[what][d] = e\n\n message = f\"Domain: {d}; Exception: {what}; Error: {e}\"\n print(message)\n\n\ndef testDomains(aList: List[str]) -> None:\n for d in aList:\n # skip empty lines\n if not d:\n continue\n\n if len(d.strip()) == 0:\n continue\n\n # skip comments\n if d.strip().startswith(\"#\"):\n continue\n\n # skip comments behind the domain\n d = d.split(\"#\")[0]\n d = d.strip()\n\n prepItem(d)\n try:\n testItem(d)\n except whois.UnknownTld as e:\n errorItem(d, e, what=\"UnknownTld\")\n except whois.FailedParsingWhoisOutput as e:\n errorItem(d, e, what=\"FailedParsingWhoisOutput\")\n except whois.UnknownDateFormat as e:\n errorItem(d, e, what=\"UnknownDateFormat\")\n except whois.WhoisCommandFailed as e:\n errorItem(d, e, what=\"WhoisCommandFailed\")\n except whois.WhoisQuotaExceeded as e:\n errorItem(d, e, what=\"WhoisQuotaExceeded\")\n except whois.WhoisPrivateRegistry as e:\n errorItem(d, e, what=\"WhoisPrivateRegistry\")\n except whois.WhoisCommandTimeout as e:\n errorItem(d, e, what=\"WhoisCommandTimeout\")\n # except Exception as e:\n # errorItem(d, e, what=\"Generic\")\n\n\ndef getTestFileOne(fPath: str, fileData: Dict[str, Any]) -> None:\n if not os.path.isfile(fPath): # only files\n return\n\n if not fPath.endswith(\".txt\"): # ending in .txt\n return\n\n bName = fPath[:-4]\n fileData[bName] = []\n xx = fileData[bName]\n\n with open(fPath, encoding=\"utf-8\") as f:\n for index, line in enumerate(f):\n line = line.strip()\n if len(line) == 0 or line.startswith(\"#\"):\n continue\n\n aa = re.split(r\"\\s+\", line)\n if aa[0] not in xx:\n xx.append(aa[0])\n\n return\n\n\ndef getTestFilesAll(\n tDir: str,\n fileData: Dict[str, Any],\n) -> None:\n for item in os.listdir(tDir):\n fPath = f\"{tDir}/{item}\"\n getTestFileOne(fPath, fileData)\n\n\ndef getAllCurrentTld() -> List[str]:\n return whois.validTlds()\n\n\ndef appendHintOrMeta(\n rr: List[str],\n allRegex: Optional[str],\n tld: str,\n) -> None:\n global TestAllTld\n global TestRunOnly\n\n if TestAllTld is True:\n hint = whois.getTestHint(tld)\n hint = hint if hint else f\"meta.{tld}\"\n rr.append(f\"{hint}\")\n else:\n rr.append(f\"meta.{tld}\")\n\n\ndef appendHint(\n rr: List[str],\n allRegex: Optional[str],\n tld: str,\n) -> None:\n global TestAllTld\n global TestRunOnly\n\n if TestAllTld is True:\n hint = whois.getTestHint(tld)\n if hint:\n rr.append(f\"{hint}\")\n\n\ndef makeMetaAllCurrentTld(\n allHaving: Optional[str] = None,\n allRegex: Optional[str] = None,\n) -> List[str]:\n rr: List[str] = []\n for tld in getAllCurrentTld():\n if allRegex is None:\n appendHintOrMeta(rr, allRegex, tld)\n continue\n\n if re.search(allRegex, tld):\n appendHintOrMeta(rr, allRegex, tld)\n\n return rr\n\n\ndef makeTestAllCurrentTld(\n allRegex: Optional[str] = None,\n) -> List[str]:\n rr: List[str] = []\n for tld in getAllCurrentTld():\n if allRegex is None:\n appendHint(rr, allRegex, tld)\n continue\n if re.search(allRegex, tld):\n appendHint(rr, allRegex, tld)\n\n return rr\n\n\ndef showAllCurrentTld() -> None:\n print(\"Tld's currently supported\")\n for tld in getAllCurrentTld():\n print(tld)\n\n\ndef ShowRuleset(tld: str) -> None:\n rr = whois.get_TLD_RE()\n if tld in rr:\n for key in sorted(rr[tld].keys()):\n rule = f\"{rr[tld][key]}\"\n if \"re.compile\" in rule:\n rule = rule.split(\"re.compile(\")[1]\n rule = rule.split(\", re.IGNORECASE)\")[0]\n print(key, rule, \"IGNORECASE\")\n\n\ndef usage() -> None:\n name = os.path.basename(sys.argv[0])\n\n print(\n f\"\"\"\n{name}\n [ -h | --usage ]\n print this text and exit\n\n [ -V | --Version ]\n print the build version string\n and exit\n\n [ -S | --SupportedTld ]\n print all known top level domains\n and exit\n\n [ -a | --all]\n test all existing tld currently supported\n and exit\n\n [ -f | --file = \" ]\n use the named file to test all domains (one domain per line)\n lines starting with # or empty lines are skipped, anything after the domain is ignored\n the option can be repeated to specify more then one file\n exits after processing all the files\n\n [ -D | --Directory = \" ]\n use the named directory, ald use all files ending in .txt as files containing domains\n files are processed as in the -f option so comments and empty lines are skipped\n the option can be repeated to specify more then one directory\n exits after processing all the dirs\n\n [ -d | --domain = \" ]\n only analyze the given domains\n the option can be repeated to specify more domain's\n\n [ -v | --verbose ]\n set verbose to True,\n verbose output will be printed on stderr only\n\n [ -j | --json ]\n print each result as json\n\n [ -I | --IgnoreReturncode ]\n sets the IgnoreReturncode to True,\n\n [ -p | --print ]\n also print text containing the raw output of the cli whois\n\n [ -R | --Ruleset ]\n dump the ruleset for the requested tld and exit\n should be combined with -d to specify tld's\n\n [ -C | --Cleanup ]\n read the input file specified and run the same cleanup as in whois.query,\n then exit\n\n # test two domains with verbose and IgnoreReturncode\n example: {name} -v -I -d meta.org -d meta.com\n\n # test all supported tld's with verbose and IgnoreReturncode\n example: {name} -v -I -a\n\n # test one specific file with verbose and IgnoreReturncode\n example: {name} -v -I -f tests/ok-domains.txt\n\n # test one specific directory with verbose and IgnoreReturncode\n example: {name} -v -I -D tests\n\n\"\"\"\n )\n\n \"\"\"\n TODO\n --all --reg \n from all tld a regex match sub selection\n\n --all --having \n from all but only the ones haveing a certain field\n \"\"\"\n sys.exit(1)\n\n\ndef showFailures() -> None:\n if len(Failures):\n print(\"\\n# ========================\")\n for i in sorted(Failures.keys()):\n for j in sorted(Failures[i].keys()):\n print(i, j, Failures[i][j])\n\n\ndef main() -> None:\n global PrintJson\n global Verbose\n global IgnoreReturncode\n global PrintGetRawWhoisResult\n global Ruleset\n global SIMPLISTIC\n global WithRedacted\n global TestAllTld\n global TestRunOnly\n global WithPublicSuffix\n global WithExtractServers\n global WithStripHttpStatus\n\n name: str = os.path.basename(sys.argv[0])\n if name == \"test2.py\":\n SIMPLISTIC = False\n else:\n SIMPLISTIC = True\n\n try:\n opts, args = getopt.getopt(\n sys.argv[1:],\n \"TtjRSpvVIhaf:d:D:r:H:C:\",\n [\n \"Testing\",\n \"test\",\n \"json\",\n \"Ruleset\",\n \"SupportedTld\",\n \"print\",\n \"verbose\",\n \"Version\",\n \"IgnoreReturncode\",\n \"all\",\n \"file=\",\n \"Directory=\",\n \"domain=\",\n \"reg=\",\n \"having=\",\n \"Cleanup=\",\n \"withRedacted\",\n \"withPublicSuffix\",\n \"extractServers\",\n \"stripHttpStatus\",\n ],\n )\n except getopt.GetoptError:\n usage()\n sys.exit(2)\n\n # TestAllTld: bool = False\n\n allHaving: Optional[str] = None # from all supported tld only process the ones having this :: TODO ::\n allRegex: Optional[str] = None # from all supported tld process only the ones matching this regex\n\n directory: Optional[str] = None\n dirs: List[str] = []\n\n filename: Optional[str] = None\n files: List[str] = []\n\n domain: Optional[str] = None\n domains: List[str] = []\n\n fileData: Dict[str, Any] = {}\n\n for opt, arg in opts:\n if opt in (\"-S\", \"SupportedTld\"):\n for tld in sorted(whois.validTlds()):\n print(tld)\n sys.exit(0)\n\n if opt in (\"-V\", \"Version\"):\n print(whois.getVersion())\n sys.exit(0)\n\n if opt == \"-h\":\n usage()\n sys.exit(0)\n\n if opt in (\"-a\", \"--all\"):\n TestAllTld = True\n\n if opt in (\"-H\", \"--having\"):\n TestAllTld = True\n allHaving = str(arg)\n\n if opt in (\"-r\", \"--reg\"):\n TestAllTld = True\n allRegex = str(arg)\n\n if opt in (\"-v\", \"--verbose\"):\n Verbose = True\n logging.basicConfig(level=\"DEBUG\")\n\n if opt in (\"-p\", \"--print\"):\n PrintGetRawWhoisResult = True\n\n if opt in (\"-j\", \"--json\"):\n PrintJson = True\n\n if opt in (\"-T\", \"--Testing\"):\n # print out all names of tld where we have _test\n TestAllTld = True\n rr = makeTestAllCurrentTld(None)\n for item in sorted(rr):\n print(item)\n sys.exit(0)\n\n if opt in (\"-t\", \"--test\"):\n # collect all _test entries defined and only run those,\n # o not run the default meta.tld\n TestAllTld = True\n TestRunOnly = True\n\n if opt in (\"-R\", \"--Ruleset\"):\n Ruleset = True\n\n if opt in (\"-D\", \"--Directory\"):\n directory = arg\n isDir = os.path.isdir(directory)\n if isDir is False:\n print(f\"{directory} cannot be found or is not a directory\", file=sys.stderr)\n sys.exit(101)\n\n if opt in (\"-C\", \"--Cleanup\"):\n inFile = arg\n isFile = os.path.isfile(arg)\n if isFile is False:\n print(f\"{inFile} cannot be found or is not a file\", file=sys.stderr)\n sys.exit(101)\n\n rc = ResponseCleaner(inFile)\n d1, rDict = rc.cleanupWhoisResponse()\n rc.printMe()\n sys.exit(0)\n\n if opt in (\"-f\", \"--file\"):\n filename = arg\n isFile = os.path.isfile(filename)\n if isFile is False:\n print(f\"{filename} cannot be found or is not a file\", file=sys.stderr)\n sys.exit(101)\n\n if filename not in files:\n files.append(filename)\n TestAllTld = False\n\n if opt in (\"-d\", \"--domain\"):\n domain = arg\n if domain not in domains:\n domains.append(domain)\n\n if opt in (\"--extractServers\"):\n WithExtractServers = True\n\n if opt in (\"--stripHttpStatus\"):\n WithStripHttpStatus = True\n\n if opt in (\"--withRedacted\"):\n WithRedacted = True\n\n if opt in (\"--withPublicSuffix\"):\n WithPublicSuffix = True\n\n msg = f\"{name} SIMPLISTIC: {SIMPLISTIC}\"\n log.debug(msg)\n\n if Ruleset is True and domains:\n for domain in domains:\n ShowRuleset(domain)\n sys.exit(0)\n\n if TestAllTld:\n if TestRunOnly is False:\n testDomains(makeMetaAllCurrentTld(allHaving, allRegex))\n else:\n testDomains(makeTestAllCurrentTld(allRegex))\n\n showFailures()\n sys.exit(0)\n\n if dirs:\n fileData = {}\n for dName in dirs:\n getTestFilesAll(dName, fileData)\n for testFile, x in fileData.items():\n testDomains(x)\n showFailures()\n sys.exit(0)\n\n if files:\n fileData = {}\n for testFile in files:\n getTestFileOne(testFile, fileData)\n for testFile, x in fileData.items():\n testDomains(x)\n showFailures()\n sys.exit(0)\n\n if domains:\n testDomains(domains)\n showFailures()\n sys.exit(0)\n\n usage()\n sys.exit(0)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "DannyCork/python-whois", "sub_path": "whois/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 20637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 280, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 67, "usage_type": "name"}, {"api_name": "re.match", "line_number": 76, "usage_type": "call"}, {"api_name": "re.match", "line_number": 81, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 94, "usage_type": "name"}, {"api_name": "re.match", "line_number": 98, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 90, "usage_type": "name"}, {"api_name": "whois.cleanupWhoisResponse", "line_number": 124, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 123, "usage_type": "name"}, {"api_name": "re.search", "line_number": 195, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 209, "usage_type": "name"}, {"api_name": "whois.ParameterContext", "line_number": 231, "usage_type": "call"}, {"api_name": "whois.query", "line_number": 244, "usage_type": "call"}, {"api_name": "whois.get_last_raw_whois_data", "line_number": 248, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 264, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 284, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 293, "usage_type": "name"}, {"api_name": "whois.UnknownTld", "line_number": 313, "usage_type": "attribute"}, {"api_name": "whois.FailedParsingWhoisOutput", "line_number": 315, "usage_type": "attribute"}, {"api_name": "whois.UnknownDateFormat", "line_number": 317, "usage_type": "attribute"}, {"api_name": "whois.WhoisCommandFailed", "line_number": 319, "usage_type": "attribute"}, {"api_name": "whois.WhoisQuotaExceeded", "line_number": 321, "usage_type": "attribute"}, {"api_name": "whois.WhoisPrivateRegistry", "line_number": 323, "usage_type": "attribute"}, {"api_name": "whois.WhoisCommandTimeout", "line_number": 325, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 331, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 331, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 348, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 357, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 357, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 359, "usage_type": "call"}, {"api_name": "whois.validTlds", "line_number": 365, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 364, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 369, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 370, "usage_type": "name"}, {"api_name": "whois.getTestHint", "line_number": 377, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 385, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 386, "usage_type": "name"}, {"api_name": "whois.getTestHint", "line_number": 393, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 399, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 400, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 402, "usage_type": "name"}, {"api_name": "re.search", "line_number": 408, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 401, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 415, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 417, "usage_type": "name"}, {"api_name": "re.search", "line_number": 422, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 416, "usage_type": "name"}, {"api_name": "whois.get_TLD_RE", "line_number": 435, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 446, "usage_type": "call"}, {"api_name": "os.path", "line_number": 446, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 446, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 526, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 551, "usage_type": "call"}, {"api_name": "os.path", "line_number": 551, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 551, "usage_type": "attribute"}, {"api_name": "getopt.getopt", "line_number": 558, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 559, "usage_type": "attribute"}, {"api_name": "getopt.GetoptError", "line_number": 584, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 586, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 590, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 591, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 593, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 594, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 596, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 597, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 599, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 600, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 602, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 602, "usage_type": "name"}, {"api_name": "whois.validTlds", "line_number": 606, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 608, "usage_type": "call"}, {"api_name": "whois.getVersion", "line_number": 611, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 612, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 616, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 631, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 645, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 658, "usage_type": "call"}, {"api_name": "os.path", "line_number": 658, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 660, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 661, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 665, "usage_type": "call"}, {"api_name": "os.path", "line_number": 665, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 667, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 668, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 673, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 677, "usage_type": "call"}, {"api_name": "os.path", "line_number": 677, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 679, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 680, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 709, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 718, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 727, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 736, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 741, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 744, "usage_type": "call"}]} +{"seq_id": "73080240082", "text": "from torch import nn\n\n\nclass Detect(nn.Module):\n\n def __init__(self, num_classes=80, anchors=(), ch=()): # detection layer\n super(Detect, self).__init__()\n self.num_classes = num_classes\n self.len_output = num_classes + 5\n self.num_layers = len(anchors)\n self.num_anchors_each_layer = len(anchors[0]) // 2\n\n self.yolo_head_P3 = nn.Conv2d(ch[0], self.num_anchors_each_layer * self.len_output, 1)\n self.yolo_head_P4 = nn.Conv2d(ch[1], self.num_anchors_each_layer * self.len_output, 1)\n self.yolo_head_P5 = nn.Conv2d(ch[2], self.num_anchors_each_layer * self.len_output, 1)\n\n # KeyPoint 其他参数初始化方法 https://arxiv.org/abs/1708.02002 section 3.3\n for m in self.modules():\n if isinstance(m, (nn.Conv2d, nn.Linear)):\n nn.init.normal_(m.weight, 0, 0.01)\n elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n nn.init.normal_(m.weight, 0, 0.01)\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6)):\n m.inplace = True\n\n def forward(self, x):\n # 第一个特征层 y1=(batch_size, 75, 20, 20)\n out0 = self.yolo_head_P5(x[2])\n # 第二个特征层 y2=(batch_size, 75, 40, 40)\n out1 = self.yolo_head_P4(x[1])\n # 第三个特征层 y3=(batch_size, 75, 80, 80)\n out2 = self.yolo_head_P3(x[0])\n\n if not self.training:\n pass\n\n return [out0, out1, out2]\n", "repo_name": "xin-pu/yolo-continuous", "sub_path": "nets/detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 1543, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 4, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn.init.normal_", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.init.normal_", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Hardswish", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU6", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "13205277986", "text": "import cPickle\nimport copy\nimport datetime\nimport logging\n\nimport twisted.internet.reactor\n\nimport deluge.component\nimport deluge.configmanager\n\nimport labelplus.common\nimport labelplus.common.config\nimport labelplus.common.label\nimport labelplus.gtkui.config\nimport labelplus.gtkui.config.convert\nimport labelplus.gtkui.common.gtklib.dnd\n\n\nfrom twisted.python.failure import Failure\n\nfrom deluge.ui.client import client\nfrom deluge.ui.client import DelugeRPCError\nfrom deluge.plugins.pluginbase import GtkPluginBase\n\nfrom labelplus.common import LabelPlusError\nfrom labelplus.gtkui.common.label_store import LabelStore\nfrom labelplus.gtkui.extensions.add_torrent_ext import AddTorrentExt\nfrom labelplus.gtkui.extensions.preferences_ext import PreferencesExt\nfrom labelplus.gtkui.extensions.sidebar_ext import SidebarExt\nfrom labelplus.gtkui.extensions.status_bar_ext import StatusBarExt\nfrom labelplus.gtkui.extensions.torrent_view_ext import TorrentViewExt\n\nfrom labelplus.gtkui import RT\n\n\nfrom labelplus.common.literals import (\n STR_UPDATE, ERR_TIMED_OUT, ERR_MAX_RETRY,\n)\n\nGTKUI_CONFIG = \"%s_ui.conf\" % labelplus.common.MODULE_NAME\n\nINIT_POLLING_INTERVAL = 3.0\nUPDATE_INTERVAL = 1.0\n\nTHROTTLED_INTERVAL = 6.0\nMAX_TRIES = 10\n\nREQUEST_TIMEOUT = 10.0\n\nEXTENSIONS = (\n AddTorrentExt,\n PreferencesExt,\n SidebarExt,\n StatusBarExt,\n TorrentViewExt,\n)\n\n\nlog = logging.getLogger(__name__)\nlabelplus.gtkui.common.gtklib.dnd.log.setLevel(logging.INFO)\n\n\nclass GtkUI(GtkPluginBase):\n\n # Section: Initialization\n\n def __init__(self, plugin_name):\n\n RT.logger.setLevel(logging.INFO)\n if __debug__: RT.register(self)\n\n super(GtkUI, self).__init__(plugin_name)\n\n self.initialized = False\n\n self.config = None\n\n self.store = LabelStore()\n self.last_updated = None\n self._tries = 0\n self._calls = []\n\n self._extensions = []\n\n self._update_funcs = []\n self._cleanup_funcs = []\n\n\n def enable(self):\n\n log.info(\"Initializing %s...\", self.__class__.__name__)\n\n self._poll_init()\n\n\n def _poll_init(self):\n\n client.labelplus.is_initialized().addCallback(self._check_init)\n\n\n def _check_init(self, result):\n\n log.debug(\"Waiting for core to be initialized...\")\n\n if result == True:\n client.labelplus.get_label_updates().addCallback(self._finish_init)\n else:\n twisted.internet.reactor.callLater(INIT_POLLING_INTERVAL,\n self._poll_init)\n\n\n def _finish_init(self, result):\n\n log.debug(\"Resuming initialization...\")\n\n try:\n info = client.connection_info()\n self.daemon = \"%s@%s:%s\" % (info[2], info[0], info[1])\n\n self._load_config()\n self._update_store(result)\n\n self.initialized = True\n\n self._load_extensions()\n\n log.info(\"%s initialized\", self.__class__.__name__)\n except:\n log.error(\"Error initializing %s\", self.__class__.__name__)\n raise\n\n twisted.internet.reactor.callLater(0, self._update_loop)\n\n\n def _load_extensions(self):\n\n log.info(\"Loading extensions...\")\n\n for ext in EXTENSIONS:\n try:\n log.debug(\"Initializing %s\", ext.__name__)\n instance = ext(self)\n self._extensions.append(instance)\n if __debug__: RT.register(instance, ext.__name__)\n log.info(\"%s initialized\", ext.__name__)\n except:\n log.exception(\"Error initializing %s\", ext.__name__)\n\n\n # Section: Deinitialization\n\n def disable(self):\n\n log.info(\"Deinitializing %s...\", self.__class__.__name__)\n\n labelplus.common.cancel_calls(self._calls)\n\n self._run_cleanup_funcs()\n self._unload_extensions()\n self._update_funcs = []\n\n self._close_config()\n self._destroy_store()\n\n self.initialized = False\n\n if __debug__: RT.report()\n\n log.info(\"%s deinitialized\", self.__class__.__name__)\n\n\n def _run_cleanup_funcs(self):\n\n while self._cleanup_funcs:\n func = self._cleanup_funcs.pop()\n try:\n func()\n except:\n log.exception(\"Failed to run %s()\", func.func_name)\n\n\n def _unload_extensions(self):\n\n log.info(\"Unloading extensions...\")\n\n while self._extensions:\n ext = self._extensions.pop()\n try:\n ext.unload()\n log.info(\"%s deinitialized\", ext.__class__.__name__)\n except:\n log.exception(\"Error deinitializing %s\", ext.__class__.__name__)\n\n\n def _destroy_store(self):\n\n if self.store:\n self.store.destroy()\n self.store = None\n\n\n # Section: Public\n\n def get_extension(self, name):\n\n for ext in self._extensions:\n if ext.__class__.__name__ == name:\n return ext\n\n return None\n\n\n def register_update_func(self, func):\n\n if func not in self._update_funcs:\n self._update_funcs.append(func)\n\n\n def deregister_update_func(self, func):\n\n if func in self._update_funcs:\n self._update_funcs.remove(func)\n\n\n def register_cleanup_func(self, func):\n\n if func not in self._cleanup_funcs:\n self._cleanup_funcs.append(func)\n\n\n def deregister_cleanup_func(self, func):\n\n if func in self._cleanup_funcs:\n self._cleanup_funcs.remove(func)\n\n\n # Section: Config\n\n def _load_config(self):\n\n config = deluge.configmanager.ConfigManager(GTKUI_CONFIG)\n\n # Workaround for 0.2.19.x that didn't use header\n if config.config.get(\"version\") == 2:\n labelplus.common.config.set_version(config, 2)\n\n labelplus.common.config.init_config(config,\n labelplus.gtkui.config.CONFIG_DEFAULTS,\n labelplus.gtkui.config.CONFIG_VERSION,\n labelplus.gtkui.config.convert.CONFIG_SPECS)\n\n self._update_daemon_config(config)\n self._normalize_config(config)\n\n self.config = config\n\n\n def _close_config(self):\n\n if self.config:\n if self.initialized:\n self.config.save()\n\n deluge.configmanager.close(GTKUI_CONFIG)\n\n\n def _update_daemon_config(self, config):\n\n saved_daemons = deluge.component.get(\"ConnectionManager\").config[\"hosts\"]\n if not saved_daemons:\n config[\"daemon\"] = {}\n else:\n daemons = [\"%s@%s:%s\" % (x[3], x[1], x[2]) for x in saved_daemons]\n\n # Remove daemons from config if not in ConnectionManager hosts\n for daemon in config[\"daemon\"].keys():\n if \"@localhost:\" in daemon or \"@127.0.0.1:\" in daemon:\n continue\n\n if daemon not in daemons and daemon != self.daemon:\n del config[\"daemon\"][daemon]\n\n if self.daemon not in config[\"daemon\"]:\n config[\"daemon\"][self.daemon] = copy.deepcopy(\n labelplus.gtkui.config.DAEMON_DEFAULTS)\n\n\n def _normalize_config(self, config):\n\n labelplus.common.normalize_dict(config.config,\n labelplus.gtkui.config.CONFIG_DEFAULTS)\n\n labelplus.common.normalize_dict(config[\"common\"],\n labelplus.gtkui.config.CONFIG_DEFAULTS[\"common\"])\n\n for daemon in config[\"daemon\"]:\n labelplus.common.normalize_dict(config[\"daemon\"][daemon],\n labelplus.gtkui.config.DAEMON_DEFAULTS)\n\n\n # Section: Update\n\n def _update_loop(self):\n\n def on_timeout():\n\n log.error(\"%s: %s\", STR_UPDATE, LabelPlusError(ERR_TIMED_OUT))\n\n if self.initialized:\n self._tries += 1\n if self._tries < MAX_TRIES:\n self._calls.append(twisted.internet.reactor.callLater(\n THROTTLED_INTERVAL, self._update_loop))\n else:\n log.error(\"%s: %s\", STR_UPDATE, LabelPlusError(ERR_MAX_RETRY))\n\n\n def process_result(result):\n\n if isinstance(result, Failure):\n if (isinstance(result.value, DelugeRPCError) and\n result.value.exception_type == \"LabelPlusError\"):\n log.error(\"%s: %s\", STR_UPDATE,\n LabelPlusError(result.value.exception_msg))\n interval = THROTTLED_INTERVAL\n else:\n return result\n else:\n self._tries = 0\n interval = UPDATE_INTERVAL\n self._update_store(result)\n\n if self.initialized:\n self._calls.append(twisted.internet.reactor.callLater(interval,\n self._update_loop))\n\n\n labelplus.common.clean_calls(self._calls)\n\n if self.initialized:\n pickled_time = cPickle.dumps(self.last_updated)\n deferred = client.labelplus.get_label_updates(pickled_time)\n labelplus.common.deferred_timeout(deferred, REQUEST_TIMEOUT, on_timeout,\n process_result, process_result)\n\n\n def _update_store(self, result):\n\n if not result:\n return\n\n update = cPickle.loads(result)\n\n log.debug(\"Update: Type: %s, Timestamp: %s\", update.type,\n update.timestamp)\n\n self.last_updated = update.timestamp\n self.store.update(update.data)\n\n for func in list(self._update_funcs):\n try:\n func(self.store)\n except:\n log.exception(\"Failed to run %s()\", func.func_name)\n", "repo_name": "ratanakvlun/deluge-labelplus", "sub_path": "labelplus/gtkui/gtkui.py", "file_name": "gtkui.py", "file_ext": "py", "file_size_in_byte": 8617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 82, "dataset": "github-code", "pt": "3", "api": [{"api_name": "labelplus.common.common", "line_number": 40, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 40, "usage_type": "name"}, {"api_name": "labelplus.gtkui.extensions.add_torrent_ext.AddTorrentExt", "line_number": 51, "usage_type": "name"}, {"api_name": "labelplus.gtkui.extensions.preferences_ext.PreferencesExt", "line_number": 52, "usage_type": "name"}, {"api_name": "labelplus.gtkui.extensions.sidebar_ext.SidebarExt", "line_number": 53, "usage_type": "name"}, {"api_name": "labelplus.gtkui.extensions.status_bar_ext.StatusBarExt", "line_number": 54, "usage_type": "name"}, {"api_name": "labelplus.gtkui.extensions.torrent_view_ext.TorrentViewExt", "line_number": 55, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 59, "usage_type": "call"}, {"api_name": "labelplus.common.gtkui.common.gtklib.dnd.log.setLevel", "line_number": 60, "usage_type": "call"}, {"api_name": "labelplus.common.gtkui", "line_number": 60, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 60, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 60, "usage_type": "attribute"}, {"api_name": "deluge.plugins.pluginbase.GtkPluginBase", "line_number": 63, "usage_type": "name"}, {"api_name": "labelplus.gtkui.RT.logger.setLevel", "line_number": 69, "usage_type": "call"}, {"api_name": "labelplus.gtkui.RT.logger", "line_number": 69, "usage_type": "attribute"}, {"api_name": "labelplus.gtkui.RT", "line_number": 69, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 69, "usage_type": "attribute"}, {"api_name": "labelplus.gtkui.RT.register", "line_number": 70, "usage_type": "call"}, {"api_name": "labelplus.gtkui.RT", "line_number": 70, "usage_type": "name"}, {"api_name": "labelplus.gtkui.common.label_store.LabelStore", "line_number": 78, "usage_type": "call"}, {"api_name": "deluge.ui.client.client.labelplus.is_initialized", "line_number": 98, "usage_type": "call"}, {"api_name": "deluge.ui.client.client.labelplus", "line_number": 98, "usage_type": "attribute"}, {"api_name": "deluge.ui.client.client", "line_number": 98, "usage_type": "name"}, {"api_name": "deluge.ui.client.client.labelplus.get_label_updates", "line_number": 106, "usage_type": "call"}, {"api_name": "deluge.ui.client.client.labelplus", "line_number": 106, "usage_type": "attribute"}, {"api_name": "deluge.ui.client.client", "line_number": 106, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.internet.reactor.callLater", "line_number": 108, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.internet", "line_number": 108, "usage_type": "attribute"}, {"api_name": "twisted.internet.reactor", "line_number": 108, "usage_type": "name"}, {"api_name": "deluge.ui.client.client.connection_info", "line_number": 117, "usage_type": "call"}, {"api_name": "deluge.ui.client.client", "line_number": 117, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.internet.reactor.callLater", "line_number": 132, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.internet", "line_number": 132, "usage_type": "attribute"}, {"api_name": "twisted.internet.reactor", "line_number": 132, "usage_type": "name"}, {"api_name": "labelplus.gtkui.RT.register", "line_number": 144, "usage_type": "call"}, {"api_name": "labelplus.gtkui.RT", "line_number": 144, "usage_type": "name"}, {"api_name": "labelplus.common.common.cancel_calls", "line_number": 156, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 156, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 156, "usage_type": "name"}, {"api_name": "labelplus.gtkui.RT.report", "line_number": 167, "usage_type": "call"}, {"api_name": "labelplus.gtkui.RT", "line_number": 167, "usage_type": "name"}, {"api_name": "deluge.component.configmanager.ConfigManager", "line_number": 241, "usage_type": "call"}, {"api_name": "deluge.component.configmanager", "line_number": 241, "usage_type": "attribute"}, {"api_name": "deluge.component", "line_number": 241, "usage_type": "name"}, {"api_name": "labelplus.common.common.config.set_version", "line_number": 245, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 245, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 245, "usage_type": "name"}, {"api_name": "labelplus.common.common.config.init_config", "line_number": 247, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 247, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 247, "usage_type": "name"}, {"api_name": "labelplus.common.gtkui", "line_number": 248, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 248, "usage_type": "name"}, {"api_name": "labelplus.common.gtkui", "line_number": 249, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 249, "usage_type": "name"}, {"api_name": "labelplus.common.gtkui", "line_number": 250, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 250, "usage_type": "name"}, {"api_name": "deluge.component.configmanager.close", "line_number": 264, "usage_type": "call"}, {"api_name": "deluge.component.configmanager", "line_number": 264, "usage_type": "attribute"}, {"api_name": "deluge.component", "line_number": 264, "usage_type": "name"}, {"api_name": "deluge.component.component.get", "line_number": 269, "usage_type": "call"}, {"api_name": "deluge.component.component", "line_number": 269, "usage_type": "attribute"}, {"api_name": "deluge.component", "line_number": 269, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 284, "usage_type": "call"}, {"api_name": "labelplus.common.gtkui", "line_number": 285, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 285, "usage_type": "name"}, {"api_name": "labelplus.common.common.normalize_dict", "line_number": 290, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 290, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 290, "usage_type": "name"}, {"api_name": "labelplus.common.gtkui", "line_number": 291, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 291, "usage_type": "name"}, {"api_name": "labelplus.common.common.normalize_dict", "line_number": 293, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 293, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 293, "usage_type": "name"}, {"api_name": "labelplus.common.gtkui", "line_number": 294, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 294, "usage_type": "name"}, {"api_name": "labelplus.common.common.normalize_dict", "line_number": 297, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 297, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 297, "usage_type": "name"}, {"api_name": "labelplus.common.gtkui", "line_number": 298, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 298, "usage_type": "name"}, {"api_name": "labelplus.common.literals.STR_UPDATE", "line_number": 307, "usage_type": "argument"}, {"api_name": "labelplus.common.LabelPlusError", "line_number": 307, "usage_type": "call"}, {"api_name": "labelplus.common.literals.ERR_TIMED_OUT", "line_number": 307, "usage_type": "argument"}, {"api_name": "twisted.internet.reactor.internet.reactor.callLater", "line_number": 312, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.internet", "line_number": 312, "usage_type": "attribute"}, {"api_name": "twisted.internet.reactor", "line_number": 312, "usage_type": "name"}, {"api_name": "labelplus.common.literals.STR_UPDATE", "line_number": 315, "usage_type": "argument"}, {"api_name": "labelplus.common.LabelPlusError", "line_number": 315, "usage_type": "call"}, {"api_name": "labelplus.common.literals.ERR_MAX_RETRY", "line_number": 315, "usage_type": "argument"}, {"api_name": "twisted.python.failure.Failure", "line_number": 320, "usage_type": "argument"}, {"api_name": "deluge.ui.client.DelugeRPCError", "line_number": 321, "usage_type": "argument"}, {"api_name": "labelplus.common.literals.STR_UPDATE", "line_number": 323, "usage_type": "argument"}, {"api_name": "labelplus.common.LabelPlusError", "line_number": 324, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.internet.reactor.callLater", "line_number": 334, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.internet", "line_number": 334, "usage_type": "attribute"}, {"api_name": "twisted.internet.reactor", "line_number": 334, "usage_type": "name"}, {"api_name": "labelplus.common.common.clean_calls", "line_number": 338, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 338, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 338, "usage_type": "name"}, {"api_name": "cPickle.dumps", "line_number": 341, "usage_type": "call"}, {"api_name": "deluge.ui.client.client.labelplus.get_label_updates", "line_number": 342, "usage_type": "call"}, {"api_name": "deluge.ui.client.client.labelplus", "line_number": 342, "usage_type": "attribute"}, {"api_name": "deluge.ui.client.client", "line_number": 342, "usage_type": "name"}, {"api_name": "labelplus.common.common.deferred_timeout", "line_number": 343, "usage_type": "call"}, {"api_name": "labelplus.common.common", "line_number": 343, "usage_type": "attribute"}, {"api_name": "labelplus.common", "line_number": 343, "usage_type": "name"}, {"api_name": "cPickle.loads", "line_number": 352, "usage_type": "call"}]} +{"seq_id": "11030044367", "text": "#!/usr/bin/env python\nimport wsgiref.handlers\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp import template\nfrom google.appengine.ext import db\n\n\nclass Shout(db.Model):\n comment = db.StringProperty(required=False)\n author = db.StringProperty(required=False)\n when = db.DateTimeProperty(auto_now_add=True)\n \n\nclass MyHandler(webapp.RequestHandler):\n def get(self):\n shouts = db.GqlQuery('Select * from Shout ORDER BY when')\n \n values = { \n 'shouts': shouts \n }\n \n self.response.out.write(template.render('main.html',values))\n \n def post(self):\n shout = Shout(author = self.request.get('author'),comment=self.request.get('comment')) \n shout.put()\n self.redirect('/')\n \ndef main():\n app = webapp.WSGIApplication([(r'.*',MyHandler)], debug=True)\n wsgiref.handlers.CGIHandler().run(app) \n \n \nif __name__ == \"__main__\":\n main()", "repo_name": "thelostrobot/GAE", "sub_path": "pandoracookie/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 997, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "google.appengine.ext.db.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 8, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.StringProperty", "line_number": 9, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 9, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.StringProperty", "line_number": 10, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 10, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.DateTimeProperty", "line_number": 11, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 11, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.RequestHandler", "line_number": 14, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp", "line_number": 14, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.GqlQuery", "line_number": 16, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 16, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.template.render", "line_number": 22, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp.template", "line_number": 22, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.WSGIApplication", "line_number": 30, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp", "line_number": 30, "usage_type": "name"}, {"api_name": "wsgiref.handlers.handlers.CGIHandler", "line_number": 31, "usage_type": "call"}, {"api_name": "wsgiref.handlers.handlers", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wsgiref.handlers", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "32395377437", "text": "# -*- codeing = utf-8 -*-\r\n# @Time : 2021/08/21 21:12\r\n# @Author : 217703 ZHANG WENXUAN\r\n# @File : main.py\r\n# @Software : PyCharm\r\nimport pynput.mouse\r\nimport win32con\r\nimport win32gui\r\nimport numpy as np\r\nimport cv2\r\nimport torch\r\nfrom cs_model import load_model\r\nfrom grabscreen import grab_screen\r\nfrom utils.general import non_max_suppression, scale_coords, xyxy2xywh\r\nfrom utils.augmentations import letterbox\r\nfrom mouse_control import lock\r\n\r\n# names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',\r\n# 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\r\n# 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\r\n# 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',\r\n# 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\r\n# 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\r\n# 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\r\n# 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',\r\n# 'hair drier', 'toothbrush']\r\n\r\n# names = ['Apple', 'Apricot', 'Avocado', 'Banana', 'Beetroot', 'Blueberry', 'Cactus', 'Cantaloupe', 'Carambula', 'Cauliflower', 'Cherry', 'Chestnut', 'Clementine', 'Cocos', 'Dates', 'Eggplant', 'Ginger', 'Granadilla', 'Grape', 'Grapefruit', 'Guava', 'Hazelnut', 'Huckleberry', 'Kaki', 'Kiwi', 'Kohlrabi', 'Kumquats', 'Lemon', 'Limes', 'Lychee', 'Mandarine', 'Mango', 'Mangostan', 'Maracuja', 'Melon', 'Mulberry', 'Nectarine', 'Nut', 'Onion', 'Orange', 'Papaya', 'Passion', 'Peach', 'Pear', 'Pepino', 'Pepper', 'Physalis', 'Pineapple', 'Pitahaya', 'Plum', 'Pomegranate', 'Pomelo', 'Potato', 'Quince', 'Rambutan', 'Raspberry', 'Redcurrant', 'Salak', 'Strawberry', 'Tamarillo', 'Tangelo', 'Tomato', 'Walnut', 'Watermelon']\r\n\r\n\r\n# detect.py VVVV\r\n\r\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\r\nhalf = device != 'cpu'\r\n\r\nimgsz = 640\r\nconf_thres = 0.4 # 物体のクラスである、confidence loss\r\niou_thres = 0.05 # 画像の重なりの割合を表す値であり\r\nx, y = (2560, 1440) # プログラムウィンドウのサイズ\r\nre_x, re_y = (2560, 1440) # スクリーンのサイズ\r\n\r\n# L76\r\nmodel = load_model()\r\nstride = int(model.stride.max()) # model stride\r\nnames = model.module.names if hasattr(model, 'module') else model.names # get class names\r\n\r\nmouse = pynput.mouse.Controller()\r\n\r\nwhile True:\r\n img0 = grab_screen(region=(0, 0, x, y)) # 左上の角から右下の角\r\n img0 = cv2.resize(img0, (re_x, re_y))\r\n\r\n # Padded resize FROM datasets.py L220\r\n img = letterbox(img0, imgsz, stride=stride)[0]\r\n\r\n # Convert FROM datasets.py L223\r\n img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB\r\n img = np.ascontiguousarray(img)\r\n\r\n img = torch.from_numpy(img).to(device)\r\n img = img.half() if half else img.float() # uint8 to fp16/32\r\n img /= 255.0 # 0 - 255 to 0.0 - 1.0\r\n if len(img.shape) == 3:\r\n img = img[None] # expand for batch dim ==[img=img.unsqueeze(0)]\r\n\r\n pred = model(img, augment=False, visualize=False)[0] # 予測\r\n\r\n pred = non_max_suppression(pred, conf_thres, iou_thres, agnostic=False)\r\n # (prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300)\r\n\r\n # print(pred)\r\n\r\n aims = []\r\n # detect.py-->L172\r\n for i, det in enumerate(pred): # detections per image\r\n s = ''\r\n s += '%gx%g ' % img.shape[2:] # print string\r\n gn = torch.tensor(img0.shape)[[1, 0, 1, 0]] # normalization gain whwh\r\n if len(det):\r\n # Rescale boxes from img_size to im0 size\r\n det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()\r\n\r\n # Print results\r\n for c in det[:, -1].unique():\r\n n = (det[:, -1] == c).sum() # detections per class\r\n s += f\"{n} {names[int(c)]}{'s' * (n > 1)}, \" # add to string\r\n\r\n # Write results\r\n # BBOX VVVVVV　　バウンディングボックス\r\n for *xyxy, conf, cls in reversed(det):\r\n # if save_txt: Write to file\r\n # save bbox:(tag,x_center,y_center,x_width,y_width)　目標検出ボックス\r\n xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh\r\n line = (cls, *xywh) # label format (only need else-->)\r\n\r\n aim = ('%g ' * len(line)).rstrip() % line\r\n # print(aim) !type-->str!\r\n\r\n # make aim type[list] len-->5\r\n # like [‘62’， ‘0.777148’，‘0.529861’， ‘0.437891’， ‘0.769444’]\r\n # [（種類番号）, アクシスx軸, アクシスy軸, ボックス長さ, ボックス高さ]\r\n # 数字 -->　(スクリーンサイズの百分比)\r\n aim = aim.split(' ')\r\n # print(aim)\r\n aims.append(aim)\r\n\r\n if len(aims): # bbox list\r\n lock(aims, mouse, x, y) # mouse cotrol\r\n\r\n for i, det in enumerate(aims):\r\n num, x_center, y_center, width, height = det # 種類番号いらない X\r\n\r\n # 　バウンディングボックスを縮小する\r\n # 元のパラメータがstr型 --> float\r\n x_center = re_x * float(x_center)\r\n y_center = re_y * float(y_center)\r\n width = re_x * float(width)\r\n height = re_y * float(height)\r\n # label = names[int(num)]\r\n\r\n # 必ずint型\r\n top_left = (int(x_center - width / 2.0), int(y_center - height / 2.0))\r\n bottom_right = (int(x_center + width / 2.0), int(y_center + height / 2.0))\r\n top_left_font = (int(x_center - width / 2.0 + 30), int(y_center - height / 2.0 + 30))\r\n\r\n color = (0, 255, 0) # RGB\r\n cv2.rectangle(img0, top_left, bottom_right, color, thickness=3) # パラメータ必ずint型 線のサイズ=3 1/3\r\n\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n cv2.putText(img0, '%s' % (names[int(num)]), top_left_font, font, 1, (255, 0, 255), 4)\r\n # 'num:%s' % (num)\r\n cv2.namedWindow('detect', cv2.WINDOW_NORMAL)\r\n cv2.resizeWindow('detect', re_x // 3, re_y // 3) # ウィンドウのサイズ 1/3\r\n cv2.imshow('detect', img0)\r\n\r\n hwnd = win32gui.FindWindow(None, 'detect') # ウィンドウを探す\r\n CVRECT = cv2.getWindowImageRect('detect')\r\n win32gui.SetWindowPos(hwnd, win32con.HWND_TOPMOST, 0, 0, 0, 0, win32con.SWP_NOMOVE | win32con.SWP_NOSIZE)\r\n # ウィンドウを常に最前面にする 、０が左上隅（すみ）に固まる 、[win32con.SWP_NOMOVE | win32con.SWP_NOSIZE]-->移動可能\r\n\r\n if cv2.waitKey(1) & 0xFF == ord('p'): # キーボードの「p」ボタン押しと、ウィンドウをしまう///ショートカットキー\r\n cv2.destroyAllWindows()\r\n break\r\n", "repo_name": "T1T3/yolov5_scd", "sub_path": "aim-csgo/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.cuda.is_available", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cs_model.load_model", "line_number": 43, "usage_type": "call"}, {"api_name": "pynput.mouse.mouse.Controller", "line_number": 47, "usage_type": "call"}, {"api_name": "pynput.mouse.mouse", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pynput.mouse", "line_number": 47, "usage_type": "name"}, {"api_name": "grabscreen.grab_screen", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.augmentations.letterbox", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.general.non_max_suppression", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.general.scale_coords", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.general.xyxy2xywh", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 93, "usage_type": "call"}, {"api_name": "mouse_control.lock", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 129, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 132, "usage_type": "attribute"}, {"api_name": "cv2.resizeWindow", "line_number": 133, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 134, "usage_type": "call"}, {"api_name": "win32gui.FindWindow", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.getWindowImageRect", "line_number": 137, "usage_type": "call"}, {"api_name": "win32gui.SetWindowPos", "line_number": 138, "usage_type": "call"}, {"api_name": "win32con.HWND_TOPMOST", "line_number": 138, "usage_type": "attribute"}, {"api_name": "win32con.SWP_NOMOVE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "win32con.SWP_NOSIZE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "74352270126", "text": "#Creating the LinearSVC model\r\nmodel = LinearSVC()\r\n\r\n#Creating the train and test sets\r\nX_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(html_feat,\r\n labels,\r\n html_df.index,\r\n test_size = 0.33,\r\n random_state = 42)\r\n\r\n#Fitting the model\r\nmodel.fit(X_train, y_train)\r\n\r\ny_pred = model.predict(X_test)\r\n\r\n#Creating the LinearSVC confusion matrix\r\nconf_mat = confusion_matrix(y_test, y_pred)\r\nfig, ax = plt.subplots(figsize = (10, 10))\r\nsns.heatmap(conf_mat, annot = True, fmt = 'd', xticklabels = catid_df.Categories.values,\r\n yticklabels = catid_df.Categories.values)\r\nplt.ylabel('Actual')\r\nplt.xlabel('Predicted')\r\nplt.show()\r\n\r\n#Determine what caused misclassifications\r\nfrom IPython.display import display\r\n\r\nfor predicted in catid_df.CatID:\r\n for actual in catid_df.CatID:\r\n if predicted != actual and conf_mat[actual, predicted] >= 10:\r\n print(\"'{}' predicted as '{}' : {} examples.\".format(id_to_cat[actual], id_to_cat[predicted],\r\n conf_mat[actual, predicted]))\r\n display(html_df.loc[indices_test[(y_test == actual) & (y_pred == predicted)\r\n ]][['Categories', 'HTML']])\r\n print('')\r\n\r\n#Creating the LineaarSVC unigrams and bigrams\r\nmodel.fit(html_feat, labels)\r\n\r\nN = 2\r\nfor Categories, CatID in sorted(cat_to_id.items()):\r\n indices = np.argsort(model.coef_[CatID])\r\n feature_names = np.array(tfidf.get_feature_names())[indices]\r\n unigrams = [v for v in reversed(feature_names) if len(v.split(' ')) == 1][:N]\r\n bigrams = [v for v in reversed(feature_names) if len(v.split(' ')) == 2][:N]\r\n print(\"# '{}':\".format(Categories))\r\n print(\" . Top unigrams:\\n . {}\".format('\\n . '.join(unigrams)))\r\n print(\" . Top bigrams:\\n . {}\".format('\\n . '.join(bigrams)))\r\n\r\n#Accuracy report\r\nprint(metrics.classification_report(y_test, y_pred,\r\n target_names = html_df['Categories'].unique()))\r\n", "repo_name": "chbrown626/College-Classifications", "sub_path": "html_linear_svc.py", "file_name": "html_linear_svc.py", "file_ext": "py", "file_size_in_byte": 2336, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "IPython.display.display", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "37033870866", "text": "import requests\nfrom urllib.parse import urlparse\nfrom exceptions import TooManyRequestsError\nimport logging\nfrom typing import Any, Dict\nimport sys\n\nlogger = logging.getLogger(__name__)\n\n\ndef request_html_page(url: str) -> Any:\n headers = {\n \"Content-Type\": \"text/html\",\n \"Accept\": \"text/html\",\n \"User-Agent\": (\n \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36\"\n \" (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\"\n ),\n \"Accept-Language\": \"en-US,en;q=0.5\",\n \"Dnt\": 1,\n \"Referer\": \"https://www.google.com\",\n \"Accept-Encoding\": \"gzip, deflate\",\n }\n return request(url, headers)\n\n\ndef request(url: str, headers: Dict = {}) -> Any:\n try:\n res = requests.get(url, headers)\n if res.status_code == 429:\n raise TooManyRequestsError()\n return res\n except TooManyRequestsError:\n logger.error(\n \"We are sending too much requests to this domain -\"\n f\" {urlparse(url).netloc}. Maybe there is a problem with the\"\n \" treading\"\n )\n except Exception as err:\n logger.error(f\"Something went wrong during I/O operation - {err}\")\n sys.exit()\n\n\ndef is_absolute_url(url: str) -> bool:\n return bool(urlparse(url).netloc)\n\n\ndef get_absolute_url(url: str, domain: str) -> str:\n if not is_absolute_url(url):\n return f\"{domain}{url}\"\n return url\n", "repo_name": "shirganot/web-crawler", "sub_path": "handlers/network_handler.py", "file_name": "network_handler.py", "file_ext": "py", "file_size_in_byte": 1444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "exceptions.TooManyRequestsError", "line_number": 31, "usage_type": "call"}, {"api_name": "exceptions.TooManyRequestsError", "line_number": 33, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "6125004034", "text": "import numpy as np\nfrom numpy.linalg import inv\n\n\nA = np.random.randint(1,10, size=(2,2))\nB = np.random.randint(1,10, size=(2,2))\n\n\nC = np.matrix(A)\nD = np.matrix(B)\n\nprint(C)\nprint(D)\n\n\n\nE = C.transpose()\nfor row in E:\n print(row)\n\nF = D.transpose()\nfor row in F:\n print(row)\n\nCinv = inv(C)\n\nfor row in Cinv:\n print(row)\n \nDinv = inv(D)\n\nfor row in Dinv:\n print(row)\n\n\nimport numpy as np\nfrom numpy import sin, cos, pi\n\nimport matplotlib.pyplot as plt\n\na = np.arange(0.0, 10.0, 0.01)\nb = np.sin(2*np.pi*a)\nc = np.cos(2*np.pi*a)\n\n\nplt.plot(a,b)\nplt.plot(a,c)\n\nplt.show()\nplt.cla()\n\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.random import normal, rand\n\n\na = np.random.randint(1, 100, size=1000)\n\n\nplt.hist(a, bins=100)\nplt.show()\nplt.cla()\n\n\n\nimport matplotlib.pyplot as plt\nfrom numpy.random import normal, rand\n\nx = normal(size=1000)\ny = normal(size=1000)\n\nplt.scatter(x,y)\nplt.show()\n", "repo_name": "Iiru/Iiru.github.io", "sub_path": "20161130oss.py", "file_name": "20161130oss.py", "file_ext": "py", "file_size_in_byte": 913, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.random.randint", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 43, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.random.normal", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 72, "usage_type": "call"}, {"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.show", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "35566540155", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 15 09:21:43 2018\n\n@author: Professor Junbin Gao\n\"\"\"\nfrom __future__ import division\nfrom sklearn.datasets import load_iris\nfrom sklearn import tree\n#import pydotplus\nfrom sklearn.externals.six import StringIO\nfrom IPython.display import Image, display\nfrom sklearn.preprocessing import LabelEncoder\nimport pandas as pd\nimport numpy as np\n\n# For extracting decision rules\nfrom sklearn.tree import _tree \ndef tree_to_code(tree, feature_names):\n\n\ttree_ = tree.tree_\n\n\tfeature_name = [\n\t\tfeature_names[i] if i != _tree.TREE_UNDEFINED else \"undefined!\"\n\t\tfor i in tree_.feature\n\t]\n\tprint (\"def tree({}):\".format(\", \".join(feature_names)))\n\n\tdef recurse(node, depth):\n\t\tindent = \" \" * depth\n\t\tif tree_.feature[node] != _tree.TREE_UNDEFINED:\n\t\t\tname = feature_name[node]\n\t\t\tthreshold = tree_.threshold[node]\n\t\t\tprint (\"{}if {} <= {}:\".format(indent, name, threshold))\n\t\t\trecurse(tree_.children_left[node], depth + 1)\n\t\t\tprint (\"{}else: # if {} > {}\".format(indent, name, threshold))\n\t\t\trecurse(tree_.children_right[node], depth + 1)\n\t\telse:\n\t\t\tprint (\"{}return {}\".format(indent, tree_.value[node]))\n\n\trecurse(0, 1)\n# End of extracting decision rules\n\n#Load and pre-process the data. Convert class labels to numeric class indices. To be updated.\npurchase_df = pd.read_csv(\"Lecture6_Data.csv\")\n\npurchase_df_x = pd.get_dummies(purchase_df.iloc[:, 1:-1])\n\nle = LabelEncoder()\ny_train = le.fit_transform(purchase_df['Purchase'])\n\n# Which of these features gives us the best split?\npurchase_df_xy = purchase_df_x\npurchase_df_xy['y'] = y_train\n\nfeature_names = purchase_df_x.columns\n\n#Define our classification critera. I have chosen the Gini metric\ndef h_function_gini (rows):\n classes = np.unique(rows.iloc[:, -1])\n gini_all = []\n N_node = len(rows)\n \n for k in classes:\n # calcualte the proportion p(x) for each class\n prop_temp= len( rows[purchase_df_xy.iloc[:, -1] == k] )/ N_node\n gini_temp = (prop_temp) *(1-prop_temp)\n gini_all.append(gini_temp)\n \n gini_all = np.array(gini_all)\n gini_final= np.sum(gini_all)\n return gini_final\n\n# For each possible branch in our tree compute the loss (or impurity). \n# We need to first split the data according to the category then compute \n# the loss value.\nloss_list = []\n\n# For each possible split\nfor i in range(purchase_df_xy.shape[1] - 1):\n \n # Find observations falling to the left (0)\n left_x = purchase_df_xy[purchase_df_xy.iloc[:, i] == 0]\n \n # Find observations falling to the right(1)\n right_x = purchase_df_xy[purchase_df_xy.iloc[:, i] == 1]\n\n # Calculate the gini\n N_branch = len(left_x) + len(right_x)\n loss = (len(left_x) / N_branch) * h_function_gini(left_x) + (len(right_x) / N_branch) * h_function_gini(right_x)\n\n loss_list.append(loss)\n\n#To find the minimum impurity split you can use argmin\nfeature_index = np.argmin(loss_list)\n\n# Split the data according to the optimal split\nfinal_left_rows = purchase_df_xy[purchase_df_xy.iloc[:, feature_index] == 0]\nfinal_right_rows = purchase_df_xy[purchase_df_xy.iloc[:, feature_index] == 1]\n\n#Calculate the number of observations from each class that was assigned to the left and right leaves\nn_classes = len(np.unique(purchase_df_xy.iloc[:,-1]))\nvalue_left = np.zeros(n_classes)\nvalue_right = np.zeros(n_classes)\n\nfor i in final_left_rows.iloc[:, -1]:\n value_left[i] = value_left[i] + 1 \n\nfor i in final_right_rows.iloc[:, -1]:\n value_right[i] = value_right[i] + 1\n \nprint(\"Left node: {0}\".format(value_left))\nprint(\"Right node: {0}\".format(value_right))\n\n#Let's compare to the SKLearn classifier.\npurchase_df_x = pd.get_dummies(purchase_df.iloc[:, 1:-1])\n\nclf = tree.DecisionTreeClassifier(max_depth = 1)\n\nclf = clf.fit(purchase_df_x, y_train)\n\n# sklearn tree class assignment\nprint(clf.tree_.value[1:3])\n\n# extract true rules\ntree_to_code(clf, feature_names)\n\n\n# If you have installed Graphviz and Pydotplus, you can uncomment this section\n\"\"\"\nimport pydotplus\nfrom sklearn.externals.six import StringIO\nfrom IPython.display import Image, display\nimport os \nos.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n\n# Create a string buffer to write to (a fake text file)\nf = StringIO()\n\n# Write the tree description data to the file\ntree.export_graphviz(clf, out_file=f)\n\n# Produce a visulation from the file\ngraph = pydotplus.graph_from_dot_data(f.getvalue())\n\n# Write visualisation to image file \ngraph.write_png(\"dtree_gini.png\")\n\n#display(Image(filename=\"dtree2.png\"))\n\ndisplay(Image(graph.create_png()))\n\n\"\"\"\n \n ", "repo_name": "weiweiECNU/QBUS", "sub_path": "qbus6850_19_1/week7/Answer_Tutorial_07_Task01.py", "file_name": "Answer_Tutorial_07_Task01.py", "file_ext": "py", "file_size_in_byte": 4603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sklearn.tree.tree_", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sklearn.tree", "line_number": 22, "usage_type": "name"}, {"api_name": "sklearn.tree._tree.TREE_UNDEFINED", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sklearn.tree._tree", "line_number": 25, "usage_type": "name"}, {"api_name": "sklearn.tree._tree.TREE_UNDEFINED", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sklearn.tree._tree", "line_number": 32, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 119, "usage_type": "name"}]} +{"seq_id": "28069413670", "text": "from PyQt5.QtWidgets import QFormLayout, QHBoxLayout\nfrom PyQt5.QtWidgets import QGridLayout\nfrom PyQt5.QtWidgets import QComboBox\nfrom PyQt5.QtWidgets import QLineEdit\nfrom PyQt5.QtWidgets import QPushButton\nfrom PyQt5.QtCore import Qt \nfrom datetime import date\n\nfrom models.editor_model import EditorData\nfrom APImodels.colour import Colour\nfrom views.label import FixedSizeLabel\n\nclass EditorView(FixedSizeLabel):\n\n def __init__(self, controller, model): \n self.controller = controller\n self.model = model\n self.width = self.model.static_data.width\n self.height = self.model.static_data.height\n super().__init__(self.width, self.height)\n \n self._init__UI()\n self.set_data()\n\n\n def _init__UI(self):\n label_title = FixedSizeLabel(self.width -6, 40, 'Problem Editor')\n label_title.setAlignment(Qt.AlignCenter | Qt.AlignVCenter)\n label_title.set_colours(Colour(30,30,30), Colour(240,240,240))\n\n label_id = FixedSizeLabel(80, 28, 'Id:')\n label_id.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n\n # buttons\n self.button_update = QPushButton('Update')\n self.button_update.clicked.connect(self._update_problem)\n\n self.button_delete = QPushButton('&Delete')\n self.button_delete.clicked.connect(self._delete_problem)\n self.button_delete.setShortcut('Ctrl+D')\n\n self.button_strip = QPushButton('Strip')\n self.button_strip.clicked.connect(self._strip_problem)\n self.button_strip.setShortcut('Ctrl+Shift+D')\n \n self.layout_button = QGridLayout()\n self.layout_button.addWidget(self.button_update, 0, 1) \n self.layout_button.addWidget(self.button_delete, 0, 0)\n self.layout_button.addWidget(self.button_strip, 0, 0)\n\n # editing side\n self.text_id = FixedSizeLabel(190, 28)\n self.text_id.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n\n self.dropdown_r = QComboBox()\n self.dropdown_r.addItems(['1','2','3','4','5'])\n\n self.dropdown_i = QComboBox()\n self.dropdown_i.addItems(['1','2','3','4','5'])\n\n self.dropdown_c = QComboBox()\n self.dropdown_c.addItems(['1','2','3','4','5'])\n self.layout_combo = QHBoxLayout()\n self.layout_combo.setContentsMargins(2,2,2,2)\n self.layout_combo.addWidget(self.dropdown_r)\n self.layout_combo.addWidget(self.dropdown_i)\n self.layout_combo.addWidget(self.dropdown_c)\n\n self.text_grade = FixedSizeLabel(190, 28)\n self.text_grade.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n\n self.text_sector = FixedSizeLabel(190, 28)\n self.text_sector.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n\n self.dropdown_hold = QComboBox() \n\n self.lineedit_styles_0 = QLineEdit()\n self.lineedit_styles_1 = QLineEdit()\n self.lineedit_styles_2 = QLineEdit()\n self.lineedit_set_by = QLineEdit()\n self.lineedit_set_date = QLineEdit()\n self.lineedit_set_date.setPlaceholderText('YYYY-MM-DD')\n self.lineedit_strip_date = QLineEdit()\n self.lineedit_strip_date.setPlaceholderText('YYYY-MM-DD')\n self.label_strip_date = FixedSizeLabel(80, 28, 'Strip Date:')\n self.label_strip_date.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n\n # layout\n self.layout = QFormLayout()\n self.layout.setFormAlignment(Qt.AlignTop)\n self.layout.setLabelAlignment(Qt.AlignRight)\n self.layout.setContentsMargins(2,2,2,2)\n self.layout.setSpacing(4)\n\n self.layout.addRow(label_title)\n self.layout.addRow(label_id, self.text_id)\n self.layout.addRow('RIC:', self.layout_combo)\n self.layout.addRow('Grade:', self.text_grade)\n self.layout.addRow('Hold Colour:', self.dropdown_hold)\n self.layout.addRow('Sector:', self.text_sector)\n self.layout.addRow('Styles:', self.lineedit_styles_0)\n self.layout.addRow('', self.lineedit_styles_1)\n self.layout.addRow('', self.lineedit_styles_2)\n self.layout.addRow('Set by:', self.lineedit_set_by)\n self.layout.addRow('Set Date:',self.lineedit_set_date)\n self.layout.addRow(self.label_strip_date, self.lineedit_strip_date)\n self.layout.addRow(self.layout_button)\n self.setLayout(self.layout)\n\n\n def set_data(self):\n data = self.model.dynamic_data\n _problem = data.problem\n self.text_id.setText(str(_problem.id))\n self.dropdown_r.setCurrentText(str(_problem.RIC.R))\n self.dropdown_i.setCurrentText(str(_problem.RIC.I))\n self.dropdown_c.setCurrentText(str(_problem.RIC.C))\n self.text_grade.setText(str(_problem.grade))\n self._set_dropdown_hold(data)\n self.text_sector.setText(_problem.sector.upper())\n self._set_lineedit_styles(_problem.styles)\n self.lineedit_set_by.setText(_problem.set_by)\n self._set_lineedit_set_date(_problem.set_date)\n \n if data.is_strippable:\n self.label_strip_date.show()\n self.lineedit_strip_date.show()\n self.button_strip.show()\n else:\n self.label_strip_date.hide()\n self.lineedit_strip_date.hide()\n self.button_strip.hide()\n\n if data.is_deletable:\n self.button_delete.show()\n else:\n self.button_delete.hide()\n\n if data.is_addable:\n self.button_update.show()\n else:\n self.button_update.hide()\n\n\n def _set_dropdown_hold(self, data:EditorData):\n self.dropdown_hold.clear()\n self.dropdown_hold.addItems(data.holds)\n self.dropdown_hold.setCurrentText(data.problem.colour)\n return True\n\n def _set_lineedit_styles(self, styles:tuple[str,...]):\n for index in range(3):\n _lineedit = getattr(self, 'lineedit_styles_' + str(index))\n if len(styles) > index:\n _lineedit.setText(styles[index]) \n else:\n _lineedit.setText('')\n\n def _set_lineedit_set_date(self, _date:date = None):\n if _date is None:\n self.lineedit_set_date.setText('')\n return True\n self.lineedit_set_date.setText(_date.isoformat())\n return True\n\n\n def _update_problem(self, event):\n self.controller.update_problem()\n\n def _delete_problem(self, event):\n self.controller.delete_problem()\n \n def _strip_problem(self, event):\n self.controller.strip_problem()\n ", "repo_name": "Supasiti/problem_manager", "sub_path": "views/editor_view.py", "file_name": "editor_view.py", "file_ext": "py", "file_size_in_byte": 6548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "views.label.FixedSizeLabel", "line_number": 13, "usage_type": "name"}, {"api_name": "views.label.FixedSizeLabel", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 28, "usage_type": "attribute"}, {"api_name": "APImodels.colour.Colour", "line_number": 29, "usage_type": "call"}, {"api_name": "views.label.FixedSizeLabel", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 46, "usage_type": "call"}, {"api_name": "views.label.FixedSizeLabel", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 63, "usage_type": "call"}, {"api_name": "views.label.FixedSizeLabel", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 70, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 70, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 70, "usage_type": "attribute"}, {"api_name": "views.label.FixedSizeLabel", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 73, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 75, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 78, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 81, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 83, "usage_type": "call"}, {"api_name": "views.label.FixedSizeLabel", "line_number": 85, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignTop", "line_number": 90, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 91, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 91, "usage_type": "name"}, {"api_name": "models.editor_model.EditorData", "line_number": 145, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 159, "usage_type": "name"}]} +{"seq_id": "23067420456", "text": "from flask import Flask, jsonify, render_template, request\nfrom flask_sqlalchemy import SQLAlchemy\nfrom random import *\napp = Flask(__name__)\n\n##Connect to Database\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///cafes.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\n\n##Cafe TABLE Configuration\nclass Cafe(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(250), unique=True, nullable=False)\n map_url = db.Column(db.String(500), nullable=False)\n img_url = db.Column(db.String(500), nullable=False)\n location = db.Column(db.String(250), nullable=False)\n seats = db.Column(db.String(250), nullable=False)\n has_toilet = db.Column(db.Boolean, nullable=False)\n has_wifi = db.Column(db.Boolean, nullable=False)\n has_sockets = db.Column(db.Boolean, nullable=False)\n can_take_calls = db.Column(db.Boolean, nullable=False)\n coffee_price = db.Column(db.String(250), nullable=True)\n\n\n@app.route(\"/\")\ndef home():\n return render_template(\"index.html\")\n\n\n## HTTP GET - Read Record\n@app.route('/random',methods=[\"GET\",\"POST\"])\ndef random():\n cafe = Cafe()\n random_cafe = choice(cafe.query.all())\n api_dict = {\n 'cafe':{\n 'id':random_cafe.id,\n 'img_url':random_cafe.img_url,\n 'location':random_cafe.location,\n 'map_url':random_cafe.map_url,\n 'name':random_cafe.name,\n 'seats':random_cafe.seats,\n },\n \"amenities\":{\n 'can_take_calls': random_cafe.can_take_calls,\n 'coffee_price': random_cafe.coffee_price,\n 'has_sockets': random_cafe.has_sockets,\n 'has_wifi': random_cafe.has_wifi,\n 'has_toilet': random_cafe.has_toilet,\n }\n }\n return jsonify(api_dict)\n #print(cafe.query.all())\n@app.route('/all')\ndef all_cafes():\n cafe = Cafe()\n all_cofes=cafe.query.all()\n api_list = []\n for cofee in all_cofes:\n api_dict={\n 'id': cofee.id,\n 'img_url': cofee.img_url,\n 'location': cofee.location,\n 'map_url': cofee.map_url,\n 'name': cofee.name,\n 'seats': cofee.seats,\n 'can_take_calls': cofee.can_take_calls,\n 'coffee_price': cofee.coffee_price,\n 'has_sockets': cofee.has_sockets,\n 'has_wifi': cofee.has_wifi,\n 'has_toilet': cofee.has_toilet,\n }\n api_list.append(api_dict)\n return jsonify(cafes=api_list)\n@app.route('/search')\ndef search():\n cafe = Cafe()\n location = request.args.get('loc')\n print(location)\n location_cafes=cafe.query.filter_by(location=location).all()\n print(location_cafes)\n if location_cafes:\n api_list = []\n for cofee in location_cafes:\n api_dict={\n 'id': cofee.id,\n 'img_url': cofee.img_url,\n 'location': cofee.location,\n 'map_url': cofee.map_url,\n 'name': cofee.name,\n 'seats': cofee.seats,\n 'can_take_calls': cofee.can_take_calls,\n 'coffee_price': cofee.coffee_price,\n 'has_sockets': cofee.has_sockets,\n 'has_wifi': cofee.has_wifi,\n 'has_toilet': cofee.has_toilet,\n }\n api_list.append(api_dict)\n return jsonify(cafes=api_list)\n else:\n return jsonify(error={\n 'Not Found': 'Sorry We dont have a caffe in that location',\n })\n\n## HTTP POST - Create Record\n@app.route('/add', methods=['GET','POST'])\ndef add():\n api_key = request.args.get('api_key')\n if api_key == \"TopSecretAPIKey\":\n new_cafe = Cafe(\n name=request.form.get(\"name\"),\n map_url=request.form.get(\"map_url\"),\n img_url=request.form.get(\"img_url\"),\n location=request.form.get(\"loc\"),\n has_sockets=bool(request.form.get(\"sockets\")),\n has_toilet=bool(request.form.get(\"toilet\")),\n has_wifi=bool(request.form.get(\"wifi\")),\n can_take_calls=bool(request.form.get(\"calls\")),\n seats=request.form.get(\"seats\"),\n coffee_price=request.form.get(\"coffee_price\"),\n )\n db.session.add(new_cafe)\n db.session.commit()\n return jsonify(response={\"success\": \"Successfully added the new cafe.\"})\n else:\n return jsonify(response={\"error\": \"Incorrect API KEY\"})\n## HTTP PUT/PATCH - Update Record\n@app.route('/update-price/',methods=['GET','POST','PUT','PATCH'])\ndef update_price(cafe_id):\n cafe = Cafe()\n coffe = cafe.query.get(cafe_id)\n new_price = request.args.get('new_price')\n if not new_price:\n return jsonify(error={\"Not Found\": \"The new_price was not given\"}),404\n if coffe:\n coffe.coffee_price = new_price\n db.session.commit()\n return jsonify({\"success\": \"Successfully added the new cafe.\"}),200\n else:\n return jsonify(error={\"Not Found\": \"Sorry the cafe with that id was not found in the database\"}) ,404\n## HTTP DELETE - Delete Record\n@app.route('/report-closed/',methods=['GET',\"DELETE\"])\ndef delete_cafe(cafe_id):\n cafe = Cafe()\n api_key = request.args.get('api_key')\n coffe = cafe.query.get(cafe_id)\n if api_key == \"TopSecretAPIKEY\":\n if coffe:\n db.session.delete(coffe)\n db.session.commit()\n return jsonify({\"success\": \"Successfully deleted cafe.\"}),200\n else:\n return jsonify(error={\"Not Found\": \"Sorry the cafe with that id was not found in the database\"}), 404\n\n else:\n return jsonify({\"error\":\"Sorry, that's not allowed. Make sure you have the correct api_key\"}), 404\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "darimachine/Creating_RestFull_API", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5778, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 119, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 147, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "17328733252", "text": "import os\nimport pickle\nimport sys\nfrom datetime import datetime\nfrom pathlib import Path\nfrom time import time\n\nimport pandas as pd\nimport torch\nfrom pytorch_widedeep import Trainer\nfrom pytorch_widedeep.callbacks import EarlyStopping, LRHistory, ModelCheckpoint\nfrom pytorch_widedeep.metrics import Accuracy, F1Score\nfrom pytorch_widedeep.models import TabMlp, WideDeep\nfrom pytorch_widedeep.preprocessing import TabPreprocessor\nfrom sklearn.metrics import accuracy_score, confusion_matrix, f1_score, roc_auc_score\n\nsys.path.append(\n os.path.abspath(\"/home/ubuntu/Projects/tabulardl-benchmark/run_experiments\")\n) # isort:skipimport pickle\nfrom general_utils.utils import load_focal_loss_params # noqa: E402\n\nfrom general_utils.utils import ( # isort:skipimport pickle # noqa: E402\n read_best_model_args, # noqa: E402\n set_lr_scheduler,\n set_optimizer,\n)\n\npd.options.display.max_columns = 100\n\nuse_cuda = torch.cuda.is_available()\n\n\nROOTDIR = Path(\"/home/ubuntu/Projects/tabulardl-benchmark\")\nWORKDIR = Path(os.getcwd())\nPROCESSED_DATA_DIR = ROOTDIR / \"processed_data/bank_marketing/\"\n\n\ndef set_dirs(model_name):\n\n models_dir = WORKDIR / \"/\".join([\"best_models\", \"bank_marketing\", model_name])\n results_dir = WORKDIR / \"/\".join([\"results\", \"bank_marketing\", model_name])\n for d in [models_dir, results_dir]:\n if not d.is_dir():\n os.makedirs(d)\n\n return results_dir, models_dir\n\n\ndef load_dataset():\n\n train = pd.read_pickle(PROCESSED_DATA_DIR / \"bankm_train.p\")\n valid = pd.read_pickle(PROCESSED_DATA_DIR / \"bankm_val.p\")\n test = pd.read_pickle(PROCESSED_DATA_DIR / \"bankm_test.p\")\n\n colnames = [c.replace(\".\", \"_\") for c in train.columns]\n train.columns = colnames\n valid.columns = colnames\n test.columns = colnames\n\n train = pd.concat([train, valid], ignore_index=True)\n\n return train, test\n\n\ndef prepare_data(results_dir):\n\n train, test = load_dataset()\n\n # All columns will be treated as categorical. The column with the highest\n # number of categories has 308\n cat_embed_cols = [c for c in train.columns if c != \"target\"]\n\n prepare_tab = TabPreprocessor(embed_cols=cat_embed_cols)\n X_train = prepare_tab.fit_transform(train)\n y_train = train.target.values\n X_test = prepare_tab.transform(test)\n y_test = test.target.values\n\n return prepare_tab, X_train, X_test, y_train, y_test\n\n\ndef set_model(args, prepare_tab):\n\n if args.mlp_hidden_dims == \"auto\":\n n_inp_dim = sum([e[2] for e in prepare_tab.embeddings_input])\n mlp_hidden_dims = [4 * n_inp_dim, 2 * n_inp_dim]\n else:\n mlp_hidden_dims = eval(args.mlp_hidden_dims)\n\n deeptabular = TabMlp(\n column_idx=prepare_tab.column_idx,\n mlp_hidden_dims=mlp_hidden_dims,\n mlp_activation=args.mlp_activation,\n mlp_dropout=args.mlp_dropout,\n mlp_batchnorm=args.mlp_batchnorm,\n mlp_batchnorm_last=args.mlp_batchnorm_last,\n mlp_linear_first=args.mlp_linear_first,\n embed_input=prepare_tab.embeddings_input,\n embed_dropout=args.embed_dropout,\n )\n model = WideDeep(deeptabular=deeptabular)\n\n return model\n\n\ndef run_experiment_and_save(\n model,\n model_name,\n results_dir,\n models_dir,\n args,\n X_train,\n X_test,\n y_train,\n y_test,\n fl_exp_indx: int = 0,\n):\n\n try:\n if args.focal_loss:\n alpha, gamma = load_focal_loss_params(results_dir, fl_exp_indx)\n focal_loss = True\n else:\n alpha = 0.25\n gamma = 2\n focal_loss = False\n except AttributeError:\n alpha = 0.25\n gamma = 2\n focal_loss = False\n\n optimizers = set_optimizer(model, args)\n\n steps_per_epoch = (X_train.shape[0] // args.batch_size) + 1\n lr_schedulers = set_lr_scheduler(optimizers, steps_per_epoch, args)\n\n early_stopping = EarlyStopping(\n monitor=args.monitor,\n min_delta=args.early_stop_delta,\n patience=args.early_stop_patience,\n )\n\n model_checkpoint = ModelCheckpoint(\n filepath=str(models_dir / \"best_model\"),\n monitor=args.monitor,\n save_best_only=True,\n max_save=1,\n )\n\n trainer = Trainer(\n model,\n objective=\"binary_focal_loss\" if focal_loss else \"binary\",\n optimizers=optimizers,\n lr_schedulers=lr_schedulers,\n reducelronplateau_criterion=args.monitor.split(\"_\")[-1],\n callbacks=[early_stopping, model_checkpoint, LRHistory(n_epochs=args.n_epochs)],\n metrics=[Accuracy, F1Score],\n alpha=alpha,\n gamma=gamma,\n )\n\n start = time()\n trainer.fit(\n X_train={\"X_tab\": X_train, \"target\": y_train},\n X_val={\"X_tab\": X_test, \"target\": y_test},\n n_epochs=args.n_epochs,\n batch_size=args.batch_size,\n validation_freq=args.eval_every,\n )\n runtime = time() - start\n\n y_pred = trainer.predict(X_tab=X_test)\n\n acc = accuracy_score(y_test, y_pred)\n auc = roc_auc_score(y_test, y_pred)\n f1 = f1_score(y_test, y_pred)\n print(f\"Accuracy: {acc}. F1: {f1}. ROC_AUC: {auc}\")\n print(confusion_matrix(y_test, y_pred))\n\n if args.save_results:\n suffix = str(datetime.now()).replace(\" \", \"_\").split(\".\")[:-1][0]\n filename = \"_\".join([\"bankm\", model_name, \"best\", suffix]) + \".p\"\n results_d = {}\n results_d[\"args\"] = args\n results_d[\"acc\"] = acc\n results_d[\"auc\"] = auc\n results_d[\"f1\"] = f1\n results_d[\"early_stopping\"] = early_stopping\n results_d[\"trainer_history\"] = trainer.history\n results_d[\"trainer_lr_history\"] = trainer.lr_history\n results_d[\"runtime\"] = runtime\n with open(results_dir / filename, \"wb\") as f:\n pickle.dump(results_d, f)\n\n\nif __name__ == \"__main__\":\n\n model_name = \"tabmlp\"\n\n results_dir, models_dir = set_dirs(model_name)\n\n prepare_tab, X_train, X_test, y_train, y_test = prepare_data(results_dir)\n\n args = read_best_model_args(results_dir, exp_idx=0)\n\n model = set_model(args, prepare_tab)\n\n run_experiment_and_save(\n model,\n model_name,\n results_dir,\n models_dir,\n args,\n X_train,\n X_test,\n y_train,\n y_test,\n fl_exp_indx=0,\n )\n", "repo_name": "jrzaurin/tabulardl-benchmark", "sub_path": "run_experiments/bank_marketing_best/bankm_tabmlp_best.py", "file_name": "bankm_tabmlp_best.py", "file_ext": "py", "file_size_in_byte": 6256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 46, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.path.append", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.options", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 33, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 34, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 34, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 60, "usage_type": "call"}, {"api_name": "pytorch_widedeep.preprocessing.TabPreprocessor", "line_number": 73, "usage_type": "call"}, {"api_name": "pytorch_widedeep.models.TabMlp", "line_number": 90, "usage_type": "call"}, {"api_name": "pytorch_widedeep.models.WideDeep", "line_number": 101, "usage_type": "call"}, {"api_name": "general_utils.utils.load_focal_loss_params", "line_number": 121, "usage_type": "call"}, {"api_name": "general_utils.utils.set_optimizer", "line_number": 132, "usage_type": "call"}, {"api_name": "general_utils.utils.set_lr_scheduler", "line_number": 135, "usage_type": "call"}, {"api_name": "pytorch_widedeep.callbacks.EarlyStopping", "line_number": 137, "usage_type": "call"}, {"api_name": "pytorch_widedeep.callbacks.ModelCheckpoint", "line_number": 143, "usage_type": "call"}, {"api_name": "pytorch_widedeep.Trainer", "line_number": 150, "usage_type": "call"}, {"api_name": "pytorch_widedeep.callbacks.LRHistory", "line_number": 156, "usage_type": "call"}, {"api_name": "pytorch_widedeep.metrics.Accuracy", "line_number": 157, "usage_type": "name"}, {"api_name": "pytorch_widedeep.metrics.F1Score", "line_number": 157, "usage_type": "name"}, {"api_name": "time.time", "line_number": 162, "usage_type": "call"}, {"api_name": "time.time", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 174, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 176, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 181, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 193, "usage_type": "call"}, {"api_name": "general_utils.utils.read_best_model_args", "line_number": 204, "usage_type": "call"}]} +{"seq_id": "35570795929", "text": "\nimport os\n\n# basic\nimport numpy as np\nimport pandas as pd\nfrom sklearn.utils import class_weight\nfrom tqdm import tqdm, trange\nimport time\nimport pprint\nimport datetime\nimport argparse\nfrom scipy.stats import gmean\nimport yaml\nimport shutil\n\n# keras\nfrom keras.optimizers import Adam\nfrom keras.models import load_model\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\n# DIY\nimport utils_classif\nfrom feat_ext import load_audio_file, get_mel_spectrogram, modify_file_variable_length\nfrom data import get_label_files, DataGeneratorPatch, PatchGeneratorPerFile\nfrom architectures import get_model_crnn_seld_tagger\nfrom eval import Evaluator\n\nimport csv\nimport sys\nsys.path.append('../')\nfrom parameters import get_params\nfrom compute_doa_metrics import compute_DOA_metrics\nfrom file_utils import write_metadata_result_file, build_result_dict_from_metadata_array, write_output_result_file\n\n\nstart = time.time()\n\nnow = datetime.datetime.now()\nprint(\"Current date and time:\")\nprint(str(now))\n\n# =========================================================================================================\n# =========================================================================================================\n\n# ==================================================================== ARGUMENTS\nparser = argparse.ArgumentParser(description='DCASE2019 Task3')\nparser.add_argument('-p', '--params_yaml',\n dest='params_yaml',\n action='store',\n required=False,\n type=str)\nargs = parser.parse_args()\nprint('\\nYaml file with parameters defining the experiment: %s\\n' % str(args.params_yaml))\n\n\n# =========================================================================Parameters, paths and variables\n# =========================================================================Parameters, paths and variables\n# =========================================================================Parameters, paths and variables\n\n# Read parameters file from yaml passed by argument\nparams = yaml.load(open(args.params_yaml))\nparams_ctrl = params['ctrl']\nparams_extract = params['extract']\nparams_learn = params['learn']\nparams_loss = params['loss']\nparams_recog = params['recognizer']\nparams_crnn = params['crnn']\n\nsuffix_in = params['suffix'].get('in')\nsuffix_out = params['suffix'].get('out')\n\n# determine loss function for stage 1 (or entire training)\nif params_loss.get('type') == 'CCE':\n params_loss['type'] = 'categorical_crossentropy'\nelif params_loss.get('type') == 'MAE':\n params_loss['type'] = 'mean_absolute_error'\n\nparams_extract['audio_len_samples'] = int(params_extract.get('fs') * params_extract.get('audio_len_s'))\n\n# vip to deploy. for public, put directly params_ctrl.gt('dataset_path') within params_path\npath_root_data = params_ctrl.get('dataset_path')\n\nparams_path = {'path_to_features': os.path.join(path_root_data, 'features'),\n # 'featuredir_dev': 'audio_dev_varup1/',\n # 'featuredir_eval': 'audio_eval_varup1/',\n 'featuredir_dev': 'audio_dev_varup2_64mel/',\n 'featuredir_eval': 'audio_eval_varup2_64mel/',\n # 'featuredir_dev_param': 'audio_dev_param_varup2_64mel/',\n # 'featuredir_eval_param': 'audio_eval_param_varup2_64mel/',\n 'featuredir_dev_param': 'audio_dev_param_Q_varup2_64mel/',\n 'featuredir_eval_param': 'audio_eval_param_Q_varup2_64mel/',\n # 'featuredir_dev': 'audio_dev_varup1_64mel/',\n # 'featuredir_eval': 'audio_eval_varup1_64mel/',\n 'path_to_dataset': path_root_data,\n 'audiodir_dev': 'wav/dev/',\n 'audiodir_eval': 'wav/eval/',\n # 'audiodir_dev_param': 'wav/dev_param/',\n # 'audiodir_eval_param': 'wav/eval_param/',\n 'audiodir_dev_param': 'wav/dev_param_Q/',\n 'audiodir_eval_param': 'wav/eval_param_Q/',\n 'audio_shapedir_dev': 'audio_dev_shapes/',\n 'audio_shapedir_eval': 'audio_eval_shapes/',\n # 'audio_shapedir_dev_param': 'audio_dev_param_shapes/',\n # 'audio_shapedir_eval_param': 'audio_eval_param_shapes/',\n 'audio_shapedir_dev_param': 'audio_dev_param_Q_shapes/',\n 'audio_shapedir_eval_param': 'audio_eval_param_Q_shapes/',\n 'gt_files': path_root_data}\n\nif params_extract.get('n_mels') == 40:\n params_path['featuredir_dev'] = 'audio_dev_varup2_40mel/'\n params_path['featuredir_eval'] = 'audio_eval_varup2_40mel/'\n # params_path['featuredir_dev_param'] = 'audio_dev_param_varup2_40mel/'\n # params_path['featuredir_eval_param'] = 'audio_eval_param_varup2_40mel/'\n params_path['featuredir_dev_param'] = 'audio_dev_param_Q_varup2_40mel/'\n params_path['featuredir_eval_param'] = 'audio_eval_param_Q_varup2_40mel/'\nelif params_extract.get('n_mels') == 96:\n params_path['featuredir_dev'] = 'audio_dev_varup2_96mel/'\n params_path['featuredir_eval'] = 'audio_eval_varup2_96mel/'\n # params_path['featuredir_dev_param'] = 'audio_dev_param_varup2_96mel/'\n # params_path['featuredir_eval_param'] = 'audio_eval_param_varup2_96mel/'\n params_path['featuredir_dev_param'] = 'audio_dev_param_Q_varup2_96mel/'\n params_path['featuredir_eval_param'] = 'audio_eval_param_Q_varup2_96mel/'\nelif params_extract.get('n_mels') == 128:\n params_path['featuredir_dev'] = 'audio_dev_varup2_128mel/'\n params_path['featuredir_eval'] = 'audio_eval_varup2_128mel/'\n # params_path['featuredir_dev_param'] = 'audio_dev_param_varup2_128mel/'\n # params_path['featuredir_eval_param'] = 'audio_eval_param_varup2_128mel/'\n params_path['featuredir_dev_param'] = 'audio_dev_param_Q_varup2_128mel/'\n params_path['featuredir_eval_param'] = 'audio_eval_param_Q_varup2_128mel/'\n\nparams_path['featurepath_dev'] = os.path.join(params_path.get('path_to_features'), params_path.get('featuredir_dev'))\nparams_path['featurepath_eval'] = os.path.join(params_path.get('path_to_features'), params_path.get('featuredir_eval'))\nparams_path['featurepath_dev_param'] = os.path.join(params_path.get('path_to_features'), params_path.get('featuredir_dev_param'))\nparams_path['featurepath_eval_param'] = os.path.join(params_path.get('path_to_features'), params_path.get('featuredir_eval_param'))\n\nparams_path['audiopath_dev'] = os.path.join(params_path.get('path_to_dataset'), params_path.get('audiodir_dev'))\nparams_path['audiopath_eval'] = os.path.join(params_path.get('path_to_dataset'), params_path.get('audiodir_eval'))\nparams_path['audiopath_dev_param'] = os.path.join(params_path.get('path_to_dataset'), params_path.get('audiodir_dev_param'))\nparams_path['audiopath_eval_param'] = os.path.join(params_path.get('path_to_dataset'), params_path.get('audiodir_eval_param'))\n\n\nparams_path['audio_shapedir_dev'] = os.path.join(params_path.get('path_to_dataset'),\n params_path.get('audio_shapedir_dev'))\nparams_path['audio_shapedir_eval'] = os.path.join(params_path.get('path_to_dataset'),\n params_path.get('audio_shapedir_eval'))\nparams_path['audio_shapedir_dev_param'] = os.path.join(params_path.get('path_to_dataset'),\n params_path.get('audio_shapedir_dev_param'))\nparams_path['audio_shapedir_eval_param'] = os.path.join(params_path.get('path_to_dataset'),\n params_path.get('audio_shapedir_eval_param'))\n\n\n# ======================================================== SPECIFIC PATHS TO SOME IMPORTANT FILES\n# ground truth, load model, save model, predictions, results\nparams_files = {'gt_eval': os.path.join(params_path.get('gt_files'), 'gt_eval.csv'),\n 'gt_dev': os.path.join(params_path.get('gt_files'), 'gt_dev.csv')}\n\npath_trained_models = utils_classif.make_sure_isdir('trained_models', params_ctrl.get('output_file'))\nparams_files['save_model'] = os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' + str(params_ctrl.get('count_trial')) + '.h5')\npath_predictions = utils_classif.make_sure_isdir('predictions', params_ctrl.get('output_file'))\nparams_files['predictions'] = os.path.join(path_predictions, params_ctrl.get('output_file') + '_v' + str(params_ctrl.get('count_trial')) + '.csv')\npath_results = utils_classif.make_sure_isdir('logs/results', params_ctrl.get('output_file'))\nparams_files['results'] = os.path.join(path_results, params_ctrl.get('output_file') + '.pickle')\n# params_files['event_durations'] = os.path.join('logs/pics', params_ctrl.get('output_file') + '_event_durations.pickle')\n\n# # ============================================= print all params to keep record in output file\nprint('\\nparams_ctrl=')\npprint.pprint(params_ctrl, width=1, indent=4)\nprint('params_files=')\npprint.pprint(params_files, width=1, indent=4)\nprint('params_extract=')\npprint.pprint(params_extract, width=1, indent=4)\nprint('params_learn=')\npprint.pprint(params_learn, width=1, indent=4)\nprint('params_loss=')\npprint.pprint(params_loss, width=1, indent=4)\nprint('params_recog=')\npprint.pprint(params_recog, width=1, indent=4)\nprint('params_crnn=')\npprint.pprint(params_crnn, width=1, indent=4)\nprint('\\n')\n\n\n# ============================================================== READ TRAIN and TEST DATA\n# ============================================================== READ TRAIN and TEST DATA\n# ============================================================== READ TRAIN and TEST DATA\n# ============================================================== READ TRAIN and TEST DATA\n\n# aim: lists with all wav files for dev, which includes train/val/test\ngt_dev = pd.read_csv(params_files.get('gt_dev'))\nsplitlist_audio_dev = gt_dev.split.values.tolist()\nfilelist_audio_dev = gt_dev.fname.values.tolist()\n\n# create dict with ground truth mapping with labels:\n# -key: path to wav\n# -value: the ground truth label too\nfile_to_label = {params_path.get('audiopath_dev') + k: v for k, v in zip(gt_dev.fname.values, gt_dev.label.values)}\n\n# ========================================================== CREATE VARS FOR DATASET MANAGEMENT\n# list with unique n_classes labels and aso_ids\nlist_labels = sorted(list(set(gt_dev.label.values)))\n\n# create dicts such that key: value is as follows\n# fixed by DCASE\nlabel_to_int = {\n 'clearthroat': 2,\n 'cough': 8,\n 'doorslam': 9,\n 'drawer': 1,\n 'keyboard': 6,\n 'keysDrop': 4,\n 'knock': 0,\n 'laughter': 10,\n 'pageturn': 7,\n 'phone': 3,\n 'speech': 5\n}\nint_to_label = {v: k for k, v in label_to_int.items()}\n\n# create ground truth mapping with categorical values\nfile_to_label_numeric = {k: label_to_int[v] for k, v in file_to_label.items()}\n\n\n#\n# ========================================================== FEATURE EXTRACTION\n# ========================================================== FEATURE EXTRACTION\n# ========================================================== FEATURE EXTRACTION\n# compute T_F representation\n# mel-spectrogram for all files in the dataset and store it\nvar_lens = {item: [] for item in label_to_int.keys()}\nvar_lens['overall'] = []\n\nvar_lens_dev_param = {}\nvar_lens_dev_param['overall'] = []\n\nif params_ctrl.get('feat_ext'):\n if params_ctrl.get('pipeline') == 'T_F':\n n_extracted_dev = 0; n_extracted_te = 0; n_failed_dev = 0; n_failed_te = 0\n n_extracted_dev_param = 0; n_failed_dev_param = 0\n\n # only if features have not been extracted, ie\n # if folder does not exist, or it exists with less than 80% of the feature files\n # create folder and extract features\n nb_files_dev = len(filelist_audio_dev)\n if not os.path.exists(params_path.get('featurepath_dev')) or \\\n len(os.listdir(params_path.get('featurepath_dev'))) < nb_files_dev*0.8:\n\n if os.path.exists(params_path.get('featurepath_dev')):\n shutil.rmtree(params_path.get('featurepath_dev'))\n os.makedirs(params_path.get('featurepath_dev'))\n\n print('\\nFeature extraction for dev set (prints enabled). Features dumped in {}.........................'.\n format(params_path.get('featurepath_dev')))\n for idx, f_name in enumerate(filelist_audio_dev):\n f_path = os.path.join(params_path.get('audiopath_dev'), f_name)\n if os.path.isfile(f_path) and f_name.endswith('.wav'):\n # load entire audio file and modify variable length, if needed\n y = load_audio_file(f_path, input_fixed_length=params_extract['audio_len_samples'], params_extract=params_extract)\n\n # keep record of the lengths, per class, for insight\n duration_seconds = len(y)/int(params_extract.get('fs'))\n var_lens[f_name.split('_')[0]].append(duration_seconds)\n var_lens['overall'].append(duration_seconds)\n\n y = modify_file_variable_length(data=y,\n input_fixed_length=params_extract['audio_len_samples'],\n params_extract=params_extract)\n # print('Considered audio length: %6.3f' % (len(y) / params_extract.get('fs')))\n # print('%-22s: [%d/%d] of %s' % ('Extracting tr features', (idx + 1), nb_files_tr, f_path))\n\n # compute log-scaled mel spec. row x col = time x freq\n mel_spectrogram = get_mel_spectrogram(audio=y, params_extract=params_extract)\n\n # save the T_F rep to a binary file (only the considered length)\n utils_classif.save_tensor(var=mel_spectrogram,\n out_path=os.path.join(params_path.get('featurepath_dev'),\n f_name.replace('.wav', '.data')), suffix='_mel')\n\n # save also label\n utils_classif.save_tensor(var=np.array([file_to_label_numeric[f_path]], dtype=float),\n out_path=os.path.join(params_path.get('featurepath_dev'),\n f_name.replace('.wav', '.data')), suffix='_label')\n\n if os.path.isfile(os.path.join(params_path.get('featurepath_dev'),\n f_name.replace('.wav', suffix_in + '.data'))):\n n_extracted_dev += 1\n print('%-22s: [%d/%d] of %s' % ('Extracted dev features', (idx + 1), nb_files_dev, f_path))\n else:\n n_failed_dev += 1\n print('%-22s: [%d/%d] of %s' % ('FAILING to extract dev features', (idx + 1), nb_files_dev, f_path))\n else:\n print('%-22s: [%d/%d] of %s' % ('this dev audio is in the csv but not in the folder', (idx + 1), nb_files_dev, f_path))\n\n print('n_extracted_dev: {0} / {1}'.format(n_extracted_dev, nb_files_dev))\n print('n_failed_dev: {0} / {1}\\n'.format(n_failed_dev, nb_files_dev))\n\n else:\n print('Dev set is already extracted in {}'.format(params_path.get('featurepath_dev')))\n\n\n # do feature extraction for dev_param (outcome of complete parametric frontend)========================================\n # do feature extraction for dev_param (outcome of complete parametric frontend)========================================\n audio_files_dev_param = [f for f in os.listdir(params_path.get('audiopath_dev_param')) if not f.startswith('.')]\n\n nb_files_dev_param = len(audio_files_dev_param)\n if not os.path.exists(params_path.get('featurepath_dev_param')) or \\\n len(os.listdir(params_path.get('featurepath_dev_param'))) < nb_files_dev_param * 0.8:\n\n if os.path.exists(params_path.get('featurepath_dev_param')):\n shutil.rmtree(params_path.get('featurepath_dev_param'))\n os.makedirs(params_path.get('featurepath_dev_param'))\n\n print(\n '\\nFeature extraction for dev set parametric (outcome of parametric frontend). Features dumped in {}.........................'.\n format(params_path.get('featurepath_dev_param')))\n for idx, f_name in enumerate(audio_files_dev_param):\n f_path = os.path.join(params_path.get('audiopath_dev_param'), f_name)\n if os.path.isfile(f_path) and f_name.endswith('.wav'):\n # load entire audio file and modify variable length, if needed\n y = load_audio_file(f_path, input_fixed_length=params_extract['audio_len_samples'],\n params_extract=params_extract)\n\n # keep record of the lengths, per class, for insight\n duration_seconds = len(y) / int(params_extract.get('fs'))\n var_lens_dev_param['overall'].append(duration_seconds)\n\n y = modify_file_variable_length(data=y,\n input_fixed_length=params_extract['audio_len_samples'],\n params_extract=params_extract)\n # print('Considered audio length: %6.3f' % (len(y) / params_extract.get('fs')))\n # print('%-22s: [%d/%d] of %s' % ('Extracting tr features', (idx + 1), nb_files_tr, f_path))\n\n # compute log-scaled mel spec. row x col = time x freq\n mel_spectrogram = get_mel_spectrogram(audio=y, params_extract=params_extract)\n\n # save the T_F rep to a binary file (only the considered length)\n utils_classif.save_tensor(var=mel_spectrogram,\n out_path=os.path.join(params_path.get('featurepath_dev_param'),\n f_name.replace('.wav', '.data')), suffix='_mel')\n\n if os.path.isfile(os.path.join(params_path.get('featurepath_dev_param'),\n f_name.replace('.wav', suffix_in + '.data'))):\n n_extracted_dev_param += 1\n print('%-22s: [%d/%d] of %s' % ('Extracted dev_param features', (idx + 1), nb_files_dev_param, f_path))\n else:\n n_failed_dev_param += 1\n print('%-22s: [%d/%d] of %s' % (\n 'FAILING to extract dev_param features', (idx + 1), nb_files_dev_param, f_path))\n else:\n print('%-22s: [%d/%d] of %s' % (\n 'this dev_param audio is in the csv but not in the folder', (idx + 1), nb_files_dev_param, f_path))\n\n print('n_extracted_dev_param: {0} / {1}'.format(n_extracted_dev_param, nb_files_dev_param))\n print('n_failed_dev_param: {0} / {1}\\n'.format(n_failed_dev_param, nb_files_dev_param))\n\n else:\n print('Dev_param set is already extracted in {}'.format(params_path.get('featurepath_dev_param')))\n\n\n# select the subset of training data to consider: all, clean, noisy, noisy_small\n# =====================================================================================================================\n# =====================================================================================================================\n\nff_list_dev = [filelist_audio_dev[i].replace('.wav', suffix_in + '.data') for i in range(len(filelist_audio_dev))]\nlabels_audio_dev = get_label_files(filelist=ff_list_dev,\n dire=params_path.get('featurepath_dev'),\n suffix_in=suffix_in,\n suffix_out=suffix_out\n )\n\nprint('Number of clips considered as dev set: {0}'.format(len(ff_list_dev)))\nprint('Number of labels loaded for dev set: {0}'.format(len(labels_audio_dev)))\n\nscalers = [None]*4\n# determine the validation setup according to the folds, and perform training / val / test for each fold\nfor kfo in range(1, 5):\n print('\\n=========================================================================================================')\n print('===Processing fold {} within the x-val setup...'.format(kfo))\n print('=========================================================================================================\\n')\n # x-val setup given by DCASE organizers\n if kfo == 1:\n splits_tr = [3, 4]\n splits_val = [2]\n splits_te = [1]\n elif kfo == 2:\n splits_tr = [4, 1]\n splits_val = [3]\n splits_te = [2]\n elif kfo == 3:\n splits_tr = [1, 2]\n splits_val = [4]\n splits_te = [3]\n elif kfo == 4:\n splits_tr = [2, 3]\n splits_val = [1]\n splits_te = [4]\n\n params_ctrl['current_fold'] = kfo\n tr_files0 = [fname for idx, fname in enumerate(ff_list_dev) if splitlist_audio_dev[idx] == splits_tr[0]]\n tr_files1 = [fname for idx, fname in enumerate(ff_list_dev) if splitlist_audio_dev[idx] == splits_tr[1]]\n tr_files = tr_files0 + tr_files1\n val_files = [fname for idx, fname in enumerate(ff_list_dev) if splitlist_audio_dev[idx] == splits_val[0]]\n te_files = [fname for idx, fname in enumerate(ff_list_dev) if splitlist_audio_dev[idx] == splits_te[0]]\n\n # SC\n if len(tr_files) + len(val_files) + len(te_files) != len(ff_list_dev):\n print('ERROR: You messed up in x-val setup for fold: {0}'.format(len(kfo)))\n print('{} is not {}'.format(len(tr_files) + len(val_files) + len(te_files), len(ff_list_dev)))\n\n # ============================================================BATCH GENERATION\n # ============================================================BATCH GENERATION\n\n tr_gen_patch = DataGeneratorPatch(feature_dir=params_path.get('featurepath_dev'),\n file_list=tr_files,\n params_learn=params_learn,\n params_extract=params_extract,\n suffix_in='_mel',\n suffix_out='_label',\n floatx=np.float32\n )\n # to predict later on on dev_param clips\n scalers[kfo-1] = tr_gen_patch.scaler\n\n print(\"Total number of instances *only* for training: %s\" % str(tr_gen_patch.nb_inst_total))\n print(\"Batch_size: %s\" % str(tr_gen_patch.batch_size))\n print(\"Number of iterations (batches) in the training subset: %s\" % str(tr_gen_patch.nb_iterations))\n print(\"\\nShape of training subset: %s\" % str(tr_gen_patch.features.shape))\n print(\"Shape of labels in training subset: %s\" % str(tr_gen_patch.labels.shape))\n\n # compute class_weigths based on the labels generated\n if params_learn.get('mode_class_weight'):\n labels_nice = np.reshape(tr_gen_patch.labels, -1) # remove singleton dimension\n class_weights = class_weight.compute_class_weight('balanced',\n np.unique(labels_nice),\n labels_nice)\n class_weights_dict = dict(enumerate(class_weights))\n else:\n class_weights_dict = None\n\n val_gen_patch = DataGeneratorPatch(feature_dir=params_path.get('featurepath_dev'),\n file_list=val_files,\n params_learn=params_learn,\n params_extract=params_extract,\n suffix_in='_mel',\n suffix_out='_label',\n floatx=np.float32,\n scaler=tr_gen_patch.scaler\n )\n\n print(\"\\nShape of validation subset: %s\" % str(val_gen_patch.features.shape))\n print(\"Shape of labels in validation subset: %s\" % str(val_gen_patch.labels.shape))\n\n # ============================================================DEFINE AND FIT A MODEL\n # ============================================================DEFINE AND FIT A MODEL\n\n tr_loss, val_loss = [0] * params_learn.get('n_epochs'), [0] * params_learn.get('n_epochs')\n # ============================================================\n if params_ctrl.get('learn'):\n if params_learn.get('model') == 'crnn_seld_tagger':\n model = get_model_crnn_seld_tagger(params_crnn=params_crnn, params_learn=params_learn,\n params_extract=params_extract)\n\n if params_learn.get('stages') == 1:\n\n opt = Adam(lr=params_learn.get('lr'))\n model.compile(optimizer=opt, loss=params_loss.get('type'), metrics=['accuracy'])\n model.summary()\n\n # callbacks\n if params_learn.get('early_stop') == \"val_acc\":\n early_stop = EarlyStopping(monitor='val_acc', patience=params_learn.get('patience'), min_delta=0.001, verbose=1)\n elif params_learn.get('early_stop') == \"val_loss\":\n early_stop = EarlyStopping(monitor='val_loss', patience=params_learn.get('patience'), min_delta=0,\n verbose=1)\n\n # save one best model for every fold, as needed for submission\n params_files['save_model'] = os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f' + str(kfo) + '.h5')\n checkpoint = ModelCheckpoint(params_files.get('save_model'), monitor='val_acc', verbose=1, save_best_only=True)\n\n reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=5, verbose=1)\n callback_list = [checkpoint, early_stop, reduce_lr]\n\n hist = model.fit_generator(tr_gen_patch,\n steps_per_epoch=tr_gen_patch.nb_iterations,\n epochs=params_learn.get('n_epochs'),\n validation_data=val_gen_patch,\n validation_steps=val_gen_patch.nb_iterations,\n class_weight=class_weights_dict,\n workers=4,\n verbose=2,\n callbacks=callback_list)\n\n # ==================================================================================================== PREDICT\n # ==================================================================================================== PREDICT\n\n print('\\nCompute predictions on test split, and save them in csv:==============================================\\n')\n\n # to store prediction probabilites\n te_preds = np.empty((len(te_files), params_learn.get('n_classes')))\n\n te_gen_patch = PatchGeneratorPerFile(feature_dir=params_path.get('featurepath_dev'),\n file_list=te_files,\n params_extract=params_extract,\n suffix_in='_mel',\n floatx=np.float32,\n scaler=tr_gen_patch.scaler\n )\n\n for i in trange(len(te_files), miniters=int(len(te_files) / 100), ascii=True, desc=\"Predicting...\"):\n patches_file = te_gen_patch.get_patches_file()\n\n preds_patch_list = model.predict(patches_file).tolist()\n preds_patch = np.array(preds_patch_list)\n\n if params_learn.get('predict_agg') == 'amean':\n preds_file = np.mean(preds_patch, axis=0)\n elif params_recog.get('aggregate') == 'gmean':\n preds_file = gmean(preds_patch, axis=0)\n else:\n print('unkown aggregation method for prediction')\n te_preds[i, :] = preds_file\n\n list_labels = np.array(list_labels)\n pred_label_files_int = np.argmax(te_preds, axis=1)\n pred_labels = [int_to_label[x] for x in pred_label_files_int]\n\n te_files_wav = [f.replace(suffix_in + '.data', '.wav') for f in te_files]\n if not os.path.isfile(params_files.get('predictions')):\n # fold 1: create the predictions file\n pred = pd.DataFrame(te_files_wav, columns=['fname'])\n pred['label'] = pred_labels\n pred['label_int'] = pred_label_files_int\n pred.to_csv(params_files.get('predictions'), index=False)\n del pred\n\n else:\n pred = pd.read_csv(params_files.get('predictions'))\n old_fname = pred.fname.values.tolist()\n old_label = pred.label.values.tolist()\n old_label_int = pred.label_int.values.tolist()\n\n new_pred_fname = old_fname + te_files_wav\n new_pred_label = old_label + pred_labels\n new_pred_label_int = old_label_int + pred_label_files_int.tolist()\n\n del pred\n pred = pd.DataFrame(new_pred_fname, columns=['fname'])\n pred['label'] = new_pred_label\n pred['label_int'] = new_pred_label_int\n pred.to_csv(params_files.get('predictions'), index=False)\n\n # deleter variables from past fold to free memory\n del tr_gen_patch\n del val_gen_patch\n # this model was trained on split X, and no need anymore\n del model\n\n# vip once we are done with all the 4 folds\n# # =================================================================================================== EVAL\n# # =================================================================================================== EVAL\nprint('\\nEvaluate ACC and print score for the cross validation setup============================================\\n')\n\n# init Evaluator object\nevaluator = Evaluator(gt_dev, pred, list_labels, params_ctrl, params_files)\n\nprint('\\n=============================ACCURACY===============================================================')\nprint('=============================ACCURACY===============================================================\\n')\nevaluator.evaluate_acc()\nevaluator.evaluate_acc_classwise()\n\nend = time.time()\nprint('\\n=============================Job finalized, but lacks DCASE metrics========================================\\n')\nprint('\\nTime elapsed for the job: %7.2f hours' % ((end - start) / 3600.0))\nprint('\\n====================================================================================================\\n')\n\n\nprint('\\n====================Starting metrics for challenge with REAL frontend=====================================')\nprint('====================Starting metrics for challenge with REAL frontend=====================================')\nprint('====================Starting metrics for challenge with REAL frontend=====================================\\n')\n\ndata_folder_path = '../data/foa_dev/'\n# Iterate over all audio files from the dev set, some are from split 1234\naudio_files = [f for f in os.listdir(data_folder_path) if not f.startswith('.')]\n\n# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n# Path stuff\n\n# This parameter will define the algorithm type\npreset_string = 'Q'\n\n# Default preset: contains path to folders\nparams = get_params(preset_string)\n\n# Dataset type:\ndataset_type_folder = params['dataset'] + \"_\" + params['mode']\ndataset_preset_folder = dataset_type_folder + '_' + preset_string\n\n# Get folder names before and after classification\ndoa_folder = params['before_classification_folder_name']\nclassif_folder = params['after_classification_folder_name']\n\n# Path to audio folder\ndataset_dir = '../data'\ndata_folder_path = os.path.join(dataset_dir, dataset_type_folder)\n\n# Path to results_metadata folder _before classification_; it should exist\nresults_metadata_doa_folder = os.path.join('.' + params['metadata_result_folder_path'],\n dataset_preset_folder,\n doa_folder)\nif not os.path.exists(results_metadata_doa_folder):\n os.mkdir(results_metadata_doa_folder)\n\n# Path to results_metadata folder _before classification_; create it if necessary\nresults_metadata_classif_folder = os.path.join('.' + params['metadata_result_folder_path'],\n dataset_preset_folder,\n classif_folder)\nif not os.path.exists(results_metadata_classif_folder):\n os.mkdir(results_metadata_classif_folder)\n\n# Path to results_output folder _before classification_; it should exist\nresults_output_doa_folder = os.path.join('.' + params['output_result_folder_path'],\n dataset_preset_folder,\n doa_folder)\nif not os.path.exists(results_output_doa_folder):\n os.mkdir(results_output_doa_folder)\n\n# Path to results_output folder _before classification_; create it if necessary\n# old: this overwrites the several trials\nresults_output_classif_folder = os.path.join('.' + params['output_result_folder_path'],\n dataset_preset_folder,\n classif_folder)\n# create just the folder classif if there is not such thing\nif not os.path.exists(results_output_classif_folder):\n os.mkdir(results_output_classif_folder)\n\n# new: a folder for each trial. This is already what we have to submit for development mode\nresults_output_classif_folder = os.path.join('.' + params['output_result_folder_path'],\n dataset_preset_folder,\n classif_folder,\n params_ctrl.get('output_file') + '_v' + str(params_ctrl.get('count_trial')))\nif not os.path.exists(results_output_classif_folder):\n os.mkdir(results_output_classif_folder)\n\n# load best model for every fold, for submission\nmodel_f1 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f1.h5'))\nmodel_f2 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f2.h5'))\nmodel_f3 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f3.h5'))\nmodel_f4 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f4.h5'))\n\nsr = 48000\nfor audio_file_name in audio_files:\n\n # Get associated metadata file\n metadata_file_name = os.path.splitext(audio_file_name)[0] + params['metadata_result_file_extension']\n\n # This is our modified metadata result array\n metadata_result_classif_array = []\n\n # Iterate over the associated doa metadata file\n with open(os.path.join(results_metadata_doa_folder, metadata_file_name), 'r') as f:\n reader = csv.reader(f, delimiter=',')\n for i, row in enumerate(reader):\n # Discard the first line (just the column titles)\n if i > 0:\n # Get values for this sound event\n sound_class_string = row[0]\n start_time_seconds = float(row[1])\n end_time_seconds = float(row[2])\n\n # Slice the b_format audio to the corresponding event length\n start_frame = int(np.floor(start_time_seconds * sr))\n end_frame = int(np.ceil(end_time_seconds * sr))\n filename = sound_class_string + '_' + str(start_frame) + '_' + str(end_frame) + '_' + metadata_file_name.split('.')[0] + '.wav'\n curent_split = int(filename.split('_')[3][-1])\n\n # Classify: this will need 4 models for 4 test splits in x-val in development mode + one model for evaluation mode\n te_preds = np.empty((1, params_learn.get('n_classes')))\n\n # only the file under question\n ff_list_dev_param = [filename.replace('.wav', suffix_in + '.data')]\n current_scaler = scalers[curent_split - 1]\n\n te_param_gen_patch = PatchGeneratorPerFile(feature_dir=params_path.get('featurepath_dev_param'),\n file_list=ff_list_dev_param,\n params_extract=params_extract,\n suffix_in='_mel',\n floatx=np.float32,\n scaler=current_scaler\n )\n\n patches_file = te_param_gen_patch.get_patches_file()\n\n # choose model accordingly\n # predicting now on the T_F patch level (not on the wav clip-level)\n if curent_split == 1:\n preds_patch_list = model_f1.predict(patches_file).tolist()\n elif curent_split == 2:\n preds_patch_list = model_f2.predict(patches_file).tolist()\n elif curent_split == 3:\n preds_patch_list = model_f3.predict(patches_file).tolist()\n elif curent_split == 4:\n preds_patch_list = model_f4.predict(patches_file).tolist()\n\n preds_patch = np.array(preds_patch_list)\n\n # aggregate softmax values across patches in order to produce predictions on the file/clip level\n if params_learn.get('predict_agg') == 'amean':\n preds_file = np.mean(preds_patch, axis=0)\n elif params_recog.get('aggregate') == 'gmean':\n preds_file = gmean(preds_patch, axis=0)\n else:\n print('unkown aggregation method for prediction')\n te_preds[0, :] = preds_file\n\n class_id = np.argmax(te_preds, axis=1)\n row[0] = class_id\n metadata_result_classif_array.append(row)\n\n # Write a new results_metadata_classif file with the modified classes\n metadata_result_classif_file_name = os.path.splitext(audio_file_name)[0] + params['metadata_result_file_extension']\n path_to_write = os.path.join(results_metadata_classif_folder, metadata_result_classif_file_name)\n write_metadata_result_file(metadata_result_classif_array, path_to_write)\n\n # Write a new result_output_classif file with the modified classes\n output_result_classif_dict = build_result_dict_from_metadata_array(metadata_result_classif_array, params['required_window_hop'])\n path_to_write = os.path.join(results_output_classif_folder, metadata_file_name)\n write_output_result_file(output_result_classif_dict, path_to_write)\n\n\nprint('-------------- COMPUTE DOA METRICS REAL--------------')\ngt_folder = os.path.join(dataset_dir, 'metadata_'+params['mode'])\ncompute_DOA_metrics(gt_folder, results_output_classif_folder)\n#\n#\n#\nprint('\\n====================Starting metrics for challenge with IDEAL frontend=====================================')\nprint('====================Starting metrics for challenge with IDEAL frontend=====================================')\nprint('====================Starting metrics for challenge with IDEAL frontend=====================================')\nprint('====================Starting metrics for challenge with IDEAL frontend=====================================\\n')\n\n\n# Path to results_metadata folder _before classification_; it should exist\nresults_metadata_doa_folder = os.path.join('.' + params['metadata_result_folder_path'],\n 'metadata_dev',\n doa_folder)\nif not os.path.exists(results_metadata_doa_folder):\n os.mkdir(results_metadata_doa_folder)\n\n# Path to results_metadata folder _before classification_; create it if necessary\nresults_metadata_classif_folder = os.path.join('.' + params['metadata_result_folder_path'],\n 'metadata_dev',\n classif_folder)\nif not os.path.exists(results_metadata_classif_folder):\n os.mkdir(results_metadata_classif_folder)\n\n# Path to results_output folder _before classification_; it should exist\nresults_output_doa_folder = os.path.join('.' + params['output_result_folder_path'],\n 'metadata_dev',\n doa_folder)\nif not os.path.exists(results_output_doa_folder):\n os.mkdir(results_output_doa_folder)\n\n# Path to results_output folder _before classification_; create it if necessary\nresults_output_classif_folder = os.path.join('.' + params['output_result_folder_path'],\n 'metadata_dev',\n classif_folder)\nif not os.path.exists(results_output_classif_folder):\n os.mkdir(results_output_classif_folder)\n\n# load best model for every fold, for submission\nmodel_f1 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f1.h5'))\nmodel_f2 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f2.h5'))\nmodel_f3 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f3.h5'))\nmodel_f4 = load_model(os.path.join(path_trained_models, params_ctrl.get('output_file') + '_v' +\n str(params_ctrl.get('count_trial')) + '_f4.h5'))\n\nsr = 48000\nfor audio_file_name in audio_files:\n\n # Get associated metadata file\n metadata_file_name = os.path.splitext(audio_file_name)[0] + params['metadata_result_file_extension']\n metadata_result_classif_array = []\n\n # Iterate over the associated doa metadata file\n with open(os.path.join(results_metadata_doa_folder, metadata_file_name), 'r') as f:\n reader = csv.reader(f, delimiter=',')\n for i, row in enumerate(reader):\n # Discard the first line (just the column titles)\n if i > 0:\n # Get values for this sound event\n sound_class_string = row[0]\n start_time_seconds = float(row[1])\n end_time_seconds = float(row[2])\n\n # Slice the b_format audio to the corresponding event length\n start_frame = int(np.floor(start_time_seconds * sr))\n end_frame = int(np.ceil(end_time_seconds * sr))\n\n # from one event entry in the csv, to its corresponding audio clip filename (that I stored previously)\n filename = sound_class_string + '_' + str(start_frame) + '_' + metadata_file_name.split('.')[0] + '.wav'\n curent_split = int(filename.split('_')[2][-1])\n\n # Classify: this will need 4 models for 4 test splits in x-val in development mode + one model for evaluation mode\n # to store prediction probabilites for one single test clip\n te_preds = np.empty((1, params_learn.get('n_classes')))\n\n # only the file under question\n ff_list_dev_ideal = [filename.replace('.wav', suffix_in + '.data')]\n current_scaler = scalers[curent_split - 1]\n te_idal_gen_patch = PatchGeneratorPerFile(feature_dir=params_path.get('featurepath_dev'),\n file_list=ff_list_dev_ideal,\n params_extract=params_extract,\n suffix_in='_mel',\n floatx=np.float32,\n scaler=current_scaler\n )\n\n patches_file = te_idal_gen_patch.get_patches_file()\n\n if curent_split == 1:\n preds_patch_list = model_f1.predict(patches_file).tolist()\n elif curent_split == 2:\n preds_patch_list = model_f2.predict(patches_file).tolist()\n elif curent_split == 3:\n preds_patch_list = model_f3.predict(patches_file).tolist()\n elif curent_split == 4:\n preds_patch_list = model_f4.predict(patches_file).tolist()\n\n preds_patch = np.array(preds_patch_list)\n\n # aggregate softmax values across patches in order to produce predictions on the file/clip level\n if params_learn.get('predict_agg') == 'amean':\n preds_file = np.mean(preds_patch, axis=0)\n elif params_recog.get('aggregate') == 'gmean':\n preds_file = gmean(preds_patch, axis=0)\n else:\n print('unkown aggregation method for prediction')\n te_preds[0, :] = preds_file\n\n class_id = np.argmax(te_preds, axis=1)\n row[0] = class_id\n metadata_result_classif_array.append(row)\n\n # Write a new results_metadata_classif file with the modified classes\n metadata_result_classif_file_name = os.path.splitext(audio_file_name)[0] + params['metadata_result_file_extension']\n path_to_write = os.path.join(results_metadata_classif_folder, metadata_result_classif_file_name)\n write_metadata_result_file(metadata_result_classif_array, path_to_write)\n\n # Write a new result_output_classif file with the modified classes\n output_result_classif_dict = build_result_dict_from_metadata_array(metadata_result_classif_array, params['required_window_hop'])\n path_to_write = os.path.join(results_output_classif_folder, metadata_file_name)\n write_output_result_file(output_result_classif_dict, path_to_write)\n\n\nprint('-------------- COMPUTE DOA METRICS IDEAL--------------')\ngt_folder = os.path.join(dataset_dir, 'metadata_'+params['mode'])\ncompute_DOA_metrics(gt_folder, results_output_classif_folder)\n\nprint('\\n=============================Job finalized==========================================================\\n')\nprint('====================================================================================================')\nprint('====================================================================================================')\n", "repo_name": "andresperezlopez/DCASE2019_task3", "sub_path": "classif/classify.py", "file_name": "classify.py", "file_ext": "py", "file_size_in_byte": 46956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.path.append", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 47, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "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.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "utils_classif.make_sure_isdir", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "utils_classif.make_sure_isdir", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "utils_classif.make_sure_isdir", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 168, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 170, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 172, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 174, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 176, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 178, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 249, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "attribute"}, {"api_name": "feat_ext.load_audio_file", "line_number": 258, "usage_type": "call"}, {"api_name": "feat_ext.modify_file_variable_length", "line_number": 265, "usage_type": "call"}, {"api_name": "feat_ext.get_mel_spectrogram", "line_number": 272, "usage_type": "call"}, {"api_name": "utils_classif.save_tensor", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path", "line_number": 276, "usage_type": "attribute"}, {"api_name": "utils_classif.save_tensor", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 284, "usage_type": "call"}, {"api_name": "os.path", "line_number": 284, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 284, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 303, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path", "line_number": 306, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 307, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 309, "usage_type": "call"}, {"api_name": "os.path", "line_number": 309, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 310, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 311, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "feat_ext.load_audio_file", "line_number": 320, "usage_type": "call"}, {"api_name": "feat_ext.modify_file_variable_length", "line_number": 327, "usage_type": "call"}, {"api_name": "feat_ext.get_mel_spectrogram", "line_number": 334, "usage_type": "call"}, {"api_name": "utils_classif.save_tensor", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 338, "usage_type": "call"}, {"api_name": "os.path", "line_number": 338, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 341, "usage_type": "call"}, {"api_name": "data.get_label_files", "line_number": 365, "usage_type": "call"}, {"api_name": "data.DataGeneratorPatch", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 419, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 432, "usage_type": "call"}, {"api_name": "sklearn.utils.class_weight.compute_class_weight", "line_number": 433, "usage_type": "call"}, {"api_name": "sklearn.utils.class_weight", "line_number": 433, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 434, "usage_type": "call"}, {"api_name": "data.DataGeneratorPatch", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 446, "usage_type": "attribute"}, {"api_name": "architectures.get_model_crnn_seld_tagger", "line_number": 460, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 465, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 471, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 473, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path", "line_number": 477, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 479, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 500, "usage_type": "call"}, {"api_name": "data.PatchGeneratorPerFile", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 506, "usage_type": "attribute"}, {"api_name": "tqdm.trange", "line_number": 510, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 514, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 517, "usage_type": "call"}, {"api_name": "scipy.stats.gmean", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 524, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 525, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 529, "usage_type": "call"}, {"api_name": "os.path", "line_number": 529, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 531, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 538, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 548, "usage_type": "call"}, {"api_name": "eval.Evaluator", "line_number": 565, "usage_type": "call"}, {"api_name": "time.time", "line_number": 572, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 584, "usage_type": "call"}, {"api_name": "parameters.get_params", "line_number": 593, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 605, "usage_type": "call"}, {"api_name": "os.path", "line_number": 605, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 608, "usage_type": "call"}, {"api_name": "os.path", "line_number": 608, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 611, "usage_type": "call"}, {"api_name": "os.path", "line_number": 611, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 612, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 615, "usage_type": "call"}, {"api_name": "os.path", "line_number": 615, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 618, "usage_type": "call"}, {"api_name": "os.path", "line_number": 618, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 619, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 622, "usage_type": "call"}, {"api_name": "os.path", "line_number": 622, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 625, "usage_type": "call"}, {"api_name": "os.path", "line_number": 625, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 626, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 630, "usage_type": "call"}, {"api_name": "os.path", "line_number": 630, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 634, "usage_type": "call"}, {"api_name": "os.path", "line_number": 634, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 635, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 638, "usage_type": "call"}, {"api_name": "os.path", "line_number": 638, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 642, "usage_type": "call"}, {"api_name": "os.path", "line_number": 642, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 643, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 646, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 646, "usage_type": "call"}, {"api_name": "os.path", "line_number": 646, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 648, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 648, "usage_type": "call"}, {"api_name": "os.path", "line_number": 648, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 650, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 650, "usage_type": "call"}, {"api_name": "os.path", "line_number": 650, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 652, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 652, "usage_type": "call"}, {"api_name": "os.path", "line_number": 652, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 659, "usage_type": "call"}, {"api_name": "os.path", "line_number": 659, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 665, "usage_type": "call"}, {"api_name": "os.path", "line_number": 665, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 666, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 676, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 677, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 682, "usage_type": "call"}, {"api_name": "data.PatchGeneratorPerFile", "line_number": 688, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 692, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 709, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 713, "usage_type": "call"}, {"api_name": "scipy.stats.gmean", "line_number": 715, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 720, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 725, "usage_type": "call"}, {"api_name": "os.path", "line_number": 725, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 726, "usage_type": "call"}, {"api_name": "os.path", "line_number": 726, "usage_type": "attribute"}, {"api_name": "file_utils.write_metadata_result_file", "line_number": 727, "usage_type": "call"}, {"api_name": "file_utils.build_result_dict_from_metadata_array", "line_number": 730, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 731, "usage_type": "call"}, {"api_name": "os.path", "line_number": 731, "usage_type": "attribute"}, {"api_name": "file_utils.write_output_result_file", "line_number": 732, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 736, "usage_type": "call"}, {"api_name": "os.path", "line_number": 736, "usage_type": "attribute"}, {"api_name": "compute_doa_metrics.compute_DOA_metrics", "line_number": 737, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 748, "usage_type": "call"}, {"api_name": "os.path", "line_number": 748, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 751, "usage_type": "call"}, {"api_name": "os.path", "line_number": 751, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 752, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 755, "usage_type": "call"}, {"api_name": "os.path", "line_number": 755, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 758, "usage_type": "call"}, {"api_name": "os.path", "line_number": 758, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 759, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 762, "usage_type": "call"}, {"api_name": "os.path", "line_number": 762, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 765, "usage_type": "call"}, {"api_name": "os.path", "line_number": 765, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 766, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 769, "usage_type": "call"}, {"api_name": "os.path", "line_number": 769, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 772, "usage_type": "call"}, {"api_name": "os.path", "line_number": 772, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 773, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 776, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 776, "usage_type": "call"}, {"api_name": "os.path", "line_number": 776, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 778, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 778, "usage_type": "call"}, {"api_name": "os.path", "line_number": 778, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 780, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 780, "usage_type": "call"}, {"api_name": "os.path", "line_number": 780, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 782, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 782, "usage_type": "call"}, {"api_name": "os.path", "line_number": 782, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 789, "usage_type": "call"}, {"api_name": "os.path", "line_number": 789, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 793, "usage_type": "call"}, {"api_name": "os.path", "line_number": 793, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 794, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 804, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 805, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 813, "usage_type": "call"}, {"api_name": "data.PatchGeneratorPerFile", "line_number": 818, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 822, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 837, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 841, "usage_type": "call"}, {"api_name": "scipy.stats.gmean", "line_number": 843, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 848, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 853, "usage_type": "call"}, {"api_name": "os.path", "line_number": 853, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 854, "usage_type": "call"}, {"api_name": "os.path", "line_number": 854, "usage_type": "attribute"}, {"api_name": "file_utils.write_metadata_result_file", "line_number": 855, "usage_type": "call"}, {"api_name": "file_utils.build_result_dict_from_metadata_array", "line_number": 858, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 859, "usage_type": "call"}, {"api_name": "os.path", "line_number": 859, "usage_type": "attribute"}, {"api_name": "file_utils.write_output_result_file", "line_number": 860, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 864, "usage_type": "call"}, {"api_name": "os.path", "line_number": 864, "usage_type": "attribute"}, {"api_name": "compute_doa_metrics.compute_DOA_metrics", "line_number": 865, "usage_type": "call"}]} +{"seq_id": "31221038930", "text": "\"\"\"\n=========================================================\nClassifier comparison\n=========================================================\n\nA comparison of a several classifiers in :mod:`imbens.ensemble` \non synthetic datasets. The point of this example is to illustrate the nature \nof decision boundaries of different imbalanced ensmeble classifiers. \nThis should be taken with a grain of salt, as the intuition conveyed by these \nexamples does not necessarily carry over to real datasets.\n\nThe plots show training points in solid colors and testing points semi-transparent. \nThe lower right shows the average precision score (AUPRC) on the test set.\n\nThis example uses:\n \n - Reweighting-based method\n - :class:`imbens.ensemble.AdaCostClassifier`\n - :class:`imbens.ensemble.AdaUBoostClassifier`\n - :class:`imbens.ensemble.AsymBoostClassifier`\n - Under-sampling-based method\n - :class:`imbens.ensemble.SelfPacedEnsembleClassifier`\n - :class:`imbens.ensemble.BalanceCascadeClassifier`\n - :class:`imbens.ensemble.BalancedRandomForestClassifier`\n - :class:`imbens.ensemble.EasyEnsembleClassifier`\n - :class:`imbens.ensemble.RUSBoostClassifier`\n - :class:`imbens.ensemble.UnderBaggingClassifier`\n - Over-sampling-based method\n - :class:`imbens.ensemble.OverBoostClassifier`\n - :class:`imbens.ensemble.SMOTEBoostClassifier`\n - :class:`imbens.ensemble.KmeansSMOTEBoostClassifier`\n - :class:`imbens.ensemble.OverBaggingClassifier`\n - :class:`imbens.ensemble.SMOTEBaggingClassifier`\n\"\"\"\n\n# Authors: Zhining Liu \n# License: MIT\n\n# %%\nprint(__doc__)\n\n# Import imbalanced-ensemble\nimport imbens\n\n# Import utilities\nimport numpy as np\nimport sklearn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import make_moons, make_circles, make_classification\nfrom imbens.datasets import make_imbalance\n\n# Import plot utilities\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\n\nRANDOM_STATE = 42\n\n# %% [markdown]\n# Preparation\n# -----------\n# **Make 3 imbalanced toy classification tasks.**\n\ndistribution = {0: 100, 1: 50}\n\n# dataset 1\nX, y = make_moons(200, noise=0.2, random_state=RANDOM_STATE)\ndataset1 = make_imbalance(\n X, y, sampling_strategy=distribution, random_state=RANDOM_STATE\n)\n# dataset 2\nX, y = make_circles(200, noise=0.2, factor=0.5, random_state=RANDOM_STATE)\ndataset2 = make_imbalance(\n X, y, sampling_strategy=distribution, random_state=RANDOM_STATE\n)\n# dataset 3\nX, y = make_classification(\n 200,\n n_features=2,\n n_redundant=0,\n n_informative=2,\n random_state=1,\n n_clusters_per_class=1,\n)\nX += 2 * np.random.RandomState(RANDOM_STATE).uniform(size=X.shape)\ndataset3 = make_imbalance(\n X, y, sampling_strategy=distribution, random_state=RANDOM_STATE\n)\n\ndatasets = [dataset1, dataset2, dataset3]\n\n\n# %% [markdown]\n# **Load all ensemble classifiers**\n\nfrom imbens.utils.testing import all_estimators\n\ninit_kwargs = {'n_estimators': 5, 'random_state': RANDOM_STATE}\nall_ensembles_clf = {\n name: ensemble(**init_kwargs) for (name, ensemble) in all_estimators('ensemble')\n}\n\nprint('{:<30s} | Class \\n{:=<120s}'.format('Method', ''))\nfor (name, ensemble) in all_estimators('ensemble'):\n print('{:<30s} | {}'.format(name, ensemble))\n\n\n# %% [markdown]\n# **Function for classifier comparison**\n\n\ndef plot_classifier_comparison(classifiers, names, datasets, figsize):\n\n h = 0.02 # step size in the mesh\n\n figure = plt.figure(figsize=figsize)\n i = 1\n # iterate over datasets\n for ds_cnt, ds in enumerate(datasets):\n # preprocess dataset, split into training and test part\n X, y = ds\n X = StandardScaler().fit_transform(X)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=0.4, random_state=42\n )\n\n x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n\n # just plot the dataset first\n cm = plt.cm.RdBu\n cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n if ds_cnt == 0:\n ax.set_title(\"Input data\")\n # Plot the training points\n ax.scatter(\n X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors='k'\n )\n # Plot the testing points\n ax.scatter(\n X_test[:, 0],\n X_test[:, 1],\n c=y_test,\n cmap=cm_bright,\n alpha=0.6,\n edgecolors='k',\n )\n ax.set_xlim(xx.min(), xx.max())\n ax.set_ylim(yy.min(), yy.max())\n ax.set_xticks(())\n ax.set_yticks(())\n i += 1\n\n # iterate over classifiers\n for name, clf in zip(names, classifiers):\n ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n clf.fit(X_train, y_train)\n score = sklearn.metrics.average_precision_score(y_test, clf.predict(X_test))\n\n # Plot the decision boundary. For that, we will assign a color to each\n # point in the mesh [x_min, x_max]x[y_min, y_max].\n if hasattr(clf, \"decision_function\"):\n Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n else:\n Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n\n # Put the result into a color plot\n Z = Z.reshape(xx.shape)\n ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)\n\n # Plot the training points\n ax.scatter(\n X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors='k'\n )\n # Plot the testing points\n ax.scatter(\n X_test[:, 0],\n X_test[:, 1],\n c=y_test,\n cmap=cm_bright,\n edgecolors='k',\n alpha=0.6,\n )\n\n ax.set_xlim(xx.min(), xx.max())\n ax.set_ylim(yy.min(), yy.max())\n ax.set_xticks(())\n ax.set_yticks(())\n if ds_cnt == 0:\n ax.set_title(name)\n ax.text(\n 0.95,\n 0.06,\n ('%.2f' % score).lstrip('0'),\n size=15,\n bbox=dict(boxstyle='round', alpha=0.8, facecolor='white'),\n transform=ax.transAxes,\n horizontalalignment='right',\n )\n i += 1\n\n plt.tight_layout()\n plt.show()\n\n\n# %% [markdown]\n# Compare all under-sampling-based ensemble algorithms\n# ----------------------------------------------------\n\nfrom imbens.ensemble._under_sampling.__init__ import __all__ as names\n\nclassifiers = [all_ensembles_clf[name] for name in names]\nplot_classifier_comparison(\n classifiers, names, datasets, figsize=(len(names) * 3 + 3, 9)\n)\n\n\n# %% [markdown]\n# Compare all over-sampling-based ensemble algorithms\n# ----------------------------------------------------\n\nfrom imbens.ensemble._over_sampling.__init__ import __all__ as names\n\nclassifiers = [all_ensembles_clf[name] for name in names]\nplot_classifier_comparison(\n classifiers, names, datasets, figsize=(len(names) * 3 + 3, 9)\n)\n\n\n# %% [markdown]\n# Compare all reweighting-based ensemble algorithms\n# ----------------------------------------------------\n\nfrom imbens.ensemble._reweighting.__init__ import __all__ as names\n\nclassifiers = [all_ensembles_clf[name] for name in names]\nplot_classifier_comparison(\n classifiers, names, datasets, figsize=(len(names) * 3 + 3, 9)\n)\n", "repo_name": "ZhiningLiu1998/imbalanced-ensemble", "sub_path": "examples/classification/plot_classifier_comparison.py", "file_name": "plot_classifier_comparison.py", "file_ext": "py", "file_size_in_byte": 7757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 265, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sklearn.datasets.make_moons", "line_number": 67, "usage_type": "call"}, {"api_name": "imbens.datasets.make_imbalance", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 72, "usage_type": "call"}, {"api_name": "imbens.datasets.make_imbalance", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.datasets.make_classification", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 85, "usage_type": "attribute"}, {"api_name": "imbens.datasets.make_imbalance", "line_number": 86, "usage_type": "call"}, {"api_name": "imbens.utils.testing.all_estimators", "line_number": 100, "usage_type": "call"}, {"api_name": "imbens.utils.testing.all_estimators", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 132, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 160, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 167, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "imbens.ensemble._under_sampling.__init__.__all__", "line_number": 214, "usage_type": "name"}, {"api_name": "imbens.ensemble._under_sampling.__init__.__all__", "line_number": 216, "usage_type": "argument"}, {"api_name": "imbens.ensemble._over_sampling.__init__.__all__", "line_number": 226, "usage_type": "name"}, {"api_name": "imbens.ensemble._over_sampling.__init__.__all__", "line_number": 228, "usage_type": "argument"}, {"api_name": "imbens.ensemble._reweighting.__init__.__all__", "line_number": 238, "usage_type": "name"}, {"api_name": "imbens.ensemble._reweighting.__init__.__all__", "line_number": 240, "usage_type": "argument"}]} +{"seq_id": "29564144095", "text": "import pytest\n\nfrom tensorbay.label import Classification, ClassificationSubcatalog\n\n\n@pytest.fixture\ndef subcatalog_classification(categories_catalog_data, attributes_catalog_data):\n return {\n \"categories\": categories_catalog_data,\n \"attributes\": attributes_catalog_data,\n \"categoryDelimiter\": \".\",\n }\n\n\nclass TestClassification:\n def test_init(self):\n classification = Classification(category=\"cat\", attributes={\"gender\": \"male\"})\n\n assert classification.category == \"cat\"\n assert classification.attributes == {\"gender\": \"male\"}\n\n def test_eq(self):\n classification1 = Classification(\"cat\", {\"gender\": \"male\"})\n classification2 = Classification(\"cat\", {\"gender\": \"male\"})\n classification3 = Classification(\"dog\", {\"gender\": \"male\"})\n\n assert classification1 == classification2\n assert classification1 != classification3\n\n def test_loads(self):\n contents = {\"category\": \"cat\", \"attributes\": {\"gender\": \"male\"}}\n classification = Classification.loads(contents)\n\n assert classification.category == \"cat\"\n assert classification.attributes == {\"gender\": \"male\"}\n\n def test_dumps(self):\n classification = Classification(category=\"cat\", attributes={\"gender\": \"male\"})\n\n assert classification.dumps() == {\"category\": \"cat\", \"attributes\": {\"gender\": \"male\"}}\n\n\nclass TestClassificationSubcatalog:\n def test_init(self):\n description = \"This is a test text.\"\n classification_subcatalog = ClassificationSubcatalog(description)\n classification_subcatalog.description = description\n\n def test_eq(self):\n contents1 = {\"category\": \"cat\", \"attributes\": [{\"name\": \"color\", \"enum\": [\"white\", \"red\"]}]}\n contents2 = {\n \"category\": \"cat\",\n \"attributes\": [{\"name\": \"color\", \"enum\": [\"white\", \"blue\"]}],\n }\n classification_subcatalog1 = ClassificationSubcatalog.loads(contents1)\n classification_subcatalog2 = ClassificationSubcatalog.loads(contents1)\n classification_subcatalog3 = ClassificationSubcatalog.loads(contents2)\n\n assert classification_subcatalog1 == classification_subcatalog2\n assert classification_subcatalog1 != classification_subcatalog3\n\n def test_loads(self, categories, attributes, subcatalog_classification):\n classification_subcatalog = ClassificationSubcatalog.loads(subcatalog_classification)\n assert classification_subcatalog.categories == categories\n assert classification_subcatalog.attributes == attributes\n assert (\n classification_subcatalog.category_delimiter\n == subcatalog_classification[\"categoryDelimiter\"]\n )\n\n def test_dumps(self, categories, attributes, subcatalog_classification):\n classification_subcatalog = ClassificationSubcatalog()\n classification_subcatalog.categories = categories\n classification_subcatalog.attributes = attributes\n classification_subcatalog.category_delimiter = subcatalog_classification[\n \"categoryDelimiter\"\n ]\n\n assert classification_subcatalog.dumps() == subcatalog_classification\n", "repo_name": "Graviti-AI/tensorbay-python-sdk", "sub_path": "tensorbay/label/tests/test_label_classification.py", "file_name": "test_label_classification.py", "file_ext": "py", "file_size_in_byte": 3185, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 74, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pytest.fixture", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tensorbay.label.Classification", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorbay.label.Classification", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorbay.label.Classification", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorbay.label.Classification", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorbay.label.Classification.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorbay.label.Classification", "line_number": 32, "usage_type": "name"}, {"api_name": "tensorbay.label.Classification", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorbay.label.ClassificationSubcatalog", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorbay.label.ClassificationSubcatalog.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorbay.label.ClassificationSubcatalog", "line_number": 55, "usage_type": "name"}, {"api_name": "tensorbay.label.ClassificationSubcatalog.loads", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorbay.label.ClassificationSubcatalog", "line_number": 56, "usage_type": "name"}, {"api_name": "tensorbay.label.ClassificationSubcatalog.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorbay.label.ClassificationSubcatalog", "line_number": 57, "usage_type": "name"}, {"api_name": "tensorbay.label.ClassificationSubcatalog.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorbay.label.ClassificationSubcatalog", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorbay.label.ClassificationSubcatalog", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "16335375079", "text": "from django.shortcuts import render\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport urllib.request\n\n# Create your views here.\n\n\ndef dictionary(request):\n isWord = False\n\n if request.method == 'POST':\n print(request.POST)\n isWord = True\n\n # get the word from the form\n word = request.POST['text']\n # get the url of the dictionary\n url = \"https://www.vocabulary.com/dictionary/\" + word\n htmlfile = urllib.request.urlopen(url)\n soup = BeautifulSoup(htmlfile, 'html.parser')\n soup1 = soup.find(class_=\"short\")\n try:\n soup1 = soup1.get_text()\n shortText = soup1\n except AttributeError:\n shortText = \"No definition found\"\n # Print short meaning\n print('Cannot find such word! Check spelling.')\n\n # Print long meaning\n soup2 = soup.find(class_=\"long\")\n try:\n soup2 = soup2.get_text()\n longText = soup2\n except AttributeError:\n longText = ''\n # Print instances like Synonyms, Antonyms, etc.\n print('Cannot find such word! Check spelling.')\n\n soup3 = soup.find(class_=\"instances\")\n try:\n txt1 = soup3.get_text()\n synonymAntonym = ' '.join(txt1.split())\n except AttributeError:\n synonymAntonym = ' '\n print('Cannot find such word! Check spelling.')\n\n return render(request, 'dictionary/dictionary.html', {'word': word, 'shortText': shortText, 'longText': longText, 'synonymAntonym': synonymAntonym, 'isWord': isWord})\n\n else:\n return render(request, 'dictionary/dictionary.html')\n", "repo_name": "Nayan-b/simple_dj", "sub_path": "dictionary/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "8078113420", "text": "# -*- coding: utf-8 -*-\n# pip install networkx\n# examples to write GEXF : https://github.com/medialab/Hypertext-Corpus-Initiative/blob/master/lib/gexf.py\n\nimport networkx as nx\nimport numpy as np\nimport numpy.matlib as npm\n\ndef run(sourceFile, targetFile):\n M, nodes, hashnodes = readGEXFintoMatrix(sourceFile)\n C = mesure(M, 10)\n saveMatrixintoGEXF(C.tolist(), nodes, hashnodes, targetFile)\n print(\"OK\")\n\ndef readGEXFintoMatrix(path):\n from cStringIO import StringIO\n sio = StringIO()\n\n for line in open(path):\n if \">sio, line\n sio.seek(0)\n\n G = nx.read_gexf(sio)\n hashnodes = {}\n nodes = [(0, {}) for _ in G.nodes()]\n\n for i, (nid, data) in enumerate(G.nodes(data=True)):\n hashnodes[nid] = i\n nodes[i] = (nid, data)\n\n ln = len(nodes)\n M = npm.zeros((ln, ln))\n\n for (aid, bid, w) in G.edges_iter(data=True):\n a = hashnodes[aid]\n b = hashnodes[bid]\n M[a,b] = 1 #w['weight']\n M[b,a] = 1 #w['weight']\n\n return (M, nodes, hashnodes)\n\ndef saveMatrixintoGEXF(M, nodes, hashnodes, path):\n G2 = nx.DiGraph()\n\n for i, (nid, data) in enumerate(nodes):\n G2.add_node(nid, data)\n\n for isource, (nsourceid, datasource) in enumerate(nodes):\n for itarget, (ntargetid, datatarget) in enumerate(nodes):\n a = hashnodes[nsourceid]\n b = hashnodes[ntargetid]\n G2.add_edge(nsourceid, ntargetid, weight=M[a][b])\n\n nx.write_gexf(G2, path)\n\ndef mesure(M,prec): #Calcul de la mesure jusqu'au rang Prec\n C = npm.identity(len(M))\n\n for i in range(prec):\n x = (M ** (i+1)) * ((len(M)-1) ** (1-i))\n C = C + x\n\n return C\n\nif __name__ == \"__main__\":\n import sys\n run(sys.argv[1], sys.argv[2])\n", "repo_name": "medialab/spatialization-quality", "sub_path": "mesure.py", "file_name": "mesure.py", "file_ext": "py", "file_size_in_byte": 1817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cStringIO.StringIO", "line_number": 17, "usage_type": "call"}, {"api_name": "networkx.read_gexf", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.matlib.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.matlib", "line_number": 33, "usage_type": "name"}, {"api_name": "networkx.DiGraph", "line_number": 44, "usage_type": "call"}, {"api_name": "networkx.write_gexf", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.matlib.identity", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.matlib", "line_number": 58, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 68, "usage_type": "attribute"}]} +{"seq_id": "30860498224", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n* Author Dominik Symonowicz\n* WWW:\thttps://dominiksymonowicz.com/welcome\n* IT BLOG:\thttps://dominiksymonowicz.blogspot.co.uk\n* GitHub:\thttps://github.com/pastorcmentarny\n* Google Play:\thttps://play.google.com/store/apps/developer?id=Dominik+Symonowicz\n* LinkedIn: https://www.linkedin.com/in/dominik-symonowicz\n\"\"\"\n\nimport csv\nimport json\nimport logging\nfrom datetime import date\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport config\nimport dom_utils\n\nENCODING = 'utf-8'\nDEFAULT_PATH = \"{}/{}{}\"\n\nlogger = logging.getLogger('ddd')\n\n\ndef save_raw_reading(reading):\n data_path = config.get_directory_path_for_aircraft()\n date_as_folders = dom_utils.get_date_as_folders_linux()\n Path(\"{}/{}\".format(data_path, date_as_folders)).mkdir(parents=True, exist_ok=True)\n airport_raw_data = DEFAULT_PATH.format(data_path, date_as_folders,\n dom_utils.get_date_as_filename(\"aircraft\", \"txt\", datetime.now()))\n try:\n with open(airport_raw_data, 'a+', encoding=ENCODING) as aircraft_raw_file:\n json.dump(reading, aircraft_raw_file, ensure_ascii=False, indent=4)\n except Exception as exception:\n logger.error('Unable to save raw reading due to {}'.format(exception), exc_info=True)\n\n\ndef save_processed_data(result):\n data_path = config.get_directory_path_for_aircraft()\n date_as_folders = dom_utils.get_date_as_folders_linux()\n Path(\"{}/{}\".format(data_path, date_as_folders)).mkdir(parents=True, exist_ok=True)\n airport_processed_data = DEFAULT_PATH.format(data_path, date_as_folders,\n dom_utils.get_date_as_filename(\"aircraft-processed\", \"csv\",\n datetime.now()))\n timestamp = datetime.now()\n try:\n with open(airport_processed_data, 'a+', encoding=ENCODING, newline='') as aircraft_processed_file:\n csv_writer = csv.writer(aircraft_processed_file)\n\n for entry in result:\n if entry['flight'] != '':\n csv_writer.writerow([timestamp,\n entry['hex'], entry['squawk'], entry['flight'].strip(), entry['lat'],\n entry['lon'], entry['validposition'], entry['altitude'],\n entry['vert_rate'], entry['track'], entry['validtrack'],\n entry['speed'], entry['messages'], entry['seen']\n ])\n except Exception as exception:\n logger.error('Unable to save processed reading due to {}'.format(exception), exc_info=True)\n\n\ndef load_processed_data() -> list:\n return load_processed_data_for(date.today())\n\n\ndef load_processed_for_yesterday() -> list:\n return load_processed_data_for(dom_utils.get_yesterday_date_as_date())\n\n\ndef load_processed_data_for(specified_data: date) -> list:\n data_path = config.get_directory_path_for_aircraft()\n date_as_folders = dom_utils.get_date_as_folders_for(specified_data)\n airport_processed_data = DEFAULT_PATH.format(data_path, date_as_folders,\n dom_utils.get_date_as_filename(\"aircraft-processed\", \"csv\",\n dom_utils.to_datetime(specified_data)))\n dom_utils.fix_nulls(airport_processed_data)\n try:\n with open(airport_processed_data) as csv_file:\n aircraft_csv = csv.reader(csv_file)\n return list(aircraft_csv)\n except Exception as exception:\n logger.error('Unable to load processed reading due to {}'.format(exception), exc_info=True)\n return []\n", "repo_name": "pastorcmentarny/denva", "sub_path": "src/ddd/aircraft_storage.py", "file_name": "aircraft_storage.py", "file_ext": "py", "file_size_in_byte": 3808, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "config.get_directory_path_for_aircraft", "line_number": 30, "usage_type": "call"}, {"api_name": "dom_utils.get_date_as_folders_linux", "line_number": 31, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "dom_utils.get_date_as_filename", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}, {"api_name": "config.get_directory_path_for_aircraft", "line_number": 43, "usage_type": "call"}, {"api_name": "dom_utils.get_date_as_folders_linux", "line_number": 44, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 45, "usage_type": "call"}, {"api_name": "dom_utils.get_date_as_filename", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 67, "usage_type": "name"}, {"api_name": "dom_utils.get_yesterday_date_as_date", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 74, "usage_type": "name"}, {"api_name": "config.get_directory_path_for_aircraft", "line_number": 75, "usage_type": "call"}, {"api_name": "dom_utils.get_date_as_folders_for", "line_number": 76, "usage_type": "call"}, {"api_name": "dom_utils.get_date_as_filename", "line_number": 78, "usage_type": "call"}, {"api_name": "dom_utils.to_datetime", "line_number": 79, "usage_type": "call"}, {"api_name": "dom_utils.fix_nulls", "line_number": 80, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "28196572310", "text": "from collections import Counter\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nfrom nltk.tokenize import word_tokenize\n\n\ndef plot_length_distribution(df, column_name) -> None:\n \"\"\"Plot the distribution of the length of a specified column of a DataFrame.\n\n The function calculates the mean and standard deviation of the column, and plots the\n distribution of the column values. It also plots the mean, and one standard deviation\n above and below the mean.\n\n Parameters\n ----------\n df (pd.DataFrame): The DataFrame containing the text data.\n column_name (str): The column of the DataFrame to analyze.\n\n Returns\n -------\n None. The function shows a plot.\n \"\"\"\n # Calculate the mean and standard deviation of the column\n mean = df[column_name].mean()\n std = df[column_name].std()\n\n # Plot the distribution\n plt.figure(figsize=(10, 6))\n sns.histplot(data=df, x=column_name, color=\"cornflowerblue\")\n\n # Plot the mean, and one standard deviation above and below the mean\n plt.axvline(mean, color=\"red\", linestyle=\"dashed\", linewidth=2)\n plt.axvline(mean + std, color=\"red\", linestyle=\"dashed\", linewidth=2)\n plt.axvline(mean - std, color=\"red\", linestyle=\"dashed\", linewidth=2)\n\n # Remove top and right spines\n sns.despine()\n\n plt.title(f\"Distribution of {column_name} Length\")\n plt.show()\n\n\ndef plot_top_words(df, column_name) -> None:\n \"\"\"Plot the top 10 words in a specified column of a DataFrame.\n\n The function tokenizes the text in the specified column, converts the tokens to lower\n case, removes non-alphabetic tokens, removes stop words, and stems the words. It then\n counts the frequency of each word, and plots the top 10 words.\n\n Parameters\n ----------\n df (pd.DataFrame): The DataFrame containing the text data.\n column_name (str): The column of the DataFrame to analyze.\n\n Returns\n -------\n None. The function shows a plot.\n \"\"\"\n # Tokenize the text\n tokens = df[column_name].apply(word_tokenize)\n\n # Convert to lower case\n tokens = tokens.apply(lambda x: [token.lower() for token in x])\n\n # Remove non-alphabetic tokens\n tokens = tokens.apply(lambda x: [token for token in x if token.isalpha()])\n\n # Remove stop words\n stop_words = stopwords.words(\"english\")\n tokens = tokens.apply(lambda x: [token for token in x if token not in stop_words])\n\n # stem words\n stemmer = PorterStemmer()\n tokens = tokens.apply(lambda x: [stemmer.stem(token) for token in x])\n\n # Count the frequency of each word\n word_counts = Counter([token for sublist in tokens.tolist() for token in sublist])\n\n # Create dataframe\n df_word_counts = pd.DataFrame.from_dict(word_counts, orient=\"index\").reset_index()\n df_word_counts.columns = [\"Word\", \"Count\"]\n\n # Sort by count and take the top 10\n df_word_counts_top10 = df_word_counts.sort_values(\"Count\", ascending=False).head(10)\n\n # Plot\n plt.figure(figsize=(10, 6))\n sns.barplot(x=\"Count\", y=\"Word\", data=df_word_counts_top10, color=\"cornflowerblue\")\n\n # Remove top and right spines\n sns.despine()\n\n plt.title(f\"Top 10 Words in {column_name}\")\n plt.show()\n", "repo_name": "TerboucheHacene/ScholarSense", "sub_path": "src/scholar_sense/viz/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3301, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "seaborn.histplot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 64, "usage_type": "argument"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 73, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 73, "usage_type": "name"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 77, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 84, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 92, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}]} +{"seq_id": "34301909196", "text": "from itertools import chain\nimport pandas as pd\nimport numpy as np\nimport datetime\nimport time\nimport os\nfrom pathlib import Path\nfrom alpha_vantage.timeseries import TimeSeries\nimport sys\n\ndf_constituents = pd.read_csv(\n \"https://raw.githubusercontent.com/fja05680/sp500/master/S%26P%20500%20Historical%20Components%20%26%20Changes(01-21-2021).csv\",\n parse_dates=True,\n index_col=0,\n).sort_index(ascending=False)\n\nALPHA_VANTAGE_DIR_PATH = Path(\"alphadata\").absolute()\n\ndef generate_monthly_stats(df):\n #print(df)\n log_return = df[\"Close\"].apply(np.log).diff()\n half_way_point = len(df) // 2\n\n return {\n \"Open\": df[\"Open\"].iloc[0],\n \"High\": df[\"High\"].max(),\n \"Low\": df[\"Low\"].min(),\n \"Close\": df[\"Close\"].iloc[-1],\n \"Volume\": df[\"Volume\"].sum(),\n \"first_half_log_return_mean\": log_return.iloc[:half_way_point].mean(),\n \"first_half_log_return_std\": log_return.iloc[:half_way_point].std(),\n \"second_half_log_return_mean\": log_return.iloc[half_way_point:].mean(),\n \"second_half_log_return_std\": log_return.iloc[half_way_point:].std(),\n \"first_second_half_log_return_diff\": (\n log_return.iloc[half_way_point:].sum()\n - log_return.iloc[:half_way_point].sum()\n ),\n \"log_return_mean\": log_return.mean(),\n \"log_return_std\": log_return.std(),\n \"log_return_min\": log_return.min(),\n \"log_return_max\": log_return.max(),\n \"month_log_return\": np.log(df[\"Close\"].iloc[-1] / df[\"Open\"].iloc[0]),\n \"pct_bull\": (log_return > 0).mean()\n }\n\ntickers = set(\",\".join(df_constituents.tickers.values).split(\",\"))\nslippage = .005 # 0.5% slippage per trade\ndict_dfs = dict()\nfor t in tickers:\n # this stock is not available on alpha vantage\n if not (ALPHA_VANTAGE_DIR_PATH / f\"{t}.csv\").is_file():\n continue\n temp = (\n pd.read_csv(ALPHA_VANTAGE_DIR_PATH / f\"{t}.csv\", index_col=0, parse_dates=True)\n .groupby(pd.Grouper(freq=\"1M\"))\n .apply(generate_monthly_stats)\n )\n print(temp)\n temp[\"next_month_log_return\"] = np.log(\n np.exp(temp[\"month_log_return\"].shift(-1)) * (1 - slippage) / (1 + slippage)\n )\n dict_dfs[t] = temp", "repo_name": "ivansmith7795/ANNRecommender", "sub_path": "featureengineer.py", "file_name": "featureengineer.py", "file_ext": "py", "file_size_in_byte": 2219, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.Grouper", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "18987497097", "text": "import os as _os\nimport os.path as _path\n\nfrom json import dump as _json_save\nfrom json import load as _json_load\nfrom logging import DEBUG as _DEBUG\nfrom logging import INFO as _INFO\nfrom logging import getLogger\n\nfrom solaar import __version__\n\n_log = getLogger(__name__)\ndel getLogger\n\n_XDG_CONFIG_HOME = _os.environ.get('XDG_CONFIG_HOME') or _path.expanduser(_path.join('~', '.config'))\n_file_path = _path.join(_XDG_CONFIG_HOME, 'solaar', 'config.json')\n\n_KEY_VERSION = '_version'\n_KEY_NAME = '_name'\n_KEY_SERIAL = '_serial'\n_KEY_MODEL_ID = '_modelId'\n_KEY_UNIT_ID = '_unitId'\n_configuration = {}\n\n\ndef _load():\n if _path.isfile(_file_path):\n loaded_configuration = {}\n try:\n with open(_file_path) as config_file:\n loaded_configuration = _json_load(config_file)\n except Exception:\n _log.error('failed to load from %s', _file_path)\n\n # loaded_configuration.update(_configuration)\n _configuration.clear()\n _configuration.update(loaded_configuration)\n\n if _log.isEnabledFor(_DEBUG):\n _log.debug('load => %s', _configuration)\n\n _cleanup(_configuration)\n _cleanup_load(_configuration)\n _configuration[_KEY_VERSION] = __version__\n return _configuration\n\n\ndef save():\n # don't save if the configuration hasn't been loaded\n if _KEY_VERSION not in _configuration:\n return\n\n dirname = _os.path.dirname(_file_path)\n if not _path.isdir(dirname):\n try:\n _os.makedirs(dirname)\n except Exception:\n _log.error('failed to create %s', dirname)\n return False\n\n _cleanup(_configuration)\n\n try:\n with open(_file_path, 'w') as config_file:\n _json_save(_configuration, config_file, skipkeys=True, indent=2, sort_keys=True)\n\n if _log.isEnabledFor(_INFO):\n _log.info('saved %s to %s', _configuration, _file_path)\n return True\n except Exception as e:\n _log.error('failed to save to %s: %s', _file_path, e)\n\n\ndef _cleanup(d):\n # remove None values from the dict\n for key in list(d.keys()):\n value = d.get(key)\n if value is None:\n del d[key]\n elif isinstance(value, dict):\n _cleanup(value)\n\n\ndef _cleanup_load(d):\n # remove boolean values for mouse-gesture and dpi-sliding\n for device in d.values():\n if isinstance(device, dict):\n for setting in ['mouse-gestures', 'dpi-sliding']:\n mg = device.get(setting, None)\n if mg is True or mg is False:\n del device[setting]\n\n\nclass _DeviceEntry(dict):\n def __init__(self, device, **kwargs):\n super().__init__(**kwargs)\n if self.get(_KEY_NAME) != device.name:\n self[_KEY_NAME] = device.name\n self.update(device)\n\n def __setitem__(self, key, value):\n super().__setitem__(key, value)\n save()\n\n def update(self, device):\n if device.modelId and device.modelId != self.get(_KEY_MODEL_ID):\n self[_KEY_MODEL_ID] = device.modelId\n if device.unitId and device.unitId != self.get(_KEY_UNIT_ID):\n self[_KEY_UNIT_ID] = device.unitId\n if device.serial and device.serial != '?' and device.serial != self.get(_KEY_SERIAL):\n self[_KEY_SERIAL] = device.serial\n\n def get_sensitivity(self, name):\n return self.get('_sensitive', {}).get(name, False)\n\n def set_sensitivity(self, name, value):\n sensitives = self.get('_sensitive', {})\n if sensitives.get(name) != value:\n sensitives[name] = value\n self.__setitem__('_sensitive', sensitives)\n\n\n# This is neccessarily complicate because the same device can be attached in several different ways.\n# All HID++ 2.0 devices have a modelId and unitId, which can be accessed when they are connected.\n# When paired via a receiver the receiver provides a WPID and a serial number.\n# The unitId and serial number are supposed to be the same, but for some models they are not\n# so even though the modelId includes the WPID it is not always possible to determine the identity of a\n# paired but not receiver-connected device for which the unitId is not known.\n# This only happens is Solaar has never seen the device while it is paired and connected through a receiver.\ndef persister(device):\n if not _configuration:\n _load()\n\n entry = {}\n key = None\n if device.wpid: # connected via receiver\n entry = _configuration.get('%s:%s' % (device.wpid, device.serial), {})\n if entry or device.protocol == 1.0: # found entry or create entry for old-style devices\n key = '%s:%s' % (device.wpid, device.serial)\n elif not entry and device.modelId: # online new-style device so look for modelId and unitId\n for k, c in _configuration.items():\n if isinstance(c, dict) and c.get(_KEY_MODEL_ID) == device.modelId and c.get(_KEY_UNIT_ID) == device.unitId:\n entry = c # use the entry that matches modelId and unitId\n key = k\n break\n if device.wpid and entry: # move entry to wpid:serial\n del _configuration[key]\n key = '%s:%s' % (device.wpid, device.serial)\n _configuration[key] = entry\n elif device.wpid and not entry: # create now with wpid:serial\n key = '%s:%s' % (device.wpid, device.serial)\n elif not entry: # create now with modelId:unitId\n key = '%s:%s' % (device.modelId, device.unitId)\n else: # defer until more is known (i.e., device comes on line)\n return\n\n if key and not isinstance(entry, _DeviceEntry):\n entry = _DeviceEntry(device, **entry)\n _configuration[key] = entry\n if isinstance(entry, _DeviceEntry):\n entry.update(device)\n\n return entry\n\n\ndef attach_to(device):\n pass\n", "repo_name": "mohamedadel666/Solaar", "sub_path": "lib/solaar/configuration.py", "file_name": "configuration.py", "file_ext": "py", "file_size_in_byte": 5838, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "name"}, {"api_name": "json.load", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 39, "usage_type": "argument"}, {"api_name": "solaar.__version__", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 67, "usage_type": "argument"}]} +{"seq_id": "37514086796", "text": "import ssl\nimport json\nimport logging\nimport dataclasses\nimport multiprocessing\n\nfrom . import crypto\nfrom . import snakesockets\n\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\nclass Sender:\n def __init__(self, mcrypto, certs, storage, my_port, queue):\n self.queue = queue\n self.storage = storage\n self.my_port = my_port\n self.offline_requested = None\n self.llsender = LowLevelSender(self.queue, mcrypto, certs, my_port)\n self.llsender_proc = multiprocessing.Process(\n target=self.llsender.run\n )\n self.llsender_proc.start()\n\n def send_message_to(self, user_key, message):\n self.broadcast(message, user_key)\n\n def request_offline_data(self):\n self.offline_requested = [\n address for address in self.storage.ipaddresses.list_all()\n ]\n \n self.broadcast(\n json.dumps(\n dict(\n type=\"request_offline_data\",\n server_port=self.my_port\n )\n )\n )\n\n def respond_offline_data(self, address):\n response = dict(\n type=\"response_offline_data\",\n server_port=self.my_port,\n ciphergrams=[]\n )\n for ciphergram in self.storage.ciphergrams.list_all():\n response[\"ciphergrams\"].append(\n dict(\n content=ciphergram.content,\n timestamp=ciphergram.timestamp\n )\n )\n self.send_to(json.dumps(response), address)\n\n def send_to(self, message, ip_address):\n self.queue.put(\n ([ip_address, ], message, None)\n )\n\n def broadcast(self, message, user_key=None):\n addresses = self.storage.ipaddresses.list_all()\n self.queue.put((addresses, message, user_key))\n\n def broadcast_from(self, message, ip_address):\n ip_addresses = self.storage.ipaddresses.list_all()\n try:\n ip_addresses.remove(ip_address)\n except ValueError:\n pass\n else:\n self.queue.put((ip_addresses, message, None))\n\n def terminate(self):\n self.llsender_proc.terminate()\n self.llsender_proc.join()\n\n\nclass LowLevelSender:\n def __init__(self, queue, mcrypto, certs, port):\n self.queue = queue\n self.mcrypto = mcrypto\n self.certs = certs\n self.my_port = port\n\n def _send_message(self, ip_addresses, message):\n context = ssl.SSLContext()\n context.verify_mode = ssl.CERT_NONE\n for ip_address in ip_addresses:\n try:\n client_socket = snakesockets.TCP()\n client_socket.sock =context.wrap_socket(client_socket.sock)\n client_socket.connect((ip_address.address, ip_address.port))\n client_socket.send(message.encode(\"utf-8\"))\n except Exception:\n pass\n logger.info(\n f\"Sending message to {ip_address} with content {message}\"\n )\n\n def run(self):\n while True:\n addresses, message, node_id = self.queue.get()\n if node_id is not None:\n try:\n ciphergram = self.mcrypto.get_ciphergram(node_id, message)\n except crypto.MessageCryptoInvalidRecipientKey:\n pass\n else:\n self._send_message(\n addresses,\n json.dumps(\n dict(\n type=\"ciphergram\",\n server_port=self.my_port,\n **dataclasses.asdict(ciphergram)\n )\n )\n )\n else:\n self._send_message(addresses, message)\n", "repo_name": "Zamony/securetalks", "sub_path": "securetalks/sender.py", "file_name": "sender.py", "file_ext": "py", "file_size_in_byte": 3878, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 20, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "ssl.SSLContext", "line_number": 88, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 89, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 113, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "3597805702", "text": "import argparse\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nimport pdo.service_client.service_data.eservice as eservice_db\n\n__all__ = ['command_eservice_db']\n\n## -----------------------------------------------------------------\n## -----------------------------------------------------------------\ndef command_eservice_db(state, bindings, pargs) :\n \"\"\"controller command to manage the enclave service database\n \"\"\"\n\n parser = argparse.ArgumentParser(prog='eservice')\n\n subparsers = parser.add_subparsers(dest='command')\n\n add_parser = subparsers.add_parser('add', description='add an eservice to the database')\n add_parser.add_argument('--url', help='URL for the enclave service to add', type=str, required=True)\n add_parser.add_argument('--name', help='Short name for the enclave service', type=str, required=True)\n\n clear_parser = subparsers.add_parser('clear', description='remove all eservices in the database')\n list_parser = subparsers.add_parser('list', description='list eservices in the database')\n\n load_parser = subparsers.add_parser('load', description='load an eservice database')\n load_parser.add_argument('--database', help='Name of the eservice database to use', type=str, required=True)\n merge_group = load_parser.add_mutually_exclusive_group(required=False)\n merge_group.add_argument('--merge', help='Merge new database with current db', dest='merge', action='store_true')\n merge_group.add_argument('--no-merge', help='Overwrite current db with new database', dest='merge', action='store_false')\n load_parser.set_defaults(merge=False)\n\n remove_parser = subparsers.add_parser('remove', description='remove eservice from the database')\n remove_group = remove_parser.add_mutually_exclusive_group(required=True)\n remove_group.add_argument('--name', help='Short name for enclave service to remove', type=str)\n\n save_parser = subparsers.add_parser('save', description='save the current eservice database')\n save_parser.add_argument('--database', help='Name of the eservice database to use', type=str, required=True)\n\n options = parser.parse_args(pargs)\n\n default_database = state.get(['Service', 'EnclaveServiceDatabaseFile'])\n ledger_config = state.get(['Ledger'])\n\n if options.command == 'add' :\n if not eservice_db.add_by_url(ledger_config, options.url, name=options.name, update=True) :\n raise Exception('failed to add eservice {0} to the database'.format(options.name))\n return\n\n if options.command == 'clear' :\n eservice_db.clear_all_data()\n return\n\n if options.command == 'list' :\n enclave_names = list(eservice_db.get_enclave_names())\n enclave_names.sort()\n\n for enclave_name in enclave_names :\n enclave_info = eservice_db.get_by_name(enclave_name)\n enclave_short_id = _hashed_identity_(enclave_info.enclave_id)\n print(\"{0:<18} {1:<18} {2}\".format(enclave_name, enclave_short_id, enclave_info.url))\n\n if options.command == 'load' :\n eservice_db.load_database(options.database, options.merge)\n return\n\n if options.command == 'remove' :\n eservice_db.remove_by_name(name=options.name)\n return\n\n if options.command == 'save' :\n eservice_db.save_database(options.database, True)\n return\n\n raise Exception('unknown subcommand')\n", "repo_name": "Yeuman/project4", "sub_path": "common/crypto/pdo/client/pdo/client/controller/commands/eservice_db.py", "file_name": "eservice_db.py", "file_ext": "py", "file_size_in_byte": 3376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice.add_by_url", "line_number": 47, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 47, "usage_type": "name"}, {"api_name": "pdo.service_client.service_data.eservice.clear_all_data", "line_number": 52, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 52, "usage_type": "name"}, {"api_name": "pdo.service_client.service_data.eservice.get_enclave_names", "line_number": 56, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 56, "usage_type": "name"}, {"api_name": "pdo.service_client.service_data.eservice.get_by_name", "line_number": 60, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 60, "usage_type": "name"}, {"api_name": "pdo.service_client.service_data.eservice.load_database", "line_number": 65, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 65, "usage_type": "name"}, {"api_name": "pdo.service_client.service_data.eservice.remove_by_name", "line_number": 69, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 69, "usage_type": "name"}, {"api_name": "pdo.service_client.service_data.eservice.save_database", "line_number": 73, "usage_type": "call"}, {"api_name": "pdo.service_client.service_data.eservice", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "28186522560", "text": "from torch.utils.data import Dataset\nimport numpy as np\nimport os\nimport cv2\nimport random\nimport torch\nfrom imgaug import augmenters as iaa\nimport imgaug\nfrom PIL import Image\nimgaug.imgaug.seed(1)\n\nclass VaihingenSegmentation(Dataset):\n\n NUM_CLASSES = 6\n\n def __init__(self,\n args,\n base_dir='/Vaihingen_dirs/',\n split='val'):\n super().__init__()\n self._base_dir = base_dir\n self.split = split\n self.args = args\n if self.split == 'train'or self.split == 'train_val':\n self._image_dir = os.path.join(self._base_dir, 'train_data', '512_top')\n self._cat_dir = os.path.join(self._base_dir, 'train_data', '512_label')\n self.images = sorted(os.listdir(self._image_dir))#[:30] # 2695\n self.cats = sorted(os.listdir(self._cat_dir))#[:30]\n if self.split == 'val':\n self._image_dir = os.path.join(self._base_dir, 'validation_data', '512_top')\n self._cat_dir = os.path.join(self._base_dir, 'validation_data', '512_label')\n self.images = sorted(os.listdir(self._image_dir)) # 2695\n self.cats = sorted(os.listdir(self._cat_dir))\n self.categories = [\n 'Imps',\n 'Building',\n 'Lowvg',\n 'Tree',\n 'Car',\n 'bg'\n ]\n\n def __len__(self):\n # print(len(self.images))\n return len(self.images)\n # return 10\n def _make_img_gt_point_pair(self, index):\n _img_path = os.path.join(self._image_dir, self.images[index])\n _target_path = os.path.join(self._cat_dir, self.cats[index])\n _img = cv2.imread(_img_path)\n _img = cv2.cvtColor(_img, cv2.COLOR_BGR2RGB)\n _target = cv2.imread(_target_path, cv2.IMREAD_GRAYSCALE)\n\n return _img, _target\n\n def __getitem__(self, index):\n _img, _target = self._make_img_gt_point_pair(index)\n sample = {'image': _img, 'label': _target}\n\n if self.split == 'train':\n sample = self.img_aug(sample, self.args)\n\n sample = self.ToTensor(sample)\n return sample\n\n def img_aug(self,sample, args):\n img, label = sample['image'], sample['label']\n flipper = iaa.Fliplr(0.5).to_deterministic()\n label = flipper.augment_image(label)\n img = flipper.augment_image(img)\n\n vflipper = iaa.Flipud(0.5).to_deterministic()\n img = vflipper.augment_image(img)\n label = vflipper.augment_image(label)\n if random.random() < 0.5:\n rot_time = random.choice([1, 2, 3])\n img = np.rot90(img, rot_time)\n label = np.rot90(label, rot_time)\n if random.random() < 0.5:\n translater = iaa.Affine( # translate_percent={\"x\": (-0.2, 0.2), \"y\": (-0.2, 0.2)},\n rotate=random.randint(0, 90),\n scale={\"x\": (1, 1.4), \"y\": (1, 1.4)},\n shear=(-8, 8),\n mode='symmetric'#\n\n ).to_deterministic()\n img = translater.augment_image(img)\n label = translater.augment_image(label)\n img = cv2.resize(img, (args.crop_size, args.crop_size))\n label = cv2.resize(label, (args.crop_size, args.crop_size))\n sample['image'], sample['label'] = img, label\n\n return sample\n\n def ToTensor(self,sample):\n img, label = sample['image'], sample['label']\n img = img.transpose((2, 0, 1))\n img = torch.from_numpy(img.astype(np.float32) / 255.0)\n label = torch.from_numpy(label.astype(np.float32))\n sample['image'], sample['label'] = img, label\n\n return sample\n\nif __name__ == '__main__':\n from torch.utils.data import DataLoader\n import matplotlib.pyplot as plt\n import argparse\n\n parser = argparse.ArgumentParser()\n args = parser.parse_args()\n args.base_size = 512\n args.crop_size = 512\n\n Vaihingen_train = VaihingenSegmentation(args, split='train')\n\n dataloader = DataLoader(Vaihingen_train, batch_size=5, shuffle=True, num_workers=8)\n\n for ii, sample in enumerate(dataloader):\n for jj in range(sample[\"image\"].size()[0]):\n img = sample['image'].numpy()\n gt = sample['label'].numpy()\n print(gt.max())\n gt = np.array(gt[jj]).astype(np.uint8)\n img_tmp = np.transpose(img[jj], axes=[1, 2, 0])\n img_tmp *= 255.0\n img_tmp = img_tmp.astype(np.uint8)\n plt.figure()\n plt.title('display')\n plt.subplot(211)\n plt.imshow(img_tmp)\n plt.subplot(212)\n plt.imshow(gt*50)\n\n if ii == 1:\n break\n\n plt.show(block=True)\n", "repo_name": "opee007/MCFINet", "sub_path": "dataloaders/datasets/isprs_Vaihingen.py", "file_name": "isprs_Vaihingen.py", "file_ext": "py", "file_size_in_byte": 4689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "imgaug.imgaug.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "imgaug.imgaug", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 12, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "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.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "imgaug.augmenters.Fliplr", "line_number": 68, "usage_type": "call"}, {"api_name": "imgaug.augmenters", "line_number": 68, "usage_type": "name"}, {"api_name": "imgaug.augmenters.Flipud", "line_number": 72, "usage_type": "call"}, {"api_name": "imgaug.augmenters", "line_number": 72, "usage_type": "name"}, {"api_name": "random.random", "line_number": 75, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 78, "usage_type": "call"}, {"api_name": "random.random", "line_number": 79, "usage_type": "call"}, {"api_name": "imgaug.augmenters.Affine", "line_number": 80, "usage_type": "call"}, {"api_name": "imgaug.augmenters", "line_number": 80, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 126, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}]} +{"seq_id": "20937642364", "text": "from PySide6.QtWidgets import (QApplication, QWidget)\nfrom libraryUI.Sign_in import Sign_in # change to main menu by your self na non , left only import sign in and from and import\n\n\nclass Remove_book(QWidget):\n\n def getSign_inPanel(self): # wait for main menu done then just import main menu page na\n self.sign_in = Sign_in()\n self.close()\n\n\nif __name__ == \"__main__\":\n app = QApplication()\n ui = Remove_book()\n app.exec()\n\n# all the connected checkbox used last function isChecked() and get their result at last na I print sentence for u to identify easier\n", "repo_name": "chananon-n/Book-Swap", "sub_path": "bookingSystem/remove_book.py", "file_name": "remove_book.py", "file_ext": "py", "file_size_in_byte": 587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "PySide6.QtWidgets.QWidget", "line_number": 5, "usage_type": "name"}, {"api_name": "libraryUI.Sign_in.Sign_in", "line_number": 8, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "4959515719", "text": "import bpy\nimport os\nfrom pathlib import Path\n\n##########################################################################################\n################################# PARAMETRES ############################\n##########################################################################################\nLeChemin = Path('J:/test') ### chemin absolu où se trouve la sequence\nPiste = 1 ### strip ou sera ajouté les images\nStripStartFrame = 1 ### on insert la séquence à partir de quelle frame dans la TimeLine ? \nStartFrame = 1 ### on démarre à partir de quelle image\nNbFrame = 1 ### Nombre de frame de décalage\nDureeFrame = 1 ### Durée en nombre d'images de chaque image insérée\n\nbpy.context.area.type = 'SEQUENCE_EDITOR'\ni = 0\nj = 1\nfor root, dirs, files in os.walk(LeChemin, topdown=False):\n for name in files[StartFrame-1:]:\n if not(i%NbFrame):\n bpy.ops.sequencer.image_strip_add(directory=str(LeChemin), files=[{\"name\":name, \"name\":name}], show_multiview=False, frame_start=j+StripStartFrame-1, frame_end=j+StripStartFrame-1+(DureeFrame-1), channel=Piste)\n j += 1 + (DureeFrame - 1)\n i += 1\nbpy.context.area.type = 'TEXT_EDITOR'\n", "repo_name": "WinZs/ImportVSE", "sub_path": "script.py", "file_name": "script.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pathlib.Path", "line_number": 8, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 18, "usage_type": "call"}, {"api_name": "bpy.ops.sequencer.image_strip_add", "line_number": 21, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "13437499946", "text": "\nfrom django.core.paginator import Paginator\nfrom django.shortcuts import get_object_or_404, redirect, render\nfrom django.views.decorators.http import require_http_methods\n\nfrom venda.forms import CriarVendaForm, ItemVendaForm\nfrom venda.models import Item, Venda\n\nNOME_ROTA_DETALHAR_VENDA = 'venda:venda_detalhar'\n\n\n@require_http_methods([\"GET\", \"POST\"])\ndef criar_venda_view(request):\n form = CriarVendaForm()\n\n if request.method == 'POST':\n form = CriarVendaForm(request.POST)\n if form.is_valid():\n nova_venda = Venda.objects.create(\n cliente=form.cleaned_data['cliente'])\n\n novo_item = form.save(commit=False)\n novo_item.venda = nova_venda\n novo_item.save()\n return redirect(NOME_ROTA_DETALHAR_VENDA, pk=nova_venda.id)\n return render(request, 'venda/venda/criar.html', {'form': form})\n\n\n@require_http_methods([\"GET\"])\ndef listar_vendas_view(request):\n vendas = Venda.objects.all().order_by(\"status\", \"-data\", \"-hora\")\n paginator = Paginator(vendas, 20)\n\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n return render(request, 'venda/venda/listar.html', {'page_obj': page_obj})\n\n\n@require_http_methods([\"GET\", \"POST\"])\ndef detalhar_venda_view(request, pk):\n venda = get_object_or_404(Venda, id=pk)\n item_form = ItemVendaForm()\n\n if request.method == 'POST':\n item_form = ItemVendaForm(request.POST)\n if (item_form.is_valid()):\n novo_item = item_form.save(commit=False)\n\n # verifica se o item já existe na venda\n item_ja_cadastrado = Item.objects.filter(\n venda=venda.id, produto=novo_item.produto).exists()\n\n if (item_ja_cadastrado):\n item_existente = Item.objects.get(\n venda=venda.id, produto=novo_item.produto)\n item_existente.quantidade = item_existente.quantidade + novo_item.quantidade\n item_existente.save()\n\n else:\n novo_item.venda = venda\n novo_item.save()\n return redirect(NOME_ROTA_DETALHAR_VENDA, pk=pk)\n\n context = {\n 'venda': venda,\n 'form': item_form\n }\n return render(request, 'venda/venda/detalhar.html', context)\n\n\n@require_http_methods([\"GET\", \"POST\"])\ndef desativar_venda_view(request, pk):\n venda = get_object_or_404(Venda, id=pk)\n if request.method == 'POST':\n venda.status = Venda.STATUS_CHOICES[1][0]\n venda.save()\n return redirect(NOME_ROTA_DETALHAR_VENDA, pk=pk)\n return render(request, 'venda/venda/desativar.html', {'venda': venda})\n\n\n@require_http_methods([\"GET\", \"POST\"])\ndef finalizar_venda_view(request, pk):\n venda = get_object_or_404(Venda, id=pk)\n if request.method == 'POST':\n venda.status = Venda.STATUS_CHOICES[2][0]\n venda.save()\n return redirect(NOME_ROTA_DETALHAR_VENDA, pk=pk)\n return render(request, 'venda/venda/finalizar.html', {'venda': venda})\n\n\n@require_http_methods([\"GET\", \"POST\"])\ndef reativar_venda_view(request, pk):\n venda = get_object_or_404(Venda, id=pk)\n if request.method == 'POST':\n venda.status = Venda.STATUS_CHOICES[0][0]\n venda.save()\n return redirect(NOME_ROTA_DETALHAR_VENDA, pk=pk)\n return render(request, 'venda/venda/reativar.html', {'venda': venda})\n", "repo_name": "Control-Cash/controlcash", "sub_path": "venda/views/venda_views.py", "file_name": "venda_views.py", "file_ext": "py", "file_size_in_byte": 3377, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "venda.forms.CriarVendaForm", "line_number": 14, "usage_type": "call"}, {"api_name": "venda.forms.CriarVendaForm", "line_number": 17, "usage_type": "call"}, {"api_name": "venda.models.Venda.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "venda.models.Venda.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "venda.models.Venda", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 12, "usage_type": "call"}, {"api_name": "venda.models.Venda.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "venda.models.Venda.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "venda.models.Venda", "line_number": 31, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 29, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "venda.models.Venda", "line_number": 41, "usage_type": "argument"}, {"api_name": "venda.forms.ItemVendaForm", "line_number": 42, "usage_type": "call"}, {"api_name": "venda.forms.ItemVendaForm", "line_number": 45, "usage_type": "call"}, {"api_name": "venda.models.Item.objects.filter", "line_number": 50, "usage_type": "call"}, {"api_name": "venda.models.Item.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "venda.models.Item", "line_number": 50, "usage_type": "name"}, {"api_name": "venda.forms.id", "line_number": 51, "usage_type": "attribute"}, {"api_name": "venda.forms", "line_number": 51, "usage_type": "name"}, {"api_name": "venda.models.Item.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "venda.models.Item.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "venda.models.Item", "line_number": 54, "usage_type": "name"}, {"api_name": "venda.forms.id", "line_number": 55, "usage_type": "attribute"}, {"api_name": "venda.forms", "line_number": 55, "usage_type": "name"}, {"api_name": "venda.forms", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 65, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 39, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 73, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 73, "usage_type": "call"}, {"api_name": "venda.models.Venda", "line_number": 73, "usage_type": "argument"}, {"api_name": "venda.forms.status", "line_number": 75, "usage_type": "attribute"}, {"api_name": "venda.forms", "line_number": 75, "usage_type": "name"}, {"api_name": "venda.models.Venda.STATUS_CHOICES", "line_number": 75, "usage_type": "attribute"}, {"api_name": "venda.models.Venda", "line_number": 75, "usage_type": "name"}, {"api_name": "venda.forms.save", "line_number": 76, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 76, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 78, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 78, "usage_type": "name"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 71, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 83, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 83, "usage_type": "call"}, {"api_name": "venda.models.Venda", "line_number": 83, "usage_type": "argument"}, {"api_name": "venda.forms.status", "line_number": 85, "usage_type": "attribute"}, {"api_name": "venda.forms", "line_number": 85, "usage_type": "name"}, {"api_name": "venda.models.Venda.STATUS_CHOICES", "line_number": 85, "usage_type": "attribute"}, {"api_name": "venda.models.Venda", "line_number": 85, "usage_type": "name"}, {"api_name": "venda.forms.save", "line_number": 86, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 86, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 88, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 88, "usage_type": "name"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 81, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 93, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 93, "usage_type": "call"}, {"api_name": "venda.models.Venda", "line_number": 93, "usage_type": "argument"}, {"api_name": "venda.forms.status", "line_number": 95, "usage_type": "attribute"}, {"api_name": "venda.forms", "line_number": 95, "usage_type": "name"}, {"api_name": "venda.models.Venda.STATUS_CHOICES", "line_number": 95, "usage_type": "attribute"}, {"api_name": "venda.models.Venda", "line_number": 95, "usage_type": "name"}, {"api_name": "venda.forms.save", "line_number": 96, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 96, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 97, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 98, "usage_type": "call"}, {"api_name": "venda.forms", "line_number": 98, "usage_type": "name"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "41467367200", "text": "import maya.cmds as cmds\r\n\r\nclass MultTool:\r\n def __init__(self):\r\n \"\"\" INIT\r\n Args:\r\n self.sel list[str] : list from selection\r\n self.attrList list[str] : list all the attributes the script can affect\r\n\r\n Returns:\r\n None\r\n \"\"\"\r\n self.sel= cmds.ls(sl=True)\r\n\r\n self.attrList = [\"tx\",\"ty\",\"tz\",\r\n \"rx\",\"ry\",\"rz\",\r\n \"sx\",\"sy\",\"sz\", \r\n ]\r\n\r\n\r\n\r\n def clickRefresh(self, *args):\r\n \"\"\" refresh the selection list\r\n Args:\r\n self.sel list[str] : list from selection\r\n Returns:\r\n None\r\n \"\"\"\r\n self.sel= cmds.ls(sl=True)\r\n\r\n def clickMultAdd(self, *args):\r\n \"\"\" refresh the selection list\r\n Args:\r\n multTx float : Translate X muliplier (Get value from UI) \r\n multTy float : Translate Y muliplier (Get value from UI)\r\n multTz float : Translate Z muliplier (Get value from UI)\r\n\r\n multRx float : Rotation X muliplier (Get value from UI)\r\n multRy float : Rotation Y muliplier (Get value from UI)\r\n multRz float : Rotation Z muliplier (Get value from UI)\r\n\r\n multSx float : Scale X muliplier (Get value from UI)\r\n multSy float : Scale Y muliplier (Get value from UI)\r\n multSz float : Scale Z muliplier (Get value from UI)\r\n\r\n addTx float : Translate X value to add (Get value from UI)\r\n addTy float : Translate Y value to add (Get value from UI)\r\n addTz float : Translate Z value to add (Get value from UI)\r\n\r\n addRx float : Rotation X value to add (Get value from UI)\r\n addRy float : Rotation Y value to add (Get value from UI)\r\n addRz float : Rotation Z value to add (Get value from UI)\r\n\r\n addSx float : Scale X value to add (Get value from UI)\r\n addSy float : Scale Y value to add (Get value from UI)\r\n addSz float : Scale Z value to add (Get value from UI)\r\n\r\n Returns:\r\n None\r\n \"\"\"\r\n\r\n multTx = cmds.floatFieldGrp(\"translateX\", q=True, value1=True )\r\n addTx = cmds.floatFieldGrp(\"translateX\", q=True, value2=True )\r\n multTy = cmds.floatFieldGrp(\"translateY\", q=True, value1=True )\r\n addTy = cmds.floatFieldGrp(\"translateY\", q=True, value2=True ) \r\n multTz = cmds.floatFieldGrp(\"translateZ\", q=True, value1=True )\r\n addTz = cmds.floatFieldGrp(\"translateZ\", q=True, value2=True ) \r\n\r\n multRx = cmds.floatFieldGrp(\"rotateX\", q=True, value1=True )\r\n addRx = cmds.floatFieldGrp(\"rotateX\", q=True, value2=True )\r\n multRy = cmds.floatFieldGrp(\"rotateY\", q=True, value1=True )\r\n addRy = cmds.floatFieldGrp(\"rotateY\", q=True, value2=True ) \r\n multRz = cmds.floatFieldGrp(\"rotateZ\", q=True, value1=True )\r\n addRz = cmds.floatFieldGrp(\"rotateZ\", q=True, value2=True ) \r\n\r\n multSx = cmds.floatFieldGrp(\"scaleX\", q=True, value1=True )\r\n addSx = cmds.floatFieldGrp(\"scaleX\", q=True, value2=True )\r\n multSy = cmds.floatFieldGrp(\"scaleY\", q=True, value1=True )\r\n addSy = cmds.floatFieldGrp(\"scaleY\", q=True, value2=True ) \r\n multSz = cmds.floatFieldGrp(\"scaleZ\", q=True, value1=True )\r\n addSz = cmds.floatFieldGrp(\"scaleZ\", q=True, value2=True ) \r\n\r\n multList= [ multTx,multTy,multTz,\r\n multRx,multRy,multRz,\r\n multSx,multSy,multSz\r\n ]\r\n\r\n addList= [ addTx,addTy,addTz,\r\n addRx,addRy,addRz,\r\n addSx,addSy,addSz\r\n ]\r\n\r\n\r\n\r\n for item in self.sel:\r\n\r\n index=0\r\n\r\n for attrib in self.attrList:\r\n\r\n\r\n cmds.setAttr(\"%s.%s\" % ( item, attrib ), ( (cmds.getAttr(\"%s.%s\"% ( item, attrib ) ) * multList[index]) + addList[index]))\r\n index +=1\r\n \r\n\r\n\r\n\r\n def multAddUI(self):\r\n \"\"\" Display the multAdd tool UI.\r\n Args:\r\n windowName (str) : The name of the window.\r\n Returns:\r\n None\r\n \"\"\"\r\n\r\n windowName = \"Multiply / Add value Tool\"\r\n\r\n #Delete the window if already exist.\r\n try: \r\n cmds.deleteUI(windowName)\r\n except:\r\n pass\r\n\r\n #Delete the window if already exist.\r\n mainWindow = cmds.window(windowName)\r\n #Create the main layout. \r\n mainLayout = cmds.columnLayout(\"mainLayout\")\r\n\r\n\r\n #create 2 fields for each attributes\r\n\r\n cmds.floatFieldGrp(\"translateX\", label=\"multiply / add translate X\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n cmds.floatFieldGrp(\"translateY\", label=\"multiply / add translate Y\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n cmds.floatFieldGrp(\"translateZ\", label=\"multiply / add translate Z\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n\r\n cmds.floatFieldGrp(\"rotateX\", label=\"multiply / add rotate X\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n cmds.floatFieldGrp(\"rotateY\", label=\"multiply / add rotate Y\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n cmds.floatFieldGrp(\"rotateZ\", label=\"multiply / add rotate Z\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n\r\n cmds.floatFieldGrp(\"scaleX\", label=\"multiply / add scale X\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n cmds.floatFieldGrp(\"scaleY\", label=\"multiply / add scale Y\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n cmds.floatFieldGrp(\"scaleZ\", label=\"multiply / add scale Z\", numberOfFields=2 , value1=1, value2=0, parent=mainLayout)\r\n\r\n\r\n #add 2 buttons, one to execute function and one to refresh selection \r\n cmds.button(\"refresh\", label=\"Refresh Selection\", command=self.clickRefresh)\r\n cmds.button(\"multAdd\", label=\"Mult / Add Values\", command=self.clickMultAdd)\r\n\r\n\r\n\r\n cmds.showWindow(mainWindow)\r\n\r\ntry1 = MultTool()\r\ntry1.multAddUI() ", "repo_name": "JsonDoe/Maya-Tools-Repository", "sub_path": "tools/OOPmultSelection.py", "file_name": "OOPmultSelection.py", "file_ext": "py", "file_size_in_byte": 6538, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "maya.cmds.ls", "line_number": 13, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 13, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 29, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 29, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 62, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 62, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 63, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 63, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 64, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 64, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 65, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 65, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 66, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 66, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 67, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 67, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 69, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 69, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 70, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 70, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 71, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 71, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 72, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 72, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 73, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 73, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 74, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 74, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 76, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 76, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 77, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 77, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 78, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 78, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 79, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 79, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 80, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 80, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 81, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 81, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 102, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 102, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 102, "usage_type": "call"}, {"api_name": "maya.cmds.deleteUI", "line_number": 120, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 120, "usage_type": "name"}, {"api_name": "maya.cmds.window", "line_number": 125, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 125, "usage_type": "name"}, {"api_name": "maya.cmds.columnLayout", "line_number": 127, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 127, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 132, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 132, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 133, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 133, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 134, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 134, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 136, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 136, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 137, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 137, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 138, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 138, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 140, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 140, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 141, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 141, "usage_type": "name"}, {"api_name": "maya.cmds.floatFieldGrp", "line_number": 142, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 142, "usage_type": "name"}, {"api_name": "maya.cmds.button", "line_number": 146, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 146, "usage_type": "name"}, {"api_name": "maya.cmds.button", "line_number": 147, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 147, "usage_type": "name"}, {"api_name": "maya.cmds.showWindow", "line_number": 151, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 151, "usage_type": "name"}]} +{"seq_id": "39436775054", "text": "from django.test import TestCase\nfrom restaurant import models\nfrom rest_framework.test import APIClient\nfrom django.urls import reverse\nfrom rest_framework import status\n\nclass MenuviewTest(TestCase):\n def setUp(self):\n models.Menu.objects.create(Title = \"noodles\" , Price =30.01, Inventory = 39)\n models.Menu.objects.create(Title = \"chicken tikka\" , Price =40.21, Inventory = 10)\n models.Menu.objects.create(Title = \"soup\" , Price =20.00, Inventory = 9)\n\n def test_getall(self):\n client = APIClient()\n\n url = reverse('menu_view')\n response = client.get(url)\n self.assertEqual(response.status_code , status.HTTP_200_OK)\n\n serialized_data = response.data\n serialized_data_without_id = [{'Title': item['Title'], 'Price': item['Price'], 'Inventory': item['Inventory']} for item in serialized_data]\n\n \n\n expected_data =[\n {'Title':'noodles','Price':'30.01','Inventory':39},\n {'Title':'chicken tikka','Price':'40.21','Inventory':10},\n {'Title':'soup','Price':'20.00','Inventory':9},\n \n ]\n\n self.assertEqual(serialized_data_without_id,expected_data)\n", "repo_name": "kid1356/Capston_Littlelemon", "sub_path": "tests/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 1199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "restaurant.models.Menu.objects.create", "line_number": 9, "usage_type": "call"}, {"api_name": "restaurant.models.Menu", "line_number": 9, "usage_type": "attribute"}, {"api_name": "restaurant.models", "line_number": 9, "usage_type": "name"}, {"api_name": "restaurant.models.Menu.objects.create", "line_number": 10, "usage_type": "call"}, {"api_name": "restaurant.models.Menu", "line_number": 10, "usage_type": "attribute"}, {"api_name": "restaurant.models", "line_number": 10, "usage_type": "name"}, {"api_name": "restaurant.models.Menu.objects.create", "line_number": 11, "usage_type": "call"}, {"api_name": "restaurant.models.Menu", "line_number": 11, "usage_type": "attribute"}, {"api_name": "restaurant.models", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "2346581527", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 30 16:51:18 2021\n\n@author: Gebruiker\n\nhttps://developer.dataplatform.knmi.nl/get-started\n\"\"\"\n\nimport logging\nimport sys\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport requests\nimport os\nimport json\n\nwith open(r'C:\\Users\\Calixte\\Desktop\\Secrets\\secrets.json') as f:\n d_secrets = json.load(f)['KMI_API']\n\nlogging.basicConfig()\nlogger = logging.getLogger(__name__)\nlogger.setLevel(\"INFO\")\n\napi_url = \"https://api.dataplatform.knmi.nl/open-data\"\napi_version = \"v1\"\n\n#https://api.dataplatform.knmi.nl/open-data/v1/datasets/Actuele10mindataKNMIstations/versions/2/files\n#https://api.dataplatform.knmi.nl/open-data/v1/datasets/Tx1/versions/2/files\n\n\n# Parameters\n#api_key = d_secrets['KEY']\napi_key = 'eyJvcmciOiI1ZTU1NGUxOTI3NGE5NjAwMDEyYTNlYjEiLCJpZCI6IjI4ZWZlOTZkNDk2ZjQ3ZmE5YjMzNWY5NDU3NWQyMzViIiwiaCI6Im11cm11cjEyOCJ9'\ndataset_name = \"Tx1\"\ndataset_version = \"2\"\nmax_keys = \"10\"\n\n# Use list files request to request first 10 files of the day.\ntimestamp = datetime.utcnow().date().strftime(\"%Y%m%d\")\nstart_after_filename_prefix = f\"KMDS__OPER_P___10M_OBS_L2_{timestamp}\"\nlist_files_response = requests.get(\n f\"{api_url}/{api_version}/datasets/{dataset_name}/versions/{dataset_version}/files\",\n headers={\"Authorization\": api_key},\n params={\"maxKeys\": max_keys, \"startAfterFilename\": start_after_filename_prefix},\n)\nlist_files = list_files_response.json()\n\nlogger.info(f\"List files response:\\n{list_files}\")\ndataset_files = list_files.get(\"files\")\n\n# Retrieve first file in the list files response\nfilename = dataset_files[0].get(\"filename\")\nlogger.info(f\"Retrieve file with name: {filename}\")\nendpoint = f\"{api_url}/{api_version}/datasets/{dataset_name}/versions/{dataset_version}/files/{filename}/url\"\nget_file_response = requests.get(endpoint, headers={\"Authorization\": api_key})\nif get_file_response.status_code != 200:\n logger.error(\"Unable to retrieve download url for file\")\n logger.error(get_file_response.text)\n sys.exit(1)\n\ndownload_url = get_file_response.json().get(\"temporaryDownloadUrl\")\ndataset_file_response = requests.get(download_url)\nif dataset_file_response.status_code != 200:\n logger.error(\"Unable to download file using download URL\")\n logger.error(dataset_file_response.text)\n sys.exit(1)\n\n# Write dataset file to disk\np = Path(filename)\np.write_bytes(dataset_file_response.content)\nlogger.info(f\"Successfully downloaded dataset file to {p}\")\n\n\n", "repo_name": "Xaintailles/weather_data", "sub_path": "api_pulls.py", "file_name": "api_pulls.py", "file_ext": "py", "file_size_in_byte": 2456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "19344443959", "text": "import os\nimport sys\nimport time\nimport datetime\nimport os.path as osp\nimport numpy as np\nimport warnings\nimport tabulate as tab\nimport pickle\n\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\n\nfrom args import argument_parser, image_dataset_kwargs, optimizer_kwargs, lr_scheduler_kwargs\nfrom data.data_manager import ImageDataManager\nfrom data.dataset_loader import read_image\nimport models\nfrom training.losses import SigmoidCrossEntropyLoss, HardnessPredictorLoss, DeepMARLoss, SplitSoftmaxCrossEntropyLoss\nfrom utils.iotools import check_isfile, save_checkpoint\nfrom utils.avgmeter import AverageMeter\nfrom utils.loggers import Logger, AccLogger\nfrom utils.torchtools import count_num_param, open_all_layers, open_specified_layers, accuracy, \\\n load_pretrained_weights, freeze_all_layers\nimport evaluation.metrics as metrics\nfrom training.optimizers import init_optimizer\nfrom training.lr_schedulers import init_lr_scheduler\nfrom utils.plot import plot_epoch_losses, show_img_grid\nfrom trainer import Trainer\nimport evaluation.rejectors as rejectors\nfrom evaluation.result_manager import ResultManager\nfrom training.calibrators import NoneCalibrator, LinearCalibrator\n\n\nclass RealisticPredictorTrainer(Trainer):\n \"\"\"\n Trainer for a baseline.\n \"\"\"\n def __init__(self, args):\n \"\"\"\n Run the trainer.\n :param args: Command line args.\n \"\"\"\n super().__init__(args)\n\n\n def init_model(self):\n print('Initializing main model: {}'.format(args.model))\n self.model_main = models.init_model(name=self.args.model, num_classes=self.dm.num_attributes,\n pretrained=not self.args.no_pretrained, use_gpu=self.use_gpu)\n print('Model size: {:.3f} M'.format(count_num_param(self.model_main)))\n\n print('Initializing HP model: {}'.format(args.hp_model))\n # Determine the size of the output vector for the HP-Net.\n num_hp_net_outputs = 1 if self.args.hp_net_simple else self.dm.num_attributes\n # Init the HP-Net\n self.model_hp = models.init_model(name=\"hp_net_\" + self.args.hp_model, num_classes=num_hp_net_outputs,\n pretrained=not self.args.no_pretrained)\n print('Model size: {:.3f} M'.format(count_num_param(self.model_hp)))\n\n if self.args.rejector == \"none\":\n self.rejector = rejectors.NoneRejector()\n elif self.args.rejector == 'macc':\n self.rejector = rejectors.MeanAccuracyRejector(self.args.max_rejection_quantile)\n elif self.args.rejector == \"median\":\n self.rejector = rejectors.MedianRejector(self.args.max_rejection_quantile)\n elif self.args.rejector == \"threshold\":\n self.rejector = rejectors.ThresholdRejector(self.args.rejection_threshold, self.args.max_rejection_quantile)\n elif self.args.rejector == \"quantile\":\n self.rejector = rejectors.QuantileRejector(self.args.max_rejection_quantile)\n elif self.args.rejector == 'f1':\n self.rejector = rejectors.F1Rejector(self.args.max_rejection_quantile)\n else:\n raise ValueError(\"Unsupported rejection strategy: '{}'\".format(self.args.rejector))\n print(\"Using rejection strategy '{}'\".format(self.args.rejector))\n\n if self.args.hp_calib == 'none':\n self.hp_calibrator = NoneCalibrator()\n elif self.args.hp_calib == 'linear':\n self.hp_calibrator = LinearCalibrator()\n else:\n raise ValueError(\"Unsupported calibrator: '{}'\".format(self.args.hp_calib))\n print(\"Using calibrator for HP-Loss '{}'\".format(self.args.hp_calib))\n\n\n # Load pretrained weights if specified in args.\n load_file = osp.join(args.save_experiment, args.load_weights)\n self.loaded_args = self.args\n if args.load_weights:\n if check_isfile(load_file):\n cp = load_pretrained_weights([self.model_main, self.model_hp], load_file)\n if \"args\" in cp:\n self.loaded_args = cp[\"args\"]\n else:\n print(\"WARNING: Could not load args. \")\n\n if \"result_dict\" in cp and cp[\"result_dict\"] is not None and self.args.evaluate:\n self.result_dict = cp[\"result_dict\"]\n self.result_manager = ResultManager(self.result_dict)\n print(\"Loaded result dict with keys: \")\n print(sorted(list(self.result_dict.keys())))\n if \"rejection_thresholds\" in self.result_dict:\n self.rejector.load_thresholds(self.result_dict[\"rejection_thresholds\"])\n if self.rejector.is_initialized():\n print(\"Loaded rejection thresholds. \")\n else:\n print(\"Loaded uninitialized (None) rejection thresholds. \")\n else:\n print(\"WARNING: Could not load rejection thresholds. \")\n else:\n print(\"WARNING: Could not load pretrained weights\")\n self.new_eval_split = self.args.eval_split != self.loaded_args.eval_split\n # Load model onto GPU if GPU is used.\n self.model_main = self.model_main.cuda() if self.use_gpu else self.model_main\n self.model = self.model_main\n self.model_hp = self.model_hp.cuda() if self.use_gpu else self.model_hp\n\n self.pos_ratio = self.dm.dataset.get_positive_attribute_ratio()\n # Select Loss function.\n # Select Loss function.\n if args.loss_func == \"deepmar\":\n\n self.criterion = DeepMARLoss(self.pos_ratio, args.train_batch_size, use_gpu=self.use_gpu,\n sigma=args.loss_func_param)\n elif args.loss_func == \"scel\":\n self.criterion = SigmoidCrossEntropyLoss(num_classes=self.dm.num_attributes, use_gpu=self.use_gpu)\n else:\n self.criterion = None\n\n self.criterion_main = self.criterion\n self.criterion_hp = HardnessPredictorLoss(self.args.use_deepmar_for_hp, self.pos_ratio, self.dm.num_attributes,\n use_gpu=self.use_gpu, sigma=self.args.hp_loss_param,\n use_visibility=self.args.use_bbs_gt,\n visibility_weight=self.args.hp_visibility_weight)\n self.f1_calibration_thresholds = None\n\n\n self.optimizer_main = init_optimizer(self.model_main, **optimizer_kwargs(args))\n self.scheduler_main = init_lr_scheduler(self.optimizer_main, **lr_scheduler_kwargs(args))\n\n self.optimizer = self.optimizer_main\n self.scheduler = self.scheduler_main\n\n op_args = optimizer_kwargs(args)\n sc_args = lr_scheduler_kwargs(args)\n op_args['lr'] *= self.args.hp_net_lr_multiplier\n self.optimizer_hp = init_optimizer(self.model_hp, **op_args)\n sc_args[\"stepsize\"] = [i + self.args.hp_epoch_offset for i in sc_args[\"stepsize\"]]\n self.scheduler_hp = init_lr_scheduler(self.optimizer_hp, **sc_args)\n\n\n self.model_main = nn.DataParallel(self.model_main) if self.use_gpu else self.model_main\n self.model = self.model_main\n self.model_hp = nn.DataParallel(self.model_hp) if self.use_gpu else self.model_hp\n\n if not self.args.evaluate:\n self.init_epochs()\n\n self.model_list = [self.model_main, self.model_hp]\n self.optimizer_list = [self.optimizer_main, self.optimizer_hp]\n self.scheduler_list = [self.scheduler_main, self.scheduler_hp]\n self.criterion_list = [self.criterion_main, self.criterion_hp]\n\n # if args.resume and check_isfile(args.resume):\n # args.start_epoch = resume_from_checkpoint(args.resume, model, optimizer=optimizer)\n\n def update_rejector_thresholds(self):\n split = self.args.rejector_thresholds_split\n self.init_f1_calibration_threshold()\n if self.result_manager.check_output_dict(split):\n labels, prediction_probs, predictions, _ = self.result_manager.get_outputs(split)\n else:\n print(\"Computing label predictions for training data. \")\n labels, prediction_probs, predictions = self.get_label_predictions(split)\n self.result_manager.update_outputs(split, prediction_probs=prediction_probs, labels=labels,\n predictions=predictions)\n if self.args.use_confidence:\n if self.args.f1_calib:\n decision_thresholds = self.f1_calibration_thresholds\n assert decision_thresholds is not None\n else:\n decision_thresholds = None\n hp_scores = 1 - metrics.get_confidence(prediction_probs, decision_thresholds)\n print(\"Using confidence scores as HP-scores. \")\n elif self.result_manager.check_output_dict(split):\n _, _, _, hp_scores = self.result_manager.get_outputs(split)\n else:\n print(\"Computing hardness scores for training data. \")\n hp_scores, _, _ = self.get_full_output(model=self.model_hp, criterion=self.criterion_hp, split=split)\n hp_scores = self.criterion_hp.broadcast(hp_scores)\n self.result_manager.update_outputs(split, hp_scores=hp_scores)\n print(\"Updating rejection thresholds based on training data. \")\n self.rejector.update_thresholds(labels, predictions, hp_scores)\n\n def update_hp_calibrator_thresholds(self, thresholds=None):\n if self.args.hp_calib == \"none\":\n return\n if self.args.hp_calib_thr == \"f1\":\n if self.hp_calibrator.is_initialized():\n return\n thresholds = self.get_baseline_f1_calibration_thresholds()\n elif self.args.hp_calib_thr == \"mean\":\n thresholds = 0.5 if thresholds is None else thresholds\n else:\n raise ValueError(\"Unsupported HP-Loss calibration threshold: '{}'\".format(self.args.hp_calib_thr))\n self.hp_calibrator.update_thresholds(thresholds)\n\n def init_epochs(self):\n # Initialize the epoch thresholds.\n if self.args.max_epoch < 0 and (self.args.main_net_train_epochs < 0 or self.args.hp_net_train_epochs < 0):\n raise ValueError(\"Neither max-epochs or not-train-epochs is defined. \")\n if self.args.main_net_train_epochs < 0:\n self.args.main_net_train_epochs = (self.args.max_epoch - self.args.hp_epoch_offset\n - self.args.main_net_finetuning_epochs)\n if self.args.hp_net_train_epochs < 0:\n self.args.hp_net_train_epochs = (self.args.max_epoch - self.args.hp_epoch_offset\n - self.args.main_net_finetuning_epochs)\n if self.args.max_epoch < 0:\n self.args.max_epoch = (max(self.args.main_net_train_epochs, self.args.hp_net_train_epochs\n + self.args.hp_epoch_offset) + self.args.main_net_finetuning_epochs)\n print(\"Training schedule: \")\n print(tab.tabulate([\n [\"Main-Net train epochs\", self.args.main_net_train_epochs],\n [\"HP-Net epoch offset\", self.args.hp_epoch_offset],\n [\"HP-Net train epochs\", self.args.hp_net_train_epochs],\n [\"Main-Net finetuning epochs\", self.args.main_net_finetuning_epochs],\n [\"Total epochs\", self.args.max_epoch]\n ]))\n\n def train(self, fixbase=False):\n \"\"\"\n Train the model for an epoch.\n :param fixbase: Is this a fixbase epoch?\n :return: Time of execution end.\n \"\"\"\n losses_main = AverageMeter()\n losses_hp = AverageMeter()\n train_main = not self.args.train_hp_only and self.epoch < self.args.main_net_train_epochs\n train_main_finetuning = (not self.args.train_hp_only and self.epoch >= self.args.max_epoch\n - self.args.main_net_finetuning_epochs)\n rejection_epoch = (not self.args.train_hp_only and self.epoch == self.args.max_epoch\n - self.args.main_net_finetuning_epochs)\n train_hp = (self.args.hp_epoch_offset <= self.epoch < self.args.hp_net_train_epochs\n + self.args.hp_epoch_offset)\n num_batch = len(self.trainloader)\n\n if rejection_epoch:\n self.update_rejector_thresholds()\n if self.args.hp_epoch_offset == self.epoch:\n self.update_hp_calibrator_thresholds()\n\n if train_main or train_main_finetuning:\n self.model_main.train()\n losses = losses_main\n else:\n self.model_main.eval()\n losses = losses_hp\n\n if train_hp:\n self.model_hp.train()\n else:\n self.model_hp.eval()\n\n # For saving results to compute mean calibration thresholds.\n positive_logits_sum = torch.zeros(self.dm.num_attributes)\n negative_logits_sum = torch.zeros(self.dm.num_attributes)\n positive_num = torch.zeros(self.dm.num_attributes)\n negative_num = torch.zeros(self.dm.num_attributes)\n if self.use_gpu:\n positive_logits_sum = positive_logits_sum.cuda()\n negative_logits_sum = negative_logits_sum.cuda()\n positive_num = positive_num.cuda()\n negative_num = negative_num.cuda()\n\n for batch_idx, (imgs, labels, _) in enumerate(self.trainloader):\n\n\n if self.use_gpu:\n imgs, labels = imgs.cuda(), labels.cuda()\n if self.use_bbs:\n visibility_labels = labels[:, self.dm.num_attributes:]\n labels = labels[:, :self.dm.num_attributes]\n assert labels.shape == visibility_labels.shape\n else:\n visibility_labels = None\n # Run the batch through both nets.\n label_prediciton_probs = self.model_main(imgs)\n label_predicitons_logits = self.criterion_main.logits(label_prediciton_probs.detach())\n\n labels_bool = labels > 0.5 # TODO: make nicer\n positive_logits_sum += label_predicitons_logits[labels_bool].sum(0)\n negative_logits_sum += label_predicitons_logits[~labels_bool].sum(0)\n positive_num += labels_bool.sum(0, dtype=torch.float)\n negative_num += (~labels_bool).sum(0, dtype=torch.float)\n\n if not self.args.use_confidence:\n hardness_predictions = self.model_hp(imgs)\n if train_main or train_main_finetuning:\n if not self.args.use_confidence:\n hardness_predictions_logits = self.criterion_hp.logits(hardness_predictions.detach())\n hardness_predictions_logits = self.criterion_hp.broadcast(hardness_predictions_logits)\n elif train_main_finetuning:\n if self.args.f1_calib:\n decision_thresholds = self.f1_calibration_thresholds\n else:\n decision_thresholds = None\n hardness_predictions_logits = 1 - metrics.get_confidence(label_predicitons_logits,\n decision_thresholds).detach()\n if self.args.no_hp_feedback or not train_hp:\n main_net_weights = label_prediciton_probs.new_ones(label_prediciton_probs.shape)\n else:\n # Make a detached version of the hp scores for computing the main loss.\n main_net_weights = hardness_predictions_logits\n if self.args.use_bbs_feedback:\n main_net_weights *= visibility_labels\n if train_main_finetuning:\n select = self.rejector(hardness_predictions_logits)\n main_net_weights = main_net_weights * select\n # Compute main loss, gradient and optimize main net.\n loss_main = self.criterion_main(label_prediciton_probs, labels, main_net_weights)\n self.optimizer_main.zero_grad()\n loss_main.backward()\n nn.utils.clip_grad_norm_(self.model_main.parameters(), max_norm=10.0)\n self.optimizer_main.step()\n\n losses_main.update(loss_main.item(), labels.size(0))\n\n if train_hp and not self.args.use_confidence:\n # Compute HP loss, gradient and optimize HP net.\n # The label predictions are calibrated.\n loss_hp = self.criterion_hp(hardness_predictions, self.hp_calibrator(label_predicitons_logits), labels, visibility_labels)\n self.optimizer_hp.zero_grad()\n loss_hp.backward()\n nn.utils.clip_grad_norm_(self.model_hp.parameters(), max_norm=10.0)\n self.optimizer_hp.step()\n\n losses_hp.update(loss_hp.item(), labels.size(0))\n # Print progress.\n if (batch_idx + 1) % args.print_freq == 0:\n print('Epoch: [{0}][{1}/{2}]\\t' \n 'Main loss {loss.avg:.4f}\\t'\n 'HP-Net loss {hp_loss.avg:.4f}'.format(\n self.epoch + 1, batch_idx + 1, num_batch,\n loss=losses_main,\n hp_loss=losses_hp\n ))\n print('Epoch: [{0}][{1}/{2}]\\t'\n 'Main loss {loss.avg:.4f}\\t'\n 'HP-Net loss {hp_loss.avg:.4f}'.format(\n self.epoch + 1, batch_idx + 1, num_batch,\n loss=losses_main,\n hp_loss=losses_hp\n ))\n\n # Update HP calibrator thresholds (mean thresholds are only used if the option is selected in args)\n positive_logits_sum /= positive_num\n negative_logits_sum /= negative_num\n self.update_hp_calibrator_thresholds((positive_logits_sum + negative_logits_sum) / 2)\n\n return losses_main.avg, losses_hp.avg\n\n def test(self, predictions=None, ground_truth=None):\n split = self.args.eval_split\n if not self.rejector.is_initialized() or self.args.no_cache:\n self.update_rejector_thresholds()\n\n # Get Hardness scores.\n\n if self.args.use_confidence:\n labels, prediction_probs, predictions = self.get_label_predictions(split)\n if self.args.f1_calib:\n decision_thresholds = self.f1_calibration_thresholds\n else:\n decision_thresholds = None\n hp_scores = 1 - metrics.get_confidence(prediction_probs, decision_thresholds)\n self.result_manager.update_outputs(split, hp_scores=hp_scores)\n print(\"Using confidence scores as HP-scores. \")\n elif self.args.evaluate and self.result_manager.check_output_dict(split) and not self.args.no_cache:\n _, _, _, hp_scores = self.result_manager.get_outputs(split)\n else:\n print(\"Computing hardness scores for testing data. \")\n hp_scores, _, _ = self.get_full_output(model=self.model_hp, criterion=self.criterion_hp)\n hp_scores = self.criterion_hp.broadcast(hp_scores)\n self.result_manager.update_outputs(split, hp_scores=hp_scores)\n\n ignore = np.logical_not(self.rejector(hp_scores))\n print(\"Rejecting the {:.2%} hardest of testing examples. \".format(ignore.mean()))\n # Run the standard accuracy testing.\n super().test(ignore)\n labels, prediction_probs, predictions, _ = self.result_manager.get_outputs(split)\n\n\n print(\"HP-Net Hardness Scores: \")\n print(tab.tabulate([\n [\"Mean\", np.mean(hp_scores)],\n [\"Variance\", np.var(hp_scores)]\n ]))\n\n # Display the hardness scores for every attribute.\n print(\"-\" * 30)\n header = [\"Attribute\", \"Positivity Ratio\", \"Accuracy\", \"Hardness Score Mean\", \"Average Precision\", \"cAP\", \"Rejection Threshold\",\n \"Rejection Quantile\"]\n mean = hp_scores.mean(0)\n var = np.sqrt(hp_scores.var(0))\n average_precision = metrics.hp_average_precision(labels, predictions, hp_scores)\n baseline_average_precision = self.get_baseline_average_precision()\n if baseline_average_precision is None:\n baseline_average_precision = 0\n comparative_average_precision = (average_precision > baseline_average_precision).astype(\"int8\")\n # mean_average_precision = metrics.hp_mean_average_precision(labels, label_predictions, hp_scores)\n\n rejection_quantiles = ignore.mean(0).flatten()\n rejection_thresholds = self.rejector.attribute_thresholds\n if rejection_thresholds is None:\n rejection_thresholds = np.ones_like(rejection_quantiles)\n else:\n rejection_thresholds = rejection_thresholds.flatten()\n data = list(zip(self.dm.attributes, self.positivity_ratio, self.acc_atts, mean, average_precision,\n comparative_average_precision, rejection_thresholds, rejection_quantiles))\n data += [[\"Total\", self.positivity_ratio.mean(), self.acc_atts.mean(), mean.mean(),\n average_precision.mean(), comparative_average_precision.mean(), rejection_thresholds.mean(),\n rejection_quantiles.mean()]]\n table = tab.tabulate(data, floatfmt='.4f', headers=header)\n print(table)\n print(\"Mean average precision of hardness prediction over attributes: {:.2%}\".format(average_precision.mean()))\n print(\"Comparative mean average precision: {:.2%}\".format(comparative_average_precision.mean()))\n csv_path = osp.join(self.args.save_experiment, self.ts + \"rp_result_table.csv\")\n np.savetxt(csv_path, np.transpose(data), fmt=\"%s\", delimiter=\"\\t\")\n print(\"Saved Table at \" + csv_path)\n\n self.result_dict.update({\n \"rejection_thresholds\": self.rejector.attribute_thresholds,\n \"calibration_thresholds\": self.hp_calibrator.thresholds_np,\n \"ignored_test_samples\": ignore,\n \"average_precision\": average_precision\n })\n self.save_result_dict()\n\n hard_att_labels = None\n hard_att_pred = None\n if self.args.num_save_hard + self.args.num_save_easy > 0:\n # This part only gets executed if the corresponding arguments are passed at the terminal.\n if self.args.hard_att in self.dm.attributes:\n # If a valid attribute is given the labels for that attribute are selected.\n print(\"Looking at Hard attribute \" + self.args.hard_att)\n att_idx = self.dm.attributes.index(self.args.hard_att)\n hard_att_labels = labels[:, att_idx]\n hard_att_pred = prediction_probs[:, att_idx]\n if not self.loaded_args.hp_net_simple:\n # If a valid attribute is given, the hardness scores for that attribute are selected, else the mean\n # over all attributes is taken.\n if self.args.hard_att in self.dm.attributes:\n hp_scores = hp_scores[:, att_idx]\n else:\n hp_scores = hp_scores.mean(1)\n hp_scores = hp_scores.flatten()\n sorted_idxs = hp_scores.argsort()\n # Select easy and hard examples as specified in the terminal.\n hard_idxs = np.concatenate((sorted_idxs[:self.args.num_save_easy],\n sorted_idxs[-self.args.num_save_hard:]))\n filename = osp.join(self.args.save_experiment, self.ts + \"hard_images.png\")\n title = \"Examples by hardness for \" + (self.args.load_weights if self.args.load_weights else self.ts)\n if hard_att_labels is not None:\n hard_att_labels = hard_att_labels[hard_idxs]\n if hard_att_pred is not None:\n hard_att_pred = hard_att_pred[hard_idxs]\n # Display the image examples.\n show_img_grid(self.dm.split_dict[self.args.eval_split], hard_idxs, filename, title, self.args.hard_att,\n hard_att_labels, hp_scores[hard_idxs], hard_att_pred)\n\n return comparative_average_precision.mean()\n\n def get_baseline_average_precision(self):\n return self.get_baseline_data(self.args.ap_baseline, \"average_precision\", \"baseline average precision\")\n\n def get_baseline_f1_calibration_thresholds(self):\n return self.get_baseline_data(self.args.f1_baseline, \"f1_thresholds\", \"baseline F1 calibration thresholds\")\n\n def get_baseline_data(self, filename, key, name):\n load_file = osp.join(self.args.save_experiment, filename)\n if filename and check_isfile(load_file):\n checkpoint = torch.load(load_file)\n\n if \"result_dict\" in checkpoint and checkpoint[\"result_dict\"] is not None:\n result_dict = checkpoint[\"result_dict\"]\n if key in result_dict and result_dict[key] is not None:\n print(\"Loaded {} from file: {}\".format(name, filename))\n return result_dict[key]\n\n print(\"WARNING: Could not load {}. \".format(name))\n return None\n\n def clear_output_cache(self):\n super().clear_output_cache()\n self.rejector.reset()\n\n\nif __name__ == '__main__':\n # global variables\n parser = argument_parser()\n args = parser.parse_args()\n trainer = RealisticPredictorTrainer(args)\n", "repo_name": "Lucas-Florin/hardness-predictor-for-par", "sub_path": "realistic_predictor_trainer.py", "file_name": "realistic_predictor_trainer.py", "file_ext": "py", "file_size_in_byte": 25456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "trainer.Trainer", "line_number": 35, "usage_type": "name"}, {"api_name": "args.model", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.init_model", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.torchtools.count_num_param", "line_number": 51, "usage_type": "call"}, {"api_name": "args.hp_model", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.init_model", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.torchtools.count_num_param", "line_number": 59, "usage_type": "call"}, {"api_name": "evaluation.rejectors.NoneRejector", "line_number": 62, "usage_type": "call"}, {"api_name": "evaluation.rejectors", "line_number": 62, "usage_type": "name"}, {"api_name": "evaluation.rejectors.MeanAccuracyRejector", "line_number": 64, "usage_type": "call"}, {"api_name": "evaluation.rejectors", "line_number": 64, "usage_type": "name"}, {"api_name": "evaluation.rejectors.MedianRejector", "line_number": 66, "usage_type": "call"}, {"api_name": "evaluation.rejectors", "line_number": 66, "usage_type": "name"}, {"api_name": "evaluation.rejectors.ThresholdRejector", "line_number": 68, "usage_type": "call"}, {"api_name": "evaluation.rejectors", "line_number": 68, "usage_type": "name"}, {"api_name": "evaluation.rejectors.QuantileRejector", "line_number": 70, "usage_type": "call"}, {"api_name": "evaluation.rejectors", "line_number": 70, "usage_type": "name"}, {"api_name": "evaluation.rejectors.F1Rejector", "line_number": 72, "usage_type": "call"}, {"api_name": "evaluation.rejectors", "line_number": 72, "usage_type": "name"}, {"api_name": "training.calibrators.NoneCalibrator", "line_number": 78, "usage_type": "call"}, {"api_name": "training.calibrators.LinearCalibrator", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "name"}, {"api_name": "args.save_experiment", "line_number": 87, "usage_type": "attribute"}, {"api_name": "args.load_weights", "line_number": 87, "usage_type": "attribute"}, {"api_name": "args.load_weights", "line_number": 89, "usage_type": "attribute"}, {"api_name": "utils.iotools.check_isfile", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.torchtools.load_pretrained_weights", "line_number": 91, "usage_type": "call"}, {"api_name": "evaluation.result_manager.ResultManager", "line_number": 99, "usage_type": "call"}, {"api_name": "args.loss_func", "line_number": 121, "usage_type": "attribute"}, {"api_name": "training.losses.DeepMARLoss", "line_number": 123, "usage_type": "call"}, {"api_name": "args.train_batch_size", "line_number": 123, "usage_type": "attribute"}, {"api_name": "args.loss_func_param", "line_number": 124, "usage_type": "attribute"}, {"api_name": "args.loss_func", "line_number": 125, "usage_type": "attribute"}, {"api_name": "training.losses.SigmoidCrossEntropyLoss", "line_number": 126, "usage_type": "call"}, {"api_name": "training.losses.HardnessPredictorLoss", "line_number": 131, "usage_type": "call"}, {"api_name": "training.optimizers.init_optimizer", "line_number": 138, "usage_type": "call"}, {"api_name": "args.optimizer_kwargs", "line_number": 138, "usage_type": "call"}, {"api_name": "training.lr_schedulers.init_lr_scheduler", "line_number": 139, "usage_type": "call"}, {"api_name": "args.lr_scheduler_kwargs", "line_number": 139, "usage_type": "call"}, {"api_name": "args.optimizer_kwargs", "line_number": 144, "usage_type": "call"}, {"api_name": "args.lr_scheduler_kwargs", "line_number": 145, "usage_type": "call"}, {"api_name": "training.optimizers.init_optimizer", "line_number": 147, "usage_type": "call"}, {"api_name": "training.lr_schedulers.init_lr_scheduler", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "evaluation.metrics.get_confidence", "line_number": 183, "usage_type": "call"}, {"api_name": "evaluation.metrics", "line_number": 183, "usage_type": "name"}, {"api_name": "tabulate.tabulate", "line_number": 222, "usage_type": "call"}, {"api_name": "utils.avgmeter.AverageMeter", "line_number": 236, "usage_type": "call"}, {"api_name": "utils.avgmeter.AverageMeter", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 293, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 294, "usage_type": "attribute"}, {"api_name": "evaluation.metrics.get_confidence", "line_number": 307, "usage_type": "call"}, {"api_name": "evaluation.metrics", "line_number": 307, "usage_type": "name"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 323, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 323, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 323, "usage_type": "name"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 334, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 334, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 334, "usage_type": "name"}, {"api_name": "args.print_freq", "line_number": 339, "usage_type": "attribute"}, {"api_name": "evaluation.metrics.get_confidence", "line_number": 375, "usage_type": "call"}, {"api_name": "evaluation.metrics", "line_number": 375, "usage_type": "name"}, {"api_name": "numpy.logical_not", "line_number": 386, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 396, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 404, "usage_type": "call"}, {"api_name": "evaluation.metrics.hp_average_precision", "line_number": 405, "usage_type": "call"}, {"api_name": "evaluation.metrics", "line_number": 405, "usage_type": "name"}, {"api_name": "numpy.ones_like", "line_number": 415, "usage_type": "call"}, {"api_name": "data.data_manager", "line_number": 418, "usage_type": "name"}, {"api_name": "data.data_manager", "line_number": 420, "usage_type": "name"}, {"api_name": "tabulate.tabulate", "line_number": 423, "usage_type": "call"}, {"api_name": "data.data_manager", "line_number": 423, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path", "line_number": 427, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 428, "usage_type": "call"}, {"api_name": "data.data_manager", "line_number": 428, "usage_type": "argument"}, {"api_name": "numpy.concatenate", "line_number": 459, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path", "line_number": 461, "usage_type": "name"}, {"api_name": "utils.plot.show_img_grid", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 480, "usage_type": "call"}, {"api_name": "os.path", "line_number": 480, "usage_type": "name"}, {"api_name": "utils.iotools.check_isfile", "line_number": 481, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 482, "usage_type": "call"}, {"api_name": "args.argument_parser", "line_number": 500, "usage_type": "call"}]} +{"seq_id": "6844915464", "text": "import psycopg2\nconn = psycopg2.connect(dbname='VicktoriaCostume', user='postgres',\n password='3041', host='localhost')\nprint(\"Database opened successfully\")\ncur = conn.cursor()\ncur.close()\n\nasync def GetUserById(id):\n cur = conn.cursor()\n cur.execute(f\"\"\"\n SELECT tg_id, fullname, is_admin, phone \n FROM public.\"user\" \n WHERE tg_id='{id}'\n\"\"\")\n rows = cur.fetchone()\n cur.close()\n return rows\nasync def GetAllUsers():\n cur = conn.cursor()\n cur.execute(\"SELECT tg_id, fullname, is_admin, phone FROM public.user;\")\n rows = cur.fetchall()\n print(rows)\n cur.close()\n return rows\n\nasync def GetAllUsersWithCostume():\n cur = conn.cursor()\n cur.execute(\"\"\"SELECT \"user\", costume\n\tFROM public.basket;\"\"\")\n rows = cur.fetchall()\n result =\"Список:\\n\"\n\n for row in rows:\n tg_id, fullname, is_admin, phone = await GetUserById(row[0])\n costume = await GetCostumeById(row[1])\n result+=f\"{fullname},{phone},{costume[0]}\\n\"\n print(result)\n if(result==\"Список:\\n\"):\n result+=\"Пуст\"\n cur.close()\n\n return result\n\n\n\nasync def GetUserCostumes(id) -> str:\n cur = conn.cursor()\n cur.execute(f\"\"\"\n SELECT costume\n\tFROM public.basket where \"user\"='{id}'\n\"\"\")\n rows = cur.fetchall()\n cur.close()\n print(rows)\n text = \"Список:\\n\"\n for row in rows:\n print(row[0])\n desc = await GetCostumeById(row[0])\n text+=f\"{desc[0]}\\n\"\n if(text==\"Список:\\n\"):\n text+=\"Пусто. Молодец!\"\n return text\n\n\nasync def GetCostumeById(id):\n cur = conn.cursor()\n cur.execute(f\"\"\"\n SELECT \"desc\"\n\tFROM public.costume Where id='{id}'\n\"\"\")\n rows = cur.fetchone()\n cur.close()\n return rows\n\n\nasync def InsertUser(tg_id, fullname,phone):\n cur = conn.cursor()\n cur.execute(\"INSERT INTO public.user(tg_id, fullname,phone) VALUES (%s, %s,%s);\",(tg_id,fullname,phone))\n conn.commit()\n cur.close()\nasync def InsertScannedCostume(tg_id, costume_id):\n cur = conn.cursor()\n cur.execute(\"\"\"\n INSERT INTO public.basket(\n\t\"user\", costume)\n\tVALUES (%s, %s);\n \"\"\",(tg_id,costume_id))\n conn.commit()\n cur.close()\nasync def UpdateUserName(tg_id, fullname):\n cur = conn.cursor()\n cur.execute(f\"\"\"\n UPDATE public.\"user\"\n\tSET fullname='{fullname}'\n\tWHERE tg_id='{tg_id}';\n \"\"\")\n conn.commit()\n cur.close()\n\nasync def UpdateUserPhone(tg_id, phone):\n cur = conn.cursor()\n cur.execute(f\"\"\"\n UPDATE public.\"user\"\n\tSET phone='{phone}'\n\tWHERE tg_id='{tg_id}';\n \"\"\")\n conn.commit()\n cur.close()\n\n\nasync def DeleteFromBasket(costume_id):\n cur = conn.cursor()\n cur.execute(f\"\"\"\n DELETE FROM public.basket\n\t WHERE basket.costume='{costume_id}';\n \"\"\")\n conn.commit()\n cur.close()\n", "repo_name": "RykivSale/VictoriaCostumer", "sub_path": "db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 2824, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "psycopg2.connect", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "20885200997", "text": "#!/usr/bin/python3\n\"\"\"4 cities by state\"\"\"\nimport MySQLdb\nimport sys\n\nif __name__ == \"__main__\":\n user = sys.argv[1]\n pswd = sys.argv[2]\n name = sys.argv[3]\n\n db = MySQLdb.connect('localhost', user, pswd, name)\n mycursor = db.cursor()\n mycursor.execute(\"SELECT cities.id, cities.name, states.name FROM cities\\\n INNER JOIN states ON states.id = cities.state_id ORDER BY cities.id\")\n result = mycursor.fetchall()\n\n for x in result:\n print(\"{}\".format(x))\n", "repo_name": "AlpagaSauvage/holbertonschool-higher_level_programming", "sub_path": "0x0F-python-object_relational_mapping/4-cities_by_state.py", "file_name": "4-cities_by_state.py", "file_ext": "py", "file_size_in_byte": 491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "MySQLdb.connect", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "3026880029", "text": "import RSA\nimport argparse\nimport gmpy2\nimport time as t\nimport random\n\n\ndef run(args):\n def exec_pollard_rho(n): # Find prime factor of n\n x = random.randrange(1, n, 2)\n i, k = 1, 2\n y = x\n while True:\n i += 1\n x2 = (gmpy2.square(x) - 1) % n\n p = gmpy2.gcd(y - x2, n)\n if (p != 1) and (p != n):\n break\n\n x = x2\n if i == k:\n y = x2\n k *= 2\n return p\n\n if args.verbose:\n print(\"---- Executing Pollard Rho algorithm ----\")\n\n public_key = RSA.PublicKey.import_from(args.public_key)\n\n t_start = t.time()\n p = exec_pollard_rho(public_key.n)\n time_passed = t.time() - t_start\n\n if args.verbose:\n print(\"---- Key broken in {} ms ----\".format(round(time_passed*1000, 5)))\n\n q = public_key.n // p # n = p * q\n phi = (p - 1)*(q - 1)\n d = RSA.get_inv_mul(public_key.x, phi)\n private_key = RSA.PrivateKey(d, public_key.n)\n\n if args.verbose:\n print(\"---- Exporting private key to {}\".format(args.output_dir))\n\n private_key.export_to(args.output_dir + \"pollard_\")\n\n if args.verbose:\n print(\"---- Done ----\")\n\n return private_key\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('-v', '--verbose', type=int, default=1)\n parser.add_argument('--public_key', type=str, default=\"public_key.der\")\n parser.add_argument('--output_dir', type=str, default='')\n args = parser.parse_args()\n\n random.seed(int(round(t.time())))\n run(args)\n", "repo_name": "lccasagrande/Textbook-RSA", "sub_path": "src/pollard_rho.py", "file_name": "pollard_rho.py", "file_ext": "py", "file_size_in_byte": 1590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "3", "api": [{"api_name": "random.randrange", "line_number": 10, "usage_type": "call"}, {"api_name": "gmpy2.square", "line_number": 15, "usage_type": "call"}, {"api_name": "gmpy2.gcd", "line_number": 16, "usage_type": "call"}, {"api_name": "RSA.PublicKey.import_from", "line_number": 29, "usage_type": "call"}, {"api_name": "RSA.PublicKey", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "RSA.get_inv_mul", "line_number": 40, "usage_type": "call"}, {"api_name": "RSA.PrivateKey", "line_number": 41, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 55, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 61, "usage_type": "call"}, {"api_name": "time.time", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "39881932663", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\nperiod_seconds = 600\ntime_unit = 1000000\nend_time = 2506198229513\nresult_cpuload_list = []\nresult_memload_list = []\n\nfor i in range(((end_time // time_unit) // period_seconds) + 1):\n result_cpuload_list.append(random.uniform(100, 500))\n result_memload_list.append(random.uniform(100, 500))\n \nfig, ax = plt.subplots()\n \nplt.plot(result_cpuload_list, label='CPU Demand')\nplt.plot(result_memload_list, label='MEM Demand')\n \n# plt.tight_layout()\n \nplt.xlabel('Time(day)')\nplt.ylabel('Resource Demand')\nplt.legend(loc='best', prop={'size':12})\n \nplt.grid(True) \nax.set_xticks(range(0, 5000, 144 * 4))\nax.set_xticklabels(range(0, 33, 4))\n\nplt.ylim(0,1000)\n \nplt.savefig(\"test.png\", dpi=300)\nplt.show()\n", "repo_name": "stevenbush/online_vm_placement", "sub_path": "script/plot/line_demo_dash_control.py", "file_name": "line_demo_dash_control.py", "file_ext": "py", "file_size_in_byte": 789, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "random.uniform", "line_number": 12, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "28120604281", "text": "\"\"\"\nBasic views for dealing with QGraphicsScene and graph views.\n\nSeveral classes are based on equivalent code in angr management. Thank you, angr devs!\n\"\"\"\n\nfrom typing import Optional\nfrom PySide6.QtWidgets import QApplication, QGraphicsScene, QGraphicsView, QGraphicsSceneMouseEvent, QStyleOptionGraphicsItem\nfrom PySide6.QtCore import QPointF, Qt, QEvent, QMarginsF, QPoint, QRectF, QSize, Signal\nfrom PySide6.QtGui import QImage, QKeyEvent, QMouseEvent, QPainter, QPointingDevice, QVector2D, QWheelEvent\n\nclass BaseGraphicsView(QGraphicsView):\n \"\"\"\n A base graphics view that keeps track of the scene rectangle and the ability to\n save the visibile scene to an image.\n \"\"\"\n\n scene_rect_changed = Signal(QRectF)\n\n def __init__(self, parent=None):\n super().__init__(parent=parent)\n self._scene_rect: QRectF = QRectF()\n self._is_extra_render_pass: bool = False\n\n @property\n def scene_rect(self):\n return self._scene_rect\n\n @property\n def is_extra_render_pass(self):\n return self._is_extra_render_pass\n\n def set_extra_render_pass(self, is_extra_pass: bool):\n \"\"\"\n Trugger any post-render callbacks.\n \"\"\"\n self._is_extra_render_pass = is_extra_pass\n\n def redraw(self):\n \"\"\"\n Redraw the scene. Do not recompute any items in the view.\"\n\n :return: None\n \"\"\"\n scene = self.scene()\n if scene:\n scene.update(self.sceneRect())\n\n def viewportEvent(self, event:QEvent) -> bool:\n scene_rect = self.mapToScene(self.viewport().geometry()).boundingRect()\n if scene_rect != self._scene_rect:\n self._scene_rect = scene_rect\n self.scene_rect_changed.emit(scene_rect)\n\n return super().viewportEvent(event)\n\n def save_image_to(self, path, left_margin=50, top_margin=50, right_margin=50, bottom_margin=50):\n \"\"\"\n Save the scene to an image.\n\n :return: None\n \"\"\"\n margins = QMarginsF(left_margin, top_margin, right_margin, bottom_margin)\n\n # Figure out rectangle for source\n old_rect = self.scene().sceneRect()\n min_rect = self.scene().itemsBoundingRect()\n img_rect = min_rect.marginsAdded(margins)\n\n image = QImage(img_rect.size().toSize(), QImage.Format_ARGB32)\n image.fill(Qt.white)\n painter = QPainter(image)\n\n painter.setRenderHints(QPainter.Antialiasing | QPainter.SmoothPixmapTransform) #type: ignore\n\n # Draw the image\n self.scene().setSceneRect(img_rect)\n self.scene().render(painter)\n image.save(path)\n\n painter.end()\n\n # Restore old scene rect\n self.scene().setSceneRect(old_rect)\n\nclass InteractiveGraphicsView(BaseGraphicsView):\n \"\"\"\n An interactive graphics view.\n \"\"\"\n\n ZOOM_X = True\n ZOOM_Y = True\n\n def __init__(self, zoom_enabled=True, parent=None):\n super().__init__(parent=parent)\n\n self._is_dragging = False\n self._is_mouse_pressed = False\n\n # Scene coordinates\n self._last_coords: Optional[QPointF] = None\n # View coordinates\n self._last_screen_pos: Optional[QPoint] = None\n\n self.setTransformationAnchor(QGraphicsView.NoAnchor)\n self.setResizeAnchor(QGraphicsView.AnchorViewCenter)\n\n self.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n self.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n\n self.zoom_enabled = zoom_enabled\n self.zoom_factor = None\n\n def _initial_position(self):\n raise NotImplementedError\n\n def _reset_view(self):\n \"\"\"\n Reset the view of the scene.\n \"\"\"\n\n self.resetTransform()\n self.centerOn(self._initial_position())\n self.zoom(restore=True)\n\n def _reset_scene(self):\n \"\"\"\n Reset the scene.\n \"\"\"\n\n if self.scene():\n self.scene().clear()\n else:\n scene = QGraphicsScene(self)\n self.setScene(scene)\n\n def sizeHint(self):\n return QSize(1000, 700)\n\n def zoom(self, out=False, at=None, reset=False, restore=False):\n if self.zoom_enabled == False:\n return\n\n if at is None:\n at = self.scene().sceneRect().center().toPoint()\n\n lod = QStyleOptionGraphicsItem.levelOfDetailFromTransform(self.transform())\n zoomInFactor = 1.25\n zoomOutFactor = 1 / zoomInFactor\n\n if reset:\n zoomFactor = 1 / lod\n elif restore:\n zoomFactor = self.zoom_factor if self.zoom_factor else 1 / lod\n elif not out:\n zoomFactor = zoomInFactor\n else:\n zoomFactor = zoomOutFactor\n # Limit scroll out\n if lod < 0.015:\n return\n\n # Save the scene position\n old_pos = self.mapToScene(at)\n\n # Perform zoom\n self.scale(zoomFactor if self.ZOOM_X else 1,\n zoomFactor if self.ZOOM_Y else 1)\n self.zoom_factor = QStyleOptionGraphicsItem.levelOfDetailFromTransform(self.transform())\n\n # Get the new position\n new_pos = self.mapToScene(at)\n\n # Translate view over to new position\n delta = new_pos - old_pos\n self.translate(delta.x(), delta.y())\n\n def wheelEvent(self, event):\n if event.modifiers() & Qt.ControlModifier == Qt.ControlModifier:\n is_zoom_out = event.angleDelta().y() < 0\n # TODO: Do we want to use cursor position (the pos() method) instead?\n self.zoom(is_zoom_out, event.globalPosition().toPoint())\n elif is_touchpad(event):\n super().wheelEvent(event)\n else:\n # Allow mouse wheel to be used for horizontal scrolling when modifier is active\n if event.modifiers() & Qt.ShiftModifier == Qt.ShiftModifier:\n event.setModifiers(event.modifiers() & ~Qt.ShiftModifier)\n self.horizontalScrollBar().wheelEvent(event)\n else:\n self.verticalScrollBar().wheelEvent(event)\n\n def _save_last_coords(self, event):\n event_pos: QPoint = event.pos()\n scene_pos = self.mapToScene(event_pos)\n self._last_coords = scene_pos\n self._last_screen_pos = event_pos\n\n def keyPressEvent(self, event: QKeyEvent) -> None:\n if event.key() == Qt.Key_Equal and is_modifier_active(event, Qt.ControlModifier):\n self.zoom(out=False)\n elif event.key() == Qt.Key_Minus and is_modifier_active(event, Qt.ControlModifier):\n self.zoom(out=True)\n elif event.key() == Qt.Key_0 and is_modifier_active(event, Qt.ControlModifier):\n self.zoom(reset=True)\n else:\n super().keyPressEvent(event)\n\n def mousePressEvent(self, event: QMouseEvent) -> None:\n if event.button() == Qt.LeftButton:\n self._is_mouse_pressed = True\n self._is_dragging = False\n\n self._save_last_coords(event)\n event.accept()\n\n super().mousePressEvent(event)\n\n def mouseMoveEvent(self, event: QMouseEvent) -> None:\n SENSITIVITY = 1.0\n if self._is_mouse_pressed:\n mouse_delta = QVector2D(event.pos() - self._last_screen_pos).length() #type: ignore\n if mouse_delta > SENSITIVITY:\n self._is_dragging = True\n scene_pos = self.mapToScene(event.pos())\n\n self.viewport().setCursor(Qt.ClosedHandCursor)\n\n delta = scene_pos - self._last_coords #type: ignore\n self.translate(delta.x(), delta.y())\n\n self._save_last_coords(event)\n event.accept()\n\n super().mouseMoveEvent(event)\n\n def dispatchMouseMoveEventToScene(self, event):\n \"\"\"\n Send unhandled events to the underlying scene.\n \"\"\"\n\n if event.type() == QEvent.MouseButtonPress:\n event_type = QEvent.GraphicsSceneMousePress\n elif event.type() == QEvent.MouseButtonRelease:\n event_type = QEvent.GraphicsSceneMouseRelease\n else:\n raise ValueError(f'Unexpected event type {event.type()}')\n\n # Pulled from angr management,\n # which pulled from QGraphicsView::mousePressEvent in Qt5\n mouse_event = QGraphicsSceneMouseEvent(event_type)\n mouse_press_view_point: QPoint = event.pos()\n mouse_press_scene_point = self.mapToScene(mouse_press_view_point)\n mouse_press_screen_point = event.globalPos()\n last_mouse_move_scene_point = mouse_press_scene_point\n last_mouse_move_screen_point = mouse_press_screen_point\n mouse_press_button = event.button()\n\n # TODO: This is from angr management code, they were unsure if needed and\n # based on comment it wasn't available in PySide2. Check out how situation\n # with PySide6.\n # mouse_event.setWidget(self.viewport())\n mouse_event.setButtonDownScenePos(mouse_press_button, mouse_press_scene_point)\n mouse_event.setButtonDownScreenPos(mouse_press_button, mouse_press_screen_point)\n mouse_event.setScenePos(mouse_press_scene_point)\n mouse_event.setScreenPos(mouse_press_screen_point)\n mouse_event.setLastScenePos(last_mouse_move_scene_point)\n mouse_event.setLastScreenPos(last_mouse_move_screen_point)\n mouse_event.setButtons(event.buttons())\n mouse_event.setButton(event.button())\n mouse_event.setModifiers(event.modifiers())\n mouse_event.setSource(event.source())\n mouse_event.setFlags(event.flags())\n mouse_event.setAccepted(False)\n QApplication.sendEvent(self.scene(), mouse_event)\n return mouse_event\n\n def mouseReleaseEvent(self, event: QMouseEvent) -> None:\n if event.button() == Qt.LeftButton:\n if self._is_dragging:\n self.viewport().setCursor(Qt.ArrowCursor)\n event.accept()\n\n if not event.isAccepted():\n gen_press_event = QMouseEvent(QEvent.MouseButtonPress,\n event.pos(),\n event.globalPos(),\n event.button(),\n event.buttons(),\n event.modifiers())\n _ = self.dispatchMouseMoveEventToScene(gen_press_event)\n\n gen_release_event = QMouseEvent(QEvent.MouseButtonRelease,\n event.pos(),\n event.globalPos(),\n event.button(),\n event.buttons(),\n event.modifiers())\n release_event = self.dispatchMouseMoveEventToScene(gen_release_event)\n\n if not release_event.isAccepted():\n # TODO: This is from angr management, but method isn't defined\n # self.on_background_click()\n release_event.accept()\n\n self._is_mouse_pressed = False\n self._is_dragging = False\n\n super().mouseReleaseEvent(event)\n\ndef is_modifier_active(event: QKeyEvent,\n modifier: Qt.KeyboardModifier) -> bool:\n \"\"\"\n Check if keyboard modifier is active for event.\n \"\"\"\n\n return event.modifiers() & modifier == modifier #type: ignore\n\ndef is_touchpad(event: QWheelEvent) -> bool:\n return event.pointerType() == QPointingDevice.PointerType.Finger\n", "repo_name": "kudu-dynamics/ember", "sub_path": "src/ember/ui/widgets/graphics.py", "file_name": "graphics.py", "file_ext": "py", "file_size_in_byte": 11495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "PySide6.QtWidgets.QGraphicsView", "line_number": 12, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Signal", "line_number": 18, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QRectF", "line_number": 18, "usage_type": "argument"}, {"api_name": "PySide6.QtCore.QRectF", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 49, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QMarginsF", "line_number": 63, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QImage", "line_number": 70, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QImage.Format_ARGB32", "line_number": 70, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt.white", "line_number": 71, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 71, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QPainter", "line_number": 72, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QPainter.Antialiasing", "line_number": 74, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui.QPainter", "line_number": 74, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QPainter.SmoothPixmapTransform", "line_number": 74, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 101, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QPointF", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 103, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QPoint", "line_number": 103, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QGraphicsView.NoAnchor", "line_number": 105, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets.QGraphicsView", "line_number": 105, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QGraphicsView.AnchorViewCenter", "line_number": 106, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets.QGraphicsView", "line_number": 106, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ScrollBarAlwaysOff", "line_number": 108, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 108, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ScrollBarAlwaysOff", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 109, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QGraphicsScene", "line_number": 134, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QSize", "line_number": 138, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QStyleOptionGraphicsItem.levelOfDetailFromTransform", "line_number": 147, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QStyleOptionGraphicsItem", "line_number": 147, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QStyleOptionGraphicsItem.levelOfDetailFromTransform", "line_number": 169, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QStyleOptionGraphicsItem", "line_number": 169, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ControlModifier", "line_number": 179, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 179, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ShiftModifier", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 187, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ShiftModifier", "line_number": 188, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 188, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QPoint", "line_number": 194, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QKeyEvent", "line_number": 199, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.Key_Equal", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 200, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ControlModifier", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt.Key_Minus", "line_number": 202, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 202, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ControlModifier", "line_number": 202, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt.Key_0", "line_number": 204, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 204, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ControlModifier", "line_number": 204, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui.QMouseEvent", "line_number": 209, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.LeftButton", "line_number": 210, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 210, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QMouseEvent", "line_number": 219, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QVector2D", "line_number": 222, "usage_type": "call"}, {"api_name": "PySide6.QtCore.Qt.ClosedHandCursor", "line_number": 227, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 227, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QEvent.MouseButtonPress", "line_number": 242, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 242, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QEvent.GraphicsSceneMousePress", "line_number": 243, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 243, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QEvent.MouseButtonRelease", "line_number": 244, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 244, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QEvent.GraphicsSceneMouseRelease", "line_number": 245, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 245, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QGraphicsSceneMouseEvent", "line_number": 251, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QPoint", "line_number": 252, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QApplication.sendEvent", "line_number": 275, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 275, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QMouseEvent", "line_number": 278, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.LeftButton", "line_number": 279, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 279, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.ArrowCursor", "line_number": 281, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 281, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QMouseEvent", "line_number": 285, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QEvent.MouseButtonPress", "line_number": 285, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 285, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QMouseEvent", "line_number": 293, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QEvent.MouseButtonRelease", "line_number": 293, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.QEvent", "line_number": 293, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QKeyEvent", "line_number": 311, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.KeyboardModifier", "line_number": 312, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 312, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QWheelEvent", "line_number": 319, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QPointingDevice.PointerType", "line_number": 320, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui.QPointingDevice", "line_number": 320, "usage_type": "name"}]} +{"seq_id": "70510566802", "text": "from asyncio.queues import QueueEmpty\nfrom ZeroTwo.config import que\nfrom pyrogram import Client, filters\nfrom pyrogram.types import Message\n\nfrom ZeroTwo.function.admins import set\nfrom ZeroTwo.helpers.channelmusic import get_chat_id\nfrom ZeroTwo.helpers.decorators import authorized_users_only, errors\nfrom ZeroTwo.helpers.filters import command, other_filters\nfrom ZeroTwo.services.callsmusic import callsmusic\n\n\n\n@Client.on_message(filters.command([\"cpause\",\"cpause@StreamMusic_Bot\"]) & filters.group & ~filters.edited)\n@errors\n@authorized_users_only\nasync def pause(_, message: Message):\n try:\n conchat = await _.get_chat(message.chat.id)\n conid = conchat.linked_chat.id\n chid = conid\n except:\n await message.reply(\"❌ __**Chat Is Not Linked!**__\")\n return \n chat_id = chid\n if (chat_id not in callsmusic.pytgcalls.active_calls) or (\n callsmusic.pytgcalls.active_calls[chat_id] == \"paused\"\n ):\n await message.reply_text(\"❗ __**Nothing Is Playing To Paused!**__\")\n else:\n callsmusic.pytgcalls.pause_stream(chat_id)\n await message.reply_text(\"⏸ __**Paused! Use `/resume` To Resume.**__\")\n\n\n@Client.on_message(filters.command([\"cresume\",\"cresume@StreamMusic_Bot\"]) & filters.group & ~filters.edited)\n@errors\n@authorized_users_only\nasync def resume(_, message: Message):\n try:\n conchat = await _.get_chat(message.chat.id)\n conid = conchat.linked_chat.id\n chid = conid\n except:\n await message.reply(\"❌ __**Chat Is Not Linked!**__\")\n return \n chat_id = chid\n if (chat_id not in callsmusic.pytgcalls.active_calls) or (\n callsmusic.pytgcalls.active_calls[chat_id] == \"playing\"\n ):\n await message.reply_text(\"❗ __**Nothing Is Paused To Resume!**__\")\n else:\n callsmusic.pytgcalls.resume_stream(chat_id)\n await message.reply_text(\"▶️ __**Resumed! Use `/pause` To Pause.**__\")\n\n\n@Client.on_message(filters.command([\"cstop\",\"cstop@StreamMusic_Bot\"]) & filters.group & ~filters.edited)\n@errors\n@authorized_users_only\nasync def stop(_, message: Message):\n try:\n conchat = await _.get_chat(message.chat.id)\n conid = conchat.linked_chat.id\n chid = conid\n except:\n await message.reply(\"❌ __**Chat Is Not Linked!**__\")\n return \n chat_id = chid\n if chat_id not in callsmusic.pytgcalls.active_calls:\n await message.reply_text(\"❗ __**Nothing Is Streaming To Stop!**__\")\n else:\n try:\n callsmusic.queues.clear(chat_id)\n except QueueEmpty:\n pass\n\n callsmusic.pytgcalls.leave_group_call(chat_id)\n await message.reply_text(\"⏹ __**Stopped & Left From Voice Chat!**__\")\n\n\n@Client.on_message(filters.command([\"cskip\",\"cskip@StreamMusic_Bot\"]) & filters.group & ~filters.edited)\n@errors\n@authorized_users_only\nasync def skip(_, message: Message):\n global que\n try:\n conchat = await _.get_chat(message.chat.id)\n conid = conchat.linked_chat.id\n chid = conid\n except:\n await message.reply(\"❌ __**Chat Is Not Linked!**__\")\n return \n chat_id = chid\n if chat_id not in callsmusic.pytgcalls.active_calls:\n await message.reply_text(\"❗ __**Queue Is Empty, Just Like Your Life!**__\")\n else:\n callsmusic.queues.task_done(chat_id)\n\n if callsmusic.queues.is_empty(chat_id):\n callsmusic.pytgcalls.leave_group_call(chat_id)\n else:\n callsmusic.pytgcalls.change_stream(\n chat_id, callsmusic.queues.get(chat_id)[\"file\"]\n )\n\n qeue = que.get(chat_id)\n if qeue:\n skip = qeue.pop(0)\n if not qeue:\n return\n await message.reply_text(f\"⏭ __**Skipped:**__ `{skip[0]}`\\n- Now Playing: `{qeue[0][0]}`\")\n\n\n@Client.on_message(filters.command([\"cadmincache\",\"admincache@StreamMusic_Bot\"]))\n@errors\nasync def admincache(client, message: Message):\n try:\n conchat = await client.get_chat(message.chat.id)\n conid = conchat.linked_chat.id\n chid = conid\n except:\n await message.reply(\"❌ __**Chat Is Not Linked!**__\")\n return\n set(\n chid,\n [\n member.user\n for member in await conchat.linked_chat.get_members(filter=\"administrators\")\n ],\n )\n await message.reply_text(\"❇️ `Admin Cache Refreshed!`\")\n", "repo_name": "TeamShizuX/zero-Two", "sub_path": "ZeroTwo/modules/channeladmin.py", "file_name": "channeladmin.py", "file_ext": "py", "file_size_in_byte": 4343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pyrogram.types.Message", "line_number": 17, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 26, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 27, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls.pause_stream", "line_number": 31, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 31, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 14, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 14, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 14, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 14, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.edited", "line_number": 14, "usage_type": "attribute"}, {"api_name": "ZeroTwo.helpers.decorators.errors", "line_number": 15, "usage_type": "name"}, {"api_name": "ZeroTwo.helpers.decorators.authorized_users_only", "line_number": 16, "usage_type": "name"}, {"api_name": "pyrogram.types.Message", "line_number": 38, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 47, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 48, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 48, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls.resume_stream", "line_number": 52, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 52, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 52, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 35, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 35, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 35, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 35, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.edited", "line_number": 35, "usage_type": "attribute"}, {"api_name": "ZeroTwo.helpers.decorators.errors", "line_number": 36, "usage_type": "name"}, {"api_name": "ZeroTwo.helpers.decorators.authorized_users_only", "line_number": 37, "usage_type": "name"}, {"api_name": "pyrogram.types.Message", "line_number": 59, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 68, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 68, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues.clear", "line_number": 72, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues", "line_number": 72, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 72, "usage_type": "name"}, {"api_name": "asyncio.queues.QueueEmpty", "line_number": 73, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls.leave_group_call", "line_number": 76, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 76, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 76, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 56, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 56, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 56, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 56, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.edited", "line_number": 56, "usage_type": "attribute"}, {"api_name": "ZeroTwo.helpers.decorators.errors", "line_number": 57, "usage_type": "name"}, {"api_name": "ZeroTwo.helpers.decorators.authorized_users_only", "line_number": 58, "usage_type": "name"}, {"api_name": "pyrogram.types.Message", "line_number": 83, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 93, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 93, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues.task_done", "line_number": 96, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues", "line_number": 96, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 96, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues.is_empty", "line_number": 98, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues", "line_number": 98, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 98, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls.leave_group_call", "line_number": 99, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 99, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 99, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls.change_stream", "line_number": 101, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.pytgcalls", "line_number": 101, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 101, "usage_type": "name"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues.get", "line_number": 102, "usage_type": "call"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic.queues", "line_number": 102, "usage_type": "attribute"}, {"api_name": "ZeroTwo.services.callsmusic.callsmusic", "line_number": 102, "usage_type": "name"}, {"api_name": "ZeroTwo.config.que.get", "line_number": 105, "usage_type": "call"}, {"api_name": "ZeroTwo.config.que", "line_number": 105, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 80, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 80, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 80, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 80, "usage_type": "name"}, {"api_name": "pyrogram.filters.group", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.edited", "line_number": 80, "usage_type": "attribute"}, {"api_name": "ZeroTwo.helpers.decorators.errors", "line_number": 81, "usage_type": "name"}, {"api_name": "ZeroTwo.helpers.decorators.authorized_users_only", "line_number": 82, "usage_type": "name"}, {"api_name": "pyrogram.types.Message", "line_number": 115, "usage_type": "name"}, {"api_name": "ZeroTwo.function.admins.set", "line_number": 123, "usage_type": "call"}, {"api_name": "pyrogram.Client.on_message", "line_number": 113, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 113, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 113, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 113, "usage_type": "name"}, {"api_name": "ZeroTwo.helpers.decorators.errors", "line_number": 114, "usage_type": "name"}]} +{"seq_id": "3388986311", "text": "from numpy.random import normal\nfrom sklearn import svm\n\nimport scipy.io\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef read_data():\n HS = scipy.io.loadmat('data/PaviaU.mat')['paviaU']\n gt = scipy.io.loadmat('data/PaviaU_gt.mat')['paviaU_gt']\n return HS, gt\n\n\ndef bit_vectors(HS, gt):\n X = []\n y = []\n\n height_gt, width_gt = gt.shape\n \n for j in range(height_gt):\n for i in range(width_gt):\n X.append(HS[j,i,:])\n y.append(gt[j,i])\n\n return np.array(X), np.array(y)\n\n\n\nclass_labels = {\n 0: \"\",\n 1: \"Asphalt - Asfalt\",\n 2: \"Meadows - Çayırlar\",\n 3: \"Gravel - Çakıl\",\n 4: \"Trees - Ağaçlar\",\n 5: \"Painted metal sheets \\nBoyalı saclar\",\n 6: \"Bare Soil - Çıplak Toprak\",\n 7: \"Bitumen - Zift\",\n 8: \"Self-Blocking Bricks \\nKendinden Blokaj Tuğlalar\",\n 9: \"Shadows - Gölgeler\"\n}\n\n\n\n\nfrom matplotlib import colors\n\n\ncmap = colors.ListedColormap(['black', \n colors.to_rgba('#c4cccd', alpha=None),\n colors.to_rgba('#02ff00', alpha=None),\n colors.to_rgba('#20fec9', alpha=None),\n colors.to_rgba('#00b800', alpha=None),\n colors.to_rgba('#e333fe', alpha=None),\n colors.to_rgba('#be3000', alpha=None),\n colors.to_rgba('#8500e5', alpha=None),\n colors.to_rgba('#fe051c', alpha=None),\n colors.to_rgba('#edfe00', alpha=None), ], N=10)\n\nticks=[0,1,2,3,4,5,6,7,8,9] \nbounds=[-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5]\nnorm = colors.BoundaryNorm(bounds, cmap.N)\n\n\n\n\n\nHS, gt = read_data()\nHS = HS/8192.0\n\n\nX, y = bit_vectors(HS, gt)\n\n# from sklearn.preprocessing import MinMaxScaler\n# scaler = MinMaxScaler(feature_range=(0, 1))\n# X = scaler.fit_transform(X)\n\n\n\n\n\n\n\n\n\n\nfrom sklearn.model_selection import train_test_split\n\nnon_zeros = np.where(y != 0)\n# y_train = np.delete(y_train, zeros)\n# X_train = np.delete(X_train, zeros)\ny = y[non_zeros]\nX = X[non_zeros]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.95, shuffle=True)\n\n\n\n\n\n\n\n\n\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score\n\nc_options = [0.0001, 0.001, 0.01, 0.1, 1, 100, 1000]\ngamma_options = [0.0001, 0.001, 0.005, 0.1, 1, 3, 5, 10, 100, 1000]\n\nresults = []\n\nfor c in c_options: \n for gamma in gamma_options:\n \n scores = []\n\n cv = KFold(n_splits=10, shuffle=False)\n \n i = 1\n for train_index, test_index in cv.split(X_train):\n\n X_train_part, X_test_part, y_train_part, y_test_part = X[train_index], X[test_index], y[train_index], y[test_index]\n\n svn = SVC(kernel='rbf', C=c, gamma=gamma)\n \n clf = svn.fit(X_train_part, y_train_part)\n \n y_pred_part = clf.predict(X_test_part)\n # scores.append(svn.score(X_test_part, y_test_part))\n current_score = accuracy_score(y_test_part, y_pred_part)\n scores.append(current_score)\n\n # print(\"Fold:\", i, \"Train Data:\", X_train_part.shape, \"Train Labels:\", y_train_part.shape, \"Test Data:\", X_test_part.shape, \"Test Labels:\", y_test_part.shape, \"Score:\", current_score)\n i = i+1\n\n print(f'c = {c}, gamma = {gamma}, avg. score = {np.array(scores).mean()}')\n results.append({ 'c': c, 'gamma': gamma, 'score': np.array(scores).mean() })\n # print('---------------------------------------------------------------')\n\n\nresult_sorted = sorted(results, key=lambda x: x['score'], reverse=True)\nfirst_result = result_sorted[0]\n\n\n\nc = first_result['c']\ngamma = first_result['gamma']\n\n\n# c = 1000\n# gamma = 3\n\nprint(f'c: {c}, gamma: {gamma}')\n\n\nfrom sklearn.svm import SVC\nsvclassifier = SVC(kernel='rbf', C=c, gamma=gamma)\nsvclassifier.fit(X_train, y_train)\n\ny_pred = svclassifier.predict(X_test)\n\n\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score, balanced_accuracy_score, cohen_kappa_score\n\nprint(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))\n\nprint(f'overall accuracy: {accuracy_score(y_test, y_pred)}')\nprint(f'average accuracy: {balanced_accuracy_score(y_test, y_pred)}')\nprint(f'kappa: {cohen_kappa_score(y_test, y_pred)}')\n\n\n\nimage_data = []\n\nheight, width, _ = HS.shape\n\nfor j in range(height):\n # print(j)\n # print(HS[j].shape)\n line = svclassifier.predict(HS[j])\n image_data.append(line)\n\n\nlabeled_data = (gt != 0) * image_data\n\n\n\nfig, (ax1, ax2, ax3)= plt.subplots(ncols=3)\n\nax1.imshow(gt, cmap=cmap, interpolation='none', norm=norm )\nax2.imshow(labeled_data, cmap=cmap, interpolation='none', norm=norm )\nax3.imshow(image_data, cmap=cmap, interpolation='none', norm=norm )\n\n\nplt.show()\n\n\n\n\n\n\nimport seaborn as sns\nimport pandas as pd\n\ndf = pd.DataFrame(confusion_matrix(y_test, y_pred), columns= list(range(1,10)), index= list(range(1,10)))\n\n\nplt.figure()\np = sns.heatmap(df,\n fmt=\"d\", \n annot=True,\n annot_kws={'size':8},\n cbar=False,\n square=True)\n\nplt.show()\n\n\n", "repo_name": "mollaf/smv-hyperspectral-image", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "scipy.io.io.loadmat", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 10, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 10, "usage_type": "name"}, {"api_name": "scipy.io.io.loadmat", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 11, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.colors.BoundaryNorm", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 62, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 139, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 159, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 167, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 168, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.balanced_accuracy_score", "line_number": 171, "usage_type": "call"}, {"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}]} +{"seq_id": "10582164673", "text": "import os\nfrom Crypto.Cipher import AES\nfrom Crypto.Util.Padding import pad, unpad\nimport base64\nimport os\nimport hashlib\nfrom datetime import datetime, timedelta\n\nfrom vanish_vault.libs.redis_utils import rclient\nfrom vanish_vault.models.message import Message\nfrom vanish_vault.models.base import db\n\nmode = AES.MODE_CBC\n\n\n# 转换为剩余时间字符串\ndef to_remaining_time(expire_at):\n remaining_time = expire_at - datetime.now()\n if remaining_time < timedelta(seconds=0):\n return '已过期'\n if remaining_time.days > 0:\n return f'{remaining_time.days}天{remaining_time.seconds // 3600}小时{remaining_time.seconds // 60 % 60}分钟'\n if remaining_time.seconds > 3600:\n return f'{remaining_time.seconds // 3600}小时{remaining_time.seconds // 60 % 60}分钟'\n if remaining_time.seconds > 60:\n return f'{remaining_time.seconds // 60}分钟'\n return f'{remaining_time.seconds}秒'\n\n\ndef encrypt(plaintext, key):\n key = hashlib.md5(key.encode('utf-8')).hexdigest().encode('utf-8')\n iv = os.urandom(16)\n cipher = AES.new(key, mode, iv)\n ciphertext = cipher.encrypt(pad(plaintext.encode('utf-8'), AES.block_size))\n return base64.b64encode(iv + ciphertext).decode('utf-8')\n\n\ndef decrypt(ciphertext, key):\n key = hashlib.md5(key.encode('utf-8')).hexdigest().encode('utf-8')\n ciphertext = base64.b64decode(ciphertext)\n iv = ciphertext[:16]\n cipher = AES.new(key, mode, iv)\n plaintext = unpad(cipher.decrypt(ciphertext[16:]), AES.block_size)\n return plaintext.decode('utf-8')\n\n\nBASE = 62\n# 打乱的 62 个字符\nCHARS = \"LWVqiarAS7bnHv2olf9d1Jeh6QYNmPRyksz3KBGU4xcEp0jItCXOgTuZwF5D8M\"\n# CHARS = \"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\"\n\n\ndef random_str(n):\n import random\n return ''.join([random.choice(CHARS) for i in range(n)])\n\n\ndef to_base62(num):\n \"\"\"10进制转62进制\"\"\"\n if num == 0:\n return CHARS[0]\n result = []\n while num > 0:\n num, rem = divmod(num, BASE)\n result.append(CHARS[rem])\n return \"\".join(result[::-1])\n\n\ndef is_id_exist(id):\n from vanish_vault.libs.redis_utils import rclient\n app = rclient.app\n prefix = app.config.setdefault('REDIS_PREFIX', 'vv_')\n client = rclient.get_redis()\n return client.exists(f'{prefix}{id}')\n\n\ndef is_key_exist(key):\n record = Message.query.filter_by(key=key, status=1).first()\n if record and record.expire_at < datetime.now():\n record.delete()\n db.session.commit()\n return False\n return record is not None\n\n\ndef delete_content2(id):\n record = Message.query.filter_by(id=id).first()\n if record is None:\n return None\n db.session.delete(record)\n db.session.commit()\n return True\n\n\ndef delete_content(id):\n from vanish_vault.libs.redis_utils import rclient\n app = rclient.app\n prefix = app.config.setdefault('REDIS_PREFIX', 'vv_')\n client = rclient.get_redis()\n return client.delete(f'{prefix}{id}')\n\n\ndef get_decrypted_content2(id, key):\n record = Message.query.filter_by(key=id).first()\n if record is None:\n return None\n if record.expire_at < datetime.now():\n record.delete()\n db.session.commit()\n return None\n try:\n return decrypt(record.content, key)\n except Exception:\n return None\n\n\ndef get_decrypted_content(id, key):\n from vanish_vault.libs.redis_utils import rclient\n app = rclient.app\n prefix = app.config.setdefault('REDIS_PREFIX', 'vv_')\n client = rclient.get_redis()\n encrypted_content = client.get(f'{prefix}{id}')\n if not encrypted_content:\n return None\n try:\n return decrypt(encrypted_content, key)\n except Exception:\n return None\n\n\ndef get_next_id():\n from vanish_vault.libs.redis_utils import rclient\n redis_next_id = to_base62(rclient.get_next_id())\n return f'{redis_next_id}{random_str(2)}'\n\n\ndef save_content(content, key, expire=10 * 60):\n app = rclient.app\n encrypted_content = encrypt(content, key)\n prefix = app.config.setdefault('REDIS_PREFIX', 'vv_')\n next_id = get_next_id()\n client = rclient.get_redis()\n client.setex(f'{prefix}{next_id}', expire, encrypted_content)\n return next_id\n\n\ndef save_content_using_mysql(content, key, user_id, expire=10 * 60):\n encrypted_content = encrypt(content, key)\n next_id = get_next_id()\n message = Message(key=next_id, content=encrypted_content, expire_at=datetime.now(\n ) + timedelta(seconds=expire), title='消息x', user_id=user_id)\n db.session.add(message)\n db.session.commit()\n return next_id\n", "repo_name": "mjhxyz/vanish-vault", "sub_path": "vanish_vault/libs/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 13, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 31, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 32, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 33, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 33, "usage_type": "name"}, {"api_name": "Crypto.Util.Padding.pad", "line_number": 34, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 34, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 34, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 35, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 39, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 40, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 42, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 42, "usage_type": "name"}, {"api_name": "Crypto.Util.Padding.unpad", "line_number": 43, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.block_size", "line_number": 43, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES", "line_number": 43, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 55, "usage_type": "call"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.app", "line_number": 71, "usage_type": "attribute"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 71, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.get_redis", "line_number": 73, "usage_type": "call"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 73, "usage_type": "name"}, {"api_name": "vanish_vault.models.message.Message.query.filter_by", "line_number": 78, "usage_type": "call"}, {"api_name": "vanish_vault.models.message.Message.query", "line_number": 78, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.message.Message", "line_number": 78, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "name"}, {"api_name": "vanish_vault.models.base.db.session.commit", "line_number": 81, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session", "line_number": 81, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.base.db", "line_number": 81, "usage_type": "name"}, {"api_name": "vanish_vault.models.message.Message.query.filter_by", "line_number": 87, "usage_type": "call"}, {"api_name": "vanish_vault.models.message.Message.query", "line_number": 87, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.message.Message", "line_number": 87, "usage_type": "name"}, {"api_name": "vanish_vault.models.base.db.session.delete", "line_number": 90, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session", "line_number": 90, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.base.db", "line_number": 90, "usage_type": "name"}, {"api_name": "vanish_vault.models.base.db.session.commit", "line_number": 91, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session", "line_number": 91, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.base.db", "line_number": 91, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.app", "line_number": 97, "usage_type": "attribute"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 97, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.get_redis", "line_number": 99, "usage_type": "call"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 99, "usage_type": "name"}, {"api_name": "vanish_vault.models.message.Message.query.filter_by", "line_number": 104, "usage_type": "call"}, {"api_name": "vanish_vault.models.message.Message.query", "line_number": 104, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.message.Message", "line_number": 104, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}, {"api_name": "vanish_vault.models.base.db.session.commit", "line_number": 109, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session", "line_number": 109, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.base.db", "line_number": 109, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.app", "line_number": 119, "usage_type": "attribute"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 119, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.get_redis", "line_number": 121, "usage_type": "call"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 121, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.get_next_id", "line_number": 133, "usage_type": "call"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 133, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.app", "line_number": 138, "usage_type": "attribute"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 138, "usage_type": "name"}, {"api_name": "vanish_vault.libs.redis_utils.rclient.get_redis", "line_number": 142, "usage_type": "call"}, {"api_name": "vanish_vault.libs.redis_utils.rclient", "line_number": 142, "usage_type": "name"}, {"api_name": "vanish_vault.models.message.Message", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 151, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session.add", "line_number": 152, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session", "line_number": 152, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.base.db", "line_number": 152, "usage_type": "name"}, {"api_name": "vanish_vault.models.base.db.session.commit", "line_number": 153, "usage_type": "call"}, {"api_name": "vanish_vault.models.base.db.session", "line_number": 153, "usage_type": "attribute"}, {"api_name": "vanish_vault.models.base.db", "line_number": 153, "usage_type": "name"}]} +{"seq_id": "2429024916", "text": "# Final Project - Image Processing: Question 1\n# Matan Yamin, ID: 311544407\nimport cv2\nimport numpy as np\nimport os # approved from Assaf for read all images from folder \"images\"\nfrom matplotlib import pyplot as plt # for show in plot figure\n\n\ndef read_image():\n \"\"\"This function reads all images that are in 'images' folder.\n doesn't matter what is the name of the images.\"\"\"\n images = []\n for hand in os.listdir('images'):\n img = cv2.imread(os.path.join('images', hand))\n if img is not None:\n images.append(img)\n return images\n\n\ndef show_image(img, title):\n \"\"\"this func will get image and show it. that is all.\n press esc to continue\"\"\"\n cv2.imshow(title, img) # show pic\n k = cv2.waitKey(0)\n if k == 27: # wait until esc\n cv2.destroyAllWindows()\n\n\ndef show_four_images(img1, img2, img3, img4, title):\n \"\"\"this func helps me show all 4 images together\"\"\"\n shape = (460, 250)\n # Get all images in same size for better display\n img1 = cv2.resize(img1, shape)\n img2 = cv2.resize(img2, shape)\n img3 = cv2.resize(img3, shape)\n img4 = cv2.resize(img4, shape)\n # combined 2 images horizontally\n numpy_horizontal1 = np.hstack((img1, img2))\n # combined the rest 2 images horizontally\n numpy_horizontal2 = np.hstack((img3, img4))\n # now combined all vertically to 1 image and display\n numpy_vertical = np.vstack((numpy_horizontal1, numpy_horizontal2))\n # final thing - show the output:\n show_image(numpy_vertical, title)\n\n\ndef action(hand_images):\n all_images = []\n blue = (255, 0, 0) # color for contours\n red = (0, 0, 255)\n for hand in hand_images:\n # convert the image to grayscale\n gray = cv2.cvtColor(hand, cv2.COLOR_BGR2GRAY)\n # Applying Canny for edge detection and contours - played with values\n canny = cv2.Canny(gray, 133, 252)\n # find contours for finding convexHull for later\n contours, hierarchy = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n marked_hand = np.copy(hand)\n drawing = np.zeros((canny.shape[0], canny.shape[1], 3), np.uint8)\n for i in range(len(contours)):\n cv2.drawContours(drawing, contours, i, blue, 3, 8, hierarchy)\n # convert to gray in order to use \"houghCircles\"\n gray = cv2.cvtColor(drawing, cv2.COLOR_BGR2GRAY)\n # finding all circles with custom parameters for fingertips, works almost for every hand\n circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, 10, param1=100, param2=32, minRadius=14, maxRadius=21)\n # if there are no circles at all:\n if circles is None:\n continue\n circles = np.uint16(np.around(circles))\n # going through the circles and mark the center\n for circle in circles[0, :]:\n cv2.circle(marked_hand, (circle[0], circle[1]), 2, red, 7)\n all_images.append(marked_hand)\n return all_images\n\n\ndef show_plot(img, title):\n \"\"\"gets an image and title and diplay it on a plot figure as requested\"\"\"\n plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n plt.title(\"Hand Number: \" + title)\n plt.show()\n\n\ndef start_program():\n \"\"\"step 1: read all images from folder\n step 2: go to action() inorder to mark the finger tips\n step 3: display all 4 images with a custom function that shows them\"\"\"\n hands_images = read_image() # read a 4 images from folder\n results = action(hands_images) # get all results into an array\n # in case we didnt find any circle on any image\n if len(hands_images) == 0:\n print(\"No luck!\")\n return -1\n # show every image in plot\n hand_number = 1\n for img in results:\n show_plot(img, str(hand_number))\n hand_number += 1\n # show all 4 images after display with plot\n if len(hands_images) == 4:\n show_four_images(results[0], results[1], results[2], results[3], \"Final Results - FingerTips: Matan Yamin\")\n\n\nif __name__ == '__main__':\n start_program()\n exit()\n", "repo_name": "MatanYamin/Hand-Detection", "sub_path": "projectIM2021_q1.py", "file_name": "projectIM2021_q1.py", "file_ext": "py", "file_size_in_byte": 4025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 79, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "71786152400", "text": "import os\nimport re\nimport shutil\n\nimport img2pdf\nfrom PyPDF2 import PdfMerger\nfrom app.config.config import config\nfrom app.manga_scripts.tools.tools import get_driver\n\n\nclass Characteristic:\n def __init__(self, property_title, description):\n self.property_title = property_title\n self.description = description\n\n\nclass MangaVolume:\n def __init__(self, volume_title, volume_url):\n self.volume_title = volume_title\n self.volume_url = volume_url\n self.page_list = []\n\n def search_page(self, driver):\n ...\n\n def download_pdf(self, volume_index, title, manga_path):\n manga_volume_path = f'{manga_path}/{volume_index}'\n manga_volume_image_path = f'{manga_volume_path}/images'\n\n if not os.path.isdir(manga_volume_path):\n os.mkdir(manga_volume_path)\n\n if not os.path.isdir(manga_volume_image_path):\n os.mkdir(manga_volume_image_path)\n\n for index in range(0, len(self.page_list)):\n print(f'{index+1}/{len(self.page_list)}')\n with open(f'{manga_volume_image_path}/{index}.jpg', 'wb') as file:\n file.write(self.page_list[index])\n if False:\n with open(f'{manga_volume_path}/{volume_index}.cfg', 'wb') as file:\n file.writelines(f'{page}\\t\\n')\n\n images = [\n f\"{manga_volume_image_path}/{img}\" for img in\n sorted(os.listdir(manga_volume_image_path), key=lambda x: int(os.path.splitext(x)[0]))\n ]\n with open(f'{manga_volume_path}/{volume_index}-{title}.pdf', \"wb\") as f:\n f.write(img2pdf.convert(images))\n\n if not config.getboolean('Settings', 'save_images'):\n shutil.rmtree(manga_volume_image_path)\n\n\nclass Manga:\n def __init__(self, url):\n self.url = url\n self.title = None\n self.type: Characteristic | None = None\n self.format: Characteristic | None = None\n self.release_date: Characteristic | None = None\n self.status: Characteristic | None = None\n self.translate_status: Characteristic | None = None\n self.publishing: Characteristic | None = None\n self.age_rating: Characteristic | None = None\n self.author: Characteristic | None = None\n self.artist: Characteristic | None = None\n self.genre: Characteristic | None = None\n self.volumes: Characteristic | None = None\n self.alter_name: Characteristic | None = None\n self.manga_volume_list = []\n self.manga_path = config.get('Settings', 'save_path')\n self.driver = get_driver()\n self.get_info_by_url(url)\n\n def get_info_by_url(self, url):\n ...\n\n def create_manga_dir(self):\n self.manga_path += self.title\n if not os.path.isdir(self.manga_path):\n os.mkdir(self.manga_path)\n\n def create_manga_info(self):\n with open(os.path.join(self.manga_path, 'info.txt'), 'w+', encoding='utf-8') as file:\n file.writelines(f'{str(self)}')\n\n def manga_volume_append(self, *manga_volume: MangaVolume):\n self.create_manga_dir()\n for index, volume in enumerate(manga_volume, start=1):\n print(f'{index}/{len(manga_volume)}')\n volume.search_page(self.driver)\n self.manga_volume_list.append(volume)\n volume.download_pdf(index, self.title, self.manga_path)\n self.pdf_merge()\n self.create_manga_info()\n\n def pdf_merge(self):\n if not config.getboolean('Settings', 'pdf_merge'):\n return\n merger = PdfMerger()\n allpdfs = [os.path.join(self.manga_path, str(index), f'{index}-{self.title}.pdf') for index in\n os.listdir(self.manga_path)]\n allpdfs.sort(key=lambda f: int(re.sub('\\D', '', f)))\n [merger.append(pdf) for pdf in allpdfs]\n with open(os.path.join(self.manga_path, f'Full_{self.title}.pdf'), \"wb\") as new_file:\n merger.write(new_file)\n\n def __str__(self):\n return f'{self.title}\\nLink: {self.url}\\n\\n' + \\\n '\\n'.join('{}:\\n{}\\n'.format(val.property_title, val.description) for key, val in self.__dict__.items()\n if val is not None and isinstance(val, Characteristic)) + \\\n '\\n' + '\\n'.join('{}:\\n{}\\n'.format(item.volume_title, item.volume_url) for item in self.manga_volume_list)\n", "repo_name": "Hikki-s/GrabManga", "sub_path": "app/manga_scripts/сlasses/manga.py", "file_name": "manga.py", "file_ext": "py", "file_size_in_byte": 4364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.isdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "img2pdf.convert", "line_number": 49, "usage_type": "call"}, {"api_name": "app.config.config.config.getboolean", "line_number": 51, "usage_type": "call"}, {"api_name": "app.config.config.config", "line_number": 51, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 52, "usage_type": "call"}, {"api_name": "app.config.config.config.get", "line_number": 72, "usage_type": "call"}, {"api_name": "app.config.config.config", "line_number": 72, "usage_type": "name"}, {"api_name": "app.manga_scripts.tools.tools.get_driver", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "app.config.config.config.getboolean", "line_number": 99, "usage_type": "call"}, {"api_name": "app.config.config.config", "line_number": 99, "usage_type": "name"}, {"api_name": "PyPDF2.PdfMerger", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 103, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}]} +{"seq_id": "34509701298", "text": "\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import figure\n\nimport matplotlib\n\nmatplotlib.rc('font', size=14)\nfigure(figsize=(12 / 2, 9 / 2), dpi=150)\n\nf, axs = plt.subplots(1, 1, sharey=True)\nf.set_size_inches(w=12 / 2, h=9 / 2)\n\nbetas = []\neners = []\n\nwith open(\"../07-UEG/00-dmrg.out\") as f:\n for l in f.readlines():\n if \"Norm =\" in l:\n betas.append(float(l.split()[2]))\n eners.append(float(l.split()[8]))\n\nbetas = np.array(betas[1:])\neners = eners[1:]\n\n# https://coolors.co/a8cbf0-4e4187-eed6bf-cbe1ce-8eb8a7\nxcolors = [\"#EED6BF\", \"#CBE1CE\", \"#7A7A7A\", \"#A8CBF0\"]\nmcolors = [\"#DDAE7E\", \"#7FB685\", \"#2E2E2E\", \"#3083DC\"]\nmarker = ['o', '^', 's', 'p']\n\nplt.grid(which='major', axis='both', alpha=0.5)\nplt.plot(1 / betas, eners, '--', marker=marker[0], mfc='white', mec=mcolors[2],\n linewidth=1.5, markersize=4, color=mcolors[2], label='Finite temperature DMRG (ancilla approach)')\nplt.xlim(0, 1 / min(betas))\nplt.ylim(0, 9)\n# plt.xscale('log')\n# plt.yscale('log')\n# plt.xticks(bdims, list(map(str, bdims)))\naxs.set_ylabel(\"Electronic energy $E(T)$\")\naxs.set_xlabel(\"Temperature $T$\")\n\naxs.text(5, 1.5, '$\\\\hat{H} = \\\\sum_{\\\\mathbf{k}} \\\\frac{|\\\\mathbf{k}|^2}{2}\\\\hat{a}^\\\\dagger_{\\\\mathbf{k}}\\\\hat{a}_{\\\\mathbf{k}}$' +\n '$\\\\ +\\\\ \\\\frac{1}{2\\\\Omega} \\\\sum_{\\\\mathbf{k}\\\\neq \\\\mathbf{0},\\\\mathbf{k}_1,\\\\mathbf{k}_2}$' +\n '$\\\\hat{a}^\\\\dagger_{\\\\mathbf{k}_1+\\\\mathbf{k}}\\\\hat{a}^\\\\dagger_{\\\\mathbf{k}_2-\\\\mathbf{k}}\\\\hat{a}_{\\\\mathbf{k}_2}\\\\hat{a}_{\\\\mathbf{k}_1}$', color='#000000', fontsize=14, zorder=999)\naxs.text(5, 0.5, '3D Uniform Electron Gas', color='#000000', fontsize=14, zorder=999)\naxs.legend(loc='upper left', fontsize=12)\nplt.subplots_adjust(left=0.11, right=0.97, top=0.95, bottom=0.14, wspace=0.0, hspace=0.0)\nplt.savefig('fig-07.pdf', dpi=600)\n", "repo_name": "hczhai/block2-example-data", "sub_path": "10-plot/07-ueg.py", "file_name": "07-ueg.py", "file_ext": "py", "file_size_in_byte": 1830, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "matplotlib.rc", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "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": "matplotlib.pyplot.ylim", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "29039750387", "text": "# coding=utf-8\nimport pytest\n\nfrom antiadblock.libs.decrypt_url.lib import resplit_using_length\nfrom antiadblock.cryprox.cryprox.url.transform.rule_evade_step import encode, construct_forward_evade_regex\n\ntest_rules = ['-ad2_',\n '/_ads/',\n '_openx/',\n '/ad2_']\n\n\n@pytest.mark.parametrize('url, encoded_etalon_url',\n [\n (\"/ads/_openx//ad2_\", \"/ads/_$openx//$ad2_\"),\n (\"/ads/_openx/ad2_\", \"/ads/_$openx/ad2_\") # правила внахлест не должны избегаться\n ])\ndef test_evader_evades(url, encoded_etalon_url):\n encoded = encode(url, construct_forward_evade_regex(test_rules))\n assert encoded == encoded_etalon_url\n length = 13\n new_url, _ = resplit_using_length(length, encoded)\n assert '$' not in new_url\n assert new_url == url.replace('/', '') # resplit_using_length убирает слеши\n", "repo_name": "Alexander-Berg/2022-tests-examples", "sub_path": "antiadblock/tests/unit/test_rule_evade_step.py", "file_name": "test_rule_evade_step.py", "file_ext": "py", "file_size_in_byte": 982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "antiadblock.cryprox.cryprox.url.transform.rule_evade_step.encode", "line_number": 19, "usage_type": "call"}, {"api_name": "antiadblock.cryprox.cryprox.url.transform.rule_evade_step.construct_forward_evade_regex", "line_number": 19, "usage_type": "call"}, {"api_name": "antiadblock.libs.decrypt_url.lib.resplit_using_length", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 13, "usage_type": "attribute"}]} +{"seq_id": "8308625255", "text": "# coding:utf-8\nfrom ExecuteActionLib.ExecuteAction import ExecuteAction\nimport logging\nimport time\nimport json\nimport base64\nimport copy\nimport random\nimport requests\nimport traceback\nfrom public.ConstantSet import Constant\nfrom public.HttpClient import HttpClient\nfrom public.websocket_clients import WebSocketClient\nfrom requests.exceptions import ConnectionError\nimport objgraph\nimport pdb\nimport gc\n\n\nclass V3ModifyinfoAction(ExecuteAction):\n def __init__(self, case_info_dict, user_pool, p_stop_signal, log_name=\"MXTest\"):\n super(ExecuteAction, self).__init__()\n self.case_info_dict = case_info_dict\n self.user_pool = user_pool\n self.p_stop_signal = p_stop_signal\n self.executor_obj = None\n self.executor_phone_num = None\n self.properly_executed = False\n self._logger = logging.getLogger(log_name)\n self._logger_ar = logging.getLogger(\"AllResult\")\n self.check_result = False\n self.attach_conf_id = None\n self.conf_obj = None\n self.conf_mid_map = None\n self.modifyinfo_mem_phone_num = None\n self.modify_mid = None\n self.info_data_dict = None\n\n def _execute_action(self):\n try :\n if \"check_result\" in self.case_info_dict:\n self.check_result = self.case_info_dict[\"check_result\"]\n if self.check_result:\n self.user_pool.cache_lock.acquire()\n self.modifyinfo_mem_phone_num = copy.deepcopy(self.user_pool.online_user_phone_num_list.pop())\n self.user_pool.cache_lock.release()\n else:\n # 在mid_list中随机选取一个元素\n self.modifyinfo_mem_phone_num = random.choice(self.user_pool.online_user_phone_num_list)\n\n self.executor_obj = self.user_pool.user_phone_num_obj_dict[self.modifyinfo_mem_phone_num]\n\n http_response = self._execute_http_request()\n if http_response.status_code == 200:\n if self.check_result:\n if not self._check_result():\n self._logger.error(\"V3ModifyinfoAction check Fail!\")\n self._logger_ar.debug(\"execute V3ModifyinfoAction: check result fail! http.status_code == 200\")\n return False\n self._logger_ar.debug(\"execute V3ModifyinfoAction: check result success! http.status_code == 200\")\n else :\n self._logger_ar.debug(\"execute V3ModifyinfoAction: http.status_code == 200\")\n\n self.user_pool.cache_lock.acquire()\n if self.modifyinfo_mem_phone_num not in self.user_pool.online_user_phone_num_list:\n self.user_pool.online_user_phone_num_list.appendleft(self.modifyinfo_mem_phone_num)\n self.user_pool.cache_lock.release()\n\n self._logger.debug(\"V3ModifyinfoAction check success!\")\n self.properly_executed = True\n return True\n else:\n self._logger.error(\"modifyinfo check Fail!\")\n if self.check_result:\n self._logger_ar.debug(\"execute modifyinfo: check result NA! http.status_code == \" + str(http_response.status_code))\n\n self._logger_ar.debug(\"execute modifyinfo: http.status_code == \" + str(http_response.status_code))\n return False\n except:\n self._logger.error(\"Return False because of except.\")\n self._logger_ar.debug(\"execute modifyinfo: except\")\n self._logger.error(traceback.format_exc())\n return False\n finally:\n sleep_after = 0\n\n if \"sleep_after\" in self.case_info_dict:\n sleep_after = self.case_info_dict[\"sleep_after\"]\n if not self.properly_executed:\n self._clean_up_when_fail()\n time.sleep(sleep_after)\n return self.properly_executed\n\n def _execute_http_request(self):\n self._logger.info(\"execute V3ModifyinfoAction\")\n self.info_data_dict = {\"name\": \"name\"+str(self.executor_obj.user_info_dict['userID']),\n \"nickName\": \"nickName\"+str(self.executor_obj.user_info_dict['userID']),\n \"sign\":\"我是一个盒子。\",\n \"mail\":\"box@mheart.com\"}\n url = Constant.HTTP_HOST_V3 + \"/mhuser/mod/profile\"\n method = 'POST'\n # 使用把参数列表中的groupshareid\n data = {\"user\":{\"name\": self.info_data_dict[\"name\"],\n \"nickName\": self.info_data_dict[\"nickName\"],\n \"sex\":\"0\",\n \"sign\": self.info_data_dict[\"sign\"],\n \"mail\": self.info_data_dict[\"mail\"]\n }\n }\n data = json.dumps(data)\n headers = {'authorization': self.executor_obj.user_info_dict[\"basicToken\"],\n \"Content-Type\": \"application/json; charset=utf-8\"}\n execute_http_client = HttpClient(url)\n r = execute_http_client.request(method=method, url=url, name=\"/mhuser/mod/profile\",\n catch_response=False, headers=headers,\n data=data)\n return r\n\n def _clean_up_when_fail(self):\n if self.user_pool.cache_lock.locked():\n self.user_pool.cache_lock.release()\n\n if self.modifyinfo_mem_phone_num not in self.user_pool.online_user_phone_num_list:\n self.user_pool.cache_lock.acquire()\n self.user_pool.online_user_phone_num_list.appendleft(self.modifyinfo_mem_phone_num)\n self.user_pool.cache_lock.release()\n\n abnormal_interrupt = False\n if \"abnormal_interrupt\" in self.case_info_dict:\n abnormal_interrupt = self.case_info_dict[\"abnormal_interrupt\"]\n\n if abnormal_interrupt:\n self.p_stop_signal.set()\n # False，设置终止整个测试信号\n else:\n pass\n\n def _check_result(self):\n r_respone = self._get_self_info()\n if r_respone.status_code != 200:\n return False\n\n get_u_info = json.loads(r_respone.text)[\"user\"]\n if get_u_info[\"name\"] == self.info_data_dict[\"name\"] and get_u_info[\"nickName\"] == self.info_data_dict[\"nickName\"] and get_u_info[\"sign\"] == self.info_data_dict[\"sign\"] and get_u_info[\"mail\"] == self.info_data_dict[\"mail\"]:\n return True\n else:\n return False\n\n def _get_self_info(self):\n url = Constant.HTTP_HOST_V3 + \"/mhuser/display/profile\"\n method = 'GET'\n headers = {'authorization': self.executor_obj.user_info_dict[\"basicToken\"],\n \"Content-Type\": \"application/json; charset=utf-8\"}\n execute_http_client = HttpClient(url)\n r = execute_http_client.request(method=method, url=url, name=\"/mhuser/display/profile\",\n catch_response=False, headers=headers)\n return r", "repo_name": "ningrulin/PerformanceTest", "sub_path": "ExecuteActionLib/V3Modifyinfo.py", "file_name": "V3Modifyinfo.py", "file_ext": "py", "file_size_in_byte": 7020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "ExecuteActionLib.ExecuteAction.ExecuteAction", "line_number": 20, "usage_type": "name"}, {"api_name": "ExecuteActionLib.ExecuteAction.ExecuteAction", "line_number": 22, "usage_type": "argument"}, {"api_name": "logging.getLogger", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 45, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 49, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "public.ConstantSet.Constant.HTTP_HOST_V3", "line_number": 100, "usage_type": "attribute"}, {"api_name": "public.ConstantSet.Constant", "line_number": 100, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 110, "usage_type": "call"}, {"api_name": "public.HttpClient.HttpClient", "line_number": 113, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 143, "usage_type": "call"}, {"api_name": "public.ConstantSet.Constant.HTTP_HOST_V3", "line_number": 150, "usage_type": "attribute"}, {"api_name": "public.ConstantSet.Constant", "line_number": 150, "usage_type": "name"}, {"api_name": "public.HttpClient.HttpClient", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "15374773742", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport json\nimport tempfile\nimport glob\nimport os\nimport subprocess\nimport sys\n\nimport numpy as np\nfrom tensorflow.compiler.tests import xla_test\nfrom tensorflow.python.platform import googletest\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.framework import ops\nfrom tensorflow.compat.v1.train import Saver\nfrom tensorflow.python.framework import errors\nfrom tensorflow.python.framework import test_util\nfrom tensorflow.python.ipu import utils\nfrom tensorflow.python.ipu.config import IPUConfig\nfrom tensorflow.python.platform import tf_logging as logging\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.ops.variables import global_variables_initializer\nfrom tensorflow.python.saved_model import saved_model\n\n\ndef filesInFolder(folder):\n return [\n name for name in os.listdir(folder)\n if os.path.isfile(os.path.join(folder, name))\n ]\n\n\nclass MyInitializer:\n def __init__(self, value):\n self.value = value\n\n def __call__(self, shape, dtype=None):\n assert dtype in [None, dtypes.float32]\n\n def generator(*args):\n return self.value + sum([10 * idx + v for idx, v in enumerate(args)])\n\n return np.fromfunction(generator, shape)\n\n\ndef instantiate_lenet():\n from tensorflow.python import keras\n from tensorflow.python.keras import layers\n model = keras.Sequential()\n\n model.add(\n layers.Conv2D(filters=6,\n kernel_size=(3, 3),\n activation='relu',\n input_shape=(32, 32, 1)))\n model.add(layers.AveragePooling2D())\n model.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))\n model.add(layers.AveragePooling2D())\n model.add(layers.Flatten())\n model.add(layers.Dense(units=120, activation='relu'))\n model.add(layers.Dense(units=84, activation='relu'))\n model.add(layers.Dense(units=10, activation='softmax'))\n\n inp = keras.Input(shape=(32, 32, 1), dtype=np.float32)\n out = model(inp)\n return out, inp, model\n\n\ndef instantiate_lenet_fix_weights():\n from tensorflow.python import keras\n from tensorflow.python.keras import layers\n model = keras.Sequential()\n\n model.add(\n layers.Conv2D(filters=6,\n kernel_size=(3, 3),\n activation='relu',\n input_shape=(32, 32, 1),\n kernel_initializer=MyInitializer(10.0)))\n model.add(layers.AveragePooling2D())\n model.add(\n layers.Conv2D(filters=16,\n kernel_size=(3, 3),\n activation='relu',\n kernel_initializer=MyInitializer(20.0)))\n model.add(layers.AveragePooling2D())\n model.add(layers.Flatten())\n model.add(\n layers.Dense(units=120,\n activation='relu',\n kernel_initializer=MyInitializer(30.0)))\n model.add(\n layers.Dense(units=84,\n activation='relu',\n kernel_initializer=MyInitializer(0.4)))\n model.add(\n layers.Dense(units=10,\n activation='softmax',\n kernel_initializer=MyInitializer(5.5)))\n\n inp = keras.Input(shape=(32, 32, 1), dtype=np.float32)\n out = model(inp)\n return out, inp, model\n\n\nclass PoplarExecutableRunnerTest(xla_test.XLATestCase):\n # Overriding abstract method.\n def cached_session(self):\n return 0\n\n # Overriding abstract method.\n def test_session(self):\n return 0\n\n def configureIPU(self, serialization_folder=None, offline_compilation=True):\n opts = IPUConfig()\n if offline_compilation:\n opts.device_connection.version = 'ipu1'\n opts.device_connection.type = utils.DeviceConnectionType.NEVER\n if serialization_folder:\n opts.serialization_output_folder = serialization_folder\n opts.configure_ipu_system()\n\n def runCommand(self, cmd):\n logging.info(\"Running: %s\", \" \".join(cmd))\n out = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n self.assertTrue(out.returncode == 0, out.stdout.decode(\"utf-8\"))\n logging.info(out.stdout.decode(\"utf-8\"))\n\n def runPythonCommand(self, cmd):\n python_cmd = cmd\n python_cmd.insert(0, sys.executable)\n self.runCommand(python_cmd)\n\n def getSingleFileWithExt(self, folder, extension):\n all_files = glob.glob(\"%s/*.%s\" % (folder, extension))\n logging.info(\"%s files in %s: %s\", extension, folder, all_files)\n self.assertEqual(\n len(all_files), 1,\n \"There should be exactly one file with the extension %s in %s: %s\" %\n (extension, folder, all_files))\n return all_files[0]\n\n @test_util.deprecated_graph_mode_only\n def testKerasLenet(self):\n \"\"\"Check that the output of PoplarExecutableRunner produces the same output as the original Graph execution.\n \"\"\"\n if utils.running_on_ipu_model():\n self.skipTest(\"PoplarExecutableRunner only works with physical IPUs\")\n\n with tempfile.TemporaryDirectory() as tmp:\n poplar_binaries_folder = os.path.join(tmp, \"poplar\")\n model_path = os.path.join(tmp, \"model\")\n weights_file = os.path.join(tmp, \"weights.bin\")\n output_path = os.path.join(tmp, \"output\")\n input_values = np.random.uniform(size=(1, 32, 32, 1))\n input_file = \"%s/input.bin\" % tmp\n\n with self.session() as sess:\n\n self.configureIPU(poplar_binaries_folder, False)\n with ops.device(\"/device:IPU:0\"):\n out, inp, model = instantiate_lenet()\n\n utils.move_variable_initialization_to_cpu()\n sess.run(global_variables_initializer())\n\n utils.export_inputs_to_file([inp], input_file, {inp: input_values})\n\n # Run the model once to generate the poplar binaries.\n reference_values = sess.run(out, {inp: input_values})\n\n # Export the model & weights.\n saved_model.save(model, model_path)\n\n metadata_file = self.getSingleFileWithExt(poplar_binaries_folder, \"json\")\n executable_file = self.getSingleFileWithExt(poplar_binaries_folder,\n \"ipu_bin\")\n\n self.runPythonCommand(\n ((\"./tensorflow/compiler/plugin/poplar/tools/\"\n \"tensorflow_weights_extractor.py -o %s -s %s -m %s\") %\n (weights_file, model_path, metadata_file)).split())\n\n self.runCommand(((\"./third_party/ipus/tools/PoplarExecutableRunner\"\n \" --binaries %s,%s,%s \"\n \"--output_folder=%s --strict\") % (\n executable_file,\n weights_file,\n input_file,\n output_path,\n )).split())\n\n output_file = self.getSingleFileWithExt(output_path, \"data\")\n with open(output_file, 'r') as f:\n runner_values = np.array(json.load(f))\n logging.info(\"Reference %s\\nRunner: %s\", reference_values,\n runner_values)\n self.assertAllClose(reference_values, runner_values)\n\n @test_util.deprecated_graph_mode_only\n def testWeightsExportersNoMetadata(self):\n \"\"\" Check that the weights extractor produces the same output with\n TF v1 and v2 models.\"\"\"\n # Disable the IPU model\n poplar_flags = os.environ.get(\"TF_POPLAR_FLAGS\",\n \"\").replace(\"--use_ipu_model\", \"\")\n with test.mock.patch.dict(\"os.environ\",\n {\"TF_POPLAR_FLAGS\": poplar_flags\n }), tempfile.TemporaryDirectory() as tmp:\n model_path_keras = os.path.join(tmp, \"model_keras\")\n model_path_session = os.path.join(tmp, \"model_session\")\n weights_keras = os.path.join(tmp, \"weights_keras.bin\")\n weights_session = os.path.join(tmp, \"weights_session.bin\")\n\n with self.session() as sess:\n self.configureIPU()\n with ops.device(\"/device:IPU:0\"):\n _, _, model = instantiate_lenet()\n utils.move_variable_initialization_to_cpu()\n sess.run(global_variables_initializer())\n\n # Export the model & weights.\n saved_model.save(model, model_path_keras)\n Saver().save(sess, model_path_session)\n\n self.runPythonCommand(((\"./tensorflow/compiler/plugin/poplar/tools/\"\n \"tensorflow_weights_extractor.py -o %s -s %s\") %\n (weights_keras, model_path_keras)).split())\n\n self.runPythonCommand(((\"./tensorflow/compiler/plugin/poplar/tools/\"\n \"tensorflow_weights_extractor.py -o %s -s %s\") %\n (weights_session, model_path_session)).split())\n\n with open(weights_session, 'rb') as s, open(weights_keras, 'rb') as k:\n self.assertEqual(s.read(), k.read())\n\n @test_util.deprecated_graph_mode_only\n def testWeightsExportersMetadataLive(self):\n \"\"\"Export weights directly from a live model.\n \"\"\"\n poplar_flags = os.environ.get(\"TF_POPLAR_FLAGS\",\n \"\").replace(\"--use_ipu_model\", \"\")\n with test.mock.patch.dict(\"os.environ\",\n {\"TF_POPLAR_FLAGS\": poplar_flags\n }), tempfile.TemporaryDirectory() as tmp:\n poplar_binaries_folder = os.path.join(tmp, \"poplar\")\n weights_keras = os.path.join(tmp, \"weights_keras.bin\")\n weights_session = os.path.join(tmp, \"weights_session.bin\")\n\n with self.session() as sess:\n self.configureIPU(poplar_binaries_folder)\n with ops.device(\"/device:IPU:0\"):\n out, inp, model = instantiate_lenet_fix_weights()\n\n utils.move_variable_initialization_to_cpu()\n sess.run(global_variables_initializer())\n\n # Run the model once to generate the poplar binaries.\n try:\n sess.run(out, {inp: np.ones((1, 32, 32, 1))})\n except errors.InvalidArgumentError:\n pass\n\n metadata_file = self.getSingleFileWithExt(poplar_binaries_folder, \"json\")\n\n with self.session() as sess:\n self.configureIPU()\n with ops.device(\"/device:IPU:0\"):\n _, _, _ = instantiate_lenet_fix_weights()\n\n utils.move_variable_initialization_to_cpu()\n sess.run(global_variables_initializer())\n\n utils.export_variables_from_live_session(sess, weights_session,\n metadata_file)\n\n with self.session() as sess:\n self.configureIPU()\n with ops.device(\"/device:IPU:0\"):\n _, _, model = instantiate_lenet_fix_weights()\n\n utils.move_variable_initialization_to_cpu()\n sess.run(global_variables_initializer())\n utils.export_variables_from_live_model(model, weights_keras,\n metadata_file)\n\n with open(weights_session, 'rb') as s, open(weights_keras, 'rb') as k:\n self.assertEqual(s.read(), k.read())\n\n\nif __name__ == \"__main__\":\n os.environ['TF_XLA_FLAGS'] = ('--tf_xla_min_cluster_size=1' +\n os.environ.get('TF_XLA_FLAGS', ''))\n googletest.main()\n", "repo_name": "graphcore/tensorflow", "sub_path": "tensorflow/compiler/plugin/poplar/tests/poplar_executable_runner_test.py", "file_name": "poplar_executable_runner_test.py", "file_ext": "py", "file_size_in_byte": 11006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 76, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.dtypes.float32", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.dtypes", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.fromfunction", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.Sequential", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.python.keras", "line_number": 51, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 54, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.AveragePooling2D", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 58, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 59, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.AveragePooling2D", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 60, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Flatten", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 61, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 62, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.Input", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.python.keras", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.Sequential", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.python.keras", "line_number": 74, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.AveragePooling2D", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 82, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 84, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.AveragePooling2D", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 88, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Flatten", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 89, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 91, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers", "line_number": 99, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.Input", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.python.keras", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 103, "usage_type": "attribute"}, {"api_name": "tensorflow.compiler.tests.xla_test.XLATestCase", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.compiler.tests.xla_test", "line_number": 108, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.config.IPUConfig", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils.DeviceConnectionType", "line_number": 121, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 121, "usage_type": "name"}, {"api_name": "tensorflow.python.platform.tf_logging.info", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.tf_logging", "line_number": 127, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 128, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 128, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.tf_logging.info", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.tf_logging", "line_number": 130, "usage_type": "name"}, {"api_name": "sys.executable", "line_number": 134, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.tf_logging.info", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.tf_logging", "line_number": 139, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.running_on_ipu_model", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 150, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.ops.device", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 164, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.move_variable_initialization_to_cpu", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 167, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.variables.global_variables_initializer", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils.export_inputs_to_file", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 170, "usage_type": "name"}, {"api_name": "tensorflow.python.saved_model.saved_model.save", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.python.saved_model.saved_model", "line_number": 176, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "json.load", "line_number": 198, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.tf_logging.info", "line_number": 199, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.tf_logging", "line_number": 199, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.test_util.deprecated_graph_mode_only", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.test_util", "line_number": 146, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 208, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 208, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.test.mock.patch.dict", "line_number": 210, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.test.mock", "line_number": 210, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.test", "line_number": 210, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.ops.device", "line_number": 220, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 220, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.move_variable_initialization_to_cpu", "line_number": 222, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 222, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.variables.global_variables_initializer", "line_number": 223, "usage_type": "call"}, {"api_name": "tensorflow.python.saved_model.saved_model.save", "line_number": 226, "usage_type": "call"}, {"api_name": "tensorflow.python.saved_model.saved_model", "line_number": 226, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.train.Saver", "line_number": 227, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.test_util.deprecated_graph_mode_only", "line_number": 203, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.test_util", "line_number": 203, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 244, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 244, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.test.mock.patch.dict", "line_number": 246, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.test.mock", "line_number": 246, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.test", "line_number": 246, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 249, "usage_type": "call"}, {"api_name": "os.path", "line_number": 249, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.ops.device", "line_number": 255, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 255, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.move_variable_initialization_to_cpu", "line_number": 258, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 258, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.variables.global_variables_initializer", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 263, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.errors.InvalidArgumentError", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.errors", "line_number": 264, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.ops.device", "line_number": 271, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 271, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.move_variable_initialization_to_cpu", "line_number": 274, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 274, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.variables.global_variables_initializer", "line_number": 275, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils.export_variables_from_live_session", "line_number": 277, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 277, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.ops.device", "line_number": 282, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 282, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.move_variable_initialization_to_cpu", "line_number": 285, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 285, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.variables.global_variables_initializer", "line_number": 286, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils.export_variables_from_live_model", "line_number": 287, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 287, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.test_util.deprecated_graph_mode_only", "line_number": 240, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.test_util", "line_number": 240, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 295, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 296, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 296, "usage_type": "attribute"}, {"api_name": "tensorflow.python.platform.googletest.main", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.googletest", "line_number": 297, "usage_type": "name"}]} +{"seq_id": "24463966232", "text": "\nimport plyer, time\nfrom winsound import Beep\n\n# Ф-ия вывода уведомления вин 10\ndef alarm(title_alarm, app_name, text):\n title_alarm = str(title_alarm)\n app_name = str(app_name)\n text = str(text)\n Beep(688, 350)\n plyer.notification.notify(message=text,\n app_name=app_name, \n title=title_alarm)\n \n \n# Ф-ия получени текущей даты\ndef current_data_time():\n current_data = time.asctime().split()\n current_data = (current_data[0] +\" \"+ current_data[3].split(\":\")[0] +\":\" +current_data[3].split(\":\")[1])\n return(current_data)\n\n\n# Ф-ия чтения расписания и заданий из файла\ndef read_shedule(file_name):\n f = open(file_name, 'r', encoding=\"utf-8\")\n inp = f.read().split(\"\\n\")\n return(inp)\n\n\ndef main():\n time_tasks = read_shedule(\"schedule.txt\")\n while True:\n if current_data_time() in time_tasks:\n task = time_tasks[time_tasks.index(current_data_time())+1].split(\"/\")\n alarm(task[0], \" \", task[1])\n time.sleep(60)\n else:\n time.sleep(5)\n \n #timer()\n \nif __name__ == \"__main__\":\n main()", "repo_name": "Atomik470/shedile_for_lessons", "sub_path": "TEMP/alarms.pyw", "file_name": "alarms.pyw", "file_ext": "pyw", "file_size_in_byte": 1227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "winsound.Beep", "line_number": 10, "usage_type": "call"}, {"api_name": "plyer.notification.notify", "line_number": 11, "usage_type": "call"}, {"api_name": "plyer.notification", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.asctime", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "8141159214", "text": "\"\"\"Results view widgets (MOVE TO OTHER MODULE!)\n\nAuthors: AiiDAlab team\n\"\"\"\n\nimport ipywidgets as ipw\nimport nglview\nimport traitlets as tl\nfrom aiida import orm\nfrom aiidalab_widgets_base import register_viewer_widget\nfrom ase import Atoms\n\nfrom .widgets import CalcJobOutputFollower, LogOutputWidget\n\n\nclass MinimalStructureViewer(ipw.VBox):\n structure = tl.Union([tl.Instance(Atoms), tl.Instance(orm.Node)], allow_none=True)\n _displayed_structure = tl.Instance(Atoms, allow_none=True, read_only=True)\n\n background = tl.Unicode()\n supercell = tl.List(tl.Int())\n\n def __init__(self, structure, *args, **kwargs):\n self._viewer = nglview.NGLWidget()\n self._viewer.camera = \"orthographic\"\n self._viewer.stage.set_parameters(mouse_preset=\"pymol\")\n ipw.link((self, \"background\"), (self._viewer, \"background\"))\n\n self.structure = structure\n\n super().__init__(\n children=[\n self._viewer,\n ],\n *args,\n **kwargs,\n )\n\n @tl.default(\"background\")\n def _default_background(self):\n return \"#FFFFFF\"\n\n @tl.default(\"supercell\")\n def _default_supercell(self):\n return [1, 1, 1]\n\n @tl.validate(\"structure\")\n def _valid_structure(self, change): # pylint: disable=no-self-use\n \"\"\"Update structure.\"\"\"\n structure = change[\"value\"]\n\n if structure is None:\n return None # if no structure provided, the rest of the code can be skipped\n\n if isinstance(structure, Atoms):\n return structure\n if isinstance(structure, orm.Node):\n return structure.get_ase()\n raise ValueError(\n \"Unsupported type {}, structure must be one of the following types: \"\n \"ASE Atoms object, AiiDA CifData or StructureData.\"\n )\n\n @tl.observe(\"structure\")\n def _update_displayed_structure(self, change):\n \"\"\"Update displayed_structure trait after the structure trait has been modified.\"\"\"\n # Remove the current structure(s) from the viewer.\n if change[\"new\"] is not None:\n self.set_trait(\"_displayed_structure\", change[\"new\"].repeat(self.supercell))\n else:\n self.set_trait(\"_displayed_structure\", None)\n\n @tl.observe(\"_displayed_structure\")\n def _update_structure_viewer(self, change):\n \"\"\"Update the view if displayed_structure trait was modified.\"\"\"\n with self.hold_trait_notifications():\n for (\n comp_id\n ) in self._viewer._ngl_component_ids: # pylint: disable=protected-access\n self._viewer.remove_component(comp_id)\n self.selection = list()\n if change[\"new\"] is not None:\n self._viewer.add_component(nglview.ASEStructure(change[\"new\"]))\n self._viewer.clear()\n self._viewer.stage.set_parameters(clipDist=0)\n self._viewer.add_representation(\"unitcell\", diffuse=\"#df0587\")\n self._viewer.add_representation(\"ball+stick\", aspectRatio=3.5)\n\n\nclass VBoxWithCaption(ipw.VBox):\n def __init__(self, caption, body, *args, **kwargs):\n super().__init__(children=[ipw.HTML(caption), body], *args, **kwargs)\n\n\n@register_viewer_widget(\"process.calculation.calcjob.CalcJobNode.\")\nclass CalcJobNodeViewerWidget(ipw.VBox):\n def __init__(self, calcjob, **kwargs):\n self.calcjob = calcjob\n self.output_follower = CalcJobOutputFollower()\n self.log_output = LogOutputWidget()\n\n self.output_follower.calcjob_uuid = self.calcjob.uuid\n self.output_follower.observe(self._observe_output_follower_lineno, [\"lineno\"])\n\n super().__init__(\n [ipw.HTML(f\"CalcJob: {self.calcjob}\"), self.log_output], **kwargs\n )\n\n def _observe_output_follower_lineno(self, _):\n with self.hold_trait_notifications():\n self.log_output.filename = self.output_follower.filename\n self.log_output.value = \"\\n\".join(self.output_follower.output)\n", "repo_name": "aiidalab/aiidalab-qe", "sub_path": "src/aiidalab_qe/common/node_view.py", "file_name": "node_view.py", "file_ext": "py", "file_size_in_byte": 4032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "3", "api": [{"api_name": "ipywidgets.VBox", "line_number": 16, "usage_type": "attribute"}, {"api_name": "traitlets.Union", "line_number": 17, "usage_type": "call"}, {"api_name": "traitlets.Instance", "line_number": 17, "usage_type": "call"}, {"api_name": "ase.Atoms", "line_number": 17, "usage_type": "argument"}, {"api_name": "aiida.orm.Node", "line_number": 17, "usage_type": "attribute"}, {"api_name": "aiida.orm", "line_number": 17, "usage_type": "name"}, {"api_name": "traitlets.Instance", "line_number": 18, "usage_type": "call"}, {"api_name": "ase.Atoms", "line_number": 18, "usage_type": "argument"}, {"api_name": "traitlets.Unicode", "line_number": 20, "usage_type": "call"}, {"api_name": "traitlets.List", "line_number": 21, "usage_type": "call"}, {"api_name": "traitlets.Int", "line_number": 21, "usage_type": "call"}, {"api_name": "nglview.NGLWidget", "line_number": 24, "usage_type": "call"}, {"api_name": "ipywidgets.link", "line_number": 27, "usage_type": "call"}, {"api_name": "traitlets.default", "line_number": 39, "usage_type": "call"}, {"api_name": "traitlets.default", "line_number": 43, "usage_type": "call"}, {"api_name": "ase.Atoms", "line_number": 55, "usage_type": "argument"}, {"api_name": "aiida.orm.Node", "line_number": 57, "usage_type": "attribute"}, {"api_name": "aiida.orm", "line_number": 57, "usage_type": "name"}, {"api_name": "traitlets.validate", "line_number": 47, "usage_type": "call"}, {"api_name": "traitlets.observe", "line_number": 64, "usage_type": "call"}, {"api_name": "nglview.ASEStructure", "line_number": 83, "usage_type": "call"}, {"api_name": "traitlets.observe", "line_number": 73, "usage_type": "call"}, {"api_name": "ipywidgets.VBox", "line_number": 90, "usage_type": "attribute"}, {"api_name": "ipywidgets.HTML", "line_number": 92, "usage_type": "call"}, {"api_name": "ipywidgets.VBox", "line_number": 96, "usage_type": "attribute"}, {"api_name": "widgets.CalcJobOutputFollower", "line_number": 99, "usage_type": "call"}, {"api_name": "widgets.LogOutputWidget", "line_number": 100, "usage_type": "call"}, {"api_name": "ipywidgets.HTML", "line_number": 106, "usage_type": "call"}, {"api_name": "aiidalab_widgets_base.register_viewer_widget", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "1962925923", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jun 6 12:06:51 2019\r\n\r\n@author: shubha.ks\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Mar 3 17:13:34 2019\r\n\r\n@author: shubha.ks\r\n\"\"\"\r\n\r\nimport psycopg2\r\nimport dbconstants\r\nimport datetime\r\ndef fun_admin_uf(execution_params):\r\n\r\n OEM = execution_params[\"OEM\"]\r\n Series = execution_params[\"Series\"]\r\n Model = execution_params[\"Model\"]\r\n Type_of_Device = execution_params[\"TypeOfDevice\"]\r\n Image_Version = execution_params[\"ImageVersion\"]\r\n Upgrade_Compatible_Version = execution_params[\"UpgradeCompatibleVersion\"]\r\n FTP_Server_IP = execution_params[\"FTPServerIP\"]\r\n Physical_File = execution_params[\"FileName\"]\r\n Path=\"C:\\\\network_automation\\\\python\\\\uploads\\\\firmwares\"\r\n\r\n conn=psycopg2.connect(**dbconstants.DBCONNECTION_PARAMS)\r\n cur=conn.cursor()\r\n sql_Insert_Mapping = \"\"\"INSERT INTO public.netauto_uf_firmware_mapping(\"OEM\",\"Series\",\"Model\",\"Type_of_Device\",\"Image_Version\",\"Upgrade_Compatible_version\",\"FTP_Server_IP\",\"Path\",\"Physical_File\",\"Submitted_Date\")\r\n \t\t VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);\"\"\"\r\n Insert_Values_Mapping = (OEM,Series,Model,Type_of_Device,Image_Version,Upgrade_Compatible_Version,FTP_Server_IP,Path,Physical_File,datetime.datetime.now())\r\n cur=conn.cursor()\r\n cur.execute(sql_Insert_Mapping, Insert_Values_Mapping)\r\n conn.commit()\r\n cur.close()\r\n conn.close()\r\n\r\n\r\n\r\n\r\n\r\n\r\n return(\"Successfull\")\r\nif __name__==\"__main__\":\r\n in1={\r\n \"FTPServerIP\": \"192.168.198.80\",\r\n \"FileName\": \"4e291cc08ff045ccbf382dc07160287c__test.bin\",\r\n \"ImageVersion\": \"15.1(4)M10\",\r\n \"Model\": 850,\r\n \"OEM\": \"Cisco\",\r\n \"Series\": 1.23,\r\n \"TypeOfDevice\": \"Switch\",\r\n \"UpgradeCompatibleVersion\": [\"v1\",\"v2\"]\r\n }\r\n out=fun_admin_uf(in1)\r\n print(out)", "repo_name": "shree8494/remote_execution", "sub_path": "admin_uf.py", "file_name": "admin_uf.py", "file_ext": "py", "file_size_in_byte": 1843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "psycopg2.connect", "line_number": 30, "usage_type": "call"}, {"api_name": "dbconstants.DBCONNECTION_PARAMS", "line_number": 30, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}]} +{"seq_id": "72458150480", "text": "#Personal Assisstant Project\r\nimport pyttsx3 \r\nimport speech_recognition as sr \r\nimport datetime \r\nimport wikipedia \r\nimport webbrowser \r\nimport os \r\nimport pyjokes \r\nimport ctypes \r\nimport time \r\nimport subprocess\r\nimport winshell\r\nimport wolframalpha \r\nfrom tkinter import *\r\n\r\n\r\nengine = pyttsx3.init('sapi5') \r\nvoices = engine.getProperty('voices') \r\nengine.setProperty('voice', voices[1].id)\r\nuname = ''\r\n\r\n\r\ndef speak(audio): \r\n engine.say(audio) \r\n engine.runAndWait() \r\n \r\ndef wishMe(): \r\n hour = int(datetime.datetime.now().hour) \r\n if hour>= 0 and hour<12: \r\n speak(\"Good Morning Sir !\") \r\n \r\n elif hour>= 12 and hour<18: \r\n speak(\"Good Afternoon Sir !\") \r\n \r\n else: \r\n speak(\"Good Evening Sir !\") \r\n \r\n assname =(\"Jennifer\") \r\n speak(f\"I am {assname}, your Assistant\")\r\n \r\n \r\ndef usrname(): \r\n speak(\"What should i call you sir\") \r\n global uname \r\n uname = takeCommand()\r\n speak(f\"Welcome Mister {uname}\") \r\n speak(f\"How can i Help you ,{uname}\") \r\n \r\ndef takeCommand(): \r\n r = sr.Recognizer() \r\n with sr.Microphone() as source: \r\n r.adjust_for_ambient_noise(source)\r\n print(\"Listening...\") \r\n r.pause_threshold = 1\r\n # audio = r.listen(source, phrase_time_limit = 5)\r\n audio = r.listen(source, timeout = 5)\r\n \r\n try: \r\n print(\"Recognizing...\") \r\n query = r.recognize_google(audio, language ='en-us') \r\n print(f\"User said: {query}\\n\") \r\n \r\n except Exception as e: \r\n print(e) \r\n print(\"Unable to Recognizing your voice.\") \r\n return \"None\"\r\n \r\n return query \r\n\r\nif __name__ == '__main__':\r\n\r\n clear = lambda: os.system('cls')\r\n clear() \r\n\r\n # wishMe()\r\n # usrname()\r\n \r\n JenniferRuntime = True\r\n while JenniferRuntime:\r\n\r\n clear()\r\n query = takeCommand().lower() \r\n\r\n if 'wikipedia' in query: \r\n speak('Searching Wikipedia...')\r\n speak('What do you want to search on Wikipedia?')\r\n query = takeCommand() \r\n results = wikipedia.summary(query, sentences = 10) \r\n speak(\"According to Wikipedia\") \r\n print(results) \r\n speak(results) \r\n \r\n elif 'open youtube' in query: \r\n speak(\"Here you go to Youtube\\n\")\r\n webbrowser.open_new_tab(\"https://youtube.com\")\r\n \r\n elif 'open maps' in query: \r\n speak(\"Here you go to Maps\\n\")\r\n webbrowser.open_new_tab(\"https://google.com/maps\")\r\n \r\n elif 'open google' in query: \r\n speak(\"Here you go to Google\\n\") \r\n webbrowser.open(\"https://google.com\") \r\n \r\n elif 'open stackoverflow' in query: \r\n speak(\"Here you go to Stack Over flow. Happy coding\") \r\n webbrowser.open(\"https://stackoverflow.com\") \r\n \r\n elif 'play music' in query or \"play song\" in query: \r\n speak(\"Here you go with music\") \r\n music_dir = \"C:\\\\Users\\\\Spedrik Webster\\\\Music\" \r\n songs = os.listdir(music_dir) \r\n print(songs) \r\n random = os.startfile(os.path.join(music_dir, songs[0])) \r\n \r\n elif 'the time' in query: \r\n hour = int(datetime.datetime.now().hour)\r\n minute = int(datetime.datetime.now().minute)\r\n if minute == 0:\r\n speak('Its {hour} O clock')\r\n elif minute in range(1,40):\r\n speak(f'Its {minute} minutes past {hour} hours')\r\n else:\r\n speak(f'Its {minute} minutes to {hour+1} hours')\r\n \r\n elif 'how are you' in query: \r\n speak(\"I am fine, Thank you\") \r\n speak(\"How are you, Sir\")\r\n\r\n query = takeCommand().lower() \r\n if 'fine' in query or \"good\" in query: \r\n speak(\"It's good to know that your fine\") \r\n \r\n elif \"change my name to\" in query: \r\n query = query.replace(\"change my name to\", \"\") \r\n assname = query \r\n \r\n elif \"change name\" in query: \r\n speak(\"What would you like to call me, Sir \") \r\n assname = takeCommand() \r\n speak(\"Thanks for naming me\") \r\n \r\n elif \"what's your name\" in query or \"What is your name\" in query: \r\n speak(\"My friends call me\") \r\n speak(assname) \r\n print(\"My friends call me\", assname) \r\n \r\n elif 'exit' in query: \r\n speak(\"Thanks for giving me your time\") \r\n exit() \r\n \r\n elif \"who made you\" in query or \"who created you\" in query: \r\n speak(\"I have been designed by Yuuvraaj, Mayank and Suuraaj.\") \r\n \r\n elif 'joke' in query:\r\n j = pyjokes.get_joke()\r\n print(j)\r\n speak(j) \r\n \r\n elif \"calculate\" in query: \r\n \r\n app_id = \"VJL6YW-E7LE749J57\" \r\n client = wolframalpha.Client(app_id) \r\n indx = query.lower().split().index('calculate') \r\n query = query.split()[indx + 1:] \r\n res = client.query(' '.join(query)) \r\n answer = next(res.results).text \r\n print(\"The answer is \" + answer) \r\n speak(\"The answer is \" + answer) \r\n \r\n elif 'search' in query: \r\n query = query.replace(\"search\", \"\") \r\n webbrowser.open('https://www.google.com/search?q='+query) \r\n \r\n elif 'play' in query: \r\n query = query.replace(\"play\", \"\") \r\n webbrowser.open('https://www.youtube.com/results?search_query='+query)\r\n \r\n elif \"who i am\" in query: \r\n speak(\"If you talk then definately your human.\") \r\n \r\n elif \"How you came to world\" in query: \r\n speak(\"Thanks to Yuvraj. further It's a secret\") \r\n \r\n elif 'powerpoint presentation' in query: \r\n speak(\"opening Power Point presentation\") \r\n webbrowser.open_new_tab(\"https://docs.google.com/presentation/\")\r\n \r\n \r\n elif \"who are you\" in query: \r\n speak(\"I am your virtual assistant created by Yuvraj\") \r\n \r\n elif 'reason for you' in query:\r\n speak(\"I was created as a Minor project by Yuvraj\") \r\n \r\n elif 'news' in query: \r\n speak('here are some of the top news') \r\n webbrowser.open('https://news.google.com/')\r\n \r\n elif 'lock window' in query: \r\n speak(\"locking the device\") \r\n ctypes.windll.user32.LockWorkStation() \r\n \r\n elif 'shutdown system' in query: \r\n speak(\"Hold On a Sec ! Your system is on its way to shut down\") \r\n subprocess.call('shutdown / p /f')\r\n \r\n elif 'empty recycle bin' in query: \r\n winshell.recycle_bin().empty(confirm = False, show_progress = False, sound = True) \r\n speak(\"Recycle Bin Recycled\") \r\n\r\n elif \"don't listen\" in query or \"stop listening\" in query: \r\n speak(\"for how much time you want to stop me from listening commands\") \r\n a = int(takeCommand()) \r\n time.sleep(a) \r\n print(a) \r\n \r\n elif \"where is\" in query: \r\n query = query.replace(\"where is\", \"\") \r\n location = query \r\n speak(\"User asked to Locate\") \r\n speak(location) \r\n webbrowser.open(\"https://www.google.com/maps/place/\" + location) \r\n \r\n elif \"restart\" in query: \r\n subprocess.call([\"shutdown\", \"/r\"]) \r\n \r\n elif \"hibernate\" in query or \"sleep\" in query: \r\n speak(\"Hibernating\") \r\n subprocess.call(\"shutdown / h\") \r\n \r\n elif \"log off\" in query or \"sign out\" in query: \r\n speak(\"Make sure all the application are closed before sign-out\") \r\n time.sleep(5) \r\n subprocess.call([\"shutdown\", \"/l\"]) \r\n \r\n elif \"write a note\" in query or 'make a note' in query or 'note down' in query: \r\n speak(\"What should i write, sir\") \r\n note = takeCommand() \r\n file = open('note.txt', 'w') \r\n file.write(note)\r\n file.close()\r\n \r\n elif \"my note\" in query: \r\n speak(\"Showing Notes\") \r\n file = open(\"note.txt\", \"r\") \r\n notes = file.read()\r\n print(notes) \r\n speak(notes) \r\n \r\n elif 'jennifer' in query: \r\n wishMe() \r\n speak(\"Jennifer 1 point o in your service\") \r\n \r\n elif \"weather\" in query: \r\n speak(\"here's the weather results\") \r\n webbrowser.open('https://www.google.com/search?q='+'weather')\r\n \r\n elif \"open wikipedia\" in query: \r\n webbrowser.open(\"wikipedia.com\") \r\n \r\n elif \"good morning\" in query: \r\n wishMe()\r\n \r\n elif \"good afternoon\" in query: \r\n wishMe()\r\n \r\n elif \"good night\" in query: \r\n wishMe()\r\n\r\n elif \"how are you\" in query: \r\n speak(\"I'm fine, glad you asked me that\") \r\n \r\n elif \"what is\" in query or \"who is\" in query: \r\n client = wolframalpha.Client(\"VJL6YW-E7LE749J57\") \r\n res = client.query(query) \r\n \r\n try: \r\n print (next(res.results).text) \r\n speak (next(res.results).text) \r\n except StopIteration: \r\n print (\"No results\") \r\n \r\n elif \"calculate\" in query: \r\n query = query.replace(\"calculate\", \"\")\r\n client = wolframalpha.Client(\"VJL6YW-E7LE749J57\") \r\n res = client.query(query) \r\n \r\n try: \r\n print (next(res.results).text) \r\n speak (next(res.results).text) \r\n except StopIteration: \r\n print (\"No results\")\r\n \r\n # print('You Said:- ',query)\r\n # speak(f'You Said:- {query}') # Testing Purpose.\r\n # speak('You Said:- ',query)\r\n JenniferRuntime = False\r\n \r\n", "repo_name": "Spedrick/Jennifer---Speech-Assistant", "sub_path": "codeTest.py", "file_name": "codeTest.py", "file_ext": "py", "file_size_in_byte": 10140, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pyttsx3.init", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "attribute"}, {"api_name": "speech_recognition.Recognizer", "line_number": 50, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 51, "usage_type": "call"}, {"api_name": "os.system", "line_number": 72, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 88, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 95, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 99, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 103, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 107, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 112, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pyjokes.get_joke", "line_number": 156, "usage_type": "call"}, {"api_name": "wolframalpha.Client", "line_number": 163, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 173, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 177, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 187, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 198, "usage_type": "call"}, {"api_name": "ctypes.windll.user32.LockWorkStation", "line_number": 202, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 202, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 206, "usage_type": "call"}, {"api_name": "winshell.recycle_bin", "line_number": 209, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 215, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 223, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 226, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 230, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 234, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 235, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 257, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 260, "usage_type": "call"}, {"api_name": "wolframalpha.Client", "line_number": 275, "usage_type": "call"}, {"api_name": "wolframalpha.Client", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "73030232082", "text": "'''\nList configuration obtained using architecture descent for different parameter sizes\n'''\n\nimport pickle\nimport numpy as np\nimport seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pylab as plt\nimport argparse\nfrom utils import compute_params\nfrom model.VGG import VGG\nfrom model.preact_resnet import PreActResNet\nfrom model.mobilenetv2 import MobileNetV2\n\nparser = argparse.ArgumentParser(description='List configuration obtained using architecture descent for different parameter sizes')\nparser.add_argument('--model', default=\"resnet18\", type=str,\n help='model selection, choices: vgg, mobilenetv2, resnet18',\n choices=[\"vgg\", \"mobilenetv2\", \"resnet18\"])\nparser.add_argument('--prune_fname', default='filename',\n help='prune save file')\nparser.add_argument('--plot', dest=\"plot\", action='store_true', default=False,\n help='Show plot of architecture descent')\nparser.add_argument('--dataset', default=\"CIFAR10\", type=str,\n help='dataset for experiment, choice: CIFAR10, CIFAR100, tinyimagenet', choices= [\"CIFAR10\", \"CIFAR100\", \"tinyimagenet\"])\nparser.add_argument('--pretrain', type=int, default=None,\n help='number of warm-up or fine-tuning epochs before pruning (default: None)')\nargs = parser.parse_args()\n\n# Settings for plot fonts\nnice_fonts = {\n # Use LaTeX to write all text\n \"text.usetex\": True,\n \"font.family\": \"Times New Roman\",\n # Use 10pt font in plots, to match 10pt font in document\n \"axes.labelsize\": 10,\n \"font.size\": 10,\n # Make the legend/label fonts a little smaller\n \"legend.fontsize\": 6,\n \"xtick.labelsize\": 6,\n \"ytick.labelsize\": 6,\n}\n\nmpl.rcParams.update(nice_fonts)\nsns.set_context(\"paper\", rc=nice_fonts)\n\n\n\nif args.dataset == 'CIFAR10':\n num_classes = 10\nelif args.dataset == 'CIFAR100':\n num_classes = 100\nelif args.dataset == \"tinyimagenet\":\n num_classes = 200\n\nplot_growth = args.plot\n\nif plot_growth:\n # width = 496.85625/2\n width = 237.13594\n def set_size(width, fraction=1):\n \"\"\" Set aesthetic figure dimensions to avoid scaling in latex.\n\n Parameters\n ----------\n width: float\n Width in pts\n fraction: float\n Fraction of the width which you wish the figure to occupy\n\n Returns\n -------\n fig_dim: tuple\n Dimensions of figure in inches\n \"\"\"\n # Width of figure\n fig_width_pt = width * fraction\n\n # Convert from pt to inches\n inches_per_pt = 1 / 72.27\n\n # Golden ratio to set aesthetic figure height\n golden_ratio = (5**.5 - 0.75) / 2\n # golden_ratio = 0.5\n\n # Figure width in inches\n fig_width_in = fig_width_pt * inches_per_pt\n # Figure height in inches\n fig_height_in = fig_width_in * golden_ratio\n\n fig_dim = (fig_width_in, fig_height_in)\n\n return fig_dim\n fig, axs = plt.subplots(2, 2, figsize=set_size(width), sharex=True, sharey=True)\n # fig, axs = plt.subplots(1, 4, figsize=set_size(width), sharex=True, sharey=True)\n # fig, axs = plt.subplots(1, 4, figsize=(25,4.5))\n\nif args.dataset == \"tinyimagenet\":\n # ratios = [0.25, 1.0]\n ratios = [0.25, 0.5, 0.75, 1.0]\nelse:\n # ratios = [0.25, 2]\n ratios = [0.25, 0.75, 1.25, 2]\nif args.model == \"vgg\":\n original_filters = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']\nelif args.model == \"resnet18\":\n original_filters = [[64],[64,64],[64,64],[128,128],[128,128],[256,256],[256,256],[512,512],[512,512]]\nelif args.model == \"mobilenetv2\":\n original_filters = [[32],[16],[24,24],[32,32,32],[64,64,64,64],[96,96,96],[160,160,160],[320],[1280]]\n\nfor idx, ratio in enumerate(ratios):\n if plot_growth:\n total_params = []\n print(\"Ratio: {}\".format(ratio))\n for iteration in range(15):\n if args.model == \"vgg\":\n filters = VGG.prepare_filters(VGG, original_filters, ratio=ratio, neuralscale=True, num_classes=num_classes, prune_fname=args.prune_fname, descent_idx=iteration)\n filters = [cfg for cfg in list(filter(lambda a: a != 'M', filters))]\n elif args.model == \"resnet18\":\n filters = PreActResNet.prepare_filters(PreActResNet, original_filters, ratio=ratio, neuralscale=True, num_classes=num_classes, prune_fname=args.prune_fname, descent_idx=iteration)\n filters = [cfg for cfg in sum(filters,[])]\n elif args.model == \"mobilenetv2\":\n filters = MobileNetV2.prepare_filters(MobileNetV2, original_filters, ratio=ratio, neuralscale=True, num_classes=num_classes, prune_fname=args.prune_fname, descent_idx=iteration)\n filters = [cfg for cfg in sum(filters,[])][:-2]\n if plot_growth:\n total_params.append(filters)\n \n if plot_growth:\n # ax = sns.heatmap(np.array(total_params).T, linewidth=0.005, xticklabels=2, yticklabels=2, ax=axs[idx])\n ax = sns.heatmap(np.array(total_params).T, linewidth=0.005, xticklabels=2, ax=axs[int(idx/2)][idx%2])\n ax.set_title(\"Ratio={}\".format(ratio))\n # if idx!=0:\n # ax.tick_params(axis='y', which='both', width=0)\n if idx==1 or idx==3:\n ax.tick_params(axis='y', which='both', width=0, length=0)\n if idx==0 or idx==1:\n ax.tick_params(axis='x', which='both', width=0, length=0)\n\nif plot_growth:\n # Bug in current version of sns heatmap\n b, t = plt.ylim()\n # for i in range(2):\n for i in range(4):\n axs[int(i/2)][i%2].set_ylim(b+0.5, t-0.5)\n # axs[i].set_ylim(b+0.5, t-0.5)\n\n # for ax in axs.flat:\n # ax.set(xlabel='Iteration', ylabel='Layer')\n # for ax in axs.flat:\n # ax.label_outer()\n # plt.suptitle(\"Architecture Descent\")\n fig.text(0.5, 0.01, \"Iteration\", ha='center')\n fig.text(0.01, 0.5, \"Layer\", va='center', rotation='vertical')\n plt.tight_layout()\n if args.pretrain == None:\n plt.savefig(\"savefigs/architecture_{}_{}.pdf\".format(args.model,args.dataset))\n else:\n plt.savefig(\"savefigs/architecture_{}_{}_{}.pdf\".format(args.pretrain,args.model,args.dataset))\n \n plt.show()\n", "repo_name": "eugenelet/NeuralScale", "sub_path": "list_architecture.py", "file_name": "list_architecture.py", "file_ext": "py", "file_size_in_byte": 6247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "3", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.rcParams.update", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 44, "usage_type": "attribute"}, {"api_name": "seaborn.set_context", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pylab.subplots", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 94, "usage_type": "name"}, {"api_name": "model.VGG.VGG.prepare_filters", "line_number": 117, "usage_type": "call"}, {"api_name": "model.VGG.VGG", "line_number": 117, "usage_type": "argument"}, {"api_name": "model.preact_resnet.PreActResNet.prepare_filters", "line_number": 120, "usage_type": "call"}, {"api_name": "model.preact_resnet.PreActResNet", "line_number": 120, "usage_type": "argument"}, {"api_name": "model.mobilenetv2.MobileNetV2.prepare_filters", "line_number": 123, "usage_type": "call"}, {"api_name": "model.mobilenetv2.MobileNetV2", "line_number": 123, "usage_type": "argument"}, {"api_name": "seaborn.heatmap", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pylab.ylim", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pylab.tight_layout", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pylab.savefig", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pylab.savefig", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 160, "usage_type": "name"}]} +{"seq_id": "71766211283", "text": "from django.http import request\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import Group\nfrom .forms import UserResisterForm, UserUpdateForm, ProfileUpdateForm\n\ndef register(request):\n if request.method == 'POST':\n form = UserResisterForm(request.POST)\n if form.is_valid():\n form.save()\n\n messages.success(request, f\"{form.cleaned_data.get('username')} Account created. Please contact your administrator to assign you a role.\")\n return redirect('profile')\n else:\n form = UserResisterForm()\n\n return render(request, 'users/register.html', {'form':form})\n\ndef terms(request):\n return render(request, 'users/terms.html')\n\n@login_required\ndef profile(request):\n if request.method == 'POST':\n u_form = UserUpdateForm(request.POST, instance=request.user)\n p_form = ProfileUpdateForm(request.POST, request.FILES, instance=request.user.profile)\n \n if u_form.is_valid() and p_form.is_valid():\n request.user.age = request.user.profile.age = u_form['age'].value()\n request.user.gender = request.user.profile.gender = u_form['gender'].value()\n request.user.email = request.user.profile.email = u_form['email'].value()\n request.user.first_name = request.user.profile.first_name = u_form['first_name'].value()\n request.user.last_name = request.user.profile.last_name = u_form['last_name'].value()\n request.user.groups.add(Group.objects.get(name='NLD'))\n request.user.groups.add(Group.objects.get(name='viewer'))\n\n u_form.save()\n p_form.save()\n\n messages.success(request, f\"Profile has been updated.\")\n return redirect('profile')\n\n else:\n u_form = UserUpdateForm(instance=request.user)\n p_form = ProfileUpdateForm(instance=request.user.profile)\n \n context = {\n 'u_form':u_form,\n 'p_form':p_form\n }\n\n return render(request, 'users/profile.html', context)", "repo_name": "openempathic/EMNS-DCT", "sub_path": "django_dataset_collection_tool/users/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.http.request.method", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 9, "usage_type": "name"}, {"api_name": "forms.UserResisterForm", "line_number": 10, "usage_type": "call"}, {"api_name": "django.http.request.POST", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 14, "usage_type": "call"}, {"api_name": "django.http.request", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.contrib.messages", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 15, "usage_type": "call"}, {"api_name": "forms.UserResisterForm", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.http.request", "line_number": 19, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.request", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.http.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 26, "usage_type": "name"}, {"api_name": "forms.UserUpdateForm", "line_number": 27, "usage_type": "call"}, {"api_name": "django.http.request.POST", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 27, "usage_type": "name"}, {"api_name": "django.http.request.user", "line_number": 27, "usage_type": "attribute"}, {"api_name": "forms.ProfileUpdateForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.request.POST", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 28, "usage_type": "name"}, {"api_name": "django.http.request.FILES", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.http.request.user", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.http.request.user", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 31, "usage_type": "name"}, {"api_name": "django.http.request.user", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 32, "usage_type": "name"}, {"api_name": "django.http.request.user", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 33, "usage_type": "name"}, {"api_name": "django.http.request.user", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.request.user", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 35, "usage_type": "name"}, {"api_name": "django.http.request.user.groups.add", "line_number": 36, "usage_type": "call"}, {"api_name": "django.http.request.user", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 36, "usage_type": "name"}, {"api_name": "django.http.request.user.groups.add", "line_number": 37, "usage_type": "call"}, {"api_name": "django.http.request.user", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 37, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 37, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 42, "usage_type": "call"}, {"api_name": "django.http.request", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.contrib.messages", "line_number": 42, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.request.user", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 46, "usage_type": "name"}, {"api_name": "forms.ProfileUpdateForm", "line_number": 47, "usage_type": "call"}, {"api_name": "django.http.request.user", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.http.request", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}, {"api_name": "django.http.request", "line_number": 54, "usage_type": "argument"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "8513231867", "text": "import hamiltorch\nimport torch \nimport numpy as np\nimport copy\nhamiltorch.set_random_seed(1)\ndef softthreshold(beta, mu):\n p = beta.shape[0]\n \n abs_w = torch.abs(beta) \n t = abs_w- mu\n \n s = torch.sign(beta)\n\n \n eta = s.t()*torch.max(t.t(), torch.zeros(p))\n return eta\np= int(200)\n\nn = int(100) \n\n#######Change correlated from 1 to 0 to produce iid design\ncorrelated = 1\nif correlated == 0:\n X = torch.randn([n,p]) \nelse: \n corr = torch.zeros(p,p)\n sig = .5\n for i in range(p):\n for j in range(p):\n corr[i,j] = .5 ** np.abs(i-j)\n L = corr.cholesky()\n X = torch.normal(torch.zeros([n,p]))@L.T\n \nd = 5 \nw0 = torch.zeros([p]) \nw0[:d] = torch.randn([d])* 0.001+ torch.tensor([1,1,1,1,1])*5.\n\ny = X@w0 + torch.randn([n])\n\nsoftplus = torch.nn.functional.softplus\ndef logjac_softplus(x):\n return x - softplus(x) \ndef log_prob_full(params): \n idx = 0\n idx1 = idx + p\n beta = params[idx: idx1]\n \n beta_log_prior = - beta.abs().sum() / .1 #(1./k+1.) * (torch.log(1+torch.abs(beta) * k / alpha)).sum() \n idx = idx1\n idx1 = idx + 1\n mu = softplus(params[idx: idx1])\n theta = softthreshold(beta, mu)\n r = theta.abs().sum()\n r_log_prior = -r / 10 #torch.log(1+r*r) #- (1./k+1.) * (torch.log(1+torch.abs(r) * k/alpha)) + logjac_softplus(params[idx: idx1])\n \n \n idx = idx1\n idx1 = idx + 1\n sigma2 = torch.ones(1)#softplus(params[idx: idx1])\n sigma2_log_prior = 0#-3*torch.log(sigma2) - sigma2 + logjac_softplus(params[idx: idx1]) \n psi = X@theta\n\n lik = -((y - psi)**2).sum()/sigma2/2.0 - n*torch.log(sigma2)/2.0\n\n total_posterior= lik + r_log_prior + sigma2_log_prior+beta_log_prior \n return [total_posterior, beta,mu, sigma2,theta] \n\n\n \ndef log_prob(params):\n return log_prob_full(params)[0]\n\n\nparams = torch.randn(p+1+1)\nparams = params.requires_grad_()\noptimizer = torch.optim.Adam([params], lr=1E-1)\n\nfor t in range(3000):\n \n\n loss = -log_prob(params)[0]\n \n if t%100==0:\n print(t, loss)\n \n optimizer.zero_grad()\n\n # Backward pass: compute gradient of the loss with respect to model\n # parameters\n loss.backward(retain_graph=True)\n\n # Calling the step function on an Optimizer makes an update to its\n # parameters\n optimizer.step()\nimport pylab as plt\ntheta = log_prob_full(params)[4] \ninv_mass = torch.ones( p+1+1)\nrep = 10\nmc_len = 3000\nlog_post = np.zeros([rep, mc_len])\ntheta_trace = np.zeros([rep, mc_len])\nfor i in range(rep):\n params_init = torch.normal(torch.zeros(params.shape)) \n if i>5:\n params_init = copy.deepcopy(params.detach()) \n params_init += torch.rand(params.shape) \n step_size = .001\n num_samples = mc_len+1 # For results in plot num_samples = 12000\n L = 50\n burn = 1 # For results in plot burn = 2000\n \n params_hmc_nuts = hamiltorch.sample(log_prob_func=log_prob,\n params_init=params_init, num_samples=num_samples,\n step_size=step_size, num_steps_per_sample=L,\n desired_accept_rate=0.6,\n sampler=hamiltorch.Sampler.HMC ,burn=burn,\n inv_mass = inv_mass\n )\n \n \n \n param_trace = torch.vstack(params_hmc_nuts)\n \n trace_np = param_trace.detach().cpu().numpy()\n theta_tracei = torch.vstack([log_prob_full(param_trace[i])[4] for i in range(mc_len)]).numpy()\n theta_trace[i] = theta_tracei[:,0]\n #plt.plot(trace_np[:,9])\n log_posti = torch.vstack([log_prob_full(param_trace[i])[0] for i in range(mc_len)]).flatten().numpy()\n log_post[i] = log_posti\n \nimport matplotlib\nfont = {'family' : 'normal',\n 'weight' : 'bold',\n 'size' : 20}\n \nmatplotlib.rc('font', **font)\n\nplt.figure(figsize = [8,6])\nfor i in range(rep):\n plt.plot(theta_trace[i,0:1000],alpha=.5, linewidth=3)\n plt.scatter(0, theta_trace[i,0],s=10)\nplt.axhline(5.,label='true model',linestyle = 'dashed', linewidth=3)\nplt.xlabel(\"iteration\")\nplt.ylabel(\"theta[1]\")\nplt.legend()\nplt.savefig(\"hmc_converge_theta_\"+str(correlated))\n\nplt.figure(figsize = [8,6])\ntrue_param = torch.hstack((w0, torch.ones(1)*0, torch.ones(1)))\ntrue_post = log_prob(true_param)[0]\nnull_param = torch.zeros(params.shape)\nnull_param[-2] = 1.\nnull_param[-1] = 1.\nnull_post = log_prob(null_param)[0]\nhigh_post = log_prob(params)[0]\nfor i in range(rep):\n plt.plot(log_post[i,0:1000],alpha=.5, linewidth=3)\n plt.scatter(0, log_post[i,0],s=10)\nplt.axhline(true_post,label='true model',linestyle = 'dashed', linewidth=3)\nplt.axhline(high_post.detach().numpy(),label='highest probability model',color='red',linestyle = 'dotted', linewidth=3)\nplt.axhline(null_post.detach().numpy(),label='null model',color='grey',linestyle = 'dashdot', linewidth=3)\nplt.xlabel(\"iteration\")\nplt.ylabel(\"log-posterior\")\nplt.legend() \nplt.savefig(\"hmc_converge_llh_\"+str(correlated))\n", "repo_name": "moran-xu/proximal_prior", "sub_path": "supplementary/hmc_converge_Figure2&3_sup.py", "file_name": "hmc_converge_Figure2&3_sup.py", "file_ext": "py", "file_size_in_byte": 5033, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "hamiltorch.set_random_seed", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.sign", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.normal", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 76, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.normal", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 106, "usage_type": "call"}, {"api_name": "hamiltorch.sample", "line_number": 112, "usage_type": "call"}, {"api_name": "hamiltorch.Sampler", "line_number": 116, "usage_type": "attribute"}, {"api_name": "torch.vstack", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.vstack", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.vstack", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 136, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 138, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 140, "usage_type": "call"}, {"api_name": "pylab.scatter", "line_number": 141, "usage_type": "call"}, {"api_name": "pylab.axhline", "line_number": 142, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 143, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 144, "usage_type": "call"}, {"api_name": "pylab.legend", "line_number": 145, "usage_type": "call"}, {"api_name": "pylab.savefig", "line_number": 146, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.hstack", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 157, "usage_type": "call"}, {"api_name": "pylab.scatter", "line_number": 158, "usage_type": "call"}, {"api_name": "pylab.axhline", "line_number": 159, "usage_type": "call"}, {"api_name": "pylab.axhline", "line_number": 160, "usage_type": "call"}, {"api_name": "pylab.axhline", "line_number": 161, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 162, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 163, "usage_type": "call"}, {"api_name": "pylab.legend", "line_number": 164, "usage_type": "call"}, {"api_name": "pylab.savefig", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "37493269066", "text": "import tensorflow as tf\nimport tensorflow_io as tfio\nimport tensorflow_probability as tfp\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nprint(tf.__version__)\nprint('Listing all GPU resources:')\nprint(tf.config.experimental.list_physical_devices('GPU'))\nprint()\nimport tensorflow.keras as keras \nprint(tfp.__version__)\nimport numpy as np\nimport datetime\nimport time\nimport matplotlib.pyplot as plt\nimport pickle\nimport os\nimport sys\nimport importlib.util\nimport git\n\nLAYER_NAME = os.getenv('LAYER_NAME')\n\nFILTERS = 32\nDATA_SIZE = 60000*144*144\n\nBATCH_SIZE = 128\nEPOCHS = 100\nVERBOSE = 2\n\nROOT_PATH = git.Repo(\"\", search_parent_directories=True).git.rev_parse(\"--show-toplevel\")\nDATA_PATH = \"/scratch/precisionhealth_owned_root/precisionhealth_owned1/snehalbp/GBM_LGG_tradeoffs/ALL/\" #Change datapath\nLAYER_PATH = ROOT_PATH + \"/models/\" + LAYER_NAME + \"/\"\nSAVE_PATH = LAYER_PATH + LAYER_NAME + \"_model_weights.h5\"\nPICKLE_PATH = LAYER_PATH + LAYER_NAME + '_history.pkl'\nMODEL_PATH = LAYER_PATH + LAYER_NAME + \"_model\"\n\nprint(\"-\" * 30)\nprint(\"Constructing model...\")\nprint(\"-\" * 30)\nmirrored_strategy = tf.distribute.MirroredStrategy()\n\nspec = importlib.util.spec_from_file_location(LAYER_NAME + \"_model\", LAYER_NAME + \"_model.py\")\nModelLoader = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(ModelLoader)\n\nwith mirrored_strategy.scope():\n model = ModelLoader.make_model()\n\nprint('Model summary:')\nprint(model.summary())\n\nprint('Model losses:')\nprint(model.losses)\n\nprint(\"-\" * 30)\nprint(\"Loading training and testing data...\")\nprint(\"-\" * 30)\n\n\nX_train = np.load(DATA_PATH + 'all_train_imgs.npy')\ny_train = np.load(DATA_PATH + 'all_train_msks.npy')\nX_val = np.load(DATA_PATH + 'all_val_imgs.npy')\ny_val = np.load(DATA_PATH + 'all_val_msks.npy')\n\n\n#-------------------------------------------------------------------------------------------------------------#\n\n#Create validation set:\nXy_val = tf.data.Dataset.zip((tf.data.Dataset.from_tensor_slices(X_val),\n tf.data.Dataset.from_tensor_slices(y_val))).cache().batch(BATCH_SIZE).prefetch(4)\n\n\n\n# we create two instances with the same arguments\ndata_gen_args = dict(\n rotation_range=25.,\n zoom_range=0.2,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.2,\n horizontal_flip=True)\n\nimage_datagen = ImageDataGenerator(**data_gen_args)\nmask_datagen = ImageDataGenerator(**data_gen_args)\n\n# Provide the same seed and keyword arguments to the fit and flow methods\nseed = 1\nimage_datagen.fit(X_train, seed=seed)\nmask_datagen.fit(y_train, seed=seed)\n\nimage_generator = image_datagen.flow(X_train, batch_size=BATCH_SIZE, seed=seed)\nmask_generator = mask_datagen.flow(y_train, batch_size=BATCH_SIZE, seed=seed)\ntrain_generator = zip(image_generator, mask_generator)\n\n\n#-------------------------------------------------------------------------------------------------------------#\n\n\n# model = actual_unet(img_rows, img_cols)\n\n# model.summary()\n# #model_checkpoint = ModelCheckpoint(\n# # 'drive/My Drive/weights_model1.h5', monitor='val_loss', save_best_only=True)\n\n# model_checkpoint = ModelCheckpoint(\n# 'weights_model1.h5', monitor='val_loss', save_best_only=True)\n\n# lrate = LearningRateScheduler(step_decay)\n\n# stopping = EarlyStopping(monitor='val_loss', patience=10)\n# #model.load_weights('drive/My Drive/weights_model1.h5')\n# model.compile(optimizer=Adam(), loss=weighted_binary_crossentropy,\n# metrics=[dice_coef, f1_m, precision_m, 'binary_accuracy', 'mae'])\n\n# n_imgs = X_train.shape[0]\n\n# model.fit_generator(\n# train_generator,\n# steps_per_epoch=sample_reps_per_epoch * n_imgs // batch_size,\n# epochs=5000,\n# verbose=1,\n# shuffle=True,\n# validation_data=(X_val, y_val),\n# callbacks=[model_checkpoint, lrate, stopping],\n# use_multiprocessing=True)\n\n\n\nprint(\"-\" * 30)\nprint(\"Fitting model with training data ...\")\nprint(\"-\" * 30)\n\nprint(\"Training the model started at {}\".format(datetime.datetime.now()))\nstart_time = time.time()\n\n#Train the model\n# model.load_weights(SAVE_PATH)\n\nmodel_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n filepath=SAVE_PATH,\n save_weights_only=True,\n monitor='loss',\n mode='min',\n save_best_only=True)\n\n# history = model.fit(Xy_train,\n# validation_data=Xy_test,\n# epochs=EPOCHS, verbose=VERBOSE,\n# callbacks=[model_checkpoint_callback])\n\nhistory = model.fit_generator(\n train_generator,\n steps_per_epoch=X_train.shape[0] // BATCH_SIZE,\n epochs=EPOCHS,\n verbose=VERBOSE,\n shuffle=True,\n validation_data=Xy_val,\n callbacks=[model_checkpoint_callback],\n use_multiprocessing=True)\n\n\nprint(\"Total time elapsed for training = {} seconds\".format(time.time() - start_time))\nprint(\"Training finished at {}\".format(datetime.datetime.now()))\n\n\n# Save the model\n# serialize weights to HDF5\nmodel.save_weights(SAVE_PATH)\nprint(\"Saved model to disk (.h5)\")\n\n#Save training history\nhistory_dict = history.history\nwith open(PICKLE_PATH, 'wb') as file_pi:\n pickle.dump(history_dict, file_pi)\nprint('Pickled training history')", "repo_name": "comp-hci-lab/BDSI_2022_ML", "sub_path": "model/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 5127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "tensorflow.__version__", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tensorflow.config.experimental.list_physical_devices", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.__version__", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.distribute.MirroredStrategy", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 41, "usage_type": "attribute"}, {"api_name": "importlib.util.util.spec_from_file_location", "line_number": 43, "usage_type": "call"}, {"api_name": "importlib.util.util", "line_number": 43, "usage_type": "attribute"}, {"api_name": "importlib.util", "line_number": 43, "usage_type": "name"}, {"api_name": "importlib.util.util.module_from_spec", "line_number": 44, "usage_type": "call"}, {"api_name": "importlib.util.util", "line_number": 44, "usage_type": "attribute"}, {"api_name": "importlib.util", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.zip", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.image.ImageDataGenerator", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.ImageDataGenerator", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 134, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 140, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "38217737264", "text": "import random,json\nfrom config.classes import OracleConnector, MySQLConnector, LDAPConnector, APIConnector,PostgreGreenplumConnector\nfrom config.models import Connection, API_CONNECTOR_TYPE, ORACLE_CONNECTOR_TYPE,LDAP_CONNECTOR_TYPE,MYSQL_CONNECTOR_TYPE,POSTGRE_CONNECTOR_TYPE\nimport numpy, traceback\n# from tasks.functions import create_alert\nfrom django.utils import timezone\n\nfrom .models import ReportHistory\n# timezone.now()\ndef random_color():\n return \"%06x\" % random.randint(0, 0xFFFFFF)\n\n# def build_chartjs_bar_data(report_history):\n# results = json.loads(report_history.data)\n# del results[0]\n# data = {'labels':[], 'datasets':[{}]}\n# data['datasets'][0]['label']=report_history.report.name\n# data['datasets'][0]['backgroundColor']=\"rgba(204, 0, 0, .4)\"\n# data['datasets'][0]['borderColor']=\"#c00\"\n# data['datasets'][0]['borderWidth']=2\n# data['datasets'][0]['hoverBackgroundColor']=\"#59597F\"\n# data['datasets'][0]['data']=[]\n# print(results)\n# for row in results:\n# data['labels'].append(row[0])\n# data['datasets'][0]['data'].append(row[1])\n#\n# return data\n\n# def build_chartjs_pie_data(report_history):\n# results = json.loads(report_history.data)\n#\n#\n# del results[0]\n# data = {'labels': [[x[0] for x in results]], 'datasets': [{}]}\n# data['datasets'][0]['label'] = report_history.report.name\n# data['datasets'][0]['backgroundColor'] = [f'#{random_color()}' for x in results]\n# data['datasets'][0]['data'] = [int(x[1]) for x in results]\n# return data\n# pass\ndef build_chartjs_line_data(report_histories):\n #presumably our data should be a list of lists in which list[n][0] is the label for the data and list[n][1] is the datapoint at the interval\n fuck = {}\n # print(len(report_histories))\n for history in report_histories:\n # print(json.loads(history.data)[1:])\n fuck[str(history.creation_date).split('.')[0]] ={x[0]: x[1] for x in json.loads(history.data)[1:]}\n labels =[]\n datasets =[]\n for key, value in fuck.items():\n labels.append(key)\n for k, v in value.items():\n if next((item for item in datasets if item[\"label\"] == k), None) is not None:\n datasets[next((i for i, item in enumerate(datasets) if item[\"label\"] == k), None)]['data'].append(v)\n\n else:\n point_color = random_color()\n color = list(numpy.random.choice(range(256), size=3))\n d = {\n \"backgroundColor\": f\"rgb({color[0]},{color[1]},{color[2]})\",\n \"borderColor\": f\"rgb({color[0]},{color[1]},{color[2]})\",\n \"fill\": False,\n # \"backgroundColor\": point_color,\n # \"borderColor\":point_color,\n \"label\":k,\n # \"fill\":False,\n \"data\": [v]\n }\n datasets.append(d)\n # print(labels)\n # print(datasets)\n return {\"datasets\":datasets, \"labels\":labels}\n\n # datasets = []\n # labels= []\n # for i in range(0, report_histories):\n # entry = report_histories[i]\n # point_color = random_color()\n # report_data = json.loads(entry.attributes)\n # datapoints = []\n #\n # d = {\n # \"label\": report_histories[i][0],\n # \"fillColor\": \"blue\",\n # \"strokeColor\": \"red\",\n # \"pointColor\": point_color,\n # \"pointStrokeColor\": point_color,\n # \"pointHighlightFill\": \"white\",\n # \"pointHighlightStroke\": \"black\",\n # \"data\": datapoints\n # }\n # datasets.append(d)\n # return {\"datasets\":datasets, \"labels\":labels}\n\ndef run_report(report):\n if report.connection.type==ORACLE_CONNECTOR_TYPE: #handle oracle case\n ora_conn = OracleConnector(Connection.objects.get(name=report.connection.name))\n ora_conn.open_connection()\n error= False\n try:\n ora_conn.run_report(report)\n ora_conn.close_connection()\n except:\n error=True\n ora_conn.close_connection()\n if error:\n raise Exception(traceback.format_exc())\n if report.connection.type==MYSQL_CONNECTOR_TYPE:\n mysql_conn = MySQLConnector(Connection.objects.get(name=report.connection.name))\n mysql_conn.open_connection()\n error = False\n try:\n mysql_conn.run_report(report)\n mysql_conn.close_connection()\n except:\n error=True\n mysql_conn.close_connection()\n if error:\n raise Exception(traceback.format_exc())\n\n\n if report.connection.type == LDAP_CONNECTOR_TYPE:\n ldap_conn = LDAPConnector(Connection.objects.get(name=report.connection.name))\n ldap_conn.open_connection()\n error=False\n try:\n ldap_conn.run_report(report)\n ldap_conn.close_connection()\n except:\n error=True\n ldap_conn.close_connection()\n if error:\n raise Exception(traceback.format_exc())\n if report.connection.type==API_CONNECTOR_TYPE:\n try:\n result = []\n connection = APIConnector(Connection.objects.get(name=report.connection.name))\n print(report.code)\n exec(report.code)\n\n except Exception as e:\n # import traceback\n traceback.print_exc()\n print('we ran into an error')\n print(traceback.format_exc())\n raise Exception(str(e))\n if report.connection.type==POSTGRE_CONNECTOR_TYPE:\n postgre_conn = PostgreGreenplumConnector(Connection.objects.get(name=report.connection.name))\n postgre_conn.open_connection()\n error = False\n try:\n postgre_conn.run_report(report)\n postgre_conn.close_connection()\n except:\n error=True\n postgre_conn.close_connection()\n if error:\n raise Exception(traceback.format_exc())\n\ndef build_tabulator_basic_data(input_data):\n data = []\n report_attrs = input_data\n # print(report_attrs[0])\n for i in range(1, len(report_attrs)):\n entry = {}\n for ii in range(0, len(report_attrs[i])):\n entry[report_attrs[0][ii]]=report_attrs[i][ii]\n data.append(entry)\n return data\n\ndef build_chartjs_pie_data(input_data):\n data = {\"labels\":[], \"datasets\":[{\"data\":[],\"label\":\"\", \"data\":[],\"backgroundColor\":[], \"hoverOffset\":4}]}\n # labels: [\n # 'Red',\n # 'Blue',\n # 'Yellow'\n # ],\n # datasets: [{\n # label: 'My First Dataset',\n # data: [300, 50, 100],\n # backgroundColor: [\n # 'rgb(255, 99, 132)',\n # 'rgb(54, 162, 235)',\n # 'rgb(255, 205, 86)'\n # ],\n # hoverOffset: 4\n # }]\n # report_attrs = input_data\n for i in range(1, len(input_data)):\n # data['labels'].append(input_data[i][0])\n data['datasets'][0]['data'].append(input_data[i][1])\n data['labels'].append(input_data[i][0])\n # point_color = random_color()\n color = list(numpy.random.choice(range(256), size=3))\n data['datasets'][0]['backgroundColor'].append(f\"rgb({color[0]},{color[1]},{color[2]})\")\n\n # for i in range(1, len(report_attrs)):\n # entry = {}\n # for ii in range(0, len(report_attrs[i])):\n # entry[report_attrs[0][ii]]=report_attrs[i][ii]\n # data.append(entry)\n # print(data)\n return data\n\ndef build_chartjs_bar_data(input_data):\n data = {\"labels\":[input_data[i][0] for i in range(1, len(input_data))], \"datasets\":[]}\n # labels: [\n # 'Red',\n # 'Blue',\n # 'Yellow'\n # ],\n # datasets: [{\n # label: 'My First Dataset',\n # data: [300, 50, 100],\n # backgroundColor: [\n # 'rgb(255, 99, 132)',\n # 'rgb(54, 162, 235)',\n # 'rgb(255, 205, 86)'\n # ],\n # hoverOffset: 4\n # }]\n # report_attrs = input_data\n # for i in range(1, len(input_data)):\n entry ={}\n color = list(numpy.random.choice(range(256), size=3))\n entry['label']=input_data[0][1]\n entry['backgroundColor']=f\"rgb({color[0]},{color[1]},{color[2]})\"\n entry['data']=[input_data[i][1] for i in range(1, len(input_data))]\n data['datasets'].append(entry)\n # data['labels'].append(input_data[i][0])\n # data['datasets'][0]['data'].append(input_data[i][1])\n # data['datasets'][0]['data'].append(input_data[i][1])\n # data['labels'].append(input_data[i][0])\n # point_color = random_color()\n\n # data['datasets'][0]['backgroundColor'].append()\n\n # for i in range(1, len(report_attrs)):\n # entry = {}\n # for ii in range(0, len(report_attrs[i])):\n # entry[report_attrs[0][ii]]=report_attrs[i][ii]\n # data.append(entry)\n # print(data)\n return data\n\n\n\ndef build_line_chart_data(label, input_data):\n #presumably our data should be a list of lists in which list[n][0] is the label for the data and list[n][1] is the datapoint at the interval\n # fuck = {}\n data = []\n # print(len(input_data))\n # for metric in input_data:\n # print(json.loads(history.data)[1:])\n # data.append([str(metric.creation_date).split('.')[0], 1 if metric.successful else -1])\n # fuck[str(metric.creation_date).split('.')[0]] ={str(metric.creation_date):1 if metric.successful else -1}\n labels =[]\n datasets =[]\n dumb = []\n for d in input_data:\n labels.append(d[0])\n dumb.append(d[1])\n\n\n print(labels)\n print(datasets)\n return {\"datasets\":[{\"label\":f\"{label}\", \"data\":dumb, 'fill':True,'fillColor':'white', \"tension\":\"0.1\",\"borderColor\": \"blue\"}], \"labels\":labels}", "repo_name": "sourdiesel1202/flowr_site", "sub_path": "reports/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 9835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "random.randint", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "config.models.ORACLE_CONNECTOR_TYPE", "line_number": 96, "usage_type": "name"}, {"api_name": "config.classes.OracleConnector", "line_number": 97, "usage_type": "call"}, {"api_name": "config.models.Connection.objects.get", "line_number": 97, "usage_type": "call"}, {"api_name": "config.models.Connection.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "config.models.Connection", "line_number": 97, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 107, "usage_type": "call"}, {"api_name": "config.models.MYSQL_CONNECTOR_TYPE", "line_number": 108, "usage_type": "name"}, {"api_name": "config.classes.MySQLConnector", "line_number": 109, "usage_type": "call"}, {"api_name": "config.models.Connection.objects.get", "line_number": 109, "usage_type": "call"}, {"api_name": "config.models.Connection.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "config.models.Connection", "line_number": 109, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 119, "usage_type": "call"}, {"api_name": "config.models.LDAP_CONNECTOR_TYPE", "line_number": 122, "usage_type": "name"}, {"api_name": "config.classes.LDAPConnector", "line_number": 123, "usage_type": "call"}, {"api_name": "config.models.Connection.objects.get", "line_number": 123, "usage_type": "call"}, {"api_name": "config.models.Connection.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "config.models.Connection", "line_number": 123, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 133, "usage_type": "call"}, {"api_name": "config.models.API_CONNECTOR_TYPE", "line_number": 134, "usage_type": "name"}, {"api_name": "config.classes.APIConnector", "line_number": 137, "usage_type": "call"}, {"api_name": "config.models.Connection.objects.get", "line_number": 137, "usage_type": "call"}, {"api_name": "config.models.Connection.objects", "line_number": 137, "usage_type": "attribute"}, {"api_name": "config.models.Connection", "line_number": 137, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 143, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 145, "usage_type": "call"}, {"api_name": "config.models.POSTGRE_CONNECTOR_TYPE", "line_number": 147, "usage_type": "name"}, {"api_name": "config.classes.PostgreGreenplumConnector", "line_number": 148, "usage_type": "call"}, {"api_name": "config.models.Connection.objects.get", "line_number": 148, "usage_type": "call"}, {"api_name": "config.models.Connection.objects", "line_number": 148, "usage_type": "attribute"}, {"api_name": "config.models.Connection", "line_number": 148, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 225, "usage_type": "attribute"}]} +{"seq_id": "7301903427", "text": "from unityagents import UnityEnvironment\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom collections import namedtuple, deque\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.tensorboard import SummaryWriter\nfrom NewReplayBuffer import ReplayBuffer, GaussianNoise, OUNoise\n\nLEARNING_RATE = 1e-2\nBUFFER_SIZE = int(1e5)\nBATCH_SIZE = 128\nTAU = 1e-2 # for soft update of target parameters\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nlogger = SummaryWriter()\n\n\n\nagent1_actor_weights = \"\"\nagent1_critic_weights = \"\"\nagent2_actor_weights = \"\"\nagent2_critic_weights = \"\"\n\nclass CriticNetwork(nn.Module):\n def __init__(self,critic_input_size, critic_output_size, seed, fc1_units = 400, fc2_units = 300):\n super(CriticNetwork, self).__init__()\n self.seed = torch.manual_seed(seed)\n self.layers = nn.Sequential(\n nn.Linear(critic_input_size, fc1_units),\n # nn.BatchNorm1d(fc1_units),\n nn.ReLU(),\n nn.Linear(fc1_units, fc2_units),\n # nn.BatchNorm1d(fc2_units),\n nn.ReLU(),\n nn.Linear(fc2_units, critic_output_size) \n )\n self.to(device)\n\n def forward(self, state):\n return self.layers(state) # TODO: Introduce batch normalization\n\nclass ActorNetwork(nn.Module):\n def __init__(self, actor_input_size, actor_output_size, seed, fc1_units = 400, fc2_units = 300):\n super(ActorNetwork, self).__init__()\n self.seed = torch.manual_seed(seed)\n self.layers = nn.Sequential(\n nn.Linear(actor_input_size, fc1_units),\n # nn.BatchNorm1d(fc1_units),\n nn.ReLU(),\n nn.Linear(fc1_units, fc2_units),\n # nn.BatchNorm1d(fc2_units),\n nn.ReLU(),\n nn.Linear(fc2_units, actor_output_size),\n nn.Tanh()\n )\n self.to(device)\n\n def forward(self, state):\n return self.layers(state) # TODO: Introduce batch normalization\n\nclass DDPGNetwork():\n def __init__(self, actor_input_size, actor_output_size, critic_input_size, critic_output_size, seed):\n self.actor_network = ActorNetwork(actor_input_size, actor_output_size, seed).to(device)\n self.critic_network = CriticNetwork(critic_input_size, critic_output_size, seed).to(device)\n self.actor_optimizer = optim.Adam(self.actor_network.parameters(), lr=LEARNING_RATE)\n self.critic_optimizer = optim.Adam(self.critic_network.parameters(), lr=LEARNING_RATE)\n \n\n def actor(self, state):\n return self.actor_network(state)\n \n def critic(self, states,actions):\n return self.critic_network(torch.cat((states, actions), 1))\n\n\nclass DDPGAgent():\n def __init__(self, actor_input_size, actor_output_size, critic_input_size, critic_output_size, seed, warmup):\n self.actor_input_size = actor_input_size\n self.actor_output_size = actor_output_size\n self.seed = random.seed(seed)\n self.action_lowest_value = -0.9\n self.action_highest_value = 0.9\n self.warump = warmup\n #self.gaussian_noise = GaussianNoise(size=action_size, std_start=0.8, std_end=0.01,steps=1000000) \n self.ou_noise = OUNoise(action_size,seed )\n\n # DDPG-Network\n # TODO: Make parameter of both networks identical at beginnings\n self.local_network = DDPGNetwork(actor_input_size, actor_output_size, critic_input_size, critic_output_size, seed)\n self.target_network = DDPGNetwork(actor_input_size, actor_output_size, critic_input_size, critic_output_size, seed)\n self.set_parameters_of_target_and_local_equal()\n \n def reset_noise(self):\n self.ou_noise.reset()\n\n def set_parameters_of_target_and_local_equal(self):\n self.soft_update(1.0)\n\n def get_acion_per_current_policy_for(self, state , number_episode, train_mode):\n if number_episode < self.warump and train_mode:\n actions = np.random.randn(self.actor_output_size) \n actions = np.clip(actions, self.action_lowest_value, self.action_highest_value) \n else: \n state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n self.local_network.actor_network.eval()\n with torch.no_grad():\n actions = self.local_network.actor(state).cpu().data.numpy()\n self.local_network.actor_network.train()\n noise_applied = self.ou_noise.noise().numpy() # TODO: Investigate noise function\n #noise_applied = self.gaussian_noise()\n actions = np.clip(actions + noise_applied, self.action_lowest_value, self.action_highest_value)\n return actions\n\n\n def soft_update(self, tau):\n \"\"\"Soft update model parameters for actor and critic of target network.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n tau (float): interpolation parameter \n \"\"\"\n for target_param, local_param in zip(self.target_network.actor_network.parameters(), self.local_network.actor_network.parameters()):\n target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n\n for target_param, local_param in zip(self.target_network.critic_network.parameters(), self.local_network.critic_network.parameters()):\n target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n\n\ndef plot_scores(scores, number):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n plt.plot(np.arange(len(scores)), scores)\n plt.ylabel('Score')\n plt.xlabel('Episode #')\n figure_name = \"scores_\" + str(number) +\".png\"\n plt.savefig(figure_name)\n \n\ndef maddpg(env, agent, n_episodes=2000, max_t=1000, gamma=0.9):\n scores = [] # list containing scores from each episode\n scores_window = deque(maxlen=100) # last 100 scores\n max_score_value = 0\n brain_name = env.brain_names[0]\n brain = env.brains[brain_name]\n noise = 2\n noise_decay = 0.9999\n for i_episode in range(1, n_episodes+1):\n env_info = env.reset(train_mode=True)[brain_name] \n state = env_info.vector_observations\n agent.reset_noise()\n score = [0,0]\n while True:\n noise *= noise_decay\n action = agent.get_acion_per_current_policy_for(state, i_episode, True)\n env_info = env.step(action)[brain_name]\n next_state = env_info.vector_observations\n reward = env_info.rewards\n done = env_info.local_done\n agent.step(state, action, reward, next_state, done, gamma)\n state = next_state\n score += reward\n if np.any(done): \n break \n logger.add_scalars('agent/scores',{'agent1': score[0], 'agent2': score[1], },i_episode)\n max_score = np.max(score)\n scores_window.append(max_score)\n scores.append(max_score)\n\n print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end=\"\")\n if i_episode % 100 == 0:\n print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))\n if np.mean(scores_window) > max_score_value + 0.1:\n print('\\nEnvironment saved in {:d} episodes!\\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n torch.save(agent.agents[0].local_network.actor_network.state_dict(), 'multi_intermediate_weight_actor1.pth')\n torch.save(agent.agents[0].local_network.critic_network.state_dict(),'multi_intermediate_weight_critic1.pth')\n torch.save(agent.agents[1].local_network.actor_network.state_dict(), 'multi_intermediate_weight_actor2.pth')\n torch.save(agent.agents[1].local_network.critic_network.state_dict(),'multi_intermediate_weight_critic2.pth')\n max_score_value = np.mean(scores_window)\n if np.mean(scores_window) >= 30:\n print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n torch.save(agent.agents[0].local_network.actor_network.state_dict(), 'multi_final_weight_actor1.pth')\n torch.save(agent.agents[0].local_network.critic_network.state_dict(), 'multi_final_weight_critic1.pth')\n torch.save(agent.agents[1].local_network.actor_network.state_dict(), 'multi_final_weight_actor2.pth')\n torch.save(agent.agents[1].local_network.critic_network.state_dict(), 'multi_final_weight_critic2.pth')\n break\n return scores\n\nclass MADDPGAgent():\n def __init__(self, actor_input_size, actor_output_size, critic_input_size, critic_output_size, seed, warmup):\n self.agents = [\n DDPGAgent(actor_input_size=actor_input_size, actor_output_size=actor_output_size, critic_input_size=critic_input_size, critic_output_size=critic_output_size, seed=seed, warmup = warmup),\n DDPGAgent(actor_input_size=actor_input_size, actor_output_size=actor_output_size, critic_input_size=critic_input_size, critic_output_size=critic_output_size, seed=seed, warmup = warmup)\n ]\n self.memory = ReplayBuffer(buffer_size=BUFFER_SIZE, batch_size=BATCH_SIZE, seed=seed)\n self.iter = 0\n self.load_weights()\n\n def load_weights(self):\n if agent1_actor_weights != \"\":\n self.agents[0].local_network.actor_network.load_state_dict(torch.load(agent1_actor_weights))\n self.agents[0].local_network.critic_network.load_state_dict(torch.load(agent1_critic_weights))\n self.agents[0].set_parameters_of_target_and_local_equal()\n\n self.agents[1].local_network.actor_network.load_state_dict(torch.load(agent2_actor_weights))\n self.agents[1].local_network.critic_network.load_state_dict(torch.load(agent2_critic_weights))\n self.agents[1].set_parameters_of_target_and_local_equal()\n\n def reset_noise(self):\n self.agents[0].reset_noise()\n self.agents[1].reset_noise()\n\n def get_acion_per_current_policy_for(self, all_states , number_episode, train_mode):\n action0 = self.agents[0].get_acion_per_current_policy_for(all_states[0],number_episode, True)\n action1 = self.agents[1].get_acion_per_current_policy_for(all_states[1],number_episode, True)\n actions = np.vstack((action0,action1))\n return actions\n\n def step(self,state, action, reward, next_state, done, gamma):\n self.memory.add(state[0], state[1] ,action[0], action[1] ,reward, next_state[0], next_state[1], done)\n self.learn(gamma)\n \n def learn(self, gamma):\n \"\"\"Update parameters.\n \"\"\"\n if len(self.memory) > BATCH_SIZE:\n self.iter += 1\n\n for i in range(0,2):\n experiences = self.memory.sample()\n states0, states1, actions0, actions1, rewards, next_states0, next_states1, dones = experiences\n next_states = torch.cat((next_states0,next_states1), 1)\n states = torch.cat((states0,states1),1)\n actions = torch.cat((actions0,actions1),1)\n next_action0 = self.agents[0].target_network.actor(next_states0)\n next_action1 = self.agents[1].target_network.actor((next_states1))\n next_actions = torch.cat((next_action0,next_action1),1)\n\n self.agents[i].local_network.critic_optimizer.zero_grad()\n with torch.no_grad():\n critic_values = torch.squeeze(self.agents[i].target_network.critic(next_states, next_actions))\n y = rewards[:,i] + (1 - dones[:,i]) * gamma * critic_values\n q_value =torch.squeeze(self.agents[i].local_network.critic(states,actions))\n huber_loss = torch.nn.SmoothL1Loss()\n critic_loss = huber_loss(y.detach() , q_value)\n critic_loss.backward()\n self.agents[i].local_network.critic_optimizer.step()\n\n estimated_action0 = self.agents[0].local_network.actor(states0)\n estimated_action1 = self.agents[1].local_network.actor(states1)\n estimated_actions = torch.cat((estimated_action0,estimated_action1),1)\n\n self.agents[i].local_network.actor_optimizer.zero_grad()\n actor_loss = -self.agents[i].local_network.critic(states, estimated_actions).mean()\n actor_loss.backward()\n self.agents[i].local_network.actor_optimizer.step()\n self.agents[i].soft_update(TAU)\n al = -actor_loss.cpu().detach().item()\n cl = critic_loss.cpu().detach().item()\n q_val = q_value.cpu().detach().mean().item()\n reward = rewards[:,i].cpu().detach().mean()\n logger.add_scalars('agent%i/losses' % i,{'critic_loss': cl, 'actor_loss': al, 'q_value' : q_val, 'reward' : reward},self.iter)\n\n\ndef test_performance(agent, env, brain_name):\n for i in range(1, 1000): # play game for 5 episodes\n env_info = env.reset(train_mode=False)[brain_name] # reset the environment \n #states = env_info.vector_observations # get the current state (for each agent)\n scores = [0,0] # initialize the score (for each agent)\n while True:\n #actions = agent.get_acion_per_current_policy_for(states , 0, False) # select an action (for each agent)\n actions_random = np.random.randn(2, 2) \n actions = actions_random\n #actions = np.clip(actions, -1, 1) # all actions between -1 and 1\n env_info = env.step(actions)[brain_name] # send all actions to tne environment\n next_states = env_info.vector_observations # get next state (for each agent)\n rewards = env_info.rewards # get reward (for each agent)\n dones = env_info.local_done # see if episode finished\n scores += env_info.rewards # update the score (for each agent)\n states = next_states # roll over states to next time step\n if np.any(dones): # exit loop if episode finished\n break\n print('Score (max over agents) from episode {}: {}'.format(i, np.max(scores)))\n \n\n\nif __name__ == '__main__': \n env = UnityEnvironment(file_name=\"/home/steffen/workspace/deep_reinforcement_learning/projects/p3_collab-compet/Tennis_Linux/Tennis.x86_64\")\n brain_name = env.brain_names[0]\n brain = env.brains[brain_name] \n\n # reset the environment\n env_info = env.reset(train_mode=True)[brain_name]\n action_size = brain.vector_action_space_size\n states = env_info.vector_observations\n state_size = states.shape[1]\n\n\n \n\n critic_input_size =2*state_size+2*action_size\n agent = MADDPGAgent(actor_input_size=state_size, actor_output_size=action_size, critic_input_size=critic_input_size, critic_output_size=1, seed=0, warmup=0)\n scores = maddpg(env=env,agent=agent, n_episodes=5000000000, gamma=0.99)\n #test_performance(agent,env,brain_name)\n logger.flush()\n logger.close()\n env.close()", "repo_name": "steffenkoerner/deep_reinforcement_learning", "sub_path": "projects/playground/Multi_Tennis.py", "file_name": "Multi_Tennis.py", "file_ext": "py", "file_size_in_byte": 15474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.device", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "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.ReLU", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 76, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 83, "usage_type": "call"}, {"api_name": "NewReplayBuffer.OUNoise", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "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.savefig", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 190, "usage_type": "call"}, {"api_name": "NewReplayBuffer.ReplayBuffer", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 277, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 288, "usage_type": "call"}, {"api_name": "unityagents.UnityEnvironment", "line_number": 293, "usage_type": "call"}]} +{"seq_id": "11036723638", "text": "#!/home/ectroudt/anaconda3/bin/python3.6\n\nimport subprocess\nimport argparse\n\n# Call 'process_salmon_SEAN.py' to carry out salmon on downloaded BAM files\ndef initiate_Salmon(split_num):\n\n # split_num is set to 6 by default, unless different argument is passed\n split_num = int(split_num)\n split_num_call = str(split_num)\n\n for j in range(1, (split_num + 1)):\n\n scr_name = \"salmon_processing_\" + str(j)\n program_cmd = \"/home/ectroudt/anaconda3/bin/python3.6\"\n program = \"/home/ectroudt/TCGA_Code/GDC_Data_Processing/process_salmon_SEAN.py\"\n program_arg_1 = str(j)\n\n subprocess.Popen([\"screen\", \"-S\", scr_name, \"-d\", \"-m\", program_cmd, program, program_arg_1, \"--split_Num\",\n split_num_call])\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description=\"Retrieve current split and split number\")\n\n # Arguments is optional, MUST be an int value between 2-6 if passed with call to 'Automate_to_Salmon.py'\n parser.add_argument(\"--split_Val\", type=int, nargs=\"?\", choices=range(2, 7), default=6,\n help=\"Split number for # of salmon processes to be ran concurrently\")\n\n split_Num_Arg = parser.parse_args()\n split_Val = split_Num_Arg.split_Val\n initiate_Salmon(split_Val)\n\n print(\"\\n Bam files being processed by Salmon in the following screen sessions: \\n\")\n subprocess.call([\"screen\", \"-ls\"])\n\n\nmain()\n\n", "repo_name": "ectroudt/Genomic_Data_Commons-Code", "sub_path": "Automate_to_Salmon.py", "file_name": "Automate_to_Salmon.py", "file_ext": "py", "file_size_in_byte": 1416, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "subprocess.Popen", "line_number": 20, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 26, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "16926389792", "text": "# -*- coding:utf-8 -*-\nimport sys\nimport urllib2\nimport subprocess\nimport threading\nimport os\n\nimport pygame\n\nimport wbui\nimport system\nimport pygamehelper\nimport gui\nimport resource_loader\n\ncanvas = None\ncursor = None\nback = None\nalpha = None\ndalpha = None\nselected = None\nactive = None\nblink_count = None\n\n# string resources\n\n\ngui.res.register(\"string_back_title_screen\",resource_loader.l({\"en\":u\"Back to the title screen\", \"ja\":u\"タイトル画面へ戻る\"}))\ngui.res.register(\"string_wbui_exit\",resource_loader.l({\"en\":u\"Exit the WBUI\", \"ja\":u\"WBUIを終了する\"}))\n\n\n\ndef init():\n global canvas, cursor, back\n canvas = pygame.Surface(gui.res.contents_panel.get_size(), pygame.SRCALPHA, 32)\n cursor = pygame.Surface((canvas.get_width(), wbui.smallfont.get_height()))\n cursor.fill((255,255,128))\n\n #self.back = main.smallfont.render(u\"何もせずにタイトルへ戻る\", True, (255,255,255))\n s = system.getSystem()\n back = wbui.smallfont.render(gui.res.string_back_title_screen if s.isRunningAsGetty() else gui.res.string_wbui_exit, True, (255,255,255))\n #self.check_update = main.smallfont.render(u\"アップデートをチェックしてから戻る\", True, (255, 255, 255))\n #self.show_demo = main.smallfont.render(u\"おまけのデモを見てから戻る\", True, (255,255,255))\n\n global alpha, dalpha, selected, active, blink_count\n alpha = 50\n dalpha = 2\n selected = 0\n active = False\n blink_count = 0\n\ndef refresh():\n return\n\ndef update():\n canvas.fill((0,0,0,0))\n canvas.blit(gui.res.contents_panel, (0, 0))\n # カーソル描画\n global alpha, dalpha, blink_count\n if active:\n cursor.set_alpha(alpha)\n canvas.blit(cursor, (0, selected * cursor.get_height()))\n # コンテンツ描画\n canvas.blit(back, (0, 0))\n #self.canvas.blit(self.check_update, (0, main.smallfont.get_height()))\n #self.canvas.blit(self.show_demo, (0, main.smallfont.get_height() * 2))\n\n # 上下矢印描画\n if active and blink_count > 15:\n if is_able_to_go_up():\n canvas.blit(wbui.up_arrow, pygamehelper.center_to_lefttop(wbui.up_arrow, (canvas.get_width() / 2, selected * cursor.get_height()) ))\n if is_able_to_go_down():\n canvas.blit(wbui.down_arrow, pygamehelper.center_to_lefttop(wbui.down_arrow, (canvas.get_width() / 2, (selected + 1) * cursor.get_height() - 1)))\n\n # カーソル点滅\n alpha += dalpha\n if alpha > 127:\n alpha = 127\n dalpha *= -1\n elif alpha < 50:\n alpha = 50\n dalpha *= -1\n\n # 上下矢印点滅\n blink_count += 1\n if blink_count >= 30:\n blink_count = 0\n\ndef get_canvas():\n return canvas\n\ndef activate():\n global active\n active = True\n return True\n\ndef deactivate():\n global active\n active = False\n\ndef is_active():\n return active\n\ndef enter():\n if selected == 0:\n pygame.quit()\n sys.exit(0)\n\ndef escape():\n return False\n\ndef is_able_to_go_up():\n return selected > 0\n\ndef is_able_to_go_down():\n #return self.selected < 2\n return False\n\ndef up():\n global selected\n if is_able_to_go_up():\n wbui.play_sound(\"move\")\n selected -= 1\n update()\n return True\n return False\n\ndef down():\n global selected\n if is_able_to_go_down():\n wbui.play_sound(\"move\")\n selected += 1\n update()\n return True\n return False\n\n", "repo_name": "shimarin/walbrix.old", "sub_path": "wbui/src/back.py", "file_name": "back.py", "file_ext": "py", "file_size_in_byte": 3427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "3", "api": [{"api_name": "gui.res.register", "line_number": 28, "usage_type": "call"}, {"api_name": "gui.res", "line_number": 28, "usage_type": "attribute"}, {"api_name": "resource_loader.l", "line_number": 28, "usage_type": "call"}, {"api_name": "gui.res.register", "line_number": 29, "usage_type": "call"}, {"api_name": "gui.res", "line_number": 29, "usage_type": "attribute"}, {"api_name": "resource_loader.l", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 35, "usage_type": "call"}, {"api_name": "gui.res.contents_panel.get_size", "line_number": 35, "usage_type": "call"}, {"api_name": "gui.res", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.SRCALPHA", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 36, "usage_type": "call"}, {"api_name": "wbui.smallfont.get_height", "line_number": 36, "usage_type": "call"}, {"api_name": "wbui.smallfont", "line_number": 36, "usage_type": "attribute"}, {"api_name": "system.getSystem", "line_number": 40, "usage_type": "call"}, {"api_name": "wbui.smallfont.render", "line_number": 41, "usage_type": "call"}, {"api_name": "wbui.smallfont", "line_number": 41, "usage_type": "attribute"}, {"api_name": "gui.res", "line_number": 41, "usage_type": "attribute"}, {"api_name": "gui.res", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wbui.up_arrow", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygamehelper.center_to_lefttop", "line_number": 71, "usage_type": "call"}, {"api_name": "wbui.down_arrow", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygamehelper.center_to_lefttop", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 107, "usage_type": "call"}, {"api_name": "wbui.play_sound", "line_number": 122, "usage_type": "call"}, {"api_name": "wbui.play_sound", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "26083031503", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\n\n\nTOTAL_WEALTH = 1_000_000\nPOPULATION = 100\nTIME_STEPS = 30\nRETURN_DISTRIB = {'name': 't', 'params': {'df': 3}}\nWEALTH_DISTRIB = {'name': 'chisq', 'params': {'df': 0.02}}\nAMT_INVESTED_DISTRIB = {'name': 'uniform', 'params': {}}\nMIN_RETURN = 0.6\nMAX_RETURN = 1.8\n#{'name': 'exp', 'params': {'scale': 30.}}\n\n\ndef main():\n wealth_distribution = (\n Returns(**WEALTH_DISTRIB).get_returns(POPULATION, normalize=True))\n invested_distribution = Returns(\n **AMT_INVESTED_DISTRIB).get_returns(POPULATION)\n # returns represented as a fraction: e.g., 1.2 = 20% gain, 0.8 = 20% loss\n returns = (\n Returns(**RETURN_DISTRIB)\n .get_returns(\n TIME_STEPS, adj_params={'min': MIN_RETURN, 'max': MAX_RETURN}))\n plot_distributions(wealth, amounts_invested, returns)\n population = Population(wealth_distribution, invested_distribution)\n # track wealth per citizen, and\n # for return in returns:\n # population.updated_wealth()\n # track\n # show distribution over time\n\n\nclass Returns:\n def __init__(self, name, params):\n self.name = name\n self.params = params\n self.distrib = {\n 'chisq': np.random.chisquare,\n 'exp': np.random.exponential,\n 't': np.random.standard_t,\n 'uniform': np.random.uniform\n }[self.name]\n\n def get_returns(self, size, adj_params=None, normalize=False):\n raw_distrib = self.distrib(size=size, **self.params)\n if adj_params is not None:\n raw_distrib = self._adjust(raw_distrib, adj_params)\n if normalize:\n return raw_distrib / raw_distrib.sum()\n return raw_distrib\n\n def _adjust(self, x, params):\n x = self._rescale(x, params)\n return x\n \n @staticmethod\n def _rescale(x, params):\n rng = params['max'] - params['min']\n x -= x.min()\n x /= x.max()\n x *= rng\n x += params['min']\n return x \n\n\nclass Citizen:\n def __init__(self, wealth, fraction_invested):\n self.wealth = wealth\n self.fraction_invested = fraction_invested\n\n # def update_wealth(self, returns):\n # self.wealth *= returns\n\n def get_wealth_as_fraction_of_population(self, total_wealth):\n return self.wealth / total_wealth\n\n\nclass Population:\n def __init__(self, wealth_distribution, invested_distribution):\n wealth_per_citizen = TOTAL_WEALTH * wealth\n population = [\n Citizen(amt, fraction_invested)\n for amt, fraction_invested in zip(\n wealth_per_citizen, invested_distribution)]\n\n def update_wealth(self, returns):\n pass\n \n\ndef plot_distributions(wealth, amounts_invested, returns):\n plt.subplot(311)\n plt.hist(wealth)\n plt.xlabel('Wealth')\n plt.subplot(312)\n plt.hist(amounts_invested)\n plt.xlabel('Fractions invested')\n plt.subplot(313)\n plt.hist(returns)\n plt.xlabel('Returns')\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "damiansp/completePython", "sub_path": "wealth_redistribution/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3066, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "40974352258", "text": "import pygame\nfrom pyo import *\nimport random\n\n# Initialize pygame\npygame.init()\n\n# Colors\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\nBLACK = (0, 0, 0)\n\n# Screen dimensions\nWIDTH = 400\nHEIGHT = 400\n\n# Set up the display\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\npygame.display.set_caption(\"Ultrasonic Sensor Simulation\")\n\n# Define the Sensor class\nclass Sensor:\n def __init__(self, x, y, orientation):\n self.x = x\n self.y = y\n self.orientation = orientation\n\n def draw(self):\n pygame.draw.circle(screen, GREEN, (self.x, self.y), 10)\n if self.orientation == \"horizontal\":\n pygame.draw.line(screen, GREEN, (self.x, self.y), (WIDTH, self.y), 2)\n else:\n pygame.draw.line(screen, GREEN, (self.x, self.y), (self.x, HEIGHT), 2)\n\n def detect(self, person):\n if self.orientation == \"horizontal\":\n if self.y - 5 < person.y < self.y + 5:\n return abs(self.x - person.x)\n else:\n if self.x - 5 < person.x < self.x + 5:\n return abs(self.y - person.y)\n return 400\n\n# Define the Person class\nclass Person:\n def __init__(self):\n self.x = random.randint(0, WIDTH)\n self.y = random.randint(0, HEIGHT)\n self.dx = random.choice([-3, 3])\n self.dy = random.choice([-3, 3])\n\n def move(self):\n self.x += self.dx\n self.y += self.dy\n\n # Change direction if person hits an edge\n if self.x <= 0 or self.x >= WIDTH:\n self.dx = -self.dx\n if self.y <= 0 or self.y >= HEIGHT:\n self.dy = -self.dy\n\n def draw(self):\n pygame.draw.circle(screen, RED, (self.x, self.y), 5)\n\n# Create sensors and people\nsensors = [\n Sensor(WIDTH // 4, 0, \"horizontal\"),\n Sensor(2 * WIDTH // 4, 0, \"horizontal\"),\n Sensor(3 * WIDTH // 4, 0, \"horizontal\"),\n Sensor(0, HEIGHT // 4, \"vertical\"),\n Sensor(0, 2 * HEIGHT // 4, \"vertical\"),\n Sensor(0, 3 * HEIGHT // 4, \"vertical\")\n]\npeople = [Person() for _ in range(5)]\n\nfont = pygame.font.SysFont(None, 25)\n\n# Start the pyo server\ns = Server().boot()\ns.start()\n\n# Initialize sound elements\nosc1 = Sine(freq=100, mul=0.3)\nosc2 = LFO(freq=50, type=3, mul=0.9)\nosc3 = Osc(table=SquareTable(), freq=25, mul=0.3)\ndrone_sound = osc1 + osc2 + osc3\nfm = FM(carrier=100, ratio=[0.2498, 0.2503], index=10, mul=0.3)\ndrone_sound += fm\nreverb = STRev(drone_sound, inpos=[0, 1], revtime=2, cutoff=5000, bal=0.3).out()\ndelay = Delay(reverb, delay=0.5, feedback=0.5).out()\n\ndef modulate_sound(measurements):\n osc1.setFreq(map_value(measurements[0], 0, 400, 100, 500))\n osc2.setFreq(map_value(measurements[1], 0, 400, 50, 250))\n osc3.setFreq(map_value(measurements[2], 0, 400, 25, 125))\n fm.setCarrier(map_value(measurements[3], 0, 400, 100, 500))\n reverb.setRevtime(map_value(measurements[4], 0, 400, 0.5, 5))\n delay.setDelay(map_value(measurements[5], 0, 400, 0.1, 1))\n\ndef map_value(value, in_min, in_max, out_min, out_max):\n return (value - in_min) * (out_max - out_min) / (in_max - in_min) + out_min\n\nrunning = True\nwhile running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n screen.fill(WHITE)\n\n for sensor in sensors:\n sensor.draw()\n\n measurements = []\n for person in people:\n person.move()\n person.draw()\n for sensor in sensors:\n distance = sensor.detect(person)\n measurements.append(distance)\n if distance != 400:\n text = font.render(f\"{distance} cm\", True, BLACK)\n screen.blit(text, (sensor.x + 10, sensor.y + 10))\n\n # Modulate sound based on measurements\n modulate_sound(measurements)\n\n pygame.display.flip()\n pygame.time.wait(50)\n\npygame.quit()\ns.stop()\n", "repo_name": "ageorg06/shameru", "sub_path": "simulation/simulation.py", "file_name": "simulation.py", "file_ext": "py", "file_size_in_byte": 3823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 34, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 48, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 49, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 50, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 129, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 130, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 130, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "23783084199", "text": "#!/usr/bin/env python3\n# encoding: utf-8\nimport json\nfrom time import time\nfrom urllib.parse import unquote_plus\n\nimport requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n\nfrom config import API_EP_DOUYIN, ROUTE_SIGN_DOUYIN\n\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\n\ndef get_original_url(action, args_dict, ts, device_info):\n install_id = device_info['install_id']\n device_id = device_info['device_id']\n uuid = device_info['uuid']\n openudid = device_info['openudid']\n\n args = \"\"\n # print(args_dict)\n for (idx, val) in args_dict.items():\n args += \"&{0}={1}\".format(idx, val)\n\n url = \"https://aweme.snssdk.com/aweme/\" + action + \"/?\" \\\n + args \\\n + \"&retry_type=no_retry&\" \\\n + \"iid=\" + str(install_id) \\\n + \"&device_id=\" + str(device_id) \\\n + \"&uuid=\" + str(uuid) \\\n + \"&openudid=\" + str(openudid) \\\n + \"&ts=\" + str(ts) \\\n + \"&ac=wifi&channel=wandoujia_zhiwei&aid=1128&app_name=aweme&version_code=290&version_name=2.9.0&device_platform=android&ssmix=a&device_type=ONEPLUS+A5000&device_brand=OnePlus&language=zh&os_api=28&os_version=9&manifest_version_code=290&resolution=1080*1920&dpi=420&update_version_code=2902&_rticket=1548672388498\"\n return url\n\n\ndef get_signed_url(action, args, ts, device_info, token=\"\"):\n original_url = get_original_url(action, args, ts, device_info)\n\n return sign(original_url, token=token)\n\n\ndef sign(original_url, token=\"\"):\n data = {\"url\": original_url}\n try:\n data = api_service(token=token, route=ROUTE_SIGN_DOUYIN, method=\"post\", data=json.dumps(data))\n # data = json.loads(content)\n return data.get(\"url\")\n except Exception as e:\n print(e)\n\n\ndef api_douyin(action, args, ts, device_info, token=\"\"):\n try:\n url = get_signed_url(action, args, ts, device_info, token=token)\n # print(url)\n # exit(0)\n\n resp = requests.get(url=url,\n headers={\n \"User-Agent\": \"okhttp/3.10.0.1\"},\n verify=False,\n cookies={'install_id': str(device_info['install_id'])})\n content = resp.content.decode(\"utf-8\")\n d = json.loads(content)\n return d\n except Exception as e:\n print(e)\n\n\ndef api_service(route, token=\"\", method=\"get\", data=None, content_type=\"application/json\"):\n resp = requests.request(method=method, url=\"{0}/{1}/{2}\".format(API_EP_DOUYIN, route, token), data=data,\n headers={\"Content-Type\": content_type}, verify=False)\n\n # print(resp.content)\n if token != \"\" and resp.headers.get(\"x-token\") != token:\n raise Exception(resp.headers.get(\"x-token\"))\n elif resp.headers.get(\"x-token-times\") == \"0\":\n raise Exception(resp.content)\n data = resp.content.decode(\"utf-8\")\n return json.loads(data)\n\n\n# ———————————————————— APIs ——————————————————————\n\n\ndef wrap_api(action, args, device_info={}, token=\"\"):\n try:\n ts = str(int(time()))\n data = api_douyin(action, args, ts, device_info, token=token)\n return data\n except Exception as e:\n print(e)\n\n\ndef request_dict(req):\n params = req.split(\"?\")[1]\n lp = params.split('&')\n di = {}\n for e in lp:\n k, v = e.split('=')\n di[k] = unquote_plus(v)\n\n return dict(di)\n", "repo_name": "303563041/spiders", "sub_path": "douyin-service/lib.py", "file_name": "lib.py", "file_ext": "py", "file_size_in_byte": 3512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.exceptions.InsecureRequestWarning", "line_number": 12, "usage_type": "argument"}, {"api_name": "requests.packages", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.ROUTE_SIGN_DOUYIN", "line_number": 47, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 60, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 73, "usage_type": "call"}, {"api_name": "config.API_EP_DOUYIN", "line_number": 73, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 82, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "urllib.parse.unquote_plus", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "7478009475", "text": "#!/usr/bin/env python\n# -*- coding:utf8 -*-\nimport json,time,os,sys\n\nfrom conf import settings\n# from .conf import settings\n# BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n# sys.path.append(BASE_DIR)\n\n\ndef db_handler():\n # connect to db\n conn_params = settings.DATABASE\n if conn_params['engine'] == 'file_storage':\n return file_db_handle(conn_params)\n elif conn_params['engine'] == 'mysql':\n pass\n\n\ndef file_db_handle(conn_params):\n # print('file db:',conn_params)\n return file_execute\n\n\ndef file_execute(sql,**kwargs):\n conn_params = settings.DATABASE\n db_path = '%s/%s' % (conn_params['path'], conn_params['name'])\n\n manager_conn_params = settings.MANAGER_DB\n manager_db_path = '%s/%s' % (manager_conn_params['path'], manager_conn_params['name'])\n\n # print(sql,db_path)\n sql_list = sql.split(\"where\")\n # print(sql_list)\n if sql_list[0].startswith(\"select\") and len(sql_list) > 1:\n column, val = sql_list[1].strip().split(\"=\")\n if column == 'account':\n account_file = '%s/%s.json' % (db_path, val)\n # print(account_file)\n if os.path.isfile(account_file):\n with open(account_file, \"r\") as f:\n account_data = json.load(f)\n return account_data\n else:\n exit(\"\\033[31;1mAccount [%s] does not exist!\\033[0m\" % val)\n\n elif column == 'manager':\n manager_file = '%s/%s.db' % (manager_db_path, val)\n if os.path.isfile(manager_file):\n with open(manager_file, 'r') as f:\n manager_data = json.load(f)\n return manager_data\n else:\n exit(\"\\033[31;1mAccount [%s] does not exist!\\033[0m\" % val)\n\n elif sql_list[0].startswith('update') and len(sql_list) > 1:\n column, val = sql_list[1].strip().split(\"=\")\n if column == 'account':\n account_file = '%s/%s.json' % (db_path, val)\n if os.path.isfile(account_file):\n account_data = kwargs.get(\"account_data\")\n with open(account_file,'w') as f:\n acc_data = json.dump(account_data,f)\n return True\n\n\ndef goods_execute(sql, *args, **kwargs):\n conn_params = settings.GOODS_DB\n db_path = '%s/%s' % (conn_params['path'],conn_params['name'])\n\n sql_list = sql.split()\n if sql_list[0].startswith('select') and len(sql_list) > 1:\n val = sql_list[-1].strip()\n if val == 'goods.db':\n goods_file = '%s/%s' % (db_path,val)\n if os.path.isfile(goods_file):\n with open(goods_file, \"r\",encoding='utf-8') as f:\n goods_data = json.load(f)\n return goods_data\n\n elif sql_list[0].startswith('update') and len(sql_list) > 1:\n val = sql_list[-1].strip()\n if val == 'goods.db':\n goods_file = '%s/%s' % (db_path, val)\n if os.path.isfile(goods_file):\n goods_data = args[0]\n with open(goods_file,'w', encoding='utf-8') as f:\n json.dump(goods_data, f, ensure_ascii=False, indent=4)\n return True\n\n\n\n\n\n\n", "repo_name": "huanggang45/atm", "sub_path": "core/db_handler.py", "file_name": "db_handler.py", "file_ext": "py", "file_size_in_byte": 3218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "conf.settings.DATABASE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 13, "usage_type": "name"}, {"api_name": "conf.settings.DATABASE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 26, "usage_type": "name"}, {"api_name": "conf.settings.MANAGER_DB", "line_number": 29, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 63, "usage_type": "call"}, {"api_name": "conf.settings.GOODS_DB", "line_number": 68, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 68, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "4832139029", "text": "\"\"\"\nThis file contains a number of custom fields to validate data with django forms\n\"\"\"\n\nimport datetime\nimport json\nfrom typing import Any, Optional, Union\n\nimport jsonschema\nimport os\nimport pytz\nimport re\nfrom django import forms\nfrom django.core.exceptions import ValidationError\nfrom django.core.validators import URLValidator\nfrom django.utils import timezone\nfrom django.utils.timezone import make_aware\n\nfrom .compatibility import to_timestamp, PatternType, string_types\nfrom .exceptions import FileSizeError, FileTypeError\nfrom .validators import URLValidatorWithUnderscoreDomain\n\n\nclass BaseField(forms.Field):\n \"\"\"\n All library fields are inherited from this base\n Adds source\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.source = kwargs.pop('source', None)\n super(BaseField, self).__init__(*args, **kwargs)\n\n\nclass InitialFixMixin(BaseField):\n \"\"\"\n Fixes problem with initial not returned by some form fields\n \"\"\"\n\n def clean(self, value): # type: (Any) -> Any\n value = super(InitialFixMixin, self).clean(value)\n return self.initial if value is None else value\n\n\nclass EmptyStringFixMixing(BaseField):\n \"\"\"\n Fixes some fields error, returning empty string (not None) when value is not provided\n \"\"\"\n # All empty values are considered by django as \"No value\" by default.\n # This causes a list of errors in REST model:\n # 1) JsonField and child classes don't allow to pass empty arrays and dicts if required=True\n # 2) RestCharField and child classes don't allow to pass empty string if required=True\n # 3) run_validators() method is not called if value is in empty_values, so min_length validation doesn't work\n # for RestCharField and value=''\n empty_values = [None]\n\n def to_python(self, value): # type: (Any) -> Optional[str]\n return None if value is None else super(EmptyStringFixMixing, self).to_python(value)\n\n def clean(self, value): # type: (Any) -> Optional[str]\n \"\"\"\n Returns initial value, when value is not provided\n :param value: value to clean\n :return: cleaned value\n \"\"\"\n if value is None:\n value = self.to_python(value)\n self.validate(value)\n # BUG I don't call self.run_validators() here, as they don't expect None, but empty string\n return self.initial\n else:\n return super(EmptyStringFixMixing, self).clean(value)\n\n def validate(self, value): # type: (Optional[str]) -> None\n \"\"\"\n Fixes None value validation when required is True\n :param value: value to validate\n :return: None\n \"\"\"\n if value is None and self.required:\n raise ValidationError(self.error_messages['required'], code='required')\n\n super(EmptyStringFixMixing, self).validate(value)\n\n\nclass RestCharField(EmptyStringFixMixing, forms.CharField):\n \"\"\"\n Wraps django.forms.forms.CharField:\n + Changes default value - None, not empty string\n + Fixes initial value bug (CharField returns empty string, ignoring 'initial' parameter)\n \"\"\"\n pass\n\n\nclass RegexField(RestCharField):\n \"\"\"\n Wraps CharField to validate via regular expression\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n self.regex = kwargs.pop('regex', None)\n self.flags = kwargs.pop('flags', 0)\n\n assert self.regex is None or isinstance(self.regex, (string_types, PatternType)), \\\n 'regex must be string if given'\n assert isinstance(self.flags, int), 'flags must be integer'\n\n super(RegexField, self).__init__(*args, **kwargs)\n self._match = None\n\n def validate(self, value): # type: (Optional[str]) -> None\n super(RegexField, self).validate(value)\n if value is not None and self.regex:\n self._match = re.match(self.regex, str(value), self.flags)\n if not self._match:\n raise ValidationError('Value don\\'t match regexp \"{0}\"'.format(self.regex))\n\n @property\n def match(self):\n return self._match\n\n\nclass RestChoiceField(EmptyStringFixMixing, forms.ChoiceField):\n \"\"\"\n Wraps django.forms.forms.ChoiceField:\n + Changes default value - None, not empty string\n + Fixes initial value bug (CharField returns empty string, ignoring 'initial' parameter)\n + Gives opportunity to set 'choices' as iterable of values, not iterable of tuples\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n choices = kwargs.pop('choices', None)\n if choices is not None:\n kwargs['choices'] = [ch if isinstance(ch, (list, tuple)) else (ch, ch) for ch in choices]\n\n EmptyStringFixMixing.__init__(self, *args, **kwargs)\n\n # This parameter was processed above, but was not removed\n kwargs.pop('source', None)\n\n forms.ChoiceField.__init__(self, *args, **kwargs)\n\n def validate(self, value):\n # Fixes bug with empty string passing choices\n if value is not None and self.choices and not self.valid_value(value):\n raise ValidationError(\n self.error_messages['invalid_choice'],\n code='invalid_choice',\n params={'value': value},\n )\n super(RestChoiceField, self).validate(value)\n\n\nclass RestIntegerField(InitialFixMixin, forms.IntegerField):\n \"\"\"\n Wraps django.forms.forms.IntegerField, fixing 'initial' property ignore\n \"\"\"\n pass\n\n\nclass RestFloatField(InitialFixMixin, forms.FloatField):\n \"\"\"\n Wraps django.forms.forms.FloatField, fixing 'initial' property ignore\n \"\"\"\n pass\n\n\nclass PositiveIntegerField(RestIntegerField):\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param with_zero: If true, than integer can be equal to zero, else - not.\n :param kwargs: Named arguments\n \"\"\"\n with_zero = kwargs.pop('with_zero', False)\n kwargs['min_value'] = kwargs.get('min_value', 0 if with_zero else 1)\n super(PositiveIntegerField, self).__init__(*args, **kwargs)\n\n\nclass IdField(PositiveIntegerField):\n \"\"\"\n This field validates id (integer >= 1)\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n kwargs['with_zero'] = kwargs.get('with_zero', False)\n super(IdField, self).__init__(*args, **kwargs)\n\n\nclass TimestampField(RestFloatField):\n \"\"\"\n Form field, containing timestamp integer. Converts given value to datetime.datetime object\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field.\n :param args: Positional arguments\n :param in_future: Boolean. If flag is false, validates, that given date is not greater than now\n :param kwargs: Named arguments\n \"\"\"\n in_future = kwargs.pop('in_future', True)\n\n assert type(in_future) is bool, \"in_future must be boolean\"\n self._in_future = in_future\n\n # Converts initial to timestamp, if it is given as datetime\n if 'initial' in kwargs:\n assert isinstance(kwargs['initial'], (datetime.datetime, int, float)), \\\n \"initial must be int, float or datetime.datetime instance\"\n dt = kwargs['initial']\n if isinstance(dt, datetime.datetime):\n kwargs['initial'] = to_timestamp(dt)\n\n super(TimestampField, self).__init__(*args, min_value=0, max_value=2147483647, **kwargs)\n\n def clean(self, value): # type: (Any) -> Optional[datetime.datetime]\n value = super(TimestampField, self).clean(value)\n if value == 0:\n # Fix python 3.6 issue with Invalid argument value=0\n return datetime.datetime(1970, 1, 1, tzinfo=pytz.utc)\n elif value is not None:\n dt = datetime.datetime.utcfromtimestamp(value)\n return make_aware(dt, pytz.utc)\n else:\n return value\n\n def validate(self, value): # type: (Optional[int]) -> None\n super(TimestampField, self).validate(value)\n if value is not None:\n if not self._in_future and value > to_timestamp(timezone.now()):\n raise ValidationError(\"You can't search time in future\")\n\n\nclass DateTimeField(RestCharField):\n \"\"\"\n Parses given string as datetime by given mask with datetime.datetime.strptime() method\n \"\"\"\n base_type = datetime.datetime\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param mask: Mask to parse datetime string with datetime.datetime.strptime() method\n :param kwargs: Named arguments\n \"\"\"\n mask = kwargs.pop(\"mask\", \"%Y-%m-%dT%H:%M:%S\")\n assert isinstance(mask, string_types), \"'mask' parameter must be string\"\n\n super(DateTimeField, self).__init__(*args, **kwargs)\n\n self.mask = mask\n\n def clean(self, value): # type: (Any) -> datetime.datetime\n value = super(DateTimeField, self).clean(value)\n if value is not None and not isinstance(value, self.base_type):\n try:\n dt = datetime.datetime.strptime(value, self.mask)\n except (ValueError, TypeError):\n raise ValidationError(\"Invalid value format (%s)\" % self.mask)\n\n return make_aware(dt, pytz.utc)\n else:\n return value\n\n\nclass DateField(DateTimeField):\n \"\"\"\n Parses month with datetime.datetime.strptime() method. Default format is %Y-%m.\n Returns datetime.date with first day of month.\n \"\"\"\n base_type = datetime.date\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param mask: Mask to parse month string with datetime.datetime.strptime() method\n :param kwargs: Named arguments\n \"\"\"\n mask = kwargs.pop(\"mask\", \"%Y-%m-%d\")\n super(DateField, self).__init__(*args, mask=mask, **kwargs)\n\n def clean(self, value): # type (Any) -> datetime.date\n value = super(DateField, self).clean(value)\n return value.date() if isinstance(value, datetime.datetime) else value\n\n\nclass MonthField(DateTimeField):\n \"\"\"\n Parses month with datetime.datetime.strptime() method. Default format is %Y-%m.\n Returns datetime.date with first day of month.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param mask: Mask to parse month string with datetime.datetime.strptime() method\n :param kwargs: Named arguments\n \"\"\"\n mask = kwargs.pop(\"mask\", \"%Y-%m\")\n super(MonthField, self).__init__(*args, mask=mask, **kwargs)\n\n def clean(self, value): # type (Any) -> datetime.date\n value = super(MonthField, self).clean(value)\n return value.date() if isinstance(value, datetime.datetime) else value\n\n\nclass TimezoneField(RestCharField):\n \"\"\"\n A field containing one of standard pytz timezone names\n \"\"\"\n\n def validate(self, value): # type: (Optional[str]) -> None\n super(TimezoneField, self).validate(value)\n if value and value not in pytz.all_timezones:\n raise ValidationError(\"Invalid timezone '{0}'\".format(value))\n\n\nclass DateUnitField(RestChoiceField):\n \"\"\"\n A field that validates one of date unit choices: hour, day or week\n \"\"\"\n UNIT_CHOICES = ('hour', 'day', 'week')\n\n def __init__(self, *args, **kwargs):\n super(DateUnitField, self).__init__(*args, choices=self.UNIT_CHOICES, **kwargs)\n\n\nclass RestBooleanField(RestCharField):\n \"\"\"\n Standard BooleanField is based on /django/forms/widgets.py CheckboxInput.value_from_datadict(value)\n It works improperly for REST model: required=True + value=False => ValidationError\n This filed fixes this issue, giving opportunity to send (required or not):\n + None as default value\n + 'false', '0', '' (ignoring case) as False\n + Everything else is parsed with standard bool function\n \"\"\"\n\n def to_python(self, value): # type: (Any) -> Optional[bool]\n \"\"\"Returns a Python boolean object.\"\"\"\n if value is None:\n return None\n elif isinstance(value, string_types) and value.lower() in ('false', '0', ''):\n value = False\n else:\n value = bool(value)\n return value\n\n\nclass LowerCaseEmailField(forms.EmailField):\n \"\"\"\n Wraps django.forms.forms.EmailField:\n + Converts all emails to lowercase\n + Fixes initial value bug (CharField returns empty string, ignoring 'initial' parameter)\n + Changes default value - None, not empty string\n \"\"\"\n\n def to_python(self, value): # type: (Any) -> Optional[str]\n return value.lower() if isinstance(value, string_types) else value\n\n def clean(self, value):\n if value is not None:\n return forms.EmailField.clean(self, value)\n elif self.required:\n raise ValidationError(self.error_messages['required'], code='required')\n else:\n return self.initial\n\n\nclass ColorField(RestCharField):\n \"\"\"\n This field validates color as six hex characters\n \"\"\"\n\n def validate(self, value): # type: (Optional[str]) -> None\n super(ColorField, self).validate(value)\n if value and not re.match('^[0-9a-f]{6}$', value):\n raise ValidationError(\"Color '{0}' is invalid\".format(value))\n\n\nclass TruncatedCharField(RestCharField):\n \"\"\"\n This field truncates string by given length\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param truncate_length: A positive integer, setting number of symbols to truncate input\n :param kwargs: Named arguments\n \"\"\"\n truncate_length = kwargs.pop('truncate_length', 255)\n assert truncate_length is None or type(truncate_length) is int and truncate_length > 0, \\\n \"'truncate_length' parameter must be positive integer\"\n\n self._truncate_length = truncate_length\n\n super(TruncatedCharField, self).__init__(*args, **kwargs)\n\n def to_python(self, value): # type: (Any) -> Optional[str]\n if value and self._truncate_length is not None:\n value = value[:self._truncate_length]\n return super(TruncatedCharField, self).to_python(value)\n\n\nclass JsonField(RestCharField):\n \"\"\"\n This field Json serialized string with jsonschema\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param json_schema: jsonschema library validation schema\n :param kwargs: Named arguments\n \"\"\"\n\n self._json_schema = kwargs.pop('json_schema', None)\n super(JsonField, self).__init__(*args, **kwargs)\n\n def to_python(self, value): # type: (Any) -> Optional[Union[dict, list]]\n if value is None or isinstance(value, (list, dict)):\n return value\n elif isinstance(value, string_types):\n try:\n return json.loads(value)\n except Exception as e:\n raise ValidationError(\"Json was not parsed [{0}]\".format(str(e)))\n else:\n raise ValidationError(\"Invalid JSON value [{0}]\".format(str(value)))\n\n def validate(self, value): # type: (Optional[Union[dict, list]]) -> None\n super(JsonField, self).validate(value)\n if self._json_schema and value is not None:\n try:\n jsonschema.validate(value, self._json_schema)\n except jsonschema.exceptions.ValidationError as ex:\n raise ValidationError(ex.message)\n\n\nclass ArrayField(JsonField):\n \"\"\"\n Field, that parses array from string. It can be passed in 2 forms:\n + strings, split by ',' symbol\n + json-encoded array\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Initializes field\n :param args: Positional arguments\n :param item_schema: jsonschema dict to validate each item of the array\n :param min_items: Minimum number of items in the array\n :param max_items: Maximum number of items in the array\n :param kwargs: Named arguments\n \"\"\"\n item_schema = kwargs.pop('item_schema', None)\n min_items = kwargs.pop('min_items', 0)\n max_items = kwargs.pop('max_items', None)\n json_schema = {\n \"type\": \"array\",\n \"minItems\": min_items\n }\n if max_items:\n json_schema['maxItems'] = max_items\n\n if item_schema:\n json_schema['items'] = item_schema\n\n kwargs['json_schema'] = json_schema\n super(ArrayField, self).__init__(*args, **kwargs)\n\n def to_python(self, value): # type: (Any) -> Optional[list]\n if value is None or isinstance(value, list):\n return super(ArrayField, self).to_python(value)\n elif isinstance(value, dict):\n raise ValidationError('Value is expected to be JSON array, not object')\n elif isinstance(value, string_types):\n if value.startswith('[') or value.endswith(']'):\n return super(ArrayField, self).to_python(value)\n elif value.startswith('{') and value.endswith('}'):\n raise ValidationError('Value is expected to be JSON array, not object')\n else:\n # Comma separated list\n data = value.split(',')\n item_schema = self._json_schema.get('items') if self._json_schema else None\n if item_schema and item_schema.get('type') == 'integer':\n try:\n return [int(item) for item in data]\n except (ValueError, TypeError):\n raise ValidationError('Array of integers')\n else:\n return data\n else:\n raise ValidationError(\"Invalid JSON value [{0}]\".format(str(value)))\n\n\nclass IdArrayField(ArrayField):\n \"\"\"\n Field, that parses array of ids from string. Each element is cleaned by IdField()\n \"\"\"\n\n def clean(self, value):\n if isinstance(value, set):\n value = list(value)\n res = super(IdArrayField, self).clean(value)\n if isinstance(res, list):\n id_field = IdField()\n res = [id_field.clean(item) for item in res]\n return res\n\n\nclass IdSetField(IdArrayField):\n \"\"\"\n Field, that parses set of ids from string. Duplicated ids are removed.\n Each element is cleaned by IdField()\n \"\"\"\n\n def clean(self, value):\n res = super(IdSetField, self).clean(value)\n if isinstance(res, list):\n res = set(res)\n return res\n\n\nclass UrlField(RegexField):\n \"\"\"\n Field validates url string\n \"\"\"\n\n def __init__(self, *args, with_underscore_domain=True, **kwargs): # type (bool) -> None\n super(UrlField, self).__init__(*args, **kwargs)\n self.with_underscore_domain = with_underscore_domain\n\n def to_python(self, value): # type: (Any) -> Optional[str]\n if isinstance(value, string_types):\n value = value.strip()\n return super(UrlField, self).to_python(value)\n\n def validate(self, value): # type: (Optional[str]) -> None\n super(UrlField, self).validate(value)\n if isinstance(value, string_types):\n validator_cls = URLValidatorWithUnderscoreDomain if self.with_underscore_domain else URLValidator\n v = validator_cls(schemes=['http', 'https'])\n v(value)\n\n\nclass HexField(RestCharField):\n \"\"\"\n Field validates hexadecimal string\n \"\"\"\n\n def validate(self, value): # type: (Optional[str]) -> None\n super(HexField, self).validate(value)\n if isinstance(value, string_types) and not re.match(r'[a-z0-9]*', value):\n raise ValidationError(\"Field can contain only hexadecimal small characters (a-z, 0-9)\")\n\n\nclass UUIDField(RegexField):\n \"\"\"\n Field validates correct UUID string\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n kwargs['regex'] = r\"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$\"\n super(UUIDField, self).__init__(*args, **kwargs)\n\n\nclass FileField(forms.FileField):\n \"\"\"\n Wraps django.forms.forms.FileField, adding:\n + file size validation\n + file extension validation\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n max_size = kwargs.pop('max_size', None)\n valid_extensions = kwargs.pop('valid_extensions', None)\n\n self.valid_extensions = valid_extensions\n self.size = max_size\n\n if self.valid_extensions is not None:\n assert isinstance(self.valid_extensions, (tuple, set, list)), \"valid_extensions must be tuple, list or set\"\n assert len(self.valid_extensions) > 0, \"valid_extensions can't be empty, if given\"\n\n if self.size is not None:\n assert type(self.size) is int and self.size > 0, \"size must be positive integer\"\n\n super(FileField, self).__init__(*args, **kwargs)\n\n def validate(self, value): # type (IO) -> None\n super(FileField, self).validate(value)\n if self.valid_extensions is not None and value:\n ext = os.path.splitext(value.name)[1][1:].lower()\n if ext not in self.valid_extensions:\n raise FileTypeError(ext, self.valid_extensions)\n\n if self.size is not None and value:\n if value.size > self.size:\n raise FileSizeError(self.size, value.size)\n\n def clean(self, data, initial=None):\n # FileField provides InitialFixMixin functionality with special parameter\n return super(FileField, self).clean(data, initial=initial if initial is not None else self.initial)\n", "repo_name": "M1ha-Shvn/django-rest-form-fields", "sub_path": "src/django_rest_form_fields/fields.py", "file_name": "fields.py", "file_ext": "py", "file_size_in_byte": 21729, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.forms.Field", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 81, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 86, "usage_type": "name"}, {"api_name": "compatibility.string_types", "line_number": 104, "usage_type": "name"}, {"api_name": "compatibility.PatternType", "line_number": 104, "usage_type": "name"}, {"api_name": "re.match", "line_number": 114, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 116, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 123, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 123, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField.__init__", "line_number": 141, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 141, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 141, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 146, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 154, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 154, "usage_type": "name"}, {"api_name": "django.forms.FloatField", "line_number": 161, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 161, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 210, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 213, "usage_type": "attribute"}, {"api_name": "compatibility.to_timestamp", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 222, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 222, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 224, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 224, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.make_aware", "line_number": 225, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 225, "usage_type": "attribute"}, {"api_name": "compatibility.to_timestamp", "line_number": 232, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 232, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 232, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 240, "usage_type": "attribute"}, {"api_name": "compatibility.string_types", "line_number": 250, "usage_type": "argument"}, {"api_name": "datetime.datetime.strptime", "line_number": 260, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 260, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 262, "usage_type": "call"}, {"api_name": "django.utils.timezone.make_aware", "line_number": 264, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 264, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 274, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 288, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pytz.all_timezones", "line_number": 319, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 320, "usage_type": "call"}, {"api_name": "compatibility.string_types", "line_number": 347, "usage_type": "argument"}, {"api_name": "django.forms.EmailField", "line_number": 354, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 354, "usage_type": "name"}, {"api_name": "compatibility.string_types", "line_number": 363, "usage_type": "argument"}, {"api_name": "django.forms.EmailField.clean", "line_number": 367, "usage_type": "call"}, {"api_name": "django.forms.EmailField", "line_number": 367, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 367, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 369, "usage_type": "call"}, {"api_name": "re.match", "line_number": 381, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 382, "usage_type": "call"}, {"api_name": "compatibility.string_types", "line_number": 430, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 432, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 434, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 436, "usage_type": "call"}, {"api_name": "jsonschema.validate", "line_number": 442, "usage_type": "call"}, {"api_name": "jsonschema.exceptions", "line_number": 443, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 444, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 483, "usage_type": "call"}, {"api_name": "compatibility.string_types", "line_number": 484, "usage_type": "argument"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 488, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 497, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 501, "usage_type": "call"}, {"api_name": "compatibility.string_types", "line_number": 542, "usage_type": "argument"}, {"api_name": "compatibility.string_types", "line_number": 548, "usage_type": "argument"}, {"api_name": "validators.URLValidatorWithUnderscoreDomain", "line_number": 549, "usage_type": "name"}, {"api_name": "django.core.validators.URLValidator", "line_number": 549, "usage_type": "name"}, {"api_name": "compatibility.string_types", "line_number": 561, "usage_type": "argument"}, {"api_name": "re.match", "line_number": 561, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 562, "usage_type": "call"}, {"api_name": "django.forms.FileField", "line_number": 575, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 575, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 601, "usage_type": "call"}, {"api_name": "os.path", "line_number": 601, "usage_type": "attribute"}, {"api_name": "exceptions.FileTypeError", "line_number": 603, "usage_type": "call"}, {"api_name": "exceptions.FileSizeError", "line_number": 607, "usage_type": "call"}]} +{"seq_id": "23271650634", "text": "import os\nfrom os import getenv\nimport pytz\nfrom sqlalchemy.orm import Session\nfrom Database import models\nfrom fastapi import HTTPException, status\nfrom sqlalchemy.sql import text\nfrom datetime import datetime\nimport json\n\n\ndef joinRoom(roomId, specialFields, tokenData, db: Session):\n roomInfo = db.execute(text(f\"\"\"\n SELECT ownerId, waitingRoomEnabled, visibility, name, specialFields\n FROM Rooms \n WHERE id = {roomId}\n \"\"\")).fetchone()\n waitingRoomEnabled = roomInfo[1]\n roomName = roomInfo[3]\n specialFieldsDB = json.loads(roomInfo[4])\n\n if not roomInfo:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f\"Invalid Room.\")\n\n if roomInfo[0] == tokenData['userId']:\n raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f\"Cannot join your own room.\")\n\n if roomInfo[2] == \"hidden\":\n raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f\"Unable to join room.\")\n\n roomMemberInfo = db.execute(text(f\"\"\"\n SELECT inWaitingRoom, isRejected \n FROM RoomMembers \n WHERE roomId = {roomId} AND userId = {tokenData['userId']};\n \"\"\")).fetchone()\n\n if roomMemberInfo:\n if roomMemberInfo[1]:\n raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f\"Unable to join room.\")\n\n if roomMemberInfo[0]:\n raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f\"You are already in waiting room.\")\n\n if len(specialFieldsDB) != len(specialFields):\n return {\"specialFields\": specialFieldsDB}\n\n newMember = models.RoomMembers(\n roomId = roomId,\n userId = tokenData['userId'],\n joinedAt = datetime.now(pytz.timezone('Asia/Kolkata')),\n inWaitingRoom = waitingRoomEnabled,\n isRejected = False,\n specialFields = specialFields\n )\n\n db.add(newMember)\n db.commit()\n db.refresh(newMember)\n\n return {\n \"enrolledRooms\": getEnrolledRooms(tokenData, db)['enrolledRooms'],\n \"roomName\": roomName,\n \"waitingRoomEnabled\": waitingRoomEnabled\n }\n\n\ndef getEnrolledRooms(tokenData, db: Session):\n enrolledRooms = db.execute(text(f\"\"\"\n SELECT RM.roomId, R.name\n FROM RoomMembers RM\n LEFT JOIN Rooms R on R.id = RM.roomId\n WHERE RM.userId = {tokenData['userId']} AND RM.inWaitingRoom = FALSE AND RM.isRejected = FALSE\n AND R.visibility <> \"hidden\"\n GROUP BY RM.roomId\n \"\"\")).fetchall()\n\n data = []\n for room in enrolledRooms:\n questions = db.execute(text(f\"\"\"\n SELECT id, _type FROM Questions \n WHERE isVisible = TRUE AND roomId = {room[0]};\n \"\"\")).fetchall()\n\n submitted = 0\n for question in questions:\n if(question[1] == \"code\"):\n submitted += min(\n db.execute(text(f\"\"\"\n SELECT COUNT(*) FROM CodeSubmissions \n WHERE questionId = {question[0]} AND userId = {tokenData['userId']} ;\n \"\"\")).fetchone()[0], 1)\n else:\n submitted += min(\n db.execute(text(f\"\"\"\n SELECT COUNT(*) FROM FileSubmissions \n WHERE questionId = {question[0]} AND userId = {tokenData['userId']} ;\n \"\"\")).fetchone()[0], 1)\n\n\n data.append({\n \"roomId\": room[0],\n \"roomName\": room[1],\n \"questions\": len(questions),\n \"submitted\": submitted\n })\n\n return {\"enrolledRooms\": data}\n\n\ndef getEnrolledRoomById(roomId, tokenData, db: Session):\n roomData = db.execute(text(f\"\"\"\n SELECT id, userId, inWaitingRoom, isRejected \n FROM RoomMembers\n WHERE roomId={roomId} AND userId={tokenData['userId']} AND isRejected = FALSE AND inWaitingRoom = FALSE\n \"\"\")).fetchone()\n\n if not roomData:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"Room not found.\")\n\n extraInfo = db.execute(text(f\"\"\"\n SELECT R.ownerId, R.name, U.fname,U.lname \n FROM Rooms R JOIN Users U on R.ownerId = U.id\n WHERE R.id = {roomId}\n GROUP BY R.ownerId\n \"\"\")).fetchone()\n\n roomQuestions = db.execute(text(f\"\"\"\n SELECT id, title, endTime, _type\n FROM Questions Q \n WHERE roomId = {roomId} AND isVisible = TRUE\n \"\"\")).fetchall()\n\n questions = []\n for row in roomQuestions:\n if row[3] == \"code\":\n submissionTime = db.execute(text(f\"\"\"\n SELECT submittedAt from CodeSubmissions \n WHERE userId = {tokenData['userId']} AND questionId = {row[0]}\n \"\"\")).fetchone()\n else:\n submissionTime = db.execute(text(f\"\"\"\n SELECT submittedAt from FileSubmissions \n WHERE userId = {tokenData['userId']} AND questionId = {row[0]}\n \"\"\")).fetchone()\n\n questions.append({\n \"questionId\": row[0],\n \"title\": row[1],\n \"endTime\": row[2],\n \"_type\": row[3],\n \"isSubmitted\": False if not submissionTime else True,\n \"submissionTime\": None if not submissionTime else submissionTime[0]\n })\n\n roomInfo = {\n \"roomId\": roomId,\n \"host\": extraInfo[2] + \" \" + extraInfo[3],\n \"roomName\": extraInfo[1]\n }\n\n return {\"roomInfo\": roomInfo, \"questions\": questions}\n\n\ndef leaveRoom(roomId, tokenData, db: Session):\n questions = db.execute(text(f\"\"\"\n SELECT id, _type FROM Questions \n WHERE roomId={roomId}\n \"\"\")).fetchall()\n\n for question in questions:\n\n if question[1] == \"code\":\n db.execute(text(f\"\"\"\n DELETE FROM CodeSubmissions\n WHERE questionId={question[0]} AND userId={tokenData['userId']}\n \"\"\"))\n db.execute(text(f\"\"\"\n DELETE FROM SavedCodes\n WHERE questionId={question[0]} AND userId={tokenData['userId']}\n \"\"\"))\n else:\n sub = db.execute(text(f\"\"\"\n SELECT id FROM FileSubmissions\n WHERE questionId={question[0]} AND userId={tokenData['userId']}\n \"\"\")).fetchone()\n\n if sub:\n os.remove(getenv(\"BASE_PATH\") + f\"/SavedFiles/Q_{question[0]}/SID_{sub[0]}.pdf\")\n db.execute(text(f\"\"\"\n DELETE FROM FileSubmissions\n WHERE id={sub[0]}\n \"\"\"))\n\n db.execute(text(f\"\"\"\n DELETE FROM RoomMembers\n WHERE roomId={roomId} AND userId={tokenData['userId']}\n \"\"\"))\n\n db.commit()\n\n return True\n\n", "repo_name": "Shlok-Zanwar/CodeRooms-Backend", "sub_path": "Functions/EnrolledFunctions.py", "file_name": "EnrolledFunctions.py", "file_ext": "py", "file_size_in_byte": 7055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sqlalchemy.orm.Session", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 23, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 23, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 23, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 26, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_406_NOT_ACCEPTABLE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 26, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 29, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_406_NOT_ACCEPTABLE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 29, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 31, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 39, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_406_NOT_ACCEPTABLE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 39, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 42, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_406_NOT_ACCEPTABLE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 42, "usage_type": "name"}, {"api_name": "Database.models.RoomMembers", "line_number": 47, "usage_type": "call"}, {"api_name": "Database.models", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 67, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 79, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 88, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 110, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 111, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 118, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 118, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 118, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 120, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 127, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 136, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 141, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 164, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 165, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 173, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 177, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 182, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 188, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 188, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 189, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.text", "line_number": 194, "usage_type": "call"}]} +{"seq_id": "18332958671", "text": "import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\n\nfrom collections import defaultdict\n\n\nclass UnionFind:\n def __init__(self, n):\n # 親要素のノード番号を格納　xが根のとき-(サイズ)を格納\n self.par = [-1 for i in range(n)]\n self.n = n\n self.roots = set(range(n))\n self.group_num = n\n self.members = defaultdict(set)\n\n for i in range(n):\n self.members[i].add(i)\n\n def find(self, x):\n # 根ならその番号を返す\n if self.par[x] < 0:\n return x\n else:\n # 親の親は親\n self.par[x] = self.find(self.par[x])\n return self.par[x]\n\n def is_same(self, x, y):\n # 根が同じならTrue\n return self.find(x) == self.find(y)\n\n def unite(self, x, y):\n x = self.find(x)\n y = self.find(y)\n if x == y: return\n\n # 木のサイズを比較し、小さいほうから大きいほうへつなぐ\n if self.par[x] > self.par[y]:\n x, y = y, x\n\n self.group_num -= 1\n self.roots.discard(y)\n assert self.group_num == len(self.roots)\n\n self.members[x] |= self.members[y]\n self.members[y] = set()\n\n self.par[x] += self.par[y]\n self.par[y] = x\n\n def size(self, x):\n return -self.par[self.find(x)]\n\n def get_members(self, x):\n root = self.find(x)\n return self.members[root]\n\n def get_roots(self):\n return self.roots\n\n def group_count(self):\n return len(self.roots)\n\n def all_group_members(self):\n return self.members\n\n def __repr__(self):\n return '\\n'.join('{}: {}'.format(r, self.members[r]) for r in self.roots)\n\n\ndef main():\n H, W = NMI()\n G = [SI() for _ in range(H)]\n uf = UnionFind(H*W)\n\n def f(h, w):\n return h * W + w\n\n DH = [0, 0, 1, -1]\n DW = [1, -1, 0, 0]\n\n for now_h in range(H):\n for now_w in range(W):\n if G[now_h][now_w] == \"#\":\n continue\n\n i = f(now_h, now_w)\n\n for dh, dw in zip(DH, DW):\n goto_h = now_h + dh\n goto_w = now_w + dw\n\n if goto_h < 0 or goto_h >= H or goto_w < 0 or goto_w >= W:\n continue\n if G[goto_h][goto_w] == \"#\":\n continue\n\n gi = f(goto_h, goto_w)\n uf.unite(i, gi)\n\n R = set()\n for now_h in range(H):\n for now_w in range(W):\n if G[now_h][now_w] == \"#\":\n continue\n\n i = f(now_h, now_w)\n R.add(uf.find(i))\n\n\n ans = 0\n for now_h in range(H):\n for now_w in range(W):\n if G[now_h][now_w] == \".\":\n continue\n\n adj = set()\n for dh, dw in zip(DH, DW):\n goto_h = now_h + dh\n goto_w = now_w + dw\n\n if goto_h < 0 or goto_h >= H or goto_w < 0 or goto_w >= W:\n continue\n if G[goto_h][goto_w] == \".\":\n adj.add(uf.find(f(goto_h, goto_w)))\n\n if len(adj) == len(R):\n ans += 1\n\n print(ans)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Mao-beta/AtCoder", "sub_path": "PAST04/PAST04G.py", "file_name": "PAST04G.py", "file_ext": "py", "file_size_in_byte": 3679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 13, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "29225836517", "text": "from decimal import Decimal\n\nimport pytest\n\nfrom maps_adv.adv_store.api.proto.billing_pb2 import Money\nfrom maps_adv.adv_store.api.proto.campaign_list_pb2 import CampaignList, CampaignListItem\nfrom maps_adv.adv_store.api.proto.campaign_pb2 import PublicationEnv\nfrom maps_adv.adv_store.api.proto.campaign_status_pb2 import CampaignStatus\nfrom maps_adv.adv_store.api.proto.order_pb2 import OrdersInput\nfrom maps_adv.adv_store.api.schemas.enums import (\n CampaignStatusEnum,\n FixTimeIntervalEnum,\n PlatformEnum,\n PublicationEnvEnum,\n)\nfrom maps_adv.adv_store.v2.tests import dt\nfrom maps_adv.common.helpers.enums import CampaignTypeEnum\n\npytestmark = [pytest.mark.asyncio]\n\nurl = \"/campaigns/list/\"\n\n\nasync def test_returns_campaigns_for_all_requested_orders(factory, api):\n campaign1_id = (await factory.create_campaign(order_id=1111))[\"id\"]\n campaign2_id = (await factory.create_campaign(order_id=1111))[\"id\"]\n campaign3_id = (await factory.create_campaign(manul_order_id=2222))[\"id\"]\n campaign4_id = (await factory.create_campaign(manul_order_id=4444))[\"id\"]\n # noise\n await factory.create_campaign(manul_order_id=4040)\n await factory.create_campaign(order_id=1010)\n\n input_pb = OrdersInput(order_ids=[1111, 9999], manul_order_ids=[2222, 4444, 8888])\n\n got = await api.post(\n url, proto=input_pb, decode_as=CampaignList, expected_status=200\n )\n\n assert sorted([campaign.id for campaign in got.campaigns]) == sorted(\n [campaign1_id, campaign2_id, campaign3_id, campaign4_id]\n )\n\n\n@pytest.mark.parametrize(\n \"billing_extra, expected_budget_value\",\n (\n (\n {\n \"cpm\": {\n \"cost\": Decimal(\"10.1234\"),\n \"budget\": Decimal(\"500\"),\n \"daily_budget\": Decimal(\"1000\"),\n \"auto_daily_budget\": False,\n }\n },\n {\"budget\": Money(value=5000000)},\n ),\n (\n {\n \"cpa\": {\n \"cost\": Decimal(\"10.1234\"),\n \"budget\": Decimal(\"300\"),\n \"daily_budget\": Decimal(\"1000\"),\n \"auto_daily_budget\": False,\n }\n },\n {\"budget\": Money(value=3000000)},\n ),\n (\n {\n \"fix\": {\n \"time_interval\": FixTimeIntervalEnum.DAILY,\n \"cost\": Decimal(\"10.1234\"),\n }\n },\n {},\n ),\n ),\n)\nasync def test_returns_data_for_all_campaign_budget_types(\n billing_extra, expected_budget_value, factory, api\n):\n campaign_id = (await factory.create_campaign(order_id=4242, **billing_extra))[\"id\"]\n\n input_pb = OrdersInput(order_ids=[4242])\n\n got = await api.post(\n url, proto=input_pb, decode_as=CampaignList, expected_status=200\n )\n\n assert got == CampaignList(\n campaigns=[\n CampaignListItem(\n id=campaign_id,\n name=\"campaign0\",\n start_datetime=dt(\"2019-01-01 00:00:00\", as_proto=True),\n end_datetime=dt(\"2019-02-01 00:00:00\", as_proto=True),\n publication_envs=[PublicationEnv.Enum.Value(\"DATA_TESTING\")],\n timezone=\"UTC\",\n status=CampaignStatus.Enum.Value(\"DRAFT\"),\n **expected_budget_value,\n )\n ]\n )\n\n\n@pytest.mark.real_db\n@pytest.mark.parametrize(\"orders_extra\", [{\"order_id\": 4242}, {\"manul_order_id\": 5555}])\nasync def test_returns_last_status_for_campaigns_in_order(orders_extra, factory, api):\n campaign1_id = (await factory.create_campaign(**orders_extra))[\"id\"]\n await factory.set_status(campaign_id=campaign1_id, status=CampaignStatusEnum.ACTIVE)\n await factory.set_status(campaign_id=campaign1_id, status=CampaignStatusEnum.DONE)\n\n input_pb = OrdersInput(order_ids=[4242], manul_order_ids=[5555])\n\n got = await api.post(\n url, proto=input_pb, decode_as=CampaignList, expected_status=200\n )\n\n assert got.campaigns[0].status == CampaignStatus.Enum.Value(\"DONE\")\n\n\n@pytest.mark.parametrize(\n \"order_extra, input_pb\",\n [\n ({\"order_id\": 4242}, OrdersInput(manul_order_ids=[4242])),\n ({\"manul_order_id\": 4242}, OrdersInput(order_ids=[4242])),\n ],\n)\nasync def test_does_not_return_campaigns_of_another_order_type(\n order_extra, input_pb, factory, api\n):\n await factory.create_campaign(**order_extra)\n\n got = await api.post(\n url, proto=input_pb, decode_as=CampaignList, expected_status=200\n )\n\n assert got == CampaignList(campaigns=[])\n\n\n@pytest.mark.real_db\nasync def test_returns_campaigns_data(factory, api):\n campaign_creation_kwargs = dict(\n name=\"campaign_name\",\n author_id=123,\n publication_envs=[PublicationEnvEnum.DATA_TESTING],\n campaign_type=CampaignTypeEnum.ZERO_SPEED_BANNER,\n start_datetime=dt(\"2019-03-01 00:00:00\"),\n end_datetime=dt(\"2019-06-01 00:00:00\"),\n timezone=\"UTC\",\n platforms=[PlatformEnum.METRO],\n order_id=4242,\n cpa={\n \"cost\": Decimal(\"10.1234\"),\n \"budget\": Decimal(\"500\"),\n \"daily_budget\": Decimal(\"1000\"),\n \"auto_daily_budget\": False,\n },\n datatesting_expires_at=dt(\"2019-04-01 00:00:00\"),\n )\n campaign_id = (await factory.create_campaign(**campaign_creation_kwargs))[\"id\"]\n await factory.set_status(\n campaign_id=campaign_id, status=CampaignStatusEnum.REJECTED\n )\n\n input_pb = OrdersInput(order_ids=[4242])\n\n got = await api.post(\n url, proto=input_pb, decode_as=CampaignList, expected_status=200\n )\n\n assert got == CampaignList(\n campaigns=[\n CampaignListItem(\n id=campaign_id,\n name=\"campaign_name\",\n start_datetime=dt(\"2019-03-01 00:00:00\", as_proto=True),\n end_datetime=dt(\"2019-06-01 00:00:00\", as_proto=True),\n publication_envs=[PublicationEnv.Enum.Value(\"DATA_TESTING\")],\n timezone=\"UTC\",\n status=CampaignStatus.Enum.Value(\"REJECTED\"),\n budget=Money(value=5000000),\n datatesting_expires_at=dt(\"2019-04-01 00:00:00\", as_proto=True),\n )\n ]\n )\n", "repo_name": "Alexander-Berg/2022-tests-examples", "sub_path": "maps/tests/api/campaigns/test_list_campaigns_for_orders.py", "file_name": "test_list_campaigns_for_orders.py", "file_ext": "py", "file_size_in_byte": 6303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pytest.mark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.order_pb2.OrdersInput", "line_number": 33, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 36, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.order_pb2.OrdersInput", "line_number": 85, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 88, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 91, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignListItem", "line_number": 93, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 96, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 97, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_pb2.PublicationEnv.Enum.Value", "line_number": 98, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_pb2.PublicationEnv.Enum", "line_number": 98, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_pb2.PublicationEnv", "line_number": 98, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus.Enum.Value", "line_number": 100, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus.Enum", "line_number": 100, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus", "line_number": 100, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 44, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 50, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 51, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 52, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.billing_pb2.Money", "line_number": 56, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 61, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 62, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 63, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.billing_pb2.Money", "line_number": 67, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.FixTimeIntervalEnum.DAILY", "line_number": 72, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.FixTimeIntervalEnum", "line_number": 72, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 73, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.CampaignStatusEnum.ACTIVE", "line_number": 111, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.CampaignStatusEnum", "line_number": 111, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.CampaignStatusEnum.DONE", "line_number": 112, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.CampaignStatusEnum", "line_number": 112, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.order_pb2.OrdersInput", "line_number": 114, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 117, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus.Enum.Value", "line_number": 120, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus.Enum", "line_number": 120, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus", "line_number": 120, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 108, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 108, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 136, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 139, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 123, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 123, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.order_pb2.OrdersInput", "line_number": 126, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.order_pb2.OrdersInput", "line_number": 127, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.PublicationEnvEnum.DATA_TESTING", "line_number": 147, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.PublicationEnvEnum", "line_number": 147, "usage_type": "name"}, {"api_name": "maps_adv.common.helpers.enums.CampaignTypeEnum.ZERO_SPEED_BANNER", "line_number": 148, "usage_type": "attribute"}, {"api_name": "maps_adv.common.helpers.enums.CampaignTypeEnum", "line_number": 148, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 149, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 150, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.PlatformEnum.METRO", "line_number": 152, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.PlatformEnum", "line_number": 152, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 155, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 156, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 157, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 160, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.CampaignStatusEnum.REJECTED", "line_number": 164, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.schemas.enums.CampaignStatusEnum", "line_number": 164, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.order_pb2.OrdersInput", "line_number": 167, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 170, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignList", "line_number": 173, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_list_pb2.CampaignListItem", "line_number": 175, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 178, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 179, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_pb2.PublicationEnv.Enum.Value", "line_number": 180, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_pb2.PublicationEnv.Enum", "line_number": 180, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_pb2.PublicationEnv", "line_number": 180, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus.Enum.Value", "line_number": 182, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus.Enum", "line_number": 182, "usage_type": "attribute"}, {"api_name": "maps_adv.adv_store.api.proto.campaign_status_pb2.CampaignStatus", "line_number": 182, "usage_type": "name"}, {"api_name": "maps_adv.adv_store.api.proto.billing_pb2.Money", "line_number": 183, "usage_type": "call"}, {"api_name": "maps_adv.adv_store.v2.tests.dt", "line_number": 184, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 142, "usage_type": "attribute"}]} +{"seq_id": "32965466369", "text": "import copy\nimport os\nimport yaml\n\nfrom swell.tasks.base.task_base import taskBase\nfrom swell.utilities.dictionary import update_dict\nfrom swell.utilities.run_jedi_executables import jedi_dictionary_iterator, run_executable\n\n\n# --------------------------------------------------------------------------------------------------\n\n\nclass RunJediUfoTestsExecutable(taskBase):\n\n # ----------------------------------------------------------------------------------------------\n\n def execute(self):\n\n # Jedi application name\n # ---------------------\n jedi_application = 'ufo_tests'\n\n # Parse configuration\n # -------------------\n window_offset = self.config.window_offset()\n window_length = self.config.window_length()\n bkg_time_offset = self.config.background_time_offset()\n observations = self.config.observations()\n generate_yaml_and_exit = self.config.generate_yaml_and_exit(False)\n\n # Compute data assimilation window parameters\n window_begin = self.da_window_params.window_begin(window_offset)\n window_begin_iso = self.da_window_params.window_begin_iso(window_offset)\n window_end_iso = self.da_window_params.window_end_iso(window_offset, window_length)\n\n # Populate jedi interface templates dictionary\n # --------------------------------------------\n background_time = self.da_window_params.background_time(window_offset, bkg_time_offset)\n self.jedi_rendering.add_key('window_begin_iso', window_begin_iso)\n self.jedi_rendering.add_key('window_end_iso', window_end_iso)\n\n # Observations\n self.jedi_rendering.add_key('background_time', background_time)\n self.jedi_rendering.add_key('crtm_coeff_dir', self.config.crtm_coeff_dir(None))\n self.jedi_rendering.add_key('window_begin', window_begin)\n\n # Open the JEDI config file and fill initial templates\n # ----------------------------------------------------\n jedi_config_dict = self.jedi_rendering.render_oops_file(f'{jedi_application}')\n\n # Perform complete template rendering\n # -----------------------------------\n jedi_dictionary_iterator(jedi_config_dict, self.jedi_rendering, '3D', observations)\n\n # Make modifications needed for testing\n # -------------------------------------\n conventional_types = [\n 'aircraft',\n 'pibal',\n 'satwind',\n 'scatwind',\n 'sfcship',\n 'sfc',\n 'sondes'\n ]\n\n # Open the ufo_tests config file\n # ------------------------------\n ufo_tests_dict = self.jedi_rendering.render_interface_observations(f'ufo_tests')\n ufo_tests_default = ufo_tests_dict['default']\n\n # Insert the GeoVaLs section\n # --------------------------\n\n # Loop over the observations\n for index in range(len(observations)):\n\n # Remove GetValues if present\n if 'get values' in jedi_config_dict['observations'][index]:\n del jedi_config_dict['observations'][index]['get values']\n\n # GeoVaLs filename\n geo_va_ls_fname = os.path.join(self.cycle_dir(),\n f'{observations[index]}_geovals.{window_begin}.nc4')\n\n # Create GeoVaLs dictionary\n geo_va_ls_dict = {}\n geo_va_ls_dict['filename'] = geo_va_ls_fname\n\n # For conventional add the GeoVaLs flip\n if observations[index] in conventional_types:\n geo_va_ls_dict['levels_are_top_down'] = False\n\n jedi_config_dict['observations'][index]['geovals'] = geo_va_ls_dict\n\n # Copies for each kind of test\n # ----------------------------\n # jedi_operator_dict = copy.deepcopy(jedi_config_dict)\n jedi_filter_dict = copy.deepcopy(jedi_config_dict)\n\n # Loop through observations and moderate based on test needs\n # ----------------------------------------------------------\n for index in range(len(observations)):\n\n # Overwrite the defaults with the values in ufo_tests_obs\n ufo_tests_obs = ufo_tests_dict[observations[index]]\n ufo_tests_obs = update_dict(ufo_tests_default, ufo_tests_obs)\n\n # Merge the ufo_tests_obs dictionary with the observation dictionary\n # jedi_operator_dict['observations'][index].update(ufo_tests_obs['operator_test'])\n jedi_filter_dict['observations'][index].update(ufo_tests_obs['filter_test'])\n\n # # Remove filters from operator test\n # if 'obs filters' in jedi_operator_dict['observations'][index]:\n # del jedi_operator_dict['observations'][index]['obs filters']\n\n # Write configuration files for the tests\n # ---------------------------------------\n # file = os.path.join(self.cycle_dir(), 'jedi_test_ObsOperator_config.yaml')\n # with open(file, 'w') as jedi_config_file_open:\n # yaml.dump(jedi_operator_dict, jedi_config_file_open, default_flow_style=False)\n\n # file = os.path.join(self.cycle_dir(), 'jedi_test_ObsOperatorTLAD_config.yaml')\n # with open(file, 'w') as jedi_config_file_open:\n # yaml.dump(jedi_operator_dict, jedi_config_file_open, default_flow_style=False)\n\n file = os.path.join(self.cycle_dir(), 'jedi_test_ObsFilters_config.yaml')\n with open(file, 'w') as jedi_config_file_open:\n yaml.dump(jedi_filter_dict, jedi_config_file_open, default_flow_style=False)\n\n # Tests to run\n # ------------\n # tests = ['test_ObsOperator', 'test_ObsOperatorTLAD', 'test_ObsFilters']\n tests = ['test_ObsFilters']\n\n # Loop over the tests\n # -------------------\n for test in tests:\n\n # Output log file\n # ---------------\n jedi_config_file = os.path.join(self.cycle_dir(), f'jedi_{test}_config.yaml')\n output_log_file = os.path.join(self.cycle_dir(), f'jedi_{test}_log.log')\n\n # Jedi executable name\n # --------------------\n jedi_executable_path = os.path.join(self.experiment_path(), 'jedi_bundle', 'build',\n 'bin', f'{test}.x')\n\n # Run the Test Obs Filters executable\n # -----------------------------------\n if not generate_yaml_and_exit:\n run_executable(self.logger, self.cycle_dir(), 36, jedi_executable_path,\n jedi_config_file, output_log_file)\n else:\n self.logger.info('YAML generated, now exiting.')\n\n# --------------------------------------------------------------------------------------------------\n", "repo_name": "GEOS-ESM/swell", "sub_path": "src/swell/tasks/run_jedi_ufo_tests_executable.py", "file_name": "run_jedi_ufo_tests_executable.py", "file_ext": "py", "file_size_in_byte": 6776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "3", "api": [{"api_name": "swell.tasks.base.task_base.taskBase", "line_number": 13, "usage_type": "name"}, {"api_name": "swell.utilities.run_jedi_executables.jedi_dictionary_iterator", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 99, "usage_type": "call"}, {"api_name": "swell.utilities.dictionary.update_dict", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "swell.utilities.run_jedi_executables.run_executable", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "71636471120", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nimport time\nfrom flask import Flask\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef hello():\n return \"Hello World! This is powered by Python backend.\"\n\nif __name__ == \"__main__\":\n print('oh hello')\n #time.sleep(5)\n app.run(host='127.0.0.1', port=5000)", "repo_name": "gollowars/python-electron", "sub_path": "hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 344, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "3", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "36978938296", "text": "import os\nfrom datetime import date\n\nfrom openpyxl import Workbook\n\nos.system('clear')\n\ninput_xl_f='sales_2013.xlsx'\noutput_xl_f='tmp3.xlsx'\n\nif os.path.exists(input_xl_f) and \\\n os.path.isfile(input_xl_f):\n input_wb=openpyxl.load_workbook(input_xl_f)\n input_ws1=input_wb['january_2013']\nelse:\n print('open error, input file !!!')\n \nif os.path.exists(output_xl_f) and \\\n os.path.isfile(output_xl_f):\n output_wb=openpyxl.load_workbook(output_xl_f)\n output_ws1=input_wb[0]\n if output_ws1.title =='filyering_cost':\n print('good !!!')\n else:\n print('output worksheet error')\n exit(1)\nelse:\n print('open error, output file !!!')\n", "repo_name": "EUGENE3329/py3_phone", "sub_path": "data_mgm_intro/p166_1.py", "file_name": "p166_1.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.system", "line_number": 6, "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.path.isfile", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "46435210968", "text": "#!/usr/bin/env python3\n#!/usr/bin/env python3\n\nfrom typing import Tuple, List, Callable, Optional, Dict, Any\nimport functools\nimport math\n\nPosition = Tuple[int, int]\nPositionWithDepth = Tuple[int, int, int]\nBoard = List[List[int]]\nPath = List[Position]\nPathWithDepth = List[PositionWithDepth]\n\n\ndef search(\n s0: PositionWithDepth,\n goal: Position,\n succ: Callable[[PositionWithDepth, Any], PathWithDepth],\n remove: Callable[\n [PathWithDepth, Position], Tuple[PositionWithDepth, PathWithDepth]\n ],\n insert: Callable[[PositionWithDepth, PathWithDepth], PathWithDepth],\n grid: Board,\n max: int,\n depth: int = 0,\n) -> Tuple[\n Optional[PositionWithDepth], Dict[PositionWithDepth, Optional[PositionWithDepth]]\n]:\n l = [s0]\n save: Dict[PositionWithDepth, Optional[PositionWithDepth]] = {s0: None}\n while l:\n s, l = remove(l, goal)\n state_without_depth: Position = (s[0], s[1])\n if state_without_depth == goal:\n return s, save\n elif s[2] > max:\n return None, save\n else:\n for s2 in succ(s, grid):\n if not s2 in save:\n save[s2] = s\n insert(s2, l)\n return None, save\n\n\ndef remove_a_star(\n l: PathWithDepth, goal: Position\n) -> Tuple[PositionWithDepth, PathWithDepth]:\n l.sort(key=functools.partial(a_star_heuristic, goal=goal))\n return l.pop(0), l\n\n\ndef insert_a_star(s: PositionWithDepth, l: PathWithDepth) -> PathWithDepth:\n l.append(s)\n return l\n\n\ndef manhattan_distance(f: Position, to: Position) -> int:\n return abs(f[0] - to[0]) + abs(f[1] - to[0])\n\n\ndef a_star_heuristic(s: PositionWithDepth, goal: Position) -> int:\n s_without_depth: Position = (s[0], s[1])\n return manhattan_distance(s_without_depth, goal) + s[2]\n\n\ndef is_free(x: int, y: int, grid: Board) -> bool:\n return grid[x][y] == 1\n\n\ndef succ_with_depth(s: PositionWithDepth, grid: Board) -> PathWithDepth:\n successors: PathWithDepth = []\n x = s[0]\n y = s[1]\n depth = s[2]\n n = len(grid)\n\n if x + 1 < n and is_free(x + 1, y, grid):\n successors.append((x + 1, y, depth + 1))\n if y + 1 < n and is_free(x, y + 1, grid):\n successors.append((x, y + 1, depth + 1))\n if x > 0 and is_free(x - 1, y, grid):\n successors.append((x - 1, y, depth + 1))\n if y > 0 and is_free(x, y - 1, grid):\n successors.append((x, y - 1, depth + 1))\n return successors\n\n\ndef dict2path(s, d):\n l = [s]\n parent = d[s]\n while not parent is None:\n l.append(parent)\n s = parent\n parent = d[s]\n\n l.reverse()\n\n l2 = []\n for elem in l:\n l2.append((elem[0], elem[1]))\n return l2\n\n\ndef a_star(s0: Position, goal: Position, grid: Board) -> Optional[Path]:\n n = len(grid)\n limit = n * (math.ceil(n // 2)) + math.ceil(n // 2)\n\n state0 = (s0[0], s0[1], 0)\n g, save = search(\n state0, goal, succ_with_depth, remove_a_star, insert_a_star, grid, limit\n )\n\n if g == None:\n return None\n\n return dict2path(g, save)\n", "repo_name": "maylisdet/nomad-game", "sub_path": "src/algorithms/astar.py", "file_name": "astar.py", "file_ext": "py", "file_size_in_byte": 3062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.Tuple", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 48, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 107, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "19572424430", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Nov 18 10:03:35 2021\r\n\r\n@author: Freddie\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport scipy as sp\r\nimport pandas as pd\r\n #Imports all relevant modules\r\nelectro=pd.read_csv(\"C:/Users/Freddie/Data/largeCV.csv\")\r\nceram_1=pd.read_csv(\"C:/Users/Freddie/Data/CERAMIC.CSV\")\r\nceram_2=pd.read_csv(\"C:/Users/Freddie/Data/Phase Shift.csv\")\r\n#Reads in the data for the electrolytic and ceramic capacitors\r\nelectro=pd.DataFrame(electro)\r\nceram_1=pd.DataFrame(ceram_1)\r\nceram_2=pd.DataFrame(ceram_2)\r\nprint(electro)\r\n#Converts all the data to datframes to make them easier to manipulate\r\nplt.plot(electro.iloc[:,0],electro.iloc[:,1])\r\nplt.title(\"Initial Check\")\r\nplt.show()\r\n#Initial plot of the voltage against time to ensure we have the correct form\r\nV_0=max(electro.iloc[:,1]) #This finds the initial voltage\r\nprint(\"The value for V_0 is\", V_0)\r\ny_t=[] #Initialises empty lists for the values of voltage and time\r\nt_t=[] \r\ny=np.log(electro.iloc[:,1])\r\nt=electro.iloc[:,0] \r\nfor m in range(0,len(y)): #Iterates through the logs of the voltages, since there is \r\n if y[m]>=-1: #massive variation in the later values which distorts our best fit\r\n y_t.append(y[m]) #line, we will only consider values greater or equal to -1 to reduce \r\n t_t.append(t[m]) #uncertainty.\r\nplt.plot(t_t,y_t,label=\"Actual Data\")\r\nfit,cov=np.polyfit(t_t,y_t,1,cov=True)\r\nfit_eq=np.poly1d(fit) #This code makes a graph with a title,\r\nplt.plot(t_t,fit_eq(t_t),color=\"red\",label=\"Best Fit\") #x and y axis labels, a grid, and a legend\r\nplt.title(\"Log graph of voltage against time\") #which distinguishes the real data from the\r\nplt.xlabel(\"Time (s)\") #fit line.\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.grid()\r\nplt.legend()\r\nplt.show()\r\nuncertainty=np.sqrt(cov[0][0]) #The square root of the first entry in the covariance matrix\r\ngrad=fit[0] #gives a good estimate of the uncertainty\r\nresistance=10000 #The resistor used in the circuit had a value of 10 kiloohms\r\ncapacitance=-1/(grad*resistance)\r\nfirst_error=np.sqrt((1/((grad**4)*(resistance**2)))*(uncertainty**2)+(1/((grad**4)*(resistance**2)))*(0.1*(resistance**2))) #This calculates the capacitance\r\nprint(\"The capacitance is\",capacitance,\"+-\",first_error,\"Farads\" )\r\n#%%\r\nprint(ceram_1)\r\nplt.plot(ceram_1.iloc[:,0],ceram_1.iloc[:,1]) #Plots an initial voltage against time graph to ensure\r\nplt.title(\"Initial Check\") #the form is as expected\r\nplt.show()\r\ny_1=ceram_1.iloc[:,1] #This assigns the time data to t_1 and the voltage data to y_1\r\nt_1=ceram_1.iloc[:,0]\r\ny_t_1=[]\r\nt_t_1=[]\r\nfor m in range(0,len(t_1)-1): #This appends all the data between t=0 and t=0.0005 to new lists\r\n if 0<=t_1[m]<0.0005: #so that we can isolate the first charging period of the capacitor\r\n y_t_1.append(y_1[m]) \r\n t_t_1.append(t_1[m])\r\nplt.plot(t_t_1,y_t_1) #The first charging period is plotted.\r\nplt.title(\"Ceramic Capacitor Charge 1\")\r\nplt.xlabel(\"Time (s)\")\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.show()\r\ny_t_11=[] #Initialises empty lists for the values of voltage and time\r\nt_t_11=[] \r\ny_log_1=np.log(max(y_t_1)+0.0000001-y_t_1) #Takes the log but adds a tiny value to prevent 0 error.\r\nt=electro.iloc[:,0] \r\nfor m in range(0,len(y_t_1)): #Iterates through the logs of the voltages, since there is \r\n if y_log_1[m]>=-1: #massive variation in the later values which distorts our best fit\r\n y_t_11.append(y_log_1[m]) #line, we will only consider values greater or equal to -1 to reduce \r\n t_t_11.append(t_t_1[m]) #uncertainty.\r\nplt.plot(t_t_11,y_t_11,label=\"Actual Data\")\r\nfit_1,cov_1=np.polyfit(t_t_11,y_t_11,1,cov=True)\r\nfit_eq_1=np.poly1d(fit_1) #This code makes a graph with a title,\r\nplt.plot(t_t_11,fit_eq_1(t_t_11),color=\"red\",label=\"Best Fit\") #x and y axis labels, a grid, and a legend\r\nplt.title(\"Log graph of voltage against time\") #which distinguishes the real data from the\r\nplt.xlabel(\"Time (s)\") #fit line.\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.grid()\r\nplt.legend()\r\nplt.show()\r\nuncertainty_1=np.sqrt(cov_1[0][0]) #The square root of the first entry in the covariance matrix\r\ngrad_1=fit_1[0] #gives a good estimate of the uncertainty\r\nresistance_1=100000 #The resistor used in the circuit had a value of 100 kiloohms\r\ncapacitance_1=-1/(grad_1*resistance_1)\r\nerror_1=np.sqrt((1/((grad_1**4)*(resistance_1**2)))*(uncertainty_1**2)+(1/((grad_1**4)*(resistance_1**2)))*(0.1*(resistance_1**2)))\r\nprint(\"The capacitance is\",capacitance_1,\"+-\",error_1,\"Farads\") \r\ny_t_2=[] \r\nt_t_2=[] \r\nfor m in range(0,len(t_1)-1): #This appends all the data between t=0.001 and t=0.0015 to new lists\r\n if 0.001<=t_1[m]<0.0015: #so that we can isolate the second charging period of the capacitor.\r\n y_t_2.append(y_1[m]) \r\n t_t_2.append(t_1[m])\r\nplt.plot(t_t_2,y_t_2) #The second charging period is plotted.\r\nplt.title(\"Ceramic Capacitor Charge 1\")\r\nplt.xlabel(\"Time (s)\")\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.show()\r\ny_t_22=[] #Initialises empty lists for the values of voltage and time\r\nt_t_22=[] \r\ny_log_2=np.log(max(y_t_2)+0.0000001-y_t_2) #Takes the log but adds a tiny value to prevent 0 error.\r\nt=electro.iloc[:,0] \r\nfor m in range(0,len(y_t_2)): #Iterates through the logs of the voltages, since there is \r\n if y_log_2[m]>=-1: #massive variation in the later values which distorts our best fit\r\n y_t_22.append(y_log_2[m]) #line, we will only consider values greater or equal to -1 to reduce \r\n t_t_22.append(t_t_2[m]) #uncertainty.\r\nplt.plot(t_t_22,y_t_22,label=\"Actual Data\")\r\nfit_2,cov_2=np.polyfit(t_t_22,y_t_22,1,cov=True)\r\nfit_eq_2=np.poly1d(fit_2) #This code makes a graph with a title,\r\nplt.plot(t_t_22,fit_eq_2(t_t_22),color=\"red\",label=\"Best Fit\") #x and y axis labels, a grid, and a legend\r\nplt.title(\"Log graph of voltage against time\") #which distinguishes the real data from the\r\nplt.xlabel(\"Time (s)\") #fit line.\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.grid()\r\nplt.legend()\r\nplt.show()\r\nuncertainty_2=np.sqrt(cov_2[0][0]) #The square root of the first entry in the covariance matrix\r\ngrad_2=fit_2[0] #gives a good estimate of the uncertainty\r\nresistance_2=100000 #The resistor used in the circuit had a value of 100 kiloohms\r\ncapacitance_2=-1/(grad_2*resistance_2)\r\nerror_2=np.sqrt((1/((grad_2**4)*(resistance_2**2)))*(uncertainty_2**2)+(1/((grad_2**4)*(resistance_2**2)))*(0.1*(resistance_2**2)))\r\nprint(\"The capacitance is\",capacitance_2,\"+-\",error_2,\"Farads\") \r\ny_t_3=[] \r\nt_t_3=[]\r\nfor m in range(0,len(t_1)-1): #This appends all the data between t=0.001 and t=0.0015 to new lists\r\n if 0.0005<=t_1[m]<0.001: #so that we can isolate the second charging period of the capacitor.\r\n y_t_3.append(y_1[m]) \r\n t_t_3.append(t_1[m])\r\nplt.plot(t_t_3,y_t_3) #The second charging period is plotted.\r\nplt.title(\"Ceramic Capacitor Discharge 1\")\r\nplt.xlabel(\"Time (s)\")\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.show()\r\ny_t_33=[] #Initialises empty lists for the values of voltage and time\r\nt_t_33=[] \r\ny_log_3=np.log(y_t_3) #Takes the log but adds a tiny value to prevent 0 error.\r\nt=electro.iloc[:,0] \r\nfor m in range(0,len(y_t_3)): #Iterates through the logs of the voltages, since there is \r\n if y_log_3[m]>=-1: #massive variation in the later values which distorts our best fit\r\n y_t_33.append(y_log_3[m]) #line, we will only consider values greater or equal to -1 to reduce \r\n t_t_33.append(t_t_3[m]) #uncertainty.\r\nplt.plot(t_t_33,y_t_33,label=\"Actual Data\")\r\nfit_3,cov_3=np.polyfit(t_t_33,y_t_33,1,cov=True)\r\nfit_eq_3=np.poly1d(fit_3) #This code makes a graph with a title,\r\nplt.plot(t_t_33,fit_eq_3(t_t_33),color=\"red\",label=\"Best Fit\") #x and y axis labels, a grid, and a legend\r\nplt.title(\"Log graph of voltage against time\") #which distinguishes the real data from the\r\nplt.xlabel(\"Time (s)\") #fit line.\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.grid()\r\nplt.legend()\r\nplt.show()\r\nuncertainty_3=np.sqrt(cov_3[0][0]) #The square root of the first entry in the covariance matrix\r\ngrad_3=fit_3[0] #gives a good estimate of the uncertainty\r\nresistance_3=100000 #The resistor used in the circuit had a value of 100 kiloohms\r\ncapacitance_3=-1/(grad_3*resistance_3)\r\nerror_3=np.sqrt((1/((grad_3**4)*(resistance_3**2)))*(uncertainty_3**2)+(1/((grad_3**4)*(resistance_3**2)))*(0.1*(resistance_3**2)))\r\nprint(\"The capacitance is\",capacitance_3,\"+-\",error_3,\"Farads\")\r\ny_t_4=[] \r\nt_t_4=[]\r\nfor m in range(0,len(t_1)-1): #This appends all the data between t=0.001 and t=0.0015 to new lists\r\n if 0.0015<=t_1[m]<0.002: #so that we can isolate the second charging period of the capacitor.\r\n y_t_4.append(y_1[m]) \r\n t_t_4.append(t_1[m])\r\nplt.plot(t_t_4,y_t_4) #The second charging period is plotted.\r\nplt.title(\"Ceramic Capacitor Discharge 2\")\r\nplt.xlabel(\"Time (s)\")\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.show()\r\ny_t_44=[] #Initialises empty lists for the values of voltage and time\r\nt_t_44=[] \r\ny_log_4=np.log(y_t_4) #Takes the log but adds a tiny value to prevent 0 error.\r\nt=electro.iloc[:,0] \r\nfor m in range(0,len(y_t_4)): #Iterates through the logs of the voltages, since there is \r\n if y_log_4[m]>=-1: #massive variation in the later values which distorts our best fit\r\n y_t_44.append(y_log_4[m]) #line, we will only consider values greater or equal to -1 to reduce \r\n t_t_44.append(t_t_4[m]) #uncertainty.\r\nplt.plot(t_t_44,y_t_44,label=\"Actual Data\")\r\nfit_4,cov_4=np.polyfit(t_t_44,y_t_44,1,cov=True)\r\nfit_eq_4=np.poly1d(fit_4) #This code makes a graph with a title,\r\nplt.plot(t_t_44,fit_eq_4(t_t_44),color=\"red\",label=\"Best Fit\") #x and y axis labels, a grid, and a legend\r\nplt.title(\"Log graph of voltage against time\") #which distinguishes the real data from the\r\nplt.xlabel(\"Time (s)\") #fit line.\r\nplt.ylabel(\"Voltage (V)\")\r\nplt.grid()\r\nplt.legend()\r\nplt.show()\r\nuncertainty_4=np.sqrt(cov_4[0][0]) #The square root of the first entry in the covariance matrix\r\ngrad_4=fit_4[0] #gives a good estimate of the uncertainty\r\nresistance_4=100000 #The resistor used in the circuit had a value of 100 kiloohms\r\ncapacitance_4=-1/(grad_4*resistance_4)\r\nerror_4=np.sqrt((1/((grad_4**4)*(resistance_4**2)))*(uncertainty_4**2)+(1/((grad_4**4)*(resistance_4**2)))*(0.1*(resistance_4**2)))\r\nprint(\"The capacitance is\",capacitance_4,\"+-\",error_4,\"Farads\")\r\nmean=(capacitance_3+capacitance_4)/2 #Takes the mean of the two discharge results.\r\nfinal_err=0.25*np.sqrt(error_3**2+error_4**2) #We discounted the charge results since the values were quite different and the uncertainties were large.\r\nprint(\"Our overall estimate for the capacitance of this capacitor is\",mean,\"+-\",final_err)\r\n#%%\r\nceram_2[\"α (Degrees)\"]=ceram_2[\"α (Degrees)\"]*(3.14159265358979/180) #Converts the angles in the data to radians\r\nprint(ceram_2)\r\ncapacitance=[]\r\nuncertainties=[] #Initialises the empty lists for the capacitances and uncertainties\r\nfor i in range(0,len(ceram_2.iloc[:,0])):\r\n v_g=ceram_2.iloc[i,0]\r\n v_x=ceram_2.iloc[i,1]\r\n alpha=ceram_2.iloc[i,2] #This iterates through the ceram_2 dataset and calculates the value\r\n vg_std=ceram_2.iloc[i,3] #of capacitance for each row using the formulas given in the lab book.\r\n vx_std=ceram_2.iloc[i,4]\r\n alpha_std=ceram_2.iloc[i,5]\r\n R_1=6800 #These are the known values of the resistance and driving frequency\r\n d_freq=50000\r\n V_R1=np.sqrt((v_g*np.cos(alpha)-v_x)**2+(v_g*np.sin(alpha))**2)\r\n i_g=V_R1/R_1\r\n phi=np.arccos((v_g*np.sin(alpha))/V_R1) \r\n x_c=v_x/(i_g*np.cos(phi)) \r\n c_t=1/(2*np.pi*x_c*d_freq)\r\n capacitance.append(c_t)\r\n tm_1=(((np.sin(alpha))**2)/(4*(np.pi**2)*(d_freq**2)*(v_x**2)*(R_1**2)))*(vg_std**2) #This chunk of code uses the error propagation formula\r\n tm_2=(((v_g**2)*((np.cos(alpha))**2))/(4*(np.pi**2)*(d_freq**2)*(v_x**2)*(R_1**2)))*(alpha_std**2) #shown in the lab book to calculate uncertainties\r\n tm_3=((v_g**2)*((np.sin(alpha))**2))/(4*((np.pi)**2)*(d_freq**2)*(v_x**4)*(R_1**2))*(vx_std**2)\r\n err=np.sqrt(tm_1+tm_2+tm_3)\r\n uncertainties.append(err)\r\nmean=np.mean(capacitance) #This takes the mean of the capacitances to find the estimate\r\nfinal_error=0.25*np.sqrt(uncertainties[0]**2+uncertainties[1]**2+uncertainties[2]**2+uncertainties[3]**2) #This combines the uncertainty terms in quadrature\r\nprint(\"Our final value for the capacitance of the ceramic capacitance is\",mean-20e-12,\"+-\",final_error) \r\n", "repo_name": "FreddieKing1/Data-Analysis", "sub_path": "Capacitance_Analysis_181121.py", "file_name": "Capacitance_Analysis_181121.py", "file_ext": "py", "file_size_in_byte": 13617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 92, "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.title", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "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"}, {"api_name": "numpy.log", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 127, "usage_type": "call"}, {"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.title", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 220, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 222, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 223, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 228, "usage_type": "call"}]} +{"seq_id": "43098457034", "text": "try:\n from math import gcd\nexcept ImportError:\n from fractions import gcd\nfrom decimal import Decimal\nimport sys\n\n\nfrom pdfautonup.pdfbackend.auto import ( # pylint: disable=no-name-in-module\n PDFFileReader,\n)\nfrom pdfautonup import LOGGER\nfrom pdfautonup import errors, options, paper, geometry, pdfbackend\n\n\ndef lcm(a, b):\n \"\"\"Return least common divisor of arguments\"\"\"\n # pylint: disable=invalid-name, deprecated-method\n return (a * b) // gcd(a, b)\n\n\ndef _none_function(*args, **kwargs): # pylint: disable=unused-argument\n \"\"\"Accept any number of arguments. and does nothing.\"\"\"\n\n\ndef _progress_printer(string):\n \"\"\"Returns a function that prints the progress message.\"\"\"\n\n def print_progress(page, total):\n \"\"\"Print progress message.\"\"\"\n try:\n text = string.format(\n page=page, total=total, percent=int(page * 100 / total)\n )\n except: # pylint: disable=bare-except\n text = string\n print(text, end=\"\")\n sys.stdout.flush()\n\n return print_progress\n\n\nclass PageIterator:\n \"\"\"Iterator over pages of several PDF files.\"\"\"\n\n def __init__(self, filenames):\n self.files = []\n self._filenames = filenames\n\n def __enter__(self):\n for name in self._filenames:\n try:\n if name == \"-\":\n self.files.append(PDFFileReader())\n else:\n self.files.append(PDFFileReader(name))\n except (FileNotFoundError, PermissionError) as error:\n raise errors.PdfautonupError(\n \"Error while reading file '{}': {}.\".format(name, error)\n )\n except RuntimeError as error:\n raise errors.PdfautonupError(\n \"Error: Malformed file '{}': {}.\".format(name, error)\n )\n return self\n\n def __exit__(self, *exc):\n for file in self.files:\n file.close()\n\n def __iter__(self):\n for pdf in self.files:\n yield from pdf\n\n def __len__(self):\n return sum(len(pdf) for pdf in self.files)\n\n def repeat(self, num):\n \"\"\"Iterator over pages, repeated `num` times.\"\"\"\n for __ in range(num):\n yield from self\n\n def metadata(self):\n \"\"\"Aggregate metadata from input files.\"\"\"\n if len(self.files) == 1:\n return self.files[0].metadata\n\n input_info = [pdf.metadata for pdf in self.files]\n output_info = dict()\n for key in pdfbackend.METADATA_KEYS:\n values = (\n data[key]\n for data in input_info\n if (key in data and (data[key] is not None))\n )\n if values:\n output_info[key] = \" / \".join([\"“{}”\".format(item) for item in values])\n return output_info\n\n\ndef nup(arguments, progress=_none_function):\n \"\"\"Build destination file.\"\"\"\n # pylint: disable=too-many-branches\n\n with PageIterator(arguments.files) as pages:\n\n if not pages:\n raise errors.PdfautonupError(\"Error: PDF files have no pages to process.\")\n\n page_sizes = list(zip(*[page.mediabox_size for page in pages]))\n source_size = (Decimal(max(page_sizes[0])), Decimal(max(page_sizes[1])))\n target_size = paper.target_papersize(arguments.target_size)\n\n if [len(set(page_sizes[i])) for i in (0, 1)] != [1, 1]:\n LOGGER.warning(\n \"Pages have different sizes. The result might be unexpected.\"\n )\n\n if arguments.algorithm is None:\n if arguments.gap[0] is None and arguments.margin[0] is None:\n fit = geometry.Fuzzy\n else:\n fit = geometry.Panelize\n else:\n fit = {\"fuzzy\": geometry.Fuzzy, \"panel\": geometry.Panelize}[\n arguments.algorithm\n ]\n\n dest = fit(source_size, target_size, arguments=arguments)\n\n if arguments.repeat == \"auto\":\n if len(pages) == 1:\n arguments.repeat = \"fit\"\n else:\n arguments.repeat = 1\n if isinstance(arguments.repeat, int):\n repeat = arguments.repeat\n elif arguments.repeat == \"fit\":\n repeat = lcm(dest.pages_per_page, len(pages)) // len(pages)\n\n pagecount = 0\n pagetotal = repeat * len(pages)\n progress(pagecount, pagetotal)\n for page in pages.repeat(repeat):\n dest.add_page(page)\n pagecount += 1\n progress(pagecount, pagetotal)\n\n dest.write(arguments.output, arguments.files[0], metadata=pages.metadata())\n\n\ndef main():\n \"\"\"Main function\"\"\"\n try:\n arguments = options.commandline_parser().parse_args(sys.argv[1:])\n\n if \"-\" in arguments.files and arguments.interactive:\n LOGGER.error(\n \"\"\"Cannot ask user input while reading files from standard input. \"\"\"\n \"\"\"Try removing the \"--interactive\" (or \"-i\") option.\"\"\"\n )\n sys.exit(1)\n\n nup(arguments, progress=_progress_printer(arguments.progress))\n if not (arguments.progress.endswith(\"\\n\") or arguments.progress == \"\"):\n print()\n except KeyboardInterrupt:\n print()\n sys.exit(1)\n except errors.PdfautonupError as error:\n LOGGER.error(error)\n sys.exit(1)\n\n sys.exit(0)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "alexdong/pdfautonup", "sub_path": "pdfautonup/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 5458, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "fractions.gcd", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pdfautonup.pdfbackend.auto.PDFFileReader", "line_number": 54, "usage_type": "call"}, {"api_name": "pdfautonup.pdfbackend.auto.PDFFileReader", "line_number": 56, "usage_type": "call"}, {"api_name": "pdfautonup.errors.PdfautonupError", "line_number": 58, "usage_type": "call"}, {"api_name": "pdfautonup.errors", "line_number": 58, "usage_type": "name"}, {"api_name": "pdfautonup.errors.PdfautonupError", "line_number": 62, "usage_type": "call"}, {"api_name": "pdfautonup.errors", "line_number": 62, "usage_type": "name"}, {"api_name": "pdfautonup.pdfbackend.METADATA_KEYS", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pdfautonup.pdfbackend", "line_number": 90, "usage_type": "name"}, {"api_name": "pdfautonup.errors.PdfautonupError", "line_number": 108, "usage_type": "call"}, {"api_name": "pdfautonup.errors", "line_number": 108, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 111, "usage_type": "call"}, {"api_name": "pdfautonup.paper.target_papersize", "line_number": 112, "usage_type": "call"}, {"api_name": "pdfautonup.paper", "line_number": 112, "usage_type": "name"}, {"api_name": "pdfautonup.LOGGER.warning", "line_number": 115, "usage_type": "call"}, {"api_name": "pdfautonup.LOGGER", "line_number": 115, "usage_type": "name"}, {"api_name": "pdfautonup.geometry.Fuzzy", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pdfautonup.geometry", "line_number": 121, "usage_type": "name"}, {"api_name": "pdfautonup.geometry.Panelize", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pdfautonup.geometry", "line_number": 123, "usage_type": "name"}, {"api_name": "pdfautonup.geometry.Fuzzy", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pdfautonup.geometry", "line_number": 125, "usage_type": "name"}, {"api_name": "pdfautonup.geometry.Panelize", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pdfautonup.options.commandline_parser", "line_number": 155, "usage_type": "call"}, {"api_name": "pdfautonup.options", "line_number": 155, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pdfautonup.LOGGER.error", "line_number": 158, "usage_type": "call"}, {"api_name": "pdfautonup.LOGGER", "line_number": 158, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 162, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 169, "usage_type": "call"}, {"api_name": "pdfautonup.errors.PdfautonupError", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pdfautonup.errors", "line_number": 170, "usage_type": "name"}, {"api_name": "pdfautonup.LOGGER.error", "line_number": 171, "usage_type": "call"}, {"api_name": "pdfautonup.LOGGER", "line_number": 171, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 172, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "6099551524", "text": "import h5py\nimport numpy as np\nimport os\n\nfiles = ['train.h5', 'train2.h5', 'train3.h5', 'train4.h5', 'train5.h5', 'train6.h5']\n\nif __name__ == '__main__':\n new_data = h5py.File('new.h5', 'w')\n # for key in new_data.keys():\n # print(new_data['data'])\n # print(new_data['label_x4'])\n for i, file in enumerate(files):\n piece = h5py.File(file, 'r')\n if i == 0:\n dat = piece['data'].value\n label_x4 = piece['label_x4'].value\n\n else:\n dat = np.concatenate((dat, piece['data'].value), axis=0)\n label_x4 = np.concatenate((label_x4, piece['label_x4'].value), axis=0)\n # print(dat.shape, label_x4.shape)\n\n data_input = new_data.create_dataset(\"data\", data=dat)\n data_label_x4 = new_data.create_dataset(\"label_x4\", data=label_x4)\n\n # test\n new_data = h5py.File('new.h5', 'r')\n for key in new_data.keys():\n print(new_data[key].value.shape)\n", "repo_name": "lby314xx/MLP-coursework", "sub_path": "prepare2/combineh5.py", "file_name": "combineh5.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "h5py.File", "line_number": 8, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 20, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "30515677992", "text": "import argparse\nimport glob\nimport json\nimport os.path as osp\nimport random\n\nfrom mmengine.runner import set_random_seed\n\nfrom tools.data.anno_txt2json import lines2dictlist\nfrom tools.data.parse_file_list import (parse_directory, parse_diving48_splits,\n parse_hmdb51_split,\n parse_jester_splits,\n parse_kinetics_splits,\n parse_mit_splits, parse_mmit_splits,\n parse_sthv1_splits, parse_sthv2_splits,\n parse_ucf101_splits)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Build file list')\n parser.add_argument(\n 'dataset',\n type=str,\n choices=[\n 'ucf101', 'kinetics400', 'kinetics600', 'kinetics700', 'thumos14',\n 'sthv1', 'sthv2', 'mit', 'mmit', 'activitynet', 'hmdb51', 'jester',\n 'diving48'\n ],\n help='dataset to be built file list')\n parser.add_argument(\n 'src_folder', type=str, help='root directory for the frames or videos')\n parser.add_argument(\n '--rgb-prefix', type=str, default='img_', help='prefix of rgb frames')\n parser.add_argument(\n '--flow-x-prefix',\n type=str,\n default='flow_x_',\n help='prefix of flow x frames')\n parser.add_argument(\n '--flow-y-prefix',\n type=str,\n default='flow_y_',\n help='prefix of flow y frames')\n parser.add_argument(\n '--num-split',\n type=int,\n default=3,\n help='number of split to file list')\n parser.add_argument(\n '--subset',\n type=str,\n default='train',\n choices=['train', 'val', 'test'],\n help='subset to generate file list')\n parser.add_argument(\n '--level',\n type=int,\n default=2,\n choices=[1, 2],\n help='directory level of data')\n parser.add_argument(\n '--format',\n type=str,\n default='rawframes',\n choices=['rawframes', 'videos'],\n help='data format')\n parser.add_argument(\n '--out-root-path',\n type=str,\n default='data/',\n help='root path for output')\n parser.add_argument(\n '--output-format',\n type=str,\n default='txt',\n choices=['txt', 'json'],\n help='built file list format')\n parser.add_argument('--seed', type=int, default=None, help='random seed')\n parser.add_argument(\n '--shuffle',\n action='store_true',\n default=False,\n help='whether to shuffle the file list')\n args = parser.parse_args()\n\n return args\n\n\ndef build_file_list(splits, frame_info, shuffle=False):\n \"\"\"Build file list for a certain data split.\n\n Args:\n splits (tuple): Data split to generate file list.\n frame_info (dict): Dict mapping from frames to path. e.g.,\n 'Skiing/v_Skiing_g18_c02': ('data/ucf101/rawframes/Skiing/v_Skiing_g18_c02', 0, 0). # noqa: E501\n shuffle (bool): Whether to shuffle the file list.\n\n Returns:\n tuple: RGB file list for training and testing, together with\n Flow file list for training and testing.\n \"\"\"\n\n def build_list(split):\n \"\"\"Build RGB and Flow file list with a given split.\n\n Args:\n split (list): Split to be generate file list.\n\n Returns:\n tuple[list, list]: (rgb_list, flow_list), rgb_list is the\n generated file list for rgb, flow_list is the generated\n file list for flow.\n \"\"\"\n rgb_list, flow_list = list(), list()\n for item in split:\n if item[0] not in frame_info:\n continue\n if frame_info[item[0]][1] > 0:\n # rawframes\n rgb_cnt = frame_info[item[0]][1]\n flow_cnt = frame_info[item[0]][2]\n if isinstance(item[1], int):\n rgb_list.append(f'{item[0]} {rgb_cnt} {item[1]}\\n')\n flow_list.append(f'{item[0]} {flow_cnt} {item[1]}\\n')\n elif isinstance(item[1], list):\n # only for multi-label datasets like mmit\n rgb_list.append(f'{item[0]} {rgb_cnt} ' +\n ' '.join([str(digit)\n for digit in item[1]]) + '\\n')\n rgb_list.append(f'{item[0]} {flow_cnt} ' +\n ' '.join([str(digit)\n for digit in item[1]]) + '\\n')\n else:\n raise ValueError(\n 'frame_info should be ' +\n '[`video`(str), `label`(int)|`labels(list[int])`')\n else:\n # videos\n if isinstance(item[1], int):\n rgb_list.append(f'{frame_info[item[0]][0]} {item[1]}\\n')\n flow_list.append(f'{frame_info[item[0]][0]} {item[1]}\\n')\n elif isinstance(item[1], list):\n # only for multi-label datasets like mmit\n rgb_list.append(f'{frame_info[item[0]][0]} ' +\n ' '.join([str(digit)\n for digit in item[1]]) + '\\n')\n flow_list.append(\n f'{frame_info[item[0]][0]} ' +\n ' '.join([str(digit) for digit in item[1]]) + '\\n')\n else:\n raise ValueError(\n 'frame_info should be ' +\n '[`video`(str), `label`(int)|`labels(list[int])`')\n if shuffle:\n random.shuffle(rgb_list)\n random.shuffle(flow_list)\n return rgb_list, flow_list\n\n train_rgb_list, train_flow_list = build_list(splits[0])\n test_rgb_list, test_flow_list = build_list(splits[1])\n return (train_rgb_list, test_rgb_list), (train_flow_list, test_flow_list)\n\n\ndef main():\n args = parse_args()\n\n if args.seed is not None:\n print(f'Set random seed to {args.seed}')\n set_random_seed(args.seed)\n\n if args.format == 'rawframes':\n frame_info = parse_directory(\n args.src_folder,\n rgb_prefix=args.rgb_prefix,\n flow_x_prefix=args.flow_x_prefix,\n flow_y_prefix=args.flow_y_prefix,\n level=args.level)\n elif args.format == 'videos':\n if args.level == 1:\n # search for one-level directory\n video_list = glob.glob(osp.join(args.src_folder, '*'))\n elif args.level == 2:\n # search for two-level directory\n video_list = glob.glob(osp.join(args.src_folder, '*', '*'))\n else:\n raise ValueError(f'level must be 1 or 2, but got {args.level}')\n frame_info = {}\n for video in video_list:\n video_path = osp.relpath(video, args.src_folder)\n # video_id: (video_relative_path, -1, -1)\n frame_info[osp.splitext(video_path)[0]] = (video_path, -1, -1)\n else:\n raise NotImplementedError('only rawframes and videos are supported')\n\n if args.dataset == 'ucf101':\n splits = parse_ucf101_splits(args.level)\n elif args.dataset == 'sthv1':\n splits = parse_sthv1_splits(args.level)\n elif args.dataset == 'sthv2':\n splits = parse_sthv2_splits(args.level)\n elif args.dataset == 'mit':\n splits = parse_mit_splits()\n elif args.dataset == 'mmit':\n splits = parse_mmit_splits()\n elif args.dataset in ['kinetics400', 'kinetics600', 'kinetics700']:\n splits = parse_kinetics_splits(args.level, args.dataset)\n elif args.dataset == 'hmdb51':\n splits = parse_hmdb51_split(args.level)\n elif args.dataset == 'jester':\n splits = parse_jester_splits(args.level)\n elif args.dataset == 'diving48':\n splits = parse_diving48_splits()\n else:\n raise ValueError(\n f\"Supported datasets are 'ucf101, sthv1, sthv2', 'jester', \"\n f\"'mmit', 'mit', 'kinetics400', 'kinetics600', 'kinetics700', but \"\n f'got {args.dataset}')\n\n assert len(splits) == args.num_split\n\n out_path = args.out_root_path + args.dataset\n\n if len(splits) > 1:\n for i, split in enumerate(splits):\n file_lists = build_file_list(\n split, frame_info, shuffle=args.shuffle)\n train_name = f'{args.dataset}_train_split_{i+1}_{args.format}.txt'\n val_name = f'{args.dataset}_val_split_{i+1}_{args.format}.txt'\n if args.output_format == 'txt':\n with open(osp.join(out_path, train_name), 'w') as f:\n f.writelines(file_lists[0][0])\n with open(osp.join(out_path, val_name), 'w') as f:\n f.writelines(file_lists[0][1])\n elif args.output_format == 'json':\n train_list = lines2dictlist(file_lists[0][0], args.format)\n val_list = lines2dictlist(file_lists[0][1], args.format)\n train_name = train_name.replace('.txt', '.json')\n val_name = val_name.replace('.txt', '.json')\n with open(osp.join(out_path, train_name), 'w') as f:\n json.dump(train_list, f)\n with open(osp.join(out_path, val_name), 'w') as f:\n json.dump(val_list, f)\n else:\n lists = build_file_list(splits[0], frame_info, shuffle=args.shuffle)\n\n if args.subset == 'train':\n ind = 0\n elif args.subset == 'val':\n ind = 1\n elif args.subset == 'test':\n ind = 2\n else:\n raise ValueError(f\"subset must be in ['train', 'val', 'test'], \"\n f'but got {args.subset}.')\n\n filename = f'{args.dataset}_{args.subset}_list_{args.format}.txt'\n if args.output_format == 'txt':\n with open(osp.join(out_path, filename), 'w') as f:\n f.writelines(lists[0][ind])\n elif args.output_format == 'json':\n data_list = lines2dictlist(lists[0][ind], args.format)\n filename = filename.replace('.txt', '.json')\n with open(osp.join(out_path, filename), 'w') as f:\n json.dump(data_list, f)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "open-mmlab/mmaction2", "sub_path": "tools/data/build_file_list.py", "file_name": "build_file_list.py", "file_ext": "py", "file_size_in_byte": 10401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3560, "dataset": "github-code", "pt": "3", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 20, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 155, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 156, "usage_type": "call"}, {"api_name": "mmengine.runner.set_random_seed", "line_number": 169, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_directory", "line_number": 172, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "name"}, {"api_name": "os.path.relpath", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "name"}, {"api_name": "tools.data.parse_file_list.parse_ucf101_splits", "line_number": 196, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_sthv1_splits", "line_number": 198, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_sthv2_splits", "line_number": 200, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_mit_splits", "line_number": 202, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_mmit_splits", "line_number": 204, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_kinetics_splits", "line_number": 206, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_hmdb51_split", "line_number": 208, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_jester_splits", "line_number": 210, "usage_type": "call"}, {"api_name": "tools.data.parse_file_list.parse_diving48_splits", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "name"}, {"api_name": "tools.data.anno_txt2json.lines2dictlist", "line_number": 235, "usage_type": "call"}, {"api_name": "tools.data.anno_txt2json.lines2dictlist", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "name"}, {"api_name": "tools.data.anno_txt2json.lines2dictlist", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 264, "usage_type": "call"}]} +{"seq_id": "40252439651", "text": "import cv2 as cv\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom skimage.draw import line\nfrom PIL import Image\nfrom line import Line\n\nleft_line = Line()\nright_line = Line()\n\ndef do_canny(frame):\n # Converts frame to grayscale because we only need the luminance channel for detecting edges - less computationally expensive\n gray = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)\n # Applies a 5x5 gaussian blur with deviation of 0 to frame - not mandatory since Canny will do this for us\n blur = cv.GaussianBlur(gray, (3, 3), 0)\n # Applies Canny edge detector with minVal of 50 and maxVal of 150\n canny = cv.Canny(blur, 30, 80)\n return canny\n\ndef do_segment(frame):\n # Since an image is a multi-directional array containing the relative intensities of each pixel in the image, we can use frame.shape to return a tuple: [number of rows, number of columns, number of channels] of the dimensions of the frame\n # frame.shape[0] give us the number of rows of pixels the frame has. Since height begins from 0 at the top, the y-coordinate of the bottom of the frame is its height\n height = frame.shape[0]\n widht = frame.shape[1]\n # Creates a triangular polygon for the mask defined by three (x, y) coordinates\n polygons = np.array([\n [(0, 210), (0, 310), (260,200), (300,200),(widht, 300), (widht,210)]\n ])\n # Creates an image filled with zero intensities with the same dimensions as the frame\n mask = np.zeros_like(frame)\n # Allows the mask to be filled with values of 1 and the other areas to be filled with values of 0\n cv.fillPoly(mask, polygons, 255)\n # A bitwise and operation between the mask and frame keeps only the triangular area of the frame\n segment = cv.bitwise_and(frame, mask)\n return segment\n\ndef calculate_lines(frame, lines):\n # Empty arrays to store the coordinates of the left and right lines\n left = []\n right = []\n #print(\"hough:\", lines)\n # Loops through every detected line\n for line in lines:\n # Reshapes line from 2D array to 1D array\n x1, y1, x2, y2 = line.reshape(4)\n\n # Fits a linear polynomial to the x and y coordinates and returns a vector of coefficients which describe the slope and y-intercept\n parameters = np.polyfit((x1, x2), (y1, y2), 1)\n slope = parameters[0]\n y_intercept = parameters[1]\n # If slope is negative, the line is to the left of the lane, and otherwise, the line is to the right of the lane\n if slope < 0:\n left.append((slope, y_intercept))\n else:\n right.append((slope, y_intercept))\n # Averages out all the values for left and right into a single slope and y-intercept value for each line\n left_avg = np.average(left, axis = 0)\n right_avg = np.average(right, axis = 0)\n # Calculates the x1, y1, x2, y2 coordinates for the left and right lines\n left_line = calculate_coordinates(frame, left_avg)\n right_line = calculate_coordinates(frame, right_avg)\n intersections = getIntersection(left_line, right_line)\n x_in = intersections[0]\n #y_in = intersections[1]\n # print(\"left:\",left_line)\n # print(left_line[1])\n #x2 = frame.shape[1]/2 - 80\n x_coor = np.arange(0, x_in)\n x = np.array([left_line[0], left_line[2]])\n y = np.array([left_line[1], left_line[3]])\n y_coor = np.interp(x_coor, x, y)\n coordinates = np.column_stack((x_coor, y_coor))\n\n #print(coordinates[-1])\n left = np.array([coordinates[0],coordinates[-1]])\n left = np.reshape(left, (1, 4))\n #print(left[0],left_line)\n #x2 = frame.shape[1]/2 + 50\n x_coor = np.arange(x_in, frame.shape[1])\n #print(right_line)\n x = np.array([right_line[2], right_line[0]])\n y = np.array([right_line[3], right_line[1]])\n #print(\"x\",x, \"y:\",y)\n y_coor = np.interp(x_coor, x, y)\n coordinates = np.column_stack((x_coor, y_coor))\n # print(coordinates[0])\n # print(coordinates[-1])\n right = np.array([coordinates[0],coordinates[-1]])\n right = np.reshape(right, (1, 4))\n #print(left[0], right[0])\n return np.array([left[0], right[0]])\n return np.array([left_line, right_line])\n\ndef calculate_coordinates(frame, parameters):\n slope, intercept = parameters\n #print(slope, intercept)\n # Sets initial y-coordinate as height from top down (bottom of the frame)\n y1 = frame.shape[0]\n # Sets final y-coordinate as 150 above the bottom of the frame\n y2 = int(y1 - 480)\n # Sets initial x-coordinate as (y1 - b) / m since y1 = mx1 + b\n x1 = int((y1 - intercept) / slope)\n # Sets final x-coordinate as (y2 - b) / m since y2 = mx2 + b\n x2 = int((y2 - intercept) / slope)\n # get coordinates\n\n return np.array([x1, y1, x2, y2])\n\ndef visualize_lines(frame, lines):\n # Creates an image filled with zero intensities with the same dimensions as the frame\n lines_visualize = np.zeros_like(frame)\n # print(lines)\n # print(lines[0][2])\n\n # Checks if any lines are detected\n if lines is not None:\n for x1, y1, x2, y2 in lines:\n # Draws lines between two coordinates with green color and 5 thickness\n cv.line(lines_visualize, (int(np.float32(x1)), int(np.float32(y1))), (int(np.float32(x2)), int(np.float32(y2))), (0, 0, 255), thickness=4)\n #cv.line(lines_visualize, (int(np.float32(lines[0][2])), int(np.float32(lines[0][3]))), (int(np.float32(lines[1][0])), int(np.float32(lines[1][1]))), (45, 255, 255), thickness=10)\n return lines_visualize\n\ndef order_points(pts):\n\t# initialzie a list of coordinates that will be ordered\n\t# such that the first entry in the list is the top-left,\n\t# the second entry is the top-right, the third is the\n\t# bottom-right, and the fourth is the bottom-left\n\trect = np.zeros((4, 2), dtype = \"float32\")\n\t# the top-left point will have the smallest sum, whereas\n\t# the bottom-right point will have the largest sum\n\ts = pts.sum(axis = 1)\n\trect[0] = pts[np.argmin(s)]\n\trect[2] = pts[np.argmax(s)]\n\t# now, compute the difference between the points, the\n\t# top-right point will have the smallest difference,\n\t# whereas the bottom-left will have the largest difference\n\tdiff = np.diff(pts, axis = 1)\n\trect[1] = pts[np.argmin(diff)]\n\trect[3] = pts[np.argmax(diff)]\n\t# return the ordered coordinates\n\treturn rect\n\ndef four_point_transform(image, pts):\n\t# obtain a consistent order of the points and unpack them\n\t# individually\n\trect = order_points(pts)\n\t(tl, tr, br, bl) = rect\n\n\t# compute the width of the new image, which will be the\n\t# maximum distance between bottom-right and bottom-left\n\t# x-coordiates or the top-right and top-left x-coordinates\n\twidthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n\twidthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n\tmaxWidth = max(int(widthA), int(widthB))\n\t# compute the height of the new image, which will be the\n\t# maximum distance between the top-right and bottom-right\n\t# y-coordinates or the top-left and bottom-left y-coordinates\n\theightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n\theightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n\tmaxHeight = max(int(heightA), int(heightB))\n\t# now that we have the dimensions of the new image, construct\n\t# the set of destination points to obtain a \"birds eye view\",\n\t# (i.e. top-down view) of the image, again specifying points\n\t# in the top-left, top-right, bottom-right, and bottom-left\n\t# order\n\tdst = np.array([\n\t\t[0, 0],\n\t\t[maxWidth - 1, 0],\n\t\t[maxWidth - 1, maxHeight - 1],\n\t\t[0, maxHeight - 1]], dtype = \"float32\")\n\t# compute the perspective transform matrix and then apply it\n\tM = cv.getPerspectiveTransform(rect, dst)\n\twarped = cv.warpPerspective(image, M, (maxWidth, maxHeight))\n\t# return the warped image\n\treturn warped\n\ndef getIntersection(line1, line2):\n #print(line1, line2)\n s1 = np.array([line1[0],line1[1]])\n\n e1 = np.array([line1[2],line1[3]])\n\n s2 = np.array([line2[0],line2[1]])\n e2 = np.array([line2[2],line2[3]])\n\n a1 = (s1[1] - e1[1]) / (s1[0] - e1[0])\n b1 = s1[1] - (a1 * s1[0])\n\n a2 = (s2[1] - e2[1]) / (s2[0] - e2[0])\n b2 = s2[1] - (a2 * s2[0])\n\n x = (b2 - b1) / (a1 - a2)\n y = a1 * x + b1\n\n return (x, y)\n\ndef illustrate_driving_lane_with_topdownview(image, left_line, right_line):\n \"\"\"\n #---------------------\n # This function illustrates top down view of the car on the road.\n # \n \"\"\"\n\n rows, cols = image.shape[:2]\n window_img = np.zeros_like(image)\n\n window_margin = 56\n left_plotx, right_plotx = left_line, right_line\n ploty = left_line\n lane_width = right_line[0] - left_line[0]\n lane_center = (right_line[0] + left_line[0]) / 2\n lane_offset = cols / 2 - (2*left_line[0] + lane_width) / 2\n car_offset = int(lane_center - 360)\n \n # Generate a polygon to illustrate the search window area\n # And recast the x and y points into usable format for cv2.fillPoly()\n left_line_window1 = np.array([np.transpose(np.vstack([right_plotx + lane_offset - lane_width - window_margin / 4, ploty]))])\n left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_plotx + lane_offset - lane_width+ window_margin / 4, ploty])))])\n left_line_pts = np.hstack((left_line_window1, left_line_window2))\n right_line_window1 = np.array([np.transpose(np.vstack([right_plotx + lane_offset - window_margin / 4, ploty]))])\n right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_plotx + lane_offset + window_margin / 4, ploty])))])\n right_line_pts = np.hstack((right_line_window1, right_line_window2))\n\n # Draw the lane onto the warped blank image\n cv.fillPoly(window_img, np.int_([left_line_pts]), (140, 0, 170))\n cv.fillPoly(window_img, np.int_([right_line_pts]), (140, 0, 170))\n\n # Recast the x and y points into usable format for cv2.fillPoly()\n pts_left = np.array([np.transpose(np.vstack([right_plotx + lane_offset - lane_width + window_margin / 4, ploty]))])\n pts_right = np.array([np.flipud(np.transpose(np.vstack([right_plotx + lane_offset - window_margin / 4, ploty])))])\n pts = np.hstack((pts_left, pts_right))\n\n # Draw the lane onto the warped blank image\n cv.fillPoly(window_img, np.int_([pts]), (0, 160, 0))\n\n #window_img[10:133,300:360] = img\n road_map = Image.new('RGBA', image.shape[:2], (0, 0, 0, 0))\n window_img = Image.fromarray(window_img)\n road_map.paste(window_img, (0, 0))\n road_map = np.array(road_map)\n road_map = cv.resize(road_map, (95, 95))\n road_map = cv.cvtColor(road_map, cv.COLOR_BGRA2BGR)\n\n return road_map\n\n# The video feed is read in as a VideoCapture object\ncap = cv.VideoCapture(\"/Users/karel/Documents/Master/IPM-F1Tenth/assets/levine_straights.mp4\")\n\nimg_array = []\ni = 0\nwhile (cap.isOpened()):\n # ret = a boolean return value from getting the frame, frame = the current frame being projected in the video\n ret, frame = cap.read()\n if ret == False:\n break\n width = 640\n height = 480\n dim = (width, height)\n \n # resize image\n resized = cv.resize(frame, dim, interpolation = cv.INTER_AREA)\n j = 0 \n \n cv.imwrite(\"frame\"+ str(j) + \".jpg\", resized)\n canny = do_canny(resized)\n #plt.imshow(canny)\n #plt.show()\n\n segment = do_segment(canny)\n # cv.imshow(\"canny\", segment)\n # plt.imshow(segment)\n # plt.show()\n # plt.imshow(segment)\n # plt.show()\n try:\n hough = cv.HoughLinesP(segment, 2, np.pi / 180, 100, np.array([]), minLineLength = 120, maxLineGap = 100)\n cdst = cv.cvtColor(segment, cv.COLOR_GRAY2BGR)\n cdstP = np.copy(cdst)\n if hough is not None:\n for i in range(0, len(hough)):\n l = hough[i][0]\n cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv.LINE_AA)\n \n #cv.imshow(\"Detected Lines (in red) - Probabilistic Line Transform\", cdstP)\n #print(hough)\n # Averages multiple detected lines from hough into one line for left border of lane and one line for right border of lane\n #print(hough)\n lines = calculate_lines(resized, hough)\n #print(lines)\n\n lines_visualize = visualize_lines(resized, lines)\n # Overlays lines on frame by taking their weighted sums and adding an arbitrary scalar value of 1 as the gamma argument\n # #print(lines[0][0],lines[0][1])\n x1 = int(lines[0][0])\n y1 = int(lines[0][1])\n A = (x1, y1)\n A_trans = (x1, y1 + 30)\n cv.circle(resized,(x1,y1),3,(0,255,0),5)\n font=cv.FONT_HERSHEY_SIMPLEX\n cv.putText(resized,'A',(x1,y1-10), font, 0.5,(0,255,0),1)\n x2 = int(lines[0][2])\n y2 = int(lines[0][3])\n cv.circle(resized,(x2,y2),3,(0,255,0),5)\n cv.putText(resized,'V',(x2,y2-10), font, 0.5,(0,255,0),1)\n # being start and end two points (x1,y1), (x2,y2)\n discrete_line_left = list(zip(*line(*(x1,y1), *(x2,y2))))\n D = discrete_line_left[len(discrete_line_left) - int(len(discrete_line_left)/5)]\n cv.circle(resized,D,3,(0,255,0),5)\n cv.putText(resized,'D',(D[0],D[1]-10), font, 0.5,(0,255,0),1)\n x = int(lines[1][2])\n y = int(lines[1][3])\n B = (x, y)\n B_trans = (x1, y1 + 30)\n cv.circle(resized,(x,y),3,(0,255,0),5)\n cv.putText(resized,'B',(x-10,y-10), font, 0.5,(0,255,0),1)\n discrete_line_right = list(zip(*line(*(x2,y2), *(x,y))))\n C = discrete_line_right[int(len(discrete_line_right)/5)]\n cv.circle(resized,C,3,(0,255,0),5)\n cv.putText(resized,'C',(C[0],C[1]-10), font, 0.5,(0,255,0),1)\n\n D_comma = np.array([discrete_line_left[len(discrete_line_left) - int(len(discrete_line_left)/5)][0], discrete_line_left[len(discrete_line_left) - int(len(discrete_line_left)/5)][1]])\n cv.circle(resized,D_comma,3,(255,0,0),5)\n cv.putText(resized,'D\\'',(D_comma[0],D_comma[1]-10), font, 0.5,(255,0,0),1)\n\n C_comma = np.array([discrete_line_right[int(len(discrete_line_right)/5)][0], discrete_line_right[int(len(discrete_line_right)/5)][1]])\n cv.circle(resized,C_comma,3,(255,0,0),5)\n cv.putText(resized,'C\\'',(C_comma[0],C_comma[1]-10), font, 0.5,(255,0,0),1)\n\n A_comma = discrete_line_left[int(len(discrete_line_left)/2)]\n cv.circle(resized,A_comma,3,(255,0,0),5)\n cv.putText(resized,'A\\'',(A_comma[0],A_comma[1]-10), font, 0.5,(255,0,0),1)\n\n B_comma = np.array([discrete_line_right[len(discrete_line_right) - int(len(discrete_line_right)/2)][0], discrete_line_right[len(discrete_line_right) - int(len(discrete_line_right)/2)][1]])\n cv.circle(resized,B_comma,3,(255,0,0),5)\n cv.putText(resized,'B\\'',(B_comma[0],B_comma[1]-10), font, 0.5,(255,0,0),1)\n #cv.line(resized, (int(np.float32(x1)), int(np.float32(y1))), (int(np.float32(discrete_line_left[len(discrete_line_left) - int(len(discrete_line_left)/3)][0])), int(np.float32(discrete_line_left[len(discrete_line_left) - int(len(discrete_line_left)/3)][1]))), (45, 255, 255), thickness=10)\n \n pts = np.array([(0,height - 60), A_comma, B_comma, (width, height - 60)])\n pts_transform = np.array([A_trans ,(D_comma[0]+30, D_comma[1]),(C_comma[0]+30,C_comma[1]),B_trans])\n #pts = np.array([(0,height), A ,D_comma,C_comma,B,(width, height)])\n overlay = resized.copy()\n cv.fillPoly(overlay, np.int_([pts]), (255, 0, 0))\n alpha = 0.5 # Transparency factor.\n\n #cv.circle(resized,(x,y),5,(255,0,255),3)\n # Following line overlays transparent rectangle over the image\n image_new = cv.addWeighted(overlay, alpha, resized, 1 - alpha, 0)\n #cv.circle(image_new,(int(x),int(y)),5,(255,0,255),3)\n output = cv.addWeighted(image_new, 0.9, lines_visualize, 1, 1)\n \n\n\n # Opens a new window and displays the output frame\n #cv.imshow(\"output\", output)\n #plt.imshow(output)\n # plt.show()\n\n birdeye_view_panel = np.zeros_like(output)\n result = output.copy()\n #info_panel[5:110, 5:325] = (255, 255, 255)\n warped = four_point_transform(resized, pts)\n birdeye_view_panel = warped.copy()\n #cv.imshow('road info', warped)\n\n scale_percent = 20\n width_bev = int(birdeye_view_panel.shape[1] * scale_percent / 100)\n height_bev = int(birdeye_view_panel.shape[0] * scale_percent / 100)\n dim_bev = (175, 175)\n birdeye_view_panel = cv.resize(birdeye_view_panel, dim_bev, interpolation = cv.INTER_AREA)\n print(birdeye_view_panel.shape)\n result[5:180, width-181:width-6,:] = birdeye_view_panel\n #road_map = illustrate_driving_lane_with_topdownview(output, lines[0], lines[1])\n #birdeye_view_panel[10:105, width-106:width-11] = road_map\n\n cv.imshow('road info', result)\n height, width, layers = result.shape\n size = (width,height)\n i = i + 1\n cv.imwrite('output_imgs/lines_alternative'+ str(i) + '.jpg', result) \n img_array.append(result)\n except Exception as e: \n print(e)\n\n # plt.imshow(output)\n # plt.show()\n # Make video\n # Frames are read by intervals of 10 milliseconds. The programs breaks out of the while loop when the user presses the 'q' key\n if cv.waitKey(10) & 0xFF == ord('q'):\n break\n\n# The following frees up resources and closes all windows\ncap.release()\ncv.destroyAllWindows()\n\nout = cv.VideoWriter('lines_edges_detector_alternative.mp4',cv.VideoWriter_fourcc(*'MP4V'), 30, size)\nfor i in range(len(img_array)):\n out.write(img_array[i])\n\n\n", "repo_name": "smejkka3/IPM-F1Tenth", "sub_path": "lane-detector-master/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 17536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "line.Line", "line_number": 8, "usage_type": "call"}, {"api_name": "line.Line", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 34, "usage_type": "call"}, {"api_name": "skimage.draw.line", "line_number": 43, "usage_type": "name"}, {"api_name": "skimage.draw.line.reshape", "line_number": 45, "usage_type": "call"}, {"api_name": "skimage.draw.line", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 222, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 225, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 231, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 234, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 237, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 237, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 238, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 238, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 241, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 242, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGRA2BGR", "line_number": 242, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 247, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 261, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 261, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 264, "usage_type": "call"}, {"api_name": "cv2.HoughLinesP", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 276, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 277, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 277, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 278, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 282, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 282, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 298, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 299, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 300, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 303, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 304, "usage_type": "call"}, {"api_name": "skimage.draw.line", "line_number": 306, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 308, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 309, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 314, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 315, "usage_type": "call"}, {"api_name": "skimage.draw.line", "line_number": 316, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 318, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 321, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 322, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 325, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 326, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 327, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 330, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 333, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 334, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 339, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 342, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 347, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 358, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 369, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 369, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 375, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 379, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 388, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 393, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 395, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 395, "usage_type": "call"}]} +{"seq_id": "73225610641", "text": "from flask import *\nfrom datetime import datetime\nimport requests\nbasic_datatype_app = Blueprint(\"basic_datatype_app\", __name__)\n\ndata_key = \"CWA-A66B5608-4EE6-4851-8CD5-CF7D4F70F4F0\"\ndata_url = \"https://opendata.cwa.gov.tw/api/v1/rest/datastore/F-D0047-091?Authorization=\"\n\n\ndef basic_data(location_name):\n response = requests.get(data_url+data_key+\"&locationName=\"+location_name)\n data = response.json()\n\n detail = data[\"records\"][\"locations\"][0][\"location\"][0]\n weather_data = detail[\"weatherElement\"][6][\"time\"]\n mintemp_data = detail[\"weatherElement\"][8][\"time\"]\n maxtemp_data = detail[\"weatherElement\"][12][\"time\"] \n\n time_detail = []\n for item in weather_data:\n date_format = \"%Y-%m-%d %H:%M:%S\"\n date_object = datetime.strptime(item[\"endTime\"], date_format)\n end_time = date_object.time()\n end_weekday = date_object.weekday()\n\n week_list = [\"星期一\", \"星期二\", \"星期三\", \"星期四\",\"星期五\", \"星期六\", \"星期日\"]\n time_data = {\n \"endDate\" : item[\"endTime\"],\n \"endWeek\" : week_list[end_weekday],\n \"endTime\" : str(end_time)\n }\n time_detail.append(time_data)\n\n weather_detail = []\n for item in weather_data:\n weather_code = item[\"elementValue\"][1][\"value\"]\n weather_detail.append(weather_code)\n\n mintemp_detail = []\n for item in mintemp_data:\n mintemp = item[\"elementValue\"][0][\"value\"]\n mintemp_detail.append(mintemp)\n\n maxtemp_detail = []\n for item in maxtemp_data:\n maxtemp = item[\"elementValue\"][0][\"value\"]\n maxtemp_detail.append(maxtemp)\n\n all_data = {\n\t\t\"locationName\" : detail[\"locationName\"],\n \"description\" : []\n\t}\n\n for time, weather, mintemp, maxtemp in zip(time_detail, weather_detail, mintemp_detail, maxtemp_detail):\n detail_data = {\n \"time\": time,\n \"weather\": weather,\n \"temperature\": mintemp + \"° - \" + maxtemp + \"°\"\n }\n all_data[\"description\"].append(detail_data)\n\n return all_data\n\ndef choose_daytime_data(all_data):\n final_data = {\n \"locationName\": all_data[\"locationName\"],\n \"detail\": []\n }\n\n for item in all_data[\"description\"]:\n if item[\"time\"][\"endTime\"] == \"18:00:00\":\n detail = {\n \"temperature\": item[\"temperature\"],\n \"endWeek\": item[\"time\"][\"endWeek\"],\n \"weatherCode\": item[\"weather\"]\n }\n final_data[\"detail\"].append(detail)\n\n return final_data\n\n@basic_datatype_app.route(\"/api/weekly_basic_data\")\ndef get_basic_data():\n location_name = str(request.args.get(\"location_name\", \"\"))\n\n all_data = basic_data(location_name)\n final_data = choose_daytime_data(all_data)\n\n return jsonify(final_data)", "repo_name": "ywyang236/wehelp-week8", "sub_path": "api/basic_datatype.py", "file_name": "basic_datatype.py", "file_ext": "py", "file_size_in_byte": 2809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "7495508106", "text": "from collections import Counter\n\n\nclass Solution:\n def canConstruct(self, ransomNote: str, magazine: str) -> bool:\n if len(ransomNote) > len(magazine):\n return False\n\n note_count = Counter(ransomNote)\n mag_count = Counter(magazine)\n\n for char in note_count:\n if note_count[char] > mag_count[char]:\n return False\n\n return True\n\n\n\"\"\"\nExplanation:\n\nInitialize dictionary note_count and mag_count to store the frequency of each character in the magazine. Iterate through each character in note_count, checking if the character frequency in note_count > the character frequency in mag_count. If so, return False. If the entire ransom note can be constructed from the magazine, return True.\n\nNotes:\n\nTime Complexity: O(m + n), where m and n are the lengths of the two input strings.\nSpace Complexity: O(1), as we never store more than 26 characters in the hashmap.\n\"\"\"\n\n# Test 1: Note length > magazine length\nnote = 'goodbye'\nmagazine = 'hello'\ncanConstruct = Solution().canConstruct(note, magazine)\nassert canConstruct == False, f\"Expected False but got {canConstruct}\"\n\n# Test 2: Can construct note from magazine\nnote = 'aa'\nmagazine = 'aab'\ncanConstruct = Solution().canConstruct(note, magazine)\nassert canConstruct == True, f\"Expected True but got {canConstruct}\"\n\n# Test 3: Cannot construct note from magazine\nnote = 'ac'\nmagazine = 'aab'\ncanConstruct = Solution().canConstruct(note, magazine)\nassert canConstruct == False, f\"Expected False but got {canConstruct}\"\n\n# Test 4: Multiple instances of note in magazine\nnote = 'apple'\nmagazine = 'apple pear apple'\ncanConstruct = Solution().canConstruct(note, magazine)\nassert canConstruct == True, f\"Expected True but got {canConstruct}\"\n", "repo_name": "garofalof/algopractice_python", "sub_path": "easy/383_Ransom_Note.py", "file_name": "383_Ransom_Note.py", "file_ext": "py", "file_size_in_byte": 1759, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "collections.Counter", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "37234759715", "text": "# test: 做出10個按照長度大小區分的長條圖\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n'''\nx = np.random.randn(1000) \nplt.hist(x,bins=20,color='b')\n \n\nplt.title(\"example\")\nplt.xlabel(\"X\")\nplt.ylabel(\"Y\")\nplt.show()\n\n\n'''\n\nnum1 = 10\nnum2 = 9\nnum3 = 8\nnum4 = 7\nnum5 = 6\nnum6 = 5\nnum7 = 4\nnum8 = 3\nnum9 = 2\nnum10 = 1\n\nlikes = [num1,num2,num3,num4,num5,num6,num7,num8,num9,num10] # 將喜愛數加入likes的陣列\n\nfor i in range(1,11): # 將喜愛度從1-10排序\n numbers = i\n \n\n# x = np.arange(len(numbers)) # 10\nx = np.arange(10) \nplt.bar(numbers, likes, \n color=['LightBlue',\n 'PowderBlue', \n 'LightSkyBlue', \n 'DodgerBlue', \n 'CornflowerBlue', \n 'RoyalBlue', \n 'Blue', \n 'MediumBlue',\n 'DarkBlue',\n 'MidnightBlue'\n ])\n# plt.xticks(numbers, likes)\nplt.xlabel('Numbers')\nplt.ylabel('Likes')\nplt.title('Dcard-Meme-Collection')\nplt.show()", "repo_name": "iamsherry000/Python", "sub_path": "CollegeHW/FinalProject/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.arange", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "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"}]} +{"seq_id": "36845567908", "text": "import enum\nclass Solution:\n def minCameraCover(self, root):\n self.cameras = 0\n \n def dfs(node):\n if node is None: return States.MONITORED_NOCAM\n left, right = dfs(node.left), dfs(node.right)\n if States.UNMONITORED in (left, right):\n self.cameras += 1\n return States.HAS_CAMERA\n if States.HAS_CAMERA in (left, right):\n return States.MONITORED_NOCAM\n return States.UNMONITORED\n\n return int(dfs(root) == States.UNMONITORED) + self.cameras\n\nclass States(enum.Enum):\n HAS_CAMERA = 1\n MONITORED_NOCAM = 2\n UNMONITORED = 3\n", "repo_name": "simonesestili/problems-dsa", "sub_path": "968.py", "file_name": "968.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "enum.Enum", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "17487873391", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('demandas', views.listar_demandas, name='listar_demandas'),\n path('demanda/', views.buscar_demanda, name='buscar_demanda'),\n path('adicionar', views.adicionar_demanda, name='adicionar_demanda'),\n path('editar', views.editar_demanda, name='editar_demanda'),\n path('excluir/', views.excluir_demanda, name='excluir_demanda'),\n path('finalizar/', views.finalizar_demanda, name='finalizar_demanda'),\n\n path('usuario', views.criar_usuario, name='criar_usuario'),\n]", "repo_name": "karolGuimaraes/droids-api", "sub_path": "droid_api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 575, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"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"}]} +{"seq_id": "7846577236", "text": "\"\"\"ICL Kubernetes runtime implementation.\n\nTODO:\n* deployment timeout\n* run timeout\n* cancel (required for integration test)\n\"\"\"\n\nfrom __future__ import annotations\n\nimport base64\nimport enum\nimport functools\nimport pathlib\nimport random\nimport string\nimport sys\nimport tempfile\nfrom typing import Any, Dict, List, Optional, Union\n\nimport fsspec\nimport s3fs\nfrom kubernetes import client, watch\n\nimport infractl.base\nimport infractl.fs\nfrom infractl import defaults, identity, kubernetes\nfrom infractl.plugins.kubernetes_runtime import engine\nfrom infractl.plugins.kubernetes_runtime.program import load\n\nKubernetesManifest = infractl.base.KubernetesManifest\n\n\nclass KubernetesRuntimeError(Exception):\n \"\"\"Kubernetes runtime error.\"\"\"\n\n\nclass KubernetesRuntimeSettings:\n \"\"\"Kubernetes runtime settings.\"\"\"\n\n namespace: str = 'default'\n\n image: str = defaults.PREFECT_IMAGE\n\n s3_base_path: str = 'prefect/infractl/kubernetes'\n\n working_dir = '/root'\n\n dependencies = ['s3cmd', 'pydantic']\n \"\"\"Required dependencies to install before downloading program.\"\"\"\n\n address = 'localtest.me'\n\n @property\n def s3_url(self):\n \"\"\"S3 endpoint.\"\"\"\n return f'http://s3.{self.address}'\n\n @property\n def prefect_api_url(self):\n \"\"\"Prefect endpoint.\"\"\"\n return f'http://prefect.{self.address}/api'\n\n @property\n def remote_fs_spec(self):\n \"\"\"Remote fs spec.\"\"\"\n return {\n 'key': 'x1miniouser',\n 'secret': 'x1miniopass',\n 'use_ssl': False,\n 'client_kwargs': {\n 'endpoint_url': self.s3_url,\n },\n }\n\n\nclass RemoteStorage:\n \"\"\"Remote storage.\"\"\"\n\n fs: fsspec.AbstractFileSystem\n base_path: str\n\n def __init__(self, fs: fsspec.AbstractFileSystem, base_path: str):\n self.fs = fs\n self.base_path = base_path\n\n\nclass KubernetesRuntimeImplementation(\n infractl.base.RuntimeImplementation, registration_name='kubernetes'\n):\n \"\"\"Kubernetes runtime implementation.\"\"\"\n\n settings = KubernetesRuntimeSettings()\n\n async def deploy(\n self,\n program: infractl.base.program.Program,\n name: Optional[str] = None,\n **kwargs,\n ) -> infractl.base.DeployedProgram:\n \"\"\"Deploys a program.\"\"\"\n\n program = load(program)\n\n program_path = pathlib.Path(program.path)\n random_part = ''.join(random.choices(string.ascii_letters + string.digits, k=5))\n if name:\n name = identity.sanitize(name)\n else:\n name = identity.generate(suffix=f'{program_path.stem}-{random_part}')\n\n base_path = f'{self.settings.s3_base_path}/{name}'\n remote_fs = s3fs.S3FileSystem(**self.settings.remote_fs_spec)\n\n code_path = f'{base_path}/code'\n data_path = f'{base_path}/data'\n\n # upload the program\n remote_fs.put(str(program_path), f'{code_path}/')\n\n # upload runtime files\n if self.runtime.files:\n with tempfile.TemporaryDirectory() as dirname:\n target_path = pathlib.Path(dirname)\n infractl.fs.prepare_to_upload(self.runtime.files, target_path)\n remote_fs.put(lpath=f'{dirname}/', rpath=f'{data_path}/', recursive=True)\n\n # upload engine\n remote_fs.put(lpath=engine.__file__, rpath=f'{data_path}/')\n\n # upload dependencies\n if self.runtime.dependencies.pip:\n with tempfile.NamedTemporaryFile(delete=False) as requirements_file:\n requirements_file.write('\\n'.join(self.runtime.dependencies.pip).encode('utf-8'))\n requirements_file.flush()\n requirements_file.close()\n remote_fs.put(lpath=requirements_file.name, rpath=f'{data_path}/requirements.txt')\n\n secret = _get_secret(name, _get_s3cmd_config())\n kubernetes.api().recreate_secret(namespace=self.settings.namespace, body=secret)\n\n job = _get_job(name, self.settings.namespace)\n job.spec.template.spec.containers[0].image = self.settings.image\n job.spec.template.spec.volumes = [\n client.V1Volume(name='s3cfg', secret=client.V1SecretVolumeSource(secret_name=name))\n ]\n job.spec.template.spec.containers[0].volume_mounts = [\n client.V1VolumeMount(name='s3cfg', read_only=True, mount_path='/secrets')\n ]\n job.spec.template.spec.containers[0].working_dir = self.settings.working_dir\n\n s3cmd_get = 's3cmd --config=/secrets/.s3cfg get --recursive --force'\n s3cmd_put = 's3cmd --config=/secrets/.s3cfg put --force'\n engine_cmd = f'python $__ICL_DATA_DIR/engine.py {program_path.name}'\n if program.name:\n engine_cmd += f' --entrypoint {program.name}'\n elif program.flow:\n engine_cmd += f' --flow {program.flow}'\n\n command_lines = []\n if self.settings.dependencies:\n command_lines = [f'pip install {\" \".join(self.settings.dependencies)}']\n\n command_lines += [\n '__ICL_DATA_DIR=$(mktemp -d)',\n 'pip install s3cmd pydantic',\n f'{s3cmd_get} s3://{code_path}/ .',\n f'{s3cmd_get} s3://{data_path}/ $__ICL_DATA_DIR/',\n 'if [[ -f \"$__ICL_DATA_DIR/requirements.txt\" ]]; then',\n ' pip install -r \"$__ICL_DATA_DIR/requirements.txt\"',\n 'fi',\n 'if [[ -f \"$__ICL_DATA_DIR/cwd.tar\" ]]; then tar xvf \"$__ICL_DATA_DIR/cwd.tar\"; fi',\n 'pwd',\n 'ls -l . $__ICL_DATA_DIR',\n 'export PYTHONPATH=$PWD',\n engine_cmd,\n 'if [[ -f \"$__ICL_DATA_DIR/result.json\" ]]; then',\n f' {s3cmd_put} \"$__ICL_DATA_DIR/result.json\" s3://{data_path}/',\n 'fi',\n ]\n job.spec.template.spec.containers[0].command = [\n '/bin/bash',\n '-xec',\n '\\n'.join(command_lines),\n ]\n\n env = self.runtime.environment.copy()\n\n if program.flow:\n env['PREFECT_API_URL'] = self.settings.prefect_api_url\n\n if env:\n job.spec.template.spec.containers[0].env = [\n client.V1EnvVar(name=key, value=value) for key, value in env.items()\n ]\n\n # TODO: use activeDeadlineSeconds for the timeout\n\n return infractl.base.DeployedProgram(\n program=program,\n runner=KubernetesRunner(job, RemoteStorage(fs=remote_fs, base_path=base_path)),\n )\n\n\nclass ProgramState(enum.Enum):\n UNKNOWN = enum.auto()\n SCHEDULED = enum.auto()\n RUNNING = enum.auto()\n COMPLETED = enum.auto()\n FAILED = enum.auto()\n\n def capitalize(self) -> str:\n \"\"\"Returns a capitalized state, for example \"Completed\".\"\"\"\n return self.name.lower().capitalize()\n\n\nclass KubernetesRunner(infractl.base.Runnable):\n \"\"\"Kubernetes runner.\"\"\"\n\n manifest: client.V1Job\n storage: RemoteStorage\n state: ProgramState\n\n def __init__(self, manifest: client.V1Job, storage: RemoteStorage):\n self.manifest = manifest\n self.storage = storage\n self.state = ProgramState.UNKNOWN\n\n @property\n def name(self):\n \"\"\"Return Job name.\"\"\"\n return self.manifest.metadata.name\n\n @property\n def namespace(self):\n \"\"\"Return Job namespace.\"\"\"\n return self.manifest.metadata.namespace\n\n @property\n def data_path(self):\n \"\"\"Returns data path.\"\"\"\n return f'{self.storage.base_path}/data'\n\n async def run(\n self,\n parameters: Union[Dict[str, Any], List[str], None] = None,\n timeout: Optional[float] = None,\n detach: bool = False,\n ) -> infractl.base.ProgramRun:\n \"\"\"Runs this runnable.\n\n Args:\n parameters: a dictionary of named arguments if a program's entry point is a function,\n a list of arguments otherwise.\n timeout: timeout in seconds to wait for a program completion, `None` (default) to wait\n forever.\n detach: `False` (default) to wait for a program completion, `True` to start the program\n and detach from it.\n \"\"\"\n\n # upload parameters\n if parameters:\n with tempfile.NamedTemporaryFile(delete=False) as requirements_file:\n requirements_file.write(engine.dumps(parameters))\n requirements_file.flush()\n requirements_file.close()\n self.storage.fs.put(\n lpath=requirements_file.name,\n rpath=f'{self.data_path}/parameters.json',\n )\n\n kubernetes.api().recreate_job(namespace=self.namespace, body=self.manifest)\n self.state = ProgramState.SCHEDULED\n\n program_run = KubernetesProgramRun(self, timeout=timeout)\n if not detach:\n await program_run.wait()\n return program_run\n\n\nclass KubernetesProgramRun(infractl.base.ProgramRun):\n \"\"\"Kubernetes program run.\"\"\"\n\n runner: KubernetesRunner\n timeout: Optional[float] = None\n\n def __init__(self, runner: KubernetesRunner, timeout: Optional[float] = None):\n self.runner = runner\n self.timeout = timeout\n\n def is_scheduled(self) -> bool:\n return self.runner.state == ProgramState.SCHEDULED\n\n def is_pending(self) -> bool:\n return False\n\n def is_running(self) -> bool:\n return self.runner.state == ProgramState.RUNNING\n\n def is_completed(self) -> bool:\n return self.runner.state == ProgramState.COMPLETED\n\n def is_failed(self) -> bool:\n return self.runner.state == ProgramState.FAILED\n\n def is_crashed(self) -> bool:\n return False\n\n def is_cancelling(self) -> bool:\n return False\n\n def is_cancelled(self) -> bool:\n return False\n\n def is_final(self) -> bool:\n return self.runner.state in (ProgramState.COMPLETED, ProgramState.FAILED)\n\n def is_paused(self) -> bool:\n return False\n\n @functools.cached_property\n def pod_name(self) -> str:\n \"\"\"Returns program pod name\"\"\"\n pod_list: client.V1PodList = (\n kubernetes.api()\n .core_v1()\n .list_namespaced_pod(\n namespace=self.runner.namespace,\n label_selector=f'job-name={self.runner.name}',\n )\n )\n if len(pod_list.items) > 1:\n raise KubernetesRuntimeError(f'Multiple pods for job {self.runner.name}')\n if len(pod_list.items) < 1:\n raise KubernetesRuntimeError(f'Pod not found for job {self.runner.name}')\n return pod_list.items[0].metadata.name\n\n async def wait(self, wait_for: Optional[ProgramState] = None) -> None:\n for event in watch.Watch().stream(\n func=kubernetes.api().core_v1().list_namespaced_pod,\n namespace=self.runner.namespace,\n timeout_seconds=3600,\n label_selector=f'job-name={self.runner.name}',\n ):\n if event['object'].status.phase == 'Succeeded':\n self.runner.state = ProgramState.COMPLETED\n return\n elif event['object'].status.phase == 'Failed':\n self.runner.state = ProgramState.FAILED\n return\n elif event['object'].status.phase == 'Running':\n self.runner.state = ProgramState.RUNNING\n if self.runner.state == wait_for:\n return\n # deleted while watching for it\n if event['type'] == 'DELETED':\n self.runner.state = ProgramState.FAILED\n return\n\n # timed out\n # TODO: timed out, stop job if it is still running\n\n async def result(self) -> Any:\n \"\"\"Returns program result.\"\"\"\n result_remote_path = f'{self.runner.data_path}/result.json'\n if not self.runner.storage.fs.exists(result_remote_path):\n return None\n with tempfile.TemporaryDirectory() as dirname:\n result_path = pathlib.Path(dirname) / 'results.json'\n self.runner.storage.fs.get(rpath=result_remote_path, lpath=str(result_path))\n with result_path.open('rb') as result_file:\n return engine.loads(result_file.read())\n\n async def logs(self) -> List[str]:\n \"\"\"Returns program logs.\"\"\"\n return (\n kubernetes.api()\n .core_v1()\n .read_namespaced_pod_log(\n name=self.pod_name,\n namespace=self.runner.namespace,\n container='program',\n )\n .splitlines()\n )\n\n async def stream_logs(self, file=None) -> None:\n \"\"\"Stream logs until the terminal state is reached.\n\n Args:\n file: a file-like object (stream); defaults to the current sys.stdout.\n \"\"\"\n await self.wait(wait_for=ProgramState.RUNNING)\n if not self.is_running():\n return\n file = file or sys.stdout\n for line in watch.Watch().stream(\n kubernetes.api().core_v1().read_namespaced_pod_log,\n name=self.pod_name,\n namespace=self.runner.namespace,\n container='program',\n ):\n print(line, file=file)\n await self.wait()\n\n def __repr__(self) -> str:\n \"\"\"Returns a string representation.\n\n Note that JupyterLab uses __repr__ instead of __str__.\n \"\"\"\n return f'{self.runner.name} ({self.runner.state.capitalize()})'\n\n\ndef _get_secret(name: str, data: str) -> client.V1Secret:\n \"\"\"Returns Kubernetes Secret.\"\"\"\n return client.V1Secret(\n api_version='v1',\n kind='Secret',\n metadata=client.V1ObjectMeta(name=name),\n data={\n '.s3cfg': base64.b64encode(data.encode('utf-8')).decode('utf-8'),\n },\n )\n\n\ndef _get_s3cmd_config() -> str:\n return '\\n'.join(\n [\n '[default]',\n 'access_key = x1miniouser',\n 'secret_key = x1miniopass',\n 'signature_v2 = False',\n 'use_https = True',\n 'check_ssl_certificate = False',\n 'host_base = minio.minio',\n 'host_bucket = minio.minio',\n ]\n )\n\n\ndef _get_job(name: str, namespace: str) -> client.V1Job:\n \"\"\"Returns Kubernetes Job.\"\"\"\n return client.V1Job(\n api_version='batch/v1',\n kind='Job',\n metadata=client.V1ObjectMeta(name=name, namespace=namespace),\n spec=client.V1JobSpec(\n template=client.V1JobTemplateSpec(\n spec=client.V1PodSpec(\n containers=[\n client.V1Container(\n name='program',\n image_pull_policy='IfNotPresent',\n ),\n ],\n restart_policy='Never',\n )\n ),\n backoff_limit=0,\n completion_mode='NonIndexed',\n completions=1,\n parallelism=1,\n ttl_seconds_after_finished=600,\n ),\n )\n", "repo_name": "intel-ai/icl", "sub_path": "src/infractl/plugins/kubernetes_runtime/runtime.py", "file_name": "runtime.py", "file_ext": "py", "file_size_in_byte": 14992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "3", "api": [{"api_name": "infractl.base.base", "line_number": 31, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 31, "usage_type": "name"}, {"api_name": "infractl.defaults.PREFECT_IMAGE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "infractl.defaults", "line_number": 43, "usage_type": "name"}, {"api_name": "fsspec.AbstractFileSystem", "line_number": 80, "usage_type": "attribute"}, {"api_name": "fsspec.AbstractFileSystem", "line_number": 83, "usage_type": "attribute"}, {"api_name": "infractl.base.base", "line_number": 89, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 89, "usage_type": "name"}, {"api_name": "infractl.base.base", "line_number": 97, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 98, "usage_type": "name"}, {"api_name": "infractl.plugins.kubernetes_runtime.program.load", "line_number": 103, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 105, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 106, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 106, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 106, "usage_type": "attribute"}, {"api_name": "infractl.identity.sanitize", "line_number": 108, "usage_type": "call"}, {"api_name": "infractl.identity", "line_number": 108, "usage_type": "name"}, {"api_name": "infractl.identity.generate", "line_number": 110, "usage_type": "call"}, {"api_name": "infractl.identity", "line_number": 110, "usage_type": "name"}, {"api_name": "s3fs.S3FileSystem", "line_number": 113, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 123, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 124, "usage_type": "call"}, {"api_name": "infractl.base.fs.prepare_to_upload", "line_number": 125, "usage_type": "call"}, {"api_name": "infractl.base.fs", "line_number": 125, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 125, "usage_type": "name"}, {"api_name": "infractl.plugins.kubernetes_runtime.engine.__file__", "line_number": 129, "usage_type": "attribute"}, {"api_name": "infractl.plugins.kubernetes_runtime.engine", "line_number": 129, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 133, "usage_type": "call"}, {"api_name": "infractl.kubernetes.api", "line_number": 140, "usage_type": "call"}, {"api_name": "infractl.kubernetes", "line_number": 140, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Volume", "line_number": 145, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 145, "usage_type": "name"}, {"api_name": "kubernetes.client.V1SecretVolumeSource", "line_number": 145, "usage_type": "call"}, {"api_name": "kubernetes.client.V1VolumeMount", "line_number": 148, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 148, "usage_type": "name"}, {"api_name": "kubernetes.client.V1EnvVar", "line_number": 194, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 194, "usage_type": "name"}, {"api_name": "infractl.base.base.DeployedProgram", "line_number": 199, "usage_type": "call"}, {"api_name": "infractl.base.base", "line_number": 199, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 199, "usage_type": "name"}, {"api_name": "infractl.base.base", "line_number": 100, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 100, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 205, "usage_type": "attribute"}, {"api_name": "enum.auto", "line_number": 206, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 207, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 208, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 209, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 210, "usage_type": "call"}, {"api_name": "infractl.base.base", "line_number": 217, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 217, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Job", "line_number": 220, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 220, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Job", "line_number": 224, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 224, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 247, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 263, "usage_type": "call"}, {"api_name": "infractl.plugins.kubernetes_runtime.engine.dumps", "line_number": 264, "usage_type": "call"}, {"api_name": "infractl.plugins.kubernetes_runtime.engine", "line_number": 264, "usage_type": "name"}, {"api_name": "infractl.kubernetes.api", "line_number": 272, "usage_type": "call"}, {"api_name": "infractl.kubernetes", "line_number": 272, "usage_type": "name"}, {"api_name": "infractl.base.base", "line_number": 249, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 249, "usage_type": "name"}, {"api_name": "infractl.base.base", "line_number": 281, "usage_type": "attribute"}, {"api_name": "infractl.base", "line_number": 281, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 285, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 287, "usage_type": "name"}, {"api_name": "kubernetes.client.V1PodList", "line_number": 324, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 324, "usage_type": "name"}, {"api_name": "infractl.kubernetes.api", "line_number": 325, "usage_type": "call"}, {"api_name": "infractl.kubernetes", "line_number": 325, "usage_type": "name"}, {"api_name": "functools.cached_property", "line_number": 321, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 338, "usage_type": "name"}, {"api_name": "kubernetes.watch.Watch", "line_number": 339, "usage_type": "call"}, {"api_name": "kubernetes.watch", "line_number": 339, "usage_type": "name"}, {"api_name": "infractl.kubernetes.api", "line_number": 340, "usage_type": "call"}, {"api_name": "infractl.kubernetes", "line_number": 340, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 368, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 369, "usage_type": "call"}, {"api_name": "infractl.plugins.kubernetes_runtime.engine.loads", "line_number": 372, "usage_type": "call"}, {"api_name": "infractl.plugins.kubernetes_runtime.engine", "line_number": 372, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 363, "usage_type": "name"}, {"api_name": "infractl.kubernetes.api", "line_number": 377, "usage_type": "call"}, {"api_name": "infractl.kubernetes", "line_number": 377, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 374, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 396, "usage_type": "attribute"}, {"api_name": "kubernetes.watch.Watch", "line_number": 397, "usage_type": "call"}, {"api_name": "kubernetes.watch", "line_number": 397, "usage_type": "name"}, {"api_name": "infractl.kubernetes.api", "line_number": 398, "usage_type": "call"}, {"api_name": "infractl.kubernetes", "line_number": 398, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Secret", "line_number": 416, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 416, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ObjectMeta", "line_number": 419, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 419, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 421, "usage_type": "call"}, {"api_name": "kubernetes.client.V1Secret", "line_number": 414, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 414, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Job", "line_number": 443, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 443, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ObjectMeta", "line_number": 446, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 446, "usage_type": "name"}, {"api_name": "kubernetes.client.V1JobSpec", "line_number": 447, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 447, "usage_type": "name"}, {"api_name": "kubernetes.client.V1JobTemplateSpec", "line_number": 448, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 448, "usage_type": "name"}, {"api_name": "kubernetes.client.V1PodSpec", "line_number": 449, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 449, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Container", "line_number": 451, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 451, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Job", "line_number": 441, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 441, "usage_type": "name"}]} +{"seq_id": "12702269060", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('tours', '0003_auto_20160209_1633'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='tour',\n name='slug',\n field=models.SlugField(unique=True, blank=True),\n ),\n ]\n", "repo_name": "MBuda03/localfriend", "sub_path": "src/tours/migrations/0004_tour_slug.py", "file_name": "0004_tour_slug.py", "file_ext": "py", "file_size_in_byte": 404, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "35769920651", "text": "import os\n\nfrom pyramid.view import view_config\nfrom pyramid.exceptions import NotFound\nfrom pyramid.exceptions import Forbidden\nfrom pyramid.response import Response\nfrom pyramid.httpexceptions import HTTPFound\n\nfrom pyramid_mailer.message import Message\nfrom pyramid_mailer.message import Attachment\n\nfrom formencode import Schema, validators\nfrom formencode.schema import SimpleFormValidator\n\nfrom pyramid_simpleform import Form\nfrom pyramid_simpleform import State\nfrom pyramid_simpleform.renderers import FormRenderer\n\nfrom pyramid.security import remember\nfrom pyramid.security import forget\n\nfrom formative.security import authenticate\n\n\n_here = os.path.dirname(__file__)\n_icon = open(os.path.join(_here, '../static', 'favicon.ico')).read()\n_fi_response = Response(content_type='image/x-icon', body=_icon)\n\n_robots = open(os.path.join(_here, '../static', 'robots.txt')).read()\n_robots_response = Response(content_type='text/plain', body=_robots)\n\n@view_config(name='favicon.ico')\ndef favicon_view(context, request):\n return _fi_response\n\n@view_config(name='robots.txt')\ndef robotstxt_view(context, request):\n return _robots_response\n\n'''@view_config(name=\"googleb9d74e8206a80fc2.html\")\ndef del_this (context, request):\n return Response(content_type='text/plain', body=\"google-site-verification: googleb9d74e8206a80fc2.html\")\n'''\n\nclass ContactSchema(Schema):\n \"\"\"\n Schema for contact form\n \"\"\"\n filter_extra_fields = True\n allow_extra_fields = True\n \n subject = validators.String(max=300, not_empty=True, messages={'empty':'Subject line cannot be empty.'})\n name = validators.String(max=300, not_empty=True)\n email = validators.Email(not_empty=True, resolve_domain=True)\n message = validators.String(not_empty=True)\n\n\n@view_config(context='formative:resources.Contact',\n renderer='/derived/contact.mak')\ndef contact_view(request):\n form = Form(request, schema=ContactSchema)\n \n if form.validate():\n \n mailer = request.registry['mailer']\n \n office_email = request.registry.settings['office.email']\n \n from_ = \"%s <%s>\" % (form.data['name'], form.data['email'])\n \n message = Message(\n subject=form.data['subject'] + ' [formative.co.za]',\n sender=form.data['email'],\n recipients=[office_email],\n body=form.data['message'],\n extra_headers = {\"From\": from_}\n )\n \n mailer.send_immediately(message)\n \n request.session.flash('Message sent.', queue='info')\n\n return HTTPFound(location=\"/\")\n \n if form.errors:\n request.session.flash('There are errors in your form.', queue='error')\n \n return {\"renderer\":FormRenderer(form)}\n\n@view_config(context='formative:resources.Root',\n renderer='/derived/home.mak')\ndef home_view(request):\n return {'project':'formative'}\n \n@view_config(context=NotFound, renderer=\"/derived/error/notfound.mak\")\ndef notfound_view(context, request):\n request.response.status_int = 404\n if request.referrer:\n return {\"referrer\" : request.referrer}\n else:\n return {\"referrer\" : \"/\"}\n \n@view_config(context=Forbidden, renderer=\"/derived/error/forbidden.mak\")\ndef forbidden_view(context, request):\n request.response.status_int = 403\n return {\"path\" : request.path}\n", "repo_name": "circlingthesun/Formative", "sub_path": "formative/views/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 3431, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pyramid.response.Response", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pyramid.response.Response", "line_number": 30, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 32, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 36, "usage_type": "call"}, {"api_name": "formencode.Schema", "line_number": 45, "usage_type": "name"}, {"api_name": "formencode.validators.String", "line_number": 52, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 52, "usage_type": "name"}, {"api_name": "formencode.validators.String", "line_number": 53, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 53, "usage_type": "name"}, {"api_name": "formencode.validators.Email", "line_number": 54, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 54, "usage_type": "name"}, {"api_name": "formencode.validators.String", "line_number": 55, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 55, "usage_type": "name"}, {"api_name": "pyramid_simpleform.Form", "line_number": 61, "usage_type": "call"}, {"api_name": "pyramid_mailer.message.Message", "line_number": 71, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 83, "usage_type": "call"}, {"api_name": "pyramid_simpleform.renderers.FormRenderer", "line_number": 88, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 58, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 90, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 95, "usage_type": "call"}, {"api_name": "pyramid.exceptions.NotFound", "line_number": 95, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 103, "usage_type": "call"}, {"api_name": "pyramid.exceptions.Forbidden", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "16218631767", "text": "from PIL import Image, ImageDraw, ImageFont, ImageOps\n\ndef get_orientation(width, height):\n if width > height:\n return \"landscape\"\n else:\n return \"portrait\"\n\ndef reduce_size(path):\n \"\"\" reduce size image \"\"\"\n src_photo = Image.open(path) \n photo = ImageOps.exif_transpose(src_photo)\n width, height = photo.size\n orientation = get_orientation(width, height)\n if orientation == 'landscape':\n photo.thumbnail((1280,720), Image.ANTIALIAS)\n else: \n photo.thumbnail((720, 1280), Image.ANTIALIAS)\n photo.save(path, quality=95, optimize=True)\n\ndef add_watermark(path):\n \"\"\" add watermark to an image\"\"\" \n watermark_txt = \"www.influ.com\" \n font = ImageFont.truetype('arial.ttf', 38)\n \n src_photo = Image.open(path) \n photo = ImageOps.exif_transpose(src_photo)\n width, height = photo.size\n \n draw = ImageDraw.Draw(photo)\n txt_w, txt_h = draw.textsize(watermark_txt, font)\n position = ((width - txt_w)/2,(height - txt_h)/2)\n draw.text(position, watermark_txt, font=font)\n\n photo.save(path)\n", "repo_name": "zhou1925/influyente_project", "sub_path": "photos/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1089, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "PIL.Image.open", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "PIL.ImageOps.exif_transpose", "line_number": 12, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 12, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 24, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 26, "usage_type": "name"}, {"api_name": "PIL.ImageOps.exif_transpose", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 27, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "5763871712", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n\n\n\n# In[1]:\n\n\n#モジュールの読み込み\nfrom __future__ import print_function\n\nimport pandas as pd\nfrom pandas import Series,DataFrame\n\nfrom sklearn import svm\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport keras\nfrom keras.datasets import fashion_mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import RMSprop\nfrom keras.optimizers import Adam\n\n\n# In[2]:\n\n\n#CSVファイルの読み込み\ndata_set = pd.read_csv(\"Sample_Data.csv\",sep=\",\",header=0)\n\ndata_set.head(3)\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[3]:\n\n\n\n# データの分割\n(train, test) = train_test_split(data_set, test_size=0.2, shuffle=True)\n\n\n#data_set.columns = ['time','country','case','cure','death','longitude','latitude']\n\n\nx_train = train.loc[:, ['x1','x2','x3','x4']]\ny_train = train.loc[:, ['S']]\n\nx_test = test.loc[:, ['x1','x2','x3','x4']]\ny_test = test.loc[:, ['S']]\n\n#データの整形\nx_train = x_train.astype(np.float)\nx_test = x_test.astype(np.float)\n\ny_train = y_train.astype(np.float)\ny_test = y_test.astype(np.float)\n\n\n# In[4]:\n\n\nx_train\n\n\n# In[6]:\n\n\nx_test\n\n\n# In[7]:\n\n\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom keras import regularizers\n\n\n#ニューラルネットワークの実装\nmodel = Sequential()\n\nmodel.add(Dense(20, activation='relu',kernel_regularizer=keras.regularizers.l2(0.001), input_shape=(4,)))\n#model.add(Dropout(0.1))\n\nmodel.add(Dense(20, activation='relu',kernel_regularizer=keras.regularizers.l2(0.001), input_shape=(4,)))\n#model.add(Dropout(0.1))\n\nmodel.add(Dense(1, activation='linear'))\n\n\n\nmodel.summary()\nprint(\"\\n\")\n\n\n\n#ニューラルネットワークの実装②\nmodel.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_squared_error'])\n\n\nearly_stopping = EarlyStopping(\n monitor='val_loss',\n min_delta=0.0,\n patience=50,\n )\n\n# val_lossの改善が20エポック見られなかったら、学習率を0.7倍する。\nreduce_lr = ReduceLROnPlateau(\n monitor='val_loss',\n factor=0.7,\n patience=20,\n min_lr=0.001\n )\n\n#ニューラルネットワークの学習\nhistory = model.fit(x_train, y_train,batch_size=10,epochs=100,verbose=1,validation_data=(x_test, y_test),callbacks=[early_stopping, reduce_lr])\n\n#ニューラルネットワークの推論\nscore = model.evaluate(x_test,y_test,verbose=1)\nprint(\"\\n\")\nprint(\"Test loss:\",score[0])\nprint(\"Test mean_squared_error\",score[1])\n\n\ndef plot_history(history):\n # print(history.history.keys())\n\n # 精度の履歴をプロット\n plt.plot(history.history['mean_squared_error'])\n plt.plot(history.history['val_mean_squared_error'])\n plt.title('model mean_squared_error')\n plt.xlabel('epoch')\n plt.ylabel('mean_squared_error')\n plt.legend(['mean_squared_error', 'val_mean_squared_error'], loc='lower right')\n plt.show()\n\n # 損失の履歴をプロット\n plt.plot(history.history['loss'])\n plt.plot(history.history['val_loss'])\n plt.title('model loss')\n plt.xlabel('epoch')\n plt.ylabel('loss')\n plt.legend(['loss', 'val_loss'], loc='lower right')\n plt.show()\n\n\n# 学習履歴をプロット\nplot_history(history)\n\n\n# In[ ]:\n\n\n#新たなデータで学習したモデルを使ってみる\n\n\n# In[8]:\n\n\n#CSVファイルの読み込み\ndata_set = pd.read_csv(\"Predict_Data.csv\",sep=\",\",header=0)\n\ndata_set.head(5)\n\n\n# In[9]:\n\n\nx_pred = data_set.loc[:, ['x1','x2','x3','x4']]\nx_pred = x_pred.astype(np.float)\n\nprint(x_pred)\n\n\n# In[10]:\n\n\n#モデルを使って予測\npredictions = model.predict(x_pred)\nprint(predictions)\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "hamachi1201/CoronaAnalysis", "sub_path": "example.py", "file_name": "example.py", "file_ext": "py", "file_size_in_byte": 3962, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 77, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 102, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 105, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 190, "usage_type": "attribute"}]} +{"seq_id": "23863765455", "text": "from collections import defaultdict\n\nimport numpy as np\nimport networkx as nx\nimport holoviews as _hv\n\nfrom bokeh.models import HoverTool\nfrom holoviews import Graph, Labels, dim\nfrom holoviews.core.options import Store\nfrom holoviews.core.util import dimension_sanitizer\nfrom holoviews.plotting.bokeh import GraphPlot, LabelsPlot\nfrom holoviews.plotting.bokeh.styles import markers\n\nfrom .backend_transforms import _transfer_opts_cur_backend\nfrom .util import process_crs\nfrom .utilities import save, show # noqa\n\nif _hv.extension and not getattr(_hv.extension, '_loaded', False):\n _hv.extension('bokeh', logo=False)\n\n\ndef _from_networkx(G, positions, nodes=None, cls=Graph, **kwargs):\n \"\"\"\n Generate a Graph element from a networkx.Graph object and networkx\n layout function or dictionary of node positions. Any keyword\n arguments will be passed to the layout function. By default it\n will extract all node and edge attributes from the networkx.Graph\n but explicit node information may also be supplied. Any non-scalar\n attributes, such as lists or dictionaries will be ignored.\n\n Parameters\n ----------\n G : networkx.Graph\n Graph to convert to Graph element\n positions : dict or callable\n Node positions defined as a dictionary mapping from node id to\n (x, y) tuple or networkx layout function which computes a\n positions dictionary.\n kwargs : dict\n Keyword arguments for the element\n\n Returns\n -------\n graph : holoviews.Graph\n Graph element\n \"\"\"\n\n # Unpack edges\n edges = defaultdict(list)\n for start, end in G.edges():\n for attr, value in sorted(G.adj[start][end].items()):\n if isinstance(value, (list, dict)):\n continue # Cannot handle list or dict attrs\n edges[attr].append(value)\n\n # Handle tuple node indexes (used in 2D grid Graphs)\n if isinstance(start, tuple):\n start = str(start)\n if isinstance(end, tuple):\n end = str(end)\n edges['start'].append(start)\n edges['end'].append(end)\n edge_cols = sorted(k for k in edges if k not in ('start', 'end')\n and len(edges[k]) == len(edges['start']))\n edge_vdims = [str(col) if isinstance(col, int) else col for col in edge_cols]\n edge_data = tuple(edges[col] for col in ['start', 'end']+edge_cols)\n\n # Unpack user node info\n xdim, ydim, idim = cls.node_type.kdims[:3]\n if nodes:\n node_columns = nodes.columns()\n idx_dim = nodes.kdims[0].name\n info_cols, values = zip(*((k, v) for k, v in node_columns.items() if k != idx_dim))\n node_info = {i: vals for i, vals in zip(node_columns[idx_dim], zip(*values))}\n else:\n info_cols = []\n node_info = None\n node_columns = defaultdict(list)\n\n # Unpack node positions\n for idx, pos in positions.items():\n node = G.nodes.get(idx)\n if node is None:\n continue\n x, y = pos\n node_columns[xdim.name].append(x)\n node_columns[ydim.name].append(y)\n for attr, value in node.items():\n if isinstance(value, (list, dict, tuple)):\n continue\n node_columns[attr].append(value)\n for i, col in enumerate(info_cols):\n node_columns[col].append(node_info[idx][i])\n if isinstance(idx, tuple):\n idx = str(idx) # Tuple node indexes handled as strings\n node_columns[idim.name].append(idx)\n node_cols = sorted(k for k in node_columns if k not in cls.node_type.kdims\n and len(node_columns[k]) == len(node_columns[xdim.name]))\n columns = [xdim.name, ydim.name, idim.name]+node_cols+list(info_cols)\n node_data = tuple(node_columns[col] for col in columns)\n\n # Construct nodes\n vdims = []\n for col in node_cols:\n if isinstance(col, int):\n dim = str(col)\n elif nodes is not None and col in nodes.vdims:\n dim = nodes.get_dimension(col)\n else:\n dim = col\n vdims.append(dim)\n nodes = cls.node_type(node_data, vdims=vdims)\n\n # Construct graph\n return cls((edge_data, nodes), vdims=edge_vdims)\n\n\ndef draw(G, pos=None, **kwargs):\n \"\"\"\n Draw the graph G using hvPlot.\n\n Draw the graph with hvPlot with options for node positions,\n labeling, titles, and many other drawing features.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values.\n If not specified a spring layout positioning will be computed.\n See :py:mod:`networkx.drawing.layout` for functions that\n compute node positions.\n arrows : bool, optional (default=True)\n For directed graphs, if True draw arrowheads.\n Note: Arrows will be the same color as edges.\n arrowhead_length : float, optional (default=0.025)\n The length of the arrows as fraction of the overall extent of\n the graph\n with_labels : bool, optional (default=True)\n Set to True to draw labels on the nodes.\n nodelist : list, optional (default G.nodes())\n Draw only specified nodes\n edgelist : list, optional (default=G.edges())\n Draw only specified edges\n node_size : scalar or array, optional (default=300)\n Size of nodes. If an array is specified it must be the\n same length as nodelist.\n node_color : color string, node attribute, or array of floats, (default='r')\n Can be a single color, the name of an attribute on the nodes or\n sequence of colors with the same length as nodelist. If the\n node_color references an attribute on the nodes or is a list of\n values they will be colormapped using the cmap and vmin, vmax\n parameters.\n node_shape : string, optional (default='o')\n The shape of the node. Specification is as valid bokeh marker.\n alpha : float, optional (default=1.0)\n The node and edge transparency\n cmap : Colormap, optional (default=None)\n Colormap for mapping intensities of nodes\n vmin,vmax : float, optional (default=None)\n Minimum and maximum for node colormap scaling\n linewidths : [None | scalar | sequence]\n Line width of symbol border (default =1.0)\n edge_width : float, optional (default=1.0)\n Line width of edges\n edge_color : color string, or array of floats (default='r')\n Can be a single color, the name of an attribute on the edges or\n sequence of colors with the same length as the edges. If the\n edge_color references an attribute on the edges or is a list of\n values they will be colormapped using the edge_cmap and\n edge_vmin, edge_vmax parameters.\n edge_cmap : Matplotlib colormap, optional (default=None)\n Colormap for mapping intensities of edges\n edge_vmin,edge_vmax : floats, optional (default=None)\n Minimum and maximum for edge colormap scaling\n style : string, optional (default='solid')\n Edge line style (solid|dashed|dotted,dashdot)\n labels : dictionary or string, optional (default=None)\n Node labels in a dictionary keyed by node of text labels or\n a string referencing a node attribute\n font_size : int, optional (default=12)\n Font size for text labels\n font_color : string, optional (default='black')\n Font color string\n font_family : string, optional (default='sans-serif')\n Font family\n label : string, optional\n Label for graph legend\n selection_policy : string, optional (default='nodes')\n Whether to select 'nodes', 'edges' or None on tap and selection\n events.\n inspection_policy : string, optional (default='nodes')\n Whether to select 'nodes', 'edges' or None on tap and selection\n events.\n geo : boolean, optional (default=False)\n Whether to return a GeoViews graph\n crs : cartopy.crs.CRS\n A cartopy coordinate reference system (enables a geographic plot)\n height : int, optional (default=400)\n The height of the plot in pixels\n width : int, optional (default=400)\n The width of the plot in pixels\n \"\"\"\n if pos is None:\n pos = nx.drawing.spring_layout\n\n if not isinstance(pos, dict):\n pos = pos(G, **kwargs.get('layout_kwargs', {}))\n\n params, label_params = {}, {}\n label_element = Labels\n if kwargs.get('geo', False) or 'crs' in kwargs:\n try:\n import geoviews\n except ImportError:\n raise ImportError('In order to use geo-related features '\n 'the geoviews library must be available. '\n 'It can be installed with pip or conda.')\n crs = process_crs(kwargs.get('crs'))\n label_element = geoviews.Labels\n params['cls'] = geoviews.Graph\n params['crs'] = crs\n label_params['crs'] = crs\n\n # Construct Graph object\n g = _from_networkx(G, pos, **params)\n\n if 'nodelist' in kwargs:\n g.nodes.data = g.nodes.data.iloc[list(kwargs['nodelist'])]\n\n if 'edgelist' in kwargs:\n edges = g.array([0, 1])\n comparisons = []\n for edge in kwargs['edgelist']:\n comparisons.append(edges == edge)\n if len(comparisons):\n selector = np.logical_and(*np.logical_or.reduce(comparisons).T)\n g = g.iloc[selector]\n else:\n g = g.iloc[:0]\n\n # Compute options\n inspection_policy = kwargs.pop('inspection_policy', 'nodes')\n opts = dict(\n axiswise=True,\n arrowhead_length=kwargs.get('arrowhead_length', 0.025),\n directed=kwargs.pop('arrows', isinstance(G, nx.DiGraph)),\n colorbar=kwargs.pop('colorbar', False),\n padding=kwargs.get('padding', 0.1),\n width=kwargs.pop('width', 400),\n height=kwargs.pop('height', 400),\n selection_policy=kwargs.pop('selection_policy', 'nodes'),\n inspection_policy=inspection_policy,\n node_fill_color='red')\n\n if '_axis_defaults':\n opts.update(xaxis=None, yaxis=None, show_frame=False)\n\n opts.update({k: kwargs.pop(k) for k in list(kwargs) if k in GraphPlot.style_opts})\n if 'node_size' in opts:\n if isinstance(opts['node_size'], str):\n opts['node_size'] = dim(opts['node_size'])\n opts['node_size'] = np.sqrt(opts['node_size'])\n if 'node_color' in opts:\n opts['node_fill_color'] = opts.pop('node_color')\n if 'edge_color' in opts:\n opts['edge_line_color'] = opts.pop('edge_color')\n if 'node_shape' in kwargs:\n marker = kwargs.pop('node_shape')\n if marker in markers:\n marker_opts = markers[marker]\n marker = marker_opts['marker']\n if 'angle' in marker_opts:\n Store.add_style_opts(Graph, ['node_angle'], 'bokeh')\n opts['node_angle'] = marker_opts['angle']\n opts['node_marker'] = marker\n if 'alpha' in kwargs:\n alpha = kwargs.pop('alpha')\n opts['node_alpha'] = alpha\n opts['edge_alpha'] = alpha\n if 'linewidths' in kwargs:\n opts['node_line_width'] = kwargs.pop('linewidths')\n if 'edge_width' in kwargs:\n opts['edge_line_width'] = kwargs.pop('edge_width')\n if 'style' in kwargs:\n opts['edge_line_dash'] = kwargs.pop('style')\n\n node_styles = ('node_fill_color', 'node_size', 'node_alpha', 'node_line_width')\n for node_style in node_styles:\n if isinstance(opts.get(node_style), (np.ndarray, list, range)):\n g = g.clone((g.data, g.nodes.add_dimension(node_style, len(g.nodes.vdims), opts[node_style], True)))\n opts[node_style] = node_style\n\n edge_styles = ('edge_line_color', 'edge_line_alpha', 'edge_alpha', 'edge_line_width')\n for edge_style in edge_styles:\n if isinstance(opts.get(edge_style), (np.ndarray, list, range)):\n g = g.add_dimension(edge_style, len(g.vdims), opts[edge_style], True)\n opts[edge_style] = edge_style\n\n if opts.get('node_fill_color') in g.nodes.dimensions():\n lims = (kwargs.get('vmin', None), kwargs.get('vmax', None))\n if lims != (None, None):\n dimension = g.nodes.get_dimension(opts.get('node_fill_color'))\n dimension.range = lims\n\n if opts.get('edge_line_color') in g.dimensions():\n lims = (kwargs.get('edge_vmin', None), kwargs.get('edge_vmax', None))\n if lims != (None, None):\n dimension = g.get_dimension(opts.get('edge_line_color'))\n dimension.range = lims\n\n if inspection_policy == 'nodes':\n tooltip_dims = [(d.label, 'index_hover' if d in g.nodes.kdims else d.name)\n for d in g.nodes.kdims[2:] + g.nodes.vdims]\n else:\n tooltip_dims = [(d.label, d.name+'_values' if d in g.kdims else d.name)\n for d in g.kdims + g.vdims]\n tooltips = [(label, '@{%s}' % dimension_sanitizer(name))\n for label, name in tooltip_dims if name not in node_styles + edge_styles]\n opts['tools'] = [HoverTool(tooltips=tooltips), 'tap']\n\n g.opts(**opts, backend='bokeh')\n\n # Construct Labels\n if kwargs.get('with_labels', kwargs.get('labels', False)):\n label_opts = {k: kwargs.pop(k) for k in list(kwargs) if k in LabelsPlot.style_opts}\n if 'xoffset' in kwargs:\n label_opts['xoffset'] = kwargs.pop('xoffset')\n if 'yoffset' in kwargs:\n label_opts['yoffset'] = kwargs.pop('yoffset')\n if 'font_size' in kwargs:\n label_opts['text_font_size'] = kwargs.pop('font_size')\n if 'font_color' in kwargs:\n label_opts['text_color'] = kwargs.pop('font_color')\n if 'font_family' in kwargs:\n label_opts['text_font'] = kwargs.pop('font_family')\n labels = kwargs.get('labels', g.nodes.kdims[2])\n if isinstance(labels, dict):\n values = g.nodes.array(g.nodes.kdims)\n data = [(x, y, labels[i]) for (x, y, i) in values if i in labels]\n labels = label_element(data, g.nodes.kdims[:2], 'text', **label_params)\n else:\n labels = label_element(g.nodes, g.nodes.kdims[:2], labels, **label_params)\n g = g * labels.opts(**label_opts, backend='bokeh')\n\n # Apply label\n if 'label' in kwargs:\n g = g.relabel(kwargs.pop('label'))\n\n # Process options\n g = _transfer_opts_cur_backend(g)\n\n return g\n\n\ndef draw_networkx(G, pos=None, **kwargs):\n \"\"\"Draw a networkx graph.\n\n Draw the graph with hvPlot with options for node positions,\n labeling, titles, and many other drawing features. See draw() for\n simple drawing without labels or axes.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values or a\n layout `networkx.drawing.layout` function. If not specified a\n spring layout positioning will be computed. See\n :py:mod:`networkx.drawing.layout` for functions that compute\n node positions.\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element\n \"\"\"\n kwargs['_axis_defaults'] = False\n return draw(G, pos, **kwargs)\n\n\ndef draw_networkx_nodes(G, pos, **kwargs):\n \"\"\"Draw networkx graph nodes.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph: holoviews.Graph or holoviews.Overlay\n Graph element\n \"\"\"\n if 'alpha' in kwargs:\n kwargs['node_alpha'] = kwargs.pop('alpha')\n kwargs.pop('edgelist', None)\n kwargs['_axis_defaults'] = False\n kwargs['edge_alpha'] = 0\n kwargs['edge_hover_alpha'] = 0\n kwargs['edge_nonselection_alpha'] = 0\n return draw(G, pos, **kwargs)\n\n\ndef draw_networkx_edges(G, pos, **kwargs):\n \"\"\"Draw networkx graph edges.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or (holoviews.Graph * holoviews.Labels)\n Graph element\n \"\"\"\n if 'alpha' in kwargs:\n kwargs['edge_alpha'] = kwargs.pop('alpha')\n kwargs.pop('nodelist', None)\n kwargs['node_alpha'] = 0\n kwargs['node_hover_alpha'] = 0\n kwargs['node_nonselection_alpha'] = 0\n kwargs['_axis_defaults'] = False\n if kwargs.get('selection_policy') is not None:\n kwargs['selection_policy'] = 'edges'\n if kwargs.get('inspection_policy') is not None:\n kwargs['inspection_policy'] = 'edges'\n return draw(G, pos, **kwargs)\n\n\ndef draw_networkx_labels(G, pos, **kwargs):\n \"\"\"Draw networkx graph node labels.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : Labels element\n Labels element\n \"\"\"\n g = draw(G, pos, **kwargs)\n return Labels(g.nodes, g.nodes.kdims[:2], g.nodes.kdims[2])\n\n\ndef draw_circular(G, **kwargs):\n \"\"\"Draw networkx graph with circular layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element or Graph and Labels\n \"\"\"\n return draw(G, pos=nx.circular_layout, **kwargs)\n\n\ndef draw_kamada_kawai(G, **kwargs):\n \"\"\"Draw networkx graph with circular layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element or Graph and Labels\n \"\"\"\n return draw(G, pos=nx.kamada_kawai_layout, **kwargs)\n\n\ndef draw_random(G, **kwargs):\n \"\"\"Draw networkx graph with random layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element\n \"\"\"\n return draw(G, nx.random_layout, **kwargs)\n\n\ndef draw_shell(G, **kwargs):\n \"\"\"Draw networkx graph with shell layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element or Graph and Labels\n \"\"\"\n nlist = kwargs.pop('nlist', None)\n if nlist is not None:\n kwargs['layout_kwargs'] = {'nlist': nlist}\n return draw(G, nx.shell_layout, **kwargs)\n\n\ndef draw_spectral(G, **kwargs):\n \"\"\"Draw networkx graph with spectral layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element or Graph and Labels\n \"\"\"\n return draw(G, nx.spectral_layout, **kwargs)\n\n\ndef draw_spring(G, **kwargs):\n \"\"\"Draw networkx graph with spring layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element or Graph and Labels\n \"\"\"\n return draw(G, nx.spring_layout, **kwargs)\n\ndef draw_planar(G, **kwargs):\n \"\"\"Draw networkx graph with planar layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n kwargs : optional keywords\n See hvplot.networkx.draw() for a description of optional\n keywords, with the exception of the pos parameter which is not\n used by this function.\n\n Returns\n -------\n graph : holoviews.Graph or holoviews.Overlay\n Graph element or Graph and Labels\n \"\"\"\n return draw(G, nx.planar_layout, **kwargs)\n", "repo_name": "holoviz/hvplot", "sub_path": "hvplot/networkx.py", "file_name": "networkx.py", "file_ext": "py", "file_size_in_byte": 21303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 822, "dataset": "github-code", "pt": "3", "api": [{"api_name": "holoviews.extension", "line_number": 18, "usage_type": "attribute"}, {"api_name": "holoviews.extension", "line_number": 19, "usage_type": "call"}, {"api_name": "holoviews.Graph", "line_number": 22, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 49, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 78, "usage_type": "call"}, {"api_name": "holoviews.dim", "line_number": 106, "usage_type": "name"}, {"api_name": "holoviews.dim", "line_number": 108, "usage_type": "name"}, {"api_name": "holoviews.dim", "line_number": 110, "usage_type": "name"}, {"api_name": "holoviews.dim", "line_number": 111, "usage_type": "argument"}, {"api_name": "networkx.drawing", "line_number": 206, "usage_type": "attribute"}, {"api_name": "holoviews.Labels", "line_number": 212, "usage_type": "name"}, {"api_name": "util.process_crs", "line_number": 220, "usage_type": "call"}, {"api_name": "geoviews.Labels", "line_number": 221, "usage_type": "attribute"}, {"api_name": "geoviews.Graph", "line_number": 222, "usage_type": "attribute"}, {"api_name": "numpy.logical_and", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.logical_or.reduce", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 238, "usage_type": "attribute"}, {"api_name": "networkx.DiGraph", "line_number": 248, "usage_type": "attribute"}, {"api_name": "holoviews.plotting.bokeh.GraphPlot.style_opts", "line_number": 260, "usage_type": "attribute"}, {"api_name": "holoviews.plotting.bokeh.GraphPlot", "line_number": 260, "usage_type": "name"}, {"api_name": "holoviews.dim", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 264, "usage_type": "call"}, {"api_name": "holoviews.plotting.bokeh.styles.markers", "line_number": 271, "usage_type": "name"}, {"api_name": "holoviews.plotting.bokeh.styles.markers", "line_number": 272, "usage_type": "name"}, {"api_name": "holoviews.core.options.Store.add_style_opts", "line_number": 275, "usage_type": "call"}, {"api_name": "holoviews.Graph", "line_number": 275, "usage_type": "argument"}, {"api_name": "holoviews.core.options.Store", "line_number": 275, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 291, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 297, "usage_type": "attribute"}, {"api_name": "holoviews.core.util.dimension_sanitizer", "line_number": 319, "usage_type": "call"}, {"api_name": "bokeh.models.HoverTool", "line_number": 321, "usage_type": "call"}, {"api_name": "holoviews.plotting.bokeh.LabelsPlot.style_opts", "line_number": 327, "usage_type": "attribute"}, {"api_name": "holoviews.plotting.bokeh.LabelsPlot", "line_number": 327, "usage_type": "name"}, {"api_name": "backend_transforms._transfer_opts_cur_backend", "line_number": 352, "usage_type": "call"}, {"api_name": "holoviews.Labels", "line_number": 464, "usage_type": "call"}, {"api_name": "networkx.circular_layout", "line_number": 484, "usage_type": "attribute"}, {"api_name": "networkx.kamada_kawai_layout", "line_number": 504, "usage_type": "attribute"}, {"api_name": "networkx.random_layout", "line_number": 524, "usage_type": "attribute"}, {"api_name": "networkx.shell_layout", "line_number": 547, "usage_type": "attribute"}, {"api_name": "networkx.spectral_layout", "line_number": 567, "usage_type": "attribute"}, {"api_name": "networkx.spring_layout", "line_number": 587, "usage_type": "attribute"}, {"api_name": "networkx.planar_layout", "line_number": 606, "usage_type": "attribute"}]} +{"seq_id": "70031849363", "text": "# Author: Luis Diego García Castro and Adolfo Enrique García Castro\n\"\"\"This module contains code to create splits.\"\"\"\nimport abc\nimport math\nfrom typing import List, Optional, Union, Any, Tuple\n\nimport numpy as np\nimport geopandas as gpd\nimport shapely\n\nfrom .common import load_geodataframe\nfrom .types import GeoDataFrameSource\n\nObjectSplitterResult = Tuple[List[int], Optional[Any]]\n\n\nclass ObjectSplitter(metaclass=abc.ABCMeta):\n \"\"\"Define an object splitter interface.\"\"\"\n\n @abc.abstractmethod\n def run(\n self,\n objects: GeoDataFrameSource,\n splits: List[float],\n *,\n seed: Optional[int] = None,\n verbose: bool = True,\n ) -> ObjectSplitterResult:\n \"\"\"Create the object splits.\n\n Parameters\n ----------\n objects\n The objects database.\n splits\n The splits distribution. Should be in [0, 1].\n seed: optional\n A seed for the random number generator.\n verbose\n Display diagnostic information.\n\n Returns\n -------\n ObjectSplitResult\n A list of split classes and optionally implementation-specific \\\n extra information.\n \"\"\"\n\n\nclass SimpleSplitter(ObjectSplitter):\n\n def __init__(self, **kwargs):\n super(SimpleSplitter, self).__init__(**kwargs)\n\n def run(\n self,\n objects: GeoDataFrameSource,\n splits: List[float],\n *,\n seed: Optional[int] = None,\n verbose: bool = True,\n ) -> ObjectSplitterResult:\n\n objects = load_geodataframe(objects, copy=False)\n n = len(objects)\n pairs = [(i, np.round(n * p)) for i, p in enumerate(splits, 1)]\n split_ids = np.zeros(size=n, dtype=int)\n next = 0\n for i, m in pairs:\n split_ids[next, m] = i\n next = m\n rng = np.random.default_rng(seed)\n rng.shuffle(split_ids)\n return list(split_ids), None\n\n\nclass LatitudeObjectSplitter(ObjectSplitter):\n \"\"\"Create latitude-balanced object splits.\n\n Examples\n --------\n >>> splitter = LatitudeObjectSplitter()\n >>> splits, sampled_areas = splitter.run(tiles, splits=(0.20, 0.20),\n ... seed=seed)\n \"\"\"\n\n def __init__(\n self,\n bins: Union[str, int] = \"auto\",\n centroid_projection: Optional[str] = \"EPSG:6933\",\n ) -> None:\n self.bins = bins\n self.centroid_projection = centroid_projection\n\n def run(\n self,\n objects: GeoDataFrameSource,\n splits: List[float],\n *,\n seed: Optional[int] = None,\n verbose: bool = True,\n ) -> ObjectSplitterResult:\n # References:\n # https://gis.stackexchange.com/a/390563\n\n total = np.sum(splits)\n if not 0.0 <= total <= 1.0:\n raise ValueError(\"Invalid splits distribution.\")\n objects = load_geodataframe(objects, copy=False)\n\n rng = np.random.default_rng(seed)\n\n # We compute a histogram of the centroid latitudes\n # to create \"latitudinal bins\" from which to sample tiles\n # uniformly at random. The target number of tiles to sample from\n # each area is proportional to the bin counts.\n if self.centroid_projection is not None:\n centroids = objects.to_crs(self.centroid_projection).centroid\n centroids = centroids.to_crs(objects.crs)\n else:\n centroids = objects.centroid\n # The latitude is the y-coordinate of the centroid\n counts, bins = np.histogram(centroids.geometry.y)\n counts = np.array(counts, dtype=float)\n # Compute probability of area selection\n p = counts / np.sum(counts)\n # Compute area boxes\n minx = objects.bounds[\"minx\"].min()\n maxx = objects.bounds[\"maxx\"].max()\n areas = [\n shapely.geometry.box(minx, start, maxx, end)\n for start, end in zip(bins, bins[1:])\n ]\n n_objects = len(objects)\n object_splits = np.zeros(n_objects, dtype=int)\n for split_id, split_rel_size in enumerate(splits, 1):\n if split_rel_size == 0:\n continue\n n_selected_target = math.ceil(n_objects * split_rel_size)\n target_count_per_area = np.round(p * n_selected_target).astype(int)\n area_indices = np.argsort(target_count_per_area)\n if np.sum(target_count_per_area) > n_selected_target:\n # Corrects possible rounding issue\n area_idx = area_indices[-1]\n target_count_per_area[area_idx] -= 1\n for area_idx in area_indices:\n n = target_count_per_area[area_idx]\n if n == 0:\n continue\n area = areas[area_idx]\n intersects = centroids.within(area).to_numpy()\n available = object_splits == 0\n candidates = np.logical_and(available, intersects)\n candidates = np.squeeze(np.argwhere(candidates))\n sample = rng.choice(candidates, size=n, replace=False)\n object_splits[sample] = split_id\n\n sampled_areas = gpd.GeoDataFrame(\n {\n \"geometry\": areas,\n \"count\": p\n },\n crs=objects.crs,\n )\n\n if verbose:\n idx = object_splits != 0\n n_selected = idx.sum()\n n_percent = n_selected / n_objects\n print(\"Selected {}/{} ({}) objects, target was {}.\".format(\n n_selected,\n n_objects,\n n_percent,\n math.ceil(n_objects * total),\n ))\n return object_splits, sampled_areas\n", "repo_name": "Emil2468/DeepLearningCounting", "sub_path": "dlc/tools/splits.py", "file_name": "splits.py", "file_ext": "py", "file_size_in_byte": 5748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 14, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 17, "usage_type": "attribute"}, {"api_name": "types.GeoDataFrameSource", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 20, "usage_type": "attribute"}, {"api_name": "types.GeoDataFrameSource", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}, {"api_name": "common.load_geodataframe", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 72, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 90, "usage_type": "name"}, {"api_name": "types.GeoDataFrameSource", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 100, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 106, "usage_type": "call"}, {"api_name": "common.load_geodataframe", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.histogram", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 126, "usage_type": "call"}, {"api_name": "shapely.geometry.box", "line_number": 131, "usage_type": "call"}, {"api_name": "shapely.geometry", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 154, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "29429243204", "text": "from flask import Flask, request, abort, send_file\nfrom os.path import join as pjoin\nimport logging\nfrom urllib.parse import unquote\n\napp = Flask(__name__)\nlogging.basicConfig(level=logging.INFO)\n\n@app.route('/download/', methods=['GET'])\ndef download(userid):\n try:\n return send_file(pjoin('jsons', userid + '.noxBackup'), mimetype=\"application/gzip\")\n except Exception as e:\n logging.error(e)\n abort(406)\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n try:\n if not request.headers['secret-key']== 'noxplayer': \n raise Exception(\"secret key invalid\", request.headers['secret_key'])\n username = unquote(request.headers['userid'])\n if not username in ['王胡桃w']: raise Exception('username invalid', username)\n with open(pjoin('jsons', username + '.noxBackup'), 'wb') as f:\n f.write(request.data)\n return \"success.\"\n except Exception as e:\n logging.error(e)\n abort(406)\n \n@app.route('/', methods=['GET'])\ndef hello():\n return \"noxbackup flask is up\"\n\nif __name__ == '__main__':\n app.run(host= '0.0.0.0', port=6666, debug=True)\n # gunicorn -w 2 'noxFlask:app' -b 0.0.0.0:9527\n", "repo_name": "lovegaoshi/fastapi-fileserv", "sub_path": "noxFlask.py", "file_name": "noxFlask.py", "file_ext": "py", "file_size_in_byte": 1210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 7, "usage_type": "attribute"}, {"api_name": "flask.send_file", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.headers", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "urllib.parse.unquote", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "40046584432", "text": "#!/usr/bin/python3\n\"\"\"Start link class to table in database\n\"\"\"\n\n\nimport sys\nfrom model_state import Base, State\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import (create_engine)\nfrom sqlalchemy import text\n\n\nif __name__ == \"__main__\":\n engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}'.format(\n sys.argv[1],\n sys.argv[2],\n sys.argv[3]),\n pool_pre_ping=True)\n Base.metadata.create_all(engine)\n\n session = Session(engine)\n searchName = sys.argv[4]\n row = session.query(State).order_by(State.id).\\\n filter(text(\"name=:name\")).params(name=searchName).first()\n if (row):\n print(\"{}\".format(row.id))\n else:\n print(\"Not found\")\n session.close()\n", "repo_name": "Profay/alx-higher_level_programming", "sub_path": "0x0F-python-object_relational_mapping/10-model_state_my_get.py", "file_name": "10-model_state_my_get.py", "file_ext": "py", "file_size_in_byte": 730, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "model_state.Base.metadata.create_all", "line_number": 19, "usage_type": "call"}, {"api_name": "model_state.Base.metadata", "line_number": 19, "usage_type": "attribute"}, {"api_name": "model_state.Base", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "model_state.State", "line_number": 23, "usage_type": "argument"}, {"api_name": "model_state.State.id", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sqlalchemy.text", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "33966928880", "text": "# -*- coding: utf-8 -*-\nfrom setuptools import setup, find_packages\n\nwith open('requirements.txt') as f:\n\tinstall_requires = f.read().strip().split('\\n')\n\n# get version from __version__ variable in amf/__init__.py\nfrom amf import __version__ as version\n\nsetup(\n\tname='amf',\n\tversion=version,\n\tdescription='ERP applications and tools for AMF',\n\tauthor='libracore AG',\n\tauthor_email='info@libracore.com',\n\tpackages=find_packages(),\n\tzip_safe=False,\n\tinclude_package_data=True,\n\tinstall_requires=install_requires\n)\n", "repo_name": "libracore/amf", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "setuptools.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "amf.__version__", "line_number": 12, "usage_type": "name"}, {"api_name": "setuptools.find_packages", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "36818202446", "text": "from collections import Counter\nimport math\n\nt=int(input())\nfor nt in range(t):\n\tn=int(input())\n\tl=list(map(int,input().split()))\n\tcnt = Counter(l)\n\tflag=0\n\tfor i in cnt:\n\t\tif cnt[i]%2==1:\n\t\t\tprint (\"NO\")\n\t\t\tflag=1\n\t\t\tbreak\n\tif flag==0:\n\t\ttemp=[]\n\t\tfor i in cnt:\n\t\t\tfor j in range(cnt[i]//2):\n\t\t\t\ttemp.append(i)\n\t\ttemp.sort()\n\t\tflag=0\n\t\tarea=temp[0]*temp[-1]\n\t\tfor i in range(len(temp)//2):\n\t\t\tif temp[i]*temp[len(temp)-i-1]!=area:\n\t\t\t\tprint (\"NO\")\n\t\t\t\tflag=1\n\t\t\t\tbreak\n\t\tif flag==0:\n\t\t\tprint (\"YES\")\n\t\t\t\n\n", "repo_name": "Naman18055/CF-Solutions", "sub_path": "Round 579/B.py", "file_name": "B.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.Counter", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "32036974933", "text": "# -----------------------------------------------------------------------------\n# pytermor [ANSI formatted terminal output toolset]\n# (C) 2022 A. Shavykin <0.delameter@gmail.com>\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nfrom copy import copy\nfrom typing import Dict, Tuple, List\n\nfrom . import build, sgr, SequenceSGR\n\n\nclass Registry:\n def __init__(self):\n self._code_to_breaker_map: Dict[int|Tuple[int, ...], SequenceSGR] = dict()\n self._complex_code_def: Dict[int|Tuple[int, ...], int] = dict()\n self._complex_code_max_len: int = 0\n\n def register_single(self, starter_code: int | Tuple[int, ...], breaker_code: int):\n if starter_code in self._code_to_breaker_map:\n raise RuntimeError(f'Conflict: SGR code {starter_code} already has a registered breaker')\n self._code_to_breaker_map[starter_code] = SequenceSGR(breaker_code)\n\n def register_complex(self, starter_codes: Tuple[int, ...], param_len: int, breaker_code: int):\n self.register_single(starter_codes, breaker_code)\n\n if starter_codes in self._complex_code_def:\n raise RuntimeError(f'Conflict: SGR complex {starter_codes} already has a registered breaker')\n self._complex_code_def[starter_codes] = param_len\n self._complex_code_max_len = max(self._complex_code_max_len, len(starter_codes) + param_len)\n\n def get_closing_seq(self, opening_seq: SequenceSGR) -> SequenceSGR:\n closing_seq_params: List[int] = []\n opening_params = copy(opening_seq.params)\n while len(opening_params):\n key_params: int|Tuple[int, ...]|None = None\n for complex_len in range(1, min(len(opening_params), self._complex_code_max_len + 1)):\n opening_complex_suggestion = tuple(opening_params[:complex_len])\n if opening_complex_suggestion in self._complex_code_def:\n key_params = opening_complex_suggestion\n complex_total_len = complex_len + self._complex_code_def[opening_complex_suggestion]\n opening_params = opening_params[complex_total_len:]\n break\n if key_params is None:\n key_params = opening_params.pop(0)\n if key_params not in self._code_to_breaker_map:\n continue\n closing_seq_params.extend(self._code_to_breaker_map[key_params].params)\n\n return build(*closing_seq_params)\n\n\nsgr_parity_registry = Registry()\n\nsgr_parity_registry.register_single(sgr.BOLD, sgr.BOLD_DIM_OFF)\nsgr_parity_registry.register_single(sgr.DIM, sgr.BOLD_DIM_OFF)\nsgr_parity_registry.register_single(sgr.ITALIC, sgr.ITALIC_OFF)\nsgr_parity_registry.register_single(sgr.UNDERLINED, sgr.UNDERLINED_OFF)\nsgr_parity_registry.register_single(sgr.DOUBLE_UNDERLINED, sgr.UNDERLINED_OFF)\nsgr_parity_registry.register_single(sgr.BLINK_SLOW, sgr.BLINK_OFF)\nsgr_parity_registry.register_single(sgr.BLINK_FAST, sgr.BLINK_OFF)\nsgr_parity_registry.register_single(sgr.INVERSED, sgr.INVERSED_OFF)\nsgr_parity_registry.register_single(sgr.HIDDEN, sgr.HIDDEN_OFF)\nsgr_parity_registry.register_single(sgr.CROSSLINED, sgr.CROSSLINED_OFF)\nsgr_parity_registry.register_single(sgr.OVERLINED, sgr.OVERLINED_OFF)\n\nfor c in [sgr.BLACK, sgr.RED, sgr.GREEN, sgr.YELLOW, sgr.BLUE, sgr.MAGENTA, sgr.CYAN, sgr.WHITE, sgr.GRAY,\n sgr.HI_RED, sgr.HI_GREEN, sgr.HI_YELLOW, sgr.HI_BLUE, sgr.HI_MAGENTA, sgr.HI_CYAN, sgr.HI_WHITE]:\n sgr_parity_registry.register_single(c, sgr.COLOR_OFF)\n\nfor c in [sgr.BG_BLACK, sgr.BG_RED, sgr.BG_GREEN, sgr.BG_YELLOW, sgr.BG_BLUE, sgr.BG_MAGENTA, sgr.BG_CYAN,\n sgr.BG_WHITE, sgr.BG_GRAY, sgr.BG_HI_RED, sgr.BG_HI_GREEN, sgr.BG_HI_YELLOW, sgr.BG_HI_BLUE,\n sgr.BG_HI_MAGENTA, sgr.BG_HI_CYAN, sgr.BG_HI_WHITE]:\n sgr_parity_registry.register_single(c, sgr.BG_COLOR_OFF)\n\n\nsgr_parity_registry.register_complex((sgr.COLOR_EXTENDED, 5), 1, sgr.COLOR_OFF)\nsgr_parity_registry.register_complex((sgr.COLOR_EXTENDED, 2), 3, sgr.COLOR_OFF)\nsgr_parity_registry.register_complex((sgr.BG_COLOR_EXTENDED, 5), 1, sgr.BG_COLOR_OFF)\nsgr_parity_registry.register_complex((sgr.BG_COLOR_EXTENDED, 2), 3, sgr.BG_COLOR_OFF)\n", "repo_name": "delameter/pytermor", "sub_path": "pytermor/registry.py", "file_name": "registry.py", "file_ext": "py", "file_size_in_byte": 4230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.Dict", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 34, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "18012724575", "text": "import sys\nfrom pathlib import Path\nfrom typing import List\n\nfrom .ingress import app\n\nfrom fastapi import FastAPI, WebSocket, WebSocketDisconnect\nfrom fastapi.responses import HTMLResponse\n\nfrom .house import connection\nfrom . import home, state\n\n\nclass Broadcast(state.MicroState):\n name = 'broadcast'\n\n def mount(self, state_machine, index=-1, parent=None):\n self.ids = {}\n return super().mount(state_machine, index, parent)\n\n\n async def inbound(self, owner, last_data=None):\n _id = id(owner)\n self.ids[_id] = owner\n print('\\n broadcast.entry client', _id, '\\n')\n # keep open, new val == True\n return 1, True\n\n async def entry(self, data, owner, micro_position):\n return await self.msg_in(data, owner, micro_position)\n\n async def concurrent(self, data, owner, micro_position):\n return await self.msg_in(data, owner, micro_position)\n\n async def msg_in(self, data, owner, micro_position):\n move_on = False\n client_count = len(self.ids)\n print(f'\\nMessage in for {client_count}', data,'\\n')\n _id = id(owner)\n d = {'from': _id,\n 'content': data,\n 'position': micro_position,\n 'client_count': client_count\n }\n for wid, ws in self.ids.items():\n if wid == _id:\n continue\n await ws.send_json(d)\n\n return move_on, True\n\n def drop(self, socket_id):\n print('\\n broadcast.drop client', socket_id, '\\n')\n del self.ids[socket_id]\n\n\nclass DropCapture(object):\n\n async def initial_entry(self, owner):\n return True\n\n async def push_message(self, data, owner):\n print(' Envelope', data)\n return data\n\n async def disconnecting_socket(self, websocket, client_id, error):\n broadcast_room.drop(id(websocket))\n\n\nbroadcast_room = Broadcast()\n\n# These plugins define the procedural list a single client should\n# walk. Each step is like a function - but built with an async waiting.\nplugins = ()\nenvelopes = ( DropCapture(), )\nentry_acceptors = ( broadcast_room,)\n\n\nstate_machine = state.StateMachine(plugins,\n entry_acceptors=entry_acceptors,\n envelopes=envelopes,\n )\n\ncon_manager = connection.Manager(state_machine)\n\nglobals()['app'] = app\n\n\n@app.on_event(\"startup\")\nasync def startup_event():\n \"\"\"Run the host manager within the async for await tools\"\"\"\n await con_manager.mount()\n\n\n@app.get(\"/\")\nasync def get():\n return HTMLResponse(home.html)\n\n\n@app.websocket(\"/\")\nasync def websocket_endpoint_master(websocket: WebSocket):\n print('Websocket on master port')\n await con_manager.master_ingress(websocket)\n", "repo_name": "Strangemother/python-websocket-server", "sub_path": "old/porthouse_1/host3.py", "file_name": "host3.py", "file_ext": "py", "file_size_in_byte": 2687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "house.connection.Manager", "line_number": 84, "usage_type": "call"}, {"api_name": "house.connection", "line_number": 84, "usage_type": "name"}, {"api_name": "ingress.app", "line_number": 86, "usage_type": "name"}, {"api_name": "ingress.app.on_event", "line_number": 89, "usage_type": "call"}, {"api_name": "ingress.app", "line_number": 89, "usage_type": "name"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 97, "usage_type": "call"}, {"api_name": "ingress.app.get", "line_number": 95, "usage_type": "call"}, {"api_name": "ingress.app", "line_number": 95, "usage_type": "name"}, {"api_name": "fastapi.WebSocket", "line_number": 101, "usage_type": "name"}, {"api_name": "ingress.app.websocket", "line_number": 100, "usage_type": "call"}, {"api_name": "ingress.app", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "38488160004", "text": "'''Create a new day directory and files for Advent of Code 2020.\n\nUsage:\n $ python3 new_day.py [-d=]\n'''\n\nfrom argparse import ArgumentParser\nfrom glob import glob\nfrom pathlib import Path\nimport os\n\nif __name__ == '__main__':\n # The next few lines glob directories to find the first day not to exist\n directory = os.path.dirname(os.path.realpath(__file__))\n path_pattern = os.path.join(directory, 'day*')\n\n day = 1\n for i, f in enumerate(sorted(glob(path_pattern))):\n if int(f[-2:]) == i+1:\n day += 1\n else:\n break\n\n # Default to the latest day that has yet to be done\n parser = ArgumentParser(description='Create daily challenge boilerplate.')\n parser.add_argument('-d', type=int, default=day, help='the puzzle day')\n args = parser.parse_args()\n\n # Overwrite if a day argument has been passed from the command line\n day = args.d\n path = Path('./day{:0>2}'.format(day))\n path.mkdir()\n\n # write stub of python file\n python_path = path / 'day{:0>2}.py'.format(day)\n with python_path.open('w') as f:\n f.write('''\\'''Day {day:0>2} of Advent of Code 2020\n\nThe prompt is ...\n\\'''\n\nfrom argparse import ArgumentParser\nfrom pathlib import PurePath\n\ndef main(file_path):\n \\'''The docstring goes here.\n \\'''\n with open(file_path, 'r') as f:\n data = [line.strip() for line in f.readlines()]\n\n solution = part1()\n print(\"Day {day:0>2} Part 1 solution: {{}}\".format(solution))\n\n solution = part2()\n print(\"Day {day:0>2} Part 2 solution: {{}}\".format(solution))\n\n\ndef part1(data):\n return None\n\n\ndef part2(data):\n return None\n\n\nif __name__ == '__main__':\n parser = ArgumentParser(description=main.__doc__)\n parser.add_argument('-f', type=PurePath, help='the input file')\n args = parser.parse_args()\n\n main(args.f)\n'''.format(day=day)\n )\n\n # create input and test input files\n input_path = path / 'input.txt'.format(day)\n input_path.touch()\n test_input_path = path / 'test_input.txt'.format(day)\n test_input_path.touch()\n\n", "repo_name": "bneb/advent-of-code-2020", "sub_path": "new_day.py", "file_name": "new_day.py", "file_ext": "py", "file_size_in_byte": 2082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"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", "line_number": 15, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "16414358767", "text": "from django.contrib import admin\nfrom .models import Faktura, FakturaLinje, Pris\n\nadmin.site.register(FakturaLinje)\nadmin.site.register(Pris)\n\n\n@admin.register(Faktura)\nclass FakturaAdmin(admin.ModelAdmin):\n list_display = [\n \"id\",\n \"bruker\",\n \"faktura_dato\",\n \"betalt\",\n \"sendt\",\n Faktura.get_total_sum,\n ]\n list_filter = [\"betalt\", \"sendt\"]\n list_editable = [\"betalt\", \"sendt\"]\n", "repo_name": "torcor-dev/veiadministrasjon", "sub_path": "invoicing/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 434, "program_lang": "python", "lang": "no", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.contrib.admin.site.register", "line_number": 4, "usage_type": "call"}, {"api_name": "models.FakturaLinje", "line_number": 4, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 5, "usage_type": "call"}, {"api_name": "models.Pris", "line_number": 5, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Faktura.get_total_sum", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Faktura", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Faktura", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "17118754277", "text": "import tensorflow.compat.v1 as tf\nimport cv2 as cv2\nimport numpy as np\n\nINPUT_TENSOR_NAME = 'input.1:0'\nOUTPUT_TENSOR_NAME = 'add_4:0'\nIMAGE_PATH = \"images/parnas3.jpg\"\nPB_PATH = \"./saved_model.pb\"\n\nimg = cv2.imread(IMAGE_PATH)\nimg = np.dot(img[..., :3], [0.299, 0.587, 0.114])\nimg = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\nimg.resize((1, 1, 28, 28))\ndef openGraph(path):\n graph = tf.Graph()\n graphDef = tf.GraphDef()\n with open(path, \"rb\") as graphFile:\n graphDef.ParseFromString(graphFile.read())\n\n with graph.as_default():\n tf.import_graph_def(graphDef)\n\n return graph\n\n\ngraph_def = openGraph(PB_PATH)\n# with tf.gfile.FastGFile(PB_PATH, 'rb') as f:\n# graph_def = tf.GraphDef()\n# graph_def.ParseFromString(f.read())\n\nwith tf.Graph().as_default() as graph:\n tf.import_graph_def(graph_def, name=\"\")\n\ninput_tensor = graph.get_tensor_by_name(INPUT_TENSOR_NAME)\noutput_tensor = graph.get_tensor_by_name(OUTPUT_TENSOR_NAME)\n\nwith tf.Session(graph=graph) as sess:\n output_vals = sess.run(output_tensor, feed_dict={input_tensor: img}) #\n\nprediction = int(np.argmax(np.array(output_vals).squeeze(), axis=0))\nprint(prediction)", "repo_name": "Smehnov/parkings", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1192, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.Graph", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 15, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.GraphDef", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 16, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.import_graph_def", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 21, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Graph", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.import_graph_def", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 32, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "32586563734", "text": "import os\nimport subprocess\nimport numpy as np\nimport cv2 as cv\nimport pytesseract\nimport pickle\nimport scipy\nimport easygui\nimport shutil\n\n#----------------------------------\n# helper functions\n#----------------------------------\n\ndef minDistance(contour, contourOther):\n distanceMin = 99999999\n for c0 in contour:\n for c1 in contourOther:\n xA=c0[0][0]\n xB=c1[0][0]\n yA=c0[0][1]\n yB=c1[0][1]\n distance = ((xB-xA)**2+(yB-yA)**2) # squared distance formula\n if (distance < distanceMin):\n distanceMin = distance\n return distanceMin**(1/2)\n\n\ndef haveIntersection(a,b):\n #a, b are 4-integer lists with edge coordinates [left, right, top, bottom]\n return (min(a[1],b[1])-max(a[0],b[0])>0) and (min(a[3],b[3])-max(a[2],b[2])>0)\n \n\ndef rectSpacing(r1,r2):\n d=max(abs(r1[0]+r1[2]/2-r2[0]-r2[2]/2)-(r1[2]+r2[2])/2,abs(r1[1]+r1[3]/2-r2[1]-r2[3]/2)-(r1[3]+r2[3])/2)\n if d<=0:\n d=0\n return d\n\ndef graphemeEdges(g,conts):\n #conts must be the dict of contours\n return [min([cv.boundingRect(conts[i])[0] for i in g]), \\\n max([cv.boundingRect(conts[i])[0] + cv.boundingRect(conts[i])[2] for i in g]), \\\n min([cv.boundingRect(conts[i])[1] for i in g]), \\\n max([cv.boundingRect(conts[i])[1] + cv.boundingRect(conts[i])[3] for i in g]) ]\n\n\ndef getDistanceMatrix(numLabels,conts,slowMode):\n #conts must be the dict of contours\n #if slowMode==True, use real distance ; other wise, just use bounding rectangles\n distances=np.zeros((numLabels,numLabels))\n for ii in range(1,numLabels):\n #print(\"Computing distances from {}\".format(ii))\n for jj in range (1,ii):\n if slowMode:\n distances[ii,jj]=minDistance(contours[ii],contours[jj])\n else:\n distances[ii,jj]=rectSpacing(cv.boundingRect(contours[ii]),cv.boundingRect(contours[jj]))\n distances[jj,ii]=distances[ii,jj]\n return distances\n\n#--------------------------------------------\n# prepare picture\n#--------------------------------------------\ndef getComponentsAndContours(bwPicture,cachefile):\n (nl, l, _, _)=cv.connectedComponentsWithStats(bwPicture,8,cv.CV_32S)\n print(\"There are {} components\".format(nl))\n #first, compute a contour for each connected component \"label\"\n if os.path.exists(cachefile):\n f=open(cachefile,\"rb\")\n conts=pickle.load(f)\n f.close()\n else:\n conts={}\n for ii in range(1,nl):\n componentMask = (l == ii).astype(\"uint8\") * 255\n #outCompFile=r\"E:\\personal\\EtudesEgyptologie\\DigitalResearch\\sandbox\\components\\{}.png\".format(ii)\n print(\"Finding contours for component {}\".format(ii))\n #cv.imwrite(outCompFile,componentMask)\n c, _ = cv.findContours(componentMask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)\n conts[ii]=c[0]\n f=open(cachefile,\"wb\")\n pickle.dump(conts,f)\n f.close()\n return nl,l,conts\n \n#-----------------------------------\n# display helper functions\n#--------------------------------------\n\ndef getViewportFactor():\n return (curViewPort[1]-curViewPort[0])/xsizeOfDisplayWindow\n\n\ndef displayInViewport(l,g,ng,di):\n #vp=[xmin,xmax,ymin,ymax]\n #g = grapheme to display in one color (list of component indexes), ng=next grapheme to display in another color\n #di = list of components to display in another color\n croppedLabels=l[curViewPort[2]:curViewPort[3],curViewPort[0]:curViewPort[1]]\n channel1 = np.zeros(croppedLabels.shape,dtype=\"uint8\")\n channel2 = np.zeros(croppedLabels.shape,dtype=\"uint8\")\n channel3 = np.zeros(croppedLabels.shape,dtype=\"uint8\")\n print(\"After defining channels\")\n for c in list(range(1,numLabels)):\n br=cv.boundingRect(contours[c])\n bre=[br[0],br[0]+br[2],br[1],br[1]+br[3]]\n if haveIntersection(curViewPort,bre):\n print(\"Before computing component mask\")\n componentMaskCropped=((labels == c).astype(\"uint8\") * 255)[curViewPort[2]:curViewPort[3],curViewPort[0]:curViewPort[1]]\n print(\"After computing component mask\")\n if c in g:\n channel1 = cv.bitwise_or(channel1, componentMaskCropped)\n elif c in ng:\n channel1 = cv.bitwise_or(channel1, componentMaskCropped)\n channel2 = cv.bitwise_or(channel2, componentMaskCropped)\n elif c in di:\n channel2 = cv.bitwise_or(channel2, componentMaskCropped)\n else:\n channel3 = cv.bitwise_or(channel3, componentMaskCropped)\n #outputImg = cv.bitwise_or(outputImg, componentMaskCropped)\n print(\"After bitwise_or\")\n print (\"Computed for {}\".format(c))\n ysize=int((curViewPort[3]-curViewPort[2])/getViewportFactor())\n outputImg=cv.merge((channel1,channel3,channel2))\n outputImg=cv.resize(outputImg,(ysize,xsize))\n cv.imshow(\"aaa\",outputImg)\n cv.waitKey()\n return outputImg\n\ndef displayInViewportLowRes(lowResLabs,g,ng,di,lowResFactor,windowName):\n #lowResLabs = labels array, in low resolution (with factor lowResFactor=4 to convert 1200 dpi to 300 dpi)\n #g = grapheme to display in one color (list of component indexes), ng=next grapheme to display in another color\n #di = list of components to display in another color\n croppedLowResLabels=lowResLabs[int(curViewPort[2]/lowResFactor):int(curViewPort[3]/lowResFactor),int(curViewPort[0]/lowResFactor):int(curViewPort[1]/lowResFactor)]\n channel1 = np.zeros(croppedLowResLabels.shape,dtype=\"uint8\")\n channel2 = np.zeros(croppedLowResLabels.shape,dtype=\"uint8\")\n channel3 = np.zeros(croppedLowResLabels.shape,dtype=\"uint8\")\n for c in list(range(1,numLabels)):\n br=cv.boundingRect(contours[c])\n bre=[br[0],br[0]+br[2],br[1],br[1]+br[3]]\n if haveIntersection(curViewPort,bre):\n componentMaskCropped=((lowResLabs == c).astype(\"uint8\") * 255)[int(curViewPort[2]/lowResFactor):int(curViewPort[3]/lowResFactor),int(curViewPort[0]/lowResFactor):int(curViewPort[1]/lowResFactor)]\n if c in g:\n channel1 = cv.bitwise_or(channel1, componentMaskCropped)\n elif c in ng:\n channel1 = cv.bitwise_or(channel1, componentMaskCropped)\n channel2 = cv.bitwise_or(channel2, componentMaskCropped)\n elif c in di:\n channel3 = cv.bitwise_or(channel3, componentMaskCropped)\n else:\n channel2 = cv.bitwise_or(channel2, componentMaskCropped)\n ysize=int((curViewPort[3]-curViewPort[2])/getViewportFactor())\n outputImg=cv.merge((channel1,channel2,channel3))\n outputImg=cv.resize(outputImg,(ysize,xsizeOfDisplayWindow))\n cv.imshow(windowName,outputImg)\n return outputImg\n\ndef displayRowOfText(img,nRow,sText,tColor):\n cv.putText(img,sText,(0,nRow*25),cv.FONT_HERSHEY_SIMPLEX,0.7,tColor,1,cv.LINE_AA,False)\n\ndef updateStatusWindow():\n img = np.zeros((800,600,3), np.uint8)\n curModeDescription=\"\"\n if curMode==\"d\":\n curModeDescription=\"Mode d=discard selected components\"\n elif curMode==\"t\":\n curModeDescription=\"Mode t=take selected components for current grapheme\"\n elif curMode==\"n\":\n curModeDescription=\"Mode n=attribute selected components to next grapheme\"\n elif curMode==\"c\":\n curModeDescription=\"Mode c=create new grapheme with selected components, set as next\"\n displayRowOfText(img,1,curModeDescription,(255,255,255))\n displayRowOfText(img,2,\"#graphemes: {}\".format(len(graphemes)),(255,255,255))\n displayRowOfText(img,3,\"#components: {}\".format(numLabels),(255,255,255))\n displayRowOfText(img,4,\"cur grapheme: {} ({} components)\".format(curGrapheme,len(graphemes[curGrapheme])),(255,0,0))\n displayRowOfText(img,5,\"next grapheme: {} ({} components)\".format(nextGraphemeNumber(),len(graphemes[nextGraphemeNumber()])),(255,255,0))\n displayRowOfText(img,6,\"#components discarded: {}\".format(len(discarded)),(0,0,255))\n displayRowOfText(img,7,\"left-right arrow = previous/next grapheme\",(255,255,255))\n displayRowOfText(img,8,\"a = discard all from cur grapheme\",(255,255,255))\n displayRowOfText(img,9,\"q = quit\",(255,255,255))\n displayRowOfText(img,10,\"n = mode 'attribute to next grapheme'\",(255,255,255))\n displayRowOfText(img,11,\"t = mode 'take for current grapheme'\",(255,255,255))\n displayRowOfText(img,12,\"d = mode 'discard'\",(255,255,255))\n displayRowOfText(img,13,\"c = mode 'create new and assign selected to it'\",(255,255,255))\n displayRowOfText(img,14,\"s = save\",(255,255,255))\n displayRowOfText(img,15,\"l = load\",(255,255,255))\n displayRowOfText(img,16,\"r = next unprocessed plate\",(255,255,255))\n displayRowOfText(img,17,\"p = choose next plate\",(255,255,255))\n displayRowOfText(img,18,\"file {}\".format(basefilename),(255,255,255))\n if justSaved:\n sSaveStatus=\"(saved)\"\n else:\n sSaveStatus=\"(updates not saved)\"\n displayRowOfText(img,19,sSaveStatus,(255,255,255)) \n \n cv.imshow(\"statusWindow\",img)\n\ndef nextGraphemeNumber():\n return (curGrapheme+1)%len(graphemes)\n\ndef computeViewPort(g):\n #g must be a grapheme, i.e. a list of component numbers\n #contours must already be defined\n ge=graphemeEdges(g,contours)\n print(\"Grapheme edges for g {} are {}\".format(g,ge))\n midX=int((ge[0]+ge[1])/2)\n midY=int((ge[2]+ge[3])/2)\n halfSpan=max(midX-ge[0]+100,midY-ge[2]+100,600)\n print(\"Viewport is {}\".format([midX-halfSpan,midX+halfSpan,midY-halfSpan,midY+halfSpan]))\n return [midX-halfSpan,midX+halfSpan,midY-halfSpan,midY+halfSpan]\n \ndef displayGrapheme(winname):\n displayInViewportLowRes(lowResLabels,graphemes[curGrapheme],graphemes[nextGraphemeNumber()],discarded,lowResFactor,winname)\n\ndef getRealCoordinates(x,y):\n #x,y are mouse coordinates -> translate into absolute coordinates on page (by taking viewport into account)\n f=getViewportFactor()\n return int(x*f+curViewPort[0]),int(y*f+curViewPort[2])\n\ndef performAction(listOfComponents,sAction):\n global curViewPort, justSaved\n print(\"Asked action {} on components {}\".format(sAction,listOfComponents))\n if len(listOfComponents)>0:\n if sAction==\"d\": #discard selected components\n for l in listOfComponents:\n print(\"Working on {}\".format(l))\n for g in graphemes:\n if l in g:\n print(\"l is in g, removing {} from {}\".format(l,g))\n g.remove(l)\n if not(l in discarded):\n discarded.append(l)\n elif sAction==\"t\" or sAction==\"n\" or sAction==\"c\": #attribute to current or next\n if sAction==\"c\":\n graphemes.insert(curGrapheme+1,[])\n if sAction==\"t\":\n nToAttributeTo=curGrapheme\n else:\n nToAttributeTo=nextGraphemeNumber()\n for l in listOfComponents:\n for idx, g in enumerate(graphemes):\n if idx!=nToAttributeTo and l in g:\n g.remove(l)\n if l in discarded:\n discarded.remove(l)\n if not(l in graphemes[nToAttributeTo]):\n graphemes[nToAttributeTo].append(l)\n cleanupEmptyGraphemes()\n justSaved=False\n updateStatusWindow()\n curViewPort=computeViewPort(graphemes[curGrapheme])\n displayGrapheme(\"main\")\n \n\n\ndef cleanupEmptyGraphemes():\n global graphemes,curGrapheme\n graphemes=[g for g in graphemes if len(g)>0]\n if curGrapheme>=len(graphemes):\n curGrapheme=0\n\ndef selectComponents(event,x,y,flags,param):\n global ix,iy,drawing\n if event == cv.EVENT_LBUTTONDOWN:\n drawing = True\n ix,iy = x,y\n elif event == cv.EVENT_LBUTTONUP:\n (ixreal,iyreal)=getRealCoordinates(ix,iy)\n (xreal,yreal)=getRealCoordinates(x,y)\n if ixreal==xreal and iyreal==yreal:\n #if we just clicked, then find the closest component\n bestComponent=-1\n shortestDistance=9999999\n for c in list(range(1,numLabels)):\n br=cv.boundingRect(contours[c])\n bre=[br[0],br[0]+br[2],br[1],br[1]+br[3]]\n if haveIntersection(curViewPort,bre):\n curDistance=-cv.pointPolygonTest(contours[c],(ixreal,iyreal),True)\n if curDistance=0:\n performAction([bestComponent],curMode)\n else:\n #if we drew a rectangle, find all components that intersect with that rectangle\n ixreal,xreal=min(ixreal,xreal),max(ixreal,xreal)\n iyreal,yreal=min(iyreal,yreal),max(iyreal,yreal)\n thisRect=[ixreal,xreal,iyreal,yreal]\n selectedComponents=[]\n for c in list(range(1,numLabels)):\n br=cv.boundingRect(contours[c])\n bre=[br[0],br[0]+br[2],br[1],br[1]+br[3]]\n if haveIntersection(thisRect,bre):\n selectedComponents.append(c)\n performAction(selectedComponents,curMode)\n\ndef saveCurrentPlate():\n global justSaved\n f=open(savefile,\"wb\")\n pickle.dump((curGrapheme,graphemes,discarded),f)\n f.close()\n platename=basefilename[:basefilename.rfind(\"_\")]\n outputdirpath=os.path.join(outGraphemesDir,platename)\n #if dir already exists, remove it\n if os.path.isdir(outputdirpath):\n shutil.rmtree(outputdirpath)\n os.mkdir(outputdirpath)\n #now, save the graphemes themselves\n for idx, g in enumerate(graphemes):\n ge=graphemeEdges(g,contours)\n croppedMaskOfThisGrapheme = np.zeros((ge[3]-ge[2],ge[1]-ge[0]), dtype=\"uint8\")\n for c in g:\n componentMaskCropped=((labels == c).astype(\"uint8\") * 255)[ge[2]:ge[3],ge[0]:ge[1]]\n croppedMaskOfThisGrapheme = cv.bitwise_or(croppedMaskOfThisGrapheme, componentMaskCropped)\n baseOutFile=platename+\"_\"+str(idx)+\"_\"+str(int((ge[0]+ge[1])/2))+\"_\"+str(int((ge[2]+ge[3])/2))+\"_\"+str(ge[1]-ge[0])+\"_\"+str(ge[3]-ge[2])+\".png\"\n cv.imwrite(os.path.join(outputdirpath,baseOutFile),croppedMaskOfThisGrapheme)\n easygui.msgbox(\"Saved\")\n justSaved=True\n\n\ndef loadCurrentPlate():\n global curGrapheme,graphemes,discarded,justSaved\n if os.path.exists(savefile):\n f=open(savefile,\"rb\")\n curGrapheme,graphemes,discarded=pickle.load(f)\n f.close()\n justSaved=True\n \n \n\n#--------------------------------------\n# main function to segment graphemes\n#---------------------------------------\n\ndef segmentGraphemes(nl,conts,slowMode,distanceCutOff):\n #pass the number of labels nl, the list of contours conts, slowMode if use real distance, and distanceCutOff in pixel\n #return graphemes = a list of list regrouping graphemes\n distances = getDistanceMatrix(nl,conts,slowMode)\n graphemes=[[i] for i in range(1,numLabels)]\n #at first, each component lives in its own cluster\n while True:\n hasRegrouped=False\n for g1 in graphemes:\n for g2 in graphemes:\n mustRegroup=False\n if not(g1==g2):\n #print(\"Comparing graphemes {} with {}\".format(g1,g2))\n for c1 in g1:\n for c2 in g2:\n #print(\"Comparing components {} with {}\".format(c1,c2))\n if distances[c1,c2] 代表读取group传递给当前消费者最新的数据，0代表读取group传给当前消费者所有未处理？的数据\r\n if consume_not_ack: # 消费未被ack的数据，故障恢复时使用\r\n response = await self.redis_client.xpending(name=stream_channel,groupname=self.groupname) # 读取未ack的消息\r\n if response.get('pending'): # 如果读取到了\r\n # await self.redis_client.xclaim() # 单消费者不能使用claim命令\r\n response = await self.redis_client.xrange(name=stream_channel,min=response.get('min'),max=response.get('min')) # min==max 只获取一条消息\r\n # [(b'1678931917148-0', {b'fdeaeb71-3419-447f-86a7-b5c92f7c9b74': b' \\xe5\\xb8\\xae\\xe6\\x88\\x91\\xe8\\xb5\\xb7\\xe4\\xb8\\x80\\xe4\\xb8\\xaa\\xe8\\x8b\\xb1\\xe6\\x96\\x87\\xe5\\x90\\x8d\\xe5\\x90\\xa7'})]\r\n response = [[stream_channel,[(response[0][0],response[0][1])]]]\r\n return response\r\n else:\r\n return []\r\n else:\r\n response = await self.redis_client.xreadgroup(groupname=self.groupname,consumername=self.consumer_name,streams={stream_channel:'>'},count=count,block=block)\r\n # >>>\r\n # [[b'message_channel_new', [(b'1675922129373-0', {b'5f31808f-19f5-4693-aaf6-e2ccf8341cb0': b'hi'})]]]\r\n # [[b'message_channel_new', [(b'1675922194051-0', {b'd8356b44-5ef2-4b31-81f0-60756f1ac65b': b'hi'})]]]\r\n return response\r\n\r\n async def ack_message(self,stream_channel,message_id):\r\n # 标记当前组中的某个消息为已处理，使用xreadgroup读取的时候将不会再次读到，实际上，只要xreadgroup的消息就不会被再读到\r\n await self.redis_client.xack(stream_channel,self.groupname,message_id)\r\n\r\n async def delete_message(self, stream_channel, message_id):\r\n # 删除消息\r\n await self.redis_client.xdel(stream_channel, message_id)\r\n", "repo_name": "Brandon-lz/chat-free", "sub_path": "worker/src/redis/stream.py", "file_name": "stream.py", "file_ext": "py", "file_size_in_byte": 3921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "aioredis.client.Redis", "line_number": 13, "usage_type": "name"}, {"api_name": "aioredis.exceptions.ResponseError", "line_number": 29, "usage_type": "name"}, {"api_name": "aioredis.exceptions.DataError", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "1207759263", "text": "\"\"\"\"\"\"\n\nfrom compas.utilities import to_valuedict\n\nimport compas_rhino\n\ntry:\n import rhinoscriptsyntax as rs\nexcept ImportError:\n import platform\n if platform.python_implementation() == 'IronPython':\n raise\n\n\n__author__ = ['Tom Van Mele', ]\n__copyright__ = 'Copyright 2016 - Block Research Group, ETH Zurich'\n__license__ = 'MIT License'\n__email__ = 'vanmelet@ethz.ch'\n\n\n__all__ = []\n\n\nclass MeshArtist(object):\n \"\"\"\"\"\"\n\n def __init__(self, mesh, layer=None):\n self.mesh = mesh\n self.layer = layer\n self.defaults = {\n 'vertex.color' : (255, 0, 0),\n 'face.color' : (255, 255, 255),\n 'edge.color' : (0, 0, 0),\n }\n\n def redraw(self):\n \"\"\"Redraw the Rhino view.\"\"\"\n rs.EnableRedraw(True)\n rs.Redraw()\n\n def clear_layer(self):\n \"\"\"Clear the main layer of the artist.\"\"\"\n if self.layer:\n compas_rhino.clear_layer(self.layer)\n\n def clear(self):\n self.clear_vertices()\n self.clear_faces()\n self.clear_edges()\n\n def clear_vertices(self, keys=None):\n if not keys:\n name = '{}.vertex.*'.format(self.mesh.attributes['name'])\n guids = compas_rhino.get_objects(name=name)\n else:\n guids = []\n for key in keys:\n name = '{}.vertex.{}'.format(self.attributes['name'], key)\n guid = compas_rhino.get_object(name=name)\n guids.append(guid)\n compas_rhino.delete_objects(guids)\n\n def clear_faces(self, keys=None):\n if not keys:\n name = '{}.face.*'.format(self.mesh.attributes['name'])\n guids = compas_rhino.get_objects(name=name)\n else:\n guids = []\n for key in keys:\n name = '{}.face.{}'.format(self.attributes['name'], key)\n guid = compas_rhino.get_object(name=name)\n guids.append(guid)\n compas_rhino.delete_objects(guids)\n\n def clear_edges(self, keys=None):\n if not keys:\n name = '{}.edge.*'.format(self.mesh.attributes['name'])\n guids = compas_rhino.get_objects(name=name)\n else:\n guids = []\n for u, v in keys:\n name = '{}.edge.{}-{}'.format(self.attributes['name'], u, v)\n guid = compas_rhino.get_object(name=name)\n guids.append(guid)\n compas_rhino.delete_objects(guids)\n\n def draw_vertices(self, keys=None, color=None):\n \"\"\"Draw a selection of vertices of the mesh.\n\n Parameters\n ----------\n keys : list\n A list of vertex keys identifying which vertices to draw.\n Default is ``None``, in which case all vertices are drawn.\n color : str, tuple, dict\n The color specififcation for the vertices.\n Colors should be specified in the form of a string (hex colors) or\n as a tuple of RGB components.\n To apply the same color to all vertices, provide a single color\n specification. Individual colors can be assigned using a dictionary\n of key-color pairs. Missing keys will be assigned the default vertex\n color (``self.defaults['vertex.color']``).\n The default is ``None``, in which case all vertices are assigned the\n default vertex color.\n\n Notes\n -----\n The vertices are named using the following template:\n ``\"{}.vertex.{}\".format(self.mesh.attributes['name'], key)``.\n This name is used afterwards to identify vertices of the meshin the Rhino model.\n\n Examples\n --------\n >>>\n\n \"\"\"\n keys = keys or list(self.mesh.vertices())\n colordict = to_valuedict(keys, color, self.defaults['vertex.color'])\n points = []\n for key in keys:\n points.append({\n 'pos' : self.mesh.vertex_coordinates(key),\n 'name' : self.mesh.vertex_name(key),\n 'color': colordict[key]\n })\n return compas_rhino.xdraw_points(points, layer=self.layer, clear=False, redraw=False)\n\n def draw_faces(self, fkeys=None, color=None):\n \"\"\"Draw a selection of faces of the mesh.\n\n Parameters\n ----------\n fkeys : list\n A list of face keys identifying which faces to draw.\n The default is ``None``, in which case all faces are drawn.\n color : str, tuple, dict\n The color specififcation for the faces.\n Colors should be specified in the form of a string (hex colors) or\n as a tuple of RGB components.\n To apply the same color to all faces, provide a single color\n specification. Individual colors can be assigned using a dictionary\n of key-color pairs. Missing keys will be assigned the default face\n color (``self.defaults['face.color']``).\n The default is ``None``, in which case all faces are assigned the\n default vertex color.\n\n Notes\n -----\n The faces are named using the following template:\n ``\"{}.face.{}\".format(self.mesh.attributes['name'], key)``.\n This name is used afterwards to identify faces of the mesh in the Rhino model.\n\n Examples\n --------\n >>>\n\n \"\"\"\n fkeys = fkeys or list(self.mesh.faces())\n colordict = to_valuedict(fkeys, color, self.defaults['face.color'])\n faces = []\n for fkey in fkeys:\n faces.append({\n 'points': self.mesh.face_coordinates(fkey),\n 'name' : \"{}.face.{}\".format(self.mesh.attributes['name'], fkey),\n 'color' : colordict[fkey],\n })\n return compas_rhino.xdraw_faces(faces, layer=self.layer, clear=False, redraw=False)\n\n def draw_edges(self, keys=None, color=None):\n \"\"\"Draw a selection of edges of the mesh.\n\n Parameters\n ----------\n keys : list\n A list of edge keys (as uv pairs) identifying which edges to draw.\n The default is ``None``, in which case all edges are drawn.\n color : str, tuple, dict\n The color specififcation for the edges.\n Colors should be specified in the form of a string (hex colors) or\n as a tuple of RGB components.\n To apply the same color to all faces, provide a single color\n specification. Individual colors can be assigned using a dictionary\n of key-color pairs. Missing keys will be assigned the default face\n color (``self.defaults['face.color']``).\n The default is ``None``, in which case all faces are assigned the\n default vertex color.\n\n Notes\n -----\n All edges are named using the following template:\n ``\"{}.edge.{}-{}\".fromat(self.mesh.attributes['name'], u, v)``.\n This name is used afterwards to identify edges of the mesh in the Rhino model.\n\n Examples\n --------\n >>> artist.draw_edges()\n >>> artist.draw_edges(color='#ff0000')\n >>> artist.draw_edges(color=(255, 0, 0))\n >>> artist.draw_edges(keys=self.mesh.edges_on_boundary())\n >>> artist.draw_edges(color={(u, v): '#00ff00' for u, v in self.mesh.edges_on_boundary()})\n\n \"\"\"\n keys = keys or list(self.mesh.edges())\n colordict = to_valuedict(keys, color, self.defaults['edge.color'])\n lines = []\n for u, v in keys:\n lines.append({\n 'start': self.mesh.vertex_coordinates(u),\n 'end' : self.mesh.vertex_coordinates(v),\n 'color': colordict[(u, v)],\n 'name' : self.mesh.edge_name(u, v)\n })\n return compas_rhino.xdraw_lines(lines, layer=self.layer, clear=False, redraw=False)\n\n def draw_vertexlabels(self, text=None, color=None):\n \"\"\"Draw labels for selected vertices of the mesh.\n\n Parameters\n ----------\n text : dict\n A dictionary of vertex labels as key-text pairs.\n The default value is ``None``, in which case every vertex of the mesh\n will be labelled with its key.\n color : str, tuple, dict\n The color sepcification of the labels.\n String values are interpreted as hex colors (e.g. ``'#ff0000'`` for red).\n Tuples are interpreted as RGB component specifications (e.g. ``(255, 0, 0) for red``.\n If a dictionary of specififcations is provided, the keys of the\n should refer to vertex keys in the mesh and the values should be color\n specifications in the form of strings or tuples.\n The default value is ``None``, in which case the labels are assigned\n the default vertex color (``self.defaults['vertex.color']``).\n\n Notes\n -----\n All labels are assigned a name using the folling template:\n ``\"{}.vertex.{}\".format(self.mesh.attributes['name'], key)``.\n\n Examples\n --------\n >>>\n\n \"\"\"\n if text is None:\n textdict = {key: str(key) for key in self.mesh.vertices()}\n elif isinstance(text, dict):\n textdict = text\n else:\n raise NotImplementedError\n colordict = to_valuedict(list(textdict.keys()), color, self.defaults['vertex.color'])\n labels = []\n for key, text in iter(textdict.items()):\n labels.append({\n 'pos' : self.mesh.vertex_coordinates(key),\n 'name' : self.mesh.vertex_name(key),\n 'color': colordict[key],\n 'text' : textdict[key],\n })\n return compas_rhino.xdraw_labels(labels, layer=self.layer, clear=False, redraw=False)\n\n def draw_facelabels(self, text=None, color=None):\n \"\"\"Draw labels for selected faces of the mesh.\n\n Parameters\n ----------\n\n Notes\n -----\n\n Examples\n --------\n\n \"\"\"\n if text is None:\n textdict = {key: str(key) for key in self.mesh.faces()}\n elif isinstance(text, dict):\n textdict = text\n else:\n raise NotImplementedError\n colordict = to_valuedict(list(textdict.keys()), color, self.defaults['face.color'])\n labels = []\n for key, text in iter(textdict.items()):\n labels.append({\n 'pos' : self.mesh.face_center(key),\n 'name' : self.mesh.face_name(key),\n 'color': colordict[key],\n 'text' : textdict[key],\n })\n return compas_rhino.xdraw_labels(labels, layer=self.layer, clear=False, redraw=False)\n\n def draw_edgelabels(self, text=None, color=None):\n \"\"\"Draw labels for selected edges of the mesh.\n\n Parameters\n ----------\n\n Notes\n -----\n\n Examples\n --------\n\n \"\"\"\n if text is None:\n textdict = {(u, v): \"{}-{}\".format(u, v) for u, v in self.mesh.edges()}\n elif isinstance(text, dict):\n textdict = text\n else:\n raise NotImplementedError\n colordict = to_valuedict(list(textdict.keys()), color, self.defaults['edge.color'])\n labels = []\n for (u, v), text in iter(textdict.items()):\n labels.append({\n 'pos' : self.mesh.edge_midpoint(u, v),\n 'name' : self.mesh.edge_name(u, v),\n 'color': colordict[(u, v)],\n 'text' : textdict[(u, v)],\n })\n return compas_rhino.xdraw_labels(labels, layer=self.layer, clear=False, redraw=False)\n\n\n# ==============================================================================\n# Debugging\n# ==============================================================================\n\nif __name__ == \"__main__\":\n\n import time\n\n from compas.datastructures.mesh import Mesh\n from compas.geometry.elements import Polyhedron\n\n from compas_rhino.artists.meshartist import MeshArtist\n\n poly = Polyhedron.generate(12)\n\n mesh = Mesh.from_vertices_and_faces(poly.vertices, poly.faces)\n\n artist = MeshArtist(mesh, layer='MeshArtist')\n\n artist.clear_layer()\n\n artist.draw_vertices()\n artist.redraw()\n time.sleep(2.0)\n\n artist.draw_faces()\n artist.redraw()\n time.sleep(2.0)\n\n artist.draw_edges()\n artist.redraw()\n time.sleep(2.0)\n\n artist.draw_vertexlabels()\n artist.draw_facelabels()\n artist.draw_edgelabels()\n\n # artist.redraw()\n", "repo_name": "dtbinh/T1_python-exercises", "sub_path": "07_ur_online/shifted_frames_setup/compas/src/compas_rhino/artists/meshartist.py", "file_name": "meshartist.py", "file_ext": "py", "file_size_in_byte": 12537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "platform.python_implementation", "line_number": 11, "usage_type": "call"}, {"api_name": "rhinoscriptsyntax.EnableRedraw", "line_number": 38, "usage_type": "call"}, {"api_name": "rhinoscriptsyntax.Redraw", "line_number": 39, "usage_type": "call"}, {"api_name": "compas_rhino.clear_layer", "line_number": 44, "usage_type": "call"}, {"api_name": "compas_rhino.get_objects", "line_number": 54, "usage_type": "call"}, {"api_name": "compas_rhino.get_object", "line_number": 59, "usage_type": "call"}, {"api_name": "compas_rhino.delete_objects", "line_number": 61, "usage_type": "call"}, {"api_name": "compas_rhino.get_objects", "line_number": 66, "usage_type": "call"}, {"api_name": "compas_rhino.get_object", "line_number": 71, "usage_type": "call"}, {"api_name": "compas_rhino.delete_objects", "line_number": 73, "usage_type": "call"}, {"api_name": "compas_rhino.get_objects", "line_number": 78, "usage_type": "call"}, {"api_name": "compas_rhino.get_object", "line_number": 83, "usage_type": "call"}, {"api_name": "compas_rhino.delete_objects", "line_number": 85, "usage_type": "call"}, {"api_name": "compas.utilities.to_valuedict", "line_number": 118, "usage_type": "call"}, {"api_name": "compas_rhino.xdraw_points", "line_number": 126, "usage_type": "call"}, {"api_name": "compas.utilities.to_valuedict", "line_number": 159, "usage_type": "call"}, {"api_name": "compas_rhino.xdraw_faces", "line_number": 167, "usage_type": "call"}, {"api_name": "compas.utilities.to_valuedict", "line_number": 204, "usage_type": "call"}, {"api_name": "compas_rhino.xdraw_lines", "line_number": 213, "usage_type": "call"}, {"api_name": "compas.utilities.to_valuedict", "line_number": 250, "usage_type": "call"}, {"api_name": "compas_rhino.xdraw_labels", "line_number": 259, "usage_type": "call"}, {"api_name": "compas.utilities.to_valuedict", "line_number": 280, "usage_type": "call"}, {"api_name": "compas_rhino.xdraw_labels", "line_number": 289, "usage_type": "call"}, {"api_name": "compas.utilities.to_valuedict", "line_number": 310, "usage_type": "call"}, {"api_name": "compas_rhino.xdraw_labels", "line_number": 319, "usage_type": "call"}, {"api_name": "compas.geometry.elements.Polyhedron.generate", "line_number": 335, "usage_type": "call"}, {"api_name": "compas.geometry.elements.Polyhedron", "line_number": 335, "usage_type": "name"}, {"api_name": "compas.datastructures.mesh.Mesh.from_vertices_and_faces", "line_number": 337, "usage_type": "call"}, {"api_name": "compas.datastructures.mesh.Mesh", "line_number": 337, "usage_type": "name"}, {"api_name": "compas_rhino.artists.meshartist.MeshArtist", "line_number": 339, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 345, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 349, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 353, "usage_type": "call"}]} +{"seq_id": "22717790391", "text": "from __future__ import print_function\nfrom glob import glob\nfrom os.path import join as pjoin\nimport os\nimport io\nHERE = os.path.abspath(os.path.dirname(__file__))\n\ndef find_packages(top=HERE):\n \"\"\"\n Find all of the packages.\n \"\"\"\n packages = []\n for d, dirs, _ in os.walk(top, followlinks=True):\n if os.path.exists(pjoin(d, '__init__.py')):\n packages.append(os.path.relpath(d, top).replace(os.path.sep, '.'))\n elif d != top:\n # Don't look for packages in subfolders if current isn't a package.\n dirs[:] = []\n return packages\n\ndef get_version(file, name='__version__'):\n \"\"\"Get the version of the package from the given file by\n executing it and extracting the given `name`.\n \"\"\"\n path = os.path.realpath(file)\n version_ns = {}\n with io.open(path, encoding=\"utf8\") as f:\n exec(f.read(), {}, version_ns)\n return version_ns[name]\n\n\nfrom setuptools import setup\n\n\n# The name of the project\nname = 'nanohub-uidl'\n\n# Ensure a valid python version ### deprecated\n#ensure_python('>=3.3')\n\n# Get our version\nversion = get_version(pjoin('nanohubuidl', '_version.py'))\n\nlong_description = \"\"\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetup_args = {\n 'name' : name,\n 'description' : 'A set of tools to run create Javascript Apps, using Teleporthq UIDL schema',\n 'long_description_content_type' : 'text/markdown',\n 'long_description':long_description,\n 'version' : version,\n 'scripts' : glob(pjoin('scripts', '*')),\n 'packages' : find_packages(),\n 'data_files' : [\n ('assets', []),\n (\n 'etc/jupyter/jupyter_notebook_config.d',\n ['nanohubuidl/jupyter-config/jupyter_server_config.d/nanohubuidl.json']\n )\n ],\n 'author' : 'Nanohub',\n 'author_email' : 'denphi@denphi.com',\n 'url' : 'https://github.com/denphi/nanohub-uidl',\n 'license' : 'BSD',\n 'platforms' : \"Linux, Mac OS X, Windows\",\n 'keywords' : ['IPython'],\n 'classifiers' : [\n 'Intended Audience :: Developers',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Framework :: Jupyter',\n ],\n 'include_package_data' : True,\n 'install_requires' : [\n 'nanohub-remote>=0.1.0',\n 'simtool',\n ],\n 'extras_require' : {\n 'test': [\n ],\n 'examples': [\n ],\n 'docs': [\n ],\n },\n 'entry_points' : {\n 'console_scripts': [\n 'run_uidl = nanohubuidl:main'\n ],\n },\n}\n\nif __name__ == '__main__':\n setup(**setup_args)\n", "repo_name": "denphi/nanohub-uidl", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2947, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "io.open", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "13230391468", "text": "import sys\nimport cx_Freeze\nfrom cx_Freeze import setup, Executable\nsys.argv.append(\"build\")\nfilename = \"gui3.py\"\nbase = None\nif sys.platform == \"win32\":\n base = \"Win32GUI\"\nsetup(\n name = \"Kelime Ogren\",\n version = \"2.0\",\n description = \"It helps you to memorize words easily!\",\n executables = [Executable(filename, base=base)])", "repo_name": "MuazKrtl/LearnWordApp", "sub_path": "gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.argv.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cx_Freeze.setup", "line_number": 9, "usage_type": "call"}, {"api_name": "cx_Freeze.Executable", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "73746985326", "text": "import unittest\nimport os\nimport time\nimport shutil\nfrom typing import List\n\nfrom airflow.models.dagrun import DagRun\nfrom airflow.utils.session import create_session\nfrom airflow.utils.state import State\nfrom notification_service.client import NotificationClient\nfrom pyflink.table import Table, DataTypes\nfrom pyflink.table.descriptors import Schema, OldCsv, FileSystem\n\nfrom ai_flow import AIFlowServerRunner, init_ai_flow_context\nfrom ai_flow.workflow.status import Status\nfrom ai_flow_plugins.job_plugins import flink\nfrom ai_flow_plugins.tests.airflow_scheduler_utils import run_ai_flow_workflow, get_dag_id, get_workflow_execution_info, \\\n set_workflow_execution_info\nfrom ai_flow_plugins.tests import airflow_db_utils\nimport ai_flow as af\nfrom ai_flow.test.util.notification_service_utils import start_notification_server, stop_notification_server\n\nproject_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))\n\n\nclass Transformer(flink.FlinkPythonProcessor):\n def process(self, execution_context: flink.ExecutionContext, input_list: List[Table] = None) -> List[Table]:\n return [input_list[0].group_by('word').select('word, count(1)')]\n\n\nclass Source(flink.FlinkPythonProcessor):\n def process(self, execution_context: flink.ExecutionContext, input_list: List[Table] = None) -> List[Table]:\n input_file = os.path.join(os.getcwd(), 'resources', 'word_count.txt')\n t_env = execution_context.table_env\n t_env.connect(FileSystem().path(input_file)) \\\n .with_format(OldCsv()\n .field('word', DataTypes.STRING())) \\\n .with_schema(Schema()\n .field('word', DataTypes.STRING())) \\\n .create_temporary_table('mySource')\n return [t_env.from_path('mySource')]\n\n\nclass Sink(flink.FlinkPythonProcessor):\n def process(self, execution_context: flink.ExecutionContext, input_list: List[Table] = None) -> List[Table]:\n output_file = os.path.join(os.getcwd(), 'output')\n if os.path.exists(output_file):\n os.remove(output_file)\n\n t_env = execution_context.table_env\n statement_set = execution_context.statement_set\n t_env.connect(FileSystem().path(output_file)) \\\n .with_format(OldCsv()\n .field_delimiter('\\t')\n .field('word', DataTypes.STRING())\n .field('count', DataTypes.BIGINT())) \\\n .with_schema(Schema()\n .field('word', DataTypes.STRING())\n .field('count', DataTypes.BIGINT())) \\\n .create_temporary_table('mySink')\n statement_set.add_insert('mySink', input_list[0])\n return []\n\n\nclass TestFlink(unittest.TestCase):\n @classmethod\n def setUpClass(cls) -> None:\n cls.ns_server = start_notification_server()\n config_file = project_path + '/master.yaml'\n cls.master = AIFlowServerRunner(config_file=config_file)\n cls.master.start()\n\n @classmethod\n def tearDownClass(cls) -> None:\n cls.master.stop()\n stop_notification_server(cls.ns_server)\n generated = '{}/generated'.format(project_path)\n if os.path.exists(generated):\n shutil.rmtree(generated)\n temp = '/tmp/aiflow'\n if os.path.exists(temp):\n shutil.rmtree(temp)\n\n def setUp(self):\n airflow_db_utils.clear_all()\n self.master._clear_db()\n af.current_graph().clear_graph()\n init_ai_flow_context()\n\n def tearDown(self):\n af.current_graph().clear_graph()\n self.master._clear_db()\n\n def test_local_flink_task(self):\n def run_workflow(client: NotificationClient):\n with af.job_config('task_1'):\n input_example = af.user_define_operation(processor=Source())\n processed = af.transform(input=[input_example], transform_processor=Transformer())\n af.user_define_operation(input=[processed], processor=Sink())\n w = af.workflow_operation.submit_workflow(workflow_name=af.current_workflow_config().workflow_name)\n wei = af.workflow_operation.start_new_workflow_execution(\n workflow_name=af.current_workflow_config().workflow_name)\n set_workflow_execution_info(wei)\n while True:\n with create_session() as session:\n dag_run = session.query(DagRun) \\\n .filter(DagRun.dag_id == 'test_project.{}'\n .format(af.current_workflow_config().workflow_name)).first()\n if dag_run is not None and dag_run.state == State.SUCCESS:\n break\n else:\n time.sleep(1)\n\n run_ai_flow_workflow(dag_id=get_dag_id(af.current_project_config().get_project_name(),\n af.current_workflow_config().workflow_name),\n test_function=run_workflow)\n workflow_execution_info = af.workflow_operation.get_workflow_execution(\n execution_id=get_workflow_execution_info().workflow_execution_id)\n self.assertEqual(Status.FINISHED, workflow_execution_info.status)\n\n job_execution_infos = af.workflow_operation.get_job_executions(job_name='task_1',\n execution_id=get_workflow_execution_info().\n workflow_execution_id)\n self.assertEqual(Status.FINISHED, job_execution_infos[0].status)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "lisy09/ffa-2021-demos", "sub_path": "aiflow/flink-ai-flow/ai_flow_plugins/tests/it_workflows/workflows/test_flink/test_flink.py", "file_name": "test_flink.py", "file_ext": "py", "file_size_in_byte": 5669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink.FlinkPythonProcessor", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink", "line_number": 26, "usage_type": "name"}, {"api_name": "ai_flow_plugins.job_plugins.flink.ExecutionContext", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "pyflink.table.Table", "line_number": 27, "usage_type": "name"}, {"api_name": "ai_flow_plugins.job_plugins.flink.FlinkPythonProcessor", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink", "line_number": 31, "usage_type": "name"}, {"api_name": "ai_flow_plugins.job_plugins.flink.ExecutionContext", "line_number": 32, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "name"}, {"api_name": "pyflink.table.Table", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 33, "usage_type": "call"}, {"api_name": "pyflink.table.descriptors.FileSystem", "line_number": 35, "usage_type": "call"}, {"api_name": "pyflink.table.descriptors.OldCsv", "line_number": 36, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes.STRING", "line_number": 37, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes", "line_number": 37, "usage_type": "name"}, {"api_name": "pyflink.table.descriptors.Schema", "line_number": 38, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes.STRING", "line_number": 39, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes", "line_number": 39, "usage_type": "name"}, {"api_name": "ai_flow_plugins.job_plugins.flink.FlinkPythonProcessor", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink", "line_number": 44, "usage_type": "name"}, {"api_name": "ai_flow_plugins.job_plugins.flink.ExecutionContext", "line_number": 45, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.job_plugins.flink", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 45, "usage_type": "name"}, {"api_name": "pyflink.table.Table", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 48, "usage_type": "call"}, {"api_name": "pyflink.table.descriptors.FileSystem", "line_number": 52, "usage_type": "call"}, {"api_name": "pyflink.table.descriptors.OldCsv", "line_number": 53, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes.STRING", "line_number": 55, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes", "line_number": 55, "usage_type": "name"}, {"api_name": "pyflink.table.DataTypes.BIGINT", "line_number": 56, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes", "line_number": 56, "usage_type": "name"}, {"api_name": "pyflink.table.descriptors.Schema", "line_number": 57, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes.STRING", "line_number": 58, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes", "line_number": 58, "usage_type": "name"}, {"api_name": "pyflink.table.DataTypes.BIGINT", "line_number": 59, "usage_type": "call"}, {"api_name": "pyflink.table.DataTypes", "line_number": 59, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 65, "usage_type": "attribute"}, {"api_name": "ai_flow.test.util.notification_service_utils.start_notification_server", "line_number": 68, "usage_type": "call"}, {"api_name": "ai_flow.AIFlowServerRunner", "line_number": 70, "usage_type": "call"}, {"api_name": "ai_flow.test.util.notification_service_utils.stop_notification_server", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 82, "usage_type": "call"}, {"api_name": "ai_flow_plugins.tests.airflow_db_utils.clear_all", "line_number": 85, "usage_type": "call"}, {"api_name": "ai_flow_plugins.tests.airflow_db_utils", "line_number": 85, "usage_type": "name"}, {"api_name": "ai_flow.current_graph", "line_number": 87, "usage_type": "call"}, {"api_name": "ai_flow.init_ai_flow_context", "line_number": 88, "usage_type": "call"}, {"api_name": "ai_flow.current_graph", "line_number": 91, "usage_type": "call"}, {"api_name": "notification_service.client.NotificationClient", "line_number": 95, "usage_type": "name"}, {"api_name": "ai_flow.job_config", "line_number": 96, "usage_type": "call"}, {"api_name": "ai_flow.user_define_operation", "line_number": 97, "usage_type": "call"}, {"api_name": "ai_flow.transform", "line_number": 98, "usage_type": "call"}, {"api_name": "ai_flow.user_define_operation", "line_number": 99, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation.submit_workflow", "line_number": 100, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation", "line_number": 100, "usage_type": "attribute"}, {"api_name": "ai_flow.current_workflow_config", "line_number": 100, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation.start_new_workflow_execution", "line_number": 101, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation", "line_number": 101, "usage_type": "attribute"}, {"api_name": "ai_flow.current_workflow_config", "line_number": 102, "usage_type": "call"}, {"api_name": "ai_flow_plugins.tests.airflow_scheduler_utils.set_workflow_execution_info", "line_number": 103, "usage_type": "call"}, {"api_name": "airflow.utils.session.create_session", "line_number": 105, "usage_type": "call"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 106, "usage_type": "argument"}, {"api_name": "airflow.models.dagrun.DagRun.dag_id", "line_number": 107, "usage_type": "attribute"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 107, "usage_type": "name"}, {"api_name": "ai_flow.current_workflow_config", "line_number": 108, "usage_type": "call"}, {"api_name": "airflow.utils.state.State.SUCCESS", "line_number": 109, "usage_type": "attribute"}, {"api_name": "airflow.utils.state.State", "line_number": 109, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "ai_flow_plugins.tests.airflow_scheduler_utils.run_ai_flow_workflow", "line_number": 114, "usage_type": "call"}, {"api_name": "ai_flow_plugins.tests.airflow_scheduler_utils.get_dag_id", "line_number": 114, "usage_type": "call"}, {"api_name": "ai_flow.current_project_config", "line_number": 114, "usage_type": "call"}, {"api_name": "ai_flow.current_workflow_config", "line_number": 115, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation.get_workflow_execution", "line_number": 117, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation", "line_number": 117, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.tests.airflow_scheduler_utils.get_workflow_execution_info", "line_number": 118, "usage_type": "call"}, {"api_name": "ai_flow.workflow.status.Status.FINISHED", "line_number": 119, "usage_type": "attribute"}, {"api_name": "ai_flow.workflow.status.Status", "line_number": 119, "usage_type": "name"}, {"api_name": "ai_flow.workflow_operation.get_job_executions", "line_number": 121, "usage_type": "call"}, {"api_name": "ai_flow.workflow_operation", "line_number": 121, "usage_type": "attribute"}, {"api_name": "ai_flow_plugins.tests.airflow_scheduler_utils.get_workflow_execution_info", "line_number": 122, "usage_type": "call"}, {"api_name": "ai_flow.workflow.status.Status.FINISHED", "line_number": 124, "usage_type": "attribute"}, {"api_name": "ai_flow.workflow.status.Status", "line_number": 124, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "24005748943", "text": "from PIL import Image\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport shutil\nimport numpy as np\nfrom pathlib import Path\nfrom torch.nn import functional\nfrom torch import nn, Tensor, squeeze, flatten, device\nfrom torch.utils.data import DataLoader, RandomSampler, Subset\nfrom torchvision import transforms, datasets\nimport torchvision\nimport torch\nfrom os import listdir\nfrom matplotlib import image\nimport time\n\nprint(os.getcwd())\n\n# # im = Image.open(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\test\\NORMAL\\IM-0001-0001.jpeg')\n# # print(im.format, im.size, im.mode)\n# # im.show();\n############################################# DATA EVALUATION ##############################################################\n# train_normal = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\train\\NORMAL'))\n# train_pneumonia = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\train\\PNEUMONIA'))\n# val_normal = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\val\\NORMAL'))\n# val_pneumonia = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\val\\PNEUMONIA'))\n# test_normal = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\test\\NORMAL'))\n# test_pneumonia = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\test\\PNEUMONIA'))\n#\n# print('BEFORE REALLOCATION:\\n', 'train_normal:', train_normal, '\\n', 'train_pneumonia:', train_pneumonia, '\\n', 'val_normal:', val_normal, '\\n', 'val_pneumonia:', val_pneumonia, '\\n', 'test_normal:', test_normal, '\\n', 'test_pneumonia:', test_pneumonia, '\\n')\n#\n# normal_sum = train_normal + test_normal + val_normal\n# pneumonia_sum = train_pneumonia + test_pneumonia + val_pneumonia\n# print('TEST/TRAIN/VAL SUM:\\n', 'normal:', normal_sum, '\\n', 'pneumonia:', pneumonia_sum, '\\n')\n# print('EXPECTED SPREAD AFTER REALLOCATION:', 'train_normal:', round(normal_sum * 0.6), '\\n', 'train_pneumonia:', round(pneumonia_sum * 0.6), '\\n', 'val_normal:', round(normal_sum * 0.2), '\\n', 'val_pneumonia:', round(pneumonia_sum * 0.2), '\\n', 'test_normal:', round(normal_sum * 0.2), '\\n', 'test_pneumona:', round(pneumonia_sum * 0.2), '\\n')\n#\n# joined_normal_dir = r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\NORMAL'\n# joined_pneumonia_dir = r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\PNEUMONIA'\n\n############################################# DATA REALLOCATION ##############################################################\n\n# import os\n# import shutil\n# src_files = os.listdir(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\test\\PNEUMONIA\")\n# for file_name in src_files:\n# full_file_name = os.path.join(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\test\\PNEUMONIA\", file_name)\n# if os.path.isfile(full_file_name):\n# shutil.copy(full_file_name, joined_pneumonia_dir)\n\n# normal = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\NORMAL'))\n# pneumonia = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\PNEUMONIA'))\n# print(\"\\nexpected vs current:\\n\", \"normal: \", normal_sum, \" vs \", normal, \"\\npneumonia: \", pneumonia_sum, \" vs \", pneumonia)\n#\n# joined_normal_dir = r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\NORMAL'\n# joined_pneumonia_dir = r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\PNEUMONIA'\n#\n# src_files_normal = os.listdir(joined_normal_dir)\n# src_files_pneumonia = os.listdir(joined_pneumonia_dir)\n#\n# train_normal_elem = []\n# train_normal_elem = src_files_normal[0:950]\n# val_normal_elem = src_files_normal[950:1267]\n# test_normal_elem = src_files_normal[1267:]\n#\n# train_pneumonia_elem = src_files_pneumonia[0:2564]\n# val_pneumonia_elem = src_files_pneumonia[2564:3419]\n# test_pneumonia_elem = src_files_pneumonia[3419:]\n#\n# print(\"\\ntrain_normal_elem:\", train_normal_elem)\n# print(\"\\nval_normal_elem:\", val_normal_elem)\n# print(\"\\ntest_normal_elem:\", test_normal_elem)\n# print(\"\\ntrain_pneumonia_elem:\", train_pneumonia_elem)\n# print(\"\\nval_pneumonia_elem:\", val_pneumonia_elem)\n# print(\"\\ntest_pneumonia_elem:\", test_pneumonia_elem)\n\n# import os\n# import shutil\n# for file_name in test_pneumonia_elem:\n# full_file_name = os.path.join(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\joined\\PNEUMONIA', file_name)\n# if os.path.isfile(full_file_name):\n# shutil.copy(full_file_name, r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\test\\PNEUMONIA')\n\n# train_normal_rearranged = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\train\\NORMAL'))\n# val_normal_rearranged = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\val\\NORMAL'))\n# test_normal_rearranged = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\test\\NORMAL'))\n#\n# train_pneumonia_rearranged = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\train\\PNEUMONIA'))\n# val_pneumonia_rearranged = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\val\\PNEUMONIA'))\n# test_pneumonia_rearranged = len(os.listdir(r'G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\test\\PNEUMONIA'))\n#\n# print(\"\\ntrain_normal:\", train_normal)\n# print(\"\\ttrain_normal_rearranged:\", train_normal_rearranged)\n#\n# print(\"\\nval_normal:\", val_normal)\n# print(\"\\tval_normal_rearranged:\", val_normal_rearranged)\n#\n# print(\"\\ntest_normal:\", test_normal)\n# print(\"\\ttest_normal_rearranged:\", test_normal_rearranged)\n#\n# print(\"\\ntrain_pneumonia:\", train_pneumonia)\n# print(\"\\ttrain_peumonia_rearranged:\", train_pneumonia_rearranged)\n#\n# print(\"\\nval_pneumonia:\", val_pneumonia)\n# print(\"\\tval_pneumonia_rearranged:\", val_pneumonia_rearranged)\n#\n# print(\"\\ntest_pneumonia:\", test_pneumonia)\n# print(\"\\ttest_pneumonia_rearranged:\", test_pneumonia_rearranged)\n\n# df = pd.DataFrame({\n# 'normal': [train_normal_rearranged, test_normal_rearranged, val_normal_rearranged],\n# 'pneumonia': [train_pneumonia_rearranged, test_pneumonia_rearranged, val_pneumonia_rearranged]\n# }, index=['train', 'test', 'val'])\n#\n# spread = df.plot.bar(legend=True)\n# plt.show()\n\n############################################# DATA LOAD ##############################################################\n\ntrain_normal_rearranged = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\train\\NORMAL\")\ntrain_pneumonia_rearranged = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\train\\PNEUMONIA\")\nval_normal_rearranged = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\val\\NORMAL\")\nval_pneumonia_rearranged = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\val\\PNEUMONIA\")\ntest_normal_rearranged = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\test\\NORMAL\")\ntest_pneumonia_rearranged = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\test\\PNEUMONIA\")\nBASE_PATH = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\")\n\ntrain_path = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\train\")\nval_path = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\val\")\ntest_path = Path(r\"G:\\17810_23812_bundle_archive\\chest_xray\\chest_xray\\rearranged\\test\")\n# train_normal_rearranged = os.path.join(train_normal_rearranged, '')\n# print(train_normal_rearranged)\n# train_normal_rearranged = train_normal_rearranged + os.path.sep\n\n# train_normal_labels = torch.ones(len(os.listdir(train_normal_rearranged)))\n# train_pneumonia_labels = torch.zeros(len(os.listdir(train_pneumonia_rearranged)))\n\n# img = Image.open(BASE_PATH + r\"\\rearranged\\train\\NORMAL\\IM-0001-0001.jpeg\")\n# print(img.getbands())\n# mask = np.array(img)\n# shape = mask.shape\n# # print(mask)\n# # print(shape)\n# trans = transforms.ToTensor()\n# test = trans(img)\n# print(test)\n\n# train_normal_images = list()\n# for filename in listdir(train_normal_rearranged):\n# \t# load image\n# \timg_data = image.imread(train_normal_rearranged + os.path.sep + filename)\n# # \t# store loaded image\n# \ttrain_normal_images.append(img_data)\n# \tprint('> loaded %s %s' % (filename, img_data.shape))\n\ndevice = torch.device('cuda')\n\nclasses = ('NORMAL', 'PNEUMONIA')\nnum_epochs = 4\nbatch_size = 4\n\ntransform = transforms.Compose([transforms.Resize(268),\n transforms.CenterCrop(268),\n transforms.RandomAffine(45),\n transforms.Grayscale(num_output_channels=1),\n transforms.ToTensor(),\n # hardcoded normalization values\n transforms.Normalize(mean = [0.5165],\n std = [0.2483])\n ])\n\ndataset_train = datasets.ImageFolder(train_path, transform=transform)\ndataset_val = datasets.ImageFolder(val_path, transform=transform)\ndataset_test = datasets.ImageFolder(test_path, transform=transform)\n\n# img_normalize = torch.stack([img_t for img_t, _ in dataset_train], dim=3)\n# img_mean = img_normalize.view(1,-1).mean(1)\n# img_std = img_normalize.view(1,-1).std(1)\n# print(f'normalized tensor shape: {img_normalize.shape}, mean value:{img_mean}, std value:{img_std}')\n\n\ntrain_loader = DataLoader(dataset_train,drop_last=True, shuffle=True, batch_size=batch_size)\nval_loader = DataLoader(dataset_val,drop_last=True, shuffle=False, batch_size=batch_size)\ntest_loader = DataLoader(dataset_test,drop_last=True, shuffle=False, batch_size=batch_size)\n\n\n\n\n\ndataiter = iter(train_loader)\nimages, labels = dataiter.next()\nprint(f'images.shape: {images.shape}')\n\n\n\nimages_test = images[0].permute(1,2,0)\nimages_test = squeeze(images_test, dim=2)\nprint(images_test.shape)\nprint(images_test, labels[0])\nplt.imshow(images_test, cmap='gray')\nplt.show()\n\n###################################### SHOW BATCH\nimages_grid = torchvision.utils.make_grid(images)\nimages_permute = images_grid.permute(1,2,0)\nprint(images.shape)\nprint(images_permute.shape)\nprint(labels)\nplt.imshow(torchvision.utils.make_grid(images_permute), cmap='gray')\nplt.show()\n\n\n############## GET MEAN/STD\n\n# train_set = datasets.ImageFolder(train_path, transform=transforms)\n# train_loader_full = DataLoader(dataset_train, batch_size=len(train_set), num_workers=1)\n#\n# def main():\n# data = next(iter(train_loader_full))\n# print(data[0].max())\n# print(data[0].min())\n# pass\n#\n# if __name__ == '__main__':\n# main()\n\n\n#### BLOCK START\nclass ConvNet(nn.Module):\n def __init__(self):\n super(ConvNet, self).__init__()\n self.conv1 = nn.Conv2d(1, 6, 5) # 264\n self.pool = nn.MaxPool2d(2, 2) # 132\n self.conv2 = nn.Conv2d(6, 16, 5) # 128\n self.pool = nn.MaxPool2d(2, 2) # 64\n self.conv3 = nn.Conv2d(16, 32, 5) # 60\n self.pool = nn.MaxPool2d(2, 2) # 30\n self.conv4 = nn.Conv2d(32, 64, 5) # 26\n self.pool = nn.MaxPool2d(2, 2) # 13\n self.conv5 = nn.Conv2d(64, 128, 5) # 9\n self.fc1 = nn.Linear(128*9*9, 1000)\n self.fc2 = nn.Linear(1000, 100)\n self.fc3 = nn.Linear(100, 1)\n\n def forward(self, x):\n x = self.pool(functional.relu(self.conv1(x)))\n x = self.pool(functional.relu(self.conv2(x)))\n x = self.pool(functional.relu(self.conv3(x)))\n x = self.pool(functional.relu(self.conv4(x)))\n x = functional.relu(self.conv5(x))\n x = x.view(-1, 128*9*9)\n x = functional.relu(self.fc1(x))\n x = functional.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\nmodel = ConvNet().to(device)\n\n# criterion = nn.CrossEntropyLoss()\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr = 0.0001)\n\nprint(len(train_loader))\nprint(f'images.shape before training loop: {images.shape}')\n# training loop\nn_total_steps = len(train_loader)\nfor epoch in range(num_epochs):\n for i, (images,labels) in enumerate(train_loader):\n images = images.to(device)\n labels = labels.to(device)\n outputs = model(images)\n loss = criterion(outputs.view(-1), labels.type_as(outputs))\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if (i + 1) % 4 == 0:\n print(f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{n_total_steps}], Loss: {loss.item():.4f}')\n\nprint('Finished Training')\nPATH = 'G:/x-ray_deeplearning/model'\n# torch.save(model.state_dict(), PATH)\n\nprint(f'images.shape after training loop: {images.shape}')\n##########################################################forward pass\n# outputs = model(images)\n# test_array = np.array([])\n# n_total_steps = len(train_loader)\n# for epoch in range(num_epochs):\n# for i, (images, labels) in enumerate(train_loader):\n# # origin shape: [4, 3, 32, 32] = 4, 3, 1024\n# # input_layer: 3 input channels, 6 output channels, 5 kernel size\n#\n# images = images.to(device)\n# labels = labels.to(device)\n###########################################################\n# for i, x in enumerate(images):\n# print(images.shape)\n\n# Forward pass\n# print(f'images.shape after forward pass: {images.shape}')\n# print(outputs.shape, labels.shape)\n# time.sleep(20)\n# print(images.shape, labels.shape)\n# print(outputs.shape, labels.shape)\n# print(outputs.view(2).shape, labels.shape)\n\n\n# Backward and optimize\n\n\n\n\nwith torch.no_grad():\n n_correct = 0\n n_samples = 0\n test_iter = 0\n n_class_correct = [0 for i in range(2)]\n n_class_samples = [0 for i in range(2)]\n for images_test, labels_test in test_loader:\n images_test = images_test.to(device)\n labels_test = labels_test.to(device)\n outputs = model(images_test)\n print(outputs)\n # max returns (value ,index)\n _, predicted = torch.max(outputs, 1)\n n_samples += labels_test.size(0)\n print(labels_test.size(0))\n n_correct += (predicted == labels_test).sum().item()\n\n for i in range(batch_size):\n label = labels_test[i]\n pred = predicted[i]\n if (label == pred):\n n_class_correct[label] += 1\n n_class_samples[label] += 1\n\n acc = 100.0 * n_correct / n_samples\n print(f'Accuracy of the network: {acc} %')\n\n for i in range(2):\n acc = 100.0 * n_class_correct[i] / n_class_samples[i]\n print(f'Accuracy of {classes[i]}: {acc} %')\n\n##### BLOCK END\n\n\n\n# # Make a grid from batch\n# out = torchvision.utils.make_grid(inputs)\n#\n# imshow(out, title=[class_names[x] for x in classes])\n\n\n# inputs, classes = next(iter(loader['NORMAL']))\n# out = torchvision.utils.make_grid(inputs)\n# imshow(out, title=[class_names[x] for x in classes])\n\n# dataset_pneumonia_train = datasets.ImageFolder(train_pneumonia_rearranged, transform=transform)\n# dataset_normal_val = datasets.ImageFolder(val_normal_rearranged, transform=transform)\n# dataset_pneumonia_val = datasets.ImageFolder(val_pneumonia_rearranged, transform=transform)\n# dataset_normal_test = datasets.ImageFolder(test_normal_rearranged, transform=transform)\n# dataset_pneumonia_test = datasets.ImageFolder(test_pneumonia_rearranged, transform=transform)\n\n\n# for images, labels in dataset_train.take(1): # only take first element of dataset\n# numpy_images = images.numpy()\n# numpy_labels = labels.numpy()\n#\n# print(numpy_images, numpy_labels)\n# dataloader = torch.utils.data.DataLoader(dataset_normal_train, batch_size=32, shuffle=True)\n# images, labels = next(iter(dataloader))\n# plt.imshow()\n# df = pd.DataFrame(im3_t[4:15,4:22])\n# df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')\n# img.show()\n\n\n############################################# NEURAL NETWORK ##############################################################\n\n\n\n", "repo_name": "roboschnoz/x-ray-deeplearning", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 15828, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "os.getcwd", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 120, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 121, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 123, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 124, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 125, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 126, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 128, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 129, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 156, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 162, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 162, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 162, "usage_type": "call"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 163, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 163, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomAffine", "line_number": 164, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 164, "usage_type": "name"}, {"api_name": "torchvision.transforms.Grayscale", "line_number": 165, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 165, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 166, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 166, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 168, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 168, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 172, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 172, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 173, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 173, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 174, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "torchvision.utils.make_grid", "line_number": 204, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 204, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "torchvision.utils.make_grid", "line_number": 209, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 209, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 229, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 229, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 232, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 233, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 234, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 235, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 236, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 237, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 238, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 239, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 241, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 242, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 247, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 248, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 250, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 252, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 257, "usage_type": "argument"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 260, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 261, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 269, "usage_type": "argument"}, {"api_name": "torch.device", "line_number": 270, "usage_type": "argument"}, {"api_name": "torch.no_grad", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 322, "usage_type": "argument"}, {"api_name": "torch.device", "line_number": 323, "usage_type": "argument"}, {"api_name": "torch.max", "line_number": 327, "usage_type": "call"}]} +{"seq_id": "35715523119", "text": "from flask import Blueprint, request\n\nfrom iso_service.cache import cache\nfrom iso_service.utils.country_matcher import CountryMatcher\nfrom iso_service.validators.match_country_validator import MatchCountryValidator\n\n\nbp = Blueprint('country', __name__)\n\n\n@bp.route('/match_country', methods=['POST'])\n@cache.cached(timeout=20)\ndef match_country():\n '''Filters out the country names corresponding to the given ISO code.'''\n validation = MatchCountryValidator().run(request)\n\n if not validation.success:\n return validation.to_response()\n\n payload = validation.data\n result = CountryMatcher(payload['iso'], payload['countries']).run()\n\n return result.to_response()\n", "repo_name": "patrotom/iso-service", "sub_path": "iso_service/blueprints/country.py", "file_name": "country.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "argument"}, {"api_name": "iso_service.validators.match_country_validator.MatchCountryValidator", "line_number": 15, "usage_type": "call"}, {"api_name": "iso_service.utils.country_matcher.CountryMatcher", "line_number": 21, "usage_type": "call"}, {"api_name": "iso_service.cache.cache.cached", "line_number": 12, "usage_type": "call"}, {"api_name": "iso_service.cache.cache", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "31216011226", "text": "# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nCompile list of files in datasets for processing\n\"\"\"\nimport json\nimport os.path as op\nfrom bids.layout import BIDSLayout\n\n\ndef get_files():\n with open('dset_config.json', 'r') as fo:\n CONFIG = json.load(fo)\n\n DATA_DIR = op.abspath('/home/data/nbc/external-datasets/ds001491/')\n\n all_info = {}\n for dset_name in list(CONFIG.keys())[:3]:\n layout = BIDSLayout(op.join(DATA_DIR, dset_name))\n cfg = CONFIG[dset_name]\n task = cfg['task']\n dset_info = {}\n for sub in layout.get_subjects():\n runs = layout.get_runs(subject=sub, task=task)\n sub_info = {}\n for run in runs:\n run_info = {}\n run_info['files'] = []\n run_info['echo_times'] = []\n for echo in sorted(layout.get_echoes(subject=sub, task=task,\n run=run)):\n raw_files = layout.get(subject=sub, task=task, run=run,\n echo=echo, extensions='.nii.gz')\n preproc_files = layout.get(subject=sub, task=task, run=run,\n root='afni-step1', echo=echo,\n extensions='.nii.gz',\n desc='realign')\n preproc_files = raw_files[:]\n if len(preproc_files) != 1:\n print(preproc_files)\n raise Exception('BAD')\n\n # Replace filename with path when using new version of bids\n run_info['files'].append(preproc_files[0].filename)\n metadata = layout.get_metadata(preproc_files[0].filename)\n run_info['echo_times'].append(metadata['EchoTime'])\n sub_info[run] = run_info\n dset_info[sub] = sub_info\n all_info[dset_name] = dset_info\n\n with open('all_files.json', 'w') as fo:\n json.dump(all_info, fo, indent=4, sort_keys=True)\n\n\nif __name__ == '__main__':\n get_files()\n", "repo_name": "ME-ICA/tedana-comparison", "sub_path": "code/get_files.py", "file_name": "get_files.py", "file_ext": "py", "file_size_in_byte": 2247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "2", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "bids.layout.BIDSLayout", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "42526401458", "text": "#!usr/bin/env python3\n# -*- coding: UTF-8 -*-\n# !usr/bin/env python3\n# -*- coding: UTF-8 -*-\n\"\"\"\n数据增强流程：\n 读取候选背景图片列表 ——> 读取图片 ——> 读取标注信息 ——> 截取区域数据 ——> 确定缩放系数 ——>确定嵌入坐标 ——>保持图片和xml文件\n可配置参数：\n 除嵌入外的其他增强方式\n 每个框合成的增强样本数量\n 是否允许多目标混合\n 是否允许多类别混合\n 最大/最小缩放系数\n文件名秒格式：\n aug + box_index + aug_index + filename\n\"\"\"\nimport os\nimport re\nimport gc\nimport time\nimport numpy as np\nfrom random import choice, shuffle\n\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom xl_tool.xl_io import file_scanning, read_json\n# from xl_tool.xl_io import l\nimport numpy as np\nfrom random import uniform\nimport imgaug.augmenters as iaa\nfrom xl_tool.data.image.config import IMAGE_FORMAT\nfrom xl_tool.data.image.annonation import get_boximgs\nfrom xl_tool.data.image.general import linear_contrast, grey_world, \\\n affine_with_rotate_scale\nfrom xl_tool.data.image.annonation import Text2XML\nfrom xl_tool.data.image.blending import PoissonBlending, PyramidBlending, DirectBlending, SegBlend, \\\n ObjectReplaceBlend\nfrom PIL import Image\nfrom cv2 import namedWindow, imshow, waitKey, WINDOW_FREERATIO, destroyAllWindows\nfrom albumentations import Flip, Blur\nimport logging\n\ndef flip(image_array):\n fl = Flip()\n return fl(image=image_array)[\"image\"]\n\n\ndef blur(image_array):\n fl = Blur(blur_limit=4)\n return fl(image=image_array)[\"image\"]\n\n\naug_dict = {\"contrast\": linear_contrast, \"grey\": grey_world, \"affine\": affine_with_rotate_scale}\n\n\ndef cv_show_image(image_file, window_name=\"图片\"):\n namedWindow(window_name, WINDOW_FREERATIO) # 0表示压缩图片，图片过大无法显示\n imshow(window_name, image_file)\n k = waitKey(0) # 无限期等待输入，需要有这个否则会死机\n if k == 27: # 如果输入ESC退出\n destroyAllWindows()\n\n\ndef blending_choose(blending_method=\"direct\", background_img_file=None, blending_img_file=None,\n background_img_array=None, blending_img_array=None,\n x=None, y=None, x_proportion=0.6, y_proportion=0.6,\n x_shift=(0.5, 1.5), y_shift=(0.5, 1.9), blending_region=None, save_img=\"\"):\n \"\"\"选择不同的合成方法\"\"\"\n if blending_method == \"poisson\":\n blender = PoissonBlending()\n elif blending_method == \"direct\":\n blender = DirectBlending()\n else:\n blender = PyramidBlending(num_pyramid=5)\n # blender = DirectBlending() if blending_method == \"direct\" else\n blending_result, [x, y, x1, y1] = blender.blending_one_image(background_img_file, blending_img_file,\n background_img_array, blending_img_array,\n x, y, x_proportion, y_proportion,\n blending_region=blending_region,\n x_shift=x_shift, y_shift=y_shift,\n save_img=save_img, )\n return blending_result, [x, y, x1, y1]\n\n\ndef blending_images(background_img_file, blending_img_files, sp_dis, save_img=\"\", blending_region=None,\n blending_method=\"direct\"):\n blender = DirectBlending() if blending_method == \"direct\" else PyramidBlending()\n background_img, image_sizes, positions = blender.blending_images(background_img_file, blending_img_files, sp_dis,\n save_img,\n blending_region)\n return background_img, image_sizes, positions\n\n\ndef get_xml_image(image_path, xml_path):\n valid_images = []\n valid_xmls = []\n image_files = file_scanning(image_path, file_format=IMAGE_FORMAT, full_path=True)\n for image_file in image_files:\n xml_file = xml_path + \"/\" + os.path.basename(image_file).split(\".\")[0] + \".xml\"\n if os.path.exists(xml_file):\n valid_images.append(image_file)\n valid_xmls.append(xml_file)\n return valid_images, valid_xmls\n\n\ndef aug_images_from_xml(cat_name, image_files, xml_files, background_path, cat_aug_path, xml_folder,\n xml_source, x_proportion=0.4, y_proportion=0.5, boxes_per_image=1, max_num=1000,\n min_box_edge=100):\n background_images = get_background_images(background_path)\n im_xmls = list(zip(image_files, xml_files))\n shuffle(im_xmls)\n pbar = tqdm(im_xmls)\n count = 0\n for image_file, xml_file in pbar:\n if count >= max_num:\n continue\n base_name = os.path.basename(image_file).split(\".\")[0]\n boximgs = get_boximgs(image_file, xml_file)\n boximgs = [boximg_info for boximg_info in boximgs if boximg_info[\"name\"] == cat_name]\n shuffle(boximgs)\n box_count = 0\n for i, boximg_info in enumerate(boximgs, 1):\n if box_count > boxes_per_image:\n break\n if sum(boximg_info[\"img\"].size) < min_box_edge:\n # print(\"图片过小，不适合增强！\",boximg_info[\"img\"].size)\n continue\n background_img_array = np.array(Image.open(choice(background_images)))\n aug_image, coordinate = blending_choose(\"\", background_img_array=background_img_array,\n blending_img_array=np.array(boximg_info[\"img\"]),\n x_proportion=x_proportion,\n y_proportion=y_proportion)\n objects_info = [[boximg_info[\"name\"]] + coordinate]\n text2xml = Text2XML()\n boximg_file = \"aug_{}_{}_{}.jpg\".format(i, 0, base_name)\n xml = text2xml.get_xml(xml_folder, boximg_file, boximg_file, xml_source, aug_image.size, objects_info)\n boxxml_file = \"aug_{}_{}_{}.xml\".format(i, 0, base_name)\n aug_image.save(cat_aug_path + \"/\" + boximg_file)\n with open(cat_aug_path + \"/\" + boxxml_file, \"w\") as f:\n f.write(xml)\n count += 1\n box_count += 1\n\n pbar.set_description(\"图片增强进度：\")\n\n\ndef get_propotions(cat):\n big_propotion_cats = {\"pizza\", \"saury\", \"chicken_brittle_bone\"}\n small_propotion_cats = {\"chicken_wing\", \"drumstick\", \"toast\", \"scallop\", \"oyster\"}\n tiny_propotion_cats = (\"egg_tart\", \"pumpkin_block\", \"prawn\", \"chicken_feet\", \"pumpkin_block\", \"prawn\")\n if cat in big_propotion_cats:\n return 0.7, 0.7\n elif cat in small_propotion_cats:\n return 0.4, 0.4\n elif cat in tiny_propotion_cats:\n return 0.3, 0.3\n else:\n return 0.5, 0.5\n\n\ndef get_background_images(path):\n files = file_scanning(path, full_path=True, file_format=IMAGE_FORMAT)\n return files\n\n\ndef replace_augmentation(object_path, real_a_path, aug_path, object_classes):\n \"\"\"替换目标增强\"\"\"\n print(\"替换目标增强\")\n blender = ObjectReplaceBlend()\n for d in [i for i in os.listdir(f\"{real_a_path}\") if i in object_classes]:\n files = file_scanning(f\"{real_a_path}/{d}\", \"jpg\", sub_scan=True)\n single_object = f\"{object_path}/{d}\"\n os.makedirs(f\"{aug_path}/{d}\", exist_ok=True)\n object_images = [Image.open(i) for i in file_scanning(single_object, \"jpg\", sub_scan=True)]\n object_images = [i for i in object_images if (i.size[0] > 8 and i.size[1] > 8)]\n aspects = [i.size[0] / i.size[1] for i in object_images]\n if not aspects:\n continue\n try:\n object_images, aspects = zip(*sorted(zip(object_images, aspects), key=lambda x: x[1]))\n except Exception as e:\n pass\n tq = tqdm(files)\n for image_file in tq:\n xml_folder = r\"Food2019\"\n xml_source = 'FoodDection'\n xml_file = image_file.replace(\"jpg\", \"xml\")\n try:\n save_img = f\"{aug_path}/{d}/{'replace_aug_' + os.path.basename(image_file)}\"\n if os.path.exists(save_img):\n continue\n aug_image, boxes = blender.blending_one_image(image_file, object_images, aspects, xml_file,\n random_choice=False, aspect_jump=1.0)\n\n aug_image.save(save_img)\n text2xml = Text2XML()\n objects_info = [[d] + coordinate for coordinate in boxes]\n boximg_file = os.path.basename(save_img)\n xml = text2xml.get_xml(xml_folder, boximg_file, boximg_file, xml_source, aug_image.size, objects_info)\n with open(save_img.replace(\"jpg\", \"xml\"), \"w\") as f:\n f.write(xml)\n except :\n pass\n tq.set_description(f\"替换增强进度 {d}:\")\n\n\ndef aug_images_from_image(blending_method, image_files, background_path, cat_aug_path, xml_folder, xml_source,\n background_config_file,\n cat_name=\"\", target_number=None, x_proportion=0.4, y_proportion=0.5, max_num=10000,\n simple_augs=(\"contrast\", \"grey\", None),\n min_size_sum=200, object_valid=True):\n \"\"\"单目标合成\"\"\"\n background_configs = read_json(background_config_file)\n if min_size_sum:\n image_files = [file for file in image_files if sum(Image.open(file).size) > min_size_sum]\n pbar = tqdm(((image_files * 10)[:target_number]))\n count = 0\n for image_file in pbar:\n if count >= max_num:\n continue\n base_name = os.path.basename(image_file).split(\".\")[0]\n base_name = cat_name.replace(\" \", \"_\") if re.search(r\"[^a-zA-Z0-9_\\-.]\", base_name) else base_name\n background_config = choice(background_configs)\n background_file, region = background_config[\"file\"], background_config['region']\n background_img_array = np.array(Image.open(background_file))\n save_img = f\"{cat_aug_path}/aug_{1}_{count}_{blending_method}_{os.path.basename(background_file).split('.')[0]}_{base_name}.jpg\"\n if not object_valid: # 是否允许生成图片用于目标检测（如果允许，则不允许小目标组合）\n blending_img_array, x_p, y_p = object_combination(image_file, x_proportion, y_proportion)\n else:\n blending_img_array, x_p, y_p = np.array(Image.open(image_file)), x_proportion, y_proportion\n # 根据范围限定最大占比\n region_proportion = (\n (region[2] - region[0]) / background_img_array.shape[1],\n (region[3] - region[1]) / background_img_array.shape[0])\n x_p = min(region_proportion[0], x_proportion)\n y_p = min(region_proportion[1], y_proportion)\n # 根据候选区域的所占比例进行浮动\n x_shift = (0.9, 1.2) if region_proportion[0] <= x_proportion else (\n max(0.8 - region_proportion[0] + x_proportion, 0.2), max(1.1 + region_proportion[0] - x_proportion, 1.9))\n y_shift = (0.9, 1.2) if region_proportion[1] <= y_proportion else (\n max(0.8 - region_proportion[1] + y_proportion, 0.2), min(1.1 + region_proportion[1] - y_proportion, 1.9))\n aug_image, coordinate = blending_choose(blending_method, background_img_array=background_img_array,\n blending_img_array=blending_img_array,\n x_proportion=x_p, x_shift=x_shift, y_shift=y_shift,\n y_proportion=y_p, save_img=save_img, blending_region=region)\n if simple_augs:\n aug_method = choice(simple_augs)\n if aug_method:\n image_array = np.array(Image.open(save_img))\n Image.fromarray(aug_dict[aug_method](image_array)).save(save_img)\n text2xml = Text2XML()\n objects_info = [[cat_name] + coordinate]\n boximg_file = os.path.basename(save_img)\n xml = text2xml.get_xml(xml_folder, boximg_file, boximg_file, xml_source, aug_image.size, objects_info)\n boxxml_file = boximg_file.replace(\"jpg\", \"xml\")\n aug_image.save(cat_aug_path + \"/\" + boximg_file)\n with open(cat_aug_path + \"/\" + boxxml_file, \"w\") as f:\n f.write(xml)\n count += 1\n pbar.set_description(\"图片增强进度：\")\n\n\ndef object_combination(image_file, x_proportion, y_proportion):\n \"\"\"小目标组合\"\"\"\n if x_proportion < 0.4:\n base = Image.open(image_file)\n size = base.size\n from random import choice\n expand = [choice([1, 2]) for i in range(2)]\n new_size = [expand[i] * size[i] for i in range(2)]\n # print(\"---------------------------------\",expand )\n new = Image.new(\"RGB\", new_size)\n for i in range(expand[0]):\n for j in range(expand[1]):\n new.paste(base, (i * size[0], j * size[1]))\n x_proportion = 0.3 if expand[0] <= 1 else 0.5\n y_proportion = 0.3 if expand[1] <= 1 else 0.5\n return np.array(new), x_proportion, y_proportion\n else:\n return np.array(Image.open(image_file)), x_proportion, y_proportion\n\n\ndef aug_images_mul_object(object_path, background_config_file, save_path, target_number=5000,\n distribute=(0.05, 0.35, 0.4, 0.15),cats=None):\n \"\"\"\n 多目标合成\n \"\"\"\n logging.info(\"开始多目标合成\")\n folder = r\"Food2019\"\n source = 'FoodDection'\n cats = os.listdir(object_path) if not cats else cats\n print(\"---类别数量：\", len(cats))\n try:\n cats.remove(\"empty\")\n except:\n logging.info(\"未找到图片\")\n pass\n background_configs = read_json(background_config_file)\n # background_config = x\n cats_files = {cat: file_scanning(f\"{object_path}/{cat}\", file_format=IMAGE_FORMAT) for cat in cats}\n count = 0\n cats_count = {cat: 0 for cat in cats}\n print(\"---开始合成\")\n while count < target_number:\n ratio = count / target_number\n if ratio <= distribute[0]:\n sp_dis = 1\n number = 1\n elif distribute[0] < ratio <= distribute[1]:\n sp_dis = choice([1, 2])\n number = 2\n elif distribute[1] < ratio <= distribute[2]:\n sp_dis = choice([1, 2])\n number = 3\n else:\n sp_dis = 1\n number = 4\n choose_cats = [choice(cats) for _ in range(number)]\n for c in choose_cats:\n cats_count[c] += 1\n choose_files = [choice(cats_files[cat]) for cat in choose_cats]\n background_config = choice(background_configs)\n background_file, region = background_config[\"file\"], background_config['region']\n save_file = f\"{save_path}/aug_{number}_{count}.jpg\"\n background_img, image_sizes, positions = blending_images(background_file, choose_files, sp_dis, save_file,\n region)\n objects_info = [[cat, ] + list(positions[i]) for i, cat in enumerate(choose_cats)]\n text2xml = Text2XML()\n boximg_file = f\"aug_{number}_{count}.jpg\"\n xml = text2xml.get_xml(folder, boximg_file, boximg_file, source, background_img.size, objects_info)\n boxxml_file = f\"{save_path}/aug_{number}_{count}.xml\"\n with open(boxxml_file, \"w\") as f:\n f.write(xml)\n count += 1\n print(count)\n print(cats_count)\n\n\ndef test_single_xml():\n data_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建\" \\\n r\"数据集\\3_公开数据集抽取\\原始标注文件\"\n aug_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建\" \\\n r\"数据集\\3_公开数据集抽取\\增强文件\"\n background_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\data\" \\\n r\"set\\自建数据集\\4_背景底图\"\n folder = r\"Food2019\"\n source = 'FoodDection'\n dirs = os.listdir(data_path)\n for dir_ in dirs:\n x_proportion, y_proportion = get_propotions(dir_)\n cat_path = data_path + \"/\" + dir_\n cat_aug_path = aug_path + \"/\" + dir_\n os.makedirs(cat_aug_path, exist_ok=True)\n image_files, xml_files = get_xml_image(cat_path, cat_path)\n print(\"类别：{}\\t有效标注文件数量：{}\".format(dir_, len(image_files)))\n aug_images_from_xml(dir_, image_files, xml_files, background_path, cat_aug_path, folder, source,\n x_proportion=x_proportion, y_proportion=y_proportion)\n\n\ndef test_single():\n data_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建数据集\\5_抽取目标\\网络\"\n aug_path_d = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建数据集\\7_增强图片\\单类别_直接融合\"\n aug_path_p = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建数据集\\7_增强图片\\单类别_金字塔融合\"\n background_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\data\" \\\n r\"set\\自建数据集\\4_背景底图\"\n folder = r\"Food2019\"\n source = 'FoodDection'\n dirs = os.listdir(data_path)\n for dir_ in dirs:\n x_proportion, y_proportion = get_propotions(dir_)\n cat_path = data_path + \"/\" + dir_\n cat_aug_path_p = aug_path_p + \"/\" + dir_\n cat_aug_path_d = aug_path_d + \"/\" + dir_\n os.makedirs(cat_aug_path_p, exist_ok=True)\n os.makedirs(cat_aug_path_d, exist_ok=True)\n image_files = file_scanning(cat_path, file_format=IMAGE_FORMAT)\n print(\"类别：{}\\t有效标注文件数量：{}\".format(dir_, len(image_files)))\n # aug_images_from_image(\"\", image_files, background_path, cat_aug_path_p, folder, source,cat_name=dir_,\n # x_proportion=x_proportion, y_proportion=y_proportion)\n aug_images_from_image(\"direct\", image_files, background_path, cat_aug_path_d, folder, source, cat_name=dir_,\n x_proportion=x_proportion, y_proportion=y_proportion)\n\n\ndef test_mul_aug():\n object_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建数据集\\5_抽取目标\"\n background_config_file = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建数据集\\4_背景底图\\background_config.json\"\n save_path = r\"E:\\Programming\\Python\\8_Ganlanz\\food_recognition\\dataset\\自建数据集\\7_增强图片\\多类别组合\"\n aug_images_mul_object(object_path, background_config_file, save_path)\n\n\ndef main():\n # test_mul_aug()\n test_single()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "Lannister-Xiaolin/data_augmentation", "sub_path": "image_augmentation/augmentation_batch.py", "file_name": "augmentation_batch.py", "file_ext": "py", "file_size_in_byte": 18869, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "albumentations.Flip", "line_number": 44, "usage_type": "call"}, {"api_name": "albumentations.Blur", "line_number": 49, "usage_type": "call"}, {"api_name": "xl_tool.data.image.general.linear_contrast", "line_number": 53, "usage_type": "name"}, {"api_name": "xl_tool.data.image.general.grey_world", "line_number": 53, "usage_type": "name"}, {"api_name": "xl_tool.data.image.general.affine_with_rotate_scale", "line_number": 53, "usage_type": "name"}, {"api_name": "cv2.namedWindow", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.WINDOW_FREERATIO", "line_number": 57, "usage_type": "argument"}, {"api_name": "cv2.imshow", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 61, "usage_type": "call"}, {"api_name": "xl_tool.data.image.blending.PoissonBlending", "line_number": 70, "usage_type": "call"}, {"api_name": "xl_tool.data.image.blending.DirectBlending", "line_number": 72, "usage_type": "call"}, {"api_name": "xl_tool.data.image.blending.PyramidBlending", "line_number": 74, "usage_type": "call"}, {"api_name": "xl_tool.data.image.blending.DirectBlending", "line_number": 87, "usage_type": "call"}, {"api_name": "xl_tool.data.image.blending.PyramidBlending", "line_number": 87, "usage_type": "call"}, {"api_name": "xl_tool.xl_io.file_scanning", "line_number": 97, "usage_type": "call"}, {"api_name": "xl_tool.data.image.config.IMAGE_FORMAT", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 111, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "xl_tool.data.image.annonation.get_boximgs", "line_number": 118, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 128, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 128, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "xl_tool.data.image.annonation.Text2XML", "line_number": 134, "usage_type": "call"}, {"api_name": "xl_tool.xl_io.file_scanning", "line_number": 162, "usage_type": "call"}, {"api_name": "xl_tool.data.image.config.IMAGE_FORMAT", "line_number": 162, "usage_type": "name"}, {"api_name": "xl_tool.data.image.blending.ObjectReplaceBlend", "line_number": 169, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 170, "usage_type": "call"}, {"api_name": "xl_tool.xl_io.file_scanning", "line_number": 171, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 173, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 174, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 174, "usage_type": "name"}, {"api_name": "xl_tool.xl_io.file_scanning", "line_number": 174, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "xl_tool.data.image.annonation.Text2XML", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "xl_tool.xl_io.read_json", "line_number": 213, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 215, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 215, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 222, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 225, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 225, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 225, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path", "line_number": 226, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 230, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 230, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 249, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 249, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 249, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 250, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 250, "usage_type": "name"}, {"api_name": "xl_tool.data.image.annonation.Text2XML", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 253, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 266, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 266, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 269, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 272, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 272, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 280, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 280, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 280, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 288, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 291, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 296, "usage_type": "call"}, {"api_name": "xl_tool.xl_io.read_json", "line_number": 298, "usage_type": "call"}, {"api_name": "xl_tool.xl_io.file_scanning", "line_number": 300, "usage_type": "call"}, {"api_name": "xl_tool.data.image.config.IMAGE_FORMAT", "line_number": 300, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 310, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 313, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 318, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 321, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 322, "usage_type": "call"}, {"api_name": "xl_tool.data.image.annonation.Text2XML", "line_number": 328, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 348, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 353, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 368, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 374, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 375, "usage_type": "call"}, {"api_name": "xl_tool.xl_io.file_scanning", "line_number": 376, "usage_type": "call"}, {"api_name": "xl_tool.data.image.config.IMAGE_FORMAT", "line_number": 376, "usage_type": "name"}]} +{"seq_id": "21501302187", "text": "# import flet as ft\r\n\r\n# def main(page: ft.Page):\r\n# page.add(ft.Text(f\"Initial route: {page.route}\"))\r\n\r\n# def route_change(route):\r\n# page.add(ft.Text(f\"New route: {route}\"))\r\n\r\n# def go_store(e):\r\n# page.route = \"/store\"\r\n# page.update()\r\n\r\n# page.on_route_change = route_change\r\n# page.add(ft.ElevatedButton(\"Go to Store\", on_click=go_store))\r\n\r\n# ft.app(target=main, view=ft.WEB_BROWSER)\r\n\r\nfrom datetime import datetime\r\n\r\nprint(datetime.now())\r\n\r\nprint(datetime.strftime(datetime.now(),f'%Y%m%d%H%M%S')+str(datetime.now()).split('.')[-1])\r\n\r\n\r\nbody = {\r\n \"pid\":2210,\r\n \"type\":\"alipay\",\r\n \"out_trade_no\":datetime.strftime(datetime.now(),f'%Y%m%d%H%M%S')+str(datetime.now()).split('.')[-1],\r\n \"notify_url\":\"http://www.pay.com/notify_url.php\",\r\n \"name\":\"服务余额充值\",\r\n \"money\":\"1\",\r\n \"clientip\":\"10.30.24.13\",\r\n}\r\n\r\nkeys = list(body.keys())\r\nkeys.sort()\r\nprint(keys)\r\n\r\n", "repo_name": "Brandon-lz/chat-free", "sub_path": "client/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 947, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "34107290474", "text": "from pyspark.context import SparkContext\nfrom pyspark.sql.session import SparkSession\n\nsc = SparkContext('local')\nspark = SparkSession(sc)\n\ntextFile = spark.read.text(\"./README.md\") # warring ./ - or ../ \ncnt = textFile.count()\nprint(cnt)\n\ncontexts = textFile.take(4)\nprint(contexts)\nlinesWithSpark = textFile.filter(textFile.value.contains(\"Spark\"))\nprint(linesWithSpark)\ncnt = linesWithSpark.count()\nprint(cnt)\n\nspark.stop()", "repo_name": "yojulab/learn_bigdata", "sub_path": "spark/vscodewithspark.py", "file_name": "vscodewithspark.py", "file_ext": "py", "file_size_in_byte": 432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pyspark.context.SparkContext", "line_number": 4, "usage_type": "call"}, {"api_name": "pyspark.sql.session.SparkSession", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "18649017180", "text": "import os\nimport sys\n\nimport discord\nfrom dotenv import load_dotenv, find_dotenv\nfrom discord.ext import commands\n\nintents = discord.Intents.default()\n#intents.members = True\nbot = commands.Bot(command_prefix='!', intents=intents)\n\npath = sys.path[2] + '/StinkRat/config/.env'\nload_dotenv(find_dotenv(path))\nstink_rat_bot_key = os.getenv('STINK_RAT_BOT_KEY')\n\n\n@bot.command(name='ping')\nasync def ping(ctx):\n await ctx.send('pong')\n\n\n@bot.event\nasync def on_ready():\n guild_count = 0\n\n for guild in bot.guilds:\n print(f\"- {guild.id} (name: {guild.name})\")\n guild_count = guild_count + 1\n\n print(\"StinkRatBot is in \" + str(guild_count) + \" guilds.\")\n\n\n@bot.event\nasync def on_message(message):\n if message.content == \"hello\":\n await message.channel.send(\"hey dirtbag\")\n\n\nbot.run(stink_rat_bot_key)\n", "repo_name": "FruitD/StinkRat", "sub_path": "src/Discord/StinkRatBotController.py", "file_name": "StinkRatBotController.py", "file_ext": "py", "file_size_in_byte": 833, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "discord.Intents.default", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.Intents", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Bot", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "dotenv.find_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "40449473980", "text": "from typing import List\n\nfrom entity.filter import Filter\n\n\nclass Task(object):\n\n def __init__(self, name, func=None, conditions: dict = None):\n self.name = name\n self.func = func\n self.filter_list: List[Filter] = []\n if conditions:\n for key, val in conditions.items():\n self.filter_list.append(Filter.create(key, val))\n", "repo_name": "olivetree123/dataset", "sub_path": "entity/task.py", "file_name": "task.py", "file_ext": "py", "file_size_in_byte": 377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "entity.filter.Filter", "line_number": 11, "usage_type": "name"}, {"api_name": "entity.filter.Filter.create", "line_number": 14, "usage_type": "call"}, {"api_name": "entity.filter.Filter", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "23412210848", "text": "# Filename: MyTMDLib.py\n# Author: Kumpee Teeravech\n# Last modified: 2018-09-22\nimport urllib.request\nfrom urllib.request import Request, urlopen\nimport json\nimport collections\nimport os\nimport datetime\nfrom MyUtilities import *\n\n# A class for collecting data from TMD server\nclass MyTMDLib:\n\tdef __init__(self):\n\t\tself.name = 'TMD'\n\t\tself.version = '1.0.2'\n\t\tself.token = ''\n\n\t# Set token key for accessing weather forecast API\n\tdef set_token(self, token):\n\t\tself.token = token;\n\t\t\n\t# -------------------------------------------------------------------------------------------\n\t# -------------------------------------------------------------------------------------------\n\tdef show_top_banner(self):\n\t\tclear_screen()\n\t\tprint('-------------------------------------------------------------------------------------------------------------------------')\n\t\tprint('RBRU-GI\\'s TMD data fetcher V-1.03')\n\t\tprint('-------------------------------------------------------------------------------------------------------------------------')\n\n\t# -------------------------------------------------------------------------------------------\n\t# -------------------------------------------------------------------------------------------\n\tdef show_table_header_weather_today(self, header):\n\t\tprint('Dataset: ' + header['Title'])\n\t\tprint('Description: ' + header['Description'])\n\t\tprint('LastBuildDate: ' + header['LastBuildDate'])\n\t\tprint('CopyRight: ' + header['CopyRight'])\n\t\tprint('Current date/time:' + str(datetime.datetime.now()))\n\t\tprint('-------------------------------------------------------------------------------------------------------------------------')\n\t\tprint('No | SID | WMO | TIME | T | T-MIN | T-MAX | MSL | RH | WD | WS | RAIN ')\n\n\t# -------------------------------------------------------------------------------------------\n\t# -------------------------------------------------------------------------------------------\n\tdef show_table_header_weather_3hours(self, header):\n\t\tprint('Dataset: ' + header['Title'])\n\t\tprint('Description: ' + header['Description'])\n\t\tprint('LastBuildDate: ' + header['LastBuildDate'])\n\t\tprint('CopyRight: ' + header['CopyRight'])\n\t\tprint('Current date/time:' + str(datetime.datetime.now()))\n\t\tprint('-------------------------------------------------------------------------------------------------------------------------')\n\t\tprint('No | SID | WMO | TIME | T | RH | SP | VP | VIS | WD | WS | R | R24')\n\n\t# -----------------------------------------------------------------------------\n\t# Get weather forecast data\n\t# http://data.tmd.go.th/nwpapi/doc/apidoc/location/forecast_daily.html\n\t# -----------------------------------------------------------------------------\n\tdef get_forecast_data(self, params):\n\t\turl = 'http://data.tmd.go.th/nwpapi/v1/forecast/location/daily/at?lat='+params['lat']+'&lon='+params['lon']+'&duration='+params['duration']+'&fields='+params['fields']\n\t\treq = Request(url)\n\t\treq.add_header('accept', 'application/json')\n\t\treq.add_header('authorization', self.token)\n\t\tdata_html = urlopen(req).read()\n\t\tdata_json = json.loads(data_html.decode('utf-8'), object_pairs_hook=collections.OrderedDict)\n\t\treturn data_json\n\t\n\t# -----------------------------------------------------------------------------\n\t# -----------------------------------------------------------------------------\n\tdef get_tmd_id_from_wmocode(stations, wmo):\n\t\tn_tmd_stations = len(stations)\n\t\tid = ''\n\t\treturn id\n\t\t\n\t# -----------------------------------------------------------------------------\n\t# Load data\n\t# http://data.tmd.go.th/api/WeatherToday/V1/\n\t# -----------------------------------------------------------------------------\n\tdef get_data(self,url):\n\t\treq = urllib.request.Request(url,None);\n\t\tdata = urllib.request.urlopen(req).read()\n\t\treturn data\n\t\n\t# -----------------------------------------------------------------------------\n\t# Get TMD stations information\n\t# -----------------------------------------------------------------------------\t\n\tdef get_stations_V1(self):\n\t\turl = \"http://data.tmd.go.th/api/Station/v1/?uid=demo&ukey=demokey&format=json\"\n\t\tdata_html = self.get_data(url)\n\t\tdata_json = json.loads(data_html.decode('utf-8'), object_pairs_hook=collections.OrderedDict)\n\t\treturn data_json\n\t\t\n\t# -----------------------------------------------------------------------------\n\t# Generate URL\n\t# -----------------------------------------------------------------------------\n\t# WeatherToday V1.0\n\tdef generate_request_url_WeatherToday_V1(self,output_type):\n\t\turl = \"http://data.tmd.go.th/api/WeatherToday/V1/?type=\"+output_type\n\t\treturn url\n\t# WeatherToday V2.0\n\tdef generate_request_url_WeatherToday_V2(self,u,pwd,output_type):\n\t\turl = \"http://data.tmd.go.th/api/WeatherToday/V2/?uid=\"+u+\"&ukey=\"+pwd+\"&format=\"+output_type\n\t\treturn url\n\t# Weather3Hours V1.0\n\tdef generate_request_url_Weather3Hours_V1(self,output_type):\n\t\turl = \"http://data.tmd.go.th/api/Weather3Hours/V1/?type=\"+output_type\n\t\treturn url\n\t# Weather3Hours V2.0\n\tdef generate_request_url_Weather3Hours_V2(self,u,pwd,output_type):\n\t\turl = \"http://data.tmd.go.th/api/Weather3Hours/V2/?uid=\"+u+\"&ukey=\"+pwd+\"&format=\"+output_type\n\t\treturn url\n\t\t\n\t# -----------------------------------------------------------------------------\n\t# Extract TMD 'WeatherTodat' V 1.0\n\t# -----------------------------------------------------------------------------\n\t# WeatherToday V1.0\n\tdef extract_WeatherToday_V1(self,input, target_WMO_station):\n\t\t#print(' extraccting data, please wait...')\n\t\tif(target_WMO_station == ''):\n\t\t\ts = input['Stations']\n\t\telse:\n\t\t\tfor s in input['Stations']:\n\t\t\t\tif(s['WmoNumber'] == target_WMO_station):\n\t\t\t\t\tobs = s['Observe']\n\t\t\t\t\tobs_time = obs['Time']\n\t\t\t\t\tdd,mm,yy = obs_time.split('/')\n\t\t\t\t\tlat = s['Latitude']['Value']\n\t\t\t\t\tlon = s['Longitude']['Value']\n\t\t\t\t\tt_min = obs['MinTemperature']['Value']\n\t\t\t\t\tt_max = obs['MaxTemperature']['Value']\n\t\t\t\t\tt_0700 = obs['Temperature']['Value']\n\t\t\t\t\trf_now = obs['Rainfall']['Value']\n\t\t\t\t\tbreak\n\t\treturn s\n\t# WeatherToday V2.0\n\tdef extract_WeatherToday_V2(self, input, target_WMO_station):\n\t\th = input['Header']\n\t\t# no data\n\t\tif(len(input['Stations']) == 0):\n\t\t\ts = []\n\t\telse:\n\t\t\tstations = input['Stations']['Station']\n\t\t\tif(target_WMO_station == ''):\n\t\t\t\t# return all stations\n\t\t\t\ts = stations\n\t\t\telse:\n\t\t\t\t# search for specific targget\n\t\t\t\tfor s in stations:\n\t\t\t\t\tif(s['WmoStationNumber'] == target_WMO_station):\n\t\t\t\t\t\tbreak\n\t\treturn h, s\n\t\t\n\t# Weather3Hours V1.0\n\tdef extract_Weather3Hours_V1(self,input, target_WMO_station):\n\t\t#print(' extraccting data, please wait...')\n\t\ts = \"\"\n\t\tfor s in input['Stations']:\n\t\t\tif(s['WmoNumber'] == target_WMO_station):\n\t\t\t\tat = s['Latitude']['Value']\n\t\t\t\tlon = s['Longitude']['Value']\n\t\t\t\tobs = s['Observe']\n\t\t\t\tobs_time = obs['Time']\n\t\t\t\tdd,mm,yy = obs_time.split('/')\n\t\t\t\tt_now = obs['Temperature']['Value']\n\t\t\t\trf_now = obs['Rainfall']['Value']\n\t\t\t\tbreak\n\t\treturn s\n\t# Weather3Hours V2.0\n\tdef extract_Weather3Hours_V2(self,input,target_WMO_station):\n\t\th = input['Header']\n\t\t# no data\n\t\tif(len(input['Stations']) == 0):\n\t\t\ts = []\n\t\telse:\n\t\t\tstations = input['Stations']['Station']\n\t\t\tif(target_WMO_station == ''):\n\t\t\t\t# return all stations\n\t\t\t\ts = stations\n\t\t\telse:\n\t\t\t\t# search for specific targget\n\t\t\t\tfor s in stations:\n\t\t\t\t\tif(s['WmoStationNumber'] == target_WMO_station):\n\t\t\t\t\t\tbreak\n\t\treturn h,s\n\t\t\n\t# -----------------------------------------------------------------------------\n\t# Get NASA data\n\t# -----------------------------------------------------------------------------\n\t# WeatherToday V1.0\n\tdef get_WeatherToday_V1(self,target_WMO_station):\n\t\tprint('Requesting, please wait...')\n\t\turl = self.generate_request_url_WeatherToday_V1('json')\n\t\tdata_html = self.get_data(url)\n\t\tdata_json = json.loads(data_html.decode('utf-8'), object_pairs_hook=collections.OrderedDict)\n\t\tdata = self.extract_WeatherToday_V1(data_json, target_WMO_station)\n\t\treturn data\n\t# WeatherToday V2.0\n\tdef get_WeatherToday_V2(self,target_WMO_station):\n\t\t#print('Requesting WeatherToday V2.0, please wait...')\n\t\turl = self.generate_request_url_WeatherToday_V2('api','api12345','json')\n\t\tdata_html = self.get_data(url)\n\t\tdata_json = json.loads(data_html.decode('utf-8'), object_pairs_hook=collections.OrderedDict)\n\t\theader,data = self.extract_WeatherToday_V2(data_json, target_WMO_station)\n\t\treturn header,data\n\t\t\n\t# Weather3Hours V1.0\n\tdef get_Weather3Hours_V1(self,target_WMO_station):\n\t\tprint('Requesting, please wait...')\n\t\turl = self.generate_request_url_Weather3Hours_V1('json')\n\t\tdata_html = self.get_data(url)\n\t\tdata_json = json.loads(data_html.decode('utf-8'), object_pairs_hook=collections.OrderedDict)\n\t\tdata = self.extract_Weather3Hours_V1(data_json, target_WMO_station)\n\t\treturn data\n\t# Weather3Hours V2.0\n\tdef get_Weather3Hours_V2(self,target_WMO_station):\n\t\turl = self.generate_request_url_Weather3Hours_V2('api','api12345','json')\n\t\tdata_html = self.get_data(url)\n\t\tdata_json = json.loads(data_html.decode('utf-8'), object_pairs_hook=collections.OrderedDict)\n\t\theader, data = self.extract_Weather3Hours_V2(data_json, target_WMO_station)\n\t\treturn header, data", "repo_name": "kameokashinichi/Thai_data", "sub_path": "tmd-weather-service-master/python/MyTMDLib.py", "file_name": "MyTMDLib.py", "file_ext": "py", "file_size_in_byte": 9136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "attribute"}, {"api_name": "urllib.request.Request", "line_number": 59, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 62, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 63, "usage_type": "attribute"}, {"api_name": "urllib.request.request.Request", "line_number": 78, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 78, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 78, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 79, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 79, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 79, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 88, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 192, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 192, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 200, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 200, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 209, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 209, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 216, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 216, "usage_type": "attribute"}]} +{"seq_id": "7354317718", "text": "# TODO: Change this file name to base.py\n\nfrom django.db import models\nfrom django.contrib.contenttypes import generic\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.auth.models import User\nfrom django.contrib.sites.managers import CurrentSiteManager\nfrom django.contrib.sites.models import Site\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom taggit.managers import TaggableManager\nfrom taggit.models import Tag\n\nfrom django.contrib.comments.models import Comment\nfrom popularity.models import ViewTracker\n\nfrom colophon.models import Publisher\nfrom features.models import Feature, FeatureSet\nfrom meta.utils import SlugifyUniquely # move to 'entropy'\n\nimport datetime\n\nclass BaseDictionaryNode(models.Model):\n \"\"\"\n Base 'Dict' Node to fulfill 'label', 'value' needs.\n \"\"\"\n label = models.CharField(max_length=200)\n value = models.TextField()\n \n class Meta:\n abstract = True\n \n def __unicode__(self):\n return u'%s: %s' % (self.label, self.value)\n \nclass BaseTupleNode(models.Model):\n \"\"\"\n For use in things like choices.\n \"\"\"\n value = models.CharField(max_length=200)\n display = models.TextField()\n \n class Meta:\n abstract = True\n \n def __unicode__(self):\n return u'%s' % (self.display)\n\n\nclass BaseType(models.Model):\n \"\"\"\n Base 'Type' field, usualy related to via FK\n \"\"\"\n name = models.CharField(\n max_length=200)\n name_plural = models.CharField(\n blank=True,\n max_length=200,\n null=True)\n slug = models.SlugField(\n editable=False,\n max_length=250,\n unique=True)\n \n def __unicode__(self):\n return u'%s' % (self.name)\n \n class Meta:\n abstract = True\n \n @property\n def handle(self):\n \"\"\"A convenience for admin\n \"\"\"\n return self.slug\n def save(self, *args, **kwargs): \n if not self.id:\n # replace self.name with your prepopulate_from field\n self.slug = SlugifyUniquely(self.name, self.__class__)\n super(BaseType, self).save(*args, **kwargs)\n \n def __unicode__(self):\n return u'%s' % self.name\n \nclass BaseCategory(BaseType):\n \"\"\"\n Base 'Category' field, usualy related to via M2M\n \"\"\"\n \n class Meta:\n abstract = True\n\nclass Base(models.Model):\n \"\"\"\n Article model\n \"\"\"\n \n title = models.CharField(\n max_length=250)\n short_title = models.CharField(\n blank=True,\n help_text=\"A short title used in listings and navigation.\",\n max_length=250)\n slug = models.SlugField(\n blank=False,\n editable=False,\n max_length=250,\n unique=True)\n summary = models.TextField(\n \"Summary\",\n blank=True,\n help_text=\"Summary used in listings & excerpts. No formatting except for linebreaks.\")\n introduction = models.TextField(\n \"Intro\",\n blank=True,\n help_text=\"Presented as a introduction to the content. No formatting except for linebreaks.\")\n content = models.TextField(\n \"Body\")\n \n created = models.DateTimeField(\n blank=True,\n editable=False,\n null=True)\n created_by = models.ForeignKey(\n User,\n blank=True,\n editable=False,\n related_name=\"%(app_label)s_%(class)s_created_by\",\n null=True)\n \n modified = models.DateTimeField(\n blank=True,\n editable=False,\n null=True)\n modified_by = models.ForeignKey(\n User,\n blank=True,\n editable=False,\n related_name=\"%(app_label)s_%(class)s_modified_by\",\n null=True)\n \n is_published = models.BooleanField(\n \"Publish\",\n help_text=\"Publish the content on sites selected below. Date and time will be added on Save.\")\n published = models.DateTimeField(\n # editable=False,\n \"Published on\",\n blank=True, \n help_text=\"Current date and time will be automatically assigned on publication if nothing else is specified.\",\n null=True)\n published_by = models.ForeignKey(\n User, \n blank=True,\n editable=False,\n related_name=\"%(app_label)s_%(class)s_published_by\",\n null=True)\n publisher = models.ForeignKey(\n Publisher,\n blank=True,\n help_text=\"Publisher or owner of this content\",\n null=True,\n verbose_name=\"Published by\")\n \n publish_on_AU = models.BooleanField()\n publish_on_NZ = models.BooleanField()\n \n sites = models.ManyToManyField(\n Site,\n blank=True, \n help_text=\"Publishes this content on the selected sites.\",\n null=True,\n verbose_name=\"Sites\")\n objects = models.Manager()\n site_objects = CurrentSiteManager('sites')\n \n tags = models.ManyToManyField(\n Tag,\n blank=True,\n null=True,\n related_name=\"%(app_label)s_%(class)s_tags\")\n \n class Meta:\n abstract = True\n \n def __unicode__(self):\n return \"%s\" % (self.title)\n \n @models.permalink\n def get_absolute_url(self):\n return (str(self.content_type()) + '_view', (), {\n 'slug': self.slug,\n })\n \n @models.permalink \n def get_list_url(self):\n return (str(self.content_type()) + 's_' + str(self.content_type()) + 's', (), {})\n \n def save(self, *args, **kwargs):\n if not self.id:\n \"\"\"\n Replace self.title with your prepopulate_from field\n \"\"\"\n self.slug = SlugifyUniquely(self.title, self.__class__)\n # else:\n # # current sites\n # sites_ids = []\n # if self.publish_on_AU:\n # sites_ids.append(1)\n # if self.publish_on_NZ:\n # sites_ids.append(2)\n # \n # sites = Site.objects.filter(pk__in=sites_ids)\n # self.sites = sites \n super(Base, self).save(*args, **kwargs)\n \n def feature_image(self):\n try:\n # Return the first image as feature\n return self.feature_images()[0]\n except:\n \"\"\"\n IndexError or others.\n \"\"\"\n return None\n \n def has_feature_image(self):\n if len(self.feature_images())>0 and self.feature_images()[0].image:\n return True\n else:\n return False\n \n def has_slideshow(self):\n \"\"\"\n Return true if the article has at least 2 slideshow images\n \"\"\"\n return True if len(self.slideshow_images()) >= 2 else False \n \n def content_type(self):\n \"\"\"\n To help in templates.\n \"\"\"\n return self._meta.module_name\n \n def comments(self):\n \"\"\"\n Get the comments for this object, if any.\n \"\"\"\n comments = []\n all_comments = Comment.objects.get_query_set()\n for comment in all_comments:\n if comment.is_removed == False and comment.content_object == self:\n comments.append(comment)\n \n return comments\n \n def comments_count(self):\n try:\n return u'%s' % len(self.comments())\n except:\n None\n \n def view_count(self):\n \"\"\"\n From django-popularity, get the view count for the object.\n This is used to sort objects in a list view.\n \"\"\"\n return ViewTracker.get_views_for(self)\n \n def popularity(self):\n \"\"\"\n Relative popularity calculated as views/age\n \"\"\"\n try:\n return self.view_count() / (datetime.datetime.now() - self.published).days\n except:\n None\n \n def photographers(self):\n \"\"\"\n Get the photographers of images attached to the object\n \"\"\"\n photographers = []\n for image in self.feature_images():\n if image.user_credit:\n photographer = image.user_credit\n try:\n \"\"\"\n Assumes user has a profile\n \"\"\"\n photographer.display_name = photographer.profile_set.all()[0].display_name()\n except IndexError:\n pass\n photographers.append(photographer)\n if image.custom_credit:\n photographers.append(image.custom_credit)\n try: \n return set(sorted(photographers, key=lambda photographer: photographer))\n except IndexError:\n return None\n \n def published_on(self):\n sites = []\n for site in self.sites.all():\n try:\n sites.append(site.siteprofile_set.all()[0].abbrieviation)\n except IndexError:\n pass \n return sites\n \n def published_on_AU(self):\n try:\n AU = Site.objects.get(pk=1)\n except:\n pass\n else:\n try: \n self.sites.get(pk=AU.id)\n except:\n return '-'\n else:\n return '✓'\n published_on_AU.allow_tags = True\n published_on_AU.short_description = 'AU'\n \n def published_on_NZ(self):\n try:\n NZ = Site.objects.get(pk=2)\n except:\n pass\n else:\n try: \n self.sites.get(pk=NZ.id)\n except:\n return '-'\n else:\n return '✓'\n published_on_NZ.allow_tags = True\n published_on_NZ.short_description = 'NZ'\n \n def published_on_URBIS(self):\n try:\n URBIS = Site.objects.get(pk=3)\n except:\n pass\n else:\n try: \n self.sites.get(pk=URBIS.id)\n except:\n return '-'\n else:\n return '✓'\n published_on_URBIS.allow_tags = True\n published_on_URBIS.short_description = 'URBIS'\n \nclass ArticleBase(Base):\n \n feature_sets = models.ManyToManyField(\n FeatureSet,\n blank=True,\n help_text=\"Feature this content on the selected sites and feature areas. Note, the content must be published on the corresponding site to take effect.\",\n null=True)\n \n # migrate this to authors\n users = models.ManyToManyField(User,\n blank=True,\n null=True,\n verbose_name=\"authors\",\n related_name=\"%(app_label)s_%(class)s_authors\")\n \n class Meta:\n abstract = True\n \n def authors(self):\n \"\"\"\n Get the authors with displayable name\n \"\"\"\n authors = []\n for author in self.users.all():\n try:\n \"\"\"\n Assumes user has a profile\n \"\"\"\n author.display_name = author.profile_set.all()[0].display_name()\n except IndexError:\n pass\n authors.append(author)\n try: \n return set(sorted(authors, key=lambda author: author))\n except IndexError:\n return None\n \n def feature_images(self):\n \"\"\"\n Resolve and return the appropriate feature images\n \"\"\"\n return self.articleimage_set.all() #feature=True)\n \n def slideshow_images(self):\n \"\"\"\n Return slideshow images.\n \"\"\"\n # return self.articleimage_set.filter(slide_show=True).order_by('-feature')\n return self.articleimage_set.all()\n \nclass ProjectBase(Base):\n\n class Meta:\n abstract = True\n \n def feature_images(self):\n \"\"\"\n Resolve and return the appropriate feature images\n \"\"\" \n return self.projectimage_set.all()#feature=True)\n \n def images(self):\n return self.projectimage_set.all()\n \nclass OrganisationBase(Base):\n \n class Meta:\n abstract = True\n \n def has_feature_image(self):\n if self.logo_image:\n return True\n else:\n return False \n \n def feature_images(self):\n \"\"\"\n Resolve and return the appropriate feature images\n \"\"\" \n return self.organisationimage_set.all()\n \n def images(self):\n return self.organisationimage_set.all()\n \nclass EventBase(Base):\n \n class Meta:\n abstract = True\n \n def feature_images(self):\n \"\"\"\n Resolve and return the appropriate feature images\n \"\"\"\n return self.eventimage_set.all()#feature=True)\n \n def images(self):\n return self.eventimage_set.all()\n\n \n", "repo_name": "eedeep/efo", "sub_path": "apps/base/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 12876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.db.models.Model", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "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.Model", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "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.CharField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "meta.utils.SlugifyUniquely", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 92, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 97, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 103, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 103, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 112, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 112, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 119, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 123, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 124, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 123, "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": 134, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 135, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 134, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 141, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 141, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 144, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 144, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 150, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 151, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 150, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 156, "usage_type": "call"}, {"api_name": "colophon.models.Publisher", "line_number": 157, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 156, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 163, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 163, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 164, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 164, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 166, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 167, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 166, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 172, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 172, "usage_type": "name"}, {"api_name": "django.contrib.sites.managers.CurrentSiteManager", "line_number": 173, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 175, "usage_type": "call"}, {"api_name": "taggit.models.Tag", "line_number": 176, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 175, "usage_type": "name"}, {"api_name": "django.db.models.permalink", "line_number": 187, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 187, "usage_type": "name"}, {"api_name": "django.db.models.permalink", "line_number": 193, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 193, "usage_type": "name"}, {"api_name": "meta.utils.SlugifyUniquely", "line_number": 202, "usage_type": "call"}, {"api_name": "django.contrib.comments.models.Comment.objects.get_query_set", "line_number": 248, "usage_type": "call"}, {"api_name": "django.contrib.comments.models.Comment.objects", "line_number": 248, "usage_type": "attribute"}, {"api_name": "django.contrib.comments.models.Comment", "line_number": 248, "usage_type": "name"}, {"api_name": "popularity.models.ViewTracker.get_views_for", "line_number": 266, "usage_type": "call"}, {"api_name": "popularity.models.ViewTracker", "line_number": 266, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 273, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 273, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site.objects.get", "line_number": 311, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 311, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 311, "usage_type": "name"}, {"api_name": "django.contrib.sites.models.Site.objects.get", "line_number": 326, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 326, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 326, "usage_type": "name"}, {"api_name": "django.contrib.sites.models.Site.objects.get", "line_number": 341, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 341, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 341, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 356, "usage_type": "call"}, {"api_name": "features.models.FeatureSet", "line_number": 357, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 356, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 363, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 363, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 363, "usage_type": "name"}]} +{"seq_id": "40484355387", "text": "from django.urls import path\nfrom django.views.generic import RedirectView\n\n# Media Access\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nfrom . import views\n\nurlpatterns = [\n path('login/', views.loginPage, name=\"login\"),\n path('logout/', views.logoutUser, name=\"logout\"),\n path('register/', views.registerPage, name=\"register\"),\n\n path('', views.home, name=\"home\"),\n path('room//', views.room, name=\"room\"),\n path('profile//', views.userProfile, name=\"user-profile\"),\n path('allphotos/', views.allphotos, name=\"all-photos\"),\n\n path('create-room/', views.createRoom, name=\"create-room\"),\n # path('success', views.success, name='success'),\n path('update-room//', views.updateRoom, name=\"update-room\"),\n path('delete-room//', views.deleteRoom, name=\"delete-room\"),\n path('delete-message//', views.deleteMessage, name=\"delete-message\"),\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)", "repo_name": "jacobjpelletier/webFinalProj", "sub_path": "base/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 28, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "10775018647", "text": "#!/usr/bin/env python\n\nbackground_image_filename = 'background.png'\nmouse_image_filename = 'flame.png'\n# 指定图像文件名称\n\nimport pygame\n# 导入pygame库\nfrom pygame.locals import *\n# 导入一些常用的函数和常量\nfrom sys import exit\n\n# 向sys模块借一个exit函数用来退出程序\n\npygame.init()\n# 初始化pygame,为使用硬件做准备\n\nscreen = pygame.display.set_mode((640, 480), 0, 32) #会返回一个Surface对象，代表了在桌面上出现的那个窗口，三个参数第一个为元祖，代表分 辨率（必须）；第二个是一个标志位，具体意思见下表，如果不用什么特性，就指定0；第三个为色深。\n# 创建了一个窗口\npygame.display.set_caption(\"Hello, World!\")\n# 设置窗口标题\n\n# convert函数是将图像数据都转化为Surface对象，每次加载完图像以后就应该做这件事件（事实上因为 它太常用了，如果你不写pygame也会帮你做）；\n# convert_alpha相比convert，保留了Alpha 通道信息（可以简单理解为透明的部分），这样我们的光标才可以是不规则的形状。\nbackground = pygame.image.load(background_image_filename).convert()\nmouse_cursor = pygame.image.load(mouse_image_filename).convert_alpha()\n# 加载并转换图像\n\nwhile True:\n # 游戏主循环\n\n for event in pygame.event.get():\n if event.type == QUIT:\n # 接收到退出事件后退出程序\n exit()\n\n # blit是个重要函数，第一个参数为一个Surface对象，第二个为左上角位置。画完以后一定记得用update更新一下，否则画面一片漆黑。\n screen.blit(background, (0, 0))\n # 将背景图画上去\n\n x, y = pygame.mouse.get_pos()\n # 获得鼠标位置\n x -= mouse_cursor.get_width() / 2\n y -= mouse_cursor.get_height() / 2\n # 计算光标的左上角位置\n screen.blit(mouse_cursor, (x, y))\n # 把光标画上去\n\n pygame.display.update()\n # 刷新一下画面", "repo_name": "CSeven19/PythonLevelUp", "sub_path": "pythonLevel3/pygame/pygamenew2old/pygameWork1.py", "file_name": "pygameWork1.py", "file_ext": "py", "file_size_in_byte": 1982, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.init", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "37164943532", "text": "from celery.result import AsyncResult\r\nfrom flask import jsonify, request\r\n\r\nfrom api.models import db, Advert\r\nfrom api.schemes import AdvertSchema\r\nfrom api.validators import is_valid, is_exist\r\n\r\nfrom server import email_task\r\n\r\nadvert_schema = AdvertSchema()\r\nadverts_schema = AdvertSchema(many=True)\r\n\r\n\r\ndef get_adverts():\r\n adverts = Advert.query.all()\r\n\r\n return adverts_schema.jsonify(adverts)\r\n\r\n\r\n@is_exist\r\ndef get_advert(advert_id):\r\n advert = Advert.query.get(advert_id)\r\n\r\n return advert_schema.jsonify(advert)\r\n\r\n\r\n@is_valid\r\ndef create_advert():\r\n new_advert_attrs = request.get_json()\r\n new_advert = Advert(**new_advert_attrs)\r\n\r\n db.session.add(new_advert)\r\n db.session.commit()\r\n\r\n return advert_schema.jsonify(new_advert)\r\n\r\n\r\n@is_exist\r\n@is_valid\r\ndef update_advert(advert_id):\r\n update_advert_attrs = request.get_json()\r\n Advert.query.filter_by(id=advert_id).update(update_advert_attrs)\r\n\r\n db.session.commit()\r\n updated_advert = Advert.query.get(advert_id)\r\n\r\n return advert_schema.jsonify(updated_advert)\r\n\r\n\r\n@is_exist\r\ndef delete_advert(advert_id):\r\n advert = Advert.query.get(advert_id)\r\n\r\n db.session.delete(advert)\r\n db.session.commit()\r\n\r\n return jsonify()\r\n\r\n\r\ndef run_task():\r\n task = email_task(request).delay()\r\n\r\n return jsonify({'task_id': task.id}), 202\r\n\r\n\r\ndef get_status(task_id):\r\n task_result = AsyncResult(task_id)\r\n result = {\r\n \"task_id\": task_id,\r\n \"task_status\": task_result.status,\r\n \"task_result\": task_result.result\r\n }\r\n\r\n return jsonify(result), 200\r\n", "repo_name": "alzex3/celery_hw", "sub_path": "api/controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 1597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "api.schemes.AdvertSchema", "line_number": 10, "usage_type": "call"}, {"api_name": "api.schemes.AdvertSchema", "line_number": 11, "usage_type": "call"}, {"api_name": "api.models.Advert.query.all", "line_number": 15, "usage_type": "call"}, {"api_name": "api.models.Advert.query", "line_number": 15, "usage_type": "attribute"}, {"api_name": "api.models.Advert", "line_number": 15, "usage_type": "name"}, {"api_name": "api.models.Advert.query.get", "line_number": 22, "usage_type": "call"}, {"api_name": "api.models.Advert.query", "line_number": 22, "usage_type": "attribute"}, {"api_name": "api.models.Advert", "line_number": 22, "usage_type": "name"}, {"api_name": "api.validators.is_exist", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "api.models.Advert", "line_number": 30, "usage_type": "call"}, {"api_name": "api.models.db.session.add", "line_number": 32, "usage_type": "call"}, {"api_name": "api.models.db.session", "line_number": 32, "usage_type": "attribute"}, {"api_name": "api.models.db", "line_number": 32, "usage_type": "name"}, {"api_name": "api.models.db.session.commit", "line_number": 33, "usage_type": "call"}, {"api_name": "api.models.db.session", "line_number": 33, "usage_type": "attribute"}, {"api_name": "api.models.db", "line_number": 33, "usage_type": "name"}, {"api_name": "api.validators.is_valid", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "api.models.Advert.query.filter_by", "line_number": 42, "usage_type": "call"}, {"api_name": "api.models.Advert.query", "line_number": 42, "usage_type": "attribute"}, {"api_name": "api.models.Advert", "line_number": 42, "usage_type": "name"}, {"api_name": "api.models.db.session.commit", "line_number": 44, "usage_type": "call"}, {"api_name": "api.models.db.session", "line_number": 44, "usage_type": "attribute"}, {"api_name": "api.models.db", "line_number": 44, "usage_type": "name"}, {"api_name": "api.models.Advert.query.get", "line_number": 45, "usage_type": "call"}, {"api_name": "api.models.Advert.query", "line_number": 45, "usage_type": "attribute"}, {"api_name": "api.models.Advert", "line_number": 45, "usage_type": "name"}, {"api_name": "api.validators.is_exist", "line_number": 38, "usage_type": "name"}, {"api_name": "api.validators.is_valid", "line_number": 39, "usage_type": "name"}, {"api_name": "api.models.Advert.query.get", "line_number": 52, "usage_type": "call"}, {"api_name": "api.models.Advert.query", "line_number": 52, "usage_type": "attribute"}, {"api_name": "api.models.Advert", "line_number": 52, "usage_type": "name"}, {"api_name": "api.models.db.session.delete", "line_number": 54, "usage_type": "call"}, {"api_name": "api.models.db.session", "line_number": 54, "usage_type": "attribute"}, {"api_name": "api.models.db", "line_number": 54, "usage_type": "name"}, {"api_name": "api.models.db.session.commit", "line_number": 55, "usage_type": "call"}, {"api_name": "api.models.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "api.models.db", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 57, "usage_type": "call"}, {"api_name": "api.validators.is_exist", "line_number": 50, "usage_type": "name"}, {"api_name": "server.email_task", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "argument"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "celery.result.AsyncResult", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "11082302917", "text": "import yfinance as yf\n\ndef get_adj_close(ticker, start_date, end_date):\n # Define the ticker symbol and date range\n ticker_symbol = ticker\n start_date = start_date\n end_date = end_date\n\n # Fetch historical data from Yahoo Finance\n data = yf.download(ticker_symbol, start=start_date, end=end_date, actions=True)\n\n # Filter for adjusted closing prices\n adj_close_data = data['Adj Close'].to_frame()\n\n # Rename the column for clarity\n adj_close_data.rename(columns={'Adj Close': 'Adjusted Close'}, inplace=True)\n\n # Print the first few rows of the DataFrame\n print(adj_close_data.head())\n\n return adj_close_data", "repo_name": "mbsuraj/publicStorageProject", "sub_path": "src/stockPriceToolkit.py", "file_name": "stockPriceToolkit.py", "file_ext": "py", "file_size_in_byte": 647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "yfinance.download", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "13012141003", "text": "import argparse\nimport random\nfrom fractions import Fraction\nfrom typing import List\nimport numpy as np\nimport xarray as xr\n\nfrom conestrip.cones import print_gambles, print_fractions\nfrom conestrip.global_settings import GlobalSettings\nfrom conestrip.optimization import generate_mass_function, incurs_sure_loss, \\\n is_mass_function, print_lower_prevision_function, generate_lower_prevision_perturbation, \\\n lower_prevision_clamped_sum, is_coherent, generate_mass_functions, linear_vacuous_lower_prevision_function, \\\n linear_lower_prevision_function\nfrom conestrip.random_cones import random_real_gambles\nfrom conestrip.utility import StopWatch\n\n\ndef info(args):\n print('--- Settings ---')\n print(f'seed = {args.seed}')\n print(f'omega-size = {args.omega_size}')\n print(f'k-size = {args.k_size}')\n print(f'coordinate-bound = {args.coordinate_bound}')\n print(f'error-magnitude = {args.error_magnitude}')\n print(f'repetitions = {args.repetitions}')\n print(f'verbose = {args.verbose}')\n print(f'pretty = {args.pretty}')\n print(f'decimals = {args.decimals}')\n print()\n\n\ndef make_default_epsilon_range() -> List[Fraction]:\n return [Fraction(f) for f in [0.000001, 0.00001, 0.0001, 0.001, 0.1, 1, 10]]\n\n\n# Returns n values of the shape [0, ..., 1/8, 1/4]\ndef make_epsilon_range(n: int) -> List[Fraction]:\n epsilon = Fraction(1, 4)\n result = []\n for i in range(n - 1):\n result.append(epsilon)\n epsilon /= 2\n return [Fraction(0)] + list(reversed(result))\n\n\ndef run_testcase1(args):\n Omega = list(range(args.omega_size))\n K = random_real_gambles(args.k_size, args.omega_size, args.coordinate_bound)\n\n for _ in range(args.repetitions):\n p = generate_mass_function(Omega, args.decimals)\n error_magnitude = Fraction(args.error_magnitude)\n assert is_mass_function(p)\n if error_magnitude > 0:\n P_p = linear_vacuous_lower_prevision_function(p, K, Fraction(error_magnitude))\n else:\n P_p = linear_lower_prevision_function(p, K)\n print('--- testcase 1 ---')\n print(f'K = {print_gambles(K, args.pretty)}\\np = {print_fractions(p, args.pretty)}\\nP_p = {print_lower_prevision_function(P_p, args.pretty)}')\n watch = StopWatch()\n result = incurs_sure_loss(P_p, Omega, args.pretty)\n print(f'incurs_sure_loss(P_p, Omega): {result} {watch.seconds():.4f}s\\n')\n assert(not result)\n\n\ndef run_testcase2(args):\n error_magnitude = Fraction(args.error_magnitude)\n Omega = list(range(args.omega_size))\n K = random_real_gambles(args.k_size, args.omega_size, args.coordinate_bound)\n\n for _ in range(args.repetitions):\n p = generate_mass_function(Omega, args.decimals)\n assert is_mass_function(p)\n if error_magnitude > 0:\n P_p = linear_vacuous_lower_prevision_function(p, K, Fraction(error_magnitude))\n else:\n P_p = linear_lower_prevision_function(p, K)\n print('--- testcase 2 ---')\n print(f'K = {print_gambles(K, args.pretty)}\\np = {print_fractions(p, args.pretty)}\\nP_p = {print_lower_prevision_function(P_p, args.pretty)}\\n')\n for epsilon in make_default_epsilon_range():\n Q_epsilon = generate_lower_prevision_perturbation(K, Fraction(epsilon))\n P_epsilon = lower_prevision_clamped_sum(P_p, Q_epsilon)\n print(f'epsilon = {float(epsilon):7.4f}\\nP = {print_lower_prevision_function(P_epsilon, args.pretty)}')\n watch = StopWatch()\n result = incurs_sure_loss(P_epsilon, Omega, args.pretty)\n print(f'incurs_sure_loss(P, Omega): {result} {watch.seconds():.4f}s\\n')\n\n\ndef print_bool(x: bool) -> str:\n return 'T' if x else 'F'\n\n\ndef print_number_list(x: List[Fraction]) -> str:\n numbers = list(f'{xi:6.4f}' for xi in x)\n numbers = ', '.join(numbers)\n return f'[{numbers}]'\n\n\ndef run_testcase3(args):\n print('--- testcase 3 ---')\n I, E, N = [int(s) for s in args.testcase3_dimensions.split(',')]\n V = 2 # the number of values per experiment\n Omega = list(range(args.omega_size))\n K = random_real_gambles(args.k_size, args.omega_size, args.coordinate_bound)\n\n p = generate_mass_functions(Omega, args.decimals)\n M = len(p)\n delta = [Fraction(i, I) for i in range(I)] # the imprecision values\n epsilon = make_epsilon_range(E) # the error magnitude values\n\n print(f'M, I, E, N = {M}, {I}, {E}, {N}')\n print(f'delta = {list(map(float, delta))}')\n print(f'epsilon = {list(map(float, epsilon))}')\n for m in range(M):\n print(f'probability mass function {m} = {list(map(float, p[m]))}')\n print('')\n\n pmf_coords = list(range(M))\n imprecision_coords = [float(d) for d in delta] # N.B. list(map(float, delta)) doesn't work!\n errmag_coords = [float(e) for e in epsilon]\n repetitions_coords = list(range(N))\n values_coords = ['sureloss', 'coherence']\n gamble_coords = list(range(len(K)))\n outcome_coords = list(range(len(Omega)))\n\n # create DataArray G containing the gambles in K\n G_data = np.empty((len(K), len(Omega)))\n G_dims = ['gamble', 'outcome']\n G_coords = [gamble_coords, outcome_coords]\n for i, f in enumerate(K):\n G_data[i] = [float(f_i) for f_i in f]\n G = xr.DataArray(G_data, G_coords, G_dims)\n\n # create DataArray A containing the probability mass functions\n A_data = np.empty((len(p), len(Omega)))\n A_dims = ['pmf', 'outcome']\n A_coords = [pmf_coords, outcome_coords]\n for i, p_i in enumerate(p):\n A_data[i] = [float(x) for x in p_i]\n A = xr.DataArray(A_data, A_coords, A_dims)\n\n # create DataArray Q containing sure loss\n Q_data = np.empty((M, I, E, N), dtype=object)\n Q_dims = ['pmf', 'imprecision', 'errmag', 'repetitions']\n Q_coords = [pmf_coords, imprecision_coords, errmag_coords, repetitions_coords]\n\n # create DataArray R containing coherence\n R_data = np.empty((M, I, E, N), dtype=object)\n R_dims = ['pmf', 'imprecision', 'errmag', 'repetitions']\n R_coords = [pmf_coords, imprecision_coords, errmag_coords, repetitions_coords]\n\n for m in range(M):\n for i in range(I):\n print(f'm, i = {m}, {i}')\n for e in range(E):\n for n in range(N):\n if delta[i] > 0:\n P_delta = linear_vacuous_lower_prevision_function(p[m], K, delta[i])\n else:\n P_delta = linear_lower_prevision_function(p[m], K)\n Q_epsilon = generate_lower_prevision_perturbation(K, epsilon[e])\n P_m_i_e_n = lower_prevision_clamped_sum(P_delta, Q_epsilon)\n Q_data[m, i, e, n] = int(incurs_sure_loss(P_m_i_e_n, Omega, args.pretty))\n R_data[m, i, e, n] = int(is_coherent(P_m_i_e_n, Omega, args.pretty))\n\n Q = xr.DataArray(Q_data, Q_coords, Q_dims)\n R = xr.DataArray(R_data, R_coords, R_dims)\n\n # Put the data arrays Q, A and G in the data set Z\n Z_data_vars = {'incurs_sure_loss': Q, 'is_coherent': R, 'mass_functions': A, 'gambles': G}\n Z = xr.Dataset(Z_data_vars)\n\n print(f'saving data set to {args.output_filename}')\n Z.to_netcdf(args.output_filename)\n\n\ndef run_testcase4(args):\n print('--- testcase 4 ---')\n Omega = list(range(args.omega_size))\n K = random_real_gambles(args.k_size, args.omega_size, args.coordinate_bound, args.decimals)\n p = generate_mass_function(Omega, args.decimals)\n\n for epsilon in make_epsilon_range(4):\n print(f'--- epsilon = {epsilon}')\n P = linear_lower_prevision_function(p, K)\n Q_epsilon = generate_lower_prevision_perturbation(K, epsilon)\n P_lower = lower_prevision_clamped_sum(P, Q_epsilon)\n\n print(f'K =\\n{print_gambles(K, args.pretty)}\\np = {print_fractions(p, args.pretty)}\\nP = {print_lower_prevision_function(P, args.pretty)}')\n if args.verbose:\n print('')\n print('----------------------------------------------------------')\n print(' calculating incurs_sure_loss')\n print('----------------------------------------------------------')\n sure_loss = incurs_sure_loss(P_lower, Omega, args.pretty)\n if args.verbose:\n print('')\n print('----------------------------------------------------------')\n print(' calculating is_coherent')\n print('----------------------------------------------------------')\n coherent = is_coherent(P_lower, Omega, args.pretty)\n print(f'incurs_sure_loss(P_lower, Omega) = {sure_loss}')\n print(f'is_coherent(P_lower, Omega) = {coherent}\\n')\n assert not sure_loss or not coherent\n\n\ndef main():\n cmdline_parser = argparse.ArgumentParser()\n cmdline_parser.add_argument(\"--seed\", help=\"the seed of the random generator\", type=int, default=0)\n cmdline_parser.add_argument('--omega-size', type=int, default=3, help='the number of elements of event set Omega')\n cmdline_parser.add_argument('--k-size', type=int, default=3, help='the number of elements of the set of gambles K')\n cmdline_parser.add_argument('--coordinate-bound', type=int, default=1, help='the maximum absolute value of the coordinates')\n cmdline_parser.add_argument('--decimals', type=int, default=2, help='the number of decimals for the random generation')\n cmdline_parser.add_argument('--error-magnitude', type=str, default='0', help='the error magnitude value used for generating lower prevision functions')\n cmdline_parser.add_argument('--repetitions', type=int, default=1, help='the number of times an experiment is repeated')\n cmdline_parser.add_argument('--test', type=int, default=1, help='the test case (1 or 2)')\n cmdline_parser.add_argument('--pretty', help='print fractions as floats', action='store_true')\n cmdline_parser.add_argument('--verbose', '-v', help='print verbose output', action='store_true')\n cmdline_parser.add_argument('--testcase3-dimensions', type=str, default='10,7,5', help='the dimensions I,E,N of test case 3 (a comma-separated list)')\n cmdline_parser.add_argument('--output-filename', type=str, help='a filename where output is stored')\n args = cmdline_parser.parse_args()\n if args.seed == 0:\n args.seed = random.randrange(0, 10000000000)\n info(args)\n\n random.seed(args.seed)\n GlobalSettings.verbose = args.verbose\n\n if args.test == 1:\n run_testcase1(args)\n elif args.test == 2:\n run_testcase2(args)\n elif args.test == 3:\n run_testcase3(args)\n elif args.test == 4:\n run_testcase4(args)\n else:\n raise RuntimeError(f'Unknown test case {args.test}')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "wiegerw/CONEstrip", "sub_path": "tests/optimization_test_cases.py", "file_name": "optimization_test_cases.py", "file_ext": "py", "file_size_in_byte": 10696, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "fractions.Fraction", "line_number": 33, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "name"}, {"api_name": "fractions.Fraction", "line_number": 32, "usage_type": "name"}, {"api_name": "fractions.Fraction", "line_number": 38, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 37, "usage_type": "name"}, {"api_name": "fractions.Fraction", "line_number": 37, "usage_type": "name"}, {"api_name": "conestrip.random_cones.random_real_gambles", "line_number": 48, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_mass_function", "line_number": 51, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 52, "usage_type": "call"}, {"api_name": "conestrip.optimization.is_mass_function", "line_number": 53, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_vacuous_lower_prevision_function", "line_number": 55, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 55, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_lower_prevision_function", "line_number": 57, "usage_type": "call"}, {"api_name": "conestrip.cones.print_gambles", "line_number": 59, "usage_type": "call"}, {"api_name": "conestrip.cones.print_fractions", "line_number": 59, "usage_type": "call"}, {"api_name": "conestrip.optimization.print_lower_prevision_function", "line_number": 59, "usage_type": "call"}, {"api_name": "conestrip.utility.StopWatch", "line_number": 60, "usage_type": "call"}, {"api_name": "conestrip.optimization.incurs_sure_loss", "line_number": 61, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 67, "usage_type": "call"}, {"api_name": "conestrip.random_cones.random_real_gambles", "line_number": 69, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_mass_function", "line_number": 72, "usage_type": "call"}, {"api_name": "conestrip.optimization.is_mass_function", "line_number": 73, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_vacuous_lower_prevision_function", "line_number": 75, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 75, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_lower_prevision_function", "line_number": 77, "usage_type": "call"}, {"api_name": "conestrip.cones.print_gambles", "line_number": 79, "usage_type": "call"}, {"api_name": "conestrip.cones.print_fractions", "line_number": 79, "usage_type": "call"}, {"api_name": "conestrip.optimization.print_lower_prevision_function", "line_number": 79, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_lower_prevision_perturbation", "line_number": 81, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 81, "usage_type": "call"}, {"api_name": "conestrip.optimization.lower_prevision_clamped_sum", "line_number": 82, "usage_type": "call"}, {"api_name": "conestrip.optimization.print_lower_prevision_function", "line_number": 83, "usage_type": "call"}, {"api_name": "conestrip.utility.StopWatch", "line_number": 84, "usage_type": "call"}, {"api_name": "conestrip.optimization.incurs_sure_loss", "line_number": 85, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 93, "usage_type": "name"}, {"api_name": "fractions.Fraction", "line_number": 93, "usage_type": "name"}, {"api_name": "conestrip.random_cones.random_real_gambles", "line_number": 104, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_mass_functions", "line_number": 106, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 127, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 135, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 148, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_vacuous_lower_prevision_function", "line_number": 158, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_lower_prevision_function", "line_number": 160, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_lower_prevision_perturbation", "line_number": 161, "usage_type": "call"}, {"api_name": "conestrip.optimization.lower_prevision_clamped_sum", "line_number": 162, "usage_type": "call"}, {"api_name": "conestrip.optimization.incurs_sure_loss", "line_number": 163, "usage_type": "call"}, {"api_name": "conestrip.optimization.is_coherent", "line_number": 164, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 166, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 167, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 171, "usage_type": "call"}, {"api_name": "conestrip.random_cones.random_real_gambles", "line_number": 180, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_mass_function", "line_number": 181, "usage_type": "call"}, {"api_name": "conestrip.optimization.linear_lower_prevision_function", "line_number": 185, "usage_type": "call"}, {"api_name": "conestrip.optimization.generate_lower_prevision_perturbation", "line_number": 186, "usage_type": "call"}, {"api_name": "conestrip.optimization.lower_prevision_clamped_sum", "line_number": 187, "usage_type": "call"}, {"api_name": "conestrip.cones.print_gambles", "line_number": 189, "usage_type": "call"}, {"api_name": "conestrip.cones.print_fractions", "line_number": 189, "usage_type": "call"}, {"api_name": "conestrip.optimization.print_lower_prevision_function", "line_number": 189, "usage_type": "call"}, {"api_name": "conestrip.optimization.incurs_sure_loss", "line_number": 195, "usage_type": "call"}, {"api_name": "conestrip.optimization.is_coherent", "line_number": 201, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 208, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 223, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 226, "usage_type": "call"}, {"api_name": "conestrip.global_settings.GlobalSettings.verbose", "line_number": 227, "usage_type": "attribute"}, {"api_name": "conestrip.global_settings.GlobalSettings", "line_number": 227, "usage_type": "name"}]} +{"seq_id": "20050413216", "text": "import serial\nimport time\nimport serial.tools.list_ports\n\nport_list = serial.tools.list_ports.comports(include_links=True)\n\nif (len(port_list)) <= 0:\n print(\"没有找到串口\")\nelse:\n port_num = str(len(port_list))\n print(\"找到\" + port_num + \"个串口\");\n i = 0;\n while (i < len(port_list)):\n # print(port_list[i]);\n port_name = port_list[i][0];\n print(port_name);\n i = i + 1\n# serial()\n com = serial.Serial(port_name, 115200);\n\n# com = serial.Serial(\"COM6\",115200);\n date_src = \"$00022123&\";\n data_encodeing = date_src.encode('utf-8')\n # print(data_encodeing);\n write_status = com.write(data_encodeing);\n # print(write_status);\n data_return = \"\";\n data_return = data_return.__add__(str(com.read_all())[3:][:-2])\n time.sleep(3);\n data_return = data_return.__add__(str(com.read_all())[3:][:-2])\n # print(data_return)\n data_return = data_return[19:]\n # print(data_return)\n print(data_return[0:2]+\".\"+data_return[3:]+\"米\")\n\n\n\n\n\n\n\n\ncom.close();\n\n\n\n# serial.serialwin32(port_name,115200);\n\n# print(serial.__version__)\n# print(port_list[0]);", "repo_name": "qtp1300/python", "sub_path": "py3_pycharm/串口激光/激光001.py", "file_name": "激光001.py", "file_ext": "py", "file_size_in_byte": 1130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "serial.tools.list_ports.comports", "line_number": 5, "usage_type": "call"}, {"api_name": "serial.tools", "line_number": 5, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 19, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "12539322392", "text": "import serial\r\nimport math as cm\r\nimport time\r\n\r\nuart = serial.Serial('/dev/ttyAMA0', 19200, timeout=0.5)\r\n\r\n\"\"\"\r\ncreated by MrWang 2018.10.16\r\n电量显示开关板\r\n\r\n初始化波特率为19200 在writedata函数中将波特率修改为了15200\r\n在每个函数结束后 将波特率重新初始化为19200\r\n\r\n\r\nmodified by Zhao Tang 2018.10.22\r\n1.修改baudrate_init()函数，波特率可调；修改write_data函数，尽可能与舵机库保持一致；增加read_data函数。\r\n2.修改其他函数名字，尽量保证通过名字知道函��功能。\r\n3.采用read_data()函数改写需要返回数据的函数。\r\n\r\n\r\n电量显示开关板函数\r\nshut_down(state=0) //无需ID 一个机器人上只有一个电量显示板\r\nset_mode(state=1) //state 取值范围1-3 表示三种闪烁样式\r\nget_voltage() //返回当前电池电压及电量值\r\n\r\n\"\"\"\r\n\r\n\r\ndef baudrate_init(baud=19200):\r\n uart.baudrate = baud\r\n\r\n\r\ndef write_data(data=[], r_n=0):\r\n baudrate_init(115200)\r\n check = 0\r\n num = len(data)\r\n if num >= 2:\r\n data[num - 2] = 0\r\n for i in range(num - 2):\r\n check += data[i + 1]\r\n data[num - 2] = check % 100\r\n uart.write(data)\r\n else:\r\n print(\"待发送的数据有误！\")\r\n if r_n == 0: # 如果不需要接收数据，则直接恢复默认串口总线波特率\r\n baudrate_init()\r\n\r\n\r\ndef read_data(num=16):\r\n i = 100 # 经过测试，发现正常接收16位耗时大概为500，这里设置1000用来保证数据接收完成\r\n byte_list = []\r\n n_s = True\r\n while uart.inWaiting() < num and i > 0: # To do:\r\n i -= 1\r\n if uart.inWaiting() > 0 and n_s:\r\n if list(uart.read(1))[0] == 123:\r\n n_s = False\r\n byte_list.append(123)\r\n while uart.inWaiting() > 0:\r\n byte_list.append(list(uart.read(1))[0])\r\n if len(byte_list) == num:\r\n baudrate_init()\r\n return byte_list\r\n else:\r\n print(\"接收的数据有误:\")\r\n print(byte_list)\r\n baudrate_init()\r\n return []\r\n\r\n\r\ndef shut_down(state=0):\r\n \"\"\"function:\r\n 开关灯板\r\n 控制机器人电源开断\r\n\r\n \"\"\"\r\n data = [0, 0, 13, 50, 0, 1, 0, 0]\r\n data[0] = 123\r\n data[1] = 0\r\n data[2] = 0x20\r\n data[3] = 0x10\r\n if state == 0:\r\n data[4] = 0x00\r\n elif state == 1:\r\n data[4] = 0x01\r\n data[5] = 0\r\n data[7] = 125\r\n write_data(data)\r\n\r\n\r\ndef set_mode(state=1):\r\n \"\"\"function:\r\n 电量显示模块LED灯控制\r\n state： 取值范围1-3\r\n 4个LED不同的闪烁样式\r\n\r\n \"\"\"\r\n data = [0, 0, 13, 50, 0, 1, 0, 0]\r\n data[0] = 123\r\n data[1] = 0\r\n data[2] = 0x20\r\n data[3] = 0x12\r\n data[4] = state\r\n data[5] = 0\r\n data[7] = 125\r\n write_data(data)\r\n\r\n\r\ndef get_voltage():\r\n \"\"\"function:\r\n 电量显示模块\r\n 读取当前电池电压及电量\r\n \"\"\"\r\n data = [0, 0, 13, 50, 0, 1, 0, 0]\r\n data[0] = 123\r\n data[1] = 0\r\n data[2] = 0x20\r\n data[3] = 0x11\r\n data[4] = 0\r\n data[5] = 0\r\n data[7] = 125\r\n write_data(data, r_n=1)\r\n byte_list = read_data(8)\r\n # print(byte_list)\r\n if len(byte_list) == 8:\r\n check = (byte_list[1] + byte_list[2] + byte_list[3] + byte_list[4] + byte_list[5]) % 100\r\n if byte_list[6] == check and byte_list[0] == 123:\r\n board_type = byte_list[1]\r\n volt = byte_list[2] + byte_list[3] / 100\r\n energy = byte_list[4] * 100 + byte_list[5]\r\n # print(\"电池电压为\", volt, \"v\")\r\n return energy\r\n else:\r\n print(\"返回的数据校验失败\")\r\n return False\r\n else:\r\n print(\"读取电量失败\")\r\n return False\r\n", "repo_name": "fsmenyao/DRrobot", "sub_path": "DRcode/app/libs/switch.py", "file_name": "switch.py", "file_ext": "py", "file_size_in_byte": 3759, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "serial.Serial", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "24656083345", "text": "# coding: utf-8\n\n\"\"\"\n EO Data Store API\n\n EO Data Store Manager indexing all data hosted on a set of storages # noqa: E501\n\n OpenAPI spec version: draft-3\n Contact: christophe.noel@gmail.com\n Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\nimport pprint\nimport re # noqa: F401\n\nimport six\n\nclass Group(object):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n \"\"\"\n \"\"\"\n Attributes:\n swagger_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n swagger_types = {\n 'group_id': 'str',\n 'dataset_type': 'str',\n 'path': 'str',\n 'metadata_type': 'str',\n 'group_properties': 'list[str]'\n }\n\n attribute_map = {\n 'group_id': 'groupId',\n 'dataset_type': 'dataset-type',\n 'path': 'path',\n 'metadata_type': 'metadata-type',\n 'group_properties': 'group-properties'\n }\n\n def __init__(self, group_id=None, dataset_type=None, path=None, metadata_type=None, group_properties=None): # noqa: E501\n \"\"\"Group - a model defined in Swagger\"\"\" # noqa: E501\n self._group_id = None\n self._dataset_type = None\n self._path = None\n self._metadata_type = None\n self._group_properties = None\n self.discriminator = None\n self.group_id = group_id\n self.dataset_type = dataset_type\n if path is not None:\n self.path = path\n if metadata_type is not None:\n self.metadata_type = metadata_type\n if group_properties is not None:\n self.group_properties = group_properties\n\n @property\n def group_id(self):\n \"\"\"Gets the group_id of this Group. # noqa: E501\n\n Group identifier used for grouping a set of datasets. Note the groupId is unique regardless the dataset type (product, datafolder) # noqa: E501\n\n :return: The group_id of this Group. # noqa: E501\n :rtype: str\n \"\"\"\n return self._group_id\n\n @group_id.setter\n def group_id(self, group_id):\n \"\"\"Sets the group_id of this Group.\n\n Group identifier used for grouping a set of datasets. Note the groupId is unique regardless the dataset type (product, datafolder) # noqa: E501\n\n :param group_id: The group_id of this Group. # noqa: E501\n :type: str\n \"\"\"\n if group_id is None:\n raise ValueError(\"Invalid value for `group_id`, must not be `None`\") # noqa: E501\n\n self._group_id = group_id\n\n @property\n def dataset_type(self):\n \"\"\"Gets the dataset_type of this Group. # noqa: E501\n\n Product or Datafolder dataset (mission specific enumeration) # noqa: E501\n\n :return: The dataset_type of this Group. # noqa: E501\n :rtype: str\n \"\"\"\n return self._dataset_type\n\n @dataset_type.setter\n def dataset_type(self, dataset_type):\n \"\"\"Sets the dataset_type of this Group.\n\n Product or Datafolder dataset (mission specific enumeration) # noqa: E501\n\n :param dataset_type: The dataset_type of this Group. # noqa: E501\n :type: str\n \"\"\"\n if dataset_type is None:\n raise ValueError(\"Invalid value for `dataset_type`, must not be `None`\") # noqa: E501\n allowed_values = [\"product\", \"dataset\"] # noqa: E501\n if dataset_type not in allowed_values:\n raise ValueError(\n \"Invalid value for `dataset_type` ({0}), must be one of {1}\" # noqa: E501\n .format(dataset_type, allowed_values)\n )\n\n self._dataset_type = dataset_type\n\n @property\n def path(self):\n \"\"\"Gets the path of this Group. # noqa: E501\n\n Optional physical root path of the group on the data storage. If provided, the submitted datasets files are verified against this path. # noqa: E501\n\n :return: The path of this Group. # noqa: E501\n :rtype: str\n \"\"\"\n return self._path\n\n @path.setter\n def path(self, path):\n \"\"\"Sets the path of this Group.\n\n Optional physical root path of the group on the data storage. If provided, the submitted datasets files are verified against this path. # noqa: E501\n\n :param path: The path of this Group. # noqa: E501\n :type: str\n \"\"\"\n\n self._path = path\n\n @property\n def metadata_type(self):\n \"\"\"Gets the metadata_type of this Group. # noqa: E501\n\n Metadata format: either a standard, or a general encoding (such as \\\"JSON\\\") which holds the set of metadata properties. In the case of the ALTIUS mission, the metadata format for products SHALL be 'OGC 10-157r4' (Earth Observation Metadata profile of Observations & Measurement (XML). The set of metadata properties is defined in the ALTIUS Product Format Definition document. The metadata format for datafolders (generic datasets) SHOULD be JSON which holds a set of JSON properties defined as a string. # noqa: E501\n\n :return: The metadata_type of this Group. # noqa: E501\n :rtype: str\n \"\"\"\n return self._metadata_type\n\n @metadata_type.setter\n def metadata_type(self, metadata_type):\n \"\"\"Sets the metadata_type of this Group.\n\n Metadata format: either a standard, or a general encoding (such as \\\"JSON\\\") which holds the set of metadata properties. In the case of the ALTIUS mission, the metadata format for products SHALL be 'OGC 10-157r4' (Earth Observation Metadata profile of Observations & Measurement (XML). The set of metadata properties is defined in the ALTIUS Product Format Definition document. The metadata format for datafolders (generic datasets) SHOULD be JSON which holds a set of JSON properties defined as a string. # noqa: E501\n\n :param metadata_type: The metadata_type of this Group. # noqa: E501\n :type: str\n \"\"\"\n\n self._metadata_type = metadata_type\n\n @property\n def group_properties(self):\n \"\"\"Gets the group_properties of this Group. # noqa: E501\n\n Name of the group (searchable) specific properties (up to three). # noqa: E501\n\n :return: The group_properties of this Group. # noqa: E501\n :rtype: list[str]\n \"\"\"\n return self._group_properties\n\n @group_properties.setter\n def group_properties(self, group_properties):\n \"\"\"Sets the group_properties of this Group.\n\n Name of the group (searchable) specific properties (up to three). # noqa: E501\n\n :param group_properties: The group_properties of this Group. # noqa: E501\n :type: list[str]\n \"\"\"\n\n self._group_properties = group_properties\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(Group, dict):\n for key, value in self.items():\n result[key] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n return pprint.pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"For `print` and `pprint`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, Group):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n", "repo_name": "christophenoel/py_datastore", "sub_path": "datastore/models/group.py", "file_name": "group.py", "file_ext": "py", "file_size_in_byte": 8357, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "six.iteritems", "line_number": 192, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "42700342342", "text": "import requests\nimport hashlib\nimport hmac\nimport json\nimport base64\n\nWEEBLY_API_KEY = 'YOUR_API_KEY'\nWEEBLY_API_SECRET = 'YOUR_API_SECRET'\nWEEBLY_TEST_ACCOUNT_ID = 'YOUR_TEST_ACCOUNT'\n\nbase_url = 'https://api.weeblycloud.com/'\n\n\ndef weebly_hash(my_content):\n my_hmac = hmac.new(WEEBLY_API_SECRET, my_content, digestmod=hashlib.sha256).hexdigest()\n my_hash = base64.b64encode(my_hmac)\n\n return my_hash\n\n\ndef post(my_url, my_data=None):\n\n # get url\n full_url = base_url + my_url\n\n # get hash\n if (my_data == None):\n my_content = 'POST' + '\\n' + my_url + '\\n'\n else:\n my_content = 'POST' + '\\n' + my_url + '\\n' + json.dumps(my_data)\n\n my_hash = weebly_hash(my_content)\n\n # get headers\n if (my_data == None):\n post_header = {\n 'X-Public-Key': WEEBLY_API_KEY,\n 'X-Signed-Request-Hash': my_hash,\n }\n else:\n post_header = {\n 'Content-Type': 'application/json',\n 'X-Public-Key': WEEBLY_API_KEY,\n 'X-Signed-Request-Hash': my_hash,\n }\n\n # send request\n if (my_data == None):\n resp = requests.post(full_url, headers=post_header)\n else:\n resp = requests.post(full_url, data=json.dumps(my_data), headers=post_header)\n\n return resp\n\n\ndef get(my_url):\n\n # get url\n full_url = base_url + my_url\n\n # get hash\n my_content = 'GET' + '\\n' + my_url + '\\n'\n my_hash = weebly_hash(my_content)\n\n # get headers\n get_header = {\n 'X-Public-Key': WEEBLY_API_KEY,\n 'X-Signed-Request-Hash': my_hash,\n }\n\n # send request\n resp = requests.get(full_url, headers=get_header)\n\n return resp\n\n\ndef put(my_url, my_data):\n # get url\n full_url = base_url + my_url\n\n # get hash\n my_content = 'PUT' + '\\n' + my_url + '\\n' + json.dumps(my_data)\n my_hash = weebly_hash(my_content)\n\n # get headers\n put_header = {\n 'Content-Type': 'application/json',\n 'X-Public-Key': WEEBLY_API_KEY,\n 'X-Signed-Request-Hash': my_hash,\n }\n\n # send request\n resp = requests.put(full_url, data=json.dumps(my_data), headers=put_header)\n\n return resp\n\n\ndef patch(my_url, my_data):\n # get url\n full_url = base_url + my_url\n\n # get hash\n my_content = 'PATCH' + '\\n' + my_url + '\\n' + json.dumps(my_data)\n my_hash = weebly_hash(my_content)\n\n # get headers\n patch_header = {\n 'Content-Type': 'application/json',\n 'X-Public-Key': WEEBLY_API_KEY,\n 'X-Signed-Request-Hash': my_hash,\n }\n\n # send request\n resp = requests.patch(full_url, data=json.dumps(my_data), headers=patch_header)\n\n return resp\n\n\ndef delete(my_url):\n full_url = base_url + my_url\n\n #get hash\n my_content = 'DELETE' + '\\n' + my_url + '\\n'\n my_hash = weebly_hash(my_content)\n\n # get headers\n post_header = {\n 'X-Public-Key': WEEBLY_API_KEY,\n 'X-Signed-Request-Hash': my_hash,\n }\n\n # send request\n resp = requests.delete(full_url, headers=post_header)\n\n return resp\n\n\n", "repo_name": "mihasK/weebly", "sub_path": "weebly/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "hmac.new", "line_number": 15, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 15, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 82, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 93, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 93, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 103, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 114, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 114, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "25755623460", "text": "\n'''\nHoughCircles设置\n第3参数默认为1\n第4参数表示圆心与圆心之间的距离（太大的话，会很多圆被认为是一个圆）\n第5参数默认为100\n第6参数根据圆大小设置(圆越小设置越小，检测的圆越多，但检测大圆会有噪点)\n第7圆最小半径\n第8圆最大半径\n'''\n\nimport cv2\nimport numpy as np\nimport pandas as pd\n\nfont = cv2.FONT_HERSHEY_SIMPLEX\nlower_red = np.array([0, 127, 128]) # 红色阈值下界\nhigher_red = np.array([10, 255, 255]) # 红色阈值上界\nlower_green = np.array([35, 110, 106]) # 绿色阈值下界\nhigher_green = np.array([77, 255, 255]) # 绿色阈值上界\n\ncap = cv2.VideoCapture(0) # 0表示第一个摄像头\nwhile (1):\n # get a frame\n ret, frame = cap.read()\n # show a frame\n frame1 = cv2.flip(frame, 1)\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n GrayImage = cv2.medianBlur(gray, 9) # 中值模糊\n th2 = cv2.adaptiveThreshold(GrayImage, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 7, 5)#二值化需要的参数是src灰度图像 倒数第二个参数越大细节越少\n kernel = np.ones((5, 5), np.uint8) # 创建全一矩阵，数值类型设置为uint8\n erosion = cv2.erode(th2, kernel, iterations=1) # 腐蚀处理\n dilation = cv2.dilate(erosion, kernel, iterations=1) # 膨胀处理\n imgray = cv2.Canny(erosion, 30, 100) # Canny算子边缘检测\n\n circles = cv2.HoughCircles(imgray, cv2.HOUGH_GRADIENT, 4, 2500, param1=100, param2=90, minRadius=250, maxRadius=258)\n\n img_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n mask_red = cv2.inRange(img_hsv, lower_red, higher_red) # 可以认为是过滤出红色部分，获得红色的掩膜\n mask_green = cv2.inRange(img_hsv, lower_green, higher_green) # 获得绿色部分掩膜\n mask_green = cv2.medianBlur(mask_green, 7) # 中值滤波\n mask_red = cv2.medianBlur(mask_red, 7) # 中值滤波\n mask = cv2.bitwise_or(mask_green, mask_red) # 三部分掩膜进行按位或运算\n cnts1, hierarchy1 = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # 轮廓检测\n cnts3, hierarchy3 = cv2.findContours(mask_green, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n if type(circles) is not type(None):\n circles = np.uint16(np.around(circles))\n\n P = circles[0] # 去掉circles数组一层外括号\n for i in P:\n # 画出外圆\n cv2.circle(gray, (i[0], i[1]), i[2], (0, 255, 0), 2)\n # 画出圆心\n cv2.circle(gray, (i[0], i[1]), 2, (0, 0, 255), 3)\n print(\"圆的个数是：\")\n print(len(P))\n for i in P:\n r = int(i[2])\n x = int(i[0])\n y = int(i[1])\n print(\"圆心坐标为：\", (x, y))\n print(\"圆的半径是：\", r)\n print('离兔子的距��', y)\n\n for cnt in cnts1:\n (x, y, w, h) = cv2.boundingRect(cnt) # 该函数返回矩阵四个点\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2) # 将检测到的颜色框起来\n cv2.putText(frame, 'red', (x, y - 5), font, 0.7, (0, 0, 255), 2)\n print('红色圆环')\n for cnt in cnts3:\n (x, y, w, h) = cv2.boundingRect(cnt) # 该函数返回矩阵四个点\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 将检测到的颜色框起来\n cv2.putText(frame, 'green', (x, y - 5), font, 0.7, (0, 255, 0), 2)\n print('绿色圆环')\n\n cv2.imshow('frame', frame)\n cv2.imshow('detected circles', gray)\n if type(circles) is type(None):\n print('picture without circles')\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\ncap.release()\ncv2.destroyAllWindows()\n", "repo_name": "githubcapture/githubcapture.github.io", "sub_path": "distinguish circles.py", "file_name": "distinguish circles.py", "file_ext": "py", "file_size_in_byte": 3751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.HoughCircles", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.bitwise_or", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "27448292508", "text": "import gym\nimport numpy as np\nfrom gym_jsbsim.tasks import Shaping, HeadingControlTask\nfrom gym_jsbsim.simulation import Simulation\nfrom gym_jsbsim.visualiser import FigureVisualiser, FlightGearVisualiser\nfrom gym_jsbsim.aircraft import Aircraft, cessna172P\nfrom typing import Type, Tuple, Dict\n\n\nclass JsbSimEnv(gym.Env):\n \"\"\"\n A class wrapping the JSBSim flight dynamics module (FDM) for simulating\n aircraft as an RL environment conforming to the OpenAI Gym Env\n interface.\n\n An JsbSimEnv is instantiated with a Task that implements a specific\n aircraft control task with its own specific observation/action space and\n variables and agent_reward calculation.\n\n ATTRIBUTION: this class implements the OpenAI Gym Env API. Method\n docstrings have been adapted or copied from the OpenAI Gym source code.\n \"\"\"\n JSBSIM_DT_HZ: int = 60 # JSBSim integration frequency\n metadata = {'render.modes': ['human', 'flightgear']}\n\n def __init__(self, task_type: Type[HeadingControlTask], aircraft: Aircraft = cessna172P,\n agent_interaction_freq: int = 5, shaping: Shaping=Shaping.STANDARD):\n \"\"\"\n Constructor. Inits some internal state, but JsbSimEnv.reset() must be\n called first before interacting with environment.\n\n :param task_type: the Task subclass for the task agent is to perform\n :param aircraft: the JSBSim aircraft to be used\n :param agent_interaction_freq: int, how many times per second the agent\n should interact with environment.\n :param shaping: a HeadingControlTask.Shaping enum, what type of agent_reward\n shaping to use (see HeadingControlTask for options)\n \"\"\"\n if agent_interaction_freq > self.JSBSIM_DT_HZ:\n raise ValueError('agent interaction frequency must be less than '\n 'or equal to JSBSim integration frequency of '\n f'{self.JSBSIM_DT_HZ} Hz.')\n self.sim: Simulation = None\n self.sim_steps_per_agent_step: int = self.JSBSIM_DT_HZ // agent_interaction_freq\n self.aircraft = aircraft\n self.task = task_type(shaping, agent_interaction_freq, aircraft)\n # set Space objects\n self.observation_space: gym.spaces.Box = self.task.get_state_space()\n self.action_space: gym.spaces.Box = self.task.get_action_space()\n # set visualisation objects\n self.figure_visualiser: FigureVisualiser = None\n self.flightgear_visualiser: FlightGearVisualiser = None\n self.step_delay = None\n\n def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, Dict]:\n \"\"\"\n Run one timestep of the environment's dynamics. When end of\n episode is reached, you are responsible for calling `reset()`\n to reset this environment's state.\n Accepts an action and returns a tuple (observation, reward, done, info).\n\n :param action: the agent's action, with same length as action variables.\n :return:\n state: agent's observation of the current environment\n reward: amount of reward returned after previous action\n done: whether the episode has ended, in which case further step() calls are undefined\n info: auxiliary information, e.g. full reward shaping data\n \"\"\"\n if not (action.shape == self.action_space.shape):\n raise ValueError('mismatch between action and action space size')\n\n state, reward, done, info = self.task.task_step(self.sim, action, self.sim_steps_per_agent_step)\n return np.array(state), reward, done, info\n\n def reset(self):\n \"\"\"\n Resets the state of the environment and returns an initial observation.\n\n :return: array, the initial observation of the space.\n \"\"\"\n init_conditions = self.task.get_initial_conditions()\n if self.sim:\n self.sim.reinitialise(init_conditions)\n else:\n self.sim = self._init_new_sim(self.JSBSIM_DT_HZ, self.aircraft, init_conditions)\n\n state = self.task.observe_first_state(self.sim)\n\n if self.flightgear_visualiser:\n self.flightgear_visualiser.configure_simulation_output(self.sim)\n\n return np.array(state)\n\n def _init_new_sim(self, dt, aircraft, initial_conditions):\n return Simulation(sim_frequency_hz=dt,\n aircraft=aircraft,\n init_conditions=initial_conditions)\n\n def render(self, mode='flightgear', flightgear_blocking=True):\n \"\"\"Renders the environment.\n The set of supported modes varies per environment. (And some\n environments do not support rendering at all.) By convention,\n if mode is:\n - human: render to the current display or terminal and\n return nothing. Usually for human consumption.\n - rgb_array: Return an numpy.ndarray with shape (x, y, 3),\n representing RGB values for an x-by-y pixel image, suitable\n for turning into a video.\n - ansi: Return a string (str) or StringIO.StringIO containing a\n terminal-style text representation. The text can include newlines\n and ANSI escape sequences (e.g. for colors).\n Note:\n Make sure that your class's metadata 'render.modes' key includes\n the list of supported modes. It's recommended to call super()\n in implementations to use the functionality of this method.\n\n :param mode: str, the mode to render with\n :param flightgear_blocking: waits for FlightGear to load before\n returning if True, else returns immediately\n \"\"\"\n if mode == 'human':\n if not self.figure_visualiser:\n self.figure_visualiser = FigureVisualiser(self.sim,\n self.task.get_props_to_output())\n self.figure_visualiser.plot(self.sim)\n elif mode == 'flightgear':\n if not self.flightgear_visualiser:\n self.flightgear_visualiser = FlightGearVisualiser(self.sim,\n self.task.get_props_to_output(),\n flightgear_blocking)\n self.flightgear_visualiser.plot(self.sim)\n else:\n super().render(mode=mode)\n\n def close(self):\n \"\"\" Cleans up this environment's objects\n\n Environments automatically close() when garbage collected or when the\n program exits.\n \"\"\"\n if self.sim:\n self.sim.close()\n if self.figure_visualiser:\n self.figure_visualiser.close()\n if self.flightgear_visualiser:\n self.flightgear_visualiser.close()\n\n def seed(self, seed=None):\n \"\"\"\n Sets the seed for this env's random number generator(s).\n Note:\n Some environments use multiple pseudorandom number generators.\n We want to capture all such seeds used in order to ensure that\n there aren't accidental correlations between multiple generators.\n Returns:\n list: Returns the list of seeds used in this env's random\n number generators. The first value in the list should be the\n \"main\" seed, or the value which a reproducer should pass to\n 'seed'. Often, the main seed equals the provided 'seed', but\n this won't be true if seed=None, for example.\n \"\"\"\n gym.logger.warn(\"Could not seed environment %s\", self)\n return\n\n\nclass NoFGJsbSimEnv(JsbSimEnv):\n \"\"\"\n An RL environment for JSBSim with rendering to FlightGear disabled.\n\n This class exists to be used for training agents where visualisation is not\n required. Otherwise, restrictions in JSBSim output initialisation cause it\n to open a new socket for every single episode, eventually leading to\n failure of the network.\n \"\"\"\n metadata = {'render.modes': ['human']}\n\n def _init_new_sim(self, dt: float, aircraft: Aircraft, initial_conditions: Dict):\n return Simulation(sim_frequency_hz=dt,\n aircraft=aircraft,\n init_conditions=initial_conditions,\n allow_flightgear_output=False)\n\n def render(self, mode='human', flightgear_blocking=True):\n if mode == 'flightgear':\n raise ValueError('flightgear rendering is disabled for this class')\n else:\n super().render(mode, flightgear_blocking)\n", "repo_name": "Gor-Ren/gym-jsbsim", "sub_path": "gym_jsbsim/environment.py", "file_name": "environment.py", "file_ext": "py", "file_size_in_byte": 8588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 124, "dataset": "github-code", "pt": "3", "api": [{"api_name": "gym.Env", "line_number": 10, "usage_type": "attribute"}, {"api_name": "typing.Type", "line_number": 26, "usage_type": "name"}, {"api_name": "gym_jsbsim.tasks.HeadingControlTask", "line_number": 26, "usage_type": "name"}, {"api_name": "gym_jsbsim.aircraft.Aircraft", "line_number": 26, "usage_type": "name"}, {"api_name": "gym_jsbsim.tasks.Shaping", "line_number": 27, "usage_type": "name"}, {"api_name": "gym_jsbsim.aircraft.cessna172P", "line_number": 26, "usage_type": "name"}, {"api_name": "gym_jsbsim.tasks.Shaping.STANDARD", "line_number": 27, "usage_type": "attribute"}, {"api_name": "gym_jsbsim.simulation.Simulation", "line_number": 43, "usage_type": "name"}, {"api_name": "gym.spaces", "line_number": 48, "usage_type": "attribute"}, {"api_name": "gym.spaces", "line_number": 49, "usage_type": "attribute"}, {"api_name": "gym_jsbsim.visualiser.FigureVisualiser", "line_number": 51, "usage_type": "name"}, {"api_name": "gym_jsbsim.visualiser.FlightGearVisualiser", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "gym_jsbsim.simulation.Simulation", "line_number": 95, "usage_type": "call"}, {"api_name": "gym_jsbsim.visualiser.FigureVisualiser", "line_number": 123, "usage_type": "call"}, {"api_name": "gym_jsbsim.visualiser.FlightGearVisualiser", "line_number": 128, "usage_type": "call"}, {"api_name": "gym.logger.warn", "line_number": 162, "usage_type": "call"}, {"api_name": "gym.logger", "line_number": 162, "usage_type": "attribute"}, {"api_name": "gym_jsbsim.aircraft.Aircraft", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 177, "usage_type": "name"}, {"api_name": "gym_jsbsim.simulation.Simulation", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "1747730948", "text": "\"\"\"A Python Module to compute PT Minimal Models and PT Entailment\n\nClasses:\n RankedModel\n Represent a ranked interpretation holding valuations on different levels\n Node\n Binary formula tree for propositional sentences\n\"\"\"\n\nimport minisolvers\nimport re\nfrom itertools import chain, combinations, product\n\n############################\n\nclass RankedModel:\n \"\"\"The RankedModel class represents a ranked interpretation or model\n holding valuations.\n It provides methods to find information on typicality of sentences and\n preferred models.\n \"\"\"\n \n def __init__(self):\n self.layers = []\n\n def copy(self):\n rm = RankedModel()\n rm.layers=self.layers\n return rm\n\n def insert_vals(self, vals, levels=-1):\n \"\"\" Insert a list of valuations into RankedModel using the arrangement levels\n \"\"\"\n if levels==-1:\n levels=\"0\"*len(vals)\n for x in range(len(set(levels))):\n self.layers.append([])\n for i in range(len(levels)):\n self.layers[int(levels[i])].append(vals[i])\n\n\n def get_typical_layer_s(self, sentence, var_list):\n \"\"\" Return lowest layer that satisfies a sentence\n \"\"\"\n for i in range(len(self.layers)):\n for val in self.layers[i]:\n if sat_kb([sentence], val, var_list):\n return i\n return \"inf\"\n\n \n def get_typical_layer_atom(self, atom_index, false_flag = False):\n \"\"\" Return lowest layer 1 atom is satisfied on\n or if false flag lowest level not atom is satisfied on\n \"\"\"\n if false_flag:\n for i in range(len(self.layers)):\n for val in self.layers[i]:\n if val[atom_index] == \"0\":\n return i\n else:\n for i in range(len(self.layers)):\n for val in self.layers[i]:\n if val[atom_index] == \"1\":\n return i\n return \"inf\"\n\n def height(self, v):\n \"\"\" Return the height of a valuation in the ranked intepretation\n \"\"\"\n for i in range(len(self.layers)):\n for val in self.layers[i]:\n if val==v:\n return i\n return \"inf\"\n\n def preferred(self, rm_2, vals):\n \"\"\"Return true if current RM is = h2:\n return False\n return True\n \n def __str__(self):\n string = \"\"\n for i in reversed(range(len(self.layers))):\n string += \"L\"+str(i)+\" \"+str(self.layers[i]) + \"\\n\"\n return string\n\nclass Node():\n \"\"\" Tree representation of propositional formula\n \"\"\"\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def copy(self):\n new_node = Node(self.value)\n if self.left:\n new_node.left = self.left.copy()\n if self.right:\n new_node.right = self.right.copy()\n return new_node\n \n def insert_left(self, left):\n self.left = left\n\n def insert_right(self, right):\n self.right = right\n\n def find_typicality(self):\n \"\"\"Return nodes with typicality children\n \"\"\"\n typ = []\n if self.left:\n if self.left.value == \"*\":\n typ.append(self)\n typ+= self.left.find_typicality()\n\n if self.right:\n if self.right.value == \"*\":\n typ.append(self)\n typ+= self.right.find_typicality()\n \n return typ\n\n def inorder_bra(self):\n \"\"\" Return inorder string with brackets\n \"\"\"\n if self.left == None and self.right == None:\n return self.value\n elif self.right == None:\n return self.value + \"(\"+self.left.inorder()+\")\"\n else:\n return \"(\"+self.left.inorder_bra()+ self.value + self.right.inorder_bra()+\")\"\n\n def inorder(self):\n \"\"\" Return inorder string of nodes\n \"\"\"\n if self.left == None and self.right == None:\n return self.value\n elif self.right == None:\n return self.value + self.left.inorder()\n else:\n return self.left.inorder()+ self.value + self.right.inorder()\n \n def __str__(self, lvl=0):\n ret = \" \"*lvl + self.value + \"\\n\"\n if self.left:\n ret += self.left.__str__(lvl+1)\n if self.right:\n ret+= self.right.__str__(lvl+1)\n return ret\n\n############################\n # Pre-processing\ndef add_brackets(s):\n \"\"\" Return string with brackets associatively right to left\n \"\"\"\n s = s.replace(\" \",\"\")\n atoms = re.split(\">|&|\\|\",s)\n ops = re.split(\"\\*|-*[a-z]\",s)\n ops = [o for o in ops if o!=\"\"]\n s = \"\"\n if len(atoms)>=2:\n s = atoms.pop() + \")\" + s\n s = ops.pop() + s\n s = \"(\" + atoms.pop() + s\n\n while len(atoms) > 0:\n s = \"(\"+atoms.pop()+ops.pop()+s+\")\"\n return s\n\ndef get_vars(kb):\n \"\"\" Return a list of atoms used in KB\n \"\"\"\n var_list = []\n for s in kb:\n atoms = re.split(\">|&|\\||-|\\*|\\(|\\)\",s)\n for atom in atoms:\n if atom not in var_list and atom!=\"\":\n var_list.append(atom)\n return var_list\n\ndef sat_format(kb,var_list):\n \"\"\" Return a list of SAT clauses\n \"\"\"\n s = \"&\".join(kb)\n clauses = []\n ors = s.split(\"&\")\n for sent in ors:\n new_clause = []\n atoms = sent.split(\"|\")\n for var in atoms:\n if \"-\" in var:\n try:\n new_clause.append(-(var_list.index(var.strip()[1:])+1))\n except ValueError:\n print(\"Error: statement not properly formatted\")\n else:\n new_clause.append(var_list.index(var.strip())+1)\n clauses.append(new_clause)\n return clauses\n############################\n\n############################\n #tree operations\ndef negate(node):\n \"\"\" Return the negation of a statement\n \"\"\"\n if node.value==\"&\":\n node.value= \"|\"\n node.left = negate(node.left)\n node.right = negate(node.right)\n elif node.value==\"|\":\n node.value=\"&\"\n node.left = negate(node.left)\n node.right = negate(node.right)\n elif node.value==\"-\":\n node=node.left.copy()\n else:\n node.left = Node(node.value)\n node.value=\"-\"\n return node\n\ndef conv_impl(node):\n \"\"\" Return a tree using | instead of >\n \"\"\"\n if node == None:\n return None\n if node.value == \">\": \n node.value = \"|\"\n if node.left.value==\">\":\n conv_impl(node.left)\n node.left = negate(node.left)\n conv_impl(node.left)\n conv_impl(node.right)\n return node\n\ndef prop_neg(node):\n \"\"\" Return formula tree with propagation of negations\n \"\"\"\n if node==None:\n return None\n if node.value ==\"-\" and node.left.value in \"&|-\":\n node = negate(node.left)\n node.left=prop_neg(node.left)\n node.right=prop_neg(node.right)\n return node\n \ndef fits_orOfAnd(node):\n \"\"\" Return true if a formula tree fits the situation of an Or of And\n \"\"\"\n if node.value == \"|\":\n if node.left.value == \"&\":\n return True\n if node.right.value ==\"&\":\n return True\n else:\n return False\n return False\n\n\ndef conv_orOfAnd(node):\n \"\"\" Return a formula tree. Find nodes that match that of \"(A and B) or C\"\n convert to \"(A or C) and (B or C)\" to be in CNF\n \"\"\"\n if fits_orOfAnd(node):\n node.value=\"&\"\n if node.left.value==\"&\":\n temp_A=node.left.left\n temp_B=node.left.right\n temp_C=node.right\n node.left = Node(\"|\")\n node.right= Node(\"|\")\n node.left.left=temp_A\n node.left.right=temp_C\n node.right.left=temp_B\n node.right.right=temp_C\n elif node.right.value==\"&\":\n temp_C=node.left\n temp_B=node.right.right\n temp_A=node.right.left\n node.left = Node(\"|\")\n node.right= Node(\"|\")\n node.left.left=temp_C\n node.left.right=temp_A\n node.right.left=temp_C\n node.right.right=temp_B\n if node.left:\n conv_orOfAnd(node.left)\n if node.right:\n conv_orOfAnd(node.right)\n return node\n\ndef create_tree(s):\n \"\"\" Return a formula tree from a sentence\n \"\"\"\n s = s.replace(\" \",\"\")\n if \"(\" not in s:\n if \"&\" in s:\n s_list = s.split(\"&\")\n if len(s_list)>2:\n s=add_brackets(s)\n new_node = create_tree(s)\n else:\n new_node = Node(\"&\")\n new_node.left = create_tree(s_list[0])\n new_node.right = create_tree(s_list[1])\n\n elif \"|\" in s:\n s_list = s.split(\"|\")\n if len(s_list)>2:\n s=add_brackets(s)\n new_node = create_tree(s)\n else:\n new_node = Node(\"|\")\n new_node.left = create_tree(s_list[0])\n new_node.right = create_tree(s_list[1])\n elif \">\" in s:\n s_list = s.split(\">\")\n if len(s_list)>2:\n s=add_brackets(s)\n new_node = create_tree(s)\n else:\n new_node = Node(\">\")\n new_node.left = create_tree(s_list[0])\n new_node.right = create_tree(s_list[1])\n\n elif s.startswith(\"*\"):\n new_node = Node(\"*\")\n new_node.left = create_tree(s[1:])\n elif s.startswith(\"-\"):\n new_node = Node(\"-\")\n new_node.left = create_tree(s[1:]) \n else :\n new_node = Node(s)\n\n else:\n if not (\">\" in s or \"|\" in s or \"&\" in s):\n s = s.replace(\"(\",\"\")\n s = s.replace(\")\",\"\")\n new_node = create_tree(s)\n else:\n counter = 0\n i=0\n for ch in s:\n if ch==\"(\":\n counter+=1\n if ch==\")\":\n counter-=1\n if counter == 0:\n if len(s) <= i+1:\n if s.startswith(\"*\"):\n new_node = Node(\"*\")\n new_node.left = create_tree(s[1:])\n elif s.startswith(\"-\"):\n new_node = Node(\"-\")\n new_node.left = create_tree(s[1:])\n else:\n new_node = create_tree(s[1:len(s)-1])\n break\n elif s[i+1] in \">&|\":\n new_node = Node(s[i+1])\n new_node.left = create_tree(s[:i+1])\n new_node.right =create_tree(s[i+2:])\n break\n i+=1\n\n try:\n return new_node\n except UnboundLocalError:\n print(\"Error: invalid sentence.\")\n\n\n#############################\n # satisfaction\ndef sat_kb(kb, val, var_list):\n \"\"\" Check a valuation satisfies a classical KB\n return true if val satisfies KB\n \"\"\"\n solver = minisolvers.MinisatSolver()\n new_kb = kb.copy()\n for i in range(len(kb)):\n tree = create_tree(kb[i])\n tree = conv_impl(tree)\n tree = prop_neg(tree)\n tree = conv_orOfAnd(tree)\n new_kb[i]=tree.inorder()\n \n val_s = []\n for i in range(len(val)):\n if val[i] ==\"0\":\n val_s.append(\"-\"+var_list[i])\n else:\n val_s.append(var_list[i])\n val=\"&\".join(val_s)\n new_kb.append(val)\n clauses = sat_format(new_kb,var_list)\n\n for i in range(len(var_list)):\n solver.new_var()\n for clause in clauses:\n solver.add_clause(clause)\n return solver.solve()\n\ndef sat_rm_val(kb, val, ranked_model, var_list):\n \"\"\"Check a valuation satisfies a KB with typicality statements wrt a ranked model\n return true if val satisfies\n \"\"\"\n new_kb=[]\n \n for s in kb:\n s_tree=create_tree(s)\n typ_roots = s_tree.find_typicality()\n if typ_roots:\n for node in typ_roots:\n if node.left.value==\"*\":\n #if typ is left child\n # Find lowest level node.left.left is satisfied on\n typ_sent = node.left.left.inorder_bra()\n if not (\">\" in typ_sent or \"&\" in typ_sent or \"|\" in typ_sent):\n if \"-\" in typ_sent:\n atom = node.left.left.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom),True)\n else:\n atom=node.left.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom))\n else:\n lowest_layer = ranked_model.get_typical_layer_s(typ_sent, var_list)\n cur_layer = ranked_model.height(val)\n\n if lowest_layer == \"inf\":\n lowest_layer = len(ranked_model.layers)\n\n if lowest_layer < cur_layer:\n # val is not most typical world, replace with false\n node.left = create_tree(\"(\"+var_list[0]+\"&\"+\"-\"+var_list[0]+\")\")\n else:\n #typical world, evaluate classically\n node.left = node.left.left\n else:\n #else typ must be right child\n # Find lowest level node.right.left is satisfied on\n typ_sent = node.right.left.inorder_bra()\n \n if not (\">\" in typ_sent or \"&\" in typ_sent or \"|\" in typ_sent):\n if \"-\" in typ_sent:\n atom=node.right.left.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom),True)\n else:\n atom=node.right.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom)) \n else:\n lowest_layer = ranked_model.get_typical_layer_s(typ_sent, var_list)\n cur_layer = ranked_model.height(val)\n \n if lowest_layer == \"inf\":\n lowest_layer = len(ranked_model.layers)\n\n if lowest_layer < cur_layer:\n # val is not most typical world, replace with false\n node.right = create_tree(\"(\"+var_list[0]+\"&\"+\"-\"+var_list[0]+\")\")\n else:\n #typical world, evaluate classically\n node.right = node.right.left\n new_kb.append(s_tree.inorder_bra())\n else:\n new_kb.append(s)\n return sat_kb(new_kb, val, var_list)\n\n\n\ndef sat_kb_rm(kb, rm, var_list):\n \"\"\" Return true if a ranked interpretation satisfies a KB\n \"\"\"\n for level in rm.layers:\n for val in level:\n if not sat_rm_val(kb, val, rm, var_list):\n return False\n return True \n\n#algorithm helper functions\n\ndef powerset(s):\n \"\"\" Return a list of subsets as set type\n \"\"\"\n result = []\n for length in range(len(s)+1):\n for subset in combinations(s,length):\n result.append(set(subset))\n return result\n \ndef valid_intr(ranking):\n \"\"\" Method to check if string ranking is a valid interpretation,\n no valuation should be >1 level above the others\n valuations should not be all on same level if lvl >1\n \"\"\"\n if len(ranking)==1:\n if ranking==\"0\":\n return True\n else:\n return False\n rnk = [int(x) for x in list(ranking)]\n rnk.sort()\n if rnk[0]!=0:\n return False\n pairwise = list(zip(rnk,rnk[1:]))\n for pair in pairwise:\n if pair[1]-pair[0]>1:\n return False\n return True\n\ndef incr_arrange(rankings):\n \"\"\" Returns a list of unique arrangements all incremented by 1 level\n \"\"\"\n temp = set()\n for a in rankings:\n for i in range(len(a)):\n temp_rank = list(a)\n temp_rank[i] = str(int(temp_rank[i])+1)\n temp.add(\"\".join(temp_rank))\n return [rank for rank in list(temp) if valid_intr(rank)]\n\n\ndef pt_ranked(kb, bound=0):\n \"\"\" Returns the set of minimal ranked models for typicality KB\n This is the main algorithm the project is concerned with\n Optional argument bound to skip if arrangements > bound\n \"\"\"\n var_list = get_vars(kb)\n #print(var_list)\n U = [\"\".join(seq) for seq in product(\"01\", repeat=len(var_list))] #every possible valuation\n # remove valuations that dn satisfy classical statements\n classical_kb = [sentence for sentence in kb if not \"*\" in sentence]\n U = [val for val in U if sat_kb(classical_kb, val, var_list)]\n #print('U',U)\n\n G = powerset(U)\n G.reverse()\n G = [list(subset) for subset in G if subset != set()]\n pt_min = [] # all minimal ranked models\n for subset in G:\n #i+=1\n rankings = [\"0\"*len(subset)]\n pt_min_s=[]\n while pt_min_s == []:\n for arr in rankings:\n #try model\n rm = RankedModel()\n rm.insert_vals(subset, arr)\n #i+=1\n if sat_kb_rm(kb, rm, var_list):\n #if ranked model with current arrangement satisfies KB\n pt_min_s.append(rm)\n\n rankings = incr_arrange(rankings)\n \n if rankings == []:\n break\n # cheap optimisation trick to skip arrangements\n if bound>0 and len(rankings)>bound:\n break\n for model in pt_min:\n for i in range(len(pt_min_s)):\n model2 = pt_min_s[i]\n if model.preferred(model2,U):\n pt_min_s.remove(model2)\n pt_min += pt_min_s\n return pt_min\n\n\ndef entail(s, val, var_list):\n \"\"\" Returns true if a statement is entailed by a valuation\n \"\"\"\n solver = minisolvers.MinisatSolver()\n s = create_tree(s)\n s = conv_impl(s)\n s = negate(s)\n s = conv_orOfAnd(s)\n s = s.inorder()\n val_s = []\n for i in range(len(val)):\n if val[i] ==\"0\":\n val_s.append(\"-\"+var_list[i])\n else:\n val_s.append(var_list[i])\n val=\"&\".join(val_s)\n kb= [s] + [val]\n clauses = sat_format(kb,var_list)\n for i in range(len(var_list)):\n solver.new_var()\n for clause in clauses:\n solver.add_clause(clause)\n return not solver.solve()\n\ndef pt_entail(s, kb, ranked_models):\n \"\"\" Returns true if a statement is entailed by a PTL KB\n input statement, knowledge base, and the minimal models from pt_ranked\n \"\"\"\n #ranked_models = pt_ranked(kb)\n var_list = get_vars(kb)\n for ranked_model in ranked_models:\n # check if classical statement\n if \"*\" not in s:\n for layer in ranked_model.layers:\n for val in layer:\n if not (entail(s,val,var_list)):\n return False\n else:\n #statement contains typicality\n for layer in ranked_model.layers:\n for val in layer:\n s_tree=create_tree(s)\n typ_roots = s_tree.find_typicality()\n for node in typ_roots:\n if node.left.value==\"*\":\n #if typ is left child\n # Find lowest level node.left.left is satisfied on\n typ_sent = node.left.left.inorder_bra()\n if not (\">\" in typ_sent or \"&\" in typ_sent or \"|\" in typ_sent):\n if \"-\" in typ_sent:\n atom=node.left.left.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom),True)\n else:\n atom=node.left.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom))\n else:\n lowest_layer = ranked_model.get_typical_layer_s(typ_sent, var_list)\n cur_layer = ranked_model.height(val)\n\n if lowest_layer == \"inf\":\n lowest_layer = len(ranked_model.layers)\n\n if lowest_layer < cur_layer:\n # val is not most typical world, replace with false\n node.left = create_tree(\"(\"+var_list[0]+\"&\"+\"-\"+var_list[0]+\")\")\n else:\n #typical world, evaluate classically\n node.left = node.left.left\n else:\n #else typ must be right child\n # Find lowest level node.right.left is satisfied on\n typ_sent = node.right.left.inorder_bra()\n if not (\">\" in typ_sent or \"&\" in typ_sent or \"|\" in typ_sent):\n if \"-\" in typ_sent:\n atom=node.right.left.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom),True)\n else:\n atom=node.right.left.inorder()\n lowest_layer = ranked_model.get_typical_layer_atom(var_list.index(atom))\n\n else:\n lowest_layer = ranked_model.get_typical_layer_s(typ_sent, var_list)\n cur_layer = ranked_model.height(val)\n \n if lowest_layer == \"inf\":\n lowest_layer = len(ranked_model.layers)\n\n if lowest_layer < cur_layer:\n # val is not most typical world, replace with false\n node.right = create_tree(\"(\"+var_list[0]+\"&\"+\"-\"+var_list[0]+\")\")\n else:\n #typical world, evaluate classically\n node.right = node.right.left\n new_s = s_tree.inorder_bra()\n if not sat_kb([new_s],val,var_list):\n return False\n \n return True\n \n\n", "repo_name": "AndrewHoweEly/PTR-PT-Entail", "sub_path": "pt_entailment.py", "file_name": "pt_entailment.py", "file_ext": "py", "file_size_in_byte": 23191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "re.split", "line_number": 169, "usage_type": "call"}, {"api_name": "re.split", "line_number": 170, "usage_type": "call"}, {"api_name": "re.split", "line_number": 187, "usage_type": "call"}, {"api_name": "minisolvers.MinisatSolver", "line_number": 390, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 498, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 541, "usage_type": "call"}, {"api_name": "minisolvers.MinisatSolver", "line_number": 584, "usage_type": "call"}]} +{"seq_id": "34662229322", "text": "#!/usr/bin/python3\n# Sample starter bot by Zac Partridge\n# Contact me at z.partridge@unsw.edu.au\n# 06/04/19\n# Feel free to use this and modify it however you wish\n\nimport socket\nimport sys\nimport numpy as np\nimport math\nimport collections\n\n# a board cell can hold:\n# 0 - Empty\n# 1 - I played here\n# 2 - They played here\n\n#class Heuristic:\n# def __init__(self):\n# self.a = None\n# self.b = None\n# self.c = None\n# self.d = None\n# self.e = None\n# self.f = None\n# self.g = None\n# self.h = None\n# self.i = None\n\n# the boards are of size 10 because index 0 isn't used\nboards = np.zeros((10, 10), dtype=\"int8\")\ns = [\".\",\"X\",\"O\"]\nS = [None,'a','b','c','d','e','f','g','h','i']\ncurr = 0 # this is the current board to play in\nsearch_depth = 4\n#scale = [0,2,1,2,1,3,1,2,1,2]\n#heuristic = Heuristic()\n\n# print a row\n# This is just ported from game.c\ndef print_board_row(board, a, b, c, i, j, k):\n print(\" \"+s[board[a][i]]+\" \"+s[board[a][j]]+\" \"+s[board[a][k]]+\" | \" \\\n +s[board[b][i]]+\" \"+s[board[b][j]]+\" \"+s[board[b][k]]+\" | \" \\\n +s[board[c][i]]+\" \"+s[board[c][j]]+\" \"+s[board[c][k]])\n\n# Print the entire board\n# This is just ported from game.c\ndef print_board(board):\n print_board_row(board, 1,2,3,1,2,3)\n print_board_row(board, 1,2,3,4,5,6)\n print_board_row(board, 1,2,3,7,8,9)\n print(\" ------+-------+------\")\n print_board_row(board, 4,5,6,1,2,3)\n print_board_row(board, 4,5,6,4,5,6)\n print_board_row(board, 4,5,6,7,8,9)\n print(\" ------+-------+------\")\n print_board_row(board, 7,8,9,1,2,3)\n print_board_row(board, 7,8,9,4,5,6)\n print_board_row(board, 7,8,9,7,8,9)\n print(\" ------+-------+------\")\n print(\" ------+-------+------\")\n print()\n\n# choose a move to play\ndef play():\n print_board(boards)\n n = alpha_beta(curr, search_depth, 1) # 1 means its us to play, in alpha_beta recursively times -1 to indicate whos turn\n place(curr, n, 1)\n return n\n\ndef killer_move(cell, target):\n global killermoves\n killermoves = []\n if (boards[cell][1:4] == [0,target,target]).all() or\\\n (boards[cell][1::3] == [0,target,target]).all() or\\\n (boards[cell][1::4] == [0,target,target]).all():\n killermoves.append(1)\n elif (boards[cell][1:4] == [target,0,target]).all() or\\\n (boards[cell][2::3] == [0,target,target]).all():\n killermoves.append(2)\n elif (boards[cell][1:4] == [target,target,0]).all() or\\\n (boards[cell][3::3] == [0,target,target]).all() or\\\n (boards[cell][3:8:2] == [0,target,target]).all():\n killermoves.append(3)\n elif (boards[cell][4:7] == [0,target,target]).all() or\\\n (boards[cell][1::3] == [target,0,target]).all():\n killermoves.append(4)\n elif (boards[cell][4:7] == [target,0,target]).all() or\\\n (boards[cell][2::3] == [target,0,target]).all() or\\\n (boards[cell][3:8:2] == [target,0,target]).all() or\\\n (boards[cell][1::4] == [target,0,target]).all():\n killermoves.append(5)\n elif (boards[cell][4:7] == [target,target,0]).all() or\\\n (boards[cell][3::3] == [target,0,target]).all():\n killermoves.append(6)\n elif (boards[cell][7:] == [0,target,target]).all() or\\\n (boards[cell][1::3] == [target,target,0]).all() or\\\n (boards[cell][3:8:2] == [target,target,0]).all():\n killermoves.append(7)\n elif (boards[cell][7:] == [target,0,target]).all() or\\\n (boards[cell][2::3] == [target,target,0]).all():\n killermoves.append(8)\n elif (boards[cell][7:] == [target,target,0]).all() or\\\n (boards[cell][3::3] == [target,target,0]).all() or\\\n (boards[cell][1::4] == [target,target,0]).all():\n killermoves.append(9)\n\ndef alpha_beta(cell, depth, player, alpha = -math.inf, beta = math.inf):\n killer_move(cell,1)\n #print(f'My potential killer moves: {killermoves}') if depth == search_depth else 0\n if depth == search_depth and killermoves:\n return killermoves[0]\n child_nodes = killermoves\n length = len(child_nodes)\n child_nodes.extend([i for i in range(1,10) if boards[cell][i]== 0 and not (i in child_nodes)])\n #print(f'Current Available moves: {child_nodes}') if depth == search_depth else 0\n remove_list = []\n for i in child_nodes:\n killer_move(i,-1)\n if killermoves and child_nodes.index(i)>=length:\n remove_list.append(i)\n #print(f'Current opponent killer moves: {remove_list}') if depth == search_depth else 0\n for i in remove_list:\n child_nodes.remove(i)\n #print(f'After elimination, my available moves left: {child_nodes}') if depth == search_depth else 0\n #if depth == search_depth:\n # killer_move(cell,-1)\n # for i in killermoves:\n # if i in child_nodes:\n # return i\n\n if depth == 0 or not child_nodes or winning(cell, -player):\n evaluate(cell)\n #for index in range(1,10):\n # total = 0\n # if index != cell:\n # total += heuristic.__dict__[S[index]]\n # else:\n # total += evaluate(cell)\n #return total if depth != search_depth else remove_list[0]\n return value if depth != search_depth else remove_list[0]\n elif player > 0:\n for i in child_nodes:\n fake_place(cell, i, player)\n #if depth == 1:\n # for index in range(1,10):\n # heuristic.__dict__[S[index]] = evaluate(index)\n if depth != search_depth:\n alpha = max(alpha, alpha_beta(i, depth-1, -player, alpha, beta))\n else:\n new_alpha = alpha_beta(i, depth-1, -player, alpha, beta)\n if new_alpha > alpha:\n alpha, move = new_alpha, i\n unplace(cell, i)\n if alpha >= beta:\n return alpha\n return alpha if depth != search_depth else move\n else:\n for i in child_nodes:\n fake_place(cell, i, player)\n #if depth == 1:\n # for index in range(1,10):\n # heuristic.__dict__[S[index]] = evaluate(index)\n beta = min(beta, alpha_beta(i, depth-1, -player, alpha, beta))\n unplace(cell, i)\n if beta <= alpha:\n return beta\n return beta\n return 'Something went wrong!!!'\n\ndef evaluate(i):\n global x, value, X2, X1, O2, O1\n X2 = 0; X1 = 0; O2 = 0; O1 = 0;\n value = 0\n x = collections.Counter(boards[i][1:4])\n sub_evaluate()\n x = collections.Counter(boards[i][4:7])\n sub_evaluate()\n x = collections.Counter(boards[i][7:])\n sub_evaluate()\n x = collections.Counter(boards[i][1::3])\n sub_evaluate()\n x = collections.Counter(boards[i][2::3])\n sub_evaluate()\n x = collections.Counter(boards[i][3::3])\n sub_evaluate()\n x = collections.Counter(boards[i][1::4])\n sub_evaluate()\n x = collections.Counter(boards[i][3:8:2])\n sub_evaluate()\n value += 3*X2+X1-(20*O2+5*O1)\n return value\n\ndef sub_evaluate():\n global value, X2, X1, O2, O1\n if x[1] == 2 and x[0] == 1:\n X2 += 1\n elif x[-1] == 2 and x[0] == 1:\n O2 += 1\n elif x[0] == 2 and x[1] == 1:\n X1 += 1\n elif x[0] == 2 and x[-1] == 1:\n O1 += 1\n elif x[1] == 3:\n value += 1000\n elif x[-1] == 3:\n value -= 10000\n\n#def sub_evaluate():\n# global value\n# if x[1] == 2 and x[0] == 1:\n# value += 300\n# elif x[-1] == 2 and x[0] == 1:\n# value -= 300\n# elif x[0] == 2 and x[1] == 1:\n# value += 10\n# elif x[0] == 2 and x[-1] == 1:\n# value -= 10\n# elif x[1] == 3:\n# value += 10000\n# elif x[-1] == 3:\n# value -= 10000\n# elif x[0] == 3:\n# value += 1\n\ndef winning(cell, player):\n if (boards[cell][1:4] == [player,player,player]).all() or (boards[cell][4:7] == [player,player,player]).all() or\\\n (boards[cell][7:] == [player,player,player]).all() or (boards[cell][1::3] == [player,player,player]).all() or\\\n (boards[cell][2::3] == [player,player,player]).all() or (boards[cell][3::3] == [player,player,player]).all() or\\\n (boards[cell][1::4] == [player,player,player]).all() or (boards[cell][3:8:2] == [player,player,player]).all():\n return True\n return False\n\ndef fake_place(cell, num, player):\n boards[cell][num] = player\n\ndef unplace(cell, i):\n boards[cell][i] = 0\n\n# place a move in the global boards\ndef place(board, num, player):\n global curr\n curr = num\n boards[board][num] = player\n\n# read what the server sent us and\n# only parses the strings that are necessary\ndef parse(string):\n if \"(\" in string:\n command, args = string.split(\"(\")\n args = args.split(\")\")[0]\n args = args.split(\",\")\n else:\n command, args = string, []\n\n if command == \"second_move\":\n place(int(args[0]), int(args[1]), -1)\n return play()\n elif command == \"third_move\":\n # place the move that was generated for us\n place(int(args[0]), int(args[1]), 1)\n # place their last move\n place(curr, int(args[2]), -1)\n return play()\n elif command == \"next_move\":\n place(curr, int(args[0]), -1)\n return play()\n elif command == \"win\":\n print_board(boards)\n print(\"Yay!! We win!! :)\")\n return -1\n elif command == \"loss\":\n print_board(boards)\n print(\"We lost :(\")\n return -1\n elif command == \"draw\":\n print_board(boards)\n print(\"Draw game :|\")\n return -1\n return 0\n\n# connect to socket\ndef main():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n port = int(sys.argv[2]) # Usage: ./agent.py -p (port)\n\n s.connect(('localhost', port))\n while True:\n text = s.recv(1024).decode()\n if not text:\n continue\n for line in text.split(\"\\n\"):\n response = parse(line)\n if response == -1:\n s.close()\n return\n elif response > 0:\n s.sendall((str(response) + \"\\n\").encode())\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "melmarsezio/Nine-Board-Tic-Tac-Toe", "sub_path": "agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 10137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 108, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 175, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 177, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 179, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 181, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 183, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 185, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 187, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 189, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 284, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 284, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 284, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 285, "usage_type": "attribute"}]} +{"seq_id": "18373634166", "text": "from selenium import webdriver\n\noptions = webdriver.ChromeOptions()\noptions.headless = True\noptions.add_argument(\"window-size=1920x1080\")\n\nbrowser = webdriver.Chrome(options=options)\nbrowser.maximize_window()\n\n# 페이지이동\n\nurl = \"https://play.google.com/store/movies/top\"\nbrowser.get(url)\n\n\nimport time\ninterval = 2 # 2초에 한번씩 스크롤 내림\n\n# 현재 문서 높이를 가져와서 저장\nprev_height = browser.execute_script(\"return document.body.scrollHeight\")\n\n# 반복수행\n\nwhile True:\n # 화면 가장 아래로 스크롤 내리기\n browser.execute_script(\"window.scrollTo(0, document.body.scrollHeight)\")\n # 페이지 로딩 대기\n time.sleep(interval)\n # 현재 문서 높이를 가져와서 저장\n curr_height = browser.execute_script(\"return document.body.scrollHeight\")\n # 스크롤을 내린 높이와 내리기 전의 높이가 같은지 비교\n if curr_height == prev_height:\n # 높이가 똑같다면 스크롤을 전부 내린 것으로 간주하고 while문을 탈출\n break\n\n prev_height = curr_height\nprint(\"스크롤 완료\")\nbrowser.get_screenshot_as_file(\"google_movie.png\")\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nsoup = BeautifulSoup(browser.page_source, \"lxml\")\n\n# movies = soup.find_all(\"div\", attrs={\"class\":[\"ImZGtf mpg5gc\",\"Vpfmgd\"]})\nmovies = soup.find_all(\"div\", attrs={\"class\":\"Vpfmgd\"})\nprint(len(movies))\n\nfor movie in movies:\n title = movie.find(\"div\", attrs={\"class\":[\"WsMG1c nnK0zc\"]}).get_text()\n #print(title)\n\n # 할인 전 가격\n original_price = movie.find(\"span\", attrs={\"class\":\"SUZt4c djCuy\"})\n if original_price:\n original_price = original_price.get_text()\n else:\n # print(title, \" <할인되지 않은 영화 제외>\")\n continue\n\n # 할인된 가격\n price = movie.find(\"span\", attrs={\"class\":\"VfPpfd ZdBevf i5DZme\"}).get_text()\n\n # 링크\n link = movie.find(\"a\", attrs={\"class\":\"JC71ub\"})['href']\n\n # 올바른 링크 : https://play.google.com + link\n\n print(f\"제목 : {title}\")\n print(f\"할이 전 금액 : {original_price}\")\n print(f\"할이 된 금액 : {price}\")\n print(\"링크 : \",\"https://play.google.com\" + link)\n print(\"-\"*100)\n\nbrowser.quit()", "repo_name": "wonyongjae/study", "sub_path": "webscraping_basic/17_headless_chrome.py", "file_name": "17_headless_chrome.py", "file_ext": "py", "file_size_in_byte": 2235, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 3, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 3, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 7, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "38694512864", "text": "from django.shortcuts import render\nfrom twitteruser.models import TwitterUser\nfrom tweet.models import Tweet\n\n# Create your views here.\n \ndef user_details(request, user_id):\n user = TwitterUser.objects.filter(id=user_id).first()\n return render(request, \"user.html\", {\"user\": user})\n \n \ndef following(request, follow_id):\n logged_in_user = request.user\n followed = TwitterUser.objects.filter(id=follow_id).first()\n logged_in_user.following.add(followed)\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n\ndef unfollowing(request, unfollow_id):\n logged_in_user = request.user\n followed = TwitterUser.objects.filter(id=unfollow_id).first()\n logged_in_user.following.remove(followed)\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))", "repo_name": "Paulracisz/twitterclone", "sub_path": "twitteruser/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "twitteruser.models.TwitterUser.objects.filter", "line_number": 8, "usage_type": "call"}, {"api_name": "twitteruser.models.TwitterUser.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "twitteruser.models.TwitterUser", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 9, "usage_type": "call"}, {"api_name": "twitteruser.models.TwitterUser.objects.filter", "line_number": 14, "usage_type": "call"}, {"api_name": "twitteruser.models.TwitterUser.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "twitteruser.models.TwitterUser", "line_number": 14, "usage_type": "name"}, {"api_name": "twitteruser.models.TwitterUser.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "twitteruser.models.TwitterUser.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "twitteruser.models.TwitterUser", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "15933134262", "text": "import os\nimport openai\nimport functools\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\n\ndef display(func):\n @functools.wraps(func)\n def wrapper_display_stats(*args, **kwargs):\n response = func(*args, **kwargs)\n print(f\"Time Taken: {response.response_ms / 1000.0}s\")\n print(f\"Token Usage: {response.usage['total_tokens']} total, {response.usage['completion_tokens']} completion, {response.usage['prompt_tokens']} prompt\")\n print(f\"Response:\\n{response.choices[0].message['content']}\")\n return response.choices[0].message[\"content\"]\n return wrapper_display_stats\n\n\n@display\ndef get_completion_from_prompt(prompt, model=\"gpt-3.5-turbo\", temperature=0, presence_penalty=0):\n messages = [{\"role\": \"user\", \"content\": prompt}]\n response = openai.ChatCompletion.create(\n model=model,\n messages=messages,\n temperature=temperature,\n presence_penalty=presence_penalty,\n )\n return response\n\n\n@display\ndef get_completion_from_messages(messages, model=\"gpt-3.5-turbo\", temperature=0):\n response = openai.ChatCompletion.create(\n model=model,\n messages=messages,\n temperature=temperature,\n )\n return response\n", "repo_name": "Daan4/ChatGPT-experiments", "sub_path": "helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 1207, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "openai.api_key", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 4, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 8, "usage_type": "call"}, {"api_name": "openai.ChatCompletion.create", "line_number": 21, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 21, "usage_type": "attribute"}, {"api_name": "openai.ChatCompletion.create", "line_number": 32, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "10968534751", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 25 08:43:23 2018\n\n@author: amandasill\n\"\"\"\n\nimport urllib.request, re, os, ssl, collections as c, time\n\ndef waiting(seconds):\n #print(\"Waiting for\", seconds, \"seconds\")\n time.sleep(seconds)\n #print('done waiting!')\n \ndef get_links(url):\n #webpage stuff\n waiting(1)\n context = ssl._create_unverified_context()\n #politeness\n req = urllib.request.Request(url, headers={'User-Agent': 'IUB-I427-asill'})\n web_page = urllib.request.urlopen(req, context = context)\n contents = web_page.read().decode(errors=\"replace\")\n web_page.close()\n \n links = re.findall('(?<=a href=[\"]).+?(?=[\"][>]?)', contents)\n return links\n \n\ndef crawler(seed, num_pages, directory, algorithm):\n\n #deque and other variables\n frontier = c.deque([seed])\n front = {seed:1}\n counter = 1\n checked = []\n out_link_count_dict = {}\n in_link_dict = {}\n\n #the chosen algorithm determines which side of the deque is popped from\n if algorithm == \"bfs\":\n #first in first out\n url = frontier.popleft()\n# checked.append(url)\n elif algorithm == \"dfs\":\n #first in last out\n url = frontier.pop()\n\n #webpage stuff\n waiting(1)\n context = ssl._create_unverified_context()\n #politeness\n req = urllib.request.Request(url, headers={'User-Agent': 'IUB-I427-asill'})\n web_page = urllib.request.urlopen(req, context = context)\n contents = web_page.read().decode(errors=\"replace\")\n web_page.close()\n checked.append(url)\n out_link_count_dict[url] = 0\n in_link_dict[url] = []\n\n #seems necessary\n base_url = os.path.dirname(url)\n\n if not base_url:\n base_url = \"index.html\"\n\n #wikipedia is weird with what os.path.dirname considers the base url -\n #it add an extra /wiki on the end\n #I haven't found a similar problem with any other site\n if \"wikipedia\" in base_url:\n #remove the /wiki\n base_url = base_url[:-5]\n\n #create file name with the counter number followed by .html\n file_name = str(counter) + \".html\"\n\n #where to save the file\n file_path = os.path.join(directory, file_name)\n\n #save the html file\n with open(file_path, \"w\", encoding=\"utf-8\") as file_out:\n file_out.write(contents)\n\n #save and update the index.dat file\n with open(os.path.join(directory, \"index.dat\"), \"a\", encoding=\"utf-8\") as record_file:\n record_file.write(file_name + \" \" + url + \"\\n\")\n\n #find all the links on the page\n links = re.findall('(?<=a href=[\"]).+?(?=[\"][>]?)', contents)\n# for link in links:\n# if link in checked:\n# out_link_count_dict[url] += 1\n\n #list of unwanted file types\n undesired = [\".jpg\", \".pdf\", \".svg\", \".png\", \".doc\", \".docx\", \".tif\", \".gif\", \".txt\", \".rtf\", \".js\"]\n #list comp to remove unwanted file types\n only_html = [link for link in links if len([extension for extension in undesired if extension in link]) == 0]\n\n #would not work unless I grabbed only full links\n full_html = []\n\n #I can't get this to work without eliminating full links\n #I tried only adding the base url to the link if http:// or https:// isn't in the link\n #but it just added it to every link instead of only the ones where the http:// or https:// was missing\n for link in only_html:\n if \"http://\" in link or \"https://\" in link:\n# full_link = base_url + link\n# full_html.append(full_link)\n# else:\n full_html.append(link)\n\n# for item in full_html:\n# if \"#\" not in item:\n# frontier.append(item)\n\n #crawler ethics\n robots = base_url + \"/robots.txt\"\n waiting(1)\n context = ssl._create_unverified_context()\n #politeness\n req = urllib.request.Request(robots, headers={'User-Agent': 'IUB-I427-asill'})\n web_page = urllib.request.urlopen(req, context = context)\n robot_contents = web_page.read().decode(errors=\"replace\")\n web_page.close()\n\n #grab everything that says disallow\n disallow = re.findall('(?<=Disallow: ).+', robot_contents)\n\n #list of things to not look at\n dont_look = []\n\n #make all list item full links and put them in the don't look list\n for item in disallow:\n ignore = base_url + item\n dont_look.append(ignore)\n\n #add things to the frontier and the dictionary (front)\n for link in full_html:\n if link not in dont_look:\n if link not in front.keys():\n front[link] = 1\n frontier.append(link)\n else:\n front[link] += 1\n\n #look for links so long as the frontier has something it in\n while len(frontier) > 0:\n\n #break condition so crawler does not run amok\n if counter == num_pages:\n break\n\n #things are just repeated from above at this point but slight edits to variable names\n #the chosen algorithm determines which side of the deque is popped from\n if algorithm == \"bfs\":\n #first in first out\n urls = frontier.popleft()\n elif algorithm == \"dfs\":\n #first in last out\n urls = frontier.pop()\n\n\n #webpage stuff\n waiting(1)\n context = ssl._create_unverified_context()\n #politeness\n req = urllib.request.Request(urls, headers={'User-Agent': 'IUB-I427-asill'})\n web_page = urllib.request.urlopen(req, context = context)\n child_contents = web_page.read().decode(errors=\"replace\")\n web_page.close()\n counter += 1\n checked.append(urls)\n out_link_count_dict[urls] = 0\n in_link_dict[urls] = []\n \n\n #seems necessary\n child_base_url = os.path.dirname(url)\n\n\n if not child_base_url:\n child_base_url = \"index.html\"\n\n #wikipedia is weird with what os.path.dirname considers the base url -\n #it add an extra /wiki on the end\n #I haven't found a similar problem with any other site\n if \"wikipedia\" in child_base_url:\n child_base_url = child_base_url[:-5]\n\n #create file anme with the counter number followed by .html\n file_name = str(counter) + \".html\"\n\n #where to save the file\n file_path = os.path.join(directory, file_name)\n\n #save the html file\n with open(file_path, \"w\", encoding=\"utf-8\") as file_out:\n file_out.write(child_contents)\n\n #save and update the index.dat file\n with open(os.path.join(directory, \"index.dat\"), \"a\", encoding=\"utf-8\") as record_file:\n record_file.write(file_name + \" \" + urls + \"\\n\")\n\n #find all the links on the child page\n child_links = re.findall('(?<=a href=[\"]).+?(?=[\"][>]?)', child_contents)\n# for link in child_links:\n# if link in checked:\n# out_link_count_dict[urls] += 1\n\n #list of unwanted file types\n child_undesired = [\".jpg\", \".pdf\", \".svg\", \".png\", \".doc\", \".docx\", \".tif\", \".gif\", \".txt\", \".rtf\", \".js\"]\n #list comp to remove unwanted file types\n child_only_html = [link for link in child_links if len([extension for extension in child_undesired if extension in link]) == 0]\n\n #would not work unless I grabbed only full links\n child_full_html = []\n\n #same issue as with above\n for link in child_only_html:\n if \"http://\" in link or \"https://\" in link:\n child_full_html.append(link)\n\n #crawler ethics\n child_robots = child_base_url + \"/robots.txt\"\n waiting(1)\n context = ssl._create_unverified_context()\n #politeness\n req = urllib.request.Request(child_robots, headers={'User-Agent': 'IUB-I427-asill'})\n web_page = urllib.request.urlopen(req, context = context)\n child_robot_contents = web_page.read().decode(errors=\"replace\")\n web_page.close()\n\n #grab everything that says disallow\n child_disallow = re.findall('(?<=Disallow: ).+', child_robot_contents)\n\n #list of things to not look at\n child_dont_look = []\n\n #make all list item full links and put them in the don't look list\n for item in child_disallow:\n child_ignore = child_base_url + item\n dont_look.append(child_ignore)\n\n #add things to the frontier and the dictionary (front)\n for link in child_full_html:\n if link not in child_dont_look:\n if link not in front.keys():\n front[link] = 1\n frontier.append(link)\n else:\n front[link] += 1\n \n #grab url we've looked at/saved \n for url in checked:\n #get all the links from it\n links = get_links(url)\n #for each link in the list of links\n for link in links:\n #if we've look at/saved the link\n if link in checked:\n #add it to the out links count for the url\n out_link_count_dict[url] += 1\n in_link_dict[link].append(url)\n \n #for page rank \n with open(os.path.join(directory, \"out_link_count.txt\"), \"a\", encoding=\"utf-8\") as out_link_count_file:\n out_link_count_file.write(str(out_link_count_dict))\n \n with open(os.path.join(directory, \"in_link.txt\"), \"a\", encoding=\"utf-8\") as in_link_file:\n in_link_file.write(str(in_link_dict))\n \n with open(os.path.join(directory, \"checked.txt\"), \"a\", encoding=\"utf-8\") as checked_file:\n checked_file.write(str(checked))\n \n# print(checked)\n# print(len(checked))\n# print(counter)\n# print(out_link_count_dict)\n# print()\n# print(in_link_dict)\n# for pair in in_link_dict.items():\n# print(pair)\n \n\n#main\n#stack overflow gave me expanduser\n#https://stackoverflow.com/questions/3324486/finding-a-files-directory-address-on-a-mac\n#bfs_folder = os.path.expanduser(\"~/Desktop/asillProgAssn1/bfs\")\n#dfs_folder = os.path.expanduser(\"~/Desktop/asillProgAssn1/dfs\")\nbfs_folder = os.path.expanduser(\"~/Desktop/ASILL FINAL PROJECT\")\n\n\n#bfs\ncrawler(\"https://www.indiana.edu/\", 200, bfs_folder, \"bfs\")\n\n#dfs\n#crawler(\"https://en.wikipedia.org/wiki/Cassowary\", 50, dfs_folder, \"dfs\")\n", "repo_name": "amsilldn/Search-Engine", "sub_path": "asillFinalComputerFiles/asillFinalCrawler.py", "file_name": "asillFinalCrawler.py", "file_ext": "py", "file_size_in_byte": 10254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "time.sleep", "line_number": 13, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 22, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 22, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 33, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 51, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 53, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 53, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 53, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 54, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 54, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 89, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 119, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 121, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 121, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 121, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 122, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 122, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 122, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 127, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 165, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 167, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 167, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 167, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 168, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 168, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 168, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 205, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 226, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 228, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 228, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 228, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 229, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 229, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 229, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 269, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path", "line_number": 290, "usage_type": "attribute"}]} +{"seq_id": "33898347611", "text": "import numpy as np\nimport skimage\nimport skimage.color\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport string\nfrom model_experiment import model_experiment\n\nletters = list(string.ascii_uppercase)\nbaseline, image = model_experiment()\n\n\n# RGB\ndiff = skimage.color.rgb2hsv(image.img) - skimage.color.rgb2hsv(baseline.img)\ndiff = -diff # to comply with the darsia definition\n\n# Regularize\nsmooth = skimage.restoration.denoise_tv_bregman(\n diff, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True\n)\n\nsamples = [\n (slice(50, 150), slice(100, 200)),\n (slice(50, 150), slice(1600, 1700)),\n]\nconcentrations = np.array([1, 0.9])\n\n# visualise patches\nfig, ax = plt.subplots()\nax.imshow(smooth) # visualise abs colours, because relative cols are neg\nax.set_xlabel(\"horizontal pixel\")\nax.set_ylabel(\"vertical pixel\")\n\n# double check number of patches\nn = np.shape(samples)[0] # number of patches\nprint(\"number of support patches: \" + str(n))\n\n# init colour vector\ncolours = np.zeros((n, 3))\n# enumerate through all patches\nfor i, p in enumerate(samples):\n # visualise patches on image\n rect = patches.Rectangle(\n (p[1].start, p[0].start),\n p[1].stop - p[1].start,\n p[0].stop - p[0].start,\n linewidth=1,\n edgecolor=\"w\",\n facecolor=\"none\",\n )\n ax.text(p[1].start + 130, p[0].start + 100, letters[i], fontsize=15, color=\"white\")\n ax.add_patch(rect)\n\n # histo analysis\n patch = smooth[p]\n # patch = skimage.color.rgb2hsv(patch)\n vals = patch[:, :, 0]\n h_hist, bins = np.histogram(vals, bins=100, range=(-1, 1))\n plt.figure(\"h\" + letters[i])\n plt.stairs(h_hist, bins)\n\n\nfig, axes = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(6, 2))\nfig.add_subplot(111, frameon=False)\nplt.tick_params(\n labelcolor=\"none\", which=\"both\", top=False, bottom=False, left=False, right=False\n)\nplt.ylabel(\"vertical pixel\")\nplt.xlabel(\"horizontal pixel\")\n\n# SIGNAL split\n# reduction blue: B\n# hsv = skimage.color.rgb2hsv(smooth)\nhsv = np.copy(smooth)\nscalar_blue = hsv[:, :, 2]\nmask_hue = np.logical_and(\n hsv[:, :, 0] > -0.5,\n hsv[:, :, 0] < -0.4,\n)\nscalar_blue[~mask_hue] = 0\n# ax1 = fig.add_subplot(211)\naxes[0].imshow(scalar_blue, vmin=0, vmax=1)\n\n# reduction green A\n# hsv = skimage.color.rgb2hsv(smooth)\nhsv = np.copy(smooth)\nscalar_green = hsv[:, :, 2]\nmask_hue = np.logical_and(\n hsv[:, :, 0] > -0.08,\n hsv[:, :, 0] < -0.04,\n)\nscalar_green[~mask_hue] = 0\n# ax2 = fig.add_subplot(212)\naxes[1].imshow(scalar_green, vmin=0, vmax=1)\n\n\nfig, axes = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(6, 2))\nfig.add_subplot(111, frameon=False)\nplt.tick_params(\n labelcolor=\"none\", which=\"both\", top=False, bottom=False, left=False, right=False\n)\nplt.ylabel(\"vertical pixel\")\nplt.xlabel(\"horizontal pixel\")\n\naxes[0].imshow(scalar_blue + scalar_green, vmin=0, vmax=1)\n\n# scale and weight scalar signals\nweighted_signal = (\n scalar_blue / np.max(scalar_blue) * 0.9 + scalar_green / np.max(scalar_green) * 1\n)\naxes[1].imshow(weighted_signal, vmin=0, vmax=1)\n\nplt.figure()\nplt.imshow(scalar_blue + scalar_green, vmin=0, vmax=1)\nplt.xlabel(\"horizontal pixel\")\nplt.ylabel(\"vertical pixel\")\n\nplt.figure(\"cut ph val\")\nplt.plot(np.average(weighted_signal, axis=0))\nplt.xlabel(\"horizontal pixel\")\nplt.ylabel(\"signal value\")\n\n\n# plt.figure(\"cut ph val\")\n# plt.plot(np.average(weighted_signal, axis=0))\n# plt.xlabel(\"horizontal pixel\")\n# plt.ylabel(\"average concentration\")\n# plt.figure(\"cut ph val\")\n# plt.plot(np.average(scalar_blue + scalar_green, axis=0))\nplt.show()\n", "repo_name": "moritzmarquardt/CA", "sub_path": "plots_hsv_reduction.py", "file_name": "plots_hsv_reduction.py", "file_ext": "py", "file_size_in_byte": 3575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "string.ascii_uppercase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "model_experiment.model_experiment", "line_number": 10, "usage_type": "call"}, {"api_name": "skimage.color.rgb2hsv", "line_number": 14, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 14, "usage_type": "attribute"}, {"api_name": "skimage.restoration.denoise_tv_bregman", "line_number": 18, "usage_type": "call"}, {"api_name": "skimage.restoration", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 58, "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.stairs", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "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.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "numpy.average", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}]} +{"seq_id": "21333201750", "text": "\nimport os, re, io\nfrom xml.etree import ElementTree\n\nfrom dap_core import common\nimport urllib.request\n\nBASE_URL = \"https://repo1.maven.org/maven2/\"\n\nclass MavenCentralInstallerOld:\n # https://search.maven.org/remotecontent?filepath=\n def __init__(self, base_url=BASE_URL, lib_dir=\"lib\"):\n self.base_url = base_url\n self.lib_dir = lib_dir\n self.latest_versions_cache = {}\n return\n\n def install(self, name, version, details):\n if not self.has_already_been_downloaded(details[\"group_id\"], name, version, \"jar\"):\n self.install_file(name, version, details, \"jar\")\n self.install_file(name, version, details, \"pom\")\n self.install_transitive_dependencies(name, version, details, \"pom\")\n\n def install_transitive_dependencies(self, name, version, details, extension):\n if self.local_pom_exists(details[\"group_id\"], name, version):\n namespaces = {'xmlns': 'http://maven.apache.org/POM/4.0.0'}\n tree = ElementTree.parse(self.local_location(details[\"group_id\"], name, version, \"pom\"))\n root = tree.getroot()\n\n deps = root.findall(\"./xmlns:dependencies/xmlns:dependency\", namespaces=namespaces)\n for d in deps:\n version = \"latest\"\n groupId = d.find(\"xmlns:groupId\", namespaces=namespaces).text\n artifactId = d.find(\"xmlns:artifactId\", namespaces=namespaces).text\n version_elem = d.find(\"xmlns:version\", namespaces=namespaces)\n if version_elem is not None:\n if self.value_references_variable(version_elem.text):\n version_var_name = re.findall(\"\\$\\{(.*?)\\}\", version_elem.text)[0]\n common.print_verbose(\"Looking for property \" + version_var_name)\n props = root.findall(\"./xmlns:properties\", namespaces=namespaces)\n for el in props[0].iter():\n if el.tag == \"{http://maven.apache.org/POM/4.0.0}\" + version_var_name:\n version = el.text\n common.print_verbose(\"Found property \" + version_var_name + \" = \" + version)\n else:\n version = version_elem.text\n self.install(artifactId, version, {\"group_id\": groupId})\n\n def value_references_variable(self, value):\n return value.startswith(\"${\")\n\n def install_file(self, name, version, details, extension):\n remote_loc = self.remote_location(details[\"group_id\"], name, version, extension)\n local_lib_dir = self.local_lib_directory(details[\"group_id\"], name, version)\n local_loc = self.local_location(details[\"group_id\"], name, version, extension)\n if not self.has_already_been_downloaded(details[\"group_id\"], name, version, extension):\n common.print_info(\"Downloading \" + remote_loc + \" to \" + local_loc)\n common.run_command([\"mkdir\", \"-p\", local_lib_dir])\n self.fetch(remote_loc, local_loc)\n else:\n common.print_verbose(\"Dependency found at \" + local_loc)\n return 0, \"\"\n\n def remote_location(self, group_id, artifact_id, version, file_extension):\n group_id_with_slashes = group_id.replace(\".\", \"/\")\n\n if version != \"latest\":\n return self.base_url + \"/\".join(\n [group_id_with_slashes, artifact_id, version, artifact_id]) + \"-\" + version + \".\" + file_extension\n else:\n metadata_file = self.metadata_local_location(group_id, artifact_id)\n # TODO: If metadata file is present, don't fetch again\n metadata_url = self.base_url + \"/\".join([group_id_with_slashes, artifact_id]) + \"/maven-metadata.xml\"\n self.fetch(metadata_url, metadata_file)\n # TODO: Parse metadata file and return the latest version.\n namespaces = {'xmlns': ''}\n tree = ElementTree.parse(metadata_file)\n root = tree.getroot()\n latest_version = root.find(\".//latest\").text\n self.add_latest_version_to_cache(group_id, artifact_id, version, file_extension, latest_version)\n return self.base_url + \"/\".join(\n [group_id_with_slashes, artifact_id, latest_version, artifact_id]) + \"-\" + latest_version + \".\" + file_extension\n\n def add_latest_version_to_cache(self, group_id, artifact_id, version, file_extension, latest_version):\n self.latest_versions_cache[\"_\".join([group_id, artifact_id, version, file_extension])] = latest_version\n\n def get_latest_version_from_cache(self, group_id, artifact_id, version, file_extension):\n key = \"_\".join([group_id, artifact_id, version, file_extension])\n if key in self.latest_versions_cache:\n return self.latest_versions_cache[key]\n else:\n return \"latest\"\n\n def metadata_local_location(self, group_id, artifact_id):\n group_id_with_slashes = group_id.replace(\".\", \"/\")\n return \"lib/java/\" + \"/\".join([group_id_with_slashes, artifact_id]) + \\\n \"/\" + \"maven-metadata.xml\"\n\n def local_location(self, group_id, artifact_id, version, file_extension):\n v = version\n if version == \"latest\":\n v = self.get_latest_version_from_cache(group_id, artifact_id, version, file_extension)\n\n return self.local_lib_directory(group_id, artifact_id, v) + \\\n \"/\" + artifact_id + \"-\" + v + \".\" + file_extension\n\n def local_lib_directory(self, group_id, artifact_id, version):\n group_id_with_slashes = group_id.replace(\".\", \"/\")\n return \"lib/java/\" + \"/\".join([group_id_with_slashes, artifact_id, version])\n\n def fetch(self, remote_loc, local_loc):\n urllib.request.urlretrieve(remote_loc, local_loc)\n\n def has_already_been_downloaded(self, group_id, artifact_id, version, file_extension):\n return os.path.exists(self.local_location(group_id, artifact_id, version, file_extension))\n\n def local_pom_exists(self, group_id, artifact_id, version):\n return os.path.exists(self.local_location(group_id, artifact_id, version, \"pom\"))\n\n\nclass MavenCentralInstaller:\n def __init__(self, base_url=BASE_URL, lib_dir=\"lib\"):\n self.base_url = base_url\n self.lib_dir = lib_dir\n self.latest_versions_cache = {}\n return\n\n def install(self, artifact_id, version, details):\n group_id = details[\"group_id\"]\n jar_dependency = JarDependency(group_id, artifact_id, version)\n\n jar_dependency.install(self.base_url)\n\n\nclass MavenCentralArtifact:\n def __init__(self, group_id, artifact_id, version):\n self.group_id = group_id\n self.artifact_id = artifact_id\n self.version = version\n self.latest_versions_cache = {}\n\n def latest_version_from_metadata(self):\n latest_version_from_cache = self.get_latest_version_from_cache()\n if latest_version_from_cache != \"latest\":\n return latest_version_from_cache\n else:\n tree = ElementTree.parse(self.metadata_file())\n root = tree.getroot()\n latest_version = root.find(\".//latest\").text\n self.add_latest_version_to_cache(latest_version)\n\n return latest_version\n\n def metadata_file(self):\n metadata_file = self.metadata_local_location(self.group_id, self.artifact_id)\n if os.path.exists(metadata_file):\n return metadata_file\n else:\n group_id_with_slashes = self.group_id.replace(\".\", \"/\")\n metadata_url = self.base_url + \"/\".join([group_id_with_slashes, self.artifact_id]) + \"/maven-metadata.xml\"\n self.fetch(metadata_url, metadata_file)\n return metadata_file\n\n def relative_remote_location(self):\n if not self.file_extension:\n common.print_error(\"File extension is not set for \" + self.artifact_id)\n self.file_extension.length\n\n group_id_with_slashes = self.group_id.replace(\".\", \"/\")\n\n return \"/\".join(\n [group_id_with_slashes, self.artifact_id, self.version, self.artifact_id]\n ) + \"-\" + self.version + \".\" + self.file_extension\n\n def local_location(self):\n return self.local_lib_directory() + \\\n \"/\" + self.artifact_id + \"-\" + self.version + \".\" + self.file_extension\n\n def local_lib_directory(self):\n group_id_with_slashes = self.group_id.replace(\".\", \"/\")\n return \"lib/java/\" + \"/\".join([group_id_with_slashes, self.artifact_id, self.version])\n\n def add_latest_version_to_cache(self, latest_version):\n self.latest_versions_cache[\"_\".join([self.group_id, self.artifact_id, self.specified_version, self.file_extension])] = latest_version\n\n def get_latest_version_from_cache(self):\n key = \"_\".join([self.group_id, self.artifact_id, self.specified_version, self.file_extension])\n if key in self.latest_versions_cache:\n return self.latest_versions_cache[key]\n else:\n return \"latest\"\n\n def has_been_installed(self):\n installed = os.path.exists(self.local_location())\n return installed\n\n def install(self, base_url):\n if not self.has_been_installed():\n common.run_command([\"mkdir\", \"-p\", self.local_lib_directory()])\n fetch(base_url + self.relative_remote_location(), self.local_location(), self.error_callback)\n\n\nclass JarDependency:\n def __init__(self, group_id, artifact_id, version):\n self.group_id = group_id\n self.artifact_id = artifact_id\n self.specified_version = version\n self.metadata = JarDependencyMetadata(group_id, artifact_id)\n self.version = self.determine_version()\n self.pom = Pom(group_id, artifact_id, self.version)\n self.jar = Jar(group_id, artifact_id, self.version)\n\n def determine_version(self):\n if self.specified_version != \"latest\":\n return self.specified_version\n return self.metadata.latest_version()\n\n def install(self, base_url):\n if not self.has_been_installed():\n common.print_info(\"Downloading \" + self.group_id + \"/\" + self.artifact_id + \" v\" + self.version)\n self.pom.install(base_url)\n self.jar.install(base_url)\n\n for d in self.dependencies():\n d.install(base_url)\n\n def has_been_installed(self):\n common.print_verbose(\"Checking if \" + self.group_id + \"/\" + self.artifact_id + \" is installed.\")\n common.print_verbose(\" v\" + self.version)\n installed = self.pom.has_been_installed() and self.jar.has_been_installed()\n if installed:\n common.print_verbose(self.group_id + \"/\" + self.artifact_id + \" v\" + self.version + \" already installed.\")\n else:\n common.print_verbose(self.group_id + \"/\" + self.artifact_id + \" v\" + self.version + \" not installed.\")\n return installed\n\n def dependencies(self):\n jar_deps = []\n namespaces = {'xmlns': 'http://maven.apache.org/POM/4.0.0'}\n if self.pom.pom_file() is None:\n return jar_deps\n\n root = self.pom.pom_tree().getroot()\n\n deps = root.findall(\"./xmlns:dependencies/xmlns:dependency\", namespaces=namespaces)\n for d in deps:\n groupId = d.find(\"xmlns:groupId\", namespaces=namespaces).text\n if groupId.startswith(\"${\"):\n groupId = self.pom.resolve_property(groupId)\n\n artifactId = d.find(\"xmlns:artifactId\", namespaces=namespaces).text\n version = \"latest\"\n\n version_elem = d.find(\"xmlns:version\", namespaces=namespaces)\n if version_elem is not None:\n version = version_elem.text\n\n if version.startswith(\"${\"):\n version = self.pom.resolve_property(version)\n\n scope_elem = d.find(\"xmlns:scope\", namespaces=namespaces)\n if scope_elem is not None:\n scope = scope_elem.text\n else:\n scope = \"unspecified\"\n\n common.print_verbose(self.group_id + \"/\" + self.artifact_id + \" v\" + self.version + \" depends on \" + groupId + \"/\" + artifactId + \" version \" + version + \" in scope \" + scope)\n if scope in [\"unspecified\", \"compile\", \"runtime\"]:\n dep = JarDependency(groupId, artifactId, version)\n jar_deps.append(dep)\n \n return jar_deps\n\n def __eq__(self, other):\n \"\"\"Overrides the default implementation\"\"\"\n if isinstance(self, other.__class__):\n return self.group_id == other.group_id and self.artifact_id == other.artifact_id and self.version == other.version\n\n return NotImplemented\n\n def __hash__(self):\n \"\"\"Overrides the default implementation\"\"\"\n return hash(tuple(sorted([self.group_id, self.artifact_id, self.version])))\n\n def __repr__(self):\n return \"JarDependency[\" + self.group_id + \",\" + self.artifact_id + \",\" + self.version + \"]\"\n\n\nclass JarDependencyMetadata:\n\n def __init__(self, group_id, artifact_id):\n self.group_id = group_id\n self.artifact_id = artifact_id\n self.latest_versions_cache = {}\n self.base_url = BASE_URL\n\n def latest_version(self):\n latest_version_from_cache = self.get_latest_version_from_cache()\n if latest_version_from_cache != \"latest\":\n return latest_version_from_cache\n else:\n tree = ElementTree.parse(self.metadata_file())\n root = tree.getroot()\n latest_version = \"cannot find\"\n latest_version_element = root.find(\".//latest\")\n version_element = root.find(\".//version\")\n if latest_version_element is not None:\n latest_version = latest_version_element.text\n elif version_element is not None:\n latest_version = version_element.text\n\n self.add_latest_version_to_cache(latest_version)\n common.print_verbose(\"Determined version of \" + self.group_id + \"/\" + self.artifact_id + \" to be \" + latest_version + \" from metadata\")\n return latest_version\n\n def metadata_file(self):\n metadata_file = self.metadata_local_location()\n if os.path.exists(metadata_file):\n return metadata_file\n else:\n common.run_command([\"mkdir\", \"-p\", self.metadata_local_directory()])\n fetch(self.metadata_remote_location(), self.metadata_local_location())\n return metadata_file\n\n def metadata_local_directory(self):\n group_id_with_slashes = self.group_id.replace(\".\", \"/\")\n return \"lib/java/\" + \"/\".join([group_id_with_slashes, self.artifact_id]) + \"/\"\n\n def metadata_local_location(self):\n return self.metadata_local_directory() + \"maven-metadata.xml\"\n\n def metadata_remote_location(self):\n group_id_with_slashes = self.group_id.replace(\".\", \"/\")\n return self.base_url + \"/\".join([group_id_with_slashes, self.artifact_id]) + \"/maven-metadata.xml\"\n\n def add_latest_version_to_cache(self, latest_version):\n self.latest_versions_cache[\"_\".join([self.group_id, self.artifact_id, \"latest\"])] = latest_version\n\n def get_latest_version_from_cache(self):\n key = \"_\".join([self.group_id, self.artifact_id, \"latest\"])\n if key in self.latest_versions_cache:\n return self.latest_versions_cache[key]\n else:\n return \"latest\"\n\n\nclass Pom(MavenCentralArtifact):\n\n def __init__(self, group_id, artifact_id, version):\n super().__init__(group_id, artifact_id, version)\n self.file_extension = \"pom\"\n self.namespaces = {'xmlns': 'http://maven.apache.org/POM/4.0.0'}\n self.tree = \"\"\n self.error_callback = common.print_warning\n\n def pom_file(self):\n if os.path.exists(self.local_location()):\n return self.local_location()\n else:\n common.print_warning(\"POM for \" + self.group_id + \"/\" + self.artifact_id + \" v\" + self.version + \" was not found locally. Assuming it has no dependencies.\")\n return None\n\n def resolve_property(self, property_name):\n prop_name = property_name.strip(\"${}\")\n prop_value = \"dunno\"\n root = self.pom_tree().getroot()\n\n common.print_verbose(\"Looking for property \" + prop_name + \" in \" + self.local_location())\n\n if prop_name.startswith(\"${project.\") or prop_name.startswith(\"project.\"):\n common.print_verbose(\"Looking for project property \" + prop_name)\n project_property = prop_name.split(\".\")[1].rstrip(\"}\")\n prop_value = self.project_property(project_property)\n common.print_verbose(\"Project property \" + prop_name + \" = \" + prop_value)\n return prop_value\n else:\n props = root.findall(\"./xmlns:properties\", namespaces=self.namespaces)\n if len(props) > 0:\n for el in props[0].iter():\n if el.tag == \"{http://maven.apache.org/POM/4.0.0}\" + prop_name:\n prop_value = el.text\n common.print_verbose(\"Found property \" + prop_name + \" = \" + prop_value)\n if prop_value.startswith(\"${\"):\n return self.resolve_property(prop_value)\n else:\n return prop_value\n common.print_verbose(\"Property \" + prop_name + \" is not defined in pom. Looking at parent.\")\n parent_pom = self.parent_pom()\n if parent_pom:\n prop_value = parent_pom.resolve_property(prop_name)\n common.print_verbose(\"Parent says \" + prop_name + \" is \" + prop_value)\n else:\n common.print_verbose(\"No parent found?!\")\n return prop_value\n\n def pom_tree(self):\n if not self.tree:\n self.tree = ElementTree.parse(self.pom_file())\n return self.tree\n\n def project_property(self, prop_name):\n root = self.pom_tree().getroot()\n # find the project version - need to figure out the xpath\n props = root.findall(\".\", namespaces=self.namespaces)\n if len(props) > 0:\n for el in props[0].iter():\n if el.tag == \"{http://maven.apache.org/POM/4.0.0}\" + prop_name:\n prop_value = el.text\n common.print_verbose(\"Found property \" + prop_name + \" = \" + prop_value)\n return prop_value\n\n return \"no clue\"\n return \"don't know\"\n\n def parent_pom(self):\n root = self.pom_tree().getroot()\n parents = root.findall(\"./xmlns:parent\", namespaces=self.namespaces)\n\n if not parents:\n common.print_verbose(\"No parent found in \" + self.local_location())\n return None\n\n parent_group_id = \"\"\n parent_artifact_id = \"\"\n parent_version = \"\"\n\n for el in parents[0].iter():\n if el.tag == \"{http://maven.apache.org/POM/4.0.0}\" + \"groupId\":\n parent_group_id = el.text\n elif el.tag == \"{http://maven.apache.org/POM/4.0.0}\" + \"artifactId\":\n parent_artifact_id = el.text\n elif el.tag == \"{http://maven.apache.org/POM/4.0.0}\" + \"version\":\n parent_version = el.text\n\n if parent_group_id:\n parent = Pom(parent_group_id, parent_artifact_id, parent_version)\n parent.install(\"https://repo1.maven.org/maven2/\")\n return parent\n else:\n return None\n\n\nclass Jar(MavenCentralArtifact):\n\n def __init__(self, group_id, artifact_id, version):\n super().__init__(group_id, artifact_id, version)\n self.file_extension = \"jar\"\n self.error_callback = common.exit_with_error_message\n\n\ndef fetch(remote_location, local_location, error_callback=common.exit_with_error_message):\n try:\n common.print_verbose(\"Fetching \" + remote_location + \" to \" + local_location)\n urllib.request.urlretrieve(remote_location, local_location)\n except urllib.error.HTTPError as err:\n error_callback(str(err.code) + \" - Could not retrieve \" + remote_location)", "repo_name": "power-daps/power-daps", "sub_path": "apps/dap_core/src/dap_core/jar_dependency_installer.py", "file_name": "jar_dependency_installer.py", "file_ext": "py", "file_size_in_byte": 18346, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 27, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 27, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 38, "usage_type": "call"}, {"api_name": "dap_core.common.print_verbose", "line_number": 39, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 39, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 44, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 44, "usage_type": "name"}, {"api_name": "dap_core.common.print_info", "line_number": 57, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 57, "usage_type": "name"}, {"api_name": "dap_core.common.run_command", "line_number": 58, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 58, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 61, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 61, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 77, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 77, "usage_type": "name"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 112, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 112, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 112, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 147, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 147, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "dap_core.common.print_error", "line_number": 166, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 166, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "dap_core.common.run_command", "line_number": 199, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 199, "usage_type": "name"}, {"api_name": "dap_core.common.print_info", "line_number": 220, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 220, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 228, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 228, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 229, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 229, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 232, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 232, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 234, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 234, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 267, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 267, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 302, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 302, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 313, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 313, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "dap_core.common.run_command", "line_number": 321, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 321, "usage_type": "name"}, {"api_name": "dap_core.common.print_warning", "line_number": 354, "usage_type": "attribute"}, {"api_name": "dap_core.common", "line_number": 354, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path", "line_number": 357, "usage_type": "attribute"}, {"api_name": "dap_core.common.print_warning", "line_number": 360, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 360, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 368, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 368, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 371, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 371, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 374, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 374, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 382, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 382, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 387, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 387, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 391, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 391, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 393, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 393, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 398, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 398, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 409, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 409, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 420, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 420, "usage_type": "name"}, {"api_name": "dap_core.common.exit_with_error_message", "line_number": 448, "usage_type": "attribute"}, {"api_name": "dap_core.common", "line_number": 448, "usage_type": "name"}, {"api_name": "dap_core.common.exit_with_error_message", "line_number": 451, "usage_type": "attribute"}, {"api_name": "dap_core.common", "line_number": 451, "usage_type": "name"}, {"api_name": "dap_core.common.print_verbose", "line_number": 453, "usage_type": "call"}, {"api_name": "dap_core.common", "line_number": 453, "usage_type": "name"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 454, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 454, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 454, "usage_type": "name"}, {"api_name": "urllib.request.error", "line_number": 455, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 455, "usage_type": "name"}]} +{"seq_id": "113752502", "text": "\"\"\"scripts/optimize_rasters.py\n\nConvert some raster files to optimized formats for use with moja flint.\n\"\"\"\n\nfrom typing import (Sequence, Iterator, Union, Dict, Tuple, TypeVar)\nimport os\nimport math\nimport warnings\nimport itertools\nimport contextlib\nimport tempfile\nimport logging\nimport json\n\nfrom pathlib import Path\n\nimport click\nimport tqdm\n\nimport rasterio\nfrom rasterio.shutil import copy\nfrom rasterio.io import DatasetReader, MemoryFile\nfrom rasterio.vrt import WarpedVRT\nfrom rasterio.enums import Resampling\nfrom rasterio.env import GDALVersion\n\nfrom affine import Affine\n\nimport flintdata.flinttile\n\nfrom flintdata.scripts.click_types import GlobbityGlob, PathlibPath\n\nlogger = logging.getLogger(__name__)\n\nIN_MEMORY_THRESHOLD = 16000 * 16000\n\nCACHEMAX = 1024 * 1024 * 512 # 512 MB\n\nGDAL_CONFIG = {\n 'GDAL_TIFF_INTERNAL_MASK': True,\n 'GDAL_TIFF_OVR_BLOCKSIZE': 400,\n 'GDAL_CACHEMAX': CACHEMAX,\n 'GDAL_SWATH_SIZE': 2 * CACHEMAX,\n 'GDAL_DISABLE_READDIR_ON_OPEN': 'EMPTY_DIR'\n}\n\nCOG_PROFILE = {\n 'count': 1,\n 'driver': 'GTiff',\n 'interleave': 'pixel',\n 'tiled': True,\n 'blockxsize': 400,\n 'blockysize': 400,\n 'photometric': 'MINISBLACK',\n 'ZLEVEL': 1,\n 'ZSTD_LEVEL': 9,\n 'BIGTIFF': 'IF_SAFER'\n}\n\nFLINT_TILE_PROFILE = {\n 'count': 1,\n 'driver': 'GTiff',\n 'interleave': 'pixel',\n 'tiled': True,\n 'blockxsize': 400,\n 'blockysize': 400,\n 'width': 4000,\n 'height': 4000,\n 'BIGTIFF': 'IF_SAFER'\n}\n\nRESAMPLING_METHODS = {\n 'average': Resampling.average,\n 'nearest': Resampling.nearest,\n 'bilinear': Resampling.bilinear,\n 'cubic': Resampling.cubic\n}\n\nNumber = TypeVar('Number', int, float)\n\n\n#@staticmethod\ndef _calculate_default_transform(src_crs: Union[Dict[str, str], str],\n _TARGET_CRS: Union[Dict[str, str], str],\n width: int,\n height: int,\n *bounds: Number) -> Tuple[Affine, int, int]:\n \"\"\"A more stable version of GDAL's default transform.\n\n Ensures that the number of pixels along the image's shortest diagonal remains\n the same in both CRS, without enforcing square pixels.\n\n Bounds are in order (west, south, east, north).\n \"\"\"\n from rasterio import warp, transform\n\n if len(bounds) != 4:\n raise ValueError('Bounds must contain 4 values')\n\n # transform image corners to target CRS\n dst_corner_sw, dst_corner_nw, dst_corner_se, dst_corner_ne = (\n list(zip(*warp.transform(\n src_crs, _TARGET_CRS,\n [bounds[0], bounds[0], bounds[2], bounds[2]],\n [bounds[1], bounds[3], bounds[1], bounds[3]]\n )))\n )\n\n # determine inner bounding box of corners in target CRS\n dst_corner_bounds = [\n max(dst_corner_sw[0], dst_corner_nw[0]),\n max(dst_corner_sw[1], dst_corner_se[1]),\n min(dst_corner_se[0], dst_corner_ne[0]),\n min(dst_corner_nw[1], dst_corner_ne[1])\n ]\n\n # compute target resolution\n dst_corner_transform = transform.from_bounds(*dst_corner_bounds, width=width, height=height)\n target_res = (dst_corner_transform.a, dst_corner_transform.e)\n\n # get transform spanning whole bounds (not just projected corners)\n dst_bounds = warp.transform_bounds(src_crs, _TARGET_CRS, *bounds)\n dst_width = math.ceil((dst_bounds[2] - dst_bounds[0]) / target_res[0])\n dst_height = math.ceil((dst_bounds[1] - dst_bounds[3]) / target_res[1])\n dst_transform = transform.from_bounds(*dst_bounds, width=dst_width, height=dst_height)\n\n return dst_transform, dst_width, dst_height\n\n\ndef _prefered_compression_method() -> str:\n if not GDALVersion.runtime().at_least('2.3'):\n return 'DEFLATE'\n\n # check if we can use ZSTD (fails silently for GDAL < 2.3)\n dummy_profile = dict(driver='GTiff', height=1, width=1, count=1, dtype='uint8')\n try:\n with MemoryFile() as memfile, memfile.open(compress='ZSTD', **dummy_profile):\n pass\n except Exception as exc:\n if 'missing codec' not in str(exc):\n raise\n else:\n return 'ZSTD'\n\n return 'DEFLATE'\n\n\ndef _get_vrt(src: DatasetReader, rs_method: int) -> WarpedVRT:\n target_crs = 'epsg:3857'\n vrt_transform, vrt_width, vrt_height = _calculate_default_transform(\n src.crs, target_crs, src.width, src.height, *src.bounds\n )\n vrt = WarpedVRT(\n src, crs=target_crs, resampling=rs_method, transform=vrt_transform,\n width=vrt_width, height=vrt_height, src_nodata=0, dst_nodata=255 )\n return vrt\n\n\n_TARGET_CRS: str = 'epsg:4326'\n\ndef _translate_type(dtype):\n return {\n rasterio.uint8: 'UInt8',\n rasterio.uint16: 'UInt16',\n rasterio.uint32: 'UInt32',\n rasterio.int16: 'Int16',\n rasterio.int32: 'Int32',\n rasterio.float32: 'Float32',\n rasterio.float64: 'Float64'\n }[dtype]\n\ndef _writeLayerInfo(src, layerName, outFld, nLayers=None):\n info = _info(src)\n layerInfo = {\n 'layer_type': 'GridLayer',\n 'layer_prefix': layerName,\n \"layer_data\": _translate_type(info['dtype']),\n 'tileLatSize': 1.0,\n 'tileLonSize': 1.0,\n 'blockLatSize': 0.1,\n 'blockLonSize': 0.1,\n 'cellLatSize': abs(info['transform'].d),\n 'cellLonSize': info['transform'].a,\n 'coordinateSystem': info['crs'],\n 'cornerCoordinates': info['bounds'],\n 'size': info[\"shape\"]\n }\n if 'nodata' in info:\n layerInfo['nodata'] = info['nodata']\n if (nLayers):\n layerInfo['nLayers'] = nLayers\n layerInfo['layer_type'] = 'StackLayer'\n\n with open(os.path.join(outFld, layerName + '.json'), 'w') as f:\n json.dump(layerInfo, f, ensure_ascii=False, sort_keys=True, indent=2)\n\n\ndef _info(src):\n info = dict(src.profile)\n info['shape'] = (info['height'], info['width'])\n info['bounds'] = src.bounds\n\n if src.crs:\n epsg = src.crs.to_epsg()\n if epsg:\n info['crs'] = 'EPSG:{}'.format(epsg)\n else:\n info['crs'] = src.crs.to_string()\n else:\n info['crs'] = None\n\n info['res'] = src.res\n info['colorinterp'] = [ci.name for ci in src.colorinterp]\n info['units'] = [units or None for units in src.units]\n info['descriptions'] = src.descriptions\n info['indexes'] = src.indexes\n info['mask_flags'] = [[\n flag.name for flag in flags] for flags in src.mask_flag_enums]\n\n if src.crs:\n info['lnglat'] = src.lnglat()\n\n gcps, gcps_crs = src.gcps\n\n if gcps:\n info['gcps'] = {'points': [p.asdict() for p in gcps]}\n if gcps_crs:\n epsg = gcps_crs.to_epsg()\n if epsg:\n info['gcps']['crs'] = 'EPSG:{}'.format(epsg)\n else:\n info['gcps']['crs'] = src.crs.to_string()\n else:\n info['gcps']['crs'] = None\n return info\n\n@contextlib.contextmanager\ndef _named_tempfile(basedir: Union[str, Path]) -> Iterator[str]:\n fileobj = tempfile.NamedTemporaryFile(dir=str(basedir), suffix='.tif')\n fileobj.close()\n try:\n yield fileobj.name\n finally:\n os.remove(fileobj.name)\n\n\nTemporaryRasterFile = _named_tempfile\n\n\n@click.command(\n 'optimize-rasters',\n short_help='Optimize a collection of raster files for use with moja Flint.'\n)\n@click.argument('raster-files', nargs=-1, type=GlobbityGlob(), required=True)\n@click.option(\n '-o', '--output-folder', required=True,\n type=PathlibPath(file_okay=False, writable=True),\n help='Output folder for optimized rasters. Subdirectories will be flattened.'\n)\n@click.option(\n '--overwrite', is_flag=True, default=False, help='Force overwrite of existing files'\n)\n@click.option(\n '--resampling-method', type=click.Choice(RESAMPLING_METHODS.keys()),\n default='nearest', help='Resampling method for overviews', show_default=True\n)\n@click.option(\n '--in-memory/--no-in-memory', default=None,\n help='Force processing raster in memory / not in memory [default: process in memory '\n f'if smaller than {IN_MEMORY_THRESHOLD // 1e6:.0f} million pixels]'\n)\n@click.option(\n '--compression', default='auto', type=click.Choice(['auto', 'deflate', 'lzw', 'zstd', 'none']),\n help='Compression algorithm to use [default: auto (ZSTD if available, DEFLATE otherwise)]'\n)\n@click.option(\n '-q', '--quiet', is_flag=True, default=False, show_default=True,\n help='Suppress all output to stdout'\n)\ndef optimize_rasters(raster_files: Sequence[Sequence[Path]],\n output_folder: Path,\n overwrite: bool = False,\n resampling_method: str = 'nearest',\n in_memory: bool = None,\n compression: str = 'auto',\n quiet: bool = False) -> None:\n \"\"\"Optimize a collection of raster files for use with moja Flint.\n\n First argument is a list of input files or glob patterns.\n\n Example:\n\n $ flintdata optimize-rasters rasters/*.tif -o optimized/\n\n Note that all rasters may only contain a single band.\n \"\"\"\n from rasterio import transform, windows\n\n raster_files_flat = sorted(set(itertools.chain.from_iterable(raster_files)))\n\n if not raster_files_flat:\n click.echo('No files given')\n return\n\n rs_method = RESAMPLING_METHODS[resampling_method]\n\n if compression == 'auto':\n compression = _prefered_compression_method()\n\n total_pixels = 0\n for f in raster_files_flat:\n if not f.is_file():\n raise click.BadParameter(f'Input raster {f!s} is not a file')\n\n with rasterio.open(str(f), 'r') as src:\n if src.count > 1 and not quiet:\n click.echo(\n f'Warning: raster file {f!s} has more than one band. '\n 'Only the first one will be used.', err=True\n )\n total_pixels += src.height * src.width\n\n output_folder.mkdir(exist_ok=True)\n\n if not quiet:\n # insert newline for nicer progress bar style\n click.echo('')\n\n sub_pbar_args = dict(\n disable=quiet,\n leave=False,\n bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}'\n )\n\n with contextlib.ExitStack() as outer_env:\n pbar = outer_env.enter_context(tqdm.tqdm(\n total=total_pixels, smoothing=0, disable=quiet,\n bar_format='{l_bar}{bar}| [{elapsed}<{remaining}{postfix}]',\n desc='Optimizing rasters'\n ))\n outer_env.enter_context(rasterio.Env(**GDAL_CONFIG))\n\n for input_file in raster_files_flat:\n if len(input_file.name) > 30:\n short_name = input_file.name[:13] + '...' + input_file.name[-13:]\n else:\n short_name = input_file.name\n\n pbar.set_postfix(file=short_name)\n\n\n path = str(input_file)\n raster_name = os.path.splitext(os.path.basename(input_file.name))[0]\n\n with contextlib.ExitStack() as file_env, warnings.catch_warnings():\n try:\n src = file_env.enter_context(rasterio.open(path))\n except OSError:\n raise IOError('error while reading file {}'.format(path))\n\n\n raster_folder = output_folder / raster_name\n raster_folder.mkdir(exist_ok=True)\n\n _writeLayerInfo(src, raster_name, raster_folder)\n\n # compute suggested resolution and bounds in target CRS\n dst_transform, _, _ = _calculate_default_transform(\n src.crs, _TARGET_CRS, src.width, src.height, *src.bounds\n )\n dst_res = (abs(dst_transform.a), abs(dst_transform.e))\n\n west, south, east, north = src.bounds\n for tile in flintdata.flinttile.tiles(west, south, east, north):\n with contextlib.ExitStack() as es, warnings.catch_warnings():\n warnings.filterwarnings('ignore', message='invalid value encountered.*')\n\n bounds = flintdata.flinttile.bounds(tile)\n\n # pad tile bounds to prevent interpolation artefacts\n num_pad_pixels = 2\n\n # compute tile VRT shape and transform\n dst_width = max(1, round((bounds[2] - bounds[0]) / dst_res[0]))\n dst_height = max(1, round((bounds[3] - bounds[1]) / dst_res[1]))\n vrt_transform = (\n transform.from_bounds(*bounds, width=dst_width, height=dst_height)\n * Affine.translation(-num_pad_pixels, -num_pad_pixels)\n )\n vrt_height, vrt_width = dst_height + 2 * num_pad_pixels, dst_width + 2 * num_pad_pixels\n\n # remove padding in output\n out_window = windows.Window(\n col_off=num_pad_pixels, row_off=num_pad_pixels, width=dst_width, height=dst_height\n )\n\n # construct VRT\n vrt = es.enter_context(\n WarpedVRT(\n src, crs=_TARGET_CRS, resampling=rs_method,\n transform=vrt_transform, width=vrt_width, height=vrt_height\n )\n )\n profile = vrt.profile.copy()\n profile.update(FLINT_TILE_PROFILE)\n\n in_memory = vrt.width * vrt.height < IN_MEMORY_THRESHOLD\n\n if in_memory:\n mem_file = es.enter_context(MemoryFile())\n dst = es.enter_context(mem_file.open(**profile))\n else:\n temp_raster = es.enter_context(TemporaryRasterFile(basedir=raster_folder))\n dst = es.enter_context(rasterio.open(temp_raster, 'w', **profile))\n\n dst_transform = (\n transform.from_bounds(*bounds, width=dst_width, height=dst_height)\n * Affine.translation(+num_pad_pixels, +num_pad_pixels)\n )\n\n vrt_dst = es.enter_context(\n WarpedVRT(\n dst, crs=_TARGET_CRS, resampling=rs_method,\n transform=dst_transform, width=vrt_width, height=vrt_height\n )\n )\n\n blockedFileName = '{0}_{1}.blk'.format(raster_name, flintdata.flinttile.name(tile))\n\n output_file = raster_folder / blockedFileName\n\n if not overwrite and output_file.is_file():\n raise click.BadParameter(\n f'Output file {output_file!s} exists (use --overwrite to ignore)'\n )\n\n blockedFile = es.enter_context(open(output_file, \"wb\"))\n\n # iterate over blocks\n block_windows = list(dst.block_windows(1))\n for _, w in tqdm.tqdm(block_windows, desc='Processing blocks', **sub_pbar_args):\n out_window = windows.Window(col_off=w.col_off+num_pad_pixels, row_off=w.row_off+num_pad_pixels, width=w.width, height=w.height)\n block_data = vrt.read(window=out_window, indexes=[1])\n dst.write(block_data, window=w)\n data = bytes(block_data) # python 3.n\n blockedFile.write(data)\n output_file = raster_folder / '{0}_{1}.tif'.format(raster_name, flintdata.flinttile.index(tile))\n copy(\n dst, str(output_file), copy_src_overviews=True,\n compress=compression, **FLINT_TILE_PROFILE\n )\n\n pbar.update(dst.height * dst.width)\n", "repo_name": "moja-global/FLINT.Data_Preprocessing", "sub_path": "flintdata/scripts/optimize_rasters.py", "file_name": "optimize_rasters.py", "file_ext": "py", "file_size_in_byte": 16100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "rasterio.enums.Resampling.average", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rasterio.enums.Resampling", "line_number": 74, "usage_type": "name"}, {"api_name": "rasterio.enums.Resampling.nearest", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rasterio.enums.Resampling", "line_number": 75, "usage_type": "name"}, {"api_name": "rasterio.enums.Resampling.bilinear", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rasterio.enums.Resampling", "line_number": 76, "usage_type": "name"}, {"api_name": "rasterio.enums.Resampling.cubic", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rasterio.enums.Resampling", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 80, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 85, "usage_type": "name"}, {"api_name": "rasterio.warp.transform", "line_number": 103, "usage_type": "call"}, {"api_name": "rasterio.warp", "line_number": 103, "usage_type": "name"}, {"api_name": "rasterio.transform.from_bounds", "line_number": 119, "usage_type": "call"}, {"api_name": "rasterio.transform", "line_number": 119, "usage_type": "name"}, {"api_name": "rasterio.warp.transform_bounds", "line_number": 123, "usage_type": "call"}, {"api_name": "rasterio.warp", "line_number": 123, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 124, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 125, "usage_type": "call"}, {"api_name": "rasterio.transform.from_bounds", "line_number": 126, "usage_type": "call"}, {"api_name": "rasterio.transform", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 88, "usage_type": "name"}, {"api_name": "affine.Affine", "line_number": 88, "usage_type": "name"}, {"api_name": "rasterio.env.GDALVersion.runtime", "line_number": 132, "usage_type": "call"}, {"api_name": "rasterio.env.GDALVersion", "line_number": 132, "usage_type": "name"}, {"api_name": "rasterio.io.MemoryFile", "line_number": 138, "usage_type": "call"}, {"api_name": "rasterio.io.DatasetReader", "line_number": 149, "usage_type": "name"}, {"api_name": "rasterio.vrt.WarpedVRT", "line_number": 154, "usage_type": "call"}, {"api_name": "rasterio.vrt.WarpedVRT", "line_number": 149, "usage_type": "name"}, {"api_name": "rasterio.uint8", "line_number": 164, "usage_type": "attribute"}, {"api_name": "rasterio.uint16", "line_number": 165, "usage_type": "attribute"}, {"api_name": "rasterio.uint32", "line_number": 166, "usage_type": "attribute"}, {"api_name": "rasterio.int16", "line_number": 167, "usage_type": "attribute"}, {"api_name": "rasterio.int32", "line_number": 168, "usage_type": "attribute"}, {"api_name": "rasterio.float32", "line_number": 169, "usage_type": "attribute"}, {"api_name": "rasterio.float64", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 196, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 239, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 239, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 240, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 245, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 238, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 281, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 281, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 282, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 300, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 300, "usage_type": "attribute"}, {"api_name": "click.echo", "line_number": 303, "usage_type": "call"}, {"api_name": "click.BadParameter", "line_number": 314, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 316, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 318, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 328, "usage_type": "call"}, {"api_name": "contextlib.ExitStack", "line_number": 336, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 337, "usage_type": "call"}, {"api_name": "rasterio.Env", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 354, "usage_type": "call"}, {"api_name": "os.path", "line_number": 354, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 354, "usage_type": "call"}, {"api_name": "contextlib.ExitStack", "line_number": 356, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 356, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 358, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile.tiles", "line_number": 375, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile", "line_number": 375, "usage_type": "attribute"}, {"api_name": "flintdata.flinttile", "line_number": 375, "usage_type": "name"}, {"api_name": "contextlib.ExitStack", "line_number": 376, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 376, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 377, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile.bounds", "line_number": 379, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile", "line_number": 379, "usage_type": "attribute"}, {"api_name": "flintdata.flinttile", "line_number": 379, "usage_type": "name"}, {"api_name": "rasterio.transform.from_bounds", "line_number": 388, "usage_type": "call"}, {"api_name": "rasterio.transform", "line_number": 388, "usage_type": "name"}, {"api_name": "affine.Affine.translation", "line_number": 389, "usage_type": "call"}, {"api_name": "affine.Affine", "line_number": 389, "usage_type": "name"}, {"api_name": "rasterio.windows.Window", "line_number": 394, "usage_type": "call"}, {"api_name": "rasterio.windows", "line_number": 394, "usage_type": "name"}, {"api_name": "rasterio.vrt.WarpedVRT", "line_number": 400, "usage_type": "call"}, {"api_name": "rasterio.io.MemoryFile", "line_number": 411, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 415, "usage_type": "call"}, {"api_name": "rasterio.transform.from_bounds", "line_number": 418, "usage_type": "call"}, {"api_name": "rasterio.transform", "line_number": 418, "usage_type": "name"}, {"api_name": "affine.Affine.translation", "line_number": 419, "usage_type": "call"}, {"api_name": "affine.Affine", "line_number": 419, "usage_type": "name"}, {"api_name": "rasterio.vrt.WarpedVRT", "line_number": 423, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile.name", "line_number": 429, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile", "line_number": 429, "usage_type": "attribute"}, {"api_name": "flintdata.flinttile", "line_number": 429, "usage_type": "name"}, {"api_name": "click.BadParameter", "line_number": 434, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 442, "usage_type": "call"}, {"api_name": "rasterio.windows.Window", "line_number": 443, "usage_type": "call"}, {"api_name": "rasterio.windows", "line_number": 443, "usage_type": "name"}, {"api_name": "flintdata.flinttile.flinttile.index", "line_number": 448, "usage_type": "call"}, {"api_name": "flintdata.flinttile.flinttile", "line_number": 448, "usage_type": "attribute"}, {"api_name": "flintdata.flinttile", "line_number": 448, "usage_type": "name"}, {"api_name": "rasterio.shutil.copy", "line_number": 449, "usage_type": "call"}, {"api_name": "click.command", "line_number": 251, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 255, "usage_type": "call"}, {"api_name": "flintdata.scripts.click_types.GlobbityGlob", "line_number": 255, "usage_type": "call"}, {"api_name": "click.option", "line_number": 256, "usage_type": "call"}, {"api_name": "flintdata.scripts.click_types.PathlibPath", "line_number": 258, "usage_type": "call"}, {"api_name": "click.option", "line_number": 261, "usage_type": "call"}, {"api_name": "click.option", "line_number": 264, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 265, "usage_type": "call"}, {"api_name": "click.option", "line_number": 268, "usage_type": "call"}, {"api_name": "click.option", "line_number": 273, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 274, "usage_type": "call"}, {"api_name": "click.option", "line_number": 277, "usage_type": "call"}]} +{"seq_id": "1386808710", "text": "__author__ = \"Gideon Juve \"\n__version__ = \"3.3\"\n\nimport datetime, os, sys\n# from io import StringIO\nfrom StringIO import StringIO\nimport codecs\nimport shlex\nimport codecs\n\nSCHEMA_NAMESPACE = \"http://pegasus.isi.edu/schema/DAX\"\nSCHEMA_LOCATION = \"http://pegasus.isi.edu/schema/dax-3.4.xsd\"\nSCHEMA_VERSION = \"3.4\"\n\nclass DAX3Error(Exception): pass\nclass DuplicateError(DAX3Error): pass\nclass NotFoundError(DAX3Error): pass\nclass FormatError(DAX3Error): pass\nclass ParseError(DAX3Error): pass\n\nclass Element:\n \"\"\"Representation of an XML element for formatting output\"\"\"\n \n def __init__(self, name, attrs=[]):\n self.name = name\n self.attrs = []\n for attr, value in attrs:\n if value is not None:\n if isinstance(value, bool):\n value = str(value).lower()\n elif not isinstance(value, str):\n value = repr(value)\n attr = attr.replace('__',':')\n self.attrs.append((attr,value))\n self.children = []\n self.flat = False\n \n def _escape(self, text):\n \"\"\"Escape special characters in XML\"\"\"\n o = []\n for c in text:\n if c == '\"': o.append(\""\")\n elif c == \"'\": o.append(\"'\")\n elif c == \"<\": o.append(\"<\")\n elif c == \">\": o.append(\">\")\n elif c == \"&\": o.append(\"&\")\n else: o.append(c)\n return ''.join(o)\n \n def element(self, element):\n self.children.append(element)\n return element\n \n def text(self, value):\n if not isinstance(value, str):\n value = str(value)\n self.children.append(self._escape(value))\n return self\n \n def comment(self, message):\n self.children.append(\"\" % self._escape(message))\n \n def flatten(self):\n self.flat = True\n return self\n \n def __unicode__(self):\n s = StringIO()\n self.write(s)\n x = s.getvalue()\n s.close()\n return unicode(x)\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def write(self, stream=sys.stdout, level=0, flatten=False):\n flat = self.flat or flatten\n \n stream.write('<%s' % self.name)\n \n for attr, value in self.attrs:\n value = self._escape(value)\n stream.write(' %s=\"%s\"' % (attr, value))\n \n if len(self.children) == 0:\n stream.write('/>')\n else:\n stream.write('>')\n if not flat:\n stream.write('\\n')\n for child in self.children:\n if not flat:\n stream.write('\\t'*(level+1))\n if isinstance(child, str):\n stream.write(child)\n else:\n child.write(stream, level+1, flat)\n if not flat:\n stream.write('\\n')\n if not flat:\n stream.write('\\t'*level)\n stream.write('' % self.name)\n \nclass Namespace:\n \"\"\"\n Namespace values recognized by Pegasus. See Executable, \n Transformation, and Job.\n \"\"\"\n PEGASUS = 'pegasus'\n CONDOR = 'condor'\n DAGMAN = 'dagman'\n ENV = 'env'\n HINTS = 'hints'\n GLOBUS = 'globus'\n SELECTOR = 'selector'\n STAT = 'stat'\n\nclass Arch:\n \"\"\"\n Architecture types. See Executable.\n \"\"\"\n X86 = 'x86'\n X86_64 = 'x86_64'\n PPC = 'ppc'\n PPC_64 = 'ppc_64'\n IA64 = 'ia64'\n SPARCV7 = 'sparcv7'\n SPARCV9 = 'sparcv9'\n AMD64 = 'amd64'\n\nclass Link:\n \"\"\"\n Linkage attributes. See File, Executable and uses().\n \"\"\"\n NONE = 'none'\n INPUT = 'input'\n OUTPUT = 'output'\n INOUT = 'inout'\n\nclass Transfer:\n \"\"\"\n Transfer types for uses. See Executable, File.\n \"\"\"\n FALSE = 'false'\n OPTIONAL = 'optional'\n TRUE = 'true'\n\nclass OS:\n \"\"\"\n OS types. See Executable.\n \"\"\"\n LINUX = 'linux'\n SUNOS = 'sunos'\n AIX = 'aix'\n MACOS = 'macos'\n WINDOWS = 'windows'\n\nclass When:\n \"\"\"\n Job states for notifications. See Job/DAX/DAG.invoke().\n \"\"\"\n NEVER = 'never'\n START = 'start'\n ON_ERROR = 'on_error'\n ON_SUCCESS = 'on_success'\n AT_END = 'at_end'\n ALL = 'all'\n\nclass Invoke:\n def __init__(self, when, what):\n if not when:\n raise FormatError(\"invalid when\", when)\n if not what:\n raise FormatError(\"invalid what\", what)\n self.when = when\n self.what = what\n \n def __unicode__(self):\n return u\"\" % (self.when, self.what)\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def __hash__(self):\n return hash((self.when, self.what))\n \n def __eq__(self, other):\n if isinstance(other, Invoke):\n return self.when == other.when and self.what == other.what\n return False\n \n def toXML(self):\n e = Element('invoke', [('when', self.when)])\n e.text(self.what)\n e.flatten()\n return e\n \nclass InvokeMixin:\n \n def addInvoke(self, invoke):\n \"\"\"Add invoke to this object\"\"\"\n if self.hasInvoke(invoke):\n raise DuplicateError(\"Duplicate Invoke\", invoke)\n self.invocations.add(invoke)\n \n def hasInvoke(self, invoke):\n \"\"\"Test to see if this object has invoke\"\"\"\n return invoke in self.invocations\n \n def removeInvoke(self, invoke):\n \"\"\"Remove invoke from this object\"\"\"\n if not self.hasInvoke(invoke):\n raise NotFoundError(\"Invoke not found\", invoke)\n self.invocations.remove(invoke)\n \n def clearInvokes(self):\n \"\"\"Remove all Invoke objects\"\"\"\n self.invocations.clear()\n \n def invoke(self, when, what):\n \"\"\"\n Invoke executable 'what' when job reaches status 'when'. The value of \n 'what' should be a command that can be executed on the submit host.\n \n The list of valid values for 'when' is:\n \n WHEN MEANING\n ========== =======================================================\n never never invoke\n start invoke just before job gets submitted.\n on_error invoke after job finishes with failure (exitcode != 0).\n on_success invoke after job finishes with success (exitcode == 0).\n at_end invoke after job finishes, regardless of exit status.\n all like start and at_end combined.\n \n Examples:\n obj.invoke('at_end','/usr/bin/mail -s \"job done\" juve@usc.edu')\n obj.invoke('on_error','/usr/bin/update_db -failure')\n \"\"\"\n self.addInvoke(Invoke(when, what))\n \n\nclass ProfileMixin:\n def addProfile(self, profile):\n \"\"\"Add a profile to this object\"\"\"\n if self.hasProfile(profile):\n raise DuplicateError(\"Duplicate profile\", profile)\n self.profiles.add(profile)\n \n def hasProfile(self, profile):\n \"\"\"Does this object have profile?\"\"\"\n return profile in self.profiles\n \n def removeProfile(self, profile):\n \"\"\"Remove profile from this object\"\"\"\n if not self.hasProfile(profile):\n raise NotFoundError(\"Profile not found\", profile)\n self.profiles.remove(profile)\n \n def clearProfiles(self):\n \"\"\"Remove all profiles from this object\"\"\"\n self.profiles.clear()\n \n def profile(self, namespace, key, value):\n \"\"\"Declarative profile addition\"\"\"\n self.addProfile(Profile(namespace, key, value))\n \n\nclass MetadataMixin:\n def addMetadata(self, metadata):\n \"\"\"Add metadata to this object\"\"\"\n if self.hasMetadata(metadata):\n raise DuplicateError(\"Duplicate Metadata\", metadata)\n self._metadata.add(metadata)\n \n def removeMetadata(self, metadata):\n \"\"\"Remove meta from this object\"\"\"\n if not self.hasMetadata(metadata):\n raise NotFoundError(\"Metadata not found\", metadata)\n self._metadata.remove(metadata)\n \n def hasMetadata(self, metadata):\n \"\"\"Does this object have metadata?\"\"\"\n return metadata in self._metadata\n \n def clearMetadata(self):\n \"\"\"Remove all metadata from this object\"\"\"\n self._metadata.clear()\n \n def metadata(self, key, type, value):\n \"\"\"Declarative metadata addition\"\"\"\n self.addMetadata(Metadata(key, type, value))\n \n\nclass PFNMixin:\n def addPFN(self, pfn):\n \"\"\"Add a PFN to this object\"\"\"\n if self.hasPFN(pfn):\n raise DuplicateError(\"Duplicate PFN\", pfn)\n self.pfns.add(pfn)\n \n def removePFN(self, pfn):\n \"\"\"Remove PFN from this object\"\"\"\n if not self.hasPFN(pfn):\n raise NotFoundError(\"PFN not found\", pfn)\n self.pfns.remove(pfn)\n \n def hasPFN(self, pfn):\n \"\"\"Does this object have pfn?\"\"\"\n return pfn in self.pfns\n \n def clearPFNs(self):\n \"\"\"Remove all PFNs from this object\"\"\"\n self.pfns.clear()\n \n def PFN(self, url, site=None):\n \"\"\"Declarative PFN addition\"\"\"\n self.addPFN(PFN(url,site))\n \n\n\nclass CatalogType(ProfileMixin, MetadataMixin, PFNMixin):\n \"\"\"Base class for File and Executable\"\"\"\n \n def __init__(self, name):\n \"\"\"\n All arguments specify the workflow-level behavior of this File. Job-level\n behavior can be defined when adding the File to a Job's uses. If the\n properties are not overridden at the job-level, then the workflow-level\n values are used as defaults.\n \n If this LFN is to be used as a job's stdin/stdout/stderr then the value\n of link is ignored when generating the tags.\n \n Arguments:\n name: The name of the file (required)\n \"\"\"\n if not name:\n raise FormatError('name required')\n self.name = name\n self.profiles = set()\n self._metadata = set()\n self.pfns = set()\n \n def innerXML(self, parent):\n for p in self.profiles:\n parent.element(p.toXML())\n for m in self._metadata:\n parent.element(m.toXML())\n for p in self.pfns:\n parent.element(p.toXML())\n \n\nclass File(CatalogType):\n \"\"\"File(name)\n \n A file entry for the DAX-level replica catalog, or a reference to a logical file\n used by the workflow.\n \n Examples:\n input = File('input.txt')\n \n Example use in job:\n input = File('input.txt')\n output = File('output.txt')\n job = Job(name=\"compute\")\n job.uses(input, link=Link.INPUT, transfer=True)\n job.uses(output, link=Link.OUTPUT, transfer=True, register=True)\n \"\"\"\n def __init__(self, name):\n \"\"\"\n All arguments specify the workflow-level behavior of this File. Job-level\n behavior can be defined when adding the File to a Job's uses. If the\n properties are not overridden at the job-level, then the workflow-level\n values are used as defaults.\n \n If this LFN is to be used as a job's stdin/stdout/stderr then the value\n of link is ignored when generating the tags.\n \n Arguments:\n name: The name of the file (required)\n \"\"\"\n CatalogType.__init__(self, name)\n \n def __unicode__(self):\n return u\"\" % self.name\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def __hash__(self):\n return hash(self.name)\n \n def __eq__(self, other):\n return isinstance(other, File) and self.name == other.name\n \n def toArgumentXML(self):\n \"\"\"Returns an XML representation of this File with no inner elements\"\"\"\n return Element('file', [('name', self.name)])\n \n def toStdioXML(self, tag):\n \"\"\"Returns an XML representation of this file as a stdin/out/err tag\"\"\"\n if tag is 'stdin':\n link = \"input\" # stdin is always input\n elif tag in ['stdout','stderr']:\n link = \"output\" # stdout/stderr are always output\n else:\n raise FormatError(\"invalid tag\",tag,\"should be one of stdin, stdout, stderr\")\n \n return Element(tag, [\n ('name',self.name),\n ('link',link)\n ])\n \n def toXML(self):\n \"\"\"Return the XML representation of this File with inner elements\"\"\"\n e = self.toArgumentXML()\n self.innerXML(e)\n return e\n \nclass Executable(CatalogType, InvokeMixin):\n \"\"\"Executable(name[,namespace][,version][,arch][,os][,osrelease][,osversion][,glibc][,installed])\n \n An entry for an executable in the DAX-level replica catalog.\n \n Examples:\n grep = Executable(\"grep\")\n grep = Executable(namespace=\"os\",name=\"grep\",version=\"2.3\")\n grep = Executable(namespace=\"os\",name=\"grep\",version=\"2.3\",arch=Arch.X86)\n grep = Executable(namespace=\"os\",name=\"grep\",version=\"2.3\",arch=Arch.X86,os=OS.LINUX)\n \"\"\"\n def __init__(self, name, namespace=None, version=None, arch=None, os=None, \n osrelease=None, osversion=None, glibc=None, installed=None):\n \"\"\"\n Arguments:\n name: Logical name of executable\n namespace: Executable namespace\n version: Executable version\n arch: Architecture that this exe was compiled for\n os: Name of os that this exe was compiled for\n osrelease: Release of os that this exe was compiled for\n osversion: Version of os that this exe was compiled for\n glibc: Version of glibc this exe was compiled against\n installed: Is the executable installed (true), or stageable (false)\n \"\"\"\n CatalogType.__init__(self, name)\n self.namespace = namespace\n self.version = version\n self.arch = arch\n self.os = os\n self.osrelease = osrelease\n self.osversion = osversion\n self.glibc = glibc\n self.installed = installed\n self.invocations = set()\n \n def __unicode__(self):\n return u\"\" % (self.namespace, self.name, self.version)\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def __hash__(self):\n return hash((self.name,\n self.namespace,\n self.version,\n self.arch,\n self.os,\n self.osrelease,\n self.osversion,\n self.glibc,\n self.installed))\n \n def __eq__(self, other):\n if isinstance(other, Executable):\n return self.name == other.name and \\\n self.namespace == other.namespace and \\\n self.version == other.version and \\\n self.arch == other.arch and \\\n self.os == other.os and \\\n self.osrelease == other.osrelease and \\\n self.osversion == other.osversion and \\\n self.glibc == other.glibc and \\\n self.installed == other.installed\n return False\n \n def toXML(self):\n \"\"\"Returns an XML representation of this file as a filename tag\"\"\"\n e = Element('executable', [\n ('name', self.name),\n ('namespace', self.namespace),\n ('version', self.version),\n ('arch', self.arch),\n ('os', self.os),\n ('osrelease', self.osrelease),\n ('osversion', self.osversion),\n ('glibc', self.glibc),\n ('installed', self.installed)\n ])\n self.innerXML(e)\n \n # Invocations\n for inv in self.invocations:\n e.element(inv.toXML())\n \n return e\n \nclass Metadata:\n \"\"\"Metadata(key,type,value)\n \n A way to add metadata to File and Executable objects. This is\n useful if you want to annotate the DAX with things like file\n sizes, application-specific attributes, etc.\n \n There is currently no restriction on the type.\n \n Examples:\n s = Metadata('size','int','12')\n a = Metadata('algorithm','string','plav')\n \"\"\"\n def __init__(self, key, type, value):\n \"\"\"\n Arguments:\n key: The key name of the item\n type: The type of the value (e.g. string, int, float)\n value: The value of the item\n \"\"\"\n if not key:\n raise FormatError(\"Invalid key\", key)\n if not type:\n raise FormatError(\"Invalid type\", type)\n if not value:\n raise FormatError(\"Invalid value\", value)\n self.key = key\n self.type = type\n self.value = value\n \n def __unicode__(self):\n return u\"\" % (self.type, self.key, self.value)\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def __hash__(self):\n return hash(self.key)\n \n def __eq__(self, other):\n return isinstance(other, Metadata) and self.key == other.key\n \n def toXML(self):\n m = Element('metadata', [\n ('key',self.key),\n ('type',self.type)\n ])\n m.text(self.value).flatten()\n return m\n \nclass PFN(ProfileMixin):\n \"\"\"PFN(url[,site])\n \n A physical file name. Used to provide URLs for files and executables\n in the DAX-level replica catalog.\n \n PFNs can be added to File and Executable.\n \n Examples:\n PFN('http://site.com/path/to/file.txt','site')\n PFN('http://site.com/path/to/file.txt',site='site')\n PFN('http://site.com/path/to/file.txt')\n \"\"\"\n def __init__(self, url, site=None):\n \"\"\"\n Arguments:\n url: The url of the file.\n site: The name of the site. [default: local]\n \"\"\"\n if not url:\n raise FormatError(\"Invalid url\", url)\n if not site:\n raise FormatError(\"Invalid site\", site)\n self.url = url\n self.site = site\n self.profiles = set()\n \n def __unicode__(self):\n return u\"\" % (self.site, self.url)\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def __hash__(self):\n return hash((self.url, self.site))\n \n def __eq__(self, other):\n return isinstance(other, PFN) and \\\n self.url == other.url and \\\n self.site == other.site\n \n def toXML(self):\n pfn = Element('pfn', [\n ('url', self.url),\n ('site', self.site)\n ])\n for p in self.profiles:\n pfn.element(p.toXML())\n return pfn\n \n\nclass Profile:\n \"\"\"Profile(namespace,key,value)\n \n A Profile captures scheduler-, system-, and environment-specific \n parameters in a uniform fashion. Each profile declaration assigns a value\n to a key within a namespace.\n \n Profiles can be added to Job, DAX, DAG, File, Executable, and PFN.\n \n Examples:\n path = Profile(Namespace.ENV,'PATH','/bin')\n vanilla = Profile(Namespace.CONDOR,'universe','vanilla')\n path = Profile(namespace='env',key='PATH',value='/bin')\n path = Profile('env','PATH','/bin')\n \"\"\"\n \n def __init__(self, namespace, key, value):\n \"\"\"\n Arguments:\n namespace: The namespace of the profile (see Namespace) \n key: The key name. Can be anything that responds to str().\n value: The value for the profile. Can be anything that responds to str().\n \"\"\"\n self.namespace = namespace\n self.key = key\n self.value = value\n \n def __unicode__(self):\n return u\"\" % (self.namespace, self.key, self.value)\n \n def __str__(self):\n return unicode(self).encode('utf-8')\n \n def __hash__(self):\n return hash((self.namespace, self.key))\n \n def __eq__(self, other):\n return isinstance(other, Profile) and \\\n self.namespace == other.namespace and \\\n self.key == other.key\n \n def toXML(self):\n \"\"\"Return an XML element for this profile\"\"\"\n p = Element(\"profile\", [\n ('namespace', self.namespace),\n ('key', self.key)\n ])\n p.text(self.value).flatten()\n return p\n \nclass Use:\n \"\"\"Use(file[,link][,register][,transfer][,optional]\n [,namespace][,version][,executable][,size])\n \n Use of a logical file name. Used for referencing files in the DAX.\n \n Attributes:\n file: A string, File or Executable representing the logical file\n link: Is this file a job input, output or both (See LFN) (optional)\n register: Should this file be registered in RLS? (True/False) (optional)\n transfer: Should this file be transferred? (True/False or See LFN) (optional)\n optional: Is this file optional, or should its absence be an error? (optional)\n namespace: Namespace of executable (optional)\n version: version of executable (optional)\n executable: Is file an executable? (True/False) (optional)\n size: The size of the file (optional)\n \n For Use objects that are added to Transformations, the attributes 'link', 'register',\n 'transfer', 'optional' and 'size' are ignored.\n \n If a File object is passed in as 'file', then the default value for executable\n is 'false'. Similarly, if an Executable object is passed in, then the default\n value for executable is 'true'.\n \"\"\"\n\n def __init__(self, name, link=None, register=None, transfer=None, \n optional=None, namespace=None, version=None, executable=None,\n size=None):\n if not name:\n raise FormatError('Invalid name', name)\n \n self.name = name\n self.link = link\n self.optional = optional\n self.register = register\n self.transfer = transfer\n self.namespace = namespace\n self.version = version\n self.executable = executable\n self.size = size\n \n def __unicode__(self):\n return u\"\" % (self.namespace, self.name, self.version)\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def __hash__(self):\n return hash((self.namespace, self.name, self.version))\n \n def __eq__(self, other):\n if isinstance(other, Use):\n return self.namespace == other.namespace and \\\n self.name == other.name and \\\n self.version == other.version\n \n def toTransformationXML(self):\n return Element('uses', [\n ('namespace',self.namespace),\n ('name',self.name),\n ('version',self.version),\n ('executable',self.executable)\n ])\n \n def toJobXML(self):\n return Element('uses', [\n ('namespace',self.namespace),\n ('name',self.name),\n ('version',self.version),\n ('link',self.link),\n ('register',self.register),\n ('transfer',self.transfer),\n ('optional',self.optional),\n ('executable',self.executable),\n ('size',self.size)\n ])\n \n\nclass UseMixin:\n def addUse(self, use):\n \"\"\"Add Use to this object\"\"\"\n if self.hasUse(use):\n raise DuplicateError(\"Duplicate Use\", use)\n self.used.add(use)\n \n def removeUse(self, use):\n \"\"\"Remove use from this object\"\"\"\n if not self.hasUse(use):\n raise NotFoundError(\"No such Use\", use)\n self.used.remove(use)\n \n def hasUse(self, use):\n \"\"\"Test to see if this object has use\"\"\"\n return use in self.used\n \n def clearUses(self):\n \"\"\"Remove all uses from this object\"\"\"\n self.used.clear()\n \n def uses(self, arg, link=None, register=None, transfer=None, \n optional=None, namespace=None, version=None, executable=None,\n size=None):\n \n if isinstance(arg, CatalogType):\n _name = arg.name\n else:\n _name = arg\n \n _namespace = None\n _version = None\n _executable = None\n \n if isinstance(arg, Executable):\n _namespace = arg.namespace\n _version = arg.version\n # We only need to set this for jobs\n # the default is True for Transformations\n if isinstance(self, AbstractJob):\n _executable = True\n \n if isinstance(arg, File):\n # We only need to set this for transformations\n # The default is False for Jobs\n if isinstance(self, Transformation):\n _executable = False\n \n if namespace is not None:\n _namespace = namespace\n if version is not None:\n _version = str(version)\n if executable is not None:\n _executable = executable\n \n use = Use(_name,link,register,transfer,optional,_namespace,\n _version,_executable,size)\n self.addUse(use)\n \n\nclass Transformation(UseMixin,InvokeMixin):\n \"\"\"Transformation((name|executable)[,namespace][,version])\n \n A logical transformation. This is basically defining one or more\n entries in the transformation catalog. You can think of it like a macro\n for adding to your jobs. You can define a transformation that\n uses several files and/or executables, and refer to it when creating\n a job. If you do, then all of the uses defined for that transformation\n will be copied to the job during planning.\n \n This code:\n in = File(\"input.txt\")\n exe = Executable(\"exe\")\n t = Transformation(namespace=\"foo\", name=\"bar\", version=\"baz\")\n t.uses(in)\n t.uses(exe)\n j = Job(t)\n \n is equivalent to:\n in = File(\"input.txt\")\n exe = Executable(\"exe\")\n j = Job(namespace=\"foo\", name=\"bar\", version=\"baz\")\n j.uses(in)\n j.uses(exe)\n \n Examples:\n Transformation(name='mDiff')\n Transformation(namespace='montage',name='mDiff')\n Transformation(namespace='montage',name='mDiff',version='3.0')\n \n Using one executable:\n mProjectPP = Executable(namespace=\"montage\",name=\"mProjectPP\",version=\"3.0\")\n x_mProjectPP = Transformation(mProjectPP)\n \n Using several executables:\n mDiff = Executable(namespace=\"montage\",name=\"mProjectPP\",version=\"3.0\")\n mFitplane = Executable(namespace=\"montage\",name=\"mFitplane\",version=\"3.0\")\n mDiffFit = Executable(namespace=\"montage\",name=\"mDiffFit\",version=\"3.0\")\n x_mDiffFit = Transformation(mDiffFit)\n x_mDiffFit.uses(mDiff)\n x_mDiffFit.uses(mFitplane)\n \n Config files too:\n conf = File(\"jbsim.conf\")\n jbsim = Executable(namespace=\"scec\",name=\"jbsim\")\n x_jbsim = Transformation(jbsim)\n x_jbsim.uses(conf)\n \"\"\"\n def __init__(self,name,namespace=None,version=None):\n \"\"\"\n The name argument can be either a string or an Executable object.\n If it is an Executable object, then the Transformation inherits\n its name, namespace and version from the Executable, and the \n Transformation is set to use the Executable with link=input,\n transfer=true, and register=False.\n \n Arguments:\n name: The name of the transformation\n namespace: The namespace of the xform (optional)\n version: The version of the xform (optional)\n \"\"\"\n self.name = None\n self.namespace = None\n self.version = None\n self.used = set()\n self.invocations = set()\n if isinstance(name, Executable):\n self.name = name.name\n self.namespace = name.namespace\n self.version = name.version\n else:\n self.name = name\n if namespace: self.namespace = namespace\n if version: self.version = version\n \n def __unicode__(self):\n return u\"\" % (self.namespace, self.name, self.version)\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def __hash__(self):\n return hash((self.namespace, self.name, self.version))\n \n def __eq__(self, other):\n if isinstance(other, Transformation):\n return self.namespace == other.namespace and \\\n self.name == other.name and \\\n self.version == other.version\n \n def toXML(self):\n \"\"\"Return an XML representation of this transformation\"\"\"\n e = Element('transformation', [\n ('namespace', self.namespace),\n ('name', self.name),\n ('version', self.version)\n ])\n \n # Uses\n for u in self.used:\n e.element(u.toTransformationXML())\n \n # Invocations\n for inv in self.invocations:\n e.element(inv.toXML())\n \n return e\n \n\nclass AbstractJob(ProfileMixin,UseMixin,InvokeMixin):\n \"\"\"The base class for Job, DAX, and DAG\"\"\"\n def __init__(self, id=None, node_label=None):\n self.id = id\n self.node_label = node_label\n \n self.arguments = []\n self.profiles = set()\n self.used = set()\n self.invocations = set()\n \n self.stdout = None\n self.stderr = None\n self.stdin = None\n \n def addArguments(self, *arguments):\n \"\"\"Add one or more arguments to the job (this will add whitespace)\"\"\"\n for arg in arguments:\n if not isinstance(arg, (File, str)):\n raise FormatError(\"Invalid argument\", arg)\n for arg in arguments:\n if len(self.arguments) > 0:\n self.arguments.append(' ')\n self.arguments.append(arg)\n \n def addRawArguments(self, *arguments):\n \"\"\"Add one or more arguments to the job (whitespace will NOT be added)\"\"\"\n for arg in arguments:\n if not isinstance(arg, (File, str)):\n raise FormatError(\"Invalid argument\", arg)\n self.arguments.extend(arguments)\n \n def clearArguments(self):\n \"\"\"Remove all arguments from this job\"\"\"\n self.arguments = []\n \n def getArguments(self):\n \"\"\"Get the arguments of this job\"\"\"\n args = []\n for a in self.arguments:\n if isinstance(a, File):\n args.append(unicode(a.toArgumentXML()))\n else:\n args.append(a)\n return ''.join(args)\n \n def setStdout(self, filename):\n \"\"\"Redirect stdout to a file\"\"\"\n if isinstance(filename, File):\n self.stdout = filename\n else:\n self.stdout = File(filename)\n \n def clearStdout(self):\n \"\"\"Remove stdout file\"\"\"\n self.stdout = None\n\n def setStderr(self, filename):\n \"\"\"Redirect stderr to a file\"\"\"\n if isinstance(filename, File):\n self.stderr = filename\n else:\n self.stderr = File(filename)\n \n def clearStderr(self):\n \"\"\"Remove stderr file\"\"\"\n self.stderr = None\n \n def setStdin(self, filename):\n \"\"\"Redirect stdin from a file\"\"\"\n if isinstance(filename, File):\n self.stdin = filename\n else:\n self.stdin = File(filename)\n \n def clearStdin(self):\n \"\"\"Remove stdin file\"\"\"\n self.stdin = None\n \n def innerXML(self, element):\n \"\"\"Return an XML representation of this job\"\"\"\n # Arguments\n if len(self.arguments) > 0:\n args = Element('argument').flatten()\n for x in self.arguments:\n if isinstance(x, File):\n args.element(x.toArgumentXML())\n else:\n args.text(x)\n element.element(args)\n\n # Profiles\n for pro in self.profiles:\n element.element(pro.toXML())\n \n # Stdin/xml/err\n if self.stdin is not None:\n element.element(self.stdin.toStdioXML('stdin'))\n if self.stdout is not None:\n element.element(self.stdout.toStdioXML('stdout'))\n if self.stderr is not None:\n element.element(self.stderr.toStdioXML('stderr'))\n \n # Uses\n for use in self.used:\n element.element(use.toJobXML())\n \n # Invocations\n for inv in self.invocations:\n element.element(inv.toXML())\n\nclass Job(AbstractJob):\n \"\"\"Job((name|Executable|Transformation)[,id][,namespace][,version][,node_label])\n \n This class defines the specifics of a job to run in an abstract manner.\n All filename references still refer to logical files. All references\n transformations also refer to logical transformations, though\n physical location hints can be passed through profiles.\n \n Examples:\n sleep = Job(id=\"ID0001\",name=\"sleep\")\n jbsim = Job(id=\"ID0002\",name=\"jbsim\",namespace=\"cybershake\",version=\"2.1\")\n merge = Job(\"jbsim\")\n \n You can create a Job based on a Transformation:\n mDiff_xform = Transformation(\"mDiff\", ...)\n mDiff_job = Job(mDiff_xform)\n \n Or an Executable:\n mDiff_exe = Executable(\"mDiff\", ...)\n mDiff_job = Job(mDiff_exe)\n \n Several arguments can be added at the same time:\n input = File(...)\n output = File(...)\n job.addArguments(\"-i\",input,\"-o\",output)\n \n Profiles are added similarly:\n job.addProfile(Profile(Namespace.ENV, key='PATH', value='/bin'))\n job.profile(Namespace.ENV, \"PATH\", \"/bin\")\n \n Adding file uses is simple, and you can override global File attributes:\n job.uses(input, Link.INPUT)\n job.uses(output, Link.OUTPUT, transfer=True, register=True)\n \"\"\"\n def __init__(self, name, id=None, namespace=None, version=None, node_label=None):\n \"\"\"The ID for each job should be unique in the DAX. If it is None, then\n it will be automatically generated when the job is added to the DAX.\n \n The name, namespace, and version should match what you have in your\n transformation catalog. For example, if namespace=\"foo\" name=\"bar\" \n and version=\"1.0\", then the transformation catalog should have an\n entry for \"foo::bar:1.0\".\n \n The name argument can be either a string, or a Transformation object. If\n it is a Transformation object, then the job will inherit the name, namespace,\n and version from the Transformation.\n \n Arguments:\n name: The transformation name or Transformation object (required)\n id: A unique identifier for the job (optional)\n namespace: The namespace of the transformation (optional)\n version: The transformation version (optional)\n node_label: The label for this job to use in graphing (optional)\n \"\"\"\n self.namespace = None\n self.version = None\n if isinstance(name, (Transformation, Executable)):\n self.name = name.name\n self.namespace = name.namespace\n self.version = name.version\n elif isinstance(name, str):\n self.name = name\n else:\n raise FormatError(\"Name must be a string, Transformation or Executable\")\n if not self.name:\n raise FormatError(\"Invalid name\", self.name)\n AbstractJob.__init__(self, id=id, node_label=node_label)\n if namespace: self.namespace = namespace\n if version: self.version = version\n \n def __unicode__(self):\n return u\"\" % (self.id, self.namespace, self.name, self.version)\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def toXML(self):\n e = Element('job',[\n ('id',self.id),\n ('namespace',self.namespace),\n ('name',self.name),\n ('version',self.version),\n ('node-label',self.node_label)\n ])\n self.innerXML(e)\n return e\n \nclass DAX(AbstractJob):\n \"\"\"DAX(file[,id][,node_label])\n \n This job represents a sub-DAX that will be planned and executed by\n the workflow.\n \n Examples:\n daxjob1 = DAX(\"foo.dax\")\n \n daxfile = File(\"foo.dax\")\n daxjob2 = DAX(daxfile)\n \"\"\"\n def __init__(self, file, id=None, node_label=None):\n \"\"\"\n \n The name argument can be either a string, or a File object. If\n it is a File object, then this job will inherit its name from the \n File and the File will be added in a with transfer=True,\n register=False, and link=input.\n \n Arguments:\n file: The logical name of the DAX file or the DAX File object\n id: The id of the DAX job [default: autogenerated]\n node_label: The label for this job to use in graphing\n \"\"\"\n if isinstance(file, File):\n self.file = file\n elif isinstance(file, str) or isinstance(file, unicode):\n self.file = File(name=file)\n else:\n raise FormatError(\"invalid file\",file)\n AbstractJob.__init__(self, id=id, node_label=node_label)\n \n def __unicode__(self):\n return u\"\" % (self.id, self.file.name)\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def toXML(self):\n \"\"\"Return an XML representation of this job\"\"\"\n e = Element('dax', [\n ('id', self.id),\n ('file', self.file.name),\n ('node-label', self.node_label)\n ])\n self.innerXML(e)\n return e\n \nclass DAG(AbstractJob):\n \"\"\"DAG(file[,id][,node_label])\n \n This job represents a sub-DAG that will be executed by this\n workflow.\n \n Examples:\n dagjob1 = DAG(file=\"foo.dag\")\n \n dagfile = File(\"foo.dag\")\n dagjob2 = DAG(dagfile)\n \"\"\"\n def __init__(self, file, id=None, node_label=None):\n \"\"\"\n The name argument can be either a string, or a File object. If\n it is a File object, then this job will inherit its name from the \n File and the File will be added in a with transfer=True,\n register=False, and link=input.\n \n Arguments:\n file: The logical name of the DAG file, or the DAG File object\n id: The ID of the DAG job [default: autogenerated]\n node_label: The label for this job to use in graphing\n \"\"\"\n if isinstance(file, File):\n self.file = file\n elif isinstance(file, str) or isinstance(file, unicode):\n self.file = File(name=file)\n else:\n raise FormatError(\"Invalid file\", file)\n AbstractJob.__init__(self, id=id, node_label=node_label)\n \n def __unicode__(self):\n return u\"\" % (self.id, self.file.name)\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def toXML(self):\n \"\"\"Return an XML representation of this DAG\"\"\"\n e = Element('dag', [\n ('id', self.id),\n ('file', self.file.name),\n ('node-label', self.node_label)\n ])\n self.innerXML(e)\n return e\n\nclass Dependency:\n \"\"\"A dependency between two nodes in the ADAG\"\"\"\n def __init__(self, parent, child, edge_label=None):\n if isinstance(parent, AbstractJob):\n if not parent.id:\n raise FormatError(\"Parent job has no id\", parent)\n self.parent = parent.id\n elif parent:\n self.parent = parent\n else:\n raise FormatError(\"Invalid parent\", parent)\n if isinstance(child, AbstractJob):\n if not child.id:\n raise FormatError(\"Child job has no id\", child)\n self.child = child.id\n elif child:\n self.child = child\n else:\n raise FormatError(\"Invalid child\", child)\n if self.parent == self.child:\n raise FormatError(\"No self edges allowed\",(self.parent,self.child))\n self.edge_label = edge_label\n \n def __unicode__(self):\n return \" %s>\" % (self.parent, self.child)\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def __hash__(self):\n return hash((self.parent,self.child))\n \n def __eq__(self, other):\n \"\"\"Equal dependencies have the same parent and child\"\"\"\n if isinstance(other, Dependency):\n return self.parent == other.parent and self.child == other.child\n return False\n \n\nclass ADAG(InvokeMixin):\n \"\"\"ADAG(name[,count][,index])\n \n Representation of a directed acyclic graph in XML (DAX).\n \n Examples:\n dax = ADAG('diamond')\n or, if you want to use the old style count/index partitioning stuff:\n part5 = ADAG('partition_5',count=10,index=5)\n \n Adding jobs:\n a = Job(...)\n dax.addJob(a)\n \n Adding parent-child control-flow dependency:\n dax.addDependency(Dependency(parent=a,child=b))\n dax.addDependency(Dependency(parent=a,child=c))\n dax.addDependency(Dependency(parent=b,child=d))\n dax.addDependency(Dependency(parent=c,child=d)) \n or:\n dax.depends(child=b, parent=a)\n \n Adding Files (not required if you have a replica catalog):\n input = File(...)\n dax.addFile(input)\n \n Adding Executables (not required if you have a transformation catalog):\n exe = Executable(...)\n dax.addExecutable(exe)\n \n Adding Transformations (not required if you have a transformation catalog):\n xform = Transformation(...)\n dax.addTransformation(xform)\n \n Writing a DAX out to a file:\n f = open('diamond.dax','w')\n dax.writeXML(f)\n f.close()\n \"\"\"\n def __init__(self, name, count=None, index=None):\n \"\"\"\n Arguments:\n name: The name of the workflow\n count: Total number of DAXes that will be created\n index: Zero-based index of this DAX\n \"\"\"\n if not name:\n raise FormatError(\"Invalid ADAG name\", name)\n self.name = name\n if count: count = int(count)\n if index: index = int(index)\n self.count = count\n self.index = index\n \n # This is used to generate unique ID numbers\n self.sequence = 1\n \n self.jobs = {}\n self.files = set()\n self.executables = set()\n self.dependencies = set()\n self.transformations = set()\n self.invocations = set()\n \n def __unicode__(self):\n return u\"\" % self.name\n \n def __str__(self):\n return unicode(self).encode(\"utf-8\")\n \n def nextJobID(self):\n \"\"\"Get an autogenerated ID for the next job\"\"\"\n next = None\n while not next or next in self.jobs:\n next = \"ID%07d\" % self.sequence\n self.sequence += 1\n return next\n \n def getJob(self, jobid):\n \"\"\"Get a Job/DAG/DAX\"\"\"\n if not jobid in self.jobs:\n raise NotFoundError(\"Job not found\",jobid)\n return self.jobs[jobid]\n \n def addJob(self, job):\n \"\"\"Add a job to this ADAG\"\"\"\n # Add an auto-generated ID if the job doesn't have one\n if job.id is None:\n job.id = self.nextJobID()\n if self.hasJob(job):\n raise DuplicateError(\"Duplicate job\",job)\n self.jobs[job.id] = job\n \n def hasJob(self, job):\n \"\"\"Test to see if job is in this ADAG\n The job parameter can be an object or a job ID\n \"\"\"\n if isinstance(job, AbstractJob):\n return job.id in self.jobs\n else:\n return job in self.jobs\n \n def removeJob(self, job):\n \"\"\"Remove job from this ADAG\"\"\"\n if not self.hasJob(job):\n raise NotFoundError(\"Job not found\", job)\n if isinstance(job, AbstractJob):\n del self.jobs[job.id]\n else:\n del self.jobs[job]\n \n def clearJobs(self):\n \"\"\"Remove all jobs\"\"\"\n self.jobs = {}\n \n def addDAX(self, dax):\n \"\"\"Add a sub-DAX (synonym for addJob)\"\"\"\n if not isinstance(dax, DAX):\n raise FormatError(\"Not a DAX\", dax)\n self.addJob(dax)\n \n def addDAG(self, dag):\n \"\"\"Add a sub-DAG (synonym for addJob)\"\"\"\n if not isinstance(dag, DAG):\n raise FormatError(\"Not a DAG\", dag)\n self.addJob(dag)\n \n def addFile(self, file):\n \"\"\"Add a file to the DAX\"\"\"\n if not isinstance(file, File):\n raise FormatError(\"Invalid File\", file)\n if self.hasFile(file):\n raise DuplicateError(\"Duplicate file\", file)\n self.files.add(file)\n \n def hasFile(self, file):\n \"\"\"Check to see if file is in this ADAG\"\"\"\n return file in self.files\n \n def removeFile(self, file):\n \"\"\"Remove file from this ADAG\"\"\"\n if not self.hasFile(file):\n raise NotFoundError(\"File not found\", file)\n self.files.remove(file)\n \n def clearFiles(self):\n \"\"\"Remove all files\"\"\"\n self.files.clear()\n \n def addExecutable(self, executable):\n \"\"\"Add an executable to this ADAG\"\"\"\n if self.hasExecutable(executable):\n raise DuplicateError(\"Duplicate executable\",executable)\n self.executables.add(executable)\n \n def hasExecutable(self, executable):\n \"\"\"Check if executable is in this ADAG\"\"\"\n return executable in self.executables\n \n def removeExecutable(self, executable):\n \"\"\"Remove executable from this ADAG\"\"\"\n if not self.hasExecutable(executable):\n raise NotFoundError(\"Executable not found\",executable)\n self.executables.remove(executable)\n \n def clearExecutables(self):\n \"\"\"Remove all executables\"\"\"\n self.executables.clear()\n \n def addTransformation(self, transformation):\n \"\"\"Add a transformation to this ADAG\"\"\"\n if self.hasTransformation(transformation):\n raise DuplicateError(\"Duplicate tranformation\",transformation)\n self.transformations.add(transformation)\n \n def hasTransformation(self, transformation):\n \"\"\"Check to see if transformation is in this ADAG\"\"\"\n return transformation in self.transformations\n \n def removeTransformation(self, transformation):\n \"\"\"Remove transformation from this ADAG\"\"\"\n if not self.hasTransformation(transformation):\n raise NotFoundError(\"Transformation not found\",transformation)\n self.transformations.remove(transformation)\n \n def clearTransformations(self):\n \"\"\"Remove all transformations\"\"\"\n self.transformations.clear()\n \n def depends(self, child, parent, edge_label=None):\n \"\"\"Add a dependency to the workflow\n Arguments:\n child: The child job/dax/dag or id\n parent: The parent job/dax/dag or id\n edge_label: A label for the edge (optional)\n \"\"\"\n d = Dependency(parent, child, edge_label)\n self.addDependency(d)\n \n def addDependency(self, dep):\n \"\"\"Add a dependency to the workflow\n \n The old way to call this method is no longer valid. Please change:\n adag.addDependency(parent=\"ID01\", child=\"ID02\", edge_label=\"E01\")\n to be:\n adag.addDependency(Dependency(parent=\"ID01\", child=\"ID02\", edge_label=\"E01\"))\n or:\n adag.depends(parent=\"ID01\", child=\"ID02\", edge_label=\"E01\")\n \n \"\"\"\n if self.hasDependency(dep):\n raise DuplicateError(\"Duplicate dependency\", dep)\n # Check the jobs\n if dep.parent not in self.jobs:\n raise NotFoundError(\"Parent not found\", dep.parent)\n if dep.child not in self.jobs:\n raise NotFoundError(\"Child not found\", dep.child)\n self.dependencies.add(dep)\n \n def hasDependency(self, dep):\n \"\"\"Check to see if dependency exists\"\"\"\n return dep in self.dependencies\n \n def removeDependency(self, dep):\n \"\"\"Remove dependency from workflow\"\"\"\n if not self.hasDependency(dep):\n raise NotFoundError(\"Dependency not found\",dep)\n self.dependencies.remove(dep)\n \n def clearDependencies(self):\n \"\"\"Remove all dependencies\"\"\"\n self.dependencies.clear()\n \n def toXML(self):\n \"\"\"Get the XML string for this ADAG\n This is primarily intended for testing. If you have a large ADAG\n you should use writeXML instead.\n \"\"\"\n s = StringIO()\n self.writeXML(s)\n xml = s.getvalue()\n s.close()\n return xml\n \n def writeXMLFile(self, filename):\n \"\"\"Write the ADAG to an XML file\"\"\"\n file = codecs.open(filename, \"w\", \"utf-8\")\n self.writeXML(file)\n file.close()\n \n def writeXML(self, out):\n \"\"\"Write the ADAG as XML to a stream\"\"\"\n # Preamble\n out.write('\\n')\n \n # Metadata\n out.write('\\n' % datetime.datetime.now())\n if os.name == 'posix':\n import pwd\n username = pwd.getpwuid(os.getuid())[0]\n elif os.name == 'nt':\n username = os.getenv(\"USERNAME\", \"N/A\")\n else:\n username = \"N/A\"\n out.write('\\n' % username) \n out.write('\\n')\n \n # Open tag\n out.write('\\n')\n \n # Invocations\n for i in self.invocations:\n out.write('\\t')\n i.toXML().write(stream=out, level=1)\n out.write('\\n')\n \n # Files\n for f in self.files:\n out.write('\\t')\n f.toXML().write(stream=out, level=1)\n out.write('\\n')\n \n # Executables\n for e in self.executables:\n out.write('\\t')\n e.toXML().write(stream=out, level=1)\n out.write('\\n')\n \n # Transformations\n for t in self.transformations:\n out.write('\\t')\n t.toXML().write(stream=out, level=1)\n out.write('\\n')\n \n # Jobs\n keys = list(self.jobs.keys())\n keys.sort()\n for job_id in keys:\n job = self.jobs[job_id]\n out.write('\\t')\n job.toXML().write(stream=out, level=1)\n out.write('\\n')\n \n # Dependencies\n # Since we store dependencies as tuples, but we need to print them as nested elements\n # we first build a map of all the children that maps child -> [(parent,label),...]\n children = {}\n for dep in self.dependencies:\n if not dep.child in children:\n children[dep.child] = []\n children[dep.child].append((dep.parent, dep.edge_label))\n \n # Now output all the xml in sorted order by child, then parent\n keys = list(children.keys())\n keys.sort()\n for child in keys:\n out.write('\\t')\n c = Element(\"child\",[(\"ref\",child)])\n parents = children[child]\n parents.sort()\n for parent, edge_label in parents:\n p = Element(\"parent\",[\n (\"ref\", parent),\n (\"edge-label\", edge_label)\n ])\n c.element(p)\n c.write(stream=out, level=1)\n out.write('\\n')\n \n # Close tag\n out.write('\\n')\n\ndef parseString(string):\n s = StringIO(string)\n return parse(s)\n\ndef parse(infile):\n try:\n import xml.etree.cElementTree as etree\n except:\n try:\n import xml.etree.ElementTree as etree\n except:\n try:\n import elementtree.ElementTree as etree\n except:\n raise Exception(\"Please install elementtree\")\n \n NS = \"{http://pegasus.isi.edu/schema/DAX}\"\n \n def QN(tag):\n return NS+tag\n \n def badattr(e, exc):\n return ParseError(\"Attribute '%s' is required for element %s\" % (exc.args[0], e.tag))\n \n def parse_invoke(e):\n try:\n return Invoke(when=e.attrib[\"when\"], what=e.text)\n except KeyError as ke:\n raise badattr(e, ke)\n \n def parse_adag(e):\n try:\n name = e.attrib['name']\n count = e.get(\"count\", None)\n index = e.get(\"index\", None)\n return ADAG(name=name, count=count, index=index)\n except KeyError as ke:\n raise badattr(e, ke)\n \n def parse_profile(e):\n try:\n return Profile(\n namespace=e.attrib[\"namespace\"],\n key=e.attrib[\"key\"],\n value=e.text)\n except KeyError as ke:\n raise badattr(e, ke)\n \n def parse_metadata(e):\n try:\n return Metadata(\n key=e.attrib['key'],\n type=e.attrib['type'],\n value=e.text)\n except KeyError as ke:\n raise badattr(e, ke)\n \n def parse_pfn(e):\n try:\n p = PFN(\n url=e.attrib['url'],\n site=e.get(\"site\", None)\n )\n except KeyError as ke:\n raise badattr(e, ke)\n for pr in e.findall(QN(\"profile\")):\n p.addProfile(parse_profile(pr))\n return p\n \n def parse_catalog(e, f):\n for p in e.findall(QN(\"profile\")):\n f.addProfile(parse_profile(p))\n for m in e.findall(QN(\"metadata\")):\n f.addMetadata(parse_metadata(m))\n for p in e.findall(QN(\"pfn\")):\n f.addPFN(parse_pfn(p))\n return f\n \n def parse_file(e):\n try:\n f = File(e.attrib['name'])\n except KeyError as ke:\n raise badattr(e, ke)\n return parse_catalog(e, f)\n \n def parse_executable(e):\n try:\n exe = Executable(\n name=e.attrib['name'],\n namespace=e.get(\"namespace\", None),\n version=e.get(\"version\", None),\n arch=e.get(\"arch\", None),\n os=e.get(\"os\", None), \n osrelease=e.get(\"osrelease\", None),\n osversion=e.get(\"osversion\", None),\n glibc=e.get(\"glibc\", None),\n installed=e.get(\"installed\", None)\n )\n except KeyError as ke:\n raise badattr(e, ke)\n parse_catalog(e, exe)\n for i in e.findall(QN(\"invoke\")):\n exe.addInvoke(parse_invoke(i))\n return exe\n \n def parse_uses(e):\n try:\n return Use(\n e.attrib['name'],\n namespace = e.get('namespace', None),\n version = e.get('version', None),\n link = e.get('link', None),\n register = e.get('register', None),\n transfer = e.get('transfer', None),\n optional = e.get('optional', None),\n executable = e.get('executable', None)\n )\n except KeyError as ke:\n raise badattr(e, ke)\n \n def parse_transformation(e):\n try:\n t = Transformation(\n namespace=e.get(\"namespace\", None),\n name=e.attrib['name'],\n version=e.get(\"version\", None))\n except KeyError as ke:\n raise badattr(e, ke)\n for u in e.findall(QN(\"uses\")):\n t.addUse(parse_uses(u))\n for i in e.findall(QN(\"invoke\")):\n t.addInvoke(parse_invoke(i))\n return t\n \n def iterelem(e):\n if e.text:\n yield e.text\n for f in e:\n if f.text:\n yield f.text\n yield f\n if f.tail:\n yield f.tail\n \n def parse_absjob(e, j):\n args = e.find(QN(\"argument\"))\n if args is not None:\n for i in iterelem(args):\n if isinstance(i, str):\n j.addRawArguments(i)\n else:\n j.addRawArguments(File(i.attrib['name']))\n \n try:\n s = e.find(QN(\"stdin\"))\n if s is not None:\n j.setStdin(s.attrib['name'])\n \n s = e.find(QN(\"stdout\"))\n if s is not None:\n j.setStdout(s.attrib['name'])\n \n s = e.find(QN(\"stderr\"))\n if s is not None:\n j.setStderr(s.attrib['name'])\n except KeyError as ke:\n raise badattr(s, ke)\n \n for p in e.findall(QN(\"profile\")):\n j.addProfile(parse_profile(p))\n \n for u in e.findall(QN(\"uses\")):\n j.addUse(parse_uses(u))\n \n for i in e.findall(QN(\"invoke\")):\n j.addInvoke(parse_invoke(i))\n \n return j\n \n def parse_job(e):\n try:\n j = Job(\n name=e.attrib[\"name\"],\n id=e.attrib[\"id\"], \n namespace=e.get(\"namespace\", None),\n version=e.get(\"version\", None),\n node_label=e.get(\"node-label\", None)\n )\n except KeyError as ke:\n raise badattr(e, ke)\n return parse_absjob(e, j)\n \n def parse_dax(e):\n try:\n d = DAX(\n file=e.attrib[\"file\"],\n id=e.attrib[\"id\"],\n node_label=e.get(\"node-label\", None)\n )\n except KeyError as ke:\n raise badattr(e, ke)\n return parse_absjob(e, d)\n \n def parse_dag(e):\n try:\n d = DAG(\n file=e.attrib[\"file\"],\n id=e.attrib[\"id\"],\n node_label=e.get(\"node-label\", None)\n )\n except KeyError as ke:\n raise badattr(e, ke)\n return parse_absjob(e, d)\n \n def parse_dependencies(e):\n try:\n child = e.attrib[\"ref\"]\n except KeyError as ke:\n raise badattr(e, ke)\n for p in e.findall(QN(\"parent\")):\n try:\n parent = p.attrib[\"ref\"]\n label = p.attrib.get(\"edge-label\", None)\n yield Dependency(parent, child, label)\n except KeyError as ke:\n raise badattr(p, ke)\n \n # We use iterparse because we don't have to read in the\n # entire document\n iterator = etree.iterparse(infile, events=(\"start\", \"end\"))\n iterator = iter(iterator)\n \n # Get the document element (should be )\n event, root = iterator.next()\n adag = parse_adag(root)\n \n # This function reads all the children of \"node\"\n def expand(node):\n event, elem = iterator.next()\n while elem != node:\n event, elem = iterator.next()\n \n # We clear the document element to prevent\n # the memory usage from growing\n root.clear()\n \n for ev, elem in iterator:\n if ev == \"end\":\n continue\n \n # Read in the entire element and children\n expand(elem)\n \n if elem.tag == QN(\"job\"):\n j = parse_job(elem)\n adag.addJob(j)\n elif elem.tag == QN(\"child\"):\n for d in parse_dependencies(elem):\n adag.addDependency(d)\n elif elem.tag == QN(\"file\"):\n f = parse_file(elem)\n adag.addFile(f)\n elif elem.tag == QN(\"executable\"):\n e = parse_executable(elem)\n adag.addExecutable(e)\n elif elem.tag == QN(\"transformation\"):\n t = parse_transformation(elem)\n adag.addTransformation(t)\n elif elem.tag == QN(\"dag\"):\n d = parse_dag(elem)\n adag.addJob(d)\n elif elem.tag == QN(\"dax\"):\n d = parse_dax(elem)\n adag.addJob(d)\n elif elem.tag == QN(\"invoke\"):\n adag.addInvoke(parse_invoke(elem))\n else:\n raise ParseError(\"Unknown tag\", elem.tag)\n \n return adag\n\ndef main():\n \"\"\"Simple smoke test\"\"\"\n # Create a DAX\n diamond = ADAG(\"diamond\")\n \n # Add input file to the DAX-level replica catalog\n a = File(\"f.a\")\n a.addPFN(PFN(\"gsiftp://site.com/inputs/f.a\",\"site\"))\n diamond.addFile(a)\n \n # Add executables to the DAX-level replica catalog\n e_preprocess = Executable(namespace=\"diamond\", name=\"preprocess\", version=\"4.0\", os=\"linux\", arch=\"x86_64\")\n e_preprocess.addPFN(PFN(\"gsiftp://site.com/bin/preprocess\",\"site\"))\n diamond.addExecutable(e_preprocess)\n \n e_findrange = Executable(namespace=\"diamond\", name=\"findrange\", version=\"4.0\", os=\"linux\", arch=\"x86_64\")\n e_findrange.addPFN(PFN(\"gsiftp://site.com/bin/findrange\",\"site\"))\n diamond.addExecutable(e_findrange)\n \n e_analyze = Executable(namespace=\"diamond\", name=\"analyze\", version=\"4.0\", os=\"linux\", arch=\"x86_64\")\n e_analyze.addPFN(PFN(\"gsiftp://site.com/bin/analyze\",\"site\"))\n diamond.addExecutable(e_analyze)\n \n # Add a preprocess job\n preprocess = Job(e_preprocess)\n b1 = File(\"f.b1\")\n b2 = File(\"f.b2\")\n preprocess.addArguments(\"-a preprocess\",\"-T60\",\"-i\",a,\"-o\",b1,b2)\n preprocess.uses(a, link=Link.INPUT)\n preprocess.uses(b1, link=Link.OUTPUT, transfer=True)\n preprocess.uses(b2, link=Link.OUTPUT, transfer=True)\n diamond.addJob(preprocess)\n \n # Add left Findrange job\n frl = Job(e_findrange)\n c1 = File(\"f.c1\")\n frl.addArguments(\"-a findrange\",\"-T60\",\"-i\",b1,\"-o\",c1)\n frl.uses(b1, link=Link.INPUT)\n frl.uses(c1, link=Link.OUTPUT, transfer=True)\n diamond.addJob(frl)\n \n # Add right Findrange job\n frr = Job(e_findrange)\n c2 = File(\"f.c2\")\n frr.addArguments(\"-a findrange\",\"-T60\",\"-i\",b2,\"-o\",c2)\n frr.uses(b2, link=Link.INPUT)\n frr.uses(c2, link=Link.OUTPUT, transfer=True)\n diamond.addJob(frr)\n \n # Add Analyze job\n analyze = Job(e_analyze)\n d = File(\"f.d\")\n analyze.addArguments(\"-a analyze\",\"-T60\",\"-i\",c1,c2,\"-o\",d)\n analyze.uses(c1, link=Link.INPUT)\n analyze.uses(c2, link=Link.INPUT)\n analyze.uses(d, link=Link.OUTPUT, transfer=True, register=True)\n diamond.addJob(analyze)\n \n # Add dependencies\n diamond.depends(parent=preprocess, child=frl)\n diamond.depends(parent=preprocess, child=frr)\n diamond.depends(parent=frl, child=analyze)\n diamond.depends(parent=frr, child=analyze)\n \n # Get generated diamond dax\n import sys\n diamond.writeXML(sys.stdout)\n\nif __name__ == '__main__':\n main()\n", "repo_name": "applicationskeleton/Skeleton", "sub_path": "src/aimes/skeleton/Pegasus/DAX3.py", "file_name": "DAX3.py", "file_ext": "py", "file_size_in_byte": 63233, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "3", "api": [{"api_name": "StringIO.StringIO", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 77, "usage_type": "attribute"}, {"api_name": "StringIO.StringIO", "line_number": 1479, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 1487, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 1497, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1497, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 1498, "usage_type": "attribute"}, {"api_name": "pwd.getpwuid", "line_number": 1500, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 1500, "usage_type": "call"}, {"api_name": "os.name", "line_number": 1501, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 1502, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 1581, "usage_type": "call"}, {"api_name": "elementtree.ElementTree.iterparse", "line_number": 1809, "usage_type": "call"}, {"api_name": "elementtree.ElementTree", "line_number": 1809, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 1927, "usage_type": "attribute"}]} +{"seq_id": "8147859826", "text": "# --------------\nimport pandas as pd\nfrom collections import Counter\n\n# Load dataset\ndata = pd.read_csv(path)\n\nprint('Null values :/n')\nprint(data.isnull().sum())\nprint('/n')\n\nprint('Statistics :/n')\nprint(data.describe())\n\n\n# --------------\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nsns.set_style(style='darkgrid')\n\n# Store the label values \nlabel = data['Activity'].copy()\n\nplt.figure(figsize=(10,5))\nchart = sns.countplot(\n data=data,\n x=label,\n)\n\nchart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n\n# plot the countplot\n\n\n\n# --------------\n# make the copy of dataset\ndata_copy = data.copy()\n\n# Create an empty column \ndata_copy['duration'] = ''\n\n# Calculate the duration\nduration_df = (data_copy.groupby([label[label.isin(['WALKING_UPSTAIRS', 'WALKING_DOWNSTAIRS'])], 'subject'])['duration'].count() * 1.28)\nduration_df = pd.DataFrame(duration_df)\n\n# Sort the values of duration\nplot_data = duration_df.reset_index().sort_values('duration', ascending=False)\nplot_data['Activity'] = plot_data['Activity'].map({'WALKING_UPSTAIRS':'Upstairs', 'WALKING_DOWNSTAIRS':'Downstairs'})\n\n\n# Plot the durations for staircase use\nplt.figure(figsize=(15,5))\nsns.barplot(data=plot_data, x='subject', y='duration', hue='Activity')\nplt.title('Participants Compared By Their Staircase Walking Duration')\nplt.xlabel('Participants')\nplt.ylabel('Total Duration [s]')\nplt.show()\n\n\n# --------------\n#exclude the Activity column and the subject column\nfeature_cols = data.columns[: -2] \n\n#Calculate the correlation values\ncorrelated_values = data[feature_cols].corr()\n#stack the data and convert to a dataframe\n\ncorrelated_values = (correlated_values.stack().to_frame().reset_index()\n .rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0:'Correlation_score'}))\n\n\n#create an abs_correlation column\ncorrelated_values['abs_correlation'] = correlated_values.Correlation_score.abs()\n\n#Picking most correlated features without having self correlated pairs\ntop_corr_fields = correlated_values.sort_values('Correlation_score', ascending = False).query('abs_correlation>0.8 ')\ntop_corr_fields = top_corr_fields[top_corr_fields['Feature_1'] != top_corr_fields['Feature_2']].reset_index(drop=True)\n\n\n\n# --------------\n# importing neccessary libraries\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import precision_recall_fscore_support as error_metric\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\n# Encoding the target variable\nle = LabelEncoder()\ndata['Activity'] = le.fit_transform(data['Activity'])\n\n# split the dataset into train and test\nX = data.drop('Activity',1)\ny = data['Activity']\n\nX_train, X_test, y_train , y_test = train_test_split(X,y,test_size = 0.3, random_state = 40)\n\n# Baseline model \nclassifier = SVC()\nclf = classifier.fit(X_train, y_train)\ny_pred = clf.predict(X_test)\n\nprecision, recall, f_score, support = error_metric(y_test, y_pred, average = 'weighted')\n\nmodel1_score = classifier.score(X_test, y_test)\n\nprint('precision',precision)\nprint('\\n')\n\nprint('recall',recall)\nprint('\\n')\n\nprint('f1_score',f_score)\nprint('\\n')\n\nprint('score',model1_score)\nprint('\\n')\n\n\n\n\n\n# --------------\n# importing libraries\nfrom sklearn.svm import LinearSVC\nfrom sklearn.feature_selection import SelectFromModel\n\nlsvc = LinearSVC(penalty = 'l1', dual = False, C = 0.01, random_state =42)\nlsvc.fit(X_train, y_train) \n\n# Feature selection using Linear SVC\nmodel_2 = SelectFromModel(lsvc, prefit=True)\n\nnew_train_features = model_2.transform(X_train)\nnew_test_features = model_2.transform(X_test)\n\nclassifier_2 = SVC()\nclf_2 = classifier_2.fit(new_train_features, y_train)\ny_pred_new = clf_2.predict(new_test_features)\n\nmodel2_score = classifier_2.score(new_test_features, y_test)\n\nprecision, recall, f_score, support = error_metric(y_test, y_pred_new, average = 'weighted')\n\nprint('precision',precision)\nprint('\\n')\n\nprint('recall',recall)\nprint('\\n')\n\nprint('f1_score',f_score)\nprint('\\n')\n\nprint('score',model2_score)\nprint('\\n')\n\n# model building on reduced set of features\n\n\n\n\n# --------------\n# Importing Libraries\nfrom sklearn.model_selection import GridSearchCV\n\n# Set the hyperparmeters\nparameters = {'kernel':['linear', 'rbf'], 'C': [100, 20, 1, 0.1]}\nselector = GridSearchCV(SVC(), parameters,'accuracy') \n\nselector.fit(new_train_features, y_train)\n\nmeans = selector.best_score_\nstds = selector.cv_results_['std_test_score'][selector.best_index_]\nprint('Params',selector.best_params_)\nprint('\\n')\n\nprint('means',means)\nprint('\\n')\n\nprint('stds',stds)\nprint('\\n')\n\n# Usage of grid search to select the best hyperparmeters\n# Model building after Hyperparameter tuning\nclassifier_3 = SVC(kernel = selector.best_params_['kernel'], C = selector.best_params_['C'])\n\nclf_3 = classifier_3.fit(new_train_features, y_train)\n\ny_pred_final = clf_3.predict(new_test_features)\n\nmodel3_score = classifier_3.score(new_test_features,y_test)\n\nprecision, recall, f_score, support = error_metric(y_test, y_pred_final, average = 'weighted')\n\nprint('precision',precision)\nprint('\\n')\n\nprint('recall',recall)\nprint('\\n')\n\nprint('f1_score',f_score)\nprint('\\n')\n\nprint('score',model3_score)\nprint('\\n')\n\n\n\n\n", "repo_name": "ninadangchekar96/ga-learner-dsmp-repo", "sub_path": "SVM-(Human-Activity-Recognition-with-Smartphones)/code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 5261, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 91, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectFromModel", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 139, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "2485641274", "text": "from django import forms\nfrom django.contrib.auth.forms import UserCreationForm, UserChangeForm\n\nfrom .models import CustomUser\n\n\nclass CustomUserCreationForm(UserCreationForm):\n class Meta:\n model = CustomUser\n fields = (\"username\", \"email\")\n\n def __init__(self, *args, **kwargs):\n super(CustomUserCreationForm, self).__init__(*args, **kwargs)\n\n self.fields[\"username\"].widget.attrs[\"placeholder\"] = \"Username\"\n self.fields[\"username\"].widget.attrs[\"style\"] = \"text-align:center;\"\n\n self.fields[\"email\"].widget.attrs[\"placeholder\"] = \"Email\"\n self.fields[\"email\"].widget.attrs[\"style\"] = \"text-align:center;\"\n\n self.fields[\"password1\"].widget.attrs[\"placeholder\"] = \"Password\"\n self.fields[\"password1\"].widget.attrs[\"style\"] = \"text-align:center;\"\n\n self.fields[\"password2\"].widget.attrs[\"placeholder\"] = \"Confirm Password\"\n self.fields[\"password2\"].widget.attrs[\"style\"] = \"text-align:center;\"\n\n\nclass CustomUserChangeForm(UserChangeForm):\n class Meta:\n model = CustomUser\n fields = (\"username\", \"email\")\n\n\nCLEANING_FREQUENCIES = [\n (\"one-off\", \"One-off\"),\n (\"weekly\", \"Weekly\"),\n (\"fortnightly\", \"Fortnightly\"),\n (\"monthly\", \"Monthly\"),\n]\nCLEANING_TYPE = [\n (\"house\", \"House\"),\n (\"office\", \"Office\"),\n (\"presale\", \"Pre-sale clean\"),\n (\"spring\", \"Spring Clean\"),\n (\"bond/exit\", \"Bond or Exit Clean\"),\n (\"mum\", \"Cleaning for First-time Mums\"),\n (\"oven\", \"Oven Clean\"),\n (\"declutter\", \"Declutter and Organise\"),\n (\"airbnb\", \"Air BnB Clean\"),\n (\"outside\", \"Outdoor Patio Clean\"),\n (\"other\", \"Other (specify below)\"),\n]\nHOME_SIZE = [\n (\"n/a\", \"N/A\"),\n (\"1-2 bed\", \"1-2 bedrooms\"),\n (\"3-5 bed\", \"3-5 bedrooms\"),\n (\">5 bed\", \"More than 5 bedrooms\"),\n]\nOFFICE_SIZE = [\n (\"n/a\", \"N/A\"),\n (\"5-10 people\", \"5-10 people\"),\n (\"11-15 people\", \"11-15 people\"),\n (\">15 people\", \"More than 15 people\"),\n]\nOTHER_SERVICES = [\n (\"yoga\", \"Private Yoga\"),\n (\"pet sit\", \"Pet Sitting\"),\n (\"meal prep\", \"Meal Preparation\"),\n (\"other\", \"Other (specify below)\"),\n]\n\n\nclass ContactForm(forms.Form):\n first_name = forms.CharField(max_length=100)\n last_name = forms.CharField(max_length=100)\n email = forms.EmailField()\n mobile = forms.CharField(max_length=100)\n street_address = forms.CharField(\n label=\"Street Address of the property\",\n max_length=250,\n )\n post_code = forms.CharField(max_length=10)\n clean_type = forms.ChoiceField(\n label=\"What type of clean are you after?\",\n choices=CLEANING_TYPE,\n widget=forms.Select(attrs={\"class\": \"form-control\"}),\n required=False,\n )\n clean_type = forms.MultipleChoiceField(\n choices=CLEANING_TYPE,\n label=\"What type of clean are you after?\",\n required=False,\n widget=forms.CheckboxSelectMultiple,\n )\n clean_frequency = forms.MultipleChoiceField(\n label=\"How often should we come?\",\n choices=CLEANING_FREQUENCIES,\n widget=forms.CheckboxSelectMultiple,\n required=False,\n )\n home_size = forms.ChoiceField(\n required=False,\n label=\"How many bedrooms in the home?\",\n widget=forms.Select(attrs={\"class\": \"form-control\"}),\n choices=HOME_SIZE,\n )\n office_size = forms.ChoiceField(\n required=False,\n label=\"How many people use the office?\",\n widget=forms.Select(attrs={\"class\": \"form-control\"}),\n choices=OFFICE_SIZE,\n )\n other_services = forms.MultipleChoiceField(\n label=\"Are you interested in any of our other services?\",\n choices=OTHER_SERVICES,\n widget=forms.CheckboxSelectMultiple,\n required=False,\n )\n\n additonal_information = forms.CharField(widget=forms.Textarea, required=False)\n\n\nFEEDBACK_CATEGORIES = [\n (\"cleaning\", \"Cleaning\"),\n (\"business_coaching\", \"Business Coaching\"),\n (\"website\", \"Website\"),\n (\"other\", \"Other\"),\n]\nSATISFACTION_RATINGS = [\n (\"\",\"\"),\n (\"very satisfied\", \"Very satisfied\"),\n (\"satisfied\", \"Satisfied\"),\n (\"neutral\", \"Neutral\"),\n (\"dissatisfied\", \"Dissatisfied\"),\n (\"very dissatisfied\", \"Very dissatisfied\"),\n]\nCLEANLINESS_RATINGS = [\n (\"\",\"\"),\n (\"excellent\", \"Excellent\"),\n (\"good\", \"Good\"),\n (\"average\", \"Average\"),\n (\"poor\", \"Poor\"),\n (\"very poor\", \"Very poor\"),\n]\nINSTRUCTIONS_FOLLOWED = [\n (\"\",\"\"),\n (\"yes\", \"Yes, completely\"),\n (\"partially\", \"Yes, but partially\"),\n (\"no\", \"No, not at all\"),\n]\nAREAS = [\n (\"\",\"\"),\n (\"dusting/vaccumming\" ,\"Dusting and vacuuming\"),\n (\"kitchen/bedroom\",\"Kitchen & Bathrooms\"),\n (\"extra/rotational\",\"Extras & rotational items\"),\n ('helpful/friendly',\"Helpful & friendly manner\"),\n ]\nRECCOMMENDATION_CHOICES = [\n (\"\",\"\"),\n (\"yes\",\"Yes, definitley\"),\n (\"maybe\",\"Yes, maybe\"),\n (\"probably not\",\"No, probably not\"),\n (\"no\",\"No, defnitley not\"),\n ]\n\n\n\nclass FeedbackForm(forms.Form):\n name = forms.CharField(max_length=100)\n email = forms.EmailField(required=False, help_text=\"(Optional)\")\n feedback_category = forms.ChoiceField(\n label=\"What is your feedback about?\",\n choices=FEEDBACK_CATEGORIES,\n widget=forms.Select(attrs={\"class\": \"form-control\"}),\n )\n feedback = forms.CharField(widget=forms.Textarea, required=False)\n satisfaction_rating = forms.ChoiceField(\n label=\"How satisfied were you with the overall cleaning service provided?\",\n choices=SATISFACTION_RATINGS,\n required=False,\n )\n cleanliness_rating = forms.ChoiceField(\n label=\"How would you rate the cleanliness of your space after the cleaning?\",\n choices=CLEANLINESS_RATINGS,\n required=False,\n )\n instructions_followed = forms.ChoiceField(\n label=\"Did the cleaner follow any specific instructions or requests you had given?\",\n choices=INSTRUCTIONS_FOLLOWED,\n required=False,\n )\n areas_for_improvement = forms.ChoiceField(\n label=\"Areas for improvement\",\n choices=AREAS,\n required=False,\n )\n areas_of_satisfaction = forms.ChoiceField(\n label=\"Areas of satisfaction\",\n choices=AREAS,\n required=False,\n )\n would_reccommend = forms.ChoiceField(\n label=\"Would you recommend our cleaning services to others?\",\n choices=RECCOMMENDATION_CHOICES,\n required=False,\n )\n additional_information = forms.CharField(\n label=\"Is there anything else you would like to share about your experience with our cleaning service?\",\n widget=forms.Textarea,\n required=False,\n )\n contact_information = forms.CharField(\n label=\"Please provide your contact information if you would like us to follow up with you regarding your feedback\",\n widget=forms.Textarea,\n required=False,\n help_text=\"Thank you for taking the time to complete this feedback form. We appreciate your input and look forward to serving you again in the future.\"\n )", "repo_name": "phyxphysio/holistic-hincher", "sub_path": "holistic_hincher/free_pages/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 7064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 7, "usage_type": "name"}, {"api_name": "models.CustomUser", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.auth.forms.UserChangeForm", "line_number": 28, "usage_type": "name"}, {"api_name": "models.CustomUser", "line_number": 30, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 73, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 74, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 75, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 75, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 76, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 77, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 77, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 78, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 78, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 82, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 82, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 83, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 86, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 86, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 89, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 89, "usage_type": "name"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 93, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 93, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 95, "usage_type": "name"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 98, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 98, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 101, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 101, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 104, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 104, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 107, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 107, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 110, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 110, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 113, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 113, "usage_type": "name"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 116, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 116, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 120, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 120, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 120, "usage_type": "attribute"}, {"api_name": "django.forms.Form", "line_number": 168, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 168, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 169, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 169, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 170, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 170, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 171, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 171, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 174, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 174, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 176, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 176, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 176, "usage_type": "attribute"}, {"api_name": "django.forms.ChoiceField", "line_number": 177, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 177, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 182, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 182, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 187, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 187, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 192, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 192, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 197, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 197, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 202, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 202, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 207, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 207, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 209, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 209, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 212, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 212, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 214, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 214, "usage_type": "name"}]} +{"seq_id": "27592188682", "text": "import asyncio\nfrom random import randint\n\nloop = asyncio.get_event_loop()\nfuture = loop.create_future()\n\ndef random_hit(future, n, cnt=1, loop=None):\n #import pdb; pdb.set_trace()\n if loop is None:\n loop = asyncio.get_event_loop()\n v = randint(1, n)\n if v == 1:\n future.set_result(cnt)\n else:\n cnt += 1\n loop.call_soon(random_hit, future, n, cnt, loop)\n\nfuture.add_done_callback(lambda f: print(\"done\"))\nloop.call_soon(random_hit, future, 100)\n\nresult = loop.run_until_complete(future)\nprint(result)\n\nloop.close()\n", "repo_name": "iihiro/test_asyncio", "sub_path": "2_example.py", "file_name": "2_example.py", "file_ext": "py", "file_size_in_byte": 558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "asyncio.get_event_loop", "line_number": 4, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 10, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "5912346267", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\nimport math\n\nwidth, height = 100, 100\ntree_prob = 0.6\nsettle_prob = 0.6\nsettle_num = 5\nneighbours = [(-2, -2), (-1, -2), (0, -2), (1, -2), (2, -2),\n (-2, -1), (-1, -1), (0, -1), (1, -1), (2, -1),\n (-2, 0), (-1, 0), (1, 0), (2, 0),\n (-2, 1), (-1, 1), (0, 1), (1, 1), (2, 1),\n (-2, 2), (-1, 2), (0, 2), (1, 2), (2, 2)]\n\nEMPTY, TREE, SETTLE, FIRE, BREAK, = 0, 1, 2, 3, 4\n\ncolours = ['white', 'green', 'brown']\n # 'orange', 'black']\ncmap = colors.ListedColormap(colours)\n\nnp.random.seed(2)\nsystem = np.random.random([width, height])\n\nsystem = np.where(system <= tree_prob, TREE, EMPTY)\n\ndef settle_neighbours(x, y):\n system[x][y] = SETTLE\n for n in neighbours:\n neigh_x = x + n[0]\n neigh_y = y + n[1]\n if((neigh_x >= 0 and neigh_x < width) and (neigh_y >= 0 and neigh_y < height)):\n if(np.random.random() <= settle_prob):\n system[neigh_x][neigh_y] = SETTLE\n\n# generate settlements\nfor settle in range(settle_num):\n x = math.trunc(np.random.random() * 100)\n y = math.trunc(np.random.random() * 100)\n settle_neighbours(x, y)\n\nplt.imshow(system, cmap=cmap)\nplt.show()\n\n# print(system)\n\nnp.savetxt('system.txt', system, fmt='%.1i')\n ", "repo_name": "cargraham/COMP6216-Group-Coursework", "sub_path": "myModel.py", "file_name": "myModel.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "matplotlib.colors.ListedColormap", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "math.trunc", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "math.trunc", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "73197908241", "text": "import webob.exc\n\nfrom neutron.tests.unit.plugins.ml2 import test_plugin\nfrom neutron.tests.unit import testlib_api\n\n# BGPVPN Table metadata should be imported before\n# sqlalchemy metadata.create_all call else tables\n# will not be created.\nfrom networking_bgpvpn.neutron.db import bgpvpn_db # noqa\nfrom networking_bgpvpn.tests.unit.services import test_plugin as bgpvpn_plugin\n\nfrom networking_odl.common import constants as odl_const\nfrom networking_odl.tests.functional import base\n\n\nclass _TestBGPVPNBase(base.OdlTestsBase):\n rds = ['100:1']\n\n def setUp(self, plugin=None, service_plugins=None,\n ext_mgr=None):\n provider = {\n 'service_type': 'BGPVPN',\n 'name': 'OpenDaylight',\n 'driver': 'networking_odl.bgpvpn.odl_v2.OpenDaylightBgpvpnDriver',\n 'default': True\n }\n self.service_providers.return_value = [provider]\n self.plugin_arg = plugin\n self.service_plugin_arg = service_plugins\n self.ext_mgr_arg = ext_mgr\n super(_TestBGPVPNBase, self).setUp()\n\n def get_ext_managers(self):\n return self.ext_mgr_arg\n\n def get_plugins(self):\n return self.plugin_arg\n\n def get_additional_service_plugins(self):\n return self.service_plugin_arg\n\n def _assert_networks_associated(self, net_ids, bgpvpn):\n response = self.get_odl_resource(odl_const.ODL_BGPVPN, bgpvpn)\n self.assertItemsEqual(net_ids,\n response[odl_const.ODL_BGPVPN]['networks'])\n\n def _assert_routers_associated(self, router_ids, bgpvpn):\n response = self.get_odl_resource(odl_const.ODL_BGPVPN, bgpvpn)\n self.assertItemsEqual(router_ids,\n response[odl_const.ODL_BGPVPN]['routers'])\n\n def test_bgpvpn_create(self):\n with self.bgpvpn() as bgpvpn:\n self.assert_resource_created(odl_const.ODL_BGPVPN, bgpvpn)\n\n def test_bgpvpn_create_with_rds(self):\n with self.bgpvpn(route_distinguishers=self.rds) as bgpvpn:\n response = self.get_odl_resource(odl_const.ODL_BGPVPN, bgpvpn)\n self.assertItemsEqual(self.rds,\n response[odl_const.ODL_BGPVPN]\n ['route_distinguishers'])\n\n def test_bgpvpn_delete(self):\n with self.bgpvpn(do_delete=False) as bgpvpn:\n self._delete('bgpvpn/bgpvpns', bgpvpn['bgpvpn']['id'])\n self.assertIsNone(\n self.get_odl_resource(odl_const.ODL_BGPVPN, bgpvpn))\n\n def test_associate_dissociate_net(self):\n with (self.network()) as net1, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn:\n net_id = net1['network']['id']\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n with self.assoc_net(bgpvpn_id, net_id):\n self._assert_networks_associated([net_id], bgpvpn)\n self._assert_networks_associated([], bgpvpn)\n\n def test_associate_multiple_networks(self):\n with (self.network()) as net1, (self.network()) as net2, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn:\n net_id1 = net1['network']['id']\n net_id2 = net2['network']['id']\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n with self.assoc_net(bgpvpn_id, net_id1), \\\n self.assoc_net(bgpvpn_id, net_id2):\n self._assert_networks_associated([net_id1, net_id2], bgpvpn)\n\n def test_assoc_multiple_networks_dissoc_one(self):\n with (self.network()) as net1, (self.network()) as net2, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn:\n net_id1 = net1['network']['id']\n net_id2 = net2['network']['id']\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n with self.assoc_net(bgpvpn_id, net_id1):\n with self.assoc_net(bgpvpn_id, net_id2):\n self._assert_networks_associated([net_id1, net_id2],\n bgpvpn)\n self._assert_networks_associated([net_id1], bgpvpn)\n\n def test_associate_dissociate_router(self):\n with (self.router(tenant_id=self._tenant_id)) as router, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn:\n router_id = router['router']['id']\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n with self.assoc_router(bgpvpn_id, router_id):\n self._assert_routers_associated([router_id], bgpvpn)\n self._assert_routers_associated([], bgpvpn)\n\n def test_associate_multiple_routers(self):\n with (self.router(tenant_id=self._tenant_id, name='r1')) as r1, (\n self.router(tenant_id=self._tenant_id, name='r2')) as r2, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn:\n router_id1 = r1['router']['id']\n router_id2 = r2['router']['id']\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n with self.assoc_router(bgpvpn_id, router_id1):\n self._assert_routers_associated([router_id1], bgpvpn)\n with testlib_api.ExpectedException(\n webob.exc.HTTPClientError) as ctx_manager:\n with self.assoc_router(bgpvpn_id, router_id2):\n pass\n self.assertEqual(webob.exc.HTTPBadRequest.code,\n ctx_manager.exception.code)\n self._assert_routers_associated([router_id1], bgpvpn)\n\n def test_assoc_router_multiple_bgpvpns(self):\n with (self.router(tenant_id=self._tenant_id, name='r1')) as router, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn1, (\n self.bgpvpn()) as bgpvpn2:\n router_id = router['router']['id']\n bgpvpn_id_1 = bgpvpn1['bgpvpn']['id']\n bgpvpn_id_2 = bgpvpn2['bgpvpn']['id']\n with (self.assoc_router(bgpvpn_id_1, router_id)), (\n self.assoc_router(bgpvpn_id_2, router_id)):\n self._assert_routers_associated([router_id], bgpvpn1)\n self._assert_routers_associated([router_id], bgpvpn2)\n\n def test_associate_router_network(self):\n with (self.router(tenant_id=self._tenant_id)) as router, (\n self.network()) as net1, (\n self.bgpvpn(route_distinguishers=self.rds)) as bgpvpn:\n router_id = router['router']['id']\n net_id = net1['network']['id']\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n with self.assoc_router(bgpvpn_id, router_id), \\\n self.assoc_net(bgpvpn_id, net_id):\n response = self.get_odl_resource(odl_const.ODL_BGPVPN, bgpvpn)\n self.assertItemsEqual([router_id],\n response[odl_const.ODL_BGPVPN]\n ['routers'])\n self.assertItemsEqual([net_id],\n response[odl_const.ODL_BGPVPN]\n ['networks'])\n\n\nclass TestBGPVPNV2Driver(base.V2DriverAdjustment,\n bgpvpn_plugin.BgpvpnTestCaseMixin,\n _TestBGPVPNBase, test_plugin.Ml2PluginV2TestCase):\n _mechanism_drivers = ['opendaylight_v2']\n", "repo_name": "Jordonchen/kolla-ansible-openstack", "sub_path": "networking_odl/tests/functional/test_bgpvpn.py", "file_name": "test_bgpvpn.py", "file_ext": "py", "file_size_in_byte": 7298, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "networking_odl.tests.functional.base.OdlTestsBase", "line_number": 16, "usage_type": "attribute"}, {"api_name": "networking_odl.tests.functional.base", "line_number": 16, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 43, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 43, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 45, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 48, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 48, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 50, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 54, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 54, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 58, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 60, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 60, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 67, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 67, "usage_type": "name"}, {"api_name": "neutron.tests.unit.testlib_api.ExpectedException", "line_number": 118, "usage_type": "call"}, {"api_name": "neutron.tests.unit.testlib_api", "line_number": 118, "usage_type": "name"}, {"api_name": "webob.exc.exc", "line_number": 119, "usage_type": "attribute"}, {"api_name": "webob.exc", "line_number": 119, "usage_type": "name"}, {"api_name": "webob.exc.exc", "line_number": 122, "usage_type": "attribute"}, {"api_name": "webob.exc", "line_number": 122, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 147, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 147, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 149, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 149, "usage_type": "name"}, {"api_name": "networking_odl.common.constants.ODL_BGPVPN", "line_number": 152, "usage_type": "attribute"}, {"api_name": "networking_odl.common.constants", "line_number": 152, "usage_type": "name"}, {"api_name": "networking_odl.tests.functional.base.V2DriverAdjustment", "line_number": 156, "usage_type": "attribute"}, {"api_name": "networking_odl.tests.functional.base", "line_number": 156, "usage_type": "name"}, {"api_name": "networking_bgpvpn.tests.unit.services.test_plugin.BgpvpnTestCaseMixin", "line_number": 157, "usage_type": "attribute"}, {"api_name": "networking_bgpvpn.tests.unit.services.test_plugin", "line_number": 157, "usage_type": "name"}, {"api_name": "neutron.tests.unit.plugins.ml2.test_plugin.Ml2PluginV2TestCase", "line_number": 158, "usage_type": "attribute"}, {"api_name": "neutron.tests.unit.plugins.ml2.test_plugin", "line_number": 158, "usage_type": "name"}]} +{"seq_id": "14901836509", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom scripts import loss_funcs\nfrom scipy.stats import norm as scipy_normal\n\n# Grab some normal points\ndistribution = loss_funcs.NormalPDFLoss()\n\nhello = {}\nto_try = np.asarray([1500] * 1)\n#colours = ['r', 'g', 'b', 'm', 'c', 'y']\n\nmean = [0.0, 0.1, 0.2, 0.3, 0.4]\nmean = [0.0, 0.0, 0.0, 0.0, 0.0]\nstd_d = [1.0, 1.1, 1.2, 1.3, 1.4]\ncolors = ['r', 'g', 'c', 'b', 'm']\n\nmeans = np.zeros((len(mean), to_try.size))\n\nfor stage_i, (a_mean, a_std_d, a_color) in enumerate(zip(mean, std_d, colors)):\n for try_i, i in enumerate(to_try):\n random_variables = scipy_normal.rvs(size=i, loc=0., scale=1.)\n\n # Evaluate the cdf at each random deviate and sort the array\n cdfs = scipy_normal.cdf(random_variables, loc=0.0, scale=a_std_d)\n\n cdfs = cdfs[np.where(np.logical_and(cdfs > a_mean, cdfs < 1 - a_mean))[0]]\n\n # Make it median-invariant\n #cdfs = np.abs(cdfs - 0.5)\n cdfs_sorted = np.sort(cdfs)\n\n # Extend the cdfs and take means\n #cdfs[:] = 0.5\n #np.random.shuffle(cdfs)\n #cdfs = np.mean(cdfs.reshape(random_variables.shape[0], -1), axis=1)\n\n # Cumulative sum and normalise so last element is 1.0\n cdfs_summed = np.cumsum(cdfs)\n cdfs_summed /= 0.5 * i\n\n # Get expected points\n cdfs_expected = np.linspace(0., 1., num=cdfs_sorted.size)\n cdfs_summed = cdfs_expected\n\n cdfs_summed = np.log(np.cosh(cdfs_summed - cdfs_sorted))\n\n # Plot shiz\n plt.plot(cdfs_sorted, cdfs_summed, '-', lw=1, ms=2, color=a_color, alpha=0.1)\n boop = np.max(cdfs_summed)\n #plt.plot([0, 1], [boop, boop], '--', color=c, label='mean^2 of {}'.format(i))\n\n means[stage_i, try_i] = boop\n\n boop = np.mean(means[stage_i, :])\n plt.plot([0, 1], [boop, boop], '--', color=a_color, label='mean^2 of {}, {}'.format(a_mean, a_std_d))\n\n\n#plt.plot([0, 1], [0, 0], 'k--')\nplt.legend()\nplt.xlabel('ci')\nplt.ylabel('(F(ci) - ci)^2')\n#plt.title('CDF stat for mean {}, std_d {} of model'.format(mean, std_d))\nplt.show()\n\nprint(\"Std deviation in means: {}\".format(np.std(means, axis=1)))\n\n", "repo_name": "emilyhunt/masters_project", "sub_path": "examples/cdf_testing.py", "file_name": "cdf_testing.py", "file_ext": "py", "file_size_in_byte": 2152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "scripts.loss_funcs.NormalPDFLoss", "line_number": 7, "usage_type": "call"}, {"api_name": "scripts.loss_funcs", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.stats.norm.rvs", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.cosh", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 55, "usage_type": "call"}, {"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.legend", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "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": "numpy.std", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "34547947448", "text": "\nimport cv2\nimport imageio\n\n# cascadeler filtre serisi bunlar yüzü ve gözü tespit etmek içi ardı ardına uygulanacak\nface_cascade = cv2.CascadeClassifier('haarcascade-frontalface-default.xml')\n\neye_cascade = cv2.CascadeClassifier('haarcascade-eye.xml')\n\n\ndef detect(frame):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # resmi gri tonuna çevirdik\n\n # bu işlem yüz tespiti yapar ve tupple döndürür bu tupple içinde x y kordinatı (dikdörtgenin sol üst köşesi\n # h(yukseklik) w (genislik) değerleri vardır. 1.3 olarak verdiğimiz değer scale dir ne kadar ölçekleneceği\n # 5 değeri ise komşu sayısı en az 5 pencere olursa oraya yüz deriz.\n\n faces = face_cascade.detectMultiScale(gray, 1.1, 5)\n\n for (x, y, w, h) in faces: # x y w h değerlerini kaç tane yüz var ise okadar alıyoruz\n cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)\n gray_face=gray[y:y+h,x:x+w] # gözü yüzün üzerinde arıyoruz yüzün seçili olduğu alanı aldık\n color_face=frame[y:y+h,x:x+w] #yüzün olsuğu alana renkli olarak aldık\n\n eyes=eye_cascade.detectMultiScale(gray_face,1.1,3) # gri yüzün üzerinde göz tespiti yaptık ve tupple dondurduk\n for (ex,ey,ew,eh) in eyes: # koordinaları aldık\n cv2.rectangle(color_face,(ex,ey),(ex+w,ey+h),(0,255,0),2) # renkkli yüzde gözlere kareler çizdik\n\n return frame\n\n\nreader=imageio.get_reader('1.mp4') #videoyu okuduk\nfps=reader.get_meta_data()['fps'] # okuduğumuz videonun fps değerini aldık\nwriter=imageio.get_writer('output.mp4',fps=fps) # yeni video oluşturmak için videonun adı ve kaç fps olucak\n\nfor i,frame in enumerate(reader): # reader ile aldığımız videodan tek tek frame burada i sayaç kaçıncı frami aldığımızı görmek için\n frame=detect(frame)#alıp bunların üzerine detect fonksiyonunu uyguladık\n writer.append_data(frame)#detect fonk uygulanmış frameleri tektek videomuza ekliyoruz\n print(i) # kaçıncı framdeyiz\n\nwriter.close() # videoyu yazmayı kapatıyoruz\n", "repo_name": "burakbaga/face-detection", "sub_path": "face_det.py", "file_name": "face_det.py", "file_ext": "py", "file_size_in_byte": 2056, "program_lang": "python", "lang": "tr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 27, "usage_type": "call"}, {"api_name": "imageio.get_reader", "line_number": 32, "usage_type": "call"}, {"api_name": "imageio.get_writer", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "8182178846", "text": "import Twitter_creds as TC\nimport twitter\nimport json\n\n\noutfile = open(\"test2.txt\",\"w\", encoding=\"utf-8\")\napi = twitter.Api(consumer_key=TC.CONSUMER_KEY,\n consumer_secret=TC.CONSUMER_SECRET,\n access_token_key=TC.ACCESS_TOKEN,\n access_token_secret=TC.ACCESS_TOKEN_SECRET,\n sleep_on_rate_limit=True)\nhash_tag_list = input(\"Please enter your search terms\\nBe sure to separate them with a comma(,).\\n>>\").split(\",\")\n#results = api.GetSearch(raw_query=\"q=%40twitterapi\")\n#results = str(api.GetSearch(raw_query=\"q=(%EF%82%A7%09%E2%80%9CHIV%E2%80%9D%20OR%20%E2%80%9CHIV%2FAIDS%E2%80%9D%20OR%20%E2%80%9CHIV%20OR%20testing%E2%80%9D%20OR%20%E2%80%9CHIV%20OR%20medication%E2%80%9D%20OR%20%E2%80%9CAIDS%20OR%20test%E2%80%9D%20OR%20%E2%80%9CHIV%20OR%20test%E2%80%9D%20OR%20%E2%80%9CHIV%2B%E2%80%9D%20OR%20%E2%80%9CHIV(%2B)%E2%80%9D%20OR%20%E2%80%9Crapid-HIV%20OR%20test%E2%80%9D%20OR%20%E2%80%9Crapid%20OR%20HIV%20OR%20test%E2%80%9D%20OR%20%E2%80%9Cora-sure%E2%80%9D%20OR%20%E2%80%9Corasure%E2%80%9D%20OR%20%E2%80%9CAcquired%20OR%20Immune%20OR%20Deficiency%20OR%20Syndrome%E2%80%9D%20OR%20%E2%80%9CAcyclovir%E2%80%9D%20OR%20%E2%80%9CADAP%E2%80%9D%20OR%20%E2%80%9CKaposi%27s%20OR%20Sarcoma%E2%80%9D%20OR%20%E2%80%9CThrush%E2%80%9D)&src=typed_query\"))\nfor terms in hash_tag_list:\n results = api.GetSearch(term=terms, count= 100, lang=\"en\")\n #, since = 2020 - 2 - 15, until = 2020 - 2 - 16\n print(results)\n for t in results:\n tweets = [t.AsDict() for t in results]\n print(t)\n #print(t[\"text\"], t[\"lang\"])\n str_result = str(results)\n outfile.write(str_result +\"\\n\")\n\n", "repo_name": "chacreton190/Social-Media-Data-Project", "sub_path": "Twitter Hist Search.py", "file_name": "Twitter Hist Search.py", "file_ext": "py", "file_size_in_byte": 1673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "twitter.Api", "line_number": 7, "usage_type": "call"}, {"api_name": "Twitter_creds.CONSUMER_KEY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "Twitter_creds.CONSUMER_SECRET", "line_number": 8, "usage_type": "attribute"}, {"api_name": "Twitter_creds.ACCESS_TOKEN", "line_number": 9, "usage_type": "attribute"}, {"api_name": "Twitter_creds.ACCESS_TOKEN_SECRET", "line_number": 10, "usage_type": "attribute"}]} +{"seq_id": "2039934973", "text": "from __future__ import print_function, division\nimport torch\nimport torch.nn as nn\nimport functools\n\ndef print_network(net):\n num_params = 0\n for param in net.parameters():\n num_params += param.numel()\n print(net)\n print('Total number of parameters: %d' % num_params)\n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n m.weight.data.normal_(0.0, 0.02)\n if hasattr(m.bias, 'data'):\n m.bias.data.fill_(0)\n elif classname.find('BatchNorm2d') != -1:\n m.weight.data.normal_(1.0, 0.02)\n # m.weight.data.fill_(1)\n m.bias.data.fill_(0)\n\ndef get_norm_layer(norm_type='instance'):\n if norm_type == 'batch':\n norm_layer = functools.partial(nn.BatchNorm2d, affine=True)\n elif norm_type == 'instance':\n norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)\n else:\n raise NotImplementedError('normalization layer [%s] is not found' % norm_type)\n return norm_layer\n\n\nclass ModelFusion(nn.Module):\n def __init__(self, config):\n super(ModelFusion, self).__init__()\n self.fc_1 = nn.Linear(config.pred_length * 256, 512)\n self.fc_2 = nn.Linear(512, config.label_size)\n self.relu = nn.ReLU(True)\n self.sig = nn.Sigmoid()\n self.config = config\n self.dis = nn.Linear(512, 1)\n if not config.resume:\n self.fc_1.weight.data.normal_(0, 0.0001)\n self.fc_1.bias.data.zero_()\n\n def forward(self, x):\n x = x.view(-1, self.config.pred_length * 256)\n net = self.fc_1(x)\n net0 = self.relu(net)\n net = self.fc_2(net0)\n # # net0 = self.dropout(net)\n #\n # dis_feature = self.sig(self.dis(net0))\n return net\n\n\nclass discriminator_audio(nn.Module):\n def __init__(self):\n super(discriminator_audio, self).__init__()\n self.fc1 = nn.Linear(256, 256)\n self.fc2 = nn.Linear(256, 1)\n self.relu = nn.ReLU(True)\n self.sig = nn.Sigmoid()\n\n def forward(self, x):\n x = x.view(-1, 256)\n net = self.fc1(x)\n net = self.fc2(self.relu(net))\n dis1 = self.sig(net)\n return dis1\n", "repo_name": "Hangz-nju-cuhk/Talking-Face-Generation-DAVS", "sub_path": "network/networks.py", "file_name": "networks.py", "file_ext": "py", "file_size_in_byte": 2195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 796, "dataset": "github-code", "pt": "2", "api": [{"api_name": "functools.partial", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "34465964408", "text": "from sqlalchemy import select\nfrom sqlalchemy import and_\nfrom database.models.Images import Images as Image\nfrom database.models.Images import engine\nfrom database.models.Objects_ import Objects_ as Object_\nfrom database.models.Coordinates import Coordinates\nfrom datetime import datetime\n\n\n\ndef getImageByFilename(filename):\n conn = engine.connect()\n\n selectStmt = select([Image]).where(Image.filename == filename)\n res = conn.execute(selectStmt).fetchone() # можно сделать fetchall и если будет больше одного результата, вернуть фолс\n if res is None:\n raise ValueError(f\"Image {filename} not found on database\")\n return dict(res)\n\n\ndef getAllFilenames():\n conn = engine.connect()\n selectStmt = select([Image.filename])\n res = conn.execute(selectStmt).fetchall()\n stringRes = [i[0] for i in res]\n return stringRes\n\n\ndef getObjects(filename):\n conn = engine.connect()\n selectStmt = select([Object_]).where(and_(Object_.imageId == Image.id, Image.filename == filename))\n objectsInfo = conn.execute(selectStmt).fetchall() # т.к. объектов может быть много\n if objectsInfo is None:\n raise ValueError(f\"Objects not found on database\")\n stringRes = [dict(i) for i in objectsInfo]\n return stringRes\n\n\ndef getImageBetweenDatesFromCamera(cameraId, startDate: datetime, endDate: datetime):\n conn = engine.connect()\n selectStmt = select([Image]).where(and_(Image.numberOfCam == cameraId,\n Image.fixationDatetime >= startDate,\n Image.fixationDatetime <= endDate))\n images = conn.execute(selectStmt).fetchall()\n stringRes = [list(i) for i in images]\n return stringRes\n\n\ndef getCoord(filename):\n conn = engine.connect()\n idImage = select([Image.id]).where(filename == Image.filename)\n coordinates = select([Coordinates.LDx, Coordinates.LDy,\n Coordinates.RUx, Coordinates.RUy])\\\n .where(and_(idImage == Object_.imageId,\n Coordinates.id == Object_.id))\n\n objectsInfo = conn.execute(coordinates).fetchall()\n stringRes = [list(i) for i in objectsInfo]\n return stringRes\n\n\n\n\n\n", "repo_name": "Sapfir0/web-premier-eye", "sub_path": "application/database/dbAPI.py", "file_name": "dbAPI.py", "file_ext": "py", "file_size_in_byte": 2266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "database.models.Images.engine.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "database.models.Images.engine", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 14, "usage_type": "call"}, {"api_name": "database.models.Images.Images", "line_number": 14, "usage_type": "name"}, {"api_name": "database.models.Images.Images.filename", "line_number": 14, "usage_type": "attribute"}, {"api_name": "database.models.Images.engine.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "database.models.Images.engine", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 23, "usage_type": "call"}, {"api_name": "database.models.Images.Images.filename", "line_number": 23, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images", "line_number": 23, "usage_type": "name"}, {"api_name": "database.models.Images.engine.connect", "line_number": 30, "usage_type": "call"}, {"api_name": "database.models.Images.engine", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 31, "usage_type": "call"}, {"api_name": "database.models.Objects_.Objects_", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 31, "usage_type": "call"}, {"api_name": "database.models.Objects_.Objects_.imageId", "line_number": 31, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images.id", "line_number": 31, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images", "line_number": 31, "usage_type": "name"}, {"api_name": "database.models.Images.Images.filename", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "database.models.Images.engine.connect", "line_number": 40, "usage_type": "call"}, {"api_name": "database.models.Images.engine", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 41, "usage_type": "call"}, {"api_name": "database.models.Images.Images", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 41, "usage_type": "call"}, {"api_name": "database.models.Images.Images.numberOfCam", "line_number": 41, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images.fixationDatetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images", "line_number": 42, "usage_type": "name"}, {"api_name": "database.models.Images.Images.fixationDatetime", "line_number": 43, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images", "line_number": 43, "usage_type": "name"}, {"api_name": "database.models.Images.engine.connect", "line_number": 50, "usage_type": "call"}, {"api_name": "database.models.Images.engine", "line_number": 50, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 51, "usage_type": "call"}, {"api_name": "database.models.Images.Images.id", "line_number": 51, "usage_type": "attribute"}, {"api_name": "database.models.Images.Images", "line_number": 51, "usage_type": "name"}, {"api_name": "database.models.Images.Images.filename", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sqlalchemy.select", "line_number": 52, "usage_type": "call"}, {"api_name": "database.models.Coordinates.Coordinates.LDx", "line_number": 52, "usage_type": "attribute"}, {"api_name": "database.models.Coordinates.Coordinates", "line_number": 52, "usage_type": "name"}, {"api_name": "database.models.Coordinates.Coordinates.LDy", "line_number": 52, "usage_type": "attribute"}, {"api_name": "database.models.Coordinates.Coordinates.RUx", "line_number": 53, "usage_type": "attribute"}, {"api_name": "database.models.Coordinates.Coordinates", "line_number": 53, "usage_type": "name"}, {"api_name": "database.models.Coordinates.Coordinates.RUy", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 54, "usage_type": "call"}, {"api_name": "database.models.Objects_.Objects_.imageId", "line_number": 54, "usage_type": "attribute"}, {"api_name": "database.models.Objects_.Objects_", "line_number": 54, "usage_type": "name"}, {"api_name": "database.models.Coordinates.Coordinates.id", "line_number": 55, "usage_type": "attribute"}, {"api_name": "database.models.Coordinates.Coordinates", "line_number": 55, "usage_type": "name"}, {"api_name": "database.models.Objects_.Objects_.id", "line_number": 55, "usage_type": "attribute"}, {"api_name": "database.models.Objects_.Objects_", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "26181280712", "text": "from typing import List\n\ndef puzzle(arr: List) -> int:\n # count number of times depth increases from one group of three measurements to the next\n group_sums = [sum(arr[i:i+3]) for i in range(0, len(arr))]\n print(group_sums)\n increases = [int(group_sums[i] > group_sums[i-1]) for i in range(1, len(group_sums))]\n return sum(increases)\n\nwith open('report.txt') as f:\n data = [int(line) for line in f.readlines()]\n\n# data = [199, 200, 208, 210, 200, 207, 240, 269, 260, 263]\n\nprint(puzzle(data))\n", "repo_name": "benrosenberg/advent-of-code-2021", "sub_path": "1/1b.py", "file_name": "1b.py", "file_ext": "py", "file_size_in_byte": 511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.List", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "71944598768", "text": "from requests_html import HTML\nimport requests\n\n\ndef parse_article_entries(doc):\n\n html = HTML(html=doc)\n\n post_entries = html.find('div.r-ent')\n\n return post_entries\n\ndef fetch(url):\n\n response = requests.get(url)\n\n response = requests.get(url, cookies={'over18': '1'})\n\n return response\n\ndef parse_article_meta(entry):\n\n meta = {'title': entry.find('div.title', first=True).text,\n 'push': entry.find('div.nrec', first=True).text,\n 'date': entry.find('div.date', first=True).text,\n 'author': entry.find('div.author', first=True).text,\n 'link': entry.find('div.title > a', first=True).attrs['href'],\n }\n try:\n meta['author'] = entry.find('div.author', first=True).text\n meta['link'] = entry.find('div.title > a', first=True).attrs['href']\n except AttributeError:\n if '(本文已被刪除)' in meta['title']:\n match_author = re.search('\\[(\\w*)\\]', meta['title'])\n if match_author:\n meta['author'] = match_author.group(1)\n elif re.search('已被\\w*刪除', meta['title']):\n match_author = re.search('\\<(\\w*)\\>', meta['title'])\n if match_author:\n meta['author'] = match_author.group(1)\n return meta\n \n return meta\n\n\nurl = 'https://www.ptt.cc/bbs/movie/index.html'\n\nresp = fetch(url) # step-1\n\npost_entries = parse_article_entries(resp.text) # step-2\n\n\nfor entry in post_entries:\n meta = parse_article_meta(entry)\n print(meta) # result of setp-3\n\n \n", "repo_name": "funew4670/pttwebcrawler", "sub_path": "pttcrawler.py", "file_name": "pttcrawler.py", "file_ext": "py", "file_size_in_byte": 1534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests_html.HTML", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "2379130792", "text": "import theano\nimport theano.tensor as T\nimport numpy as np\nimport lasagne\n\n# compute vector average\nclass AverageWordLayer(lasagne.layers.MergeLayer):\n def __init__(self, incomings, **kwargs):\n super(AverageWordLayer, self).__init__(incomings, **kwargs)\n\n #embedding layer is batch_size x max_post_length x max_sentence_length x d\n #mask layer is batch_size x max_post_length x max_sentence_length \n def get_output_for(self, inputs, **kwargs):\n emb_sums = T.sum(inputs[0] * inputs[1][:, :, :, None], axis=2)\n\n mask_sums = T.sum(inputs[1], axis=2)\n\n #need to avoid dividing by zero\n mask_sums += T.eq(mask_sums, T.as_tensor_variable(0))\n \n return emb_sums / mask_sums[:,:,None]\n\n # output is batch_size x max_post_length x d\n def get_output_shape_for(self, input_shapes):\n \n return (None,input_shapes[0][1],input_shapes[0][-1])\n\nclass AverageSentenceLayer(lasagne.layers.MergeLayer):\n def __init__(self, incomings, **kwargs):\n super(AverageSentenceLayer, self).__init__(incomings, **kwargs)\n\n #sentence layer is batch_size x max_post_length x d\n #mask layer is batch_size x max_post_length \n def get_output_for(self, inputs, **kwargs):\n emb_sums = T.sum(inputs[0] * inputs[1][:, :, None], axis=1)\n mask_sums = T.sum(inputs[1], axis=1)\n\n return emb_sums / mask_sums[:,None]\n\n # output is batch_size x d\n def get_output_shape_for(self, input_shapes):\n \n return (None,input_shapes[0][-1])\n\nclass AttentionWordLayer(lasagne.layers.MergeLayer):\n #uses either a fixed \"query\" for the important words or another layer\n #this returns weights that can be used in the averaging layer in place of the mask\n def __init__(self, incomings, d, W_w=lasagne.init.Normal(),\n u_w=lasagne.init.Normal(), b_w=lasagne.init.Normal(),\n custom_query=None, normalized=True, **kwargs):\n super(AttentionWordLayer, self).__init__(incomings, **kwargs)\n self.W_w = self.add_param(W_w, (incomings[0].output_shape[-1],d))\n self.b_w = self.add_param(b_w, (d,))\n self.normalized = normalized\n\n self.fixed_query = True\n if custom_query is not None:\n self.fixed_query = False\n self.u_w = lasagne.layers.get_output(custom_query)\n else:\n self.u_w = self.add_param(u_w, (d,)) \n \n def get_output_for(self, inputs, **kwargs):\n #u = T.sum(inputs[0], axis=-1)\n if self.fixed_query:\n u = T.dot(T.tanh(T.dot(inputs[0], self.W_w) + self.b_w), self.u_w)\n else:\n u = T.batched_dot(T.tanh(T.dot(inputs[0], self.W_w) + self.b_w), self.u_w)\n \n # set masked positions to large negative value\n u = u*inputs[1] - (1-inputs[1])*10000\n \n #now batch_size x post_length x sentence_length x 1 but need to normalize via softmax\n #over 2nd axis, and also multiply by the sentence mask\n\n # normalize over sentence_length (->large negative values = 0)\n if not self.normalized:\n return T.reshape(u, (inputs[0].shape[0], inputs[0].shape[1], inputs[0].shape[2]))\n u = T.reshape(u, (inputs[0].shape[0]*inputs[0].shape[1], inputs[0].shape[2]))\n alpha = T.nnet.softmax(u)\n alpha = T.reshape(alpha, (inputs[0].shape[0], inputs[0].shape[1], inputs[0].shape[2]))\n\n #now return the weighted sum\n #return T.sum(inputs[0] * alpha[:,:,:, None], axis=2)\n\n return alpha\n \n def get_output_shape_for(self, input_shapes):\n \n #return (None,input_shapes[0][1],input_shapes[0][-1])\n return (None,input_shapes[0][1],input_shapes[0][2])\n\nclass AttentionSentenceLayer(lasagne.layers.MergeLayer):\n #uses either a fixed \"query\" for the important words or another layer\n #this returns weights that can be used in the averaging layer in place of the mask\n def __init__(self, incomings, d, W_s=lasagne.init.Normal(),\n u_s=lasagne.init.Normal(), b_s=lasagne.init.Normal(),\n custom_query=None, nonlinearity=T.tanh,\n hidden_layers=1, **kwargs):\n super(AttentionSentenceLayer, self).__init__(incomings, **kwargs)\n self.W_s = [self.add_param(W_s, (incomings[0].output_shape[-1], d)) for i in range(hidden_layers)]\n self.b_s = [self.add_param(b_s, (d,)) for i in range(hidden_layers)]\n \n self.fixed_query = True\n if custom_query is not None:\n self.fixed_query = False\n self.u_s = lasagne.layers.get_output(custom_query)\n else:\n self.u_s = self.add_param(u_s, (d,)) \n self.nonlinearity = nonlinearity\n self.hidden_layers = hidden_layers\n \n def get_output_for(self, inputs, **kwargs):\n #u = T.sum(inputs[0], axis=-1)\n tmp = inputs[0]\n for i in range(self.hidden_layers):\n tmp = self.nonlinearity(T.dot(tmp, self.W_s[i]) + self.b_s[i][None, None, :])\n \n if self.fixed_query:\n u = T.dot(tmp, self.u_s) \n else:\n u = T.batched_dot(tmp, self.u_s)\n \n # set masked positions to large negative value\n if len(inputs) > 1:\n u = u*inputs[1] - (1-inputs[1])*10000\n \n #now batch_size x post_length x 1 but need to normalize via softmax\n\n # normalize over post_length (->large negative values = 0)\n u = T.reshape(u, (inputs[0].shape[0], inputs[0].shape[1]))\n alpha = T.nnet.softmax(u)\n\n #now return the weights\n\n return alpha\n \n def get_output_shape_for(self, input_shapes):\n \n #return (None,input_shapes[0][1],input_shapes[0][-1])\n return (None,input_shapes[0][-1])\n \nclass WeightedAverageWordLayer(lasagne.layers.MergeLayer):\n def __init__(self, incomings, **kwargs):\n super(WeightedAverageWordLayer, self).__init__(incomings, **kwargs)\n\n def get_output_for(self, inputs, **kwargs):\n return T.sum(inputs[0] * inputs[1][:,:,:,None], axis=2)\n\n def get_output_shape_for(self, input_shapes):\n return (None, input_shapes[0][1], input_shapes[0][-1])\n\nclass WeightedAverageSentenceLayer(lasagne.layers.MergeLayer):\n def __init__(self, incomings, **kwargs):\n super(WeightedAverageSentenceLayer, self).__init__(incomings, **kwargs)\n \n def get_output_for(self, inputs, **kwargs):\n return T.sum(inputs[0] * inputs[1][:,:,None], axis=1)\n\n def get_output_shape_for(self, input_shapes):\n return (None, input_shapes[0][-1])\n \nclass HighwayLayer(lasagne.layers.Layer):\n def __init__(self, incoming, num_units, W_h=lasagne.init.GlorotUniform(),\n b_h=lasagne.init.Constant(0.), W_t=lasagne.init.GlorotUniform(),\n b_t=lasagne.init.Constant(-2.),\n nonlinearity=lasagne.nonlinearities.rectify,\n num_leading_axes=1, **kwargs):\n \n super(HighwayLayer, self).__init__(incoming, **kwargs)\n self.nonlinearity = (nonlinearities.identity if nonlinearity is None else nonlinearity)\n\n self.num_units = num_units\n\n if num_leading_axes >= len(self.input_shape):\n raise ValueError(\n \"Got num_leading_axes=%d for a %d-dimensional input, \"\n \"leaving no trailing axes for the dot product.\" %\n (num_leading_axes, len(self.input_shape)))\n elif num_leading_axes < -len(self.input_shape):\n raise ValueError(\n \"Got num_leading_axes=%d for a %d-dimensional input, \"\n \"requesting more trailing axes than there are input \"\n \"dimensions.\" % (num_leading_axes, len(self.input_shape)))\n self.num_leading_axes = num_leading_axes\n\n if any(s is None for s in self.input_shape[num_leading_axes:]):\n raise ValueError(\n \"A DenseLayer requires a fixed input shape (except for \"\n \"the leading axes). Got %r for num_leading_axes=%d.\" %\n (self.input_shape, self.num_leading_axes))\n num_inputs = int(np.prod(self.input_shape[num_leading_axes:]))\n\n assert(num_inputs == num_units)\n \n self.W_h = self.add_param(W_h, (num_inputs, num_units), name=\"W_h\")\n if b_h is None:\n self.b_h = None\n else:\n self.b_h = self.add_param(b_h, (num_units,), name=\"b_h\",\n regularizable=False)\n\n self.W_t = self.add_param(W_t, (num_inputs, num_units), name=\"W_t\")\n if b_t is None:\n self.b_t = None\n else:\n self.b_t = self.add_param(b_t, (num_units,), name=\"b_t\",\n regularizable=False)\n \n def get_output_shape_for(self, input_shape):\n return input_shape[:self.num_leading_axes] + (self.num_units,)\n\n def get_output_for(self, input, **kwargs):\n num_leading_axes = self.num_leading_axes\n if num_leading_axes < 0:\n num_leading_axes += input.ndim\n if input.ndim > num_leading_axes + 1:\n # flatten trailing axes (into (n+1)-tensor for num_leading_axes=n)\n input = input.flatten(num_leading_axes + 1)\n\n t = lasagne.nonlinearities.sigmoid(T.dot(input, self.W_t) + self.b_t)\n g = self.nonlinearity(T.dot(input, self.W_h) + self.b_h)\n\n return T.mul(t,g) + T.mul(1-t, input)\n\nclass MemoryLayer(lasagne.layers.MergeLayer):\n def __init__(self, incomings, W_r=lasagne.init.GlorotUniform(),\n hops=3, q=lasagne.init.Normal(), query=None, **kwargs):\n \n super(MemoryLayer, self).__init__(incomings, **kwargs)\n\n d = incomings[0].output_shape[-1]\n self.W_r = self.add_param(W_r, (d, d), name=\"W_r\")\n self.hops = hops\n self.d = d\n \n self.fixed_query = True\n if query is not None:\n self.fixed_query = False\n self.q = lasagne.layers.get_output(query)\n else:\n self.q = self.add_param(q, (d,)) \n\n def get_output_shape_for(self, input_shape):\n #B x D\n return (None, self.d)\n \n def get_output_for(self, inputs, **kwargs):\n q = self.q\n for i in range(self.hops):\n if self.fixed_query and not i:\n u = T.dot(inputs[0], q) \n else:\n u = T.batched_dot(inputs[0], q)\n\n # set masked positions to large negative value\n if len(inputs) > 1:\n u = u*inputs[1] - (1-inputs[1])*10000\n\n #now batch_size x post_length x 1 but need to normalize via softmax\n\n # normalize over post_length (->large negative values = 0)\n u = T.reshape(u, (inputs[0].shape[0], inputs[0].shape[1]))\n alpha = T.nnet.softmax(u)\n\n #now B x S\n o = T.dot(T.sum(inputs[0] * alpha[:,:,None], axis=1), self.W_r)\n if self.fixed_query:\n q = q + o\n else:\n q = q + o\n\n return q\n\n\nclass MyConcatLayer(lasagne.layers.MergeLayer):\n '''\n for concatenating a MxN tensor and an MxNxO tensor\n '''\n def __init__(self, incomings, **kwargs):\n super(MyConcatLayer, self).__init__(incomings, **kwargs) # MergeLayer constructor requires list of incoming layers\n \n def get_output_shape_for(self, input_shapes):\n lstm_shape, other_shape = input_shapes\n return (lstm_shape[0], lstm_shape[1], lstm_shape[2] + other_shape[-1])\n \n def get_output_for(self, inputs, **kwargs):\n lstm_input, other_input = inputs\n other_input = T.repeat(other_input.dimshuffle(0, 'x', 1), lstm_input.shape[1], axis=1) # repeat along time dimension\n return T.concatenate((lstm_input, other_input), axis=-1)\n\n", "repo_name": "chridey/cmv", "sub_path": "cmv/rnn/layers.py", "file_name": "layers.py", "file_ext": "py", "file_size_in_byte": 11854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "3", "api": [{"api_name": "lasagne.layers", "line_number": 7, "usage_type": "attribute"}, {"api_name": "theano.tensor.sum", "line_number": 14, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 14, "usage_type": "name"}, {"api_name": "theano.tensor.sum", "line_number": 16, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 16, "usage_type": "name"}, {"api_name": "theano.tensor.eq", "line_number": 19, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 19, "usage_type": "name"}, {"api_name": "theano.tensor.as_tensor_variable", "line_number": 19, "usage_type": "call"}, {"api_name": "lasagne.layers", "line_number": 28, "usage_type": "attribute"}, {"api_name": "theano.tensor.sum", "line_number": 35, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 35, "usage_type": "name"}, {"api_name": "theano.tensor.sum", "line_number": 36, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 36, "usage_type": "name"}, {"api_name": "lasagne.layers", "line_number": 45, "usage_type": "attribute"}, {"api_name": "lasagne.init.Normal", "line_number": 48, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 48, "usage_type": "attribute"}, {"api_name": "lasagne.init.Normal", "line_number": 49, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 49, "usage_type": "attribute"}, {"api_name": "lasagne.layers.get_output", "line_number": 59, "usage_type": "call"}, {"api_name": "lasagne.layers", "line_number": 59, "usage_type": "attribute"}, {"api_name": "theano.tensor.dot", "line_number": 66, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 66, "usage_type": "name"}, {"api_name": "theano.tensor.tanh", "line_number": 66, "usage_type": "call"}, {"api_name": "theano.tensor.batched_dot", "line_number": 68, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 68, "usage_type": "name"}, {"api_name": "theano.tensor.tanh", "line_number": 68, "usage_type": "call"}, {"api_name": "theano.tensor.dot", "line_number": 68, "usage_type": "call"}, {"api_name": "theano.tensor.reshape", "line_number": 78, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 78, "usage_type": "name"}, {"api_name": "theano.tensor.reshape", "line_number": 79, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 79, "usage_type": "name"}, {"api_name": "theano.tensor.nnet.softmax", "line_number": 80, "usage_type": "call"}, {"api_name": "theano.tensor.nnet", "line_number": 80, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 80, "usage_type": "name"}, {"api_name": "theano.tensor.reshape", "line_number": 81, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 81, "usage_type": "name"}, {"api_name": "lasagne.layers", "line_number": 93, "usage_type": "attribute"}, {"api_name": "lasagne.init.Normal", "line_number": 96, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 96, "usage_type": "attribute"}, {"api_name": "lasagne.init.Normal", "line_number": 97, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 97, "usage_type": "attribute"}, {"api_name": "theano.tensor.tanh", "line_number": 98, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 98, "usage_type": "name"}, {"api_name": "lasagne.layers.get_output", "line_number": 107, "usage_type": "call"}, {"api_name": "lasagne.layers", "line_number": 107, "usage_type": "attribute"}, {"api_name": "theano.tensor.dot", "line_number": 117, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 117, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 120, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 120, "usage_type": "name"}, {"api_name": "theano.tensor.batched_dot", "line_number": 122, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 122, "usage_type": "name"}, {"api_name": "theano.tensor.reshape", "line_number": 131, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 131, "usage_type": "name"}, {"api_name": "theano.tensor.nnet.softmax", "line_number": 132, "usage_type": "call"}, {"api_name": "theano.tensor.nnet", "line_number": 132, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 132, "usage_type": "name"}, {"api_name": "lasagne.layers", "line_number": 143, "usage_type": "attribute"}, {"api_name": "theano.tensor.sum", "line_number": 148, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 148, "usage_type": "name"}, {"api_name": "lasagne.layers", "line_number": 153, "usage_type": "attribute"}, {"api_name": "theano.tensor.sum", "line_number": 158, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 158, "usage_type": "name"}, {"api_name": "lasagne.layers", "line_number": 163, "usage_type": "attribute"}, {"api_name": "lasagne.init.GlorotUniform", "line_number": 164, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 164, "usage_type": "attribute"}, {"api_name": "lasagne.init.Constant", "line_number": 165, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 165, "usage_type": "attribute"}, {"api_name": "lasagne.init.GlorotUniform", "line_number": 165, "usage_type": "call"}, {"api_name": "lasagne.init.Constant", "line_number": 166, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 166, "usage_type": "attribute"}, {"api_name": "lasagne.nonlinearities", "line_number": 167, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 192, "usage_type": "call"}, {"api_name": "lasagne.nonlinearities.sigmoid", "line_number": 221, "usage_type": "call"}, {"api_name": "lasagne.nonlinearities", "line_number": 221, "usage_type": "attribute"}, {"api_name": "theano.tensor.dot", "line_number": 221, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 221, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 222, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 222, "usage_type": "name"}, {"api_name": "theano.tensor.mul", "line_number": 224, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 224, "usage_type": "name"}, {"api_name": "lasagne.layers", "line_number": 226, "usage_type": "attribute"}, {"api_name": "lasagne.init.GlorotUniform", "line_number": 227, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 227, "usage_type": "attribute"}, {"api_name": "lasagne.init.Normal", "line_number": 228, "usage_type": "call"}, {"api_name": "lasagne.init", "line_number": 228, "usage_type": "attribute"}, {"api_name": "lasagne.layers.get_output", "line_number": 240, "usage_type": "call"}, {"api_name": "lasagne.layers", "line_number": 240, "usage_type": "attribute"}, {"api_name": "theano.tensor.dot", "line_number": 252, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 252, "usage_type": "name"}, {"api_name": "theano.tensor.batched_dot", "line_number": 254, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 254, "usage_type": "name"}, {"api_name": "theano.tensor.reshape", "line_number": 263, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 263, "usage_type": "name"}, {"api_name": "theano.tensor.nnet.softmax", "line_number": 264, "usage_type": "call"}, {"api_name": "theano.tensor.nnet", "line_number": 264, "usage_type": "attribute"}, {"api_name": "theano.tensor", "line_number": 264, "usage_type": "name"}, {"api_name": "theano.tensor.dot", "line_number": 267, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 267, "usage_type": "name"}, {"api_name": "theano.tensor.sum", "line_number": 267, "usage_type": "call"}, {"api_name": "lasagne.layers", "line_number": 276, "usage_type": "attribute"}, {"api_name": "theano.tensor.repeat", "line_number": 289, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 289, "usage_type": "name"}, {"api_name": "theano.tensor.concatenate", "line_number": 290, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 290, "usage_type": "name"}]} +{"seq_id": "33597040517", "text": "# coding: utf-8\n\nimport os,sys\nimport copy\nimport argparse\nimport numpy as np\nfrom scipy import stats\nimport Bio.Cluster\nfrom sklearn import metrics\nimport pandas as pd\nfrom ranking import RankingMeasures\n\n# parser settings\nparser = argparse.ArgumentParser()\nparser.add_argument('-test','--test-metric', \\\n action='store', \\\n nargs=None, \\\n const=None, \\\n default='cor', \\\n type=str, \\\n choices=None, \\\n help='Test metric option which you\\'d like to set.', \\\n metavar=None)\nparser.add_argument('-k','--top-k', \\\n action='store', \\\n nargs=None, \\\n const=None, \\\n default=10, \\\n type=int, \\\n choices=None, \\\n help='Number of top-k value which you\\'d like to set.', \\\n metavar=None)\nparser.add_argument('-thre','--threshold', \\\n action='store', \\\n nargs=None, \\\n const=None, \\\n default=4, \\\n type=int, \\\n choices=None, \\\n help='Number of threshold value which you\\'d like to set.', \\\n metavar=None)\n\n# config\nNUM_FOLDS = 5\nGROUNDTRUTH = '../dataset/open_peer_review_v3/peer_review/translated_groundtruth.csv'\nPEER_REVIEW = '../dataset/open_peer_review_v3/peer_review/peer_review_forPG3.csv'\n\n# load true ability\ngDF = pd.read_csv(GROUNDTRUTH)\ntrue_ability = gDF['grade'].get_values()\n\n# calculate mean grades\nrDF = pd.read_csv(PEER_REVIEW)\nmean_grades = rDF[['receiver_id','value']].groupby(['receiver_id']).mean()['value'].get_values()\nmean_corrected = rDF[['receiver_id','corrected']].groupby(['receiver_id']).mean()['corrected'].get_values()\nmean_diff = rDF[['receiver_id','diff']].groupby(['receiver_id']).mean()['diff'].get_values()\n\n# generate random permutation and fold that index\nnp.random.seed(12345678)\npermu =np.random.permutation(len(true_ability))\nidx_inFold = np.array_split(permu, NUM_FOLDS)\n\n# set metric\ncorrcoef = lambda true,estimated: np.corrcoef(true, estimated)[0,1]\nkendalltau = lambda true,estimated: stats.kendalltau(true, estimated)[0]\nspearmanrho = lambda true,estimated: 1-Bio.Cluster.distancematrix((true,estimated), dist=\"s\")[1][0]\ndef precisionAtK(true,estimated,top_k,threshold):\n top_ranker_ture = np.array((true >= threshold))\n id_top_k = estimated.argsort()[::-1][:top_k]\n TP = top_ranker_ture[id_top_k].sum()\n return TP/float(top_k)\ndef auc(true,estimated,threshold):\n fpr, tpr, thresholds = metrics.roc_curve(true >= threshold, estimated, pos_label=1)\n return metrics.auc(fpr, tpr)\ndef nDCG(true,estimated,top_k):\n rm = RankingMeasures(estimated, true)\n return rm.nDCG(k=top_k)\n\n#argparse\nargs = parser.parse_args()\n# set test metric\nif args.test_metric == 'cor':\n func_metric = corrcoef\nelif args.test_metric == 'ktau':\n func_metric = kendalltau\nelif args.test_metric == 'srho':\n func_metric = spearmanrho\nelif args.test_metric == 'preck':\n top_k = args.top_k\n threshold = args.threshold\n func_metric = lambda true,estimated: precisionAtK(true,estimated,top_k,threshold)\nelif args.test_metric == 'auc':\n threshold = args.threshold\n func_metric = lambda true,estimated: auc(true,estimated,threshold)\nelif args.test_metric == 'ndcg':\n top_k = args.top_k\n func_metric = lambda true,estimated: nDCG(true,estimated,top_k)\nelse:\n print('Error: set test metrics [cor|ktau|srho|preck|auc|ndcg]')\n sys.exit()\n\nstatistic_test = np.empty(0)\nfor loop in xrange(NUM_FOLDS):\n buf_list = copy.copy(idx_inFold)\n idx_train = buf_list.pop(loop)\n idx_test = np.concatenate(buf_list)\n #test\n true_test = true_ability[idx_test]\n mean_grade_test = mean_grades[idx_test]\n corrcoef_test = func_metric(true_test, mean_grade_test)\n statistic_test = np.append(statistic_test,corrcoef_test)\nprint('-------- result mean grade --------')\nprint('mean:{0}, std:{1}'.format(statistic_test.mean(),statistic_test.std()))\n\nstatistic_test = np.empty(0)\nfor loop in xrange(NUM_FOLDS):\n buf_list = copy.copy(idx_inFold)\n idx_train = buf_list.pop(loop)\n idx_test = np.concatenate(buf_list)\n #test\n true_test = true_ability[idx_test]\n mean_corrected_test = mean_corrected[idx_test]\n corrcoef_test = func_metric(true_test, -mean_corrected_test)\n statistic_test = np.append(statistic_test,corrcoef_test)\nprint('-------- result mean corrected --------')\nprint('mean:{0}, std:{1}'.format(statistic_test.mean(),statistic_test.std()))\n\nstatistic_test = np.empty(0)\nfor loop in xrange(NUM_FOLDS):\n buf_list = copy.copy(idx_inFold)\n idx_train = buf_list.pop(loop)\n idx_test = np.concatenate(buf_list)\n #test\n true_test = true_ability[idx_test]\n mean_diff_test = mean_diff[idx_test]\n corrcoef_test = func_metric(true_test, -mean_diff_test)\n statistic_test = np.append(statistic_test,corrcoef_test)\nprint('-------- result mean diff --------')\nprint('mean:{0}, std:{1}'.format(statistic_test.mean(),statistic_test.std()))\n", "repo_name": "takerun/PeerCorrection", "sub_path": "tools/calculateMeanGrade.py", "file_name": "calculateMeanGrade.py", "file_ext": "py", "file_size_in_byte": 4906, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.array_split", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.stats.kendalltau", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 65, "usage_type": "name"}, {"api_name": "Bio.Cluster.Cluster.distancematrix", "line_number": 66, "usage_type": "call"}, {"api_name": "Bio.Cluster.Cluster", "line_number": 66, "usage_type": "attribute"}, {"api_name": "Bio.Cluster", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 73, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 74, "usage_type": "name"}, {"api_name": "ranking.RankingMeasures", "line_number": 76, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 102, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 115, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 128, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "41087921093", "text": "import collections\ninventory = collections.OrderedDict()\nN = int(input())\nfor i in range(0, N):\n input_line = input()\n item_name, price = input_line.split()[:-1], int(input_line.split()[-1])\n if str(' '.join(item_name)) in inventory:\n inventory[' '.join(item_name)] += price\n else:\n inventory[' '.join(item_name)] = price\n \n \nfor key in inventory:\n print(key, inventory[key])\n ", "repo_name": "raleighlittles/Python-HackerRank", "sub_path": "Collections/Collections Ordered Dict.py", "file_name": "Collections Ordered Dict.py", "file_ext": "py", "file_size_in_byte": 420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 47, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.OrderedDict", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "25698014494", "text": "from django.shortcuts import render, redirect\nfrom principal.models import *\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.generic.base import View\nfrom principal.forms import *\nfrom django.http import HttpResponseRedirect, HttpResponse\n\n@login_required\ndef index(request):\n usuarioLogado = get_perfil_logado(request)\n turmas_aluno = Turma.objects.filter(alunos__id=usuarioLogado.id) \n turmas_adm = Turma.objects.all().filter(administrador=usuarioLogado.id)\n return render(request, 'index.html', {'turmas_adm': turmas_adm,'usuarioLogado':usuarioLogado, 'turmas_aluno': turmas_aluno})\n\n@login_required\ndef acessarTurma(request, id):\n ranking = []\n usuarioLogado = get_perfil_logado(request)\n turma = Turma.objects.get(id=id)\n atividades = Atividade.objects.filter(turma=turma)\n respostas = RespostaAtividade.objects.filter(atividade__turma__id = turma.id)\n if(len(turma.alunos.all()) > 0):\n for aluno in turma.alunos.all(): \n soma = 0\n for res in RespostaAtividade.objects.filter(aluno__id=aluno.id):\n soma = soma + res.nota\n r = Ranking(aluno.id,aluno.nome, soma, turma.id)\n ranking.append(r)\n rankingOrdenado = sorted(ranking, key=lambda Ranking:Ranking.resultado, reverse=True)\n else:\n rankingOrdenado = None\n return render(request, 'turmaDetalhes.html', {'turma': turma, 'atividades': atividades, 'usuarioLogado':usuarioLogado, 'ranking': rankingOrdenado})\n\n@login_required\ndef cadastrarVideo(request):\n usuarioLogado = get_perfil_logado(request)\n video = Video()\n urlVideo = request.POST.get('url')\n video.embedCode = urlVideo.replace('watch?v=', 'embed/')\n video.data_entrega = request.POST.get('dataEntrega')\n video.titulo = request.POST.get('titulo')\n video.valor = request.POST.get('valor')\n turmaId = request.POST.get('turma')\n turma = Turma.objects.get(id=turmaId)\n video.turma = turma\n video.save()\n return HttpResponseRedirect('/video/lista')\n\n@login_required\ndef listarVideos(request):\n usuarioLogado = get_perfil_logado(request)\n turmas = Turma.objects.filter(administrador__id=usuarioLogado.id)\n videos = Video.objects.filter(turma__administrador__id=usuarioLogado.id)\n videosVencido = []\n videosPrazo = []\n for v in videos:\n if(v.comparaData == False):\n videosVencido.append(v)\n else:\n videosPrazo.append(v) \n return render(request, 'listaVideos.html', {'videos':videosPrazo, 'videoVencido': videosVencido, 'usuarioLogado':usuarioLogado, 'turmas':turmas})\n\n@login_required\ndef verResposta(request, id):\n resposta = RespostaAtividade.objects.get(id=id)\n usuarioLogado = get_perfil_logado(request)\n return render(request, 'verResposta.html', {'resposta': resposta, 'usuarioLogado':usuarioLogado})\n\n@login_required\ndef listarAtividades(request):\n usuarioLogado = get_perfil_logado(request)\n turmas = Turma.objects.filter(administrador__id=usuarioLogado.id)\n atividadesProfessor = Atividade.objects.filter(turma__administrador__id=usuarioLogado.id)\n atividadesAluno = Atividade.objects.filter(turma__alunos__id=usuarioLogado.id)\n return render(request, 'listaAtividades.html', {'usuarioLogado': usuarioLogado, 'atividadesProfessor': atividadesProfessor, 'atividadesAluno': atividadesAluno, 'turmas':turmas})\n\n@login_required\ndef alterarNota(request, id, idPagina): \n novaNota = request.POST.get('nota')\n resposta = RespostaAtividade.objects.get(id=id)\n resposta.nota = novaNota\n resposta.save()\n usuarioLogado = get_perfil_logado\n if(idPagina == 1):\n return render(request, 'verResposta.html', {'resposta': resposta, 'usuarioLogado':usuarioLogado})\n else:\n respostas = RespostaAtividade.objects.filter(atividade__id=resposta.atividade.id)\n atividade = Atividade.objects.get(id=resposta.atividade.id)\n return render(request, 'respostasAtividade.html', {'respostas': respostas, 'atividade':atividade, 'usuarioLogado':usuarioLogado})\n\n@login_required\ndef responderAtividade(request, id, idAluno):\n usuarioLogado = get_perfil_logado(request)\n if(request.method == 'GET'):\n return render(request, 'respostaAtividadeForm.html', {'usuarioLogado': usuarioLogado})\n elif(request.method == \"POST\"):\n resposta = request.POST.get('froala-editor')\n atividade = Atividade.objects.get(id=id)\n aluno = Usuario.objects.get(id=idAluno)\n r = RespostaAtividade()\n r.aluno = aluno\n r.atividade = atividade\n r.nota = 0\n r.resposta = resposta\n r.save()\n return render(request, 'minhasRespostas.html', {'usuarioLogado': usuarioLogado})\n\n@login_required\ndef acessarTurmaAluno(request, idTurma):\n usuarioLogado = get_perfil_logado(request)\n atividades = Atividade.objects.filter(turma__id=idTurma)\n respostas = RespostaAtividade.objects.filter(aluno__id=1000).filter(atividade__turma__id=idTurma)\n return render(request, 'turmasDetalheAluno.html', {'respostas': respostas, 'atividades' : atividades, 'usuarioLogado': usuarioLogado})\n\n@login_required\ndef acessarDetalhesAluno(request, id):\n usuarioLogado = get_perfil_logado(request)\n aluno = Usuario.objects.get(id=usuarioLogado.id)\n return render(request, 'alunoDetalhes.html', {'aluno': aluno, 'usuarioLogado': usuarioLogado}) \n\n@login_required\ndef acessarNotasAluno(request, id, idTurma):\n usuarioLogado = get_perfil_logado(request)\n atividades = Atividade.objects.filter(turma__id=idTurma)\n respostas = RespostaAtividade.objects.filter(atividade__turma__id=idTurma).filter(aluno__id=id)\n return render(request, 'notasAluno.html', {'respostas':respostas, 'atividades':atividades, 'usuarioLogado':usuarioLogado})\n\n@login_required\ndef getRespostasAtividade(request, id):\n usuarioLogado = get_perfil_logado(request)\n respostas = RespostaAtividade.objects.filter(atividade=id)\n from ast import literal_eval\n gruposString = Grupo.objects.filter(atividade=id)\n grupos = literal_eval(gruposString[0].grupo) if gruposString else []\n atividade = Atividade.objects.get(id=id)\n return render(request, 'respostasAtividade.html', {'respostas': respostas, 'atividade':atividade, 'usuarioLogado':usuarioLogado, 'grupos': grupos})\n\n@login_required\ndef detalhesAtividade(request,id):\n usuarioLogado = get_perfil_logado(request)\n atividade = Atividade.objects.get(id=id)\n respostas = RespostaAtividade.objects.filter(atividade__id=atividade.id).filter(aluno__id=usuarioLogado.id)\n return render(request, 'atividadeDetalhes.html', {'atividade': atividade, 'usuarioLogado':usuarioLogado, 'respostas':respostas})\n\n@login_required\ndef get_perfil_logado(request):\n return request.user.usuario \n\n@login_required\ndef cadastrarTurma(request):\n usuarioLogado = get_perfil_logado(request)\n turma = Turma()\n turma.titulo = request.POST.get('nomeTurma')\n turma.descricao = request.POST.get('descricaoTurma')\n turma.codigo = request.POST.get('codigoTurma')\n turma.administrador = usuarioLogado\n turma.save()\n return HttpResponseRedirect('/index') \n\n@login_required\ndef cadastrarAtividade(request):\n usuarioLogado = get_perfil_logado(request)\n atividade = Atividade()\n atividade.titulo = request.POST.get('nomeAtividade')\n atividade.data_entrega = request.POST.get('dataEntrega')\n atividade.valor = request.POST.get('valorAtividade')\n atividade.url = request.POST.get('urlAtividade')\n atividade.individual = True if request.POST.get(\"individualAtividade\") == 'True' else False\n grupos = []\n t = request.POST.get('turmaAtividade')\n turma = Turma.objects.get(id=t)\n atividade.turma = turma\n if(atividade.individual == False):\n lenGrupo = request.POST.get('tamanhoGrupo')\n grupos = gerarGrupos(turma.alunos.all(), lenGrupo if lenGrupo and int(lenGrupo) > 1 else 1)\n atividade.save()\n grupo = Grupo()\n grupo.atividade = Atividade.objects.get(id=atividade.id)\n grupo.grupo = str(grupos)\n grupo.save()\n print(str(grupos))\n return HttpResponseRedirect('/atividade/lista')\n\n@login_required\ndef entrarTurma(request, id):\n msgErro = None\n usuarioLogado = get_perfil_logado(request)\n valor = request.POST.get('valorConsultado')\n turmas = Turma.objects.filter(codigo__icontains=valor)\n turma = Turma.objects.get(id=id)\n for aluno in turma.alunos.all():\n if(aluno.id == usuarioLogado.id):\n msgErro = \"Você já é aluno desta turma\"\n break\n else:\n msgErro = None\n turma.alunos.add(usuarioLogado)\n turma.save()\n return render(request, 'resultadoConsultaTurma.html', {'turmas':turmas, 'usuarioLogado':usuarioLogado, 'msgErro': msgErro})\n\n@login_required\ndef consultaTurmaByCodigo(request):\n valor = request.POST.get('valorBuscado')\n turmas = Turma.objects.filter(codigo__icontains=valor)\n usuarioLogado = get_perfil_logado(request) \n return render(request, 'resultadoConsultaTurma.html', {'turmas':turmas, 'usuarioLogado':usuarioLogado, 'valorConsultado': valor})\n\nclass RegistrarUsuarioView(View):\n template = 'cadastroUsuario.html'\n def get(self, request):\n return render(request, self.template)\n def post(self,request):\n #preenche o from\n form = RegistrarUsuarioForm(request.POST)\n\n #verifica se eh valido\n if form.is_valid():\n\n dados_form = form.data\n\n #cria o usuario\n usuario = User.objects.create_user(dados_form['nome'], dados_form['email'], dados_form['senha']) \n\n #cria o perfil\n perfil = Usuario(nome=dados_form['nome'],\n matricula=dados_form['matricula'],\n usuario=usuario)\n\n #grava no banco\n perfil.save()\n\n #redireciona para index\n return redirect('login')\n\n #so chega aqui se nao for valido\n #vamos devolver o form para mostrar o formulario preenchido \n return render(request, self.template_name, {'form' : form})\n\ndef gerarGrupos(alunos, lenGrupo):\n valores = []\n for aluno in alunos:\n valores.append(aluno.nome)\n countGrupo = 1\n grupos = {}\n tamanhoGrupo = int(lenGrupo)\n tamTotal = int(len(valores) / tamanhoGrupo)\n for x in range(0, tamTotal):\n pos = 0\n contador = 0\n grupo = []\n while(contador < tamanhoGrupo):\n try: \n grupo.append(valores[pos])\n del valores[pos]\n except IndexError:\n break\n contador += 1\n pos = 0 if pos == -1 else -1\n nome = 'Grupo ' + str(countGrupo)\n grupos[nome] = grupo\n countGrupo += 1\n maiorGrupo = len(grupos)\n if(len(valores) > 0):\n if(len(valores) == tamanhoGrupo):\n grupos['Grupo ' + str(countGrupo)] = valores\n else: \n for valor in valores:\n try:\n grupos['Grupo ' + str(countGrupo)].append(valor)\n except KeyError:\n grupos['Grupo ' + str(maiorGrupo)].append(valor)\n countGrupo -= 1\n countGrupo = countGrupo - 1 if countGrupo > 0 else maiorGrupo\n return grupos\n\nclass Ranking(object):\n idAluno = 0\n nomeAluno = ''\n resultado = 0.0\n idTurma = 0\n\n def __init__(self, idAluno, nome, resultado, idTurma):\n self.idAluno = idAluno\n self.nomeAluno = nome\n self.resultado = resultado\n self.idTurma = idTurma", "repo_name": "Hallessandro/unione-ifrn", "sub_path": "principal/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 11638, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 15, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 49, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 63, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 69, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 77, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 95, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 106, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 91, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 113, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 108, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 115, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 126, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 121, "usage_type": "name"}, {"api_name": "ast.literal_eval", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 136, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 128, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 143, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 138, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 145, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 158, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 149, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 182, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 160, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 199, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 184, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 206, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 201, "usage_type": "name"}, {"api_name": "django.views.generic.base.View", "line_number": 208, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 211, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 233, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 237, "usage_type": "call"}]} +{"seq_id": "23051562729", "text": "import time\r\nimport requests\r\nimport hashlib\r\nimport hmac\r\n\r\nTICK_INTERVAL = 60 # seconds\r\nAPI_KEY = 'my-api-key'\r\nAPI_SECRET_KEY = b'my-secret-key'\r\n\r\n\r\ndef main():\r\n print('Starting trader bot...')\r\n\r\n while True:\r\n start = time.time()\r\n tick()\r\n end = time.time()\r\n\r\n # Sleep the thread if needed\r\n if end - start < TICK_INTERVAL:\r\n time.sleep(TICK_INTERVAL - (end - start))\r\n\r\n\r\ndef tick():\r\n print('Running routine')\r\n\r\n market_summaries = simple_request('https://bittrex.com/api/v1.1/public/getmarketsummaries')\r\n for summary in market_summaries['result']:\r\n market = summary['MarketName']\r\n day_close = summary['PrevDay']\r\n last = summary['Last']\r\n\r\n if day_close > 0:\r\n percent_chg = ((last / day_close) - 1) * 100\r\n else:\r\n print('day_close zero for ' + market)\r\n\r\n print(market + ' changed ' + str(percent_chg))\r\n\r\n if 40 < percent_chg < 60:\r\n # Fomo strikes! Let's buy some\r\n if has_open_order(market, 'LIMIT_BUY'):\r\n print('Order already opened to buy 5 ' + market)\r\n else:\r\n print('Purchasing 5 units of ' + market + ' for ' + str(format_float(last)))\r\n res = buy_limit(market, 5, last)\r\n print(res)\r\n\r\n if percent_chg < -20:\r\n # Do we have any to sell?\r\n balance_res = get_balance_from_market(market)\r\n current_balance = balance_res['result']['Available']\r\n\r\n if current_balance > 5:\r\n # Ship is sinking, get out!\r\n if has_open_order(market, 'LIMIT_SELL'):\r\n print('Order already opened to sell 5 ' + market)\r\n else:\r\n print('Selling 5 units of ' + market + ' for ' + str(format_float(last)))\r\n res = sell_limit(market, 5, last)\r\n print(res)\r\n else:\r\n print('Not enough ' + market + ' to open a sell order')\r\n\r\n\r\ndef buy_limit(market, quantity, rate):\r\n url = 'https://bittrex.com/api/v1.1/market/buylimit?apikey=' + API_KEY + '&market=' + market + '&quantity=' + str(quantity) + '&rate=' + format_float(rate)\r\n return signed_request(url)\r\n\r\n\r\ndef sell_limit(market, quantity, rate):\r\n url = 'https://bittrex.com/api/v1.1/market/selllimit?apikey=' + API_KEY + '&market=' + market + '&quantity=' + str(quantity) + '&rate=' + format_float(rate)\r\n return signed_request(url)\r\n\r\n\r\ndef get_balance_from_market(market_type):\r\n markets_res = simple_request('https://bittrex.com/api/v1.1/public/getmarkets')\r\n markets = markets_res['result']\r\n for market in markets:\r\n if market['MarketName'] == market_type:\r\n return get_balance(market['MarketCurrency'])\r\n\r\n # Return a fake response of 0 if not found\r\n return {'result': {'Available': 0}}\r\n\r\n\r\ndef get_balance(currency):\r\n url = 'https://bittrex.com/api/v1.1/account/getbalance?apikey=' + API_KEY + '¤cy=' + currency\r\n res = signed_request(url)\r\n\r\n if res['result'] is not None and len(res['result']) > 0:\r\n return res\r\n\r\n # If there are no results, than your balance is 0\r\n return {'result': {'Available': 0}}\r\n\r\n\r\ndef get_open_orders(market):\r\n url = 'https://bittrex.com/api/v1.1/market/getopenorders?apikey=' + API_KEY + '&market=' + market\r\n return signed_request(url)\r\n\r\n\r\ndef has_open_order(market, order_type):\r\n orders_res = get_open_orders(market)\r\n orders = orders_res['result']\r\n\r\n if orders is None or len(orders) == 0:\r\n return False\r\n\r\n # Check all orders for a LIMIT_BUY\r\n for order in orders:\r\n if order['OrderType'] == order_type:\r\n return True\r\n\r\n return False\r\n\r\n\r\ndef signed_request(url):\r\n now = time.time()\r\n url += '&nonce=' + str(now)\r\n signed = hmac.new(API_SECRET_KEY, url.encode('utf-8'), hashlib.sha512).hexdigest()\r\n headers = {'apisign': signed}\r\n r = requests.get(url, headers=headers)\r\n return r.json()\r\n\r\n\r\ndef simple_request(url):\r\n r = requests.get(url)\r\n return r.json()\r\n\r\n\r\ndef format_float(f):\r\n return \"%.8f\" % f\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n\r\n", "repo_name": "tmstieff/BittrexBot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4232, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "2", "api": [{"api_name": "time.time", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 119, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 121, "usage_type": "call"}, {"api_name": "hashlib.sha512", "line_number": 121, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 123, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "7702113198", "text": "from utils import *\nfrom tqdm import tqdm\nfrom urllib3.exceptions import ProtocolError, NewConnectionError\nfrom selenium.common.exceptions import InvalidArgumentException\n\nif __name__ == \"__main__\":\n with open('non-images.txt', 'r') as non_img_file:\n urls = [u for u in non_img_file.read().split('\\n') if u]\n non_img_file.close()\n\n log, psw = (input('Login? '), input('Password? '))\n\n driver = start_driver(log, psw)\n\n towrite = open('for_manual_download', 'a')\n\n actual_non_images = []\n\n for url in tqdm(urls):\n try:\n link = links_exist(driver, url)\n if link:\n towrite.write(f'{link}\\n')\n except (ConnectionRefusedError, ProtocolError, NewConnectionError):\n restart_driver(log, psw)\n link = links_exist(driver, url)\n if link:\n towrite.write(f'{link}\\n')\n except (AttributeError, InvalidArgumentException):\n towrite.write(f'{url}\\n')\n\n driver.quit()\n towrite.close()\n", "repo_name": "Pythonimous/python-miscellaneous", "sub_path": "reddit_imgs_selenium/removed_posts_handler.py", "file_name": "removed_posts_handler.py", "file_ext": "py", "file_size_in_byte": 1018, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "tqdm.tqdm", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib3.exceptions.ProtocolError", "line_number": 24, "usage_type": "name"}, {"api_name": "urllib3.exceptions.NewConnectionError", "line_number": 24, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.InvalidArgumentException", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "1545625871", "text": "from fastapi import APIRouter, Depends, HTTPException, status, Query, Path, BackgroundTasks\n\nfrom typing import Optional, Any\nfrom app import schemas, models, database, oauth2\nfrom sqlalchemy.orm import Session\nfrom ..controllers import crud_analytic\n\nrouter = APIRouter(\n prefix=\"/analytics\",\n tags=[\"Analytics\"],\n # dependencies=[Depends(get_token_header)],\n responses={404: {\"description\": \"Analytic data not found\"}},\n)\n\n\n@router.get(\"/\", tags=[])\ndef index(\n db: Session = Depends(database.get_db),\n skip: int = Query(0, description=\"Apply offset to the query\"),\n limit: int = Query(\n 10, description=\"Set a limit of data retrieved\"),\n):\n \"\"\"\n Retrieve product analytics.\n \"\"\"\n return crud_analytic.index(db, skip=skip, limit=limit)\n", "repo_name": "JulioJair/catalog-fastapi", "sub_path": "backend/app/app/routers/analytics.py", "file_name": "analytics.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "fastapi.APIRouter", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 18, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 18, "usage_type": "call"}, {"api_name": "app.database.get_db", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.database", "line_number": 18, "usage_type": "name"}, {"api_name": "fastapi.Query", "line_number": 19, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 20, "usage_type": "call"}, {"api_name": "controllers.crud_analytic.index", "line_number": 26, "usage_type": "call"}, {"api_name": "controllers.crud_analytic", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "14137085556", "text": "import godunovfunctions\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom sklearn.model_selection import KFold\nimport csv\nimport time\nfrom tqdm import tqdm\n\n\n# Loading all data\ndata_name = \"short_highway\"\ndatafolder = os.path.join(os.getcwd(), \"data\", data_name)\ndf = godunovfunctions.load_data(datafolder, print_logs=True)\nX_df = df[[1, 2, 3, 4, 5, 6, \"gem_dichtheid\"]]\nY_df = df[\"gem_intensiteit\"]\n\n\ndef train_and_save(\n model_linear_stack = nn.Sequential(\n nn.Linear(6, 4),\n nn.Softplus(),\n nn.Linear(4, 3),\n nn.Softplus(),\n ),\n X_min_normalizer = 0.0,\n X_max_normalizer = 100.0,\n Y_normalizer = 10000.0,\n criterion_function = nn.MSELoss,\n optimizer_function = optim.Adam,\n bias_init_function = nn.init.zeros_,\n weights_init_function = nn.init.xavier_uniform_,\n lr = 0.01,\n epochs = 1000,\n batch_size = 1000,\n k_folds = 5,):\n # ----------- From here, the hyperparameter search loop starts -------------\n # Normalize data, create tensors and create dataloader\n X = (torch.tensor(X_df.values, dtype=torch.float32) - X_min_normalizer) / (X_max_normalizer - X_min_normalizer)\n Y = torch.tensor(Y_df.values, dtype=torch.float32).view(-1) / Y_normalizer\n dataset = TensorDataset(X, Y)\n\n # We will save all results in logging_data\n logging_data = []\n\n kfold = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n for fold, (train_ids, test_ids) in enumerate(kfold.split(dataset)):\n train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids)\n test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids)\n\n # Define data loaders for training and testing data in this fold\n trainloader = torch.utils.data.DataLoader(\n dataset, \n batch_size=batch_size, sampler=train_subsampler)\n testloader = torch.utils.data.DataLoader(\n dataset,\n batch_size=batch_size, sampler=test_subsampler)\n \n # Set up model and optimizer\n torch.manual_seed(42)\n model = godunovfunctions.NeuralNetwork(\n lin_stack=model_linear_stack,\n bias_init_function=bias_init_function,\n weights_init_function=weights_init_function)\n optimizer = optimizer_function(model.parameters(), lr=lr)\n criterion = criterion_function()\n\n # Run epochs\n starttime = time.time()\n for epoch in tqdm(range(epochs), desc=f\"Epochs in fold {fold}\", ncols=100):\n epoch_starttime = time.time()\n\n # Loop over all training batches\n train_loss_total = 0\n tot_batches = 0\n for x_batch, y_batch in trainloader:\n # Get train loss\n y_batch_pred = model(x_batch)\n loss = criterion(y_batch_pred , y_batch)\n\n # Save that loss\n train_loss_total += loss.item()\n tot_batches += 1\n\n # Take a step\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n # Scale loss according to batches\n train_loss_total = train_loss_total / tot_batches\n\n # Loop over all test batches\n test_loss_total = 0\n tot_batches = 0\n for x_batch, y_batch in testloader:\n # Get test loss\n y_batch_pred = model(x_batch)\n loss = criterion(y_batch_pred, y_batch)\n\n # Save that loss\n test_loss_total += loss.item()\n tot_batches += 1\n \n # Scale loss according to batches\n test_loss_total = test_loss_total / tot_batches\n\n # Save the epoch results\n logging_data.append([fold, epoch, train_loss_total, test_loss_total, time.time() - epoch_starttime, time.time() - starttime])\n\n # Save the logging_data in a csv\n setting_data = [\n data_name,\n str(model_linear_stack).replace(\"\\n\", \"\"),\n X_min_normalizer,\n X_max_normalizer,\n Y_normalizer,\n str(criterion_function),\n str(optimizer_function),\n str(bias_init_function),\n str(weights_init_function),\n lr,\n epochs,\n batch_size,\n k_folds\n ]\n\n with open(\"hyperparameter_logs.csv\", \"a\", newline=\"\") as f_object:\n writer_object = csv.writer(f_object)\n for row in logging_data:\n writer_object.writerow(setting_data + row)\n f_object.close()\n\n\npossible_lin_stacks = [\n nn.Sequential(\n nn.Linear(6, 3),\n nn.Softplus(),\n ),\n nn.Sequential(\n nn.Linear(6, 10),\n nn.Softplus(),\n nn.Linear(10, 3),\n nn.Softplus(),\n ),\n nn.Sequential(\n nn.Linear(6, 20),\n nn.Softplus(),\n nn.Linear(20, 10),\n nn.Softplus(),\n nn.Linear(10, 3),\n nn.Softplus(),\n ),\n nn.Sequential(\n nn.Linear(6, 40),\n nn.Softplus(),\n nn.Linear(40, 20),\n nn.Softplus(),\n nn.Linear(20, 10),\n nn.Softplus(),\n nn.Linear(10, 3),\n nn.Softplus(),\n ),\n nn.Sequential(\n nn.Linear(6, 50),\n nn.Softplus(),\n nn.Linear(50, 40),\n nn.Softplus(),\n nn.Linear(40, 20),\n nn.Softplus(),\n nn.Linear(20, 10),\n nn.Softplus(),\n nn.Linear(10, 3),\n nn.Softplus()\n )\n]\n\nsetupcounter = 1\nfor lin_stack in possible_lin_stacks:\n print(f\"\\nTraining and testing setup {setupcounter}\")\n train_and_save(model_linear_stack=lin_stack)\n setupcounter += 1\n\n\n# # Settings\n# model_linear_stack = nn.Sequential(\n# nn.Linear(6, 50),\n# nn.Softplus(),\n# nn.Linear(50, 25),\n# nn.Softplus(),\n# nn.Linear(25, 10),\n# nn.Softplus(),\n# nn.Linear(10, 3),\n# nn.Softplus(),\n# )\n# X_min_normalizer = 0.0\n# X_max_normalizer = 100.0\n# Y_normalizer = 10000.0\n# criterion_function = nn.MSELoss\n# optimizer_function = optim.Adam\n# bias_init_function = nn.init.zeros_\n# weights_init_function = nn.init.xavier_uniform_\n# lr = 0.01\n# epochs = 20\n# batch_size = 1000\n# k_folds = 5\n\n", "repo_name": "vossemeijssen/macroscopic_traffic_model", "sub_path": "hyperparameter_tuning.py", "file_name": "hyperparameter_tuning.py", "file_ext": "py", "file_size_in_byte": 6353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 15, "usage_type": "call"}, {"api_name": "godunovfunctions.load_data", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.init", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.init", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.utils.data.SubsetRandomSampler", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.utils.data.SubsetRandomSampler", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 62, "usage_type": "call"}, {"api_name": "godunovfunctions.NeuralNetwork", "line_number": 63, "usage_type": "call"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 111, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 138, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 156, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 163, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 164, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 171, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}]} +{"seq_id": "69900055408", "text": "import argparse\nimport gc\nimport os\nimport time\nimport random\nimport sys\nimport importlib\nsys.path.append('.')\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport tqdm\n# from apex import amp\nfrom apex import parallel as apex_parallel\n\n# import data.dtu as dtu, data.sceneflow as sceneflow, data.blended as bld\n# from core.model_cas import Model, Loss\nfrom utils.io_utils import load_model, save_model\nfrom utils.preproc import recursive_apply\nfrom utils.utils import NanError\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--num_workers', type=int, default=8, help='The number of workers for the dataloader. 0 to disable the async loading.')\n# parser.add_argument('--num_gpus', type=int, default=1)\n\nparser.add_argument('--data_root', type=str, help='The root dir of the data.')\nparser.add_argument('--dataset_name', type=str, default='blended', help='The name of the dataset. Should be identical to the dataloader source file. e.g. blended refers to data/blended.py.')\nparser.add_argument('--model_name', type=str, default='model_cas', help='The name of the model. Should be identical to the model source file. e.g. model_cas refers to core/model_cas.py.')\n\nparser.add_argument('--num_src', type=int, default=3, help='The number of source views.')\nparser.add_argument('--max_d', type=int, default=128, help='The standard max depth number.')\nparser.add_argument('--interval_scale', type=float, default=1., help='The standard interval scale.')\nparser.add_argument('--cas_depth_num', type=str, default='32,16,8', help='The depth number for each stage.')\nparser.add_argument('--cas_interv_scale', type=str, default='4,2,1', help='The interval scale for each stage.')\nparser.add_argument('--resize', type=str, default='768,576', help='The size of the preprocessed input resized from the original one.')\nparser.add_argument('--crop', type=str, default='640,512', help='The size of the preprocessed input cropped from the resized one.')\n\nparser.add_argument('--mode', type=str, default='soft', choices=['soft', 'hard', 'uwta', 'maxpool', 'average'], help='The fusion strategy.')\nparser.add_argument('--occ_guide', action='store_true', default=False, help='Deprecated')\n\nparser.add_argument('--lr', type=str, default='1e-3,.5e-3,.25e-3,.125e-3', help='Learning rate under piecewise constant scheme.')\nparser.add_argument('--boundaries', type=str, default='.625,.75,.875', help='Boundary percentage for changing the learning rate.')\nparser.add_argument('--weight_decay', type=float, default=0, help='Weight decay factor.')\nparser.add_argument('--num_samples', type=int, default=160000, help='Total number =total_step*batch_size of samples for training.')\nparser.add_argument('--batch_size', type=int, default=2, help='Batch size.')\n\nparser.add_argument('--load_path', type=str, default=None, help='The dir of the folder containing the pretrained checkpoints.')\nparser.add_argument('--load_step', type=int, default=-1, help='The step to load. -1 for the latest one.')\nparser.add_argument('--reset_step', action='store_true', default=True, help='Set to reset the global step. Otherwise resume from the step of the checkpoint.')\n\nparser.add_argument('--job_name', type=str, default='temp', help='Job name for the name of the saved checkpoint.')\n\nparser.add_argument('--save_dir', type=str, help='The dir for saving the checkpoints.')\n\nparser.add_argument('--snapshot', type=int, default=5000, help='Step interval to save a checkpoint.')\nparser.add_argument('--max_keep', type=int, default=1000, help='Max number of checkpoints kept.')\n\nargs = parser.parse_args()\n\nif __name__ == '__main__':\n torch.backends.cudnn.benchmark = True\n\n # seed = 0\n # torch.backends.cudnn.benchmark = False\n # torch.backends.cudnn.deterministic = True\n # torch.manual_seed(seed)\n # np.random.seed(seed)\n # random.seed(seed)\n # torch.cuda.manual_seed(seed)\n # torch.cuda.manual_seed_all(seed)\n\n total_steps = args.num_samples // args.batch_size\n [resize_width, resize_height], [crop_width, crop_height] = [[int(v) for v in arg_str.split(',')] for arg_str in [args.resize, args.crop]]\n cas_depth_num = [int(v) for v in args.cas_depth_num.split(',')]\n cas_interv_scale = [float(v) for v in args.cas_interv_scale.split(',')]\n\n Model = importlib.import_module(f'core.{args.model_name}').Model\n Loss = importlib.import_module(f'core.{args.model_name}').Loss\n get_train_loader = importlib.import_module(f'data.{args.dataset_name}').get_train_loader\n\n dataset, loader = get_train_loader(\n args.data_root, args.num_src, total_steps, args.batch_size,\n {\n 'interval_scale': args.interval_scale,\n 'max_d': args.max_d,\n 'resize_width': resize_width,\n 'resize_height': resize_height,\n 'crop_width': crop_width,\n 'crop_height': crop_height\n },\n num_workers=args.num_workers\n )\n\n model = Model()\n model.cuda()\n model = apex_parallel.convert_syncbn_model(model)\n print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters() if p.requires_grad])))\n compute_loss = Loss()\n\n model = nn.DataParallel(model)\n\n if args.load_path is None:\n for m in model.modules():\n if any([isinstance(m, T) for T in [nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d, nn.ConvTranspose3d]]):\n if m.weight.requires_grad:\n nn.init.xavier_uniform_(m.weight)\n elif any([isinstance(m, T) for T in [nn.BatchNorm2d, nn.BatchNorm3d]]):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n global_step = 0\n else:\n global_step = load_model(model, args.load_path, args.load_step)\n if args.reset_step: global_step = 0\n print(f'load {os.path.join(args.load_path, str(args.load_step))}')\n\n lr = [float(v) for v in args.lr.split(',')]\n boundaries = args.boundaries\n if boundaries is not None:\n boundaries = [int(total_steps * float(b)) for b in boundaries.split(',')]\n optimizer = optim.Adam(model.parameters(), lr=lr[0], weight_decay=args.weight_decay)\n\n # model, optimizer = amp.initialize(model, optimizer, opt_level='O0')\n\n def piecewise_constant():\n if boundaries is None: return lr[0]\n i = 0\n for b in boundaries:\n if global_step < b: break\n i += 1\n curr_lr = lr[i]\n for param_group in optimizer.param_groups:\n param_group['lr'] = curr_lr\n return curr_lr\n\n model.train()\n\n pbar = tqdm.tqdm(loader, dynamic_ncols=True)\n if global_step != 0: pbar.update(global_step)\n for sample in pbar:\n if global_step >= total_steps: break\n if sample.get('skip') is not None and np.any(sample['skip']): continue\n\n curr_lr = piecewise_constant()\n\n recursive_apply(sample, lambda x: torch.from_numpy(x).float().cuda())\n ref, ref_cam, srcs, srcs_cam, gt, masks = [sample[attr] for attr in ['ref', 'ref_cam', 'srcs', 'srcs_cam', 'gt', 'masks']]\n\n loss, uncert_loss, less1, less3, l1, losses, outputs, refined_depth, prob_maps = None, None, None, None, None, None, None, None, None\n try:\n # est_depth, prob_map, pair_results = model([ref, ref_cam, srcs, srcs_cam], args.max_d, mode=args.mode)\n outputs, refined_depth, prob_maps = model(sample, cas_depth_num, cas_interv_scale, mode=args.mode)\n\n # losses = compute_loss([est_depth, pair_results], gt, masks, ref_cam, args.max_d, occ_guide=args.occ_guide, mode=args.mode)\n losses = compute_loss([outputs, refined_depth], gt, masks, ref_cam, args.max_d, occ_guide=args.occ_guide, mode=args.mode)\n \n loss, uncert_loss, less1, less3, l1 = losses[:5] #MVS\n # loss, less1, less3, l1 = losses[:4]\n\n if np.isnan(loss.item()):\n raise NanError\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n losses_np = [v.item() for v in losses[:5]] #MVS\n loss, uncert_loss, less1, less3, l1 = losses_np #MVS\n # loss, less1, less3, l1 = losses_np\n\n stats = losses[5]\n stats_np = [(l1.item(), less1.item(), less3.item()) for l1, less1, less3 in stats]\n stats_str = ''.join([f'({l1:.3f} {less1*100:.2f} {less3*100:.2f})' for l1, less1, less3 in stats_np])\n\n pbar.set_description(f'{loss:.3f}{stats_str}{l1:.3f}')\n # pbar.set_description(f'{loss:.4f} {less1:.3f} {less3:.3f} {l1:.4f}') #MVS\n # pbar.set_description(f'{less1:.3f} {less3:.3f} {l1:.4f}')\n except NanError:\n print(f'nan: {global_step}/{total_steps}')\n gc.collect()\n torch.cuda.empty_cache()\n # optimizer.zero_grad()\n # optimizer.step()\n\n if global_step != 0 and global_step % args.snapshot == 0:\n save_model({\n 'global_step': global_step,\n 'state_dict': model.state_dict()\n }, args.save_dir, args.job_name, global_step, args.max_keep)\n\n global_step += 1\n del loss, uncert_loss, less1, less3, l1, losses, outputs, refined_depth, prob_maps\n\n save_model({\n 'global_step': global_step,\n 'state_dict': model.state_dict()\n }, args.save_dir, args.job_name, global_step, args.max_keep)\n", "repo_name": "jzhangbs/Vis-MVSNet", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 9439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 211, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 66, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 82, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 83, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 84, "usage_type": "call"}, {"api_name": "apex.parallel.convert_syncbn_model", "line_number": 101, "usage_type": "call"}, {"api_name": "apex.parallel", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn.ConvTranspose3d", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 112, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm3d", "line_number": 112, "usage_type": "attribute"}, {"api_name": "torch.nn.init.constant_", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 114, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "utils.io_utils.load_model", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 125, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 146, "usage_type": "call"}, {"api_name": "utils.preproc.recursive_apply", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.utils.NanError", "line_number": 165, "usage_type": "name"}, {"api_name": "utils.utils.NanError", "line_number": 182, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 185, "usage_type": "attribute"}, {"api_name": "utils.io_utils.save_model", "line_number": 190, "usage_type": "call"}, {"api_name": "utils.io_utils.save_model", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "42081835737", "text": "import datetime\nimport json\nimport re\n\nimport requests.utils\n\nimport sonar.issue_changelog as changelog\nimport sonar.utilities as util\nfrom sonar import env, findings, projects, users\n\nSYNC_IGNORE_COMPONENTS = 'ignore_components'\nSYNC_ADD_LINK = 'add_link'\nSYNC_ADD_COMMENTS = 'add_comments'\nSYNC_COMMENTS = 'sync_comments'\nSYNC_ASSIGN = 'sync_assignments'\nSYNC_SERVICE_ACCOUNTS = 'sync_service_accounts'\n\n_TOO_MANY_ISSUES_MSG = \"Too many issues, recursing...\"\n\n_ISSUES = {}\n\n\nclass TooManyIssuesError(Exception):\n \"\"\"When a call to api/issues/search returns too many issues.\"\"\"\n\n def __init__(self, nbr_issues, message):\n super().__init__()\n self.nbr_issues = nbr_issues\n self.message = message\n\n\nclass Issue(findings.Finding):\n \"\"\"SonarQube Issue.\"\"\"\n\n SEARCH_API = 'issues/search'\n MAX_PAGE_SIZE = 500\n MAX_SEARCH = 10000\n OPTIONS_SEARCH = ['additionalFields', 'asc', 'assigned', 'assignees', 'authors', 'componentKeys',\n 'createdAfter', 'createdAt', 'createdBefore', 'createdInLast', 'directories',\n 'facetMode', 'facets', 'files', 'branch', 'fileUuids',\n 'issues', 'languages', 'onComponentOnly', 'p', 'ps', 'resolutions', 'resolved',\n 'rules', 's', 'severities', 'sinceLeakPeriod', 'statuses', 'tags', 'types']\n\n def __init__(self, key, endpoint, data=None, from_export=False):\n super().__init__(key, endpoint, data, from_export)\n self._debt = None\n if data is not None:\n self.component = data.get('component', None)\n util.logger.debug(\"Loaded issue: %s\", util.json_dump(data))\n _ISSUES[self.uuid()] = self\n\n def __str__(self):\n return f\"Issue key '{self.key}'\"\n\n def __format__(self, format_spec=''):\n return f\"Key: {self.key} - Type: {self.type} - Severity: {self.severity}\" \\\n f\" - File/Line: {self.component}/{self.line} - Rule: {self.rule} - Project: {self.projectKey}\"\n\n def to_string(self):\n \"\"\"Dumps the object in a string.\"\"\"\n return util.json_dump(self._json)\n\n def url(self):\n branch = ''\n if self.branch is not None:\n branch = f'&branch={requests.utils.quote(self.branch)}'\n elif self.pull_request is not None:\n branch = f'pullRequest={requests.utils.quote(self.pull_request)}&'\n return f'{self.endpoint.url}/project/issues?id={self.projectKey}{branch}&issues={self.key}'\n\n def debt(self):\n if self._debt is not None:\n return self._debt\n if 'debt' in self._json:\n kdays, days, hours, minutes = 0, 0, 0, 0\n debt = self._json['debt']\n m = re.search(r'(\\d+)kd', debt)\n if m:\n kdays = int(m.group(1))\n m = re.search(r'(\\d+)d', debt)\n if m:\n days = int(m.group(1))\n m = re.search(r'(\\d+)h', debt)\n if m:\n hours = int(m.group(1))\n m = re.search(r'(\\d+)min', debt)\n if m:\n minutes = int(m.group(1))\n self._debt = ((kdays * 1000 + days) * 24 + hours) * 60 + minutes\n elif 'effort' in self._json:\n self._debt = 0\n if self._json['effort'] != 'null':\n self._debt = int(self._json['effort'])\n return self._debt\n\n def to_json(self):\n data = super().to_json()\n data['url'] = self.url()\n data['effort'] = self.debt()\n return data\n\n def read(self):\n resp = self.get(Issue.SEARCH_API, params={'issues': self.key, 'additionalFields': '_all'})\n self._load(resp.issues[0])\n\n def changelog(self, force_api=False):\n if (force_api or self._changelog is None):\n resp = self.get('issues/changelog', {'issue': self.key, 'format': 'json'})\n data = json.loads(resp.text)\n util.json_dump_debug(data['changelog'], f\"{str(self)} Changelog = \")\n self._changelog = {}\n seq = 1\n for l in data['changelog']:\n d = changelog.Changelog(l)\n if d.is_technical_change():\n # Skip automatic changelog events generated by SonarSource itself\n util.logger.debug('Changelog is a technical change: %s', str(d))\n continue\n util.json_dump_debug(l, \"Changelog item Changelog ADDED = \")\n seq += 1\n self._changelog[f\"{d.date()}_{seq:03d}\"] = d\n return self._changelog\n\n def has_changelog(self):\n util.logger.debug('Issue %s had %d changelog', self.key, len(self.changelog()))\n return len(self.changelog()) > 0\n\n def can_be_synced(self, user_list):\n util.logger.debug(\"Issue %s: Checking if modifiers %s are different from user %s\",\n str(self), str(self.modifiers()), str(user_list))\n if user_list is None:\n return not self.has_changelog()\n for u in self.modifiers():\n if u not in user_list:\n return False\n return True\n\n def get_all_events(self, event_type='changelog'):\n if event_type == 'comments':\n events = self.comments()\n util.logger.debug('Issue %s has %d comments', self.key, len(events))\n else:\n events = self.changelog()\n util.logger.debug('Issue %s has %d changelog', self.key, len(events))\n bydate = {}\n for e in events:\n bydate[e['date']] = e\n return bydate\n\n def comments(self):\n if 'comments' not in self._json:\n self._comments = {}\n elif self._comments is None:\n self._comments = {}\n for c in self._json['comments']:\n self._comments[c['createdAt']] = {'date': c['createdAt'], 'event': 'comment',\n 'value': c['markdown'], 'user': c['login'], 'userName': c['login']}\n return self._comments\n\n def has_comments(self):\n comments = self.comments()\n return len(comments) > 0\n\n def has_changelog_or_comments(self):\n return self.has_changelog() or self.has_comments()\n\n def add_comment(self, comment, really=True):\n util.logger.debug(\"Adding comment %s to issue %s\", comment, self.key)\n if really:\n return self.post('issues/add_comment', {'issue': self.key, 'text': comment})\n else:\n return None\n\n def set_severity(self, severity):\n util.logger.debug(\"Changing severity of issue %s from %s to %s\", self.key, self.severity, severity)\n return self.post('issues/set_severity', {'issue': self.key, 'severity': severity})\n\n def assign(self, assignee):\n util.logger.debug(\"Assigning issue %s to %s\", self.key, assignee)\n return self.post('issues/assign', {'issue': self.key, 'assignee': assignee})\n\n def set_tags(self, tags):\n util.logger.debug(\"Setting tags %s to issue %s\", tags, self.key)\n return self.post('issues/set_tags', {'issue': self.key, 'tags': tags})\n\n def set_type(self, new_type):\n util.logger.debug(\"Changing type of issue %s from %s to %s\", self.key, self.type, new_type)\n return self.post('issues/set_type', {'issue': self.key, 'type': new_type})\n\n def modifiers(self):\n \"\"\"Returns list of users that modified the issue.\"\"\"\n item_list = []\n for c in self.changelog().values():\n util.logger.debug(\"Checking author of changelog %s\", str(c))\n author = c.author()\n if author is not None and author not in item_list:\n item_list.append(author)\n return item_list\n\n def commenters(self):\n \"\"\"Returns list of users that commented the issue.\"\"\"\n return util.unique_dict_field(self.comments(), 'user')\n\n def modifiers_excluding_service_users(self, service_users):\n mods = []\n for u in self.modifiers():\n if u not in service_users:\n mods.append(u)\n return mods\n\n def search_siblings(self, issue_list, allowed_users=None, ignore_component=False, **kwargs):\n exact_matches = []\n approx_matches = []\n match_but_modified = []\n for key, issue in issue_list.items():\n if key == self.key:\n continue\n if issue.strictly_identical_to(self, ignore_component, **kwargs):\n util.logger.debug(\"Issues %s and %s are strictly identical\", self.key, key)\n if issue.can_be_synced(allowed_users):\n exact_matches.append(issue)\n else:\n match_but_modified.append(issue)\n elif issue.almost_identical_to(self, ignore_component, **kwargs):\n util.logger.debug(\"Issues %s and %s are almost identical\", self.key, key)\n if issue.can_be_synced(allowed_users):\n approx_matches.append(issue)\n else:\n match_but_modified.append(issue)\n else:\n util.logger.debug(\"Issues %s and %s are not siblings\", self.key, key)\n return (exact_matches, approx_matches, match_but_modified)\n\n def is_wont_fix(self):\n return self.__has_been_marked_as_statuses([\"WONTFIX\"])\n\n def is_false_positive(self):\n return self.__has_been_marked_as_statuses([\"FALSE-POSITIVE\"])\n\n def __has_been_marked_as_statuses(self, statuses):\n for log in self.changelog():\n for diff in log['diffs']:\n if diff[\"key\"] != \"resolution\":\n continue\n for status in statuses:\n if diff[\"newValue\"] == status:\n return True\n return False\n\n def strictly_identical_to(self, another_issue, ignore_component=False):\n return (\n self.rule == another_issue.rule and\n self.hash == another_issue.hash and\n self.message == another_issue.message and\n self.debt() == another_issue.debt() and\n self.file() == another_issue.file() and\n (self.component == another_issue.component or ignore_component)\n )\n\n def almost_identical_to(self, another_issue, ignore_component=False, **kwargs):\n if self.rule != another_issue.rule or self.hash != another_issue.hash:\n return False\n score = 0\n if self.message == another_issue.message or kwargs.get('ignore_message', False):\n score += 2\n if self.file() == another_issue.file():\n score += 2\n if self.debt() == another_issue.debt() or kwargs.get('ignore_debt', False):\n score += 1\n if self.line == another_issue.line or kwargs.get('ignore_line', False):\n score += 1\n if self.component == another_issue.component or ignore_component:\n score += 1\n if self.author == another_issue.author or kwargs.get('ignore_author', False):\n score += 1\n if self.type == another_issue.type or kwargs.get('ignore_type', False):\n score += 1\n if self.severity == another_issue.severity or kwargs.get('ignore_severity', False):\n score += 1\n # Need at least 8 / 10 to match\n return score >= 8\n\n def __do_transition(self, transition):\n return self.post('issues/do_transition', {'issue': self.key, 'transition': transition})\n\n def reopen(self):\n util.logger.debug(\"Reopening %s\", str(self))\n return self.__do_transition('reopen')\n\n def mark_as_false_positive(self):\n util.logger.debug(\"Marking %s as false positive\", str(self))\n return self.__do_transition('falsepositive')\n\n def confirm(self):\n util.logger.debug(\"Confirming %s\", str(self))\n return self.__do_transition('confirm')\n\n def unconfirm(self):\n util.logger.debug(\"Unconfirming %s\", str(self))\n return self.__do_transition('unconfirm')\n\n def resolve_as_fixed(self):\n util.logger.debug(\"Marking %s as fixed\", str(self))\n return self.__do_transition('resolve')\n\n def mark_as_wont_fix(self):\n util.logger.debug(\"Marking %s as won't fix\", str(self))\n return self.__do_transition('wontfix')\n\n def close(self):\n util.logger.debug(\"Closing %s\", str(self))\n return self.__do_transition('close')\n\n def mark_as_reviewed(self):\n if self.is_hotspot():\n util.logger.debug(\"Marking hotspot %s as reviewed\", self.key)\n return self.__do_transition('resolveasreviewed')\n elif self.is_vulnerability():\n util.logger.debug(\"Marking vulnerability %s as won't fix in replacement of 'reviewed'\", self.key)\n self.add_comment(\"Vulnerability marked as won't fix to replace hotspot 'reviewed' status\")\n return self.__do_transition('wontfix')\n\n util.logger.debug(\"Issue %s is neither a hotspot nor a vulnerability, cannot mark as reviewed\", self.key)\n return False\n\n def __apply_event(self, event, settings):\n util.logger.debug(\"Applying event %s\", str(event))\n # origin = f\"originally by *{event['userName']}* on original branch\"\n (event_type, data) = event.changelog_type()\n if event_type == 'SEVERITY':\n self.set_severity(data)\n # self.add_comment(f\"Change of severity {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'TYPE':\n self.set_type(data)\n # self.add_comment(f\"Change of issue type {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'REOPEN':\n if event.previous_state() == 'CLOSED':\n util.logger.info(\"Reopen from closed issue won't be applied, issue was never closed\")\n else:\n self.reopen()\n # self.add_comment(f\"Issue re-open {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'FALSE-POSITIVE':\n self.mark_as_false_positive()\n # self.add_comment(f\"False positive {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'WONT-FIX':\n self.mark_as_wont_fix()\n # self.add_comment(f\"Won't fix {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'CONFIRM':\n self.confirm()\n # self.add_comment(f\"Won't fix {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'UNCONFIRM':\n self.unconfirm()\n # self.add_comment(f\"Won't fix {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'REVIEWED':\n self.mark_as_reviewed()\n # self.add_comment(f\"Hotspot review {origin}\")\n elif event_type == 'ASSIGN':\n if settings[SYNC_ASSIGN]:\n u = users.get_login_from_name(data, endpoint=self.endpoint)\n if u is None:\n u = settings[SYNC_SERVICE_ACCOUNTS][0]\n self.assign(u)\n # self.add_comment(f\"Issue assigned {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'TAG':\n self.set_tags(data)\n # self.add_comment(f\"Tag change {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'FIXED':\n self.resolve_as_fixed()\n # self.add_comment(f\"Change of issue type {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'CLOSED':\n util.logger.info(\"Changelog event is a CLOSE issue, it cannot be applied... %s\",\n str(event))\n # self.add_comment(f\"Change of issue type {origin}\", settings[SYNC_ADD_COMMENTS])\n elif event_type == 'INTERNAL':\n util.logger.info(\"Changelog %s is internal, it will not be applied...\", str(event))\n # self.add_comment(f\"Change of issue type {origin}\", settings[SYNC_ADD_COMMENTS])\n else:\n util.logger.error(\"Event %s can't be applied\", str(event))\n return False\n return True\n\n def apply_changelog(self, source_issue, settings):\n events = source_issue.changelog()\n if events is None or not events:\n util.logger.debug(\"Sibling %s has no changelog, no action taken\", source_issue.key)\n return False\n\n change_nbr = 0\n start_change = len(self.changelog()) + 1\n util.logger.debug(\"Issue %s: Changelog = %s\", str(self), str(self.changelog()))\n util.logger.info(\"Applying changelog of issue %s to issue %s, from change %d\",\n source_issue.key, self.key, start_change)\n for key in sorted(events.keys()):\n change_nbr += 1\n if change_nbr < start_change:\n util.logger.debug(\"Skipping change already applied in a previous sync: %s\", str(events[key]))\n continue\n self.__apply_event(events[key], settings)\n\n comments = source_issue.comments()\n if len(self.comments()) == 0 and settings[SYNC_ADD_LINK]:\n util.logger.info(\"Target issue has 0 comments\")\n start_change = 1\n self.add_comment(f\"Automatically synchronized from [this original issue]({source_issue.url()})\")\n else:\n start_change = len(self.comments())\n util.logger.info(\"Target issue already has %d comments\", start_change)\n util.logger.info(\"Applying comments of issue %s to issue %s, from comment %d\",\n source_issue.key, self.key, start_change)\n change_nbr = 0\n for key in sorted(comments.keys()):\n change_nbr += 1\n if change_nbr < start_change:\n util.logger.debug(\"Skipping comment already applied in a previous sync: %s\", str(comments[key]))\n continue\n util.logger.debug(\"Applying comment %s\", comments[key]['value'])\n # origin = f\"originally by *{event['userName']}* on original branch\"\n self.add_comment(comments[key]['value'])\n return True\n\n# ------------------------------- Static methods --------------------------------------\n\ndef __search_all_by_directories(params, endpoint=None):\n new_params = params.copy()\n facets = _get_facets(new_params['componentKeys'], facets='directories', params=new_params, endpoint=endpoint)\n issue_list = {}\n for d in facets['directories']:\n util.logger.info('Search by directory %s', d['val'])\n new_params['directories'] = d['val']\n issue_list.update(_search_all(new_params, endpoint, raise_error=False))\n return issue_list\n\n\ndef __search_all_by_types(params, endpoint=None):\n issue_list = {}\n new_params = params.copy()\n for issue_type in ('BUG', 'VULNERABILITY', 'CODE_SMELL'):\n try:\n util.logger.info('Search by type %s', issue_type)\n new_params['types'] = issue_type\n issue_list.update(_search_all(new_params, endpoint))\n except TooManyIssuesError:\n util.logger.info(_TOO_MANY_ISSUES_MSG)\n issue_list.update(__search_all_by_directories(params=new_params, endpoint=endpoint))\n return issue_list\n\n\ndef __search_all_by_severities(params, endpoint=None):\n issue_list = {}\n new_params = params.copy()\n for sev in ('BLOCKER', 'CRITICAL', 'MAJOR', 'MINOR', 'INFO'):\n util.logger.info('Search by severity %s', sev)\n new_params['severities'] = sev\n try:\n issue_list.update(_search_all(params=new_params, endpoint=endpoint))\n except TooManyIssuesError:\n util.logger.info(_TOO_MANY_ISSUES_MSG)\n issue_list.update(__search_all_by_types(params=new_params, endpoint=endpoint))\n util.logger.info('Total: %d for %s', len(issue_list), str(params))\n return issue_list\n\n\ndef __search_all_by_date(params, date_start=None, date_stop=None, endpoint=None):\n new_params = params.copy()\n if date_start is None:\n date_start = get_oldest_issue(endpoint=endpoint,\n params=new_params).replace(hour=0, minute=0, second=0, microsecond=0)\n if date_stop is None:\n date_stop = get_newest_issue(endpoint=endpoint,\n params=new_params).replace(hour=0, minute=0, second=0, microsecond=0)\n util.logger.info(\"Search by date between [%s - %s]\",\n util.date_to_string(date_start, False), util.date_to_string(date_stop, False))\n issue_list = {}\n new_params.update({'createdAfter': date_start, 'createdBefore': date_stop})\n try:\n issue_list = _search_all(params=new_params, endpoint=endpoint)\n except TooManyIssuesError as e:\n util.logger.info(\"Too many issues (%d), splitting time window\", e.nbr_issues)\n diff = (date_stop - date_start).days\n if diff == 0:\n util.logger.info(_TOO_MANY_ISSUES_MSG)\n issue_list = __search_all_by_severities(new_params, endpoint=endpoint)\n elif diff == 1:\n issue_list.update(\n __search_all_by_date(new_params, date_start=date_start, date_stop=date_start, endpoint=endpoint))\n issue_list.update(\n __search_all_by_date(new_params, date_start=date_stop, date_stop=date_stop, endpoint=endpoint))\n else:\n date_middle = date_start + datetime.timedelta(days=diff//2)\n issue_list.update(\n __search_all_by_date(new_params, date_start=date_start, date_stop=date_middle, endpoint=endpoint))\n date_middle = date_middle + datetime.timedelta(days=1)\n issue_list.update(\n __search_all_by_date(new_params, date_start=date_middle, date_stop=date_stop, endpoint=endpoint))\n if date_start is not None and date_stop is not None:\n util.logger.debug(\"Project %s has %d issues between %s and %s\", new_params['componentKeys'], len(issue_list),\n util.date_to_string(date_start, False), util.date_to_string(date_stop, False))\n return issue_list\n\n\ndef _search_all_by_project(project_key, params, endpoint=None):\n if project_key is None:\n key_list = projects.search(endpoint).keys()\n else:\n key_list = util.csv_to_list(project_key)\n issue_list = {}\n for k in key_list:\n params['componentKeys'] = k\n try:\n issue_list.update(_search_all(params, endpoint))\n except TooManyIssuesError:\n util.logger.info(_TOO_MANY_ISSUES_MSG)\n issue_list.update(__search_all_by_date(params=params, endpoint=endpoint))\n return issue_list\n\n\ndef _search_all(params, endpoint=None, raise_error=True):\n new_params = params.copy()\n new_params['ps'] = Issue.MAX_PAGE_SIZE\n issue_list = {}\n p, nbr_pages = 1, 20\n util.logger.debug(\"Search all with %s\", str(params))\n while p <= nbr_pages:\n new_params['p'] = p\n resp = env.get(Issue.SEARCH_API, params=new_params, ctxt=endpoint)\n data = json.loads(resp.text)\n for i in data['issues']:\n i['branch'] = params.get('branch', None)\n i['pullRequest'] = params.get('pullRequest', None)\n issue_list[i['key']] = get_object(i['key'], endpoint=endpoint, data=i)\n nbr_issues = data['paging']['total']\n #util.logger.info(\"nbr_issues = %d max = %d raise error = %s\", nbr_issues, Issue.MAX_SEARCH, str(raise_error))\n if nbr_issues > Issue.MAX_SEARCH and raise_error:\n raise TooManyIssuesError(nbr_issues, f'{nbr_issues} issues returned by api/issues/search, '\n f'this is more than the max {Issue.MAX_SEARCH} possible')\n nbr_pages = (nbr_issues + Issue.MAX_PAGE_SIZE - 1) // Issue.MAX_PAGE_SIZE\n p += 1\n util.logger.info('Collected %d issues', len(issue_list))\n return issue_list\n\n\ndef search_by_project(project_key, endpoint=None, branch=None, pull_request=None, params=None, search_findings=False):\n if params is None:\n params = {}\n if branch is not None:\n params['branch'] = branch\n if pull_request is not None:\n params['pullRequest'] = pull_request\n if project_key is None:\n key_list = projects.search(endpoint).keys()\n else:\n key_list = util.csv_to_list(project_key)\n issue_list = {}\n for k in key_list:\n util.logger.info(\"Issue search by project %s branch %s\", k, str(branch))\n if endpoint.version() >= (9, 1, 0) and endpoint.edition() in ('enterprise', 'datacenter') and search_findings:\n util.logger.info('Using new export findings to speed up issue export')\n issue_list.update(projects.Project(k, endpoint=endpoint).get_findings(branch, pull_request))\n else:\n util.logger.info('Traditional issue search by project')\n issue_list.update(_search_all_by_project(k, params, endpoint=endpoint))\n return issue_list\n\n\ndef search(endpoint=None, page=None, params=None):\n if params is None:\n new_params = {}\n else:\n new_params = params.copy()\n new_params = __get_issues_search_params(new_params)\n util.logger.debug(\"Search params = %s\", str(new_params))\n if 'ps' not in new_params:\n new_params['ps'] = Issue.MAX_PAGE_SIZE\n p = 1\n issue_list = {}\n while True:\n if page is None:\n new_params['p'] = p\n else:\n new_params['p'] = page\n resp = env.get(Issue.SEARCH_API, params=new_params, ctxt=endpoint)\n data = json.loads(resp.text)\n nbr_issues = data['paging']['total']\n nbr_pages = (nbr_issues + new_params['ps']-1) // new_params['ps']\n util.logger.debug(\"Number of issues: %d - Page: %d/%d\", nbr_issues, new_params['p'], nbr_pages)\n if page is None and nbr_issues > Issue.MAX_SEARCH:\n raise TooManyIssuesError(nbr_issues,\n f'{nbr_issues} issues returned by api/{Issue.SEARCH_API}, '\n f'this is more than the max {Issue.MAX_SEARCH} possible')\n\n for i in data['issues']:\n i['branch'] = new_params.get('branch', None)\n i['pullRequest'] = new_params.get('pullRequest', None)\n issue_list[i['key']] = get_object(i['key'], endpoint=endpoint, data=i)\n if page is not None or p >= nbr_pages:\n break\n p += 1\n return issue_list\n\n\ndef search_all_issues(params=None, endpoint=None):\n util.logger.info('searching issues for %s', str(params))\n if params is None:\n params = {}\n params['ps'] = 500\n page = 1\n nbr_pages = 1\n issues = []\n while page <= nbr_pages and page <= 20:\n params['p'] = page\n returned_data = search(endpoint=endpoint, params=params)\n issues = issues + returned_data['issues']\n page = returned_data['page']\n nbr_pages = returned_data['pages']\n page = page + 1\n util.logger.debug(\"Total number of issues: %d\", len(issues))\n return issues\n\n\ndef _get_facets(project_key, facets='directories', endpoint=None, params=None):\n if params is None:\n parms = {}\n else:\n parms = params.copy()\n parms['componentKeys'] = project_key\n parms['facets'] = facets\n parms['ps'] = 500\n parms = __get_issues_search_params(parms)\n resp = env.get(Issue.SEARCH_API, params=parms, ctxt=endpoint)\n data = json.loads(resp.text)\n util.json_dump_debug(data['facets'], 'FACETS = ')\n l = {}\n facets_list = util.csv_to_list(facets)\n for f in data['facets']:\n if f['property'] in facets_list:\n l[f['property']] = f['values']\n return l\n\n\ndef __get_one_issue_date(endpoint=None, asc_sort='false', params=None):\n \"\"\"Returns the date of one issue found\"\"\"\n if params is None:\n parms = {}\n else:\n parms = params.copy()\n parms['s'] = 'CREATION_DATE'\n parms['asc'] = asc_sort\n parms['ps'] = 1\n issue_list = search(endpoint=endpoint, page=1, params=parms)\n if not issue_list:\n return None\n for _, i in issue_list.items():\n date = i.creation_date\n util.logger.debug('Date: %s Issue %s', str(date), str(i))\n break\n return date\n\n\ndef get_oldest_issue(endpoint=None, params=None):\n \"\"\"Returns the oldest date of all issues found\"\"\"\n return __get_one_issue_date(endpoint=endpoint, asc_sort='true', params=params)\n\n\ndef get_newest_issue(endpoint=None, params=None):\n \"\"\"Returns the newest date of all issues found\"\"\"\n return __get_one_issue_date(endpoint=endpoint, asc_sort='false', params=params)\n\n\ndef _search_project_daily_issues(key, day, sqenv=None, **kwargs):\n util.logger.debug(\"Searching daily issues for project %s on day %s\", key, day)\n kw = kwargs.copy()\n kw['componentKeys'] = key\n if kwargs is None or 'severities' not in kwargs:\n severities = {'INFO', 'MINOR', 'MAJOR', 'CRITICAL', 'BLOCKER'}\n else:\n severities = util.csv_to_list(kwargs['severities'])\n util.logger.debug(\"Severities = %s\", str(severities))\n if kwargs is None or 'types' not in kwargs:\n types = {'CODE_SMELL', 'VULNERABILITY', 'BUG', 'SECURITY_HOTSPOT'}\n else:\n types = util.csv_to_list(kwargs['types'])\n util.logger.debug(\"Types = %s\", str(types))\n kw['createdAfter'] = day\n kw['createdBefore'] = day\n issues = []\n for severity in severities:\n kw['severities'] = severity\n for issue_type in types:\n kw['types'] = issue_type\n issues = issues + search_all_issues(sqenv=sqenv, **kw)\n util.logger.info(\"%d daily issues for project key %s on %s\", len(issues), key, day)\n return issues\n\n\ndef count(endpoint=None, **kwargs):\n \"\"\"Returns number of issues of a search\"\"\"\n returned_data = search(endpoint=endpoint, params=kwargs.copy().update({'ps': 1}))\n util.logger.debug(\"Issue search %s would return %d issues\", str(kwargs), returned_data['total'])\n return returned_data['total']\n\n\ndef search_project_issues(key, sqenv=None, **kwargs):\n kwargs['componentKeys'] = key\n oldest = get_oldest_issue(endpoint=sqenv, **kwargs)\n if oldest is None:\n return []\n startdate = oldest\n enddate = get_newest_issue(endpoint=sqenv, **kwargs)\n\n nbr_issues = count(sqenv=sqenv, **kwargs)\n days_slice = abs((enddate - startdate).days)+1\n if nbr_issues > Issue.MAX_SEARCH:\n days_slice = (Issue.MAX_SEARCH * days_slice) // (nbr_issues * 4)\n util.logger.debug(\"For project %s, slicing by %d days, between %s and %s\", key, days_slice, startdate, enddate)\n\n issues = []\n window_start = startdate\n while window_start <= enddate:\n current_slice = days_slice\n sliced_enough = False\n while not sliced_enough:\n window_size = datetime.timedelta(days=current_slice)\n kwargs['createdAfter'] = util.format_date(window_start)\n window_stop = window_start + window_size\n kwargs['createdBefore'] = util.format_date(window_stop)\n found_issues = search_all_issues(endpoint=sqenv, **kwargs)\n if len(found_issues) < Issue.MAX_SEARCH:\n issues = issues + found_issues\n util.logger.debug(\"Got %d issue, OK, go to next window\", len(found_issues))\n sliced_enough = True\n window_start = window_stop + datetime.timedelta(days=1)\n elif current_slice == 0:\n found_issues = _search_project_daily_issues(key, kwargs['createdAfter'], sqenv, **kwargs)\n issues = issues + found_issues\n sliced_enough = True\n util.logger.error(\"Project key %s has many issues on %s, showing only the first %d\",\n key, window_start, len(found_issues))\n window_start = window_stop + datetime.timedelta(days=1)\n else:\n sliced_enough = False\n current_slice = current_slice // 2\n util.logger.debug(\"Reslicing with a thinner slice of %d days\", current_slice)\n\n util.logger.debug(\"For project %s, %d issues found\", key, len(issues))\n return issues\n\n\ndef identical_attributes(o1, o2, key_list):\n for key in key_list:\n if o1[key] != o2[key]:\n return False\n return True\n\n\ndef __get_issues_search_params(params):\n outparams = {'additionalFields': 'comments'}\n for key in params:\n if params[key] is not None and key in Issue.OPTIONS_SEARCH:\n outparams[key] = params[key]\n return outparams\n\n\ndef get_object(key, data=None, endpoint=None, from_export=False):\n if key not in _ISSUES:\n _ = Issue(key=key, data=data, endpoint=endpoint, from_export=from_export)\n return _ISSUES[key]\n", "repo_name": "gamesh411/sonarqube-tools", "sub_path": "sonar/issues.py", "file_name": "issues.py", "file_ext": "py", "file_size_in_byte": 32410, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "2", "api": [{"api_name": "sonar.findings.Finding", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sonar.findings", "line_number": 32, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 49, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 49, "usage_type": "name"}, {"api_name": "sonar.utilities.json_dump", "line_number": 49, "usage_type": "call"}, {"api_name": "sonar.utilities.json_dump", "line_number": 61, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 61, "usage_type": "name"}, {"api_name": "requests.utils.utils.quote", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.utils.utils", "line_number": 66, "usage_type": "attribute"}, {"api_name": "requests.utils", "line_number": 66, "usage_type": "name"}, {"api_name": "requests.utils.utils.quote", "line_number": 68, "usage_type": "call"}, {"api_name": "requests.utils.utils", "line_number": 68, "usage_type": "attribute"}, {"api_name": "requests.utils", "line_number": 68, "usage_type": "name"}, {"api_name": "re.search", "line_number": 77, "usage_type": "call"}, {"api_name": "re.search", "line_number": 80, "usage_type": "call"}, {"api_name": "re.search", "line_number": 83, "usage_type": "call"}, {"api_name": "re.search", "line_number": 86, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 109, "usage_type": "call"}, {"api_name": "sonar.utilities.json_dump_debug", "line_number": 110, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 110, "usage_type": "name"}, {"api_name": "sonar.issue_changelog.Changelog", "line_number": 114, "usage_type": "call"}, {"api_name": "sonar.issue_changelog", "line_number": 114, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 117, "usage_type": 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{"api_name": "sonar.projects", "line_number": 503, "usage_type": "name"}, {"api_name": "sonar.utilities.csv_to_list", "line_number": 505, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 505, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.info", "line_number": 512, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 512, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 512, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 522, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 522, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 522, "usage_type": "name"}, {"api_name": "sonar.env.get", "line_number": 525, "usage_type": "call"}, {"api_name": "sonar.env", "line_number": 525, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 526, "usage_type": "call"}, {"api_name": "sonar.utilities.logger.info", 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"usage_type": "name"}, {"api_name": "sonar.projects.Project", "line_number": 558, "usage_type": "call"}, {"api_name": "sonar.projects", "line_number": 558, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.info", "line_number": 560, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 560, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 560, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 571, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 571, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 571, "usage_type": "name"}, {"api_name": "sonar.env.get", "line_number": 581, "usage_type": "call"}, {"api_name": "sonar.env", "line_number": 581, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 582, "usage_type": "call"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 585, "usage_type": "call"}, {"api_name": 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631, "usage_type": "name"}, {"api_name": "sonar.utilities.csv_to_list", "line_number": 633, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 633, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 654, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 654, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 654, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 670, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 670, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 670, "usage_type": "name"}, {"api_name": "sonar.utilities.csv_to_list", "line_number": 676, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 676, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 677, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 677, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 677, "usage_type": "name"}, {"api_name": "sonar.utilities.csv_to_list", "line_number": 681, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 681, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 682, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 682, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 682, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.info", "line_number": 691, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 691, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 691, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 698, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 698, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 698, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 714, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 714, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 714, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 722, "usage_type": "call"}, {"api_name": "sonar.utilities.format_date", "line_number": 723, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 723, "usage_type": "name"}, {"api_name": "sonar.utilities.format_date", "line_number": 725, "usage_type": "call"}, {"api_name": "sonar.utilities", "line_number": 725, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 729, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 729, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 729, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 731, "usage_type": "call"}, {"api_name": "sonar.utilities.logger.error", "line_number": 736, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 736, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 736, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 738, "usage_type": "call"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 742, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 742, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 742, "usage_type": "name"}, {"api_name": "sonar.utilities.logger.debug", "line_number": 744, "usage_type": "call"}, {"api_name": "sonar.utilities.logger", "line_number": 744, "usage_type": "attribute"}, {"api_name": "sonar.utilities", "line_number": 744, "usage_type": "name"}]} +{"seq_id": "28551028226", "text": "from numbers import Number\nfrom typing import Tuple, Optional, Generic, List\n\n\nclass Node(object):\n def __init__(self, key: Number, val: Generic) -> None:\n self.key = key\n self.val = val\n\n\nclass BinarySearchTree(object):\n def __init__(self, list_of_values: List[Tuple]) -> None:\n list_of_values.sort(key=lambda x: x[0])\n self.data = list(map(lambda x: Node(*x), list_of_values))\n self.n = len(self.data)\n\n def search(self, target: Number) -> Optional[Node]:\n left, right = 0, self.n - 1\n while left < right:\n mid = (left + right) // 2\n current_node = self.data[mid]\n if current_node.key == target:\n return current_node\n elif current_node.key < target:\n left = mid + 1\n else:\n right = mid - 1\n return None\n\n def search_closest(self, target: Number) -> Node:\n left, right = 0, self.n - 1\n if target >= self.data[right].key:\n return self.data[right]\n\n if target <= self.data[left].key:\n return self.data[left]\n\n while left < right:\n mid = (left + right) // 2\n current_node = self.data[mid]\n\n if current_node.key == target:\n return current_node\n\n if target < current_node.key:\n if mid > 0 and target > self.data[mid - 1].key:\n return self._get_closest(current_node, self.data[mid - 1], target)\n right = mid\n\n else:\n if mid < self.n - 1 and target < self.data[mid + 1].key:\n return self._get_closest(current_node, self.data[mid + 1], target)\n\n left = mid\n\n return current_node\n\n @staticmethod\n def _get_closest(x: Node, y: Node, target: Number):\n if abs(x.key - target) <= abs(y.key - target):\n return x\n return y\n\n\nclass UnionFind(object):\n def __init__(self, n: int) -> None:\n self.items = list(range(n))\n self.sizes = [1 for _ in range(n)]\n self.n_nodes = n\n self.n_connected_sets = n\n\n def find(self, p: int) -> int:\n root = self.items[p]\n while root != self.items[root]:\n self.items[root] = self.items[self.items[root]]\n root = self.items[root]\n self.items[p] = root\n return root\n\n def connected(self, p: int, q: int) -> bool:\n return p == q or self.find(p) == self.find(q)\n\n def union(self, p: int, q: int) -> None:\n if self.connected(p, q):\n return None\n\n p_root = self.find(p)\n q_root = self.find(q)\n\n smaller, larger = sorted([p_root, q_root], key=lambda z: self.sizes[z])\n self.items[smaller] = larger\n self.sizes[larger] += self.sizes[smaller]\n\n self.n_connected_sets -= 1\n\n", "repo_name": "jiduque/rosalind", "sub_path": "rosalind/_data_structures.py", "file_name": "_data_structures.py", "file_ext": "py", "file_size_in_byte": 2861, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numbers.Number", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 12, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 17, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 30, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 59, "usage_type": "name"}]} +{"seq_id": "862527270", "text": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nfrom datadog_api_client.model_utils import (\n ModelNormal,\n cached_property,\n)\n\n\nif TYPE_CHECKING:\n from datadog_api_client.v2.model.opsgenie_service_create_data import OpsgenieServiceCreateData\n\n\nclass OpsgenieServiceCreateRequest(ModelNormal):\n @cached_property\n def openapi_types(_):\n from datadog_api_client.v2.model.opsgenie_service_create_data import OpsgenieServiceCreateData\n\n return {\n \"data\": (OpsgenieServiceCreateData,),\n }\n\n attribute_map = {\n \"data\": \"data\",\n }\n\n def __init__(self_, data: OpsgenieServiceCreateData, **kwargs):\n \"\"\"\n Create request for an Opsgenie service.\n\n :param data: Opsgenie service data for a create request.\n :type data: OpsgenieServiceCreateData\n \"\"\"\n super().__init__(kwargs)\n\n self_.data = data\n", "repo_name": "DataDog/datadog-api-client-python", "sub_path": "src/datadog_api_client/v2/model/opsgenie_service_create_request.py", "file_name": "opsgenie_service_create_request.py", "file_ext": "py", "file_size_in_byte": 919, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 79, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 11, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.ModelNormal", "line_number": 15, "usage_type": "name"}, {"api_name": "datadog_api_client.v2.model.opsgenie_service_create_data.OpsgenieServiceCreateData", "line_number": 21, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.cached_property", "line_number": 16, "usage_type": "name"}, {"api_name": "datadog_api_client.v2.model.opsgenie_service_create_data.OpsgenieServiceCreateData", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "19397679148", "text": "from typing import Any, Callable, Dict, List, Tuple\nimport numpy as np\n\nimport torch\nfrom torch import Tensor\nimport torchmetrics\nimport torch.nn as nn\nfrom torch.nn import functional as F, Module\nfrom transformers import BertModel, AdamW\n\nfrom pytorch_lightning import LightningModule\n\nfrom utilities import utils\n\n\nAMLBatch = Tuple[Dict[str, Tensor], Tensor]\n\n\nclass LongTextClassifier(LightningModule):\n \"\"\"\n RoBERT model for AML article classification\n \"\"\"\n\n def __init__(\n self, \n num_classes: int,\n config_path: str,\n num_epochs_freeze_pretrained: int,\n ):\n super().__init__()\n self.save_hyperparameters()\n self.num_classes = num_classes\n \n self.num_epochs_freeze_pretrained = num_epochs_freeze_pretrained\n self.config = utils.load_config(config_path)\n self.dropout_rate = self.config.DROPOUT_RATE\n self.pretrained_weights_frozen = False\n\n # define the model architecture:\n self._setup_feature_extractor()\n self._setup_aggregating_network()\n self._setup_predictor()\n\n # define evaluation metrics:\n self.accuracy = torchmetrics.Accuracy(num_classes=self.num_classes).to(self.device)\n\n def _setup_feature_extractor(self):\n \"\"\"\n Defines the base feature extraction model\n \"\"\"\n self.feature_extractor = BertModel.from_pretrained(self.config.BERT_MODEL)\n\n def _setup_aggregating_network(self):\n \"\"\"\n Defines the feature collating network\n \"\"\"\n self.aggregating_network = nn.LSTM(\n self.config.BERT_MODEL_OUTPUT_SIZE, \n self.config.AGGREGATING_NETWORK_OUTPUT_SIZE,\n num_layers=1,\n bidirectional=False,\n )\n\n def _setup_predictor(self):\n \"\"\"\n Defines the model predictor\n \"\"\"\n self.predictor = nn.Sequential(\n nn.Dropout(p=self.dropout_rate),\n nn.Linear(\n self.config.AGGREGATING_NETWORK_OUTPUT_SIZE, 30\n ),\n nn.ReLU(),\n nn.Linear(30, self.num_classes)\n ) \n\n def forward(self, article_batch):\n \"\"\"\n Defines the model forward pass\n \"\"\"\n output_embeddings = []\n # TODO: refactor to allow batch processing:\n article_part_features = self.feature_extractor(\n article_batch['input_ids'].squeeze(0),\n attention_mask=article_batch['attention_mask'].squeeze(0),\n token_type_ids=article_batch['token_type_ids'].squeeze(0)\n )\n output_embeddings = article_part_features['pooler_output'].unsqueeze(0)\n aggregated_outputs, _ = self.aggregating_network(output_embeddings)\n article_idxs = np.arange(len(aggregated_outputs))\n last_time_step_idx = article_batch['num_splits'] - 1\n last_time_step = aggregated_outputs[article_idxs, last_time_step_idx]\n return self.predictor(last_time_step)\n\n def _loss_step(\n self, \n batch: AMLBatch, \n eval: bool, \n criterion: Module = F.cross_entropy\n ) -> Tensor:\n \"\"\"\n Definition of a standard loss step\n \"\"\"\n tokens, labels = batch\n logits = self(tokens)\n loss = criterion(logits, labels)\n if eval:\n self.accuracy.update(logits, labels)\n return loss\n\n def training_step(self, batch: AMLBatch, batch_idx: int) -> Tensor:\n loss = self._loss_step(batch, eval=False)\n self.log('train/loss', loss)\n return loss\n \n def validation_step(self, batch: AMLBatch, batch_idx: int) -> Tensor:\n loss = self._loss_step(batch, eval=True)\n self.log('val/loss', loss)\n\n def validation_epoch_end(self, outputs) -> None:\n accuracy = self.accuracy.compute()\n self.log('val/accuracy', accuracy)\n print('val/accuracy', accuracy.item())\n self.accuracy.reset()\n\n @property\n def optimizer(self) -> Callable:\n \"\"\"\n Returns the configured optimizer\n \"\"\"\n optimizer = self.config.OPTIMIZER\n if optimizer == 'Adam':\n return torch.optim.Adam\n if optimizer == 'AdamW':\n return AdamW\n if optimizer == 'SGD':\n return torch.optim.SGD\n raise NotImplementedError\n\n def configure_optimizers(self):\n parameter_groups = [\n {'params': self.feature_extractor.parameters(), 'weight_decay': float(self.config.FEATURE_EXTRACTOR_WEIGHT_DECAY)},\n {'params': self.aggregating_network.parameters(), 'weight_decay': float(self.config.AGGREGATING_NETWORK_WEIGHT_DECAY)},\n {'params': self.predictor.parameters(), 'weight_decay': float(self.config.PREDICTOR_WEIGHT_DECAY)}\n ]\n return self.optimizer(parameter_groups, lr=float(self.config.LEARNING_RATE))\n\n def predict(self, tokens: Dict[str, Tensor]) -> List[Tuple[str, float]]:\n \"\"\"\n Returns prediction results on a given batch\n \"\"\"\n self.eval()\n logits = self(tokens)\n probabilities = F.softmax(logits)\n predicted_classes = probabilities.argmax(dim=1)\n output_mapping = utils.invert_dictionary(self.config.CLASS_MAPPING)\n output = [\n (\n output_mapping[predicted_class.item()], \n probabilities[idx][predicted_class.item()].item()\n )\n for idx, predicted_class in enumerate(predicted_classes)\n ]\n return output\n", "repo_name": "TomaszKaleczyc/AML_news_detection", "sub_path": "src/model/long_text_classifier.py", "file_name": "long_text_classifier.py", "file_ext": "py", "file_size_in_byte": 5536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.Tuple", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 16, "usage_type": "name"}, {"api_name": "pytorch_lightning.LightningModule", "line_number": 19, "usage_type": "name"}, {"api_name": "utilities.utils.load_config", "line_number": 35, "usage_type": "call"}, {"api_name": "utilities.utils", "line_number": 35, "usage_type": "name"}, {"api_name": "torchmetrics.Accuracy", "line_number": 45, "usage_type": "call"}, {"api_name": "transformers.BertModel.from_pretrained", "line_number": 51, "usage_type": "call"}, {"api_name": "transformers.BertModel", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.optim", "line_number": 133, "usage_type": "attribute"}, {"api_name": "transformers.AdamW", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.optim", "line_number": 137, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 154, "usage_type": "name"}, {"api_name": "utilities.utils.invert_dictionary", "line_number": 156, "usage_type": "call"}, {"api_name": "utilities.utils", "line_number": 156, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 148, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 148, "usage_type": "name"}]} +{"seq_id": "25121348080", "text": "#%%\nimport os\nimport geopandas\n\ngeojson_in = os.path.join(\"..\", \"layers\", \"mental_health.geojson\")\ndata = geopandas.read_file(geojson_in)\noutdir = os.path.dirname(geojson_in)\n\ncol = \"AUDIENCE\"\nval = \"Children and Youth\"\n# %%\n\n\nout = data[getattr(data, col)==val]\nout.to_file(os.path.join(outdir, \"child_youth_mental_health.geojson\"), driver=\"GeoJSON\")\n", "repo_name": "bcgov/smk-moh-cymh", "sub_path": "scripts/geopandas_subset_on_attribute_value.py", "file_name": "geopandas_subset_on_attribute_value.py", "file_ext": "py", "file_size_in_byte": 352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "geopandas.read_file", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}]} +{"seq_id": "36579319692", "text": "import datetime\nimport pytz\nimport pandas as pd\nimport xlrd\nfrom openpyxl import Workbook\n\ndef timp():\n utc_now = pytz.utc.localize(datetime.datetime.utcnow())\n local_now = utc_now.astimezone(pytz.timezone(\"Europe/Chisinau\"))\n current_time = local_now.strftime(\"%H:%M:%S\")\n return current_time\n#strftime = string format time\n\n\nlista = pd.DataFrame(columns=['Nume','Ora sosirii','Ora plecarii'])\n\n\ndef venit():\n \n for row in range(lista.shape[0],75):\n timp()\n nume = input(\"A venit: \")\n if nume == \"/\":\n menu()\n break\n for i,index in lista.iterrows():\n unique = i\n name = index[\"Nume\"]\n if nume ==name:\n lista.loc[unique,\"Ora plecarii\"] = \"\"\n row +=1\n venit()\n lista.loc[row] = [nume,timp(),\"\"]\n print(lista)\n row +=1\n venit()\n\n\ndef plecat():\n left = input(\"A plecat: \")\n for i,row in lista.iterrows():\n unique = i\n name = row[\"Nume\"]\n if left == name:\n lista.loc[unique,\"Ora plecarii\"] =timp()\n \n #print(str(unique) +\". \"+ name)\n \n\ndef menu():\n print(\"1. A venit cineva nou\")\n print(\"2. A plecat cineva\")\n print(\"3. Vezi lista\")\n print(\"4. Ședința s-a terminat\")\n pick = input(\"Pick one: \")\n if pick == str(1):\n venit()\n elif pick== str(2):\n plecat()\n elif pick == str(3):\n print(lista)\n else:\n print(\"Please pick one of above\")\n menu()\n\n\nmenu()", "repo_name": "DanielVorobiov/Prezenta-App", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1368, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pytz.utc.localize", "line_number": 8, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 8, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "1454015228", "text": "import tempfile\nimport base64\nimport os\nimport tarfile\nimport tempfile\nfrom typing import List, Union\n\nfrom app.db_models.jobcontainer import db_JobContainer\nfrom app.db_models.mljob import db_MLJob\nfrom app.db_models.model import db_Model\nfrom app.providers.model import ModelProvider\nfrom flask import send_file\n\nfrom . import frontend\n\n\n@frontend.route('/session//model', methods=['GET'])\ndef get_model(session_id):\n \"\"\" Returns the last used model of the session\"\"\"\n # first get the most recent model\n model_id = db_Model.query.\\\n join(db_MLJob).\\\n filter(db_Model.job_id == db_MLJob.uid).\\\n join(db_JobContainer).\\\n filter(db_MLJob.container_id == db_JobContainer.uid).\\\n filter(db_JobContainer.session_id == session_id).\\\n order_by(db_Model.edited.desc()).first().uid\n\n MP = ModelProvider()\n model = MP.get(model_id)\n\n # split the binary and wrap both models(b64 encoded and \n # separated using a ',')\n ner, nel = model['binary'].split(',', 1)\n\n ner_wrapper = B64TarWrapper(\"ner wrapper\", ner)\n nel_wrapper = B64TarWrapper(\"nel wrapper\", nel)\n\n try:\n os.mkdir(\"./tmp\")\n os.mkdir(\"./tmp/ner\")\n os.mkdir(\"./tmp/nel\")\n except FileExistsError:\n pass\n\n ner_dir = ner_wrapper.get_extract(\"./tmp/ner\")\n nel_dir = nel_wrapper.get_extract(\"./tmp/nel\")\n\n with open(\"/tmp/archive.tar.gz\", \"w\") as f:\n f.write(\"\")\n\n with tarfile.open(\"/tmp/archive.tar.gz\", mode='w:gz') as tar:\n tar.add(ner_dir, arcname=\"ner\")\n tar.add(nel_dir, arcname=\"nel\")\n\n try:\n return send_file(\"/tmp/archive.tar.gz\", as_attachment=True, attachment_filename='models.tar.gz')\n except FileNotFoundError:\n abort(404)\n\n\nclass B64TarWrapper:\n\n def __init__(self, identifier: str, tar_base64: str):\n self.identifier: str = identifier\n self.tar_base64: str = tar_base64\n\n @classmethod\n def from_files(cls, paths: List[str], identifier: Union[str, None] = None):\n tar_file = tempfile.mktemp()\n with tarfile.open(tar_file, 'x') as tar:\n for path in paths:\n tar.add(path, arcname=os.path.basename(path))\n with open(tar_file, 'rb') as f:\n encoded = base64.b64encode(f.read()).decode('utf-8')\n return cls(identifier=identifier, tar_base64=encoded)\n\n def get_extract(self, directory: Union[str, None] = None) -> str:\n \"\"\"\n Extracts the content into a temporary directory and returns the path as string\n :param directory: If given, extraction will take place in this directory\n :return: Path as string\n \"\"\"\n temp_tar_file = tempfile.NamedTemporaryFile()\n with open(temp_tar_file.name, 'wb') as f:\n f.write(base64.b64decode(self.tar_base64))\n\n if directory is None:\n directory = tempfile.mkdtemp()\n\n with tarfile.open(temp_tar_file.name, 'r') as tar:\n for member in tar.getmembers():\n tar.extract(member, directory)\n\n return directory\n\n def get_base64_str(self) -> str:\n return self.tar_base64\n\n", "repo_name": "DATEXIS/TrainX", "sub_path": "TrainX-Backend/app/routes/frontend_backend/get_model.py", "file_name": "get_model.py", "file_ext": "py", "file_size_in_byte": 3162, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "app.db_models.jobcontainer.db_JobContainer", "line_number": 24, "usage_type": "argument"}, {"api_name": "app.db_models.model.db_Model.query.join", "line_number": 21, "usage_type": "call"}, {"api_name": "app.db_models.mljob.db_MLJob", "line_number": 22, "usage_type": "argument"}, {"api_name": "app.db_models.model.db_Model.query", "line_number": 21, "usage_type": "attribute"}, {"api_name": "app.db_models.model.db_Model", "line_number": 21, "usage_type": "name"}, {"api_name": "app.db_models.model.db_Model.job_id", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.db_models.model.db_Model", "line_number": 23, "usage_type": "name"}, {"api_name": "app.db_models.mljob.db_MLJob.uid", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.db_models.mljob.db_MLJob", "line_number": 23, "usage_type": "name"}, {"api_name": "app.db_models.mljob.db_MLJob.container_id", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.db_models.mljob.db_MLJob", "line_number": 25, "usage_type": "name"}, {"api_name": "app.db_models.jobcontainer.db_JobContainer.uid", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.db_models.jobcontainer.db_JobContainer", "line_number": 25, "usage_type": "name"}, {"api_name": "app.db_models.jobcontainer.db_JobContainer.session_id", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.db_models.jobcontainer.db_JobContainer", "line_number": 26, "usage_type": "name"}, {"api_name": "app.db_models.model.db_Model.edited.desc", "line_number": 27, "usage_type": "call"}, {"api_name": "app.db_models.model.db_Model.edited", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.db_models.model.db_Model", "line_number": 27, "usage_type": "name"}, {"api_name": "app.providers.model.ModelProvider", "line_number": 29, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 40, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 42, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 69, "usage_type": "name"}, {"api_name": "tempfile.mktemp", "line_number": 70, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 75, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 78, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 84, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 86, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 89, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "16030251066", "text": "import requests\nfrom dateutil import parser\nfrom datetime import datetime as DT\nimport binascii\nfrom db import Мemorizer\nfrom app_user import User\nimport time\n\n\nclass Finder():\n def __init__(self, user, token):\n self.token = token\n self.user = user\n self.attribute_list = user.attribute_list\n self.param_dict = self.request_param()\n self.match_index = 0\n\n def request_param(self) -> dict:\n param_dict = {}\n param_dict['age_from'] = (self.age_calc(self.attribute_list['bdate'])) - 5\n param_dict['age_to'] = (self.age_calc(self.attribute_list['bdate'])) + 5\n param_dict['sex'] = 1 if self.attribute_list['sex'] == 2 else 2\n param_dict['city'] = self.attribute_list['city']['id']\n return param_dict\n\n def age_calc(self, date) -> int:\n result = (DT.date(DT.now()) - (DT.date(parser.parse(date)))).days // 365\n return result\n\n def request(self, status) -> list:\n age_request = 9\n respons_list = []\n for i in range(age_request):\n time.sleep(1)\n url = 'https://api.vk.com/method/users.search/'\n params = {'access_token': self.token, 'sort': 1, 'offset': 0, 'count': 1000,\n 'common_count': 0, 'sex': self.param_dict['sex'], 'status': status,\n 'age_from': self.param_dict['age_from'] + i, 'age_to': self.param_dict['age_to'],\n 'city': self.param_dict['city'],\n 'fields': 'interests, about, books, music, connections, people_main, life_main, personal, political',\n 'v': '5.131'}\n respons = requests.get(url, params=params)\n respons_list = respons_list + respons.json()['response']['items']\n return respons_list\n\n def matcher(self, user_profile, candidate_profile) -> bool:\n try:\n self.match_index += self._direct_matching(user_profile['personal']['religion_id'],\n candidate_profile['personal']['religion_id'])\n except:\n self.match_index += 2\n try:\n self.match_index += self._direct_matching(user_profile['personal']['political'],\n candidate_profile['personal']['political'])\n except:\n self.match_index += 2\n try:\n self.match_index += self._direct_matching(user_profile['personal']['life_main'],\n candidate_profile['personal']['life_main'])\n except:\n self.match_index += 2\n try:\n self.match_index += self._not_direct_match(user_profile['personal']['smoking'],\n candidate_profile['personal']['smoking'], 'habits')\n except:\n self.match_index += 2\n try:\n self.match_index += self._not_direct_match(user_profile['personal']['life_main'],\n candidate_profile['personal']['life_main'])\n except:\n self.match_index += 2\n try:\n self.match_index += self.compaire(user_profile['personal']['inspired_by'],\n candidate_profile['personal']['inspired_by'])\n except:\n self.match_index += 2\n try:\n self.match_index += self.lang_macth(user_profile['personal']['langs'],\n candidate_profile['personal']['langs'])\n except:\n self.match_index += 2\n try:\n self.match_index += self.compaire(user_profile['about'], candidate_profile['about'])\n except:\n self.match_index += 2\n try:\n self.match_index += self.compaire(user_profile['interests'], candidate_profile['interests'])\n except:\n self.match_index += 2\n try:\n self.match_index += self.compaire(user_profile['books'], candidate_profile['books'])\n except:\n self.match_index += 2\n\n if self.match_index >= 190:\n return True\n else:\n return False\n\n def lang_macth(self, value1, value2) -> int:\n result = 0\n for i in value1:\n if i in value2:\n result += 3\n return result\n\n def canonize_text(self, value) -> list:\n symbols = '.,!?:;-\\n\\r()'\n words = (u'это', u'как', u'так',\n u'и', u'в', u'над',\n u'к', u'до', u'не',\n u'на', u'но', u'за',\n u'то', u'с', u'ли',\n u'а', u'во', u'от',\n u'со', u'для', u'о',\n u'же', u'ну', u'вы',\n u'бы', u'что', u'кто',\n u'он', u'она')\n return ([x for x in [y.strip(symbols) for y in value.lower().split()] if x and (x not in words)])\n\n def shingle(self, value) -> list:\n shingleLen = 3\n out = []\n for i in range(len(value) - (shingleLen - 1)):\n out.append(binascii.crc32(' '.join([x for x in value[i:i + shingleLen]]).encode('utf-8')))\n\n return out\n\n def compaire(self, value1, value2) -> int:\n same = 0\n for i in range(len(value1)):\n if value1[i] in value2:\n same += 1\n\n result = int(same * 2 / float(len(value1) + len(value2)) * 10)\n if result >= 9:\n return 9\n elif result >= 7:\n return 5\n elif result > 5:\n return 3\n else:\n return 0\n\n def _direct_matching(self, value1, value2) -> int:\n if value1 == value2:\n return 9\n else:\n return 0\n\n def _not_direct_match(self, value1, value2, flag='') -> int:\n m_list = ['56', '65', '82', '28']\n if flag == 'habits':\n if abs(value1 - value2) <= 1:\n return 9\n if abs(value1 - value2) <= 3:\n return 5\n else:\n return 0\n else:\n if value1 == value2:\n return 9\n elif str(value1) + str(value2) in m_list:\n return 5\n else:\n return 0\n\n def check_common_subscriptions(self, item) -> None:\n try:\n candidate = User(item['id'])\n for group in candidate.group_list():\n if group in self.user.group_list():\n self.match_index += 2\n for friend in candidate.friends_list:\n if friend in self.user.friends_list:\n self.match_index += 9\n except:\n self.match_index += 0\n\n def sorter(self) -> dict:\n search_list = []\n memorizer = Мemorizer()\n req_list = self.request(6) + self.request(1) + self.request(5)\n for item in req_list:\n if self.matcher(self.attribute_list, item):\n self.check_common_subscriptions(item)\n if self.match_index >= 210:\n if memorizer.find_previos_value(item) == []:\n search_list.append(item)\n self.match_index = 0\n else:\n continue\n return search_list\n\n\n\n", "repo_name": "GusevADresume/Course_Work_2", "sub_path": "search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 7338, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datetime.datetime.date", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 27, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 27, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "binascii.crc32", "line_number": 125, "usage_type": "call"}, {"api_name": "app_user.User", "line_number": 170, "usage_type": "call"}, {"api_name": "db.Мemorizer", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "8726763880", "text": "from django.conf.urls import patterns, include, url\nfrom django.views.generic.simple import direct_to_template\nfrom django.conf import settings\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^accounts/login/$', 'django.contrib.auth.views.login'),\n # url(r'^$', 'IntentoTreiky.views.home', name='home'),\n # url(r'^IntentoTreiky/', include('IntentoTreiky.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n url(r'^admin/', include(admin.site.urls)),\n # MediaURL\n url(r'^media/(?P.*)$', 'django.views.static.serve', {\n 'document_root': settings.MEDIA_ROOT,\n }),\n\n\n # Mi URL:\n url(r'^$', direct_to_template,\n {'template': 'index.html'}, \"home\"),\n url(r'^accounts/profile/$', direct_to_template,\n {'template': 'index.html'}, \"home\"),\n url(r'^home$', direct_to_template,\n {'template': 'index.html'}, \"home\"),\n url(r'^write_req/$',\n 'apps.requerimiento.views.write_req', name='write_req'),\n url(r'^view_req/$',\n 'apps.requerimiento.views.view_req', name=\"view_req\"),\n url(r'^write_project/$',\n 'apps.requerimiento.views.write_project', name='write_project'),\n url(r'^new_user/$',\n 'apps.requerimiento.views.new_user', name='new_user'),\n url(r'^resultado_usuario/$',\n 'apps.requerimiento.views.resultado_alta_usuario',\n name='resultado_alta_usuario'),\n url(r'^resultado_proyecto/$',\n 'apps.requerimiento.views.resultado_alta_proyecto',\n name='resultado_alta_proyecto'),\n url(r'^logout/$',\n 'apps.requerimiento.views.logoutuser', name='logoutuser'),\n url(r'^search_project/$',\n 'apps.requerimiento.views.searchProject', name='searchProject'),\n url(r'^update_project/$',\n 'apps.requerimiento.views.update_project', name='updateproject'),\n url(r'^asigned_user/$',\n 'apps.requerimiento.views.asig_user', name='asiguser'),\n url(r'^edit_user/$',\n 'apps.requerimiento.views.edit_user', name='editUserForm'),\n)\n", "repo_name": "Treiky/Treiky", "sub_path": "Treiky/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "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.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": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.views.generic.simple.direct_to_template", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "django.views.generic.simple.direct_to_template", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.views.generic.simple.direct_to_template", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 40, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 50, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "8986101314", "text": "from flask import Flask, render_template, request, redirect, session\nimport os\n\nTask07 = Flask(__name__)\n\nTask07.config['SECRET_KEY'] = os.urandom(24)\n\n\n@Task07.route('/')\ndef index():\n if 'username' in session:\n return render_template('index07.html', username=str(session['username']))\n else:\n return render_template('login.html')\n\n\n@Task07.route('/login', methods=['POST'])\ndef login():\n if request.form.get('username'):\n session['username'] = request.form.get('username')\n return redirect('/')\n\n\n@Task07.route('/logout')\ndef logout():\n session.pop('username', None)\n return render_template('login.html')\n\n\nif __name__ == \"__main__\":\n Task07.debug = True\n Task07.run()\n", "repo_name": "k018c1072/flaskworks", "sub_path": "Task07.py", "file_name": "Task07.py", "file_ext": "py", "file_size_in_byte": 716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.form.get", "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.session", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.session.pop", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "36869234528", "text": "import logging\nimport os\nfrom functools import wraps\nimport re\n\nfrom dotenv import load_dotenv\nfrom flask import Flask, jsonify, make_response, request\nfrom flask_pymongo import PyMongo\n\n\ndef init_logger(app):\n gunicorn_error_logger = logging.getLogger('gunicorn.error')\n app.logger.handlers.extend(gunicorn_error_logger.handlers)\n app.logger.setLevel(logging.DEBUG)\n\n\ndef cross_orgin(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n if request.method == 'OPTIONS':\n response = make_response()\n response.headers.add(\"Access-Control-Allow-Origin\", \"*\")\n response.headers.add('Access-Control-Allow-Headers', \"*\")\n response.headers.add('Access-Control-Allow-Methods', \"*\")\n return response\n\n resp = func(*args, **kwargs)\n resp.headers.add(\"Access-Control-Allow-Origin\", \"*\")\n return resp\n\n return wrapper\n\n\ndef create_app():\n load_dotenv()\n app = Flask(__name__)\n app.config[\"MONGO_URI\"] = os.environ.get(\"MONGO_URI\")\n mongo = PyMongo(app)\n db = mongo.db\n init_logger(app)\n\n @app.route('/', methods=['GET', 'OPTIONS'])\n @cross_orgin\n def index():\n return make_response(\n \"\"\"

Manga Crawler Server

Repo\"\"\",\n 200)\n\n @app.route(\"/get_index\", methods=[\"GET\", \"OPTIONS\"])\n @cross_orgin\n def get_index():\n index = db[\"content\"].find_one({\"_meta\": 0}, {\n \"_meta\": 0,\n \"_id\": 0,\n })\n return make_response(jsonify(index), 200)\n\n @app.route(\"/change_index\", methods=[\"PUT\", \"PATCH\", \"OPTIONS\"])\n @cross_orgin\n def change_index():\n data = request.get_json()\n if not data:\n return make_response(jsonify({\"message\": \"No data found\"}), 400)\n\n if request.method == \"PUT\":\n if data.get(\"_id\", \"no_id\") == \"no_id\":\n return make_response(jsonify({\"message\": \"No id found\"}), 400)\n\n if data.get(\"_meta\", \"no_meta\") == \"no_meta\":\n data.update({\"_meta\": 0})\n\n db[\"content\"].replace_one({\"_meta\": 0}, data)\n app.logger.info(\"Index Regenerated\")\n return make_response(jsonify({\"message\": \"Index Regenerated\"}),\n 200)\n\n if request.method == \"PATCH\":\n db[\"content\"].update_one({\"_meta\": 0}, {\"$set\": data})\n app.logger.info(\"Index Altered\")\n return make_response(jsonify({\"message\": \"Index Altered\"}), 200)\n\n @app.route('/get_metadata', methods=['GET', \"OPTIONS\"])\n @cross_orgin\n def get_file():\n file_id = request.args.get('file_id', \"\")\n resp = {}\n\n if file_id == '':\n resp = make_response(jsonify({\"message\": \"No file found\"}), 400)\n\n try:\n resp = db[\"content\"].find_one({\"_id\": file_id}, {\n \"_meta\": 0,\n \"_id\": 0,\n })\n resp = jsonify(resp)\n\n except Exception as e:\n app.logger.error(f\"Error: {e}\")\n resp = make_response(jsonify({\"message\": \"No file found\"}), 400)\n\n else:\n resp = make_response(\n resp,\n 200,\n )\n\n return resp\n\n @app.route(\"/add_manga\", methods=[\"PUT\", \"OPTIONS\"])\n @cross_orgin\n def put_file():\n data = request.get_json()\n if not data:\n return make_response(jsonify({\"message\": \"No data found\"}), 400)\n\n if data.get(\"_id\", \"no_id\") == \"no_id\":\n return make_response(jsonify({\"message\": \"No _id found\"}), 400)\n\n if data.get(\"_meta\", \"no_meta\") == \"no_meta\":\n data.update({\"_meta\": 1})\n\n db[\"content\"].insert_one(data)\n app.logger.info(\"Manga Added\")\n return make_response(jsonify({\"message\": \"success\"}), 200)\n\n @app.errorhandler(404)\n def Error(error):\n return make_response(\"

404 Error: Page Not Found

\", 404)\n\n return app\n", "repo_name": "shambu09/manga-utils-server", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 21, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 18, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 37, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "6324674494", "text": "import numpy as np \nfrom numpy import array as a \nfrom multiprocessing_generator import ParallelGenerator\nfrom random import random as r\nfrom random import gauss as g\nfrom random import shuffle\nimport matplotlib.pyplot as plt\nfrom time import sleep\n\n# with ParallelGenerator(\n# \tself.xygen(wn1a,wn2a),\n# \tmax_lookahead=200) as xyg:\n# \tfor x,y in xyg:\n# \t\tself.model.fit(x,y,epochs=1)\n\nclass agent:\n\tc = a([0,0])\t# Row,Col\n\tE = []\t\t\t# Sensing Matrix\n\tenv = ''\t\t# Environment Generator\n\tf = 0\t\t\t# Fitness Score\n\n\tG = []\t\t\t# Gene Matrix\n\tP = []\t\t\t# Next Step Policy\n\tcfg = ''\n\n\tdef __init__(self,env,cfg):\n\t\tself.c = [int(g(0,10)),int(g(0,10))]\n\t\tself.env = env\n\t\tself.E = env.getSensingMatrix(self.c)\n\t\tself.G = gene(cfg)\n\t\tself.cfg = cfg\n\n\tdef step(self):\n\n\t\tif np.linalg.norm(self.c) < self.cfg.B + 10:\n\n\n\t\t\tself.E = self.env.getSensingMatrix(self.c)\n\n\t\t\tself.P = self.G.A @ self.E @ self.G.B\n\t\t\tfor i in range(len(self.P)):\n\t\t\t\tif self.P[i] == np.array(self.P).max():\n\t\t\t\t\tbreak\n\t\t\t# Up\n\t\t\tif i == 0:\n\t\t\t\tself.c += a([1,0])\n\t\t\t# Down\n\t\t\telif i == 1:\n\t\t\t\tself.c += a([-1,0])\n\t\t\t# Left\n\t\t\telif i == 2:\n\t\t\t\tself.c += a([0,-1])\n\t\t\t# Right\n\t\t\telif i == 3:\n\t\t\t\tself.c += a([0,1])\n\t\t\t# Up Right\n\t\t\telif i == 4:\n\t\t\t\tself.c += a([1,1])\n\t\t\t# Down Right\n\t\t\telif i == 5:\n\t\t\t\tself.c += a([-1,1])\n\t\t\t# Down Left\n\t\t\telif i == 6:\t\n\t\t\t\tself.c += a([-1,-1])\n\t\t\t# Up Left\n\t\t\telif i == 7:\n\t\t\t\tself.c += a([1,-1])\n\n\t\t\tself.f += self.env.consume(self.c)\n\nclass genePool:\n\n\tP = []\t# Gene Pool [N_A,Dir,Vision]\n\n\tdef getShuffleReducePool(self,agents):\n\t\ttmp = []\n\n\t\tfs = []\n\t\tfor agent in agents:\n\t\t\tfs.append(agent.f)\n\t\tidx = sorted(range(len(fs)), key=lambda k: fs[k])\n\t\tidx = idx[int(len(idx)/2):]\n\n\n\t\tfor i in idx:\n\t\t\ttmp.append(agents[i].G.W)\n\t\ttmp = a(tmp)\n\t\tfor i in range(tmp.shape[1]):\n\t\t\tfor j in range(tmp.shape[2]):\n\t\t\t\tnp.random.shuffle(tmp[:,i,j])\n\n\t\tself.P = tmp\n\n\tdef mutate(self):\n\t\tself.P += np.random.normal(0,1,self.P.shape)\n\n\tdef pltAvg(self):\n\t\tplt.figure('Weights')\n\t\tplt.clf()\n\t\tplt.imshow(self.P.mean(axis=0),aspect='equal')\n\t\tplt.show(block=False)\n\t\tplt.pause(.001)\n\n\tdef reproduce(self,agents):\n\n\t\tself.getShuffleReducePool(agents)\n\t\tself.mutate()\n\n\t\tfs = []\n\t\tfor agent in agents:\n\t\t\tfs.append(agent.f)\n\t\tidx = sorted(range(len(fs)), key=lambda k: fs[k])\n\t\ttry:\n\t\t\tidx = idx[:int(len(idx)/2)]\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\tprint('Use Even Number of Agents')\n\n\t\tPCTR = 0\n\t\tfor i in range(len(agents)):\n\t\t\tif i in idx:\n\t\t\t\tagents[i].G.W = self.P[PCTR]\n\t\t\t\tagents[i].G.A = agents[i].G.W[:7,:]\n\t\t\t\tagents[i].G.B = agents[i].G.W[8,:].T\n\t\t\t\tPCTR += 1\n\n\t\treturn(agents)\n\nclass gene:\n\tA = []\t# Weight Matrix Vertical\n\tB = []\t# Weight Matrix Horizontal\n\tW = []\t# Weigh Matrix [A B.T].T\n\n\tdef __init__(self,c):\n\t\t# [up down left right UR DR DL UL]\n\n\t\t# P = A@E@B\n\t\t# 8X1 = 8XV VXV VX1\n\t\tself.W = np.random.normal(0,1,[9,c.V])\n\t\tself.A = self.W[:7,:]\n\t\tself.B = self.W[8,:].T\n\nclass environment:\n\n\tmaster = {}\n\tc = ''\n\n\tdef __init__(self,c):\n\t\tself.c = c\n\n\tdef getSensingMatrix(self,coord):\n\t\tE = np.zeros([self.c.V,self.c.V])\n\t\tfor i in range(int(coord[0]-(self.c.V-1)/2),int(coord[0]+(self.c.V-1)/2+1)):\n\t\t\tfor j in range(int(coord[1]-(self.c.V-1)/2),int(coord[1]+(self.c.V-1)/2+1)):\n\t\t\t\tir = int(i - coord[0] + (self.c.V-1)/2)\n\t\t\t\tjr = int(j - coord[1] + (self.c.V-1)/2)\n\t\t\t\tcstr = '['+str(i)+','+str(j)+']'\n\t\t\t\tif cstr not in self.master:\n\t\t\t\t\tif r()*100 <= self.c.S and np.linalg.norm([coord[0],coord[1]]) xma:\n\t\t\t\txma = x \n\t\t\tif x < xm:\n\t\t\t\txm = x\n\t\t\tif y > yma:\n\t\t\t\tyma = y \n\t\t\tif y < ym:\n\t\t\t\tym = y\n\n\t\tgrid = np.zeros([xma-xm+1,yma-ym+1])\n\n\t\tfor key in master:\n\t\t\txr = eval(key)[0] - xm\n\t\t\tyr = eval(key)[1] - ym\n\t\t\tgrid[xr,yr] = master[key]\n\n\t\tfor ag in A:\n\t\t\txr = ag.c[0]-xm \n\t\t\tyr = ag.c[1]-ym \n\t\t\tgrid[xr,yr] = -1\n\n\t\tplt.figure('Environment')\n\t\tplt.clf()\n\t\tplt.imshow(grid,aspect='equal')\n\t\tplt.show(block=False)\n\t\tplt.pause(.001)\n\nclass splice:\n\n\tA = []\n\tc = ''\n\tGP = ''\n\n\tdef __init__(self,c):\n\n\t\tself.c = c\n\t\tE = environment(c)\n\t\tself.A = [agent(E,c) for i in range(c.N_A)]\n\t\tself.GP = genePool()\n\n\tdef sharedEnvironmentTrain(self):\n\n\t\tfor j in range(300000):\n\t\t\t# initialize environment\n\t\t\tself.E = environment(c)\n\t\t\tself.E.master = {}\n\t\t\tfor ag in self.A:\n\t\t\t\tag.env = self.E\n\t\t\t\tag.c = [int(g(0,10)),int(g(0,10))]\n\n\t\t\t# For each agent, take step\n\t\t\tfor i in range(self.c.L):\n\t\t\t\tfor a in self.A:\n\t\t\t\t\ta.step()\n\t\t\t\tself.E.plot(self.A)\n\n\t\t\t# Reproduce\n\t\t\tself.A = self.GP.reproduce(self.A)\n\n\t\t\tself.GP.pltAvg()\n\n\t\t\tavg = 0\n\t\t\tmaxv = 0\n\t\t\tfor ag in self.A:\n\t\t\t\tavg += ag.f\n\t\t\t\tif ag.f > maxv:\n\t\t\t\t\tmaxv = ag.f\n\t\t\t\tag.f = 0\n\n\t\t\tavg /= self.c.N_A\n\n\t\t\tprint('gen'+str(j)+': ')\n\t\t\tprint(avg)\n\t\t\tprint(maxv)\n\n\t\tplt.imshow(ag.G.W,aspect='auto')\n\t\tplt.show(block=False)\n\nclass cfg:\n\n\tN_A = 10\t# Num Agents\n\tS \t= 20\t# Sparsity per 100\n\tV\t= 10\t# Vision / Size of sensing matrix\n\tB\t= 1000\t# Resource Boundary || x,y || > 1000\n\tL \t= 100 # Lifespan\n\n\tdef __init__(self,ID):\n\t\tif ID == 1:\n\t\t\tself.N_A \t= 99\n\t\t\tself.S \t\t= 5\n\t\t\tself.V \t\t= 10\n\t\t\tself.B \t\t= 100\n\n\nif __name__ == '__main__':\n\n\n\tc = cfg(1)\n\t\n\ts = splice(c)\n\ts.sharedEnvironmentTrain()\n\n\tplt.show()\n\n\n\n", "repo_name": "griffinjb/splice", "sub_path": "splice.py", "file_name": "splice.py", "file_ext": "py", "file_size_in_byte": 5545, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "random.gauss", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 95, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.random.normal", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "random.random", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "random.gauss", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 242, "usage_type": "name"}, {"api_name": "numpy.array.step", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "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.show", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}]} +{"seq_id": "16609066631", "text": "import matplotlib.pyplot as plt\nimport statistics\n\nμ = 1\nb1 = 0.85\nb0 = 0.1\nσ = 1\nn = 30\n\neps = statistics.NormalDist(μ, σ**2).samples(n, seed=30)\nvals = [i for i in range(1, n)]\n\nh = []\nfor i in range(n-1):\n h.append(μ+b0*eps[i+1] + b1*eps[i])\n\nplt.plot(vals, h, label=\"Сгенерированная\")\n\npred_h = [b1*h[0], 0]\nfor i in range(1, n-2):\n for j in range(1, i+1):\n pred_h[i] += h[j]*(b1**(i-j+1))*(-1)**(i-j)\n pred_h.append(0)\n\nplt.plot(vals, pred_h, label=\"Предсказанная\")\n\nplt.legend()\nplt.savefig(\"graphic.png\")\nplt.show()\n\n\n\n", "repo_name": "BarmaJley/Code-examples", "sub_path": "Лабораторные по Эконометрике/2_lab/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "statistics.NormalDist", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "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": "matplotlib.pyplot.legend", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "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": "37206872570", "text": "import matplotlib.pyplot as plt\r\nimport matplotlib.animation as animation\r\nfrom matplotlib import style\r\nimport time\r\n\r\nstyle.use(\"ggplot\")\r\n\r\nfig = plt.figure()\r\nax1 = fig.add_subplot(1,1,1)\r\n\r\ndef animate(i):\r\n pullData = open(\"twitter-out.txt\",\"r\").read()\r\n lines = pullData.split('\\n')\r\n\r\n xar = []\r\n yar = []\r\n zar=[]\r\n\r\n x = 0\r\n y = 0\r\n z = 0\r\n\r\n for l in lines[:]:\r\n x += 1\r\n if \"pos\" in l:\r\n y += 1\r\n elif \"neg\" in l:\r\n #y -= 1\r\n z+=1\r\n\r\n xar.append(x)\r\n yar.append(y)\r\n zar.append(z)\r\n \r\n ax1.clear()\r\n ax1.plot(xar,yar,label=\"positive\",)\r\n ax1.plot(xar,zar,label=\"negative\")\r\n plt.xlabel('Total number of tweets')\r\n plt.ylabel('Respective count of each sentiment');\r\n plt.legend(loc='best')\r\nani = animation.FuncAnimation(fig, animate, interval=1000)\r\nplt.show()\r\n", "repo_name": "Zualemo-xo/Sentiment-Analysis-of-COVID-19-tweets", "sub_path": "plot_graph.py", "file_name": "plot_graph.py", "file_ext": "py", "file_size_in_byte": 901, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "2", "api": [{"api_name": "matplotlib.style.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "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.legend", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.animation", "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": "18269759373", "text": "import pandas as pd\r\nimport dash\r\nfrom dash import dcc, html\r\nfrom dash.dependencies import Input, Output\r\n\r\n# Assuming your dataset is stored in a CSV file named 'your_data.csv'\r\n# Replace 'your_data.csv' with the actual file name or provide the DataFrame directly if you have it loaded\r\nfile_path = 'data1.csv'\r\ndf = pd.read_csv(file_path)\r\n\r\n# Removing non-numeric characters and converting 'kWh' column to numeric\r\ndf['kWh'] = pd.to_numeric(df['kWh'].replace('[^\\d.]', '', regex=True), errors='coerce')\r\n\r\n# Convert 'kWh' to 'MWh'\r\ndf['MWh'] = df['kWh'] / 1000 # 1 MWh = 1000 kWh\r\n\r\n# Rounding the 'MWh' values to 3 significant figures\r\ndf['MWh'] = df['MWh'].round(3)\r\n\r\n# Remove 'London' data\r\ndf = df[df['Area'] != 'London']\r\n\r\n# Create a Dash web application\r\napp = dash.Dash(__name__)\r\n\r\n# Define layout of the web application\r\napp.layout = html.Div([\r\n html.H1(\"Energy Consumption Dashboard\"),\r\n\r\n # Tabs for different pages\r\n dcc.Tabs([\r\n dcc.Tab(label='Borough Comparison', children=[\r\n # Dropdown for selecting the years\r\n dcc.Dropdown(\r\n id='year-dropdown',\r\n options=[\r\n {'label': str(year), 'value': year} for year in df['LEGGI_Year'].unique()\r\n ],\r\n multi=True,\r\n value=list(df['LEGGI_Year'].unique()), # Set the default to all years\r\n style={'width': '50%'}\r\n ),\r\n\r\n # Graph to display the comparison\r\n dcc.Graph(id='borough-comparison-graph'),\r\n ]),\r\n\r\n dcc.Tab(label='Sector Pie Chart', children=[\r\n # Dropdown for selecting the year\r\n dcc.Dropdown(\r\n id='pie-chart-year-dropdown',\r\n options=[\r\n {'label': str(year), 'value': year} for year in df['LEGGI_Year'].unique()\r\n ],\r\n multi=False,\r\n value=df['LEGGI_Year'].min(), # Set the default to the minimum year\r\n style={'width': '50%'}\r\n ),\r\n\r\n # Graph to display the pie chart\r\n dcc.Graph(id='pie-chart'),\r\n ]),\r\n\r\n dcc.Tab(label='Fuel-wise MWh per Year', children=[\r\n # Graph to display the fuel-wise MWh per year\r\n dcc.Graph(id='fuel-wise-mwh'),\r\n ]),\r\n ]),\r\n])\r\n\r\n# Define callback to update the borough comparison graph based on user input\r\n@app.callback(\r\n Output('borough-comparison-graph', 'figure'),\r\n [Input('year-dropdown', 'value')]\r\n)\r\ndef update_borough_comparison(selected_years):\r\n filtered_df = df[df['LEGGI_Year'].isin(selected_years)]\r\n\r\n borough_comparison_data = []\r\n\r\n for year in selected_years:\r\n year_data = filtered_df[filtered_df['LEGGI_Year'] == year]\r\n borough_comparison_data.append({'x': year_data['Area'], 'y': year_data['MWh'], 'type': 'bar', 'name': year})\r\n\r\n fig = {\r\n 'data': borough_comparison_data,\r\n 'layout': {\r\n 'title': f'MWh Comparison ({\", \".join(map(str, selected_years))})',\r\n 'barmode': 'group'\r\n }\r\n }\r\n\r\n return fig\r\n\r\n# Define callback to update the pie chart based on the selected year\r\n@app.callback(\r\n Output('pie-chart', 'figure'),\r\n [Input('pie-chart-year-dropdown', 'value')]\r\n)\r\ndef update_pie_chart(selected_year):\r\n filtered_df = df[df['LEGGI_Year'] == selected_year]\r\n\r\n # Exclude 'Total' from the pie chart\r\n filtered_df = filtered_df[filtered_df['Sector'] != 'Total']\r\n\r\n # Calculate total MWh for each sector\r\n total_mwh_per_sector = filtered_df.groupby('Sector')['MWh'].sum().reset_index()\r\n\r\n # Create the pie chart\r\n fig = {\r\n 'data': [\r\n {\r\n 'labels': total_mwh_per_sector['Sector'],\r\n 'values': total_mwh_per_sector['MWh'],\r\n 'type': 'pie',\r\n 'name': 'MWh distribution'\r\n }\r\n ],\r\n 'layout': {\r\n 'title': f'MWh Distribution by Sector for {selected_year}'\r\n }\r\n }\r\n\r\n return fig\r\n\r\n# Define callback to update the fuel-wise MWh per year graph\r\n@app.callback(\r\n Output('fuel-wise-mwh', 'figure'),\r\n [Input('year-dropdown', 'value')]\r\n)\r\ndef update_fuel_wise_mwh(selected_years):\r\n try:\r\n filtered_df = df[df['LEGGI_Year'].isin(selected_years)]\r\n\r\n fuel_wise_mwh_data = []\r\n\r\n max_y_per_fuel = {} # Dictionary to store the maximum y-axis value for each fuel type\r\n\r\n for fuel_type in filtered_df['Fuel'].unique():\r\n # Exclude 'Total' from fuel types\r\n if fuel_type != 'Total':\r\n fuel_data = filtered_df[filtered_df['Fuel'] == fuel_type]\r\n max_y_per_fuel[fuel_type] = fuel_data['MWh'].max() # Store the maximum value for the current fuel type\r\n fuel_wise_mwh_data.append({'x': selected_years, 'y': fuel_data['MWh'], 'type': 'bar', 'name': fuel_type})\r\n\r\n max_y = max(max_y_per_fuel.values(), default=0) # Get the overall maximum y-axis value\r\n\r\n fig = {\r\n 'data': fuel_wise_mwh_data,\r\n 'layout': {\r\n 'title': 'Fuel-wise MWh per Year',\r\n 'xaxis': {'title': 'Year'},\r\n 'yaxis': {'title': 'MWh', 'range': [0, max_y * 1.1]}, # Set the y-axis range based on max_y\r\n 'shapes': [], # Ensure shapes are defined to avoid potential issues\r\n }\r\n }\r\n\r\n # Add faint lines between different years\r\n for i in range(1, len(selected_years)):\r\n fig['layout']['shapes'].append({\r\n 'type': 'line',\r\n 'x0': selected_years[i],\r\n 'y0': 0,\r\n 'x1': selected_years[i],\r\n 'y1': max_y,\r\n 'line': {\r\n 'color': 'rgba(128, 128, 128, 0.5)',\r\n 'width': 2,\r\n 'dash': 'dash',\r\n },\r\n })\r\n\r\n return fig\r\n\r\n except Exception as e:\r\n print(e)\r\n return {'data': [], 'layout': {}}\r\n\r\n# Run the web application\r\nif __name__ == '__main__':\r\n app.run_server(debug=True)\r\n", "repo_name": "Nimosteve88/ACHACK", "sub_path": "csv KWH data per borough.py", "file_name": "csv KWH data per borough.py", "file_ext": "py", "file_size_in_byte": 6149, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 12, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 24, "usage_type": "call"}, {"api_name": "dash.html.Div", "line_number": 27, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 27, "usage_type": "name"}, {"api_name": "dash.html.H1", "line_number": 28, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 28, "usage_type": "name"}, {"api_name": "dash.dcc.Tabs", "line_number": 31, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 31, "usage_type": "name"}, {"api_name": "dash.dcc.Tab", "line_number": 32, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 32, "usage_type": "name"}, {"api_name": "dash.dcc.Dropdown", "line_number": 34, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 34, "usage_type": "name"}, {"api_name": "dash.dcc.Graph", "line_number": 45, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 45, "usage_type": "name"}, {"api_name": "dash.dcc.Tab", "line_number": 48, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 48, "usage_type": "name"}, {"api_name": "dash.dcc.Dropdown", "line_number": 50, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 50, "usage_type": "name"}, {"api_name": "dash.dcc.Graph", "line_number": 61, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 61, "usage_type": "name"}, {"api_name": "dash.dcc.Tab", "line_number": 64, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 64, "usage_type": "name"}, {"api_name": "dash.dcc.Graph", "line_number": 66, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 66, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 73, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 74, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 97, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 98, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 128, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "28708116159", "text": "# 1) Design model (input, output size, forward pass)\n# 2) Construct loss and optimizer\n# 3) Training loop\n# - forward pass: compute prediction\n# - backward pass: gradients\n# - update gradients\n\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom sklearn import datasets\nimport matplotlib.pyplot as plt\n\n# 0) Prepare data\n# X_numpy = [[2.0, 6.0, 5.0, 1.0], [7.0, 9.0, 3.0, 2.0]]\nX_numpy = [[2.0, 7.0], [6.0, 9.0], [5.0, 3.0], [1.0, 2.0]]\ny_numpy = [52.8, 96.7, 21.2, 6.0]\n\n\n# astype(将numpy转换为float样式，以进行后续操作)\nX = torch.from_numpy(np.array(X_numpy)).float()\ny = torch.from_numpy(np.array(y_numpy)).float()\n\n# X = X.transpose(0,1)\ny = y.view(y.shape[0],1)\n\nn_samples, n_features = X.shape\nn_y = y.shape[1]\n# print(X.shape)\n\n# 1) Model\ninput_size = n_features\noutput_size = n_y\nmodel = nn.Linear(input_size, output_size)\n\n# 2) Loss and optimizer\nlearning_rate = 0.01\n# MSE均方误差\ncriterion = nn.MSELoss()\noptimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)\n# print(model.parameters)\n# \n\n# 3) Training loop\nnum_epochs = 100\nfor epoch in range(num_epochs):\n # forward pass and loss\n y_predicted = model(X)\n loss = criterion(y_predicted, y)\n\n # backward pass\n loss.backward()\n\n #update\n optimizer.step()\n\n # zero grad before new step\n optimizer.zero_grad()\n\n if (epoch+1) % 10 == 0:\n print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')\n\n# Plot\n# detach()分离函数， .numpy()作用是将tensor格式转成numpy格式，让matplotlib可以直接用\npredicted = model(X).detach().numpy()\n# print(predicted)\n\n\n\n\nfig1 = plt.figure()\ncol_1 = [[row[0]] for row in X_numpy]\nplt.plot(col_1, y_numpy, 'ro')\nplt.plot(col_1, predicted, 'r')\n\n\nfig2 = plt.figure()\ncol_2 = [[row[1]] for row in X_numpy]\nplt.plot(col_2, y_numpy, 'bo')\nplt.plot(col_2, predicted, 'b')\nplt.show()", "repo_name": "memory009/pytorch_learning", "sub_path": "linear_regression.py", "file_name": "linear_regression.py", "file_ext": "py", "file_size_in_byte": 1941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.from_numpy", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 41, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "43027567290", "text": "import os\nimport os.path as osp\nimport tempfile\nimport warnings\nfrom collections import OrderedDict, defaultdict\n\nimport json_tricks as json\nimport numpy as np\nfrom mmcv import deprecated_api_warning\n\nfrom ....core.post_processing import oks_nms, soft_oks_nms\nfrom ...builder import DATASETS\nfrom ..base import Kpt2dSviewRgbVidTopDownDataset\n\ntry:\n from poseval import eval_helpers\n from poseval.evaluateAP import evaluateAP\n has_poseval = True\nexcept (ImportError, ModuleNotFoundError):\n has_poseval = False\n\n\n@DATASETS.register_module()\nclass TopDownPoseTrack18VideoDataset(Kpt2dSviewRgbVidTopDownDataset):\n \"\"\"PoseTrack18 dataset for top-down pose estimation.\n\n \"Posetrack: A benchmark for human pose estimation and tracking\", CVPR'2018.\n More details can be found in the `paper\n `__ .\n\n The dataset loads raw features and apply specified transforms\n to return a dict containing the image tensors and other information.\n\n PoseTrack2018 keypoint indexes::\n\n 0: 'nose',\n 1: 'head_bottom',\n 2: 'head_top',\n 3: 'left_ear',\n 4: 'right_ear',\n 5: 'left_shoulder',\n 6: 'right_shoulder',\n 7: 'left_elbow',\n 8: 'right_elbow',\n 9: 'left_wrist',\n 10: 'right_wrist',\n 11: 'left_hip',\n 12: 'right_hip',\n 13: 'left_knee',\n 14: 'right_knee',\n 15: 'left_ankle',\n 16: 'right_ankle'\n\n Args:\n ann_file (str): Path to the annotation file.\n img_prefix (str): Path to a directory where videos/images are held.\n Default: None.\n data_cfg (dict): config\n pipeline (list[dict | callable]): A sequence of data transforms.\n dataset_info (DatasetInfo): A class containing all dataset info.\n test_mode (bool): Store True when building test or\n validation dataset. Default: False.\n ph_fill_len (int): The length of the placeholder to fill in the\n image filenames, default: 6 in PoseTrack18.\n \"\"\"\n\n def __init__(self,\n ann_file,\n img_prefix,\n data_cfg,\n pipeline,\n dataset_info=None,\n test_mode=False,\n ph_fill_len=6):\n super().__init__(\n ann_file,\n img_prefix,\n data_cfg,\n pipeline,\n dataset_info=dataset_info,\n test_mode=test_mode)\n\n self.use_gt_bbox = data_cfg['use_gt_bbox']\n self.bbox_file = data_cfg['bbox_file']\n self.det_bbox_thr = data_cfg.get('det_bbox_thr', 0.0)\n self.use_nms = data_cfg.get('use_nms', True)\n self.soft_nms = data_cfg['soft_nms']\n self.nms_thr = data_cfg['nms_thr']\n self.oks_thr = data_cfg['oks_thr']\n self.vis_thr = data_cfg['vis_thr']\n self.frame_weight_train = data_cfg['frame_weight_train']\n self.frame_weight_test = data_cfg['frame_weight_test']\n self.frame_weight = self.frame_weight_test \\\n if self.test_mode else self.frame_weight_train\n\n self.ph_fill_len = ph_fill_len\n\n # select the frame indices\n self.frame_index_rand = data_cfg.get('frame_index_rand', True)\n self.frame_index_range = data_cfg.get('frame_index_range', [-2, 2])\n self.num_adj_frames = data_cfg.get('num_adj_frames', 1)\n self.frame_indices_train = data_cfg.get('frame_indices_train', None)\n self.frame_indices_test = data_cfg.get('frame_indices_test',\n [-2, -1, 0, 1, 2])\n\n if self.frame_indices_train is not None:\n self.frame_indices_train.sort()\n self.frame_indices_test.sort()\n\n self.db = self._get_db()\n\n print(f'=> num_images: {self.num_images}')\n print(f'=> load {len(self.db)} samples')\n\n def _get_db(self):\n \"\"\"Load dataset.\"\"\"\n if (not self.test_mode) or self.use_gt_bbox:\n # use ground truth bbox\n gt_db = self._load_coco_keypoint_annotations()\n else:\n # use bbox from detection\n gt_db = self._load_posetrack_person_detection_results()\n return gt_db\n\n def _load_coco_keypoint_annotations(self):\n \"\"\"Ground truth bbox and keypoints.\"\"\"\n gt_db = []\n for img_id in self.img_ids:\n gt_db.extend(self._load_coco_keypoint_annotation_kernel(img_id))\n return gt_db\n\n def _load_coco_keypoint_annotation_kernel(self, img_id):\n \"\"\"load annotation from COCOAPI.\n\n Note:\n bbox:[x1, y1, w, h]\n Args:\n img_id: coco image id\n Returns:\n dict: db entry\n \"\"\"\n img_ann = self.coco.loadImgs(img_id)[0]\n width = img_ann['width']\n height = img_ann['height']\n num_joints = self.ann_info['num_joints']\n\n file_name = img_ann['file_name']\n nframes = int(img_ann['nframes'])\n frame_id = int(img_ann['frame_id'])\n\n ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)\n objs = self.coco.loadAnns(ann_ids)\n\n # sanitize bboxes\n valid_objs = []\n for obj in objs:\n if 'bbox' not in obj:\n continue\n x, y, w, h = obj['bbox']\n x1 = max(0, x)\n y1 = max(0, y)\n x2 = min(width - 1, x1 + max(0, w - 1))\n y2 = min(height - 1, y1 + max(0, h - 1))\n if ('area' not in obj or obj['area'] > 0) and x2 > x1 and y2 > y1:\n obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]\n valid_objs.append(obj)\n objs = valid_objs\n\n bbox_id = 0\n rec = []\n for obj in objs:\n if 'keypoints' not in obj:\n continue\n if max(obj['keypoints']) == 0:\n continue\n if 'num_keypoints' in obj and obj['num_keypoints'] == 0:\n continue\n joints_3d = np.zeros((num_joints, 3), dtype=np.float32)\n joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)\n\n keypoints = np.array(obj['keypoints']).reshape(-1, 3)\n joints_3d[:, :2] = keypoints[:, :2]\n joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])\n\n center, scale = self._xywh2cs(*obj['clean_bbox'][:4])\n\n image_files = []\n cur_image_file = osp.join(self.img_prefix, self.id2name[img_id])\n image_files.append(cur_image_file)\n\n # \"images/val/012834_mpii_test/000000.jpg\" -->> \"000000.jpg\"\n cur_image_name = file_name.split('/')[-1]\n ref_idx = int(cur_image_name.replace('.jpg', ''))\n\n # select the frame indices\n if not self.test_mode and self.frame_indices_train is not None:\n indices = self.frame_indices_train\n elif not self.test_mode and self.frame_index_rand:\n low, high = self.frame_index_range\n indices = np.random.randint(low, high + 1, self.num_adj_frames)\n else:\n indices = self.frame_indices_test\n\n for index in indices:\n if self.test_mode and index == 0:\n continue\n # the supporting frame index\n support_idx = ref_idx + index\n support_idx = np.clip(support_idx, 0, nframes - 1)\n sup_image_file = cur_image_file.replace(\n cur_image_name,\n str(support_idx).zfill(self.ph_fill_len) + '.jpg')\n\n if osp.exists(sup_image_file):\n image_files.append(sup_image_file)\n else:\n warnings.warn(\n f'{sup_image_file} does not exist, '\n f'use {cur_image_file} instead.', UserWarning)\n image_files.append(cur_image_file)\n rec.append({\n 'image_file': image_files,\n 'center': center,\n 'scale': scale,\n 'bbox': obj['clean_bbox'][:4],\n 'rotation': 0,\n 'joints_3d': joints_3d,\n 'joints_3d_visible': joints_3d_visible,\n 'dataset': self.dataset_name,\n 'bbox_score': 1,\n 'bbox_id': bbox_id,\n 'nframes': nframes,\n 'frame_id': frame_id,\n 'frame_weight': self.frame_weight\n })\n bbox_id = bbox_id + 1\n\n return rec\n\n def _load_posetrack_person_detection_results(self):\n \"\"\"Load Posetrack person detection results.\n\n Only in test mode.\n \"\"\"\n num_joints = self.ann_info['num_joints']\n all_boxes = None\n with open(self.bbox_file, 'r') as f:\n all_boxes = json.load(f)\n\n if not all_boxes:\n raise ValueError('=> Load %s fail!' % self.bbox_file)\n\n print(f'=> Total boxes: {len(all_boxes)}')\n\n kpt_db = []\n bbox_id = 0\n for det_res in all_boxes:\n if det_res['category_id'] != 1:\n continue\n\n score = det_res['score']\n if score < self.det_bbox_thr:\n continue\n\n box = det_res['bbox']\n\n # deal with different bbox file formats\n if 'nframes' in det_res and 'frame_id' in det_res:\n nframes = int(det_res['nframes'])\n frame_id = int(det_res['frame_id'])\n elif 'image_name' in det_res:\n img_id = self.name2id[det_res['image_name']]\n img_ann = self.coco.loadImgs(img_id)[0]\n nframes = int(img_ann['nframes'])\n frame_id = int(img_ann['frame_id'])\n else:\n img_id = det_res['image_id']\n img_ann = self.coco.loadImgs(img_id)[0]\n nframes = int(img_ann['nframes'])\n frame_id = int(img_ann['frame_id'])\n\n image_files = []\n if 'image_name' in det_res:\n file_name = det_res['image_name']\n else:\n file_name = self.id2name[det_res['image_id']]\n\n cur_image_file = osp.join(self.img_prefix, file_name)\n image_files.append(cur_image_file)\n\n # \"images/val/012834_mpii_test/000000.jpg\" -->> \"000000.jpg\"\n cur_image_name = file_name.split('/')[-1]\n ref_idx = int(cur_image_name.replace('.jpg', ''))\n\n indices = self.frame_indices_test\n for index in indices:\n if self.test_mode and index == 0:\n continue\n # the supporting frame index\n support_idx = ref_idx + index\n support_idx = np.clip(support_idx, 0, nframes - 1)\n sup_image_file = cur_image_file.replace(\n cur_image_name,\n str(support_idx).zfill(self.ph_fill_len) + '.jpg')\n\n if osp.exists(sup_image_file):\n image_files.append(sup_image_file)\n else:\n warnings.warn(f'{sup_image_file} does not exist, '\n f'use {cur_image_file} instead.')\n image_files.append(cur_image_file)\n\n center, scale = self._xywh2cs(*box[:4])\n joints_3d = np.zeros((num_joints, 3), dtype=np.float32)\n joints_3d_visible = np.ones((num_joints, 3), dtype=np.float32)\n kpt_db.append({\n 'image_file': image_files,\n 'center': center,\n 'scale': scale,\n 'rotation': 0,\n 'bbox': box[:4],\n 'bbox_score': score,\n 'dataset': self.dataset_name,\n 'joints_3d': joints_3d,\n 'joints_3d_visible': joints_3d_visible,\n 'bbox_id': bbox_id,\n 'nframes': nframes,\n 'frame_id': frame_id,\n 'frame_weight': self.frame_weight\n })\n bbox_id = bbox_id + 1\n print(f'=> Total boxes after filter '\n f'low score@{self.det_bbox_thr}: {bbox_id}')\n return kpt_db\n\n @deprecated_api_warning(name_dict=dict(outputs='results'))\n def evaluate(self, results, res_folder=None, metric='mAP', **kwargs):\n \"\"\"Evaluate posetrack keypoint results. The pose prediction results\n will be saved in ``${res_folder}/result_keypoints.json``.\n\n Note:\n - num_keypoints: K\n\n Args:\n results (list[dict]): Testing results containing the following\n items:\n\n - preds (np.ndarray[N,K,3]): The first two dimensions are \\\n coordinates, score is the third dimension of the array.\n - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \\\n scale[1],area, score]\n - image_paths (list[str]): For example, ['val/010016_mpii_test\\\n /000024.jpg']\n - heatmap (np.ndarray[N, K, H, W]): model output heatmap.\n - bbox_id (list(int))\n res_folder (str, optional): The folder to save the testing\n results. If not specified, a temp folder will be created.\n Default: None.\n metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.\n\n Returns:\n dict: Evaluation results for evaluation metric.\n \"\"\"\n metrics = metric if isinstance(metric, list) else [metric]\n allowed_metrics = ['mAP']\n for metric in metrics:\n if metric not in allowed_metrics:\n raise KeyError(f'metric {metric} is not supported')\n\n if res_folder is not None:\n tmp_folder = None\n else:\n tmp_folder = tempfile.TemporaryDirectory()\n res_folder = tmp_folder.name\n\n gt_folder = osp.join(\n osp.dirname(self.ann_file),\n osp.splitext(self.ann_file.split('_')[-1])[0])\n\n kpts = defaultdict(list)\n\n for result in results:\n preds = result['preds']\n boxes = result['boxes']\n image_paths = result['image_paths']\n bbox_ids = result['bbox_ids']\n\n batch_size = len(image_paths)\n for i in range(batch_size):\n if not isinstance(image_paths[i], list):\n image_id = self.name2id[image_paths[i]\n [len(self.img_prefix):]]\n else:\n image_id = self.name2id[image_paths[i][0]\n [len(self.img_prefix):]]\n\n kpts[image_id].append({\n 'keypoints': preds[i],\n 'center': boxes[i][0:2],\n 'scale': boxes[i][2:4],\n 'area': boxes[i][4],\n 'score': boxes[i][5],\n 'image_id': image_id,\n 'bbox_id': bbox_ids[i]\n })\n kpts = self._sort_and_unique_bboxes(kpts)\n\n # rescoring and oks nms\n num_joints = self.ann_info['num_joints']\n vis_thr = self.vis_thr\n oks_thr = self.oks_thr\n valid_kpts = defaultdict(list)\n for image_id in kpts.keys():\n img_kpts = kpts[image_id]\n for n_p in img_kpts:\n box_score = n_p['score']\n kpt_score = 0\n valid_num = 0\n for n_jt in range(0, num_joints):\n t_s = n_p['keypoints'][n_jt][2]\n if t_s > vis_thr:\n kpt_score = kpt_score + t_s\n valid_num = valid_num + 1\n if valid_num != 0:\n kpt_score = kpt_score / valid_num\n # rescoring\n n_p['score'] = kpt_score * box_score\n\n if self.use_nms:\n nms = soft_oks_nms if self.soft_nms else oks_nms\n keep = nms(img_kpts, oks_thr, sigmas=self.sigmas)\n valid_kpts[image_id].append(\n [img_kpts[_keep] for _keep in keep])\n else:\n valid_kpts[image_id].append(img_kpts)\n\n self._write_keypoint_results(valid_kpts, gt_folder, res_folder)\n\n info_str = self._do_keypoint_eval(gt_folder, res_folder)\n name_value = OrderedDict(info_str)\n\n if tmp_folder is not None:\n tmp_folder.cleanup()\n\n return name_value\n\n @staticmethod\n def _write_keypoint_results(keypoint_results, gt_folder, pred_folder):\n \"\"\"Write results into a json file.\n\n Args:\n keypoint_results (dict): keypoint results organized by image_id.\n gt_folder (str): Path of directory for official gt files.\n pred_folder (str): Path of directory to save the results.\n \"\"\"\n categories = []\n\n cat = {}\n cat['supercategory'] = 'person'\n cat['id'] = 1\n cat['name'] = 'person'\n cat['keypoints'] = [\n 'nose', 'head_bottom', 'head_top', 'left_ear', 'right_ear',\n 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',\n 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee',\n 'right_knee', 'left_ankle', 'right_ankle'\n ]\n cat['skeleton'] = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13],\n [6, 12], [7, 13], [6, 7], [6, 8], [7, 9], [8, 10],\n [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5],\n [4, 6], [5, 7]]\n categories.append(cat)\n\n json_files = [\n pos for pos in os.listdir(gt_folder) if pos.endswith('.json')\n ]\n for json_file in json_files:\n\n with open(osp.join(gt_folder, json_file), 'r') as f:\n gt = json.load(f)\n\n annotations = []\n images = []\n\n for image in gt['images']:\n im = {}\n im['id'] = image['id']\n im['file_name'] = image['file_name']\n images.append(im)\n\n img_kpts = keypoint_results[im['id']]\n\n if len(img_kpts) == 0:\n continue\n for track_id, img_kpt in enumerate(img_kpts[0]):\n ann = {}\n ann['image_id'] = img_kpt['image_id']\n ann['keypoints'] = np.array(\n img_kpt['keypoints']).reshape(-1).tolist()\n ann['scores'] = np.array(ann['keypoints']).reshape(\n [-1, 3])[:, 2].tolist()\n ann['score'] = float(img_kpt['score'])\n ann['track_id'] = track_id\n annotations.append(ann)\n\n info = {}\n info['images'] = images\n info['categories'] = categories\n info['annotations'] = annotations\n\n with open(osp.join(pred_folder, json_file), 'w') as f:\n json.dump(info, f, sort_keys=True, indent=4)\n\n def _do_keypoint_eval(self, gt_folder, pred_folder):\n \"\"\"Keypoint evaluation using poseval.\"\"\"\n\n if not has_poseval:\n raise ImportError('Please install poseval package for evaluation'\n 'on PoseTrack dataset '\n '(see requirements/optional.txt)')\n\n argv = ['', gt_folder + '/', pred_folder + '/']\n\n print('Loading data')\n gtFramesAll, prFramesAll = eval_helpers.load_data_dir(argv)\n\n print('# gt frames :', len(gtFramesAll))\n print('# pred frames:', len(prFramesAll))\n\n # evaluate per-frame multi-person pose estimation (AP)\n # compute AP\n print('Evaluation of per-frame multi-person pose estimation')\n apAll, _, _ = evaluateAP(gtFramesAll, prFramesAll, None, False, False)\n\n # print AP\n print('Average Precision (AP) metric:')\n eval_helpers.printTable(apAll)\n\n stats = eval_helpers.getCum(apAll)\n\n stats_names = [\n 'Head AP', 'Shou AP', 'Elb AP', 'Wri AP', 'Hip AP', 'Knee AP',\n 'Ankl AP', 'Total AP'\n ]\n\n info_str = list(zip(stats_names, stats))\n\n return info_str\n", "repo_name": "ViTAE-Transformer/ViTPose", "sub_path": "mmpose/datasets/datasets/top_down/topdown_posetrack18_video_dataset.py", "file_name": "topdown_posetrack18_video_dataset.py", "file_ext": "py", "file_size_in_byte": 20232, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 978, "dataset": "github-code", "pt": "2", "api": [{"api_name": "base.Kpt2dSviewRgbVidTopDownDataset", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 179, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 217, "usage_type": "call"}, {"api_name": "json_tricks.load", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path", "line_number": 306, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 314, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 315, "usage_type": "attribute"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path", "line_number": 376, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path", "line_number": 378, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 380, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 412, "usage_type": "call"}, {"api_name": "core.post_processing.soft_oks_nms", "line_number": 430, "usage_type": "name"}, {"api_name": "core.post_processing.oks_nms", "line_number": 430, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 440, "usage_type": "call"}, {"api_name": "mmcv.deprecated_api_warning", "line_number": 336, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 475, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 479, "usage_type": "call"}, {"api_name": "os.path", "line_number": 479, "usage_type": "name"}, {"api_name": "json_tricks.load", "line_number": 480, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 500, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 511, "usage_type": "call"}, {"api_name": "os.path", "line_number": 511, "usage_type": "name"}, {"api_name": "json_tricks.dump", "line_number": 512, "usage_type": "call"}, {"api_name": "poseval.eval_helpers.load_data_dir", "line_number": 525, "usage_type": "call"}, {"api_name": "poseval.eval_helpers", "line_number": 525, "usage_type": "name"}, {"api_name": "poseval.evaluateAP.evaluateAP", "line_number": 533, "usage_type": "call"}, {"api_name": "poseval.eval_helpers.printTable", "line_number": 537, "usage_type": "call"}, {"api_name": "poseval.eval_helpers", "line_number": 537, "usage_type": "name"}, {"api_name": "poseval.eval_helpers.getCum", "line_number": 539, "usage_type": "call"}, {"api_name": "poseval.eval_helpers", "line_number": 539, "usage_type": "name"}, {"api_name": "builder.DATASETS.register_module", "line_number": 23, "usage_type": "call"}, {"api_name": "builder.DATASETS", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "23163654852", "text": "# -*- coding:utf-8 -*-\n__author__ = 'Windows'\n\nimport scrapy\nfrom scrapy.http import Request\nfrom dangdang2.items import Dangdang2Item\n\nclass DangSpider(scrapy.Spider):\n name = \"dangdang\"\n allowed_domains = ['dangdang.com']\n start_urls = ['http://3c.dangdang.com/pc']\n\n def parse(self,response):\n categroy = response.xpath('//div[@class=\"level_one \"]/dl/dt/a/@href').extract()\n for url in categroy:\n yield Request(url, callback=self.parse_detail)\n\n def parse_detail(self, response):\n link = response.xpath('//a[@class=\"pic\"]/@href').extract()\n if link:\n for detail_url in link:\n yield Request(detail_url, callback=self.parse_item)\n next_link = response.xpath('//li[@class=\"next\"]/a/@href').extract()\n #print next_link\n if next_link:\n next_link = next_link[0]\n yield Request('http://category.dangdang.com'+next_link,callback=self.parse_detail)\n\n\n\n def parse_item(self,response):\n item = Dangdang2Item()\n\n item[\"title\"]=response.xpath('//div[@class=\"name_info\"]/h1/@title').extract()\n #print item['title'][0].encode('utf-8')\n\n item[\"comment_num\"]=response.xpath('//a[@id=\"comm_num_down\"]/text()').extract()\n #print item['comment_num']\n\n item[\"price\"]=response.xpath('//p[@id=\"dd-price\"]/text()').extract()\n #print item['price']\n\n item[\"link\"]=response.url\n #print item['link']\n\n item[\"img_url\"]=response.xpath('//img[@id=\"modalBigImg\"]/@src').extract()\n #print item['img_url']\n\n yield item\n", "repo_name": "caiyingyi/scrapy_dangdang", "sub_path": "dangdang2/dangdang2/spiders/DangSpider.py", "file_name": "DangSpider.py", "file_ext": "py", "file_size_in_byte": 1618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "scrapy.Spider", "line_number": 8, "usage_type": "attribute"}, {"api_name": "scrapy.http.Request", "line_number": 16, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 22, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "dangdang2.items.Dangdang2Item", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "35740908649", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 25 17:07:45 2019\n\n@author: datacore\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 25 12:01:13 2019\n\n@author: datacore\n\"\"\"\n\nfrom flask import Flask, request, redirect, url_for, flash, jsonify\nfrom flask_restful import Resource\nimport os\nimport pandas as pd\nimport h2o\nfrom h2o.automl import H2OAutoML\n\napp = Flask(__name__)\n\nROOT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n@app.route('/api/WS_create_h2o', methods=['POST'])\ndef WSCreate():\n ip = request.json['ip']\n port = request.json['port']\n nthreads = request.json['nthreads']\n max_mem_size = request.json['max_mem_size']\n \n ##Existing \\ new workspace\n ws = h2o.init(ip = ip, port = port , nthreads = nthreads, max_mem_size = max_mem_size )\n print('Found existing Workspace.')\n return \"existing Workspace\"\n return ws\n\n \n@app.route('/api/upload_data_h2o', methods=['POST'])\ndef UploadCSV():\n file = request.json['file_path']\n print(file)\n data = h2o.import_file(file)\n #print(data)\n return data \n\n@app.route('/api/split_h20', methods=['POST'])\ndef SplitCSV():\n file = request.json['file_path']\n print(file)\n data = h2o.import_file(file)\n #print(data)\n stock_split = data.split_frame(ratios = [0.8], seed = 1234)\n stock_train = stock_split[0] # using 80% for training\n stock_test = stock_split[1] #rest 20% for testingprint(wine_train.shape, wine_test.shape)\n print(stock_train.shape, stock_test.shape)\n return stock_train, stock_test\n\n\n \nif __name__ == '__main__':\n app.run(host='localhost', debug=True, port=54321)", "repo_name": "rajibstats/AutoML_Azure-Google-H2o", "sub_path": "Flask_API_H2o/WS_H2o.py", "file_name": "WS_H2o.py", "file_ext": "py", "file_size_in_byte": 1717, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Flask", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "h2o.init", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "h2o.import_file", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "h2o.import_file", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "11718689565", "text": "import sys\nsys.path += [\"../\"]\n\nimport smbh\nimport numpy as np\nimport matplotlib.pyplot as plt\n\npos = 1e-3 * np.ones(3) / 3 ** 0.5\n\nv = 60\ntheta = np.pi / 4\nphi = np.pi / 4\n\nx = v * np.sin(theta) * np.cos(phi)\ny = v * np.sin(theta) * np.sin(phi)\nz = v * np.cos(theta)\n\nspeeds = [x, y, z]\nls = np.logspace(1, 3, 10).astype(int)\nlya_s = np.zeros((len(ls), 7))\nlya_t = np.zeros((len(ls), 7))\n\nT = 1e-5\ndq = 1e-8\n\nwith open('lyapunov_s.dat', 'w') as file_s: pass\nwith open('lyapunov_t.dat', 'w') as file_t: pass\nfor (i, l) in enumerate(ls):\n lya_s[i] = l, *smbh.lyapunov(speeds, l = l, triaxial = False)\n lya_t[i] = l, *smbh.lyapunov(speeds, l = l, triaxial = True)\n\n with open('lyapunov_s.dat', 'a') as file_s:\n txt = \"%d %f %f %f %f %f %f\\n\" % tuple(lya_s[i])\n file_s.write(txt)\n with open('lyapunov_t.dat', 'a') as file_t:\n txt = \"%d %f %f %f %f %f %f\\n\" % tuple(lya_t[i])\n file_t.write(txt)\n\n# lya_s = np.genfromtxt('lyapunov_s.dat')\n# lya_t = np.genfromtxt('lyapunov_t.dat')\n\nfig, ax = plt.subplots(figsize = (8, 4.5))\nax.plot(lya_s[:, 0] * T, lya_s[:, 1], label = \"Spherical\")\nax.plot(lya_t[:, 0] * T, lya_t[:, 1], label = \"Triaxial\")\n# fig, axes = plt.subplots(1, 3, figsize = (8, 4.5))\n# for i in range(3):\n# axes[i].plot(lya_s[:, 0] * T, lya_s[:, i * 2 + 1], label = 'Spherical +')\n# axes[i].plot(lya_t[:, 0] * T, lya_t[:, i * 2 + 1], label = 'Triaxial +')\n# axes[i].plot(lya_s[:, 0] * T, lya_s[:, i * 2 + 2], '--o', label = 'Spherical -')\n# axes[i].plot(lya_t[:, 0] * T, lya_t[:, i * 2 + 2], '--o', label = 'Triaxial -')\n#\n# axes[i].set_xlabel('$lT$')\n# # axes[i].set_yscale('log')\n# axes[i].set_xscale('log')\n# print(lya_s[-1, 0] * T)\n\n# axes[0].set_ylabel(r'$\\vec{x}$')\n\n# ax.grid()\n# axes[0].legend()\n\nax.set_ylabel(r'$\\mathcal{L}$')\nax.set_xlabel('$l$ $T$ (Gyr)')\n\nax.set_xscale('log')\nax.legend()\nax.grid()\n\nfig.tight_layout()\nfig.savefig('lyapunov.png', dpi = 300)\n\nplt.show()\n\n# results = smbh.run(speeds, pot_type = smbh.SYMMETRIC)\n# print(results.RETURN_TIME)\n", "repo_name": "jsbarbosa/TesisFisica", "sub_path": "Files/Week 12/lyapunov.py", "file_name": "lyapunov.py", "file_ext": "py", "file_size_in_byte": 2043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "smbh.lyapunov", "line_number": 29, "usage_type": "call"}, {"api_name": "smbh.lyapunov", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "41564287900", "text": "import sys\nimport glob\nimport socket\nimport argparse\nimport urllib.request\nimport os\nimport os.path\nimport subprocess\nimport platform\nimport json\nimport pandas as pd\nfrom time import sleep\nfrom sys import exit\nfrom html.parser import HTMLParser\n\ndef report():\n return \"report.html\"\n\n# BEGIN: Check if file is text\nENC = {}\ndef is_binary(file_name):\n try:\n with open(file_name, 'tr', encoding='cp1251') as check_file: # try open file in text mode\n check_file.read()\n ENC[file_name] = 'cp1251'\n return False\n except: # if fail then file is non-text (binary)\n try:\n with open(file_name, 'tr', encoding='utf-8-sig') as check_file: # try open file in text mode\n check_file.read()\n ENC[file_name] = 'utf-8-sig'\n return False\n except: # if fail then file is non-text (binary)\n return True\n# END: Check if file is text\n\n\nclass TableHTMLParser(HTMLParser):\n\n def __init__(self):\n self.table = False\n self.data = {\n 'Source': [],\n 'Compared': [],\n 'Percent': [],\n 'Lines': [],\n 'URL': []\n }\n self.tind = -1\n self.tag = None\n super().__init__()\n\n def handle_starttag(self, tag, attrs):\n self.tag = tag\n if self.table:\n if tag == 'tr':\n self.tind = -1\n elif tag == 'td':\n self.tind += 1\n elif tag == 'a' and self.tind == 0:\n self.data['URL'].append(attrs[0][1])\n elif tag == 'table':\n self.table = True\n\n def handle_endtag(self, tag):\n self.tag = ''\n if tag == 'table':\n self.table = False\n self.tind = -1\n\n def handle_data(self, data):\n if self.table:\n if self.tind == 2 and self.tag == 'td':\n self.data['Lines'].append(int(data))\n elif self.tind == 0 and self.tag == 'a':\n _, p = data.split()\n self.data['Source'].append(data)\n self.data['Percent'].append(int(p[1:-2]))\n elif self.tind == 1 and self.tag == 'a':\n _, p = data.split()\n self.data['Compared'].append(data)\n self.data['Percent'][-1] = max(self.data['Percent'][-1], int(p[1:-2]))\n\ndef get_args_parser():\n parser = argparse.ArgumentParser(description='Run MOSS')\n parser.add_argument('files', type=str, nargs='+',\n help='files to send')\n parser.add_argument('-X', action='store_true',\n help='use experimental server')\n parser.add_argument('-d', action='store_true',\n help='specifies that submissions are by directory, not by file')\n parser.add_argument('-uid', type=int, default=939277019,\n help='ID to login')\n parser.add_argument('-m', type=int, default=100,\n help='maximum number of times a given passage may appear before it is ignored')\n parser.add_argument('-n', type=int, default=250,\n help='the number of matching files to show in the results')\n parser.add_argument('-l', type=str, default='c',\n help='the source language of the tested programs')\n parser.add_argument('-b', type=str, nargs='*', default=[],\n help='base files - code that appears in the base files is not counted in matches')\n parser.add_argument('-c', type=str, default='',\n help='comment string that is attached to the generated report')\n\n return parser\n\n\ndef load_args(fname='moss.json'):\n with open(fname, 'r') as f:\n args = json.load(f)\n return args\n\n\ndef call_moss(**args):\n \"\"\"\n Arguments same as for argparse. See get args_parser.\n \"\"\"\n\n noreq = 'Request not sent.';\n\n print('Checking files . . .')\n b = []\n for f in args['b']:\n for arg in glob.glob(f, recursive=True):\n if not os.path.isfile(arg):\n print('Base file %s does not exist. %s' % (arg, noreq))\n exit()\n if not os.access(arg, os.R_OK):\n print('Base file %s is not readable. %s' % (arg, noreq))\n exit()\n if is_binary(arg):\n print('Base file %s is not a text file. %s' % (arg, noreq))\n exit()\n b.append(arg)\n\n files = []\n for f in args['files']:\n for arg in glob.glob(f, recursive=True):\n if not os.path.isfile(arg):\n print('File %s does not exist. %s' % (arg, noreq))\n exit()\n if not os.access(arg, os.R_OK):\n print('File %s is not readable. %s' % (arg, noreq))\n exit()\n if is_binary(arg):\n print('File %s is not a text file. %s' % (arg, noreq))\n exit()\n files.append(arg)\n\n if not files:\n print('No files submitted.')\n exit()\n\n print('OK')\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.settimeout(600)\n server_address = ('moss.stanford.edu', 7690) # 171.64.78.49\n\n try:\n sock.connect(server_address)\n except Exception:\n print('Could not connect to server %s: %s' % server_address)\n exit()\n\n\n def upload_file(filename, i, lang):\n name = filename.replace(' ', '_')\n name = os.path.join(\n os.path.basename(os.path.dirname(name)),\n os.path.basename(name)).replace('\\\\', '/')\n print('Uploading %s ...' % name)\n res = b''\n with open(filename, 'rt', encoding=ENC[filename]) as file:\n v = (file.read() + '\\r\\n').encode(ENC[filename]) #.decode('utf8').encode('utf8')\n size = len(v)\n res += ('file %s %s %s %s\\n' % (i, lang, size, name)).encode('utf8')\n res += v\n print('done')\n return res\n\n\n sock.sendall((('moss %s\\n' % (args['uid'])) +\n ('directory %s\\n' % (int(args['d']))) +\n ('directory %s\\n' % (int(args['d']))) +\n ('X %s\\n' % (int(args['X']))) +\n ('maxmatches %s\\n' % (args['m'])) +\n ('show %s\\n' % (args['n'])) +\n ('language %s\\n' % (args['l']))).encode('utf8'))\n\n res = sock.recv(3600)\n if res == b'no\\n':\n print('Unrecognized language %s' % (args['l']))\n sock.sendall(b'end\\n')\n sock.close()\n exit()\n\n to_send = b''\n for f in b:\n to_send += upload_file(f, 0, args['l'])\n\n for i, f in enumerate(files):\n to_send += upload_file(f, i+1, args['l'])\n\n to_send += ('query 0 %s\\n' % (args['c'])).encode('utf8')\n sock.sendall(to_send)\n print(\"Query submitted. Waiting for the server's response.\")\n url = sock.recv(100).decode('utf-8')\n print(url)\n sock.sendall(b'end\\n')\n sock.close()\n return url\n\n\ndef sort_moss(url):\n page = urllib.request.urlopen(url).read().decode('utf8')\n parserHTML = TableHTMLParser()\n parserHTML.feed(page)\n table = pd.DataFrame(parserHTML.data)\n return page, table.sort_values('Percent', ascending=False)\n \n\ndef report_moss(page, sortedtable, url):\n prepage = page.split('')[0] + url\n\n body = '\\n
\\n'\n body += '
SourceComparedLines Matched\\n'\n for i in range(len(sortedtable)):\n row = sortedtable.iloc[i]\n body += '
%s\\n' % (row['URL'], row['Source'])\n body += ' %s\\n' % (row['URL'], row['Compared'])\n body += '%s\\n' % (row['Lines'])\n body += '
\\n'\n\n postpage = page.split('\\n\\n')[1]\n\n with open(report(), \"w\") as f:\n f.write(prepage + body + postpage)\n\ndef main():\n args = get_args_parser().parse_args()\n url = call_moss(**vars(args))\n print('Sorting...')\n page, sortedtable = sort_moss(url)\n print('done.')\n print('Creating report')\n report_moss(page, sortedtable, url)\n print('done')\n print(os.path.abspath(report()))\n \n if platform.system() == 'Darwin': # macOS\n subprocess.call(('open', report()))\n elif platform.system() == 'Windows': # Windows\n os.startfile(report())\n else: # linux variants\n subprocess.call(('xdg-open', report()))\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "voloshinbogdan/AllTheMoodleMMCS", "sub_path": "MoodleAssistant/moss.py", "file_name": "moss.py", "file_ext": "py", "file_size_in_byte": 7970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "html.parser.HTMLParser", "line_number": 38, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 85, "usage_type": "call"}, {"api_name": "json.load", "line_number": 110, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 127, "usage_type": "call"}, {"api_name": "os.access", "line_number": 128, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 133, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 141, "usage_type": "call"}, {"api_name": "os.access", "line_number": 142, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 142, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 144, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 147, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 152, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 156, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 156, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 156, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 196, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 216, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 216, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 216, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 249, "usage_type": "call"}, {"api_name": "os.path", "line_number": 249, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 251, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 252, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 253, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 254, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 256, "usage_type": "call"}]} +{"seq_id": "71565621488", "text": "\"\"\"APIs for action plugins.\"\"\"\n\nfrom django.utils.translation import gettext_lazy as _\n\nfrom rest_framework import permissions\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom plugin import registry\n\n\nclass ActionPluginView(APIView):\n \"\"\"Endpoint for running custom action plugins.\"\"\"\n\n permission_classes = [\n permissions.IsAuthenticated,\n ]\n\n def post(self, request, *args, **kwargs):\n \"\"\"This function checks if all required info was submitted and then performs a plugin_action or returns an error.\"\"\"\n action = request.data.get('action', None)\n\n data = request.data.get('data', None)\n\n if action is None:\n return Response({\n 'error': _(\"No action specified\")\n })\n\n action_plugins = registry.with_mixin('action')\n for plugin in action_plugins:\n if plugin.action_name() == action:\n plugin.perform_action(request.user, data=data)\n return Response(plugin.get_response(request.user, data=data))\n\n # If we got to here, no matching action was found\n return Response({\n 'error': _(\"No matching action found\"),\n \"action\": action,\n })\n", "repo_name": "inventree/InvenTree", "sub_path": "InvenTree/plugin/base/action/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 1254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3204, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 26, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 27, "usage_type": "call"}, {"api_name": "plugin.registry.with_mixin", "line_number": 30, "usage_type": "call"}, {"api_name": "plugin.registry", "line_number": 30, "usage_type": "name"}, {"api_name": "plugin.action_name", "line_number": 32, "usage_type": "call"}, {"api_name": "plugin.perform_action", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "plugin.get_response", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 37, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "3770188900", "text": "import telebot\r\nfrom telebot import types\r\n\r\nfrom texhandler import *\r\n\r\nfrom pylatex import Document\r\n\r\nfrom entities_handler import ent2latex, only_quots\r\n\r\nimport shutil\r\n\r\nbot = telebot.TeleBot('6120587308:AAGy1kJpZM3v9JFwqMaJhI1NUjuxRTTeUdo')\r\nprint('Bot started.\\nPress Ctr-C to terminate the proccess\\n')\r\n\r\nusers = {}\r\n\r\ndef start_reply_kb():\r\n reply_kb = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n btn_yes = types.KeyboardButton(text='Да')\r\n btn_no = types.KeyboardButton(text='Нет')\r\n\r\n reply_kb.add(btn_yes, btn_no)\r\n\r\n return reply_kb\r\n\r\ndef type_reply_kb():\r\n reply_kb = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n btn_issl = types.KeyboardButton(text='Исследовательский')\r\n btn_prog = types.KeyboardButton(text='Программный')\r\n\r\n reply_kb.add(btn_issl, btn_prog)\r\n\r\n return reply_kb\r\n\r\n@bot.callback_query_handler(func=lambda callback: callback.data)\r\ndef check_choice(callback):\r\n if callback.data == 'doc_name':\r\n bot.send_message(callback.message.chat.id, 'Введите название файла')\r\n bot.register_next_step_handler(callback.message, change_doc_name)\r\n elif callback.data == 'doc_font_size':\r\n bot.send_message(callback.message.chat.id, 'Введите размер шрифта (pt)')\r\n bot.register_next_step_handler(callback.message, change_doc_font_size)\r\n elif callback.data == 'doc_geometry':\r\n bot.send_message(callback.message.chat.id, 'Введи размеры полей через пробел\\n(левое правое верхнее нижнее)', parse_mode='HTML')\r\n bot.register_next_step_handler(callback.message, change_doc_geometry)\r\n elif callback.data == 'online':\r\n users[\"{0}\".format(callback.message.chat.id)].bib_refs.append(BibOnline())\r\n\r\n curr_bib = users[\"{0}\".format(callback.message.chat.id)].bib_refs[-1]\r\n\r\n bot.send_message(callback.message.chat.id, \r\n '⚠️ Введите краткое название источника (на английском)\\n\\n'\\\r\n 'Например: chirkova18_arxiv', parse_mode='HTML')\r\n bot.register_next_step_handler(callback.message, bib_set_label, curr_bib)\r\n elif callback.data == 'book':\r\n users[\"{0}\".format(callback.message.chat.id)].bib_refs.append(BibBook())\r\n\r\n curr_bib = users[\"{0}\".format(callback.message.chat.id)].bib_refs[-1]\r\n\r\n bot.send_message(callback.message.chat.id, \r\n '⚠️ Введите краткое название источника (на английском)\\n\\n'\\\r\n 'Например: chirkova18_arxiv', parse_mode='HTML')\r\n bot.register_next_step_handler(callback.message, bib_set_label, curr_bib)\r\n elif callback.data == 'inbook':\r\n users[\"{0}\".format(callback.message.chat.id)].bib_refs.append(BibInbook())\r\n\r\n curr_bib = users[\"{0}\".format(callback.message.chat.id)].bib_refs[-1]\r\n\r\n bot.send_message(callback.message.chat.id, \r\n '⚠️ Введите краткое название источника (на английском)\\n\\n'\\\r\n 'Например: chirkova18_arxiv', parse_mode='HTML')\r\n bot.register_next_step_handler(callback.message, bib_set_label, curr_bib)\r\n elif callback.data == 'article':\r\n users[\"{0}\".format(callback.message.chat.id)].bib_refs.append(BibArticle())\r\n\r\n curr_bib = users[\"{0}\".format(callback.message.chat.id)].bib_refs[-1]\r\n\r\n bot.send_message(callback.message.chat.id, \r\n '⚠️ Введите краткое название источника (на английском)\\n\\n'\\\r\n 'Например: chirkova18_arxiv', parse_mode='HTML')\r\n bot.register_next_step_handler(callback.message, bib_set_label, curr_bib)\r\n \r\ndef change_doc_name(message):\r\n # name : string\r\n users[\"{0}\".format(message.chat.id)].default_filepath_ = message.text\r\n bot.send_message(message.chat.id, 'Требуется ли изменить параметры?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, is_change)\r\n\r\ndef change_doc_font_size(message):\r\n # size : string, int\r\n size = message.text\r\n users[\"{0}\".format(message.chat.id)].font_size_ = f'{size}pt'\r\n bot.send_message(message.chat.id, 'Требуется ли изменить параметры?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, is_change)\r\n\r\ndef change_doc_geometry(message):\r\n # all : int\r\n l = list(map(int, message.text.split()))\r\n left = l[0]\r\n right = l[1]\r\n top = l[2]\r\n bottom = l[3]\r\n\r\n users[\"{0}\".format(message.chat.id)].geometry_options_ = {\r\n 'left': f'{left}mm',\r\n 'right': f'{right}mm',\r\n 'top': f'{top}mm',\r\n 'bottom': f'{bottom}mm'}\r\n \r\n bot.send_message(message.chat.id, 'Требуется ли изменить параметры?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, is_change)\r\n\r\ndef add_dirs(message):\r\n print('Adding dirs...')\r\n\r\n # Добавляем папку, где будет храниться информация пользователя\r\n usr_dir_path = 'users/dir-' + str(message.chat.id)\r\n os.mkdir(usr_dir_path)\r\n\r\n # Добавляем папку, где будут храниться картинки пользователя\r\n usr_graphics_path = os.path.join(usr_dir_path, 'graphics')\r\n os.mkdir(usr_graphics_path)\r\n\r\n users[\"{0}\".format(message.chat.id)].default_filepath_ = usr_dir_path\r\n users[\"{0}\".format(message.chat.id)].title_name_ = os.path.join(usr_dir_path, 'title')\r\n users[\"{0}\".format(message.chat.id)].chat_id = message.chat.id\r\n users[\"{0}\".format(message.chat.id)].path = usr_dir_path\r\n users[\"{0}\".format(message.chat.id)].graphics_path = usr_graphics_path\r\n users[\"{0}\".format(message.chat.id)].bib_path = os.path.join(usr_dir_path, 'refs')\r\n\r\n\r\n@bot.message_handler(commands=['start'])\r\ndef start(message):\r\n bot.send_message(message.chat.id, '👋 Добро пожаловать!\\n\\n'\\\r\n 'Бот помогает заполнить отчет по курсовому проекту, автоматически добавляя все рекомендации по оформлению.'\\\r\n 'В конце работы вы получите ваш pdf-файл\\n\\n'\r\n 'Перед ра��отой с ботом, советуем прочитать рекомендации по содержанию: '\\\r\n r'https://docs.google.com/document/d/1Mjhw5jVO1bv-XD1PrSyE2nhg8F-z1W9b/edit', parse_mode='HTML')\r\n \r\n bot.send_message(message.chat.id, 'ℹ️ Доступные комманды: \\n\\n'\\\r\n '''/start - Начать\r\n/makefile - Создать документ\r\n/section - Добавить главу\r\n/subsection - Добавить подглаву\r\n/subsubsection - Добавить подподглаву\r\n/paragraph - Начать новый параграф\r\n/table - Добавить таблицу\r\n/list - Добавить список\r\n/math - Добавить простое уравнение\r\n/cite - Добавить ссылку на библиографию\r\n/next - Перейти к заполнению нового раздела''', parse_mode='HTML')\r\n \r\n bot.send_message(message.chat.id, '📝 Для того, чтобы добавить текст, просто введите его и отправьте.\\n\\n'\\\r\n 'Бот поддерживает жирный шрифт, курсив, зачеркивание и подчеркивание прямо из Telegram')\r\n \r\n bot.send_message(message.chat.id, '🖼 Для отправки изображения, просто отправьте его\\n\\n'\r\n '❗️ В десктопной версии приложения потребуется добавить пункт сжать изображение', parse_mode='HTML')\r\n \r\n bot.send_message(message.chat.id, '🖊 Для того, чтобы приступить к заполнению отчета, введите комманду /makefile', parse_mode='HTML')\r\n\r\n@bot.message_handler(commands=['makefile'])\r\ndef makefile(message):\r\n users[\"{0}\".format(message.chat.id)] = UserInfo2()\r\n add_dirs(message)\r\n \r\n bot.send_message(message.chat.id, '⚠️ Сейчас вам предстоит ввести параметры документа')\r\n bot.send_message(message.chat.id, \r\n 'Стандартные параметры документа:\\n'\\\r\n '◽️ Имя файла: your_file\\n'\\\r\n '◽️ Размер страницы: A4\\n'\\\r\n '◽️ Размер шрифта: 12 пт\\n'\\\r\n '◽️ Поля:\\n'\\\r\n ' ▫️ Левое: 25 мм\\n'\\\r\n ' ▫️ Правое: 10 мм\\n'\\\r\n ' ▫️ Верхнее: 20 мм\\n'\\\r\n ' ▫️ Нижнее: 20 мм\\n',\r\n parse_mode='HTML')\r\n\r\n bot.send_message(message.chat.id, '⚠️ Требуется ли изменить параметры?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, is_change)\r\n\r\ndef is_change(message):\r\n if message.text == 'Да':\r\n kb = types.InlineKeyboardMarkup(row_width=1)\r\n btn1 = types.InlineKeyboardButton(text='Имя файла', callback_data='doc_name')\r\n btn2 = types.InlineKeyboardButton(text='Размер шрифта', callback_data='doc_font_size')\r\n btn3 = types.InlineKeyboardButton(text='Поля', callback_data='doc_geometry')\r\n kb.add(btn1, btn2, btn3)\r\n\r\n bot.send_message(message.chat.id, '...', reply_markup=types.ReplyKeyboardRemove())\r\n bot.send_message(message.chat.id, '⚠️ Выберите параметр, который требуется изменить', reply_markup=kb)\r\n elif message.text == 'Нет':\r\n bot.send_message(message.chat.id, '✅ Переходим к созданию титульного листа', reply_markup=types.ReplyKeyboardRemove())\r\n bot.send_message(message.chat.id,\r\n '⚠️ Введите ФИО студента\\n\\n'\\\r\n 'Введите в формате: Фамилия Имя Отчество', parse_mode='HTML')\r\n bot.register_next_step_handler(message, set_title_student)\r\n else:\r\n bot.send_message(message.chat.id, '❗️ Выберите ответ на кнопке')\r\n bot.send_message(message.chat.id, '⚠️ Требуется ли изменить параметры?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, is_change)\r\n\r\ndef set_title_student(message):\r\n users[\"{0}\".format(message.chat.id)].student_ = message.text\r\n bot.send_message(message.chat.id, '⚠️ Выберите тип вашего проекта', reply_markup=type_reply_kb())\r\n bot.register_next_step_handler(message, set_title_type)\r\n\r\ndef set_title_type(message):\r\n if message.text == 'Исследовательский':\r\n users[\"{0}\".format(message.chat.id)].type_ = 'б исследовательском'\r\n elif message.text == 'Программный':\r\n users[\"{0}\".format(message.chat.id)].type_ = ' программном'\r\n else:\r\n bot.send_message(message.chat.id, '❗️ Выберите ответ на кнопке')\r\n bot.send_message(message.chat.id, '⚠️ Введите тип вашего проекта', reply_markup=type_reply_kb())\r\n bot.register_next_step_handler(message, set_title_type) \r\n\r\n if message.text == 'Исследовательский' or message.text == 'Программный':\r\n bot.send_message(message.chat.id, '⚠️ Введите тему вашего проекта', reply_markup=types.ReplyKeyboardRemove())\r\n bot.register_next_step_handler(message, set_title_topic)\r\n\r\ndef set_title_topic(message):\r\n users[\"{0}\".format(message.chat.id)].topic_ = only_quots(message.text)\r\n bot.send_message(message.chat.id, '⚠️ Введите номер вашей группы')\r\n bot.register_next_step_handler(message, set_title_group)\r\n\r\ndef set_title_group(message):\r\n users[\"{0}\".format(message.chat.id)].group_ = message.text\r\n bot.send_message(message.chat.id,\r\n '⚠️ Введите ФИО руководителя проекта\\n\\n'\\\r\n 'Введите в формате: Фамилия Имя Отчество', parse_mode='HTML')\r\n bot.register_next_step_handler(message, set_title_assistant)\r\n\r\ndef set_title_assistant(message):\r\n users[\"{0}\".format(message.chat.id)].assistant_ = message.text\r\n bot.send_message(message.chat.id, '⚠️ Введите должность вашего руководителя')\r\n bot.register_next_step_handler(message, set_title_post)\r\n\r\ndef set_title_post(message):\r\n users[\"{0}\".format(message.chat.id)].post_ = message.text\r\n bot.send_message(message.chat.id,\r\n '⚠️ Введите место работы вашего руководителя в родительном падеже\\n\\n'\\\r\n 'Например: Факультета компьютерных наук НИУ ВШЭ')\r\n bot.register_next_step_handler(message, set_title_work)\r\n\r\ndef set_title_work(message):\r\n users[\"{0}\".format(message.chat.id)].work_ = message.text\r\n set_title(\r\n title_name=users[\"{0}\".format(message.chat.id)].title_name_,\r\n type = users[\"{0}\".format(message.chat.id)].type_,\r\n topic=users[\"{0}\".format(message.chat.id)].topic_,\r\n student=users[\"{0}\".format(message.chat.id)].student_,\r\n group=users[\"{0}\".format(message.chat.id)].group_,\r\n assistant=users[\"{0}\".format(message.chat.id)].assistant_,\r\n post=users[\"{0}\".format(message.chat.id)].post_,\r\n work=users[\"{0}\".format(message.chat.id)].work_)\r\n \r\n bot.send_message(message.chat.id, '✅ Переходим к заполнению библиографии')\r\n bot_add_bib(message)\r\n\r\ndef set_annot(message):\r\n users[\"{0}\".format(message.chat.id)].next = set_key_words\r\n\r\n users[\"{0}\".format(message.chat.id)].document_ = set_tex_document(users[\"{0}\".format(message.chat.id)])\r\n add_tex_packages(users[\"{0}\".format(message.chat.id)].document_)\r\n add_tex_preamble(users[\"{0}\".format(message.chat.id)].document_)\r\n add_tex_title(users[\"{0}\".format(message.chat.id)].document_, 'title')\r\n add_tex_table_of_contents(users[\"{0}\".format(message.chat.id)].document_)\r\n\r\n users[\"{0}\".format(message.chat.id)].annotation_ = add_tex_annotation(users[\"{0}\".format(message.chat.id)].document_)\r\n\r\n bot.send_message(message.chat.id, '⚠️ Введите текст аннотации')\r\n bot.send_message(message.chat.id, '🪄 Чтобы ввести текст с нового абзаца введите команду /paragraph\\n\\n'\\\r\n '🪄 Чтобы перейти заполнению нового раздела введите команду /next')\r\n \r\ndef set_key_words(message):\r\n users[\"{0}\".format(message.chat.id)].next = None\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить Список ключевых слов', parse_mode='HTML')\r\n bot.send_message(message.chat.id, \r\n '❔ Подсказка\\n\\n'\\\r\n '5-10 слов или фраз, характеризующих содержание '\\\r\n '(на том же языке, на котором написан текст работы)', parse_mode='HTML')\r\n bot.send_message(message.chat.id, '⚠️ Введите ключевые слова через запятую с пробелом\\n\\n'\\\r\n 'Например: Глубинное обучение, разреживание моделей, рекуррентные нейронные сети', parse_mode='HTML')\r\n bot.register_next_step_handler(message, set_intro)\r\n\r\ndef set_intro(message):\r\n users[\"{0}\".format(message.chat.id)].next = None\r\n add_tex_key_words(users[\"{0}\".format(message.chat.id)].document_, message.text)\r\n\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить Введение', parse_mode='HTML')\r\n bot.send_message(message.chat.id, \r\n '❔ Подсказка\\n\\n'\\\r\n 'По смыслу, это одновременно неформальное введение '\\\r\n 'в работу и пересказ работы длиной 1-2 страницы. '\\\r\n 'В введении обычно дается описание предметной области, '\\\r\n 'неформально формулируется постановка задачи, описывается '\\\r\n 'ее актуальность и значимость, неформально описываются '\\\r\n 'основные результаты работы, в том числе их новизна и значимость. '\\\r\n 'При выполнении группового проекта в конце введения стоит описать '\\\r\n 'структуру деления задач между участниками проекта.', parse_mode='HTML')\r\n \r\n users[\"{0}\".format(message.chat.id)].next = set_literature\r\n\r\n bot_add_section(message, 'Введение')\r\n bot.send_message(message.chat.id, '⚠️ Начинайте заполнять этот раздел')\r\n\r\ndef set_literature(message):\r\n users[\"{0}\".format(message.chat.id)].next = bot_add_chapters\r\n\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить Обзор литературы', parse_mode='HTML')\r\n bot.send_message(message.chat.id, \r\n '❔ Подсказка\\n\\n'\\\r\n 'Краткое описание и характеристика релевантных работ. Для исследовательского проекта: позиционирование вашей работы относительно других современных работ (к примеру: предложенный метод эффективнее работы [1] потому-то, в работе исследуется дополнительный случай, который не исследуется в [2] и т.п.). Для программного проекта: обзор похожих программных решений, их сравнительный анализ и описание почему их нельзя использовать для решения поставленной задачи. Обзор литературы не должен выглядеть как перечисление релевантных работ, он должен включать в себя анализ этих работ и позиционировать вашу работу относительно других существующих работ.', parse_mode='HTML')\r\n\r\n bot_add_section(message, 'Обзор литературы')\r\n bot.send_message(message.chat.id, '⚠️ Начинайте заполнять этот раздел')\r\n\r\n\r\n# Image adder\r\n@bot.message_handler(content_types=['photo'])\r\ndef bot_set_image(message):\r\n photo_id = message.photo[-1].file_id\r\n file_photo = bot.get_file(photo_id)\r\n\r\n filedir_and_name, file_extencion = os.path.splitext(file_photo.file_path)\r\n downloaded_file_photo = bot.download_file(file_photo.file_path)\r\n\r\n filedir, filename = os.path.split(filedir_and_name)\r\n print(filedir)\r\n print(filename, file_extencion)\r\n src = os.path.join(users[\"{0}\".format(message.chat.id)].graphics_path, filename + file_extencion)\r\n no_dir_src = filename + file_extencion\r\n with open(src, 'wb') as local_file:\r\n local_file.write(downloaded_file_photo)\r\n\r\n bot.send_message(message.chat.id, '❔ Подсказка\\n\\n'\\\r\n 'В описании поясните, что происходит на изображении', parse_mode='HTML')\r\n bot.send_message(message.chat.id, '⚠️ Введите описание изображения', parse_mode='HTML')\r\n bot.register_next_step_handler(message, bot_add_image, no_dir_src, filename)\r\n\r\ndef bot_add_image(message, path, label):\r\n add_image(users[\"{0}\".format(message.chat.id)].document_, path, message.text, label_name=label)\r\n bot.send_message(message.chat.id, '✅ Изображение было успешно добавлено')\r\n\r\n# Table adder\r\n@bot.message_handler(commands=['table'])\r\ndef bot_set_table(message):\r\n bot.send_message(message.chat.id, '⚠️ Введите размер таблицы через пробел\\n\\n'\\\r\n 'Например, если хотите ввести таблицу размера 4 на 5, введите 4 5', parse_mode='HTML')\r\n \r\n bot.register_next_step_handler(message, bot_add_table)\r\n\r\ndef set_table_data(message, num_of_rows, num_of_columns, i, j):\r\n print(i, j)\r\n if i < num_of_rows:\r\n if j < num_of_columns:\r\n users[\"{0}\".format(message.chat.id)].table_data[i][j] = message.text\r\n if i + 1 == num_of_rows and j + 1 == num_of_columns:\r\n bot.send_message(message.chat.id, '⚠️ Введите описание таблицы')\r\n bot.register_next_step_handler(message, set_table_caption, num_of_rows, num_of_columns)\r\n return\r\n if j + 1 == num_of_columns:\r\n bot.send_message(message.chat.id,\r\n '⚠️ Введите данные в\\n'\\\r\n f'{i + 1 + 1} строку, 1 столбец')\r\n else:\r\n bot.send_message(message.chat.id,\r\n '⚠️ Введите данные в\\n'\\\r\n f'{i + 1} строку, {j + 1 + 1} столбец')\r\n bot.register_next_step_handler(message, set_table_data, num_of_rows, num_of_columns, i, j + 1)\r\n else:\r\n set_table_data(message, num_of_rows, num_of_columns, i + 1, 0)\r\n else:\r\n bot.send_message(message.chat.id, '⚠️ Введите описание таблицы')\r\n bot.register_next_step_handler(message, set_table_caption, num_of_rows, num_of_columns)\r\n\r\ndef set_table_caption(message, num_of_rows, num_of_columns):\r\n add_table(users[\"{0}\".format(message.chat.id)].document_, message.text, num_of_rows, num_of_columns, users[\"{0}\".format(message.chat.id)].table_data)\r\n users[\"{0}\".format(message.chat.id)].table_data.clear()\r\n bot.send_message(message.chat.id, '✅ Таблица была успешно добавлена')\r\n\r\n\r\ndef bot_add_table(message):\r\n rows, clmns = map(int, message.text.split())\r\n users[\"{0}\".format(message.chat.id)].table_data = [['' for i in range(clmns)] for j in range(rows)]\r\n\r\n bot.send_message(message.chat.id,\r\n '⚠️ Введите данные в\\n'\\\r\n '1 строку, 1 столбец')\r\n bot.register_next_step_handler(message, set_table_data, rows, clmns, 0, 0)\r\n\r\n# List adder\r\n@bot.message_handler(commands=['list'])\r\ndef bot_set_list(message):\r\n reply_kb = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n btn1 = types.KeyboardButton(text='Нумерованный список')\r\n btn2 = types.KeyboardButton(text='Маркированный список (список с точками)')\r\n reply_kb.add(btn1, btn2)\r\n\r\n bot.send_message(message.chat.id, '⚠️ Выберите, какой список хотите добавить', reply_markup=reply_kb)\r\n bot.register_next_step_handler(message, bot_set_list_items)\r\n\r\ndef add_list_items(message, list_type):\r\n if message.text == '0':\r\n if list_type == 'enum':\r\n add_list_enumerate(users[\"{0}\".format(message.chat.id)].document_, users[\"{0}\".format(message.chat.id)].items)\r\n users[\"{0}\".format(message.chat.id)].items.clear()\r\n elif list_type == 'itemize':\r\n add_list_itemize(users[\"{0}\".format(message.chat.id)].document_, users[\"{0}\".format(message.chat.id)].items)\r\n users[\"{0}\".format(message.chat.id)].items.clear()\r\n\r\n bot.send_message(message.chat.id, '✅ Список был успешно добавлен')\r\n else:\r\n users[\"{0}\".format(message.chat.id)].items.append(message.text)\r\n bot.send_message(message.chat.id, '⚠️ Введите элемент списка\\n\\n'\\\r\n 'Чтобы закончить ввод, введите 0', reply_markup=types.ReplyKeyboardRemove())\r\n bot.register_next_step_handler(message, add_list_items, list_type)\r\n\r\ndef bot_set_list_items(message):\r\n if message.text == 'Нумерованный список':\r\n bot.send_message(message.chat.id, '⚠️ Введите элемент списка\\n\\n'\\\r\n 'Чтобы закончить ввод, введите 0', reply_markup=types.ReplyKeyboardRemove())\r\n bot.register_next_step_handler(message, add_list_items, 'enum')\r\n elif message.text == 'Маркированный список (список с точками)':\r\n bot.send_message(message.chat.id, '⚠️ Введите элемент списка\\n\\n'\\\r\n 'Чтобы закончить ввод, введите 0', reply_markup=types.ReplyKeyboardRemove())\r\n bot.register_next_step_handler(message, add_list_items, 'itemize')\r\n else:\r\n print('hz')\r\n\r\n# Math adder\r\n@bot.message_handler(commands=['math'])\r\ndef bot_set_math(message):\r\n bot.send_message(message.chat.id, '⚠️ Введите простое уравнение\\n\\n'\\\r\n 'Например, y = 5x^2 + 3', parse_mode='HTML')\r\n \r\n bot.register_next_step_handler(message, bot_add_math)\r\n\r\ndef bot_add_math(message):\r\n add_math(users[\"{0}\".format(message.chat.id)].document_, NoEscape(message.text))\r\n bot.send_message(message.chat.id, '✅ Уравнение было успешно добавлено')\r\n\r\n# Cite adder\r\ndef txt_arr_cites(message):\r\n result = ''\r\n for key in users[\"{0}\".format(message.chat.id)].cites_:\r\n if not users[\"{0}\".format(message.chat.id)].cites_[key].is_triggered:\r\n result += (key + '\\n')\r\n\r\n return result\r\n\r\n@bot.message_handler(commands=['cite'])\r\ndef bot_add_cite(message):\r\n if users[\"{0}\".format(message.chat.id)].triggered_len > 0:\r\n bot.send_message(message.chat.id, 'ℹ️ Вы еще не сослались на следующие источники:\\n\\n{0}'.format(txt_arr_cites(message)))\r\n\r\n cites_reply_kb = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n \r\n for key in users[\"{0}\".format(message.chat.id)].cites_:\r\n cites_reply_kb.add(types.KeyboardButton(text=key))\r\n\r\n bot.send_message(message.chat.id, '⚠️ Выберите, на какой источник хотите сослаться', reply_markup=cites_reply_kb)\r\n bot.register_next_step_handler(message, bot_cite_handler)\r\n\r\ndef bot_cite_handler(message):\r\n if message.text not in users[\"{0}\".format(message.chat.id)].cites_:\r\n cites_reply_kb = types.ReplyKeyboardMarkup(resize_keyboard=True)\r\n \r\n for key in users[\"{0}\".format(message.chat.id)].cites_:\r\n cites_reply_kb.add(types.KeyboardButton(text=key))\r\n\r\n bot.send_message(message.chat.id, '❗️ Выберите ответ на кнопке!')\r\n bot.send_message(message.chat.id, '⚠️ Выберите, на какой источник хотите сослаться', reply_markup=cites_reply_kb)\r\n bot.register_next_step_handler(message, bot_cite_handler)\r\n\r\n add_cite(users[\"{0}\".format(message.chat.id)].document_, message.text)\r\n users[\"{0}\".format(message.chat.id)].cites_[message.text].is_triggered = True\r\n users[\"{0}\".format(message.chat.id)].triggered_len -= 1\r\n bot.send_message(message.chat.id, f'✅ Была добавлена ссылка на {message.text}', reply_markup=types.ReplyKeyboardRemove())\r\n\r\n\r\ndef bot_add_chapters(message):\r\n users[\"{0}\".format(message.chat.id)].next = bot_add_conclusion\r\n bot_send_hint_sections(message)\r\n bot_send_hint_subsections(message)\r\n bot_send_hint_subsubsections(message)\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить ваши главы\\n\\n'\\\r\n 'Выше были даны указания, как правильно добавлять главы, подглавы и подподглавы. '\\\r\n 'Все что вы введете после комманды, будет добавлено в выбранную секцию.\\n\\n'\\\r\n 'Например вы введете /section, после этого введете называние главы.'\\\r\n 'Комманда subsection, добавит подгаву этой главы. Если после этого вы начнете вводить текст или пришлете '\\\r\n 'картинку, то они добавятся в эту подглаву.\\n\\n'\\\r\n 'Если же вы захотите, начать новую подглаву или главу, введите команды /subsection или /section соответственно\\n\\n', parse_mode='HTML')\r\n \r\n bot.send_message(message.chat.id, '❔ Подсказка\\n\\n'\\\r\n 'Главы (обычно от 2 до 5). Здесь структура сильно зависит от темы проекта.'\\\r\n 'Например, работа, предлагающая некий новый метод решения какой-то задачи, может содержать следующие главы: формальная постановка задачи и анализ ее особенностей, описание предлагаемого метода, теоретический анализ метода, экспериментальное исследование и сравнение с аналогами.\\n\\n'\\\r\n 'Например, работа, исследующая особенности применения некоторого метода для различных задач, может содержать следующие главы: описание метода, обзор применимости метода для различных задач с описанием этих задач, анализом и обоснованием выбора конкретных задач для вашего исследования, экспериментальный анализ применимости метода к задаче 1 в сравнении с аналогами, то же для задачи 2 и т.д. \\n\\n'\\\r\n 'Например, работа, посвященная разработке программной системы для решения практической задачи, может содержать следующие главы: описание и обоснование всех выбранных архитектурных решений/алгоритмов/технологий, описание подхода к тестированию разработанного решения и обоснование выбранных метрик качества, результаты тестирования разработанной системы и ее сравнение с известными аналогами. \\n\\n'\\\r\n 'Каждую главу, для которой это уместно, стоит завершать кратким заключением с основными выводами. Это поможет выделить основные результаты текущей главы и плавно перейти к следующей главе.',\r\n parse_mode='HTML')\r\n bot.send_message(message.chat.id, '⚠️ Если захотите перейти к разделу Заключение, воспользуйтесь коммандой /next', parse_mode='HTML')\r\n\r\ndef bot_add_conclusion(message):\r\n users[\"{0}\".format(message.chat.id)].next = bot_set_bib\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить заключение\\n\\n', parse_mode='HTML')\r\n bot.send_message(message.chat.id, '❔ Подсказка\\n\\n'\\\r\n 'Перечисление и характеристика результатов работы (как положительных, так и отрицательных, если таковые есть), перспективы дальнейшей деятельности. ', parse_mode='HTML')\r\n bot_add_section(message, 'Заключение')\r\n bot.send_message(message.chat.id, '⚠️ Введите содержание заключения')\r\n\r\ndef bot_set_bib(message):\r\n add_tex_literature(users[\"{0}\".format(message.chat.id)].document_)\r\n end_doc(message)\r\n\r\n# End document\r\ndef end_doc(message):\r\n bot.send_message(message.chat.id, '✅ Заполнение работы закончено!', parse_mode='HTML')\r\n bot.send_message(message.chat.id, '⏳ Ваш файл...', parse_mode='HTML')\r\n\r\n users[\"{0}\".format(message.chat.id)].document_.generate_tex(os.path.join(users[\"{0}\".format(message.chat.id)].path, 'your_file'))\r\n compile_tex(message)\r\n\r\n bot.send_document(message.chat.id, open(os.path.join(users[\"{0}\".format(message.chat.id)].path, 'your_file.pdf'), 'rb'))\r\n shutil.rmtree(users[\"{0}\".format(message.chat.id)].path)\r\n bot.send_message(message.chat.id, '🖊 Для того, чтобы приступить к новому заполнению отчета, введите комманду /makefile', parse_mode='HTML')\r\n\r\n\r\ndef compile_tex(message):\r\n os.chdir(users[\"{0}\".format(message.chat.id)].path)\r\n os.system('pdflatex ' + 'your_file')\r\n\r\n os.system('biber ' + 'your_file')\r\n\r\n os.system('pdflatex ' + 'your_file')\r\n os.system('pdflatex ' + 'your_file')\r\n os.system('pdflatex ' + 'your_file')\r\n\r\n os.chdir('../../')\r\n\r\n# Section adder\r\ndef bot_add_section(message, title):\r\n if title == None:\r\n title = message.text\r\n bot.send_message(message.chat.id, f'✅ Был добавлен раздел {title}')\r\n add_tex_section(users[\"{0}\".format(message.chat.id)].document_, title)\r\n\r\n@bot.message_handler(commands=['section'])\r\ndef bot_new_sect(message):\r\n bot_send_hint_sections(message)\r\n bot.send_message(message.chat.id, '⚠️ Введите имя раздела')\r\n bot.register_next_step_handler(message, bot_add_section, None)\r\n\r\n\r\n# Subsection adder\r\ndef bot_add_subsection(message, title):\r\n if title == None:\r\n title = message.text\r\n bot.send_message(message.chat.id, f'✅ Был добавлен подраздел {title}')\r\n add_tex_subsection(users[\"{0}\".format(message.chat.id)].document_, title)\r\n\r\n@bot.message_handler(commands=['subsection'])\r\ndef bot_new_subsect(message):\r\n bot_send_hint_subsections(message)\r\n bot.send_message(message.chat.id, '⚠️ Введите имя подраздела')\r\n bot.register_next_step_handler(message, bot_add_subsection, None)\r\n\r\n\r\n# Subsubsection adder\r\ndef bot_add_subsubsection(message, title):\r\n if title == None:\r\n title = message.text\r\n bot.send_message(message.chat.id, f'✅ Был добавлен подподраздел {title}')\r\n add_tex_subsubsection(users[\"{0}\".format(message.chat.id)].document_, title)\r\n\r\n@bot.message_handler(commands=['subsubsection'])\r\ndef bot_new_subsubsect(message):\r\n bot_send_hint_subsubsections(message)\r\n bot.send_message(message.chat.id, '⚠️ Введите имя подподраздела')\r\n bot.register_next_step_handler(message, bot_add_subsubsection, None)\r\n\r\n\r\n# Next adder\r\n@bot.message_handler(commands=['next'])\r\ndef bot_next(message):\r\n bot.send_message(message.chat.id, '✅ Переходим к заполению следующего раздела')\r\n users[\"{0}\".format(message.chat.id)].next(message)\r\n\r\n\r\n# Paragraph adder\r\n@bot.message_handler(commands=['paragraph'])\r\ndef bot_new_par(message):\r\n bot.send_message(message.chat.id, '✅ Был добавлен новый параграф')\r\n add_paragraph(users[\"{0}\".format(message.chat.id)].document_)\r\n\r\n# Text adder\r\n@bot.message_handler(content_types=['text'])\r\ndef bot_add_text(message):\r\n \r\n add_text(users[\"{0}\".format(message.chat.id)].document_, only_quots(ent2latex(message)))\r\n bot.send_message(message.chat.id, '✅ Текст был успешно добавлен')\r\n bot_send_hint_text(message)\r\n\r\ndef bot_gen_tex(message):\r\n bot.send_message(message.chat.id, 'Сгенерирован tex-файл')\r\n users[\"{0}\".format(message.chat.id)].document_.generate_tex('simpletex')\r\n\r\n\r\n# Hint adders\r\ndef bot_send_hint_sections(message):\r\n bot.send_message(message.chat.id, '🪄 Чтобы добавить раздел введите комманду /section\\n\\n'\\\r\n 'Все, что вы введете после этой команды будет добавлено в этот раздел (в том числе и посекции и подподсекции)\\n\\n'\\\r\n '🪄 Чтобы перейти к заполнению нового раздела введите комманду /next')\r\n \r\ndef bot_send_hint_subsections(message):\r\n bot.send_message(message.chat.id, '🪄 Чтобы добавить подраздел введите комманду /subsection\\n\\n'\\\r\n 'Все, что вы введете после этой команды будет добавлено в этот раздел (в том числе и подподсекции)\\n\\n'\\\r\n '🪄 Чтобы перейти к заполнению нового раздела введите комманду /next')\r\n \r\ndef bot_send_hint_subsubsections(message):\r\n bot.send_message(message.chat.id, '🪄 Чтобы добавить подподраздел введите комманду /subsubsection\\n\\n'\\\r\n 'Все, что вы введете после этой команды будет добавлено в этот раздел\\n\\n'\\\r\n '🪄 Чтобы перейти к заполнению нового раздела введите комманду /next')\r\n\r\ndef bot_send_hint_text(message):\r\n bot.send_message(message.chat.id, '🪄 Чтобы ввести текст с нового абзаца введите комманду /paragraph\\n\\n'\\\r\n '🪄 Чтобы перейти к заполнению нового раздела введите комманду /next')\r\n\r\n\r\n# -------------------------------------------------------------------------------------------------------------------------------------------\r\n\r\ndef bib_inline_kb():\r\n kb = types.InlineKeyboardMarkup(row_width=1)\r\n btn1 = types.InlineKeyboardButton(text='🌐 Онлайн источник', callback_data='online')\r\n btn2 = types.InlineKeyboardButton(text='📚 Книга', callback_data='book')\r\n btn3 = types.InlineKeyboardButton(text='📖 Глава из книги', callback_data='inbook')\r\n btn4 = types.InlineKeyboardButton(text='🧾 Статья', callback_data='article')\r\n btn5 = types.InlineKeyboardButton(text='🔬 Конференция', callback_data='inproceedings')\r\n kb.add(btn1, btn2, btn3, btn4, row_width=1)\r\n\r\n return kb\r\n\r\n# @bot.callback_query_handler(func=lambda callback: callback.data)\r\n# def check_choice(callback):\r\n# bot.send_message(callback.message.chat.id, '📍 Приступим к заполнению библиографии')\r\n# if callback.data == 'online':\r\n# users[\"{0}\".format(message.chat.id)].bib_refs.append(BibOnline())\r\n# elif callback.data == 'book':\r\n# users[\"{0}\".format(message.chat.id)].bib_refs.append(BibBook())\r\n# elif callback.data == 'inbook':\r\n# users[\"{0}\".format(message.chat.id)].bib_refs.append(BibInbook())\r\n# elif callback.data == 'article':\r\n# users[\"{0}\".format(message.chat.id)].bib_refs.append(BibArticle())\r\n\r\n# curr_bib = users[\"{0}\".format(message.chat.id)].bib_refs[-1]\r\n\r\n# bot.send_message(callback.message.chat.id, \r\n# '⚠️ Введите краткое название источника (на английском)\\n\\n'\\\r\n# 'Например: chirkova18_arxiv', parse_mode='HTML')\r\n# bot.register_next_step_handler(callback.message, bib_set_label, curr_bib)\r\n\r\n# Bib adders\r\ndef bib_set_label(message, bib_ref):\r\n bib_ref.label = message.text.lower()\r\n\r\n bot.send_message(message.chat.id, \r\n '⚠️ Введите Автора\\n\\n'\\\r\n 'Например: Nadezhda Chirkova или Donald E. Knuth\\n'\\\r\n 'Если конкретного автора нет, введите 0\\n', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_author, bib_ref)\r\n\r\ndef bib_set_author(message, bib_ref):\r\n if message.text != '0':\r\n bib_ref.author = message.text\r\n else:\r\n bib_ref.author = ''\r\n\r\n bot.send_message(message.chat.id, \r\n '⚠️ Введите название\\n\\n'\\\r\n 'Например: Knuth: Computers and Typesetting', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_title, bib_ref)\r\n\r\ndef bib_set_title(message, bib_ref):\r\n bib_ref.title = message.text\r\n\r\n if isinstance(bib_ref, BibOnline):\r\n bot.send_message(message.chat.id, \r\n '⚠️ Введите Ссылку на материал\\n\\n'\\\r\n 'Например: http://www-cs-faculty.stanford.edu/~uno/abcde.html', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_url, bib_ref)\r\n elif isinstance(bib_ref, BibBook) or isinstance(bib_ref, BibInbook):\r\n bot.send_message(message.chat.id, '⚠️ Введите Издательство\\n\\n'\\\r\n 'Например: Addison-Wesley', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_publisher, bib_ref)\r\n elif isinstance(bib_ref, BibArticle):\r\n bot.send_message(message.chat.id, '⚠️ Введите Год выпуска материала\\n\\n'\\\r\n 'Например: 1968', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_year, bib_ref)\r\n\r\ndef bib_set_publisher(message, bib_ref):\r\n bib_ref.publisher = message.text\r\n\r\n bot.send_message(message.chat.id, '⚠️ Введите Год выпуска материала\\n\\n'\\\r\n 'Например: 1968', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_year, bib_ref)\r\n\r\ndef bib_set_year(message, bib_ref):\r\n bib_ref.year = message.text\r\n\r\n if isinstance(bib_ref, BibBook):\r\n bot.send_message(message.chat.id, '⚠️ Введите серию книги\\n\\n'\\\r\n 'Например: Four volumes\\n\\n'\\\r\n 'Если такой нет, введите 0', \r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_series, bib_ref)\r\n elif isinstance(bib_ref, BibInbook):\r\n bot.send_message(message.chat.id, '⚠️ Введите номер главы, на которую хотите сослаться\\n\\n'\\\r\n 'Например: 1.2\\n\\n',\r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_chapter, bib_ref)\r\n elif isinstance(bib_ref, BibArticle):\r\n bot.send_message(message.chat.id, '⚠️ Введите название журнала, в котором опубликовалась статья\\n\\n'\\\r\n 'Например: TUGBoat\\n\\n',\r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_journal, bib_ref)\r\n\r\ndef bib_set_journal(message, bib_ref):\r\n bib_ref.journal = message.text\r\n\r\n bot.send_message(message.chat.id, '⚠️ Введите общий номер выпуска журнала (Volume.), в котором опубликовалась статья\\n\\n'\\\r\n 'Например, Vol.59 No.4 означает, что это четвертый выпуск журнала за год, который имеет общий номер 59.\\n\\n',\r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_volume, bib_ref)\r\n\r\ndef bib_set_volume(message, bib_ref):\r\n bib_ref.volume = message.text\r\n\r\n bot.send_message(message.chat.id, '⚠️ Введите номер выпуска журнала за год (No.), в котором опубликовалась статья\\n\\n'\\\r\n 'Например, Vol.59 No.4 означает, что это четвертый выпуск журнала за год, который имеет общий номер 59.\\n\\n',\r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_number, bib_ref)\r\n\r\ndef bib_set_number(message, bib_ref):\r\n bib_ref.number = message.text\r\n\r\n bot.send_message(message.chat.id, '⚠️ Введите номера страниц, на которые хотите сослаться\\n\\n'\\\r\n 'Введите номера страниц через --, например 342--351',\r\n parse_mode='HTML')\r\n bot.register_next_step_handler(message, bib_set_pages, bib_ref)\r\n\r\ndef bib_set_pages(message, bib_ref):\r\n bib_ref.pages = message.text\r\n\r\n\r\n users[\"{0}\".format(message.chat.id)].cites_[bib_ref.label] = bib_ref\r\n bib_add_article(users[\"{0}\".format(message.chat.id)].bib_, bib_ref)\r\n\r\n bot.send_message(message.chat.id, '✅ Заполнили данные для журнальной статьи')\r\n bot.send_message(message.chat.id, 'Требуется ли еще добавить источник?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, bib_is_change)\r\n\r\ndef bib_set_chapter(message, bib_ref):\r\n bib_ref.chapter = message.text\r\n\r\n users[\"{0}\".format(message.chat.id)].cites_[bib_ref.label] = bib_ref\r\n bib_add_inbook(users[\"{0}\".format(message.chat.id)].bib_, bib_ref)\r\n\r\n bot.send_message(message.chat.id, '✅ Заполнили данные для главы из книги')\r\n bot.send_message(message.chat.id, 'Требуется ли еще добавить источник?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, bib_is_change)\r\n\r\ndef bib_set_series(message, bib_ref):\r\n if message.text != '0':\r\n bib_ref.series = message.text\r\n else:\r\n bib_ref.series = ''\r\n\r\n users[\"{0}\".format(message.chat.id)].cites_[bib_ref.label] = bib_ref\r\n bib_add_book(users[\"{0}\".format(message.chat.id)].bib_, bib_ref)\r\n\r\n bot.send_message(message.chat.id, '✅ Заполнили данные для книги')\r\n bot.send_message(message.chat.id, 'Требуется ли еще добавить источник?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, bib_is_change)\r\n\r\ndef bib_set_url(message, bib_ref):\r\n bib_ref.url = message.text\r\n\r\n bot.send_message(message.chat.id, \r\n '⚠️ Введите Дату обращения к источнику\\n\\n'\\\r\n 'Введите дату в формате ГОД-МЕСЯЦ-ДЕНЬ\\n\\n'\r\n 'Например: 2013-05-16', \r\n parse_mode='HTML')\r\n \r\n bot.register_next_step_handler(message, bib_set_urldate, bib_ref)\r\n\r\ndef bib_set_urldate(message, bib_ref):\r\n bib_ref.urldate = message.text\r\n\r\n users[\"{0}\".format(message.chat.id)].cites_[bib_ref.label] = bib_ref\r\n bib_add_online(users[\"{0}\".format(message.chat.id)].bib_, bib_ref)\r\n\r\n bot.send_message(message.chat.id, '✅ Заполнили данные для онлайн источника')\r\n bot.send_message(message.chat.id, 'Требуется ли еще добавить источник?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, bib_is_change)\r\n\r\ndef bib_is_change(message):\r\n if message.text == 'Да':\r\n bot.send_message(message.chat.id, '...', reply_markup=types.ReplyKeyboardRemove())\r\n bot.send_message(message.chat.id, '⚠️ Выберите, на какой тип какого источника хотите сослаться', \r\n reply_markup=bib_inline_kb())\r\n elif message.text == 'Нет':\r\n users[\"{0}\".format(message.chat.id)].bib_.close()\r\n users[\"{0}\".format(message.chat.id)].triggered_len = len(users[\"{0}\".format(message.chat.id)].cites_)\r\n bot.send_message(message.chat.id, '.bib сгенерирован')\r\n\r\n bot.send_message(message.chat.id, '✅ Заполнили библиографию', reply_markup=types.ReplyKeyboardRemove())\r\n bot.send_message(message.chat.id, '✅ Переходим к заполнению основного документа')\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить Аннотацию', parse_mode='HTML')\r\n bot.send_message(message.chat.id, \r\n '❔ Подсказка\\n\\n'\\\r\n 'По смыслу, аннотация это очень краткий пересказ вашей работы, '\\\r\n 'из которого релевантный человек должен быть способен понять, '\\\r\n 'что вы делали идейно. Она обычно описывает постановку задачи '\\\r\n 'и основные результаты работы в достаточно неформальной формулировке.\\n\\n'\\\r\n 'Объем до 2000 знаков', parse_mode='HTML')\r\n set_annot(message)\r\n\r\n else:\r\n bot.send_message(message.chat.id, '❗️ Выберите ответ на кнопке')\r\n bot.send_message(message.chat.id, 'Требуется ли еще добавить источник?', reply_markup=start_reply_kb())\r\n bot.register_next_step_handler(message, bib_is_change)\r\n \r\ndef bot_add_bib(message):\r\n users[\"{0}\".format(message.chat.id)].bib_ = make_bib_file(users[\"{0}\".format(message.chat.id)].bib_path)\r\n bot.send_message(message.chat.id, 'ℹ️ Теперь вам предстоит заполнить Библиографию', parse_mode='HTML')\r\n\r\n bot.send_message(message.chat.id, '⚠️ Выберите, на какой тип какого источника хотите сослаться', reply_markup=bib_inline_kb())\r\n\r\n\r\nbot.polling()\r\n", "repo_name": "avabramovv/MJ-Bot", "sub_path": "app/bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 53080, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "telebot.TeleBot", "line_number": 12, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 18, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 18, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 19, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 19, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 20, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 20, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 27, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 27, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 28, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 28, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 29, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 29, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 184, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 184, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 185, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 185, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 186, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 186, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 187, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 187, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 190, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 190, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 193, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 193, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 219, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 219, "usage_type": "name"}, {"api_name": "entities_handler.only_quots", "line_number": 223, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 396, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 396, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 397, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 397, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 398, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 398, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 417, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 417, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 423, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 423, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 427, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 427, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 458, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 458, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 461, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 461, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 468, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 468, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 471, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 471, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 480, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 480, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 526, "usage_type": "call"}, {"api_name": "entities_handler.only_quots", "line_number": 601, "usage_type": "call"}, {"api_name": "entities_handler.ent2latex", "line_number": 601, "usage_type": "call"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 634, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 634, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 635, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 635, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 636, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 636, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 637, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 637, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 638, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 638, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 639, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 639, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 815, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 815, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 823, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 823, "usage_type": "name"}]} +{"seq_id": "71548714606", "text": "from typing import Optional\n\nimport cv2\n\nimport glfw\nimport imgui\n\nfrom app.core import VideoCaptureCVStream\nfrom app.core.tracker import TargetManager\n\nfrom app.core.autopilot import Port\nfrom app.core.autopilot.fg_controller import FGController\n\nfrom app.core.autopilot.fg_brain import BrainBase\nfrom app.core.autopilot.fg_brain import ManualBrain\nfrom app.core.autopilot.fg_brain import AutopilotBrain\nfrom app.core.autopilot.fg_brain import TrackingAutopilotBrain\n\nfrom app.gui import ImGuiApp\n\nfrom app.gui.objects.windows.control_windows import UserWindow\nfrom app.gui.objects.windows.image_windows import ZoomImageWindow\nfrom app.gui.objects.windows.image_windows import TrackerImageWindow\n\n\nclass FGApp(ImGuiApp):\n def __init__(self, window_width, window_height, fullscreen):\n super().__init__(window_width, window_height, fullscreen)\n\n self._video_capture = VideoCaptureCVStream(src=2)\n\n #self._target_manager = TargetManager()\n self._is_tracking = False\n\n self._brains = {\n BrainBase.BrainType.MANUAL: ManualBrain(),\n BrainBase.BrainType.AUTOPILOT: AutopilotBrain(),\n BrainBase.BrainType.TRACKING: TrackingAutopilotBrain()\n }\n self._brain = BrainBase()\n\n self._controller: Optional[FGController] = None\n\n self._settings_window = UserWindow(self._on_connect_clicked, self._on_stop_clicked, self._on_brain_changed)\n\n self._image_window = ZoomImageWindow()\n\n def _terminate(self):\n super()._terminate()\n\n self._video_capture.stop()\n\n if self._controller:\n self._controller.stop()\n\n def _on_connect_clicked(self,\n host: str,\n fdm_out_port: int, fdm_in_port: int,\n ctrls_out_port: int, ctrls_in_port: int):\n\n if type(self._brain) is BrainBase:\n self._brain = self._brains[BrainBase.BrainType.MANUAL]\n \n if self._controller:\n self._controller.stop()\n\n self._controller = FGController(self._brain)\n self._controller.connect(host=host,\n fdm_port=Port(fdm_out_port, fdm_in_port),\n ctrls_port=Port(ctrls_out_port, ctrls_in_port),\n disconnect_callback=self._disconnect_callback)\n self._controller.start()\n\n if isinstance(self._brain, TrackingAutopilotBrain):\n self._image_window = TrackerImageWindow(self._on_roi_selected)\n else:\n self._image_window = ZoomImageWindow()\n \n def _disconnect_callback(self, disconnect):\n if disconnect:\n print(\"=====================+NO CONNECTION==========================\")\n\n def _on_stop_clicked(self):\n if self._controller:\n self._controller.stop()\n\n def _on_brain_changed(self, brain_type):\n self._brain = self._brains[brain_type]\n self._settings_window.set_brain(self._brain)\n\n def _on_roi_selected(self, selected_roi):\n if not isinstance(self._brain, TrackingAutopilotBrain):\n return\n\n if not selected_roi:\n return\n\n grabbed, frame = self._video_capture.read()\n if grabbed:\n self._target_manager.init_tracker(frame, selected_roi)\n self._brain.set_target_location(self._target_manager.target_location)\n\n def _update_target_manager(self, frame):\n if not isinstance(self._brain, TrackingAutopilotBrain):\n return\n\n if self._target_manager.is_tracker_initialized:\n score, roi = self._target_manager.update_tracker(frame)\n\n if score < 0.6 and self._is_tracking:\n self._toggle_tracking()\n return\n\n if self._is_tracking:\n self._brain.set_object_bbox(roi)\n\n self._image_window.selected_roi = roi\n\n def _update_image_window(self, frame):\n rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n self._image_window.upload_image(rgb_frame)\n\n def _toggle_tracking(self):\n if not isinstance(self._brain, TrackingAutopilotBrain):\n return\n if not isinstance(self._image_window, TrackerImageWindow):\n return\n\n if self._target_manager.is_tracker_initialized:\n self._is_tracking = not self._is_tracking\n\n if self._is_tracking:\n self._image_window.set_bbox_color(self._image_window.TRACKING_BBOX_COLOR)\n else:\n self._brain.set_object_bbox(None)\n self._image_window.set_bbox_color(self._image_window.DEFAULT_BBOX_COLOR)\n else:\n print(\"Tracker is not initialized.\")\n\n def _cancel_tracking(self):\n if not isinstance(self._brain, TrackingAutopilotBrain):\n return\n if not isinstance(self._image_window, TrackerImageWindow):\n return\n\n self._is_tracking = False\n\n self._target_manager.reset_tracker()\n\n self._brain.set_object_bbox(None)\n\n self._image_window.reset_roi()\n\n def keyboard_input(self):\n super().keyboard_input()\n\n if isinstance(self._brain, TrackingAutopilotBrain):\n if imgui.is_key_pressed(glfw.KEY_T):\n self._toggle_tracking()\n\n if imgui.is_key_pressed(glfw.KEY_C):\n self._cancel_tracking()\n\n def draw_content(self):\n display_size = imgui.get_io().display_size\n\n image_window_width_scale = 0.8\n self._image_window.size = imgui.Vec2(display_size[0] * image_window_width_scale, display_size[1])\n self._settings_window.position = imgui.Vec2(display_size[0] * image_window_width_scale, 0)\n self._settings_window.size = imgui.Vec2(display_size[0] * (1 - image_window_width_scale), display_size[1])\n\n grabbed, frame = self._video_capture.read()\n if grabbed:\n #self._update_target_manager(frame)\n self._update_image_window(frame)\n self._image_window.draw()\n\n self._settings_window.draw()\n\n\nif __name__ == \"__main__\":\n app = FGApp(1280, 720, fullscreen=False)\n app.run()\n", "repo_name": "maratsher/Flightgear-controller", "sub_path": "app/fg_app.py", "file_name": "fg_app.py", "file_ext": "py", "file_size_in_byte": 6157, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "app.gui.ImGuiApp", "line_number": 26, "usage_type": "name"}, {"api_name": "app.core.VideoCaptureCVStream", "line_number": 30, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase.BrainType", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase", "line_number": 36, "usage_type": "name"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase.BrainType", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase", "line_number": 37, "usage_type": "name"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase.BrainType", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase", "line_number": 38, "usage_type": "name"}, {"api_name": "app.core.autopilot.fg_brain.ManualBrain", "line_number": 36, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.AutopilotBrain", "line_number": 37, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 38, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 42, "usage_type": "name"}, {"api_name": "app.core.autopilot.fg_controller.FGController", "line_number": 42, "usage_type": "name"}, {"api_name": "app.gui.objects.windows.control_windows.UserWindow", "line_number": 44, "usage_type": "call"}, {"api_name": "app.gui.objects.windows.image_windows.ZoomImageWindow", "line_number": 46, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase", "line_number": 61, "usage_type": "name"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase.BrainType", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app.core.autopilot.fg_brain.BrainBase", "line_number": 62, "usage_type": "name"}, {"api_name": "app.core.autopilot.fg_controller.FGController", "line_number": 67, "usage_type": "call"}, {"api_name": "app.core.autopilot.Port", "line_number": 69, "usage_type": "call"}, {"api_name": "app.core.autopilot.Port", "line_number": 70, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 74, "usage_type": "argument"}, {"api_name": "app.gui.objects.windows.image_windows.TrackerImageWindow", "line_number": 75, "usage_type": "call"}, {"api_name": "app.gui.objects.windows.image_windows.ZoomImageWindow", "line_number": 77, "usage_type": "call"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 92, "usage_type": "argument"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 104, "usage_type": "argument"}, {"api_name": "cv2.cvtColor", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 120, "usage_type": "attribute"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 124, "usage_type": "argument"}, {"api_name": "app.gui.objects.windows.image_windows.TrackerImageWindow", "line_number": 126, "usage_type": "argument"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 141, "usage_type": "argument"}, {"api_name": "app.gui.objects.windows.image_windows.TrackerImageWindow", "line_number": 143, "usage_type": "argument"}, {"api_name": "app.core.autopilot.fg_brain.TrackingAutopilotBrain", "line_number": 157, "usage_type": "argument"}, {"api_name": "imgui.is_key_pressed", "line_number": 158, "usage_type": "call"}, {"api_name": "glfw.KEY_T", "line_number": 158, "usage_type": "attribute"}, {"api_name": "imgui.is_key_pressed", "line_number": 161, "usage_type": "call"}, {"api_name": "glfw.KEY_C", "line_number": 161, "usage_type": "attribute"}, {"api_name": "imgui.get_io", "line_number": 165, "usage_type": "call"}, {"api_name": "imgui.Vec2", "line_number": 168, "usage_type": "call"}, {"api_name": "imgui.Vec2", "line_number": 169, "usage_type": "call"}, {"api_name": "imgui.Vec2", "line_number": 170, "usage_type": "call"}, {"api_name": "app.core", "line_number": 182, "usage_type": "name"}, {"api_name": "app.core.run", "line_number": 183, "usage_type": "call"}, {"api_name": "app.core", "line_number": 183, "usage_type": "name"}]} +{"seq_id": "34530505576", "text": "from ruffus import *\nimport yaml\nfrom joblib import Parallel, delayed\nimport json\nimport datetime\nimport secrets\nfrom argparse import ArgumentParser\nimport tqdm\nimport math\nimport numpy as np\nfrom dismal_random_sims.simulate import Simulation\nfrom dismal_random_sims.functions import random_param_value\nfrom dismal.models import three_epoch_gim, three_epoch_iim, three_epoch_sec, three_epoch_iso\n\n\n# new pipeline design:\n# 1. Read YAML\n# 2. @transform YAML into random parameter values for n replicates; save as npy where each row is a parameter set\n# 3. @split into each simulation replicate, saving a compressed simulation.npz ON SCRATCH containing [params, s1, s2, s3]\n# 4. @transform each replicate into a compressed modelled.npz containing [true_params, inferred_params, s1, s2, s3, expected_s1, expected_s2, expected_s3]\n# 5. @merge all outputs into single \n#\n# cleanup: delete files on scratch\n\nparser = ArgumentParser()\nparser.add_argument(\"--yaml-spec\", help=\"YAML specifying parameters for random simulations\")\nparser.add_argument(\"--threads\", help=\"Number of threads to use; use -1 for all threads. Defaults to 1 (no parallelisation).\", default=1, type=int)\nparser.add_argument(\"--simulation-id\", help=\"Simulation ID, either to control name of simulation or to rerun failed sim. Default is {DATE}_{UNIQUE_HEX}\", default=None)\nargs = parser.parse_args()\n\nif args.simulation_id is None:\n date = datetime.datetime.now().strftime(\"%Y%m%d\")\n unique_hex = secrets.token_hex(5)\n SIMULATION_ID = f\"{date}_{unique_hex}\"\nelse:\n SIMULATION_ID = args.simulation_id\n\n@split(args.yaml_spec, [f\"{SIMULATION_ID}.s1.npz\",\n f\"{SIMULATION_ID}.s2.npz\",\n f\"{SIMULATION_ID}.s3.npz\",\n f\"{SIMULATION_ID}.sim_params.json\"], \n args.threads)\ndef simulate(yaml_spec, outfiles, threads):\n \n with open(yaml_spec, \"r\") as f:\n yaml_spec = yaml.safe_load(f)\n\n def _sim_wrapper(yaml_spec):\n\n block_thetas = [random_param_value(yaml_spec[\"thetas\"][\"distribution\"], distr_params) \n for distr_params in \n [yaml_spec[\"thetas\"][\"epoch1\"][\"pop1\"], yaml_spec[\"thetas\"][\"epoch1\"][\"pop2\"],\n yaml_spec[\"thetas\"][\"epoch2\"][\"pop1\"], yaml_spec[\"thetas\"][\"epoch2\"][\"pop2\"],\n yaml_spec[\"thetas\"][\"epoch3\"][\"pop1\"]]]\n \n epoch_durations = [random_param_value(yaml_spec[\"epoch_durations\"][\"distribution\"],\n yaml_spec[\"epoch_durations\"][\"epoch1\"]),\n random_param_value(yaml_spec[\"epoch_durations\"][\"distribution\"],\n yaml_spec[\"epoch_durations\"][\"epoch2\"])]\n \n migration_rates = [random_param_value(yaml_spec[\"migration_rates\"][\"distribution\"], distr_params) \n for distr_params in \n [yaml_spec[\"migration_rates\"][\"epoch1\"][\"rate1\"], yaml_spec[\"migration_rates\"][\"epoch1\"][\"rate2\"],\n yaml_spec[\"migration_rates\"][\"epoch2\"][\"rate1\"], yaml_spec[\"migration_rates\"][\"epoch2\"][\"rate2\"]]]\n\n return Simulation(block_thetas, epoch_durations, migration_rates,\n yaml_spec[\"blocklen\"], yaml_spec[\"mutation_rate\"], \n blocks_per_state=yaml_spec[\"blocks_per_state\"], recombination_rate=yaml_spec[\"recombination_rate\"])\n\n sims = Parallel(n_jobs=threads, prefer=\"threads\")(\n delayed(_sim_wrapper)(yaml_spec) \n for _ in tqdm.tqdm(range(yaml_spec[\"num_replicates\"])))\n \n np.savez(outfiles[0], [sim.s1 for sim in sims])\n np.savez(outfiles[1], [sim.s2 for sim in sims])\n np.savez(outfiles[2], [sim.s3 for sim in sims])\n\n simulated_params = [{\"block_thetas\": list(sim.block_thetas),\n \"epoch_durations\": list(sim.epoch_durations),\n \"migration_rates_fraction\": list(sim.migration_rates_fraction),\n \"recombination_rate\": sim.recombination_rate} for sim in sims]\n \n with open(outfiles[3], \"w\") as f:\n json.dump(simulated_params, f)\n \n\n@merge(simulate,\n f\"{SIMULATION_ID}_results.json\", args.yaml_spec, args.threads)\ndef infer(infiles, outfile, yaml_spec, threads):\n\n with open(yaml_spec, \"r\") as f:\n yaml_spec = yaml.safe_load(f)\n\n model_type = yaml_spec[\"infer_with\"]\n blocklen = yaml_spec[\"blocklen\"]\n\n if model_type.lower() == \"iso\":\n mod = three_epoch_iso()\n elif model_type.lower() == \"iim\":\n mod = three_epoch_iim()\n elif model_type.lower() == \"sec\":\n mod = three_epoch_sec()\n elif model_type.lower() == \"gim\":\n mod = three_epoch_gim()\n else:\n raise ValueError(f\"{model_type} not recognised as valid model\")\n \n s1s, s2s, s3s = [np.load(infiles[i])[\"arr_0\"] for i in range(3)]\n assert s1s.shape[0] == s2s.shape[0] == s3s.shape[0]\n assert s1s.shape[0] == yaml_spec[\"num_replicates\"]\n\n try:\n\n if threads == 1:\n mods = [mod.fit(s1s[i], s2s[i], s3s[i], \n yaml_spec[\"blocklen\"], None, None, None, False) \n for i in tqdm.tqdm(range(yaml_spec[\"num_replicates\"]))]\n else:\n mods = Parallel(n_jobs=threads, prefer=\"threads\")(\n delayed(mod.fit)(s1s[i], s2s[i], s3s[i], \n yaml_spec[\"blocklen\"], None, None, None, False) \n for i in tqdm.tqdm(range(yaml_spec[\"num_replicates\"])))\n \n except Exception:\n pass\n \n results = [{\n \"thetas_block\": list(mod.thetas_block),\n \"thetas_site\": list(mod.thetas_site),\n \"migration_rates\": list(mod.migration_rates),\n \"epoch_durations\": list(mod.ts_2n),\n \"fitted_s1\": list(mod.fitted_s1),\n \"fitted_s2\": list(mod.fitted_s2),\n \"fitted_s3\": list(mod.fitted_s3),\n \"optimiser\": mod.optimiser,\n \"negll\": mod.negll,\n \"claic\": mod.claic\n } for mod in mods]\n \n with open(outfile, \"w\") as f:\n json.dump(results, f)\n\n\ndef main():\n pipeline_run()\n\nif __name__ == \"__main__\":\n main()\n \n\n# @merge\n# def analyse_results()", "repo_name": "simonharnqvist/dismal-random-sims", "sub_path": "dismal_random_sims/random_simulation_pipeline.py", "file_name": "random_simulation_pipeline.py", "file_ext": "py", "file_size_in_byte": 6173, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "attribute"}, {"api_name": "secrets.token_hex", "line_number": 33, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 46, "usage_type": "call"}, {"api_name": "dismal_random_sims.functions.random_param_value", "line_number": 50, "usage_type": "call"}, {"api_name": "dismal_random_sims.functions.random_param_value", "line_number": 56, "usage_type": "call"}, {"api_name": "dismal_random_sims.functions.random_param_value", "line_number": 58, "usage_type": "call"}, {"api_name": "dismal_random_sims.functions.random_param_value", "line_number": 61, "usage_type": "call"}, {"api_name": "dismal_random_sims.simulate.Simulation", "line_number": 66, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 70, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 71, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 76, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 84, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 92, "usage_type": "call"}, {"api_name": "dismal.models.three_epoch_iso", "line_number": 98, "usage_type": "call"}, {"api_name": "dismal.models.three_epoch_iim", "line_number": 100, "usage_type": "call"}, {"api_name": "dismal.models.three_epoch_sec", "line_number": 102, "usage_type": "call"}, {"api_name": "dismal.models.three_epoch_gim", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 108, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 117, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 119, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 120, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 122, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "15580305621", "text": "import pygame as pg\nfrom math import sin,cos,floor,ceil,pi,factorial,pow,sqrt,tan\n\n\npressed_letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o','p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'COMMA', '1', '2', '3','4', '5', '6', '7', '8', '9', '0']\npressed = []\nlast_letters = []\n\nfor i in pressed_letters:\n pressed.append(False)\n last_letters.append(False)\n\n\ndef update_pressed():\n global last_letters\n global pressed\n global pressed_letters\n for x, i in enumerate(pressed_letters):\n test = eval('pg.K_' + i)\n press = key_down(test)\n if press and (not last_letters[x]):\n pressed[x] = True\n else:\n pressed[x] = False\n if press:\n last_letters[x] = True\n else:\n last_letters[x] = False\n\n\ndef key_press(key: str):\n return pressed[pressed_letters.index(key)]\n\n\ndef key_down(key: pg.key) -> bool:\n return pg.key.get_pressed()[key]\n\ndef draw_rect(pos,width,height,color):\n pg.draw.rect(screen,color,pg.Rect(pos[0]+(sw/2),sh-(pos[1]+(sh/2)),width,height))\n\n\ndef equation_red(x,y):\n '''\n Output shold from 0 - 255\n '''\n try:\n return (0.003*(x**2)+0.2*x+100)*sqrt(abs(y)/10)\n except:\n return 0\n\n\ndef equation_green(x,y):\n '''\n Output shold from 0 - 255\n '''\n return tan(x/100)*cos(y/10)\n\n\ndef equation_blue(x,y):\n '''\n Output shold from 0 - 255\n '''\n return sin((x+y)/1000)*sin((x*y)/10000)*400\n\n\npg.init()\n\n\nsw = 1920\nsh = 1080\nscreen = pg.display.set_mode((sw, sh), pg.FULLSCREEN)\n\n\n\n\nfor ix in range(1920):\n x = ix-960\n for iy in range(1080):\n y = iy - 540\n red = equation_red(x,y)\n green = equation_green(x,y)\n blue = equation_blue(x,y)\n color = (int(red)%255,int(green)%255,int(blue)%255)\n draw_rect((x,y),1,1,color)\n\npg.display.update()\n\n\n\nrunning = True\nwhile running:\n events = pg.event.get()\n for event in events:\n if event.type == pg.QUIT:\n running = False\n update_pressed()\n\n if key_down(pg.K_BACKSPACE):\n running = False\npg.quit()", "repo_name": "des54321/Python_Project", "sub_path": "_Simulations/grid stuff/cool_images.py", "file_name": "cool_images.py", "file_ext": "py", "file_size_in_byte": 2125, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.key", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 39, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 47, "usage_type": "call"}, {"api_name": "math.tan", "line_number": 56, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 56, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.FULLSCREEN", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.K_BACKSPACE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "3157651409", "text": "from flask import Blueprint, render_template, send_from_directory, jsonify\n\n\ndownload = Blueprint('download', __name__, template_folder='templates')\n\n\n@download.route('/download/')\ndef download_all():\n return render_template('download/index.html')\n\n\n@download.route('/download/sample/')\ndef download_sample():\n try:\n return send_from_directory('static/sample/',\n 'chorale_F.zip',\n as_attachment=True)\n except FileNotFoundError:\n return jsonify({'error': 'sample files not available'}), 500\n", "repo_name": "ruixuantan/FourPartsWeb", "sub_path": "fourpartsweb/blueprints/download/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Blueprint", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "34319278612", "text": "from keras.models import Model\nfrom keras.layers import Input, Conv2D, MaxPooling2D, concatenate, Conv2DTranspose, Dropout\n\nfrom multiclass_semantic_segmentation.math_utils import jacard_coef, jacard_coef_loss\n\n\ndef multi_unet_model(config, input_sz: tuple[int, int, int]):\n # Build the model\n inputs = Input(input_sz)\n s = inputs\n\n # Contraction path\n c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)\n c1 = Dropout(0.1)(c1)\n c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)\n p1 = MaxPooling2D((2, 2))(c1)\n\n c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)\n c2 = Dropout(0.1)(c2)\n c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)\n p2 = MaxPooling2D((2, 2))(c2)\n\n c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)\n c3 = Dropout(0.2)(c3)\n c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)\n p3 = MaxPooling2D((2, 2))(c3)\n\n c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)\n c4 = Dropout(0.2)(c4)\n c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)\n p4 = MaxPooling2D(pool_size=(2, 2))(c4)\n\n c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)\n c5 = Dropout(0.3)(c5)\n c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)\n\n # Expansive path\n u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)\n u6 = concatenate([u6, c4])\n c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)\n c6 = Dropout(0.2)(c6)\n c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)\n\n u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)\n u7 = concatenate([u7, c3])\n c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)\n c7 = Dropout(0.2)(c7)\n c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)\n\n u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)\n u8 = concatenate([u8, c2])\n c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)\n c8 = Dropout(0.1)(c8)\n c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)\n\n u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)\n u9 = concatenate([u9, c1], axis=3)\n c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)\n c9 = Dropout(0.1)(c9)\n c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)\n\n outputs = Conv2D(config.n_classes, (1, 1), activation='softmax')(c9)\n\n model = Model(inputs=[inputs], outputs=[outputs])\n\n model.compile(\n optimizer=config.optimizer,\n loss=[jacard_coef_loss],\n metrics=[jacard_coef])\n return model\n", "repo_name": "michalgonet/ML_imaging", "sub_path": "multiclass_semantic_segmentation/unet_structure_iou.py", "file_name": "unet_structure_iou.py", "file_ext": "py", "file_size_in_byte": 3251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "keras.layers.Input", "line_number": 9, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 64, "usage_type": "call"}, {"api_name": "multiclass_semantic_segmentation.math_utils.jacard_coef_loss", "line_number": 68, "usage_type": "name"}, {"api_name": "multiclass_semantic_segmentation.math_utils.jacard_coef", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "38236868274", "text": "from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('',views.index_view,name = \"game_index\"),\n path('/detail/',views.DetailGameView.as_view(),name ='detail-game'),\n path('create/',views.CreateGameView.as_view(),name = 'create-game'),\n path('/delete/',views.DeleteGameView.as_view(),name = 'delete-game'),\n path('/update/',views.UpdateGameView.as_view(),name = 'update-game'),\n path('/review/',views.CreateReviewView.as_view(),name = 'game_review'),\n path('search/',views.post_search,name = 'game_search'),\n]", "repo_name": "okunoo/myreviewsite", "sub_path": "myreviewsite/game/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "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": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "1838077292", "text": "\r\nimport pygame\r\nfrom multiprocessing import Queue\r\nimport time\r\nimport threading\r\nimport random\r\nfrom os.path import abspath, expanduser\r\nimport nltk\r\n\r\n\r\nclass Image:\r\n def __init__(self):\r\n pygame.init()\r\n gameLoop = False\r\n #self.clock = pygame.time.Clock()\r\n self.myfont = pygame.font.SysFont(\"Helvetica\", 30)\r\n\r\n # display window properties.\r\n filepath = abspath(expanduser(\"~/\") + \"/Documents/Studies/NLP/Project/pyGameDialog/images/\")\r\n display_width = 1265\r\n display_height= 550\r\n self.gameDisplay = pygame.display.set_mode((display_width, display_height))\r\n #pygame.display.set_caption(\"Find The Treasure\")\r\n self.imageBackground= pygame.image.load(filepath+\"/backWithDialog.png\")\r\n self.imageBackground=pygame.transform.scale(self.imageBackground, (1265,550))\r\n self.herodown=pygame.image.load(filepath+\"/herodown.png\")\r\n self.herodown=pygame.transform.scale(self.herodown, (40,40))\r\n self.lightoff= pygame.image.load(filepath+\"/lightcauldronoff.png\")\r\n self.lighton= pygame.image.load(filepath+\"/lightcauldronon.png\")\r\n self.kapow= pygame.image.load(filepath+\"/kapow.png\")\r\n self.kapow=pygame.transform.scale(self.kapow, (60,50))\r\n self.ctreasure=pygame.image.load(filepath+\"/closedTreasure.png\")\r\n self.rockwithoutsword= pygame.image.load(filepath+\"/rockwithoutsword.png\")\r\n self.swordrock=pygame.image.load(filepath+\"/swordRock.png\")\r\n self.swordrock=pygame.transform.scale(self.swordrock, (79,73))\r\n self.armor=pygame.image.load(filepath+\"/armor1.png\")\r\n self.armor=pygame.transform.scale(self.armor, (79,73))\r\n self.dragon=pygame.image.load(filepath+\"/dragon.png\")\r\n self.dragonback=pygame.image.load(filepath+\"/dragon.png\")\r\n self.dragonbackback=pygame.image.load(filepath+\"/dragonCry1.png\")\r\n self.dragonbackfront=pygame.image.load(filepath+\"/dragonCry2.png\")\r\n self.hammer=pygame.image.load(filepath+\"/hammer.png\")\r\n self.hammer=pygame.transform.scale(self.hammer,(100,100))\r\n self.key=pygame.image.load(filepath+\"/key5.png\")\r\n self.scratchwall=pygame.image.load(filepath+\"/scratchwall.png\")\r\n self.scratchwall=pygame.transform.scale(self.scratchwall,(62,71))\r\n self.red1 = pygame.image.load(filepath+\"/red1.png\")\r\n self.red2 = pygame.image.load(filepath+\"/red2.png\")\r\n def displayObject(self, image, x, y):\r\n \"\"\"This method is used to load the image on the screen.\"\"\"\r\n self.gameDisplay.blit(image, (x,y))\r\n def image(self):\r\n gameLoop=False\r\n while not gameLoop:\r\n for event in pygame.event.get():\r\n #print(event)\r\n if event.type == pygame.QUIT:\r\n gameLoop = True\r\n\r\n self.gameDisplay.blit(self.imageBackground,(0,0))\r\n self.displayObject(self.herodown,320,490)\r\n self.displayObject(self.hammer,440,485)\r\n self.displayObject(self.scratchwall,432,427)\r\n self.displayObject(self.armor,420,40)\r\n self.displayObject(self.swordrock,440,90)\r\n self.displayObject(self.dragon,580,200)\r\n self.displayObject(self.ctreasure,250,220)\r\n self.displayObject(self.key,540,180)\r\n self.displayObject(self.lightoff,530,390)\r\n self.displayObject(self.lighton,250,330)\r\n self.displayObject(self.lighton,250,60)\r\n self.displayObject(self.lighton,640,330)\r\n self.displayObject(self.lighton,640,60)\r\n self.displayObject(self.kapow,432,437)\r\n self.displayObject(self.kapow,540,220)\r\n self.text_y1 = 0\r\n self.text_y2 = 0\r\n self.white = (255, 255, 255)\r\n self.text_array = [\"pick up the sword\", \"use the hammer to break the door\", \"kill the dragon\"]\r\n if True:\r\n for item in self.text_array:\r\n word_list = nltk.word_tokenize(item)\r\n word_count = 0;\r\n display_text = \"\"\r\n for word in word_list:\r\n word_count = word_count + 1\r\n display_text = display_text + \" \" + word\r\n if word_count == 5:\r\n text = self.myfont.render(display_text, 1, self.white)\r\n self.gameDisplay.blit(text, (940, (30 + self.text_y1 + self.text_y2)))\r\n word_count = 0\r\n display_text = \"\"\r\n self.text_y1 = self.text_y1 + 25\r\n if word_count > 0:\r\n text = self.myfont.render(display_text, 1, self.white)\r\n self.gameDisplay.blit(text, (940, (30 + self.text_y1 + self.text_y2)))\r\n self.text_y1 = self.text_y1 + 25\r\n display_text = \"\"\r\n word_count = 0\r\n self.text_y2 = self.text_y2 + 10\r\n self.displayObject(self.red2, 1030, 400)\r\n pygame.display.update()\r\n\r\n pygame.quit()\r\n quit()\r\n\r\nif __name__ == \"__main__\":\r\n g = Image()\r\n g.image()\r\n", "repo_name": "rohan-pat/pyGameDialog", "sub_path": "source/fGame.py", "file_name": "fGame.py", "file_ext": "py", "file_size_in_byte": 5259, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.init", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.image", "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.transform.scale", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 41, "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.transform.scale", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 44, "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": "pygame.transform.scale", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 48, "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": 57, "usage_type": "attribute"}, {"api_name": "nltk.word_tokenize", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "42373222098", "text": "import sys\r\nimport pygame\r\n\r\n\r\ndef show_text(screen, pos, text, color, font_bold=False, font_size=60, font_italic=False):\r\n # 获取系统字体，设置字体的大小\r\n current_font = pygame.font.SysFont('宋体', font_size)\r\n # 设置是否加粗\r\n current_font.set_bold(font_bold)\r\n # 设置是否斜体\r\n current_font.set_italic(font_italic)\r\n # 文字内容(反锯齿)\r\n text_fmt = current_font.render(text, True, color)\r\n # 绘制字体\r\n screen.blit(text_fmt, pos)\r\n\r\n\r\ndef check_event(screen, settings, snake, food):\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n sys.exit()\r\n elif event.type == pygame.KEYDOWN:\r\n snake.change_direction(event.key)\r\n if event.key == pygame.K_p and settings.run: # 按p暂停\r\n if not settings.pause: # 游戏暂停\r\n settings.pause = not settings.pause\r\n show_text(screen, (165, 200), 'GAME PAUSE!', (227, 29, 18))\r\n show_text(screen, (135, 260), \"press 'P' to start...\", (0, 0, 22))\r\n pygame.display.flip() # 让绘制的东西显示在屏幕上\r\n else: # 游戏继续\r\n settings.reset()\r\n elif event.key == pygame.K_SPACE and not settings.run: # 死后按space重新开始\r\n replay(screen, settings, snake, food)\r\n\r\n\r\ndef get_result(screen):\r\n show_text(screen, (165, 200), 'GAME OVER!', (227, 29, 18))\r\n show_text(screen, (75, 260), \"press 'SPACE' to restart...\", (0, 0, 22))\r\n pygame.display.flip() # 让绘制的东西显示在屏幕上\r\n\r\n\r\ndef update_food(settings, snake, food):\r\n if food.rect in snake.body: # 食物被吃\r\n settings.scores += 100\r\n food.remove()\r\n snake.add_node()\r\n food.set()\r\n\r\n\r\ndef update_drawing(screen, snake, food):\r\n # 画蛇\r\n for rect in snake.body:\r\n pygame.draw.rect(screen, (20, 220, 39), rect, 0)\r\n # 画食物\r\n pygame.draw.rect(screen, (136, 0, 21), food.rect, 0)\r\n\r\n\r\ndef update_screen(screen, settings, snake, food):\r\n screen.fill((255, 255, 255)) # 填充颜色\r\n update_drawing(screen, snake, food) # 绘制蛇和食物\r\n # 显示分数\r\n show_text(screen, (25, 550), 'Scores:' + str(settings.scores), (223, 223, 223))\r\n # 暂停提示\r\n show_text(screen, (510, 10), '暂停:P', (223, 223, 223))\r\n pygame.display.flip() # 让绘制的东西显示在屏幕上\r\n\r\n\r\ndef replay(screen, settings, snake, food):\r\n # 重置游戏设置\r\n settings.run = True\r\n settings.is_dead = False\r\n # 重新绘制画面\r\n snake.draw()\r\n # 重置游戏统计信息\r\n settings.initialize()\r\n # 更新画面\r\n update_screen(screen, settings, snake, food)\r\n", "repo_name": "hide-in-cloud/python-test", "sub_path": "贪吃蛇/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2808, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.font.SysFont", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.K_p", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 53, "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.display.flip", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 65, "usage_type": "attribute"}]} +{"seq_id": "9274619955", "text": "from genericpath import exists\nimport cv2\nimport os\nimport PIL\nimport copy\nimport numpy as np\nimport torch\nfrom PIL import Image\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\n\nfrom detectron2.data import detection_utils as utils\nfrom detectron2.data import transforms as T\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.utils.visualizer import Visualizer, ColorMode, _create_text_labels\nimport albumentations as A\n\n\nimport pickle\nimport pandas as pd\n\n\n\n\n\nclass Visualizer2 (Visualizer):\n \"\"\"\n Same as Visualize\n Implements draw_instances\n \"\"\"\n\n def __init__(self, img_rgb, metadata=None, scale=1.0,):\n super().__init__(img_rgb,metadata=metadata,scale=1.0)\n\n def draw_instances(self, instances):\n \"\"\"\n Draw instance-level prediction results on an image.\n\n Args:\n predictions (Instances): the output of an instance detection/segmentation\n model. Following fields will be used to draw:\n \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n boxes = instances.gt_boxes if instances.has(\"gt_boxes\") else None\n scores = instances.scores if instances.has(\"scores\") else None\n classes = instances.gt_classes.tolist() if instances.has(\"gt_classes\") else None\n labels = _create_text_labels(classes, scores, self.metadata.get(\"thing_classes\", None))\n keypoints = instances.pred_keypoints if instances.has(\"pred_keypoints\") else None\n\n masks = None\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"thing_colors\"):\n colors = [\n self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes\n ]\n alpha = 0.8\n else:\n colors = None\n alpha = 0.5\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(\n self._create_grayscale_image(\n (instances.pred_masks.any(dim=0) > 0).numpy()\n if instances.has(\"pred_masks\")\n else None\n )\n )\n alpha = 0.3\n\n self.overlay_instances(\n masks=masks,\n boxes=boxes,\n labels=labels,\n keypoints=keypoints,\n assigned_colors=colors,\n alpha=1.0,\n )\n return self.output\n\n\n\n\ndef load_paisley_dict(split):\n with open(f'datasets/paisley/paisley_{split}_dicts.pkl', 'rb') as f:\n dicts = pickle.load(f)\n return dicts\n\ndataset_dicts = load_paisley_dict('train')\nfor d in [\"train\", \"test\"]: \n if f'paisley_{d}' not in DatasetCatalog.keys():\n DatasetCatalog.register(\"paisley_\" + d, lambda d=d: load_paisley_dict(d))\n MetadataCatalog.get(\"paisley_\" + d).set(thing_classes=[\"Paisley\"])\n\npaisley_metadata = MetadataCatalog.get(\"paisley_train\")\n \n\ndef my_imshow(a):\n a = a.clip(0, 255).astype('uint8')\n # cv2 stores colors as BGR; convert to RGB\n if a.ndim == 3:\n if a.shape[2] == 4:\n a = cv2.cvtColor(a, cv2.COLOR_BGRA2RGBA)\n else:\n a = cv2.cvtColor(a, cv2.COLOR_BGR2RGB)\n a = PIL.Image.fromarray(a)\n return(a)\n\ndef build_augmentation(is_train):\n \"\"\"\n This is default augmentation in the detectron2 \n \"\"\"\n if is_train:\n min_size = (440, 472, 504, 536, 568, 600)\n max_size = 1000\n sample_style = \"choice\"\n else:\n min_size = 600\n max_size = 1000\n sample_style = \"choice\"\n \n #T.ResizeShortestEdge(min_size, max_size, sample_style)\n augmentation = [T.RandomRotation(angle=[0,25]), T.ResizeShortestEdge(min_size, max_size, sample_style) ]\n # augmentation = [A.HorizontalFlip(p=0.5)]\n # augmentation.append(\n # T.RandomFlip(\n # horizontal=1,\n # vertical=0,\n # )\n # )\n augmentation.extend([\n T.RandomBrightness(0.1, 2),\n T.RandomContrast(0.1, 4),\n T.RandomSaturation(0.1, 4),\n T.RandomFlip(prob=0.5, horizontal=True, vertical=False),\n T.RandomFlip(prob=0.5, horizontal=False, vertical=True)\n ])\n\n return augmentation\n\n\nsave_dir = 'Augmentation_checks_albumentation/'\nos.makedirs(save_dir, exist_ok=True)\n\naugs = build_augmentation(True)\nrecompute_boxes= False\n\n\nfor jj in range(3):\n for ii, dataset_dict_tmp in enumerate(dataset_dicts):\n dataset_dict = copy.deepcopy(dataset_dict_tmp) # it will be modified by code below\n fn = dataset_dict_tmp[\"image_id\"]\n # Plot the original image and annotations\n img1 = cv2.imread(dataset_dict_tmp[\"file_name\"])\n visualizer = Visualizer(img1[:, :, ::-1], metadata=paisley_metadata, scale=1)\n vis = visualizer.draw_dataset_dict(dataset_dict_tmp)\n img1 = vis.get_image()[:, :, ::-1]\n print(f'Original Image Size: {img1.shape} ')\n img1 = my_imshow(img1)\n \n # PArt of code from data_mapper in detectron2\n image = utils.read_image(dataset_dict[\"file_name\"], format='BGR') # returns image BGR numpy\n utils.check_image_size(dataset_dict, image)\n \n \n # Detectron 2 \n # aug_input = T.AugInput(image)\n # augs_list = T.AugmentationList(augs)\n # transforms = augs_list(aug_input) # inplace \n # image = aug_input.image\n # image_shape = image.shape[:2]\n \n # annos = [\n # utils.transform_instance_annotations(\n # obj, transforms, image_shape, keypoint_hflip_indices=None\n # )\n # for obj in dataset_dict.pop(\"annotations\")\n # if obj.get(\"iscrowd\", 0) == 0\n # ]\n \n # instances = utils.annotations_to_instances(\n # annos, image_shape, mask_format='polygon'\n # )\n \n \n # # FewX type\n image, transforms = T.apply_transform_gens(augs, image)\n image_shape = image.shape[:2]\n annos = [\n utils.transform_instance_annotations(\n obj, transforms, image_shape, keypoint_hflip_indices=None\n )\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n \n instances = utils.annotations_to_instances(\n annos, image_shape, mask_format='polygon'\n )\n\n v = Visualizer2(image[:, :, ::-1], metadata=paisley_metadata, scale=1)\n v = v.draw_instances(instances)\n img = v.get_image()[:, :, ::-1]\n print(f'Transformed Image Size: {img.shape} ')\n img = my_imshow(img)\n\n print(transforms)\n \n with open('transforms2.txt', 'a') as f:\n f.write(f'{jj}-{ii} \\t{transforms}\\n')\n \n \n ## Matplotlib\n fig, ax = plt.subplots(1,2)\n plt.setp(ax, xticklabels=[], yticklabels=[])\n ax[0].imshow(img1)\n ax[0].axis('off')\n ax[1].imshow(img)\n ax[1].axis('off') \n \n print('Done')\n \n #plt.title(transforms)\n plt.tight_layout()\n \n #plt.show() \n #print(dataset_dict_tmp[\"file_name\"])\n plt.savefig(f'{save_dir}{jj}-{ii}.png', bbox_inches='tight', pad_inches = 0, dpi = 300)\n plt.close()\n \n \n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n if recompute_boxes:\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n dataset_dict[\"instances\"] = utils.filter_empty_instances(instances)\n \n", "repo_name": "dips4717/design_motif_detection", "sub_path": "FewX/scripts/test_augmentations.py", "file_name": "test_augmentations.py", "file_ext": "py", "file_size_in_byte": 8049, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "matplotlib.use", "line_number": 10, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 27, "usage_type": "name"}, {"api_name": "detectron2.utils.visualizer._create_text_labels", "line_number": 51, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.ColorMode.SEGMENTATION", "line_number": 55, "usage_type": "attribute"}, {"api_name": "detectron2.utils.visualizer.ColorMode", "line_number": 55, "usage_type": "name"}, {"api_name": "detectron2.utils.visualizer.ColorMode.IMAGE_BW", "line_number": 64, "usage_type": "attribute"}, {"api_name": "detectron2.utils.visualizer.ColorMode", "line_number": 64, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 89, "usage_type": "call"}, {"api_name": "detectron2.data.DatasetCatalog.keys", "line_number": 94, "usage_type": "call"}, {"api_name": "detectron2.data.DatasetCatalog", "line_number": 94, "usage_type": "name"}, {"api_name": "detectron2.data.DatasetCatalog.register", "line_number": 95, "usage_type": "call"}, {"api_name": "detectron2.data.DatasetCatalog", "line_number": 95, "usage_type": "name"}, {"api_name": "detectron2.data.MetadataCatalog.get", "line_number": 96, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog", "line_number": 96, "usage_type": "name"}, {"api_name": "detectron2.data.MetadataCatalog.get", "line_number": 98, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog", "line_number": 98, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGRA2RGBA", "line_number": 106, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 108, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 109, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 109, "usage_type": "attribute"}, {"api_name": "detectron2.data.transforms.RandomRotation", "line_number": 126, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 126, "usage_type": "name"}, {"api_name": "detectron2.data.transforms.ResizeShortestEdge", "line_number": 126, "usage_type": "call"}, {"api_name": "detectron2.data.transforms.RandomBrightness", "line_number": 135, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 135, "usage_type": "name"}, {"api_name": "detectron2.data.transforms.RandomContrast", "line_number": 136, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 136, "usage_type": "name"}, {"api_name": "detectron2.data.transforms.RandomSaturation", "line_number": 137, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 137, "usage_type": "name"}, {"api_name": "detectron2.data.transforms.RandomFlip", "line_number": 138, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 138, "usage_type": "name"}, {"api_name": "detectron2.data.transforms.RandomFlip", "line_number": 139, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 139, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 146, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 157, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 158, "usage_type": "call"}, {"api_name": "detectron2.data.detection_utils.read_image", "line_number": 165, "usage_type": "call"}, {"api_name": "detectron2.data.detection_utils", "line_number": 165, "usage_type": "name"}, {"api_name": "detectron2.data.detection_utils.check_image_size", "line_number": 166, "usage_type": "call"}, {"api_name": "detectron2.data.detection_utils", "line_number": 166, "usage_type": "name"}, {"api_name": "detectron2.data.transforms.apply_transform_gens", "line_number": 190, "usage_type": "call"}, {"api_name": "detectron2.data.transforms", "line_number": 190, "usage_type": "name"}, {"api_name": "detectron2.data.detection_utils.transform_instance_annotations", "line_number": 193, "usage_type": "call"}, {"api_name": "detectron2.data.detection_utils", "line_number": 193, "usage_type": "name"}, {"api_name": "detectron2.data.detection_utils.annotations_to_instances", "line_number": 200, "usage_type": "call"}, {"api_name": "detectron2.data.detection_utils", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "detectron2.data.detection_utils.filter_empty_instances", "line_number": 242, "usage_type": "call"}, {"api_name": "detectron2.data.detection_utils", "line_number": 242, "usage_type": "name"}]} +{"seq_id": "36830713818", "text": "import ddt\nimport unittest\n\nfrom midonetclient.protobuf import utils\nfrom . import test_pb2\n\n_msg_with_req_fields = dict(\n b = True,\n by = b'\\x0bo',\n d = 1.1e+211,\n e = test_pb2.Exhaustive.Third,\n i32 = 2 ** 31 - 1,\n i64 = 2 ** 63 - 1,\n f32 = 2 ** 31 - 1,\n f64 = 2 ** 63 - 1,\n f = 11.11,\n nested = test_pb2.Exhaustive.Nested(nest = 'i am nested'),\n s = 'stringy thing',\n u32 = (2 ** 32) - 1,\n u64 = (2 ** 64) - 1)\n\n_req_fields = _msg_with_req_fields.copy()\n_req_fields.update(dict(\n e = 'Third',\n nested = {'nest': u'i am nested'}))\n\n\n_msg_with_rep_fields = _msg_with_req_fields.copy()\n_msg_with_rep_fields.update(dict(\n tags = ['networking', 'virtualization'],\n nests = [test_pb2.Exhaustive.Nested(nest='1st'),\n test_pb2.Exhaustive.Nested(nest='2nd'),\n test_pb2.Exhaustive.Nested(nest='3rd')]))\n\n_rep_fields = _req_fields.copy()\n_rep_fields.update(dict(\n tags = ['networking', 'virtualization'],\n nests = [{'nest': '1st'}, {'nest': '2nd'}, {'nest': '3rd'}]))\n\n_msg_with_ext_fields = _msg_with_rep_fields.copy()\n_msg_with_ext_fields.update(dict(\n Extensions = {test_pb2.number: 11, test_pb2.name: 'abiko'}))\n\n\n# NOTE(yamamoto): This class provides __name__ attribute to make ddt\n# use consistent names. It's important especially when test enumuration\n# and execution are done in separate processes.\nclass _Case(object):\n def __init__(self, name, values):\n self.__name__ = name\n self.values = values\n\n\n@ddt.ddt\nclass TestProtobuf(unittest.TestCase):\n @ddt.data(\n _Case(\"req\",\n (test_pb2.Exhaustive(**_msg_with_req_fields), _req_fields)),\n _Case(\"rep\",\n (test_pb2.Exhaustive(**_msg_with_rep_fields), _rep_fields)),\n )\n def test_proto_dict(self, data):\n msg, expected_dict = data.values\n self.assertEqual(expected_dict, utils.proto_to_dict(msg))\n\n @ddt.data(\n _Case(\"req\",\n (test_pb2.Exhaustive(**_msg_with_req_fields), _req_fields)),\n _Case(\"rep\",\n (test_pb2.Exhaustive(**_msg_with_rep_fields), _rep_fields)),\n )\n def test_proto_dict_with_exts(self, data):\n msg, expected_dict = data.values\n\n msg.Extensions[test_pb2.number] = 11\n msg.Extensions[test_pb2.name] = 'abiko'\n\n expected_dict['extensions'] = {'number': 11, 'name': 'abiko'}\n self.assertEqual(expected_dict, utils.proto_to_dict(msg))\n\n\ndef main():\n unittest.main()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "midonet/midonet", "sub_path": "python-midonetclient/src/tests/test_protobuf.py", "file_name": "test_protobuf.py", "file_ext": "py", "file_size_in_byte": 2514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 237, "dataset": "github-code", "pt": "2", "api": [{"api_name": "unittest.TestCase", "line_number": 55, "usage_type": "attribute"}, {"api_name": "midonetclient.protobuf.utils.proto_to_dict", "line_number": 64, "usage_type": "call"}, {"api_name": "midonetclient.protobuf.utils", "line_number": 64, "usage_type": "name"}, {"api_name": "ddt.data", "line_number": 56, "usage_type": "call"}, {"api_name": "midonetclient.protobuf.utils.proto_to_dict", "line_number": 79, "usage_type": "call"}, {"api_name": "midonetclient.protobuf.utils", "line_number": 79, "usage_type": "name"}, {"api_name": "ddt.data", "line_number": 66, "usage_type": "call"}, {"api_name": "ddt.ddt", "line_number": 54, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "73847663405", "text": "from asyncio import create_task, ensure_future, get_event_loop\nfrom operator import setitem\nfrom os import environ\n\nfrom .Challenger import Challenger\nfrom .Issuer import Issuer\nfrom .Monitor import Monitor\nfrom .Router import Router\nfrom .Scheduler import Scheduler\nfrom .Storage import Storage\nfrom .Terminator import Terminator\nfrom .utils.call import call\nfrom .utils.gen_ecc import gen_ecc\n\nCONTEXT = environ.get('CONTEXT', '.')\nDIRECTORY = environ.get('DIRECTORY', 'https://acme-v02.api.letsencrypt.org/directory')\nHTTP_PORT = int(environ.get('HTTP_PORT', 80))\nHTTPS_PORT = int(environ.get('HTTPS_PORT', 443))\n\n\n@ensure_future\n@call\nasync def main():\n\tstorage = Storage(CONTEXT)\n\tmonitor = Monitor()\n\tscheduler = Scheduler()\n\tissuer = Issuer(DIRECTORY, 'key' not in storage and setitem(storage, 'key', gen_ecc()) or storage['key'])\n\tchallenger = Challenger(HTTP_PORT)\n\tterminator = Terminator(HTTPS_PORT)\n\trouter = Router()\n\n\tasync def issue(domain):\n\t\tasync with issuer:\n\t\t\tasync with issuer(gen_ecc(), domain) as issuance:\n\t\t\t\tchallenger[f'/.well-known/acme-challenge/{issuance:token}'] = f'{issuance:token}.{issuance:auth}'\n\t\t\t\tstorage[domain] = await issuance()\n\n\t@create_task\n\t@call\n\tasync def monitor_handler():\n\t\tasync for domain, target in monitor:\n\t\t\tif domain not in storage:\n\t\t\t\tawait issue(domain)\n\t\t\tterminator[domain] = storage(domain)\n\t\t\trouter[domain] = target\n\t\t\tcreate_task(scheduler(storage[domain]))\n\n\t@create_task\n\t@call\n\tasync def scheduler_handler():\n\t\tasync for domain in scheduler:\n\t\t\tawait issue(domain)\n\t\t\tcreate_task(scheduler(storage[domain]))\n\n\t@create_task\n\t@call\n\tasync def terminator_handler():\n\t\tasync for domain, reader, writer in terminator:\n\t\t\tcreate_task(router(domain, reader, writer))\n\n\nget_event_loop().set_exception_handler(lambda loop, context: None)\nget_event_loop().run_forever()\n", "repo_name": "majkrzak/tunel", "sub_path": "src/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 1831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "name"}, {"api_name": "Storage.Storage", "line_number": 24, "usage_type": "call"}, {"api_name": "Monitor.Monitor", "line_number": 25, "usage_type": "call"}, {"api_name": "Scheduler.Scheduler", "line_number": 26, "usage_type": "call"}, {"api_name": "Issuer.Issuer", "line_number": 27, "usage_type": "call"}, {"api_name": "operator.setitem", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.gen_ecc.gen_ecc", "line_number": 27, "usage_type": "call"}, {"api_name": "Challenger.Challenger", "line_number": 28, "usage_type": "call"}, {"api_name": "Terminator.Terminator", "line_number": 29, "usage_type": "call"}, {"api_name": "Router.Router", "line_number": 30, "usage_type": "call"}, {"api_name": "utils.gen_ecc.gen_ecc", "line_number": 34, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 46, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 38, "usage_type": "name"}, {"api_name": "utils.call.call", "line_number": 39, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 53, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.call.call", "line_number": 49, "usage_type": "name"}, {"api_name": "asyncio.create_task", "line_number": 59, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 55, "usage_type": "name"}, {"api_name": "utils.call.call", "line_number": 56, "usage_type": "name"}, {"api_name": "asyncio.ensure_future", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.call.call", "line_number": 22, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 62, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "34805474591", "text": "import os\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import KFold\n\nfrom titanic.titanic_common import age_encoding, sex_encoding\n\n\ndef load_data():\n train_df = pd.read_csv(os.path.dirname(__file__) + \"/data/train.csv\", index_col=\"PassengerId\")\n return train_df\n\n\ndef missing_data_filling(df):\n # https://www.encyclopedia-titanica.org/titanic-survivor/martha-evelyn-stone.html\n df['Embarked'].fillna(\"S\", inplace=True)\n df.drop(\"Cabin\", inplace=True, axis=1)\n df['Age'] = df.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))\n return df\n\n\ndef get_family_id(x):\n return \"{}_{}_{}\".format(x['Surename'], x['Pclass'], x['Embarked'])\n\n\ndef extract_common_features(df):\n df['AgeEncoded'] = df['Age'].map(age_encoding)\n df['SexEncoded'] = df['Sex'].map(sex_encoding)\n df['Surename'] = df['Name'].apply(lambda x: x.split(\",\")[0].strip().lower())\n df['Family'] = df.apply(lambda x: get_family_id(x), axis=1)\n return df\n\n\ndef generate_family_survive_rate(x):\n x_filtered = x[(x['Sex'] == 'female') | (x['AgeEncoded'] == 1)]\n if x_filtered.shape[0] >= 0:\n return np.mean(x_filtered['Survived'])\n else:\n return None\n\n\ndef cal_family_survive_rate(df):\n family_survive_rate = df.groupby('Family').apply(lambda x: generate_family_survive_rate(x)).to_frame()\n family_survive_rate.reset_index(inplace=True)\n family_survive_rate.columns = ['Family', 'Family_Survive_Rate']\n return family_survive_rate\n\n\ndef model(x, family_survive_column=\"Family_Survive_Rate\"):\n if x['Sex'] == 'female':\n if 0 <= x[family_survive_column] <= 0.5:\n return 0\n else:\n return 1\n else:\n if x['AgeEncoded'] == 1:\n if x[family_survive_column] > 0.5:\n return 1\n else:\n return 0\n else:\n return 0\n\n\ndef model_accuracy(X_train, X_test):\n family_survive_rate_train = cal_family_survive_rate(X_train)\n # default_survive_rate = np.mean(X_train['Survived'])\n default_survive_rate = -1\n\n temp = X_train.merge(family_survive_rate_train, left_on='Family', right_on='Family', how='left')\n temp['Family_Survive_Rate'].fillna(default_survive_rate, inplace=True)\n X_train['Family_Survive_Rate'] = temp['Family_Survive_Rate'].values\n\n temp2 = X_test.merge(family_survive_rate_train, left_on='Family', right_on='Family', how='left')\n temp2['Family_Survive_Rate'].fillna(default_survive_rate, inplace=True)\n X_test['Family_Survive_Rate'] = temp2['Family_Survive_Rate'].values\n\n train_y = X_train['Survived'].values\n prediction_train_y = X_train.apply(lambda x: model(x), axis=1).values\n\n test_y = X_test['Survived'].values\n prediction_test_y = X_test.apply(lambda x: model(x), axis=1).values\n\n return accuracy_score(train_y, prediction_train_y), accuracy_score(test_y, prediction_test_y)\n\n\ndef cross_validate(k=5):\n train_df = load_data()\n train_df = missing_data_filling(train_df)\n train_df = extract_common_features(train_df)\n\n kf3 = KFold(n_splits=k, shuffle=True)\n train_accuracy_list = []\n test_accuracy_list = []\n\n for tune_train_index, tune_test_index in kf3.split(train_df):\n X_train = train_df.iloc[tune_train_index].copy()\n X_test = train_df.iloc[tune_test_index].copy()\n train_accuracy, test_accuracy = model_accuracy(X_train, X_test)\n train_accuracy_list.append(train_accuracy)\n test_accuracy_list.append(test_accuracy)\n\n print({\n \"train_accuracy\": train_accuracy_list,\n \"mean_train_accuracy\": np.mean(train_accuracy_list),\n \"test_accuracy\": test_accuracy_list,\n \"mean_test_accuracy\": np.mean(test_accuracy_list),\n })\n\n\n# example:\n# training accuracy: 0.8978670595836551\n# testing accuracy: 0.8417362375243236\n# leaderboard score: 0.79\nif __name__ == \"__main__\":\n cross_validate()\n", "repo_name": "xuyannus/kaggles", "sub_path": "titanic/family_model_cross_validation.py", "file_name": "family_model_cross_validation.py", "file_ext": "py", "file_size_in_byte": 3950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "titanic.titanic_common.age_encoding", "line_number": 28, "usage_type": "argument"}, {"api_name": "titanic.titanic_common.sex_encoding", "line_number": 29, "usage_type": "argument"}, {"api_name": "numpy.mean", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "4561150925", "text": "import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.utils import to_categorical\nimport sys\nimport matplotlib.pyplot as plt\nfrom master_data_functions.functions import *\nfrom master_models.prediction import *\nfrom sklearn.model_selection import train_test_split, StratifiedKFold\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Activation\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend\n\n\n# ================== Custom Functions ==================\n# Define R2 score for metrics since it's not available by default\ndef r2_keras(y_true, y_pred):\n SS_res = backend.sum(backend.square(y_true - y_pred)) \n SS_tot = backend.sum(backend.square(y_true - backend.mean(y_true))) \n return ( 1 - SS_res/(SS_tot + backend.epsilon()) )\n\n# Load model\nMODEL_PATH = \"../../data/output/models/\"\nFIGURE_PATH = \"../../\"\nDATA_PATH = \"../../data/simulated/\"\n\n# Load data\nimages = np.load(DATA_PATH + \"images_1M.npy\")\npositions = np.load(DATA_PATH + \"positions_1M.npy\")\nenergies = np.load(DATA_PATH + \"energies_1M.npy\")\ntargets = np.concatenate((positions/16, energies), axis=1)\nwith tf.device('/GPU:2'):\n name = \"cnn_pos_energy-r2-0.89.hdf5\"\n loaded_model = tf.keras.models.load_model(MODEL_PATH+name, custom_objects={'r2_keras': r2_keras})\n \n \n y_pred = loaded_model.predict(images)\n y_resid = targets - y_pred\n \nsingle, double, close = event_indices(positions)\nrel_dist = relative_distance(positions)/16\nrel_E = relative_energy(energies)\n\nindices = np.random.choice(double, 10000, replace=False)\n\n# Residuals in pos 1 vs residuals in energy 1\npos1_resid = np.sum(y_resid[:, :2], axis=1)\nplt.scatter(y_resid[indices, 4], pos1_resid[indices], alpha=0.2)\nplt.xlabel(\"Residual energy\")\nplt.ylabel(\"Residual sum(x1,y1)\")\nplt.savefig(\"resid_e1_pos1.pdf\")\nplt.clf()\n\n# Residuals in pos 2 vs residuals in energy 2\npos2_resid = np.sum(y_resid[:, 2:4], axis=1)\nplt.scatter(y_resid[indices, 5], pos2_resid[indices], alpha=0.2)\nplt.xlabel(\"Residual energy\")\nplt.ylabel(\"Residual sum(x2,y2)\")\nplt.savefig(\"resid_e2_pos2.pdf\")\nplt.clf()\n\n#\n#plt.scatter(rel_E[indices], y_resid[indices], alpha=0.2)\n#plt.xlabel(\"rel_E\")\n#plt.ylabel(\"sum_residuals\")\n#plt.savefig(\"pos_energy_residuals_rel_e.pdf\")\n#plt.clf()\n# \n#plt.scatter(rel_dist[indices], y_resid[indices], alpha=0.2)\n#plt.xlabel(\"rel_dist\")\n#plt.ylabel(\"sum_residuals\")\n#plt.savefig(\"pos_energy_residuals_rel_dist.pdf\")\n", "repo_name": "achartley/temp", "sub_path": "scripts/regression/check_predictions.py", "file_name": "check_predictions.py", "file_ext": "py", "file_size_in_byte": 2444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "keras.backend.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 18, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 19, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.backend.epsilon", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "27353857177", "text": "# Backing a folder into a ZIP file\n\n# copies an entire folder and its contents\n# into a ZIP file whose filename increments\n\nimport os\nimport zipfile\nfrom pathlib import Path\n\n\ndef backup_to_zip(folder):\n # folder = os.path.abspath(folder)\n\n # Step 1: figure out the ZIP file's name\n p = Path.cwd()\n folder = os.path.join(p, str(folder))\n\n n = 1\n\n while True:\n zip_filename = os.path.basename(folder) + '_' + str(n) + '.zip'\n\n if not os.path.exists(zip_filename):\n break\n n += 1\n\n # Step 2: create the new ZIP file\n\n print(f'\\n Creating {zip_filename} ...')\n backup_zip = zipfile.ZipFile(zip_filename, 'w')\n\n # Step 3: walk the directory and add to the ZIP file\n\n for foldername, subfolders, filenames in os.walk(folder):\n print(f'\\n Adding files in ... ')\n print(foldername)\n backup_zip.write(foldername)\n\n for filename in filenames:\n new_base = os.path.basename(folder) + '_'\n if filename.startswith(new_base) and filename.endswith('.zip'):\n continue\n backup_zip.write(os.path.join(foldername, filename))\n\n backup_zip.close()\n print('Done.')\n\n\nbackup_to_zip('TEST')\n", "repo_name": "HernanMagallanes/Python_sandbox", "sub_path": "organizing files/backUpToZip.py", "file_name": "backUpToZip.py", "file_ext": "py", "file_size_in_byte": 1215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pathlib.Path.cwd", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 30, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}]} +{"seq_id": "39437095079", "text": "import os\nimport cv2\nimport matplotlib\n# from matplotlib import pyplot as plt\n\n\ndef access_pixels(frame,img):\n height = frame.shape[0]\n weight = frame.shape[1]\n channels = frame.shape[2]\n print(\"weight : %s, height : %s, channel : %s\" %(weight, height, channels))\n intersection=0\n union=0\n for row in range(height): #遍历高\n for col in range(weight): #遍历宽\n for c in range(channels): #便利通道\n pv1 = frame[row, col, c]\n pv2 =img[row,col,c]\n if(pv1<250 and pv2<250):\n intersection+=1\n union+=1\n elif(pv1<250 or pv2<250):\n union+=1\n\n return intersection,union,intersection/union\n\n\nif __name__ == \"__main__\":\n\n root_path='test_result'\n\n image_path=os.path.join(root_path,'gt')\n\n image_paths=os.listdir(image_path)\n \n output_path=os.path.join(root_path,'processed')\n output_paths=os.listdir(output_path)\n\n print(image_paths[:5])\n print(output_paths[:5])\n\n precision=0.0\n for input_image,output_image in zip(image_paths,output_paths):\n\n output_image_path=os.path.join(output_path,output_image)\n input_image_path=os.path.join(image_path,input_image)\n \n\n target=cv2.imread(output_image_path)\n img=cv2.imread(input_image_path)\n\n _,_,ratio=access_pixels(target,img)\n precision+=ratio\n print(precision/len(image_paths))\n\n", "repo_name": "w5688414/TensorFlow-Defocus-Map-Estimation-Network", "sub_path": "calculate.py", "file_name": "calculate.py", "file_ext": "py", "file_size_in_byte": 1485, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "36804292543", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\ntry:\n from importlib import import_module\nexcept ImportError:\n # Python 2.6 fallback\n from django.utils.importlib import import_module\n\nfrom django.conf import settings\nfrom django.conf.urls import include, url\n\n_cache = {}\n\n\nclass BaseAddon(object):\n\n \"\"\"Add-ons should inherit from this\"\"\"\n # Must fill in!\n slug = None\n # fontawesome 3.2.1 icon class name\n iconclass = 'fa fa-circle-blank'\n # General urlpatterns that will reside in /background/addon-slug/...\n urlpatterns = []\n navs = {\n # Like this: _('Users'): {\"url_name\":\"wxrbot:user_list\", \"iconclass\": \"fa fa-user\", \"nav_id\":\"nav-user\"},\n }\n sort_id = 0\n\n class RenderMedia:\n js = []\n css = {}\n\n\ndef register(AddonsClass):\n \"\"\"\n Register a add-on class. This function will call back your add-on's\n constructor.\n \"\"\"\n if AddonsClass in list(_cache.keys()):\n raise Exception(\"Addons class already registered\")\n addon = AddonsClass()\n _cache[AddonsClass] = addon\n\n\ndef get_addons():\n \"\"\"Get loaded addons - do not call before all addons are loaded.\"\"\"\n return _cache\n\n\ndef get_addons_urls():\n urlpatterns = []\n for addon in list(get_addons().values()):\n slug = getattr(addon, 'slug', None)\n if slug:\n urlpatterns += [\n url('^' + slug + '/', include(addon.urlpatterns)),\n ]\n else:\n urlpatterns += [\n url('^', include(addon.urlpatterns)),\n ]\n return urlpatterns\n\n\ndef get_module(app, modname, verbose=False, failfast=False, success=True):\n \"\"\"\n Internal function to load a module from a single app.\n \"\"\"\n module_name = '%s.%s' % (app, modname)\n try:\n module = import_module(module_name)\n except ImportError as e:\n if failfast:\n if 'No module named' in str(e):\n pass\n else:\n raise e\n elif verbose:\n print(\"Could not load %r from %r: %s\" % (modname, app, e))\n return None\n if success or verbose:\n print(\"Loaded %r from %r\" % (modname, app))\n return module\n\n\ndef load(modname, verbose, failfast, success):\n \"\"\"\n Loads all modules with name 'modname' from all installed apps.\n If verbose is True, debug information will be printed to stdout.\n If failfast is True, import errors will not be surpressed.\n \"\"\"\n for app in settings.INSTALLED_APPS:\n get_module(app, modname, verbose, failfast, success)\n\n\ndef load_bgframework_addons(verbose=False, failfast=False, success=True):\n load('bgf_addons', verbose, failfast, success)\n", "repo_name": "tkliuxing/bgframework", "sub_path": "src/bgframework/add_ons.py", "file_name": "add_ons.py", "file_ext": "py", "file_size_in_byte": 2766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.conf.urls.url", "line_number": 59, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 59, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 63, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 63, "usage_type": "call"}, {"api_name": "django.utils.importlib.import_module", "line_number": 74, "usage_type": "call"}, {"api_name": "django.conf.settings.INSTALLED_APPS", "line_number": 95, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 95, "usage_type": "name"}]} +{"seq_id": "8635274285", "text": "\"\"\"Time Series Analysis\n\"\"\"\nimport numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt \n\n# pip install statsmodels \nfrom statsmodels.tsa.seasonal import seasonal_decompose\n\n# generating sample time series data\nnp.random.seed(0)\ntime = pd.date_range(start='2022-01-01', periods=365, freq='D')\ndata = np.cumsum(np.random.randn(365))\n\n# create a pandas dataframe\nts_data = pd.DataFrame({'Date':time, 'value': data})\nprint(ts_data)\n\n\n# Decompose time series into trend, seasonal, residual components\nresult = seasonal_decompose(ts_data['value'], model='additive', period=30)\n\nprint(result)\n\n# plot the original time series, tren, seasonal, and residual components\nplt.figure(figsize=(10, 6))\nplt.plot(ts_data['Date'], ts_data['value'], label='Original', linewidth=2)\nplt.plot(ts_data['Date'], result.trend, label='Trend', linewidth=2)\nplt.plot(ts_data['Date'], result.seasonal, label='Seasonal', linewidth=2)\nplt.plot(ts_data['Date'], result.resid, label='Residual', linewidth=2)\nplt.title('Time Series Analaysis')\nplt.xlabel('Date')\nplt.ylabel('Value')\nplt.legend()\nplt.savefig('ts.png')\nplt.show()", "repo_name": "epythonlab/scripts", "sub_path": "timeseries.py", "file_name": "timeseries.py", "file_ext": "py", "file_size_in_byte": 1109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.random.seed", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "statsmodels.tsa.seasonal.seasonal_decompose", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.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.title", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "27614830215", "text": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.linear_model import LogisticRegression\n\nbase = pd.read_csv('risco-credito2.csv')\nprevisores = base.iloc[:, 0:4].values\nclasse = base.iloc[:, 4].values\n\n# Transformação de variáveis categóricas em numéricas\nlabelencoder = LabelEncoder()\nprevisores[:, 0] = labelencoder.fit_transform(previsores[:, 0])\nprevisores[:, 1] = labelencoder.fit_transform(previsores[:, 1])\nprevisores[:, 2] = labelencoder.fit_transform(previsores[:, 2])\nprevisores[:, 3] = labelencoder.fit_transform(previsores[:, 3])\n\n# Nota: para o atributo classe, normalmente o primeiro valor encontrado\n# será o 0, nesta base de dados, o risco alto é o 0.\n\n# Criando instância do classificador de Regressão Logística\nclassificador = LogisticRegression()\nclassificador.fit(previsores, classe)\n\n# Retorna o valor do coeficiente b0\nprint(classificador.intercept_)\n\n# Retorna o coeficiente de cada atributo previsor, de acordo com sua ordem\nprint(classificador.coef_)\n\n# história boa, dívida alta, garantias nenhuma, renda > 35\n# história ruim, dívida alta, garantias adequada, renda < 15\nresultado = classificador.predict([[0, 0, 1, 2], [3, 0, 0, 0]])\nprint(resultado)\n\n# Armazena a probabilidade da predição de cada uma das classes\nresultado2 = classificador.predict_proba([[0, 0, 1, 2], [3, 0, 0, 0]])\nprint(resultado2)\n\n# NOTA: veja que é retornado uma matriz 2 por 2, ou seja, temos 2 novos\n# registros classificados (linhas 0 e 1)\n# nas colunas, temos as PROBABILIDADES de ser da classe 0 (alto) ou da classe\n# 1 (baixo)\n# Veja no resultado da primeira linha, temos 2 colunas\n# esse registro teve 0.1856032% de probabilidade de ser risco 0 (alto)\n# e 0.8143968% de probabilidade de ser risco 1 (baixo)\n# Portanto, a maior probabilidade irá prevalecer, portanto, será classificado\n# como risco 0 (baixo)\n# O mesmo vale pro segundo registro, ou seja, a segunda linha, onde a maior\n# probabilidade é de ser risco 0 (alto), que é de 0.90683435%\n", "repo_name": "ldynczuki/ml-python-curso", "sub_path": "classificacao/regressao_logistica_risco_credito.py", "file_name": "regressao_logistica_risco_credito.py", "file_ext": "py", "file_size_in_byte": 2031, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "29087288064", "text": "# Importing CV Library\nimport cv2\n\n# Initialising Camera\nvs = cv2.VideoCapture(0)\n\n# Infinite While Loop\nwhile True:\n _, img = vs.read() # Reading each frame\n cv2.imshow(\"Video Stream\", img) # Show each frame image to user\n\n # Reading Key\n key = cv2.waitKey(1) & 0xFF\n\n # Exit the Camera is pressed Q\n if key == ord(\"q\"):\n break\n\n# Release the Camera and destroy all windows\nvs.release()\ncv2.destroyAllWindows()\n", "repo_name": "yanukadeneth99/Simple-Image-Processing", "sub_path": "CameraStream.py", "file_name": "CameraStream.py", "file_ext": "py", "file_size_in_byte": 439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "43798876543", "text": "# 34268 KB / 92 ms\n\nfrom collections import deque\ngear = [deque([int(x) for x in input()]) for _ in range(4)]\nK = int(input())\nfor _ in range(K):\n index, dir = map(int, input().split())\n index -= 1\n\n left = gear[index][6]\n left_dir = dir\n\n right = gear[index][2]\n right_dir = dir\n\n gear[index].rotate(dir)\n\n for i in range(index-1, -1, -1):\n if gear[i][2] != left:\n left = gear[i][6]\n gear[i].rotate(left_dir * (-1))\n left_dir *= (-1)\n else:\n break\n \n for i in range(index+1, 4):\n if gear[i][6] != right:\n right = gear[i][2]\n gear[i].rotate(right_dir * (-1))\n right_dir *= (-1)\n else:\n break\n \nres = 0\nscore = 1\nfor i in gear:\n if i[0] == 1:\n res += score\n score *= 2\nprint(res)", "repo_name": "KDT-02-Algorithm-Study/Algorithm-Study", "sub_path": "week15_230420/14891_톱니바퀴/14891_박현준.py", "file_name": "14891_박현준.py", "file_ext": "py", "file_size_in_byte": 845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "3", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "25493662999", "text": "import cv2\r\nimport numpy as np\r\n\r\nif __name__ == \"__main__\":\r\n video=cv2.VideoCapture('imgs\\性格--错觉.mp4')\r\n face_detector=cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')\r\n \r\n while True:\r\n retval,image=video.read() #retval boolean表明是否获得了图片，true\r\n image=cv2.resize(image,(215,162))\r\n if retval == False:\r\n print('收到v')\r\n break\r\n gray=cv2.cvtColor(image,code=cv2.COLOR_BGR2GRAY)\r\n faces=face_detector.detectMultiScale(gray) #耗时操作\r\n for x,y,w,h in faces:\r\n cv2.rectangle(image,pt1=(x,y),pt2=(x+w,y+h),color=[0,0,255],thickness=2) \r\n cv2.imshow('img',image)\r\n key=cv2.waitKey(1) #1ms\r\n if key == ord('q'):\r\n print('退出')\r\n break\r\n cv2.destroyAllWindows()\r\n video.release() #释放内存\r\n", "repo_name": "mygithubma/opencv", "sub_path": "视频人脸检测.py", "file_name": "视频人脸检测.py", "file_ext": "py", "file_size_in_byte": 874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "36054414962", "text": "from datetime import datetime, date\n\n\ndef newyear_time():\n NEWYEAR = datetime(year=date.today().year, month=12, day=31, hour=23,\n minute=59, second=59, microsecond=999999)\n today = datetime.today()\n days_left = NEWYEAR - today\n hours_left = NEWYEAR.hour - today.hour\n minutes_left = NEWYEAR.minute - today.minute\n seconds_left = NEWYEAR.second - today.second\n\n return days_left.days, hours_left, minutes_left, seconds_left\n", "repo_name": "abrikk/time-birthday-bot", "sub_path": "tgbot/functions/newyear_func.py", "file_name": "newyear_func.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "datetime.datetime", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 5, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "29147280441", "text": "import torch\nimport torch.nn as nn\nimport cv2\nimport torchvision\nfrom torch.autograd import Variable\nimport torchvision.transforms as transforms\n\n\nclass carNet(nn.Module):\n def __init__(self, num_classes = 2):\n super(carNet, self).__init__()\n self.feature=nn.Sequential(nn.Conv2d(3,96,kernel_size=11,dilation=2,padding=10),#[64, 96, 54, 54]\n nn.ReLU(inplace=True),\n nn.MaxPool2d(2,stride=2),\n nn.Conv2d(96,192,kernel_size=11,dilation=2,padding=10),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(2,stride=2),\n nn.Conv2d(192,384,kernel_size=11,dilation=2,padding=10),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(2,stride=2)\n )\n\n self.classifier=nn.Sequential(nn.Dropout(0.5),\n nn.Linear(7*7*384,4096),\n nn.ReLU(),\n nn.Dropout(0.5),\n nn.Linear(4096,4096),\n nn.ReLU(),\n nn.Dropout(0.5),\n nn.Linear(4096,2))\n def forward(self,x):\n x=self.feature(x)\n # print(x.shape)\n # exit()\n x = torch.flatten(x,1)\n x=self.classifier(x)\n m = nn.Softmax(dim=1)\n x = m(x)\n return x\n# transforms = torchvision.transforms.Compose([\n# transforms.ToTensor(), # normalize to [0, 1]\n# transforms.Resize((56,56)),\n# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n# ])\n# img=cv2.imread(\"../example.jpg\")\n# x=transforms(img)\n# x=Variable(x.unsqueeze(0))\n# net=carNet()\n# out=net(x)", "repo_name": "adept-thu/parkinglot-detection", "sub_path": "model/carnet.py", "file_name": "carnet.py", "file_ext": "py", "file_size_in_byte": 1922, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.flatten", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "74498867282", "text": "from typing import Any, Callable, Dict, Tuple\n\nimport numpy as np\nimport numpy.typing as npt\n\n\nclass EmptyBufferException(Exception):\n pass\n\n\ndef mean_merge(x1: Any,\n x2: Any) -> Any:\n \"\"\"Return the average of two values.\n\n Args:\n x1 (Any): The first value.\n x2 (Any): The second value.\n\n Returns:\n Any: The average of the two values.\n \"\"\"\n return (x1 + x2) / 2\n\n\ndef max_merge(x1: Any,\n x2: Any) -> Any:\n \"\"\"Return the maximum of two values.\n\n Args:\n x1 (Any): The first value.\n x2 (Any): The second value.\n\n Returns:\n Any: The maximum of the two values.\n \"\"\"\n return max(x1, x2)\n\n\ndef min_merge(x1: Any,\n x2: Any) -> Any:\n \"\"\"Return the minimum of two values.\n\n Args:\n x1 (Any): The first value.\n x2 (Any): The second value.\n\n Returns:\n Any: The minimum of the two values.\n \"\"\"\n return min(x1, x2)\n\n\nmerge_methods = {\n 'mean_merge': mean_merge,\n 'max_merge': max_merge,\n 'min_merge': min_merge\n}\n\n\nclass Buffer:\n def __init__(self,\n merge_method: Callable[[Any, Any], Any] = mean_merge) -> None:\n \"\"\"Create an empty buffer.\n\n Args:\n merge_method (Callable[[Any, Any], Any], optional): The merging method. Defaults to `mean_merge`.\n \"\"\"\n self._xs, self._ys = [], []\n self._merge = merge_method\n\n def _contains(self,\n x: Any) -> int:\n \"\"\"Check whether the element is present in the buffer. If it is, the index of the element is returned, otherwise `-1` is returned.\n\n Args:\n x (Any): The element to check.\n\n Returns:\n int: The index of the element if it is present, otherwise `-1`.\n \"\"\"\n for i, _x in enumerate(self._xs):\n if np.array_equal(x, _x):\n return i\n return -1\n\n def insert(self,\n x: Any,\n y: npt.NDArray[np.float32]) -> None:\n \"\"\"Add a datapoint to the buffer.\n\n Args:\n x (Any): The input data.\n y (Any): The label data.\n \"\"\"\n i = self._contains(x)\n if i > -1:\n y0 = self._ys[i]\n self._ys[i] = self._merge(y0, y)\n else:\n self._xs.append(np.asarray(x))\n self._ys.append(y)\n\n def get(self) -> Tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]:\n \"\"\"Get the array representation of the buffer.\n\n Raises:\n EmptyBufferException: Raised if the buffer is empty.\n\n Returns:\n Tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: The input data and label data as NumPy arrays.\n \"\"\"\n if len(self._xs) > 0:\n xs = np.empty((len(self._xs), len(self._xs[0])))\n for i, X in enumerate(self._xs):\n xs[i, :] = X\n ys = np.asarray(self._ys)\n return xs, ys\n else:\n raise EmptyBufferException('Buffer is empty!')\n\n def clear(self) -> None:\n \"\"\"Clear the buffer.\"\"\"\n self._xs, self._ys = [], []\n\n def to_json(self) -> Dict[str, Any]:\n return {\n 'xs': [x.tolist() for x in self._xs],\n 'ys': [y.tolist() for y in self._ys],\n 'merge_method': self._merge.__name__\n }\n\n @staticmethod\n def from_json(my_args: Dict[str, Any]) -> 'Buffer':\n b = Buffer(merge_method=merge_methods[my_args['merge_method']])\n b._xs = [np.asarray(x) for x in my_args['xs']]\n b._ys = [np.asarray(y) for y in my_args['ys']]\n return b\n", "repo_name": "arayabrain/space-engineers-ai-spaceship-generator", "sub_path": "pcgsepy/mapelites/buffer.py", "file_name": "buffer.py", "file_ext": "py", "file_size_in_byte": 3610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "3", "api": [{"api_name": "typing.Any", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.array_equal", "line_number": 82, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.typing.NDArray", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.typing", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 116, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.typing.NDArray", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.typing", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 103, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 133, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "29413415597", "text": "import ctypes\nfrom ctypes import wintypes\n\nGetKeyState = ctypes.windll.user32.GetKeyState\nGetKeyState.argtypes = (ctypes.c_int,)\nGetKeyState.restype = wintypes.USHORT # !!! It's actually wt.SHORT, but chose unsigned for display purposes !!!\nscancodes = [\n'', '', '', '', '', '', '', '', # 8 unused\n'backspace', # 8\n'tab', # 9\n'', '', '', # 10,11,12\n'enter', # 13\n'', '', # 14, 15\n'shift', # 16\n'control', #17\n'alt', # 18\n'pause/break', # pause/break\t19\n'capslock', # 20\n'', '', '', '', '', '', # 21-26\n'escape', #\t27\n'', '', '', '',\n'space', #32\n'page up', #\t33\n'page down', #\t34\n'end', #\t35\n'home', #\t36\n'arrow left', #\t37\n'arrow up', #\t38\n'arrow right', #\t39\n'arrow down', #\t40\n'','','',\n'printscreen', #\t44\n'insert', #\t45\n'delete', #\t46\n'', # 47\n'0','1','2','3','4','5','6','7','8','9', # 48-57\n'','','','','','','', # 58-64\n'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z',\n'', # left window key\t91\n'', # right window key\t92\n'', # select key\t93\n'', '',\n'numpad 0', # numpad 0\t96\n'numpad 1', # numpad 1\t97\n'numpad 2', # numpad 2\t98\n'numpad 3', # numpad 3\t99\n'numpad 4', # numpad 4\t100\n'numpad 5', # numpad 5\t101\n'numpad 6', # numpad 6\t102\n'numpad 7', # numpad 7\t103\n'numpad 8', # numpad 8\t104\n'numpad 9', # numpad 9\t105\n'*', # multiply\t106\n'+', # add\t107\n'',\n'-', # subtract\t109\n'.', # decimal point\t110\n'/', # divide\t111\n# f1-f12, 112-123\n'f1','f2','f3','f4','f5','f6','f7','f8','f9','f10','f11','f12',\n'', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',\n'num lock', # num lock\t144\n'scroll lock', # scroll lock\t145\n*['' for i in range(146, 186)],\n# My Computer (multimedia keyboard)\t182\n# My Calculator (multimedia keyboard)\t183\n';', # 186\n'=', # equal sign\t187\n',', # comma\t188\n'-', # dash\t189\n'.', # period\t190\n'/', # forward slash\t191\n'(' # open bracket\t219\n'\\\\', # back slash\t220\n')', # close braket\t221\n'\\'' # single quote\t222\n]\nfor i, e in enumerate(scancodes):\n print(i, e)\n\nfrom collections import defaultdict\nheld_keys = defaultdict(lambda: 0)\n\nwhile True:\n for i, key in enumerate(scancodes):\n # vkc = 65 # 65, 'A'\n # ks = GetKeyState(vkc)\n is_pressed = int(GetKeyState(i) > 1)\n if not held_keys[key] and is_pressed:\n print(key)\n elif held_keys[key] and not is_pressed:\n print(key + ' up')\n held_keys[key] = is_pressed\n # print(ks)\n # print(held_keys)\n # if any(held_keys.values()):\n # for key, value in held_keys.items():\n # if value:\n # print(key,value)\n # else:\n # print(' ')\n", "repo_name": "standardgalactic/ursina", "sub_path": "ursina/keyboard.py", "file_name": "keyboard.py", "file_ext": "py", "file_size_in_byte": 2612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "3", "api": [{"api_name": "ctypes.windll", "line_number": 4, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 5, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes.USHORT", "line_number": 6, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "25994465587", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom flask import render_template, flash, redirect, url_for, session, request\nfrom configparser import ConfigParser\nfrom . import admin\nfrom app.admin.forms import LoginForm, CommandForm, EmailForm\nfrom config import *\nimport uuid\nimport time\nfrom utils import write_cfg, list_data_file, remove_cfg, get_data_config, get_config\nfrom data import ProductDB, EmailInfo\n\n\n@admin.route('/login', methods=[\"GET\", \"POST\"])\ndef login():\n form = LoginForm()\n if form.validate_on_submit():\n data = form.data\n if data[\"name\"] != USER_NAME or data[\"pwd\"] != PASS_WD:\n flash(\"账号或密码错误\", \"err\")\n return redirect(url_for('admin.login'))\n session[\"user_name\"] = data['name']\n session[\"user_id\"] = uuid.uuid1().hex\n return render_template('admin/admin.html')\n return render_template('admin/login.html', form=form)\n\n\n@admin.route('/email/add/', methods=[\"GET\", \"POST\"])\ndef email_add():\n form = EmailForm()\n if form.validate_on_submit():\n data = form.data\n username = data['username']\n password = data['password']\n smtpserver = data['smtpserver']\n smtpport = data['smtpport']\n Config = ConfigParser()\n Config.add_section('email')\n Config.set(\"email\", \"username\", username)\n Config.set(\"email\", \"password\", password)\n Config.set(\"email\", \"smtpserver\", smtpserver)\n Config.set(\"email\", \"smtpport\", smtpport)\n write_cfg(filename='mini.cfg', data_path='.', cfg=Config)\n flash(\"增加项目成功\", \"success\")\n return redirect(url_for('admin.email_add'))\n return render_template(\"admin/email_add.html\", form=form)\n\n\n@admin.route('/email/list/', methods=[\"GET\", \"POST\"])\ndef email_list():\n # Render file\n cfg = get_config()\n page_data = EmailInfo(\n username=cfg.get(\"email\", 'username'),\n password=cfg.get(\"email\", 'password'),\n smtpserver=cfg.get(\"email\", 'smtpserver'),\n smtpport=cfg.get(\"email\", 'smtpport'),\n )\n return render_template(\"admin/email_list.html\", v=page_data)\n\n\n# 编辑邮箱\n@admin.route(\"/email/edit/\", methods=[\"GET\", \"POST\"])\ndef email_edit(name='mini.cfg'):\n form = EmailForm()\n # 获得当前的编辑object\n cfg = get_config(name)\n if request.method == \"GET\":\n form.username.data = cfg.get('email', 'username')\n form.password.data = cfg.get('email', 'password')\n form.smtpserver.data = cfg.get('email', 'smtpserver')\n form.smtpport.data = cfg.get('email', 'smtpport')\n if form.validate_on_submit():\n data = form.data\n Config = ConfigParser()\n Config.add_section(\"email\")\n Config.set(\"email\", \"username\", data['username'])\n Config.set(\"email\", \"password\", data['password'])\n Config.set(\"email\", \"smtpserver\", data['smtpserver'])\n Config.set(\"email\", \"smtpport\", data['smtpport'])\n write_cfg(filename='mini.cfg', data_path='.', cfg=Config)\n flash(\"修改项目成功！\", \"success\")\n return redirect(url_for('admin.email_edit', name=name))\n return render_template(\"admin/email_edit.html\", form=form, name=name)\n\n\n@admin.route('/product/add/', methods=[\"GET\", \"POST\"])\ndef product_add():\n \"\"\"\n 对提交的项目数据进行更新\n \"\"\"\n form = CommandForm()\n if form.validate_on_submit():\n data = form.data\n # 判断编辑的信息是否存在\n # 项目名字是否存在\n if data['name'] in list_data_file():\n flash(\"项目名已经存在\", \"err\")\n return redirect(url_for('admin.product_add'))\n # webhooks的url 是否存在\n pdb = ProductDB()\n if data['url'] in [i.url for i in pdb.products]:\n flash(\"webhooks触发URl已经存在\", \"err\")\n return redirect(url_for('admin.product_add'))\n\n url = data['url']\n command = str(data['command'].split('\\n'))\n email = data['email']\n section_name = data['name']\n Config = ConfigParser()\n Config.add_section(section_name)\n Config.set(section_name, \"name\", section_name)\n Config.set(section_name, \"time\", time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n Config.set(section_name, \"url\", url)\n Config.set(section_name, \"command\", command)\n Config.set(section_name, \"email\", email)\n write_cfg(filename=section_name, cfg=Config)\n flash(\"增加项目成功\", \"success\")\n return redirect(url_for('admin.product_add'))\n return render_template(\"admin/product_add.html\", form=form)\n\n\n@admin.route('/product/list//', methods=[\"GET\", \"POST\"])\ndef product_list(page=None):\n \"\"\"\n 列出全部的项目信息\n 问题：\n 1、如果对data 目录下面的cfg进行读取合并为一个变量进行前端的绚染\n 2、如何分页\n :return:\n \"\"\"\n if page is None:\n page = 1\n # Render file\n pdb = ProductDB()\n page_data = pdb.paginate(page=page, per_page=10)\n return render_template(\"admin/product_list.html\", page_data=page_data)\n\n\n# 删除项目\n@admin.route(\"/product/del//\", methods=[\"GET\", \"POST\"])\ndef product_del(name=None):\n # 删除 data 下面的某个项目\n assert name is not None, '删除项目不存在'\n remove_cfg(name)\n flash(\"删除项目成功！\", \"success\")\n return redirect(url_for('admin.product_list', page=1))\n\n\n# 编辑项目\n@admin.route(\"/product/edit//\", methods=[\"GET\", \"POST\"])\ndef product_edit(name=None):\n form = CommandForm()\n # 获得当前的编辑object\n cfg = get_data_config(name)\n if request.method == \"GET\":\n form.name.data = name\n form.command.data = cfg.get(name, 'command')\n form.email.data = cfg.get(name, 'email')\n form.url.data = cfg.get(name, 'url')\n if form.validate_on_submit():\n data = form.data\n # 判断编辑的信息是否存在\n # 项目名字是否存在\n if data['name'] in list_data_file():\n flash(\"项目名已经存在\", \"err\")\n return redirect(url_for('admin.product_edit', name=name))\n # webhooks的url 是否存在\n pdb = ProductDB()\n if data['url'] in [i.url for i in pdb.products]:\n flash(\"webhooks触发URl已经存在\", \"err\")\n return redirect(url_for('admin.product_edit', name=name))\n\n Config = ConfigParser()\n Config.add_section(name)\n Config.set(name, \"name\", name)\n Config.set(name, \"time\", time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n Config.set(name, \"url\", data['url'])\n Config.set(name, \"command\", data['command'])\n Config.set(name, \"email\", data['email'])\n write_cfg(filename=name, cfg=Config)\n flash(\"修改项目成功！\", \"success\")\n return redirect(url_for('admin.product_edit', name=name))\n return render_template(\"admin/product_edit.html\", form=form, name=name)\n", "repo_name": "bingryan/mini", "sub_path": "app/admin/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "app.admin.forms.LoginForm", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 23, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}, {"api_name": "app.admin.forms.EmailForm", "line_number": 30, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.write_cfg", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.get_config", "line_number": 52, "usage_type": "call"}, {"api_name": "data.EmailInfo", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "app.admin.forms.EmailForm", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.get_config", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 75, "usage_type": "call"}, {"api_name": "utils.write_cfg", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 84, "usage_type": "call"}, {"api_name": "app.admin.forms.CommandForm", "line_number": 92, "usage_type": "call"}, {"api_name": "utils.list_data_file", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 99, "usage_type": "call"}, {"api_name": "data.ProductDB", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 104, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 110, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 113, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.write_cfg", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "data.ProductDB", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.remove_cfg", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 147, "usage_type": "call"}, {"api_name": "app.admin.forms.CommandForm", "line_number": 153, "usage_type": "call"}, {"api_name": "utils.get_data_config", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 156, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 156, "usage_type": "name"}, {"api_name": "utils.list_data_file", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 166, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 167, "usage_type": "call"}, {"api_name": "data.ProductDB", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 172, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 172, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 174, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 177, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 177, "usage_type": "call"}, {"api_name": "utils.write_cfg", "line_number": 181, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 182, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 183, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 183, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 184, "usage_type": "call"}]} +{"seq_id": "24835296453", "text": "import sys\nsys.path.append('../MLMC/src')\n\nimport subprocess\nimport yaml\nimport attr\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport copy\n\n#import MLMC.src.gmsh_io as gmsh_io\nfrom bgem.gmsh import gmsh_io\n\n\n@attr.s(auto_attribs=True)\nclass ValueDesctription:\n time: float\n position: str\n quantity: str\n unit: str\n\n\ndef substitute_placeholders(file_in, file_out, params):\n \"\"\"\n Substitute for placeholders of format '' from the dict 'params'.\n :param file_in: Template file.\n :param file_out: Values substituted.\n :param params: { 'name': value, ...}\n \"\"\"\n used_params = []\n with open(file_in, 'r') as src:\n text = src.read()\n for name, value in params.items():\n placeholder = '<%s>' % name\n n_repl = text.count(placeholder)\n if n_repl > 0:\n used_params.append(name)\n text = text.replace(placeholder, str(value))\n with open(file_out, 'w') as dst:\n dst.write(text)\n return used_params\n\n\ndef compute_hm(config_dict):\n \"\"\"\n :param config_dict: Parsed config.yaml. see key comments there.\n \"\"\"\n substitute_placeholders('01_hm_tmpl.yaml', '01_hm.yaml', config_dict['hm_params'])\n arguments = config_dict['flow_executable'].copy()\n arguments.extend(['--output_dir', 'output_hm', '01_hm.yaml'])\n print(\"Running: \", \" \".join(arguments))\n subprocess.call(arguments)\n\n\ndef prepare_th_input(config_dict):\n \"\"\"\n Prepare FieldFE input file for the TH simulation.\n :param config_dict: Parsed config.yaml. see key comments there.\n \"\"\"\n # pass\n # we have to read region names from the input mesh\n # input_mesh = gmsh_io.GmshIO(config_dict['hm_params']['mesh'])\n #\n # is_bc_region = {}\n # for name, (id, _) in input_mesh.physical.items():\n # unquoted_name = name.strip(\"\\\"'\")\n # is_bc_region[id] = (unquoted_name[0] == '.')\n\n # read mesh and mechanichal output data\n mesh = gmsh_io.GmshIO('output_hm/mechanics.msh')\n\n n_bulk = len(mesh.elements)\n ele_ids = np.zeros(n_bulk, dtype=int)\n for i, id_bulk in zip(range(n_bulk), mesh.elements.items()):\n ele_ids[i] = id_bulk[0]\n\n init_fr_cs = float(config_dict['hm_params']['fr_cross_section'])\n init_fr_K = float(config_dict['hm_params']['fr_conductivity'])\n init_bulk_K = float(config_dict['hm_params']['bulk_conductivity'])\n\n field_cs = mesh.element_data['cross_section_updated'][1]\n\n K = np.zeros((n_bulk, 1), dtype=float)\n cs = np.zeros((n_bulk, 1), dtype=float)\n for i, valcs in zip(range(n_bulk), field_cs[1].values()):\n cs_el = valcs[0]\n cs[i, 0] = cs_el\n if cs_el != 1.0: # if cross_section == 1, i.e. 3d bulk\n K[i, 0] = init_fr_K * (cs_el*cs_el) / (init_fr_cs*init_fr_cs)\n else:\n K[i, 0] = init_bulk_K\n\n # mesh.write_fields('output_hm/th_input.msh', ele_ids, {'conductivity': K})\n th_input_file = 'output_hm/th_input.msh'\n with open(th_input_file, \"w\") as fout:\n mesh.write_ascii(fout)\n mesh.write_element_data(fout, ele_ids, 'conductivity', K)\n mesh.write_element_data(fout, ele_ids, 'cross_section_updated', cs)\n\n # create field for K (copy cs)\n # posun dat K do casu 0\n # read original K = oK (define in config yaml)\n # read original cs = ocs (define in config yaml)\n # compute K = oK * (cs/ocs)^2\n # write K\n\n # posun dat cs do casu 0\n # write cs\n\n # mesh.element_data.\n\n\ndef compute_th(config_dict):\n \"\"\"\n :param config_dict: Parsed config.yaml. see key comments there.\n \"\"\"\n substitute_placeholders('02_th_tmpl.yaml', '02_th.yaml', config_dict['th_params'])\n arguments = config_dict['flow_executable'].copy()\n arguments.extend(['--output_dir', 'output_th', '02_th.yaml'])\n print(\"Running: \", \" \".join(arguments))\n subprocess.call(arguments)\n\n\ndef get_result_description():\n \"\"\"\n :return:\n \"\"\"\n end_time = 30\n values = [ [ValueDesctription(time=t, position=\"extraction_well\", quantity=\"power\", unit=\"MW\"),\n ValueDesctription(time=t, position=\"extraction_well\", quantity=\"temperature\", unit=\"Celsius deg.\")\n ] for t in np.linspace(0, end_time, 0.1)]\n power_series, temp_series = zip(*values)\n return power_series + temp_series\n\n\ndef extract_time_series(yaml_stream, regions, extract):\n \"\"\"\n\n :param yaml_stream:\n :param regions:\n :return: times list, list: for every region the array of value series\n \"\"\"\n data = yaml.safe_load(yaml_stream)['data']\n times = set()\n reg_series = {reg: [] for reg in regions}\n\n for time_data in data:\n region = time_data['region']\n if region in reg_series:\n times.add(time_data['time'])\n power_in_time = extract(time_data)\n reg_series[region].append(power_in_time)\n times = list(times)\n times.sort()\n series = [np.array(region_series) for region_series in reg_series.values()]\n return np.array(times), series\n\n\ndef extract_results(config_dict):\n \"\"\"\n :param config_dict: Parsed config.yaml. see key comments there.\n : return\n \"\"\"\n abs_zero_temp = 273.15\n year_sec = 60 * 60 * 24 *365\n bc_regions = ['.left_fr_left_well', '.left_well', '.right_fr_right_well', '.right_well']\n out_regions = bc_regions[2:]\n with open(\"output_th/energy_balance.yaml\", \"r\") as f:\n power_times, reg_powers = extract_time_series(f, bc_regions, extract=lambda frame: frame['data'][0])\n power_series = -sum(reg_powers)\n\n with open(\"output_th/Heat_AdvectionDiffusion_region_stat.yaml\", \"r\") as f:\n temp_times, reg_temps = extract_time_series(f, out_regions, extract=lambda frame: frame['average'][0])\n with open(\"output_th/water_balance.yaml\", \"r\") as f:\n flux_times, reg_fluxes = extract_time_series(f, out_regions, extract=lambda frame: frame['data'][0])\n sum_flux = sum(reg_fluxes)\n avg_temp = sum([temp * flux for temp, flux in zip(reg_temps, reg_fluxes)]) / sum_flux\n\n fig, ax1 = plt.subplots()\n temp_color = 'red'\n ax1.set_xlabel('time [y]')\n ax1.set_ylabel('Temperature [C deg]', color=temp_color)\n ax1.plot(temp_times[1:] / year_sec, avg_temp[1:] - abs_zero_temp, color=temp_color)\n ax1.tick_params(axis='y', labelcolor=temp_color)\n\n ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n pow_color = 'blue'\n ax2.set_ylabel('Power [MW]', color=pow_color) # we already handled the x-label with ax1\n ax2.plot(power_times[1:] / year_sec, power_series[1:] / 1e6, color=pow_color)\n ax2.tick_params(axis='y', labelcolor=pow_color)\n\n fig.tight_layout() # otherwise the right y-label is slightly clipped\n plt.show()\n\n\nif __name__ == \"__main__\":\n with open(\"config.yaml\", \"r\") as f:\n config_dict = yaml.safe_load(f)\n compute_hm(config_dict)\n prepare_th_input(config_dict)\n compute_th(config_dict)\n extract_results(config_dict)\n", "repo_name": "jbrezmorf/WGC2020-THM-MC", "sub_path": "fixed-3-frac/process.py", "file_name": "process.py", "file_ext": "py", "file_size_in_byte": 6918, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "attr.s", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 52, "usage_type": "call"}, {"api_name": "bgem.gmsh.gmsh_io.GmshIO", "line_number": 70, "usage_type": "call"}, {"api_name": "bgem.gmsh.gmsh_io", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 73, "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": "subprocess.call", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 131, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "yaml.safe_load", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "35753672790", "text": "#Nama : febriyadi\n#Nim : F5521082\n#Kelas : A Informatika universitas Tadulako\n\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom PIL import Image, ImageTk\nimport cv2\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter\n\n\n# fungsi untuk memproses citra dengan metode Median filter\ndef median_filter(img):\n median_img = cv2.medianBlur(img, 5)\n return median_img\n\n# fungsi untuk memproses citra dengan metode difference_image\ndef difference_image(img):\n # konversi citra ke grayscale\n gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # proses perbaikan citra menggunakan metode Difference Image\n blur_img = cv2.GaussianBlur(gray_img, (5, 5), 0)\n diff_img = cv2.absdiff(gray_img, blur_img)\n _, thresh_img = cv2.threshold(diff_img, 30, 255, cv2.THRESH_BINARY)\n\n return thresh_img\n\n# fungsi untuk memproses citra dengan metode grayscale\ndef grayscale(img):\n gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n return gray_img\n\n# fungsi untuk memperbaiki citra dengan metode thresholding\ndef thresholding_correction(img):\n gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n _, threshold_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n return threshold_img\n\ndef sharpening(img):\n kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])\n sharpened_img = cv2.filter2D(img, -1, kernel)\n return sharpened_img\n\ndef noise_reduction(img):\n denoised_img = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)\n return denoised_img\n\n# fungsi untuk memperbaiki citra dengan metode peningkatan kecerahan\ndef brightness_correction(img):\n brightness = 50\n corrected_img = cv2.add(img, brightness)\n return corrected_img\n\n# fungsi untuk menampilkan gambar dalam kotak\ndef show_image(img, x, y, title):\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = Image.fromarray(img)\n img = ImageTk.PhotoImage(img)\n label = tk.Label(root, image=img)\n label.image = img\n label.place(x=x, y=y)\n title_label = tk.Label(root, text=title)\n title_label.place(x=x, y=y-20)\n\n# fungsi untuk memproses citra dan menampilkan hasilnya\ndef process_image(method):\n global original_img\n if method == 'grayscale':\n corrected_img = grayscale(original_img)\n show_image(corrected_img, 360, 170, 'Hasil Metode grayscale')\n elif method == 'thresholding_correction':\n corrected_img = thresholding_correction(original_img)\n show_image(corrected_img, 620, 170, 'Hasil Metode thresholding_correction')\n elif method == 'brightness':\n corrected_img = brightness_correction(original_img)\n show_image(corrected_img, 880, 170, 'Hasil Metode brightness_correction')\n elif method == 'difference_image':\n corrected_img = difference_image(original_img)\n show_image(corrected_img, 70, 450, 'Hasil Metode difference image')\n elif method == 'sharpening':\n corrected_img = sharpening(original_img)\n show_image(corrected_img, 360, 450, 'Hasil Metode sharpening image')\n elif method == 'noise_reduction':\n corrected_img = noise_reduction(original_img)\n show_image(corrected_img, 620, 450, 'Hasil Metode noise_reduction image')\n elif method == 'median_filter':\n corrected_img = median_filter(original_img)\n show_image(corrected_img, 880, 450, 'Hasil Metode median_filter image')\n\n\n# fungsi untuk menampilkan informasi pembuat program\ndef show_creator():\n creator_label = tk.Label(root, text='NAMA : FEBRIYADI | NIM : F55121082 | KELAS : A')\n creator_label.place(x=480, y=100)\n\n# fungsi untuk membuka gambar\ndef open_image():\n global original_img\n file_path = filedialog.askopenfilename()\n if file_path:\n original_img = cv2.imread(file_path)\n original_img = cv2.resize(original_img, (250, 250))\n show_image(original_img, 70, 170, 'Gambar Original')\n size_label.config(format(original_img.shape[1], original_img.shape[0]))\n\n# membuat jendela utama\nroot = tk.Tk()\nroot.geometry('1200x900')\nroot.title('GUI Aplikasi Pengolahan Citra')\n\n# menambahkan judul gambar original\ntitle_label = tk.Label(root, text='Original image')\ntitle_label.place(x=50, y=20)\n\n# menambahkan tombol untuk membuka gambar\nopen_button = tk.Button(root, text='Select an image', command=open_image)\nopen_button.place(x=50, y=50)\n\n# menambahkan kotak untuk metode perbaikan citra\ncorrection_box = tk.LabelFrame(root, text='Metode Perbaikan Citra', padx=5, pady=5)\ncorrection_box.place(x=50, y=760, width=1100, height=70)\n\n# tombol untuk metode Transformasi Negatif\ngrayscale_button = tk.Button(correction_box, text='grayscale', command=lambda: process_image('grayscale'))\ngrayscale_button.pack(side=tk.LEFT, padx=5)\n\n# tombol untuk perbaikan metode smoothing\nthresholding_correction_button = tk.Button(correction_box, text='thresholding_correction', command=lambda: process_image('thresholding_correction'))\nthresholding_correction_button.pack(side=tk.LEFT, padx=5)\n\n# tombol untuk perbaikan metode Peningkatan Kecerahan\nbrightness_button = tk.Button(correction_box, text='Peningkatan Kecerahan', command=lambda: process_image('brightness'))\nbrightness_button.pack(side=tk.LEFT, padx=5)\n\n# tombol untuk perbaikan metode difference_image\ndifference_image_button = tk.Button(correction_box, text='difference_image', command=lambda: process_image('difference_image'))\ndifference_image_button.pack(side=tk.LEFT, padx=5)\n\n# tombol untuk perbaikan metode sharpening\nsharpening_button = tk.Button(correction_box, text='sharpening', command=lambda: process_image('sharpening'))\nsharpening_button.pack(side=tk.LEFT, padx=5)\n\n# tombol untuk perbaikan metode noise_reduction\nnoise_reduction_button = tk.Button(correction_box, text='noise_reduction', command=lambda: process_image('noise_reduction'))\nnoise_reduction_button.pack(side=tk.LEFT, padx=5)\n\n# tombol untuk perbaikan metode noise_reduction\nmedian_filter_button = tk.Button(correction_box, text='median_filter', command=lambda: process_image('median_filter'))\nmedian_filter_button.pack(side=tk.LEFT, padx=5)\n\n# menambahkan kotak untuk menampilkan hasil perbaikan citra\nresult_box = tk.LabelFrame(root, text='Output Perbaikan Citra', padx=5, pady=5)\nresult_box.place(x=50, y=100, width=1100, height=650)\n\n# menambahkan kotak untuk informasi pembuat program\ncreator_box = tk.LabelFrame(root, text='Creator', padx=5, pady=5)\ncreator_box.place(x=460, y=80, width=315, height=60)\n\n# menampilkan informasi pembuat program\nshow_creator()\n\n# menjalankan aplikasi\nroot.mainloop()", "repo_name": "febriyadi/F55121082_FEBRIYADI_A_TUGAS-PCD", "sub_path": "Ujiian_Pcd/F55121082_FEBRIYADI_A.py", "file_name": "F55121082_FEBRIYADI_A.py", "file_ext": "py", "file_size_in_byte": 6508, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.medianBlur", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.absdiff", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.fastNlMeansDenoisingColored", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.add", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 58, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 59, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 60, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 60, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 64, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 95, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 101, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 101, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 104, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 109, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 114, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 118, "usage_type": "call"}, {"api_name": "tkinter.LabelFrame", "line_number": 122, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 126, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 127, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 130, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 131, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 134, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 138, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 142, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 146, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 150, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tkinter.LabelFrame", "line_number": 154, "usage_type": "call"}, {"api_name": "tkinter.LabelFrame", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "14250313396", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 28 16:55:24 2018\r\n\r\n@author: Administrator\r\n\"\"\"\r\n\r\nimport jieba\r\nfrom gensim import corpora,models,similarities\r\nimport getData \r\n\r\n#获得原数据（已分词，去停词，加语料\r\ngetdata = getData.getData()\r\ndata_origin = getdata.read_text(word_type=1)\r\ngetdata.jiebaNWored()\r\n \r\ndata_seg = getdata.pretreatment(data_type=2)\r\ndata_seg = data_seg.split(\"\\n\")\r\nfor i in range(0,len(data_seg)):\r\n data_seg[i] = data_seg[i].split(\" \")\r\n data_seg[i].remove(\"\")\r\n \r\n#获取预处理后的问题 \r\ntest_test = \"CRM宽带资源的地址在哪里\"\r\nsentence = getdata.testWord(sentence=test_test)\r\n\r\n\r\ndictionary = corpora.Dictionary(data_seg)#这就是一个词袋，就是以序号为键以单词为值的字典\r\ndictionary.keys()\r\ndictionary.token2id\r\n\r\ncorpus = [dictionary.doc2bow(doc) for doc in data_seg]#编号、频次\r\ntest_corpus = dictionary.doc2bow(sentence)#测试句子的词表示\r\n\r\ntfidf = models.TfidfModel(corpus)\r\ntfidf[test_corpus]#词tiidf值\r\n\r\nindex = similarities.SparseMatrixSimilarity(tfidf[corpus], num_features=len(dictionary.keys()))\r\nsim = index[tfidf[test_corpus]]\r\nsim\r\nsorted(enumerate(sim), key=lambda item: -item[1])", "repo_name": "Peterzwb/tfidfClassify", "sub_path": "sTClassify.py", "file_name": "sTClassify.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "getData.getData", "line_number": 13, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 28, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 28, "usage_type": "name"}, {"api_name": "gensim.models.TfidfModel", "line_number": 35, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 35, "usage_type": "name"}, {"api_name": "gensim.similarities.SparseMatrixSimilarity", "line_number": 38, "usage_type": "call"}, {"api_name": "gensim.similarities", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "39317331249", "text": "# Zed-Thon\r\r\n# Copyright (C) 2022 Zed-Thon . All Rights Reserved\r\r\n#\r\r\n# This file is a part of < https://github.com/Zed-Thon/ZelZal/ >\r\r\n# PLease read the GNU Affero General Public License in\r\r\n# .\r\r\n#يبووووووووووووووووووو\r\r\n#هههههههههههههههههههههههههههههههههههههه\r\r\nimport os\r\r\nimport re\r\r\nimport telethon\r\r\nfrom telethon.events import CallbackQuery, InlineQuery\r\r\nfrom telethon import Button, events, functions\r\r\nfrom telethon.tl import functions, types\r\r\nfrom telethon.errors.rpcerrorlist import UserNotParticipantError\r\r\nfrom telethon.tl.functions.channels import EditBannedRequest, GetFullChannelRequest, GetParticipantRequest\r\r\nfrom telethon.tl.functions.messages import ExportChatInviteRequest\r\r\nfrom telethon.tl.functions.users import GetFullUserRequest\r\r\nfrom telethon.tl.types import ChatBannedRights\r\r\n\r\r\nfrom ..sql_helper.fsub_sql import *\r\r\nfrom zthon import zedub\r\r\nfrom . import BOTLOG, BOTLOG_CHATID, admin_groups, get_user_from_event\r\r\n\r\r\n# =========================================================== #\r\r\n# الملـــف كتـــابـــة - T.me/ZThon #\r\r\n# =========================================================== #\r\r\nWarn = \"تخمـط بـدون ذكـر المصـدر - راح توثقهـا فضيحـه ع نفسـك\"\r\r\n# =========================================================== #\r\r\n# زلـــزال الهيبـــه - T.me/zzzzl1l #\r\r\n# =========================================================== #\r\r\n# تـاريـخ كتابـة الملـف - 30 اكتوبر/2022 #\r\r\n# الملف كان مدفوع وتم تنزيله مجاني #\r\r\n# الدليل https://t.me/ZThon/260 #\r\r\n# =========================================================== #\r\r\n\r\r\nzilzal = zedub.uid\r\r\nMUTE_RIGHTS = ChatBannedRights(until_date=None, send_messages=True)\r\r\nUNMUTE_RIGHTS = ChatBannedRights(until_date=None, send_messages=False)\r\r\nANTI_DDDD_ZEDTHON_MODE = ChatBannedRights(\r\r\n until_date=None, view_messages=None, send_messages=True, send_media=True, send_stickers=True, send_gifs=True\r\r\n)\r\r\n\r\r\nasync def is_admin(event, user):\r\r\n try:\r\r\n sed = await event.client.get_permissions(event.chat_id, user)\r\r\n if sed.is_admin:\r\r\n is_mod = True\r\r\n else:\r\r\n is_mod = False\r\r\n except:\r\r\n is_mod = False\r\r\n return is_mod\r\r\n\r\r\n\r\r\nasync def check_him(channel, user):\r\r\n try:\r\r\n result = await bot(\r\r\n functions.channels.GetParticipantRequest(channel, user)\r\r\n )\r\r\n return True\r\r\n except telethon.errors.rpcerrorlist.UserNotParticipantError:\r\r\n return False\r\r\n\r\r\n\r\r\nasync def rights(event):\r\r\n result = await bot(\r\r\n functions.channels.GetParticipantRequest(\r\r\n channel=event.chat_id,\r\r\n user_id=zilzal,\r\r\n )\r\r\n )\r\r\n p = result.participant\r\r\n return isinstance(p, types.ChannelParticipantCreator) or (\r\r\n isinstance(p, types.ChannelParticipantAdmin) and p.admin_rights.ban_users\r\r\n )\r\r\n\r\r\n\r\r\n@zedub.zed_cmd(pattern=\"(ضع اشتراك الكروب|وضع اشتراك الكروب) ?(.*)\")\r\r\nasync def fs(event):\r\r\n permissions = await bot.get_permissions(event.chat_id, event.sender_id)\r\r\n if not permissions.is_admin:\r\r\n return await event.reply(\r\r\n \"**⌔╎عـذرًا .. عـزيـزي\\n**⌔╎لا أمـلك صلاحيات المشـرف هنـا**\"\r\r\n )\r\r\n if not await is_admin(event, zilzal):\r\r\n return await event.reply(\"**⌔╎عـذرًا .. عـزيـزي\\n**⌔╎لا أمـلك صلاحيات المشـرف هنـا**\")\r\r\n if event.is_private:\r\r\n await edit_or_reply(event, \"**✾╎عـذرًا .. هـذا الامـر خـاص بالمجمـوعـات فقـط**\")\r\r\n return\r\r\n ahmed = event.pattern_match.group(1)\r\r\n if not ahmed:\r\r\n return await edit_delete(event, \"**✾╎استخـدم الامـر هكـذا**\\n**✾╎.اشتراك الكروب + معـرف القنـاة**\")\r\r\n args = event.pattern_match.group(2)\r\r\n channel = args.replace(\"@\", \"\")\r\r\n if args == \"تفعيل\" or args == \"تشغيل\":\r\r\n return await event.reply(\"**⌔╎عـذرًا .. يرجى التحقق من معـرف القنـاة**\")\r\r\n if args in (\"off\", \"تعطيل\", \"ايقاف\"):\r\r\n rm_fsub(event.chat_id)\r\r\n await event.reply(\"**✾╎تـم إيقـاف الاشتـراك الإجبـاري هنـا .. بنجـاح ✓**\")\r\r\n else:\r\r\n try:\r\r\n ch_full = await bot(GetFullChannelRequest(channel=channel))\r\r\n except Exception as e:\r\r\n await event.reply(f\"{e}\")\r\r\n return await event.reply(\"**⌔╎عـذرًا .. معـرف القنـاة غيـر موجـود**\")\r\r\n rip = await check_him(channel, zilzal)\r\r\n if rip is False:\r\r\n return await event.reply(\r\r\n f\"**⌔╎عـذرًا .. عـزيـزي**\\n**⌔╎لـ تمكين الاشتـراك الإجبـاري**\\n**⌔╎يجب أن تـكون مشرفًا في** [القنـاة](https://t.me/{args}).\",\r\r\n link_preview=False,\r\r\n )\r\r\n add_fsub(event.chat_id, str(channel))\r\r\n await event.reply(f\"**✾╎تم تفعيل الاشتراك الاجباري .. بنجاح ☑️**\\n**✾╎قناة الاشتراك ~** @{channel}.\")\r\r\n\r\r\n\r\r\n@zedub.on(events.NewMessage(pattern=None))\r\r\nasync def f(event):\r\r\n chat_id = event.chat_id\r\r\n chat_db = is_fsub(event.chat_id)\r\r\n event.sender_id\r\r\n user = await event.get_sender()\r\r\n zed_dev = (1260465030, 9256472505)\r\r\n zelzal = event.sender_id\r\r\n if isinstance(user, telethon.types.User) and user.bot:\r\r\n return\r\r\n if zelzal in zed_dev:\r\r\n return\r\r\n if not await is_admin(event, zilzal):\r\r\n return\r\r\n if not chat_db:\r\r\n return\r\r\n if chat_db:\r\r\n try:\r\r\n channel = chat_db.channel\r\r\n chat_id = event.chat_id\r\r\n chat_db = is_fsub(event.chat_id)\r\r\n channel = chat_db.channel\r\r\n user = await event.get_sender()\r\r\n grp = f\"t.me/{channel}\"\r\r\n rip = await check_him(channel, event.sender_id)\r\r\n if rip is False:\r\r\n await bot.send_message(\r\r\n event.chat_id, f\"[ᯓ 𝗦𝗢𝗨𝗥𝗖𝗘 𝗧𝗘𝗣𝗧𝗛𝗢𝗡 - الاشتࢪاك الإجباࢪي](t.me/Tepthon)\\n⋆┄─┄─┄─┄┄─┄─┄─┄─┄┄⋆\\n\\n⌔╎**مࢪحـبًا عـزيـزي 👋** [{user.first_name}](tg://user?id={user.id}) \\n⌔╎**لـ إلغـاء كتمـك 🔊**\\n⌔╎**يُࢪجـى الإشتـࢪاك بالقنـاة @{channel} **\", link_preview=False\r\r\n )\r\r\n await event.delete()\r\r\n except:\r\r\n if not await rights(event):\r\r\n await bot.send_message(\r\r\n event.chat_id,\r\r\n \"**⌔╎عـذرًا .. عـزيـزي\\n**⌔╎لا أمـلك صلاحيات المشـرف هنـا**\",\r\r\n )\r\r\n\r\r\n\r\r\n@zedub.zed_cmd(pattern=\"تعطيل اشتراك الكروب$\")\r\r\nasync def removef(event):\r\r\n if is_fsub(event.chat_id):\r\r\n rm_fsub(event.chat_id)\r\r\n await edit_or_reply(event, \"**✾╎تـم إيقـاف الاشتـراك الإجبـاري هنـا .. بنجـاح ✓**\")\r\r\n else:\r\r\n return await edit_delete(event, \"**✾╎عـذرًا .. الاشتـراك الإجبـاري غيـر مفعـل هنـا**\")\r\r\n #شـكرًا زلـزال الهـيـبـة .", "repo_name": "Tepthonee/PPF22", "sub_path": "zthon/plugins/اشتراك المجموعة.py", "file_name": "اشتراك المجموعة.py", "file_ext": "py", "file_size_in_byte": 8061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "zthon.zedub.uid", "line_number": 73, "usage_type": "attribute"}, {"api_name": "zthon.zedub", "line_number": 73, "usage_type": "name"}, {"api_name": "telethon.tl.types.ChatBannedRights", "line_number": 75, "usage_type": "call"}, {"api_name": "telethon.tl.types.ChatBannedRights", "line_number": 77, "usage_type": "call"}, {"api_name": "telethon.tl.types.ChatBannedRights", "line_number": 79, "usage_type": "call"}, {"api_name": "telethon.tl.functions.channels.GetParticipantRequest", "line_number": 117, "usage_type": "call"}, {"api_name": "telethon.tl.functions.channels", "line_number": 117, "usage_type": "attribute"}, {"api_name": "telethon.tl.functions", "line_number": 117, "usage_type": "name"}, {"api_name": "telethon.errors", "line_number": 123, "usage_type": "attribute"}, {"api_name": "telethon.tl.functions.channels.GetParticipantRequest", "line_number": 135, "usage_type": "call"}, {"api_name": "telethon.tl.functions.channels", "line_number": 135, "usage_type": "attribute"}, {"api_name": "telethon.tl.functions", "line_number": 135, "usage_type": "name"}, {"api_name": "telethon.tl.types.ChannelParticipantCreator", "line_number": 147, "usage_type": "attribute"}, {"api_name": "telethon.tl.types", "line_number": 147, "usage_type": "name"}, {"api_name": "telethon.tl.types.ChannelParticipantAdmin", "line_number": 149, "usage_type": "attribute"}, {"api_name": "telethon.tl.types", "line_number": 149, "usage_type": "name"}, {"api_name": "telethon.tl.functions.channels.GetFullChannelRequest", "line_number": 205, "usage_type": "call"}, {"api_name": "zthon.zedub.zed_cmd", "line_number": 157, "usage_type": "call"}, {"api_name": "zthon.zedub", "line_number": 157, "usage_type": "name"}, {"api_name": "telethon.types", "line_number": 249, "usage_type": "attribute"}, {"api_name": "zthon.zedub.on", "line_number": 233, "usage_type": "call"}, {"api_name": "zthon.zedub", "line_number": 233, "usage_type": "name"}, {"api_name": "telethon.events.NewMessage", "line_number": 233, "usage_type": "call"}, {"api_name": "telethon.events", "line_number": 233, "usage_type": "name"}, {"api_name": "zthon.zedub.zed_cmd", "line_number": 309, "usage_type": "call"}, {"api_name": "zthon.zedub", "line_number": 309, "usage_type": "name"}]} +{"seq_id": "1574807169", "text": "# coding=utf-8\nimport json\nimport os\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.core.files.storage import default_storage\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.urls import reverse_lazy, reverse\nfrom django.views.generic import ListView, DetailView, FormView\nfrom django_filters import rest_framework\nfrom rest_framework import viewsets, status\nfrom rest_framework.decorators import action\nfrom rest_framework.filters import SearchFilter\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAdminUser\n\nfrom metamodel.filters import InstanceFilterSet, MetaFieldFilterSet, \\\n InstanceFieldFilterSet\nfrom metamodel.forms.meta_field_form import MetaFieldForm\nfrom metamodel.forms.meta_field_make_non_nullable_meta_field_form import \\\n MetaFieldMakeNonNullableMetaFieldForm\nfrom metamodel.forms.meta_field_make_non_nullable_primitive_form import \\\n MetaFieldMakeNonNullablePrimitiveForm\nfrom metamodel.forms.meta_model_add_field_form import MetaModelAddFieldForm\nfrom metamodel.forms.meta_model_form import MetaModelForm\nfrom metamodel.models import MetaModel, MetaField, InstanceModel, InstanceField\nfrom metamodel.pagination import InstancePagination\nfrom metamodel.plugin import Plugin\nfrom metamodel.serializers import MetaModelWithoutFieldsSerializer, \\\n MetaModelSerializer, InstanceModelSerializer, MetaFieldSerializer, \\\n MetaModelAddFieldSerializer, InstanceFieldSerializer, \\\n InstanceModelWithoutMetamodelSerializer\nfrom solotodo.permissions import IsSuperuser\n\n\nclass ModelListView(ListView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_list.html'\n\n\nclass ModelDetailView(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_detail.html'\n\n\nclass ModelAddView(FormView):\n form_class = MetaModelForm\n template_name = 'metamodel/model_add.html'\n success_url = reverse_lazy('metamodel_model_list')\n\n def form_valid(self, form):\n form.save()\n\n self.meta_model = form.instance\n\n return super(ModelAddView, self).form_valid(form)\n\n def get_success_url(self):\n return reverse('metamodel_model_meta',\n kwargs={'pk': self.meta_model.pk})\n\n\nclass ModelEditView(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_edit.html'\n\n def get_context_data(self, **kwargs):\n context = super(ModelEditView, self).get_context_data(**kwargs)\n\n form = MetaModelForm(instance=self.object)\n context['form'] = form\n\n return context\n\n def post(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n\n form = MetaModelForm(request.POST, instance=meta_model)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(\n reverse('metamodel_model_meta',\n kwargs={'pk': self.get_object().pk}))\n else:\n self.object = meta_model\n context = self.get_context_data(**kwargs)\n context['form'] = form\n return self.render_to_response(context)\n\n\nclass ModelDeleteView(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_delete.html'\n\n def post(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n meta_model.delete()\n\n return HttpResponseRedirect(reverse('metamodel_model_list'))\n\n\nclass ModelMetaView(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_meta.html'\n\n def post(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n\n for field in meta_model.fields.all():\n if field.name in request.POST:\n field.ordering = int(request.POST[field.name])\n field.save()\n\n return HttpResponseRedirect(reverse('metamodel_model_meta',\n kwargs={'pk': meta_model.pk}))\n\n\nclass ModelAddInstance(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_add_instance.html'\n\n def is_popup(self):\n return bool(self.request.GET.get('popup', False))\n\n def get_context_data(self, **kwargs):\n context = super(ModelAddInstance, self).get_context_data(**kwargs)\n context['form'] = self.object.get_form()()\n is_popup = self.is_popup()\n\n form_action = reverse('metamodel_model_add_instance',\n kwargs={'pk': self.object.pk})\n\n if is_popup:\n form_action += '?popup=1'\n\n context['hide_nav'] = is_popup\n context['form_action'] = form_action\n return context\n\n def post(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n\n form = meta_model.get_form()(request.POST, request.FILES)\n\n if form.is_valid():\n instance_model = InstanceModel()\n instance_model.model = meta_model\n\n instance_model.save(initial=True)\n instance_model.update_fields(\n form.cleaned_data,\n request.POST,\n creator_id=request.user.id)\n\n if self.is_popup():\n return HttpResponseRedirect(reverse(\n 'metamodel_instance_popup_redirect',\n kwargs={'pk': instance_model.pk}))\n else:\n messages.success(\n request,\n u'{1} creada correctamente'.format(\n reverse('metamodel_instance_detail',\n kwargs={'pk': instance_model.pk}),\n str(instance_model)\n ))\n return HttpResponseRedirect(reverse(\n 'metamodel_model_detail', kwargs={'pk': meta_model.pk}))\n else:\n self.object = meta_model\n context = self.get_context_data(**kwargs)\n context['form'] = form\n return self.render_to_response(context)\n\n\nclass ModelUsagesView(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_usages.html'\n\n\nclass MetaModelAddFieldView(DetailView):\n queryset = MetaModel.get_non_primitive()\n template_name = 'metamodel/model_add_field.html'\n\n def get_context_data(self, **kwargs):\n context = super(MetaModelAddFieldView, self).get_context_data(**kwargs)\n\n form = MetaModelAddFieldForm()\n context['form'] = form\n\n return context\n\n def get(self, request, *args, **kwargs):\n if request.headers.get('X-Requested-With', None) == 'XMLHttpRequest':\n # Used to populate the \"default\" choices in the old metamodel UI\n # when adding a new field to a model\n model_type = request.GET['model']\n nullable = request.GET['nullable'] == 'true'\n multiple = request.GET['multiple'] == 'true'\n\n meta_model = self.get_object()\n\n requires_default = True\n\n if nullable or multiple:\n requires_default = False\n\n if not meta_model.instancemodel_set.all():\n requires_default = False\n\n default_choices = None\n\n if model_type:\n meta_model = MetaModel.objects.get(pk=model_type)\n if meta_model.is_primitive():\n default_choices = meta_model.html_input_type()\n else:\n default_choices = [(e.pk, str(e)) for e\n in meta_model.instancemodel_set.all()]\n\n return HttpResponse(json.dumps([requires_default,\n default_choices]))\n\n else:\n return super(MetaModelAddFieldView, self).get(request, *args,\n **kwargs)\n\n def post(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n\n form = MetaModelAddFieldForm(data=request.POST, files=request.FILES)\n\n if form.is_valid():\n meta_field = form.instance\n meta_field.parent = meta_model\n\n if meta_field.requires_default_value_for_saving():\n if meta_field.model.name == 'FileField':\n default_value = request.FILES['default'].name\n cleaned_default = meta_field.clean_value(default_value)\n\n if hasattr(settings, 'METAMODEL'):\n path = os.path.join(\n settings.METAMODEL.get('MEDIA_PATH', ''),\n cleaned_default)\n else:\n path = cleaned_default\n\n cleaned_default = default_storage.save(\n path, request.FILES['default'])\n else:\n default_value = request.POST.get('default')\n cleaned_default = meta_field.clean_value(default_value)\n\n meta_field.save(default=cleaned_default)\n else:\n meta_field.save()\n\n return HttpResponseRedirect(reverse('metamodel_model_meta',\n kwargs={'pk': meta_model.pk}))\n\n self.object = meta_model\n context = self.get_context_data(**kwargs)\n context['form'] = form\n return self.render_to_response(context)\n\n\nclass MetaFieldDetailView(DetailView):\n model = MetaField\n template_name = 'metamodel/field_detail.html'\n\n def get_context_data(self, **kwargs):\n context = super(MetaFieldDetailView, self).get_context_data(**kwargs)\n\n context['form'] = MetaFieldForm(instance=self.object)\n\n return context\n\n def post(self, request, pk, *args, **kwargs):\n meta_field = self.get_object()\n form = MetaFieldForm(request.POST, instance=meta_field)\n\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse(\n 'metamodel_model_meta',\n kwargs={'pk': meta_field.parent.pk}))\n else:\n self.object = meta_field\n context = self.get_context_data()\n context['form'] = form\n\n return self.render_to_response(context)\n\n\nclass MetaFieldDeleteView(DetailView):\n model = MetaField\n template_name = 'metamodel/field_delete.html'\n\n def post(self, request, pk, *args, **kwargs):\n meta_field = self.get_object()\n\n meta_model = meta_field.parent\n meta_field.delete()\n\n return HttpResponseRedirect(reverse(\n 'metamodel_model_meta',\n kwargs={'pk': meta_model.pk}))\n\n\nclass MetaFieldMakeNullableView(DetailView):\n queryset = MetaField.objects.filter(nullable=False)\n template_name = 'metamodel/field_make_nullable.html'\n\n def post(self, request, pk, *args, **kwargs):\n meta_field = self.get_object()\n meta_field.nullable = True\n meta_field.save()\n\n return HttpResponseRedirect(reverse(\n 'metamodel_model_meta',\n kwargs={'pk': meta_field.parent.pk}))\n\n\nclass MetaFieldMakeNonNullableView(DetailView):\n queryset = MetaField.objects.filter(nullable=True)\n template_name = 'metamodel/field_make_non_nullable.html'\n\n def get_form(self, data=None):\n meta_field = self.get_object()\n form = None\n if not meta_field.multiple:\n if meta_field.model.is_primitive():\n form = MetaFieldMakeNonNullablePrimitiveForm(meta_field,\n data=data)\n else:\n form = MetaFieldMakeNonNullableMetaFieldForm(meta_field,\n data=data)\n\n return form\n\n def get_context_data(self, **kwargs):\n context = super(MetaFieldMakeNonNullableView, self).get_context_data(\n **kwargs)\n\n form = self.get_form()\n context['form'] = form\n\n return context\n\n def post(self, request, pk, *args, **kwargs):\n meta_field = self.get_object()\n\n form = self.get_form(data=self.request.POST)\n\n if form:\n if form.is_valid():\n default = form.cleaned_data['default']\n meta_field.nullable = False\n meta_field.save(default=default)\n else:\n self.object = meta_field\n context = self.get_context_data()\n context['form'] = form\n\n return self.render_to_response(context)\n else:\n meta_field.nullable = False\n meta_field.save()\n\n return HttpResponseRedirect(reverse(\n 'metamodel_model_meta',\n kwargs={'pk': meta_field.parent.pk}))\n\n\nclass MetaFieldMakeMultipleView(DetailView):\n queryset = MetaField.objects.filter(multiple=False)\n template_name = 'metamodel/field_make_multiple.html'\n\n def post(self, request, pk, *args, **kwargs):\n meta_field = self.get_object()\n meta_field.multiple = True\n meta_field.save()\n\n return HttpResponseRedirect(reverse(\n 'metamodel_model_meta',\n kwargs={'pk': meta_field.parent.pk}))\n\n\nclass InstanceModelDetailView(DetailView):\n queryset = InstanceModel.get_non_primitive()\n template_name = 'metamodel/instance_detail.html'\n\n def get_context_data(self, **kwargs):\n context = super(InstanceModelDetailView, self).get_context_data(\n **kwargs)\n\n instance_model = self.get_object()\n form_klass = instance_model.get_form()\n\n context['form'] = form_klass()\n\n plugin_context = []\n for PluginKlass in Plugin.registry.values():\n plugin_value = PluginKlass.on_instance_model_detail_view(\n instance_model)\n if plugin_value is not None:\n plugin_context.append(plugin_value)\n\n context['plugin_context'] = plugin_context\n\n return context\n\n def post(self, request, pk, *args, **kwargs):\n instance_model = self.get_object()\n\n form = instance_model.get_form()(request.POST, request.FILES)\n\n if form.is_valid():\n instance_model.update_fields(form.cleaned_data, request.POST)\n messages.success(\n request,\n u'{1} actualizado correctamente'.format(\n reverse('metamodel_instance_detail',\n kwargs={'pk': instance_model.pk}),\n str(instance_model)\n ))\n if 'save' in request.POST:\n return HttpResponseRedirect(\n reverse('metamodel_model_detail',\n kwargs={'pk': instance_model.model.pk}))\n\n return HttpResponseRedirect(\n reverse('metamodel_instance_detail',\n kwargs={'pk': instance_model.pk}))\n\n else:\n messages.error(request, u'Formulario no válido')\n self.object = instance_model\n context = self.get_context_data(**kwargs)\n context['form'] = form\n\n return self.render_to_response(context)\n\n\nclass InstanceModelDeleteView(DetailView):\n queryset = InstanceModel.get_non_primitive()\n template_name = 'metamodel/instance_delete.html'\n\n def post(self, request, pk, *args, **kwargs):\n instance_model = self.get_object()\n model = instance_model.model\n\n instance_model.delete()\n messages.success(request, u'Instancia borrada correctamente')\n\n return HttpResponseRedirect(reverse('metamodel_model_detail',\n kwargs={'pk': model.id}))\n\n\nclass InstanceModelPopupRedirect(DetailView):\n queryset = InstanceModel.get_non_primitive()\n template_name = 'metamodel/instance_popup_redirect.html'\n\n def get_context_data(self, **kwargs):\n context = super(InstanceModelPopupRedirect, self).get_context_data(\n **kwargs)\n\n instance_dict = {\n 'id': self.object.id,\n 'name': str(self.object),\n 'model': self.object.model.id\n }\n\n context['instance_json'] = json.dumps(instance_dict)\n\n return context\n\n\n# api\nclass MetaModelViewSet(viewsets.ModelViewSet):\n queryset = MetaModel.objects.all()\n\n def get_serializer_class(self):\n if self.action == 'list' or self.action == 'create' or self.action == \\\n 'partial_update':\n return MetaModelWithoutFieldsSerializer\n if self.action == 'retrieve':\n return MetaModelSerializer\n\n def get_permissions(self):\n if self.action in ['list', 'retrieve', 'add_instance']:\n permission_classes = [IsAdminUser]\n else:\n permission_classes = [IsSuperuser]\n\n return [permission() for permission in permission_classes]\n\n @action(detail=True, methods=['POST'])\n def add_instance(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n form = meta_model.get_form()(request.data, request.FILES)\n\n if form.is_valid():\n instance_model = InstanceModel()\n instance_model.model = meta_model\n\n instance_model.save(initial=True)\n instance_model.update_fields(\n form.cleaned_data,\n request.data,\n creator_id=request.user.id)\n instance_values = list(\n InstanceModel.objects.filter(id=instance_model.id).values())\n\n return Response(instance_values[0])\n else:\n return Response(form.errors, status=status.HTTP_400_BAD_REQUEST)\n\n @action(detail=True, methods=['POST'])\n def add_field(self, request, *args, **kwargs):\n meta_model = self.get_object()\n\n form = MetaModelAddFieldForm(data=request.data, files=request.FILES)\n\n if form.is_valid():\n meta_field = form.instance\n meta_field.parent = meta_model\n if 'default' in request.data:\n default_value = request.data['default']\n cleaned_default = meta_field.clean_value(default_value)\n meta_field.save(default=cleaned_default)\n else:\n meta_field.save()\n serializer = MetaFieldSerializer(\n meta_field, context={'request': request})\n return Response(serializer.data)\n\n @action(detail=True, methods=['GET'])\n def get_dependencies(self, request, pk, *args, **kwargs):\n meta_model = self.get_object()\n dependencies = MetaField.objects.select_related('model').filter(\n model__id=meta_model.id)\n serializer = MetaFieldSerializer(dependencies,\n context={'request': request},\n many=True)\n return Response(serializer.data)\n\n def destroy(self, request, *args, **kwargs):\n meta_model = self.get_object()\n dependencies = MetaField.objects.select_related('model').filter(\n model__id=meta_model.id).count()\n if not meta_model.is_primitive() and dependencies == 0:\n meta_model.delete()\n return Response({'status': 'ok'})\n else:\n return Response({'errors': 'this meta model has dependencies'},\n status=status.HTTP_400_BAD_REQUEST)\n\n\nclass InstanceModelViewSet(viewsets.ReadOnlyModelViewSet):\n queryset = InstanceModel.objects.select_related('model').prefetch_related(\n 'model__fields__model', 'model__fields__parent',\n 'fields__field__model', 'fields__field__parent', 'fields__value')\n pagination_class = InstancePagination\n filter_backends = (rest_framework.DjangoFilterBackend, SearchFilter)\n search_fields = ['unicode_representation']\n filterset_class = InstanceFilterSet\n permission_classes = [IsAdminUser]\n\n def get_serializer_class(self):\n if self.action == 'list':\n return InstanceModelWithoutMetamodelSerializer\n if self.action == 'retrieve':\n return InstanceModelSerializer\n\n @action(detail=True, methods=['POST'])\n def edit(self, request, pk, *args, **kwargs):\n instance_model = self.get_object()\n form = instance_model.get_form()(request.data, request.FILES)\n if form.is_valid():\n instance_model.update_fields(form.cleaned_data, request.POST)\n instance_model = self.queryset.get(id=instance_model.id)\n\n return Response(InstanceModelSerializer(\n instance_model,\n context={'request': request}).data)\n else:\n return Response(form.errors, status=status.HTTP_400_BAD_REQUEST)\n\n @action(detail=True, methods=['GET'])\n def get_dependencies(self, request, pk, *args, **kwargs):\n instance_model = self.get_object()\n dependencies = InstanceField.objects.select_related('parent', 'field',\n 'value').filter(\n value__id=instance_model.id)\n serializer = InstanceFieldSerializer(dependencies,\n context={'request': request},\n many=True)\n return Response(serializer.data)\n\n def destroy(self, request, *args, **kwargs):\n instance_model = self.get_object()\n dependencies = InstanceField.objects.filter(\n value__id=instance_model.id).count()\n if not instance_model.is_model_primitive() and dependencies == 0:\n instance_model.delete()\n return Response({'status': 'ok'})\n else:\n return Response({'errors': 'this instance model has dependencies'},\n status=status.HTTP_400_BAD_REQUEST)\n\n\nclass InstanceFieldViewSet(viewsets.ModelViewSet):\n queryset = InstanceField.objects.select_related('field', 'field__model',\n 'field__parent', 'value')\n pagination_class = InstancePagination\n serializer_class = InstanceFieldSerializer\n filter_backends = (rest_framework.DjangoFilterBackend,)\n filterset_class = InstanceFieldFilterSet\n permission_classes = [IsSuperuser]\n\n\nclass MetaFieldViewSet(viewsets.ModelViewSet):\n queryset = MetaField.objects.select_related('model', 'parent')\n permission_classes = [IsSuperuser]\n filter_backends = (rest_framework.DjangoFilterBackend,)\n filterset_class = MetaFieldFilterSet\n\n def get_serializer_class(self):\n if self.action == 'create':\n return MetaModelAddFieldSerializer\n else:\n return MetaFieldSerializer\n\n def destroy(self, request, *args, **kwargs):\n meta_field = self.get_object()\n meta_model = meta_field.parent\n meta_field.delete()\n return Response(\n MetaModelSerializer(meta_model, context={'request': request}).data)\n\n def get_form(self, data=None):\n meta_field = self.get_object()\n form = None\n if not meta_field.multiple:\n if meta_field.model.is_primitive():\n form = MetaFieldMakeNonNullablePrimitiveForm(meta_field,\n data=data)\n else:\n form = MetaFieldMakeNonNullableMetaFieldForm(meta_field,\n data=data)\n\n return form\n\n @action(detail=True, methods=['POST'])\n def make_non_nullable(self, request, pk, *args, **kwargs):\n meta_field = self.get_object()\n\n form = self.get_form(data=request.data)\n\n if form:\n if form.is_valid():\n default = form.cleaned_data['default']\n meta_field.nullable = False\n meta_field.save(default=default)\n else:\n return Response(form.errors,\n status=status.HTTP_400_BAD_REQUEST)\n else:\n meta_field.nullable = False\n meta_field.save()\n\n serializer = self.get_serializer(instance=meta_field)\n\n return Response(serializer.data)\n", "repo_name": "SoloTodo/solotodo_core", "sub_path": "metamodel/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 24251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.views.generic.ListView", "line_number": 36, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.get_non_primitive", "line_number": 37, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel", "line_number": 37, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 41, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.get_non_primitive", "line_number": 42, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel", "line_number": 42, "usage_type": "name"}, {"api_name": "django.views.generic.FormView", "line_number": 46, "usage_type": "name"}, {"api_name": "metamodel.forms.meta_model_form.MetaModelForm", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 59, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 63, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.get_non_primitive", "line_number": 64, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel", "line_number": 64, "usage_type": "name"}, {"api_name": "metamodel.forms.meta_model_form.MetaModelForm", "line_number": 70, "usage_type": "call"}, {"api_name": "metamodel.forms.meta_model_form.MetaModelForm", "line_number": 78, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 81, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 82, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 91, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.get_non_primitive", "line_number": 92, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel", "line_number": 92, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 99, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 99, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 102, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.get_non_primitive", "line_number": 103, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel", "line_number": 103, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 114, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 114, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 118, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.get_non_primitive", "line_number": 119, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel", "line_number": 119, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 130, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 146, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 156, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 160, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 160, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 163, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 167, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 167, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 176, "usage_type": "name"}, {"api_name": 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"usage_type": "call"}, {"api_name": "metamodel.forms.meta_model_add_field_form.MetaModelAddFieldForm", "line_number": 231, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 242, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "django.conf.settings.METAMODEL.get", "line_number": 244, "usage_type": "call"}, {"api_name": "django.conf.settings.METAMODEL", "line_number": 244, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 244, "usage_type": "name"}, {"api_name": "django.core.files.storage.default_storage.save", "line_number": 249, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 249, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 259, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 259, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 268, "usage_type": "name"}, {"api_name": "metamodel.models.MetaField", "line_number": 269, "usage_type": "name"}, {"api_name": "metamodel.forms.meta_field_form.MetaFieldForm", "line_number": 275, "usage_type": "call"}, {"api_name": "metamodel.forms.meta_field_form.MetaFieldForm", "line_number": 281, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 285, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 285, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 296, "usage_type": "name"}, {"api_name": "metamodel.models.MetaField", "line_number": 297, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 306, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 306, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 311, "usage_type": "name"}, {"api_name": "metamodel.models.MetaField.objects.filter", "line_number": 312, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects", "line_number": 312, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaField", "line_number": 312, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 320, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 320, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 325, "usage_type": "name"}, {"api_name": "metamodel.models.MetaField.objects.filter", "line_number": 326, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects", "line_number": 326, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaField", "line_number": 326, "usage_type": "name"}, {"api_name": "metamodel.forms.meta_field_make_non_nullable_primitive_form.MetaFieldMakeNonNullablePrimitiveForm", "line_number": 334, "usage_type": "call"}, {"api_name": "metamodel.forms.meta_field_make_non_nullable_meta_field_form.MetaFieldMakeNonNullableMetaFieldForm", "line_number": 337, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 371, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 371, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 376, "usage_type": "name"}, {"api_name": "metamodel.models.MetaField.objects.filter", "line_number": 377, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects", "line_number": 377, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaField", "line_number": 377, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 385, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 385, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 390, "usage_type": "name"}, {"api_name": "metamodel.models.InstanceModel.get_non_primitive", "line_number": 391, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 391, "usage_type": "name"}, {"api_name": "metamodel.plugin.Plugin.registry.values", "line_number": 404, "usage_type": "call"}, {"api_name": "metamodel.plugin.Plugin.registry", "line_number": 404, "usage_type": "attribute"}, {"api_name": "metamodel.plugin.Plugin", "line_number": 404, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 421, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 421, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 424, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 429, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 430, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 433, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 434, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 438, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 438, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 446, "usage_type": "name"}, {"api_name": "metamodel.models.InstanceModel.get_non_primitive", "line_number": 447, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 447, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 455, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 455, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 457, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 457, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 461, "usage_type": "name"}, {"api_name": "metamodel.models.InstanceModel.get_non_primitive", "line_number": 462, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 462, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 475, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 481, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 481, "usage_type": "name"}, {"api_name": "metamodel.models.MetaModel.objects.all", "line_number": 482, "usage_type": "call"}, {"api_name": "metamodel.models.MetaModel.objects", "line_number": 482, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaModel", "line_number": 482, "usage_type": "name"}, {"api_name": "metamodel.serializers.MetaModelWithoutFieldsSerializer", "line_number": 487, "usage_type": "name"}, {"api_name": "metamodel.serializers.MetaModelSerializer", "line_number": 489, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 493, "usage_type": "name"}, {"api_name": "solotodo.permissions.IsSuperuser", "line_number": 495, "usage_type": "name"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 505, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel.objects.filter", "line_number": 514, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel.objects", "line_number": 514, "usage_type": "attribute"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 514, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 516, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 518, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 518, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 518, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 499, "usage_type": "call"}, {"api_name": "metamodel.forms.meta_model_add_field_form.MetaModelAddFieldForm", "line_number": 524, "usage_type": "call"}, {"api_name": "metamodel.serializers.MetaFieldSerializer", "line_number": 535, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 537, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 520, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects.select_related", "line_number": 542, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects", "line_number": 542, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaField", "line_number": 542, "usage_type": "name"}, {"api_name": "metamodel.serializers.MetaFieldSerializer", "line_number": 544, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 547, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 539, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects.select_related", "line_number": 551, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects", "line_number": 551, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaField", "line_number": 551, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 555, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 557, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 558, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 558, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ReadOnlyModelViewSet", "line_number": 561, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 561, "usage_type": "name"}, {"api_name": "metamodel.models.InstanceModel.objects.select_related", "line_number": 562, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceModel.objects", "line_number": 562, "usage_type": "attribute"}, {"api_name": "metamodel.models.InstanceModel", "line_number": 562, "usage_type": "name"}, {"api_name": "metamodel.pagination.InstancePagination", "line_number": 565, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 566, "usage_type": "attribute"}, {"api_name": "django_filters.rest_framework", "line_number": 566, "usage_type": "name"}, {"api_name": "rest_framework.filters.SearchFilter", "line_number": 566, "usage_type": "name"}, {"api_name": "metamodel.filters.InstanceFilterSet", "line_number": 568, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 569, "usage_type": "name"}, {"api_name": "metamodel.serializers.InstanceModelWithoutMetamodelSerializer", "line_number": 573, "usage_type": "name"}, {"api_name": "metamodel.serializers.InstanceModelSerializer", "line_number": 575, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 585, "usage_type": "call"}, {"api_name": "metamodel.serializers.InstanceModelSerializer", "line_number": 585, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 589, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 589, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 589, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 577, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceField.objects.select_related", "line_number": 594, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceField.objects", "line_number": 594, "usage_type": "attribute"}, {"api_name": "metamodel.models.InstanceField", "line_number": 594, "usage_type": "name"}, {"api_name": "metamodel.serializers.InstanceFieldSerializer", "line_number": 597, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 600, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 591, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceField.objects.filter", "line_number": 604, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceField.objects", "line_number": 604, "usage_type": "attribute"}, {"api_name": "metamodel.models.InstanceField", "line_number": 604, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 608, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 610, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 611, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 611, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 614, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 614, "usage_type": "name"}, {"api_name": "metamodel.models.InstanceField.objects.select_related", "line_number": 615, "usage_type": "call"}, {"api_name": "metamodel.models.InstanceField.objects", "line_number": 615, "usage_type": "attribute"}, {"api_name": "metamodel.models.InstanceField", "line_number": 615, "usage_type": "name"}, {"api_name": "metamodel.pagination.InstancePagination", "line_number": 617, "usage_type": "name"}, {"api_name": "metamodel.serializers.InstanceFieldSerializer", "line_number": 618, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 619, "usage_type": "attribute"}, {"api_name": "django_filters.rest_framework", "line_number": 619, "usage_type": "name"}, {"api_name": "metamodel.filters.InstanceFieldFilterSet", "line_number": 620, "usage_type": "name"}, {"api_name": "solotodo.permissions.IsSuperuser", "line_number": 621, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 624, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 624, "usage_type": "name"}, {"api_name": "metamodel.models.MetaField.objects.select_related", "line_number": 625, "usage_type": "call"}, {"api_name": "metamodel.models.MetaField.objects", "line_number": 625, "usage_type": "attribute"}, {"api_name": "metamodel.models.MetaField", "line_number": 625, "usage_type": "name"}, {"api_name": "solotodo.permissions.IsSuperuser", "line_number": 626, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 627, "usage_type": "attribute"}, {"api_name": "django_filters.rest_framework", "line_number": 627, "usage_type": "name"}, {"api_name": "metamodel.filters.MetaFieldFilterSet", "line_number": 628, "usage_type": "name"}, {"api_name": "metamodel.serializers.MetaModelAddFieldSerializer", "line_number": 632, "usage_type": "name"}, {"api_name": "metamodel.serializers.MetaFieldSerializer", "line_number": 634, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 640, "usage_type": "call"}, {"api_name": "metamodel.serializers.MetaModelSerializer", "line_number": 641, "usage_type": "call"}, {"api_name": "metamodel.forms.meta_field_make_non_nullable_primitive_form.MetaFieldMakeNonNullablePrimitiveForm", "line_number": 648, "usage_type": "call"}, {"api_name": "metamodel.forms.meta_field_make_non_nullable_meta_field_form.MetaFieldMakeNonNullableMetaFieldForm", "line_number": 651, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 668, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 669, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 669, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 676, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 656, "usage_type": "call"}]} +{"seq_id": "37502143370", "text": "import csv\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ncorpora=\"corpus-kab.txt\"\ndef tagset (corpora):\n tags=[]\n for sentence in open(corpora,encoding='utf-8'):\n tagged_sentence=sentence.replace('\\ufeff',\"\").replace('\\n',\"\").split()\n for tagged_word in tagged_sentence:\n tag=tagged_word.split(\"/\")[1]\n if tag not in tags:\n tags.append(tag)\n return tags\n\ntags=tagset(corpora)\n\n\nheader = tags\ndata=[]\n\ndef initilialize (tags):\n words=[]\n for i in tags:\n words.append('')\n return words\n\n\n\nfor sentence in open(corpora,encoding='utf-8'):\n words=initilialize (tags)\n\n tagged_sentence=sentence.replace('\\ufeff',\"\").replace('\\n',\"\").split()\n for tagged_word in tagged_sentence:\n word=tagged_word.split(\"/\")[0]\n words[tags.index(tagged_word.split(\"/\")[1])]=word\n data.append(words)\n#print (data)\n\n\nwith open('postag.csv', 'w', encoding='UTF8', newline='\\n') as f:\n writer = csv.writer(f,delimiter='\\t')\n\n # write the header\n writer.writerow(header)\n for i in data:\n writer.writerow(i)\n\n\ndf = pd.read_csv ('postag.csv',delimiter='\\t')\n\nVerbs=['VAF', #aoriste futur\n 'VAI', # aoriste impératif\n 'VAIT', #aoriste intensif\n 'VII', #impératif intensif\n 'VP', # prétérit\n 'VPA', #participe aoriste\n 'VPAIN', #participe aoriste intensif négatif\n 'VPAIP', #participe aoriste intensif positif\n 'VPN', # prétérit négatif\n 'VPPN', #participe prétérit négatif\n 'VPPP', # participe prétérit positif\n 'VS' # verbe subjonctif\n ]\noccurences=[]\nfor i in Verbs:\n\n occurences.append(df[i].count())\n\npatches, texts, autotexts = plt.pie(occurences,\n labels=Verbs, autopct='%.0f%%',\n shadow=False, radius=1)\nfor t in texts:\n t.set_size('smaller')\nautotexts[0].set_color('y')\n\nplt.xlabel('Ifmiḍen n yimyagen s tmeẓri deg uḍris n ulmad')\n\nplt.show()\n\n\n##noms Verbes\n\nVerbes=['VAF', #aoriste futur\n 'VAI', # aoriste impératif\n 'VAIT', #aoriste intensif\n 'VII', #impératif intensif\n 'VP', # prétérit\n 'VPA', #participe aoriste\n 'VPAIN', #participe aoriste intensif négatif\n 'VPAIP', #participe aoriste intensif positif\n 'VPN', # prétérit négatif\n 'VPPN', #participe prétérit négatif\n 'VPPP', # participe prétérit positif\n 'VS' # verbe subjonctif\n ]\n\nVerbs=['Imyagen','Ismawen']\n\noccurences=[]\nnb=0\nfor i in Verbes:\n nb=nb+df[i].count()\n\n\noccurences.append(nb)\n\nNames=['NMC', #nom commun\n 'NMP', # nom propre\n 'NCM', #nom cardinal\n]\n\nnb=0\nfor i in Names:\n nb=nb+df[i].count()\n\n\noccurences.append(nb)\n\n\npatches, texts, autotexts = plt.pie(occurences,\n labels=Verbs, autopct='%.0f%%',\n shadow=False, radius=1)\nfor t in texts:\n t.set_size('smaller')\nautotexts[0].set_color('y')\n\nplt.xlabel('Ismawen d Imyagen')\n\nplt.show()\n\n", "repo_name": "MohammedBelkacem/POSTAG-Kabyle", "sub_path": "PosTagAnalysis.py", "file_name": "PosTagAnalysis.py", "file_ext": "py", "file_size_in_byte": 3085, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "3", "api": [{"api_name": "csv.writer", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}]} +{"seq_id": "143143536", "text": "import copy\nimport regex as re\n\nfrom fsm_tools.parsers.expression_ast import Expression\nfrom fsm_tools.parsers.expression_parser import ExpressionParser\nfrom fsm_tools.parsers.transition_parser import TransitionParser\nfrom fsm_tools.fsm_types import State, Transition, Event\n\n''\n'EvConfig / isAction(arg0, arg1) \\n { arg0 = arg1; [ arg0 == arg1 ] } //'\n\n_state_declaration_regex = r\"((?P\\\".*\\\")\\s*as\\s+)?\\s*?(?P\\w+)\\s*\"\n_state_declaration = re.compile(_state_declaration_regex)\n_nested_state_regex = r\"state\\s*\" + \\\n _state_declaration_regex + \\\n r\"(?P\\{((?:[^\\{\\}]|(?&body))*)\\})\"\n_nested_state = re.compile(_nested_state_regex)\n\n_start_end_point_regex = r\"\\[\\*\\]\"\n_start_end_point = re.compile(_start_end_point_regex)\n_state_name_regex = r\"(\\[\\*\\]|[\\.\\d\\w]+)\"\n_state_name = re.compile(_state_name_regex)\n_arrow_regex = r\"-(down|right|left|up)?-?>\"\n_arrow = re.compile(_arrow_regex)\n\n_transition_regex = r\"(?P\" + _state_name_regex + r\")\\s*?\" + \\\n _arrow_regex + \\\n r\"\\s*?(?P\" + _state_name_regex + r\")\" + \\\n r\"(\\s*:\\s*(?P.+))?\"\n_transition = re.compile(_transition_regex)\n# _event_regex = r\"(?P[\\.\\w]+)\\s*?\\(\\s*?(?P.*?)\\s*?\\)\"\n# _action_regex = r\"(?P[\\.\\w]+)\\s*\\(\\s*(?P.*)\\s*\\)\"\n# _action = re.compile(_action_regex)\n# _actions_regex = r\"(?P(\" + _action_regex + r\")+)\"\n# _condition_regex = r\"\\[\\s*(?P.*)\\s*\\]\"\n# _transition_meta_info_regex = _event_regex + r\"\\s*\\/?\\s*\" + \\\n# r\"(\" + _actions_regex + r\")?\\s*\" + \\\n# r\"(\" + _condition_regex + r\")?\"\n# _transition_meta_info = re.compile(_transition_meta_info_regex)\n\n_comment_regex = r\"(\\<\\*\\*(?P(\\<\\*\\*(*PRUNE)(*FAIL)|.|\\n)*?)\\*\\*\\>)?\"\n_comment = re.compile(_comment_regex)\n_type_regex = r\"(?P([\\.\\w]+)\\s*(\\[\\])?)\"\n_parameter_regex = _comment_regex + \\\n r\"\\s*((\" + _type_regex + r\")\\s+(?P\\w+)\\s*)\\s*\"\n\n\ndef _find_least_common_acesentor(root, states=[]):\n if root is None:\n return None\n\n if root in states:\n return root\n\n least_common_acesentors = set()\n for sub_state in root.sub_states:\n acesentor = _find_least_common_acesentor(sub_state, states)\n least_common_acesentors.add(acesentor)\n\n if len(least_common_acesentors) > 0 and root.sub_states == least_common_acesentors:\n if len(least_common_acesentors) == 1:\n return least_common_acesentors.pop()\n else:\n return root\n else:\n return None\n\n\nclass PlantUMLParser:\n\n def _parse_events(self, events):\n parser = ExpressionParser(events)\n expression = parser.get_ast()\n return [Event(exp) for exp in expression] if isinstance(expression, Expression) else [Event(expression)]\n\n def _parse_actions(self, actions):\n parser = ExpressionParser(actions)\n expression = parser.get_ast()\n return [exp for exp in expression] if isinstance(expression, Expression) else [expression]\n\n def _parse_condition(self, condition):\n parser = ExpressionParser(condition)\n return parser.get_ast()\n\n def _parse_transition(self, instructions, parent_state=None):\n \"\"\"\n\n :param instructions:\n :param parent_state:\n :return:\n \"\"\"\n states = set()\n transitions = set()\n transition_meta = _transition.finditer(instructions)\n for transition in transition_meta:\n from_state_name = transition.group('state_from')\n to_state_name = transition.group('state_to')\n comment = transition.group('comment')\n events = []\n actions = []\n condition = None\n if comment is not None:\n parser = TransitionParser(comment)\n raw_events, raw_condition, raw_actions = parser.get_transition_items()\n events = self._parse_events(raw_events)\n actions = self._parse_actions(raw_actions)\n condition = self._parse_condition(raw_condition)\n if from_state_name != '[*]':\n from_state = State.create_state(from_state_name, parent_state)\n for state in states:\n if from_state == state:\n state.sub_states.update(from_state.sub_states)\n state.transitions.update(from_state.transitions)\n break\n else:\n states.add(from_state)\n else:\n from_state = parent_state\n if to_state_name != '[*]':\n to_state = State.create_state(to_state_name, parent_state)\n for state in states:\n if to_state == state:\n state.sub_states.update(to_state.sub_states)\n state.transitions.update(to_state.transitions)\n break\n else:\n states.add(to_state)\n else:\n to_state = parent_state\n transitions.add(Transition(from_state, to_state, events, actions, condition))\n return states, transitions\n\n def _parse_instructions(self, instructions, parent_state=None):\n \"\"\"\n Parsing the platuml\n :param instructions:\n :param parent_state:\n :return:\n \"\"\"\n states = set()\n transitions = set()\n simple_instructions = re.sub(_nested_state_regex, '', copy.copy(instructions))\n new_states, new_transitions = self._parse_transition(simple_instructions, parent_state)\n states.update(new_states)\n transitions.update(new_transitions)\n nested_state_meta = _nested_state.finditer(instructions)\n for nested_state in nested_state_meta:\n state_name = nested_state.group('name')\n state_comment = nested_state.group('comment')\n new_state = State.create_state(state_name, parent_state, state_comment)\n body = nested_state.group('body')\n new_states, new_transitions = self._parse_instructions(body, new_state)\n for state in states:\n if new_state == state:\n state.sub_states.update(new_state.sub_states)\n state.transitions.update(new_state.transitions)\n break\n else:\n states.add(new_state)\n transitions.update(new_transitions)\n return states, transitions\n\n def _tie_transitions_to_states(self, transitions, root):\n for transition in transitions:\n transition_owner = _find_least_common_acesentor(root, [transition.from_state, transition.to_state])\n if transition_owner is not None:\n transition_owner.transitions.add(transition)\n\n def parse_uml_file(self, file_name):\n \"\"\"\n Function for parsing PlantUML State Machine\n :param file_name: Name of file with State Machine\n :return: states, transitions: State and Transitions of State Machine\n \"\"\"\n with open(file_name, 'r') as file:\n import os\n instructions = file.read()\n state_machine_name = os.path.basename(file_name)\n state_machine_name = state_machine_name.split('.')[0]\n root = State.create_state(state_machine_name)\n states, transitions = self._parse_instructions(instructions, root)\n self._tie_transitions_to_states(transitions, root)\n return states, transitions\n\n\nif __name__ == '__main__':\n parser = PlantUMLParser()\n states, transitions = parser.parse_uml_file('/home/redra/Projects/FSMTools/tests/simple_fsm/FSM.txt')\n for state in states:\n print(\"=================\")\n print(\"State name is \" + state.name)\n if state.parent_state is not None:\n print(\"State parent is \" + str(state.parent_state))\n print(\"State parent name is \" + str(state.parent_state.name))\n print(\"State parent comment is \" + str(state.parent_state.comment))\n print(\"State comment is \" + str(state.comment))\n print(\"=================\")\n for transition in transitions:\n print(\"Transition from: \" + str(transition.from_state))\n print(\"Transition to: \" + str(transition.to_state))\n print(\"Transition events:\" + str(transition.event))\n for ev in transition.event:\n print(\" Event: \" + str(ev))\n print(\"Transition actions: \" + str(transition.action))\n for ac in transition.action:\n print(\" Action: \" + str(ac))\n print(\"Transition condition: \" + str(transition.condition))\n\n # TODO(redra): Should be considered if needed to generate *.png\n # from subprocess import call\n # call(['java', '-jar', './plantuml.jar'])\n", "repo_name": "redradist/hsm_tools", "sub_path": "src/fsm_tools/parsers/plantuml_parser.py", "file_name": "plantuml_parser.py", "file_ext": "py", "file_size_in_byte": 8921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "regex.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 17, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 20, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 24, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 30, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 42, "usage_type": "call"}, {"api_name": "fsm_tools.parsers.expression_parser.ExpressionParser", "line_number": 72, "usage_type": "call"}, {"api_name": "fsm_tools.parsers.expression_ast.Expression", "line_number": 74, "usage_type": "argument"}, {"api_name": "fsm_tools.fsm_types.Event", "line_number": 74, "usage_type": "call"}, {"api_name": "fsm_tools.parsers.expression_parser.ExpressionParser", "line_number": 77, "usage_type": "call"}, {"api_name": "fsm_tools.parsers.expression_ast.Expression", "line_number": 79, "usage_type": "argument"}, {"api_name": "fsm_tools.parsers.expression_parser.ExpressionParser", "line_number": 82, "usage_type": "call"}, {"api_name": "fsm_tools.parsers.transition_parser.TransitionParser", "line_number": 103, "usage_type": "call"}, {"api_name": "fsm_tools.fsm_types.State.create_state", "line_number": 109, "usage_type": "call"}, {"api_name": "fsm_tools.fsm_types.State", "line_number": 109, "usage_type": "name"}, {"api_name": "fsm_tools.fsm_types.State.create_state", "line_number": 120, "usage_type": "call"}, {"api_name": "fsm_tools.fsm_types.State", "line_number": 120, "usage_type": "name"}, {"api_name": "fsm_tools.fsm_types.Transition", "line_number": 130, "usage_type": "call"}, {"api_name": "regex.sub", "line_number": 142, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 142, "usage_type": "call"}, {"api_name": "fsm_tools.fsm_types.State.create_state", "line_number": 150, "usage_type": "call"}, {"api_name": "fsm_tools.fsm_types.State", "line_number": 150, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "fsm_tools.fsm_types.State.create_state", "line_number": 180, "usage_type": "call"}, {"api_name": "fsm_tools.fsm_types.State", "line_number": 180, "usage_type": "name"}]} +{"seq_id": "32812311382", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb 10 19:41:31 2019\r\n\r\n@author: Nick Langan\r\nCSC 5930\r\nThis program performs the following:\r\n 1. Places baby names within the Social Security Administration dataset CSV whose births occurred first decade of the 2000s \r\n (2000-2009) into a dataframe. \r\n 2. Groups the data by name, excludes those names with less than 5 occurrences\r\n in an effort to preserve useful data. \r\n 3. Creates a pivot table from the filtered names, the number of births for each\r\n sex is tallied. \r\n\r\nThe steps are repeated from Homework #3 to devise a list of gender neutral names from 2000-2009:\r\n 4. Copy the quantities of births by male and female into a quantity data frame. \r\n 5. We use the div function to return a decimal that reflects the ratio (or percentage) of \r\n a name being either female or male. The female and male ratio columns are appended to original dataframe.\r\n 6. A totalBirths column is introduced for each name, with those names netting less than 5,000 births\r\n being excluded. \r\n 7. We create a genderNeutral data frame, extracting those names from the names data frame that have a female name ratio\r\n between 1/3 and 2/3 (0.333 and 0.666).\r\n 8. A new distance column is added to the genderNeutral data frame, reflecting how prominent the name is in either gender.\r\n The lower the value, the more gender neutral the name is. \r\n\r\nThen we eliminate the 2000-2009 gender-netural names from the data frame: \r\n 9. We create a list of names extracted from the genderNeutral data frame. This list is then compared with our original\r\n names data frame that was imported from the SSA. We create a new dataset featuring unique names not contained in the genderNeutral data frame.\r\n 10. The uniqueNames data frame is split up into two lists of names, one containing unique boys names and one containing unique girls names.\r\n Again, this excludes all names considered gender-neutral in the 2000-2009 decade. The first 20 and last 20 boys and girls names from each\r\n list (alphabetically) are printed to the output.\r\n 11. Two pivot tables are created using the boyNames and girlNames data frames. They are pivoted by births, indexed by name, with the sum\r\n of the births for each name by year tabulated. A fill value of 0 is issued for those years with an NaN value for number of births. \r\n 12. A separate data frame is created (and eventually deleted) for each gender, using the diff function to calculate the increase or decrease\r\n of births for each name compared with the year prior. The absolute value of each difference is taken and then placed back in the respective\r\n gender data frame. Eventually, the 'Largest_Diff' column is appended to each gender data frame, using the idxmax method to show the year\r\n where the largest change compared with its prior year occurred for each of the names.\r\n 13. The idxmax method is used to store the year that had the maximum amount of births for a baby name in a 'Max_Year' column.\r\n 14. An 'Average' column is added to each data frame, showing the mean value for the birth totals from 2000-2009 for each name.\r\n 15. Two new data frames (one for each gender) are created to sort the names by average births, highest to lowest. \r\n 16. A total number of births for each name is also shown, using the margins=true function upon pivot table creation.\r\n 17. The Tabulate package is used to produce tables for each gender for the top 20 names in terms of\r\n average births. The data produced is the total births from 2000-2004 and 2005-2009, as well as the total birth number for the decade,\r\n average births, maximum year, and largest difference values for each of the names. The tables are split\r\n into 3 to be more friendly for output review. Tabulate seems to be the updated version of the previously used PrettyTable package.\r\n 18. Two plots are produced for each gender. The first plot shows the totals each year for the Top 10 most used Boy names in the decade,\r\n with a subsequent plot also shown for Girl names. The second plot shows the year with the highest amount of births for the Top 10 most used\r\n Boy names, with a subsequent plot also shown for Girl names. \r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Here, the Tabulate package is loaded through easy_install, so that the\r\n# pre-requsite is in place regardless of the Python environment. The source\r\n# for the idea for this script can be found at:\r\n# https://stackoverflow.com/a/5944496\r\nfrom setuptools.command import easy_install\r\nimport pkg_resources\r\neasy_install.main( ['tabulate'] )\r\npkg_resources.require('tabulate') #This line ensures the module can be imported\r\n\r\nfrom tabulate import tabulate #Note, in assignment #2 I used the PrettyTable package to format a printed table. Upon further investigation,\r\n#it appears that package is \"Abandonware\", as it has not been updated since 2013. I am using a reasonable facsimile, Tabulate, which is newly updated.\r\n#Inspiration found here: https://stackoverflow.com/questions/18528533/pretty-printing-a-pandas-dataframe/24079771\r\n\r\n#The SSA dataset is imported. This location is hard coded to a location on my laptop (this should be able to be safely amended).\r\nyears = range(2000,2010)\r\npieces = []\r\ncolumns = ['name','sex','births']\r\nfor year in years: \r\n path = 'C:/Users/nick.langan.NLANGAN-LT/OneDrive - Villanova University/Spring 19/CSC5930/Week 3/names/yob%d.txt'%year\r\n frame = pd.read_csv(path,names=columns) \r\n frame['year'] = year\r\n pieces.append(frame) \r\n\r\n#The initial names data frame is created.\r\nnames = pd.concat(pieces, ignore_index=True)\r\n\r\n#Group the data frame by name.\r\nnamesGrouped = names.groupby('name')\r\n\r\n#We filter out all name occurrences with a quantity less than 100.\r\nnewNames = namesGrouped.filter(lambda x: len(x)>=5)\r\n\r\n#Pivot the data frame to create birth quantities for each name by sex.\r\nnamesDF = pd.pivot_table(newNames, values='births', index='name', columns='sex', fill_value=0, aggfunc=sum)\r\n\r\n#Male and female birth total columns are added.\r\nnamesDF.columns = ['F', 'M']\r\n \r\nnamesDF.reset_index(drop=False, inplace=True)\r\n\r\n#The male and female birth quantities are copied into a new data frame to normalize.\r\nquantityDF = namesDF[['M','F']].copy()\r\n\r\n#Since we can safely assume the sum of a ratio of a name of female and male births equals to 1, we calculate\r\n#the ratio for each name being used as a male or female in decimal format.\r\nquantityDF = quantityDF.div(quantityDF.sum(axis=1), axis=0)\r\n\r\n#The female and male ratios are appended to the namesDF.\r\nnamesDF['FemaleRatio'] = quantityDF['F']\r\nnamesDF['MaleRatio'] = quantityDF['M']\r\n\r\n#Delete unnecessary data frames to relieve memory\r\ndel quantityDF\r\n\r\n#A total births column is also appended to the namesDF.\r\nnamesDF['totalBirths'] = namesDF['M'] + namesDF['F']\r\n\r\n#The names \"Unknown\" and \"Baby\" are not meaningful, in my opinion, and I am excluding them from the dataset.\r\nnamesDF = namesDF[(namesDF['name'] != 'Unknown') & (namesDF['name'] != 'Baby')]\r\n\r\n#We exclude all names with a total birth quantity less than 5000.\r\nnamesDF = namesDF[namesDF['totalBirths'] >= 5000]\r\n\r\n#A gender neutral data frame is created. We consider a name gender neutral if its ratio for being used as a female\r\n#name lies between one-third and two-thirds.\r\ngenderNeutral = namesDF[(namesDF['FemaleRatio'] >= 0.3333) & (namesDF['FemaleRatio'] <= 0.6666)].copy() \r\n#NOTE: The copy() function was used to avoid chained assignment. More:\r\n#https://www.dataquest.io/blog/settingwithcopywarning/\r\n\r\n#A variable distance is created, the difference between the name's use as a female versus a male. The lower the distance,\r\n#the more gender neutral the name is. \r\ngenderNeutral['distance'] = np.abs(genderNeutral['FemaleRatio'] - 0.5)\r\n\r\ngenderNeutral = genderNeutral.sort_values(by='distance', ascending=True)\r\n\r\n#We extract all names from the genderNeutral list. This will be used to feed our master data frame. \r\nnameList = genderNeutral['name'].tolist()\r\n\r\ndel genderNeutral\r\n\r\n#We create a new data frame for all of the unique boys and girls names. This will use the names from our master data frame,\r\n#but only those names that DO NOT appear in the list created from our genderNeutral data frame. \r\nuniqueNames = names[names['name'].isin(nameList) == False]\r\n\r\n#Create two new data frames split by gender\r\nboyNames = uniqueNames[uniqueNames.sex == 'M']\r\ngirlNames = uniqueNames[uniqueNames.sex == 'F']\r\n\r\n#Here, we make a list of unique boy names and unique girl names from each data frame\r\nboyNamesList = boyNames['name'].tolist()\r\ngirlNamesList = girlNames['name'].tolist()\r\n\r\n#Ensure that the lists of names are sorted alphabetically\r\nboyNamesList.sort()\r\ngirlNamesList.sort()\r\n\r\n#The first and last 20 names from the boy and girl lists are printed to the output\r\nprint()\r\nprint(\"Here are the first 20 names in our list of unique boy baby names, 2000-2009:\")\r\nprint()\r\nprint(boyNamesList[:20])\r\nprint()\r\nprint(\"Here are the last 20 names in our list of unique boy baby names, 2000-2009:\")\r\nprint()\r\nprint(boyNamesList[-20:])\r\nprint()\r\nprint(\"Here are the first 20 names in our list of unique girl baby names, 2000-2009:\")\r\nprint()\r\nprint(girlNamesList[:20])\r\nprint()\r\nprint(\"Here are the last 20 names in our list of unique girl baby names, 2000-2009:\")\r\nprint()\r\nprint(girlNamesList[-20:])\r\nprint()\r\n\r\ndel uniqueNames\r\ndel boyNamesList\r\ndel girlNamesList\r\n\r\n#We pivot the boyNames data frame, to create quantities of births for the dataset by year. The sum of births for each name is calculated\r\n#via margins=true. We substitute NaN values for years without births with the fill value of 0. \r\nboy_births = boyNames.pivot_table('births', index='name', columns='year', aggfunc=sum, margins=True).fillna(0)\r\n\r\n#A separate data frame is created. This holds a differential value for the birth total of each name versus its previous year.\r\nboyColumn_difference = boy_births.drop('All', axis=1).diff(axis=1)\r\n\r\n#The absolute value of the differential value is substituted for the initial value. This will help us find the greatest change between years.\r\nboyColumn_difference = boyColumn_difference.abs()\r\n\r\n#Not counting the sum of all 10 years, the idxmax method is used to store the year that had the maximum amount of births for a baby name in a separate column.\r\nboy_births['Max_Year'] = boy_births.drop('All', axis=1).idxmax(axis=1)\r\n\r\n#Ensure all numeric data in the data frame is type integer\r\nboy_births = boy_births.astype(int)\r\n\r\n#The average total births for each name is calculated by taking the mean of each row (with only the columns with the yearly totals included)\r\nboy_births['Average'] = boy_births.iloc[:,0:10].mean(axis=1)\r\nboy_births = boy_births.drop(['All'])\r\n\r\n#A Largest_Diff column is created in the boy_births data frame from the differential. Using the idxmax method, it holds the year that had the greatest change in terms\r\n#of number of births for each name compared with its PREVIOUS year (consecutive)\r\nboy_births['Largest_Diff'] = boyColumn_difference.idxmax(axis=1).astype(int)\r\n\r\ndel boyColumn_difference\r\n\r\n#Before we print the table values, we sort the values by the highest average amount of births for the name.\r\nboyBirthsSort = boy_births.sort_values(by='Average', ascending=False)\r\n\r\ndel boy_births\r\n\r\n#We pivot the girlNames data frame, to create quantities of births for the dataset by year. The sum of births for each name is calculated\r\n#via margins=true. We substitute NaN values for years without births with the fill value of 0. \r\ngirl_births = girlNames.pivot_table('births', index='name', columns='year', aggfunc=sum, margins=True).fillna(0)\r\n\r\n#A separate data frame is created. This holds a differential value for the birth total of each name versus its previous year.\r\ngirlColumn_difference = girl_births.drop('All', axis=1).diff(axis=1)\r\n\r\n#The absolute value of the differential value is substituted for the initial value. This will help us find the greatest change between years.\r\ngirlColumn_difference = girlColumn_difference.abs()\r\n\r\n#Not counting the sum of all 10 years, the idxmax method is used to store the year that had the maximum amount of births for a baby name in a separate column.\r\ngirl_births['Max_Year'] = girl_births.drop('All', axis=1).idxmax(axis=1)\r\n\r\n#Ensure all numeric data in the data frame is type integer\r\ngirl_births = girl_births.astype(int)\r\n\r\n#The average total births for each name is calculated by taking the mean of each row (with only the columns with the yearly totals included)\r\ngirl_births['Average'] = girl_births.iloc[:,0:10].mean(axis=1)\r\ngirl_births = girl_births.drop(['All'])\r\n\r\n#A Largest_Diff column is created in the boy_births data frame from the differential. Using the idxmax method, it holds the year that had the greatest change in terms\r\n#of number of births for each name compared with its PREVIOUS year (consecutive)\r\ngirl_births['Largest_Diff'] = girlColumn_difference.idxmax(axis=1).astype(int)\r\n\r\ndel girlColumn_difference\r\n\r\n#Before we print the table values, we sort the values by the highest average amount of births for the name.\r\ngirlBirthsSort = girl_births.sort_values(by='Average', ascending=False)\r\n\r\ndel girl_births\r\n\r\ngirlBirthsSort.rename(columns={'All':'Total'}, inplace=True)\r\n\r\nprint()\r\n\r\n#Plot is printed to the output showing the top 10 boy names from 2000-2009 and their totals for each of the years\r\nboyBirthsSort.iloc[:10,:10].plot(kind='bar', figsize=(10, 10), title=\"The totals for the Top 10 most used Boy Names, 2000-2009\")\r\n\r\n#Plot is printed to the output showing the year which each of the top 10 names had their most amount of births\r\nmaxBoyPlot = boyBirthsSort.iloc[:10].plot(y='Max_Year', kind='bar', ylim=(1999, 2010), figsize=(10,10), yticks=range(2000, 2010, 1), legend=False,\r\ntitle=\"The year with the highest amount of births for most used Boy Names, 2000-2009\")\r\nprint()\r\n\r\n#Tabulate is utilized to print yearly totals, a sum, max_year, average, and the largest difference fromt he previous year for the top 10 boys names. \r\n#To help formatting, I've split the tables into 3 entries (data from 2000-2004, data from 2005-2009, overall data for the decade)\r\nprint(\"A table showing the the totals of the top 10 boys names from the decade in the years 2000-2004:\")\r\nprint(tabulate(boyBirthsSort.iloc[:10, :5], headers='keys', tablefmt='fancy_grid'))\r\nprint()\r\nprint(\"A table showing the the totals of the top 10 boys names from the decade in the years 2005-2009:\")\r\nprint(tabulate(boyBirthsSort.iloc[:10, 5:10], headers='keys', tablefmt='fancy_grid'))\r\nprint()\r\nprint(\"A table showing the decade total, maximum year, average, and the year that had the greatest change from the year prior for the top 10 boys names 2000-2009:\")\r\nprint(tabulate(boyBirthsSort.iloc[:10, 10:], headers='keys', tablefmt='fancy_grid'))\r\nprint()\r\n\r\n#Plot is printed to the output showing the top 10 girl names from 2000-2009 and their totals for each of the years\r\ngirlBirthsSort.iloc[:10,:10].plot(kind='bar', figsize=(10, 10), title=\"The totals for the Top 10 most used Girl Names, 2000-2009\")\r\n\r\n#Plot is printed to the output showing the year which each of the top 10 names had their most amount of births\r\nmaxGirlPlot = girlBirthsSort.iloc[:10].plot(y='Max_Year', kind='bar', figsize=(10,10), ylim=(1999, 2010), yticks=range(2000, 2010, 1), legend=False,\r\ntitle=\"The year with the highest amount of births for most used Girl Names, 2000-2009\")\r\nprint()\r\n\r\n#Tabulate is utilized to print yearly totals, a sum, max_year, average, and the largest difference fromt he previous year for the top 10 girls names. \r\n#To help formatting, I've split the tables into 3 entries (data from 2000-2004, data from 2005-2009, overall data for the decade)\r\nprint(\"A table showing the the totals of the top 10 girls names from the decade in the years 2000-2004:\")\r\nprint(tabulate(girlBirthsSort.iloc[:10, :5], headers='keys', tablefmt='fancy_grid'))\r\nprint()\r\nprint(\"A table showing the the totals of the top 10 girls names from the decade in the years 2005-2009:\")\r\nprint(tabulate(girlBirthsSort.iloc[:10, 5:10], headers='keys', tablefmt='fancy_grid'))\r\nprint()\r\nprint(\"A table showing the decade total, maximum year, average, and the year that had the greatest change from the year prior for the top 10 girls names 2000-2009:\")\r\nprint(tabulate(girlBirthsSort.iloc[:10, 10:], headers='keys', tablefmt='fancy_grid'))\r\n\r\n", "repo_name": "W2NJL/Villanova", "sub_path": "UniqueBabyNames.py", "file_name": "UniqueBabyNames.py", "file_ext": "py", "file_size_in_byte": 16494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "setuptools.command.easy_install.main", "line_number": 60, "usage_type": "call"}, {"api_name": "setuptools.command.easy_install", "line_number": 60, "usage_type": "name"}, {"api_name": "pkg_resources.require", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 125, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 250, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 253, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 256, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 270, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 273, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 276, "usage_type": "call"}]} +{"seq_id": "31532912247", "text": "import yaml\nimport torch\nimport h5py as h5\nimport os\nimport datetime\nimport functools\n\n\ndef get_new_session_id():\n sess_id = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n if not os.path.exists('out'):\n os.makedirs('out')\n sess_dir = 'out/{}'.format(sess_id)\n if not os.path.exists(sess_dir):\n os.mkdir(sess_dir)\n return sess_id\n\n\ndef load_weights(model, path):\n state_dict = {}\n with h5.File(path, 'r') as file:\n for key, val in file.items():\n state_dict[key] = torch.from_numpy(val[...])\n model.load_state_dict(state_dict)\n\n\ndef store_weights(model, path):\n state_dict = model.state_dict()\n with h5.File(path, 'w') as f:\n for key, val in state_dict.items():\n f.create_dataset(key, data=val.numpy())\n\n\ndef timer(process_name):\n def decorator_timer(func):\n functools.wraps(func)\n def _wrapper(*args, **kwargs):\n tic = datetime.datetime.now()\n date_time = tic.strftime('%Y-%m-%d %H:%M:%S')\n print(f'{process_name} started at time {date_time}')\n func(*args, **kwargs)\n toc = datetime.datetime.now()\n date_time = toc.strftime('%Y-%m-%d %H:%M:%S')\n print(f'{process_name} ended at time {date_time}')\n print(f'{process_name} executed in {tic - toc}')\n return _wrapper\n return decorator_timer\n\n\ndef session(func):\n functools.wraps(func)\n def _wrapper(*args, **kwargs):\n sess_id = get_new_session_id()\n with open('out/{}/args.yaml'.format(sess_id), 'w') as args_file:\n yaml.dump(kwargs, args_file)\n func(session_id=sess_id, *args, **kwargs)\n return _wrapper\n", "repo_name": "Bartolo1024/img_to_img_baseline", "sub_path": "utils/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "datetime.datetime.now", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "attribute"}, {"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.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 15, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 23, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 29, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 51, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "10327768682", "text": "from django.shortcuts import render\nfrom django.shortcuts import get_object_or_404\n\nfrom .models import *\n\n\ndef post(request, p_id):\n context = {}\n post = get_object_or_404(Post, pk=p_id)\n next_post = post.next()\n previous_post = post.previous()\n\n context[\"post\"] = post\n context[\"next_post\"] = next_post\n context[\"previous_post\"] = previous_post\n\n return render(request, \"post.html\", context)\n\n\ndef category(request, c_id):\n context = {}\n category = Category.objects.get(pk=c_id)\n categories = Category.objects.all()\n context[\"category\"] = category\n context[\"catiegories\"] = categories\n \n posts = Post.objects.filter(category=category)\n context[\"posts\"] = posts\n\n return render(request, \"category.html\", context)\n\ndef blog(request):\n context = {}\n posts = Post.objects.all().order_by('-date')[:10]\n context[\"posts\"] = posts\n return render(request, \"blog.html\", context)\n\n\n", "repo_name": "linxuan2016/photoblog", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 936, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "django.shortcuts.get_object_or_404", "line_number": 9, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "26943890774", "text": "import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef initialize_membership_matrix(n_samples, n_clusters):\n membership_matrix = np.random.rand(n_samples, n_clusters)\n membership_matrix = np.divide(membership_matrix, np.sum(membership_matrix, axis=1, keepdims=True))\n return membership_matrix\n\ndef update_cluster_centers(X, membership_matrix, fuzziness):\n numerator = np.dot(membership_matrix.T ** fuzziness, X)\n denominator = np.sum(membership_matrix.T ** fuzziness, axis=1, keepdims=True)\n new_centers = numerator / denominator\n return new_centers\n\ndef update_membership_matrix(X, cluster_centers, fuzziness):\n distances = np.linalg.norm(X[:, np.newaxis] - cluster_centers, axis=2)\n distances = np.fmax(distances, np.finfo(np.float64).eps) # Avoid division by zero\n membership_matrix = 1.0 / distances ** (2 / (fuzziness - 1))\n membership_matrix = np.divide(membership_matrix, np.sum(membership_matrix, axis=1, keepdims=True))\n return membership_matrix\n\ndef fuzzy_c_means(X, n_clusters, fuzziness, max_iter=100, tol=1e-4, verbose=False):\n n_samples, n_features = X.shape\n\n # Initialize cluster centers randomly\n cluster_centers = X[np.random.choice(n_samples, n_clusters, replace=False)]\n\n # Initialize membership matrix randomly\n membership_matrix = initialize_membership_matrix(n_samples, n_clusters)\n\n for iteration in range(max_iter):\n # Update cluster centers\n new_cluster_centers = update_cluster_centers(X, membership_matrix, fuzziness)\n\n # Update membership matrix\n new_membership_matrix = update_membership_matrix(X, new_cluster_centers, fuzziness)\n\n # Calculate the change in cluster centers and membership matrix\n center_change = np.linalg.norm(new_cluster_centers - cluster_centers)\n membership_change = np.linalg.norm(new_membership_matrix - membership_matrix)\n\n # Update cluster centers and membership matrix\n cluster_centers = new_cluster_centers\n membership_matrix = new_membership_matrix\n\n if verbose:\n print(f\"Iteration {iteration + 1}: Center Change = {center_change}, Membership Change = {membership_change}\")\n\n # Check for convergence\n if center_change < tol and membership_change < tol:\n break\n\n return cluster_centers, membership_matrix\n\ndef apply_fuzzy_c_means_to_image(image_path, n_clusters, fuzziness):\n # Load the image\n img = cv2.imread(image_path)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img_lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)\n img_shape = img_lab.shape\n\n # Extract the L channel\n L = img_lab[:, :, 0]\n\n # Rescale pixel values to the range [0, 1]\n pixels = L / 255.0\n\n # Reshape the L channel to a 1D array\n pixels = pixels.reshape(-1, 1)\n\n # Run Fuzzy C-Means clustering\n cluster_centers, membership_matrix = fuzzy_c_means(pixels, n_clusters, fuzziness, verbose=True)\n\n # Assign each pixel to the cluster with the highest membership value\n labels = np.argmax(membership_matrix, axis=1)\n\n # Replace pixel values with cluster center values\n segmented_img = cluster_centers[labels].reshape(img_shape[0], img_shape[1])\n\n # Display the original and segmented images\n plt.figure(figsize=(10, 5))\n plt.subplot(1, 2, 1)\n plt.imshow(img)\n plt.title('Original Image')\n plt.axis('off')\n\n plt.subplot(1, 2, 2)\n plt.imshow(segmented_img, cmap='gray')\n plt.title('Segmented Image (Fuzzy C-Means)')\n plt.axis('off')\n\n plt.show()\n\n# Example usage:\nif __name__ == \"__main__\":\n np.random.seed(42)\n\n # Image path\n image_path = 'PIA11420~orig.jpg'\n\n # Number of clusters\n n_clusters = 3\n\n # Fuzziness parameter (greater values make the clusters fuzzier)\n fuzziness = 2.0\n\n apply_fuzzy_c_means_to_image(image_path, n_clusters, fuzziness)\n", "repo_name": "MuhammadAreebKazmi2/ADIP-Project", "sub_path": "fcm_code.py", "file_name": "fcm_code.py", "file_ext": "py", "file_size_in_byte": 3854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "numpy.random.rand", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.fmax", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2LAB", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 97, "usage_type": "attribute"}]} +{"seq_id": "23204849791", "text": "#! /usr/bin/env python\n\"\"\" `mujoco_engine.py`\n\n @author: Jack (Jianxiang) Xu\n Contacts : projectbyjx@gmail.com\n Last edits : July 27, 2022\n\n @description:\n This library will initiate an Engine that handles updates and rendering\n\"\"\"\n#===================================#\n# I M P O R T - L I B R A R I E S #\n#===================================#\n\n# python libraries:\nimport os\nimport threading\nimport time\nimport signal\n\nfrom enum import Enum\nfrom datetime import timedelta\n\n\n# python 3rd party libraries:\nimport numpy as np\nimport mujoco\nimport cv2\n\n# custom libraries:\nimport mujoco_viewer\n\n# local libraries:\nfrom mujoco_engine.core_engine.wrapper.core import MjData, MjModel\n\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n# # ___ ___ ___ ___ ___ ___ # #\n# # /__/\\ /__/\\ / /\\ / /\\ / /\\ / /\\ # #\n# # | |::\\ \\ \\:\\ / /:/ / /::\\ / /:/ / /::\\ # #\n# # | |:|:\\ \\ \\:\\ /__/::\\ / /:/\\:\\ / /:/ / /:/\\:\\ # #\n# # __|__|:|\\:\\ ___ \\ \\:\\ \\__\\/\\:\\ / /:/ \\:\\ / /:/ ___ / /:/ \\:\\ # #\n# # /__/::::| \\:\\ /__/\\ \\__\\:\\ \\ \\:\\ /__/:/ \\__\\:\\ /__/:/ / /\\ /__/:/ \\__\\:\\ # #\n# # \\ \\:\\~~\\__\\/ \\ \\:\\ / /:/ \\__\\:\\ \\ \\:\\ / /:/ \\ \\:\\ / /:/ \\ \\:\\ / /:/ # #\n# # \\ \\:\\ \\ \\:\\ /:/ / /:/ \\ \\:\\ /:/ \\ \\:\\ /:/ \\ \\:\\ /:/ # #\n# # \\ \\:\\ \\ \\:\\/:/ /__/:/ \\ \\:\\/:/ \\ \\:\\/:/ \\ \\:\\/:/ # #\n# # \\ \\:\\ \\ \\::/ \\__\\/ \\ \\::/ \\ \\::/ \\ \\::/ # #\n# # \\__\\/ \\__\\/ \\__\\/ \\__\\/ \\__\\/ # #\n# # # #\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n#=======================#\n# D E F I N I T I O N #\n#=======================#\nclass MuJoCo_Engine_InterruptException(Exception):\n pass\n\nclass Mujoco_Engine:\n #===================#\n # C O N S T A N T #\n #===================#\n _camera_config = {\n \"camera/zed/L\": {\"width\": 1280, \"height\":720, \"fps\": 60, \"id\":1},\n \"camera/zed/R\": {\"width\": 1280, \"height\":720, \"fps\": 60, \"id\":0},\n \"camera/intel/rgb\": {\"width\": 1280, \"height\":720, \"fps\": 60, \"id\":2},\n }\n _camera_views = {}\n _IC_state = None\n _core = None\n \n #===============================#\n # I N I T I A L I Z A T I O N #\n #===============================#\n def __init__(self, \n xml_path, rate_Hz, \n camera_config=None, \n name=\"DEFAULT\", \n CAMERA_V_FACTOR=3\n ):\n signal.signal(signal.SIGTERM, self._signal_handler)\n signal.signal(signal.SIGINT, self._signal_handler)\n ## Init Configs:\n if camera_config:\n self._camera_config = (camera_config) # override if given\n self._name = name\n self._rate_Hz = rate_Hz\n \n ## Initiate MJ\n # self.mj_model = mujoco.MjModel.from_xml_path(xml_path)\n # self.mj_data = mujoco.MjData(self.mj_model)\n self.mj_model = MjModel.from_xml_path(xml_path=xml_path)\n self.mj_data = MjData(self.mj_model)\n \n ## MJ Viewer:\n self.mj_viewer = mujoco_viewer.MujocoViewer(self.mj_model._model, self.mj_data._data, \n title=\"Mujoco-Engine\", \n sensor_config=self._camera_config,\n window_size=(1280,720),\n )\n # if len(self._camera_config):\n # self.mj_viewer_off = mujoco_viewer.MujocoViewer(self.mj_model, self.mj_data, width=800, height=800, title=\"camera-view\")\n self._t_update = time.time()\n \n # cv2 window\n cv2.startWindowThread()\n self.h_min = np.Infinity\n for camera, config in self._camera_config.items():\n self.h_min = int(min(config[\"height\"]/CAMERA_V_FACTOR, self.h_min))\n \n #==================================#\n # P U B L I C F U N C T I O N #\n #==================================#\n def shutdown(self):\n print(\"[Job_Engine::{}] Program killed: running cleanup code\".format(self._name))\n self.mj_viewer.terminate_safe()\n cv2.destroyAllWindows()\n \n def is_shutdown(self):\n try:\n return False\n except MuJoCo_Engine_InterruptException:\n self.shutdown()\n return True\n \n #====================================#\n # P R I V A T E F U N C T I O N #\n #====================================# \n def _signal_handler(self, signum, frame):\n raise MuJoCo_Engine_InterruptException\n \n def _internal_engine_update(self):\n self._update()\n\n def _update(self, if_camera_preview=True):\n delta_t = time.time() - self._t_update\n # print(\"FPS: {0}\".format(1/delta_t))\n # - command joint control angles:\n self.mj_data.actuator(\"wam/J1/P\").ctrl = -1.92\n self.mj_data.actuator(\"wam/J2/P\").ctrl = 1.88\n\n # process GUI interrupts\n self.mj_viewer.process_safe()\n \n # stepping if needed\n if not self.mj_viewer.is_key_registered_to_pause_program_safe() or \\\n self.mj_viewer.is_key_registered_to_step_to_next_safe():\n # - render current view:\n for i in range(10):\n mujoco.mj_step(self.mj_model._model, self.mj_data._data)\n \n self.mj_viewer.reset_key_registered_to_step_to_next_safe()\n\n # render current view with glfw:\n self.mj_viewer.update_safe()\n self.mj_viewer.render_safe()\n self.mj_viewer.render_sensor_cameras_safe()\n # self.mj_viewer_off.render()\n\n # - capture view:\n camera_sensor_data = self.mj_viewer.acquire_sensor_camera_frames_safe()\n print(camera_sensor_data[\"frame_stamp\"])\n \n # render captured views on cv2\n if if_camera_preview:\n cv2_capture_window = []\n for camera_name, camera_buf in camera_sensor_data[\"frame_buffer\"].items():\n img = cv2.cvtColor(camera_buf, cv2.COLOR_RGB2BGR)\n img = cv2.flip(img, 0)\n img = cv2.resize(img, (int(img.shape[1] * self.h_min / img.shape[0]), self.h_min))\n cv2_capture_window.append(img)\n \n cv2.imshow(\"camera views\", cv2.hconcat(cv2_capture_window))\n \n # - update:\n self._t_update = time.time()\n \n \n", "repo_name": "jaku-jaku/jx-mujoco-python-engine", "sub_path": "mujoco_engine/core_engine/engine.py", "file_name": "engine.py", "file_ext": "py", "file_size_in_byte": 6804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "signal.signal", "line_number": 80, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 80, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 81, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 81, "usage_type": "attribute"}, {"api_name": "mujoco_engine.core_engine.wrapper.core.MjModel.from_xml_path", "line_number": 91, "usage_type": "call"}, {"api_name": "mujoco_engine.core_engine.wrapper.core.MjModel", "line_number": 91, "usage_type": "name"}, {"api_name": "mujoco_engine.core_engine.wrapper.core.MjData", "line_number": 92, "usage_type": "call"}, {"api_name": "mujoco_viewer.MujocoViewer", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.startWindowThread", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.Infinity", "line_number": 106, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 116, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "mujoco.mj_step", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 167, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.hconcat", "line_number": 172, "usage_type": "call"}, {"api_name": "time.time", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "5775686286", "text": "\"\"\"Analyze.\"\"\"\n\nimport os\n\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport torch\nimport gym\nimport neurogym as ngym\nfrom neurogym.wrappers.block import MultiEnvs\n\nfrom models import RNNNet, get_performance\n\nimport argparse\nimport numpy as np\nimport random\n\nparser = argparse.ArgumentParser(description='seed parser')\nparser.add_argument('--seed', type=int, default=42,\n help='random seed')\n\nargs = parser.parse_args()\n\n\ndef set_seed(seed):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(seed)\n print(f\"Running with seed {seed}!\")\n\nset_seed(args.seed)\n\n\n# Environment\ntiming = {'fixation': ('constant', 500)}\nkwargs = {'dt': 100, 'timing': timing}\nseq_len = 100\ntasks = ngym.get_collection('yang19')\nenvs = [gym.make(task, **kwargs) for task in tasks]\nenv = MultiEnvs(envs, env_input=True)\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nnet = RNNNet(input_size=53, hidden_size=256, output_size=17,\n dt=env.dt).to(device)\nfname = os.path.join('files', 'model.pt')\nnet.load_state_dict(torch.load(fname, map_location=torch.device(device)))\n\n\ndef get_activity(net, env, num_trial=1000):\n \"\"\"Get activity of equal-length trials\"\"\"\n trial_list = list()\n activity_list = list()\n for i in range(num_trial):\n env.new_trial()\n ob = env.ob\n ob = ob[:, np.newaxis, :] # Add batch axis\n inputs = torch.from_numpy(ob).type(torch.float).to(device)\n\n action_pred, activity = net(inputs)\n activity = activity.detach().numpy()\n trial_list.append(env.trial)\n activity_list.append(activity)\n\n activity = np.concatenate(activity_list, axis=1)\n return activity, trial_list\n\n# Get performance\nfor i in range(20):\n env.set_i(i)\n perf = get_performance(net, env, num_trial=200)\n print('Average performance {:0.2f} for task {:s}'.format(perf, tasks[i]))\n\nprint(\"Computing and plotting task variance\")\n# Compute and Plot task variance\ntask_variance_list = list()\nfor i in range(20):\n env.set_i(i)\n activity, trial_list = get_activity(net, env, num_trial=500)\n # Compute task variance\n task_variance = np.var(activity, axis=1).mean(axis=0)\n task_variance_list.append(task_variance)\ntask_variance = np.array(task_variance_list) # (n_task, n_units)\nthres = 1e-6\ntask_variance = task_variance[:, task_variance.sum(axis=0)>thres]\n\nnorm_task_variance = task_variance / np.max(task_variance, axis=0)\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.metrics import silhouette_score\nX = norm_task_variance.T\nsilhouette_scores = list()\nn_clusters = np.arange(2, 20)\nfor n in n_clusters:\n cluster_model = AgglomerativeClustering(n_clusters=n)\n labels = cluster_model.fit_predict(X)\n silhouette_scores.append(silhouette_score(X, labels))\nplt.figure()\nplt.plot(n_clusters, silhouette_scores, 'o-')\nplt.xlabel('Number of clusters')\nplt.ylabel('Silhouette score')\n\nn_cluster = n_clusters[np.argmax(silhouette_scores)]\ncluster_model = AgglomerativeClustering(n_clusters=n_cluster)\nlabels = cluster_model.fit_predict(X)\n\n# Sort clusters by its task preference (important for consistency across nets)\nlabel_prefs = [np.argmax(norm_task_variance[:, labels==l].sum(axis=1)) for l in set(labels)]\n\nind_label_sort = np.argsort(label_prefs)\nlabel_prefs = np.array(label_prefs)[ind_label_sort]\n# Relabel\nlabels2 = np.zeros_like(labels)\nfor i, ind in enumerate(ind_label_sort):\n labels2[labels==ind] = i\nlabels = labels2\n\n# Sort neurons by labels\nind_sort = np.argsort(labels)\nlabels = labels[ind_sort]\nnorm_task_variance = norm_task_variance[:, ind_sort]\n\n\n# Plot Normalized Variance\nfigsize = (3.5,2.5)\nrect = [0.25, 0.2, 0.6, 0.7]\nrect_color = [0.25, 0.15, 0.6, 0.05]\nrect_cb = [0.87, 0.2, 0.03, 0.7]\ntick_names = [task[len('yang19.'):-len('-v0')] for task in tasks]\nfs = 6\nlabelpad = 13\n\nvmin, vmax = 0, 1\nfig = plt.figure(figsize=figsize)\nax = fig.add_axes(rect)\nim = ax.imshow(norm_task_variance, cmap='magma',\n aspect='auto', interpolation='nearest', vmin=vmin, vmax=vmax)\n\nplt.yticks(range(len(tick_names)), tick_names,\n rotation=0, va='center', fontsize=fs)\nplt.xticks([])\nplt.title('Units', fontsize=7, y=0.97)\nplt.xlabel('Clusters', fontsize=7, labelpad=labelpad)\nax.tick_params('both', length=0)\nfor loc in ['bottom','top','left','right']:\n ax.spines[loc].set_visible(False)\nax = fig.add_axes(rect_cb)\ncb = plt.colorbar(im, cax=ax, ticks=[vmin,vmax])\ncb.outline.set_linewidth(0.5)\nclabel = 'Normalized Task Variance'\n\ncb.set_label(clabel, fontsize=7, labelpad=0)\nplt.tick_params(axis='both', which='major', labelsize=7)\n\n\n# Plot color bars indicating clustering\ncmap = matplotlib.cm.get_cmap('tab10')\nax = fig.add_axes(rect_color)\nfor il, l in enumerate(np.unique(labels)):\n color = cmap(il % 10)\n ind_l = np.where(labels==l)[0][[0, -1]]+np.array([0,1])\n ax.plot(ind_l, [0,0], linewidth=4, solid_capstyle='butt',\n color=color)\n ax.text(np.mean(ind_l), -0.5, str(il+1), fontsize=6,\n ha='center', va='top', color=color)\nax.set_xlim([0, len(labels)])\nax.set_ylim([-1, 1])\nax.axis('off')\n\nplt.savefig(os.path.join('files','clusterplot.png'),bbox_inches='tight',dpi=280)\nfig.show()", "repo_name": "neurogym/ngym_usage", "sub_path": "yang19/analyze.py", "file_name": "analyze.py", "file_ext": "py", "file_size_in_byte": 5293, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "3", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 31, "usage_type": "attribute"}, {"api_name": "neurogym.get_collection", "line_number": 41, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 42, "usage_type": "call"}, {"api_name": "neurogym.wrappers.block.MultiEnvs", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.RNNNet", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 67, "usage_type": "call"}, {"api_name": "models.get_performance", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"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.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}]} +{"seq_id": "16942126008", "text": "from flask import Flask\nfrom datetime import datetime\nimport csv\nimport requests\napp = Flask(__name__)\n\n@app.route('/master')\ndef master():\n url = \"http://localhost:5000/monitor\"\n k = 1 # default interval value\n response = requests.get(url, params={\"k\": k})\n data = response.text.split(\" | \")\n cpu_percent = float(data[0].split(\": \")[1].strip(\"%\"))\n mem_percent = float(data[1].split(\": \")[1].strip(\"%\"))\n\n timestamp = datetime.now().strftime('%m-%d-%Y %H:%M:%S')\n\n # Write data to CSV file\n with open('monitor_log.csv', mode='a') as file:\n writer = csv.writer(file)\n writer.writerow([timestamp, cpu_percent, mem_percent])\n\n return \"Resource utilization logged successfully\"\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=8000)\n time.sleep= (10)", "repo_name": "Hmza-Bilal/Oscar", "sub_path": "master.py", "file_name": "master.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "10789907888", "text": "import psycopg2\nfrom com.mhttp import Request\nimport json\nfrom datetime import datetime\n\nconn = psycopg2.connect(database=\"appservice\", user=\"photo_monitor\", password=\"xxx\", host=\"xxx.rds.cn-north-1.amazonaws.com.cn\", port=\"5432\")\n\nrequest = Request()\ncur = conn.cursor()\n\ncur.execute('''SELECT table1.id AS table1_id,\n table1.photo_album_id AS table1_photo_album_id,\n table1.photo_name AS table1_photo_name,\n table1.update_time AS table1_update_time\nFROM table1\n LEFT OUTER JOIN table2\n ON table1.photo_album_id = table2.photo_album_id AND\n table1.photo_name = table2.photo_name\nWHERE table1.photo_name IS NOT NULL\n AND table2.status IS NULL\nORDER BY table1.update_time DESC;''')\n\nrows = cur.fetchall()\n\n\nrequest.post('https://hooks.slack.com/services/xxx/xxx/xxx',\n headers={'Content-Type': 'application/json'},\n data=json.dumps({'message': 'photo',\n 'username': 'PUBLISHED_PHOTO',\n 'text': str(datetime.now())+ ' ：统计结果\\n'+'photo count: '+ str(len(rows) if rows else 0)}))", "repo_name": "MaxwellXX/api_sql_to_influxdb", "sub_path": "py_query_rds.py", "file_name": "py_query_rds.py", "file_ext": "py", "file_size_in_byte": 1182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "psycopg2.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "com.mhttp.Request", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "39416968500", "text": "from sklearn.metrics import ndcg_score, dcg_score\nimport numpy as np\n# Relevance scores in Ideal order\ntrue_relevance = np.asarray([[5, 5, 5, 5, 3, 3, 3, 3, 3, 3]])\n\n# Relevance scores in output order\nrelevance_score = np.asarray([[5, 5, 3, 3, 5, 5, 2, 3, 3, 3]])\n\ndcg = dcg_score(true_relevance, relevance_score)\nprint(\"DCG score : \", dcg)\n\n# IDCG score\nidcg = dcg_score(true_relevance, true_relevance)\nprint(\"IDCG score : \", idcg)\n\n# Normalized DCG score\nndcg = dcg / idcg\nprint(\"nDCG score : \", ndcg)", "repo_name": "Calebdee/IR-RealTimeTwitterQuery", "sub_path": "dcg.py", "file_name": "dcg.py", "file_ext": "py", "file_size_in_byte": 503, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.asarray", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.metrics.dcg_score", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.metrics.dcg_score", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "40458307940", "text": "from flask import Flask\r\nfrom flask_script import Manager\r\nfrom peewee import MySQLDatabase\r\nfrom peewee import CharField, BooleanField, IntegerField\r\n\r\nfrom flask_autoapi import AutoAPI\r\nfrom flask_autoapi.model import ApiModel\r\nfrom flask_autoapi.command import GenerateDoc\r\n\r\n\r\ndb = MySQLDatabase(\r\n \"test\", \r\n host=\"localhost\", \r\n port=3306, \r\n user=\"root\", \r\n password=\"1q2w3e\"\r\n)\r\n\r\nclass Album(ApiModel):\r\n name = CharField(null=False, index=True, verbose_name=\"相册名称\")\r\n desc = CharField(null=True, verbose_name=\"说明\")\r\n cover = CharField(null=True, verbose_name=\"封面\")\r\n dirname = CharField(null=True, verbose_name=\"存储目录\")\r\n \r\n @staticmethod\r\n def name_in_handler(content):\r\n # 示例：如何自定义处理字段\r\n return content+\"-olivetree\"\r\n\r\n class Meta:\r\n database = db\r\n group = \"Album\"\r\n # 指定别名，用于显示在 API 文档上。默认为 Model 的名称\r\n verbose_name = \"相册\"\r\n # filter_fields 用于指定 list 接口的参数\r\n filter_fields = (\"dirname\", \"name\", )\r\n\r\n\r\n\r\nMODEL_LIST = [Album, ]\r\ndb.create_tables(MODEL_LIST)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app = Flask(__name__)\r\n api = AutoAPI()\r\n api.init_app(app, MODEL_LIST)\r\n\r\n manager = Manager(app)\r\n manager.add_command(\"doc\", GenerateDoc(MODEL_LIST))\r\n manager.run()", "repo_name": "olivetree123/flask_autoapi", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1411, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "peewee.MySQLDatabase", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_autoapi.model.ApiModel", "line_number": 19, "usage_type": "name"}, {"api_name": "peewee.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "peewee.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 45, "usage_type": "call"}, {"api_name": "flask_autoapi.AutoAPI", "line_number": 46, "usage_type": "call"}, {"api_name": "flask_script.Manager", "line_number": 49, "usage_type": "call"}, {"api_name": "flask_autoapi.command.GenerateDoc", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "44162064027", "text": "# utf-8\nimport os\nimport importlib\nimport numpy as np\nimport cv2\n\nfrom ..util import check\nimportlib.reload(check)\nfrom ..core import trans\nimportlib.reload(trans)\n\nfrom . import train_ticket\nimportlib.reload(train_ticket)\n\nclass _TrainTicketPipeline(object):\n '''\n textline extraction and template match for blue train ticket\n '''\n def __init__(self, detect_surface, is_upside_down,\n detect_textlines, match_template, debug=False):\n '''\n Args:\n debug : for debuging only\n '''\n self.reset()\n\n # 创建 发票票面 检测器 （无需再使用 Adjust 类）\n self.detect_surface = detect_surface\n # 检查是否上下颠倒火车票\n self.check_upside_down = is_upside_down\n\n # 创建 字符行检测器 （检测结果为：若干可能为字符行的矩形框）\n self.detect_textlines = detect_textlines\n\n # 创建模板匹配器\n self.match_template = match_template\n\n self.debug = dict() if debug else None\n\n def reset(self):\n self.is_blue = None\n self.surface_image = None\n self.textlines = None\n self.template = None\n self.guess = None\n self.exit_msg = None\n\n def __call__(self, image, no_background=False):\n '''\n Args:\n image : input color image\n no_background : set to True if no background in input image\n '''\n assert check.valid_image(image, colored=1)\n self.reset()\n\n if not no_background:\n # 检测提取\n surface_image = self.detect_surface(image)\n if surface_image is None:\n self.exit_msg = 'Detected zero ticket'\n return False\n else:\n if image.shape[0] < image.shape[1]:\n surface_image = image\n else:\n surface_image = trans.rotate90(image)\n\n # 检查是否蓝色票\n self.is_blue = train_ticket.is_blue(surface_image)\n # 是否上下颠倒，是则旋转180°\n if self.check_upside_down(surface_image):\n surface_image = trans.rotate180(surface_image)\n self.surface_image = surface_image \n \n gray_surface_image = cv2.cvtColor(surface_image, cv2.COLOR_BGR2GRAY)\n std_size = surface_image.shape[1], surface_image.shape[0]\n\n detected_rects = self.detect_textlines(gray_surface_image)\n self.textlines = detected_rects\n if len(detected_rects) == 0:\n self.exit_msg = 'Detected zero textlines'\n return False\n\n # 匹配模板\n if self.match_template is None:\n return True\n new_named_rects, succeed = self.match_template(detected_rects=detected_rects,\n image_size=std_size, # image size of detection\n para_init=None, update=False)\n self.template = new_named_rects\n self.guess = [self.match_template.data.keys[j] for j, i in enumerate(succeed) if i == 0]\n\n return True", "repo_name": "Ulquiorracifa/Ocr2", "sub_path": "fp/train_ticket/__train_ticket_pipeline.py", "file_name": "__train_ticket_pipeline.py", "file_ext": "py", "file_size_in_byte": 3105, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "importlib.reload", "line_number": 8, "usage_type": "call"}, {"api_name": "util.check", "line_number": 8, "usage_type": "argument"}, {"api_name": "importlib.reload", "line_number": 10, "usage_type": "call"}, {"api_name": "core.trans", "line_number": 10, "usage_type": "argument"}, {"api_name": "importlib.reload", "line_number": 13, "usage_type": "call"}, {"api_name": "util.check.valid_image", "line_number": 54, "usage_type": "call"}, {"api_name": "util.check", "line_number": 54, "usage_type": "name"}, {"api_name": "core.trans.rotate90", "line_number": 67, "usage_type": "call"}, {"api_name": "core.trans", "line_number": 67, "usage_type": "name"}, {"api_name": "core.trans.rotate180", "line_number": 73, "usage_type": "call"}, {"api_name": "core.trans", "line_number": 73, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "43427193599", "text": "import unittest\nimport tornado.ioloop\n\nfrom decimal import Decimal\nfrom valutakrambod.services import Service\n\nclass Coinbase(Service):\n baseurl = \"https://api.coinbase.com/v2/\"\n def servicename(self):\n return \"Coinbase\"\n\n def ratepairs(self):\n return [\n ('BTC', 'NOK'),\n ('BTC', 'EUR'),\n ('BTC', 'USD'),\n ]\n \n async def fetchRates(self, pairs = None):\n if pairs is None:\n pairs = self.ratepairs()\n res = {}\n for p in pairs:\n f = p[0]\n t = p[1]\n sellurl = \"%sprices/sell?currency=%s\" % (self.baseurl, t)\n buyurl = \"%sprices/buy?currency=%s\" % (self.baseurl, t)\n (sj, sr) = await self._jsonget(sellurl)\n #print(sj)\n (bj, br) = await self._jsonget(buyurl)\n #print(bj)\n ask = Decimal(bj['data']['amount'])\n bid = Decimal(sj['data']['amount'])\n self.updateRates(p, ask, bid, None)\n res[p] = self.rates[p]\n return res\n\n def websocket(self):\n \"\"\"Coinbase do not provide websocket API 2018-06-27.\"\"\"\n return None\n\nclass TestCoinbase(unittest.TestCase):\n \"\"\"\nRun simple self test.\n\"\"\"\n def setUp(self):\n self.s = Coinbase()\n self.ioloop = tornado.ioloop.IOLoop.current()\n def checkTimeout(self):\n print(\"check timed out\")\n self.ioloop.stop()\n def runCheck(self, check):\n to = self.ioloop.call_later(30, self.checkTimeout)\n self.ioloop.add_callback(check)\n self.ioloop.start()\n self.ioloop.remove_timeout(to)\n async def checkCurrentRates(self):\n res = await self.s.currentRates()\n for pair in self.s.ratepairs():\n self.assertTrue(pair in res)\n ask = res[pair]['ask']\n bid = res[pair]['bid']\n self.assertTrue(ask >= bid)\n self.ioloop.stop()\n def testCurrentRates(self):\n self.runCheck(self.checkCurrentRates)\n\nif __name__ == '__main__':\n t = TestCoinbase()\n unittest.main()\n", "repo_name": "petterreinholdtsen/valutakrambod", "sub_path": "valutakrambod/service/coinbase.py", "file_name": "coinbase.py", "file_ext": "py", "file_size_in_byte": 2079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "valutakrambod.services.Service", "line_number": 7, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 32, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 33, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tornado.ioloop.ioloop.IOLoop.current", "line_number": 48, "usage_type": "call"}, {"api_name": "tornado.ioloop.ioloop", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 48, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "14919786877", "text": "import sys\nfrom uiClass import ui_form\nfrom PyQt5.QtWidgets import QApplication, QWidget\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n w = QWidget()\n ui = ui_form.Ui_Form(w)\n # ui.setupUi(w)\n w.setWindowTitle('通讯测试自动化工具')\n w.show()\n sys.exit(app.exec_())", "repo_name": "AdamLee1121/UIDesign", "sub_path": "run_testToolDemo.py", "file_name": "run_testToolDemo.py", "file_ext": "py", "file_size_in_byte": 364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 8, "usage_type": "call"}, {"api_name": "uiClass.ui_form.Ui_Form", "line_number": 9, "usage_type": "call"}, {"api_name": "uiClass.ui_form", "line_number": 9, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "38898557080", "text": "import sys\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Check that at least one argument is provided\nif len(sys.argv) < 2:\n print(f\"Usage: python3 {sys.argv[0]} ...\")\n exit(1)\n\n# Load all csv files, (real, imaginary) pairs \nsequences = []\nfor i in range(1, len(sys.argv)):\n sequences.append(np.genfromtxt(sys.argv[i], delimiter=','))\n\n# Plot each sequence\nfor i in range(len(sequences)):\n plt.subplot(len(sequences), 1, i + 1)\n plt.plot([x[0] for x in sequences[i]])\n plt.plot([x[1] for x in sequences[i]])\n plt.title(\"Sequence \" + str(i + 1) + \": \" + sys.argv[i + 1])\n plt.xlabel(\"Index\")\n plt.ylabel(\"Magnitude\")\n plt.legend([\"Real\", \"Imaginary\"])\n\nplt.tight_layout()\nplt.show()", "repo_name": "AMSC22-23/FFT-Pesce-Miotti-Allahakbari", "sub_path": "tools/visualize-fft.py", "file_name": "visualize-fft.py", "file_ext": "py", "file_size_in_byte": 746, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.argv", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "12715544294", "text": "from zipfile import ZipFile\nimport os\nfrom PIL import Image\nimport numpy as np\n\nfrom utils.fileio import verify_dir, is_dir_empty, clear_dir, files_of_type\n\n\ndef zip_to_png(source_zip, rgb_dir, ir_dir=None, img_ext=\"png\",\n overwrite=False, verbose=True):\n \"\"\"\n Process ZIP files from New York City GIS data download\n (https://gis.ny.gov/gateway/mg/2018/new_york_city/)\n\n Convert 4 channel JPEG2000 images into 3 channel RGB images and separate\n single channel images of the Infrared channel.\n\n Parameters\n ----------\n source_zip: str\n full path to the zip file downloaded from the database\n rgb_dir: str\n full path output directory for the rgb images\n ir_dir: str or None (default None)\n full path output directory for the alpha images. If None, alpha images\n are not saved.\n img_ext: str (default \"png\")\n Extension of the image file name to save as. Must be supported by PIL.\n overwrite: bool (default False)\n If output path(s) exist, should the operation be run anyway?\n verbose: bool (default True)\n Print a status update file by file?\n \"\"\"\n\n # Make sure our output directories exist\n verify_dir(rgb_dir)\n if not is_dir_empty(rgb_dir):\n if not overwrite:\n print(\"RGB Output path not empty. Skipping entire operation.\")\n return\n else:\n clear_dir(rgb_dir)\n\n if ir_dir is not None:\n verify_dir(ir_dir)\n if not is_dir_empty(ir_dir):\n if not overwrite:\n print(\"IR Output path not empty. Skipping entire operation.\")\n return\n else:\n clear_dir(ir_dir)\n\n # Open the zip file for internal access\n with ZipFile(source_zip, \"r\") as zipdat:\n\n # Get a list of all files within the zip\n fns = zipdat.namelist()\n\n # loop over each file within the zip\n for fn in fns:\n\n # Separate the file name and its extension for this file\n fn_short, ext = os.path.splitext(fn)\n\n # Check to see if it's an image\n if ext == \".jp2\":\n\n if verbose:\n # Just output a status indicator\n print(fn)\n\n # Get access to this individual picture file within the zip\n with zipdat.open(fn) as file:\n\n # Read it in as a PIL object\n pic = Image.open(file)\n\n # Images have 4 layers (RGB + Infrared).\n # Convert to NumPy Array for manipulation\n x, y = pic.size\n a = np.array(pic.getdata()).reshape(x, y, 4)\n\n # Separate RGB and Infrared channels and save each into\n # a PNG file\n # Layers 0-2 are RGB\n im = Image.fromarray(a[:, :, :-1].astype('uint8'))\n im.save(os.path.join(rgb_dir, fn_short + \".\" + img_ext))\n\n # Layer 3 is Infrared\n if ir_dir is not None:\n alpha = Image.fromarray(a[:, :, -1].astype('uint8'))\n alpha.save(os.path.join(ir_dir, fn_short +\n \".\" + img_ext))\n\n\ndef tif_to_png(tif_dir, overwrite=False, verbose=True, delete=False):\n \"\"\"\n Convert a directory of tif images to pngs\n\n Parameters\n ----------\n tif_dir: str\n directory where tif images are stored\n overwrite: bool (default: False)\n Overwrite, or skip if exists\n verbose: bool (default: True)\n Print filenames while running\n delete: bool (default: False)\n Delete tif file when done?\n \"\"\"\n fns = files_of_type(tif_dir, \"*.tif\")\n for fn in fns:\n if verbose:\n print(fn)\n with Image.open(fn) as f:\n pfn = fn.replace(\".tif\", \".png\")\n if os.path.exists(pfn) and not overwrite:\n print(f\"File exists, skipping: {pfn}\")\n continue\n else:\n f.save(pfn)\n\n if delete:\n os.remove(fn)\n\n\n# Example dirs for testing\nsourcefile = \"c:\\\\nycdata\\\\boro_queens_sp18.zip\"\noutdir = \"c:\\\\nycdata\\\\boro_queens_sp18_png\"\noutdir_a = \"c:\\\\nycdata\\\\boro_queens_sp18_alpha\"\n\nif __name__ == \"__main__\":\n zip_to_png(sourcefile, outdir, outdir_a)\n", "repo_name": "jranalli/solarpvnet", "sub_path": "utils/convert_to_png.py", "file_name": "convert_to_png.py", "file_ext": "py", "file_size_in_byte": 4375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "utils.fileio.verify_dir", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.fileio.is_dir_empty", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.fileio.clear_dir", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.fileio.verify_dir", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.fileio.is_dir_empty", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.fileio.clear_dir", "line_number": 51, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 86, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 86, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "utils.fileio.files_of_type", "line_number": 111, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 115, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 115, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "15001022010", "text": "from DataImporter import DataImporter\nimport xgboost as xgb\nfrom sklearn.model_selection import KFold, cross_val_score, train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.ensemble import AdaBoostRegressor\nfrom sklearn.ensemble import ExtraTreesRegressor\nfrom sklearn.linear_model import BayesianRidge, HuberRegressor\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.kernel_ridge import KernelRidge\nfrom sklearn import linear_model\nimport pandas as pd\nimport numpy as np\n\n\nEXPERIMENT = False\n\ndef main():\n #########################\n # Initialize data #\n #########################\n importer = DataImporter()\n importer.ProcessData()\n\n x_train_scaled, y_train = importer.GetExperimentScaledTrainPair()\n\n #########################\n # Experiment #\n #########################\n if EXPERIMENT:\n models = [\n ('MLPR', MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', hidden_layer_sizes=9, learning_rate='constant', learning_rate_init=0.001, max_iter=500, solver='lbfgs')),\n ('EmptyXGBoost', xgb.XGBRegressor()),\n ('EmptyXGBRFRegressor', xgb.XGBRFRegressor),\n ('RandomForestRegressor', RandomForestRegressor()),\n ('GradientBoostingRegressor', GradientBoostingRegressor()),\n ('BayesianRidge', BayesianRidge()),\n ('HuberRegressor', HuberRegressor()),\n ('AdaBoostRegressor', AdaBoostRegressor()),\n ('ExtraTreesRegressor', ExtraTreesRegressor()),\n ('LassoLars', linear_model.LassoLars()),\n ('KernelRidge', KernelRidge())\n ]\n\n for modelname, model in models:\n score = cross_val_score(model, x_train_scaled, y=y_train, cv = 5, scoring=\"r2\")\n print(modelname + \" raw results (r2): \")\n print(score)\n print(modelname + \" score: {:.4f} std: ({:.4f})\\n\".format(score.mean(), score.std()))\n\n #####################################\n # Replicaate best models #\n #####################################\n\n regressor = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n max_depth=4, max_features='sqrt',\n min_samples_leaf=15, min_samples_split=10,\n loss='huber', random_state =5)\n x_test_scaled, ids = importer.GetExperimentScaledTestData()\n regressor.fit(x_train_scaled, y_train)\n y_predicted_reg1 = np.expm1(regressor.predict(x_test_scaled))\n\n regressor = RandomForestRegressor()\n x_test_scaled, ids = importer.GetExperimentScaledTestData()\n regressor.fit(x_train_scaled, y_train)\n y_predicted_reg2 = np.expm1(regressor.predict(x_test_scaled))\n\n regressor = BayesianRidge()\n x_test_scaled, ids = importer.GetExperimentScaledTestData()\n regressor.fit(x_train_scaled, y_train)\n y_predicted_reg3 = np.expm1(regressor.predict(x_test_scaled))\n\n regressor = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,\n learning_rate=0.05, max_depth=3,\n min_child_weight=1.7817, n_estimators=2200,\n reg_alpha=0.4640, reg_lambda=0.8571,\n subsample=0.5213, silent=1,\n random_state =7, nthread = -1)\n\n x_test_scaled, ids = importer.GetExperimentScaledTestData()\n regressor.fit(x_train_scaled, y_train)\n y_predicted_reg4 = np.expm1(regressor.predict(x_test_scaled))\n\n y_predicted = []\n for i in range(len(y_predicted_reg1)):\n y1 = y_predicted_reg1[i]\n y2 = y_predicted_reg2[i]\n y3 = y_predicted_reg3[i]\n y4 = y_predicted_reg4[i]\n y_predicted.append(0.70 * y1 + 0.05 * y2 + 0.00 * y3 + 0.25 * y4)\n\n #####################################\n # Serialize submission #\n #####################################\n\n sub = pd.DataFrame()\n sub['Id'] = ids\n sub['SalePrice'] = y_predicted\n sub.to_csv('submission.csv',index=False)\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "razvanra2/ssl_house_price_prediction_experiment", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4155, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "DataImporter.DataImporter", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 32, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 33, "usage_type": "call"}, {"api_name": "xgboost.XGBRFRegressor", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.linear_model.BayesianRidge", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.linear_model.HuberRegressor", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostRegressor", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.ensemble.ExtraTreesRegressor", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LassoLars", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 41, "usage_type": "name"}, {"api_name": "sklearn.kernel_ridge.KernelRidge", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.linear_model.BayesianRidge", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 71, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "9076068446", "text": "\"\"\"\nTitle : 【Python × スクレイピング入門②】\nTheme : 10分で理解！BeautifulSoupで複数の要素を取得する方法(find_all)をマスターしよう！\nURL : https://www.youtube.com/watch?v=8ZmQo8WiAis&list=PL4Y-mUWLK2t1LehwHVwAqxXTXw5xd-Yq8&index=4\n\"\"\"\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\n# URLにアクセス\nurl = 'https://www.python.org/'\nr = requests.get(url)\n\n# HTML解析\nsoup = BeautifulSoup(r.text, \"lxml\")\n\n# 全てのh2タグの取得\nsoup.find_all('h2')\n\n# 最初の要素を確認\nsoup.find_all('h2')[0]\nsoup.find('h2') == soup.find_all('h2')[0]\n\n# テキストのループ表示\n# h2_tag = soup.find_all('h2')[0]\nfor i, h2_tag in enumerate(soup.find_all('h2')):\n print(i, h2_tag.text)\n\n# テキストの格納\nh2_tag_list = []\nfor i, h2_tag in enumerate(soup.find_all('h2')):\n h2_tag_list.append(h2_tag.text)\n\n# テキスト表示\nh2_tag_list\n", "repo_name": "delta0726/py-scraping", "sub_path": "youtube/hayatasu/sec04_複数の要素を取得する方法をマスターしよう.py", "file_name": "sec04_複数の要素を取得する方法をマスターしよう.py", "file_ext": "py", "file_size_in_byte": 900, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "22992689297", "text": "from playwright.sync_api import sync_playwright\nfrom time import perf_counter\n\n# with sync_playwright() as playwright:\n# browser = playwright.chromium.launch(headless=False, slow_mo=1000)\n# page = browser.new_page()\n# page.goto('https://www.scrapethissite.com/pages/ajax-javascript/')\n# link = page.get_by_role('link', name='2015')\n# link.click()\n\n# print('2015 loading....')\n# start = perf_counter()\n# spotlight = page.locator('td.film-title').first\n# spotlight.wait_for()\n \n# time_taken = perf_counter() - start\n\n# print(f\"... loaded with {round(time_taken, 2)} seconds\")\n\ndef on_load(page):\n print(\"Page loaded:\", page)\nwith sync_playwright() as playwright:\n browser = playwright.chromium.launch(headless=False, slow_mo=1000)\n page = browser.new_page()\n\n page.on(\"domcontentloaded\", on_load)\n page.goto(\"https://bootswatch.com/default\")\n\n browser.close()", "repo_name": "Abdifatahibrahi/Playwright-for-automation", "sub_path": "app4.py", "file_name": "app4.py", "file_ext": "py", "file_size_in_byte": 922, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "playwright.sync_api.sync_playwright", "line_number": 22, "usage_type": "call"}, {"api_name": "playwright.sync_api", "line_number": 22, "usage_type": "name"}, {"api_name": "playwright.sync_api.chromium.launch", "line_number": 23, "usage_type": "call"}, {"api_name": "playwright.sync_api.chromium", "line_number": 23, "usage_type": "attribute"}, {"api_name": "playwright.sync_api", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "71211568367", "text": "from django import forms\nfrom django.conf import settings\nfrom django.utils.translation import gettext\nfrom django.utils.translation import gettext_lazy as _\n\nfrom users.models import DepartmentUser\nfrom users.models import Lageruser\n\n\nclass SettingsForm(forms.ModelForm):\n error_css_class = 'has-error'\n\n class Meta:\n model = Lageruser\n fields = [\"pagelength\", \"timezone\", \"theme\", \"main_department\"]\n help_texts = {\n \"pagelength\": _(\"The number of items displayed on one page in a list.\"),\n \"main_department\": _(\"Your Main department determines, which department devices you create are assigned to.\"),\n }\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields[\"timezone\"].choices[0] = (\"\", gettext(\"Default ({0})\".format(settings.TIME_ZONE)))\n self.fields[\"timezone\"].widget.choices[0] = (\"\", gettext(\"Default ({0})\".format(settings.TIME_ZONE)))\n\n\nclass AvatarForm(forms.ModelForm):\n error_css_class = 'has-error'\n avatar_clear = forms.BooleanField(required=False)\n\n class Meta:\n model = Lageruser\n fields = [\"avatar\"]\n widgets = {\n \"avatar\": forms.FileInput()\n }\n\n\nclass DepartmentAddUserForm(forms.ModelForm):\n error_css_class = 'has-error'\n\n class Meta:\n model = DepartmentUser\n widgets = {\n \"department\": forms.HiddenInput()\n }\n fields = '__all__'\n", "repo_name": "MPIB/Lagerregal", "sub_path": "users/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.forms.ModelForm", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "users.models.Lageruser", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 18, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.settings.TIME_ZONE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.settings.TIME_ZONE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "users.models.Lageruser", "line_number": 32, "usage_type": "name"}, {"api_name": "django.forms.FileInput", "line_number": 35, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 35, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "users.models.DepartmentUser", "line_number": 43, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "28033407760", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[24]:\n\n\n# -*- coding:utf-8 -*-\n\nimport codecs\nimport imgkit\nimport pandas as pd\nimport pdfkit as pdfkit\n\n\nclass ExcelToImage:\n def __init__(self):\n super(ExcelToImage, self).__init__()\n self.config = imgkit.config(wkhtmltoimage='G:/pdf/wkhtmltopdf/bin/wkhtmltoimage.exe')\n self.html_head = \"\"\"\n \n \n \n \n \n Document\n \n \"\"\"\n\n self.html_end = \"\"\"\n \"\"\"\n \n\n def excel_html(self, excel_path, html_path):\n \"\"\"\n excel to html\n :param excel_path: excel 路径\n :param html_path: html 存放 路径\n :return: html 路径集合\n \"\"\"\n html_paths = []\n\n excel_obj = pd.ExcelFile(excel_path) # excel 文件对象\n\n excel_sheets = excel_obj.sheet_names # 获取 excel 所有单元\n\n # 将每个单元转换为 html 文件\n for index, sheet in enumerate(excel_sheets):\n html_path_own = html_path + sheet + \".html\"\n print()\n\n # 获取本单元 excel 信息\n excel_data = excel_obj.parse(excel_obj.sheet_names[index])\n\n with codecs.open(html_path_own, 'w', 'utf-8') as html:\n # 加上头尾部, 防止中文乱码\n html_data = self.html_head + excel_data.to_html(header=True, index=True) + self.html_end\n\n html.write(html_data)\n\n html_paths.append(html_path_own)\n\n return html_paths\n\n# @staticmethod\n# def html_pdf(html_paths, pdf_path, config):\n# \"\"\"\n# html to pdf\n# :param html_paths: html 路径\n# :param pdf_path: pdf 存放 结果 路径\n# :return:\n# \"\"\"\n\n# for index, html_path in enumerate(html_paths):\n# pdf_obj = pdf_path + str(index) + \".pdf\"\n# with open(html_path, \"r\", encoding=\"utf-8\") as html_file:\n# pdfkit.from_file(html_file, pdf_obj,config=config)\n\n# @staticmethod\n def html_image(self,html_paths, image_path):\n \"\"\"\n html to image\n :param html_paths: html 路径\n :param image_path: image 存放 结果 路径\n :return:\n \"\"\"\n for index, html_path in enumerate(html_paths):\n img_obj = image_path + str(index) + \".png\"\n with open(html_path, \"r\", encoding=\"utf-8\") as html_file:\n imgkit.from_file(html_file, img_obj,config=self.config)\n\n\nif __name__ == '__main__':\n ReportImage = ExcelToImage()\n config = imgkit.config(wkhtmltoimage='G:/pdf/wkhtmltopdf/bin/wkhtmltoimage.exe')\n # excel 转 html\n html_paths = ReportImage.excel_html(\"D:/GoodsStatistics/2020-10-14_detail_morning_1602644318.xls\", \"D:/GoodsStatistics/html/\")\n # html 转 pdf\n# ReportImage.html_pdf(html_paths, \"D:/GoodsStatistics/pdf/\",config)\n # html 转 image\n ReportImage.html_image(html_paths, \"D:/GoodsStatistics/image/\")\n\n\n\n# In[2]:\n\nconfig = imgkit.config(wkhtmltoimage='G:/pdf/wkhtmltopdf/bin/wkhtmltoimage.exe')\n\nimgkit.from_string(html_string, output_file, config=config)\n\nself.wkhtmltoimage = subprocess.Popen(['which', 'wkhtmltoimage'], stdout=subprocess.PIPE).communicate()[0].strip()\n\n\n\n\n\n\n", "repo_name": "ouyanjiayao/DAILY_EVENT", "sub_path": "excel_to_image.py", "file_name": "excel_to_image.py", "file_ext": "py", "file_size_in_byte": 3650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "imgkit.config", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.ExcelFile", "line_number": 42, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 54, "usage_type": "call"}, {"api_name": "imgkit.from_file", "line_number": 89, "usage_type": "call"}, {"api_name": "imgkit.config", "line_number": 94, "usage_type": "call"}, {"api_name": "imgkit.config", "line_number": 106, "usage_type": "call"}, {"api_name": "imgkit.from_string", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "70651158768", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\n\r\ntry:\r\n url = input(\"Enter URL: \")\r\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:90.0) Gecko/20100101 Firefox/90.0'}\r\n req = requests.get(url, headers= headers )\r\n page = BeautifulSoup(req.text,'html.parser')\r\n print(page.prettify())\r\n print(\"===============================================================================================\")\r\n\r\n find_tag = input(\"Enter tag to find: \")\r\n\r\n soup4 = page.find_all(find_tag)\r\n for line in soup4:\r\n print(line)\r\nexcept Exception as e:\r\n print(e)", "repo_name": "tmeir/my_script", "sub_path": "WebFindTags.py", "file_name": "WebFindTags.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "33323483575", "text": "import os\nimport sys\nimport unittest2 as unittest\n\nimport aimseqtk.src.recomb.aa_events as aaevents\nimport aimseqtk.lib.clone as lclone\n\nclass TestAaeventsFuncs(unittest.TestCase):\n #def setUp(self):\n \n def test_get_all_nts(self):\n codon_lists = [['a', 'b'], ['c'], ['d', 'efg']]\n expected = ['acd', 'acefg', 'bcd', 'bcefg']\n received = aaevents.get_all_nts(codon_lists)\n self.assertEqual(set(expected), set(received))\n #self.assertRaises\n \n def test_left_max_match(self):\n seq1 = 'ATCGGG'\n seq2 = 'ATCGCCATCGGT'\n expected = 'ATCG'\n received = aaevents.left_max_match(seq1, seq2)\n self.assertEqual(expected, received)\n\n def test_right_max_match(self):\n seq1 = 'ATCGTGCA'\n seq2 = 'ATCGTTGCCCGCA'\n e = 'GCA'\n r = aaevents.right_max_match(seq1, seq2)\n self.assertEqual(e, r)\n seq3 = 'AAAAAAATCGTATCGTGCA'\n r3 = aaevents.right_max_match(seq1, seq3)\n self.assertEqual(seq1, r3)\n\n def test_find_min_vdel(self):\n vnt = 'tgtgccagcagcttaga'.upper()\n cdr3aa = 'CASSLTGCHQTF'\n e = 2\n r = aaevents.find_min_vdel(vnt, cdr3aa)\n self.assertEqual(e, r)\n cdr3aa = 'CASSLDATG'\n r = aaevents.find_min_vdel(vnt, cdr3aa)\n self.assertEqual(0, r)\n cdr3aa = 'CASSLEATG'\n r = aaevents.find_min_vdel(vnt, cdr3aa)\n self.assertEqual(0, r)\n cdr3aa = 'CASSSVATG'\n r = aaevents.find_min_vdel(vnt, cdr3aa)\n self.assertEqual(4, r)\n\n def test_find_min_jdel(self):\n jnt = 'tgaacactgaagctttcttt'.upper()\n cdr3aa = 'NTEAFF'\n r = aaevents.find_min_jdel(jnt, cdr3aa)\n self.assertEqual(2, r)\n\n cdr3aa = 'HKAFF'\n r = aaevents.find_min_jdel(jnt, cdr3aa)\n self.assertEqual(9, r)\n\n cdr3aa = 'HVAFF'\n r = aaevents.find_min_jdel(jnt, cdr3aa)\n self.assertEqual(10, r)\n\n def test_find_dmatches(self):\n daa = 'GCT'\n cdr3aa = 'CASSHTLGCTF'\n matches = aaevents.find_dmatches(daa, cdr3aa)\n e = [(7, 10)]\n self.assertEqual(e, matches)\n\n daa = 'AGCT'\n cdr3aa = 'CASSHTLGCTF'\n matches = aaevents.find_dmatches(daa, cdr3aa)\n e = []\n self.assertEqual(e, matches)\n\n daa = 'GC'\n cdr3aa = 'CASSGCHTLGCTF'\n matches = aaevents.find_dmatches(daa, cdr3aa)\n e = [(4, 6), (9, 11)]\n self.assertEqual(set(e), set(matches))\n \n def test_find_devents(self):\n d_nt = 'GGGACA' # frame0 = GT, frame1 = G, frame2 = D\n cdr3aa = 'AGTT'\n \n devents = aaevents.find_devents(d_nt, cdr3aa)\n #for d in devents:\n # print \"%d\\t%d\\t*%s*\\t%d\\t%d\\t*%s*\" % (d.d5del, d.d3del, d.left_nts, d.cdr3aa_dstart, d.cdr3aa_dend, d.right_nts)\n #print len(devents)\n #d1 = aaevents.Devent(0, 0, '', 1, 3, '')\n \n def test_get_vdins_events(self):\n vnt = 'tgtgccagcagcttaga'.upper()\n cdr3aa = 'CASSLAGTQTF'\n vdel = 2\n devent = aaevents.Devent(0, 0, '', 6, 8, '')\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n e = ['AGCA', 'AGCC', 'AGCG', 'AGCT']\n self.assertEqual(set(e), set(vdins))\n\n vdel = 0\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n self.assertTrue(vdins is None)\n \n cdr3aa = 'CASSLDGTQTF'\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n e = ['AT', 'AC']\n self.assertEqual(set(e), set(vdins))\n\n cdr3aa = 'CASSLDAGTQTF'\n devent = aaevents.Devent(0, 0, '', 7, 9, '')\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n e = ['ATGCT', 'ATGCC', 'ATGCA', 'ATGCG', 'ACGCT', 'ACGCC', 'ACGCA', 'ACGCG']\n self.assertEqual(set(e), set(vdins))\n\n cdr3aa = 'CASSLDGTQTF'\n devent = aaevents.Devent(1, 0, 'T', 6, 8, '')\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n e = ['A']\n self.assertEqual(set(e), set(vdins))\n\n cdr3aa = 'CASSLDGTQTF'\n devent = aaevents.Devent(1, 0, 'TGG', -1, -1, '')\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n self.assertTrue( vdins is None)\n\n cdr3aa = 'CASSLDAGTQTF'\n devent = aaevents.Devent(1, 3, 'A', 7, 9, '')\n vdins = aaevents.get_vdins_events(vdel, vnt, devent, cdr3aa)\n e = ['ATGC', 'ACGC']\n self.assertEqual(set(e), set(vdins))\n\n def test_get_djins_events(self):\n #jnt = 'tga ac act gaa gct ttc ttt'.upper()\n jnt = 'tgaacactgaagctttcttt'.upper()\n cdr3aa = 'AGTVNTEAFF'\n devent = aaevents.Devent(2, 4, '', 1, 3, '')\n jdel = 0\n djins = aaevents.get_djins_events(jdel, jnt, devent, cdr3aa)\n e = ['GT']\n self.assertEqual(set(e), set(djins))\n\n #cdr3aa = 'AGTVN TEAFF'\n devent = aaevents.Devent(2, 4, '', 1, 3, '')\n jdel = 3\n djins = aaevents.get_djins_events(jdel, jnt, devent, cdr3aa)\n e = ['GTTAA', 'GTCAA', 'GTAAA', 'GTGAA']\n self.assertEqual(set(e), set(djins))\n\n devent = aaevents.Devent(2, 4, '', 1, 3, 'GT')\n jdel = 3\n djins = aaevents.get_djins_events(jdel, jnt, devent, cdr3aa)\n e = ['TAA', 'CAA', 'AAA', 'GAA']\n self.assertEqual(set(e), set(djins))\n\n \n def test_get_vjins_emptyd(self):\n vnt = 'tgtgccagcagcttaga'.upper()\n jnt = 'tgaacactgaagctttcttt'.upper()\n cdr3aa = 'CASSLDVNTEAFF'\n vdel = 0\n jdel = 0\n dnts = '' \n e = ['ATGT', 'ACGT']\n vjins = aaevents.get_vjins_emptyd(vnt, vdel, jnt, jdel, dnts, cdr3aa)\n self.assertEqual(set(e), set(vjins))\n\n cdr3aa = 'CASSLDNVNTEAFF'\n e = ['ATAATGT', 'ACAATGT', 'ATAACGT', 'ACAACGT']\n vjins = aaevents.get_vjins_emptyd(vnt, vdel, jnt, jdel, dnts, cdr3aa)\n self.assertEqual(set(e), set(vjins))\n \n dnts = 'TAA'\n e = ['ATAATGT', 'ATAACGT']\n vjins = aaevents.get_vjins_emptyd(vnt, vdel, jnt, jdel, dnts, cdr3aa)\n self.assertEqual(set(e), set(vjins))\n\nif __name__ == '__main__':\n unittest.main()\n\n\n", "repo_name": "ngannguyen/aimseqtk", "sub_path": "tests/aa_events_test.py", "file_name": "aa_events_test.py", "file_ext": "py", "file_size_in_byte": 6171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "unittest2.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_all_nts", "line_number": 14, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 14, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.left_max_match", "line_number": 22, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 22, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.right_max_match", "line_number": 29, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 29, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.right_max_match", "line_number": 32, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 32, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_vdel", "line_number": 39, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 39, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_vdel", "line_number": 42, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 42, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_vdel", "line_number": 45, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 45, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_vdel", "line_number": 48, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 48, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_jdel", "line_number": 54, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 54, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_jdel", "line_number": 58, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 58, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_min_jdel", "line_number": 62, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 62, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_dmatches", "line_number": 68, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 68, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_dmatches", "line_number": 74, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 74, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_dmatches", "line_number": 80, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 80, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.find_devents", "line_number": 88, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 88, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 98, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 98, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 99, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 99, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 104, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 104, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 108, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 108, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 113, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 113, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 114, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 114, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 119, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 119, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 120, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 120, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 125, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 125, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 126, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 126, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 130, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 130, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vdins_events", "line_number": 131, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 131, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 139, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 139, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_djins_events", "line_number": 141, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 141, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 146, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 146, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_djins_events", "line_number": 148, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 148, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.Devent", "line_number": 152, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 152, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_djins_events", "line_number": 154, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 154, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vjins_emptyd", "line_number": 167, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 167, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vjins_emptyd", "line_number": 172, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 172, "usage_type": "name"}, {"api_name": "aimseqtk.src.recomb.aa_events.get_vjins_emptyd", "line_number": 177, "usage_type": "call"}, {"api_name": "aimseqtk.src.recomb.aa_events", "line_number": 177, "usage_type": "name"}, {"api_name": "unittest2.main", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "37318897813", "text": "# Standaard imports\nimport os\nimport argparse\nimport json \nfrom time import sleep\nfrom math import log, pi, exp \nimport random \n\n\nimport torch\nimport torch.nn as nn \nimport torch.nn.functional as F \nimport torch.utils as utils\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms, utils\nfrom loaderCelebA import CelebALoader\nfrom loader import DataLoader\n\nfrom torch import optim\nfrom tqdm import tqdm\nfrom ipywidgets import IntProgress\n\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Plotting imports \nimport matplotlib.pyplot as plt\n\nfrom flows import Glow\nfrom loader import DefaultLoader\n\ndef get_current_lr(optimizer, group_idx, parameter_idx):\n group = optimizer.param_groups[group_idx]\n p = group['params'][parameter_idx]\n\n beta1, _ = group['betas']\n state = optimizer.state[p]\n\n bias_correction1 = 1 - beta1 ** state['step']\n current_lr = group['lr'] / bias_correction1\n return current_lr\n\n\ndef train(device, parser, writer):\n args = parser.parse_args()\n root = f'./Data'\n save_path = f'./checkpoints/checkpoint.pt'\n args_path = f'./checkpoints/commandline_args.txt'\n\n with open(args_path, 'w') as f:\n json.dump(args.__dict__, f, indent=2)\n\n with open(args_path, 'r') as f:\n args.__dict__ == json.load(f) \n\n if not os.path.exists(root):\n os.mkdir(root)\n\n if args.print_dict:\n for arg in vars(args):\n print(arg, getattr(args, arg))\n print('Using device: ', device, '\\n')\n\n data_loader = DefaultLoader(root)\n if args.split:\n train_loader, val_loader = data_loader.dataloader(args.batch_size, args.split, args.dataset)\n else:\n train_loader = data_loader.dataloader(args.batch_size, args.split, args.dataset)\n glow = Glow(args.c, args.n_bits, args.n_blocks, args.levels, args.affine, args.lu, args.resNet, args.actnorm, args.batchnorm)\n \n optimizer = optim.Adam(\n params=glow.parameters(), \n lr=args.learning_rate\n )\n\n scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n optimizer,\n mode='min',\n patience = 500, \n verbose=True,\n min_lr=1e-8\n )\n \n if os.path.exists(save_path):\n print('loading model...')\n checkpoint = torch.load(save_path)\n glow.load_state_dict(checkpoint['model_state_dict'])\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n scheduler.load_state_dict(checkpoint['schedular_state_dict'])\n epoch = checkpoint['epoch']\n loss = checkpoint['loss']\n \n global_steps = 0\n running_loss = 0.0\n # Training loop \n for epoch in range(1,args.epoch):\n epoch_loss = []\n \n index = 0\n # with tqdm(train_loader, unit=\"batch\") as tepoch:\n for i, batch in enumerate (train_loader):\n x = batch[0]\n x = x.to(device)\n x = x.requires_grad_(False)\n if not glow.calculated:\n glow.pixels(x)\n x = glow.discretize(x)\n x = glow.dequantize(x)\n x = x - 0.5\n x, log_det, shapes = glow.forward(x)\n bpd = glow.log_pz(x, log_det, True)\n \n optimizer.zero_grad()\n \n loss = -bpd.mean(0)\n epoch_loss.append(loss)\n scheduler.step(loss)\n loss.backward()\n torch.nn.utils.clip_grad_value_(glow.parameters(),2)\n torch.nn.utils.clip_grad_norm_(glow.parameters(), 50)\n \n optimizer.step()\n # tepoch.set_description(f\" Epoch {epoch}\")\n z_std = [0., 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] \n if index % 250 == 0:\n with torch.no_grad():\n rand_idx = random.randint(0, len(z_std)-1)\n eps = z_std[rand_idx]\n sample = glow.sample(shapes=shapes, eps=eps)\n sample = glow.inverse(sample)\n sample = glow.quantize(sample)\n sample_grid = utils.make_grid(sample[:10], nrow=15)\n sample_grid = sample_grid.detach().numpy()\n # writer.add_image('sample'+str(global_steps), sample_grid)\n utils.save_image(\n sample,\n f'./samples/sample{str(epoch+index+1).zfill(6)}.png',\n normalize=True, \n nrow=10,\n range=(-0.5,0.5),\n )\n if index % 500 == 0:\n x = glow.inverse(x)\n x = glow.quantize(x)\n check_grid = utils.make_grid(x[:10], nrow=15)\n check_grid = check_grid.detach().numpy()\n # writer.add_image('check'+str(global_steps), check_grid)\n utils.save_image(\n x,\n f'./check/checked{str(epoch+index+1).zfill(6)}.png',\n normalize=True, \n nrow=10,\n range=(-0.5,0.5),\n )\n\n writer.add_scalar('training loss', running_loss/100, epoch * global_steps + index)\n group_idx, param_idx = 0, 0\n current_lr = get_current_lr(optimizer, group_idx, param_idx)\n writer.add_scalar('learning rate ', current_lr, epoch * global_steps + index)\n writer.add_scalar('current loss ', loss.item(), epoch * global_steps + index)\n\n index += 1\n global_steps += 1\n running_loss += loss\n \n # tepoch.set_postfix(loss = loss.item(), log_det=log_det.mean(0).item()) \n # sleep(0.001)\n # print(' Current learning rate (g:%d, p:%d): %.10f | Loss: %.6f'%(group_idx, param_idx, current_lr, loss.item()))\n \n mean_loss = sum(epoch_loss)/len(epoch_loss)\n writer.add_scalar('mean eocpch loss', mean_loss, epoch)\n print('epoch: ', epoch, 'epoch avg loss ', mean_loss.item())\n # scheduler.step(mean_loss)\n if epoch % 2 == 0: \n torch.save({\n 'epoch' : epoch,\n 'model_state_dict' : glow.state_dict(),\n 'optimizer_state_dict' : optimizer.state_dict(),\n 'schedular_state_dict' : scheduler.state_dict(),\n 'loss': epoch_loss\n },save_path \n )\n\n \n\n if args.print_dict:\n print('Models state.dict:')\n for param_tensor in glow.state_dict():\n print(param_tensor, '\\t', glow.state_dict()[param_tensor].size())\n print('Optimizers state_dict') \n for var_name in optimizer.state_dict():\n print(var_name, '\\t', optimizer.state_dict()[var_name])\n\nif __name__ == '__main__':\n device = 'cpu'\n parser = argparse.ArgumentParser(description=\"Glow Model\")\n parser.add_argument('--batch_size',default=16, type=int, help='size of the batch')\n parser.add_argument('--epoch', default=10, type=int, help='Number of epochs')\n parser.add_argument('--learning_rate', default=1e-4, type=float, help='The learning rate for the optimizer')\n parser.add_argument('--actnorm', default=True, type=bool, help='Use activation normalization')\n parser.add_argument('--batchnorm', default=False, type=bool, help='Use batch normalization')\n parser.add_argument('--resNet', default=False, type=bool, help='Use ResNet normalization')\n parser.add_argument('--lu', default=True, type=bool, help='Use lu decompostion for the inverse convolution')\n parser.add_argument('--n_blocks', default=20, type=int, help='Number of blocks of the flow')\n parser.add_argument('--levels', default=2, type=int, help='Number of levels of flow')\n parser.add_argument('--n_bits', default=5, type=int, help='number of bits') \n parser.add_argument('--n_samples', default=10, type=int, help=' Number of images sampled')\n parser.add_argument('--split', default=False, type=bool, help='Split the training data')\n parser.add_argument('--dataset', default='MNIST', type=str, help='Choose a dataset, MNIST, FashionMNIST, CIFAR10')\n parser.add_argument('--affine', default=False, type=bool, help='Choose affine coupling if True or Addittive if False')\n parser.add_argument('--c', default=1, type=int, help='Number of channels')\n parser.add_argument('--print_dict', default=False, type=bool, help='Print model parameters')\n parser.add_argument('--dequantize', default=False, type=bool, help='True to dequantize the data')\n\n writer = SummaryWriter(\"runs/MNIST\")\n \n train(device, parser, writer)\n writer.close()", "repo_name": "liverom017/BachelorThesisNormalizingFlows", "sub_path": "training.py", "file_name": "training.py", "file_ext": "py", "file_size_in_byte": 7915, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "3", "api": [{"api_name": "json.dump", "line_number": 50, "usage_type": "call"}, {"api_name": "json.load", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 56, "usage_type": "call"}, {"api_name": "loader.DefaultLoader", "line_number": 63, "usage_type": "call"}, {"api_name": "flows.Glow", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 75, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_value_", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 125, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 126, "usage_type": "call"}, {"api_name": "torchvision.utils.make_grid", "line_number": 131, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 131, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 134, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 134, "usage_type": "name"}, {"api_name": "torchvision.utils.make_grid", "line_number": 144, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 144, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 147, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 174, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "16158908384", "text": "\"\"\"webapp URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n https://docs.djangoproject.com/en/4.1/topics/http/urls/\r\nExamples:\r\nFunction views\r\n 1. Add an import: from my_app import views\r\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\r\nClass-based views\r\n 1. Add an import: from other_app.views import Home\r\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n 1. Import the include() function: from django.urls import include, path\r\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.contrib import admin\r\nfrom django.urls import path\r\nfrom . import views\r\nfrom django.contrib.auth import views as auth_views\r\n\r\nurlpatterns = [\r\n path(\"\",views.index),\r\n path(\"upload/\",views.upload,name='upload'),\r\n path('stu_per/',views.student_performance,name='student_performance'),\r\n path('sub_per/',views.subject_performance,name='subject_performance'),\r\n path('overall_per/',views.overall_per,name='overall_performance'),\r\n path('gpa_per/',views.gpa_performance,name='gpa_performance'),\r\n path(\"login/\",auth_views.LoginView.as_view(template_name='login.html'),name='login'),\r\n path(\"logout/\",auth_views.LogoutView.as_view(template_name='logout.html'),name='logout'),\r\n path('register/',views.register,name='register'),\r\n path('profile/',views.profile,name='profile'),\r\n path('download_file',views.download_file,name='download_file'),\r\n path('download_stu_file',views.download_stu_file,name='download_stu_file'),\r\n path('download_sub_file',views.download_sub_file,name='download_sub_file'),\r\n path('download_overall_file',views.download_overall_file,name='download_overall_file'),\r\n path('download_gpa_file',views.download_gpa_file,name='download_gpa_file'),\r\n]\r\n", "repo_name": "ChanBotCoder/Result_Analysis_and_Visualization", "sub_path": "weebapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "7575010699", "text": "from django.db import models\n\nfrom Petstagram.photos.models import PetPhoto\n\n\nclass Comment(models.Model):\n comment_text = models.TextField(\n max_length=300\n )\n\n date_and_time_of_publication = models.DateTimeField(\n auto_now_add=True\n )\n\n to_photo = models.ForeignKey(\n PetPhoto,\n on_delete=models.CASCADE\n )\n\n class Meta:\n ordering = ['-date_and_time_of_publication', ]\n\n\nclass Like(models.Model):\n to_photo = models.ForeignKey(\n PetPhoto,\n on_delete=models.CASCADE\n )\n", "repo_name": "kristiyanradoslavov/Petstagram", "sub_path": "Petstagram/common/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 15, "usage_type": "call"}, {"api_name": "Petstagram.photos.models.PetPhoto", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 25, "usage_type": "call"}, {"api_name": "Petstagram.photos.models.PetPhoto", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "21417460263", "text": "import os.path\nfrom os.path import join, splitext\nfrom os import listdir\n\nimport csv\nfrom functools import lru_cache\n\ndef get_smart_title(filename):\n\tonlyfirst = splitext(filename)[0]\n\t\t\n\tnamedate = onlyfirst.split(\"_\")\n\tif len(namedate) >= 2:\n\t\tdatepart = namedate[1].split('-')\n\t\tdatepart = '(' + '.'.join(reversed(datepart)) + ')'\n\telse:\n\t\tdatepart = ''\n\n\tnamepart = namedate[0].split('-')\n\tif len(namepart) >= 2:\n\t\tpismeno = namepart[1]\n\telse:\n\t\tpismeno = ''\n\n\tcorrection = {'pis': 'Písomka', 'pisomka': 'Písomka', 'skuskova': 'Skúšková'}\n\tfirstpart = namepart[0]\n\ttry:\n\t\tfirstpart = correction[firstpart]\n\texcept KeyError:\n\t\tpass\n\t\n\treturn \"{firstpart} {pismeno} {datepart}\" .format(\n\t\t\tfirstpart=firstpart, pismeno=pismeno, datepart=datepart)\n\n\ndef get_rich_files(dirpath, fresh_titlehelper):\n\tforbidden_extensions = ['.csv']\t\n\tonlyfiles = [ f for f in listdir(dirpath) if os.path.isfile(join(dirpath, f)) ]\n\n\tonlyfiles = [f for f in onlyfiles \n\t\tif splitext(join(dirpath, f))[1] not in forbidden_extensions]\n\n\t\"\"\" (title, path, extension) \"\"\"\n\trichfiles = [(get_smart_title(f) if f not in fresh_titlehelper else fresh_titlehelper[f], join(dirpath, f), splitext(join(dirpath, f))[1]) for f in onlyfiles]\n\treturn richfiles\n\ndef get_dirs(dirpath):\n\treturn [d for d in listdir(dirpath) if os.path.isdir(join(dirpath, d))]\n\n@lru_cache(maxsize=256)\ndef read_external_titlehelper(dirpath):\n\tmydict = dict()\n\thelper_file_name = 'titles.csv'\n\thelper_file_path = join(dirpath, helper_file_name)\n\tif os.path.isfile(helper_file_path):\n\t\twith open(helper_file_path, newline='') as f:\n\t\t\trows = csv.reader(f, delimiter=',')\n\t\t\tfor row in rows:\n\t\t\t\tmydict[row[0]] = row[1]\n\treturn mydict\n\ndef dir_to_struct(dirpath, dirname, titlehelper):\n\texternal_titlehelper = read_external_titlehelper(dirpath)\n\tfresh_titlehelper = dict()\n\tfresh_titlehelper.update(titlehelper)\n\tfresh_titlehelper.update(external_titlehelper)\n\n\tmydir = {\n\t\t'name': dirname,\n\t\t'path': dirpath,\n\t\t'files': get_rich_files(dirpath, fresh_titlehelper),\n\t\t'dirs': list(), }\n\tmydir['title'] = titlehelper[dirname] if dirname in titlehelper else dirname\n\t\n\tfor subdir_name in get_dirs(dirpath):\n\t\tsubdir = dir_to_struct(join(dirpath, subdir_name), subdir_name, fresh_titlehelper)\n\t\tmydir['dirs'].append(subdir)\n\n\treturn mydir\n", "repo_name": "radomirbosak/fmfi-tests", "sub_path": "fmfi/aux.py", "file_name": "aux.py", "file_ext": "py", "file_size_in_byte": 2285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.splitext", "line_number": 9, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 43, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.path.isdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 47, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 54, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 56, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "73603253486", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport os\nimport ray\nfrom baselines.common.vec_env import VecEnvWrapper\n\nimport gym\n\n_initialized = False\n# ray.init(redirect_output=True)\n\n\n@ray.remote\nclass RayEnv(object):\n def __init__(self, fn):\n # Tell numpy to only use one core. If we don't do this, each actor may\n # try to use all of the cores and the resulting contention may result\n # in no speedup over the serial version. Note that if numpy is using\n # OpenBLAS, then you need to set OPENBLAS_NUM_THREADS=1, and you\n # probably need to do it from the command line (so it happens before\n # numpy is imported).\n os.environ[\"MKL_NUM_THREADS\"] = \"1\"\n self.env = fn()\n\n def reset(self):\n return self.env.reset()\n\n def step(self, action):\n return self.env.step(action)\n\n def _call(self, name, args, kwargs):\n return getattr(self.env, name)(*args, **kwargs)\n\n def action_space(self):\n return self.env.action_space\n\n def observation_space(self):\n return self.env.observation_space\n\n\nclass VecEnv(gym.Env):\n def __init__(self, env_create_fn, num_envs=4):\n self._envs = [RayEnv.remote(env_create_fn) for _ in range(num_envs)]\n\n def _reset(self):\n return np.stack(ray.get([e.reset.remote() for e in self._envs]))\n\n def _step(self, actions):\n results = ray.get([e.step.remote(a) for e, a in zip(self._envs, actions)])\n obs_next, rewards, done, info = zip(*results)\n return np.stack(obs_next), np.array(rewards), np.array(done), list(info)\n\n @property\n def action_space(self):\n return ray.get(self._envs[0].action_space.remote())\n\n @property\n def observation_space(self):\n return ray.get(self._envs[0].observation_space.remote())\n\n\nclass RunningMeanStd(object):\n # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm\n def __init__(self, epsilon=1e-4, shape=()):\n self.mean = np.zeros(shape, 'float64')\n self.var = np.ones(shape, 'float64')\n self.count = epsilon\n\n def update(self, x):\n batch_mean = np.mean(x, axis=0)\n batch_var = np.var(x, axis=0)\n batch_count = x.shape[0]\n self.update_from_moments(batch_mean, batch_var, batch_count)\n\n def update_from_moments(self, batch_mean, batch_var, batch_count):\n delta = batch_mean - self.mean\n tot_count = self.count + batch_count\n\n new_mean = self.mean + delta * batch_count / tot_count\n m_a = self.var * (self.count)\n m_b = batch_var * (batch_count)\n M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)\n new_var = M2 / (self.count + batch_count)\n\n new_count = batch_count + self.count\n\n self.mean = new_mean\n self.var = new_var\n self.count = new_count\n\n\nclass VecNormalize(VecEnvWrapper):\n \"\"\"\n Vectorized environment base class\n \"\"\"\n def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):\n VecEnvWrapper.__init__(self, venv)\n self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None\n self.ret_rms = RunningMeanStd(shape=()) if ret else None\n self.clipob = clipob\n self.cliprew = cliprew\n self.ret = np.zeros(self.num_envs)\n self.gamma = gamma\n self.epsilon = epsilon\n\n def step_wait(self):\n \"\"\"\n Apply sequence of actions to sequence of environments\n actions -> (observations, rewards, news)\n\n where 'news' is a boolean vector indicating whether each element is new.\n \"\"\"\n obs, rews, news, infos = self.venv.step()\n self.ret = self.ret * self.gamma + rews\n obs = self._obfilt(obs)\n if self.ret_rms:\n self.ret_rms.update(self.ret)\n rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)\n return obs, rews, news, infos\n\n def _obfilt(self, obs):\n if self.ob_rms:\n self.ob_rms.update(obs)\n obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)\n return obs\n else:\n return obs\n\n\n def reset(self):\n \"\"\"\n Reset all environments\n \"\"\"\n obs = self.venv.reset()\n return self._obfilt(obs)\n\n\nif __name__ == '__main__':\n envs = VecEnv()\n print(envs.reset())\n actions = np.array([envs.action_space.sample() for _ in envs._envs])\n next_obs, rewards, done, info = envs.step(actions)\n\n print(next_obs)\n print(rewards)\n print(done)\n print(info)", "repo_name": "ethanluoyc/pytorch-rl", "sub_path": "pytorch_rl/a2c/vecenv.py", "file_name": "vecenv.py", "file_ext": "py", "file_size_in_byte": 4783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ray.remote", "line_number": 16, "usage_type": "attribute"}, {"api_name": "gym.Env", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 49, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 49, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 58, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 85, "usage_type": "call"}, {"api_name": "baselines.common.vec_env.VecEnvWrapper", "line_number": 95, "usage_type": "name"}, {"api_name": "baselines.common.vec_env.VecEnvWrapper.__init__", "line_number": 100, "usage_type": "call"}, {"api_name": "baselines.common.vec_env.VecEnvWrapper", "line_number": 100, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}]} +{"seq_id": "40237948518", "text": "'''Utility functions'''\n\nimport collections\n\ndef flatten(parent_dict, parent_key='', sep='_'):\n '''Flatten a nested dict into a single layer'''\n items = []\n for key, val in parent_dict.items():\n new_key = parent_key + sep + key if parent_key else key\n if isinstance(val, collections.MutableMapping):\n items.extend(flatten(val, new_key, sep=sep).items())\n else:\n items.append((new_key, val))\n return dict(items)\n", "repo_name": "colebrumley/des", "sub_path": "des/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.MutableMapping", "line_number": 10, "usage_type": "attribute"}]} +{"seq_id": "34863261090", "text": "import logging\n\nfrom huaweisms.api.config import MODEM_HOST\nfrom huaweisms.api.common import get_from_url\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_session_token_info(base_url=None):\n # type: (str) -> ...\n \"\"\"\n Get session token information\n\n :param base_url: base url for the modem api\n :return:\n \"\"\"\n if base_url is None:\n logger.warning(\n 'calling %s.get_session_token_info without base_url argument is deprecated',\n __name__\n )\n base_url = 'http://{}/api'.format(MODEM_HOST)\n\n url = \"{}/webserver/SesTokInfo\".format(base_url)\n return get_from_url(url, timeout=30)\n", "repo_name": "Brisseta/SmartHome", "sub_path": "venv/Lib/site-packages/huaweisms/api/webserver.py", "file_name": "webserver.py", "file_ext": "py", "file_size_in_byte": 645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "huaweisms.api.config.MODEM_HOST", "line_number": 23, "usage_type": "argument"}, {"api_name": "huaweisms.api.common.get_from_url", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "22707845286", "text": "import logging\nimport os\nimport traceback\n\nimport flask\n\nfrom .ml_layer_finder_engine import process_structure_core\nfrom .utils.structures import parse_structure\nfrom .utils.response import FlaskRedirectException\n\n# Ignoring the import error because it's from tools-barebone\n# Not ideal, but works for now\nfrom web_module import get_config # pylint: disable=import-error\nimport header\n\nVALID_EXAMPLES = {\n \"WTe2\": \"WTe2-02f1827d-f339-436f-baf6-66d1cf142fcf_structure.xsf\",\n \"ZnCl2\": \"ZnCl2-e5f429a4-3b02-4fb0-8921-0a7ab05078ed_structure.xsf\",\n # MoS2 bulk from Materials Cloud:\n # https://www.materialscloud.org/explore/2dstructures/details/6e58409f-4ab2-4883-9686-87d4d89c0bf9\n # (Originally from COD, 9007660, P6_3/mmc)\n \"MoS2\": \"MoS2-6e58409f-4ab2-4883-9686-87d4d89c0bf9_structure.xsf\",\n # black P bulk from Materials Cloud:\n # https://www.materialscloud.org/explore/2dstructures/details/904c1f0e-da23-42f0-95b4-a4fee98e6d04\n # (Originally from COD, 9012486, Cmce)\n \"blackP\": \"P-904c1f0e-da23-42f0-95b4-a4fee98e6d04_structure.xsf\",\n \"graphite\": \"graphite-544d62e4-8ebe-404c-aa17-b99be62ea70b.xsf\",\n \"BN\": \"BN-P6_3mmc-f7e2ff32-27ed-4c89-9c3c-4acbaffbb897.xsf\",\n # \"Sr2Nb5O9\": \"Sr2Nb5O9-866e918e-7a5f-41e3-980e-038852391b5a_structure.xsf\",\n \"KGaSe2\": \"KGaSe2-1e35f667-92e1-4b90-bfc8-daf3c5d1f7b0_structure.xsf\",\n \"Na2TiS2O\": \"Na2TiS2O-8d5eb648-9aa3-409f-9e72-861e38e11f30_structure.xsf\",\n \"B2N2\": \"B2N2-8f2e38e9-01d5-4208-adaf-daa461ac8139_structure.xsf\",\n \"AgBiTe3\": \"AgBiTe3-0bddefb1-3b12-4d9b-bd77-2491d5d9fdb9_structure.xsf\",\n}\n\nlogger = logging.getLogger(\"tool-ml-layer-finder-tool-app\")\nblueprint = flask.Blueprint(\"compute\", __name__, url_prefix=\"/compute\")\n\n\n@blueprint.route(\"/process_structure/\", methods=[\"GET\", \"POST\"])\ndef process_structure():\n if flask.request.method == \"POST\":\n # check if the post request has the file part\n if \"structurefile\" not in flask.request.files:\n return flask.redirect(flask.url_for(\"input_data\"))\n structurefile = flask.request.files[\"structurefile\"]\n fileformat = flask.request.form.get(\"fileformat\", \"unknown\")\n filecontent = structurefile.read().decode(\"utf-8\")\n\n try:\n structure = parse_structure(\n filecontent=filecontent,\n fileformat=fileformat,\n extra_data=dict(flask.request.form),\n )\n except Exception as exc:\n traceback.print_exc()\n flask.flash(\n \"Unable to parse the structure, sorry... ({}, {})\".format(\n str(type(exc)), str(exc)\n )\n )\n return flask.redirect(flask.url_for(\"input_data\"))\n\n try:\n data_for_template = process_structure_core(\n structure=structure,\n logger=logger,\n flask_request=flask.request,\n )\n config = get_config()\n tvars = header.template_vars\n return flask.render_template(\n \"user_templates/visualizer_header.j2\",\n **data_for_template,\n **config,\n **tvars,\n )\n except FlaskRedirectException as e:\n flask.flash(str(e))\n return flask.redirect(flask.url_for(\"input_data\"))\n except Exception as exc:\n traceback.print_exc()\n flask.flash(\n \"Unable to process the structure, sorry... ({}, {})\".format(\n str(type(exc)), str(exc)\n )\n )\n return flask.redirect(flask.url_for(\"input_data\"))\n else: # GET Request\n return flask.redirect(flask.url_for(\"input_data\"))\n\n\n@blueprint.route(\"/process_example_structure/\", methods=[\"GET\", \"POST\"])\ndef process_example_structure():\n \"\"\"\n Process an example structure (example name from POST request)\n \"\"\"\n if flask.request.method == \"POST\":\n examplestructure = flask.request.form.get(\"examplestructure\", \"\")\n fileformat = \"xsf-ase\"\n\n try:\n filename = VALID_EXAMPLES[examplestructure]\n except KeyError:\n flask.flash(\"Invalid example structure '{}'\".format(examplestructure))\n return flask.redirect(flask.url_for(\"input_data\"))\n\n # I expect that the valid_examples dictionary already filters only\n # existing files, so I don't try/except here\n with open(\n os.path.join(\n os.path.dirname(__file__),\n \"xsf-examples\",\n filename,\n )\n ) as structurefile:\n filecontent = structurefile.read()\n\n try:\n structure = parse_structure(\n filecontent=filecontent,\n fileformat=fileformat,\n )\n except Exception as exc:\n flask.flash(\n \"Unable to parse the example structure, sorry... ({}, {})\".format(\n str(type(exc)), str(exc)\n )\n )\n return flask.redirect(flask.url_for(\"input_data\"))\n\n try:\n data_for_template = process_structure_core(\n structure=structure, logger=logger, flask_request=flask.request\n )\n config = get_config()\n tvars = header.template_vars\n return flask.render_template(\n \"user_templates/visualizer_header.j2\",\n **data_for_template,\n **config,\n **tvars,\n )\n except FlaskRedirectException as e:\n flask.flash(str(e))\n return flask.redirect(flask.url_for(\"input_data\"))\n except Exception as exc:\n traceback.print_exc()\n flask.flash(\n \"Unable to process the structure, sorry... ({}, {})\".format(\n str(type(exc)), str(exc)\n )\n )\n return flask.redirect(flask.url_for(\"input_data\"))\n else: # GET Request\n return flask.redirect(flask.url_for(\"input_data\"))\n", "repo_name": "epfl-theos/tool-ml-layer-finder", "sub_path": "compute/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 6067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request.form.get", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "attribute"}, {"api_name": "utils.structures.parse_structure", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 63, "usage_type": "call"}, {"api_name": "ml_layer_finder_engine.process_structure_core", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "attribute"}, {"api_name": "web_module.get_config", "line_number": 71, "usage_type": "call"}, {"api_name": "header.template_vars", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.response.FlaskRedirectException", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 81, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request.form.get", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "utils.structures.parse_structure", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 131, "usage_type": "call"}, {"api_name": "ml_layer_finder_engine.process_structure_core", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "attribute"}, {"api_name": "web_module.get_config", "line_number": 137, "usage_type": "call"}, {"api_name": "header.template_vars", "line_number": 138, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 139, "usage_type": "call"}, {"api_name": "utils.response.FlaskRedirectException", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 147, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "34806550511", "text": "#!/usr/bin/env python3\n\"\"\"\nAuthor : Jon Walters \nDate : 2023-08-21\nPurpose: Howl at people\n\"\"\"\n\nimport argparse\nimport io\nimport os\nimport sys\n\n\n# --------------------------------------------------\ndef get_args():\n \"\"\"Get command-line arguments\"\"\"\n\n parser = argparse.ArgumentParser(\n description=\"Howl at people\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n\n parser.add_argument(\n \"text\", metavar=\"TEXT\", help=\"Input string(s) or file name(s)\", type=str, nargs=\"+\",\n )\n\n parser.add_argument(\n \"-o\",\n \"--outdir\",\n help=\"Output directory\",\n metavar=\"STRING\",\n type=str,\n default=\"\",\n )\n\n parser.add_argument(\"-l\", \"--lower\", help=\"Output in lower case\", action=\"store_true\")\n\n args = parser.parse_args()\n\n\n \"\"\"\n for text in args.text:\n if os.path.isfile(text):\n text = open(text)\n else:\n text = io.StringIO(initial_value=text + \"\\n\")\n \"\"\"\n return args\n\n\n# --------------------------------------------------\ndef main():\n \"\"\"Start doing stuff here.\"\"\"\n\n args = get_args()\n if args.outdir and not os.path.isdir(args.outdir):\n os.mkdir(\"./\" + args.outdir)\n\n for text in args.text:\n text_in = open(text) if os.path.isfile(text) else io.StringIO(initial_value=text + \"\\n\")\n basename = os.path.basename(text)\n out_fh = open(os.path.join(\"./\", args.outdir, basename), \"wt\") if args.outdir else sys.stdout\n for line in text_in:\n out_line = line.lower() if args.lower else line.upper()\n out_fh.write(out_line)\n out_fh.close()\n\n\n# --------------------------------------------------\nif __name__ == \"__main__\":\n main()\n", "repo_name": "ringbolt60/tiny_python_projects", "sub_path": "05_howler/howler.py", "file_name": "howler.py", "file_ext": "py", "file_size_in_byte": 1763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 62, "usage_type": "attribute"}]} +{"seq_id": "38628467622", "text": "\"\"\" Tests for the energy value widget. \"\"\"\n\nfrom PySide2.QtWidgets import QLabel\n\nfrom nutrition.recipe_builder_widget.widgets.energy_value import EnergyValueWidget\nfrom nutrition.recipe_builder_widget.widgets.utils import energy_data_str\n\nfrom tests.helpers import UsesQApplication, random_energy_value\n\n\nclass TestEnergyValueWidget(UsesQApplication):\n \"\"\" Tests for the energy value widget. \"\"\"\n\n def test_energy_value_has_qlabel(self):\n \"\"\" Tests the widget layout. \"\"\"\n widget = EnergyValueWidget()\n\n self.assertTrue(hasattr(widget, \"widget\"))\n self.assertTrue(isinstance(widget.widget, QLabel))\n\n def test_set_energy_value(self):\n \"\"\" Tests the set energy value method. \"\"\"\n widget = EnergyValueWidget()\n\n energy_value = random_energy_value()\n ingredient_mass = 100\n\n widget.set_energy_value(energy_value, ingredient_mass)\n\n self.assertEqual(widget.widget.text(), energy_data_str(energy_value, ingredient_mass))\n", "repo_name": "healty-diet/nutrition", "sub_path": "tests/recipe_builder_widget/test_energy_value_widget.py", "file_name": "test_energy_value_widget.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "tests.helpers.UsesQApplication", "line_number": 11, "usage_type": "name"}, {"api_name": "nutrition.recipe_builder_widget.widgets.energy_value.EnergyValueWidget", "line_number": 16, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 19, "usage_type": "argument"}, {"api_name": "nutrition.recipe_builder_widget.widgets.energy_value.EnergyValueWidget", "line_number": 23, "usage_type": "call"}, {"api_name": "tests.helpers.random_energy_value", "line_number": 25, "usage_type": "call"}, {"api_name": "nutrition.recipe_builder_widget.widgets.utils.energy_data_str", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "17045876796", "text": "from string import Template\n\nimport numpy as np\nimport pycuda.autoinit\nfrom pycuda.compiler import SourceModule\nfrom pycuda.gpuarray import GPUArray, to_gpu\n\nfrom ..cuda_implementations.utils import all_arrays_to_gpu\n\n\ndef batch_depth_to_voxels_mapping(M, D, grid_shape):\n cu_file_path = \"../raynet/cuda_implementations/planes_voxels_mapping.cu\"\n with open(cu_file_path, \"r\") as f:\n cu_file = f.read()\n tpl = Template(cu_file)\n\n mod = SourceModule(tpl.substitute(\n max_voxels=M,\n depth_planes=D,\n grid_x=grid_shape[0],\n grid_y=grid_shape[1],\n grid_z=grid_shape[2]\n ))\n cuda_pvm = mod.get_function(\"batch_planes_voxels_mapping\")\n cuda_pvm.prepare(\"i\" + \"P\"*7)\n\n @all_arrays_to_gpu\n def pvm(\n voxel_grid,\n ray_voxel_indices,\n ray_voxel_count,\n ray_start,\n ray_end,\n S,\n S_new,\n threads=2048\n ):\n # Assert everything is the right size, shape and dtype\n assert S.shape[1] == D\n assert S_new.shape[1] == M\n assert len(ray_voxel_count.shape) == 1\n assert np.float32 == S.dtype\n assert np.float32 == S_new.dtype\n assert np.int32 == ray_voxel_count.dtype\n assert np.float32 == ray_start.dtype\n assert np.float32 == ray_end.dtype\n\n # Determine the grid and block arguments\n n_rays = len(S)\n blocks = n_rays / threads + int(n_rays % threads != 0)\n\n cuda_pvm.prepared_call(\n (threads, 1),\n (blocks, 1, 1),\n np.int32(n_rays),\n voxel_grid.gpudata,\n ray_voxel_indices.gpudata,\n ray_voxel_count.gpudata,\n ray_start.gpudata,\n ray_end.gpudata,\n S.gpudata,\n S_new.gpudata\n )\n\n return S_new\n\n return pvm\n\n\ndef depth_to_voxels(\n ray_voxel_count,\n ray_voxel_indices,\n rays_idxs,\n voxel_grid,\n points,\n S,\n S_new,\n batch_size=20000\n):\n \"\"\"Compute the depth probability of each voxel based on S the probability\n distribution on points.\n\n Arguments\n ---------\n ray_voxel_count: array(shape=(N,), int), The number of voxels\n intersected by each ray\n ray_voxel_indices: array(shape=(N, M, 3), int), The indices in the\n voxel grid per ray\n rays_idxs: array(shape=(N,), int), The indices of the valid rays\n voxel_grid: array(shape=(3, D1, D2, D3)), The coordinates of the\n centers of all voxels in the voxel grid of size\n (D1, D2, D3)\n points: array(shape=(4, N, D), float32), D points sampled on each of\n the N rays\n S: array(shape=(N, D), float32), A depth probability distribution for\n each of the N rays\n \"\"\"\n # Extract the numbers N, M, D\n N, M, _ = ray_voxel_indices.shape\n _, _, D = points.shape\n\n # Fill the output array to 0\n S_new.fill(0)\n # Move to GPU to save some time frome copying\n points_start_gpu = to_gpu(points[:-1, rays_idxs, 0].T)\n points_end_gpu = to_gpu(points[:-1, rays_idxs, -1].T)\n ray_voxel_count = to_gpu(ray_voxel_count[rays_idxs])\n\n pvm = batch_depth_to_voxels_mapping(M, D, np.array(voxel_grid.shape[1:]))\n voxel_grid = voxel_grid.transpose(1, 2, 3, 0).ravel()\n # Start iterationg over the batch of rays\n for i in range(0, len(rays_idxs), batch_size):\n s = pvm(\n voxel_grid,\n ray_voxel_indices[rays_idxs[i:i+batch_size]],\n ray_voxel_count[i:i+batch_size],\n points_start_gpu[i:i+batch_size],\n points_end_gpu[i:i+batch_size],\n S[rays_idxs[i:i+batch_size]],\n S_new[rays_idxs[i:i+batch_size]]\n )\n S_new[rays_idxs[i:i+batch_size]] = s.get()\n\n return S_new\n", "repo_name": "paschalidoud/raynet", "sub_path": "raynet/planes_voxels_mapping/planes_voxels_mapping_cuda.py", "file_name": "planes_voxels_mapping_cuda.py", "file_ext": "py", "file_size_in_byte": 3834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 72, "dataset": "github-code", "pt": "2", "api": [{"api_name": "string.Template", "line_number": 15, "usage_type": "call"}, {"api_name": "pycuda.compiler.SourceModule", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 55, "usage_type": "call"}, {"api_name": "cuda_implementations.utils.all_arrays_to_gpu", "line_number": 27, "usage_type": "name"}, {"api_name": "pycuda.gpuarray.to_gpu", "line_number": 105, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.to_gpu", "line_number": 106, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.to_gpu", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "23079084294", "text": "#!/usr/bin/env python3\n\"\"\"\nFile: astro_functions.py\nAuthor: Jack Runburg\nEmail: jack.runburg@gmail.com\nGithub: https://github.com/runburg\nDescription: Functions to compute astrophysical quantities.\n\n\nFunctions:\n - compute_z_from_mag: Compute the redshift z of a galaxy with apparent magnitude mag_app and absolute magnitude mag_abs.\n - compute_z_func: Return dummy function for newton's method computation of z.\n - compute_mag_abs: Compute the absolute magnitude for a given apparent magnitude and redshift.\n - K_correction_func: NOT IMPLEMENTED\n - setup_cosmology: Setup the assumed cosmology.\n\n\nTODO:\n - Write K-corrections for the different channels.\n - Central absolute magnitude values assume a fixed redshift. Fix this?\n - Is Newton's method a stupid way to calculate the redshift? Probably.\n\"\"\"\nfrom astropy import constants\nfrom astropy import cosmology\nfrom astropy import units as u\nfrom scipy.optimize import newton\nimport numpy as np\n\n\ndef setup_cosmology(H0=67.4, omegam0=0.32, omegade0=0.68):\n \"\"\"Setup the assumed cosmology.\"\"\"\n cosmo = cosmology.FlatLambdaCDM(H0=H0, Om0=omegam0)\n\n return cosmo\n\n\ndef K_correction_func(z):\n return 0\n\n\ndef compute_mag_abs(z, mag_app, cosmo, k_correction=K_correction_func):\n \"\"\"Compute the absolute magnitude for a given apparent magnitude and redshift.\n\n\n Input:\n - z: redshift\n - mag_app: apparent magnitude\n - cosmo: astropy cosmology\n - k_correction: function of z calculating the k-correction\n \"\"\"\n # Eq form given in Eales 93 after eq (2)\n return mag_app - 5 * np.log10(cosmo.luminosity_distance(z).to(u.pc).value / 10) - k_correction(z)\n\n\ndef compute_z_func(z, mag_abs, mag_app, cosmo):\n \"\"\"Return dummy function for newton's method computation of z.\"\"\"\n\n return mag_abs - compute_mag_abs(z, mag_app, cosmo)\n\n\ndef compute_z_from_mag(mag_abs, mag_app, z_guess, cosmo):\n \"\"\"Compute the redshift z of a galaxy with apparent magnitude mag_app and absolute magnitude mag_abs.\n\n\n Input:\n - mag_abs: absolute magnitude\n - mag_app: apparent magnitude\n - z_guess: initial guess of z, redshift from mag_abs value should be good I think\n - cosmo: astropy cosmology\n\n Return:\n - z_newton: The calculated redshift for the function\n \"\"\"\n # Define constants\n c = constants.c\n H0 = cosmo.H0\n omega0 = cosmo.Om0\n\n # Choose K-correction\n K = lambda z: 0\n\n # Look for zero of magnitude, luminosity distance equation\n try:\n z_newton = newton(compute_z_func, z_guess, args=(mag_abs, mag_app, cosmo))\n except RuntimeError:\n print(mag_abs, mag_app, z_guess)\n z_newton = None\n\n return z_newton\n\n\ndef double_power_law(L, A, Lstar, gamma1, gamma2, L_multiplier=1, evolution_multiplier=1, z=[]):\n \"\"\"Compute value of broken double power law.\n\n Can give a muliplier on L and on the whole function to test different evolution models.\n \"\"\"\n\n return A / (((L * L_multiplier) / Lstar)**gamma1 + ((L * L_multiplier) / Lstar)**gamma2) * evolution_multiplier\n\n\ndef dpl_fit(lfvals, lferrvals, l_bins, beta0s, ifixb=[1, 1, 1, 1]):\n \"\"\"Fit double power law to binned LF estimate.\"\"\"\n from scipy import odr\n\n # if len(lfvals.shape) == 1:\n # lfvals = [lfvals]\n # lferrvals = [lferrvals]\n\n def dpl_func(params, L):\n A, gamma1, gamma2, Lstar = np.array(params).astype(np.float64).astype(complex)\n # if A > -4.0:\n # return 1e90\n # if Lstar > 46.5:\n # return 1e90\n return (10**A / ((L / 10**Lstar)**gamma1 + (L / 10**Lstar)**gamma2)).real\n\n lum_errors = (l_bins[1:] - l_bins[:-1]) / 2\n bin_centers = (l_bins[:-1] + l_bins[1:]) / 2\n\n dpl_fits = []\n for lf, lferr, beta0 in zip(lfvals, lferrvals, beta0s):\n dpl = odr.Model(dpl_func)\n lferr[lferr == 0.0] = np.nan\n mydata = odr.RealData(bin_centers, lf * bin_centers * np.log(10), sx=lum_errors, sy=lferr * np.log(10) * bin_centers)\n myodr = odr.ODR(mydata, dpl, beta0=beta0, ifixb=ifixb, maxit=300, stpb=[0.1, 0.1, 0.1, 1])\n # myodr = odr.ODR(mydata, dpl, beta0=[np.log10(1e-4), 0.3, 2.8, np.log10(4e44)], ifixb=[1, 0, 0, 1], maxit=300)\n # myodr = odr.ODR(mydata, dpl, beta0=[4e-5, 0.4, 3.3, 6e44])\n myoutput = myodr.run()\n # print('param for this z bin', myoutput.beta)\n\n dpl_fits.append(lambda L, paramss=myoutput.beta: dpl_func(paramss, L))\n\n return dpl_fits\n\n\ndef LDDE(L, z, *, A, gamma1, gamma2, Lstar, zcstar, p1, p2, alpha, La):\n \"\"\"Compute LF with LADE evolution.\"\"\"\n ldde_vals_at_z = []\n\n def zc(l, zcstar=zcstar, La=La, alpha=alpha):\n if l < La:\n return zcstar * (l / La)**alpha\n else:\n return zcstar\n\n def ez(z, l, p1=p1, p2=p2):\n if z <= zc(l):\n return (1 + z)**p1\n else:\n return (1 + zc(l))**p1 * ((1 + z) / (1 + zc(l)))**p2\n\n ldde_vals_at_z = []\n\n for zz in z:\n zbin_lf = np.zeros(len(L))\n for j, ll in enumerate(L):\n zbin_lf[j] = double_power_law(ll, A, Lstar, gamma1, gamma2, z=zz) * ez(zz, ll)\n # ez = lambda l, red: (1 + red)**p1 if red < zc(l) else # wtf goes here\n ldde_vals_at_z.append(np.array([L, zbin_lf]).T)\n # print(lade_vals_at_z)\n\n return np.array(ldde_vals_at_z)\n\n\ndef LADE(L, z, *, A, gamma1, gamma2, Lstar, zc, p1, p2, d, no_k=False):\n \"\"\"Compute LF with LADE evolution.\"\"\"\n k = (1 + zc)**p1 + (1 + zc)**p2\n if no_k is True:\n k = 1\n\n lade_vals_at_z = []\n for zz in z:\n eta1 = 1 / k * (((1 + zc) / (1 + zz))**p1 + ((1 + zc) / (1 + zz))**p2)\n etad = 10**(d * (1 + zz))\n\n lade_vals_at_z.append(np.array([L, double_power_law(L, A, Lstar, gamma1, gamma2, z=zz, L_multiplier=eta1, evolution_multiplier=etad)]).T)\n # print(lade_vals_at_z)\n\n return lade_vals_at_z\n\n\ndef IR_evol(L, z, *, A, gamma1, gamma2, zref, Lstar, k1, k2, k3, limits=None):\n \"\"\"Compute LF with IR evolution.\"\"\"\n lade_vals_at_z = []\n for zz in z:\n eps = np.log10((1 + zz) / (1 + zref))\n L_mult = 10**-(k1 * eps + k2 * eps**2 + k3 * eps**3)\n\n if limits is None:\n lade_vals_at_z.append(np.array([L, double_power_law(L, A, Lstar, gamma1, gamma2, z=zz, L_multiplier=L_mult)]).T)\n else:\n for l in limits:\n low_index = np.argwhere(L > l[0]) - 1\n high_index = np.argwhere(L > l[1])\n lade_vals_at_z.append(np.array([L[low_index:high_index], double_power_law(L, A, Lstar, gamma1, gamma2, z=zz, L_multiplier=L_mult)][low_index:high_index]).T)\n # print(lade_vals_at_z)\n\n return np.array(lade_vals_at_z)\n", "repo_name": "runburg/agn_lf", "sub_path": "source/astro_functions.py", "file_name": "astro_functions.py", "file_ext": "py", "file_size_in_byte": 6697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "3", "api": [{"api_name": "astropy.cosmology.FlatLambdaCDM", "line_number": 32, "usage_type": "call"}, {"api_name": "astropy.cosmology", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 52, "usage_type": "call"}, {"api_name": "astropy.units.pc", "line_number": 52, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 52, "usage_type": "name"}, {"api_name": "astropy.constants.c", "line_number": 75, "usage_type": "attribute"}, {"api_name": "astropy.constants", "line_number": 75, "usage_type": "name"}, {"api_name": "scipy.optimize.newton", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 110, "usage_type": "attribute"}, {"api_name": "scipy.odr.Model", "line_number": 122, "usage_type": "call"}, {"api_name": "scipy.odr", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 123, "usage_type": "attribute"}, {"api_name": "scipy.odr.RealData", "line_number": 124, "usage_type": "call"}, {"api_name": "scipy.odr", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 124, "usage_type": "call"}, {"api_name": "scipy.odr.ODR", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.odr", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "20289102024", "text": "\"\"\"\n\n题目：你有一个目录，装了很多照片，把它们的尺寸变成都不大于 iPhone5 分辨率的大小\n\n实现：\n os内置模块有很多实用的方法\n\n os.getcwd()：查看当前所在路径。\n\n os.listdir(path):列举目录下的所有文件。返回的是列表类型\n\n os.path.abspath(path):返回path的绝对路径\n\n os.path.split(path):将路径分解为(文件夹,文件名)，返回的是元组类型。可以看出，若路径字符串最后一个字符是\\,则只有文件夹部分有值；\n 若路径字符串中均无\\,则只有文件名部分有值。若路径字符串有\\，且不在最后，则文件夹和文件名均有值。且返回的文件夹的结果不包含\\.\n\n os.path.join(path1,path2,...):将path进行组合，若其中有绝对路径，则之前的path将被删除\n\n os.path.dirname(path):返回path中的文件夹部分，结果不包含'\\'\n\n os.path.basename(path):返回path中的文件名。\n\n os.path.getmtime(path):文件或文件夹的最后修改时间，从新纪元到访问时的秒数。\n\n os.path.getatime(path):文件或文件夹的最后访问时间，从新纪元到访问时的秒数。\n\n os.path.getctime(path):文件或文件夹的创建时间，从新纪元到访问时的秒数\n\n os.path.getsize(path):文件或文件夹的大小，若是文件夹返回0\n\n os.path.exists(path):文件或文件夹是否存在，返回True 或 False\n\n os中定义了一组文件、路径在不同操作系统中的表现形式参数\n\n\n\n 我们只需要根据传入的路径，找到以“jpg、jpeg、png”后缀的文件\n os.listdir(path) 返回的是目录下所有文件，所以还需要判断一下文件的后缀\n\n 获取文件的长宽\n Image.open(path)\n\n 判断文件的长宽是否大于1136*640\n 如果大于就\n 文件的最大值对应1136\n 文件的最小值对应640\n 小于就不管了\n\n 不管修不修改都保存到另外一个新文件夹中\n os.makedirs()\n\"\"\"\nimport os\nfrom PIL import Image\n\ndef modifyPic(modifyAgoPath, modifyAfterPath, width = 1136, height = 640):\n \"\"\"\n :param modifyAgoPath: 存放待修改的图片路径\n :param modifyAfterPath: 修改后存放图片的路径\n :param width: 指定图片的长\n :param height: 指定图片的高\n :return:\n \"\"\"\n #根据modifyAgoPath，去该路径下找到所有的以“jpg、jpeg、png”后缀的文件，并保存到一个list中\n modifyFile = [x for x in os.listdir(modifyAgoPath) if x.find('.JPG') >= 0 or x.find('.PNG') >= 0 or x.find('.JPEG') >= 0]\n\n #拿到图片后，循环修改图片\n for x in modifyFile:\n im = Image.open(modifyAgoPath + '\\\\' + x)\n\n width, height = im.size\n\n if width >= height:\n if width > 1136:\n width = 1136\n else:\n if height > 640:\n height = 640\n\n #im.thumbnail((str(width), str(height)))\n im.thumbnail((width, height))\n\n im.save(modifyAfterPath + '\\\\' + x )\n\nif __name__ == '__main__':\n modifyPic(r'E:\\Git-codeBase\\show-me-the-code\\day-five\\test', r'E:\\Git-codeBase\\show-me-the-code\\day-five\\test2')", "repo_name": "TestSmallWhite/show-me-the-code", "sub_path": "day-five/day-five.py", "file_name": "day-five.py", "file_ext": "py", "file_size_in_byte": 3221, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 68, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "858594490", "text": "from __future__ import annotations\n\nfrom typing import Union\n\nfrom datadog_api_client.model_utils import (\n ModelNormal,\n cached_property,\n unset,\n UnsetType,\n)\n\n\nclass DistributionWidgetXAxis(ModelNormal):\n @cached_property\n def openapi_types(_):\n return {\n \"include_zero\": (bool,),\n \"max\": (str,),\n \"min\": (str,),\n \"scale\": (str,),\n }\n\n attribute_map = {\n \"include_zero\": \"include_zero\",\n \"max\": \"max\",\n \"min\": \"min\",\n \"scale\": \"scale\",\n }\n\n def __init__(\n self_,\n include_zero: Union[bool, UnsetType] = unset,\n max: Union[str, UnsetType] = unset,\n min: Union[str, UnsetType] = unset,\n scale: Union[str, UnsetType] = unset,\n **kwargs,\n ):\n \"\"\"\n X Axis controls for the distribution widget.\n\n :param include_zero: True includes zero.\n :type include_zero: bool, optional\n\n :param max: Specifies maximum value to show on the x-axis. It takes a number, percentile (p90 === 90th percentile), or auto for default behavior.\n :type max: str, optional\n\n :param min: Specifies minimum value to show on the x-axis. It takes a number, percentile (p90 === 90th percentile), or auto for default behavior.\n :type min: str, optional\n\n :param scale: Specifies the scale type. Possible values are ``linear``.\n :type scale: str, optional\n \"\"\"\n if include_zero is not unset:\n kwargs[\"include_zero\"] = include_zero\n if max is not unset:\n kwargs[\"max\"] = max\n if min is not unset:\n kwargs[\"min\"] = min\n if scale is not unset:\n kwargs[\"scale\"] = scale\n super().__init__(kwargs)\n", "repo_name": "DataDog/datadog-api-client-python", "sub_path": "src/datadog_api_client/v1/model/distribution_widget_x_axis.py", "file_name": "distribution_widget_x_axis.py", "file_ext": "py", "file_size_in_byte": 1771, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 79, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datadog_api_client.model_utils.ModelNormal", "line_number": 13, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.cached_property", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 32, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 33, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 34, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 35, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 35, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 32, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 33, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 34, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 35, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 53, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 55, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 57, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 59, "usage_type": "name"}]} +{"seq_id": "8028133307", "text": "import os\nfrom itertools import cycle\nfrom time import sleep\n\n\nclass Processor:\n x: int = 1\n cycle: int = 1\n crt_pos: int = 0\n\n interesting_x = {\n 20: None,\n 60: None,\n 100: None,\n 140: None,\n 180: None,\n 220: None,\n }\n\n def crt_cycle(self):\n EMTPY_CHAR = \" \" # is \".\" in the original task\n FULL_CHAR = \"█\" # is \"#\" in the original\n if abs(self.x - self.crt_pos) < 2:\n print(FULL_CHAR, end=\"\")\n else:\n print(EMTPY_CHAR, end=\"\")\n self.crt_pos += 1\n if self.crt_pos == 40:\n self.crt_pos = 0\n print()\n\n def execute(self, line: str):\n\n self.crt_cycle()\n self._check_interesting()\n\n parts = line.strip().split(\" \")\n cmd = parts[0]\n arg = None\n if len(parts) > 1:\n arg = int(parts[1])\n if cmd == \"noop\":\n self.cycle += 1\n return\n elif cmd == \"addx\":\n self.cycle += 1\n self._check_interesting()\n self.cycle += 1\n self.crt_cycle()\n self._check_interesting()\n self.x += arg\n\n def _check_interesting(self):\n if self.cycle in self.interesting_x:\n self.interesting_x[self.cycle] = self.x\n\n def get_sum_strengths(self):\n res = 0\n for (cycle, x_val) in self.interesting_x.items():\n res += cycle * x_val\n return res\n\n\ndef main():\n\n input_path = os.path.join(os.path.dirname(__file__), \"input.txt\")\n\n with open(input_path, \"r\") as f:\n lines = f.readlines()\n\n proc = Processor()\n print(\"PART 2: (read letters)\")\n for line in lines:\n proc.execute(line)\n print()\n print()\n print(\n \"=========================================================================== \\n\"\n )\n print(\"Part 1:\")\n print(\">>\", proc.get_sum_strengths(), \"<<\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Nighmared/AoC", "sub_path": "2022/day10/solve.py", "file_name": "solve.py", "file_ext": "py", "file_size_in_byte": 1967, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "itertools.cycle", "line_number": 8, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 59, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 60, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "4266055239", "text": "import os\nimport unittest\n\nimport utils.Configuration\nfrom utils.Configuration import ConfigurationItem\nfrom handlers import addsection\n\n\nclass testAddSection(unittest.TestCase):\n def test_ShouldDefaultSectionNameToEmptyString(self):\n page = addsection.AddSection((), {})\n page.OutputPage()\n self.assertEqual('', page.page.page.Nests['']['SectionName'])\n\n def test_ShouldUseFilenameGiven(self):\n page = addsection.AddSection((), {\n 'filename': 'test.ini'\n })\n page.OutputPage()\n self.assertEqual('test.ini', page.page.page.Nests['']['ConfigFilename'])\n\n def test_ShouldSaveSectionWhenSaveClicked(self):\n global sectionSaved\n sectionSaved = False\n\n def SaveSection():\n global sectionSaved\n sectionSaved = True\n\n page = addsection.AddSection((), {'saveSection': 'Value'})\n page._SaveSection = SaveSection\n page.OutputPage()\n self.assertTrue(sectionSaved)\n\n def test_ShouldAddNewSectionToConfigurationWhenSaveSectionCalled(self):\n global config\n config = None\n def TrueFunc(var1=None):\n return True\n\n def FalseFunc(var1=None):\n return False\n\n def LoadConfig(inst, filename):\n inst.config = ConfigurationItem('', '')\n \n\n def CaptureConfig(inst, filename):\n global config\n config = inst.config\n\n page = addsection.AddSection((), {'sectionName': 'NewSection', 'saveSection': 'Value'})\n page.isValidFilename = FalseFunc\n pathExists = os.path.exists\n os.path.exists = TrueFunc\n utils.Configuration.Configuration.LoadFile = LoadConfig\n utils.Configuration.Configuration.WriteFile = CaptureConfig\n \n page._SaveSection()\n\n os.path.exists = pathExists\n\n self.assertEqual(config, ConfigurationItem('', '', [ConfigurationItem('NewSection', '')]))\n\n\n", "repo_name": "James226/BrowserConfigurationUtility", "sub_path": "tests/testAddSection.py", "file_name": "testAddSection.py", "file_ext": "py", "file_size_in_byte": 1956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "unittest.TestCase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "handlers.addsection.AddSection", "line_number": 11, "usage_type": "call"}, {"api_name": "handlers.addsection", "line_number": 11, "usage_type": "name"}, {"api_name": "handlers.addsection.AddSection", "line_number": 16, "usage_type": "call"}, {"api_name": "handlers.addsection", "line_number": 16, "usage_type": "name"}, {"api_name": "handlers.addsection.AddSection", "line_number": 30, "usage_type": "call"}, {"api_name": "handlers.addsection", "line_number": 30, "usage_type": "name"}, {"api_name": "utils.Configuration.ConfigurationItem", "line_number": 45, "usage_type": "call"}, {"api_name": "handlers.addsection.AddSection", "line_number": 52, "usage_type": "call"}, {"api_name": "handlers.addsection", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "utils.Configuration.Configuration", "line_number": 56, "usage_type": "attribute"}, {"api_name": "utils.Configuration", "line_number": 56, "usage_type": "name"}, {"api_name": "utils.Configuration.Configuration", "line_number": 57, "usage_type": "attribute"}, {"api_name": "utils.Configuration", "line_number": 57, "usage_type": "name"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "utils.Configuration.ConfigurationItem", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "959284058", "text": "import scrapy\r\n#构造虚拟代理\r\nfrom faker import Factory\r\nfrom urllib import parse\r\nfrom doubanTop.items import DoubanCommentItem\r\nf = Factory.create()\r\n\r\nclass commentSpider(scrapy.Spider):\r\n name = \"doubanCommentSpider\"\r\n\r\n start_urls=[\r\n 'https://www.douban.com/'\r\n ]\r\n\r\n #构造请求头\r\n headers = {\r\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\r\n 'Accept-Encoding': 'gzip, deflate, br',\r\n 'Accept-Language': 'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3',\r\n 'Connection': 'keep-alive',\r\n 'Host': 'accounts.douban.com',\r\n 'User-Agent': f.user_agent()\r\n }\r\n\r\n\r\n #构造表单数据\r\n formdata={\r\n 'form_email': '17746913064',\r\n 'form_password': 'cgz12345678',\r\n # 'captcha-solution': '',\r\n # 'captcha-id': '',\r\n 'login': '登录',\r\n 'redir': 'https://www.douban.com/',\r\n 'source': 'index_nav'\r\n }\r\n\r\n ####################################################\r\n # name : start_requests\r\n # function : 爬虫启动时，会首先执行start_requests函数的调用，执行成功之后，再去执行parse()函数进行解析\r\n # 发送登录豆瓣的请求\r\n # parameters_in:\r\n #\r\n # parameters_out:\r\n # scrapy.Request:请求登录\r\n ####################################################\r\n def start_requests(self):\r\n return [scrapy.Request(url='https://www.douban.com/accounts/login',\r\n headers=self.headers,\r\n meta={'cookiejar': 1},\r\n callback=self.loginParse)]\r\n\r\n ####################################################\r\n # name : loginParse\r\n # function : 处理验证码\r\n # parameters_in:\r\n # response:\r\n # parameters_out:\r\n # scrapy.FormRequest.from_response:提交表单的登录信息\r\n ####################################################\r\n def loginParse(self,response):\r\n if b'captcha_image' in response.body:\r\n link=response.xpath('//img[@class=\"captcha_image\"]/@src').extract()[0]\r\n print(link)\r\n captcha_solution=input('请输入验证码：')\r\n captcha_id=parse.parse_qs(parse.urlparse(link).query,True)['id']\r\n self.formdata['captcha-solution']=captcha_solution\r\n self.formdata['captcha-id'] = captcha_id\r\n #提交表单信息\r\n return [scrapy.FormRequest.from_response(response,\r\n headers=self.headers,\r\n formdata=self.formdata,\r\n meta={'cookiejar':response.meta['cookiejar']},\r\n callback=self.afterLogin\r\n )]\r\n ####################################################\r\n # name : afterLogin\r\n # function : 成功登录之后，开始爬取网页的数据\r\n # parameters_in:\r\n # response:\r\n # parameters_out:\r\n #\r\n ####################################################\r\n def afterLogin(self,response):\r\n print(response.status)\r\n self.headers['Host']='www.douban.com'\r\n yield scrapy.Request(url='https://movie.douban.com/subject/22266320/reviews',\r\n headers=self.headers,\r\n meta={'cookiejar':response.meta['cookiejar']},\r\n callback=self.commentUrlParse,\r\n dont_filter=True)\r\n yield scrapy.Request(url='https://movie.douban.com/subject/22266320/reviews',\r\n headers=self.headers,\r\n meta={'cookiejar':response.meta['cookiejar']},\r\n callback=self.nextpageParse,\r\n dont_filter=True)\r\n ####################################################\r\n # name : commentUrlParse\r\n # function : 爬取评论的链接\r\n # parameters_in:\r\n # response:\r\n # parameters_out:\r\n #\r\n ####################################################\r\n def commentUrlParse(self,response):\r\n for item in response.xpath('//div[@class=\"main review-item\"]/div[1]/h2'):\r\n commentUrl=item.xpath('a/@href').extract_first()\r\n commentTitle=item.xpath('a/text()').extract_first()\r\n # print(\"*************\")\r\n # print(commentTitle,commentUrl)\r\n yield scrapy.Request(commentUrl,\r\n headers=self.headers,\r\n meta={'cookiejar': response.meta['cookiejar']},\r\n callback=self.commentParse\r\n )\r\n ####################################################\r\n # name : commentParse\r\n # function : 爬取评论\r\n # parameters_in:\r\n # response:\r\n # parameters_out:\r\n #\r\n ####################################################\r\n def commentParse(self,response):\r\n content=response.xpath('//header[@class=\"main-hd\"]')\r\n authorUrl=content.xpath('a/@href').extract_first()\r\n author=content.xpath('a/span/text()').extract_first()\r\n print(author,authorUrl)\r\n\r\n\r\n ####################################################\r\n # name : nextpageParse\r\n # function : 爬取评论下一页\r\n # parameters_in:\r\n # response:\r\n # parameters_out:\r\n #\r\n ####################################################\r\n def nextpageParse(self,response):\r\n try:\r\n nextPage=response.xpath('//span[@class=\"next\"]/a/@href').extract()[0]\r\n nextPage=response.urljoin(nextPage)\r\n print(\"****************************下一页：\")\r\n print(nextPage)\r\n yield scrapy.Request(url=nextPage,\r\n headers=self.headers,\r\n meta={\"cookiejar\":response.meta['cookiejar']},\r\n callback=self.commentUrlParse,\r\n dont_filter=True) #默认去重，必须加这个，不去重\r\n yield scrapy.Request(url=nextPage,\r\n headers=self.headers,\r\n meta={\"cookiejar\":response.meta['cookiejar']},\r\n callback=self.nextpageParse,\r\n dont_filter=True)\r\n except:\r\n print(\"Error:no page\")\r\n return\r\n\r\n\r\n\r\n", "repo_name": "YellowMapleLeaf/crawler", "sub_path": "doubanTop/doubanTop/spiders/doubanCommentSpider.py", "file_name": "doubanCommentSpider.py", "file_ext": "py", "file_size_in_byte": 6715, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "faker.Factory.create", "line_number": 6, "usage_type": "call"}, {"api_name": "faker.Factory", "line_number": 6, "usage_type": "name"}, {"api_name": "scrapy.Spider", "line_number": 8, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 65, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 65, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 65, "usage_type": "call"}, {"api_name": "scrapy.FormRequest.from_response", "line_number": 69, "usage_type": "call"}, {"api_name": "scrapy.FormRequest", "line_number": 69, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 86, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 91, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 110, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 144, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "36105877175", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport configparser\n\n\ndef load_config_data(config_dir):\n config = configparser.ConfigParser()\n config.read(config_dir)\n return config\n\n\ndef str_2_array(str_state_shape, type_n='int'):\n sep_str_state_shape = str_state_shape.split(',')\n state_n_dim = len(sep_str_state_shape)\n state_shape = []\n for i in range(state_n_dim):\n if type_n == 'int':\n state_shape.append(int(sep_str_state_shape[i]))\n elif type_n == 'float':\n state_shape.append(float(sep_str_state_shape[i]))\n else:\n print('Selected type for str_2_array not implemented.')\n exit()\n\n return state_shape\n\n\ndef observation_to_gray(observation, image_size):\n observation = np.array(observation).reshape(1, image_size, image_size, 3)\n observation_gray = np.mean(observation, axis=3)\n observation_gray = observation_gray.reshape(\n (-1, image_size, image_size, 1))\n observation_gray_norm = observation_gray / 255.0\n\n return observation_gray_norm\n\n\nclass FastImagePlot:\n def __init__(self, fig_num, observation, image_size, title_name, vmin=0, vmax=1):\n self.window = plt.figure(fig_num)\n self.image_size = image_size\n self.im = plt.imshow(np.reshape(observation, [self.image_size, self.image_size]),\n cmap='gray', vmin=vmin, vmax=vmax)\n plt.show(block=False)\n self.window.canvas.set_window_title(title_name)\n self.window.canvas.draw()\n\n def refresh(self, observation):\n self.im.set_data(np.reshape(observation, [self.image_size, self.image_size]))\n self.window.draw_artist(self.im)\n self.window.canvas.blit()\n self.window.canvas.flush_events()\n", "repo_name": "rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks", "sub_path": "D-COACH/tools/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 1762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "2", "api": [{"api_name": "configparser.ConfigParser", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "38346299992", "text": "#Pacman in Python with PyGame\n#https://github.com/hbokmann/Pacman\n \nimport pygame\nimport AIPacSupport as ai\nfrom random import randint\nimport sys\n \nblack = (0,0,0)\nwhite = (255,255,255)\nblue = (0,0,255)\ngreen = (0,255,0)\ngray = (90, 90, 90)\nred = (255,0,0)\npurple = (255,0,255)\nyellow = (255, 255, 0)\nbrown = (139, 69, 19)\nwalls = [ [0,0,6,600],\n [0,0,600,6],\n [0,600,606,6],\n [600,0,6,606],\n [300,0,6,66],\n [60,60,186,6],\n [360,60,186,6],\n [60,120,66,6],\n [60,120,6,126],\n [180,120,246,6],\n [300,120,6,66],\n [480,120,66,6],\n [540,120,6,126],\n [120,180,126,6],\n [120,180,6,126],\n [360,180,126,6],\n [480,180,6,126],\n [180,240,6,126],\n [180,360,246,6],\n [420,240,6,126],\n [240,240,42,6],\n [324,240,42,6],\n [240,240,6,66],\n [240,300,126,6],\n [360,240,6,66],\n [0,300,66,6],\n [540,300,66,6],\n [60,360,66,6],\n [60,360,6,186],\n [480,360,66,6],\n [540,360,6,186],\n [120,420,366,6],\n [120,420,6,66],\n [480,420,6,66],\n [180,480,246,6],\n [300,480,6,66],\n [120,540,126,6],\n [360,540,126,6]\n ]\n\n# This class represents the bar at the bottom that the player controls\nclass Wall(pygame.sprite.Sprite):\n # Constructor function\n def __init__(self,x,y,width,height, color):\n # Call the parent's constructor\n pygame.sprite.Sprite.__init__(self)\n \n # Make a blue wall, of the size specified in the parameters\n self.image = pygame.Surface([width, height])\n self.image.fill(color)\n \n # Make our top-left corner the passed-in location.\n self.rect = self.image.get_rect()\n self.rect.top = y\n self.rect.left = x\n\n# This creates all the walls in room 1\ndef setupRoomOne(all_sprites_list):\n # Make the walls. (x_pos, y_pos, width, height)\n wall_list=pygame.sprite.RenderPlain()\n \n # This is a list of walls. Each is in the form [x, y, width, height]\n walls = [ [0,0,6,600],\n [0,0,600,6],\n [0,600,606,6],\n [600,0,6,606],\n [300,0,6,66],\n [60,60,186,6],\n [360,60,186,6],\n [60,120,66,6],\n [60,120,6,126],\n [180,120,246,6],\n [300,120,6,66],\n [480,120,66,6],\n [540,120,6,126],\n [120,180,126,6],\n [120,180,6,126],\n [360,180,126,6],\n [480,180,6,126],\n [180,240,6,126],\n [180,360,246,6],\n [420,240,6,126],\n [240,240,42,6],\n [324,240,42,6],\n [240,240,6,66],\n [240,300,126,6],\n [360,240,6,66],\n [0,300,66,6],\n [540,300,66,6],\n [60,360,66,6],\n [60,360,6,186],\n [480,360,66,6],\n [540,360,6,186],\n [120,420,366,6],\n [120,420,6,66],\n [480,420,6,66],\n [180,480,246,6],\n [300,480,6,66],\n [120,540,126,6],\n [360,540,126,6]\n ]\n \n # Loop through the list. Create the wall, add it to the list\n for item in walls:\n wall=Wall(item[0],item[1],item[2],item[3],blue)\n wall_list.add(wall)\n all_sprites_list.add(wall)\n \n # return our new list\n return wall_list\n\ndef setupRoomFoomba(all_sprites_list):\n # Make the walls. (x_pos, y_pos, width, height)\n wall_list=pygame.sprite.RenderPlain()\n \n # This is a list of walls. Each is in the form [x, y, width, height]\n walls = [ [0,0,6,600],\n [0,0,600,6],\n [0,600,606,6],\n [600,0,6,606],\n [300,0,6,66],\n [60,60,186,6],\n [360,60,186,6],\n [60,120,66,6],\n [60,120,6,126],\n [180,120,246,6],\n [300,120,6,66],\n [480,120,66,6],\n [540,120,6,126],\n [120,180,126,6],\n [120,180,6,126],\n [360,180,126,6],\n [480,180,6,126],\n [180,240,6,126],\n [180,360,246,6],\n [420,240,6,126],\n [240,240,42,6],\n [324,240,42,6],\n [240,240,6,66],\n [240,300,126,6],\n [360,240,6,66],\n [0,300,66,6],\n [540,300,66,6],\n [60,360,66,6],\n [60,360,6,186],\n [480,360,66,6],\n [540,360,6,186],\n [120,420,366,6],\n [120,420,6,66],\n [480,420,6,66],\n [180,480,246,6],\n [300,480,6,66],\n [120,540,126,6],\n [360,540,126,6]\n ]\n \n # Loop through the list. Create the wall, add it to the list\n for item in walls:\n wall=Wall(item[0],item[1],item[2],item[3], gray)\n wall_list.add(wall)\n all_sprites_list.add(wall)\n \n # return our new list\n return wall_list\n\ndef setupGate(all_sprites_list):\n gate = pygame.sprite.RenderPlain()\n gate.add(Wall(282,242,42,2,white))\n all_sprites_list.add(gate)\n return gate\n\n# This class represents the ball \n# It derives from the \"Sprite\" class in Pygame\nclass Block(pygame.sprite.Sprite):\n \n # Constructor. Pass in the color of the block, \n # and its x and y position\n def __init__(self, color, width, height):\n # Call the parent class (Sprite) constructor\n pygame.sprite.Sprite.__init__(self) \n \n # Create an image of the block, and fill it with a color.\n # This could also be an image loaded from the disk.\n self.image = pygame.Surface([width, height])\n self.image.fill(white)\n self.image.set_colorkey(white)\n pygame.draw.ellipse(self.image,color,[0,0,width,height])\n \n # Fetch the rectangle object that has the dimensions of the image\n # image.\n # Update the position of this object by setting the values \n # of rect.x and rect.y\n self.rect = self.image.get_rect() \n\n# This class represents the bar at the bottom that the player controls\nclass Player(pygame.sprite.Sprite):\n \n # Set speed vector\n change_x=0\n change_y=0\n last_move = [0, 0]\n num_moves = 0\n calcpathmove = 0\n next_moves = []\n eating = False\n \n # Constructor function\n def __init__(self,x,y, _image):\n # Call the parent's constructor\n pygame.sprite.Sprite.__init__(self)\n \n # Set height, width\n self.image = _image\n \n # Make our top-left corner the passed-in location.\n self.rect = self.image.get_rect()\n self.rect.top = y\n self.rect.left = x\n self.prev_x = x\n self.prev_y = y\n\n # Clear the speed of the player\n def prevdirection(self):\n self.prev_x = self.change_x\n self.prev_y = self.change_y\n\n # Change the speed of the player\n def changespeed(self,x,y):\n self.change_x+=x\n self.change_y+=y\n \n # Make the player speed 0\n def speedzero(self):\n self.change_x = 0\n self.change_y = 0\n \n def resetpos(self):\n self.rect.left = w\n self.rect.top = p_h\n # Find a new position for the player\n def update(self,walls,gate = None):\n # Get the old position, in case we need to go back to it\n self.num_moves += 1\n old_x=self.rect.left\n new_x=old_x+self.change_x\n prev_x=old_x+self.prev_x\n self.rect.left = new_x\n \n old_y=self.rect.top\n new_y=old_y+self.change_y\n prev_y=old_y+self.prev_y\n\n # Did this update cause us to hit a wall?\n x_collide = pygame.sprite.spritecollide(self, walls, False)\n if x_collide:\n # Whoops, hit a wall. Go back to the old position\n self.rect.left=old_x\n # self.rect.top=prev_y\n # y_collide = pygame.sprite.spritecollide(self, walls, False)\n # if y_collide:\n # # Whoops, hit a wall. Go back to the old position\n # self.rect.top=old_y\n # print('a')\n else:\n\n self.rect.top = new_y\n\n # Did this update cause us to hit a wall?\n y_collide = pygame.sprite.spritecollide(self, walls, False)\n if y_collide:\n # Whoops, hit a wall. Go back to the old position\n self.rect.top=old_y\n # self.rect.left=prev_x\n # x_collide = pygame.sprite.spritecollide(self, walls, False)\n # if x_collide:\n # # Whoops, hit a wall. Go back to the old position\n # self.rect.left=old_x\n # print('b')\n\n # if gate != False:\n # gate_hit = pygame.sprite.spritecollide(self, gate, False)\n # if gate_hit:\n # self.rect.left=old_x\n # self.rect.top=old_y\n #TODO: consolidate the following into one func based on move\n #save potential future moves\n def ai_eat(self, layout, ghosts, blocks, num_moves):\n if len(self.next_moves) == 0 or num_moves is not self.num_moves: \n # or self.num_moves + 15 > self.calcpathmove:\n path = ai.closestpillpath(layout, ghosts, self.rect.left, self.rect.top, blocks)\n self.calcpathmove = self.num_moves + 1\n self.rect.left += path[0][0]\n self.rect.top += path[0][1]\n self.last_move = path[0]\n self.next_moves = path[1:]\n else:\n self.rect.left += self.next_moves[0][0]\n self.rect.top += self.next_moves[0][1]\n self.last_move = self.next_moves[0]\n self.next_moves = self.next_moves[1:]\n \n self.num_moves += 1\n #TODO: make more efficient by saving previous moves\n #change if not resetting speed\n #print(path[0][0])\n #print(path[0][1])\n #print(\"last move was \", self.last_move)\n #print(\"move is \", path[0])\n \n #self.last_move = path[0]\n \n def ai_chase(self, layout, ghosts, threshold):\n path = ai.closeghostdist(layout, ghosts, self.rect.left, self.rect.top, threshold)\n\n self.rect.left += path[0][0]\n self.rect.top += path[0][1]\n self.num_moves += 1\n\n def ai_avoid(self, layout, ghosts, threshold):\n path = ai.avoider(layout, ghosts, self.rect.left, self.rect.top, threshold)\n print(\"this is avoid: \", path)\n self.rect.left += path[0][0]\n self.rect.top += path[0][1]\n self.last_move = path[0]\n # if num_moves is not self.num_moves or len(self.next_moves) == 0:\n # path = ai.allghostavoid(layout, ghosts, self.rect.left, self.rect.top, threshold)\n # self.rect.left += path[0][0]\n # self.rect.top += path[0][1]\n # self.last_move = path[0]\n # self.next_moves = path[1:]\n # else:\n # self.rect.left += self.next_moves[0][0]\n # self.rect.top += self.next_moves[0][1]\n # self.last_move = self.next_moves[0]\n # self.next_moves = self.next_moves[1:]\n # path = ai.closeghostdist(layout, ghosts, self.rect.left, self.rect.top, threshold)\n # #TODO: make better solution, take into account other ghosts\n # made_move = False\n # possible = ai.possiblepacmoves(layout, ghosts, self.rect.left, self.rect.top, False)\n # for name, change in possible.items():\n # if change[0] == path[0][0] * -1 and change[1] == path[0][1] * -1:\n # self.rect.left += path[0][0] * -1\n # self.rect.top += path[0][1] * -1\n # made_move = True\n # #TODO: make so doesnt accidentally bring closer to ghost\n # if not made_move:\n # if path[0][0] == 0:\n # if [30, 0] in list(possible.values()):\n # self.rect.left += 30\n # elif [-30, 0] in list(possible.values()):\n # self.rect.left += -30\n # elif path[0][1] == 0:\n # if [0, 30] in list(possible.values()):\n # self.rect.top += 30\n # elif [0, -30] in list(possible.values()):\n # self.rect.top += -30\n # closeghost = ai.closestghost(layout, ghosts, self.rect.left, self.rect.top, threshold)\n # minimum = ai.euclid_dist(self.rect.left, self.rect.top, closeghost[1], closeghost[2])\n # move = [0, 0]\n # for name, change in possible.items():\n # if ai.euclid_dist(self.rect.left + change[0], self.rect.top + change[1], closeghost[1], closeghost[2]) < minimum:\n # move = change\n # minimium = ai.euclid_dist(self.rect.left + change[0], self.rect.top + change[1], closeghost[1], closeghost[2])\n # self.rect.left += move[0]\n # self.rect.top += move[1]\n # random = randint(0, len(possible) - 1)\n # for i, name, change in enumerate(possible.items()):\n # if i == random:\n # self.rect.left += change[0]\n # self.rect.top += change[1]\n # change = list(possible.values())[random]\n # self.rect.left += change[0]\n # self.rect.top += change[1]\n # self.rect.left += possible.values()[random][0]\n # self.rect.top += possible.values()[random][1]\n print(\"this is avoid: \", self.last_move)\n self.num_moves += 1\n\n def stop_eating(self):\n self.eating = False\n def get_num_moves(self):\n return self.num_moves\n \n\n\n\n#Inheritime Player klassist\nclass Ghost(Player):\n # Change the speed of the ghost\n def changespeed(self,list,ghost,turn,steps,l):\n try:\n z=list[turn][2]\n if steps < z:\n self.change_x=list[turn][0]\n self.change_y=list[turn][1]\n steps+=1\n else:\n if turn < l:\n turn+=1\n elif ghost == \"clyde\":\n turn = 2\n else:\n turn = 0\n self.change_x=list[turn][0]\n self.change_y=list[turn][1]\n steps = 0\n return [turn,steps]\n except IndexError:\n return [0,0]\n \n def resetpos(self, name):\n self.steps = 0\n self.turn = 0\n self.rect.left = w\n self.rect.top = m_h\n if name == \"Clyde\":\n self.rect.left = c_w\n elif name == \"Inky\":\n self.rect.left = i_w\n elif name == \"Blinky\":\n self.rect.top = b_h\n \n def moveoffgrid(self):\n self.rect.left = 1000\n self.rect.top = 1000\n # def resetdir(self, name):\n # if name == \"Pinky\":\n #\n\n\nPinky_directions = [\n[0,-30,4],\n[15,0,9],\n[0,15,11],\n[-15,0,23],\n[0,15,7],\n[15,0,3],\n[0,-15,3],\n[15,0,19],\n[0,15,3],\n[15,0,3],\n[0,15,3],\n[15,0,3],\n[0,-15,15],\n[-15,0,7],\n[0,15,3],\n[-15,0,19],\n[0,-15,11],\n[15,0,9]\n]\n\nBlinky_directions = [\n[0,-15,4],\n[15,0,9],\n[0,15,11],\n[15,0,3],\n[0,15,7],\n[-15,0,11],\n[0,15,3],\n[15,0,15],\n[0,-15,15],\n[15,0,3],\n[0,-15,11],\n[-15,0,3],\n[0,-15,11],\n[-15,0,3],\n[0,-15,3],\n[-15,0,7],\n[0,-15,3],\n[15,0,15],\n[0,15,15],\n[-15,0,3],\n[0,15,3],\n[-15,0,3],\n[0,-15,7],\n[-15,0,3],\n[0,15,7],\n[-15,0,11],\n[0,-15,7],\n[15,0,5]\n]\n\nInky_directions = [\n[30,0,2],\n[0,-15,4],\n[15,0,10],\n[0,15,7],\n[15,0,3],\n[0,-15,3],\n[15,0,3],\n[0,-15,15],\n[-15,0,15],\n[0,15,3],\n[15,0,15],\n[0,15,11],\n[-15,0,3],\n[0,-15,7],\n[-15,0,11],\n[0,15,3],\n[-15,0,11],\n[0,15,7],\n[-15,0,3],\n[0,-15,3],\n[-15,0,3],\n[0,-15,15],\n[15,0,15],\n[0,15,3],\n[-15,0,15],\n[0,15,11],\n[15,0,3],\n[0,-15,11],\n[15,0,11],\n[0,15,3],\n[15,0,1],\n]\n\nClyde_directions = [\n[-30,0,2],\n[0,-15,4],\n[15,0,5],\n[0,15,7],\n[-15,0,11],\n[0,-15,7],\n[-15,0,3],\n[0,15,7],\n[-15,0,7],\n[0,15,15],\n[15,0,15],\n[0,-15,3],\n[-15,0,11],\n[0,-15,7],\n[15,0,3],\n[0,-15,11],\n[15,0,9],\n]\n\n# Call this function so the Pygame library can initialize itself\npygame.init()\n\n\n#default locations for Pacman and monstas\nw = 303-16 #Width\np_h = (7*60)+19 #Pacman height\nm_h = (4*60)+19 #Monster height\nb_h = (3*60)+19 #Binky height\ni_w = 303-16-32 #Inky width\nc_w = 303+(32-16) #Clyde width\n\n\ndef doNext(message,left,all_sprites_list,block_list,monsta_list,pacman_collide,wall_list,gate):\n while True:\n # ALL EVENT PROCESSING SHOULD GO BELOW THIS COMMENT\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_ESCAPE:\n pygame.quit()\n if event.key == pygame.K_RETURN:\n del all_sprites_list\n del block_list\n del monsta_list\n del pacman_collide\n del wall_list\n del gate\n startGame()\n\n #Grey background\n w = pygame.Surface((400,200)) # the size of your rect\n w.set_alpha(10) # alpha level\n w.fill((128,128,128)) # this fills the entire surface\n screen.blit(w, (100,200)) # (0,0) are the top-left coordinates\n\n #Won or lost\n text1=font.render(message, True, white)\n screen.blit(text1, [left, 233])\n\n text2=font.render(\"To play again, press ENTER.\", True, white)\n screen.blit(text2, [135, 303])\n text3=font.render(\"To quit, press ESCAPE.\", True, white)\n screen.blit(text3, [165, 333])\n\n pygame.display.flip()\n", "repo_name": "lf-lang/playground-lingua-franca", "sub_path": "experiments/Python/src/Pac-Man/include/hbpacman.py", "file_name": "hbpacman.py", "file_ext": "py", "file_size_in_byte": 17817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pygame.sprite", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.sprite.RenderPlain", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.sprite.RenderPlain", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.sprite.RenderPlain", "line_number": 184, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 197, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 201, "usage_type": "call"}, {"api_name": "pygame.draw.ellipse", "line_number": 204, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 213, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 227, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 227, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 271, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 271, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 286, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 286, "usage_type": "attribute"}, {"api_name": "AIPacSupport.closestpillpath", "line_number": 307, "usage_type": "call"}, {"api_name": "AIPacSupport.closeghostdist", "line_number": 330, "usage_type": "call"}, {"api_name": "AIPacSupport.avoider", "line_number": 337, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 555, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 570, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 570, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 571, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 572, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 573, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 574, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 575, "usage_type": "call"}, {"api_name": "pygame.K_RETURN", "line_number": 576, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 586, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 600, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 600, "usage_type": "attribute"}]} +{"seq_id": "41861454981", "text": "#!/usr/bin/python3\n\nimport re\nimport argparse\nimport random\n\ndef splitLine (line):\n toks = line.split(' ')\n return [int(toks[0]), int(toks[1]), float(toks[2])]\n\ndef readInput(in_file):\n fh = open(in_file)\n N = 0\n for line in fh:\n if re.match(\"%\", line):\n continue\n\n if N == 0:\n cell = splitLine(line)\n N = cell[1]\n fh.close()\n return N\n\n else:\n fh.close()\n return 0\n\ndef createVec(num, out_file):\n VEC = []\n for i in range(num):\n #VEC.append(1)\n VEC.append(random.randint(1, 9))\n\n import sys\n with open(out_file, 'w') as fh:\n sys.stdout = fh\n print(\"%BEGIN VEC\", len(VEC))\n for val in VEC:\n print(val)\n\n#===============================================\n#MAIN\n#===============================================\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Sparse Matrix Parser')\n parser.add_argument('--input', type=str, help='input data file')\n args = parser.parse_args()\n\n num = readInput(args.input)\n createVec(num, args.input + \".vector\") \n \n", "repo_name": "mrwillzou/rehs", "sub_path": "src/VEC.py", "file_name": "VEC.py", "file_ext": "py", "file_size_in_byte": 1161, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "re.match", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 36, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "39681995455", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\n\nDATA_X_FP = \"Assignment3_DataCode/Dataset3.3_X.csv\"\nDATA_Y_FP = \"Assignment3_DataCode/Dataset3.3_Y.csv\"\nINCLUDE_BIAS_TERM = False\n\nX = pd.read_csv(DATA_X_FP, header=None)\ny = pd.read_csv(DATA_Y_FP, header=None)\n\n\npenalty_values = [0.5, 10, 100]\nplt.figure()\n\nlog_clf = LogisticRegression(penalty=\"none\")\nlog_clf.fit(X, y)\n\nweights = (\n np.hstack((log_clf.intercept_[:, None], log_clf.coef_))\n if INCLUDE_BIAS_TERM\n else log_clf.coef_\n)\nx_weights = [i for i in range(log_clf.coef_.shape[1])]\nplt.scatter(x_weights, weights.ravel())\nplt.plot(x_weights, weights.ravel(), label=\"0\")\nfor C in penalty_values:\n log_clf = LogisticRegression(penalty=\"l1\", solver=\"liblinear\", C=1 / C)\n log_clf.fit(X, y)\n\n weights = (\n np.hstack((log_clf.intercept_[:, None], log_clf.coef_))\n if INCLUDE_BIAS_TERM\n else log_clf.coef_\n )\n plt.scatter(x_weights, weights.ravel())\n plt.plot(x_weights, weights.ravel(), label=C)\n\nplt.ylabel(\"Value\")\nplt.xlabel(\"W_i\")\nplt.legend()\nplt.show()\n", "repo_name": "WatchmeDoc/CS577-Machine-Learning", "sub_path": "hw3_logistic_regression/run_lr_penalty_experiments.py", "file_name": "run_lr_penalty_experiments.py", "file_ext": "py", "file_size_in_byte": 1135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "27200583584", "text": "import datetime as dt\nimport csv\nimport logging\nfrom typing import Any, List\n\nfrom prettytable import PrettyTable\n\nfrom constants import BASE_DIR, DATETIME_FORMAT, NAME_DIR_RESULTS,\\\n OUTPUT_FILE, OUTPUT_TABLE\n\n\ndef control_output(results: List[List[str]], cli_args: Any) -> None:\n \"\"\"\n Определяет тип вывода результатов парсинга PEP документов в зависимости\n от выбранного режима и вызывает соответствующую функцию вывода.\n Если тип вывода не задан, то используется функция default_output.\n\n :param results: список результатов парсинга\n :param cli_args: аргументы командной строки\n :return: None\n \"\"\"\n output = cli_args.output\n if output == OUTPUT_TABLE:\n pretty_output(results)\n elif output == OUTPUT_FILE:\n file_output(results, cli_args)\n else:\n default_output(results)\n\n\ndef default_output(results: List[List[str]]) -> None:\n \"\"\"\n Выводит результаты парсинга в консоль в формате,\n где каждый элемент строки разделен пробелом.\n\n :param results: список результатов парсинга\n :return: None\n \"\"\"\n for row in results:\n print(*row)\n\n\ndef pretty_output(results: List[List[str]]) -> None:\n \"\"\"\n Выводит результаты парсинга в консоль в виде таблицы\n с помощью библиотеки PrettyTable.\n\n :param results: список результатов парсинга\n :return: None\n \"\"\"\n table = PrettyTable()\n table.field_names = results[0]\n table.align = 'l'\n table.add_rows(results[1:])\n print(table)\n\n\ndef file_output(results: List[List[str]], cli_args: Any) -> None:\n \"\"\"\n Сохраняет результаты парсинга в файл формата csv в директории results\n с названием, содержащим текущую дату и время и выбранный режим работы.\n Также функция записывает информацию о сохранении файла в лог.\n\n :param results: список результатов парсинга\n :param cli_args: аргументы командной строки\n :return: None\n \"\"\"\n results_dir = BASE_DIR / NAME_DIR_RESULTS\n results_dir.mkdir(exist_ok=True)\n now = dt.datetime.now().strftime(DATETIME_FORMAT)\n file_name = f'{cli_args.mode}_{now}.csv'\n file_path = results_dir / file_name\n\n with open(file_path, 'w', encoding='UTF-8') as file:\n writer = csv.writer(file, dialect='unix')\n writer.writerows(results)\n logging.info(f'Файл с результатами был сохранён: {file_path}')\n", "repo_name": "AlexBatanov/bs4_parser_pep", "sub_path": "src/outputs.py", "file_name": "outputs.py", "file_ext": "py", "file_size_in_byte": 2962, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 12, "usage_type": "name"}, {"api_name": "constants.OUTPUT_TABLE", "line_number": 23, "usage_type": "name"}, {"api_name": "constants.OUTPUT_FILE", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "prettytable.PrettyTable", "line_number": 51, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 58, "usage_type": "name"}, {"api_name": "constants.BASE_DIR", "line_number": 68, "usage_type": "name"}, {"api_name": "constants.NAME_DIR_RESULTS", "line_number": 68, "usage_type": "name"}, {"api_name": "constants.DATETIME_FORMAT", "line_number": 70, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "72180685167", "text": "import math\nimport argparse\nimport matplotlib\nmatplotlib.use('GTK3Agg')\n\nfrom qiskit import QuantumCircuit, execute, QuantumRegister, Aer, BasicAer, transpile\nfrom qiskit.tools.visualization import plot_histogram\nfrom qiskit.extensions import UnitaryGate\nfrom qiskit.circuit.library.standard_gates import XGate\n\nfrom utility import CustomGate\n\nclass HASH:\n def __init__(self, n) -> None:\n self.n = n\n self.output = [i for i in range(0, 2**n)]\n\nclass UtilityGates:\n def __init__(self, n) -> None:\n self.custom_gate = CustomGate(n)\n # self.custom_gate.generate_random_permutation()\n try:\n self.custom_gate.load_random_permutation()\n except:\n self.custom_gate.generate_random_permutation()\n self.custom_gate.save_random_permutation()\n\n self.Uf = UnitaryGate(self.custom_gate.matrix, label=\"Uf\")\n self.Uf_rev = UnitaryGate(self.custom_gate.matrix_rev, label=\"Uf_rev\")\n\n @staticmethod\n def _n_hadamard(n):\n # return a n qubit hadamard gate\n circuit = QuantumCircuit(QuantumRegister(n, \"q\"))\n circuit.h(range(0, n))\n circuit.name = \"Hn\"\n # print(circuit)\n return circuit\n \n @staticmethod\n def _n_diffuser(n):\n circuit = QuantumCircuit(QuantumRegister(n, \"q\"))\n circuit.append(UtilityGates._n_hadamard(n-1), range(0, n-1))\n\n # apply anti control\n for i in range(0, n-1):\n circuit.x(i)\n\n circuit.append(XGate().control(n-1), range(0, n))\n\n # apply anti control\n for i in range(0, n-1):\n circuit.x(i)\n\n circuit.append(UtilityGates._n_hadamard(n-1), range(0, n-1))\n circuit.name = \"Dn\"\n # print(circuit)\n return circuit\n\n \n @staticmethod\n def _n_toffoli(n, query):\n _bitstring_query = \"{0:08b}\".format(query)\n _bitstring_query = _bitstring_query[::-1][:n-1]\n circuit = QuantumCircuit(QuantumRegister(n-1, \"q\"), QuantumRegister(1, \"y\"))\n\n for i in range(0, n-1):\n if _bitstring_query[i] == \"0\":\n circuit.x(i)\n ncx = XGate().control(n-1)\n circuit.append(ncx, range(0, n))\n for i in range(0, n-1):\n if _bitstring_query[i] == \"0\":\n circuit.x(i)\n circuit.name = \"nCX\"\n # print(circuit)\n return circuit\n \n\n\nclass GroversHashCracker:\n def __init__(self, n) -> None:\n self.n = n\n self.circuit = None\n self.itretion = int(math.pi/4 * math.sqrt(2**n))\n self.utitlity = UtilityGates(n)\n self._build_circuit()\n\n self.simulator = Aer.get_backend('qasm_simulator')\n\n def _build_circuit(self, query = 0):\n self.query = query\n self.circuit = QuantumCircuit(QuantumRegister(self.n, \"q\"), QuantumRegister(1, \"y\"))\n # initialization\n self.circuit.append(UtilityGates._n_hadamard(self.n), range(0, self.n))\n self.circuit.x(self.n)\n self.circuit.h(self.n)\n\n for _ in range(self.itretion):\n # oracle\n self.circuit.append(self.utitlity.Uf, range(0, self.n))\n self.circuit.append(self.utitlity._n_toffoli(self.n + 1, query), range(0, self.n + 1))\n self.circuit.append(self.utitlity.Uf_rev, range(0, self.n))\n\n # defuser\n self.circuit.append(self.utitlity._n_diffuser(self.n + 1), range(0, self.n + 1))\n\n self.circuit.measure_all()\n\n def run(self):\n compiled_circuit = transpile(self.circuit, self.simulator)\n job = self.simulator.run(compiled_circuit, shots=1000)\n result = job.result()\n counts = result.get_counts(circ.circuit)\n print(counts)\n\n # get 2 most frequent results\n max1 = max(counts, key=counts.get)\n print(f\"Query: {self.query}, Result: {int(max1[1:],2)}\")\n\n\n def show(self):\n self.circuit.draw(\"mpl\")\n print(self.circuit)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--n\", type=int, default=4)\n parser.add_argument(\"--iteration\", type=int, default=1)\n\n args = parser.parse_args()\n circ = GroversHashCracker(args.n)\n circ.show()\n\n for j in range(args.iteration):\n print(\"Iteration: {}, query (hash) : \".format(j))\n try:\n query = int(input())\n except:\n print(\"Query must be an integer between 0 and 2^n\")\n \n circ._build_circuit(query)\n circ.run()\n", "repo_name": "RupakBiswas-2304/CS689", "sub_path": "assignment2/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "matplotlib.use", "line_number": 4, "usage_type": "call"}, {"api_name": "utility.CustomGate", "line_number": 20, "usage_type": "call"}, {"api_name": "qiskit.extensions.UnitaryGate", "line_number": 28, "usage_type": "call"}, {"api_name": "qiskit.extensions.UnitaryGate", "line_number": 29, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 34, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 34, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 42, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 42, "usage_type": "call"}, {"api_name": "qiskit.circuit.library.standard_gates.XGate", "line_number": 49, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 65, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 65, "usage_type": "call"}, {"api_name": "qiskit.circuit.library.standard_gates.XGate", "line_number": 70, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 85, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 85, "usage_type": "call"}, {"api_name": "qiskit.Aer.get_backend", "line_number": 89, "usage_type": "call"}, {"api_name": "qiskit.Aer", "line_number": 89, "usage_type": "name"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 93, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 93, "usage_type": "call"}, {"api_name": "qiskit.transpile", "line_number": 111, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "13550692544", "text": "import networkx as nx\nfrom bokeh.io import output_file, show\nfrom bokeh.models import (BoxZoomTool, Circle, HoverTool, LabelSet, ColumnDataSource,\n MultiLine, Plot, Range1d, ResetTool,)\nfrom bokeh.palettes import Spectral4\nfrom bokeh.plotting import from_networkx\n\nDIRECT_PEER, OTHER_COLOR = \"black\", \"red\"\n\nclass GraphPlotter(object):\n def __init__(self, asn: int) -> None:\n self.main_as = str(asn)\n\n def plot(self, graph, fname):\n \"\"\"Fetch BGP data and build the AS graph\"\"\"\n # Plot graph\n # Prepare Data\n graph.normalize_weights()\n graph.trim(0)\n G = graph.graph.copy()\n\n\n for start_node, end_node, data in G.edges(data=True):\n data['edge_color'] = DIRECT_PEER if start_node == self.main_as else OTHER_COLOR\n\n # Show with Bokeh\n plot = Plot(plot_width=1024, plot_height=1024,\n x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))\n plot.title.text = f\"AS Graph for AS{self.main_as}\"\n \n node_hover_tool = HoverTool(tooltips=[(\"AS\", \"@index\"), (\"weight\", \"@weight\")])\n plot.add_tools(node_hover_tool, BoxZoomTool(), ResetTool())\n\n graph_renderer = from_networkx(G, nx.spring_layout, scale=1, center=(0, 0))\n\n graph_renderer.node_renderer.glyph = Circle(size=\"weight\", fill_color=Spectral4[0], )\n graph_renderer.edge_renderer.glyph = MultiLine(line_color=\"edge_color\", line_alpha=0.8, \n line_width=\"weight\")\n plot.renderers.append(graph_renderer)\n\n # Add node labels\n x, y = zip(*graph_renderer.layout_provider.graph_layout.values())\n node_labels = [f'AS{node}' for node in G.nodes()]\n source = ColumnDataSource({'x': x, 'y': y,\n 'ASN': [node_labels[i] for i in range(len(x))]})\n labels = LabelSet(x='x', y='y', text='ASN', source=source,)\n plot.renderers.append(labels)\n\n output_file(fname)\n show(plot)\n\n", "repo_name": "romain-fontugne/pear", "sub_path": "pear/graph_plotter.py", "file_name": "graph_plotter.py", "file_ext": "py", "file_size_in_byte": 1996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "bokeh.models.Plot", "line_number": 27, "usage_type": "call"}, {"api_name": "bokeh.models.Range1d", "line_number": 28, "usage_type": "call"}, {"api_name": "bokeh.models.HoverTool", "line_number": 31, "usage_type": "call"}, {"api_name": "bokeh.models.BoxZoomTool", "line_number": 32, "usage_type": "call"}, {"api_name": "bokeh.models.ResetTool", "line_number": 32, "usage_type": "call"}, {"api_name": "bokeh.plotting.from_networkx", "line_number": 34, "usage_type": "call"}, {"api_name": "networkx.spring_layout", "line_number": 34, "usage_type": "attribute"}, {"api_name": "bokeh.models.Circle", "line_number": 36, "usage_type": "call"}, {"api_name": "bokeh.palettes.Spectral4", "line_number": 36, "usage_type": "name"}, {"api_name": "bokeh.models.MultiLine", "line_number": 37, "usage_type": "call"}, {"api_name": "bokeh.models.ColumnDataSource", "line_number": 44, "usage_type": "call"}, {"api_name": "bokeh.models.LabelSet", "line_number": 46, "usage_type": "call"}, {"api_name": "bokeh.io.output_file", "line_number": 49, "usage_type": "call"}, {"api_name": "bokeh.io.show", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "8996699997", "text": "import time, os\nfrom selenium import webdriver\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom app import app\n\n\ndef burrp(filter):\n try:\n # chromedriver = \"/Documents/chromedriver\"\n # os.environ[\"webdriver.chrome.driver\"] = chromedriver\n # driver = webdriver.Chrome(executable_path=r\"/home/repusight/Documents/chromedriver\")\n driver = webdriver.Chrome()\n driver.get(filter[0][\"url1\"])\n mores = driver.find_elements_by_link_text(\"Read more\")\n for more in mores:\n driver.execute_script(\"arguments[0].click();\", more)\n time.sleep(2)\n html = driver.page_source\n soup = BeautifulSoup(html, \"lxml\")\n restaurantName = filter[0][\"propertyName\"]\n restaurantPlace = filter[0][\"Place\"]\n soup = soup.find_all(\"div\", class_=\"review-pane-inner clearfix\")\n data = []\n for div in soup:\n date = div.find_next(\"div\", class_=\"author-name\").find_next(\"span\").getText()\n pattern = '%B %d,%Y'\n date = int(time.mktime(time.strptime(date, pattern)))\n if filter[0][\"lastCrawl\"] != \"\" and date <= int(filter[0][\"lastCrawl\"]):\n if data:\n dtaFrm = pd.DataFrame(data)\n driver.close()\n if not dtaFrm.empty:\n app.config[\"PROPERTY_COLLECTION\"].update({\"_id\": filter[0][\"_id\"]},\n {\"$set\": {\"lastCrawl\": round(time.time())}})\n filename = (str(filter[0][\"propertyName\"]) + \"_\" + str(filter[0][\"Place\"]) + \"_\" + str(\n filter[0][\"source\"]) + \"_\" + str(time.strftime(\"%d-%m-%Y\")) + \".csv\").replace(\" \", \"\").replace(\"'\", \"\")\n with open('/home/repusight/data/' + filename, 'a') as f:\n dtaFrm.to_csv(f, sep='|', encoding='utf-8', index=False, header=True)\n print(\"1-Crawled\")\n return \"Crawled\"\n else:\n driver.close()\n print(\"2-Already Updated !!\")\n return \"Already Updated !!\"\n rimg = div.find_next(\"div\", class_=\"author-pic\").find_next(\"img\")['src']\n if rimg == \"http://www.burrp.com/images/default_user.jpg\":\n rimg = filter[0][\"Logo\"]\n rname = div.find_next(\"div\", class_=\"author-name\").find_next(\"a\").getText()\n comment = div.find_next(\"div\", class_=\"review_con\").getText()\n rating = div.find_next(\"span\", class_=\"badge badge-md right\").getText()\n if rating == \"--\":\n rating = 0\n replydiv = div.find(\"div\", class_=\"rev_reply_box\")\n if replydiv != None:\n Replied = \"R1\"\n else:\n Replied = \"R0\"\n data.append(\n {\"Name\": restaurantName, \"Place\": restaurantPlace, \"Date\": date, \"Rname\": rname,\n \"Rimg\": rimg, \"Comment\": comment, \"ReviewID\": \"\",\n \"Rating\": rating, \"Channel\": filter[0][\"source\"], \"icon\": \"/static/images/burrp-new-logo.jpg\",\n \"Replied\": Replied, \"Logo\": filter[0][\"Logo\"], \"URL\": filter[0][\"revertURL\"],\n \"City\": filter[0][\"City\"], \"State\": filter[0][\"State\"], \"Country\": filter[0][\"Country\"]})\n except Exception as e:\n print(e)\n return \"Something went wrong !!\"\n if data:\n dtaFrm = pd.DataFrame(data)\n driver.close()\n if not dtaFrm.empty:\n app.config[\"PROPERTY_COLLECTION\"].update({\"_id\": filter[0][\"_id\"]}, {\"$set\": {\"lastCrawl\": round(time.time())}})\n filename = (\n str(filter[0][\"propertyName\"]) + \"_\" + str(filter[0][\"Place\"]) + \"_\" + str(filter[0][\"source\"]) + \"_\" + str(\n time.strftime(\"%d-%m-%Y\")) + \".csv\").replace(\" \", \"\").replace(\"'\", \"\")\n with open('/home/repusight/data/' + filename, 'a') as f:\n dtaFrm.to_csv(f, sep='|', encoding='utf-8', index=False, header=True)\n print(\"3-Crawled\")\n return \"Crawled\"", "repo_name": "Jerricks/Crawlers-for-Hotels-and-Restaurants", "sub_path": "BurrpCrawler.py", "file_name": "BurrpCrawler.py", "file_ext": "py", "file_size_in_byte": 4037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 34, "usage_type": "name"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 72, "usage_type": "name"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "43187385568", "text": "import bpy\nimport os\n\n# format integer with leading zeros\ndef formatNumbers(number, length):\n return '%0*d' % (length, number)\n\n# get the scene\nscn = bpy.context.scene\n\n# get the output path\noutput_path = scn.render.filepath\n\n# set filename\nfilename = \"\"\n\n# set render frames\nrender_frames = range(1, 2)\n\n# iterate through render frames\nfor f in render_frames:\n\n # set the frames\n scn.frame_set(f)\n\n # set filepath\n scn.render.filepath = os.path.join(\n output_path,\n filename + formatNumbers(f, 4) + \".png\",\n )\n\n # render\n bpy.ops.render.render(use_viewport=True, write_still=True)\n\n# reset internal filepath\nbpy.context.scene.render.filepath = output_path\n", "repo_name": "valera-rozuvan-archive/blender-demos", "sub_path": "scientific-equipment/scripts/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "bpy.context", "line_number": 9, "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": "bpy.ops.render.render", "line_number": 33, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "20174938422", "text": "import urllib\nfrom bs4 import BeautifulSoup\nfrom Utilities import checkElement, checkVal, getTime\n\ndef scrapeArticle(URL):\n #Indexes title, date, body, and author of given article\n #returns values in a dictionary\n bText = ''\n soup = BeautifulSoup(urllib.urlopen(URL))\n linkList = map(lambda x: x.get_text(), soup.find_all('a'))\n pageStatus = soup.find('div', attrs = {'class':'singlepage'})\n if pageStatus != None:\n soup = BeautifulSoup(urllib.urlopen('http://abcnews.go.com' + pageStatus.a.get('href')))\n for paragraph in soup.find_all('p', attrs = {'itemprop':'articleBody'}):\n if paragraph.a not in linkList:\n bText += paragraph.get_text()\n date = checkElement(soup.find('div', attrs = {'class':'date'}), 'date')\n image = soup.find('div', attrs = {'class' : 'main_media'})\n ArticleDict = {'title' : checkElement(soup.find('h1', True), 'title'),\n 'author' : \" \".join(checkElement(\n soup.find('div', attrs = {'class':'byline'}), 'author')\n .split('\\n\\n')[0].lower().title().split())\n .replace('And', 'and').replace('Abc', 'Source'),\n 'body_text' : bText.replace('\\n', '\\n\\n').strip(),\n 'URL' : URL,\n 'source' : 'ABC News',\n 'date' : date,\n 'timestamp' : getTime(date, [',',':','.'], [], '%b %d %Y')} \n if image != None and image.img != None:\n if image.img.get('src') != None:\n ArticleDict['image'] = image.img.get('src')\n return ArticleDict\n\ndef scrapeSection(URL):\n #Scrapes given section, returns a list of article links\n linkList = []\n soup = BeautifulSoup(urllib.urlopen(URL))\n header = soup.find('div', attrs = {'id':'s4a_headline'})\n if header != None:\n headerLink = soup.find('div', attrs = {'id':'s4a_headline'}).a.get('href').encode('utf-8')\n if 'slideshow' not in headerLink and 'blogs' not in headerLink:\n linkList.append(headerLink)\n soup = BeautifulSoup(str(soup.find('div', attrs = {'class':'b_col'})))\n soup = BeautifulSoup(str(soup.find('div', attrs = {'class':'midcontainer'})))\n for story in soup.find_all(id='h_default'):\n story = story.a.get('href').encode('utf-8')\n if 'slideshow' not in story and 'blogs' not in story:\n linkList.append(story)\n return linkList\n\ndef scrapeLongSection(URL):\n #scrapes section with a big photo and header (Tech, Living etc.)\n #returns a list of article links\n linkList = []\n soup = BeautifulSoup(urllib.urlopen(URL))\n headerLink = soup.find('div', attrs = {'class':'headline spev8-medium'}).a.get('href').encode('utf-8')\n if 'slideshow' not in headerLink:\n linkList.append(headerLink)\n soup = BeautifulSoup(str(soup.find('div', attrs = {'class':'midcontainer'})))\n for story in soup.find_all(id='h_default'):\n story = story.a.get('href').encode('utf-8')\n if 'slideshow' not in story and 'entertainment' not in story:\n if 'http' not in story:\n linkList.append(story)\n return linkList\n\ndef scrapeFrontPage(URL):\n #scrapes front page, returns a list of article links\n linkList = []\n soup = BeautifulSoup(urllib.urlopen(URL))\n carousel = BeautifulSoup(str(soup.find('div', attrs = {'class':'carousel carousel-center'})))\n for link in carousel.find_all('a'):\n curCell = link.get('href')\n if curCell not in linkList:\n linkList.append(curCell.encode('utf-8'))\n soup = BeautifulSoup(str(soup.find('div', attrs = {'class':'a_cont'})))\n for story in soup.find_all('div', attrs = {'class':'h'}):\n story = story.a.get('href').encode('utf-8')\n if 'slideshow' not in story and 'video' not in story:\n if 'social-climber' not in story and 'blogs' not in story:\n if 'http' not in story:\n linkList.append(story)\n return linkList\n \n", "repo_name": "cnwalker/newsfinder", "sub_path": "ABCScraper.py", "file_name": "ABCScraper.py", "file_ext": "py", "file_size_in_byte": 4037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 13, "usage_type": "call"}, {"api_name": "Utilities.checkElement", "line_number": 17, "usage_type": "call"}, {"api_name": "Utilities.checkElement", "line_number": 19, "usage_type": "call"}, {"api_name": "Utilities.checkElement", "line_number": 20, "usage_type": "call"}, {"api_name": "Utilities.getTime", "line_number": 28, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 37, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 43, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 44, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 55, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 55, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 59, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 70, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 71, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "6727469712", "text": "from io import RawIOBase\nfrom time import localtime\nfrom zipfile import ZipFile, ZipInfo\n\nfrom aiopath import AsyncPath\n\nfrom .utils import read_file\n\n\nclass Stream(RawIOBase):\n \"\"\"An unseekable stream for the ZipFile to write to\"\"\"\n\n def __init__(self):\n self._buffer = bytearray()\n self._closed = False\n\n def close(self):\n self._closed = True\n\n def write(self, b):\n if self._closed:\n raise ValueError(\"Can't write to a closed stream\")\n self._buffer += b\n return len(b)\n\n def readall(self):\n chunk = bytes(self._buffer)\n self._buffer.clear()\n return chunk\n\n\nclass AioZipInfo(ZipInfo):\n @classmethod\n async def from_file(cls, filename, arcname=None, *, strict_timestamps=True):\n filename = AsyncPath(filename)\n st = await filename.stat()\n isdir = await filename.is_dir()\n mtime = localtime(st.st_mtime)\n date_time = mtime[0:6]\n if not strict_timestamps and date_time[0] < 1980:\n date_time = (1980, 1, 1, 0, 0, 0)\n elif not strict_timestamps and date_time[0] > 2107:\n date_time = (2107, 12, 31, 23, 59, 59)\n if arcname is None:\n arcname = str(filename.relative_to(filename.anchor))\n if isdir:\n arcname += '/'\n zinfo = cls(arcname, date_time)\n zinfo.external_attr = (st.st_mode & 0xFFFF) << 16 # Unix attributes\n if isdir:\n zinfo.file_size = 0\n zinfo.external_attr |= 0x10 # MS-DOS directory flag\n else:\n zinfo.file_size = st.st_size\n return zinfo\n\n\nasync def zipstream(files):\n files = [\n (f, await AioZipInfo.from_file(f, a))\n for f, a in files\n ]\n stream = Stream()\n with ZipFile(stream, mode='w') as zf:\n for f, zinfo in files:\n with zf.open(zinfo, mode='w') as fp:\n if zinfo.is_dir():\n continue\n async for buf in read_file(AsyncPath(f)):\n fp.write(buf)\n yield stream.readall()\n yield stream.readall()", "repo_name": "tritium21/Webindex2", "sub_path": "webindex2/zipstream.py", "file_name": "zipstream.py", "file_ext": "py", "file_size_in_byte": 2109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "io.RawIOBase", "line_number": 10, "usage_type": "name"}, {"api_name": "zipfile.ZipInfo", "line_number": 32, "usage_type": "name"}, {"api_name": "aiopath.AsyncPath", "line_number": 35, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 38, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 64, "usage_type": "call"}, {"api_name": "utils.read_file", "line_number": 69, "usage_type": "call"}, {"api_name": "aiopath.AsyncPath", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "9444708653", "text": "from __future__ import annotations\n\nfrom arcade import Window\n\nfrom constants import SCREEN_HEIGHT, SCREEN_WIDTH, START_FULLSCREEN\nfrom global_input import GlobalInput\n\n\nclass FullscreenController:\n \"\"\"\n Turns fullscreen on and off, both at the start of the game, and when\n keybinds are hit to toggle it. Sets viewport to preserve aspect ratio.\n\n Viewport is described in game coordinates. It is scaled to match the\n screen's resolution. This means our game's logic is written against a\n fixed screen resolution declared in `constants.py`. The GPU stretches the\n fixed resolution to fill the physical screen.\n\n For example, if the game is coded against a resolution of 800 by 600, and\n its running on a 1920x1080 television, then we set the viewport to:\n\n left = -240\n right = 1040\n bottom = 0\n top = 600\n \"\"\"\n\n def __init__(self, window: Window, input: GlobalInput):\n self._window = window\n self._input = input\n self.is_fullscreen = START_FULLSCREEN\n self._dirty = True\n\n def update(self):\n if self._input.fullscreen_toggle.pressed:\n self.is_fullscreen = not self.is_fullscreen\n self._dirty = True\n if self._dirty:\n self._update_viewport()\n self._dirty = False\n\n def _update_viewport(self):\n if self.is_fullscreen:\n self._window.set_fullscreen(True)\n (w, h) = self._window.get_size()\n w_ratio = w / SCREEN_WIDTH\n h_ratio = h / SCREEN_HEIGHT\n if w_ratio > h_ratio:\n # black bars on left and right\n w_scaled = w * SCREEN_HEIGHT / h\n bar_size = (w_scaled - SCREEN_WIDTH) / 2\n print((w_scaled, -bar_size, SCREEN_WIDTH + bar_size, 0, SCREEN_HEIGHT))\n self._window.set_viewport(\n -bar_size, SCREEN_WIDTH + bar_size, 0, SCREEN_HEIGHT\n )\n else:\n # black bars on top and bottom\n h_scaled = h * SCREEN_WIDTH / w\n bar_size = h_scaled / 2\n self._window.set_viewport(\n 0, SCREEN_WIDTH, -bar_size, SCREEN_HEIGHT + bar_size\n )\n else:\n self._window.set_fullscreen(False)\n self._window.set_viewport(0, SCREEN_WIDTH, 0, SCREEN_HEIGHT)\n", "repo_name": "CastIronChat/twisted-metal", "sub_path": "src/fullscreen.py", "file_name": "fullscreen.py", "file_ext": "py", "file_size_in_byte": 2380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "arcade.Window", "line_number": 28, "usage_type": "name"}, {"api_name": "global_input.GlobalInput", "line_number": 28, "usage_type": "name"}, {"api_name": "constants.START_FULLSCREEN", "line_number": 31, "usage_type": "name"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 46, "usage_type": "name"}, {"api_name": "constants.SCREEN_HEIGHT", "line_number": 47, "usage_type": "name"}, {"api_name": "constants.SCREEN_HEIGHT", "line_number": 50, "usage_type": "name"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 51, "usage_type": "name"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 52, "usage_type": "name"}, {"api_name": "constants.SCREEN_HEIGHT", "line_number": 52, "usage_type": "name"}, {"api_name": "constants.SCREEN_HEIGHT", "line_number": 54, "usage_type": "argument"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 54, "usage_type": "name"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 58, "usage_type": "name"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 61, "usage_type": "argument"}, {"api_name": "constants.SCREEN_HEIGHT", "line_number": 61, "usage_type": "name"}, {"api_name": "constants.SCREEN_WIDTH", "line_number": 65, "usage_type": "argument"}, {"api_name": "constants.SCREEN_HEIGHT", "line_number": 65, "usage_type": "argument"}]} +{"seq_id": "21554967597", "text": "import subprocess\nimport os\nimport shutil\nimport sys\n\nfrom sfaira.commands.questionary import sfaira_questionary\nfrom rich import print\n\n\nclass PullRequestHandler:\n\n def __init__(self, path_loader):\n self.WD = os.path.dirname(__file__)\n self.path_loader = path_loader\n self.loader_name_list = []\n self.loader_name = None\n self.gh_token = \"\"\n\n def submit_pr(self):\n self._check_container_and_loaders()\n self._gh_authenticate()\n self._clone_sfaira_and_move_dataloader()\n self._submit_pr()\n\n def _check_container_and_loaders(self):\n sfaira_container = os.getenv('SFAIRA_DOCKER')\n if sfaira_container and self.path_loader and os.path.isdir(self.path_loader):\n for d in os.listdir(self.path_loader):\n if os.path.isdir(os.path.join(self.path_loader, d)) and d.startswith((\"d10_\", \"dno_doi_\")):\n self.loader_name_list.append(d)\n if not self.loader_name_list:\n print(\"[bold red]No data loaders found in loader directory. Aborting.\")\n sys.exit()\n else:\n print('[bold red]This function can only be called when running inside the sfaira CLI docker container. '\n 'Aborting.')\n sys.exit()\n\n def _gh_authenticate(self) -> None:\n \"\"\"\n Guides the user to authenticate with the GitHub CLI\n \"\"\"\n # Skip login procedure if already logged in\n if subprocess.run([\"gh\", \"auth\", \"status\"], check=False, text=True, shell=False).returncode == 0:\n subprocess.run([\"gh\", \"auth\", \"setup-git\"], check=True, text=True, shell=False)\n print(\"[bold green]Already authenticated with GitHub.\")\n return\n print(\n \"[bold blue]Please authenticate with GitHub. Hint: you can generate a Personal Access Token here: \"\n \"https://github.com/settings/tokens\\nThe minimum required scopes are 'repo', 'read:org', 'workflow'.\"\n )\n returncode = 1\n while returncode != 0:\n gh_token = sfaira_questionary(function='password',\n question='Please enter your GitHub token. '\n 'Leave blank to interactively authenticate with GitHub.',\n default='')\n if gh_token == \"\":\n gh_call = subprocess.run(\n [\"gh\", \"auth\", \"login\", \"--hostname\", \"github.com\"], check=False, text=True, shell=False)\n else:\n gh_call = subprocess.run(\n f\"echo {gh_token} | gh auth login --with-token\", check=False, text=True, shell=True)\n returncode = gh_call.returncode\n self.gh_token = gh_token\n subprocess.run([\"gh\", \"auth\", \"setup-git\"], check=True, text=True, shell=False)\n print(\"[bold green]Successfully authenticated with GitHub.\")\n\n def _clone_sfaira_and_move_dataloader(self) -> None:\n \"\"\"\n Clones the sfaira repo, creates a new branch and moves dataloader into the right location.\n \"\"\"\n # Clone sfaira\n subprocess.run([\"rm\", \"-rf\", \"/root/sfaira\"], check=False, text=True, shell=False)\n subprocess.run(\n [\"gh\", \"repo\", \"clone\", \"theislab/sfaira\", \"/root/sfaira/\"], check=True, text=True, shell=False)\n # Get loader name\n if len(self.loader_name_list) == 1:\n self.loader_name = self.loader_name_list[0]\n else:\n self.loader_name = sfaira_questionary(\n function='select',\n question='Multiple dataloaders detected in the loader directory. '\n 'Which one do you want to submit as a pull request?',\n choices=self.loader_name_list\n )\n # Create new branch in sfaira git repo\n subprocess.run(\n [\"git\", \"checkout\", \"-b\", f\"dataset/{self.loader_name}\"],\n check=True, text=True, shell=False, cwd=\"/root/sfaira/\"\n )\n # Copy loader\n shutil.copytree(\n src=os.path.join(self.path_loader, self.loader_name),\n dst=os.path.join(\"/root/sfaira/sfaira/data/dataloaders/loaders\", self.loader_name)\n )\n\n def _submit_pr(self):\n \"\"\"\n Adds and commits the dataloader in git and creates a pull request\n \"\"\"\n # Make sure git credentials are set and get them from github api if not\n current_email = subprocess.run([\"git\", \"config\", \"user.email\"], check=False, text=True, shell=False,\n stdout=subprocess.PIPE).stdout\n if not current_email:\n git_email = subprocess.run([\"gh\", \"api\", \"/user/public_emails\", \"-q\", \".[0].email\"], check=True, text=True,\n shell=False, stdout=subprocess.PIPE).stdout.strip()\n subprocess.run([\"git\", \"config\", \"--global\", \"user.email\", git_email], check=True, text=True, shell=False)\n current_user = subprocess.run([\"git\", \"config\", \"user.name\"], check=False, text=True, shell=False,\n stdout=subprocess.PIPE).stdout\n if not current_user:\n git_user = subprocess.run([\"gh\", \"api\", \"/user\", \"-q\", \".login\"], check=True, text=True, shell=False,\n stdout=subprocess.PIPE).stdout.strip()\n subprocess.run([\"git\", \"config\", \"--global\", \"user.name\", git_user], check=True, text=True, shell=False)\n # Add and commit the dataloader\n subprocess.run([\"git\", \"add\", \"*\"], check=True, text=True, shell=False, cwd=\"/root/sfaira/\")\n subprocess.run([\"git\", \"commit\", \"-m\", f\"[from sfaira cli] add dataloader {self.loader_name}\"],\n check=True, text=True, shell=False, cwd=\"/root/sfaira/\")\n # Create pullrequest (authenticate again beforehand if gh_token was provided before)\n create_pr_str = f\"gh pr create --base dev --title {self.loader_name} \" \\\n f\"--body 'This PR was created by the sfaira CLI adding dataset {self.loader_name}' \" \\\n f\"--label dataset\"\n if self.gh_token != \"\":\n subprocess.run(f\"echo {self.gh_token} | gh auth login --with-token && {create_pr_str}\",\n check=True, text=True, shell=True, cwd=\"/root/sfaira/\")\n else:\n subprocess.run(create_pr_str, check=True, text=True, shell=True, cwd=\"/root/sfaira/\")\n subprocess.run(\n [\"rm\", \"-rf\", os.path.join(self.path_loader, self.loader_name)], check=False, text=True, shell=False)\n print(\"[bold green]Your PR was successfully submitted. Feel free to add further comments to it using the URL \"\n \"in the line above.\")\n", "repo_name": "theislab/sfaira", "sub_path": "sfaira/commands/submit_pullrequest.py", "file_name": "submit_pullrequest.py", "file_ext": "py", "file_size_in_byte": 6798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 132, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 33, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 44, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 45, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 46, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 48, "usage_type": "call"}, {"api_name": "sfaira.commands.questionary.sfaira_questionary", "line_number": 54, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 59, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 62, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 66, "usage_type": "call"}, {"api_name": "rich.print", "line_number": 67, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 74, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 75, "usage_type": "call"}, {"api_name": "sfaira.commands.questionary.sfaira_questionary", "line_number": 81, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 88, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 103, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 104, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 106, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 107, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 108, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 109, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 110, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 112, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 114, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 116, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 117, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 124, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 127, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 128, "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": "rich.print", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "36806538788", "text": "#simple command line animation viewer\nimport pygame\nfrom pygame.locals import *\nimport sys\nimport os\n\nsys.path.append(\"..\")\n\nimport animation\nimport settings\n\nscreen = pygame.display.set_mode((512, 512))\n\nscreen = pygame.display.set_mode((settings.screen_x*settings.screen_scale, \\\n\tsettings.screen_y*settings.screen_scale))\n\nanim_dest = pygame.Surface((settings.screen_x, settings.screen_y))\n\nanim = animation.PartAnimationSet(None, sys.argv[1])\nanim.set_animation(sys.argv[2])\n\nposx, posy = int(sys.argv[3]), int(sys.argv[4])\n\nrunning = True\n\nclock = pygame.time.Clock()\n\nwhile running: #loop while we are still running\n\tfor event in pygame.event.get(): #process events\n\t\tif event.type == QUIT: #if we're being told to quit\n\t\t\trunning = False #stop running\n\t\t\tbreak #and stop processing events\n\t\telif event.type == KEYDOWN: #if a key has been pressed\n\t\t\tif event.key == K_ESCAPE: #if it's one we care about\n\t\t\t\trunning = False\n\t\t\telif event.key == ord(\"r\"):\n\t\t\t\tanim = animation.PartAnimationSet(None, sys.argv[1])\n\t\t\t\tanim.set_animation(sys.argv[2])\n\tif running == False: #if we aren't supposed to be running any more\n\t\tbreak #stop running\n\tanim_dest.fill((255, 255, 255))\n\tanim.update(anim_dest, posx, posy)\n\tpygame.transform.scale(anim_dest, (settings.screen_x*settings.screen_scale, \\\n\t\tsettings.screen_y*settings.screen_scale), screen) #draw the screen scaled properly\n\tpygame.display.flip() #flip double buffers\n\tclock.tick(30)\n", "repo_name": "tpwrules/pokeclone", "sub_path": "animation_view.py", "file_name": "animation_view.py", "file_ext": "py", "file_size_in_byte": 1436, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"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_mode", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "settings.screen_x", "line_number": 14, "usage_type": "attribute"}, {"api_name": "settings.screen_scale", "line_number": 14, "usage_type": "attribute"}, {"api_name": "settings.screen_y", "line_number": 15, "usage_type": "attribute"}, {"api_name": "settings.screen_scale", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 17, "usage_type": "call"}, {"api_name": "settings.screen_x", "line_number": 17, "usage_type": "attribute"}, {"api_name": "settings.screen_y", "line_number": 17, "usage_type": "attribute"}, {"api_name": "animation.PartAnimationSet", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 29, "usage_type": "attribute"}, {"api_name": "animation.PartAnimationSet", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.screen_x", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.screen_scale", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.screen_y", "line_number": 44, "usage_type": "attribute"}, {"api_name": "settings.screen_scale", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 45, "usage_type": "attribute"}]} +{"seq_id": "40812171969", "text": "from tkinter import *\r\nfrom tkinter.messagebox import * \r\nimport sqlite3\r\nimport re\r\nregex = '^\\w+([\\.-]?\\w+)*@\\w+([\\.-]?\\w+)*(\\.\\w{2,3})+$'\r\ncon=sqlite3.Connection(\"phonebook\")\r\ncur=con.cursor()\r\ncur.execute(\"create table if not exists contact(id integer primary key autoincrement,fn varchar(20),mn varchar(20),ln varchar(20),company varchar(20),address varchar(20),city varchar(20),pin varchar(20),website varchar(20),dob varchar(20),ptype number,phno number,etype varchar(20),email_no varchar(20))\")\r\nroot0=Tk()\r\nroot0.geometry(\"5000x5000\")\r\nLabel(root0,text='Project Title: Phonebook',font='arial 20').grid(row=0,column=0)\r\nLabel(root0,text='Project of Python and Database',font='arial 20',fg='BLUE').grid(row=1,column=5)\r\nLabel(root0,text='Developed By: Divyansh Singh Sengar',font='arial 15',fg='red').grid(row=2,column=5)\r\nLabel(root0,text='---------------------------------------',font='arial 15').grid(row=3,column=5)\r\nLabel(root0,text='make mouse movement over screen to close',font='arial 15').grid(row=4,column=5)\r\ndef shut(e=1):\r\n root0.destroy()\r\nroot0.bind('',shut)\r\nroot0.mainloop()\r\nroot=Tk()\r\na=PhotoImage(file='phonebook image.gif')\r\na=a.subsample(1,1)\r\nLabel(root,image=a).grid(row=0,column=2)\r\nLabel(root,text=\"First Name :\",font=\"times 15 bold\").grid(row=1,column=1)\r\nfn=Entry(root)\r\nfn.grid(row=1,column=3)\r\n\r\nLabel(root,text=\"Middle Name :\",font=\"times 15 bold\").grid(row=2,column=1)\r\nmn=Entry(root)\r\nmn.grid(row=2,column=3)\r\n\r\nLabel(root,text=\"Last Name :\",font=\"times 15 bold\").grid(row=3,column=1)\r\nln=Entry(root)\r\nln.grid(row=3,column=3)\r\n\r\nLabel(root,text=\"Company Name :\",font=\"times 15 bold\").grid(row=4,column=1)\r\ncn=Entry(root)\r\ncn.grid(row=4,column=3)\r\n\r\nLabel(root,text=\"Address :\",font=\"times 15 bold\").grid(row=5,column=1)\r\nadd=Entry(root)\r\nadd.grid(row=5,column=3)\r\n\r\nLabel(root,text=\"City :\",font=\"times 15 bold\").grid(row=6,column=1)\r\nct=Entry(root)\r\nct.grid(row=6,column=3)\r\n\r\nLabel(root,text=\"Pin Code :\",font=\"times 15 bold\").grid(row=7,column=1)\r\npc=Entry(root)\r\npc.grid(row=7,column=3)\r\n\r\nLabel(root,text=\"Website URL :\",font=\"times 15 bold\").grid(row=8,column=1)\r\nws=Entry(root)\r\nws.grid(row=8,column=3)\r\n\r\nLabel(root,text=\"Date Of Birth :\",font=\"times 15 bold\").grid(row=9,column=1)\r\ndob=Entry(root)\r\ndob.grid(row=9,column=3)\r\n\r\nv1=StringVar()\r\nLabel(root,text=\"Select Phone type :\",font=\"times 15 bold\").grid(row=10,column=1)\r\nRadiobutton(root,text=\"Office\",variable=v1,value='office',tristatevalue=\"x\").grid(row=10,column=2)\r\nRadiobutton(root,text=\"Home\",variable=v1,value='home',tristatevalue=\"x\").grid(row=10,column=3)\r\nRadiobutton(root,text=\"Mobile\",variable=v1,value='mobile',tristatevalue=\"x\").grid(row=10,column=4)\r\nLabel(root,text=\"Phone Number :\").grid(row=11,column=1)\r\npn=Entry(root)\r\npn.grid(row=11,column=2)\r\nButton(root,text=\"+\").grid(row=11,column=3)\r\n\r\nv2=StringVar()\r\nLabel(root,text=\"Select Email type :\",font=\"times 15 bold\").grid(row=12,column=1)\r\nRadiobutton(root,text=\"Office\",variable=v2,value='office',tristatevalue=\"x\").grid(row=12,column=2)\r\nRadiobutton(root,text=\"Home\",variable=v2,value='home',tristatevalue=\"x\").grid(row=12,column=3)\r\nLabel(root,text=\"Email id :\").grid(row=13,column=1)\r\nem=Entry(root)\r\nem.grid(row=13,column=2)\r\nButton(root,text=\"+\").grid(row=13,column=3)\r\noff='office'\r\nhm='home'\r\nmob='mobile'\r\nv1.set(\"mobile\")\r\nv2.set(\"home\")\r\ndef check(email):\r\n if(re.search(regex,email)):\r\n return 1\r\n else:\r\n return 0\r\ndef save():\r\n if (fn.get()=='' and mn.get()=='' and ln.get()=='' and cn.get()=='' and add.get()=='' and ct.get()=='' and pc.get()=='' and ws.get()=='' and dob.get()=='' and pn.get()=='' and em.get()==''):\r\n def fun():\r\n showerror('error','Enter atleast one field')\r\n fun()\r\n if((fn.get()==mn.get()==ln.get()) and (fn.get()==mn.get()==ln.get()!='')):\r\n def fun():\r\n showerror('error','Firstname,Middlename and Lastname should not be same')\r\n fun()\r\n if(len(pn.get())>14):\r\n def fun():\r\n showerror('error','digits length exceeded')\r\n fun()\r\n if(check(em.get())==0):\r\n def fun():\r\n showerror('error','Not a valid email id')\r\n fun()\r\n else:\r\n if v1.get()=='office' and v2.get()=='office':\r\n cur.execute(\"insert into contact (fn,mn,ln,company,address,city,pin,website,dob,ptype,phno,etype,email_no) values(?,?,?,?,?,?,?,?,?,?,?,?,?)\",(fn.get(),mn.get(),ln.get(),cn.get(),add.get(),ct.get(),pc.get(),ws.get(),dob.get(),off,pn.get(),off,em.get()))\r\n if v1.get()=='office' and v2.get()=='home':\r\n cur.execute(\"insert into contact (fn,mn,ln,company,address,city,pin,website,dob,ptype,phno,etype,email_no) values(?,?,?,?,?,?,?,?,?,?,?,?,?)\",(fn.get(),mn.get(),ln.get(),cn.get(),add.get(),ct.get(),pc.get(),ws.get(),dob.get(),off,pn.get(),hm,em.get()))\r\n if v1.get()=='home' and v2.get()=='office':\r\n cur.execute(\"insert into contact (fn,mn,ln,company,address,city,pin,website,dob,ptype,phno,etype,email_no) values(?,?,?,?,?,?,?,?,?,?,?,?,?)\",(fn.get(),mn.get(),ln.get(),cn.get(),add.get(),ct.get(),pc.get(),ws.get(),dob.get(),hm,pn.get(),off,em.get()))\r\n if v1.get()=='home' and v2.get()=='home':\r\n cur.execute(\"insert into contact (fn,mn,ln,company,address,city,pin,website,dob,ptype,phno,etype,email_no) values(?,?,?,?,?,?,?,?,?,?,?,?,?)\",(fn.get(),mn.get(),ln.get(),cn.get(),add.get(),ct.get(),pc.get(),ws.get(),dob.get(),hm,pn.get(),hm,em.get()))\r\n if v1.get()=='mobile' and v2.get()=='office':\r\n cur.execute(\"insert into contact (fn,mn,ln,company,address,city,pin,website,dob,ptype,phno,etype,email_no) values(?,?,?,?,?,?,?,?,?,?,?,?,?)\",(fn.get(),mn.get(),ln.get(),cn.get(),add.get(),ct.get(),pc.get(),ws.get(),dob.get(),mob,pn.get(),off,em.get()))\r\n if v1.get()=='mobile' and v2.get()=='home':\r\n cur.execute(\"insert into contact (fn,mn,ln,company,address,city,pin,website,dob,ptype,phno,etype,email_no) values(?,?,?,?,?,?,?,?,?,?,?,?,?)\",(fn.get(),mn.get(),ln.get(),cn.get(),add.get(),ct.get(),pc.get(),ws.get(),dob.get(),mob,pn.get(),hm,em.get()))\r\n cur.execute(\"select * from contact\")\r\n root2=Tk()\r\n root2.geometry(\"600x600\")\r\n Label(root2,text=\"First Name :\",font=\"times 15 bold\").grid(row=0,column=1)\r\n Label(root2,text=fn.get()).grid(row=0,column=3)\r\n\r\n Label(root2,text=\"Middle Name :\",font=\"times 15 bold\").grid(row=1,column=1)\r\n Label(root2,text=mn.get()).grid(row=1,column=3)\r\n \r\n Label(root2,text=\"Last Name :\",font=\"times 15 bold\").grid(row=2,column=1)\r\n Label(root2,text=ln.get()).grid(row=2,column=3)\r\n\r\n Label(root2,text=\"Company Name :\",font=\"times 15 bold\").grid(row=3,column=1)\r\n Label(root2,text=cn.get()).grid(row=3,column=3)\r\n\r\n Label(root2,text=\"Address :\",font=\"times 15 bold\").grid(row=4,column=1)\r\n Label(root2,text=add.get()).grid(row=4,column=3)\r\n\r\n Label(root2,text=\"City :\",font=\"times 15 bold\").grid(row=5,column=1)\r\n Label(root2,text=ct.get()).grid(row=5,column=3)\r\n\r\n Label(root2,text=\"Pin Code :\",font=\"times 15 bold\").grid(row=6,column=1)\r\n Label(root2,text=pc.get()).grid(row=6,column=3)\r\n \r\n Label(root2,text=\"Website URL :\",font=\"times 15 bold\").grid(row=7,column=1)\r\n Label(root2,text=ws.get()).grid(row=7,column=3)\r\n\r\n Label(root2,text=\"Date Of Birth :\",font=\"times 15 bold\").grid(row=8,column=1)\r\n Label(root2,text=dob.get()).grid(row=8,column=3)\r\n\r\n Label(root2,text=\"Phone type :\",font=\"times 15 bold\").grid(row=9,column=1)\r\n Label(root2,text=v1.get()).grid(row=9,column=3)\r\n\r\n Label(root2,text=\"Phone Number :\",font=\"times 15 bold\").grid(row=10,column=1)\r\n Label(root2,text=pn.get()).grid(row=10,column=3)\r\n\r\n Label(root2,text=\"Email type :\",font=\"times 15 bold\").grid(row=11,column=1)\r\n Label(root2,text=v2.get()).grid(row=11,column=3)\r\n \r\n Label(root2,text=\"Email Id :\",font=\"times 15 bold\").grid(row=12,column=1)\r\n Label(root2,text=em.get()).grid(row=12,column=3)\r\n\r\n Label(root2,text=\"CONTACT SAVED!\",font=\"times 15 bold\").grid(row=13,column=2)\r\n\r\n fn.delete(0,END)\r\n mn.delete(0,END)\r\n ln.delete(0,END)\r\n cn.delete(0,END)\r\n add.delete(0,END)\r\n ct.delete(0,END)\r\n pc.delete(0,END)\r\n ws.delete(0,END)\r\n dob.delete(0,END)\r\n fn.delete(0,END)\r\n pn.delete(0,END)\r\n em.delete(0,END)\r\n v1.set(\"mobile\")\r\n v2.set(\"home\")\r\n def close2(x):\r\n root2.destroy()\r\n root2.bind(\"\",close2)\r\n Label(root2,text=\"press any key to continue...\",font=\"times 15 bold\").grid(row=14,column=2)\r\n root2.mainloop()\r\ndef search():\r\n root3=Tk()\r\n root3.geometry(\"600x600\")\r\n Label(root3,text=\"Search Box \",font=\"times 20 bold\").grid(row=0,column=0)\r\n sb=Entry(root3)\r\n sb.grid(row=0,column=1)\r\n def submit(x):\r\n lb=Listbox(root3,height=50)\r\n lb.grid(row=1,column=0)\r\n i=0\r\n j=0\r\n cur.execute(\"select fn\" + \" from contact where fn like (?) order by fn\",('%'+sb.get()+'%',))\r\n record=cur.fetchall()\r\n while(i!=len(record)):\r\n j=0\r\n while(j!=len(record[i])):\r\n lb.insert(END,record[i][j])\r\n j=j+1\r\n i=i+1\r\n def get_list(x):\r\n index=lb.curselection()\r\n text=lb.get(index)\r\n cur.execute(\"select id from contact where fn=(?)\",(text,))\r\n id_no=cur.fetchall()\r\n try:\r\n if len(id_no)>1:\r\n cur.execute(\"select * from contact where fn=(?) and id=(?) order by id\",(text,id_no[index[0]][0]))\r\n elif len(id_no)==1:\r\n cur.execute(\"select * from contact where fn=(?) order by id\",(text,))\r\n except IndexError:\r\n cur.execute(\"select * from contact where fn=(?) order by id\",(text,))\r\n record=cur.fetchall()\r\n root4=Tk()\r\n root4.geometry(\"600x600\")\r\n Label(root4,text=\"FIRST NAME: \").grid(row=0,column=0)\r\n Label(root4,text=record[0][1]).grid(row=0,column=1)\r\n Label(root4,text=\"MIDDLE NAME: \").grid(row=1,column=0)\r\n Label(root4,text=record[0][2]).grid(row=1,column=1)\r\n Label(root4,text=\"LAST NAME\").grid(row=2,column=0)\r\n Label(root4,text=record[0][3]).grid(row=2,column=1)\r\n Label(root4,text=\"COMPANY NAME\").grid(row=3,column=0)\r\n Label(root4,text=record[0][4]).grid(row=3,column=1)\r\n Label(root4,text=\"ADDRESS\").grid(row=4,column=0)\r\n Label(root4,text=record[0][5]).grid(row=4,column=1)\r\n Label(root4,text=\"CITY\").grid(row=5,column=0)\r\n Label(root4,text=record[0][6]).grid(row=5,column=1)\r\n Label(root4,text=\"PIN\").grid(row=6,column=0)\r\n Label(root4,text=record[0][7]).grid(row=6,column=1)\r\n Label(root4,text=\"WEBSITE URL\").grid(row=7,column=0)\r\n Label(root4,text=record[0][8]).grid(row=7,column=1)\r\n Label(root4,text=\"DOB\").grid(row=8,column=0)\r\n Label(root4,text=record[0][9]).grid(row=8,column=1)\r\n Label(root4,text=\"PHONETYPE\").grid(row=9,column=0)\r\n Label(root4,text=record[0][10]).grid(row=9,column=1)\r\n Label(root4,text=\"PHONE NUMBER\").grid(row=10,column=0)\r\n Label(root4,text=record[0][11]).grid(row=10,column=1)\r\n Label(root4,text=\"EMAILTYPE\").grid(row=11,column=0)\r\n Label(root4,text=record[0][12]).grid(row=11,column=1)\r\n Label(root4,text=\"EMAIL ID\").grid(row=12,column=0)\r\n Label(root4,text=record[0][13]).grid(row=12,column=1)\r\n def edit():\r\n new_fname=Entry(root4)\r\n new_fname.grid(row=0,column=2)\r\n new_mname=Entry(root4)\r\n new_mname.grid(row=1,column=2)\r\n new_lname=Entry(root4)\r\n new_lname.grid(row=2,column=2)\r\n new_cname=Entry(root4)\r\n new_cname.grid(row=3,column=2)\r\n new_add=Entry(root4)\r\n new_add.grid(row=4,column=2)\r\n new_city=Entry(root4)\r\n new_city.grid(row=5,column=2)\r\n new_pin=Entry(root4)\r\n new_pin.grid(row=6,column=2)\r\n new_website=Entry(root4)\r\n new_website.grid(row=7,column=2)\r\n new_dob=Entry(root4)\r\n new_dob.grid(row=8,column=2)\r\n new_ptype=Entry(root4)\r\n new_ptype.grid(row=9,column=2)\r\n new_pnumber=Entry(root4)\r\n new_pnumber.grid(row=10,column=2)\r\n new_etype=Entry(root4)\r\n new_etype.grid(row=11,column=2)\r\n new_email=Entry(root4)\r\n new_email.grid(row=12,column=2)\r\n def new_f():\r\n cur.execute(\"update contact set fn=(?) where id=(?)\",(new_fname.get(),record[0][0]))\r\n new_fname.delete(0,END)\r\n def new_m():\r\n cur.execute(\"update contact set mn=(?) where id=(?)\",(new_mname.get(),record[0][0]))\r\n new_mname.delete(0,END)\r\n def new_l():\r\n cur.execute(\"update contact set ln=(?) where id=(?)\",(new_lname.get(),record[0][0]))\r\n new_lname.delete(0,END)\r\n def new_c():\r\n cur.execute(\"update contact set company=(?) where id=(?)\",(new_cname.get(),record[0][0]))\r\n new_cname.delete(0,END)\r\n def new_a():\r\n cur.execute(\"update contact set address=(?) where id=(?)\",(new_add.get(),record[0][0]))\r\n new_add.delete(0,END)\r\n def new_ct():\r\n cur.execute(\"update contact set city=(?) where id=(?)\",(new_city.get(),record[0][0]))\r\n new_city.delete(0,END)\r\n def new_p():\r\n cur.execute(\"update contact set pin=(?) where id=(?)\",(new_pin.get(),record[0][0]))\r\n new_pin.delete(0,END)\r\n def new_w():\r\n cur.execute(\"update contact set website=(?) where id=(?)\",(new_website.get(),record[0][0]))\r\n new_website.delete(0,END)\r\n def new_d():\r\n cur.execute(\"update contact set dob=(?) where id=(?)\",(new_dob.get(),record[0][0]))\r\n new_dob.delete(0,END)\r\n def new_pt():\r\n cur.execute(\"update contact set ptype=(?) where id=(?)\",(new_ptype.get(),record[0][0]))\r\n new_ptype.delete(0,END)\r\n def new_pnum():\r\n cur.execute(\"update contact set phno=(?) where id=(?)\",(new_pnumber.get(),record[0][0]))\r\n new_pnumber.delete(0,END)\r\n def new_et():\r\n cur.execute(\"update contact set etype=(?) where id=(?)\",(new_etype.get(),record[0][0]))\r\n new_etype.delete(0,END)\r\n def new_em():\r\n cur.execute(\"update contact set email_no=(?) where id=(?)\",(new_email.get(),record[0][0]))\r\n new_email.delete(0,END)\r\n Button(root4,text='EDIT',command=new_f).grid(row=0,column=3)\r\n Button(root4,text='EDIT',command=new_m).grid(row=1,column=3)\r\n Button(root4,text='EDIT',command=new_l).grid(row=2,column=3)\r\n Button(root4,text='EDIT',command=new_c).grid(row=3,column=3)\r\n Button(root4,text='EDIT',command=new_a).grid(row=4,column=3)\r\n Button(root4,text='EDIT',command=new_ct).grid(row=5,column=3)\r\n Button(root4,text='EDIT',command=new_p).grid(row=6,column=3)\r\n Button(root4,text='EDIT',command=new_w).grid(row=7,column=3)\r\n Button(root4,text='EDIT',command=new_d).grid(row=8,column=3)\r\n Button(root4,text='EDIT',command=new_pt).grid(row=9,column=3)\r\n Button(root4,text='EDIT',command=new_pnum).grid(row=10,column=3)\r\n Button(root4,text='EDIT',command=new_et).grid(row=11,column=3)\r\n Button(root4,text='EDIT',command=new_em).grid(row=12,column=3)\r\n Button(root4,text='EDIT',command=edit).grid(row=13,column=0)\r\n Button(root4,text='CLOSE',command=root4.destroy).grid(row=14,column=1)\r\n root4.mainloop()\r\n lb.bind(\"<>\",get_list)\r\n def delete_contact():\r\n index=lb.curselection()\r\n text=lb.get(index)\r\n cur.execute(\"select * from contact where fn=(?)\",(text,))\r\n record=cur.fetchall()\r\n cur.execute(\"delete from contact where fn=(?) and id=(?)\",(text,record[0][0]))\r\n lb.delete(index)\r\n Button(root3,text=\"delete\",command=delete_contact).grid(row=0,column=3)\r\n root3.bind(\"\",submit)\r\n Button(root3,text=\"CLOSE\",command=root3.destroy).grid(row=0,column=4)\r\n root3.mainloop()\r\nButton(root,text=\"SAVE\",font=\"times 15 bold\",command=save).grid(row=15,column=1)\r\nButton(root,text=\"SEARCH\",font=\"times 15 bold\",command=search).grid(row=15,column=2)\r\nButton(root,text=\"CLOSE\",font=\"times 15 bold\",command=root.destroy).grid(row=15,column=3)\r\nroot.geometry(\"800x800\")\r\nroot.mainloop()\r\ncon.commit()\r\n", "repo_name": "divyansh894/Python-Phonebook", "sub_path": "Phonebook.pyw", "file_name": "Phonebook.pyw", "file_ext": "pyw", "file_size_in_byte": 17431, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sqlite3.Connection", "line_number": 6, "usage_type": "call"}, {"api_name": "re.search", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "27355277412", "text": "import asyncio\nimport functools\nimport inspect\nimport logging\nimport sys\nimport time\nfrom typing import Any, List, Tuple, Union\n\nimport cachetools\nfrom async_timeout import timeout\nfrom environs import Env\n\nlog = logging.getLogger(__name__)\nenv = Env()\nenv.read_env() # read .env file, if it exists\nSHOULD_INSPECT = env.bool(\"INSPECT_CALLERS\", False)\n\n\ndef async_ttl_cache(ttl: int = 3600, maxsize: int = 1) -> Any:\n \"\"\"Decorator to cache a coroutine result using :py:class:`cachetools.TTLCache`.\"\"\"\n cache: cachetools.TTLCache = cachetools.TTLCache(ttl=ttl, maxsize=maxsize)\n\n def decorator(fn):\n @functools.wraps(fn)\n async def memoize(*args, **kwargs):\n key = str((args, kwargs))\n try:\n return cache[key]\n except KeyError:\n result = await fn(*args, **kwargs)\n cache[key] = result\n return result\n\n return memoize\n\n return decorator\n\n\ndef get_callers(stack_size: int = 5) -> List[Tuple[int, str, Any]]:\n stack = inspect.stack()\n modules = [(index, inspect.getmodule(stack[index][0])) for index in reversed(range(1, min(stack_size, len(stack))))]\n callers = []\n for index, module in modules:\n try:\n callers.append((index, module.__name__, stack[index][3])) # type: ignore\n except AttributeError:\n # AttributeError: 'NoneType' object has no attribute '__name__'\n callers.append((index, \"unknown\", stack[index][3]))\n return sorted(callers)\n\n\ndef safe_ensure_future(coro, old_naming_style=False, call_loop_exception_handler=False, *args, **kwargs):\n \"\"\"\n Run a coroutine in a wrapper, catching and logging unexpected exception.\n\n :param old_naming_style: don't modify wrapped coroutine name if True\n :param call_loop_exception_handler: immediately call loop-wide exception handler on exception. This is needed to\n work-around python behavior: normally loop-wide exception handler is called when the finished task is\n garbage-collected. But if there is a reference, it prevents such call. See https://bugs.python.org/issue28274\n for details\n :envvar: INSPECT_CALLERS: if true, show callers on failure\n \"\"\"\n caller_names = \"\"\n if SHOULD_INSPECT:\n caller_names = \"\\n\" + \"\\n\".join([str(t) for t in get_callers()])\n\n async def safe_wrapper(c):\n try:\n return await c\n except asyncio.CancelledError:\n raise\n except Exception as exc:\n logging.getLogger().error(\n f\"Unhandled error in background task: {str(exc)} {caller_names}\",\n exc_info=True,\n )\n if call_loop_exception_handler:\n loop = asyncio.get_event_loop()\n loop.call_exception_handler({\"message\": \"exception in background task\", \"exception\": exc})\n\n wrapped_coro = safe_wrapper(coro)\n\n if old_naming_style:\n return asyncio.ensure_future(wrapped_coro, *args, **kwargs)\n\n if coro.__name__ and isinstance(coro.__name__, str) and coro.__name__.isidentifier():\n wrapped_coro.__qualname__ = f\"safe_{coro.__qualname__}\"\n\n return asyncio.ensure_future(wrapped_coro, *args, **kwargs)\n\n\nasync def safe_gather(*args, **kwargs):\n \"\"\"\n Gather an awaitables logging unexpected exceptions.\n\n .. note::\n\n Exception is logged and re-raised!\n\n :envvar: INSPECT_CALLERS: if true, show callers on failure\n \"\"\"\n caller_names = \"\"\n if SHOULD_INSPECT:\n caller_names = \"\\n\" + \"\\n\".join([str(t) for t in get_callers()])\n try:\n return await asyncio.gather(*args, **kwargs)\n except Exception as e:\n logging.getLogger().debug(\n f\"Unhandled error in background task: {str(e)} {caller_names}\",\n exc_info=True,\n )\n raise\n\n\nasync def safe_cancel(task: asyncio.Task, info: str, cancel_wait_timeout: Union[int, float] = 10) -> int:\n \"\"\"\n Cancel asyncio task and retry cancel if task didn't finished.\n\n :param task: Task to cancel\n :param info: some additional info to log when cancelling a task\n :param cancel_wait_timeout: how many seconds wait for task to cancel before retrying\n :return: how many attempts has been made to cancel a task\n \"\"\"\n retries = 0\n\n while True:\n task.cancel()\n log.debug(f\"Task cancel request sent to task: {task}, additional info: {info}\")\n\n try:\n async with timeout(cancel_wait_timeout):\n while not task.done():\n await asyncio.sleep(cancel_wait_timeout / 10)\n except asyncio.TimeoutError:\n log.warning(f\"Task failed to stop: {task}, additional task info: {info}, retries: {retries}\")\n else:\n log.info(f\"Task cancelled: {task}, additional info: {info}\")\n return retries\n\n retries += 1\n\n\ndef calc_delay_til_next_tick(seconds: float) -> float:\n \"\"\"Calculate the delay to next tick.\"\"\"\n now: float = time.time()\n current_tick: int = int(now // seconds)\n delay_til_next_tick: float = (current_tick + 1) * seconds - now\n return delay_til_next_tick\n\n\nasync def wait_til_next_tick(seconds: float = 1.0) -> None:\n \"\"\"Wait until the end of quantized time interval.\"\"\"\n delay = calc_delay_til_next_tick(seconds)\n await asyncio.sleep(delay)\n\n\ndef task_callback(task: asyncio.Task) -> None:\n \"\"\"\n Helper to terminate background asyncio task in test, on failure.\n\n Usage:\n\n .. code-block:: python\n\n task = asyncio.create_task(some_task())\n task.add_done_callback(task_callback)\n \"\"\"\n try:\n # Re-raises an exception\n task.result()\n except asyncio.CancelledError:\n pass\n except Exception:\n log.exception(\"Force-terminating test due to unhandled exception in task:\")\n # Terminates only current testcase, not the whole test run. Allows pytest fixture to teardown.\n sys.exit(1)\n", "repo_name": "CoinAlpha/birdfeeder", "sub_path": "birdfeeder/async_utils.py", "file_name": "async_utils.py", "file_ext": "py", "file_size_in_byte": 5950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "environs.Env", "line_number": 14, "usage_type": "call"}, {"api_name": "cachetools.TTLCache", "line_number": 21, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 24, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 19, "usage_type": "name"}, {"api_name": "inspect.stack", "line_number": 40, "usage_type": "call"}, {"api_name": "inspect.getmodule", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 39, "usage_type": "name"}, {"api_name": "asyncio.CancelledError", "line_number": 70, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 73, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 78, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 84, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 89, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 106, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 108, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 115, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 115, "usage_type": "name"}, {"api_name": "async_timeout.timeout", "line_number": 131, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 133, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 134, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 157, "usage_type": "attribute"}, {"api_name": "asyncio.CancelledError", "line_number": 171, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "41779097849", "text": "from __future__ import unicode_literals\n\nimport csv\nimport os\nimport glob\n\nfrom collections import OrderedDict\n\nfrom django.contrib import messages\nfrom django.http import HttpResponse\n# from django.http import QueryDict\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils.html import format_html, strip_tags\n\nfrom .models import Contact, Company\nfrom .forms import ContactForm, CompanyForm, CompanyContactForm, DeleteContactForm, DeleteCompanyForm\n\nfrom ..notes.models import Notes\n\nfrom ..csv_import.models import FileImport\nfrom ..csv_import.forms import CSVImportForm\n\n# ----------------------------------------#\n# ----- CONTACT & COMPANY LIST VIEW ----- #\n# ----------------------------------------#\n\n\ndef connections_list(request):\n contact_list = Contact.objects.order_by('first_name')\n company_list = Company.objects.order_by('company_name')\n\n if request.method == 'GET':\n slug = request.GET.get('content')\n if slug is None:\n try:\n contact = contact_list[0]\n company = company_list[0]\n except IndexError:\n contact = None\n company = None\n elif 'contact_sum' in request.GET.get('name'):\n contact = get_object_or_404(Contact, slug=slug)\n company = None\n elif 'company_sum' in request.GET.get('name'):\n company = get_object_or_404(Company, slug=slug)\n contact = None\n\n csv_import_form = CSVImportForm(request.POST, request.FILES)\n if request.method == \"POST\":\n if 'csv_upload' in request.POST:\n if csv_import_form.is_valid():\n csv_import_form = csv_import_form.save(commit=False)\n csv_import = csv_import_form\n csv_import_form.save()\n # Create file path for csv_import file\n filename = \"sniffersapp/media/\" + str(csv_import.csv_import)\n with open(filename, 'r') as f:\n reader = csv.reader(f)\n for row in reader:\n if row[0] != 'First Name' or row[0] != 'FirstName':\n _, created = Company.objects.get_or_create(\n company_name=row[2],\n )\n company_list = Company.objects.all()\n company_name_list = list(Company.objects.all().values_list('company_name', flat=True))\n _, created = Contact.objects.get_or_create(\n first_name=row[0],\n last_name=row[1],\n company=company_list[company_name_list.index(row[2])],\n company_name=row[2],\n job_title=row[3],\n email_work=row[4],\n phone_mobile=row[5],\n phone_work=row[6],\n linkedin=row[7],\n facebook=row[8],\n twitter=row[9],\n location=row[10],\n additional_info=row[11],\n contact_status=row[12],\n )\n # At the end of the import process delete any files in the FileImport model\n FileImport.objects.all().delete()\n # At the end of the import process delete any files in the csv_import folder\n csv_import_files = glob.glob(\"sniffersapp/media/csv_import/*\")\n for files in csv_import_files:\n os.remove(files)\n return redirect('connections:list')\n\n context = {\n 'contact_list': contact_list,\n 'company_list': company_list,\n 'contact': contact,\n 'company': company,\n 'csv_import_form': csv_import_form,\n }\n template = 'connections/connection_list.html'\n return render(request, template, context)\n\n\n# ----------------------------------------#\n# ------------ CONTACT VIEWS ------------ #\n# ----------------------------------------#\n\ndef contact_detail(request, slug, *args, **kwargs):\n contact = get_object_or_404(Contact, slug=slug)\n\n context = {\n 'contact': contact,\n }\n template = 'connections/contact_detail.html'\n return render(request, template, context)\n\n\ndef contact_new(request, *args, **kwargs):\n contact_list = Contact.objects.all()\n company_list = Company.objects.all()\n company_name_list = list(company_list.values_list('company_name', flat=True))\n\n contact_form = ContactForm(request.POST or None)\n\n if request.method == \"POST\":\n if 'new_contact' in request.POST:\n if contact_form.is_valid():\n contact_form = contact_form.save(commit=False)\n for contact in contact_list:\n # If the first_name, last_name and company already exist show an Alert Message\n if contact_form.first_name == contact.first_name and contact_form.last_name == \\\n contact.last_name and contact_form.job_title == contact.job_title:\n messages.warning(request, format_html('A contact with the same details already exists'))\n return redirect('connections:list')\n\n contact_form.save()\n messages.success(request, format_html('New contact Added'))\n return redirect('connections:list')\n\n\n context = {\n 'contact_list': contact_list,\n 'company_list': company_list,\n 'contact_form': contact_form,\n }\n template = 'connections/contact_new.html'\n return render(request, template, context)\n\n\ndef contact_edit(request, slug, *args, **kwargs):\n contact = get_object_or_404(Contact, slug=slug)\n\n contact_form = ContactForm(request.POST or None, instance=contact)\n delete_contact_form = DeleteContactForm(request.POST or None, instance=contact)\n\n if request.method == \"POST\":\n if 'edit_contact' in request.POST:\n if contact_form.is_valid():\n contact_form.save()\n return redirect('connections:list')\n\n elif 'delete_contact' in request.POST:\n if delete_contact_form.is_valid():\n contact.delete()\n return redirect('connections:list')\n\n context = {\n 'contact': contact,\n 'contact_form': contact_form,\n 'delete_contact_form': delete_contact_form,\n }\n template = 'connections/contact_edit.html'\n return render(request, template, context)\n\n\ndef export_contacts_csv(request):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"people.csv\"'\n writer = csv.writer(response)\n writer.writerow(['Company',\n 'First Name',\n 'Last Name',\n 'Job Title',\n 'Location',\n 'Email',\n 'Mobile Phone',\n 'Office Phone',\n 'Linkedin',\n 'Facebook',\n 'Twitter',\n 'Additional Info'])\n\n contact_list = Contact.objects.all().values_list('company_name',\n 'first_name',\n 'last_name',\n 'job_title',\n 'location',\n 'email_work',\n 'phone_mobile',\n 'phone_work',\n 'linkedin',\n 'facebook',\n 'twitter',\n 'additional_info')\n for contact in contact_list:\n writer.writerow(contact)\n return response\n\n# ----------------------------------------#\n# ------------ COMPANY VIEWS ------------ #\n# ----------------------------------------#\n\n\ndef company_detail(request, slug, *args, **kwargs):\n company = get_object_or_404(Company, slug=slug)\n contact_list = Contact.objects.all().filter(company=company)\n note_list = Notes.objects.all().filter(company=company)\n\n if request.method == 'GET':\n slug = request.GET.get('content')\n if slug is None:\n try:\n contact = contact_list[0]\n note = note_list[0]\n except IndexError:\n contact = None\n note = None\n elif 'contact_sum' in request.GET.get('name'):\n contact = get_object_or_404(Contact, slug=slug)\n note = None\n elif 'note_sum' in request.GET.get('name'):\n note = get_object_or_404(Notes, slug=slug)\n print(note.slug)\n contact = contact_list[0]\n\n context = {\n 'company': company,\n 'contact': contact,\n 'note': note,\n 'contact_list': contact_list,\n 'note_list': note_list,\n }\n template = 'connections/company_detail.html'\n return render(request, template, context)\n\n\ndef company_new(request, *args, **kwargs):\n contact_list = Contact.objects.all()\n company_list = Company.objects.all()\n\n if request.method == \"POST\":\n company_form = CompanyForm(request.POST)\n if company_form.is_valid():\n company_form.save()\n return redirect('connections:list')\n else:\n company_form = CompanyForm()\n context = {\n 'contact_list': contact_list,\n 'company_list': company_list,\n 'company_form': company_form,\n }\n template = 'connections/company_new.html'\n return render(request, template, context)\n\n\ndef export_companies_csv(request):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"companies.csv\"'\n writer = csv.writer(response)\n writer.writerow(['Company Name ',\n 'Website',\n 'Office Email',\n 'Office Phone',\n 'Linkedin',\n 'Facebook',\n 'Twitter',\n 'Additional Info'])\n company_list = Company.objects.all().values_list('company_name',\n 'webaddress_company',\n 'company_email',\n 'company_phone',\n 'linkedin',\n 'facebook',\n 'twitter',\n 'additional_info')\n for company in company_list:\n writer.writerow(company)\n return response\n\n\ndef company_edit(request, slug, *args, **kwargs):\n company = get_object_or_404(Company, slug=slug)\n contact_list = Contact.objects.filter(company=company)\n\n company_form = CompanyForm(request.POST or None, instance=company)\n delete_company_form = DeleteCompanyForm(request.POST or None, instance=company)\n\n if request.method == \"POST\":\n if 'edit_company' in request.POST:\n if company_form.is_valid():\n company = company_form.save()\n return redirect('connections:list')\n elif 'delete_company' in request.POST:\n\n contact_form = ContactForm(request.POST)\n if delete_company_form.is_valid():\n company.delete()\n for contact in contact_list:\n if contact.company is None:\n contact_form.company_name = None\n contact = contact_form.save()\n return redirect('connections:list')\n\n context = {\n 'company_form': company_form,\n 'delete_company_form': delete_company_form,\n }\n template = 'connections/company_edit.html'\n return render(request, template, context)\n", "repo_name": "jamesokane/Oneworksite-Application", "sub_path": "sniffersapp/connections/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 12355, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "models.Contact.objects.order_by", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Company.objects.order_by", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Contact", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Company", "line_number": 45, "usage_type": "argument"}, {"api_name": "csv_import.forms.CSVImportForm", "line_number": 48, "usage_type": "call"}, {"api_name": "csv_import.models", "line_number": 53, "usage_type": "name"}, {"api_name": "csv_import.models.csv_import", "line_number": 56, "usage_type": "attribute"}, {"api_name": "csv_import.models", "line_number": 56, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Company.objects.get_or_create", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Company.objects.all", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 64, "usage_type": "name"}, {"api_name": "models.Company.objects.all", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Contact.objects.get_or_create", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 66, "usage_type": "name"}, {"api_name": "csv_import.models.FileImport.objects.all", "line_number": 83, "usage_type": "call"}, {"api_name": "csv_import.models.FileImport.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "csv_import.models.FileImport", "line_number": 83, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 85, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 98, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 106, "usage_type": "call"}, {"api_name": "models.Contact", "line_number": 106, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 112, "usage_type": "call"}, {"api_name": "models.Contact.objects.all", "line_number": 116, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 116, "usage_type": "name"}, {"api_name": "models.Company.objects.all", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 117, "usage_type": "name"}, {"api_name": "forms.ContactForm", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 130, "usage_type": "name"}, {"api_name": "django.utils.html.format_html", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 131, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 134, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 134, "usage_type": "name"}, {"api_name": "django.utils.html.format_html", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 135, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 144, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 148, "usage_type": "call"}, {"api_name": "models.Contact", "line_number": 148, "usage_type": "argument"}, {"api_name": "forms.ContactForm", "line_number": 150, "usage_type": "call"}, {"api_name": "forms.DeleteContactForm", "line_number": 151, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 157, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 162, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 170, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 174, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 176, "usage_type": "call"}, {"api_name": "models.Contact.objects.all", "line_number": 190, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 190, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 190, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 212, "usage_type": "call"}, {"api_name": "models.Company", "line_number": 212, "usage_type": "argument"}, {"api_name": "models.Contact.objects.all", "line_number": 213, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 213, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 213, "usage_type": "name"}, {"api_name": "notes.models.Notes.objects.all", "line_number": 214, "usage_type": "call"}, {"api_name": "notes.models.Notes.objects", "line_number": 214, "usage_type": "attribute"}, {"api_name": "notes.models.Notes", "line_number": 214, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 226, "usage_type": "call"}, {"api_name": "models.Contact", "line_number": 226, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 229, "usage_type": "call"}, {"api_name": "notes.models.Notes", "line_number": 229, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 241, "usage_type": "call"}, {"api_name": "models.Contact.objects.all", "line_number": 245, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 245, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 245, "usage_type": "name"}, {"api_name": "models.Company.objects.all", "line_number": 246, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 246, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 246, "usage_type": "name"}, {"api_name": "forms.CompanyForm", "line_number": 249, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 252, "usage_type": "call"}, {"api_name": "forms.CompanyForm", "line_number": 254, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 261, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 265, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 267, "usage_type": "call"}, {"api_name": "models.Company.objects.all", "line_number": 276, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 276, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 276, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 290, "usage_type": "call"}, {"api_name": "models.Company", "line_number": 290, "usage_type": "argument"}, {"api_name": "models.Contact.objects.filter", "line_number": 291, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 291, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 291, "usage_type": "name"}, {"api_name": "forms.CompanyForm", "line_number": 293, "usage_type": "call"}, {"api_name": "forms.DeleteCompanyForm", "line_number": 294, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 300, "usage_type": "call"}, {"api_name": "forms.ContactForm", "line_number": 303, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 310, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 317, "usage_type": "call"}]} +{"seq_id": "30521133026", "text": "# This file will help to serve as a starting point for using the rest of the tools\n# Things we want to figure out\n# 1) Is your key active?\n# 2) If active, can you read monitoring configs, can you write?\n# 3) Okay, you can read monitoring configs. We recommend things to avoid. Want to go further? Use write access to disable (if applicable)\n# 4) Don't want to do anything with monitoring? That's fine, let's guide you through figuring out what your access looks like\n# 5) Help with a printout of options from this point forward\n\nimport boto3\nimport argparse\nimport os\nfrom botocore.exceptions import ClientError\nfrom botocore.exceptions import ConfigParseError\nfrom modules import *\nimport sys\nimport builtins\nimport re\nfrom tabulate import tabulate\nimport textwrap\n\n# Let a user set .aws/credentials or another file as the credentials source\n# If user-defined, must be an absolute path\nif 'AWS_SHARED_CREDENTIALS_FILE' not in os.environ:\n try:\n # print(\"loading .env into our ENV\")\n os.environ['AWS_SHARED_CREDENTIALS_FILE'] = '.env'\n except Exception as e:\n print(\"Error: {}\".format(e))\n sys.exit(\"fix your credentials file -exiting...\")\n\n# If you want to use a transparent + supports SSL proxy you can put it here\n# os.environ['HTTPS_PROXY'] = 'https://127.0.0.1:3128'\n\nsys.path.append(\"modules\")\nfor module in all_modules:\n exec(\"from %s import *\" % module)\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-m\", \"--module\", help=\"list the module you would like to run\", action=\"store\", type=str, required=False)\nparser.add_argument(\"-t\", \"--target\", help=\"Give your target a name so we can track results\", action=\"store\", type=str, required=False)\nparser.add_argument(\"-a\", \"--arguments\", help=\"Provide a list of arguments, comma separated. Ex: arg1,arg2,arg3\", action=\"store\", type=str, required=False)\nparser.add_argument(\"-l\", \"--list\", help=\"list modules\", required=False, action=\"store_true\")\nparser.add_argument(\"-v\", \"--verbosity\", help=\"increase output verbosity\", action=\"store_true\")\nargs = parser.parse_args()\n\n# Provides us with a global var \"db_name\" we can access anywhere\nbuiltins.db_name = \"weirdAAL.db\"\n\n\ndef perform_credential_check():\n '''\n Check that the AWS keys work before we go any further. It picks the keys up from the local .env file\n We are letting boto3 do all the work that way we can handle session tokens natively\n '''\n\n try:\n client = boto3.client(\"sts\")\n account_id = client.get_caller_identity()[\"Account\"]\n except (botocore.exceptions.NoCredentialsError) as e:\n print(\"Error: Unable to locate credentials\")\n sys.exit(\"fix your credentials file -exiting...\")\n except ClientError as e:\n print(\"[X] The AWS Access Keys are not valid/active [X]\")\n sys.exit(1)\n \n\ndef method_create():\n try:\n arg = globals()[\"module_\" + args.module]\n return arg\n except KeyError:\n print(\"That module does not exist\")\n exit(1)\n\nbuiltins.aws_module_methods_info = {}\nbuiltins.gcp_module_methods_info = {}\n\ndef get_methods_for_classname(classname):\n methods = []\n all_methods = dir(sys.modules[classname])\n for meth in all_methods:\n if meth.startswith(\"module_\"):\n narg = \"{}.__doc__\".format(meth)\n narg = eval(narg)\n nhash = {}\n nhash[meth] = narg\n methods.append(nhash)\n return methods\n\n\ndef make_list_of_methods(cloud_service, mod):\n meths = get_methods_for_classname(mod)\n if cloud_service == 'aws':\n new_mod_name = re.sub(\"modules.aws.\", \"\", mod)\n aws_module_methods_info[new_mod_name.upper()] = meths\n elif cloud_service == 'gcp':\n new_mod_name = re.sub(\"modules.gcp.\", \"\", mod)\n gcp_module_methods_info[new_mod_name.upper()] = meths\n\n\ndef make_the_list():\n for m in sys.modules.keys():\n if (m.startswith(\"modules.aws\")\n and not (m == \"modules.aws\")):\n make_list_of_methods(\"aws\", m)\n elif ((m.startswith(\"modules.gcp\"))\n and not (m == \"modules.gcp\")):\n make_list_of_methods(\"gcp\", m)\n\ndef normalize_comments(string):\n string = textwrap.fill(string.strip(), 40)\n return string\n\n\ndef make_tabulate_rows(hash, cloud_provider):\n entire_contents = []\n for (key) in hash:\n for item in hash[key]:\n for (k,v) in item.items():\n normalized_comment = normalize_comments(v)\n k = re.sub(\"module_\", \"\", k)\n entire_contents.append([cloud_provider, key, k, normalized_comment])\n\n return entire_contents\n\n\n\ndef print_the_list():\n aws_rows = make_tabulate_rows(aws_module_methods_info, 'AWS')\n gcp_rows = make_tabulate_rows(gcp_module_methods_info, 'GCP')\n print(tabulate(aws_rows, headers=['Cloud Provider', 'Service', 'Mod', 'Desc']))\n print(tabulate(gcp_rows, headers=['Cloud Provider', 'Service', 'Mod', 'Desc']))\n\nif (args.list):\n make_the_list()\n print_the_list()\n sys.exit(1)\n\n# Need to figure out if we have keys in the ENV or not\ntry:\n perform_credential_check()\nexcept:\n print(\"[-] Check the above error message and fix to use weirdAAL [-]\")\n sys.exit(1)\n\n\n# arg_list has to be defined otherwise will cause an exception\narg_list = None\n\nif (args.arguments):\n arg_list = args.arguments.split(',')\n\n# We need the user to tell us the module they want to proceed on\nif (args.module):\n if not (args.target):\n print(\"Use -t to give your target a name so we can track results!!!\")\n sys.exit(1)\n else:\n # Provides us with a global var \"target\" we can access anywhere\n builtins.target = args.target\n arg = method_create()\n if callable(arg):\n if arg_list:\n arg(arg_list)\n else:\n arg()\n\n\n# Allow the user to specify verbosity for debugging\nif (args.verbosity):\n print(\"Verbosity is enabled\")\n", "repo_name": "carnal0wnage/weirdAAL", "sub_path": "weirdAAL.py", "file_name": "weirdAAL.py", "file_ext": "py", "file_size_in_byte": 5921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 711, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}, {"api_name": "builtins.db_name", "line_number": 49, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 59, "usage_type": "call"}, {"api_name": "botocore.exceptions.exceptions", "line_number": 61, "usage_type": "attribute"}, {"api_name": "botocore.exceptions", "line_number": 61, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 63, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 64, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 66, "usage_type": "call"}, {"api_name": "builtins.aws_module_methods_info", "line_number": 77, "usage_type": "attribute"}, {"api_name": "builtins.gcp_module_methods_info", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 82, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 96, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 99, "usage_type": "call"}, {"api_name": "sys.modules.keys", "line_number": 104, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 104, "usage_type": "attribute"}, {"api_name": "textwrap.fill", "line_number": 113, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 123, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 133, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 134, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 139, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 146, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 159, "usage_type": "call"}, {"api_name": "builtins.target", "line_number": 162, "usage_type": "attribute"}]} +{"seq_id": "39254680884", "text": "from typing import List\n\nfrom pandas import Series\n\nfrom BL.datatypes import TradeAction\n\n\nclass TradeResult:\n def __init__(self):\n self.action:str = TradeAction.NONE\n self.result:str = \"\"\n self.last_df_time = \"\"\n\n\n\nclass EvalResult:\n\n def __init__(self, trades_results:List = [], reward: float = 0.0,\n trades: int = 0, len_df: int = 0, trade_minutes: int = 0, wins: int = 0):\n self._reward = reward\n self._trades = trades\n self._len_df = len_df\n self._trade_minutes = trade_minutes\n self._wins = wins\n self._trade_results:List = trades_results\n\n def get_reward(self):\n return self._reward\n\n def get_trades(self):\n return self._trades\n\n def get_average_reward(self):\n if self._trades == 0:\n return 0\n return round(self._reward / self._trades, 7)\n\n def get_trade_frequency(self):\n if self._len_df == 0:\n return 0\n return round(self._trades / self._len_df, 7)\n\n def get_win_loss(self):\n if self._trades == 0:\n return 0\n return round(self._wins / self._trades, 7)\n\n def get_average_minutes(self):\n if self._trades == 0:\n return 0\n return round(self._trade_minutes / self._trades, 7)\n\n def get_data(self):\n return Series([\n self._reward,\n self._trades,\n self._wins,\n self._len_df,\n self._trade_minutes\n ],\n index=[\"_reward\",\n \"_trades\",\n \"_wins\",\n \"_len_df\",\n \"_trade_minutes\",\n ])\n\n def __repr__(self):\n return f\"Reward {self.get_reward()}\" + \\\n f\"success {self.get_average_reward()} \" \\\n f\"trade_freq {self.get_trade_frequency()} \" \\\n f\"win_loss {self.get_win_loss()} \" \\\n f\"trades {self.get_trades()} \" \\\n f\"avg_minutes {self.get_average_minutes()} \"\n\n\nclass EvalResultCollection:\n _items = []\n\n def add(self, r: EvalResult):\n self._items.append(r)\n\n def get_avg_profit(self):\n win_loss_overall = 0\n market_measures = 0\n for i in self._items:\n if i.get_trades() != 0:\n market_measures = market_measures + 1\n win_loss_overall = win_loss_overall + i.get_win_loss()\n\n return win_loss_overall / market_measures\n\n def get_avg_trades(self):\n win_loss_trades = 0\n market_measures = 0\n for i in self._items:\n if i.get_trades() != 0:\n market_measures = market_measures + 1\n win_loss_trades = win_loss_trades + i.get_trades()\n\n return win_loss_trades / market_measures\n\n def __repr__(self):\n text = f\"Avg Win_loss {self.get_avg_profit()} \\r\\n\"\n text += f\"Avg Trades {self.get_avg_trades()}\"\n\n return text\n", "repo_name": "hacky1610/EmmanuelProject", "sub_path": "BL/eval_result.py", "file_name": "eval_result.py", "file_ext": "py", "file_size_in_byte": 2935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "BL.datatypes.TradeAction.NONE", "line_number": 10, "usage_type": "attribute"}, {"api_name": "BL.datatypes.TradeAction", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "43000694186", "text": "import datetime\nfrom pathlib import Path\n\nimport pandas as pd\n\n\ndef read_file(filename):\n \"\"\"Load in the different debts from input.csv\"\"\"\n\n debts = pd.read_csv(filename, encoding=\"utf-8\", skiprows=3, index_col=\"Name\")\n debts = debts.replace(r\"[^.0-9]\", \"\", regex=True).astype(float)\n debts[\"Adjusted Payment\"] = 0\n debts[\"Interest\"] = 0\n\n inputs = pd.read_csv(filename, encoding=\"utf-8\", nrows=1, skipinitialspace=True)\n inputs[\"Monthly Payment\"] = (\n inputs[\"Monthly Payment\"].replace(r\"[^.0-9]\", \"\", regex=True).astype(float)\n )\n\n totalfunds = inputs.loc[0, \"Monthly Payment\"]\n try:\n date = datetime.datetime.strptime(\n str(inputs.loc[0, \"Start Date (YYYY-MM)\"]).strip(), \"%Y-%m\"\n )\n except ValueError:\n date = datetime.date.today()\n print(\n \"Invalid date format entered for Start Date. Must be YYYY-MM format. Using today's date instead.\"\n )\n\n strategy = inputs.loc[0, \"Strategy (Avalanche or Snowball)\"].strip().lower()\n\n if strategy == \"snowball\":\n debts = debts.sort_values(\"Principal\", ascending=True)\n else:\n debts = debts.sort_values(\"Rate\", ascending=False)\n\n return debts, totalfunds, date\n\n\ndef compound_daily(date, principal, rate):\n \"\"\"Calculate the principal and interest using daily compounding.\"\"\"\n\n daysinyear = 366 if (pd.Period(f\"{date}\").is_leap_year) else 365\n dailyrate = (rate / 100) / daysinyear\n days = pd.Period(f\"{date}\").days_in_month\n\n new_principal = principal * (1 + dailyrate) ** days\n interest = new_principal - principal\n\n return new_principal, interest\n\n\ndef pay_minimums(principal, payment):\n \"\"\"Make the minimum payments first.\"\"\"\n\n if principal - payment <= 0:\n payment = principal\n return principal - payment, payment\n\n\ndef pay_excess(principal, minimum, remainder):\n \"\"\"Pay any excess remaining after making minimum payments.\"\"\"\n\n excess = remainder\n\n if principal - excess <= 0:\n excess = principal\n remainder = remainder - principal\n else:\n remainder = 0\n return principal - excess, minimum + excess, remainder\n\n\ndef update_schedule():\n path = Path(__file__).resolve().parent / \"input.csv\"\n debts, totalfunds, date = read_file(path)\n\n initial_date = date\n payments = debts[[\"Adjusted Payment\"]].transpose()\n interest = debts[[\"Interest\"]].transpose()\n\n while debts[\"Principal\"].sum() > 0:\n if debts[\"Minimum Payment\"].sum() > totalfunds:\n print(\"not enough for minimum monthly payments\")\n break\n else:\n\n # Update the principal and paid interest using daily compounding\n debts[\"Principal\"], debts[\"Interest\"] = zip(\n *debts.apply(\n lambda x: compound_daily(date, x[\"Principal\"], x[\"Rate\"]), axis=1,\n )\n )\n\n # If principal balance is zero, set it's payment to zero.\n debts[\"Minimum Payment\"] = debts.apply(\n lambda x: 0 if x[\"Principal\"] <= 0 else x[\"Minimum Payment\"], axis=1\n )\n\n # Make minimum payments\n debts[\"Principal\"], debts[\"Adjusted Payment\"] = zip(\n *debts.apply(\n lambda x: pay_minimums(x[\"Principal\"], x[\"Minimum Payment\"]), axis=1\n )\n )\n\n remainder = totalfunds - debts[\"Minimum Payment\"].sum()\n\n # Make excess payments and update the adjusted payment amount\n for _, debt in debts.iterrows():\n if debt[\"Principal\"] > 0:\n (\n debt[\"Principal\"],\n debt[\"Adjusted Payment\"],\n remainder,\n ) = pay_excess(\n debt[\"Principal\"], debt[\"Minimum Payment\"], remainder,\n )\n\n payments = payments.append(debts[[\"Adjusted Payment\"]].transpose())\n interest = interest.append(debts[[\"Interest\"]].transpose())\n\n date = date + pd.DateOffset(months=1)\n\n payments.index = pd.date_range(\n start=initial_date, periods=len(payments), freq=\"M\", name=\"Date\"\n )\n\n # The initial payment row is set to zero.\n # Shift up one and drop the last row before writing to csv.\n payments = payments.shift(-1).drop(payments.tail(1).index)\n path = Path(__file__).resolve().parent / \"payment_schedule.csv\"\n payments.to_csv(\n path, index=True, header=True, encoding=\"utf-8\", float_format=\"%.2f\"\n )\n\n print()\n print(f\"Debt Free: {payments.index[-1].strftime('%B %Y')}\")\n print()\n print(f\"Total Payments: {payments.values.sum():,.2f}\")\n print(f\"Total Principal: {payments.values.sum()-interest.values.sum():,.2f}\")\n print(f\"Total Interest: {interest.values.sum():,.2f}\")\n print()\n print(\"payment_schedule.csv generated!\")\n\n\nif __name__ == \"__main__\":\n update_schedule()\n", "repo_name": "jscoughlin/gazelle", "sub_path": "gazelle/gazelle.py", "file_name": "gazelle.py", "file_ext": "py", "file_size_in_byte": 4932, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pandas.Period", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.Period", "line_number": 46, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.DateOffset", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 126, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "23308635180", "text": "import os\nimport random\nimport sys\nimport urllib.request\n\nimport cv2\nimport numpy as np\nimport requests\nimport torch\nfrom torch.nn import functional as F\nfrom torchvision.transforms import Grayscale\n\nfrom models.modules.sam.sam_inference import predict_sam_edges\nfrom models.modules.sketch_generation.hed import HEDdetector\nfrom models.modules.sketch_generation.mlsd import MLSDdetector\nfrom models.modules.utils import download_midas_weight, predict_depth\nfrom util.util import im2tensor\n\nsys.path.append(\"./../\")\n\n\ndef fill_img_with_sketch(img, mask, **kwargs):\n \"\"\"Fill the masked region with sketch edges.\"\"\"\n\n grayscale = Grayscale(3)\n gray = grayscale(img)\n\n threshold = torch.tensor((120 / 255) * 2 - 1)\n\n thresh = (gray < threshold) * 1.0 # thresh = ((gray < threshold) * 1.0) * 2 - 1\n\n mask = torch.clamp(mask, 0, 1)\n\n return mask * thresh + (1 - mask) * img\n\n\ndef fill_img_with_canny(\n img,\n mask,\n low_threshold=None,\n high_threshold=None,\n **kwargs,\n):\n \"\"\"Fill the masked region with canny edges.\"\"\"\n low_threshold_random = kwargs[\"low_threshold_random\"]\n high_threshold_random = kwargs[\"high_threshold_random\"]\n max_value = 255 * 3\n if high_threshold is None and low_threshold is None:\n threshold_1 = random.randint(low_threshold_random, high_threshold_random)\n threshold_2 = random.randint(low_threshold_random, high_threshold_random)\n high_threshold = max(threshold_1, threshold_2)\n low_threshold = min(threshold_1, threshold_2)\n elif high_threshold is None and low_threshold is not None:\n high_threshold = random.randint(low_threshold, max_value)\n elif high_threshold is not None and low_threshold is None:\n low_threshold = random.randint(0, high_threshold)\n\n device = img.device\n edges_list = []\n for cur_img in img:\n cur_img = (\n (torch.einsum(\"chw->hwc\", cur_img).cpu().numpy() + 1) * 255 / 2\n ).astype(np.uint8)\n edges = cv2.Canny(cur_img, low_threshold, high_threshold)\n edges = (\n (((torch.tensor(edges, device=device) / 255) * 2) - 1)\n .unsqueeze(0)\n .unsqueeze(0)\n )\n edges_list.append(edges)\n edges = torch.cat(edges_list, dim=0)\n mask = torch.clamp(mask, 0, 1)\n\n return mask * edges + (1 - mask) * img\n\n\ndef fill_img_with_hed(img, mask, **kwargs):\n \"\"\"Fill the masked region with HED edges from the ControlNet paper.\"\"\"\n\n apply_hed = HEDdetector()\n device = img.device\n edges_list = []\n for cur_img in img:\n cur_img = (\n (torch.einsum(\"chw->hwc\", cur_img).cpu().numpy() + 1) * 255 / 2\n ).astype(np.uint8)\n detected_map = apply_hed(cur_img)\n detected_map = (\n (((torch.tensor(detected_map, device=device) / 255) * 2) - 1)\n .unsqueeze(0)\n .unsqueeze(0)\n )\n edges_list.append(detected_map)\n edges = torch.cat(edges_list, dim=0)\n mask = torch.clamp(mask, 0, 1)\n\n return mask * edges + (1 - mask) * img\n\n\ndef fill_img_with_hough(\n img,\n mask,\n value_threshold=1e-05,\n distance_threshold=10.0,\n with_canny=False,\n **kwargs,\n):\n \"\"\"Fill the masked region with Hough lines detection from the ControlNet paper.\"\"\"\n\n if with_canny:\n img = fill_img_with_canny(img, mask, **kwargs)\n\n device = img.device\n apply_mlsd = MLSDdetector()\n edges_list = []\n for cur_img in img:\n cur_img = (\n (torch.einsum(\"chw->hwc\", cur_img).cpu().numpy() + 1) * 255 / 2\n ).astype(np.uint8)\n detected_map = apply_mlsd(\n cur_img, thr_v=value_threshold, thr_d=distance_threshold\n )\n detected_map = (\n (((torch.tensor(detected_map, device=device) / 255) * 2) - 1)\n .unsqueeze(0)\n .unsqueeze(0)\n )\n edges_list.append(detected_map)\n edges = torch.cat(edges_list, dim=0)\n mask = torch.clamp(mask, 0, 1)\n\n return mask * edges + (1 - mask) * img\n\n\ndef fill_img_with_depth(img, mask, depth_network=\"DPT_SwinV2_T_256\", **kwargs):\n \"\"\"Fill the masked region with depth map.\"\"\"\n\n device = img.device\n midas_w = download_midas_weight(model_type=depth_network)\n edges_list = []\n for cur_img in img:\n cur_img = torch.from_numpy(\n np.transpose(\n ((torch.einsum(\"chw->hwc\", cur_img).cpu() + 1) * 255 / 2).numpy(),\n (2, 0, 1),\n )\n ).float()\n depth_map = predict_depth(\n img=cur_img.unsqueeze(0), midas=midas_w, model_type=depth_network\n )\n if (depth_map.shape[0], depth_map.shape[1]) != (\n cur_img.shape[1],\n cur_img.shape[2],\n ):\n depth_map = torch.nn.functional.interpolate(\n depth_map.unsqueeze(1),\n size=cur_img.shape[1:],\n mode=\"bilinear\",\n ).squeeze()\n depth_map = (\n ((torch.tensor(depth_map, device=device) / 255) * 2) - 1\n ).unsqueeze(0)\n edges_list.append(depth_map)\n edges = torch.cat(edges_list, dim=0)\n mask = torch.clamp(mask, 0, 1)\n\n return mask * edges + (1 - mask) * img\n\n\ndef fill_img_with_sam(img, mask, sam, opt):\n crops = []\n for i, cur_img in enumerate(img):\n cur_img = (\n (torch.einsum(\"chw->hwc\", cur_img).cpu().numpy() + 1) * 255 / 2\n ).astype(np.uint8)\n cur_mask = mask[i].cpu().squeeze(0).numpy()\n cur_mask[cur_mask > 0] = 1\n bbox = np.argwhere(cur_mask == 1)\n crop_delta = opt.alg_palette_sam_crop_delta\n if bbox.shape[0] != 0:\n x_min = np.min(bbox[:, 0])\n x_max = np.max(bbox[:, 0])\n y_min = np.min(bbox[:, 1])\n y_max = np.max(bbox[:, 1])\n else:\n x_min = 0\n x_max = cur_img.shape[0]\n y_min = 0\n y_max = cur_img.shape[1]\n x_min_to_crop = max(0, x_min - crop_delta)\n x_max_to_crop = min(cur_img.shape[0], x_max + crop_delta)\n y_min_to_crop = max(0, y_min - crop_delta)\n y_max_to_crop = min(cur_img.shape[1], y_max + crop_delta)\n img_cropped = cur_img[\n x_min_to_crop:x_max_to_crop, y_min_to_crop:y_max_to_crop, :\n ]\n crops.append(\n (\n img_cropped,\n (x_min, x_max, y_min, y_max),\n (x_min_to_crop, x_max_to_crop, y_min_to_crop, y_max_to_crop),\n )\n )\n edges_list = predict_sam_edges(\n [crop[0] for crop in crops],\n sam,\n use_gaussian_filter=opt.alg_palette_sam_use_gaussian_filter,\n use_sobel_filter=opt.alg_palette_sam_no_sobel_filter,\n output_binary_sam=opt.alg_palette_sam_no_output_binary_sam,\n redundancy_threshold=opt.alg_palette_sam_redundancy_threshold,\n sobel_threshold=opt.alg_palette_sam_sobel_threshold,\n final_canny=opt.alg_palette_sam_final_canny,\n min_mask_area=opt.alg_palette_sam_min_mask_area,\n max_mask_area=opt.alg_palette_sam_max_mask_area,\n points_per_side=opt.alg_palette_sam_points_per_side,\n sample_points_in_ellipse=opt.alg_palette_sam_no_sample_points_in_ellipse,\n )\n\n output_list = []\n for k in range(len(img)):\n edges = edges_list[k]\n x_min, x_max, y_min, y_max = crops[k][1]\n x_min_to_crop, x_max_to_crop, y_min_to_crop, y_max_to_crop = crops[k][2]\n\n crop_x_min = abs(x_min_to_crop - x_min)\n crop_x_max = abs(x_max_to_crop - x_max)\n crop_y_min = abs(y_min_to_crop - y_min)\n crop_y_max = abs(y_max_to_crop - y_max)\n\n cur_img = (\n (torch.einsum(\"chw->hwc\", img[k]).cpu().numpy() + 1) * 255 / 2\n ).astype(np.uint8)\n\n cur_img[x_min:x_max, y_min:y_max, :] = edges[\n crop_x_min : edges.shape[0] - crop_x_max,\n crop_y_min : edges.shape[1] - crop_y_max,\n ][..., np.newaxis]\n\n output_list.append(im2tensor(cur_img).unsqueeze(0).to(sam.device))\n\n output_list = torch.cat(output_list, dim=0)\n\n return output_list\n\n\ndef random_edge_mask(fn_list):\n edge_fns = []\n for fn in fn_list:\n if fn == \"canny\":\n edge_fns.append(fill_img_with_canny)\n elif fn == \"hed\":\n edge_fns.append(fill_img_with_hed)\n elif fn == \"hough\":\n edge_fns.append(fill_img_with_hough)\n elif fn == \"depth\":\n edge_fns.append(fill_img_with_depth)\n elif fn == \"sketch\":\n edge_fns.append(fill_img_with_sketch)\n elif fn == \"sam\":\n edge_fns.append(fill_img_with_sam)\n else:\n raise NotImplementedError(f\"Unknown edge function {fn}\")\n return random.choice(edge_fns)\n", "repo_name": "jolibrain/joliGEN", "sub_path": "util/mask_generation.py", "file_name": "mask_generation.py", "file_ext": "py", "file_size_in_byte": 8711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 161, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sys.path.append", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Grayscale", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 49, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 54, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 72, "usage_type": "call"}, {"api_name": "models.modules.sketch_generation.hed.HEDdetector", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 95, "usage_type": "call"}, {"api_name": "models.modules.sketch_generation.mlsd.MLSDdetector", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 130, "usage_type": "call"}, {"api_name": "models.modules.utils.download_midas_weight", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 144, "usage_type": "call"}, {"api_name": "models.modules.utils.predict_depth", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.argwhere", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 184, "usage_type": "call"}, {"api_name": "models.modules.sam.sam_inference.predict_sam_edges", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 232, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 237, "usage_type": "attribute"}, {"api_name": "util.util.im2tensor", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 241, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 263, "usage_type": "call"}]} +{"seq_id": "72427954605", "text": "from django.db import migrations\n\nfrom gatherer.models import *\nfrom ds.models import *\n\n\ndef remove_russian_lemmas(apps, schema_editor):\n russian_records = DigestRecord.objects.filter(language=Language.RUSSIAN.name)\n russian_records_lemmas = DigestRecordLemma.objects.filter(digest_record__in=russian_records)\n drl: DigestRecordLemma\n removed_connections_count = 0\n removed_lemmas_count = 0\n lemmas_to_remove = []\n for drl in russian_records_lemmas:\n remove_only_connection = False\n if DigestRecordLemma.objects.filter(lemma=drl.lemma, digest_record__in=DigestRecord.objects.filter(language=Language.ENGLISH.name)):\n remove_only_connection = True\n lemma = drl.lemma\n drl.delete()\n removed_connections_count += 1\n if not remove_only_connection:\n lemmas_to_remove.append(lemma)\n for lemma in lemmas_to_remove:\n lemma.delete()\n removed_lemmas_count += 1\n print(f'Removed {removed_connections_count} connections')\n print(f'Removed {removed_lemmas_count} lemmas')\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('ds', '0005_fill_lemmas_and_connections_to_digest_records'),\n ]\n\n operations = [\n migrations.RunPython(remove_russian_lemmas, migrations.RunPython.noop),\n ]\n", "repo_name": "Gim6626/foss-news-gathering-server", "sub_path": "fngs/ds/migrations/0006_remove_russian_lemmas.py", "file_name": "0006_remove_russian_lemmas.py", "file_ext": "py", "file_size_in_byte": 1313, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.migrations.RunPython", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "26560500948", "text": "# scraper and parser for Meritalk Events\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n# ---------------------\n\nurl = \"https://www.meritalk.com/events/\"\npage = requests.get(url)\n\nsoup = BeautifulSoup(page.content, \"html.parser\")\ncards = soup.find_all('div', {'class', 'wpb_wrapper'})[7]\n\n# ---------------------\n\nupcoming_title = cards.find_all('a')\n\nupcoming_title_final = []\nfor item in upcoming_title[0::2]:\n upcoming_title_final.append(item.text)\n\n# ---\n\n# upcoming event date\nupcoming_date = cards('div', {'class', 'event-date'})\n\nupcoming_date_final = []\nfor item in upcoming_date:\n upcoming_date_final.append(item.text)\n \n# ---\n\n# upcoming event description\nupcoming_description = cards('div', {'class', 'event-excerpt'})\n\nupcoming_description_final = [getattr(event.find('p'), 'text', None) for event in upcoming_description]\n\n# ---\n\n# upcoming learn more\n\nupcoming_url_final = []\nfor link in upcoming_title[0::2]:\n upcoming_url_final.append(link.get('href'))", "repo_name": "opensource-joe/python_scraper_parser_projects", "sub_path": "fed-events/events_meritalk.py", "file_name": "events_meritalk.py", "file_ext": "py", "file_size_in_byte": 981, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "21190461836", "text": "\"\"\"Wheel serializer.\"\"\"\nfrom wheel.models import FrontWheel, RearWheel\nfrom rest_framework import serializers\n\n\nclass GenericWheelSerializer(serializers.Serializer):\n \"\"\"Generic wheel serializer for all wheels list view.\"\"\"\n\n type = serializers.SerializerMethodField()\n id = serializers.IntegerField(read_only=True)\n manufacturer = serializers.CharField(\n required=True,\n allow_blank=False,\n max_length=30,\n )\n model = serializers.CharField(\n required=True,\n allow_blank=False,\n max_length=30,\n )\n\n def get_type(self, obj):\n \"\"\"Return class name of an object for identification.\"\"\"\n return obj.__class__.__name__\n\n\nclass FrontWheelSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for FrontWheel object.\"\"\"\n\n class Meta:\n model = FrontWheel\n exclude = ['created_at', 'modified_at']\n\n\nclass RearWheelSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for RearWheel object.\"\"\"\n\n def validate(self, data):\n \"\"\"Declare extra validations.\"\"\"\n if (data.get('fixed') or data.get('single_speed_only')\n and data['driver'] != 6):\n raise serializers.ValidationError(\n \"Invalid combination of rear hub drivers.\"\n )\n if data.get('fixed') and data.get('single_speed_only'):\n raise serializers.ValidationError(\n \"Fixed hubs are not the same as single speed hubs.\"\n )\n return data\n\n class Meta:\n model = RearWheel\n exclude = ['created_at', 'modified_at']\n", "repo_name": "mikez321/bike", "sub_path": "wheel/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 9, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 10, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 27, "usage_type": "name"}, {"api_name": "wheel.models.FrontWheel", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 42, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 46, "usage_type": "name"}, {"api_name": "wheel.models.RearWheel", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "23248373715", "text": "import time\nimport threading\nimport tkinter as tk\nfrom tkinter import *\nfrom tkinter import ttk\nfrom PIL import ImageTk, Image\nimport yaml\nimport os\n\nwith open(\"data/questions.yaml\", 'rt', encoding='utf8') as yml:\n yaml_content = yaml.safe_load(yml)\n\n\nquestion_counter = 0\nscore = 0\nfilename = \"scoreboard\"\nfile = filename +\".yaml\"\n\nif os.path.exists(\"data/scoreboard.yaml\"):\n with open(\"data/scoreboard.yaml\", 'rt', encoding='utf8') as yml:\n scoreboard = yaml.safe_load(yml)\nelse:\n scoreboard = []\n\nroot = Tk()\nroot.resizable(False, False)\n\nroot.title(\"Quiz App\")\nroot.geometry(\"600x400\")\nroot.iconbitmap(\"./image.ico\")\n\nframe = Frame(root)\nframe.pack(side=\"top\", expand=True, fill=\"both\")\n\nfrage = Label(frame, text=\"Welches Logo ist das?\", width=20, height=5, font=(\"Ubuntu\", 15))\nfrage.pack()\n\nlogo = ImageTk.PhotoImage(Image.open(f\"data/images/{yaml_content['questions'][question_counter]['image']}\"))\nimglabel = Label(frame, image=logo, height=120, width=120)\nimglabel.pack()\n\ndef refreshCnfg(question_counter):\n btnsConfig = {\n \"ans1\": {\n \"y\": \"250\",\n \"x\": \"100\",\n \"text\": yaml_content[\"questions\"][question_counter]['answers'][0],\n \"height\": 1,\n \"width\": 12,\n \"bg\": \"#EEEEEE\"\n },\n \"ans2\": {\n \"y\": \"310\",\n \"x\": \"100\",\n \"text\": yaml_content[\"questions\"][question_counter]['answers'][1],\n \"height\": 1,\n \"width\": 12,\n \"bg\": \"#EEEEEE\"\n },\n \"ans3\": {\n \"y\": \"250\",\n \"x\": \"410\",\n \"text\": yaml_content[\"questions\"][question_counter]['answers'][2],\n \"height\": 1,\n \"width\": 12,\n \"bg\": \"#EEEEEE\"\n },\n \"ans4\": {\n \"y\": \"310\",\n \"x\": \"410\",\n \"text\": yaml_content[\"questions\"][question_counter]['answers'][3],\n \"height\": 1,\n \"width\": 12,\n \"bg\": \"#EEEEEE\"\n }\n }\n return btnsConfig\n\ndef changeImg():\n img2 = ImageTk.PhotoImage(Image.open(f\"data/images/{yaml_content['questions'][question_counter]['image']}\"))\n imglabel.configure(image=img2)\n imglabel.image = img2\n\ndef createOrRefreshFrame():\n global scorelbl\n global question_counter\n if question_counter == len(yaml_content['questions']):\n ask()\n return\n\n scorelbl = tk.Label(frame, text=\"Score: \", font=(\"Arial\", 12))\n scorelbl.pack()\n scorelbl.place(x=10, y=10)\n\n print(f'counter before: {question_counter}')\n changeImg()\n refreshCnfg(question_counter)\n Ans = {\"rigthAns\": yaml_content['questions'][question_counter]['right_answer'],\n \"ansOpt\": yaml_content['questions'][question_counter]['answers']}\n\n btnsConfig = refreshCnfg(question_counter)\n global ans1, ans2, ans3, ans4\n ans1 = tk.Button(frame, text=btnsConfig[\"ans1\"][\"text\"], height=btnsConfig[\"ans1\"][\"height\"],\n width=btnsConfig[\"ans1\"][\"width\"], bg=btnsConfig[\"ans1\"][\"bg\"],\n command=lambda: onclick(Ans[\"ansOpt\"][0], Ans[\"rigthAns\"]))\n ans1.pack()\n ans1.place(x=btnsConfig[\"ans1\"][\"x\"], y=btnsConfig[\"ans1\"][\"y\"])\n\n ans2 = tk.Button(frame, text=btnsConfig[\"ans2\"][\"text\"], height=btnsConfig[\"ans2\"][\"height\"],\n width=btnsConfig[\"ans2\"][\"width\"], bg=btnsConfig[\"ans2\"][\"bg\"],\n command=lambda: onclick(Ans[\"ansOpt\"][1], Ans[\"rigthAns\"]))\n ans2.place(x=btnsConfig[\"ans2\"][\"x\"], y=btnsConfig[\"ans2\"][\"y\"])\n\n ans3 = tk.Button(frame, text=btnsConfig[\"ans3\"][\"text\"], height=btnsConfig[\"ans3\"][\"height\"],\n width=btnsConfig[\"ans3\"][\"width\"], bg=btnsConfig[\"ans3\"][\"bg\"],\n command=lambda: onclick(Ans[\"ansOpt\"][2], Ans[\"rigthAns\"]))\n ans3.pack()\n ans3.place(x=btnsConfig[\"ans3\"][\"x\"], y=btnsConfig[\"ans3\"][\"y\"])\n\n ans4 = tk.Button(frame, text=btnsConfig[\"ans4\"][\"text\"], height=btnsConfig[\"ans4\"][\"height\"],\n width=btnsConfig[\"ans4\"][\"width\"], bg=btnsConfig[\"ans4\"][\"bg\"],\n command=lambda: onclick(Ans[\"ansOpt\"][3], Ans[\"rigthAns\"]))\n ans4.pack()\n ans4.place(x=btnsConfig[\"ans4\"][\"x\"], y=btnsConfig[\"ans4\"][\"y\"])\n\n question_counter += 1\n scorelbl.configure(text=\"Score: \" + str(score))\n print(f'counter after: {question_counter}')\n print(score)\n\n\n def revealWrong():\n next.configure(state=\"disabled\")\n time.sleep(1)\n wrong()\n ans1.configure(state=\"disabled\")\n ans2.configure(state=\"disabled\")\n ans3.configure(state=\"disabled\")\n ans4.configure(state=\"disabled\")\n next.configure(state=\"normal\")\n\n def revealRight():\n next.configure(state=\"disabled\")\n global score\n time.sleep(1)\n right()\n score -= 1\n ans1.configure(state=\"disabled\")\n ans2.configure(state=\"disabled\")\n ans3.configure(state=\"disabled\")\n ans4.configure(state=\"disabled\")\n next.configure(state=\"normal\")\n\n def right():\n global score\n if (Ans[\"rigthAns\"] == Ans[\"ansOpt\"][0]):\n ans1.configure(bg=\"green\")\n score += 1\n if (Ans[\"rigthAns\"] == Ans[\"ansOpt\"][1]):\n ans2.configure(bg=\"green\")\n score += 1\n if (Ans[\"rigthAns\"] == Ans[\"ansOpt\"][2]):\n ans3.configure(bg=\"green\")\n score += 1\n if (Ans[\"rigthAns\"] == Ans[\"ansOpt\"][3]):\n ans4.configure(bg=\"green\")\n score += 1\n\n def wrong():\n if (Ans[\"rigthAns\"] != Ans[\"ansOpt\"][0]):\n ans1.configure(bg=\"red\")\n if (Ans[\"rigthAns\"] != Ans[\"ansOpt\"][1]):\n ans2.configure(bg=\"red\")\n if (Ans[\"rigthAns\"] != Ans[\"ansOpt\"][2]):\n ans3.configure(bg=\"red\")\n if (Ans[\"rigthAns\"] != Ans[\"ansOpt\"][3]):\n ans4.configure(bg=\"red\")\n\n def onclick(args, right_answer):\n if args == right_answer:\n t1 = threading.Thread(target=right)\n t1.start()\n t2 = threading.Thread(target=revealWrong)\n t2.start()\n\n else:\n t1 = threading.Thread(target=wrong)\n t1.start()\n t2 = threading.Thread(target=revealRight)\n t2.start()\n\n\ncreateOrRefreshFrame()\n\ndef ask():\n for widgets in frame.winfo_children():\n widgets.destroy()\n\n namelbl = tk.Label(root, text=\"Wie heißt du?\")\n namelbl.pack()\n namelbl.place(x=250, y=10)\n\n nameentr = tk.Entry()\n nameentr.pack()\n nameentr.place(x=230, y=50)\n\n def getName():\n name = nameentr.get()\n print(name)\n scoreboard.append({\"name\": name, \"score\": score})\n with open(\"data/scoreboard.yaml\", 'w', encoding='utf8') as file:\n yaml.dump(scoreboard, file)\n namelbl.destroy()\n nameentr.destroy()\n nextbtn.destroy()\n printScorebrd()\n\n nextbtn = tk.Button(root, text=\"weiter\", command=getName)\n nextbtn.pack\n nextbtn.place(x=270, y=80)\n\nnext = tk.Button(frame, text=\"Nächste Frage\", height=1, width=12, command=createOrRefreshFrame, state=\"normal\") #create function that enables this button after one was clicked\nnext.pack()\nnext.place(x=250, y=350)\n\ndef printScorebrd():\n again = tk.Button(frame, text=\"Quiz wiederholen ↺\")\n again.pack()\n again.place(x=40, y=10)\n\n exit = tk.Button(frame, text=\"Beenden ❌\", command=lambda: root.quit())\n exit.pack()\n exit.place(x=450, y=10)\n\n headings =[\"User\", \"Score\"]\n table = ttk.Treeview(frame, columns=headings, show=\"headings\")\n table.pack(fill=\"both\", expand=True,pady=50)\n\n for heading in headings:\n table.heading(heading, text=heading)\n\n for i, row in enumerate(scoreboard):\n table.insert(\"\", i, values=[row[\"name\"], row[\"score\"]])\n\n\ndef board():\n frage.destroy()\n imglabel.destroy()\n ans1.destroy()\n ans2.destroy()\n ans3.destroy()\n ans4.destroy()\n next.destroy()\n scorelbl.destroy()\n scorebrd.destroy()\n printScorebrd()\n\nscorebrd = tk.Button(frame, text=\"Scoreboard\", command=board)\nscorebrd.pack()\nscorebrd.place(x=510, y=10)\n\nframe.mainloop()\n", "repo_name": "WallnussJonas/LogoQuizApp", "sub_path": "Main.py", "file_name": "Main.py", "file_ext": "py", "file_size_in_byte": 8129, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "yaml.safe_load", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 80, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 80, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 80, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 80, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 91, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 103, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 109, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 114, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 120, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 134, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 145, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 181, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 183, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 187, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 189, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 199, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 203, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 212, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 218, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 222, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 227, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 231, "usage_type": "call"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 236, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 236, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 258, "usage_type": "call"}]} +{"seq_id": "2510797875", "text": "#!/usr/bin/python3\nimport logging\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom src.data.dataloader import TestDataset\n\n\nclass KGEModel(nn.Module):\n def __init__(self, model_name, nentity, nrelation, hidden_dim, gamma,\n double_entity_embedding=False,\n double_relation_embedding=False):\n super(KGEModel, self).__init__()\n self.model_name = model_name\n self.nentity = nentity\n self.nrelation = nrelation\n self.hidden_dim = hidden_dim\n self.epsilon = 2.0\n\n self.gamma = nn.Parameter(\n torch.Tensor([gamma]),\n requires_grad=False\n )\n\n self.embedding_range = nn.Parameter(\n torch.Tensor([(self.gamma.item() + self.epsilon) / hidden_dim]),\n requires_grad=False\n )\n\n if double_entity_embedding:\n self.entity_dim = hidden_dim*2\n else:\n self.entity_dim = hidden_dim\n\n if double_relation_embedding:\n self.relation_dim = hidden_dim*2\n else:\n self.relation_dim = hidden_dim\n\n self.entity_embedding = nn.Parameter(torch.zeros(nentity,\n self.entity_dim))\n nn.init.uniform_(\n tensor=self.entity_embedding,\n a=-self.embedding_range.item(),\n b=self.embedding_range.item()\n )\n\n self.relation_embedding = nn.Parameter(torch.zeros(nrelation,\n self.relation_dim))\n nn.init.uniform_(\n tensor=self.relation_embedding,\n a=-self.embedding_range.item(),\n b=self.embedding_range.item()\n )\n\n if model_name == 'pRotatE':\n tmp = [[0.5 * self.embedding_range.item()]]\n self.modulus = nn.Parameter(torch.Tensor(tmp))\n\n # Do not forget to modify this line when you add a new model\n # in the \"forward\" function\n if model_name not in ['RotatE']:\n raise ValueError('model %s not supported' % model_name)\n\n if model_name == 'RotatE' and (not double_entity_embedding\n or double_relation_embedding):\n raise ValueError('RotatE should use --double_entity_embedding')\n\n def change_entity_embedding(self, new_embedding):\n self.entity_embedding = new_embedding\n\n def change_relation_embedding(self, new_embedding):\n self.relation_embedding = new_embedding\n\n #pylint: disable=arguments-differ\n def forward(self, sample, mode='single'):\n '''\n Forward function that calculate the score of a batch of triples.\n In the 'single' mode, sample is a batch of triple.\n In the 'head-batch' or 'tail-batch' mode, sample consists two part.\n The first part is usually the positive sample.\n And the second part is the entities in the negative samples.\n Because negative samples and positive samples usually share 2 elements\n in their triple ((head, relation) or (relation, tail)).\n '''\n\n if mode == 'single':\n batch_size, neg_sample_size = sample.size(0), 1\n\n head = torch.index_select(\n self.entity_embedding,\n dim=0,\n index=sample[:, 0]\n ).unsqueeze(1)\n\n relation = torch.index_select(\n self.relation_embedding,\n dim=0,\n index=sample[:, 1]\n ).unsqueeze(1)\n\n tail = torch.index_select(\n self.entity_embedding,\n dim=0,\n index=sample[:, 2]\n ).unsqueeze(1)\n\n elif mode == 'head-batch':\n tail_part, head_part = sample\n batch_size, neg_sample_size = head_part.size(0), head_part.size(1)\n\n head = torch.index_select(\n self.entity_embedding,\n dim=0,\n index=head_part.view(-1)\n ).view(batch_size, neg_sample_size, -1)\n\n relation = torch.index_select(\n self.relation_embedding,\n dim=0,\n index=tail_part[:, 1]\n ).unsqueeze(1)\n\n tail = torch.index_select(\n self.entity_embedding,\n dim=0,\n index=tail_part[:, 2]\n ).unsqueeze(1)\n\n elif mode == 'tail-batch':\n head_part, tail_part = sample\n batch_size, neg_sample_size = tail_part.size(0), tail_part.size(1)\n\n head = torch.index_select(\n self.entity_embedding,\n dim=0,\n index=head_part[:, 0]\n ).unsqueeze(1)\n\n relation = torch.index_select(\n self.relation_embedding,\n dim=0,\n index=head_part[:, 1]\n ).unsqueeze(1)\n\n tail = torch.index_select(\n self.entity_embedding,\n dim=0,\n index=tail_part.view(-1)\n ).view(batch_size, neg_sample_size, -1)\n\n else:\n raise ValueError('mode %s not supported' % mode)\n\n model_func = {\n 'RotatE': self.RotatE,\n # 'pRotatE': self.pRotatE\n }\n\n if self.model_name in model_func:\n score = model_func[self.model_name](head, relation, tail, mode)\n else:\n raise ValueError('model %s not supported' % self.model_name)\n\n return score\n\n def RotatE(self, head, relation, tail, mode): # often just named \"model\"\n pi = 3.14159265358979323846\n\n re_head, im_head = torch.chunk(head, 2, dim=2)\n re_tail, im_tail = torch.chunk(tail, 2, dim=2)\n\n # Make phases of relations uniformly distributed in [-pi, pi]\n phase_relation = relation/(self.embedding_range.item()/pi)\n\n re_relation = torch.cos(phase_relation)\n im_relation = torch.sin(phase_relation)\n\n if mode == 'head-batch':\n re_score = re_relation * re_tail + im_relation * im_tail\n im_score = re_relation * im_tail - im_relation * re_tail\n re_score = re_score - re_head\n im_score = im_score - im_head\n else:\n re_score = re_head * re_relation - im_head * im_relation\n im_score = re_head * im_relation + im_head * re_relation\n re_score = re_score - re_tail\n im_score = im_score - im_tail\n\n score = torch.stack([re_score, im_score], dim=0)\n score = score.norm(dim=0)\n\n score = self.gamma.item() - score.sum(dim=2)\n return score\n\n # def pRotatE(self, head, relation, tail, mode):\n # pi = 3.14159262358979323846\n\n # # Make phases of entities and relations uniformly\n # # distributed in [-pi, pi]\n\n # phase_head = head/(self.embedding_range.item()/pi)\n # phase_relation = relation/(self.embedding_range.item()/pi)\n # phase_tail = tail/(self.embedding_range.item()/pi)\n\n # if mode == 'head-batch':\n # score = phase_head + (phase_relation - phase_tail)\n # else:\n # score = (phase_head + phase_relation) - phase_tail\n\n # score = torch.sin(score)\n # score = torch.abs(score)\n\n # score = self.gamma.item() - score.sum(dim = 2) * self.modulus\n # return score\n\n @staticmethod\n def train_step(model, optimizer, train_iterator, args):\n '''\n A single train step. Apply back-propation and return the loss\n '''\n\n model.train()\n\n optimizer.zero_grad()\n\n pos_sample, neg_sample, subsampling_weight, mode = next(train_iterator)\n if args.cuda:\n pos_sample = pos_sample.cuda()\n neg_sample = neg_sample.cuda()\n subsampling_weight = subsampling_weight.cuda()\n\n negative_score = model((pos_sample, neg_sample), mode=mode)\n\n if args.negative_adversarial_sampling:\n # In self-adversarial sampling, we do not apply back-propagation\n # on the sampling weight\n negative_score = (F.softmax(negative_score\n * args.adversarial_temperature, dim=1).detach()\n * F.logsigmoid(-negative_score)).sum(dim=1)\n else:\n negative_score = F.logsigmoid(-negative_score).mean(dim=1)\n\n positive_score = model(pos_sample)\n\n positive_score = F.logsigmoid(positive_score).squeeze(dim=1)\n\n value1 = (subsampling_weight * positive_score).sum()\n value2 = subsampling_weight.sum()\n pos_sample_loss = - value1 / value2\n value1 = (subsampling_weight * negative_score).sum()\n neg_sample_loss = - value1 / value2\n\n loss = (pos_sample_loss + neg_sample_loss)/2\n regularization_log = {}\n loss.backward()\n optimizer.step()\n\n log = {\n **regularization_log,\n 'pos_sample_loss': pos_sample_loss.item(),\n 'neg_sample_loss': neg_sample_loss.item(),\n 'loss': loss.item()\n }\n\n return log\n\n @staticmethod\n def test_step(model, test_triples, all_true_triples, args,\n save_scores=False):\n '''\n Evaluate the model on test or valid datasets\n '''\n model.eval()\n\n test_dataloader_head = DataLoader(\n TestDataset(\n test_triples,\n all_true_triples,\n args.nentity,\n args.nrelation,\n 'head-batch'\n ),\n batch_size=args.test_batch_size,\n num_workers=max(1, args.cpu_num//2),\n collate_fn=TestDataset.collate_fn\n )\n\n test_dataloader_tail = DataLoader(\n TestDataset(\n test_triples,\n all_true_triples,\n args.nentity,\n args.nrelation,\n 'tail-batch'\n ),\n batch_size=args.test_batch_size,\n num_workers=max(1, args.cpu_num//2),\n collate_fn=TestDataset.collate_fn\n )\n\n test_dataset_list = [test_dataloader_head, test_dataloader_tail]\n\n logs = []\n all_scores = []\n all_scores_filter_bias = []\n step = 0\n total_steps = sum([len(dataset) for dataset in test_dataset_list])\n\n with torch.no_grad():\n for test_dataset, missing in zip(test_dataset_list, [0, 2]):\n # missing: 0 = head, 2 = tail\n line = 0\n for pos_sample, neg_sample, filter_bias, mode in test_dataset:\n if args.cuda:\n pos_sample = pos_sample.cuda() # shape = (4,3)\n # 3 because its a triple\n neg_sample = neg_sample.cuda() # shape = (4,14541)\n filter_bias = filter_bias.cuda() # shape = (4,14541)\n batch_size = pos_sample.size(0)\n\n # score: for each entity a score (negative value or 0!)\n # index of score = entity\n # score.shape = (4, 14541) (14541 = num different entities)\n score_without_bias = model((pos_sample, neg_sample), mode)\n score = score_without_bias + filter_bias\n\n if save_scores:\n for i in range(batch_size):\n all_scores.append((line, missing,\n score_without_bias[i, :]))\n all_scores_filter_bias.append((line, missing,\n score[i, :]))\n line += 1\n\n # \"argsort\" returns the indices that sort a tensor along\n # a given dimension by value (score)\n # -> first value = index of entity with highest score\n argsort = torch.argsort(score, dim=1, descending=True)\n\n if mode == 'head-batch':\n positive_arg = pos_sample[:, 0]\n else: # mode == 'tail-batch'\n positive_arg = pos_sample[:, 2]\n # else:\n # raise ValueError('mode %s not supported' % mode)\n\n for i in range(batch_size):\n # positive_arg[i] = index of true entity\n # rank = position of true entity in models ranking\n # (lower = better)\n # nonzero returns indices of elements that are non-zero\n rank = (argsort[i, :] == positive_arg[i]).nonzero()\n assert rank.size(0) == 1\n\n # rank + 1 is the true rank used in eval. metrics\n rank = 1 + rank.item()\n logs.append({\n 'MRR': 1.0/rank, # higher is better\n 'MR': float(rank), # lower is better\n # higher is better for all HITS Scores\n 'HITS@1': 1.0 if rank <= 1 else 0.0,\n 'HITS@3': 1.0 if rank <= 3 else 0.0,\n 'HITS@10': 1.0 if rank <= 10 else 0.0,\n })\n if step % args.test_log_steps == 0:\n logging.info('Evaluating the model... (%d/%d)',\n step, total_steps)\n step += 1\n\n ranks = [int(log['MR']) for log in logs]\n metrics = {}\n for metric in logs[0].keys():\n metrics[metric] = sum([log[metric] for log in logs])/len(logs)\n\n return metrics, all_scores, all_scores_filter_bias, ranks\n", "repo_name": "wang-yuhao/Practical-Big-Data-Science-ADL-AI", "sub_path": "src/models/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 13775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.nn.Module", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.chunk", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.chunk", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 239, "usage_type": "name"}, {"api_name": "torch.nn.functional.logsigmoid", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 241, "usage_type": "name"}, {"api_name": "torch.nn.functional.logsigmoid", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.functional.logsigmoid", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 247, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 277, "usage_type": "call"}, {"api_name": "src.data.dataloader.TestDataset", "line_number": 278, "usage_type": "call"}, {"api_name": "src.data.dataloader.TestDataset.collate_fn", "line_number": 287, "usage_type": "attribute"}, {"api_name": "src.data.dataloader.TestDataset", "line_number": 287, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 290, "usage_type": "call"}, {"api_name": "src.data.dataloader.TestDataset", "line_number": 291, "usage_type": "call"}, {"api_name": "src.data.dataloader.TestDataset.collate_fn", "line_number": 300, "usage_type": "attribute"}, {"api_name": "src.data.dataloader.TestDataset", "line_number": 300, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 311, "usage_type": "call"}, {"api_name": "torch.argsort", "line_number": 340, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 368, "usage_type": "call"}]} +{"seq_id": "27347737422", "text": "import json\nimport logging\nimport re\nimport sys\n\nfrom imgbased.bootloader import BootConfiguration\n\nfrom .utils import string_types\n\nlog = logging.getLogger()\n\n\nclass Info(object):\n \"\"\"Fetches and displays some information about the running node:\n\n Bootloader information\n Layout information\n \"\"\"\n\n results = dict()\n\n def __init__(self, app, machine=False):\n self.app = app\n self.machine = machine\n self._fetch_information()\n\n def _fetch_information(self):\n self._get_bootloader_info()\n self._get_layout()\n\n def _get_bootloader_info(self):\n b = BootConfiguration()\n bootinfo = dict()\n\n bootinfo[\"default\"] = b.get_default()\n bootinfo[\"entries\"] = dict()\n for k, v in b.list().items():\n # FIXME: this isn't very nice. GrubbyEntry should present\n # a clean way for a dict which can be JSON serializable.\n # json chokes with __repr__, so maybe a custom decoder?\n for entry in v:\n bootinfo[\"entries\"][entry.title] = entry.__dict__\n\n self.results[\"bootloader\"] = bootinfo\n\n def _get_layout(self):\n layout = LayoutParser(self.app.imgbase.layout()).parse()\n self.results[\"layers\"] = layout\n self.results[\"current_layer\"] = \\\n str(self.app.imgbase.current_layer())\n\n def write(self):\n def pretty_print(k, indent=0):\n sys.stdout.write('{0}{1}: '.format(' ' * indent, k[0]))\n if isinstance(k[1], string_types):\n sys.stdout.write('{0}\\n'.format(k[1]))\n\n elif isinstance(k[1], dict):\n sys.stdout.write('\\n')\n items = list(k[1].items())\n if k[0] == \"entries\": # bootloader entries\n items.sort(key=lambda x: x[1][\"index\"])\n for item in items:\n pretty_print(item, indent+2)\n\n elif isinstance(k[1], list):\n sys.stdout.write('\\n')\n for item in k[1]:\n print('{0}{1}'.format(' ' * (indent + 2), item))\n\n sys.stdout.flush()\n\n if self.machine:\n print(json.dumps(self.results))\n else:\n # Neither JSON nor YAML gives a very nice output here, so use\n # our own formatter, since pprint includes sigils\n for k in self.results.items():\n pretty_print(k)\n\n\nclass LayoutParser(object):\n \"\"\"This parser grabs the output of \"imgbase layout\" and turns it into\n something which is easily consumable by regular Python (until imgbased\n itself can get some tweaking to make this better\n \"\"\"\n\n layout = None\n\n def __init__(self, layout):\n self.layout = layout\n\n def parse(self):\n result = dict()\n layouts = re.split(r'\\n(?=\\w)', self.layout, re.M)\n\n for current_layout in layouts:\n lines = current_layout.splitlines()\n parent = lines.pop(0)\n result[parent] = []\n for line in lines:\n line = re.sub(r'^.*?(\\w+)', r'\\1', line)\n result[parent].append(line)\n\n return result\n", "repo_name": "oVirt/ovirt-node-ng", "sub_path": "src/nodectl/info.py", "file_name": "info.py", "file_ext": "py", "file_size_in_byte": 3157, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "imgbased.bootloader.BootConfiguration", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 54, "usage_type": "attribute"}, {"api_name": "utils.string_types", "line_number": 55, "usage_type": "argument"}, {"api_name": "sys.stdout.write", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 71, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 74, "usage_type": "call"}, {"api_name": "re.split", "line_number": 95, "usage_type": "call"}, {"api_name": "re.M", "line_number": 95, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "42764972178", "text": "#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport codecs\nimport collections\nimport fnmatch\nimport os.path\nimport subprocess\nimport sys\n\n\ndef find_all(a_str, sub):\n for i, line in enumerate(a_str.splitlines()):\n column = 0\n while True:\n column = line.find(sub, column)\n if column == -1:\n break\n yield i, column\n column += len(sub)\n\n\ncommand = ['git', 'ls-files', '-s']\nproc = subprocess.Popen(command, stdout=subprocess.PIPE)\noutput, err = proc.communicate()\nlines = [x.split() for x in output.decode('utf-8').splitlines()]\nEXECUTABLE_BIT = {\n s[3].strip(): int(s[0]) for s in lines\n}\nfiles = [s[3].strip() for s in lines]\nfiles.sort()\n\nfile_types = ('.h', '.c', '.cpp', '.tcc', '.yaml', '.yml', '.ini', '.txt', '.ico',\n '.py', '.html', '.js', '.md', '.sh', '.css', '.proto', '.conf', '.cfg')\ncpp_include = ('*.h', '*.c', '*.cpp', '*.tcc')\nignore_types = ('.ico',)\n\nLINT_FILE_CHECKS = []\nLINT_CONTENT_CHECKS = []\n\n\ndef run_check(lint_obj, fname, *args):\n include = lint_obj['include']\n exclude = lint_obj['exclude']\n func = lint_obj['func']\n if include is not None:\n for incl in include:\n if fnmatch.fnmatch(fname, incl):\n break\n else:\n return None\n for excl in exclude:\n if fnmatch.fnmatch(fname, excl):\n return None\n return func(*args)\n\n\ndef run_checks(lints, fname, *args):\n for lint in lints:\n add_errors(fname, run_check(lint, fname, *args))\n\n\ndef _add_check(checks, func, include=None, exclude=None):\n checks.append({\n 'include': include,\n 'exclude': exclude or [],\n 'func': func,\n })\n\n\ndef lint_file_check(**kwargs):\n def decorator(func):\n _add_check(LINT_FILE_CHECKS, func, **kwargs)\n return func\n return decorator\n\n\ndef lint_content_check(**kwargs):\n def decorator(func):\n _add_check(LINT_CONTENT_CHECKS, func, **kwargs)\n return func\n return decorator\n\n\ndef lint_content_find_check(find, **kwargs):\n decor = lint_content_check(**kwargs)\n\n def decorator(func):\n def new_func(fname, content):\n find_ = find\n if callable(find):\n find_ = find(fname, content)\n for line, col in find_all(content, find_):\n err = func(fname)\n return \"{err} See line {line}:{col}.\".format(err=err, line=line+1, col=col+1)\n return decor(new_func)\n return decorator\n\n\n@lint_file_check(include=['*.ino'])\ndef lint_ino(fname):\n return \"This file extension (.ino) is not allowed. Please use either .cpp or .h\"\n\n\n@lint_file_check(exclude=['*{}'.format(f) for f in file_types] + [\n '.clang-*', '.dockerignore', '.editorconfig', '*.gitignore', 'LICENSE', 'pylintrc',\n 'MANIFEST.in', 'docker/Dockerfile*', 'docker/rootfs/*', 'script/*'\n])\ndef lint_ext_check(fname):\n return \"This file extension is not a registered file type. If this is an error, please \" \\\n \"update the script/ci-custom.py script.\"\n\n\n@lint_file_check(exclude=[\n 'docker/rootfs/*', 'script/*', 'setup.py'\n])\ndef lint_executable_bit(fname):\n ex = EXECUTABLE_BIT[fname]\n if ex != 100644:\n return 'File has invalid executable bit {}. If running from a windows machine please ' \\\n 'see disabling executable bit in git.'.format(ex)\n return None\n\n\n@lint_content_find_check('\\t', exclude=[\n 'esphome/dashboard/static/ace.js', 'esphome/dashboard/static/ext-searchbox.js',\n 'script/.neopixelbus.patch',\n])\ndef lint_tabs(fname):\n return \"File contains tab character. Please convert tabs to spaces.\"\n\n\n@lint_content_find_check('\\r')\ndef lint_newline(fname):\n return \"File contains windows newline. Please set your editor to unix newline mode.\"\n\n\n@lint_content_check()\ndef lint_end_newline(fname, content):\n if content and not content.endswith('\\n'):\n return \"File does not end with a newline, please add an empty line at the end of the file.\"\n return None\n\n\ndef relative_cpp_search_text(fname, content):\n parts = fname.split('/')\n integration = parts[2]\n return '#include \"esphome/components/{}'.format(integration)\n\n\n@lint_content_find_check(relative_cpp_search_text, include=['esphome/components/*.cpp'])\ndef lint_relative_cpp_import(fname):\n return (\"Component contains absolute import - Components must always use \"\n \"relative imports.\\n\"\n \"Change:\\n\"\n ' #include \"esphome/components/abc/abc.h\"\\n'\n 'to:\\n'\n ' #include \"abc.h\"\\n\\n')\n\n\ndef relative_py_search_text(fname, content):\n parts = fname.split('/')\n integration = parts[2]\n return 'esphome.components.{}'.format(integration)\n\n\n@lint_content_find_check(relative_py_search_text, include=['esphome/components/*.py'])\ndef lint_relative_py_import(fname):\n return (\"Component contains absolute import - Components must always use \"\n \"relative imports within the integration.\\n\"\n \"Change:\\n\"\n ' from esphome.components.abc import abc_ns\"\\n'\n 'to:\\n'\n ' from . import abc_ns\\n\\n')\n\n\n@lint_content_find_check('\"esphome.h\"', include=cpp_include, exclude=['tests/custom.h'])\ndef lint_esphome_h(fname):\n return (\"File contains reference to 'esphome.h' - This file is \"\n \"auto-generated and should only be used for *custom* \"\n \"components. Please replace with references to the direct files.\")\n\n\n@lint_content_check(include=['*.h'])\ndef lint_pragma_once(fname, content):\n if '#pragma once' not in content:\n return (\"Header file contains no 'pragma once' header guard. Please add a \"\n \"'#pragma once' line at the top of the file.\")\n return None\n\n\n@lint_content_find_check('ESP_LOG', include=['*.h', '*.tcc'], exclude=[\n 'esphome/components/binary_sensor/binary_sensor.h',\n 'esphome/components/cover/cover.h',\n 'esphome/components/display/display_buffer.h',\n 'esphome/components/i2c/i2c.h',\n 'esphome/components/mqtt/mqtt_component.h',\n 'esphome/components/output/binary_output.h',\n 'esphome/components/output/float_output.h',\n 'esphome/components/sensor/sensor.h',\n 'esphome/components/stepper/stepper.h',\n 'esphome/components/switch/switch.h',\n 'esphome/components/text_sensor/text_sensor.h',\n 'esphome/core/component.h',\n 'esphome/core/esphal.h',\n 'esphome/core/log.h',\n 'tests/custom.h',\n])\ndef lint_log_in_header(fname):\n return ('Found reference to ESP_LOG in header file. Using ESP_LOG* in header files '\n 'is currently not possible - please move the definition to a source file (.cpp)')\n\n\nerrors = collections.defaultdict(list)\n\n\ndef add_errors(fname, errs):\n if not isinstance(errs, list):\n errs = [errs]\n errs = [x for x in errs if x is not None]\n for err in errs:\n if not isinstance(err, str):\n raise ValueError(\"Error is not instance of string!\")\n if not errs:\n return\n errors[fname].extend(errs)\n\n\nfor fname in files:\n _, ext = os.path.splitext(fname)\n run_checks(LINT_FILE_CHECKS, fname, fname)\n if ext in ('.ico',):\n continue\n try:\n with codecs.open(fname, 'r', encoding='utf-8') as f_handle:\n content = f_handle.read()\n except UnicodeDecodeError:\n add_errors(fname, \"File is not readable as UTF-8. Please set your editor to UTF-8 mode.\")\n continue\n run_checks(LINT_CONTENT_CHECKS, fname, fname, content)\n\nfor f, errs in sorted(errors.items()):\n print(\"\\033[0;32m************* File \\033[1;32m{}\\033[0m\".format(f))\n for err in errs:\n print(err)\n print()\n\nsys.exit(len(errors))\n", "repo_name": "tsunglung/esphome", "sub_path": "script/ci-custom.py", "file_name": "ci-custom.py", "file_ext": "py", "file_size_in_byte": 7692, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "subprocess.Popen", "line_number": 24, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "fnmatch.fnmatch", "line_number": 48, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 53, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 230, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 235, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 248, "usage_type": "call"}]} +{"seq_id": "32677628024", "text": "#!/usr/bin/python\n\nimport os, numpy as np\nimport platform\nimport argparse\nimport read_transcription as rt\nimport read_SVG as svgread\nimport crop_svg_outline as svgcrop\nimport resize_images as resize\nimport binarization as binary\n# import feature_extraction as features\n# import scan_image_features as scan\nfrom matplotlib import pyplot as plt\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--preprocessing', default=True, type=bool)\nparser.add_argument('--id_linking', default=True, type=bool)\nparser.add_argument('--word_width', default=212, type=int)\nparser.add_argument('--word_height', default=94, type=int)\nargs = parser.parse_args()\n\n# ----- paths and folders ----#\nwork_dir = os.getcwd()\nif (work_dir[-14:] != \"4.5_biologists\" and work_dir[-3:] != \"kws\"):\n print(\"get back to main directory, or cd into src/main/py/kws, sucker !\")\n exit()\n\npaths = {}\n\n# directories\npaths[\"images\"] = os.path.join('data', 'images')\npaths[\"binarized_images\"] = os.path.join('data', 'binarized_images')\npaths[\"word_images\"] = os.path.join('data', 'word_images')\npaths[\"resized_word_images\"] = os.path.join('data', 'resized_word_images')\npaths[\"svg\"] = os.path.join('data', 'ground-truth', 'locations')\n\n# files\npaths[\"transcription.txt\"] = os.path.join('data', 'ground-truth', 'transcription.txt')\n\n# adapt if run from 4.5_biologists\nif (os.getcwd()[-14:] == \"4.5_biologists\"):\n for k in paths:\n paths[k] = os.path.join('src', 'main', 'py', 'kws', paths[k])\n\n# adapt for Windows\nif (platform.system() == \"Windows\"):\n for k in paths:\n # paths[k] = re.sub(\"/\", \"\\\\\\\\\", paths[k])\n # or\n paths[k] = os.path.normpath(paths[k])\n\n# and create directories if these don't exist\nfor k in paths:\n if (not os.path.exists(paths[k]) and paths[k][:-4] != \".txt\"):\n os.makedirs(paths[k])\n\n\nlist_of_images = sorted(os.listdir(paths[\"images\"]))\nlist_of_svg = sorted(os.listdir(paths[\"svg\"]))\n\n\n# ----- pre-processing ----#\nif args.preprocessing:\n if not os.listdir(paths[\"binarized_images\"]) or not os.listdir(paths[\"word_images\"]):\n # ----- ID linking----#\n ID_dict = rt.read_transcription(file_name=paths[\"transcription.txt\"], output=\"ID_dict\")\n\n # --- processing pages (binarization and cropping out words) --- #\n i = 0\n for page_no, page in enumerate(list_of_images):\n print(\"processing page \", i+1, \" out of \", len(list_of_images))\n\n image = plt.imread(os.path.join(paths[\"images\"], page))\n svg = os.path.join(paths[\"svg\"], list_of_svg[page_no])\n coord_list = svgread.extract_SVG_masks(svg)\n\n img_name = page[:-4] + \".png\"\n image_out = os.path.join(paths[\"binarized_images\"], img_name)\n\n image_bin = binary.binarize_image(image, block_size=101) # binarize image using local thresholding\n binary.save_image_png(image_out, image_bin)\n\n svg_in = os.path.join(paths[\"binarized_images\"], img_name)\n svgcrop.crop_svg_outline(svg_in, ID_dict=ID_dict, svg_coordinates=coord_list, output_path=paths[\"word_images\"]) # crop individual words by polygon outline\n\n i += 1\n\n\n # --- get median word width and height (for resizing) --- #\n base = os.getcwd()\n list_of_wordimages = sorted(os.listdir(paths[\"word_images\"]))\n list_of_wordimages = [word for word in list_of_wordimages if not word.startswith('.')] # ignore special files starting with '.'\n os.chdir(paths[\"word_images\"])\n median_word_width, median_word_height = resize.median_wh(list_of_wordimages) # word_lengths = [len(word) for word in word_dict]\n os.chdir(base)\n\n\n # --- processing individual word images (resizing) --- #\n if not os.listdir(paths[\"resized_word_images\"]):\n i = 0\n for file in list_of_wordimages:\n if i%100 == 0:\n print(\"processing word-image \", i+1, \" out of \", len(list_of_wordimages))\n file_in = os.path.join(paths[\"word_images\"], file)\n resize.resize_image(file_in, height_new=args.word_height, width_new=args.word_width, output_path=paths[\"resized_word_images\"])\n\n i += 1\n\n print(\"Binary images of individual words extracted and rescaled to\", args.word_width, \"x\", args.word_height, \"pixel (width x height).\")\n print(\"Medium is\", median_word_width, \"x\", median_word_height, \"pixel (width x height).\")\n", "repo_name": "Afanc/4.5_biologists", "sub_path": "src/main/py/kws/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 23, "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.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "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": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 55, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "read_transcription.read_transcription", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "read_SVG.extract_SVG_masks", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "binarization.binarize_image", "line_number": 80, "usage_type": "call"}, {"api_name": "binarization.save_image_png", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "crop_svg_outline.crop_svg_outline", "line_number": 84, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 90, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 93, "usage_type": "call"}, {"api_name": "resize_images.median_wh", "line_number": 94, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 95, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "resize_images.resize_image", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "34924687420", "text": "from django.apps import AppConfig\n\n# Copyright (c) 2017-2020, Mikhail Podgurskiy\n# All Rights Reserved.\n\n# This work is dual-licensed under AGPL defined in file 'LICENSE' with\n# LICENSE_EXCEPTION and the Commercial license defined in file 'COMM_LICENSE',\n# which is part of this source code package.\n\n\nclass ViewflowAdminConfig(AppConfig):\n \"\"\"Default application config.\"\"\"\n\n name = \"viewflow.contrib.admin\"\n label = \"viewflow_admin\"\n", "repo_name": "viewflow/viewflow", "sub_path": "viewflow/contrib/admin/apps.py", "file_name": "apps.py", "file_ext": "py", "file_size_in_byte": 444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2468, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.apps.AppConfig", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "14760791565", "text": "from odoo import _, models\nimport logging\n\n_logger = logging.getLogger(__name__)\n\n\nclass EmployeeXlsx(models.AbstractModel):\n _name = 'report.report_employee_xlsx'\n _inherit = 'report.account_financial_report.abstract_report_xlsx'\n\n def _get_report_name(self, report):\n report_name = _('Employees Report')\n return self._get_report_complete_name(report, report_name)\n\n def _get_report_columns(self, report):\n\n\n res = {\n 0: {'header': _('Employee'),\n 'field': 'employee_id',\n 'type': 'many2one',\n 'width': 30},\n 1: {'header': _('Type'),\n 'field': 'type_id',\n 'type': 'many2one',\n 'width': 30},\n 2: {'header': _('Settlement Type'),\n 'field': 'settlement_type',\n 'width': 20},\n 3: {'header': _('Date'),\n 'field': 'date',\n 'width': 20},\n 4: {'header': _('Description'),\n 'field': 'description',\n 'width': 20},\n 5: {'header': _('Amount'),\n 'field': 'total',\n 'type': 'amount',\n 'width': 20},\n }\n\n return res\n\n def _get_col_count_filter_name(self):\n return 2\n\n def _get_col_count_filter_value(self):\n return 2\n\n def _get_report_filters(self, report):\n accounts = []\n companies = []\n for account in report.filter_account_ids:\n accounts.append([_('Account'),\n _('%s') % (account.name)])\n for company in report.filter_company_ids:\n companies.append([_('Company'),\n _('%s') % (company.name)])\n filters = []\n filters.append([_('Date range filter'),\n _('From: %s To: %s') % (report.date_from, report.date_to)])\n if len(companies) > 0:\n for company in companies:\n filters.append(company)\n if len(accounts) > 0:\n for account in accounts:\n filters.append(account)\n return filters\n\n def _generate_report_content(self, workbook, report):\n # Display array header for account lines\n self.write_array_header()\n\n for _type in report.type_ids:\n self.write_type_line(_type)\n if report.detail:\n for employee in _type.employee_ids:\n self.write_employee_line(employee)\n for account in employee.move_ids:\n self.write_move_line(account)\n\n def write_employee_line(self, line_object):\n \"\"\"Write a line on current line using all defined columns field name.\n Columns are defined with `_get_report_columns` method.\n \"\"\"\n\n cell_format = self.format_amount\n for col_pos, column in self.columns.items():\n\n if column.get('field', False) in ('employee_id'):\n self.sheet.write_string(\n self.row_pos, col_pos, line_object.employee_id.name, self.format_bold)\n \n if column.get('field', False) in ('total'):\n value = line_object.total or 0.0\n self.sheet.write_number(\n self.row_pos, col_pos, float(value), self.format_bold\n )\n\n self.row_pos += 1\n\n def write_type_line(self, line_object):\n \"\"\"Write a line on current line using all defined columns field name.\n Columns are defined with `_get_report_columns` method.\n \"\"\"\n\n cell_format = self.format_amount\n for col_pos, column in self.columns.items():\n\n if column.get('field', False) in ('type_id'):\n self.sheet.write_string(\n self.row_pos, col_pos, line_object.type_id.name or _('Concept Type not assigned'), self.format_bold)\n \n if column.get('field', False) in ('total'):\n value = line_object.total or 0.0\n self.sheet.write_number(\n self.row_pos, col_pos, float(value), self.format_bold\n )\n\n self.row_pos += 1\n\n def write_move_line(self, line_object):\n \"\"\"Write a line on current line using all defined columns field name.\n Columns are defined with `_get_report_columns` method.\n \"\"\"\n\n cell_format = self.format_amount\n for col_pos, column in self.columns.items():\n\n if column.get('field', False) in ('employee_id'):\n self.sheet.write_string(\n self.row_pos, col_pos, line_object.employee_id.employee_id.name)\n if column.get('field', False) in ('type_id'):\n self.sheet.write_string(\n self.row_pos, col_pos, line_object.employee_id.type_id.type_id.name)\n if column.get('field', False) in ('settlement_type'):\n settlement_type = line_object.settlement_type or ''\n self.sheet.write_string(\n self.row_pos, col_pos, settlement_type)\n if column.get('field', False) in ('description'):\n self.sheet.write_string(\n self.row_pos, col_pos, line_object.description)\n if column.get('field', False) in ('date'):\n self.sheet.write_string(\n self.row_pos, col_pos, line_object.date)\n if column.get('field', False) in ('total'):\n value = line_object.total or 0.0\n self.sheet.write_number(\n self.row_pos, col_pos, float(value)\n )\n\n self.row_pos += 1\n", "repo_name": "calyx-servicios/easy-invoice", "sub_path": "easy_invoice_employee_report/report/employee_report_xlsx.py", "file_name": "employee_report_xlsx.py", "file_ext": "py", "file_size_in_byte": 5650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "odoo.models.AbstractModel", "line_number": 7, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 7, "usage_type": "name"}, {"api_name": "odoo._", "line_number": 12, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 19, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 23, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 27, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 33, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 36, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 54, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 55, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 57, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 58, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 60, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 61, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "41623648037", "text": "import datetime\r\nimport streamlit as st \r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nst.title('Belajar Analisis Data');\r\nst.header('Pengembangan Dashboard');\r\nst.subheader('Pengembangan Dashboard');\r\nst.caption('Copyright (c) 2023');\r\n\r\ncode = \"\"\"def hello():\r\n print(\"Hello, Streamlit!\")\"\"\"\r\nst.code(code, language='python')\r\n\r\nst.write(\r\n \"\"\"\r\n # My first app\r\n Hello juan ganteng, para calon praktisi data masa depan!\r\n \"\"\"\r\n)\r\nst.write(pd.DataFrame({\r\n 'First Column':[1,2,3,4,5],\r\n 'Second Column':[6,7,8,9,10]\r\n}))\r\n\r\n\r\nst.latex(r'''\r\n a + ar + a r^2 + a r^3 + \\cdots + a r^{n-1} =\r\n \\sum_{k=0}^{n-1} ar^k =\r\n a \\left(\\frac{1-r^{n}}{1-r}\\right)\r\n ''')\r\nst.markdown(\r\n \"\"\"\r\n # My first app\r\n Hello, para calon praktisi data masa depan!\r\n \"\"\"\r\n)\r\n\r\ndf = pd.DataFrame({\r\n 'c1': [1, 2, 3, 4],\r\n 'c2': [10, 20, 30, 40],\r\n})\r\n \r\nst.dataframe(data=df, width=500, height=150)\r\nst.table(data=df)\r\nst.metric(label=\"Temperature\", value=\"28 °C\", delta=\"1.2 °C\")\r\n\r\nx = np.random.normal(15, 5, 250)\r\n \r\nfig, ax = plt.subplots()\r\nax.hist(x=x, bins=15)\r\nst.pyplot(fig)\r\n\r\nname = st.text_input(label='Nama lengkap', value='')\r\nst.write('Nama: ', name)\r\n\r\ntext = st.text_area('Feedback')\r\nst.write('Feedback: ', text)\r\nnumber = st.number_input(label='Umur')\r\nst.write('Umur: ', int(number), ' tahun')\r\n\r\ndate = st.date_input(label='Tanggal lahir', min_value=datetime.date(1900, 1, 1))\r\nst.write('Tanggal lahir:', date)\r\n\r\nuploaded_file = st.file_uploader('Choose a CSV file')\r\n \r\nif uploaded_file:\r\n df = pd.read_csv(uploaded_file)\r\n st.dataframe(df)\r\n \r\npicture = st.camera_input('Take a picture')\r\nif picture:\r\n st.image(picture)\r\n \r\nif st.button('Say hello'):\r\n st.write('Hello there')\r\n\r\nagree = st.checkbox('I agree')\r\n \r\nif agree:\r\n st.write('Welcome to MyApp')\r\n \r\ngenre = st.radio(\r\n label=\"What's your favorite movie genre\",\r\n options=('Comedy', 'Drama', 'Documentary'),\r\n horizontal=False\r\n)\r\nif genre:\r\n st.write('wlcop')\r\n\r\nka = st.selectbox(\r\n label=\"What's your favorite movie genre\",\r\n options=('Comedy', 'Drama', 'Documentary')\r\n)\r\n\r\nwith st.sidebar:\r\n \r\n st.text('Ini merupakan sidebar')\r\n st.image(\"https://static.streamlit.io/examples/cat.jpg\")\r\n \r\n values = st.slider(\r\n label='Select a range of values',\r\n min_value=0, max_value=100, value=(0, 100)\r\n )\r\n st.write('Values:', values)\r\n \r\nst.title('Belajar Analisis Data')\r\n\r\ntab1, tab2, tab3,tab4, tab5, tab6 = st.tabs([\"Tab 1\", \"Tab 2\", \"Tab 3\", \"Tab 4\", \"Tab 5\", \"Tab 6\"])\r\n \r\nwith tab1:\r\n st.header(\"Tab 1\")\r\n st.image(\"https://static.streamlit.io/examples/cat.jpg\")\r\n \r\nwith tab2:\r\n st.header(\"Tab 2\")\r\n st.image(\"https://static.streamlit.io/examples/dog.jpg\")\r\n \r\nwith tab3:\r\n st.header(\"Tab 3\")\r\n st.image(\"https://static.streamlit.io/examples/owl.jpg\")\r\n \r\nwith tab4:\r\n st.header(\"Tab 1\")\r\n st.image(\"https://static.streamlit.io/examples/cat.jpg\")\r\n \r\nwith tab5:\r\n st.header(\"Tab 2\")\r\n st.image(\"https://static.streamlit.io/examples/dog.jpg\")\r\n \r\nwith tab6:\r\n st.header(\"Tab 3\")\r\n st.image(\"https://static.streamlit.io/examples/owl.jpg\")\r\n\r\nwith st.container():\r\n tab1, tab2, tab3 = st.tabs([\"Tab 1\", \"Tab 2\", \"Tab 3\"])\r\n with tab1:\r\n st.header(\"Tab 1\")\r\n st.image(\"https://static.streamlit.io/examples/cat.jpg\")\r\n \r\n with tab2:\r\n st.header(\"Tab 2\")\r\n st.image(\"https://static.streamlit.io/examples/dog.jpg\")\r\n \r\n with tab3:\r\n st.header(\"Tab 3\")\r\n st.image(\"https://static.streamlit.io/examples/owl.jpg\")\r\n st.write(\"Inside the container\")\r\n \r\n x = np.random.normal(15, 5, 250)\r\n \r\n fig, ax = plt.subplots()\r\n ax.hist(x=x, bins=15)\r\n st.pyplot(fig) \r\n st.title('Belajar Analisis Data')\r\n st.write(\"Outside the container\")\r\n \r\n \r\nwith st.container(): \r\n st.title('Belajar menggunakan container')\r\n col1, col2, col3 = st.columns([2, 1, 1])\r\n with col1:\r\n st.header(\"Kolom 1\")\r\n st.image(\"https://static.streamlit.io/examples/cat.jpg\")\r\n \r\n with col2:\r\n st.header(\"Kolom 2\")\r\n st.image(\"https://static.streamlit.io/examples/dog.jpg\")\r\n \r\n with col3:\r\n st.header(\"Kolom 3\")\r\n st.image(\"https://static.streamlit.io/examples/owl.jpg\")\r\n \r\nx = np.random.normal(15, 5, 250)\r\n \r\nfig, ax = plt.subplots()\r\nax.hist(x=x, bins=15)\r\nst.pyplot(fig) \r\n \r\nwith st.expander(\"See explanation\"):\r\n st.write(\r\n \"\"\"Lorem ipsum dolor sit amet, consectetur adipiscing elit, \r\n sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. \r\n Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris \r\n nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor \r\n in reprehenderit in voluptate velit esse cillum dolore eu fugiat \r\n nulla pariatur. Excepteur sint occaecat cupidatat non proident, \r\n sunt in culpa qui officia deserunt mollit anim id est laborum.\r\n \"\"\"\r\n )", "repo_name": "donjuan136/Streamlit-App", "sub_path": "hello-juan.py", "file_name": "hello-juan.py", "file_ext": "py", "file_size_in_byte": 5054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "streamlit.title", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 8, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 9, "usage_type": "call"}, {"api_name": "streamlit.caption", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.code", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "call"}, {"api_name": "streamlit.latex", "line_number": 28, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 45, "usage_type": "call"}, {"api_name": "streamlit.table", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 55, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 56, "usage_type": "call"}, {"api_name": "streamlit.text_area", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.date_input", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 63, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.camera_input", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 74, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.radio", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 90, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 92, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 97, "usage_type": "attribute"}, {"api_name": "streamlit.text", "line_number": 99, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 100, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 102, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 106, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 108, "usage_type": "call"}, {"api_name": "streamlit.tabs", "line_number": 110, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 114, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 117, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 118, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 121, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 122, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 125, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 126, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 129, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 130, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 133, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 134, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 136, "usage_type": "call"}, {"api_name": "streamlit.tabs", "line_number": 137, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 139, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 140, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 143, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 144, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 147, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 148, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 151, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 155, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 156, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 157, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 160, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 161, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 162, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 164, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 165, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 168, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 169, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 172, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 175, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 179, "usage_type": "call"}, {"api_name": "streamlit.expander", "line_number": 181, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "40274750408", "text": "import logging\nfrom unittest.mock import patch\n\nimport pytest\nimport requests\nfrom assertpy import assert_that\n\nfrom sentinelpy import SentinelProductRequest, query_sentinel_hub\nfrom sentinelpy.exceptions import QuerySentinelProductsError\nfrom sentinelpy.query_sentinel_products_response import QuerySentinelProductsResponse\nfrom tests.utils import get_query_parameters_of_url\n\n\n@pytest.fixture()\ndef requests_mock():\n with patch(\"sentinelpy.main.requests\") as mock:\n yield mock\n\n\nclass TestQuerySentinelHub:\n @pytest.fixture(autouse=True)\n def setup_fixtures(self, requests_mock):\n self.requests_mock = requests_mock\n self.sentinel_product_request = SentinelProductRequest(\n \"*\", 30, None, 0, \"test-user\", \"test-password\"\n )\n self.sentinel_product_request_no_rows = SentinelProductRequest(\n \"*\", None, None, 0, \"test-user\", \"test-password\"\n )\n self.ordered_sentinel_product_request = SentinelProductRequest(\n \"*\", 30, \"beginposition asc\", 0, \"test-user\", \"test-password\"\n )\n self.sentinel_product_request_with_query = SentinelProductRequest(\n \"platformname:Sentinel-1 AND cloudcoverpercentage:5\",\n 30,\n None,\n 0,\n \"test-user\",\n \"test-password\",\n )\n\n def _get_called_url(self):\n\n calls = self.requests_mock.get.call_args_list\n\n assert_that(len(calls)).is_equal_to(1)\n\n call = calls[0]\n\n positional_args, _ = call\n return positional_args[0]\n\n def test_when_called_then_calls_api(self):\n query_sentinel_hub(self.sentinel_product_request)\n\n self.requests_mock.get.assert_called_once()\n\n def test_when_query_called_then_calls_are_authenticated(self):\n expected_auth = (\n self.sentinel_product_request.username,\n self.sentinel_product_request.password,\n )\n\n query_sentinel_hub(self.sentinel_product_request)\n\n calls = self.requests_mock.get.call_args_list\n\n assert_that(len(calls)).is_equal_to(1)\n\n call = calls[0]\n\n _, kwargs = call\n\n assert_that(kwargs[\"auth\"]).is_equal_to(expected_auth)\n\n def test_when_query_called_without_ordering_then_calls_correct_url(self):\n expected_params = {\n \"q\": [\"*\"],\n \"start\": [\"0\"],\n \"rows\": [\"30\"],\n \"format\": [\"json\"],\n }\n\n query_sentinel_hub(self.sentinel_product_request)\n\n url = self._get_called_url()\n\n assert_that(get_query_parameters_of_url(url)).is_equal_to(expected_params)\n assert_that(url).starts_with(\"https://scihub.copernicus.eu/dhus/search?\")\n\n def test_when_query_called_without_rows_then_calls_correct_url(self):\n expected_params = {\"q\": [\"*\"], \"start\": [\"0\"], \"format\": [\"json\"]}\n\n query_sentinel_hub(self.sentinel_product_request_no_rows)\n\n url = self._get_called_url()\n\n assert_that(get_query_parameters_of_url(url)).is_equal_to(expected_params)\n assert_that(url).starts_with(\"https://scihub.copernicus.eu/dhus/search?\")\n\n def test_when_query_called_with_ordering_then_calls_correct_url(self):\n expected_params = {\n \"q\": [\"*\"],\n \"start\": [\"0\"],\n \"format\": [\"json\"],\n \"orderby\": [\"beginposition asc\"],\n \"rows\": [\"30\"],\n }\n\n query_sentinel_hub(self.ordered_sentinel_product_request)\n\n url = self._get_called_url()\n\n assert_that(get_query_parameters_of_url(url)).is_equal_to(expected_params)\n\n def test_when_query_supplied_then_the_url_contains_query(self):\n expected_params = {\n \"q\": [\"platformname:Sentinel-1 AND cloudcoverpercentage:5\"],\n \"start\": [\"0\"],\n \"format\": [\"json\"],\n \"rows\": [\"30\"],\n }\n\n query_sentinel_hub(self.sentinel_product_request_with_query)\n\n url = self._get_called_url()\n\n assert_that(get_query_parameters_of_url(url)).is_equal_to(expected_params)\n\n def test_when_request_successful_then_returns_status_code_and_json(self):\n self.requests_mock.get.return_value.status_code = 200\n self.requests_mock.get.return_value.json.return_value = {\"content\": {}}\n\n result = query_sentinel_hub(self.sentinel_product_request)\n\n assert_that(result).is_equal_to(\n QuerySentinelProductsResponse(200, {\"content\": {}}, None)\n )\n\n def test_when_request_has_non_200_status_code_then_returns_status_code_and_json(\n self,\n ):\n self.requests_mock.get.return_value.status_code = 400\n self.requests_mock.get.return_value.json.return_value = {\"content\": {}}\n\n result = query_sentinel_hub(self.sentinel_product_request)\n\n assert_that(result).is_equal_to(\n QuerySentinelProductsResponse(400, {\"content\": {}}, None)\n )\n\n def test_when_requests_raises_error_then_returns_error(self):\n error = requests.ConnectionError()\n self.requests_mock.get.side_effect = error\n\n result = query_sentinel_hub(self.sentinel_product_request)\n\n assert_that(result).is_equal_to(\n QuerySentinelProductsResponse(None, None, error)\n )\n\n def test_when_response_is_not_json_then_returns_content_as_string_with_error(self):\n original_error = ValueError()\n self.requests_mock.get.return_value.status_code = 200\n self.requests_mock.get.return_value.json.side_effect = original_error\n self.requests_mock.get.return_value.content = \"Not json\".encode()\n\n result = query_sentinel_hub(self.sentinel_product_request)\n\n assert_that(result).is_equal_to(\n QuerySentinelProductsResponse(\n 200, None, QuerySentinelProductsError(original_error, 200, \"Not json\")\n )\n )\n\n @patch(\"sentinelpy.main.urlencode\")\n def test_when_called_then_encodes_parameters(self, urlencode_mock):\n query_sentinel_hub(self.sentinel_product_request_with_query)\n\n urlencode_mock.assert_called_once_with(\n {\n \"q\": \"platformname:Sentinel-1 AND cloudcoverpercentage:5\",\n \"start\": 0,\n \"format\": \"json\",\n \"rows\": 30,\n }\n )\n\n @patch(\"sentinelpy.main.logging\")\n def test_when_query_sentinel_hub_called_then_it_logs_at_the_set_log_level(\n self, logging_mock\n ):\n query_sentinel_hub(self.sentinel_product_request, log_level=logging.DEBUG)\n\n logging_mock.getLogger.assert_called_once()\n logging_mock.getLogger.return_value.setLevel.assert_called_once_with(\n logging.DEBUG\n )\n", "repo_name": "UKHO/sentinelpy", "sub_path": "tests/test_query_sentinel_hub.py", "file_name": "test_query_sentinel_hub.py", "file_ext": "py", "file_size_in_byte": 6667, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "unittest.mock.patch", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 14, "usage_type": "call"}, {"api_name": "sentinelpy.SentinelProductRequest", "line_number": 24, "usage_type": "call"}, {"api_name": "sentinelpy.SentinelProductRequest", "line_number": 27, "usage_type": "call"}, {"api_name": "sentinelpy.SentinelProductRequest", "line_number": 30, "usage_type": "call"}, {"api_name": "sentinelpy.SentinelProductRequest", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 46, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 54, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 64, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 68, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 74, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 84, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 88, "usage_type": "call"}, {"api_name": "tests.utils.get_query_parameters_of_url", "line_number": 88, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 89, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 94, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 98, "usage_type": "call"}, {"api_name": "tests.utils.get_query_parameters_of_url", "line_number": 98, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 99, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 110, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 114, "usage_type": "call"}, {"api_name": "tests.utils.get_query_parameters_of_url", "line_number": 114, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 124, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 128, "usage_type": "call"}, {"api_name": "tests.utils.get_query_parameters_of_url", "line_number": 128, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 134, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 136, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_products_response.QuerySentinelProductsResponse", "line_number": 137, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 146, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 148, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_products_response.QuerySentinelProductsResponse", "line_number": 149, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 153, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 156, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 158, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_products_response.QuerySentinelProductsResponse", "line_number": 159, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 168, "usage_type": "call"}, {"api_name": "assertpy.assert_that", "line_number": 170, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_products_response.QuerySentinelProductsResponse", "line_number": 171, "usage_type": "call"}, {"api_name": "sentinelpy.exceptions.QuerySentinelProductsError", "line_number": 172, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 178, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 176, "usage_type": "call"}, {"api_name": "sentinelpy.query_sentinel_hub", "line_number": 193, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 193, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 197, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "70400019568", "text": "import configparser\nimport os.path\n\nfrom biskit.errors import BiskitError\nfrom biskit import EHandler\n\nimport biskit.tools as T\n\nclass ExeConfigError( BiskitError ):\n pass\n\nclass CaseSensitiveConfigParser( configparser.SafeConfigParser ):\n \"\"\"\n Change ConfigParser so that it doesn't convert option names to lower case.\n \"\"\"\n def optionxform(self, optionstr):\n return optionstr\n\n\nclass ExeConfig( object ):\n\n \"\"\"\n ExeConfig\n =========\n \n Manage the settings that Executor needs for calling an external\n program.\n\n ExeConfig is initialised with a program name and expects to find\n the configuration for this program in\n C{ ~/.biskit/exe_|name|.dat }. Only if nothing is found there, it looks\n for C{ Biskit/data/defaults/exe_|name|.dat } in the biskit installation\n folder. If neither of the two files are found, an error is raised\n (strict=1) or the binary is assumed to be |name| and must be\n accessible in the search path (strict=0).\n\n\n Example\n -------\n \n The configuration file (exe_name.dat) should look like this::\n\n ---- start example configuration file ----\n [BINARY]\n\n comment=the emacs editor\n bin=emacs\n cwd=\n shell=0\n shellexe=\n pipes=0\n ## Use new environment containing only variables given below\n replaceEnv=0\n\n [ENVIRONMENT]\n\n HOME=\n EMACS_CONFIG=~/.emacs/config.dat\n ---- end of example file ----\n\n\n This example config would ask Executor to look for an executable\n called 'emacs' in the local search path. Before running it,\n executor should check that a variable $HOME exists in the local\n shell environment (or raise an error otherwise) and set the\n variable $EMACS_CONFIG to the given path.\n\n The other settings specify how the program call is done (see also\n Python 2.4 subprocess.Popen() ):\n\n - cwd ... working directory (empty -- current working directory)\n - shell ... wrap process in separate shell\n - shellexe ... which shell (empty -- sh)\n - pipes ... paste input via STDIN, collect output at STDOUT\n\n Missing options are reset to their default value; See\n :class:` ExeConfig.reset() `. All entries in section BINARY are put into the\n name space of the ExeConfig object. That means an ExeConfig object x\n created from the above file can be used as follows:\n\n >>> x = ExeConfig( 'emacs' )\n >>> x.cwd is None\n >>> True\n >>> print x.comment\n >>> the emacs editor\n \"\"\"\n\n ## default search path for exe_...config files\n CONFIG_PATH = [ os.path.expanduser('~/.biskit'), \n os.path.join( T.dataRoot(), 'defaults' ) ]\n\n SECTION_BIN = 'BINARY'\n SECTION_ENV = 'ENVIRONMENT'\n\n def __init__( self, name, strict=True, configpath=None):\n \"\"\"\n :param name: unique name of the program\n :type name: str\n :param strict: insist on a config file exe_name.dat\n and do not tolerate missing environment variables\n (default: True)\n :type strict: bool\n :param configpath: list of pathnames where configuration file should\n be searched, None means use default:\n ['~/.biskit', '.../biskit/data/defaults']\n :type configpath: [str]\n \n :raise ExeConfigError: if strict==1 and config file incomplete/missing\n \"\"\"\n self.name = name #: identifier\n self.strict = strict\n self.dat = '' #: configuration file path\n \n searchpath = configpath or self.CONFIG_PATH #: [str]\n p = searchpath.copy() # don't empty out original CONFIG_PATH with pop() !\n if len(p) < 1:\n raise ExeConfigError('Path(s) for exe config files missing.')\n \n while p and not os.path.exists(self.dat):\n self.dat = os.path.join( p.pop(0), 'exe_%s.dat' % name )\n\n if self.strict and not os.path.exists(self.dat) :\n raise ExeConfigError(\n 'Could not find configuration file %s for program %s.\\n'\\\n %(self.dat, self.name) +\\\n 'Searching in: %r'% searchpath)\n \n self.env_checked = 0 ## environment was verified\n\n self.conf = CaseSensitiveConfigParser()\n self.conf.read( self.dat )\n\n self.reset()\n self.update()\n\n\n def reset( self ):\n \"\"\"\n Reset all required parameters. Called at creation\n \"\"\"\n ## default values\n self.comment = 'no comment or missing configuration file'\n self.bin = self.name\n self.shell = 0\n self.shellexe = None\n self.pipes = 0\n self.cwd = None #'./'\n\n self.replaceEnv = 0\n self.env = None\n\n\n def update( self ):\n \"\"\"\n Load settings from associated configuration file (if available).\n Is automatically called at creation.\n \n :raise ExeConfigError: if section [BINARY] was not found in the file\n \"\"\"\n ## get parameters from config file if available; type-cast values \n try:\n dconf = self.conf.items( self.SECTION_BIN )\n\n for key, value in dconf:\n\n ## default type is string\n t = type( self.__dict__.get( key, '' ) )\n if t is type(None):\n t = str\n\n ## leave default value if None is given\n if value is not '':\n self.__dict__[ key ] = t( value )\n\n except configparser.NoSectionError:\n if self.strict:\n raise ExeConfigError('Could not find BINARY section in %s.' % self.dat)\n\n try:\n self.env = dict( self.conf.items( self.SECTION_ENV ) )\n except:\n pass\n\n\n def validate( self ):\n \"\"\"\n Validate the path to the binary.\n \n :raise ExeConfigError: if environment is not fit for running\n the program\n \"\"\"\n try:\n self.bin = T.absbinary( self.bin ) ## raises IOError if not found\n\n missing = self.update_environment()\n report = '%s is missing environment variables: %r'\\\n % (self.name, missing )\n\n if missing and self.strict:\n raise ExeConfigError(report)\n\n if missing:\n EHandler.warning( report )\n\n except IOError as e:\n ## re-raise but silence reporting of IOError in stack trace\n raise ExeConfigError(str(e) + ' Check %s!' % self.dat) from None \n\n\n def environment( self ):\n \"\"\"\n Get needed environment variables.\n \n :return: dictionary with environment for subprocess.Popen;\n empty, if no environment was specified\n :rtype: {str:str} OR None\n\n :raise ExeConfigError: if env was not yet checked by update_environment\n \"\"\"\n if not self.env_checked:\n raise ExeConfigError('Environment not yet checked, validate()!')\n\n return self.env\n\n\n def update_environment( self ):\n \"\"\"\n Check for missing environment settings.\n \n :return: names of required but missing environment variables\n :rtype: [str]\n \"\"\"\n missing = []\n\n if self.env:\n\n for key,value in self.env.items():\n\n if value is '':\n\n if os.getenv( key ) is None:\n missing += [ key ]\n else:\n self.env[ key ] = os.getenv( key )\n\n self.env_checked = 1\n\n return missing\n\n\n def __repr__( self ):\n s = 'ExeConfig for %s' % self.name\n for k,v in self.__dict__.items():\n s += '\\n%10s \\t%r' % (k,v)\n return s\n\n def __str__( self ):\n return self.__repr__()\n\n\n\n#############\n## TESTING \n#############\nimport biskit.test as BT\n\nclass Test(BT.BiskitTest):\n \"\"\"ExeConfig test\"\"\"\n \n def test_ExeConfig( self ):\n \"\"\"ExeConfig test (validate ls)\"\"\"\n\n x = ExeConfig( 'ls', strict=True )\n x.validate()\n\n if self.local:\n print(x.bin)\n\n self.assertEqual( True, 'ls' in x.bin )\n \n def test_ExeConfig_externalPath(self):\n \"\"\"ExeConfig using external path\"\"\"\n \n x = ExeConfig('ls',configpath=[T.testRoot('exe')])\n x.validate()\n \n if self.local:\n print(x.bin)\n self.assertTrue('ls' in x.bin)\n \nif __name__ == '__main__':\n\n BT.localTest()\n", "repo_name": "graik/biskit", "sub_path": "biskit/exe/exeConfig.py", "file_name": "exeConfig.py", "file_ext": "py", "file_size_in_byte": 8590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 53, "dataset": "github-code", "pt": "2", "api": [{"api_name": "biskit.errors.BiskitError", "line_number": 9, "usage_type": "name"}, {"api_name": "configparser.SafeConfigParser", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.path.expanduser", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 89, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 90, "usage_type": "name"}, {"api_name": "biskit.tools.dataRoot", "line_number": 90, "usage_type": "call"}, {"api_name": "biskit.tools", "line_number": 90, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 119, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 120, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 122, "usage_type": "name"}, {"api_name": "configparser.NoSectionError", "line_number": 175, "usage_type": "attribute"}, {"api_name": "biskit.tools.absbinary", "line_number": 193, "usage_type": "call"}, {"api_name": "biskit.tools", "line_number": 193, "usage_type": "name"}, {"api_name": "biskit.EHandler.warning", "line_number": 203, "usage_type": "call"}, {"api_name": "biskit.EHandler", "line_number": 203, "usage_type": "name"}, {"api_name": "os.path.getenv", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "name"}, {"api_name": "os.path.getenv", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "name"}, {"api_name": "biskit.test.BiskitTest", "line_number": 267, "usage_type": "attribute"}, {"api_name": "biskit.test", "line_number": 267, "usage_type": "name"}, {"api_name": "biskit.tools.testRoot", "line_number": 284, "usage_type": "call"}, {"api_name": "biskit.tools", "line_number": 284, "usage_type": "name"}, {"api_name": "biskit.test.localTest", "line_number": 293, "usage_type": "call"}, {"api_name": "biskit.test", "line_number": 293, "usage_type": "name"}]} +{"seq_id": "38523926074", "text": "import os\r\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\r\nimport logging\r\nfrom datetime import datetime\r\n\r\nfrom experiments.run_context import RunContext\r\nfrom datasets.svhn_embed import SVHN\r\nfrom method.model_svhn_1000labels_embed import Model\r\nfrom method import minibatching\r\n\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\nLOG = logging.getLogger('main')\r\n\r\n\r\ndef run(data_seed=0):\r\n n_labeled = 1000\r\n n_extra_unlabeled = 0\r\n context_size = 500\r\n max_iter = 2000\r\n\r\n batch_size = 100\r\n n_labeled_per_batch = 'vary'\r\n\r\n model = Model(batch_size, context_size, n_labeled_per_batch, RunContext(__file__, 0))\r\n\r\n tensorboard_dir = model.save_tensorboard_graph()\r\n LOG.info(\"Saved tensorboard graph to %r\", tensorboard_dir)\r\n\r\n svhn = SVHN(data_seed, n_labeled, n_extra_unlabeled, context_size, max_iter, test_phase=True)\r\n training_batches = minibatching.training_batches(svhn.training, batch_size, n_labeled_per_batch)\r\n evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation, batch_size=200)\r\n\r\n model.train(training_batches, evaluation_batches_fn, svhn.context_training)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n run()\r\n", "repo_name": "hellotem/SSL_graph_embedding", "sub_path": "train_svhn_1000labels_embed.py", "file_name": "train_svhn_1000labels_embed.py", "file_ext": "py", "file_size_in_byte": 1224, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 3, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "method.model_svhn_1000labels_embed.Model", "line_number": 26, "usage_type": "call"}, {"api_name": "experiments.run_context.RunContext", "line_number": 26, "usage_type": "call"}, {"api_name": "datasets.svhn_embed.SVHN", "line_number": 31, "usage_type": "call"}, {"api_name": "method.minibatching.training_batches", "line_number": 32, "usage_type": "call"}, {"api_name": "method.minibatching", "line_number": 32, "usage_type": "name"}, {"api_name": "method.minibatching.evaluation_epoch_generator", "line_number": 33, "usage_type": "call"}, {"api_name": "method.minibatching", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "43130892699", "text": "import statistics\nfrom logging import INFO, WARNING\nfrom typing import Callable, Dict, List, Optional, Tuple, cast\n\nimport numpy as np\n\nfrom flwr.common import (\n EvaluateRes,\n FitIns,\n FitRes,\n MetricsAggregationFn,\n Parameters,\n Scalar,\n Weights,\n parameters_to_weights,\n weights_to_parameters,\n)\nfrom flwr.common.logger import log\nfrom flwr.server.client_manager import ClientManager\nfrom flwr.server.client_proxy import ClientProxy\n\nfrom .aggregate import aggregate, weighted_loss_avg\nfrom .fast_and_slow import is_fast_round, normalize_and_sample\nfrom .fedavg import FedAvg\n\nE = 0.001\nWAIT_TIMEOUT = 600\n\n\nclass FedFSv0(FedAvg):\n \"\"\"Strategy implementation which alternates between fast and slow\n rounds.\"\"\"\n\n # pylint: disable=too-many-arguments,too-many-instance-attributes,too-many-locals\n def __init__(\n self,\n fraction_fit: float = 0.1,\n fraction_eval: float = 0.1,\n min_fit_clients: int = 1,\n min_eval_clients: int = 1,\n min_available_clients: int = 1,\n eval_fn: Optional[\n Callable[[Weights], Optional[Tuple[float, Dict[str, Scalar]]]]\n ] = None,\n min_completion_rate_fit: float = 0.5,\n min_completion_rate_evaluate: float = 0.5,\n on_fit_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,\n on_evaluate_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,\n r_fast: int = 1,\n r_slow: int = 1,\n t_fast: int = 10,\n t_slow: int = 10,\n initial_parameters: Optional[Parameters] = None,\n fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,\n evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,\n ) -> None:\n super().__init__(\n fraction_fit=fraction_fit,\n fraction_eval=fraction_eval,\n min_fit_clients=min_fit_clients,\n min_eval_clients=min_eval_clients,\n min_available_clients=min_available_clients,\n eval_fn=eval_fn,\n on_fit_config_fn=on_fit_config_fn,\n on_evaluate_config_fn=on_evaluate_config_fn,\n initial_parameters=initial_parameters,\n fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,\n evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,\n )\n self.min_completion_rate_fit = min_completion_rate_fit\n self.min_completion_rate_evaluate = min_completion_rate_evaluate\n self.r_fast = r_fast\n self.r_slow = r_slow\n self.t_fast = t_fast\n self.t_slow = t_slow\n self.contributions: Dict[str, List[Tuple[int, int, int]]] = {}\n self.fit_metrics_aggregation_fn = fit_metrics_aggregation_fn\n self.evaluate_metrics_aggregation_fn = evaluate_metrics_aggregation_fn\n\n def __repr__(self) -> str:\n rep = f\"FedFSv0(r_fast={self.r_fast}, r_slow={self.r_slow}, \"\n rep += f\"t_fast={self.t_fast}, t_slow={self.t_slow})\"\n return rep\n\n # pylint: disable=too-many-locals\n def configure_fit(\n self, rnd: int, parameters: Parameters, client_manager: ClientManager\n ) -> List[Tuple[ClientProxy, FitIns]]:\n \"\"\"Configure the next round of training.\"\"\"\n\n # Block until `min_num_clients` are available\n sample_size, min_num_clients = self.num_fit_clients(\n client_manager.num_available()\n )\n success = client_manager.wait_for(\n num_clients=min_num_clients, timeout=WAIT_TIMEOUT\n )\n if not success:\n # Do not continue if not enough clients are available\n log(\n INFO,\n \"FedFS: not enough clients available after timeout %s\",\n WAIT_TIMEOUT,\n )\n return []\n\n # Sample clients\n clients = self._contribution_based_sampling(\n sample_size=sample_size, client_manager=client_manager\n )\n\n # Prepare parameters and config\n config = {}\n if self.on_fit_config_fn is not None:\n # Use custom fit config function if provided\n config = self.on_fit_config_fn(rnd)\n\n # Set timeout for this round\n use_fast_timeout = is_fast_round(rnd - 1, self.r_fast, self.r_slow)\n config[\"timeout\"] = str(self.t_fast if use_fast_timeout else self.t_slow)\n\n # Fit instructions\n fit_ins = FitIns(parameters, config)\n\n # Return client/config pairs\n return [(client, fit_ins) for client in clients]\n\n def _contribution_based_sampling(\n self, sample_size: int, client_manager: ClientManager\n ) -> List[ClientProxy]:\n \"\"\"Sample clients depending on their past contributions.\"\"\"\n # Get all clients and gather their contributions\n all_clients: Dict[str, ClientProxy] = client_manager.all()\n cid_idx: Dict[int, str] = {}\n raw: List[float] = []\n for idx, (cid, _) in enumerate(all_clients.items()):\n cid_idx[idx] = cid\n penalty = 0.0\n if cid in self.contributions:\n contribs: List[Tuple[int, int, int]] = self.contributions[cid]\n penalty = statistics.mean([c / m for _, c, m in contribs])\n # `p` should be:\n # - High for clients which have never been picked before\n # - Medium for clients which have contributed,\n # but not used their entire budget\n # - Low (but not 0) for clients which have been picked and used their budget\n raw.append(1.1 - penalty)\n\n # Sample clients\n return normalize_and_sample(\n all_clients=all_clients,\n cid_idx=cid_idx,\n raw=np.array(raw),\n sample_size=sample_size,\n use_softmax=False,\n )\n\n def aggregate_fit(\n self,\n rnd: int,\n results: List[Tuple[ClientProxy, FitRes]],\n failures: List[BaseException],\n ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:\n \"\"\"Aggregate fit results using weighted average.\"\"\"\n if not results:\n return None, {}\n\n # Check if enough results are available\n completion_rate = len(results) / (len(results) + len(failures))\n if completion_rate < self.min_completion_rate_fit:\n # Not enough results for aggregation\n return None, {}\n\n # Convert results\n weights_results = [\n (parameters_to_weights(fit_res.parameters), fit_res.num_examples)\n for client, fit_res in results\n ]\n weights_prime = aggregate(weights_results)\n\n # Track contributions to the global model\n for client, fit_res in results:\n cid = client.cid\n\n assert \"num_examples_ceil\" in fit_res.metrics\n num_examples_ceil: int = cast(int, fit_res.metrics[\"num_examples_ceil\"])\n\n contribution: Tuple[int, int, int] = (\n rnd,\n fit_res.num_examples,\n num_examples_ceil,\n )\n if cid not in self.contributions:\n self.contributions[cid] = []\n self.contributions[cid].append(contribution)\n\n parameters_aggregated = weights_to_parameters(weights_prime)\n\n # Aggregate custom metrics if aggregation fn was provided\n metrics_aggregated = {}\n if self.fit_metrics_aggregation_fn:\n fit_metrics = [(res.num_examples, res.metrics) for _, res in results]\n metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)\n elif rnd == 1:\n log(WARNING, \"No fit_metrics_aggregation_fn provided\")\n\n return parameters_aggregated, metrics_aggregated\n\n def aggregate_evaluate(\n self,\n rnd: int,\n results: List[Tuple[ClientProxy, EvaluateRes]],\n failures: List[BaseException],\n ) -> Tuple[Optional[float], Dict[str, Scalar]]:\n \"\"\"Aggregate evaluation losses using weighted average.\"\"\"\n if not results:\n return None, {}\n\n # Check if enough results are available\n completion_rate = len(results) / (len(results) + len(failures))\n if completion_rate < self.min_completion_rate_evaluate:\n # Not enough results for aggregation\n return None, {}\n\n # Aggregate loss\n loss_aggregated = weighted_loss_avg(\n [\n (evaluate_res.num_examples, evaluate_res.loss)\n for _, evaluate_res in results\n ]\n )\n\n # Aggregate custom metrics if aggregation fn was provided\n metrics_aggregated = {}\n if self.evaluate_metrics_aggregation_fn:\n eval_metrics = [(res.num_examples, res.metrics) for _, res in results]\n metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics)\n elif rnd == 1:\n log(WARNING, \"No evaluate_metrics_aggregation_fn provided\")\n\n return loss_aggregated, metrics_aggregated\n", "repo_name": "sylvia969301/Blockchain-based-Federated-Learning", "sub_path": "BCFL/flwr/server/strategy/fedfs_v0.py", "file_name": "fedfs_v0.py", "file_ext": "py", "file_size_in_byte": 8963, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "2", "api": [{"api_name": "fedavg.FedAvg", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 43, "usage_type": "name"}, {"api_name": "flwr.common.Weights", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 43, "usage_type": "name"}, {"api_name": "flwr.common.Scalar", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 47, "usage_type": "name"}, {"api_name": "flwr.common.Scalar", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 48, "usage_type": "name"}, {"api_name": "flwr.common.Scalar", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 53, "usage_type": "name"}, {"api_name": "flwr.common.Parameters", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 54, "usage_type": "name"}, {"api_name": "flwr.common.MetricsAggregationFn", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "flwr.common.MetricsAggregationFn", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 76, "usage_type": "name"}, {"api_name": "flwr.common.Parameters", "line_number": 87, "usage_type": "name"}, {"api_name": "flwr.server.client_manager.ClientManager", "line_number": 87, "usage_type": "name"}, {"api_name": "flwr.common.logger.log", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 101, "usage_type": "argument"}, {"api_name": "fast_and_slow.is_fast_round", "line_number": 119, "usage_type": "call"}, {"api_name": "flwr.common.FitIns", "line_number": 123, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 88, "usage_type": "name"}, {"api_name": "flwr.server.client_proxy.ClientProxy", "line_number": 88, "usage_type": "name"}, {"api_name": "flwr.common.FitIns", "line_number": 88, "usage_type": "name"}, {"api_name": "flwr.server.client_manager.ClientManager", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 133, "usage_type": "name"}, {"api_name": "flwr.server.client_proxy.ClientProxy", "line_number": 133, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 134, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 135, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 140, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 141, "usage_type": "call"}, {"api_name": "fast_and_slow.normalize_and_sample", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 130, "usage_type": "name"}, {"api_name": "flwr.server.client_proxy.ClientProxy", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 161, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 161, "usage_type": "name"}, {"api_name": "flwr.server.client_proxy.ClientProxy", "line_number": 161, "usage_type": "name"}, {"api_name": "flwr.common.FitRes", "line_number": 161, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 162, "usage_type": "name"}, {"api_name": "flwr.common.parameters_to_weights", "line_number": 176, "usage_type": "call"}, {"api_name": "aggregate.aggregate", "line_number": 179, "usage_type": "call"}, {"api_name": "typing.cast", "line_number": 186, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 188, "usage_type": "name"}, {"api_name": "flwr.common.weights_to_parameters", "line_number": 197, "usage_type": "call"}, {"api_name": "flwr.common.logger.log", "line_number": 205, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 205, "usage_type": "argument"}, {"api_name": "typing.Tuple", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 163, "usage_type": "name"}, {"api_name": "flwr.common.Parameters", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 163, "usage_type": "name"}, {"api_name": "flwr.common.Scalar", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 212, "usage_type": "name"}, {"api_name": "flwr.server.client_proxy.ClientProxy", "line_number": 212, "usage_type": "name"}, {"api_name": "flwr.common.EvaluateRes", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 213, "usage_type": "name"}, {"api_name": "aggregate.weighted_loss_avg", "line_number": 226, "usage_type": "call"}, {"api_name": "flwr.common.logger.log", "line_number": 239, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 239, "usage_type": "argument"}, {"api_name": "typing.Tuple", "line_number": 214, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 214, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 214, "usage_type": "name"}, {"api_name": "flwr.common.Scalar", "line_number": 214, "usage_type": "name"}]} +{"seq_id": "40362180610", "text": "'''\n2023-11-24\nThis script is to plot the difference between two periods (1960 and 1900) in precipitation over Indian\n\nFollowing Massimo advice:\nThe stippling principle: when 2/3 members hold the same difference, then stipple it\n\n2023-11-27 update\nchange the object to the fixEU experiment\n'''\nimport xarray as xr\nimport numpy as np\n\n# =============== File information ====================\nclass f1:\n '''\n This class save file location for the files\n '''\n\n CESM_path = '/mnt/d/samssd/precipitation/CESM/ensemble_JJAS/'\n #CESM_name = 'CESM_BTAL_esemble_JJAS_precipitation.nc'\n # 20231127 update\n CESM_name = 'CESM_noEU_esemble_JJAS_precipitation.nc'\n\nf0 = xr.open_dataset(f1.CESM_path + f1.CESM_name)\n\nperiodA_1 = 1900 ; periodA_2 = 1920\nperiodB_1 = 1940 ; periodB_2 = 1960\n\n# =============== Calculation for two period =================\ndef calculate_period_diff(pa1, pa2, pb1, pb2, ncfile, varname):\n '''\n This function is to calculate difference between two periods\n '''\n\n ncfile1 = ncfile.sel(time=slice(pa1, pa2))\n ncfile2 = ncfile.sel(time=slice(pb1, pb2))\n\n period_diff = np.average(ncfile2[varname].data, axis=0) - np.average(ncfile1[varname].data, axis=0)\n\n return period_diff\n\ndef check_sign(lists, num, size0):\n '''\n This function is to check whether this difference is same among the ensemble members\n '''\n\n # 1. Claim the array save the information of sign\n sign_array = np.zeros(size0)\n\n # 2. create a loop for calculation\n for i in range(size0[0]):\n for j in range(size0[1]):\n count_positive = 0\n count_negative = 0\n for k in range(num):\n if lists[k][i, j] >= 0:\n count_positive += 1\n else:\n count_negative += 1\n \n if abs(count_negative) >= 6 or abs(count_positive) >= 6:\n sign_array[i, j] = 1\n else:\n continue\n\n return sign_array\n\ndef save_ncfile(array, sign):\n ncfile = xr.Dataset(\n {\n \"JJAS_prect_diff\": ([\"member\", \"lat\", \"lon\"], array),\n \"sign\": ([\"lat\", \"lon\"], sign),\n },\n coords={\n \"member\": ([\"member\"], np.linspace(1, 8, 8)),\n \"lat\": ([\"lat\"], f0['lat'].data),\n \"lon\": ([\"lon\"], f0['lon'].data),\n },\n )\n\n return ncfile\n\n# =================== Main calculation ==========================\ndiff_list = []\ndiff_result = np.zeros((8, 96, 144))\nfor i in range(8):\n diff_list.append(calculate_period_diff(pa1=periodA_1, pa2=periodA_2, pb1=periodB_1, pb2=periodB_2, ncfile=f0, varname=\"JJAS_prect_{}\".format(i + 1)))\n diff_result[i] = calculate_period_diff(pa1=periodA_1, pa2=periodA_2, pb1=periodB_1, pb2=periodB_2, ncfile=f0, varname=\"JJAS_prect_{}\".format(i + 1))\n\nprint(\"Difference calculation completed\")\n\nsigns = check_sign(lists=diff_list, num=8, size0=(96, 144))\n\n# Save to the netcdf file\n#print(np.average(f0[\"JJAS_prect_1\"]))\ndiff_ncfile = save_ncfile(array=diff_result, sign=signs)\n\nout_path = \"/mnt/d/samssd/precipitation/processed/\"\n\ndiff_ncfile.attrs['description'] = 'This file save the JJAS precipitation difference between two periods (1901-1920 and 1941-1961). The sign array is the agreement among all the ensemble members.'\ndiff_ncfile.to_netcdf(out_path + \"EUI_CESM_fixEU_precipitation_difference_period_1901_1960_JJAS.nc\")", "repo_name": "sunweihao1997/local_code", "sub_path": "calculate/cal_CESM_difference_1960_1900_Indian_precipitation_stippling_231124.py", "file_name": "cal_CESM_difference_1960_1900_Indian_precipitation_stippling_231124.py", "file_ext": "py", "file_size_in_byte": 3432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "xarray.open_dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "2663086837", "text": "from sqlalchemy import sql, Column\n\nfrom utils.db.database import db\nfrom utils.db.models.user import User\n\n\nclass Referral(db.Model):\n __tablename__ = \"referrals\"\n query: sql.Select\n\n user_id = Column(None, db.ForeignKey(\"users.id\"), primary_key=True)\n referrer_id = Column(None, db.ForeignKey(\"users.id\"))\n\n\nasync def get_referrer_user_by_user_id(user_id) -> User:\n row = await Referral.query.where(Referral.user_id == int(user_id)).gino.first()\n\n if row:\n return await User.query.where(User.id == row.referrer_id).gino.first()\n else:\n return None\n\n\nasync def add_new_referrer_for_user(user_id, referrer_id):\n return await Referral(user_id=user_id, referrer_id=referrer_id).create()\n\n\nasync def get_referrals_count_by_referrer_id(user_id):\n query = db.text(\n f\"SELECT count(r.*) \"\n f\"FROM users u \"\n f\"JOIN referrals r \"\n f\"ON r.referrer_id = u.id \"\n f\"WHERE u.id = {int(user_id)}\"\n )\n result = await db.first(query)\n\n if result[0]:\n return result[0]\n else:\n return 0\n\n\nasync def get_referrals_cost_by_referrer_id(user_id):\n query = db.text(\n f\"SELECT sum(t.cost) \"\n f\"FROM referrals r \"\n f\"JOIN users u \"\n f\"ON u.id = r.user_id \"\n f\"JOIN transactions t \"\n f\"ON t.user_id = u.id \"\n f\"WHERE r.referrer_id = {int(user_id)}\"\n )\n result = await db.first(query)\n\n if result[0]:\n return result[0]\n else:\n return 0\n", "repo_name": "suchimauz/mr.suchimauz-bot", "sub_path": "utils/db/models/referral.py", "file_name": "referral.py", "file_ext": "py", "file_size_in_byte": 1490, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "utils.db.database.db.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "utils.db.database.db", "line_number": 7, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.Select", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sqlalchemy.sql", "line_number": 9, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "utils.db.database.db.ForeignKey", "line_number": 11, "usage_type": "call"}, {"api_name": "utils.db.database.db", "line_number": 11, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "utils.db.database.db.ForeignKey", "line_number": 12, "usage_type": "call"}, {"api_name": "utils.db.database.db", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.db.models.user.User.query.where", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.db.models.user.User.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "utils.db.models.user.User", "line_number": 19, "usage_type": "name"}, {"api_name": "utils.db.models.user.User.id", "line_number": 19, "usage_type": "attribute"}, {"api_name": "utils.db.models.user.User", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.db.database.db.text", "line_number": 29, "usage_type": "call"}, {"api_name": "utils.db.database.db", "line_number": 29, "usage_type": "name"}, {"api_name": "utils.db.database.db.first", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.db.database.db", "line_number": 36, "usage_type": "name"}, {"api_name": "utils.db.database.db.text", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.db.database.db", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.db.database.db.first", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.db.database.db", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "12308323941", "text": "from argparse import ArgumentParser\nfrom copy import deepcopy\nfrom itertools import product\nfrom pathlib import Path\nfrom re import sub\nfrom sys import exit, stderr\nfrom pkg_resources import resource_string\n\nimport math\nimport xmlschema\nimport yaml\n\n\ndef iota(start, step, stop):\n tol = 1e-10\n if math.isclose(step, 0.0, abs_tol=tol):\n yield start\n return\n while (start < stop if step > 0 else start > stop) or math.isclose(\n start, stop, abs_tol=tol\n ):\n yield start\n start = start + step\n\n\nclass MacroExpander:\n def __init__(self, rules, schema):\n\n self.rules = rules\n\n self.schema = schema\n\n self.specs = []\n\n if rules is not None:\n for each in rules[\"ScenarioModifier\"]:\n name = each[\"name\"]\n if \"list\" in each:\n self.specs.append(list(map(lambda x: (name, x), each[\"list\"])))\n else:\n self.specs.append(\n list(\n map(\n lambda x: (name, x),\n iota(each[\"start\"], each[\"step\"], each[\"stop\"]),\n )\n )\n )\n\n def __call__(self, xosc: str, output: Path, basename: str):\n paths = []\n\n for index, bindings in enumerate(product(*self.specs)):\n target = deepcopy(xosc)\n\n for binding in bindings:\n target = sub(str(binding[0]), str(binding[1]), target)\n\n if self.specs:\n paths.append(output.joinpath(basename + \"_\" + str(index) + \".xosc\"))\n else:\n paths.append(output.joinpath(basename + \".xosc\"))\n\n with paths[-1].open(mode=\"w\") as file:\n file.write(target)\n\n try:\n self.schema.validate(target)\n\n except xmlschema.XMLSchemaValidationError as exception:\n print(\"File: \" + str(paths[-1]), file=stderr)\n print(\"\", file=stderr)\n print(\"Error: \" + str(exception), file=stderr)\n exit()\n\n return paths\n\n\ndef load_yaml(path):\n if path.exists():\n with path.open(\"r\") as file:\n return yaml.safe_load(file)\n else:\n print(\n \"\\x1b[31mNo such file or directory: \" + str(path) + \"\\x1b[0m\", file=stderr\n )\n exit()\n\n\ndef from_yaml(keyword, node):\n\n if isinstance(node, dict):\n #\n # ???: { ... }\n #\n result = {}\n\n for tag, value in node.items():\n\n if isinstance(value, list) and len(value) == 0:\n #\n # Tag: []\n #\n # => REMOVE\n #\n continue\n\n if str.islower(tag[0]):\n #\n # tag: { ... }\n #\n # => @tag: { ... }\n #\n result[\"@\" + tag] = str(value)\n else:\n #\n # Tag: { ... }\n #\n # => NO CHANGES\n #\n result[tag] = from_yaml(tag, value)\n\n return result\n\n elif isinstance(node, list):\n #\n # ???: [ ... ]\n #\n result = []\n\n for index, item in enumerate(node):\n result.append(from_yaml(keyword, item))\n\n return result\n\n elif isinstance(node, str):\n return node\n\n else:\n return None\n\n\ndef convert(input: Path, output: Path, verbose: bool = True):\n\n if output.exists():\n for each in output.iterdir():\n each.resolve().unlink()\n else:\n output.mkdir(parents=True, exist_ok=True)\n\n schema = xmlschema.XMLSchema(\n resource_string(__name__, \"resources/OpenSCENARIO-1.2.xsd\").decode(\"utf-8\")\n )\n\n yaml = load_yaml(input)\n\n macroexpand = MacroExpander(yaml.pop(\"ScenarioModifiers\", None), schema)\n\n xosc, errors = schema.encode(\n from_yaml(\"OpenSCENARIO\", yaml),\n indent=2,\n preserve_root=True,\n unordered=True, # Reorder elements\n validation=\"lax\", # The \"strict\" mode is too strict than we would like.\n )\n\n if not schema.is_valid(xosc) and len(errors) != 0:\n print(\n \"Error: \" + str(errors[0]), file=stderr\n ) # Other than the first is not important.\n exit()\n\n else:\n paths = macroexpand(\n xmlschema.XMLResource(xosc)\n .tostring()\n .replace(\"True\", \"true\")\n .replace(\"False\", \"false\"),\n output,\n input.stem,\n )\n\n if verbose:\n for each in paths:\n print(each)\n\n return paths\n\n\ndef main():\n parser = ArgumentParser(description=\"Convert OpenSCENARIO.yaml into .xosc\")\n\n parser.add_argument(\"--input\", type=str, required=True)\n parser.add_argument(\"--output\", type=str, default=Path(\"/tmp\").joinpath(\"xosc\"))\n\n args = parser.parse_args()\n\n convert(Path(args.input).resolve(), Path(args.output).resolve())\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "tier4/scenario_simulator_v2", "sub_path": "openscenario/openscenario_utility/openscenario_utility/conversion.py", "file_name": "conversion.py", "file_ext": "py", "file_size_in_byte": 5155, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 97, "dataset": "github-code", "pt": "2", "api": [{"api_name": "math.isclose", "line_number": 16, "usage_type": "call"}, {"api_name": "math.isclose", "line_number": 19, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 50, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 53, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 54, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 57, "usage_type": "call"}, {"api_name": "xmlschema.XMLSchemaValidationError", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 71, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 72, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 73, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 82, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 85, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 87, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 143, "usage_type": "name"}, {"api_name": "xmlschema.XMLSchema", "line_number": 151, "usage_type": "call"}, {"api_name": "pkg_resources.resource_string", "line_number": 152, "usage_type": "call"}, {"api_name": "yaml.pop", "line_number": 157, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 169, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 171, "usage_type": "call"}, {"api_name": "xmlschema.XMLResource", "line_number": 175, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 191, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 194, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "3002645631", "text": "import pytest\nfrom seaice import *\nfrom firedrake import PeriodicSquareMesh, SpatialCoordinate, as_vector, pi, sin\n\n\n@pytest.mark.parametrize(\n \"state, family, theta\",\n [(a, b, c) for a in [True, False] for b in [\"CR\", \"CG\"] for c in [0, 0.5, 1]],\n)\ndef test_srt_model_compile(state, family, theta):\n timestep = 1\n dumpfreq = 10 ** 6\n timescale = 1\n\n zero = Constant(0)\n zero_vector = Constant(as_vector([0, 0]))\n\n dirname = \"./output/test-output/u.pvd\"\n number_of_triangles = 35\n length = 5 * 10 ** 5\n mesh = PeriodicSquareMesh(number_of_triangles, number_of_triangles, length)\n\n x, y = SpatialCoordinate(mesh)\n\n pi_x = pi / length\n v_exp = as_vector([-sin(pi_x * x) * sin(pi_x * y), -sin(pi_x * x) * sin(pi_x * y)])\n\n ic = {\"u\": v_exp, \"a\": 1, \"h\": 1}\n stabilised = {\"state\": state, \"alpha\": 1}\n conditions = Conditions(ic=ic, family=family, theta=theta)\n timestepping = TimesteppingParameters(timescale=timescale, timestep=timestep)\n output = OutputParameters(dirname=dirname, dumpfreq=dumpfreq)\n solver = SolverParameters()\n zero = Constant(0)\n params = SeaIceParameters(\n rho=1, rho_a=zero, C_a=zero, rho_w=zero, C_w=zero, cor=zero\n )\n\n srt = ViscousPlastic(\n mesh=mesh,\n conditions=conditions,\n timestepping=timestepping,\n output=output,\n params=params,\n solver_params=solver,\n )\n\n zeta = srt.zeta(srt.h, srt.a, params.Delta_min)\n sigma = zeta * srt.strain(grad(srt.u1))\n sigma_exp = zeta * srt.strain(grad(conditions.ic[\"u\"]))\n\n eqn = srt.momentum_equation(\n srt.h,\n srt.u1,\n srt.u0,\n srt.p,\n sigma,\n params.rho,\n zero_vector,\n conditions.ocean_curr,\n params.rho_a,\n params.C_a,\n params.rho_w,\n params.C_w,\n conditions.geo_wind,\n params.cor,\n timestep,\n )\n eqn -= inner(div(sigma_exp), srt.p) * dx\n\n srt.assemble(eqn, srt.u1, srt.bcs, solver.srt_params)\n\n t = 0\n\n while t < timescale - 0.5 * timestep:\n srt.solve(srt.usolver)\n srt.update(srt.u0, srt.u1)\n t += timestep\n\n assert t > 0\n\n\nif __name__ == \"__main__\":\n import sys\n\n pytest.main(sys.argv)\n", "repo_name": "elma16/floe", "sub_path": "tests/test_srt_model_compile.py", "file_name": "test_srt_model_compile.py", "file_ext": "py", "file_size_in_byte": 2242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "firedrake.as_vector", "line_number": 16, "usage_type": "call"}, {"api_name": "firedrake.PeriodicSquareMesh", "line_number": 21, "usage_type": "call"}, {"api_name": "firedrake.SpatialCoordinate", "line_number": 23, "usage_type": "call"}, {"api_name": "firedrake.pi", "line_number": 25, "usage_type": "name"}, {"api_name": "firedrake.as_vector", "line_number": 26, "usage_type": "call"}, {"api_name": "firedrake.sin", "line_number": 26, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 6, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pytest.main", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 86, "usage_type": "attribute"}]} +{"seq_id": "17302373505", "text": "#! /usr/bin/env python3\n\nfrom typing import Optional, Iterator\nimport socket\nimport json\n\nfrom .signalCommon import __socket_receive__, __socket_send__, __type_error__\nfrom .signalGroup import Group\nfrom .signalContacts import Contacts\n\n# from signalSyncMessage import SyncMessage\n\nDEBUG: bool = False\n\n\nclass Groups(object):\n \"\"\"Object containing all the groups acting like a list.\"\"\"\n def __init__(self,\n sync_socket: socket.socket,\n config_path: str,\n account_id: str,\n account_contacts: Contacts,\n from_dict: Optional[dict] = None,\n do_sync: bool = False\n ) -> None:\n # Arg checks:\n if not isinstance(sync_socket, socket.socket):\n __type_error__(\"sync_socket\", \"socket.socket\", sync_socket)\n if not isinstance(config_path, str):\n __type_error__(\"config_path\", \"str\", config_path)\n if not isinstance(account_id, str):\n __type_error__(\"account_id\", \"str\", account_id)\n if not isinstance(account_contacts, Contacts):\n __type_error__(\"account_contacts\", \"Contacts\", account_contacts)\n if from_dict is not None and not isinstance(from_dict, dict):\n __type_error__(\"from_dict\", \"Optional[dict]\", from_dict)\n if not isinstance(do_sync, bool):\n __type_error__(\"do_sync\", \"bool\", do_sync)\n # Set internal vars:\n self._sync_socket: socket.socket = sync_socket\n self._config_path: str = config_path\n self._account_id: str = account_id\n self._contacts: Contacts = account_contacts\n self._groups: list[Group] = []\n # Load from dict:\n if from_dict is not None:\n self.__from_dict__(from_dict)\n # Load from signal\n if do_sync:\n self.__sync__()\n return\n\n ############################\n # Overrides:\n ############################\n def __iter__(self) -> Iterator[Group]:\n return iter(self._groups)\n\n ############################\n # To / From Dict:\n ############################\n def __to_dict__(self) -> dict[str, object]:\n groups_dict = {\n \"groups\": []\n }\n for group in self._groups:\n groups_dict[\"groups\"].append(group.__to_dict__())\n return groups_dict\n\n def __from_dict__(self, from_dict: dict):\n self._groups = []\n for groupDict in from_dict['groups']:\n group = Group(sync_socket=self._sync_socket, config_path=self._config_path, account_id=self._account_id,\n account_contacts=self._contacts, from_dict=groupDict)\n self._groups.append(group)\n return\n\n ###################################\n # Sync with signal:\n ##################################\n def __sync__(self) -> bool:\n # Create command object and json command string:\n list_groups_command_obj = {\n \"jsonrpc\": \"2.0\",\n \"id\": 0,\n \"method\": \"listGroups\",\n \"params\": {\n \"account\": self._account_id,\n }\n }\n json_command_str = json.dumps(list_groups_command_obj) + '\\n'\n # Communicate with signal:\n __socket_send__(self._sync_socket, json_command_str)\n response_string = __socket_receive__(self._sync_socket)\n # Parse response:\n response_obj: dict = json.loads(response_string)\n # print(responseObj)\n # Check for error:\n if 'error' in response_obj.keys():\n return False\n # Parse result:\n group_added = False\n for raw_group in response_obj['result']:\n new_group = Group(sync_socket=self._sync_socket, config_path=self._config_path, account_id=self._account_id,\n account_contacts=self._contacts, raw_group=raw_group)\n old_group = self.get_by_id(new_group.id)\n if old_group is None:\n self._groups.append(new_group)\n group_added = True\n else:\n old_group.__merge__(new_group)\n\n return group_added\n\n ##############################\n # Helpers:\n ##############################\n def __parse_sync_message__(self, sync_message) -> None: # sync_message type SyncMessage\n if sync_message.sync_type == 5: # SyncMessage.TYPE_BLOCKED_SYNC\n for group_id in sync_message.blocked_groups:\n added, group = self.__get_or_add__(\"\", group_id)\n group.is_blocked = True\n else:\n errorMessage = \"groups can only parse sync message of type: SyncMessage.TYPE_BLOCKED_SYNC.\"\n raise TypeError(errorMessage)\n\n ##############################\n # Getters:\n ##############################\n def get_by_id(self, group_id: str) -> Optional[Group]:\n \"\"\"\n Get a group by id.\n :param group_id: str: The id to search for.\n :returns: Optional[group]: The group, or None if not found.\n :raises: TypeError: If group_id is not a string.\n \"\"\"\n if not isinstance(group_id, str):\n __type_error__(\"group_id\", \"str\", group_id)\n for group in self._groups:\n if group.id == group_id:\n return group\n return None\n\n def get_by_name(self, name: str) -> Optional[Group]:\n \"\"\"\n Get a group given a name.\n :param name: str: The name of the group to search for.\n :returns: Optional[group]: The group, or None if not found.\n :raises: TypeError: If name is not a string.\n \"\"\"\n if not isinstance(name, str):\n __type_error__(\"name\", \"str\", name)\n for group in self._groups:\n if group.name == name:\n return group\n return None\n\n ####################################\n # Helpers:\n ###################################\n def __get_or_add__(self, name: str, group_id: str) -> tuple[bool, Group]:\n # self.__sync__()\n old_group = self.get_by_id(group_id)\n if old_group is not None:\n return False, old_group\n new_group = Group(sync_socket=self._sync_socket, config_path=self._config_path, account_id=self._account_id,\n account_contacts=self._contacts, name=name, group_id=group_id)\n self._groups.append(new_group)\n return True, new_group\n", "repo_name": "pnearing/SignalCliApi", "sub_path": "src/SignalCliApi/signalGroups.py", "file_name": "signalGroups.py", "file_ext": "py", "file_size_in_byte": 6415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "socket.socket", "line_number": 19, "usage_type": "attribute"}, {"api_name": "signalContacts.Contacts", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 27, "usage_type": "attribute"}, {"api_name": "signalCommon.__type_error__", "line_number": 28, "usage_type": "call"}, {"api_name": "signalCommon.__type_error__", "line_number": 30, "usage_type": "call"}, {"api_name": "signalCommon.__type_error__", "line_number": 32, "usage_type": "call"}, {"api_name": "signalContacts.Contacts", "line_number": 33, "usage_type": "argument"}, {"api_name": "signalCommon.__type_error__", "line_number": 34, "usage_type": "call"}, {"api_name": "signalCommon.__type_error__", "line_number": 36, "usage_type": "call"}, {"api_name": "signalCommon.__type_error__", "line_number": 38, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 40, "usage_type": "attribute"}, {"api_name": "signalContacts.Contacts", "line_number": 43, "usage_type": "name"}, {"api_name": "signalGroup.Group", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 56, "usage_type": "name"}, {"api_name": "signalGroup.Group", "line_number": 56, "usage_type": "name"}, {"api_name": "signalGroup.Group", "line_number": 73, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "signalCommon.__socket_send__", "line_number": 93, "usage_type": "call"}, {"api_name": "signalCommon.__socket_receive__", "line_number": 94, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 96, "usage_type": "call"}, {"api_name": "signalGroup.Group", "line_number": 104, "usage_type": "call"}, {"api_name": "signalCommon.__type_error__", "line_number": 138, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 130, "usage_type": "name"}, {"api_name": "signalGroup.Group", "line_number": 130, "usage_type": "name"}, {"api_name": "signalCommon.__type_error__", "line_number": 152, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 144, "usage_type": "name"}, {"api_name": "signalGroup.Group", "line_number": 144, "usage_type": "name"}, {"api_name": "signalGroup.Group", "line_number": 166, "usage_type": "call"}, {"api_name": "signalGroup.Group", "line_number": 161, "usage_type": "name"}]} +{"seq_id": "17236355521", "text": "from flask_wtf import FlaskForm\nfrom wtforms import StringField,SubmitField\n\n\nclass NounForm(FlaskForm):\n Noun_word=StringField('ಪದ') \n Genders=StringField('ಲಿಂಗ ') \n Noun_categories=StringField('ನಾಮಪದದ ವರ್ಗ ') \n submit=SubmitField('ಸೇರಿಸಿ')\n \nclass VerbForm(FlaskForm):\n Verb_word=StringField('ಪದ') \n Present_tense=StringField('ವರ್ತಮಾನ ಕಾಲದ ವರ್ಗ ') \n Future_tense=StringField('ಭವಿಷ್ಯತ್ಕಾಲದ ವರ್ಗ ')\n Past_tense=StringField('ಭೂತಕಾಲದ ವರ್ಗ')\n submit=SubmitField('ಸೇರಿಸಿ') \n \n\n \nclass OtherForm(FlaskForm):\n Other_word=StringField('ಪದ') \n Other_categories=StringField('ವರ್ಗ') \n submit=SubmitField('ಸೇರಿಸಿ')", "repo_name": "Ashish4n8/Inflection-and-Specific-word-form-generator", "sub_path": "forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 818, "program_lang": "python", "lang": "kn", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask_wtf.FlaskForm", "line_number": 5, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 6, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 7, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 8, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 11, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 12, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 13, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 14, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 20, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 21, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 22, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "35505070876", "text": "\nfrom django.urls import path\nfrom . import views\n\napp_name = 'administracion'\n\nurlpatterns = [\n path('crearCarrera', views.crearCarrera, name = 'crearCarrera'),\n path('obtenerCarreras/', views.obtenerCarreras, name='obtenerCarreras'),\n path('obtenerIdCarrerasSegunAlumno/', views.obtenerIdCarrerasSegunAlumno, name='obtenerIdCarrerasSegunAlumno'),\n path('eliminarCarrera', views.eliminarCarrera, name = 'eliminarCarrera'),\n path('crearDocente', views.crearDocente, name = 'crearDocente'),\n path('obtenerDocentes/', views.obtenerDocentes, name='obtenerDocentes'),\n path('eliminarDocente', views.eliminarDocente, name = 'eliminarDocente'),\n path('crearAlumno', views.crearAlumno, name = 'crearAlumno'),\n path('obtenerAlumnos/', views.obtenerAlumnos, name='obtenerAlumnos'),\n path('eliminarAlumno', views.eliminarAlumno, name = 'eliminarAlumno'),\n path('crearMateria', views.crearMateria, name = 'crearMateria'),\n path('obtenerMaterias/', views.obtenerMaterias, name='obtenerMaterias'),\n path('eliminarMateria', views.eliminarMateria, name = 'eliminarMateria'),\n path('correlativas/', views.correlativas, name = 'correlativas'),\n path('obtenerCorrelativas/', views.obtenerCorrelativas, name = 'obtenerCorrelativas'),\n path('guardarCorrelativas/', views.guardarCorrelativas, name = 'guardarCorrelativas'),\n path('obtenerIdCarreraDocenteSegunMateria/', views.obtenerIdCarreraDocenteSegunMateria, name='obtenerIdCarreraDocenteSegunMateria'),\n path('error/', views.error, name = 'error'),\n path('alumnosCursando/', views.alumnosCursando, name = 'alumnosCursando'),\n path('obtenerAlumnos/carrera/', views.obtenerAlumnosSegunCarrera, name = 'obtenerAlumnosSegunCarrera'),\n path('obtenerAlumnos/materia/', views.obtenerAlumnosSegunMateria, name = 'obtenerAlumnosSegunMateria'),\n \n]", "repo_name": "locodasi/GestionUniversitariaWeb", "sub_path": "administracion/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1899, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "71828221808", "text": "from concurrent.futures import thread\nimport socket\nimport pickle\nfrom scipy import signal\nfrom scipy.io import wavfile\nimport time\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport sys\nimport pyaudio\nimport struct\nfrom spotify_integration import SpotifyPlayingMonitor\nfrom colorthief import ColorThief\nimport requests\nimport io\nimport threading\nimport spotipy\nfrom spotipy.oauth2 import SpotifyOAuth\nfrom dotenv import load_dotenv\nimport os\nfrom sty import Style, RgbFg, fg\n\nUDP_IP = \"192.168.1.126\"\nUDP_PORT = 5005\n \nFORMAT = pyaudio.paInt16\nCHANNELS = 1\nRATE = 44100\nCHUNK = int(44100 / 20)\n\nSPOTIFY_ENABLED = True\nSPOTIFY_CHECK_RATE = 3\n\np = pyaudio.PyAudio()\n\nprint(\"====================== AUDIO DEVICES ======================\")\nfor i in range(p.get_device_count()):\n dev = p.get_device_info_by_index(i)\n print((i,dev['name'],dev['maxInputChannels']))\n \n# start Recording\nstream = p.open(format=FORMAT, channels=CHANNELS,\n rate=RATE, input=True,\n frames_per_buffer=CHUNK, input_device_index=10)\n\nfrequency_bins = [\n 0,\n 50,\n 75,\n 125,\n 250,\n 500,\n 1000,\n 2000,\n 4000,\n 6000,\n 10000,\n 20000\n]\n\n# default colours\ncolour_gradient = np.array([[0, 255, 0], [125, 255, 125], [255, 255, 255]])\n\ndef fetch_album_colours(album_art_url):\n response = requests.get(album_art_url)\n if response.status_code != 200:\n raise ConnectionError(\"Failed to fetch album art\")\n ct = ColorThief(io.BytesIO(response.content))\n colours = ct.get_palette(color_count=5, quality=1)\n colours = sorted(filter(lambda c: sum(c) > 100, colours), key=lambda x: max(x) - min(x), reverse=True)\n brightest_colour = colours[0]\n colour_differences = [sum(map(lambda x, y: abs(x - y), brightest_colour, c)) for c in colours[1:]]\n brightness_sort = [x for x, _ in sorted(zip(colours[1:], colour_differences), key=lambda pair: pair[1], reverse=True)]\n print(brightest_colour)\n print(colour_differences)\n print(colours[1:])\n return [brightest_colour] + brightness_sort\n\ndef spotify_monitor(colours_list, c_id, c_sec, redir):\n print(\"Starting spotify monitor thread\")\n original_colours = colours_list.copy()\n sp = spotipy.Spotify(auth_manager=SpotifyOAuth(\n scope=\"user-read-currently-playing\", \n client_id=c_id, \n client_secret=c_sec, \n redirect_uri=redir\n ))\n prev_track_id = None\n while True:\n try:\n track = sp.current_user_playing_track()\n except requests.exceptions.ConnectionError:\n print(\"Spotify API error\")\n time.sleep(SPOTIFY_CHECK_RATE)\n continue\n if track is None:\n if not (colours_list == original_colours).all():\n for i in range(colours_list.shape[0]):\n colours_list[i] = original_colours[i]\n time.sleep(SPOTIFY_CHECK_RATE)\n continue\n if track[\"item\"] is None:\n time.sleep(SPOTIFY_CHECK_RATE)\n continue\n track_id = track[\"item\"][\"id\"]\n art_url = track[\"item\"][\"album\"][\"images\"][0][\"url\"]\n if track_id != prev_track_id:\n colours = fetch_album_colours(art_url)\n for col in colours:\n col_str = str(col)\n print(f\"{col}{' ' * (16 - len(col_str))}{fg(*col)}████████████████████████████████████████████{fg.rs}\")\n print()\n colours_list[0] = colours[0]\n colours_list[2] = colours[1]\n colours_list[1] = ((colours_list[0] + colours_list[2]) / 2).astype(int)\n # for i in range(colours_list.shape[0]):\n # colours_list[i] = colours[i]\n prev_track_id = track_id\n time.sleep(SPOTIFY_CHECK_RATE)\n \nif SPOTIFY_ENABLED:\n load_dotenv(\"spotify.env\")\n t = threading.Thread(target=spotify_monitor, args=(\n colour_gradient,\n os.environ.get(\"SPOTIFY_CLIENT_ID\"),\n os.environ.get(\"SPOTIFY_CLIENT_SECRET\"),\n os.environ.get(\"SPOTIPY_REDIRECT_URI\")\n ))\n t.start()\n\ndef encode_tuple(tup):\n return \"|\".join(map(str, tup)).encode()\n\ndef softmax(x):\n e = np.exp(x)\n return e / e.sum()\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nsock.sendto(\"wave\".encode(), (UDP_IP, UDP_PORT))\n\nwhile True:\n data = stream.read(CHUNK)\n data = np.frombuffer(data, dtype=np.int16)\n\n fourier = np.abs(np.fft.rfft(data))\n # p = np.where(fourier > 0, np.log(fourier), 0)\n p = fourier\n freqs = np.fft.rfftfreq(data.size, d=1./RATE)\n \n # plt.plot(freqs, p)\n # plt.show()\n \n p[freqs < 150] *= 2\n\n bass = np.sum(p[freqs < 150])\n mids = np.sum(p[(freqs > 150) & (freqs < 3000)])\n treble = np.sum(p[freqs > 3000])\n all_freqs = np.sum(p)\n \n if all_freqs != 0:\n bass_ratio = bass / all_freqs\n mids_ratio = mids / all_freqs\n treble_ratio = treble / all_freqs\n \n ratios = np.array([bass_ratio, mids_ratio, treble_ratio])\n # print(\"Ratios:\", (ratios * 100).astype(int))\n sm = softmax(ratios * 5)\n # print(\"Softmax:\", (sm * 100).astype(int))\n ratios = sm\n colour = np.nan_to_num(ratios.reshape(-1, 1) * colour_gradient).sum(axis=0)\n else:\n colour = np.array([0, 0, 0])\n \n rms_amp = (np.sqrt(np.mean((data * 1./32768) ** 2)) / 0.6).item()\n if rms_amp > 1.0:\n rms_amp = 1.0\n # log_rms = 20 * np.log10(rms_amp)\n # rms_amp = np.tanh(1.3 * rms_amp)\n # print(rms_amp, type(rms_amp))\n\n scaled_colour = rms_amp * colour\n \n # if scaled_colour[1:3].sum() > 130:\n # scaled_colour -= [0, 30, 30]\n # scaled_colour[scaled_colour < 0] = 0\n \n int_colour_tuple = tuple(scaled_colour.astype(int))\n int_colour_tuple = tuple(x.item() for x in int_colour_tuple)\n \n # print(sys.getsizeof(int_colour_tuple))\n # print(type(int_colour_tuple), type(int_colour_tuple[0]))\n\n # sock.sendto(pickle.dumps(frequencies.tolist()), (UDP_IP, UDP_PORT))\n sock.sendto(encode_tuple(int_colour_tuple), (UDP_IP, UDP_PORT))\n \n# plt.pcolormesh(times, frequencies, 10*np.log10(spectrogram))\n# plt.show()\n\nspectrogram = 5*np.log10(spectrogram)\n\nfor i in range(129):\n spectrogram[i] /= spectrogram[i].max()\n spectrogram[i] *= 255\n\nspectrogram[spectrogram < 0] = 0\n\n# print(spectrogram[0].max())\n# plt.plot(spectrogram[0])\n# plt.show()\n\nprev_time = 0\n\nfor i, t in enumerate(times):\n spec = [int(spectrogram[x][i]) for x in range(129)]\n spec = np.interp(np.linspace(0, len(spec) - 1, num=288), np.arange(len(spec)), spec)\n data = {\n \"leds\": list(range(288)),\n \"values\": [(x, x, x) for x in spec]\n }\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n sock.sendto(pickle.dumps(data), (UDP_IP, UDP_PORT))\n\n # print(sys.getsizeof(pickle.dumps(data)))\n\n time.sleep(t - prev_time)\n prev_time = t\n\n# data = {\n# \"leds\": [0, 25, 200],\n# \"values\": [(255, 0, 0), (0, 255, 255), (255, 255, 0)]\n# }\n\n# sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n# sock.sendto(pickle.dumps(data), (UDP_IP, UDP_PORT))", "repo_name": "Ivaneres/ws281x-rpi-playground", "sub_path": "rpi_led_client.py", "file_name": "rpi_led_client.py", "file_ext": "py", "file_size_in_byte": 7175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pyaudio.paInt16", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pyaudio.PyAudio", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 65, "usage_type": "call"}, {"api_name": "colorthief.ColorThief", "line_number": 68, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 68, "usage_type": "call"}, {"api_name": "spotipy.Spotify", "line_number": 82, "usage_type": "call"}, {"api_name": "spotipy.oauth2.SpotifyOAuth", "line_number": 82, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 92, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "sty.fg", "line_number": 111, "usage_type": "call"}, {"api_name": "sty.fg.rs", "line_number": 111, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 122, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 123, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 125, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 126, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 127, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 135, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 138, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 138, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.fft.rfft", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.fft.rfftfreq", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 215, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 221, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 221, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 222, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 226, "usage_type": "call"}]} +{"seq_id": "8309412981", "text": "from zeep import Client, AsyncClient\nfrom zeep.transports import Transport, AsyncTransport\nfrom zeep.plugins import HistoryPlugin\nfrom requests import Session\nfrom requests.auth import HTTPBasicAuth\nimport xmltodict\nfrom lxml import etree\nfrom loguru import logger\nimport yaml\nimport httpx\n\n\n# Urllib3 Disable Warnings\nimport urllib3\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n# Import Security data\nwith open(r\"config/config.yaml\", encoding=\"utf8\") as f:\n config = yaml.safe_load(f)\n\n\ndef parse_content(content):\n \"\"\"-\"\"\"\n data = xmltodict.parse(content)\n print(data)\n\n\nclass WssZeep:\n \"\"\"\n This is main class for providing communication with the WSS WebServices\n \"\"\"\n\n def __init__(self, end_point=None, async_client=False):\n if end_point is None:\n end_point = config[\"accept_solution\"][\"end_point\"]\n self.end_point = end_point\n self.user_name = config[\"accept_solution\"][\"user_name\"]\n self.user_pswd = config[\"accept_solution\"][\"user_pswd\"]\n self.history = HistoryPlugin()\n self.async_client = async_client\n self.client = None\n self.factory = None\n\n if self.async_client is True:\n httpx_client = httpx.AsyncClient(\n verify=False,\n auth=(self.user_name, self.user_pswd),\n )\n self.client = AsyncClient(\n self.end_point, transport=AsyncTransport(client=httpx_client)\n )\n else:\n session = Session()\n session.auth = HTTPBasicAuth(self.user_name, self.user_pswd)\n session.verify = False\n self.history = HistoryPlugin()\n self.client = Client(\n self.end_point,\n transport=Transport(session=session),\n plugins=[self.history],\n )\n\n # Try to initialize the factory for SVC or for ASMX\n try:\n self.factory = self.client.type_factory(\"ns2\") # SVC\n except ValueError:\n self.factory = self.client.type_factory(\"ns0\") # ASMX\n\n def get_raw_xml(self, method: str, parameters=None):\n \"\"\"\n Generate raw XML\n\n \"\"\"\n xml_str = \"\"\n try:\n node = self.client.create_message(self.client.service, method, parameters)\n xml_str = etree.tostring(node, pretty_print=True).decode()\n except BaseException as base_exception:\n logger.error(base_exception)\n return xml_str\n\n @staticmethod\n def find_value_by_field_name(dicts_list: list, field_name: str):\n \"\"\"Find Value by Field Name in list of dictionary\n\n Args:\n dicts_list (list): list of dictionary fields from zeep response\n field_name (str): Field name for search\n\n Returns:\n string: Value of Field\n \"\"\"\n finded_value = next(\n item[\"Value\"] for item in dicts_list if item[\"Name\"] == field_name\n )\n return finded_value\n\n def get_base_document_field(self, name: str, value: str):\n \"\"\"\n Generate DocumentField for WSS WebServices\n\n Args:\n name (str): Name of the field\n value (str): Value for the field\n\n Returns:\n Dict: DocumentField for WSS WebServices\n \"\"\"\n # Try to initialize the factory for SVC or for ASMX\n try:\n df_type = self.client.get_type(\"ns2:DocumentField\")\n except ValueError:\n df_type = self.client.get_type(\"ns0:DocumentField\")\n document_field = df_type(Name=name, Value=value)\n return document_field\n\n def get_lookup_document_field(self, name: str, search_field: str, value: str):\n \"\"\"\n Generate DocumentLookupField for WSS WebServices\n\n Args:\n name (str): Name of field(system)\n search_field (str): Name of field for LookUp list search\n value (str): Value for LookUp field search\n\n Returns:\n zeep_object: DocumentLookupField for WSS WebServices\n \"\"\"\n # Try to initialize the factory for SVC or for ASMX\n try:\n df_type = self.client.get_type(\"ns2:DocumentLookupField\")\n except ValueError:\n df_type = self.client.get_type(\"ns0:DocumentLookupField\")\n document_field = df_type(Name=name, SearchField=search_field, Value=value)\n return document_field\n\n def get_file_document_field(self, field_name: str, file_name: str, base64_str: str):\n \"\"\"Generate DocumentFilesField for WSS WebServices\n\n Args:\n field_name (str): Name of field\n file_name (str): Name of file\n base64_str (str): File content in base64 format\n\n Returns:\n _type_: _description_\n \"\"\"\n\n base64_str = base64_str.encode(\"UTF-8\")\n\n # Try to initialize the factory for SVC or for ASMX\n try:\n df_type = self.client.get_type(\"ns2:DocumentFilesField\")\n except ValueError:\n df_type = self.client.get_type(\"ns0:DocumentFilesField\")\n\n try:\n if_type = self.client.get_type(\"ns2:ItemFile\")\n except ValueError:\n if_type = self.client.get_type(\"ns0:ItemFile\")\n\n file_data = if_type(Content=base64_str, Name=file_name)\n file_field = df_type(Name=field_name, Files=file_data)\n return file_field\n\n def get_param_get_document_by_fields(\n self,\n list_name: str = \"Users\",\n user_mail: str = \"test2@wss-consulting.ru\",\n searchable_field: dict = None,\n ):\n \"\"\"\n Generate 'document' for soap request\n\n Args:\n client (Client): zeep client\n\n list_name (str, optional): The NAME of list in which the search will be performed.\n Defaults to 'Users'.\n\n user_mail (str, optional): UserMail from what name will the search be performed.\n Defaults to 'test2@wss-consulting.ru'.\n\n searchable_field (dict, optional): {FieldName: SearchValue}.\n Defaults to { 'Имя пользователя': 'Кижапкина Елена' }.\n \"\"\"\n\n if searchable_field is None:\n searchable_field = {\"Имя пользователя\": \"Кижапкина Елена\"}\n\n factory = self.factory\n\n # Generete list of search parameters [Value, Data] for SearchableField\n searchable_field_list = self.dict_to_searchable_field_list(\n factory, searchable_field\n )\n\n # Generate parameters body\n param = factory.SearchParameters(\n Fields=factory.ArrayOfSearchableField(\n SearchableField=searchable_field_list\n ),\n ListName=list_name,\n UserMail=user_mail,\n )\n\n return param\n\n def get_param_accept_document_solution(\n self,\n comment_to_solution: str = None,\n emails_to_notify: str = None,\n reg_number: str = None,\n solution: str = None,\n user_email: str = None,\n base_fields: list = None,\n # files_files:list = None,\n ):\n \"\"\"Generate parameters for AcceptDocumentRequestParameters (method WSS Webserives)\n\n Args:\n comment_to_solution (str, optional): Comment to solution. Defaults to None.\n emails_to_notify (str, optional): To whom the message will be sent. Defaults to None.\n reg_number (str, optional): RegNumber of documents in the WssDocs. Defaults to None.\n solution (str, optional): System name of solution which will be used. Defaults to None.\n user_email (str, optional): On whose behalf accept solution. Defaults to None.\n base_fields (list, optional): ns2:DocumentField structure\n can be generate by get_base_document_field. Defaults to None.\n files_files (list, optional): ns2:DocumentFilesField # Don't work. Defaults to None.\n lookup_fields (list, optional): ns2:DocumentLookupField # Don't work. Defaults to None.\n\n Returns:\n ZeepObject: Structure of data for WssZeep\n \"\"\"\n\n # Set the default value to the variable\n if comment_to_solution is None:\n comment_to_solution = \"Тестовый комментарий\"\n if emails_to_notify is None:\n emails_to_notify = config[\"web_service\"][\"test_email\"]\n if reg_number is None:\n reg_number = \"Пр-ТК-2021-0003\"\n if solution is None:\n solution = \"Разослать\"\n if user_email is None:\n user_email = config[\"web_service\"][\"test_email\"]\n\n factory = self.factory\n\n # Generate parameters body\n param = factory.AcceptDocumentRequestParameters(\n CommentToSolution=comment_to_solution,\n # DocumentFields=[{'BaseDocumentField':[df,df2]},],\n DocumentFields=[{\"BaseDocumentField\": base_fields}],\n Emails=emails_to_notify,\n RegNumber=reg_number,\n SolutionName=solution,\n User=user_email,\n )\n\n return param\n\n def get_param_create_document(\n self,\n doc_type: str,\n user_email: str,\n base_fields: list = None,\n ):\n\n factory = self.factory\n\n param = factory.RequestParameters(\n AgreementInfo=\"\",\n DocType=doc_type,\n DocumentFiles=\"\",\n FieldValues=[{\"BaseDocumentField\": base_fields}],\n UserMail=user_email,\n )\n\n return param\n\n def get_param_for_update_element(self, structure: dict):\n \"\"\"Generate parameters for the update element\n\n Example structure:\n upd_srructure = {\n 'ListID': 1,\n 'Upd_items': [\n {\n 'ID': 1,\n 'Field':[\n {'Name': '',\n 'Value': ''}\n ],\n 'Lookup_Field': [\n {'Name': '',\n 'Value': ''}\n ],\n 'Multi_Lookup_Field':[\n {'Name': '',\n 'Value': ''}\n ]\n }\n ]\n }\n\n Example:\n upd_param = update_parameters(\n ListID=869,\n UpdateItems=array_of_update_item([update_item(\n LookupItemId=8986,\n LookupValue=array_of_item_field([\n item_field('Префикс', 'qwe'),\n item_lookup_field('Руководитель',\n Item = '1'\n ),\n item_lookup_multi_field('Вид коммунальных услуг',\n Items=array_of_lookup_item(\n LookupItem = [ 1,2 ]\n ))\n ])\n )])\n )\n\n ## Tips\n For Clear value in `item_lookup_field` need send Item = 0\n For Clear value in `item_lookup_multi_field` need send LookupItem = []\n \"\"\"\n\n ns7 = self.client.type_factory(\"ns7\")\n ns6 = self.client.type_factory(\"ns6\")\n update_parameters = ns7.UpdateParameters\n array_of_update_item = ns7.ArrayOfUpdateItem\n update_item = ns7.UpdateItem\n array_of_item_field = ns6.ArrayOfItemField\n item_field = ns6.ItemField\n item_lookup_field = ns6.ItemLookupField\n array_of_lookup_item = ns6.ArrayOfLookupItem\n item_lookup_multi_field = ns6.ItemLookupMultiField\n\n upd_param = update_parameters(\n ListID=869,\n UpdateItems=array_of_update_item(\n [\n update_item(\n LookupItemId=8986,\n LookupValue=array_of_item_field(\n [\n item_field(\"Префикс\", \"qwe\"),\n item_lookup_field(\"Руководитель\", Item=\"1\"),\n item_lookup_multi_field(\n \"Вид коммунальных услуг\",\n Items=array_of_lookup_item(LookupItem=[1, 2]),\n ),\n ]\n ),\n )\n ]\n ),\n )\n return upd_param\n\n def get_param_from_structure_update(self, structure: dict):\n \"\"\"Generate parameters for the update element\n\n Example structure:\n upd_srructure = {\n 'ListID': 1,\n 'Upd_items': [\n {\n 'ID': 1,\n 'Field':[\n {'Name': '',\n 'Value': ''}\n ],\n 'Lookup_Field': [\n {'Name': '',\n 'Value': ''}\n ],\n 'Multi_Lookup_Field':[\n {'Name': '',\n 'Value': ''}\n ]\n }\n ]\n }\n\n Example:\n upd_param = update_parameters(\n ListID=869,\n UpdateItems=array_of_update_item([update_item(\n LookupItemId=8986,\n LookupValue=array_of_item_field([\n item_field('Префикс', 'qwe'),\n item_lookup_field('Руководитель',\n Item = '1'\n ),\n item_lookup_multi_field('Вид коммунальных услуг',\n Items=array_of_lookup_item(\n LookupItem = [ 1,2 ]\n ))\n ])\n )])\n )\n\n ## Tips\n For Clear value in `item_lookup_field` need send Item = 0\n For Clear value in `item_lookup_multi_field` need send LookupItem = []\n \"\"\"\n\n ns7 = self.client.type_factory(\"ns7\")\n ns6 = self.client.type_factory(\"ns6\")\n update_parameters = ns7.UpdateParameters\n array_of_update_item = ns7.ArrayOfUpdateItem\n update_item = ns7.UpdateItem\n array_of_item_field = ns6.ArrayOfItemField\n item_field = ns6.ItemField\n item_lookup_field = ns6.ItemLookupField\n array_of_lookup_item = ns6.ArrayOfLookupItem\n item_lookup_multi_field = ns6.ItemLookupMultiField\n\n # print(structure)\n\n update_items = []\n\n for item in structure[\"Upd_items\"]:\n upd_fields = []\n\n # for field in item['Field']:\n # print(field)\n # upd_fields.append(item_field(field['Name'], field['Value']))\n\n # add simple fields\n upd_fields += list(\n [item_field(Name=i[\"Name\"], Value=i[\"Value\"]) for i in item[\"Field\"]]\n )\n\n # add LookUp fields\n upd_fields += list(\n [\n item_lookup_field(Name=i[\"Name\"], Item=i[\"Value\"])\n for i in item[\"Lookup_Field\"]\n ]\n )\n\n # add MultiLookUp fields\n upd_fields += list(\n [\n item_lookup_multi_field(\n Name=i[\"Name\"],\n Items=array_of_lookup_item(LookupItem=i[\"Value\"]),\n )\n for i in item[\"Multi_Lookup_Field\"]\n ]\n )\n\n # print(upd_fields)\n\n update_items.append(\n update_item(\n LookupItemId=item[\"ID\"], LookupValue=array_of_item_field(upd_fields)\n )\n )\n\n upd_param = update_parameters(\n ListID=structure[\"ListID\"], UpdateItems=array_of_update_item(update_items)\n )\n return upd_param\n\n def send_request(self, method_str, param):\n \"\"\"\n Spend request to WebServices and try catch Error\n\n Args:\n method_str (str): Name one of the available methods:\n AcceptDocumentSolution,\n CreateDocument,\n CreateElement,\n FindObjects,\n GetDocumentsByFields,\n GetFile,\n UpdateElement,\n param (dict): Structure of parameters of wsszeep for web services\n \"\"\"\n client = self.client\n return_result = True\n\n factory_method = {\n \"AcceptDocumentSolution\": client.service.AcceptDocumentSolution,\n \"CreateDocument\": client.service.CreateDocument,\n \"CreateElement\": client.service.CreateElement,\n \"FindObjects\": client.service.FindObjects,\n \"GetDocumentsByFields\": client.service.GetDocumentsByFields,\n \"GetFile\": client.service.GetFile,\n \"UpdateElement\": client.service.UpdateElement,\n }\n\n if method_str in factory_method and param is not None:\n method = factory_method[method_str]\n try:\n result = method(param)\n return_result = result\n except ConnectionError:\n logger.info(\"ConnectionError\")\n return_result = False\n\n error = self.check_result(result)\n if error:\n logger.info(error)\n return_result = False\n\n else:\n logger.info(\"Incorrect params\")\n return_result = False\n\n return return_result\n\n def find_objects(\n self,\n client: Client,\n start_date: str = \"2020-11-18T23:59:59\",\n end_date: str = \"2020-11-18T00:00:00\",\n ais_number: str = \"ТО02КО0200001187\",\n ):\n \"\"\"\n # ? Need to test\n \"\"\"\n\n try:\n factory = client.type_factory(\"ns4\")\n except ValueError:\n factory = client.type_factory(\"ns0\")\n\n param = factory.SearchObjectsRequestParameters(\n StartDate=start_date, EndDate=end_date, AISNumber=ais_number\n )\n\n return param\n\n def dict_to_searchable_field_list(self, factory, dict_value: dict):\n \"\"\"\n Change structure data by dict to list format [Name:{name}, Value:{value}]\n \"\"\"\n result = []\n keys = dict_value.keys()\n\n for key in keys:\n result.append(factory.SearchableField(Name=key, Value=dict_value[key]))\n\n return result\n\n def check_result(self, result):\n \"\"\"\n Check result from Zeep\n If return None all is good\n Or It's error msg\n \"\"\"\n if result is None:\n return result\n\n # ? There are Result and OperationResult in result, but i just skip parse it\n\n error = \"Can't parse result from Zeep\"\n\n try:\n error = result.ErrorMessage\n except AttributeError:\n pass\n\n try:\n error = result.ErrorText\n except AttributeError:\n pass\n\n return error\n\n def print_history(self):\n \"\"\"\n Print last request and response. This function only for debug\n \"\"\"\n try:\n for hist in [self.history.last_sent, self.history.last_received]:\n print(\n etree.tostring(\n hist[\"envelope\"], encoding=\"unicode\", pretty_print=True\n )\n )\n except (IndexError, TypeError):\n # catch cases where it fails before being put on the wire\n pass\n\n def pares_last_response(self):\n \"\"\"\n # ! Don't work\n \"\"\"\n try:\n data = xmltodict.parse(self.history.last_received)\n print(data)\n except (IndexError, TypeError):\n # catch cases where it fails before being put on the wire\n logger.info(\"None\")\n\n\ndef test_1():\n \"\"\"Test work on WssDocs\"\"\"\n wsszeep = WssZeep()\n bdf = wsszeep.get_base_document_field(\"Содержание\", \"Здесь был тест\")\n param = wsszeep.get_param_accept_document_solution(\n comment_to_solution=\"\",\n emails_to_notify=\"\",\n reg_number=\"\",\n solution=\"\",\n user_email=\"\",\n base_fields=[bdf],\n )\n zeep_result = wsszeep.send_request(\"AcceptDocumentSolution\", param)\n logger.info(zeep_result)\n return zeep_result\n # wsszeep.print_history()\n # wsszeep.pares_last_response()\n\n\ndef test_2():\n \"\"\"Test update Element\"\"\"\n wsszeep = WssZeep()\n param = None\n\n upd_srructure = {\n \"ListID\": 821,\n \"Upd_items\": [\n {\n \"ID\": 64841,\n \"Field\": [\n # {\"Name\": \"Номер для печати\", \"Value\": \"123\"}\n ],\n \"Lookup_Field\": [{\"Name\": \"Адресат\", \"Value\": 5782}],\n \"Multi_Lookup_Field\": [\n # {\"Name\": \"Вид коммунальных услуг\", \"Value\": [1, 2, 3]}\n ],\n }\n ],\n }\n\n # param = wsszeep.get_param_for_update_element(upd_srructure)\n param = wsszeep.get_param_from_structure_update(upd_srructure)\n zeep_result = wsszeep.send_request(\"UpdateElement\", param)\n logger.info(zeep_result)\n # print(param)\n # xml = wsszeep.get_raw_xml('UpdateElement', param)\n # print(xml)\n\n\ndef main():\n \"\"\"\n Main function for test class\n\n Available methods:\n AcceptDocumentSolution\n CreateDocument\n CreateElement\n FindObjects\n GetDocumentsByFields\n GetFile\n UpdateElement\n\n 1) Create wsszeep client:\n wsszeep = WssZeep()\n\n 2) Create param for one of the methods (example GetDocumentsByFields):\n param = wsszeep.get_param_get_document_by_fields()\n\n 2.1) Some methods need to specify parameters:\n DocumentField, DocumentFilesField, DocumentLookupField\n You can get it for special method:\n\n\n 3) CHOOSE:\n -- create Raw XML:\n xml = wsszeep.get_raw_xml('GetDocumentsByFields', param)\n\n -- Spend request and parse response:\n wsszeep.send_request('GetDocumentsByFields', param)\n wsszeep.print_history()\n wsszeep.pares_last_response()\n \"\"\"\n # wsszeep = WssZeep()\n # param = wsszeep.get_param_get_document_by_fields()\n\n # example how generate Raw XML request\n # xml = wsszeep.get_raw_xml('GetDocumentsByFields', param)\n # print(xml)\n\n # Example how use WebServices\n # wsszeep.send_request('GetDocumentsByFields', param)\n # wsszeep.print_history()\n # wsszeep.pares_last_response()\n\n # df = wsszeep.get_base_document_field(name='Содержани', value='qaz')\n # param = wsszeep.get_param_accept_document_solution(base_fields=[df])\n # xml = wsszeep.get_raw_xml('AcceptDocumentSolution', parameters=param)\n # print(xml)\n\n # TEST WORK\n\n # test_1()\n test_2()\n\n\nif __name__ == \"__main__\":\n main()\n # print(config['accept_solution'])\n", "repo_name": "Ads369/WSS_Helper", "sub_path": "services/wss_webservices_controller_zeep.py", "file_name": "wss_webservices_controller_zeep.py", "file_ext": "py", "file_size_in_byte": 23300, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 16, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 20, "usage_type": "call"}, {"api_name": "xmltodict.parse", "line_number": 25, "usage_type": "call"}, {"api_name": "zeep.plugins.HistoryPlugin", "line_number": 40, "usage_type": "call"}, {"api_name": "httpx.AsyncClient", "line_number": 46, "usage_type": "call"}, {"api_name": "zeep.AsyncClient", "line_number": 50, "usage_type": "call"}, {"api_name": "zeep.transports.AsyncTransport", "line_number": 51, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 55, "usage_type": "call"}, {"api_name": "zeep.plugins.HistoryPlugin", "line_number": 57, "usage_type": "call"}, {"api_name": "zeep.Client", "line_number": 58, "usage_type": "call"}, {"api_name": "zeep.transports.Transport", "line_number": 60, "usage_type": "call"}, {"api_name": "lxml.etree.tostring", "line_number": 78, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 78, "usage_type": "name"}, {"api_name": "loguru.logger.error", "line_number": 80, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 80, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 505, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 505, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 510, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 510, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 514, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 514, "usage_type": "name"}, {"api_name": "zeep.Client", "line_number": 521, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 585, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 585, "usage_type": "name"}, {"api_name": "xmltodict.parse", "line_number": 598, "usage_type": "call"}, {"api_name": "loguru.logger.info", "line_number": 602, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 602, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 618, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 618, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 648, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 648, "usage_type": "name"}]} +{"seq_id": "41034239538", "text": "import concurrent.futures\nimport socket\nimport threading\nimport datetime\nfrom termcolor import colored\nfrom main import url\n\n\nstart_time = datetime.datetime.now()\n\n# Threading - We can Perform multiple task simultaneously\nprint_lock = threading.Lock()\nprint(\"\\n\")\nprint(colored(\"Port Scanning Process\", 'red'))\nprint(\"\\n\")\n# Take input from the user\nserver = (url)\nip = socket.gethostbyname(server)\nprint(\"The ip address of host is\", colored(ip, 'green'))\n\n# To print new line\nprint(\"\\n\")\n\n#To print the time when service started.\nprint(\"This process will check all the port from 1 to 65,535.\\n\")\nprint(\"The service started at {}\".format(start_time.strftime(\"%c\")))\nprint(\"\\n\")\n\n# Pass the ip and port into the function.\ndef scan_port(ip, port):\n # The socket in the Internet domain, and\n # configures it for stream-oriented communication with default TCP protocol\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(1)\n try:\n s.connect((ip, port))\n s.close()\n with print_lock:\n print('Port', colored(port, 'green'), 'and service', colored(socket.getservbyport(port), 'green'),\n ' is open')\n except:\n pass\n\n\nwith concurrent.futures.ThreadPoolExecutor(max_workers=5000) as executor:\n for port in range(65535):\n executor.submit(scan_port, ip, port + 1)\n\nend_time = datetime.datetime.now()\n\n# To print the time when service ended.\nprint(\"\\nThe service ended at {}\".format(end_time.strftime(\"%c\")))\nprint(\"\\n\")\n", "repo_name": "SHAHKRISHS/The-Inspection-Tool", "sub_path": "portscanner.py", "file_name": "portscanner.py", "file_ext": "py", "file_size_in_byte": 1508, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "datetime.datetime.now", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 12, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 14, "usage_type": "call"}, {"api_name": "main.url", "line_number": 17, "usage_type": "name"}, {"api_name": "socket.gethostbyname", "line_number": 18, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 19, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 33, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 33, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 33, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 39, "usage_type": "call"}, {"api_name": "socket.getservbyport", "line_number": 39, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 45, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 45, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "8617011789", "text": "import rasterio.warp\nfrom osgeo import osr\nimport sys\nimport click\nimport yaml\nfrom pathlib import Path\n\nfrom datacube.index.hl import Doc2Dataset\nimport datacube\n\n# Construct metadata dict\n# import uuid\nfrom xml.etree import ElementTree # should use cElementTree..\nfrom dateutil import parser\nimport os\n\n# Global variable\nbands = ['vh', 'vv']\n\ndef get_geometry(path):\n with rasterio.open(path) as img:\n# t0=parser.parse(img.get_tag_item('ACQUISITION_START_TIME'))\n# t1=parser.parse(img.get_tag_item('ACQUISITION_STOP_TIME'))\n t0=parser.parse(path.split('/')[-1].split('_')[4])\n t1=parser.parse(path.split('/')[-1].split('_')[5])\n left, bottom, right, top = img.bounds\n crs = str(str(getattr(img, 'crs_wkt', None) or img.crs.wkt))\n corners = {\n 'ul': {\n 'x': left,\n 'y': top\n },\n 'ur': {\n 'x': right,\n 'y': top\n },\n 'll': {\n 'x': left,\n 'y': bottom\n },\n 'lr': {\n 'x': right,\n 'y': bottom\n }\n }\n projection = {'spatial_reference': crs, 'geo_ref_points': corners}\n\n spatial_ref = osr.SpatialReference(crs)\n t = osr.CoordinateTransformation(spatial_ref, spatial_ref.CloneGeogCS())\n\n def transform(p):\n lon, lat, z = t.TransformPoint(p['x'], p['y'])\n return {'lon': lon, 'lat': lat}\n\n extent = {key: transform(p) for key, p in corners.items()}\n\n return projection, extent, (t0, t1)\n\n\ndef ingest_sentinel1_grd_50m_beta0(path, uuid):\n dc = datacube.Datacube()\n resolver = Doc2Dataset(dc.index)\n projection, extent, (t0, t1) = get_geometry(path)\n images = {v: {'path': path, 'layer': i+1} for i, v in enumerate(bands)}\n p = Path(path)\n scene_name = p.stem[:-11]\n \n result = {\n # 'id': str(uuid.uuid4()), # Generate random uuid\n 'id': str(uuid),\n 'processing_level': \"Level-1\",\n 'product_type': \"sentinel_1_grd_50m_beta0\",\n 'creation_dt': t0,\n 'platform': {\n 'code': 'SENTINEL_1A'\n },\n 'instrument': {\n 'name': 'SAR'\n },\n 'extent': {\n 'coord': extent,\n 'from_dt': str(t0),\n 'to_dt': str(t1),\n 'center_dt': str(t0 + (t1 - t0) / 2)\n },\n 'format': {\n 'name': 'GeoTIFF'\n }, # ENVI or BEAM-DIMAP ?\n 'grid_spatial': {\n 'projection': projection\n },\n 'image': {\n 'bands': images\n },\n 'lineage': {\n 'source_datasets': {},\n 'ga_label': scene_name\n } \n }\n print(result)\n dataset, _ = resolver(result,'')\n dc.index.datasets.add(dataset) \n return True", "repo_name": "new20121439/datacube", "sub_path": "apps/download/ingest_sentinel1.py", "file_name": "ingest_sentinel1.py", "file_ext": "py", "file_size_in_byte": 2861, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rasterio.warp.open", "line_number": 21, "usage_type": "call"}, {"api_name": "rasterio.warp", "line_number": 21, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 24, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 24, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 25, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 25, "usage_type": "name"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 48, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 48, "usage_type": "name"}, {"api_name": "osgeo.osr.CoordinateTransformation", "line_number": 49, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 49, "usage_type": "name"}, {"api_name": "datacube.Datacube", "line_number": 61, "usage_type": "call"}, {"api_name": "datacube.index.hl.Doc2Dataset", "line_number": 62, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "30354275021", "text": "import pandas as pd\n\nfrom sklearn.cluster import MeanShift\n\nif __name__ == \"__main__\":\n # Cargamos los datos del dataframe de pandas\n dt = pd.read_csv('data/candy-data.csv')\n# print(dt.head())\n\nX = dt.drop('competitorname', axis=1)\n\nmeanshift = MeanShift().fit(X)\nprint(max(meanshift.labels_))\nprint('='*64)\nprint(meanshift.cluster_centers_)\n\ndt['meanshift']= meanshift.labels_\nprint(dt.head())\n", "repo_name": "kwuan95/Curso_profesional_de_ML_con_Scikit_Learn", "sub_path": "scrips/mean-shift.py", "file_name": "mean-shift.py", "file_ext": "py", "file_size_in_byte": 403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.cluster.MeanShift", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "11272902594", "text": "import json\nimport yaml\nimport requests\nimport urllib\nimport pandas as pd\nfrom collections import defaultdict\n\nfrom pathlib import Path\nimport sys\n\ntmp_dir = Path(__file__).parent / '..' / 'tmp'\nselenium_dir = (tmp_dir / 'selenium').resolve(strict=False)\n\nauth_data = {}\napi_cookies = defaultdict(str)\napi_debug = False\n\nquery_tmpl = \"{url}/api/v1/query?query={q}\"\nquery_range_tmpl = \"{url}/api/v1/query_range?query={q}&start={start}&end={end}&step={step}\"\nmetrics_tmpl = \"{url}/api/v1/label/__name__/values\"\n# metrics_tmpl = \"{url}/api/v1/targets/metadata\"\n\n\ndef dates_range(period='1w', step='1h', start=None):\n if start is None:\n start = pd.Timestamp('now') - pd.Timedelta(period)\n return {'period': pd.Timedelta(period), 'step': pd.Timedelta(step), 'start': pd.Timestamp(start)}\n\n\ndef request_data(cloud, url):\n if cloud in api_cookies.keys():\n req_headers = {'cookie': api_cookies[cloud]}\n else:\n req_headers = {}\n if api_debug:\n print(url)\n r = requests.get(url, headers=req_headers, verify=auth_data[cloud]['verify_cert'])\n if r.url.startswith('https://keycloak'):\n hack_url = query_tmpl.format(\n url=auth_data[cloud]['url'], q=urllib.parse.quote('1'),\n )\n api_cookies[cloud] = hack_keycloak_cookies(hack_url, auth_data[cloud]['keystone_auth'])\n r = requests.get(url, headers={'cookie': api_cookies[cloud]}, verify=auth_data[cloud]['verify_cert'])\n return json.loads(r.content)\n\n\ndef q(query, cloud, period=None, step='1h', start=None, output_format='json', metric=None):\n if period:\n if type(period) == dict:\n start = period['start']\n step = period['step']\n period = period['period']\n if start:\n date_start = pd.Timestamp(start)\n else:\n date_start = pd.Timestamp('now') - pd.Timedelta(period)\n date_end = date_start + pd.Timedelta(period)\n date_step = pd.Timedelta(step)\n url = query_range_tmpl.format(\n url=auth_data[cloud]['url'], q=urllib.parse.quote(query),\n start=date_start.timestamp(),\n end=date_end.timestamp(),\n step=date_step.total_seconds()\n )\n else:\n url = query_tmpl.format(url=auth_data[cloud]['url'], q=urllib.parse.quote(query))\n data = request_data(cloud, url)\n if output_format == 'json':\n return data\n if output_format == 'df':\n return data_to_df(data, column_name_field=metric)\n raise \"Unknown output format\"\n\n\ndef hack_keycloak_cookies(url, auth_data):\n import os\n from selenium import webdriver\n from selenium.webdriver.common.by import By\n import urllib\n import platform\n import zipfile\n import io\n import stat\n chrome_options = webdriver.ChromeOptions()\n prefs = {\"profile.default_content_setting_values.notifications\": 2}\n chrome_options.add_experimental_option(\"prefs\", prefs)\n chrome_options.add_argument(f\"user-data-dir={selenium_dir}/user-data-dir\")\n chrome_options.add_argument('--headless')\n chrome_options.add_argument('--disable-gpu') # Last I checked this was necessary.\n chrome_options.add_argument(\"--remote-debugging-port=9222\")\n chrome_options.add_argument(\"--no-first-run\")\n chrome_options.add_argument(\"--no-sandbox\")\n chrome_options.add_argument(\"--autoplay-policy=no-user-gesture-required\")\n # chrome_options.add_argument(\"--use-fake-ui-for-media-stream\")\n # chrome_options.add_argument(\"--use-fake-device-for-media-stream\")\n chrome_options.add_argument(\"--disable-sync\")\n chrome_options.add_argument('ignore-certificate-errors')\n\n if selenium_dir not in sys.path:\n sys.path.append(str(selenium_dir))\n\n try:\n driver = webdriver.Chrome(options=chrome_options)\n except:\n if api_debug:\n print(\"Some issues with webdriver. Downloading newest version\")\n release_id_page = urllib.request.urlopen(\"https://chromedriver.storage.googleapis.com/LATEST_RELEASE\")\n release_id = release_id_page.read().decode(\"utf8\")\n release_id_page.close()\n file_extensions = {\n 'Linux': 'linux64',\n 'Darwin': 'mac64',\n 'Windows': 'win32'\n }\n chromedriver_url = \"https://chromedriver.storage.googleapis.com/{}/chromedriver_{}.zip\".format(\n release_id, file_extensions[platform.system()])\n zip_source = zipfile.ZipFile(io.BytesIO(urllib.request.urlopen(chromedriver_url).read()))\n Path(selenium_dir).mkdir(parents=True, exist_ok=True)\n zip_source.extract(\"chromedriver\", selenium_dir)\n st = os.stat(selenium_dir / 'chromedriver')\n os.chmod(selenium_dir / 'chromedriver', st.st_mode | stat.S_IEXEC)\n driver = webdriver.Chrome(options=chrome_options)\n\n driver.get(url)\n if driver.current_url.startswith('https://keycloak.'):\n driver.find_element(By.ID, \"username\").send_keys(auth_data['username'])\n driver.find_element(By.ID, \"password\").send_keys(auth_data['password'])\n driver.find_element(By.NAME, \"login\").click()\n cookies = '; '.join(['{}={}'.format(x['name'], x['value']) for x in driver.get_cookies()])\n return cookies\n\n\ndef data_to_df(data, column_name_field=None, raw_data=False):\n def serialize_data(data, name):\n (i, d) = zip(*data)\n return pd.Series(pd.to_numeric(d), index=pd.to_datetime(pd.to_numeric(i) * 1000 ** 3), name=name)\n\n columns_data = []\n df_metric = pd.DataFrame()\n\n if data['status'] == 'success':\n if data['data']['resultType'] == 'vector':\n for c in data['data']['result']:\n if not column_name_field:\n column_name = 'result'\n else:\n try:\n column_name = c['metric'][column_name_field]\n except KeyError:\n print(c['metric'].keys())\n raise\n columns_data.append(serialize_data([c['value']], name=column_name))\n if raw_data:\n df_metric[column_name] = pd.Series(c['metric'])\n if data['data']['resultType'] == 'matrix':\n for c in data['data']['result']:\n if not column_name_field:\n column_name = 'result'\n else:\n try:\n column_name = c['metric'][column_name_field]\n except KeyError:\n print(\"Not found metric {} in {}, using 'result'\".format(column_name_field, c['metric'].keys()))\n column_name = 'result'\n columns_data.append(serialize_data(c['values'], column_name))\n if raw_data:\n df_metric[column_name] = pd.Series(c['metric'])\n df_result = pd.DataFrame(columns_data).T\n if raw_data:\n return [df_result, df_metric]\n return df_result\n\n\ndef init(auth_file, selenium_path=None):\n with open(auth_file) as f:\n globals()['auth_data'] = yaml.safe_load(f)\n for c in auth_data.keys():\n if 'verify_cert' not in auth_data[c]:\n auth_data[c]['verify_cert'] = True\n print('ping', c, '-', q('1', c)['status'])\n\n\ndef get_metrics(cloud):\n url = metrics_tmpl.format(url=auth_data[cloud]['url'])\n data = request_data(cloud, url)\n return data\n\n\nQUERIES = {\n 'EU_OVERLOADED_NODES': '(avg(quantile_over_time(0.8, node_load15[2w])) by (node)) / (sum(label_replace(openstack_nova_vcpus, \"node\", \"$1\", \"hostname\", \"(.*)\")) by (node)) > 1.2',\n 'US_OVERLOADED_NODES': 'avg(quantile_over_time (0.8, system_load15{host=~\"cmp.*\"}[2w])) by (host) / sum(label_replace(openstack_nova_vcpus, \"host\", \"$1\", \"hostname\", \"(.*)\")) by (host) > 0.8',\n 'EU_NODES_RAM_ALLOC': 'sum(openstack_nova_ram - openstack_nova_free_ram) / sum(openstack_nova_ram)',\n 'EU_NODES_RAM_USAGE': 'sum(node_memory_MemTotal_bytes - node_memory_MemFree_bytes - node_memory_Buffers_bytes - node_memory_Cached_bytes)/1024/1024 / sum(openstack_nova_ram)',\n 'EU_NODES_CPU_ALLOC': 'sum(openstack_nova_used_vcpus) / sum(openstack_nova_vcpus)',\n 'EU_NODES_CPU_USAGE': 'sum(node_load15) / sum(openstack_nova_vcpus)',\n 'US_NODES_RAM_ALLOC': 'sum(openstack_nova_ram - openstack_nova_free_ram) / sum(openstack_nova_ram)',\n 'US_NODES_RAM_USAGE': 'sum(mem_used) / sum(openstack_nova_ram) / 1024 / 1024',\n 'US_NODES_CPU_ALLOC': 'sum(openstack_nova_used_vcpus) / sum(openstack_nova_vcpus)',\n 'US_NODES_CPU_USAGE': 'sum(system_load15) / sum(openstack_nova_vcpus)',\n 'EU_PROJECT_QUOTA_RAM': 'avg(openstack_nova_quota_ram{project_name=~\".*team\"}) by (project_name)',\n}\n", "repo_name": "dmi-try/python-misc-lib", "sub_path": "misc/prometheus.py", "file_name": "prometheus.py", "file_ext": "py", "file_size_in_byte": 8645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 40, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 58, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 60, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 60, "usage_type": "attribute"}, {"api_name": "urllib.parse.quote", "line_number": 66, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 66, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 84, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 84, "usage_type": "name"}, {"api_name": "sys.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 103, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 103, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 107, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 107, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 116, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 117, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 117, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 117, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 118, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 120, "usage_type": "call"}, {"api_name": "os.chmod", "line_number": 121, "usage_type": "call"}, {"api_name": "stat.S_IEXEC", "line_number": 121, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 122, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 122, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 126, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 126, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 127, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 127, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.NAME", "line_number": 128, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 128, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 139, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 154, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 168, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "19575391220", "text": "import cv2\nimport socket\nimport pickle\nimport struct\nimport time\n\n# This streams the camera over udp\n# You can change the Quality and Size of the JPG you are sending to test tradeoffs on packet size\n\n# Define the UDP IP address and port to send the stream to\nUDP_IP = '127.0.0.1' # Change this to the IP address of the receiving machine\nUDP_PORT = 12345 # Change this to an available UDP port on the receiving machine\n\n# Before creating the socket, set the buffer size\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nsock.setsockopt(socket.SOL_SOCKET, socket.SO_SNDBUF, 65536) # Set buffer size to 65536 bytes\n\n\n# Open the laptop's camera\ncap = cv2.VideoCapture(0)\nQUALITY = 20\n#create trackbar for quality\ncv2.namedWindow('frame')\ncv2.createTrackbar('QUALITY', 'frame', 1, 100, lambda x: None)\ncv2.setTrackbarPos('QUALITY', 'frame', QUALITY)\nRESIZE = 50\n#trackbar for resize\ncv2.createTrackbar('RESIZE', 'frame', 1, 100, lambda x: None)\ncv2.setTrackbarPos('QUALITY', 'frame', RESIZE)\n\n#trackbar for len\ncv2.createTrackbar('PACKET_SIZE', 'frame', 1, 10000, lambda x: None)\n\n\nwhile True:\n try:\n # Capture a frame from the camera\n ret, frame = cap.read()\n #show\n #cv2.imshow('frame',frame)\n #print resolution\n #print(frame.shape)\n frame = cv2.resize(frame, (0,0), fx=RESIZE/100, fy=RESIZE/100)\n # send frame as jpg over udp\n encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), QUALITY]\n frame = cv2.imencode('.jpg', frame, encode_param)[1].tobytes()\n print(len(frame))\n #if len > 60000 dont sent\n if len(frame) < 60000:\n sock.sendto(frame, (UDP_IP, UDP_PORT))\n #string saying \"time \" and current time\n timestring = \"time \" + str(time.time())\n #send string \"time\"+ time now\n sock.sendto(timestring.encode(), (UDP_IP, UDP_PORT))\n \n #slider to set quality\n QUALITY = cv2.getTrackbarPos('QUALITY', 'frame')\n RESIZE = cv2.getTrackbarPos('RESIZE', 'frame')\n if(RESIZE == 0):\n RESIZE = 1\n #set PACKET_SIZE trackbar value to len(frame)\n cv2.setTrackbarPos('PACKET_SIZE', 'frame', len(frame)) \n \n #print image size in bytes\n # sleep 0.05\n cv2.waitKey(50)\n except:\n print(\"Error sending frame\")\n \n\n# Release the camera and close the socket when done\ncap.release()\nsock.close()\n", "repo_name": "AIRLab-POLIMI/PhysicalMetaverse", "sub_path": "PhysicalMetaverseUnity/Assets/Code and Prefabs/Python Scripts/udp_camera_stream.py", "file_name": "udp_camera_stream.py", "file_ext": "py", "file_size_in_byte": 2409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "socket.socket", "line_number": 15, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 15, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 15, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 16, "usage_type": "attribute"}, {"api_name": "socket.SO_SNDBUF", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.IMWRITE_JPEG_QUALITY", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 46, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "27130129876", "text": "# By : @Codexun\n# By : Pavan Magar\n\nimport asyncio\nimport json\nimport logging\nimport platform\nimport re\nimport socket\nimport time\nimport uuid\nfrom datetime import datetime\nfrom sys import version as pyver\n\nfrom pyrogram.types import (\n CallbackQuery,\n InlineKeyboardButton,\n InlineKeyboardMarkup,\n InputMediaPhoto,\n Message,\n)\nfrom m8n import BOT_NAME, BOT_USERNAME\nfrom m8n.config import BOT_NAME\nfrom m8n.config import IMG_1\n\nimport psutil\nfrom pyrogram import Client\nfrom pyrogram import __version__ as pyrover\nfrom pyrogram import filters\nfrom pyrogram.types import Message\nfrom pytgcalls import __version__ as pytover\n\nfrom m8n import (BOT_ID, BOT_NAME, SUDO_USERS, app, boottime)\nfrom m8n import client as userbot\nfrom m8n.database.chats import get_served_chats\nfrom m8n.database.sudo import get_sudoers\nfrom m8n.database.ping import get_readable_time\n\ndef dashmarkup():\n buttons = [\n [\n InlineKeyboardButton(text=\"UpTime\", callback_data=\"UPT\"),\n InlineKeyboardButton(text=\"RAM\", callback_data=\"RAT\"),\n ],\n [\n InlineKeyboardButton(text=\"CPU\", callback_data=\"CPT\"),\n InlineKeyboardButton(text=\"DISK\", callback_data=\"DIT\"),\n ],\n [InlineKeyboardButton(text=\"🔙 BACK\", callback_data=\"settingm\")],\n ]\n return f\"🔧 **{BOT_NAME} Settings**\", buttons\n\n\nstats1 = InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton(\n text=\"System 🖥️\", callback_data=f\"sys_stats\"\n ),\n InlineKeyboardButton(\n text=\"Bots 🤖\", callback_data=f\"bot_stats\"\n ),\n ],\n [\n InlineKeyboardButton(\n text=\"Assist 🙋🏻‍♂️\", callback_data=f\"assis_stats\"\n ),\n InlineKeyboardButton(\n text=\"Storage 🔋\", callback_data=f\"sto_stats\"\n )\n ],\n [\n InlineKeyboardButton(\n text=\"Close Stats 🗑️\", callback_data=f\"statsclose\"\n ),\n ],\n ]\n)\n\nstatsback = InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton(\n text=\"🔙 Back Home\", callback_data=f\"gen_stats\"\n ),\n ],\n ]\n)\n\nstatswait = InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton(\n text=\"Getting Bot's Stats....\",\n callback_data=f\"wait_stats\",\n )\n ]\n ]\n)\n\nasync def bot_sys_stats():\n bot_uptime = int(time.time() - boottime)\n cpu = psutil.cpu_percent(interval=0.5)\n mem = psutil.virtual_memory().percent\n disk = psutil.disk_usage(\"/\").percent\n stats = f\"\"\"\n**• Uptime :** {get_readable_time((bot_uptime))}\n**• CPU :** {cpu}%\n**• RAM :** {mem}%\n**• Disk : **{disk}%\"\"\"\n return stats\n\n\n@app.on_message(filters.command(\"stats\") & ~filters.edited)\nasync def gstats(_, message):\n start = datetime.now()\n try:\n await message.delete()\n except:\n pass\n uptime = await bot_sys_stats()\n response = await message.reply_photo(\n photo=f\"{IMG_1}\",\n caption=f\"\"\"Getting Stats...\"\"\"\n )\n end = datetime.now()\n resp = (end - start).microseconds / 1000\n smex = f\"\"\"\n**{BOT_NAME} General Stats 🤖**\n \nPing: `{resp} ms`\n{uptime}\n\n**Get your needed stats from the options given below**\n \"\"\"\n await response.edit_text(smex, reply_markup=stats1)\n return\n\n\n@app.on_callback_query(\n filters.regex(\n pattern=r\"^(sys_stats|sto_stats|bot_stats|Dashboard|mongo_stats|gen_stats|assis_stats|wait_stats|stats_close)$\"\n )\n)\nasync def stats_markup(_, CallbackQuery):\n command = CallbackQuery.matches[0].group(1)\n if command == \"sys_stats\":\n await CallbackQuery.edit_message_text(\n \"Getting System Stats.. Please Wait...\", reply_markup=statswait\n )\n sc = platform.system()\n arch = platform.machine()\n ram = (\n str(round(psutil.virtual_memory().total / (1024.0 ** 3))) + \" GB\"\n )\n bot_uptime = int(time.time() - boottime)\n uptime = f\"{get_readable_time((bot_uptime))}\"\n smex = f\"\"\"\n**{BOT_NAME} System Stats 🖥️**\n\n**• Uptime :** {uptime}\n**• System Proc :** Online\n**• Platform :** {sc}\n**• Architecture:** {arch}\n**• Ram :** {ram}\n**• PyTgCalls Version :** {pytover.__version__}\n**• Python Ver :** {pyver.split()[0]}\n**• Pyrogram Ver :** {pyrover}\"\"\"\n await CallbackQuery.edit_message_text(smex, reply_markup=statsback)\n if command == \"sto_stats\":\n await CallbackQuery.edit_message_text(\n \"Getting Storage Stats.. Please Wait...\", reply_markup=statswait\n )\n hdd = psutil.disk_usage(\"/\")\n total = hdd.total / (1024.0 ** 3)\n total = str(total)\n used = hdd.used / (1024.0 ** 3)\n used = str(used)\n free = hdd.free / (1024.0 ** 3)\n free = str(free)\n smex = f\"\"\"\n**{BOT_NAME} Storage Stats 🔋**\n\n**• Storage Avail :** {total[:4]} GiB \n**• Storage Used :** {used[:4]} GiB\n**• Storage Left :** {free[:4]} GiB\"\"\"\n await CallbackQuery.edit_message_text(smex, reply_markup=statsback)\n if command == \"bot_stats\":\n await CallbackQuery.edit_message_text(\n \"Getting Bot Stats.. Please Wait...\", reply_markup=statswait\n )\n served_chats = []\n chats = await get_served_chats()\n for chat in chats:\n served_chats.append(int(chat[\"chat_id\"]))\n sudoers = await get_sudoers()\n modules_loaded = \"20\"\n j = 0\n for count, user_id in enumerate(sudoers, 0):\n try:\n user = await app.get_users(user_id)\n j += 1\n except Exception:\n continue\n smex = f\"\"\"\n**{BOT_NAME} Bot Stats 🤖**\n\n**• Modules Loaded :** {modules_loaded}\n**• Sudo Users :** {j}\n**• Served Chats :** {len(served_chats)}\"\"\"\n await CallbackQuery.edit_message_text(smex, reply_markup=statsback)\n if command == \"assis_stats\":\n await CallbackQuery.edit_message_text(\n \"Getting Assistant Stats.. Please Wait...\", reply_markup=statswait\n )\n groups_ub = channels_ub = bots_ub = privates_ub = total_ub = 0\n async for i in userbot.iter_dialogs():\n t = i.chat.type\n total_ub += 1\n if t in [\"supergroup\", \"group\"]:\n groups_ub += 1\n elif t == \"channel\":\n channels_ub += 1\n elif t == \"bot\":\n bots_ub += 1\n elif t == \"private\":\n privates_ub += 1\n\n smex = f\"\"\"\n**{BOT_NAME} Assistant Stats 🚶🏻**\n\n**• Dialogs :** {total_ub}\n**• Groups :** {groups_ub} \n**• Channels :** {channels_ub} \n**• Bots :** {bots_ub}\n**• Users :** {privates_ub}\"\"\"\n await CallbackQuery.edit_message_text(smex, reply_markup=statsback)\n if command == \"gen_stats\":\n start = datetime.now()\n uptime = await bot_sys_stats()\n end = datetime.now()\n resp = (end - start).microseconds / 1000\n smex = f\"\"\"\n**{BOT_NAME} General Stats 🤖**\n\n**Ping :** `{resp} ms`\n{uptime}\n\n**Get your needed stats from the options given below**\"\"\"\n await CallbackQuery.edit_message_text(smex, reply_markup=stats1)\n if command == \"wait_stats\":\n await CallbackQuery.answer()\n\n@app.on_callback_query(filters.regex(\"statsclose\"))\nasync def statsclose(_, query: CallbackQuery):\n await query.message.delete()\n", "repo_name": "UnknownMortal/M8N-Music-Bot", "sub_path": "m8n/modules/stats.py", "file_name": "stats.py", "file_ext": "py", "file_size_in_byte": 7521, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 42, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 43, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 46, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 47, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 49, "usage_type": "call"}, {"api_name": "m8n.BOT_NAME", "line_number": 51, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 54, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 57, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 60, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 65, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 68, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 73, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 80, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 83, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 90, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 93, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "m8n.boottime", "line_number": 102, "usage_type": "name"}, {"api_name": "psutil.cpu_percent", "line_number": 103, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 104, "usage_type": "call"}, {"api_name": "psutil.disk_usage", "line_number": 105, "usage_type": "call"}, {"api_name": "m8n.database.ping.get_readable_time", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "name"}, {"api_name": "m8n.config.IMG_1", "line_number": 123, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 126, "usage_type": "name"}, {"api_name": "m8n.BOT_NAME", "line_number": 129, "usage_type": "name"}, {"api_name": "m8n.app.on_message", "line_number": 114, "usage_type": "call"}, {"api_name": "m8n.app", "line_number": 114, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 114, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 114, "usage_type": "name"}, {"api_name": "pyrogram.filters.edited", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pyrogram.types.CallbackQuery.matches", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 146, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 148, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 148, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 151, "usage_type": "call"}, {"api_name": "platform.machine", "line_number": 152, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 154, "usage_type": "call"}, {"api_name": "time.time", "line_number": 156, "usage_type": "call"}, {"api_name": "m8n.boottime", "line_number": 156, "usage_type": "name"}, {"api_name": "m8n.database.ping.get_readable_time", "line_number": 157, "usage_type": "call"}, {"api_name": "m8n.BOT_NAME", "line_number": 159, "usage_type": "name"}, {"api_name": "pytgcalls.__version__.__version__", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pytgcalls.__version__", "line_number": 166, "usage_type": "name"}, {"api_name": "sys.version.split", "line_number": 167, "usage_type": "call"}, {"api_name": "sys.version", "line_number": 167, "usage_type": "name"}, {"api_name": "pyrogram.__version__", "line_number": 168, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 169, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 169, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 171, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 171, "usage_type": "name"}, {"api_name": "psutil.disk_usage", "line_number": 174, "usage_type": "call"}, {"api_name": "m8n.BOT_NAME", "line_number": 182, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 187, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 187, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 189, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 189, "usage_type": "name"}, {"api_name": "m8n.database.chats.get_served_chats", "line_number": 193, "usage_type": "call"}, {"api_name": "m8n.database.sudo.get_sudoers", "line_number": 196, "usage_type": "call"}, {"api_name": "m8n.app.get_users", "line_number": 201, "usage_type": "call"}, {"api_name": "m8n.app", "line_number": 201, "usage_type": "name"}, {"api_name": "m8n.BOT_NAME", "line_number": 206, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 211, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 211, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 213, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 213, "usage_type": "name"}, {"api_name": "m8n.client.iter_dialogs", "line_number": 217, "usage_type": "call"}, {"api_name": "m8n.client", "line_number": 217, "usage_type": "name"}, {"api_name": "m8n.BOT_NAME", "line_number": 230, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 237, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 237, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 239, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 239, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 241, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 241, "usage_type": "name"}, {"api_name": "m8n.BOT_NAME", "line_number": 244, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.edit_message_text", "line_number": 250, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 250, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery.answer", "line_number": 252, "usage_type": "call"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 252, "usage_type": "name"}, {"api_name": "m8n.app.on_callback_query", "line_number": 140, "usage_type": "call"}, {"api_name": "m8n.app", "line_number": 140, "usage_type": "name"}, {"api_name": "pyrogram.filters.regex", "line_number": 141, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 141, "usage_type": "name"}, {"api_name": "pyrogram.types.CallbackQuery", "line_number": 255, "usage_type": "name"}, {"api_name": "m8n.app.on_callback_query", "line_number": 254, "usage_type": "call"}, {"api_name": "m8n.app", "line_number": 254, "usage_type": "name"}, {"api_name": "pyrogram.filters.regex", "line_number": 254, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 254, "usage_type": "name"}]} +{"seq_id": "5870503140", "text": "import calendar\r\nimport requests\r\nimport xlrd\r\nfrom requests_html import HTMLSession\r\n\r\nurl = \"https://bashesk.ru/corporate/tariffs/unregulated/\"\r\nsearch_string = \"г) объем фактического пикового потребления гарантирующего поставщика на оптовом рынке, МВт\"\r\nparams = (\"filter_date_from\", \"filter_date_to\")\r\nstart_year = 2019\r\nstart_month_idx = 6\r\nmonth_period = 12\r\n\r\n# Корректные значение для заданных параметров (извлечены вручную)\r\ncorrect_values = [1422.71, 1491.433, 1693.642, 1792.235, 2054.841, 2146.946,\r\n 2066.84, 2045.08, 1873.812, 1638.088, 1397.15, 1328.968]\r\n\r\nvalues = []\r\nsession = HTMLSession()\r\nfor month in range(month_period):\r\n month_idx = (start_month_idx + month) % 12 + 1\r\n year = start_year + ((start_month_idx + month) // 12)\r\n\r\n # Первый и последний день месяца\r\n date_from = \"1.\" + str(month_idx) + \".\" + str(year)\r\n date_to = str(calendar.monthrange(year, month_idx)[1]) + \".\" + str(month_idx) + \".\" + str(year)\r\n print(\"Month:\", str(month_idx) + \".\" + str(year))\r\n\r\n response = session.post(url, params={params[0]: date_from, params[1]: date_to})\r\n links = response.html.absolute_links\r\n # Поиск нужного файла\r\n for link in links:\r\n if link.find(\"ПУНЦЭМ_до 670кВт\") != -1:\r\n download_url = link\r\n break\r\n else:\r\n print(\"Xls file not found\")\r\n continue\r\n # Скачивание и запись файла\r\n print(\"Url for download:\", download_url)\r\n resp = requests.get(download_url)\r\n if resp.status_code != 200:\r\n print(\"Unsuccessful get-attempt, status code:\", resp.status_code)\r\n continue\r\n excel_name = str(date_from) + \".xls\"\r\n output = open(excel_name, \"wb\")\r\n output.write(resp.content)\r\n output.close()\r\n\r\n # Поиск нужной строки\r\n excel_file = xlrd.open_workbook(excel_name)\r\n sheet = excel_file.sheet_by_index(0)\r\n for rx in range(sheet.nrows):\r\n if sheet.row(rx)[0].value.strip() == search_string:\r\n row_idx = rx\r\n break\r\n else:\r\n print(\"Xls search string not found\")\r\n continue\r\n \r\n for i in range(sheet.ncols):\r\n val = sheet.row(row_idx)[i].value\r\n if type(val) is float:\r\n print(\"Value:\", val)\r\n values.append(val)\r\n break\r\n else:\r\n print(\"Value not found\")\r\n print()\r\n\r\nprint(\"All values:\", values)\r\nassert values == correct_values\r\n", "repo_name": "ISBogatyreva/TestTasks", "sub_path": "site_parse/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests_html.HTMLSession", "line_number": 18, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "20553835856", "text": "from typing import AbstractSet, Dict, Optional, Set, Tuple\n\nfrom third_party.pddl.pddl.core import Action, Domain, Problem, Requirements\nfrom third_party.pddl.pddl.logic import Constant\nfrom third_party.pddl.pddl.logic.base import And, Not\nfrom third_party.pddl.pddl.logic.effects import AndEffect, When\nfrom third_party.pddl.pddl.logic.predicates import DerivedPredicate, Predicate\nfrom pylogics.syntax.base import Formula, Logic\nfrom pylogics.syntax.pltl import Atomic as PLTLAtomic\nfrom pylogics.utils.to_string import to_string\n\nfrom third_party.planning_with_past.helpers.utils import (\n add_val_prefix,\n default_mapping,\n replace_symbols,\n)\nfrom third_party.planning_with_past.utils.derived_visitor import derived_predicates\nfrom third_party.planning_with_past.utils.predicates_visitor import predicates\nfrom third_party.planning_with_past.utils.val_predicates_visitor import val_predicates\n\n\nclass Compiler:\n \"\"\"Compiler of PLTLf goals into PDDL.\"\"\"\n\n def __init__(\n self,\n domain: Domain,\n problem: Problem,\n formula: Formula,\n from_atoms_to_fluent: Optional[Dict[PLTLAtomic, Predicate]] = None,\n ) -> None:\n \"\"\"\n Initialize the compiler.\n\n :param domain: the domain\n :param problem: the problem\n :param formula: the formula\n :param from_atoms_to_fluent: optional mapping from atoms to fluent\n \"\"\"\n self.domain = domain\n self.problem = problem\n self.formula = formula\n if from_atoms_to_fluent:\n self.from_atoms_to_fluent = from_atoms_to_fluent\n self.validate_mapping(domain, formula, from_atoms_to_fluent)\n else:\n self.from_atoms_to_fluent = default_mapping(self.formula)\n\n assert self.formula.logic == Logic.PLTL, \"only PLTL is supported!\"\n\n self._nondeterministic: bool = self._is_deterministic(self.domain)\n self._executed: bool = False\n self._result_domain: Optional = None\n self._result_problem: Optional = None\n\n self._derived_predicates: Set[DerivedPredicate] = set()\n\n @staticmethod\n def _is_deterministic(domain: Domain):\n \"\"\"Check if domain is non-deterministic.\"\"\"\n return True if Requirements.NON_DETERMINISTIC in domain.requirements else False\n\n @classmethod\n def validate_mapping(\n cls,\n domain: Domain,\n formula: Formula,\n from_atoms_to_fluent: Dict[PLTLAtomic, Predicate],\n ):\n \"\"\"\n Check that the mapping is valid wrt the problem instance.\n\n In particular:\n - check that all the formula atoms are covered (TODO)\n - check that all the atoms are legal wrt the formula\n - check that the fluents are legal wrt the domain\n\n :param domain:\n :param formula:\n :param from_atoms_to_fluent:\n :return:\n \"\"\"\n for atom, fluent in from_atoms_to_fluent.items():\n assert all(isinstance(t, Constant) for t in fluent.terms)\n\n @property\n def result(self) -> Tuple[Domain, Problem]:\n \"\"\"Get the result.\"\"\"\n if self._result_domain and self._result_problem is None:\n raise ValueError(\"compilation not executed yet\")\n return self._result_domain, self._result_problem\n\n def compile(self):\n \"\"\"Compute the new domain and the new problem.\"\"\"\n if self._executed:\n return self.result\n self._executed = True\n\n self._compile_domain()\n self._compile_problem()\n\n def _compile_domain(self):\n \"\"\"Compute the new domain.\"\"\"\n new_predicates = predicates(self.formula).union(val_predicates(self.formula))\n new_derived_predicates = derived_predicates(\n self.formula, self.from_atoms_to_fluent\n )\n new_whens = _compute_whens(self.formula)\n domain_actions = _update_domain_actions_det(self.domain.actions, new_whens)\n\n self._result_domain = Domain(\n name=self.domain.name,\n requirements=[\n *self.domain.requirements,\n Requirements.DERIVED_PREDICATES,\n Requirements.CONDITIONAL_EFFECTS,\n Requirements.NEG_PRECONDITION,\n ],\n types=self.domain.types,\n constants=self.domain.constants,\n predicates=[*self.domain.predicates, *new_predicates],\n derived_predicates=[\n *self.domain.derived_predicates,\n *new_derived_predicates,\n ],\n actions=domain_actions,\n )\n\n def _compile_problem(self):\n \"\"\"Compute the new problem.\"\"\"\n new_init = set(self.problem.init)\n\n self._result_problem = Problem(\n name=self.problem.name,\n domain=self._result_domain,\n requirements=self.problem.requirements,\n objects=[*self.problem.objects],\n init=new_init,\n goal=And(\n Predicate(add_val_prefix(replace_symbols(to_string(self.formula))))\n ),\n )\n\n\ndef _compute_whens(formula: Formula) -> Set[When]:\n \"\"\"Compute conditional effects for formula progression.\"\"\"\n return {When(Predicate(add_val_prefix(p.name)), p) for p in predicates(formula)}\n\n\ndef _update_domain_actions_det(\n actions: AbstractSet[Action], progression: Set[When]\n) -> Set[Action]:\n \"\"\"Update domain action when domain is deterministic.\"\"\"\n new_actions = set()\n for action in actions:\n new_actions.add(\n Action(\n name=action.name,\n parameters=[*action.parameters],\n precondition=And(action.precondition),\n effect=AndEffect(action.effect, *progression),\n )\n )\n return new_actions\n", "repo_name": "EmanueleMusumeci/RoboCup-Coach", "sub_path": "robocup_spl_temporal_goals/third_party/planning_with_past/compiler.py", "file_name": "compiler.py", "file_ext": "py", "file_size_in_byte": 5748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "third_party.pddl.pddl.core.Domain", "line_number": 27, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Problem", "line_number": 28, "usage_type": "name"}, {"api_name": "pylogics.syntax.base.Formula", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 30, "usage_type": "name"}, {"api_name": "pylogics.syntax.pltl.Atomic", "line_number": 30, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.predicates.Predicate", "line_number": 30, "usage_type": "name"}, {"api_name": "third_party.planning_with_past.helpers.utils.default_mapping", "line_number": 47, "usage_type": "call"}, {"api_name": "pylogics.syntax.base.Logic.PLTL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pylogics.syntax.base.Logic", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 56, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.predicates.DerivedPredicate", "line_number": 56, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Domain", "line_number": 59, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Requirements.NON_DETERMINISTIC", "line_number": 61, "usage_type": "attribute"}, {"api_name": "third_party.pddl.pddl.core.Requirements", "line_number": 61, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Domain", "line_number": 66, "usage_type": "name"}, {"api_name": "pylogics.syntax.base.Formula", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 68, "usage_type": "name"}, {"api_name": "pylogics.syntax.pltl.Atomic", "line_number": 68, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.predicates.Predicate", "line_number": 68, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.Constant", "line_number": 84, "usage_type": "argument"}, {"api_name": "typing.Tuple", "line_number": 87, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Domain", "line_number": 87, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Problem", "line_number": 87, "usage_type": "name"}, {"api_name": "third_party.planning_with_past.utils.predicates_visitor.predicates", "line_number": 104, "usage_type": "call"}, {"api_name": "third_party.planning_with_past.utils.val_predicates_visitor.val_predicates", "line_number": 104, "usage_type": "call"}, {"api_name": "third_party.planning_with_past.utils.derived_visitor.derived_predicates", "line_number": 105, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.core.Domain", "line_number": 111, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.core.Requirements.DERIVED_PREDICATES", "line_number": 115, "usage_type": "attribute"}, {"api_name": "third_party.pddl.pddl.core.Requirements", "line_number": 115, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Requirements.CONDITIONAL_EFFECTS", "line_number": 116, "usage_type": "attribute"}, {"api_name": "third_party.pddl.pddl.core.Requirements", "line_number": 116, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Requirements.NEG_PRECONDITION", "line_number": 117, "usage_type": "attribute"}, {"api_name": "third_party.pddl.pddl.core.Requirements", "line_number": 117, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Problem", "line_number": 133, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.logic.base.And", "line_number": 139, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.logic.predicates.Predicate", "line_number": 140, "usage_type": "call"}, {"api_name": "third_party.planning_with_past.helpers.utils.add_val_prefix", "line_number": 140, "usage_type": "call"}, {"api_name": "third_party.planning_with_past.helpers.utils.replace_symbols", "line_number": 140, "usage_type": "call"}, {"api_name": "pylogics.utils.to_string.to_string", "line_number": 140, "usage_type": "call"}, {"api_name": "pylogics.syntax.base.Formula", "line_number": 145, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.effects.When", "line_number": 147, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.logic.predicates.Predicate", "line_number": 147, "usage_type": "call"}, {"api_name": "third_party.planning_with_past.helpers.utils.add_val_prefix", "line_number": 147, "usage_type": "call"}, {"api_name": "third_party.planning_with_past.utils.predicates_visitor.predicates", "line_number": 147, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 145, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.effects.When", "line_number": 145, "usage_type": "name"}, {"api_name": "typing.AbstractSet", "line_number": 151, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Action", "line_number": 151, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 151, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.logic.effects.When", "line_number": 151, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Action", "line_number": 157, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.logic.base.And", "line_number": 160, "usage_type": "call"}, {"api_name": "third_party.pddl.pddl.logic.effects.AndEffect", "line_number": 161, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 152, "usage_type": "name"}, {"api_name": "third_party.pddl.pddl.core.Action", "line_number": 152, "usage_type": "name"}]} +{"seq_id": "15800815501", "text": "import time\nfrom argparse import ArgumentParser\nfrom http_client_selenium import SeleniumBrowser\n\nCHROME = 'chrome'\nWGET = 'wget'\nURLLIB = 'urllib'\n\n\ndef get_crawler(crawler_type):\n if crawler_type == CHROME:\n from selenium import webdriver\n chrome_options = webdriver.ChromeOptions()\n # running as root -> must be set this\n chrome_options.add_argument('--no-sandbox')\n # page view window size\n chrome_options.add_argument('window-size=1420,1080')\n # do not show window\n chrome_options.add_argument('headless')\n # disable GPU\n chrome_options.add_argument('disable-gpu')\n driver = webdriver.Chrome(chrome_options=chrome_options)\n return SeleniumBrowser(driver=driver)\n\n elif crawler_type == WGET:\n print('Selected wget as client.')\n from http_client_wget import WgetDownloader\n return WgetDownloader()\n elif crawler_type == URLLIB:\n print('Selected URLlib as client.')\n from http_client_urllib import UrllibDownloader\n return UrllibDownloader()\n else:\n print('Using default client - URLlib.')\n from http_client_urllib import UrllibDownloader\n return UrllibDownloader()\n\n\nparser = ArgumentParser()\nparser.add_argument(\"-p\", \"--page\", dest=\"page\", help=\"URL address to crawl.\", metavar=\"URL\", required=True)\nparser.add_argument(\"-c\", \"--crawler\", dest=\"crawler\", help=\"Define crawler (chrome/wget/urllib)\",\n metavar=\"URL\", required=False, default='urllib')\n\nargs = parser.parse_args()\ntime.sleep(5)\nprint('Initializing crawler.')\ncrawler = get_crawler(args.crawler)\nprint('Crawler initialized.')\ncrawler.start_crawling(args.page)\n", "repo_name": "StaryVena/pcap_generator", "sub_path": "src/http_client/app/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 1705, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 22, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 22, "usage_type": "name"}, {"api_name": "http_client_selenium.SeleniumBrowser", "line_number": 23, "usage_type": "call"}, {"api_name": "http_client_wget.WgetDownloader", "line_number": 28, "usage_type": "call"}, {"api_name": "http_client_urllib.UrllibDownloader", "line_number": 32, "usage_type": "call"}, {"api_name": "http_client_urllib.UrllibDownloader", "line_number": 36, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "73933895725", "text": "from rest_framework import serializers\nfrom rest_framework.metadata import BaseMetadata\nfrom django.db import transaction\n\nfrom recipes_models.models import *\n\n\nclass IngredientMetadata(BaseMetadata):\n def determine_metadata(self, request, view):\n return {\n 'display_name': view.get_view_name(),\n 'value': view.id\n }\n\n\nclass IngredientSerializer(serializers.ModelSerializer):\n metadata_class = IngredientMetadata\n class Meta:\n model = Ingredient\n fields = ('id', 'url', 'name',)\n\n\nclass CategorySerializer(serializers.ModelSerializer):\n class Meta:\n model = Category\n fields = ('url', 'name',)\n\n\nclass QuantitySerializer(serializers.ModelSerializer):\n ingredient = serializers.SlugRelatedField(\n many=False,\n read_only=False,\n slug_field='name',\n queryset=Ingredient.objects.all()\n )\n\n class Meta:\n model = Quantity\n fields = ('amount', 'unit', 'ingredient',)\n\n\nclass MomentSerializer(serializers.ModelSerializer):\n ingredients = QuantitySerializer(source='quantity_set', many=True)\n extra_ingredients = serializers.SlugRelatedField(\n many=True,\n read_only=False,\n slug_field='name',\n queryset=Ingredient.objects.all()\n )\n\n class Meta:\n model = Moment\n fields = ('id', 'url', 'name', 'ingredients', 'extra_ingredients', 'instructions', )\n\n\nclass DynamicFieldsModelSerializer(serializers.ModelSerializer):\n \"\"\"\n A ModelSerializer that takes an additional `fields` argument that\n controls which fields should be displayed.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n fields = kwargs.pop('fields', None)\n\n super(DynamicFieldsModelSerializer, self).__init__(*args, **kwargs)\n\n if fields is not None:\n allowed = set(fields)\n existing = set(self.fields.keys())\n for field_name in existing - allowed:\n self.fields.pop(field_name)\n\n\nclass RecipeSerializer(DynamicFieldsModelSerializer):\n\n creator = serializers.ReadOnlyField(source='creator.username')\n moments = MomentSerializer(many=True)\n time_unit = serializers.SlugRelatedField(\n many=False,\n read_only=False,\n slug_field='name',\n queryset=TimeUnit.objects.all()\n )\n category = serializers.SlugRelatedField(\n many=True,\n read_only=False,\n slug_field='name',\n queryset=Category.objects.all()\n )\n\n @transaction.atomic\n def create(self, validated_data, commit=True, *args, **kwargs):\n moments = validated_data.pop('moments')\n categories = validated_data.pop('category')\n recipe = Recipe.objects.create(commit=commit, **validated_data)\n recipe.category = categories\n for jsonMoment in moments:\n quantities = jsonMoment.pop('quantity_set')\n extra_ingredients = jsonMoment.pop('extra_ingredients')\n moment = Moment.objects.create(commit=commit, recipe=recipe, **jsonMoment)\n for ingredient in extra_ingredients:\n moment.extra_ingredients.add(Ingredient.objects.get(name=ingredient))\n for quantity in quantities:\n Quantity.objects.create(commit=commit, moment=moment, **quantity)\n moment.save(commit=commit)\n recipe.save(commit=commit)\n return recipe\n\n class Meta:\n model = Recipe\n fields = ('id', 'url', 'name', 'description', 'creator', 'time', 'time_unit', 'category', 'moments')\n\n\nclass UserSerializer(serializers.ModelSerializer):\n recipes = RecipeSerializer(many=True)\n\n class Meta:\n model = User\n fields = ('id', 'username', 'recipes')\n", "repo_name": "magnlun/recipes", "sub_path": "recipes_rest/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 3711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "rest_framework.metadata.BaseMetadata", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 29, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 30, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 42, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ReadOnlyField", "line_number": 76, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 78, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 78, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 84, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 91, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 114, "usage_type": "name"}]} +{"seq_id": "2713174723", "text": "#Importing the necessary libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport termcolor\nimport sys\n\n\n#for colored text\nfrom termcolor import colored\ntext=colored('MEDIUM','blue')\n\n\n#splitting the dataset into dependent and independent variables\ndataset = pd.read_csv('achal5.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 6].values\n\n\n\n#encoding column 1 as reading strings is not possible\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\nlabelencoder = LabelEncoder()\nX[:, 0] = labelencoder.fit_transform(X[:, 0])\nonehotencoder = OneHotEncoder(categorical_features = [0])\nX = onehotencoder.fit_transform(X).toarray()\n\n\n\n\n#avoiding the trap\nX = X[:, 1:]\n\n\n\n\n#splitting the dataset into test and training set\nfrom sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n\n\n#training the model(object)\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)\n\n\n\n#using the object to predict y_test\ny_pred = regressor.predict(X_test)\n\n\n\n\n\n#Visualization\ny1, y2, y3, y4,x1,x2 = train_test_split(y_pred, y_test,X_test[:,4], test_size = 0.005, random_state = 0)\nplt.scatter(x2, y2, color = 'blue')\nplt.scatter(x2, y4, color = 'red')\nplt.title('predicted vs actual values (wrt to length in kms)')\nplt.xlabel('length in kms')\nplt.ylabel('prediction/testing')\nplt.show()\n\ny1, y2, y3, y4,x1,x2 = train_test_split(y_pred, y_test,X_test[:,5], test_size = 0.005, random_state = 0)\nplt.scatter(x2, y2, color = 'blue')\nplt.scatter(x2, y4, color = 'red')\nplt.title('predicted vs actual values (motorcycle congestion)')\nplt.xlabel('motorcycles congestion')\nplt.ylabel('prediction/testing')\nplt.show()\n\ny1, y2, y3, y4,x1,x2 = train_test_split(y_pred, y_test,X_test[:,6], test_size = 0.005, random_state = 0)\nplt.scatter(x2, y2, color = 'blue')\nplt.scatter(x2, y4, color = 'red')\nplt.title('predicted vs actual values (cars/taxis)')\nplt.xlabel('cars/taxis')\nplt.ylabel('prediction/testing')\nplt.show()\n\ny1, y2, y3, y4,x1,x2 = train_test_split(y_pred, y_test,X_test[:,7], test_size = 0.005, random_state = 0)\nplt.scatter(x2, y2, color = 'blue')\nplt.scatter(x2, y4, color = 'red')\nplt.title('predicted vs actual values (buses)')\nplt.xlabel('buses')\nplt.ylabel('prediction/testing')\nplt.show()\n\ny1, y2, y3, y4,x1,x2 = train_test_split(y_pred, y_test,X_test[:,8], test_size = 0.005, random_state = 0)\nplt.scatter(x2, y2, color = 'blue')\nplt.scatter(x2, y4, color = 'red')\nplt.title('predicted vs actual values (mini vans)')\nplt.xlabel('mini vans')\nplt.ylabel('prediction/testing')\nplt.show()\n\n#y_pred , y_test vs x\nax=np.array([i for i in range(0,32)])\nplt.plot(ax, y4, color = 'red')#ytest\nplt.plot(ax,y2,color='blue')#ypred\nplt.title('predicted vs actual values')\nplt.xlabel('index')\nplt.ylabel('performance of our model')\nplt.show()\n\n#predicting congestion based on input/real time \nnew_pred=regressor.predict(np.array([[0,0,0,0,0.8,112,5000,70,200]]))\nprint(new_pred)\nif new_pred <5000 :\n print('\\033[0;32m'+'The Traffic on this Road is : LOW'+'\\033[00m')\nelif new_pred>= 5000 and new_pred<=20000:\n print('The Traffic on this Road is : '+text)\nelse:\n print('\\x1b[1;31m'+'The Traffic on this Road is : HIGH'+'\\x1b[0m') \n\n\n\n\n\n\n\n", "repo_name": "VivekDosaya/MiniProject1", "sub_path": "viv.py", "file_name": "viv.py", "file_ext": "py", "file_size_in_byte": 3321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "termcolor.colored", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 73, "usage_type": "call"}, {"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.scatter", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"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.title", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "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"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "2417129470", "text": "# Importing opencv library\nimport cv2\nsize = 4\n\n# The internal webcam is called a 0, so we will use that.\nwebcam = cv2.VideoCapture(0)\n\n# Load the face classifier file.\nclassifier = cv2.CascadeClassifier('face.xml')\n\nwhile True:\n (rval, im) = webcam.read()\n # Flip to act as a mirror.\n im=cv2.flip(im,1,1)\n\n # This is for resizing the image to speed up detection.\n mini = cv2.resize(im, (im.shape[1] // size, im.shape[0] // size))\n\n # This line code is for detecting faces.\n faces = classifier.detectMultiScale(mini)\n\n # This draws rectangles around each faces.\n for f in faces:\n (x, y, w, h) = [v * size for v in f] #Scale the shapesize backup\n cv2.rectangle(im, (x, y), (x + w, y + h),(0,255,0),thickness=4)\n\n # This shows the output.\n cv2.imshow('Face Detection for SMART ENTRY', im)\n key = cv2.waitKey(10)\n\n\n", "repo_name": "zara4444/Face-detection", "sub_path": "face_detection.py", "file_name": "face_detection.py", "file_ext": "py", "file_size_in_byte": 864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "13723121459", "text": "import argparse\nimport numpy as np\nimport cv2\nimport os\n\nimport torch\nimport torch.nn.functional as F\nfrom torchvision import models, transforms\n\n\ndef GetArgs():\n parser = argparse.ArgumentParser()\n parser.add_argument('-M', '--modelPth', type=str, default='../model/resnet34-333f7ec4.pth',\n help='the network model path')\n\n parser.add_argument('-P', '--readImgUrl', type=str, default='../data/spider.png',\n help='Url of the image for attention map visualization')\n\n parser.add_argument('-S', '--savePth', type=str, default='../results/CAM/',\n help='the path to save attention maps and overlapped images')\n arg = parser.parse_args()\n return arg\n\n\ndef LoadNet(modelpath):\n net = models.resnet34(pretrained=False)\n net.load_state_dict(torch.load(modelpath))\n net.eval()\n net.cuda()\n return net\n\n\n# hook the feature extractor\ndef hook_features(module, input, output):\n features_blobs[0] = output.squeeze()\n\n\nnormMean = [0.485, 0.456, 0.406]\nnormStd = [0.229, 0.224, 0.225]\npreprocess = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize(normMean, normStd)])\n\n\n# get the corresponding weight to the predicted class\ndef GetWeights(network, prediction):\n params = list(network.parameters())\n weights = params[-2]\n return weights[prediction, :]\n\n\n# generate the CAM map\ndef GetCAM(featsmap, weights):\n weights = torch.reshape(weights, (-1, 1, 1))\n cam = torch.sum(torch.mul(featsmap, weights), dim=0)\n # cam = F.relu(cam)\n cam = cam - torch.min(cam)\n cam_img = cam / torch.max(cam)\n\n return cam_img.detach().cpu().numpy()\n\n\ndef main(net, imgUrl, position='layer4'):\n net._modules[position].register_forward_hook(hook_features) # get feature maps\n \n img = cv2.imread(imgUrl, 1)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n H, W = img.shape[:2]\n torch_img = preprocess(img).unsqueeze(0).cuda()\n\n with torch.no_grad():\n prob_output = F.softmax(net(torch_img), dim=1)\n pred = torch.argmax(prob_output, dim=1).squeeze().cpu().numpy()\n\n torch_cls_weights = GetWeights(net, pred)\n CAM = GetCAM(features_blobs[0], torch_cls_weights)\n outCAM = cv2.resize(np.uint8(CAM*255), (W, H))\n\n print(imgUrl, ' finished generation !')\n return pred, outCAM\n\n\n# save CAM to savepth\ndef saveCAM(imgUrl, savepath, pred, CAM):\n if not os.path.exists(savepath):\n os.mkdir(savepath)\n\n name = imgUrl.split('/')[-1]\n savename = name.split('.')[0] + '_' + str(pred)\n\n img = cv2.imread(imgUrl, 1)\n\n attentionmap = cv2.applyColorMap(CAM, cv2.COLORMAP_JET)\n overlap = attentionmap * 0.5 + img * 0.5\n\n cv2.imwrite(os.path.join(savepath, savename + '_CAM.png'), attentionmap)\n cv2.imwrite(os.path.join(savepath, savename + '_overlap.png'), overlap)\n\n\nif __name__ == '__main__':\n features_blobs = [0]\n Args = GetArgs()\n\n model = LoadNet(Args.modelPth) # load model\n Prediction, CAMap = main(model, Args.readImgUrl, position='layer4') # get CAM\n\n saveCAM(Args.readImgUrl, Args.savePth, Prediction, CAMap) # save CAM and overlap\n", "repo_name": "gatsby2016/FeatsVisDL", "sub_path": "codes/1_CAM.py", "file_name": "1_CAM.py", "file_ext": "py", "file_size_in_byte": 3158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.models.resnet34", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.reshape", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.argmax", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 92, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}]} +{"seq_id": "11703601178", "text": "from flask import Flask, Response, request, render_template, url_for\r\nimport cv2\r\nfrom flask_socketio import SocketIO\r\nimport random\r\nfrom flask import url_for\r\n\r\napp = Flask(__name__)\r\nsocketio = SocketIO(app)\r\n\r\ncascade_filename = (\r\n \"C:\\\\MyMoble\\\\OpenCV\\\\opencv\\\\data\\\\haarcascades\\\\haarcascade_frontalface_alt.xml\"\r\n)\r\ncascade = cv2.CascadeClassifier(cascade_filename)\r\n\r\n\r\ndef imgDetector(img, cascade):\r\n img = cv2.resize(img, dsize=None, fx=1.0, fy=1.0)\r\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n results = cascade.detectMultiScale(\r\n gray, # 입력 이미지\r\n scaleFactor=1.1, # 이미지 피라미드 스케일 factor\r\n minNeighbors=2, # 인접 객체 최소 거리 픽셀\r\n minSize=(20, 20), # 탐지 객체 최소 크기\r\n )\r\n return img\r\n\r\n\r\ndef gen():\r\n camera = cv2.VideoCapture(cv2.CAP_DSHOW + 0)\r\n camera.set(3, 640)\r\n camera.set(4, 480)\r\n while 1:\r\n _, frame = camera.read()\r\n retimg = imgDetector(frame, cascade)\r\n cv2.imwrite(\"pic.jpg\", retimg)\r\n yield (\r\n b\"--frame\\r\\n\"\r\n b\"Content-Type: image/jpeg\\r\\n\\r\\n\" + open(\"pic.jpg\", \"rb\").read() + b\"\\r\\n\"\r\n )\r\n camera.release()\r\n cv2.destroyAllWindows()\r\n\r\n\r\n@socketio.on(\"connect\")\r\ndef handle_connect():\r\n print(\"Client connected\")\r\n socketio.emit(\"connection_response\", {\"data\": \"Connected\"})\r\n\r\n\r\n@socketio.on(\"disconnect\")\r\ndef handle_disconnect():\r\n print(\"Client disconnected\")\r\n\r\n\r\n@socketio.on(\"message\")\r\ndef handle_message(text):\r\n socketio.emit(\"chat\", text)\r\n\r\n\r\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\r\ndef index():\r\n if request.method == \"POST\":\r\n print(\"/ post in\")\r\n camera = cv2.VideoCapture(cv2.CAP_DSHOW + 0)\r\n camera.set(3, 640)\r\n camera.set(4, 480)\r\n _, frame = camera.read()\r\n retimg = imgDetector(frame, cascade)\r\n cv2.imwrite(\"pic.jpg\", retimg)\r\n camera.release()\r\n imgs = [\r\n \"C:/MyMoble/OpenCV/pic.jpg\",\r\n \"C:/MyMoble/OpenCV/pic.jpg\",\r\n \"C:/MyMoble/OpenCV/pic.jpg\",\r\n \"/video_feed\",\r\n ]\r\n random.shuffle(imgs)\r\n print(imgs)\r\n return render_template(\"index.html\", imgs=imgs)\r\n \"\"\"Video streaming .\"\"\"\r\n return render_template(\"index.html\")\r\n\r\n\r\n@app.route(\"/video_feed\")\r\ndef video_feed():\r\n \"\"\"video straming rout. put this in the src attribute of an img tag.\"\"\"\r\n return Response(gen(), mimetype=\"multipart/x-mixed-replace; boundary=frame\")\r\n\r\n\r\n@app.route(\"/check_image\")\r\ndef check_image():\r\n print(\"/checki_image in\")\r\n image_param = request.args.get(\"image\")\r\n print(image_param)\r\n if image_param == \"/video_feed\":\r\n return \"이미지는 /video_feed와 동일합니다.\"\r\n else:\r\n return \"이미지는 /video_feed와 다릅니다.\"\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run(\"0.0.0.0\", port=9000, debug=True, threaded=True)\r\n", "repo_name": "ibamin/Portfolio", "sub_path": "Moble/Moble-Opencv/ch04/live_streaming_game/live_video_streaming.py", "file_name": "live_video_streaming.py", "file_ext": "py", "file_size_in_byte": 2949, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.CAP_DSHOW", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.CAP_DSHOW", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 69, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "28477370558", "text": "#!/usr/bin/env python\n\nimport os\nimport os.path as osp\nfrom collections import OrderedDict\nfrom typing import Any, Callable, Dict, Union\n\nimport torch\nimport torch.nn as nn\nfrom omegaconf import DictConfig\n\nfrom ..io import S3Client\nfrom ..logger import logger\nfrom ..utils import model_name\n\n__all__ = ['load_weights', 'load_weights_from_s3', 'load_weights_from_file']\n\n\ndef _remap_keys(weight_dict) -> OrderedDict:\n new_wpth = OrderedDict()\n for key in weight_dict:\n new_key = key.replace('module.', '') if 'module.' in key else key\n new_wpth[new_key] = weight_dict[key]\n return new_wpth\n\n\ndef get_weights(cfg: DictConfig, **kwargs: Dict[str, Any]) -> Union[Dict[str, Any], None]:\n weight_path = cfg.build[model_name(cfg)].get('weights', None) if isinstance(cfg, DictConfig) else cfg\n if not weight_path or len(weight_path) == 0:\n logger.warning('Weight is empty!'.upper())\n return None\n\n state_dict = (\n load_weights_from_s3(weight_path, kwargs.get('decoder', None)) # type: ignore\n if 's3://' in weight_path\n else load_weights_from_file(weight_path)\n )\n assert state_dict is not None, 'Weight dict is None'\n return state_dict\n\n\ndef load_all(engine, cfg: DictConfig, **kwargs: Dict[str, Any]):\n state_dict = get_weights(cfg, **kwargs)\n if not state_dict:\n return\n\n engine._model.load_state_dict(state_dict['model'])\n\n if 'optimizer' in state_dict and engine._optimizer:\n engine._optimizer.load_state_dict(state_dict['optimizer'])\n\n if 'scheduler' in state_dict and engine._scheduler:\n engine._scheduler.load_state_dict(state_dict['scheduler'])\n\n if 'state' in state_dict:\n engine.state = state_dict['state']\n\n\ndef load_weights(model: nn.Module, cfg: DictConfig, **kwargs):\n weight_dict = get_weights(cfg, **kwargs)\n if not weight_dict:\n return\n\n state_dict = model.state_dict()\n wpth = _remap_keys(weight_dict['model'])\n\n for key in state_dict:\n if key not in wpth or state_dict[key].shape == wpth[key].shape:\n continue\n logger.info(f'Removing shape missmatch key {key}')\n wpth.pop(key)\n\n load_status = model.load_state_dict(wpth, strict=kwargs.get('strict', False))\n logger.info(f'{load_status}')\n\n\ndef load_weights_from_s3(path: str, decoder: Union[Callable[..., Any], str, None] = None) -> Dict[str, Any]:\n bucket_name = path[5:].split('/')[0]\n assert len(bucket_name) > 0, 'Invalid bucket name'\n\n path = path[5 + len(bucket_name) + 1 :]\n # check if weight is in cache\n root = osp.join(os.environ['HOME'], f'.cache/torch/{path}')\n if osp.isfile(root):\n logger.info(f'Cache found in cache, loading from {root}')\n return load_weights_from_file(root)\n\n s3_client = S3Client(bucket_name=bucket_name)\n os.makedirs('/'.join(root.split('/')[:-1]), exist_ok=True)\n\n logger.info(f'Loading weights from {path}')\n if decoder:\n weights = s3_client(path, decoder=decoder)\n torch.save(weights, root)\n else:\n s3_client.download(path, root)\n weights = load_weights_from_file(root) # type: ignore\n\n logger.info(f'Saved model weight to cache: {root}')\n\n return weights # type: ignore\n\n\ndef load_weights_from_file(path: str) -> Dict[str, torch.Tensor]:\n assert osp.isfile(path), f'Not weight found {path}'\n return torch.load(path, map_location='cpu')\n", "repo_name": "iKrishneel/igniter", "sub_path": "igniter/engine/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3410, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.OrderedDict", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 19, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 27, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 28, "usage_type": "argument"}, {"api_name": "utils.model_name", "line_number": 28, "usage_type": "call"}, {"api_name": "logger.logger.warning", "line_number": 30, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 27, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 59, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 70, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 70, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 74, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 77, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 85, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 85, "usage_type": "name"}, {"api_name": "io.S3Client", "line_number": 88, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 89, "usage_type": "call"}, {"api_name": "logger.logger.info", "line_number": 91, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 94, "usage_type": "call"}, {"api_name": "logger.logger.info", "line_number": 99, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 77, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 106, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 104, "usage_type": "attribute"}]} +{"seq_id": "3435829867", "text": "# სავარჯიშო_1\r\n\r\nimport sqlite3\r\n\r\n\r\ndef db_connection(file):\r\n return sqlite3.connect(file)\r\n\r\n\r\ndef moc_2(conn):\r\n cursor = conn.cursor()\r\n count = cursor.execute(\"SELECT COUNT(*) FROM students WHERE SelfStudyHour < 2 \")\r\n for i in count:\r\n print(i)\r\n\r\n\r\ndef moc_3(conn):\r\n device = input(\"მოწყობილობა: \")\r\n age = int(input(\"ასაკი: \"))\r\n cursor = conn.cursor()\r\n count = cursor.execute(\"SELECT COUNT(*) FROM students WHERE Device = ? and Age = ?\", (device, age))\r\n for i in count:\r\n print(i)\r\n\r\n\r\ndef moc_4(conn):\r\n cursor = conn.cursor()\r\n cursor.execute(\"\"\"INSERT INTO students(AGE,\r\n OnlineClassTime, Device, SelfStudyHour,\r\n FitnessTime, Sleep, SocialMedia, SocialMediaPlatform)\r\n VALUES (18, 20, \"phone\", 5, 4, 8, 7, \"google\");\r\n \"\"\")\r\n conn.commit()\r\n\r\n\r\ndef mpc_5(conn):\r\n cursor = conn.cursor()\r\n cursor.execute(\"UPDATE students SET SocialMediaPlatform = 'data' WHERE age <> 21\")\r\n conn.commit()\r\n\r\n\r\ndef main():\r\n conn = db_connection(\"survey.sqlite\")\r\n moc_2(conn)\r\n moc_4(conn)\r\n moc_3(conn)\r\n mpc_5(conn)\r\n conn.close()\r\n\r\n\r\nmain()\r\n\r\n# სავარჯიშო_2\r\n\r\n\r\nimport json\r\n\r\n\r\nwith open('sample (1).json') as user_file:\r\n file_contents = user_file.read()\r\nuser_file.close()\r\nprint(file_contents)\r\n\r\nparsed_json = json.loads(file_contents)\r\nprint(parsed_json[\"person\"][\"address\"])\r\n\r\nfor i in range(len(parsed_json[\"person\"][\"friends\"])):\r\n print(parsed_json[\"person\"][\"friends\"][i][\"name\"])\r\n\r\n", "repo_name": "davitkarchava/exam2", "sub_path": "shualeduri_2.py", "file_name": "shualeduri_2.py", "file_ext": "py", "file_size_in_byte": 1566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "sqlite3.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "70229702768", "text": "import requests\nimport pandas as pd\nimport time\nfrom functools import partial\nfrom property_finder.tfl_credentials import TFL_CREDENTIALS\nfrom ratelimit import limits, sleep_and_retry\nfrom tqdm import tqdm\nfrom datetime import datetime\n\n\n# TODO remove hardcoded parameters from URL\nTFL_URL = 'https://api.tfl.gov.uk/Journey/JourneyResults/' \\\n '%f%%2C%%20%f/to/%f%%2C%%20%f?nationalSearch=true&date=20190527&time=%s&timeIs=%s&app_id=%s&app_key=%s'\n\n\n@sleep_and_retry\n@limits(calls=500, period=60)\ndef get_single_journey(origin, destination, time_str, time_is):\n loaded = False\n retries = 0\n\n while not loaded:\n try:\n result = requests.get(TFL_URL % (origin[0], origin[1],\n destination[0], destination[1],\n time_str, time_is, TFL_CREDENTIALS['app_id'],\n TFL_CREDENTIALS['app_key'])).json()\n\n journey = result['journeys'][0]\n loaded = True\n except:\n print(\"[%s] Could not connect to TFL API\" % datetime.now())\n retries += 1\n if retries == 10:\n raise Exception(\"Could not match journey\")\n\n output = dict()\n output['JourneyDuration'] = journey['duration']\n if 'fare' in journey.keys():\n output['JourneyFare'] = journey['fare']['totalCost']\n else:\n output['JourneyFare'] = None\n\n output['Walking'] = 0\n output['Train'] = 0\n output['Underground'] = 0\n output['Bus'] = 0\n for leg in journey['legs']:\n leg_mode = leg['mode']['name']\n if leg_mode == 'walking':\n mode = 'Walking'\n elif leg_mode == 'national-rail':\n mode = 'Train'\n elif leg_mode == 'tube':\n mode = 'Underground'\n elif leg_mode == 'bus':\n mode = 'Bus'\n\n output[mode] += leg['duration']\n\n return output\n\n\ndef get_monthly_journey(origin, destination, outbound_arrival, inbound_departure):\n journey1 = get_single_journey(origin, destination, time_str=outbound_arrival, time_is='Arriving')\n journey2 = get_single_journey(destination, origin, time_str=inbound_departure, time_is='Departing')\n result = pd.DataFrame([journey1, journey2]).sum()\n\n # average monthly working days\n result.JourneyFare = (result.JourneyFare/100.0) * 22\n\n return result\n\n\ndef process_row(destination, database, config, row):\n result = {'ID': [],\n 'JourneyDuration': [],\n 'JourneyFare': [],\n 'Walking': [],\n 'Train': [],\n 'Underground': [],\n 'Bus': []}\n\n origin = (row.Latitude, row.Longitude)\n\n try:\n journey = get_monthly_journey(origin, destination,\n '0845', '1715')\n for key in [x for x in result.keys() if x != 'ID']:\n result[key].append(journey[key])\n\n result['ID'].append(row.ID)\n\n result = pd.DataFrame(result)\n database.write_table(result, config['database']['TravelTable'])\n except Exception as e:\n print('ID: %s %s' % (row.ID, str(e)))\n\n\ndef update_travel_info(database, config):\n location_data = database.read_table(table=config['database']['LocationTable'])\n travel_data = database.read_table(table=config['database']['TravelTable'])\n\n location_data = location_data[~location_data.ID.isin(travel_data.ID)]\n\n destination = (float(config['journey']['destination_lat']), float(config['journey']['destination_lon']))\n get_journey = partial(process_row, destination, database, config)\n\n tqdm.pandas()\n location_data.progress_apply(get_journey, axis=1)\n", "repo_name": "yosifovemil/london-property-finder", "sub_path": "property_finder/journey.py", "file_name": "journey.py", "file_ext": "py", "file_size_in_byte": 3680, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "property_finder.tfl_credentials.TFL_CREDENTIALS", "line_number": 26, "usage_type": "name"}, {"api_name": "property_finder.tfl_credentials.TFL_CREDENTIALS", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "ratelimit.sleep_and_retry", "line_number": 16, "usage_type": "name"}, {"api_name": "ratelimit.limits", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 94, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 107, "usage_type": "call"}, {"api_name": "tqdm.tqdm.pandas", "line_number": 109, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 109, "usage_type": "name"}]} +{"seq_id": "28048030596", "text": "import os\nimport uuid\nfrom expects import expect, equal\nfrom mamba import before, description, it\nfrom sdcclient import SdSecureClient, SdMonitorClient\nfrom specs import be_successful_api_call\nfrom collections import defaultdict\n\nTEAM_PREFIX_NAME = 'sysdig-sdk - '\n\nwith description(\"Teams\", \"integration\", \"teams\") as self:\n with before.all:\n self.secure_client = SdSecureClient(\n sdc_url=os.getenv(\"SDC_SECURE_URL\", \"https://secure.sysdig.com\"),\n token=os.getenv(\"SDC_SECURE_TOKEN\")\n )\n self.monitor_client = SdMonitorClient(\n sdc_url=os.getenv(\"SDC_MONITOR_URL\", \"https://app.sysdigcloud.com\"),\n token=os.getenv(\"SDC_MONITOR_TOKEN\")\n )\n\n with before.each:\n self.team_name = f'{TEAM_PREFIX_NAME}{uuid.uuid4()}'\n\n with it(\"it should list all teams\"):\n ok, teams_monitor = self.monitor_client.get_teams()\n expect((ok, teams_monitor)).to(be_successful_api_call)\n\n ok, teams_secure = self.secure_client.get_teams()\n expect((ok, teams_secure)).to(be_successful_api_call)\n\n count_monitor = defaultdict(int)\n count_secure = defaultdict(int)\n\n def count_products(teams, count):\n for team in teams:\n for product in team['products']:\n count[product] += 1\n\n count_products(teams_monitor, count_monitor)\n count_products(teams_secure, count_secure)\n\n expect(len(count_secure)).to(equal(len(count_monitor)))\n for k, v in count_monitor.items():\n expect(count_secure[k]).to(equal(v))\n expect(len(teams_secure)).to(equal(len(teams_monitor)))\n\n with it(\"it should list only monitor teams\"):\n ok, team = self.secure_client.create_team(self.team_name)\n expect((ok, team)).to(be_successful_api_call)\n\n ok, teams = self.monitor_client.get_teams(product_filter='SDC')\n expect((ok, teams)).to(be_successful_api_call)\n\n secure_teams = [t for t in teams if 'SDS' in t['products']]\n expect(len(secure_teams)).to(equal(0))\n\n ok, res = self.secure_client.delete_team(self.team_name)\n expect((ok, res)).to(be_successful_api_call)\n\n with it(\"it should list only secure teams\"):\n ok, team = self.monitor_client.create_team(self.team_name)\n expect((ok, team)).to(be_successful_api_call)\n\n ok, teams = self.secure_client.get_teams(product_filter='SDS')\n expect((ok, teams)).to(be_successful_api_call)\n\n monitor_teams = [t for t in teams if 'SDC' in t['products']]\n expect(len(monitor_teams)).to(equal(0))\n\n ok, res = self.monitor_client.delete_team(self.team_name)\n expect((ok, res)).to(be_successful_api_call)\n", "repo_name": "sysdiglabs/sysdig-sdk-python", "sub_path": "specs/_common/team_spec.py", "file_name": "team_spec.py", "file_ext": "py", "file_size_in_byte": 2741, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 57, "dataset": "github-code", "pt": "2", "api": [{"api_name": "mamba.description", "line_number": 11, "usage_type": "call"}, {"api_name": "mamba.before.all", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mamba.before", "line_number": 12, "usage_type": "name"}, {"api_name": "sdcclient.SdSecureClient", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "sdcclient.SdMonitorClient", "line_number": 17, "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": "mamba.before.each", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mamba.before", "line_number": 22, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 23, "usage_type": "call"}, {"api_name": "mamba.it", "line_number": 25, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 27, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 27, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 30, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 33, "usage_type": "call"}, {"api_name": "expects.expect", "line_number": 43, "usage_type": "call"}, {"api_name": "expects.equal", "line_number": 43, "usage_type": "call"}, {"api_name": "expects.expect", "line_number": 45, "usage_type": "call"}, {"api_name": "expects.equal", "line_number": 45, "usage_type": "call"}, {"api_name": "expects.expect", "line_number": 46, "usage_type": "call"}, {"api_name": "expects.equal", "line_number": 46, "usage_type": "call"}, {"api_name": "mamba.it", "line_number": 48, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 50, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 50, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 53, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 53, "usage_type": "call"}, {"api_name": "expects.expect", "line_number": 56, "usage_type": "call"}, {"api_name": "expects.equal", "line_number": 56, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 59, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 59, "usage_type": "call"}, {"api_name": "mamba.it", "line_number": 61, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 63, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 63, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 66, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 66, "usage_type": "call"}, {"api_name": "expects.expect", "line_number": 69, "usage_type": "call"}, {"api_name": "expects.equal", "line_number": 69, "usage_type": "call"}, {"api_name": "specs.be_successful_api_call", "line_number": 72, "usage_type": "argument"}, {"api_name": "expects.expect", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "15073477337", "text": "import collections\n\nT = int(input())\n\n\ndef solution(p, n, nums):\n if n == 0 and 'D' in p:\n return 'error'\n elif n == 0 and 'D' not in p:\n return '[]'\n else:\n nums = list(map(int, nums))\n nums = collections.deque(nums)\n pointer = 0\n\n for cmd in p:\n if cmd == 'R':\n if pointer == 0:\n pointer = 1\n elif pointer == 1:\n pointer = 0\n elif cmd == 'D':\n try:\n if pointer == 0:\n nums.popleft()\n elif pointer == 1:\n nums.pop()\n except IndexError:\n return 'error'\n nums = list(nums)\n if pointer == 1:\n nums.reverse()\n nums = str(nums).replace(' ', '')\n return nums\n\n\nfor _ in range(T):\n p = input()\n n = int(input())\n nums = input().lstrip('[').rstrip(']').split(',')\n print(solution(p, n, nums))\n", "repo_name": "gudwh14/algorithm", "sub_path": "boj/string/AC.py", "file_name": "AC.py", "file_ext": "py", "file_size_in_byte": 927, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.deque", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "13295903637", "text": "from django.shortcuts import render\nfrom models import Tiempo\nimport numpy as np\nfrom django.http import HttpResponse\nfrom django.core import serializers\nimport json\n\n# Create your views here.\ndef view(request):\n context = {\n }\n return render(request, 'tiempo/view.html', context)\n\ndef resumen(request):\n temperaturasData = Tiempo.objects.values_list(\"temperatura\", flat=True)\n temperaturas = np.array(list(temperaturasData)) \n\n dataJson = {\n \"media\" : np.mean(temperaturas),\n \"maxima\" : np.max(temperaturas),\n \"minima\" : np.min(temperaturas)\n }\n\n return HttpResponse(json.dumps(dataJson), content_type=\"application/json\") \n\ndef data(request):\n\n records = Tiempo.objects.filter(temperatura__range = (16, 27))\n results = [record.as_json() for record in records]\n dataJson = json.dumps(results)\n\n return HttpResponse(dataJson, content_type=\"application/json\")\n\ndef pordia(request):\n\n diasData = Tiempo.objects.values_list(\"fecha\", flat=True).filter(temperatura__range = (16, 27))\n arrayDias = np.array(list(diasData))\n\n dias = np.array(arrayDias).astype('datetime64[D]')\n\n datetimeToDate = []\n\n for dia in dias:\n datetimeToDate.append(str(dia))\n\n count = np.unique(datetimeToDate, return_counts=True)\n\n returnJson = {\n \"day\" : list(count[0].astype('str')),\n \"count\" : list(count[1].astype('str')),\n }\n\n return HttpResponse(json.dumps(returnJson), content_type=\"application/json\")\n \n", "repo_name": "carloscba/AgroBuzz", "sub_path": "apps/tiempo/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Tiempo.objects.values_list", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Tiempo.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Tiempo", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 21, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Tiempo.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Tiempo.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Tiempo", "line_number": 28, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Tiempo.objects.values_list", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Tiempo.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Tiempo", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 53, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "38914627052", "text": "\"\"\"\nExample of choropleth using geojson files.\nBased on by Mike Bostock's unemployment choropleth http://bl.ocks.org/mbostock/4060606\n\"\"\"\n\nimport geoplotlib\nfrom geoplotlib.utils import BoundingBox\nfrom geoplotlib.colors import ColorMap\nimport json\n\n\n# find the unemployment rate for the selected county, and convert it to color\ndef get_color(properties):\n key = str(int(properties['STATE'])) + properties['COUNTY']\n if key in unemployment:\n return cmap.to_color(unemployment.get(key), .15, 'lin')\n else:\n return [0, 0, 0, 0]\n\n\nwith open('data/unemployment.json') as fin:\n unemployment = json.load(fin)\n\ncmap = ColorMap('Blues', alpha=255, levels=10)\ngeoplotlib.geojson('data/gz_2010_us_050_00_20m.json', fill=True, color=get_color, f_tooltip=lambda properties: properties['NAME'])\ngeoplotlib.geojson('data/gz_2010_us_050_00_20m.json', fill=False, color=[255, 255, 255, 64])\ngeoplotlib.set_bbox(BoundingBox.USA)\ngeoplotlib.show()\n", "repo_name": "andrea-cuttone/geoplotlib", "sub_path": "examples/choropleth.py", "file_name": "choropleth.py", "file_ext": "py", "file_size_in_byte": 956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1009, "dataset": "github-code", "pt": "2", "api": [{"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "geoplotlib.colors.ColorMap", "line_number": 24, "usage_type": "call"}, {"api_name": "geoplotlib.geojson", "line_number": 25, "usage_type": "call"}, {"api_name": "geoplotlib.geojson", "line_number": 26, "usage_type": "call"}, {"api_name": "geoplotlib.set_bbox", "line_number": 27, "usage_type": "call"}, {"api_name": "geoplotlib.utils.BoundingBox.USA", "line_number": 27, "usage_type": "attribute"}, {"api_name": "geoplotlib.utils.BoundingBox", "line_number": 27, "usage_type": "name"}, {"api_name": "geoplotlib.show", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "16982594367", "text": "# -*- coding: UTF-8 -*-\nimport re\nimport hashlib\nimport random\nimport string\nimport urllib.request\nimport urllib.parse\nimport json\nimport hashlib\nimport ssl\nimport base64\nfrom PIL import Image, ImageEnhance, ImageFilter\n\n\ndef sendRequest2(url, headers, body):\n req = urllib.request.Request(url)\n req.set_proxy('proxy.sin.sap.corp:8080', 'http')\n for k, v in headers.items():\n req.add_header(k, v)\n response = urllib.request.urlopen(req)\n print(response.read())\n return (response.code, '', response.read())\n\n\ndef sendRequest(url, headers, body):\n ssl._create_default_https_context = ssl._create_unverified_context\n req = urllib.request.Request(url)\n req.set_proxy('proxy.sin.sap.corp:8080', 'http')\n for k, v in headers.items():\n req.add_header(k, v)\n opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor)\n try:\n response = opener.open(req, body)\n return (response.code, '', response.read())\n except urllib.request.HTTPError as e:\n return (e.code, e.reason, e.read())\n except urllib.request.URLError as e:\n return (None, e.reason, None)\n\n\ndef getWXToken(appId, secret):\n url = \"https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=%s&secret=%s\"\n url = url % (appId, secret)\n headers = {}\n headers['Content-Type'] = 'application/json'\n body = b\"\"\n (code, reason, result) = sendRequest(url, headers, body)\n return json.loads(result.decode('utf-8'))\n\n\ndef getWXJSAPITicket(accessToken):\n url = \"https://api.weixin.qq.com/cgi-bin/ticket/getticket?access_token=%s&type=jsapi\"\n url = url % accessToken\n headers = {}\n headers['Content-Type'] = 'application/json'\n body = b\"\"\n (code, reason, result) = sendRequest(url, headers, body)\n return json.loads(result.decode('utf-8'))\n\n\n# access_token = getWXToken('wx9f248acc2b1a683b','e26c5dac6f18a94a11cb1bd72ec5b897')['access_token']\n# print(access_token)\n# jsapiticket = getWXJSAPITicket(access_token)\n# print(jsapiticket)\n\ndef getSign(noncestr, ticket, timestamp, url):\n parameters = {\n 'jsapi_ticket': ticket,\n 'noncestr': noncestr,\n 'timestamp': timestamp,\n 'url': url\n }\n sortedParameters = [(k, parameters[k]) for k in sorted(parameters.keys())]\n parameterString = urllib.parse.urlencode(sortedParameters, safe=':/?=')\n print(parameterString)\n result = hashlib.sha1(parameterString.encode('utf-8'))\n print(result.hexdigest())\n return result.hexdigest()\n\n\nchars = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\",\n \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\",\n \"u\", \"v\", \"w\", \"x\", \"y\", \"z\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\",\n \"6\", \"7\", \"8\", \"9\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\",\n \"I\", \"J\", \"K\", \"L\", \"M\", \"N\", \"O\", \"P\", \"Q\", \"R\", \"S\", \"T\",\n \"U\", \"V\", \"W\", \"X\", \"Y\", \"Z\"\n ]\ns = 'http://www.young-sun.com'\n\n\ndef randomChar(length):\n return ''.join([random.choice(string.ascii_letters) for x in range(length)])\n\n\ndef hand(seg):\n v = string.atol(seg, 16)\n v = v & 0x3FFFFFFF\n char = ''\n for i in range(6):\n idx = v & 0x0000003D\n char += chars[int(idx)]\n v = v >> 5\n return char\n\n\ndef sendRequest(url, headers, body):\n # ssl._create_default_https_context = ssl._create_unverified_context\n req = urllib.request.Request(url)\n for k, v in headers.items():\n req.add_header(k, v)\n opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor)\n try:\n response = opener.open(req, body)\n return (response.code, '', response.read())\n except urllib.request.HTTPError as e:\n return (e.code, e.reason, e.read())\n except urllib.request.URLError as e:\n return (None, e.reason, None)\n\n\ndef getIpInfo(ip):\n url = \"http://ip.taobao.com/service/getIpInfo.php?ip=%s\" % ip\n headers = {}\n headers['Content-Type'] = 'application/json'\n print(url)\n body = b\"\"\n (code, reason, result) = sendRequest(url, headers, body)\n return json.loads(result.decode('utf-8'))\n\n\ndef getIpLocation(ip):\n result = getIpInfo(ip)\n if result['code'] == 0:\n location = '%s%s%s%s%s' % (\n result['data']['country'],\n result['data']['area'],\n result['data']['region'],\n result['data']['city'],\n result['data']['isp']\n )\n return location\n else:\n return 'N/A'\n\n\n# print(getIpLocation('114.61.230.73'))\n\n\n\ndef getBaiduAPIToken(apiKey, secretKey):\n url = \"https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=%(clientId)s&client_secret=%(clientSecret)s\" % {\n 'clientId': apiKey,\n 'clientSecret': secretKey\n }\n print(url)\n headers = {}\n # headers['Content-Type'] = 'application/json'\n body = b\"\"\n (code, reason, result) = sendRequest(url, headers, body)\n print(code)\n print(reason)\n print(result)\n return result\n\n\ndef postAIPDectect(imgData, accessToken):\n url = \"https://aip.baidubce.com/rest/2.0/face/v1/detect?access_token=%(accessToken)s\" % {\n 'accessToken': accessToken\n }\n print(url)\n headers = {}\n headers['Content-Type'] = 'application/x-www-form-urlencoded'\n body = b\"image=%s\" % imgData\n print(body)\n (code, reason, result) = sendRequest(url, headers, body)\n print(code)\n print(reason)\n print(result)\n if code == 200:\n return json.loads(result.decode('utf-8'))\n return {}\n\ndef test():\n baiduApiKey = 'MtxmcKOG44KGoxngiFHneaTz'\n baiduSecretKey = '9vk2KnoLOchxqVOavFsgOqtY8G2OGaGZ'\n\n # result = getBaiduAPIToken(baiduApiKey, baiduSecretKey)\n # print(result)\n\n # b'{\"access_token\":\"24.8263e1ea1626e1343ee995e71dad0288.2592000.1504230788.282335-9958853\",\"session_key\":\"9mzdA5gmmov7X2D6FSHYCiSZvw9zVmhN7exVs+HUWrsKJnRMDl0HCuO3J2xv3bKDaRFc3jbGOppQKeyMxcd7Cd5AKxNv\",\"scope\":\"public vis-faceverify_faceverify vis-faceattribute_faceattribute vis-faceverify_faceverify_v2 brain_all_scope wise_adapt lebo_resource_base lightservice_public hetu_basic lightcms_map_poi kaidian_kaidian wangrantest_test wangrantest_test1 bnstest_test1 bnstest_test2 vis-classify_flower\",\"refresh_token\":\"25.1c2618eceeb270b6dd2bc940f6a86bab.315360000.1816998788.282335-9958853\",\"session_secret\":\"00c3d619d02c3ba5fe822e6c1925560e\",\"expires_in\":2592000}\\n'\n baiduAccessToken = '24.4f4834808c48b1a20a26c3f310c484ab.2592000.1504243948.282335-9958853'\n filename = 'mirei.jpg'\n f = open(filename, 'rb')\n im = Image.open(f)\n faceIm = Image.open('face.jpg')\n im.thumbnail((400, 400))\n im.save('updated.jpg', 'JPEG')\n f = open('updated.jpg', 'rb')\n data = f.read()\n base64edData = base64.b64encode(data)\n urlencodedBase64edData = urllib.parse.quote(base64edData)\n # for l in f:\n # # print(l)\n # data = data + l\n # print(str(data,'utf-8'))\n result = postAIPDectect(urlencodedBase64edData.encode(), baiduAccessToken)\n # location = result['result'][0]['location']\n # rotationAngle = result['result'][0]['rotation_angle']\n location = {\"left\": 94, \"top\": 96, \"width\": 95, \"height\": 87}\n # rotationAngle = -5\n\n # if rotationAngle:\n # im = im.rotate(rotationAngle, expand=True)\n # size = im.size\n # location = {\n # 'top': location['left'],\n # 'left': size[0] - location['top'],\n # 'width': location['width'],\n # 'height': location['height']\n # }\n box = (location['left'], location['top'], location['left'] + location['width'],\n location['top'] + location['height'])\n im = im.crop(box)\n im = ImageEnhance.Contrast(im).enhance(2)\n im = im.convert('L')\n im = im.filter(ImageFilter.CONTOUR)\n\n p = (101, 82)\n s = im.size\n loc = (p[0], p[1], p[0] + s[0], p[1] + s[1])\n faceIm.paste(im, loc)\n # print(faceIm.size)\n faceIm.show()\n # im.show()\n # # im.filter(ImageFilter.GaussianBlur)\n # im = im.filter(ImageFilter.MedianFilter)\n # r, g, b = im.split()\n # r = r.point(lambda i: 1 if i > 100 else 255)\n # g = g.point(lambda i: 1 if i > 100 else 255)\n # b = b.point(lambda i: 1 if i > 100 else 255)\n #\n # def adjust(i):\n # print(i)\n # return i * 0.8 if i < 100 else i * 1.7\n # # im = im.point(adjust)\n # # im = Image.merge(im.mode, (r, g, b))\n # # im.convert('1',colors=1)\n # im.save('updated.jpg', 'JPEG')\n\n #\n # im90 = im.rotate(-90, expand=True)\n #\n # # newBox = (location['top'], location['left'], box[2], box[3])\n #\n # size = im90.size\n # print(size)\n #\n # newLocation = {\n # 'top': location['left'],\n # 'left': size[0] - location['top'],\n # 'width': location['width'],\n # 'height': location['height']\n # }\n #\n # # box = (newLocation['top'], newLocation['left'], newLocation['left'] + newLocation['width'], newLocation['top'] + newLocation['height'] )\n # box = (newLocation['left'],newLocation['top'], newLocation['left'] + newLocation['width'], newLocation['top'] + newLocation['height'] )\n #\n # print(box)\n # im90 = im90.crop(box)\n # im90.save('updated.jpg', 'JPEG')\n # # newIm = im.rotate(-90, expand=True).save('updated.jpg', 'JPEG')\n # # newIm = Image.open(open('updated.jpg', 'rb'))\n # # newIm.crop(box)\n # # print(newIm.size)\n # # newIm.save('updated.jpg', 'JPEG')\n\n\ndef merge(file1, file2):\n im1 = Image.open(open(file1,'rb'))\n im2 = Image.open(open(file2,'rb'))\n size1 = im1.size\n size2 = im2.size\n print('file1 size %d %d' % size1)\n print('file2 size %d %d' % size2)\n # newSize = (size1[0] + size2[0], size1[1] if size1[1] > size2[1] else size2[1])\n newSize = (size1[0] if size1[0] > size2[0] else size2[0], size1[1] + size2[1])\n print('new size %d %d' % newSize)\n newImg = Image.new('RGBA', newSize)\n newImg.paste(im1, (0,0, size1[0], size1[1]))\n newImg.paste(im2, (0,size1[1], size2[0], size1[1]+ size2[1]))\n newImg.save('id-updated.jpg','JPEG')\n # newImg.show()\n\nmerge('id1.jpg','id2.jpg')", "repo_name": "xiaoyexu/python", "sub_path": "test3.py", "file_name": "test3.py", "file_ext": "py", "file_size_in_byte": 10055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "urllib.request.request.Request", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 16, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 16, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 20, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 20, "usage_type": "name"}, {"api_name": "ssl._create_default_https_context", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ssl._create_unverified_context", "line_number": 26, "usage_type": "attribute"}, {"api_name": "urllib.request.request.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 27, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.request.request.build_opener", "line_number": 31, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 31, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 31, "usage_type": "name"}, {"api_name": "urllib.request.request", "line_number": 35, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 35, "usage_type": "name"}, {"api_name": "urllib.request.request", "line_number": 37, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 37, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 58, "usage_type": "call"}, {"api_name": "urllib.request.parse.urlencode", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 74, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 74, "usage_type": "name"}, {"api_name": "hashlib.sha1", "line_number": 76, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 92, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 92, "usage_type": "attribute"}, {"api_name": "string.atol", "line_number": 96, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 108, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 108, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 108, "usage_type": "name"}, {"api_name": "urllib.request.request.build_opener", "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": "urllib.request.request", "line_number": 115, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 115, "usage_type": "name"}, {"api_name": "urllib.request.request", "line_number": 117, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 117, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 128, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 180, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 194, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 194, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 195, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 195, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 200, "usage_type": "call"}, {"api_name": "urllib.request.parse.quote", "line_number": 201, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 201, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 201, "usage_type": "name"}, {"api_name": "PIL.ImageEnhance.Contrast", "line_number": 224, "usage_type": "call"}, {"api_name": "PIL.ImageEnhance", "line_number": 224, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.CONTOUR", "line_number": 226, "usage_type": "attribute"}, {"api_name": "PIL.ImageFilter", "line_number": 226, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 279, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 279, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 280, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 280, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 288, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 288, "usage_type": "name"}]} +{"seq_id": "44139312007", "text": "\"\"\"Authentication Resource.\"\"\"\n\nfrom muria.common.resource import Resource\nfrom falcon import (\n HTTP_SERVICE_UNAVAILABLE,\n HTTPMissingParam,\n HTTPBadRequest\n)\n\n\nclass Authentication(Resource):\n \"\"\"Authenticate user with their credentials and issue token for them.\"\"\"\n\n def on_get(self, req, resp):\n # verify token\n if req.context.auth:\n req.context.auth.verify(req, resp)\n else:\n resp.status = HTTP_SERVICE_UNAVAILABLE\n\n def on_post(self, req, resp):\n # acquire token\n if req.context.auth:\n req.context.auth.acquire(req, resp)\n else:\n resp.status = HTTP_SERVICE_UNAVAILABLE\n\n def on_patch(self, req, resp):\n # refresh token\n if req.context.auth:\n req.context.auth.refresh(req, resp)\n else:\n resp.status = HTTP_SERVICE_UNAVAILABLE\n\n def on_delete(self, req, resp):\n # revoke token\n if req.context.auth:\n req.context.auth.revoke(req, resp)\n else:\n resp.status = HTTP_SERVICE_UNAVAILABLE\n", "repo_name": "xakiy/muria", "sub_path": "muria/resource/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 1082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "muria.common.resource.Resource", "line_number": 11, "usage_type": "name"}, {"api_name": "falcon.HTTP_SERVICE_UNAVAILABLE", "line_number": 19, "usage_type": "name"}, {"api_name": "falcon.HTTP_SERVICE_UNAVAILABLE", "line_number": 26, "usage_type": "name"}, {"api_name": "falcon.HTTP_SERVICE_UNAVAILABLE", "line_number": 33, "usage_type": "name"}, {"api_name": "falcon.HTTP_SERVICE_UNAVAILABLE", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "12710688760", "text": "import io\nimport os\n\nimport lxml.html\nimport pytest\n\nfrom testutil import xmlparse_testcase, get_metadata_from_build\n\n\n@pytest.mark.parametrize(\"builder\", [\n pytest.mark.sphinx('html', 'html', testroot='testdefaults-not-used'),\n pytest.mark.sphinx('xmlexport', 'xmlexport', testroot='testdefaults-not-used'),\n ])\ndef test_directive_not_used(app, status, warning, builder):\n \"\"\"\n Check that build doesn't fail without the feature.\n \"\"\"\n app.builder.build_all()\n\n\n@pytest.mark.sphinx('html', testroot='testdefaults-flat')\ndef test_directive_html_content(app, status, warning):\n \"\"\"\n Check that test_defaults directive doesn't produce any content.\n \"\"\"\n app.builder.build_all()\n # get content of index.html file\n with io.open(os.path.join(app.outdir, 'index.html'), encoding='utf-8') as html_file:\n html_str = html_file.read()\n assert len(html_str) > 0\n # parse index.html and get div element with content\n html_tree = lxml.html.fromstring(html_str)\n div_tree_list = html_tree.xpath('//div[@class=\"body\" and @role=\"main\"]')\n assert len(div_tree_list) == 1\n div_tree = div_tree_list[0]\n # check that there is nothing in the content div\n div_b = lxml.html.tostring(div_tree, method=\"text\", encoding=\"utf-8\")\n div_str = div_b.decode('utf-8').strip()\n assert div_str == u\"Test of pylatest_defaults¶\"\n\n\n@pytest.mark.parametrize(\"builder\", [\n pytest.mark.sphinx('html', 'html', testroot='testdefaults-flat'),\n pytest.mark.sphinx('xmlexport', 'xmlexport', testroot='testdefaults-flat'),\n ])\ndef test_testcasemetadata_html_flat(app, status, warning, builder):\n app.builder.build_all()\n # parse test case document builds\n foo_tree = xmlparse_testcase(app.outdir, \"test_foo\", builder)\n bar_tree = xmlparse_testcase(app.outdir, \"test_bar\", builder)\n # get metadata\n foo_meta = get_metadata_from_build(foo_tree, builder)\n bar_meta = get_metadata_from_build(bar_tree, builder)\n # check metadata directly included in the files\n assert ('author', 'joe.foo@example.com') in foo_meta\n assert ('author', 'joe.bar@example.com') in bar_meta\n # check metadata added by test_defaults directive (in index.rst file)\n assert ('component', 'foobar') in foo_meta\n assert ('importance', 'high') in foo_meta\n assert ('component', 'foobar') in bar_meta\n assert ('importance', 'high') in bar_meta\n # there are no other metadata\n assert len(foo_meta) == 3\n assert len(bar_meta) == 3\n\n\n@pytest.mark.parametrize(\"builder\", [\n pytest.mark.sphinx('html', 'html', testroot='testdefaults-flat-override'),\n pytest.mark.sphinx('xmlexport', 'xmlexport', testroot='testdefaults-flat-override'),\n ])\ndef test_testcasemetadata_html_flat_override(app, status, warning, builder):\n \"\"\"\n Check that values from test_defaults directive can override\n values specified directly in a test case.\n \"\"\"\n app.builder.build_all()\n # get metadata\n foo_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"test_foo\", builder),\n builder)\n bar_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"test_bar\", builder),\n builder)\n # check metadata overriden by testdefaults directive,\n # in index.rst file, we set/override 'component' to value 'actium'\n for meta in foo_meta, bar_meta:\n comp_list = [val for (key, val) in meta if key == 'component']\n assert len(comp_list) == 1\n assert comp_list[0] == 'actium'\n\n\n@pytest.mark.parametrize(\"builder\", [\n pytest.mark.sphinx('html', 'html', testroot='testdefaults-nested'),\n pytest.mark.sphinx('xmlexport', 'xmlexport', testroot='testdefaults-nested'),\n ])\ndef test_testcasemetadata_html_nested(app, status, warning, builder):\n \"\"\"\n Given 2 directories with test cases (foo and bar), check that\n all test cases inside has component metadata value set properly,\n as defined in index.rst file (via test_defaults directive).\n \"\"\"\n app.builder.build_all()\n for tc_name in (\n \"foo/test_one\",\n \"foo/test_two\",\n \"bar/test_ten\",\n \"bar/test_elewen\"):\n meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, tc_name, builder),\n builder)\n component = tc_name.split(\"/\")[0]\n assert (\"component\", component) in meta\n\n\n@pytest.mark.parametrize(\"builder\", [\n pytest.mark.sphinx('html', 'html', testroot='testdefaults-nested-multiple'),\n pytest.mark.sphinx('xmlexport', 'xmlexport', testroot='testdefaults-nested-multiple'),\n ])\ndef test_testcasemetadata_html_nested_multiple(app, status, warning, builder):\n app.builder.build_all()\n # get metadata\n one_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"foo/test_one\", builder),\n builder)\n ten_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"foo/bar/test_ten\", builder),\n builder)\n # check metadata defined in test_defaults of root index.rst file\n assert ('note', 'test') in one_meta\n assert ('note', 'test') in ten_meta\n # check metadata defined in test_defaults of foo/index.rst file\n assert ('component', 'foo') in one_meta\n assert ('component', 'foo') in ten_meta\n # check metadata defined in test_defaults of foo/bar/index.rst file\n assert ('subcomponent', 'bar') in ten_meta\n\n\n@pytest.mark.parametrize(\"builder\", [\n pytest.mark.sphinx('html', 'html', testroot='testdefaults-nested-multiple'),\n pytest.mark.sphinx('xmlexport', 'xmlexport', testroot='testdefaults-nested-multiple'),\n ])\ndef test_testcasemetadata_html_nested_multiple_override(app, status, warning, builder):\n app.builder.build_all()\n # get metadata\n two_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"foo/test_two\", builder),\n builder)\n ten_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"foo/bar/test_ten\", builder),\n builder)\n elewen_meta = get_metadata_from_build(\n xmlparse_testcase(app.outdir, \"foo/bar/test_elewen\", builder),\n builder)\n # check metadata defined both in test_defaults of root index.rst file and\n # the test case itself, default value should be used\n assert ('note', 'test') in two_meta\n # check metadata defined both in test_defaults of bar's index.rst file and\n # the test case itself, default value should be used\n assert ('subcomponent', 'bar') in elewen_meta\n # check metadata defined both in test_defaults of bar's and foo's index.rst\n # file default value from foo's index.rst file should be used\n assert ('type', 'functional') in ten_meta\n assert ('type', 'functional') in elewen_meta\n", "repo_name": "mbukatov/pylatest", "sub_path": "tests/xsphinx/test_testdefaults.py", "file_name": "test_testdefaults.py", "file_ext": "py", "file_size_in_byte": 6709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pytest.mark.parametrize", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "io.open", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "lxml.html.html.fromstring", "line_number": 32, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 32, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 32, "usage_type": "name"}, {"api_name": "lxml.html.html.tostring", "line_number": 37, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 37, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 37, "usage_type": "name"}, {"api_name": "pytest.mark.sphinx", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 21, "usage_type": "attribute"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 49, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 50, "usage_type": "call"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 52, "usage_type": "call"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 53, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 44, "usage_type": "attribute"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 78, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 79, "usage_type": "call"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 81, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 82, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 67, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 69, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 69, "usage_type": "attribute"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 108, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 109, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 93, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 94, "usage_type": "attribute"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 122, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 123, "usage_type": "call"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 125, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 126, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 115, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 116, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 117, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 117, "usage_type": "attribute"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 145, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 146, "usage_type": "call"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 148, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 149, "usage_type": "call"}, {"api_name": "testutil.get_metadata_from_build", "line_number": 151, "usage_type": "call"}, {"api_name": "testutil.xmlparse_testcase", "line_number": 152, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 138, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 139, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pytest.mark.sphinx", "line_number": 140, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 140, "usage_type": "attribute"}]} +{"seq_id": "30765957999", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.preprocessing import MinMaxScaler\nfrom scipy import stats\nimport os\nimport sys\nimport functools\nfrom sklearn.neighbors import KDTree,NearestNeighbors\n\npath = os.path.abspath(__file__)\ndir_path = os.path.dirname(path)\nsys.path.append(dir_path)\n\nfrom generalrandom import GeneralRandom\nfrom distributions import *\nfrom plot import *\nfrom parameters import *\nfrom getmags import *\nimport intNN \n\nimport torch\nimport sbi\nfrom sbi import utils as utils\nfrom sbi.utils import user_input_checks\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\n\nimport extinction\n\nfrom joblib.externals.loky import set_loky_pickler\nset_loky_pickler(\"dill\")\n\nimport logging\n\nclass StarWave:\n \"\"\"\n StarWave: fitting the stellar birth function of resolved stellar populations \n with approximate Bayesian computation.\n This is the main class that performs the CMD fitting. The class is instantiated with\n an isochrone dataframe and artifical star database, as well as the type of IMF and\n SFH you want to fit/sample from.\n \n \"\"\"\n\n def __init__(self, isodf, asdf, bands, band_lambdas, imf_type, sfh_type = 'gaussian',\n sfh_grid = None, Rv = 3.1, params_kwargs = None):\n \"\"\"\n Initializes the StarWave object\n Parameters\n ----------\n isodf : pandas DataFrame\n Multi-indexed dataframe containing isochrone data for the required photometric bands.\n Should be indexed in Age, [Fe/H], and mass. # add more details\n asdf : pandas DataFrame\n Artifical star database containing input and output magnitudes for artifically injected\n stars, in all the required photometric bands\n bands : list\n list of strings containing the names of the photometric bands used. These names must be consistent\n in the isodf and the asdf\n imf_type : str\n whether to fit an 'spl', 'bpl', or 'ln' IMF parameterization\n sfh_type : str\n whether to fit a single-burst Gaussian SFH ('gaussian') or sample from a grid-based SFH ('grid')\n sfh_grid : dict\n if sfh_type is 'grid', then this dictionary contains the SFH with the following keys:\n 'mets' : array of M [Fe/H] grid points\n\t\t 'ages' : array of A age (Gyr) grid points\n\t\t 'probabilities' : M x A matrix with probability (or some weight) of each SFH bin\n params_kwargs : dict\n dictionary for printing/saving prior parameters\n\n \"\"\"\n\n if sfh_type == 'grid' and sfh_grid is None:\n print('please pass an sfh_grid if you want to use grid-based SFH sampling!')\n raise\n\n self.imf_type = imf_type\n self.sfh_type = sfh_type\n self.params_kwargs = params_kwargs\n self.params = make_params(imf_type, sfh_type, self.params_kwargs)\n self.make_prior(self.params) ## INITIALIZE FIXED PARAMS VECTOR\n self.bands = bands\n self.iso_int = intNN.intNN(isodf, self.bands)\n self.asdf = asdf\n self.return_inputmags = False\n\n self.bands_in = [band + '_in' for band in bands]\n self.bands_out = [band + '_out' for band in bands]\n\n self.asdf_noise = self.asdf[self.bands_out].to_numpy() - self.asdf[self.bands_in].to_numpy()\n\n self.kdtree = KDTree(asdf[self.bands_in])\n self.trgb = -100\n self.lim_logmass = np.log(0.1)\n self.sfh_grid = sfh_grid\n\n self.Rv = Rv\n self.band_lambdas = band_lambdas\n\n self.debug = False\n \n print('initalized starwave with %s bands, %s IMF, and default priors' % (str(bands), imf_type))\n print('using Rv = %.1f' % (self.Rv))\n self.params.summary()\n\n def init_scaler(self, observed_cmd, gamma = 0.5):\n \"\"\"\n initialize min-max scaling of CMD, along with the Nystroem kernel\n Parameters\n ----------\n observed_cmd : array\n gamma : float\n\n Returns\n -------\n array\n unit-scaled CMD\n \"\"\"\n self.cmd_scaler = MinMaxScaler()\n self.cmd_scaler.fit(observed_cmd);\n scaled_observed_cmd = self.cmd_scaler.transform(observed_cmd)\n Phi_approx = Nystroem(kernel = 'rbf', n_components=50, gamma = gamma) \n Phi_approx.fit(scaled_observed_cmd)\n self.mapping = Phi_approx.transform\n print('scaler initialized and mapping defined!')\n return scaled_observed_cmd\n\n def get_cmd(self, nstars, gr_dict, pdict):\n \"\"\"\n get a sampled CMD for a set of input parameters and total number of stars\n Parameters\n ----------\n nstars : int\n total number of sampled stars\n gr_dict : dict\n dictionary containing the IMF parameter distributions as GeneralRandom objects\n pdict : dict\n dictionary containing the current starwave parameters\n\n Returns\n -------\n\n \"\"\"\n\n input_mags = np.empty((nstars, len(self.bands)))\n input_mags[:] = np.nan\n\n masses = gr_dict['logM'].sample(nstars)\n binqs = gr_dict['BinQ'].sample(nstars)\n sfhs = gr_dict['SFH'].sample(nstars)\n dms = gr_dict['DM'].sample(nstars)\n\n\n for ii in range(nstars):\n\n mass = masses[ii]\n binq = binqs[ii]\n age, feh = sfhs[ii]\n dm = dms[ii]\n\n if mass < self.lim_logmass or np.isnan(age) or np.isnan(feh):\n continue\n\n input_mag = get_absolute_mags(mass, age, feh, binq, self.iso_int, self.bands)\n\n input_mags[ii, :] = input_mag + dm\n\n nans = (np.isnan(input_mags) + (input_mags < self.trgb)).any(axis = 1)\n\n input_mags = input_mags[~nans]\n\n if len(input_mags) == 0:\n return input_mags, input_mags\n\n exts = np.array([extinction.ccm89(np.array([band_lambda]),pdict['av'],self.Rv)[0] for band_lambda in self.band_lambdas])\n input_mags += exts\n\n idxs = self.kdtree.query(input_mags)[1][:, 0]\n\n output_mags = input_mags + self.asdf_noise[idxs]\n\n nans = np.isnan(output_mags).any(axis = 1)\n\n output_mags = output_mags[~nans]\n\n\n return input_mags, output_mags \n \n def make_cmd(self, mags):\n \"\"\"\n convert magnitudes to a cmd\n Parameters\n ----------\n mags : array\n\n Returns\n -------\n array\n \"\"\"\n cmd = mags\n for ii in range(mags.shape[1] - 1):\n cmd[:, ii + 1] -= cmd[:, 0]\n\n return cmd\n\n def best_gamma(self, cmd, q = 0.68, fac = 1, NN = 5):\n \"\"\"\n find best gamma value using Mario's heuristic\n Parameters\n ----------\n cmd : array\n input CMD\n q : float\n quantile to use in the heuristic\n fac : float\n fudge factor to scale up distances\n NN : int\n number of nearest neighbours to use\n\n Returns\n -------\n float\n best gamma value\n \"\"\"\n nbr = NearestNeighbors(n_neighbors = NN, algorithm = 'kd_tree', metric = 'minkowski', p = 2)\n nbr.fit(cmd)\n dst, idx = nbr.kneighbors(cmd, return_distance = True)\n dst = dst[:, -1] # pick NNth distance\n\n best_dist = np.quantile(dst, q)\n gamma = 1 / (2 * (fac * best_dist)**2)\n\n return gamma\n\n\n def set_sfh_dist(self, pdict, sfh_type):\n \"\"\"\n initialize and return the SFH distribution so that it can be sampled\n Parameters\n ----------\n pdict : dict\n parameter dictionary containing SFH parameters\n sfh_type : str\n type of SFH being fitted/sampled from ('gaussian' or 'grid')\n\n Returns\n -------\n object\n A starwave SFH object that can be sampled from\n \"\"\"\n\n if sfh_type == 'gaussian':\n cov = pdict['age_feh_corr'] * pdict['sig_age'] * pdict['sig_feh']\n covmat = np.array([[pdict['sig_age']**2, cov], [cov, pdict['sig_feh']**2]])\n\n\n if not isPD(covmat):\n covmat = nearestPD(covmat)\n print('found nearest SFH covmat...')\n\n means = np.array([pdict['age'], pdict['feh']])\n\n return SW_SFH(stats.multivariate_normal(mean = means, cov = covmat, allow_singular = True))\n\n elif sfh_type == 'grid':\n\n if self.sfh_grid is None:\n print('must pass an sfh_grid to use grid-based sampling!')\n raise\n\n else:\n return GridSFH(self.sfh_grid)\n\n\n def make_prior(self, parameters):\n \"\"\"\n initialize priors for all sampled parameters\n Parameters\n ----------\n parameters : object\n starwave parameters object\n\n Returns\n -------\n list\n list of prior distributions in torch format\n \"\"\"\n \n priors = [];\n self.fixed_params = {};\n self.param_mapper = {};\n idx = 0\n\n for ii,(name, param) in enumerate(parameters.dict.items()):\n \n if param.fixed:\n self.fixed_params[name] = param.value\n continue\n \n \n lower = param.bounds[0]\n upper = param.bounds[1]\n \n if param.distribution == 'uniform':\n distribution = torch.distributions.Uniform(lower*torch.ones(1), upper*torch.ones(1))\n priors.append(distribution)\n \n elif param.distribution == 'norm':\n try:\n mean = param.dist_kwargs['mean']\n sigma = param.dist_kwargs['sigma']\n except:\n raise ValueError('please pass valid distribution arguments!')\n distribution = torch.distributions.Normal(torch.tensor(mean), torch.tensor(sigma))\n priors.append(distribution)\n \n else:\n raise ValueError('invalid distribution name')\n\n self.param_mapper[name] = idx\n idx += 1 # IDX maps the vector of sampled parameters, leaving apart the fixed ones. \n \n return priors\n\n def sample_cmd(self, params, model):\n \"\"\"\n wrapper function to sample a CMD for a given set of starwave parameters\n Parameters\n ----------\n params : SWParameters object\n model : str\n 'spl', 'bpl', or 'ln' IMF model\n\n Returns\n -------\n list\n list of two arrays, one for the noiseless CMD and one for the noisy CMD\n \"\"\"\n\n is_pdict = False\n\n if isinstance(params, torch.FloatTensor):\n params = params.detach().cpu().numpy()\n elif isinstance(params, (list, np.ndarray)):\n pass\n elif isinstance(params, dict):\n pdict = params\n is_pdict = True\n\n\n self.make_prior(self.params) # Re-initialize priors, check fixed parameters\n\n pdict = {};\n for name in self.params.keys():\n if name in self.fixed_params.keys():\n pdict[name] = self.fixed_params[name]\n else:\n if is_pdict:\n pdict[name] = params[name] # if params are dictionary \n else:\n pdict[name] = params[self.param_mapper[name]] # if params are array or tensor\n\n # if self.debug:\n # print('param dictionary in sample_cmd:' + str(pdict))\n\n if model == 'spl':\n gr_dict = {'logM':set_GR_spl(pdict['slope'])}\n elif model == 'bpl':\n gr_dict = {'logM':set_GR_bpl(pdict['alow'], pdict['ahigh'], pdict['bm'])}\n elif model == 'ln':\n gr_dict = {'logM':set_GR_ln10full(pdict['mean'], pdict['sigma'], pdict['bm'], pdict['slope'])}\n else:\n print('Unrecognized model!')\n\n gr_dict['BinQ'] = set_GR_unif(pdict['bf'])\n gr_dict['SFH'] = self.set_sfh_dist(pdict, self.sfh_type)\n gr_dict['DM'] = SWDist(stats.norm(loc = pdict['dm'], scale = pdict['sig_dm']))\n\n intensity = 10**pdict['log_int']\n nstars = int(stats.poisson.rvs(intensity))\n\n mags_in, mags_out = self.get_cmd(nstars, gr_dict, pdict)\n\n cmd_in = self.make_cmd(mags_in)\n cmd_out = self.make_cmd(mags_out)\n\n return cmd_in, cmd_out\n\n def sample_norm_cmd(self, params, model):\n \"\"\"\n wrapper function to sample unit-normalized CMD\n Parameters\n ----------\n params : SWParameters object\n model : str\n 'spl', 'bpl', or 'ln' IMF model\n\n Returns\n -------\n list\n list of two arrays, one for the noiseless CMD and one for the noisy CMD, unit-scaled\n \"\"\"\n in_cmd, out_cmd = self.sample_cmd(params, model)\n if len(in_cmd) == 0 or len(out_cmd) == 0:\n print('empty cmd!')\n return self.dummy_cmd, self.dummy_cmd\n return self.cmd_scaler.transform(in_cmd), self.cmd_scaler.transform(out_cmd)\n\n def kernel_representation(self, P, mapping):\n \"\"\"\n project a given array (CMD) onto the kernal space\n Parameters\n ----------\n P : array\n CMD to be projected\n mapping : array\n kernel mapping from Nystroem\n\n Returns\n -------\n array\n projected representation of CMD\n \"\"\"\n Phi_P = mapping(P).sum(axis=0)\n return Phi_P\n\n def cmd_sim(self, params, imf_type):\n \"\"\"\n wrapper function to simulate kernel-represented CMD given parameters\n Parameters\n ----------\n params : SWParameters object\n imf_type : str\n 'spl', 'bpl', or 'ln' IMF model\n\n Returns\n -------\n array\n sampled CMD in kernel representation form\n \"\"\"\n in_cmd, out_cmd = self.sample_norm_cmd(params, model = imf_type)\n if self.return_inputmags:\n return self.kernel_representation(in_cmd, self.mapping)\n else:\n return self.kernel_representation(out_cmd, self.mapping)\n\n def fit_cmd(self, observed_cmd,\n n_rounds = 5,\n n_sims = 100,\n savename = 'starwave',\n min_acceptance_rate = 0.0001,\n gamma = None,\n cores = 1, alpha = 0.5,\n statistic = 'output',\n gamma_kw = {}):\n\n \"\"\"\n main function to fit an observed CMD using an instatiated StarWave object\n Parameters\n ----------\n observed_cmd :\n n_rounds :\n n_sims :\n savename :\n min_acceptance_rate :\n gamma :\n cores :\n alpha :\n statistic :\n gamma_kw :\n\n Returns\n -------\n\n \"\"\"\n\n\n if cores == 1:\n pass # IMPLEMENT SBI MULTICORE\n\n scaled_observed_cmd = self.init_scaler(observed_cmd, gamma = gamma)\n obs = torch.tensor(self.kernel_representation(scaled_observed_cmd, self.mapping))\n self.obs = obs\n\n if gamma is None:\n print('finding optimal kernel width...')\n gamma = self.best_gamma(scaled_observed_cmd, **gamma_kw)\n print('setting gamma = %i' % gamma)\n\n self.dummy_cmd = np.zeros(observed_cmd.shape)\n \n def simcmd(imf_type):\n return lambda params: self.cmd_sim(params, imf_type = imf_type)\n\n Nobs = len(scaled_observed_cmd)\n\n #self.params['log_int'].set(value = np.log10(Nobs), bounds = [np.log10(Nobs/2) , np.log10(Nobs*10)])\n\n print_prior_summary(self.params)\n \n prior = user_input_checks.MultipleIndependent(self.make_prior(self.params))\n simulator = simcmd(self.imf_type)\n\n self.simulator,self.prior = prepare_for_sbi(simulator,prior)\n\n inference = SNPE(prior = self.prior)\n\n self.posteriors = [];\n proposal = self.prior\n\n for _ in range(n_rounds):\n print('Starting round %i of neural inference...' % (_+1))\n theta, x = simulate_for_sbi(self.simulator, proposal, num_simulations=n_sims, num_workers = cores)\n density_estimator = inference.append_simulations(theta, x, proposal=proposal).train()\n posterior = inference.build_posterior(density_estimator)\n self.posteriors.append(posterior)\n proposal = posterior.set_default_x(obs)\n\n return self.posteriors[-1]\n \nif __name__ == '__main__':\n sw = StarWave()\n sw.params.pretty_print()\n", "repo_name": "vedantchandra/starwave", "sub_path": "starwave/starwave.py", "file_name": "starwave.py", "file_ext": "py", "file_size_in_byte": 16672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "joblib.externals.loky.set_loky_pickler", "line_number": 32, "usage_type": "call"}, {"api_name": "intNN.intNN", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KDTree", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.kernel_approximation.Nystroem", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "extinction.ccm89", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.quantile", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 263, "usage_type": "call"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 265, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 265, "usage_type": "name"}, {"api_name": "parameters.dict.items", "line_number": 296, "usage_type": "call"}, {"api_name": "parameters.dict", "line_number": 296, "usage_type": "attribute"}, {"api_name": "torch.distributions.Uniform", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 307, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.distributions.Normal", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 316, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 344, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 346, "usage_type": "attribute"}, {"api_name": "scipy.stats.norm", "line_number": 379, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 379, "usage_type": "name"}, {"api_name": "scipy.stats.poisson.rvs", "line_number": 382, "usage_type": "call"}, {"api_name": "scipy.stats.poisson", "line_number": 382, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 382, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 492, "usage_type": "call"}, {"api_name": "sbi.utils.user_input_checks.MultipleIndependent", "line_number": 503, "usage_type": "call"}, {"api_name": "sbi.utils.user_input_checks", "line_number": 503, "usage_type": "name"}, {"api_name": "sbi.inference.prepare_for_sbi", "line_number": 506, "usage_type": "call"}, {"api_name": "sbi.inference.SNPE", "line_number": 508, "usage_type": "call"}, {"api_name": "sbi.inference.simulate_for_sbi", "line_number": 515, "usage_type": "call"}]} +{"seq_id": "4828695551", "text": "from bs4 import BeautifulSoup\nimport datetime\nfrom datetime import date, timedelta\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\n\n\nclass TWQueryMngr:\n # init method or constructor\n def __init__(self):\n self.search_url = \"https://twitter.com/search?q=\"\n self.dates = []\n\n self.driver = webdriver.Chrome()\n self.search_words = []\n self.lang = 'es'\n self.output = None\n\n def set_driver(self,_driver):\n self.driver = _driver\n\n def set_lang(self,_lang):\n self.lang = _lang\n\n def scroll(self,words, lang, max_time=180):\n\n languages = {1: 'en', 2: 'it', 3: 'es', 4: 'fr', 5: 'de', 6: 'ru', 7: 'zh'}\n url = self.search_url\n for w in words[:-1]:\n url += \"{}%20OR\".format(w)\n url += \"{}%20\".format(words[-1])\n url += \"&src=typed_query&f=live\"\n if lang != 0:\n url += \"l={}&\".format(languages[lang])\n print(url)\n live = \"css-4rbku5 css-18t94o4 css-1dbjc4n r-1awozwy r-18p3no4 r-rull8r r-wgabs5 r-1loqt21 r-6koalj r-eqz5dr r-16y2uox r-1777fci r-1ny4l3l r-1oqcu8e r-o7ynqc r-6416eg\"\n\n self.driver.get(url)\n start_time = time.time() # remember when we started\n\n time.sleep(5)\n element =self.driver.find_element_by_xpath(\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[1]/div[2]/nav/div[2]/div[2]/a/div/span\").click()\n\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(3)\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(10)\n\n\n def scrape_tweets(self):\n \n sql = \"\"\n\n try:\n\n obj = BeautifulSoup(self.driver.page_source, \"html.parser\")\n\n \n for a in obj.find_all(\"article\"):\n\n tw_text = a.find(\"div\", {\"lang\": \"es\"})\n\n if tw_text != None:\n\n tw_obj = BeautifulSoup(str(tw_text), \"html.parser\")\n tw_user = a.find(\"a\", {\"role\": \"link\"})['href'].strip(\"/\")\n tw_datetime = a.find(\"time\")['datetime']\n\n\n sql = \"('%s', '%s', '%s')\"%(tw_obj.text,tw_user,tw_datetime)\n print(\"====================================\")\n print(sql)\n\n except Exception as e:\n print(\"Something went wrong!\")\n print(e)\n \n print(sql)\n\n\n def tw_search(self,_search_str):\n\n wordsToSearch = []\n wordsToSearch.append(_search_str)\n self.output = open(\"output.json\",\"w\")\n\n self.driver = webdriver.Chrome()\n self.scroll(wordsToSearch, 1)\n self.scrape_tweets()\n time.sleep(3)\n #print(\"The tweets for {} are ready!\".format(all_dates[i]))\n self.driver.quit()\n\n self.output.close()\n", "repo_name": "filip7575/Scrapy-Crawler", "sub_path": "Selenium/TwitterSearch/twquerymngr.py", "file_name": "twquerymngr.py", "file_ext": "py", "file_size_in_byte": 3075, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 17, "usage_type": "name"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 59, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 68, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 90, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 90, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "38741261926", "text": "import numpy as np\nimport glfw\nfrom OpenGL.GL import *\n\nglobal input_key\ninput_key = 100\n\ndef render():\n glClear(GL_COLOR_BUFFER_BIT)\n glLoadIdentity()\n glBegin(GL_LINE_LOOP)\n for i in range(0, 12):\n glVertex2f(np.cos(2*i*np.pi/12), np.sin(2*i*np.pi/12))\n glEnd()\n\n glBegin(GL_LINES)\n \n if(input_key == 100):\n glVertex2f(0,0)\n glVertex2f(0,1)\n elif(input_key>=49 and input_key<=57):\n glVertex2f(0,0)\n glVertex2f(np.cos(np.pi*(0.5-(int(input_key)-48)/6)), np.sin(np.pi*(0.5-(int(input_key)-48)/6)))\n elif(input_key == 48):\n glVertex2f(0,0)\n glVertex2f(np.cos(5*np.pi/6), np.sin(5*np.pi/6))\n elif(input_key == 81):\n glVertex2f(0,0)\n glVertex2f(np.cos(2*np.pi/3), np.sin(2*np.pi/3))\n elif(input_key == 87):\n glVertex2f(0,0)\n glVertex2f(0,1)\n\n glEnd()\n\ndef key_callback(window, key, scnacode, action, mods):\n if(action == glfw.PRESS and ((key>=48 and key<=57) or key==81 or key==87)):\n global input_key\n input_key = key\n\ndef main():\n if not glfw.init():\n return\n\n window = glfw.create_window(480, 480, \"2018008804\", None, None)\n if not window:\n glfw.terminate()\n return\n\n glfw.set_key_callback(window, key_callback)\n \n glfw.make_context_current(window)\n \n while not glfw.window_should_close(window):\n glfw.poll_events()\n \n render()\n\n glfw.swap_buffers(window)\n\n glfw.terminate()\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "ilovetayy/2021_1_computer-graphics", "sub_path": "lab/2-2.py", "file_name": "2-2.py", "file_ext": "py", "file_size_in_byte": 1523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "numpy.cos", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 29, "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": "glfw.PRESS", "line_number": 37, "usage_type": "attribute"}, {"api_name": "glfw.init", "line_number": 42, "usage_type": "call"}, {"api_name": "glfw.create_window", "line_number": 45, "usage_type": "call"}, {"api_name": "glfw.terminate", "line_number": 47, "usage_type": "call"}, {"api_name": "glfw.set_key_callback", "line_number": 50, "usage_type": "call"}, {"api_name": "glfw.make_context_current", "line_number": 52, "usage_type": "call"}, {"api_name": "glfw.window_should_close", "line_number": 54, "usage_type": "call"}, {"api_name": "glfw.poll_events", "line_number": 55, "usage_type": "call"}, {"api_name": "glfw.swap_buffers", "line_number": 59, "usage_type": "call"}, {"api_name": "glfw.terminate", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "72517116206", "text": "from datetime import date, timedelta, datetime\n\nfrom aiogram import types, Router, Bot, F\nfrom aiogram.filters import StateFilter\nfrom aiogram.types import Message, CallbackQuery\nfrom aiogram.fsm.state import default_state\nfrom aiogram.fsm.context import FSMContext\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\n\nfrom tg_bot.filters.filters import TimeFormatFilter\nfrom tg_bot.scheduler.notification import send_message_question_implementation\nfrom tg_bot.services.base_service import get_event, get_event_data, preparation_text_notification, \\\n calculate_time_hour_reminder_timedelta, process_input_time\nfrom tg_bot.keyboards.notification_keyboards import request_postponement, \\\n processing_postponement_request, event_validation\nfrom tg_bot.services.notification_services import update_completed, change_deadline\nfrom tg_bot.states.notification_state import FSMFillFormNotif\nfrom tg_bot.keyboards.calendar_keyboard import SimpleCalendar\n\nnotif_router = Router()\n\n\nsearch_callback_calendar = \"notif\"\n\n\n# Валидация задачи КНОПКА ДА:ВЫПОЛНЕНО-НЕТ:НЕ ВЫПОЛНЕНО\n@notif_router.callback_query(lambda c: c.data.startswith('event_done'), StateFilter(default_state))\nasync def notif_event_done(call: types.CallbackQuery, bot: Bot):\n trash, event_id = call.data.split(\":\")\n await bot.edit_message_text(text='Отлично, отправляем задачу на валидацию!',\n chat_id=call.message.chat.id, message_id=call.message.message_id)\n event = await get_event(event_id)\n dict_event = await get_event_data(event)\n prepare = preparation_text_notification(dict_event, \"validation\")\n await bot.send_message(chat_id=prepare['chat_id'], text=prepare['text'], reply_markup=event_validation(event_id))\n\n\n# Подтверждение выполнения задачи НЕ ОТПРАВЛЯЕМ КНОПКИ\n@notif_router.callback_query(lambda c: c.data.startswith('event_completed'), StateFilter(default_state))\nasync def notif_event_validation(call: types.CallbackQuery, bot: Bot):\n trash, event_id = call.data.split(\":\")\n await bot.edit_message_text(text='Задача выполнена, готовим оповещение!',\n chat_id=call.message.chat.id, message_id=call.message.message_id)\n await update_completed(event_id)\n event = await get_event(event_id)\n dict_event = await get_event_data(event)\n prepare = preparation_text_notification(dict_event, \"completed\")\n print(prepare)\n await bot.send_message(chat_id=call.message.chat.id, text=prepare['text'])\n await bot.send_message(chat_id=prepare['chat_id'], text=prepare['text'])\n\n\n# Задача не выполнена, вопрос о запросе на перенос срока КНОПКИ-- ДА:ПЕРЕНСТИ-НЕТ:НЕ ПЕРЕНОСИМ\n@notif_router.callback_query(lambda c: c.data.startswith('event_not_done'), StateFilter(default_state))\nasync def notif_event_not_done(call: types.CallbackQuery, bot: Bot):\n trash, event_id = call.data.split(\":\")\n await bot.edit_message_text(text='Задача не выполнена\\n\\n'\n 'Запросить перенос срока?',\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n reply_markup=request_postponement(event_id))\n\n\n# Вопрос о запросе на перенос срока отклонен НЕТ КНОПОК\n@notif_router.callback_query(lambda c: c.data.startswith('event_not_change'), StateFilter(default_state))\nasync def notif_event_not_change(call: types.CallbackQuery, bot: Bot):\n await bot.edit_message_text(text='Не переносим срок задачи',\n chat_id=call.message.chat.id,\n message_id=call.message.message_id)\n\n\n# Отправка запроса на перенос срока\n@notif_router.callback_query(lambda c: c.data.startswith('event_req_new_deadline'), StateFilter(default_state))\nasync def notif_event_req_new_deadline(call: types.CallbackQuery, bot: Bot):\n trash, event_id = call.data.split(\":\")\n await bot.edit_message_text(text='Задача не выполнена\\n\\n',\n chat_id=call.message.chat.id,\n message_id=call.message.message_id)\n event = await get_event(event_id)\n dict_event = await get_event_data(event)\n prepare = preparation_text_notification(dict_event, \"new_deadline_req\")\n await bot.send_message(chat_id=prepare['chat_id'], text=prepare['text'],\n reply_markup=processing_postponement_request(event_id))\n\n\n# Отправка запроса на перенос срока\n@notif_router.callback_query(lambda c: c.data.startswith('event_new_deadline'), StateFilter(default_state))\nasync def event_new_deadline(call: types.CallbackQuery, bot: Bot, state: FSMContext):\n trash, event_id = call.data.split(\":\")\n await bot.edit_message_text(text='Переносим срок задачи',\n chat_id=call.message.chat.id,\n message_id=call.message.message_id)\n await state.update_data(event_id=event_id)\n await call.message.answer(\"Введите время:\\n\"\n \"Формат времени ЧЧ:ММ\")\n await state.set_state(FSMFillFormNotif.time)\n\n\n@notif_router.message(StateFilter(FSMFillFormNotif.time), TimeFormatFilter())\nasync def process_age_sent(msg: Message, state: FSMContext):\n await state.update_data(time=msg.text)\n await msg.answer(\"Спасибо, теперь введите дату:\",\n reply_markup=await SimpleCalendar().start_calendar(search_callback_calendar))\n await state.set_state(FSMFillFormNotif.date)\n\n\n@notif_router.message(StateFilter(FSMFillFormNotif.time))\nasync def warning_not_age(message: Message):\n await message.answer(\n text='Введите корректное время в формате ЧЧ:ММ\\n\\n'\n 'Попробуйте еще раз\\n\\n'\n )\n\n\n@notif_router.message(F.data.startswith(f'simple_calendar{search_callback_calendar}'))\nasync def process_age_sent(call: CallbackQuery, state: FSMContext, bot: Bot,\n apscheduler: AsyncIOScheduler):\n selected, date_choice = await SimpleCalendar().process_selection(call, search_callback_calendar)\n if selected:\n await call.message.answer(\"Обновляем задачу...:\")\n await state.update_data(date=date_choice)\n fill_data = await state.get_data()\n await state.clear()\n\n await change_deadline(fill_data['event_id'], fill_data['date'], fill_data['time'])\n\n event = await get_event(fill_data['event_id'])\n dict_event = await get_event_data(event)\n prepare = preparation_text_notification(dict_event, \"change\")\n\n await bot.send_message(prepare['chat_id'], prepare['text'])\n await bot.send_message(call.from_user.id, prepare['text'])\n\n if fill_data['date'].date() == date.today():\n prepare_event = preparation_text_notification(dict_event, \"check_ex\")\n seconds = await calculate_time_hour_reminder_timedelta(process_input_time(fill_data['time']))\n apscheduler.add_job(send_message_question_implementation, trigger='date',\n run_date=datetime.now() + timedelta(seconds=seconds),\n kwargs={'bot': bot,\n 'chat_id': prepare_event['chat_id'],\n 'text': prepare_event['text'],\n 'event_id': event.id})\n\n\n@notif_router.message(StateFilter(FSMFillFormNotif.date))\nasync def warning_not_age(message: Message):\n await message.answer(\n text='Введите корректное дату в формате дд.мм\\n\\n'\n 'Попробуйте еще раз\\n\\n'\n )\n", "repo_name": "TBWTK/TaskManager", "sub_path": "tg_bot/handlers/notification_handler.py", "file_name": "notification_handler.py", "file_ext": "py", "file_size_in_byte": 8137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "aiogram.Router", "line_number": 20, "usage_type": "call"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 28, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 28, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 28, "usage_type": "name"}, {"api_name": "tg_bot.services.base_service.get_event", "line_number": 32, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.get_event_data", "line_number": 33, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.preparation_text_notification", "line_number": 34, "usage_type": "call"}, {"api_name": "tg_bot.keyboards.notification_keyboards.event_validation", "line_number": 35, "usage_type": "call"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 27, "usage_type": "call"}, {"api_name": "aiogram.fsm.state.default_state", "line_number": 27, "usage_type": "argument"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 40, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 40, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 40, "usage_type": "name"}, {"api_name": "tg_bot.services.notification_services.update_completed", "line_number": 44, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.get_event", "line_number": 45, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.get_event_data", "line_number": 46, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.preparation_text_notification", "line_number": 47, "usage_type": "call"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 39, "usage_type": "call"}, {"api_name": "aiogram.fsm.state.default_state", "line_number": 39, "usage_type": "argument"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 55, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 55, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 55, "usage_type": "name"}, {"api_name": "tg_bot.keyboards.notification_keyboards.request_postponement", "line_number": 61, "usage_type": "call"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 54, "usage_type": "call"}, {"api_name": "aiogram.fsm.state.default_state", "line_number": 54, "usage_type": "argument"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 66, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 66, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 66, "usage_type": "name"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 65, "usage_type": "call"}, {"api_name": "aiogram.fsm.state.default_state", "line_number": 65, "usage_type": "argument"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 74, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 74, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 74, "usage_type": "name"}, {"api_name": "tg_bot.services.base_service.get_event", "line_number": 79, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.get_event_data", "line_number": 80, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.preparation_text_notification", "line_number": 81, "usage_type": "call"}, {"api_name": "tg_bot.keyboards.notification_keyboards.processing_postponement_request", "line_number": 83, "usage_type": "call"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 73, "usage_type": "call"}, {"api_name": "aiogram.fsm.state.default_state", "line_number": 73, "usage_type": "argument"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 88, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 88, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 88, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 88, "usage_type": "name"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif.time", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif", "line_number": 96, "usage_type": "name"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 87, "usage_type": "call"}, {"api_name": "aiogram.fsm.state.default_state", "line_number": 87, "usage_type": "argument"}, {"api_name": "aiogram.types.Message", "line_number": 100, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 100, "usage_type": "name"}, {"api_name": "tg_bot.keyboards.calendar_keyboard.SimpleCalendar", "line_number": 103, "usage_type": "call"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif.date", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif", "line_number": 104, "usage_type": "name"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 99, "usage_type": "call"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif.time", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif", "line_number": 99, "usage_type": "name"}, {"api_name": "tg_bot.filters.filters.TimeFormatFilter", "line_number": 99, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 108, "usage_type": "name"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 107, "usage_type": "call"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif.time", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif", "line_number": 107, "usage_type": "name"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 116, "usage_type": "name"}, {"api_name": "aiogram.fsm.context.FSMContext", "line_number": 116, "usage_type": "name"}, {"api_name": "aiogram.Bot", "line_number": 116, "usage_type": "name"}, {"api_name": "apscheduler.schedulers.asyncio.AsyncIOScheduler", "line_number": 117, "usage_type": "name"}, {"api_name": "tg_bot.keyboards.calendar_keyboard.SimpleCalendar", "line_number": 118, "usage_type": "call"}, {"api_name": "tg_bot.services.notification_services.change_deadline", "line_number": 125, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.get_event", "line_number": 127, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.get_event_data", "line_number": 128, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.preparation_text_notification", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 134, "usage_type": "name"}, {"api_name": "tg_bot.services.base_service.preparation_text_notification", "line_number": 135, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.calculate_time_hour_reminder_timedelta", "line_number": 136, "usage_type": "call"}, {"api_name": "tg_bot.services.base_service.process_input_time", "line_number": 136, "usage_type": "call"}, {"api_name": "apscheduler.schedulers.asyncio.add_job", "line_number": 137, "usage_type": "call"}, {"api_name": "tg_bot.scheduler.notification.send_message_question_implementation", "line_number": 137, "usage_type": "argument"}, {"api_name": "apscheduler.schedulers.asyncio", "line_number": 137, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 138, "usage_type": "call"}, {"api_name": "aiogram.F.data.startswith", "line_number": 115, "usage_type": "call"}, {"api_name": "aiogram.F.data", "line_number": 115, "usage_type": "attribute"}, {"api_name": "aiogram.F", "line_number": 115, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 146, "usage_type": "name"}, {"api_name": "aiogram.filters.StateFilter", "line_number": 145, "usage_type": "call"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif.date", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tg_bot.states.notification_state.FSMFillFormNotif", "line_number": 145, "usage_type": "name"}]} +{"seq_id": "23495014432", "text": "# -*- coding: UTF-8 -*-\n\n\nimport os\nimport logging\n\nfrom suntest.tools import TOOLS_PATH\nimport suntest.core.command as Command\n\nlogger = logging.getLogger(__name__)\n\nclass Automator2JunitXml(object):\n \"\"\"automator-log-converter\"\"\"\n tool_path = os.path.join(TOOLS_PATH, 'automator-log-converter-1.5.0.jar')\n\n @staticmethod\n def converter(log_file):\n \"\"\"\n Read Android UI automator file output and write a JUNIT xml to same dir.\n\n log_file: file with output from automator.\n \"\"\"\n\n if not os.path.exists(log_file):\n logger.error('测试程序结果log文件 %s不存在' % log_file)\n return\n automator_log_coverter_cmd = 'java -jar %s %s' % (Automator2JunitXml.tool_path, log_file)\n logger.debug('测试程序结果log文件转为xml文件命令: %s ' % automator_log_coverter_cmd)\n if not Command.shell_command(automator_log_coverter_cmd):\n logger.error(\"测试程序结果log文件转为xml文件失败\")\n else:\n logger.info('测试程序结果log文件转为xml文件成功')", "repo_name": "berniehuang/autotest", "sub_path": "suntest/suntest_unittest/report/automator_report.py", "file_name": "automator_report.py", "file_ext": "py", "file_size_in_byte": 1095, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "suntest.tools.TOOLS_PATH", "line_number": 14, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "suntest.core.command.shell_command", "line_number": 29, "usage_type": "call"}, {"api_name": "suntest.core.command", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "14732866848", "text": "#coding=utf-8\n#用中文文本做词云\n\nimport jieba\nfrom wordcloud import WordCloud\nimport matplotlib.pyplot as plt\n\n#读取中文文本，并对其做分词\nzh_text=open(\"./txt/coder.txt\").read()\nmytext=\" \".join(jieba.cut(zh_text))\n\nwordcloud = WordCloud(font_path=\"./font/simsun.ttf\").generate(mytext)\n\nplt.imshow(wordcloud,interpolation=\"bilinear\")\nwordcloud.to_file('./img/simple_zh.png')\nplt.axis(\"off\")\nplt.show()", "repo_name": "JamesBaiyong/python_pracatice", "sub_path": "pracaticeProgram/word_cloud_study/simple_zh.py", "file_name": "simple_zh.py", "file_ext": "py", "file_size_in_byte": 422, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "jieba.cut", "line_number": 10, "usage_type": "call"}, {"api_name": "wordcloud.WordCloud", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "wordcloud.to_file", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "41146022486", "text": "from json import loads,dumps\nimport httplib\ndef check_pasward(name,pas,conection):\n conection.request(\"GET\", \"/check_pasward?pasward=\"+pas+\"&user=\"+name)\n\n res = conection.getresponse()\n if res.status==200:\n data = res.read()\n return (data==\"True\")\n else:\n return (\"there was a conection eror\")\n\ndef create_user(name,pas,groups,conection):\n\n conection.request(\"POST\", \"/create_user?pasward=\"+pas+\"&user=\"+name+\"&groups=\"+dumps(list(groups)))\n res = conection.getresponse()\n if res.status==200:\n return (res.read())\n else:\n return (\"there was a conection eror\")\n\n#/get_file//\ndef get_file_list(username,path,conection):\n filename=path.split('/')[-1]\n conection.request(\"GET\", \"/get_file/\"+username+path)\n\n res=conection.getresponse()\n if res.status==200:\n return (loads(res.read()))\n else:\n return (\"there was a conection eror\")\n\ndef get_file(username,path,conection):\n filename=path.split('/')[-1]\n conection.request(\"GET\", \"/get_file/\"+username+path)\n\n res=conection.getresponse()\n if res.status==200:\n\n open(filename, 'wb').write(res.read())\n return ('')\n\n return (loads(res.read()))\n else:\n return (\"there was a conection eror\")\n\nget_file_list('yuval','/root/admin', httplib.HTTPConnection(\"localhost\", port=5000))\n\ndef rename_file(username,path,new_name,conection):\n conection.request(\"POST\", \"/rename_file/\"+username+path+\"?new_name=\"+new_name)\n res=conection.getresponse()\n if res.status==200:\n\n return (res.read())\n else:\n return (\"there was a conection eror\")\n\ndef delete_file(username,path,conection):\n conection.request(\"DEL\", \"/del_file/\"+username+path)\n res=conection.getresponse()\n if res.status==200:\n\n return (res.read())\n else:\n return (\"there was a conection eror\")\n\ndef replace(username,path,new_path,conn):\n print(username,path,new_path)\n conn.request(\"GET\", \"/replace/\"+username+path+\"?new_place=\"+new_path)\n res = conn.getresponse()\n if res.status==200:\n return (res.read())\n else:\n return (\"there was a conection eror\")\ndef get_type(path,conection):\n conection.request(\"GET\", \"/get_type/\"+path)\n res = conection.getresponse()\n if res.status==200:\n return (res.read())\n else:\n return (\"there was a conection eror\")\n\ndef change_per(username,path,per_list,conn):\n conn.request(\"GET\", \"/change_per/\"+username+path+\"?names=\"+dumps(per_list))\n res = conn.getresponse()\n if res.status==200:\n return (res.read())\n else:\n return (\"there was a conection eror\")\n\ndef get_per(path,conn):\n conn.request(\"GET\", \"/get_per\"+path)\n res = conn.getresponse()\n if res.status==200 or res.read()=='the file didnt found' :\n return (loads(res.read()))\n else:\n return (\"there was a conection eror\")\n\ndef get_log(conn):\n conn.request(\"GET\", \"/get_log\")\n res = conn.getresponse()\n\n\n if res.status==200:\n x=loads(res.read())\n if type(x)!=str:\n return (x['log'])\n\n else:\n return (\"there was a conection eror\")\ndef put_dir(user,path,name,conn):\n conn.request(\"POST\", \"/put_dir/\"+user+path+\"?name=\"+name)\n res = conn.getresponse()\n\n\n if res.status==200:\n x=loads(res.read())\n if type(x)!=str:\n return (x['log'])", "repo_name": "yuvalorp/dropbox", "sub_path": "src/client/client_http.py", "file_name": "client_http.py", "file_ext": "py", "file_size_in_byte": 3396, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "json.dumps", "line_number": 15, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "httplib.HTTPConnection", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "12532266529", "text": "import sys\nimport itertools as it\nfrom collections import Counter\n\n\nSTATES = {j: i for i, j in enumerate(('A', 'C', 'G', 'T'))}\ndef parsePatterns(fn):\n patterns = {}\n c = it.count()\n with open(fn) as f:\n for i, j in it.combinations(map(lambda l: l.strip().split(','), filter(lambda l: not l.isspace(), f)), 2):\n a, A, T, G, C, x = i\n b, _, _, _, _, y = j\n counter = Counter(zip(map(STATES.get, x), map(STATES.get, y)))\n for i, n in enumerate(map(int, (A, C, G, T))):\n counter[(i, i)] += n\n patterns[tuple(sorted((a, b)))] = dict(counter)\n return patterns\n\nfor fn in sys.argv[1:]:\n print(parsePatterns(fn))", "repo_name": "armanbilge/pySSGD", "sub_path": "SSGD/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "itertools.count", "line_number": 9, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "44331447969", "text": "from django.shortcuts import render\nfrom media_manager.repositories import MediaFileRepository\n\n\n# Create your views here.\n\ndef home(request):\n if request.method == \"GET\":\n audios = MediaFileRepository.find_approved_media(media_type=\"audio\",count=10)\n videos = MediaFileRepository.find_approved_media(media_type=\"video\",count=10)\n context = {\n \"audios\": audios,\n \"videos\": videos\n }\n return render(request, \"portal/index.html\", context)\n\n\ndef list_videos(request,branche_name):\n\n if request.method == \"GET\":\n if branche_name == 'all':\n videos = MediaFileRepository.find_approved_media(\"video\")\n else:\n videos = MediaFileRepository.filter_by_branche(media_type=\"video\",branche_name=branche_name)\n\n context = {\n \"videos\": videos\n }\n return render(request, \"portal/video_list.html\", context)\n\n\ndef list_audios(request,branche_name):\n print(branche_name)\n if request.method == \"GET\":\n if branche_name == 'all':\n audios = MediaFileRepository.find_approved_media(\"audio\")\n else:\n audios = MediaFileRepository.filter_by_branche(media_type=\"audio\", branche_name=branche_name)\n\n context = {\n \"audios\": audios\n }\n\n return render(request, \"portal/audio_list.html\", context)\n\n\ndef media_file_detail(request, media_id):\n context = {\n \"media\" : MediaFileRepository.find_one(media_id=media_id)\n }\n return render(request, \"portal/fichier_media.html\",context)\n", "repo_name": "kayscode/theway", "sub_path": "portal/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1564, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "media_manager.repositories.MediaFileRepository.find_approved_media", "line_number": 9, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 9, "usage_type": "name"}, {"api_name": "media_manager.repositories.MediaFileRepository.find_approved_media", "line_number": 10, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 10, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository.find_approved_media", "line_number": 22, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 22, "usage_type": "name"}, {"api_name": "media_manager.repositories.MediaFileRepository.filter_by_branche", "line_number": 24, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository.find_approved_media", "line_number": 36, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 36, "usage_type": "name"}, {"api_name": "media_manager.repositories.MediaFileRepository.filter_by_branche", "line_number": 38, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository.find_one", "line_number": 49, "usage_type": "call"}, {"api_name": "media_manager.repositories.MediaFileRepository", "line_number": 49, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "12804424679", "text": "from pathlib import Path\nimport subprocess\n\nfrom spack import *\n\n\nclass Chaco(MakefilePackage):\n \"\"\"Chaco: Software for Partitioning Graphs\n\n Chaco contains a wide variety of algorithms and options, many of\n which were invented by the authors. Some of the algorithms exploit\n the geometry of the mesh, others its local connectivity or its global\n structure as captured by eigenvectors of a related matrix. These\n methods can be mixed and matched in several ways, and combinations\n often prove to be more effective than any single technique in\n isolation. All these algorithms are accessed via a simple user\n interface, or a call from other software. Innovations in Chaco\n include\n - Development of multilevel graph partitioning. This widely\n imitated approach has become the premiere algorithm combining very\n high quality with short calculation times.\n - Extension of spectral partitioning to enable the use of 2 or 3\n Laplacian eigenvectors to quadrisect of octasect a graph.\n # Highly efficient and robust eigensolvers for use with spectral\n graph algorithms.\n - Generalization of the Kernighan-Lin/Fiduccia-Mattheyses algorithm\n to handle weighted graphs, arbitrary number of sets and lazy\n initiation.\n - Development of skewed partitioning to improve the mapping of a\n graph onto a target parallel architecture.\n - Various post-processing options to improve partitions in a number\n of ways.\n \"\"\"\n\n homepage = \"https://www3.cs.stonybrook.edu/~algorith/implement/chaco/implement.shtml\"\n url = \"https://www3.cs.stonybrook.edu/~algorith/implement/chaco/distrib/Chaco-2.2.tar.gz\"\n\n maintainers = [\"connorjward\"]\n\n version(\"petsc\", git=\"https://bitbucket.org/petsc/pkg-chaco.git\", branch=\"master\")\n version(\"2.2\", sha256=\"e7c1ab187b2dbd4b682da3dbb99097143b773f6b68b39be989442c176e9bcee1\")\n\n build_directory = \"code\"\n\n def edit(self, spec, prefix):\n cflags = [\"-O2\", self.compiler.cc_pic_flag]\n\n makefile = FileFilter(\"{}/Makefile\".format(self.build_directory))\n makefile.filter(r\"^DEST\\s*=.*\", \"DEST=../bin/chaco\")\n\n if self.spec.version == Version(\"petsc\"):\n with open(\"make.inc\", \"w\") as inc:\n inc.write(\"CC={}\\n\".format(spack_cc))\n inc.write(\"CFLAGS={}\\n\".format(\" \".join(cflags)))\n inc.write(\"OFLAGS={}\\n\".format(\" \".join(cflags)))\n else:\n makefile.filter(r\"^CC\\s*=.*\", \"CC={}\".format(spack_cc))\n makefile.filter(r\"^CFLAGS\\s*=.*\", \"CFLAGS={}\".format(cflags))\n makefile.filter(r\"^OFLAGS\\s*=.*\", \"OFLAGS={}\".format(cflags))\n\n def build(self, spec, prefix):\n with working_dir(\"code\"):\n mkdirp(\"../bin\")\n make()\n\n if self.spec.version == Version(\"petsc\"):\n # See https://gitlab.com/petsc/petsc/-/blob/main/config/BuildSystem/config/packages/Chaco.py\n mkdirp(\"../lib\")\n cwd = Path()\n object_list = [\n str(ofile.relative_to(cwd.absolute())) for ofile in cwd.cwd().glob(\"*/*.o\")\n ]\n subprocess.run([\"ar\", \"cr\", \"libchaco.a\", *object_list], check=True)\n subprocess.run([\"ranlib\", \"libchaco.a\"], check=True)\n subprocess.run([\"cp\", \"libchaco.a\", \"../lib/\"], check=True)\n\n def install(self, spec, prefix):\n install_tree(\"bin\", prefix.bin)\n if self.spec.version == Version(\"petsc\"):\n install_tree(\"lib\", prefix.lib)\n", "repo_name": "firedrakeproject/firedrake-spack", "sub_path": "packages/chaco/package.py", "file_name": "package.py", "file_ext": "py", "file_size_in_byte": 3537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pathlib.Path", "line_number": 69, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 73, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 74, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "18780536748", "text": "import os\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nfrom PIL import Image\nfrom faceai_bgimpact.data_processing.paths import data_folder\n\n\nclass FFHQDataset(Dataset):\n \"\"\"FFHQ dataset that returns blended images at two resolutions.\"\"\"\n\n def __init__(self, root_dir, resolution, alpha=1.0):\n self.root_dir = root_dir\n try:\n self.image_files = [f for f in os.listdir(root_dir) if os.path.isfile(os.path.join(root_dir, f))]\n except FileNotFoundError:\n raise FileNotFoundError(\"Please download the data first, using the download-all-ffhq command.\")\n self.resolution = resolution\n self.alpha = alpha\n self._update_transforms()\n\n def _update_transforms(self):\n self.transform = transforms.Compose(\n [\n transforms.Resize((self.resolution, self.resolution), antialias=True),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]\n )\n self.low_res_transform = transforms.Compose(\n [\n transforms.Resize((self.resolution // 2, self.resolution // 2), antialias=True),\n transforms.Resize((self.resolution, self.resolution), antialias=True), # Scale back up\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]\n )\n\n def update_alpha(self, new_alpha):\n \"\"\"Update the alpha value for progressive growing.\"\"\"\n self.alpha = new_alpha\n\n def update_resolution(self, new_resolution):\n \"\"\"Update the resolution of the images.\"\"\"\n self.resolution = new_resolution\n self._update_transforms()\n\n def __getitem__(self, idx):\n \"\"\"Get the blended image at the specified index.\"\"\"\n image_path = os.path.join(self.root_dir, self.image_files[idx])\n image = Image.open(image_path)\n\n high_res_image = self.transform(image)\n low_res_image = self.low_res_transform(image)\n\n blended_image = self.alpha * high_res_image + (1 - self.alpha) * low_res_image\n return blended_image\n\n def __len__(self):\n \"\"\"Length of the dataset.\"\"\"\n return len(self.image_files)\n\n\ndef get_dataloader(dataset_name, batch_size, shuffle=True, resolution=128, alpha=1.0):\n \"\"\"\n Create a DataLoader for the specified FFHQ dataset.\n\n Parameters\n ----------\n dataset_name : str\n The name of the dataset to load.\n batch_size : int\n The batch size to use for the DataLoader.\n shuffle : bool\n Whether to shuffle the dataset.\n resolution : int\n The resolution of the images in the dataset.\n alpha : float\n The alpha value for progressive growing.\n \"\"\"\n # Load the dataset with blended images\n root_dir = f\"{data_folder}/{dataset_name}\"\n dataset = FFHQDataset(root_dir=root_dir, resolution=resolution, alpha=alpha)\n\n # Create DataLoader\n loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=os.cpu_count())\n\n return dataset, loader\n\n\ninv_normalize = transforms.Compose(\n [\n transforms.Normalize(mean=[0.0, 0.0, 0.0], std=[2.0, 2.0, 2.0]),\n transforms.Normalize(mean=[-0.5, -0.5, -0.5], std=[1.0, 1.0, 1.0]),\n ]\n)\n\n\ndef denormalize_image(tensor):\n \"\"\"\n Reverses the ImageNet normalization applied to images.\n\n Parameters:\n ----------\n tensor : torch.Tensor\n The normalized tensor.\n\n Returns:\n -------\n torch.Tensor\n The denormalized tensor.\n \"\"\"\n return inv_normalize(tensor)\n", "repo_name": "thomktz/AML-VAE", "sub_path": "faceai_bgimpact/models/data_loader.py", "file_name": "data_loader.py", "file_ext": "py", "file_size_in_byte": 3641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 8, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 25, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 26, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "faceai_bgimpact.data_processing.paths.data_folder", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 85, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 85, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 90, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 90, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 92, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 92, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 93, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "12225291714", "text": "import psycopg2\nimport datetime\nimport requests\nimport time\nfrom tabulate import tabulate\n\n\nclass PostgreSQL:\n def __init__(self, ip, user, password, database):\n self.ip = ip\n self.user = user\n self.password = password\n self.database = database\n self.pgsConnection = None\n self.nodeExporterUrl=f'''http://{ip}:9100/metrics'''\n self.pgsExporterUrl=f'''http://{ip}:9187/metrics'''\n self.checkNodeExporter()\n self.checkPostgresqlExporter()\n self.connect()\n if self.connectedPostgresql:\n self.inactiveReplicationSlot()\n self.longrunningQuery()\n self.activeSessionCount()\n self.calculateBloat()\n self.lastAnalyze()\n self.checkDatabaseLocks()\n self.prepareResults()\n else:\n self.allTablesMaintained=False\n self.inactivereplication=False\n self.longrunningquery=False\n self.sumofactivesessionslessthan50=False\n self.allTablesMaintained=False\n self.nobloattableexists=False\n self.noSeriousLocking=False\n self.prepareResults()\n\n\n def connect(self):\n try:\n self.pgsConnection = psycopg2.connect(\n host=self.ip,\n user=self.user,\n password=self.password,\n database=self.database\n )\n print(\"Connected to PostgreSQL successfully.\")\n self.connectedPostgresql=True\n except psycopg2.Error as e:\n self.connectedPostgresql=False\n\n\n def execute_query(self, query):\n if not self.pgsConnection:\n print(\"Not connected to PostgreSQL. Call connect() first.\")\n return\n\n cursor = self.pgsConnection.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n return result\n\n def inactiveReplicationSlot(self):\n query = \"\"\"\n SELECT active FROM pg_replication_slots WHERE active = false;\n \"\"\"\n result = self.execute_query(query)\n self.inactivereplication = False if result else True\n\n def longrunningQuery(self):\n query = \"\"\"\n SELECT pid, usename, state, query_start\n FROM pg_stat_activity\n WHERE state != 'idle'\n AND usename NOT IN ('postgres', 'test')\n AND query_start < NOW() - INTERVAL '1 minute';\n \"\"\"\n result = self.execute_query(query)\n self.longrunningquery = False if result else True\n\n def activeSessionCount(self):\n query = \"\"\"\n SELECT COUNT(*)\n FROM pg_stat_activity\n WHERE state != 'idle'\n AND usename NOT IN ('postgres', 'test');\n \"\"\"\n result = self.execute_query(query)\n self.sumofactivesessionslessthan50 = True if result[0][0] <= 50 else False\n\n def calculateBloat(self):\n query = \"\"\"\n SELECT schemaname, tablename, (pg_total_relation_size(schemaname || '.' || tablename) - pg_relation_size(schemaname || '.' || tablename)) / pg_total_relation_size(schemaname || '.' || tablename) * 100 AS bloat_ratio\n FROM pg_tables\n WHERE schemaname NOT IN ('pg_catalog', 'information_schema');\n \"\"\"\n result = self.execute_query(query)\n self.nobloattableexists = all(row[2] <= 50 for row in result)\n\n def checkDatabaseLocks(self):\n # it is not the best way to analyze locking in postgresql. But will be improved.\n importantLockTypes=['RowExclusiveLock','ShareUpdateExclusiveLock','ShareLock','AccessExclusiveLock']\n realAverageCount=0\n for i in range(3):\n averageCount=0\n lockingQery=f'''SELECT locktype, relation::regclass, mode, transactionid AS tid,\n virtualtransaction AS vtid, pid, granted FROM pg_catalog.pg_locks l LEFT JOIN pg_catalog.pg_database db\n ON db.oid = l.database WHERE (db.datname = '{self.database}')\n AND NOT pid = pg_backend_pid();'''\n lockingResult=self.execute_query(lockingQery)\n for lockType in lockingResult:\n lockTypeController=lockType[2]\n if lockTypeController in importantLockTypes:\n averageCount=averageCount+1\n realAverageCount=realAverageCount+averageCount\n time.sleep(5)\n realAverageCount=int(realAverageCount/3)\n if realAverageCount > 10:\n self.noSeriousLocking=False\n else:\n self.noSeriousLocking=True\n def lastAnalyze(self):\n cursor = self.pgsConnection.cursor()\n\n # Get the list of tables in the database\n cursor.execute(\"SELECT table_name FROM information_schema.tables WHERE table_schema='public'\")\n tables = cursor.fetchall()\n\n # Dictionary to store the results\n table_results = {}\n self.allTablesMaintained=True\n\n for table in tables:\n table_name = table[0]\n\n # Retrieve the last analyze date for the table\n cursor.execute(f\"SELECT last_analyze FROM pg_stat_all_tables WHERE relname = '{table_name}'\")\n last_analyze_result = cursor.fetchone()\n last_analyze_date = last_analyze_result[0] if last_analyze_result else None\n\n # Retrieve the last autovacuum date for the table\n cursor.execute(f\"SELECT last_autovacuum FROM pg_stat_all_tables WHERE relname = '{table_name}'\")\n last_autovacuum_result = cursor.fetchone()\n last_autovacuum_date = last_autovacuum_result[0] if last_autovacuum_result else None\n\n # Determine if the dates are older than 5 days\n current_date = datetime.date.today()\n five_days_ago = current_date - datetime.timedelta(days=5)\n if (last_autovacuum_date is None) or (last_analyze_date is None):\n pass\n else:\n try:\n if (last_analyze_date.date() < five_days_ago) or (last_autovacuum_date.date() < five_days_ago):\n self.allTablesMaintained=False\n except Exception as testExcep:\n print(testExcep)\n print(f'''Failed to collect stats for table {table_name}''')\n\n cursor.close()\n\n def checkNodeExporter(self):\n try:\n response = requests.get(self.nodeExporterUrl)\n result=response.status_code\n if result==200:\n self.nodeExporterWorking=True\n \n else:\n self.nodeExporterWorking=False\n except Exception as nodeExporterException:\n self.nodeExporterWorking=False\n\n return True\n \n\n def checkPostgresqlExporter(self):\n try:\n response = requests.get(self.pgsExporterUrl)\n result=response.status_code\n if result==200:\n self.pgsExporterWorking=True\n \n else:\n self.pgsExporterWorking=False\n except Exception as nodeExporterException:\n self.pgsExporterWorking=False\n\n return True\n\n def prepareResults(self):\n connection_text= \"✓\" if self.connectedPostgresql else \"✗\"\n inactivereplication_text = \"✓\" if self.inactivereplication else \"✗\"\n longrunningquery_text = \"✓\" if self.longrunningquery else \"✗\"\n sumofactivesessionslessthan50_text = \"✓\" if self.sumofactivesessionslessthan50 else \"✗\"\n lastautovacuumoranalyzeinthisweek_text = \"✓\" if self.allTablesMaintained else \"✗\"\n nobloattableexists_text = \"✓\" if self.nobloattableexists else \"✗\"\n exporterworking = \"✓\" if self.nodeExporterWorking else \"✗\"\n pgsexporterworking = \"✓\" if self.pgsExporterWorking else \"✗\"\n pgslocking = \"✓\" if self.noSeriousLocking else \"✗\"\n\n\n table_data = [\n [\"Connected To PostgreSQL\", connection_text,'PostgreSQL connection test.'],\n [\"Long Running Query\", longrunningquery_text,'There is no running query older than 1 minute'],\n [\"Sum of Active Sessions < 50\", sumofactivesessionslessthan50_text,'Active session count is less than 50'],\n [\"Average Important Lock Count < 10\", pgslocking,'Average of RowExclusiveLock,ShareUpdateExclusiveLock,ShareLock,AccessExclusiveLock is less than 10'],\n [\"Last Analyze/Autovacuum in the Last Week\", lastautovacuumoranalyzeinthisweek_text,'All tables are maintained this week.'],\n [\"No Bloat Table Exists\", nobloattableexists_text,'There is no table with bloat ratio greater than 50'],\n [\"No Inactive Replication Slot\", inactivereplication_text,'All replication slots are working and active.'],\n [\"Node Exporter Working\", exporterworking,'Node exporter are working and running'],\n [\"PostgreSQL Exporter Working\", pgsexporterworking,'PostgreSQL exporter are working and running']\n ]\n headers = [\"Issue\", \"Result\",\"Description\"]\n table = tabulate(table_data, headers, tablefmt=\"grid\")\n print(table)", "repo_name": "adiosamig/insidepgs", "sub_path": "src/postgresql/postgresql.py", "file_name": "postgresql.py", "file_ext": "py", "file_size_in_byte": 9089, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "psycopg2.connect", "line_number": 41, "usage_type": "call"}, {"api_name": "psycopg2.Error", "line_number": 49, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 147, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 148, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 163, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 178, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "40189859299", "text": "\n## --- Functions for Use in NN Architectures or in General ---\n\nimport torch\nimport math\n#import torchvision.transforms as T\n\n\ndef FieldMap(xindices, yindices):\n\n field = torch.meshgrid(xindices, yindices, indexing = 'ij')\n\n return field[0], field[1]\n\n\ndef SpatialVariance(P, fh, fw):\n\n\n mh = math.floor(fh/2)\n mw = math.floor(fw/2)\n\n x = torch.linspace(-mw, mw, steps = fw)\n y = torch.linspace(-mh, mh, steps = fh)\n\n fieldx, fieldy = FieldMap(x, y)\n\n field = torch.stack((fieldx, fieldy), dim = 0)\n\n field = torch.flatten(field, start_dim = 1, end_dim = -1)\n\n field = field.unsqueeze(0).unsqueeze(0).unsqueeze(-1)\n\n field = field.to('cuda:0')\n\n # Uncomment line below to run all below lines in a single line\n # Var = torch.sum(P*torch.sum(torch.square(field - torch.sum(P.unsqueeze(2)*field, dim = 3).unsqueeze(3)), dim = 2), dim = 2)\n\n P = P.unsqueeze(2)\n\n mu = torch.sum(P*field, dim = 3)\n\n mu = mu.unsqueeze(3)\n\n Fsq = torch.square(field - mu)\n\n Dsq = torch.sum(Fsq, dim = 2)\n\n P = P.squeeze(2)\n\n Var = torch.sum(P*Dsq, dim = 2)\n\n return Var\n\n\ndef InvVarWeighting(P, fh, fw, k1, k2):\n\n # Uncomment line below to run all below lines in a single line\n # weight = (k1 / (SpatialVariance(P, fh, fw) + k2)).unsqueeze(2)\n\n Var = SpatialVariance(P, fh, fw)\n\n weight = k1 / (Var + k2)\n\n weight = weight.unsqueeze(2)\n\n return weight\n\n\n\ndef calc_iou(outputs: torch.Tensor, labels: torch.Tensor):\n eps = 1e-6\n # You can comment out this line if you are passing tensors of equal shape\n # But if you are passing output from UNet or something it will most probably\n # be with the BATCH x 1 x H x W shape\n outputs = outputs.squeeze(1) # BATCH x 1 x H x W => BATCH x H x W\n \n intersection = (outputs & labels).float().sum((1, 2)) # Will be zero if Truth=0 or Prediction=0\n union = (outputs | labels).float().sum((1, 2)) # Will be zzero if both are 0\n \n iou = (intersection + eps) / (union + eps) # We smooth our devision to avoid 0/0\n \n thresholded = torch.clamp(20 * (iou - 0.5), 0, 10).ceil() / 10 # This is equal to comparing with thresolds\n \n return thresholded # Or thresholded.mean() if you are interested in average across the batch\n\ndef iou_loss(outputs, labels, threshold):\n\n outputs = (outputs >= threshold)\n\n iou = calc_iou(outputs, labels)\n\n return iou\n ", "repo_name": "gmm0050/SNN", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 2403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.meshgrid", "line_number": 11, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 19, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.flatten", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.square", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.clamp", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "17055644209", "text": "from fastapi import APIRouter\nfrom app.projects.crud import projectCrud\n\nfrom app.serializers.projects import projectSerializer, projectsSerializer\n\n\nproject_router = APIRouter()\n\n\n@project_router.get(\"/\")\nasync def get_projects():\n return projectsSerializer(projectCrud.get_all())\n\n\n@project_router.get(\"/{project_id}\")\nasync def get_project(project_id: str):\n return projectSerializer(projectCrud.get(project_id))\n\n\n@project_router.post(\"/\") \nasync def add_project():\n return {\"message\": \"Hello World\"}\n\n\n", "repo_name": "anandukch/DigiLib-backend", "sub_path": "app/projects/endpoints.py", "file_name": "endpoints.py", "file_ext": "py", "file_size_in_byte": 516, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "fastapi.APIRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "app.serializers.projects.projectsSerializer", "line_number": 12, "usage_type": "call"}, {"api_name": "app.projects.crud.projectCrud.get_all", "line_number": 12, "usage_type": "call"}, {"api_name": "app.projects.crud.projectCrud", "line_number": 12, "usage_type": "name"}, {"api_name": "app.serializers.projects.projectSerializer", "line_number": 17, "usage_type": "call"}, {"api_name": "app.projects.crud.projectCrud.get", "line_number": 17, "usage_type": "call"}, {"api_name": "app.projects.crud.projectCrud", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "6702555107", "text": "import os\nimport hashlib\nimport json\nimport gzip\nimport argparse\n\n\nclass Packager:\n output = {\n \"files\": {},\n \"host\": None,\n \"version\": None\n }\n hosts = {\n \"odin\": \"http://odin.nordinvasion.com/mod\",\n \"thor\": \"http://thor.nordinvasion.com/mod\"\n }\n input_dir = None\n output_dir = None\n output_file = None\n exclude_items = [\".git\", \".revision\"]\n\n def __init__(self):\n # Set up the accepted arguments\n parser = argparse.ArgumentParser(description=\"NI Update Packager Args\")\n parser.add_argument(\"-i\", \"--input\", help=\"Input Directory\", required=True)\n parser.add_argument(\"-o\", \"--output\", help=\"Output Directory\", required=True)\n parser.add_argument(\"-v\", \"--version\", help=\"Module Version\", required=True)\n parser.add_argument(\"-H\", \"--host\", help=\"Host Name\", required=True)\n parser.add_argument(\"-f\", \"--file\", help=\"File Name\", required=False)\n # Store the arguments\n args = parser.parse_args()\n self.input_dir = os.path.abspath(args.input)\n self.output_dir = os.path.abspath(args.output)\n self.output[\"version\"] = args.version\n if args.file:\n self.output_file = args.file\n else:\n self.output_file = \"master.json\"\n if args.host:\n if args.host in self.hosts:\n self.output[\"host\"] = self.hosts[args.host]\n else:\n print(\"Error: The host \" + args.host + \" does not exist!\")\n else:\n if len(self.hosts) is 1:\n self.output[\"host\"] = list(self.hosts.values())[0]\n exit()\n else:\n print(\"Error: The host must be specified!\")\n exit()\n # Begin standard operations\n self.check_input_dir()\n self.check_output_dir()\n self.process_dir(self.input_dir)\n self.create_output_file()\n\n def check_input_dir(self):\n # Check that the input directory exists\n if not os.path.exists(self.input_dir):\n print(\"Error: Input directory \" + self.input_dir + \" does not exist!\")\n exit()\n\n def check_output_dir(self):\n # Check that the output directory exists\n if not os.path.exists(self.output_dir):\n os.mkdir(self.output_dir)\n if not os.path.exists(os.path.join(self.output_dir, self.output[\"version\"])):\n os.mkdir(os.path.join(self.output_dir, self.output[\"version\"]))\n\n def compress_file(self, path):\n # Compress the file and put it in the output directory\n original_file = open(path, \"rb\")\n relative_path = os.path.relpath(path, self.input_dir)\n print(\"Compressing \" + relative_path)\n gz_file = gzip.open(os.path.join(self.output_dir, self.output[\"version\"], relative_path + \".gz\"), \"wb\")\n gz_file.writelines(original_file)\n gz_file.close()\n original_file.close()\n\n def create_output_file(self):\n # Save the hashes to a file\n file = open(os.path.join(self.output_dir, self.output_file), \"wt\")\n file.write(json.dumps(self.output, separators=(\",\", \":\"), sort_keys=True, indent=4))\n file.close()\n print(\"Hash generation complete.\")\n exit()\n\n def process_dir(self, path):\n # Loop through the input directory to generate hashes\n for item in os.listdir(path):\n if item in self.exclude_items:\n continue\n absolute_path = os.path.join(path, item)\n relative_path = os.path.relpath(absolute_path, self.input_dir)\n if os.path.isdir(absolute_path):\n output_path = os.path.join(self.output_dir, self.output[\"version\"], relative_path)\n if not os.path.exists(output_path):\n os.mkdir(output_path)\n self.process_dir(absolute_path)\n else:\n file_hash = generate_hash(absolute_path)\n self.record_hash(relative_path, file_hash)\n self.compress_file(absolute_path)\n\n def record_hash(self, relative_path, file_hash):\n # Record a file's hash to the output array\n path = split_path(relative_path)\n _current_key = self.output[\"files\"]\n for i in range(len(path)):\n if not path[i] in _current_key:\n _current_key[path[i]] = {}\n _current_key = _current_key[path[i]]\n _current_key[\"hash\"] = file_hash\n\n\ndef generate_hash(path):\n # Return the hash of the file\n return hashlib.sha1(open(path, 'rb').read()).hexdigest()\n\n\ndef split_path(path):\n # Split a path into an array of directories\n path = os.path.normcase(path)\n return path.split(os.path.sep)\n\nPackager()\n", "repo_name": "Naozumi/updater", "sub_path": "packager.py", "file_name": "packager.py", "file_ext": "py", "file_size_in_byte": 4739, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"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.abspath", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 99, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.normcase", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}]} +{"seq_id": "23429475385", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\n# Import dependencies\n\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\nimport sqlalchemy \nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func, inspect\nfrom flask import Flask, jsonify\n\n\n# In[4]:\n\n\n# Set up database\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\n\nBase = automap_base()\n\nBase.prepare(engine, reflect=True)\n\n\n# In[10]:\n\n\n# Create references\n\nmeasurement = Base.classes.measurement\nstation = Base.classes.station\n\n# print(measurement)\n# print(station)\n\n\n# In[11]:\n\n\n# Create session link \n\nsession = Session(engine)\n\n\n# In[13]:\n\n\n# Flast setup\n\napp = Flask(__name__)\n\n@app.route(\"/\")\n\ndef welcome():\n return (\n f\"Hawaii Climate Analysis
\"\n f\"Available Routes
\"\n f\"/api/v1.0/precipitation
\"\n f\"/api/v1.0/stations
\"\n f\"/api/v1.0/tobs
\"\n f\"/api/v1.0/temp/start/end
\"\n )\n\n@app.route(\"/api/v1.0/precipitation\")\n\ndef climate():\n last_year = dt.date(2017,8,23) - dt.timedelta(days=365)\n climate = session.query(measurement.date, measurement.prcp).filter(measurement.date >= last_year).all()\n precipitation = {date: prcp for date, prcp in climate}\n return jsonify(precipitation)\n\n@app.route(\"/api.v1.0/station\")\n\ndef stations():\n results = session.query(station.station).all()\n stations = list(np.ravel(results))\n return jsonify(stations)\n\n@app.route(\"/api/v1.0/tobs\")\ndef temp_obs():\n last_year = dt.date(2017,8,23) - dt.timedelta(days=365)\n results = session.query(measurement.tobs).filer(measurement.station == 'USC00519281').filter(measurement.date >= last_year).all()\n temps = list(np.ravel(results))\n return jsonify(temps)\n\n@app.route(\"/api/v1.0/temp/start/end
\")\n\ndef temps():\n temp = [func.min(measurement.tobs), func.avg(measurement.tobs), func.max(measurement.tobs)]\n results = session.query(measurement.date >= start).all()\n temps = list(np.ravel(results))\n return jsonify(temps)\n\n\n# In[ ]:\n\n\nif __name__ == '__main__':\n app.run()\n\n", "repo_name": "loripepper/sqlalchemy-challenge", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2112, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.func.min", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 94, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlalchemy.func.max", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "7605822140", "text": "import os\nimport argparse\nimport logging\nimport itertools\nfrom collections import OrderedDict\nfrom typing import Optional\n\nimport path_to_kapture_localization # noqa: F401\nimport kapture_localization.utils.path_to_kapture # noqa: F401\n\nfrom kapture_colmap_build_map import colmap_build_map_from_loaded_data\nfrom kapture_colmap_localize import colmap_localize_from_loaded_data\nfrom kapture_run_colmap_gv import run_colmap_gv_from_loaded_data\nfrom kapture_compute_matches import compute_matches_from_loaded_data\n\nimport kapture\nimport kapture.utils.logging\nfrom kapture.io.csv import kapture_from_dir, kapture_to_dir, table_from_file, table_to_file, get_all_tar_handlers\nfrom kapture.core.Trajectories import rigs_remove_inplace\nfrom kapture.io.records import TransferAction\nfrom kapture.converter.colmap.import_colmap import import_colmap\nfrom kapture.utils.paths import safe_remove_file, safe_remove_any_path\nfrom kapture.io.tar import TarCollection, TarHandler, retrieve_tar_handler_from_collection, get_feature_tar_fullpath\nfrom kapture.utils.Collections import try_get_only_key_from_collection\n\nlogger = logging.getLogger()\n\n\ndef sub_kapture_from_img_list(kdata, img_list, pairs, keypoints_type, descriptors_type):\n trajectories = kapture.Trajectories()\n sensors = kapture.Sensors()\n records = kapture.RecordsCamera()\n keypoints = kapture.Keypoints(kdata.keypoints[keypoints_type].type_name,\n kdata.keypoints[keypoints_type].dtype,\n kdata.keypoints[keypoints_type].dsize)\n if kdata.descriptors is not None and descriptors_type in kdata.descriptors:\n descriptors = kapture.Descriptors(kdata.descriptors[descriptors_type].type_name,\n kdata.descriptors[descriptors_type].dtype,\n kdata.descriptors[descriptors_type].dsize,\n kdata.descriptors[descriptors_type].keypoints_type,\n kdata.descriptors[descriptors_type].metric_type)\n else:\n descriptors = None\n matches = kapture.Matches()\n\n timestamp_sensor_id_from_image_name = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in\n kapture.flatten(kdata.records_camera)}\n for img in img_list:\n timestamp, sensor_id = timestamp_sensor_id_from_image_name[img]\n sensors[sensor_id] = kdata.sensors[sensor_id]\n records[timestamp, sensor_id] = img\n if (timestamp, sensor_id) in kdata.trajectories:\n pose = kdata.trajectories[timestamp][sensor_id]\n trajectories[timestamp, sensor_id] = pose\n keypoints.add(img)\n if kdata.descriptors is not None:\n descriptors.add(img)\n\n for i in pairs:\n if i in kdata.matches[keypoints_type]:\n matches.add(i[0], i[1])\n matches.normalize()\n\n return kapture.Kapture(sensors=sensors, trajectories=trajectories, records_camera=records,\n descriptors={descriptors_type: descriptors},\n keypoints={keypoints_type: keypoints},\n matches={keypoints_type: matches})\n\n\ndef add_image_to_kapture(kdata_src,\n kdata_trg, img_name, pairs,\n keypoints_type, descriptors_type,\n add_pose=False):\n timestamp_sensor_id_from_image_name = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in\n kapture.flatten(kdata_src.records_camera)}\n timestamp, sensor_id = timestamp_sensor_id_from_image_name[img_name]\n kdata_trg.sensors[sensor_id] = kdata_src.sensors[sensor_id]\n kdata_trg.records_camera[timestamp, sensor_id] = img_name\n kdata_trg.keypoints[keypoints_type].add(img_name)\n if kdata_trg.descriptors is not None and descriptors_type in kdata_trg.descriptors:\n kdata_trg.descriptors[descriptors_type].add(img_name)\n\n if add_pose:\n kdata_trg.trajectories[timestamp, sensor_id] = kdata_src.trajectories[timestamp, sensor_id]\n\n if len(pairs) != 0:\n if kdata_trg.matches is None:\n kdata_trg.matches = {}\n kdata_trg.matches[keypoints_type] = kapture.Matches()\n for i in pairs:\n if i in kdata_src.matches[keypoints_type]:\n kdata_trg.matches[keypoints_type].add(i[0], i[1])\n kdata_trg.matches[keypoints_type].normalize()\n return kdata_trg\n\n\ndef pose_found(kdata, img_name):\n timestamp_sensor_id_from_image_name = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in\n kapture.flatten(kdata.records_camera)}\n if img_name in timestamp_sensor_id_from_image_name:\n timestamp, sensor_id = timestamp_sensor_id_from_image_name[img_name]\n if (timestamp, sensor_id) in kdata.trajectories:\n return True\n else:\n return False\n else:\n logger.info(f'something is wrong with {img_name} (it will be skipped), check input kapture data')\n return True\n\n\ndef add_pose_to_query_kapture(kdata_src, kdata_trg, img_name):\n timestamp_sensor_id_from_image_name_src = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in\n kapture.flatten(kdata_src.records_camera)}\n if img_name not in timestamp_sensor_id_from_image_name_src:\n logger.info(f'{img_name} was not found in localized kapture, that should not be possible, something went wrong')\n return False\n timestamp_src, sensor_id_src = timestamp_sensor_id_from_image_name_src[img_name]\n timestamp_sensor_id_from_image_name_trg = {img_name: (timestamp, sensor_id) for timestamp, sensor_id, img_name in\n kapture.flatten(kdata_trg.records_camera)}\n if img_name not in timestamp_sensor_id_from_image_name_trg:\n logger.info(f'{img_name} not found in query kapture')\n return False\n timestamp_trg, sensor_id_trg = timestamp_sensor_id_from_image_name_trg[img_name]\n\n if not (timestamp_src, sensor_id_src) in kdata_src.trajectories:\n logger.info(f'{img_name} was not localized')\n return False\n kdata_trg.trajectories[timestamp_trg, sensor_id_trg] = kdata_src.trajectories[timestamp_src, sensor_id_src]\n\n return True\n\n\ndef get_pairfile_from_img_list(img_list):\n image_pairs = [(i, j, 0) if i < j else (j, i, 0) for i, j in itertools.product(img_list, img_list)]\n image_pairs = list(OrderedDict.fromkeys(image_pairs))\n image_pairs_filtered = []\n for i in image_pairs:\n if i[0] != i[1]:\n image_pairs_filtered.append(i)\n return image_pairs_filtered\n\n\ndef get_pairfile_img_vs_img_list(img, img_list):\n image_pairs = []\n for i in img_list:\n if img < i:\n image_pairs.append((img, i, 0))\n else:\n image_pairs.append((i, img, 0))\n return image_pairs\n\n\ndef local_sfm(map_plus_query_path: str,\n map_plus_query_gv_path: str,\n query_path: str,\n descriptors_type: Optional[str],\n pairsfile_path: str,\n output_path_root: str,\n colmap_binary: str,\n force: bool):\n \"\"\"\n Localize query images in a COLMAP model built from topk retrieved images.\n\n :param map_plus_query_path: path to the kapture data consisting of mapping and query data (sensors and reconstruction)\n :param map_plus_query_gv_path: path to the kapture data consisting of mapping and query data after geometric verification (sensors and reconstruction)\n :param query_path: path to the query kapture data (sensors)\n :param descriptors_type: type of descriptors, name of the descriptors subfolder\n :param pairsfile_path: path to the pairsfile that contains the topk retrieved mapping images for each query image\n :param output_path_root: root path where outputs should be stored\n :param colmap_binary: path to the COLMAP binary\n :param force: silently overwrite already existing results\n \"\"\"\n kdata_query = kapture_from_dir(query_path)\n with get_all_tar_handlers(map_plus_query_path,\n mode={kapture.Keypoints: 'r',\n kapture.Descriptors: 'r',\n kapture.GlobalFeatures: 'r',\n kapture.Matches: 'a'}) as tar_handlers_map:\n kdata_map = kapture_from_dir(map_plus_query_path, tar_handlers=tar_handlers_map)\n with get_all_tar_handlers(map_plus_query_gv_path,\n mode={kapture.Keypoints: 'r',\n kapture.Descriptors: 'r',\n kapture.GlobalFeatures: 'r',\n kapture.Matches: 'a'}) as tar_handlers_map_gv:\n kdata_map_gv = kapture_from_dir(map_plus_query_gv_path, tar_handlers=tar_handlers_map_gv)\n local_sfm_from_loaded_data(kdata_map, kdata_map_gv, kdata_query,\n map_plus_query_path, map_plus_query_gv_path,\n tar_handlers_map,\n tar_handlers_map_gv,\n descriptors_type,\n pairsfile_path,\n output_path_root,\n colmap_binary,\n force)\n\n\ndef local_sfm_from_loaded_data(kdata_map: kapture.Kapture,\n kdata_map_gv: kapture.Kapture,\n kdata_query: kapture.Kapture,\n map_plus_query_path: str,\n map_plus_query_gv_path: str,\n tar_handlers_map: Optional[TarCollection],\n tar_handlers_map_gv: Optional[TarCollection],\n descriptors_type: Optional[str],\n pairsfile_path: str,\n output_path_root: str,\n colmap_binary: str,\n force: bool):\n \"\"\"\n Localize query images in a COLMAP model built from topk retrieved images.\n\n :param map_plus_query_path: path to the kapture data consisting of mapping and query data (sensors and reconstruction)\n :param map_plus_query_gv_path: path to the kapture data consisting of mapping and query data after geometric verification (sensors and reconstruction)\n :param query_path: path to the query kapture data (sensors)\n :param descriptors_type: type of descriptors, name of the descriptors subfolder\n :param pairsfile_path: path to the pairsfile that contains the topk retrieved mapping images for each query image\n :param output_path_root: root path where outputs should be stored\n :param colmap_binary: path to the COLMAP binary\n :param force: silently overwrite already existing results\n \"\"\"\n\n # load query kapture (we use query kapture to reuse sensor_ids etc.)\n if kdata_query.trajectories:\n logger.warning(\"Query data contains trajectories: they will be ignored\")\n kdata_query.trajectories.clear()\n else:\n kdata_query.trajectories = kapture.Trajectories()\n\n # clear query trajectories in map_plus_query\n kdata_map_cleared_trajectories = kapture.Trajectories()\n query_image_list = set(kdata_query.records_camera.data_list())\n for timestamp, subdict in kdata_map.records_camera.items():\n for sensor_id, image_name in subdict.items():\n if image_name in query_image_list:\n continue\n if (timestamp, sensor_id) in kdata_map.trajectories:\n pose = kdata_map.trajectories.get(timestamp)[sensor_id]\n kdata_map_cleared_trajectories.setdefault(timestamp, {})[sensor_id] = pose\n kdata_map.trajectories = kdata_map_cleared_trajectories\n\n # load output kapture\n output_path = os.path.join(output_path_root, 'localized')\n if os.path.exists(os.path.join(output_path, 'sensors/trajectories.txt')):\n kdata_output = kapture_from_dir(output_path)\n if kdata_query.records_camera == kdata_output.records_camera and len(\n kdata_output.trajectories) != 0 and not force:\n kdata_query.trajectories = kdata_output.trajectories\n\n if kdata_map.rigs is not None:\n rigs_remove_inplace(kdata_map.trajectories, kdata_map.rigs)\n if kdata_map_gv.rigs is not None:\n rigs_remove_inplace(kdata_map_gv.trajectories, kdata_map_gv.rigs)\n\n # load pairsfile\n pairs = {}\n with open(pairsfile_path, 'r') as fid:\n table = table_from_file(fid)\n for img_query, img_map, _ in table:\n if img_query not in pairs:\n pairs[img_query] = []\n pairs[img_query].append(img_map)\n\n kdata_sub_colmap_path = os.path.join(output_path_root, 'colmap')\n kdata_reg_query_path = os.path.join(output_path_root, 'query_registered')\n sub_kapture_pairsfile_path = os.path.join(output_path_root, 'tmp_pairs.txt')\n\n if descriptors_type is None:\n descriptors_type = try_get_only_key_from_collection(kdata_map.descriptors)\n assert descriptors_type is not None\n assert descriptors_type in kdata_map.descriptors\n keypoints_type = kdata_map.descriptors[descriptors_type].keypoints_type\n\n # init matches for kdata_map and kdata_map_gv\n if kdata_map.matches is None:\n kdata_map.matches = {}\n if keypoints_type not in kdata_map.matches:\n kdata_map.matches[keypoints_type] = kapture.Matches()\n if kdata_map_gv.matches is None:\n kdata_map_gv.matches = {}\n if keypoints_type not in kdata_map_gv.matches:\n kdata_map_gv.matches[keypoints_type] = kapture.Matches()\n\n # run all matching\n # loop over query images\n img_skip_list = set()\n for img_query, img_list_map in pairs.items():\n if pose_found(kdata_query, img_query):\n logger.info(f'{img_query} already processed, skipping...')\n img_skip_list.add(img_query)\n continue\n else:\n map_pairs = get_pairfile_from_img_list(img_list_map)\n query_pairs = get_pairfile_img_vs_img_list(img_query, img_list_map)\n with open(sub_kapture_pairsfile_path, 'w') as fid:\n logger.info(f'matching for {img_query}')\n table_to_file(fid, map_pairs)\n table_to_file(fid, query_pairs)\n\n pairs_all = map_pairs + query_pairs\n pairs_all = [(i, j) for i, j, _ in pairs_all]\n # match missing pairs\n # kdata_map.matches is being updated by compute_matches_from_loaded_data\n compute_matches_from_loaded_data(map_plus_query_path,\n tar_handlers_map,\n kdata_map,\n descriptors_type,\n pairs_all)\n\n # if kdata_map have matches in tar, they need to be switched to read mode\n matches_handler = retrieve_tar_handler_from_collection(kapture.Matches, keypoints_type, tar_handlers_map)\n if matches_handler is not None:\n matches_handler.close()\n tarfile_path = get_feature_tar_fullpath(kapture.Matches, keypoints_type, map_plus_query_path)\n tar_handlers_map.matches[keypoints_type] = TarHandler(tarfile_path, 'r')\n\n # run all gv\n # loop over query images\n for img_query, img_list_map in pairs.items():\n if img_query in img_skip_list:\n continue\n else:\n # recompute the pairs\n map_pairs = get_pairfile_from_img_list(img_list_map)\n query_pairs = get_pairfile_img_vs_img_list(img_query, img_list_map)\n with open(sub_kapture_pairsfile_path, 'w') as fid:\n logger.info(f'geometric verification of {img_query}')\n table_to_file(fid, map_pairs)\n table_to_file(fid, query_pairs)\n\n pairs_all = map_pairs + query_pairs\n pairs_all = [(i, j) for i, j, _ in pairs_all]\n\n if all(pair in kdata_map_gv.matches[keypoints_type] for pair in pairs_all):\n continue\n\n # create a sub kapture in order to minimize the amount of data exported to colmap\n # kdata_sub needs to be re-created to add the new matches\n kdata_sub = sub_kapture_from_img_list(kdata_map, img_list_map + [img_query], pairs_all,\n keypoints_type, descriptors_type)\n\n kdata_sub_gv = sub_kapture_from_img_list(kdata_map_gv, img_list_map + [img_query], pairs_all,\n keypoints_type, descriptors_type)\n # run colmap gv on missing pairs\n run_colmap_gv_from_loaded_data(kdata_sub,\n kdata_sub_gv,\n map_plus_query_path,\n map_plus_query_gv_path,\n tar_handlers_map,\n tar_handlers_map_gv,\n colmap_binary,\n keypoints_type,\n [],\n True)\n # update kdata_map_gv.matches\n kdata_map_gv.matches[keypoints_type].update(kdata_sub_gv.matches[keypoints_type])\n\n # if kdata_map_gv have matches in tar, they need to be switched to read mode\n matches_gv_handler = retrieve_tar_handler_from_collection(kapture.Matches, keypoints_type, tar_handlers_map_gv)\n if matches_gv_handler is not None:\n print(matches_gv_handler)\n matches_gv_handler.close()\n tarfile_path = get_feature_tar_fullpath(kapture.Matches, keypoints_type, map_plus_query_gv_path)\n tar_handlers_map_gv.matches[keypoints_type] = TarHandler(tarfile_path, 'r')\n\n # loop over query images\n for img_query, img_list_map in pairs.items():\n if img_query in img_skip_list:\n continue\n else:\n map_pairs = get_pairfile_from_img_list(img_list_map)\n with open(sub_kapture_pairsfile_path, 'w') as fid:\n logger.info(f'mapping and localization for {img_query}')\n table_to_file(fid, map_pairs)\n map_pairs = [(i, j) for i, j, _ in map_pairs]\n kdata_sub_gv = sub_kapture_from_img_list(kdata_map_gv, img_list_map, map_pairs,\n keypoints_type, descriptors_type)\n # sanity check\n if len(map_pairs) != len(kdata_sub_gv.matches[keypoints_type]):\n logger.info(f'not all mapping matches available')\n\n # build COLMAP map\n try:\n colmap_build_map_from_loaded_data(\n kdata_sub_gv,\n map_plus_query_gv_path,\n tar_handlers_map_gv,\n kdata_sub_colmap_path,\n colmap_binary,\n keypoints_type,\n False,\n [],\n ['model_converter'],\n True)\n except ValueError:\n logger.info(f'{img_query} was not localized')\n continue\n\n if not os.path.exists(os.path.join(kdata_sub_colmap_path, 'reconstruction/images.bin')):\n logger.info(f'colmap mapping for {img_query} did not work, image was not localized')\n continue\n\n query_pairs = get_pairfile_img_vs_img_list(img_query, img_list_map)\n with open(sub_kapture_pairsfile_path, 'w') as fid:\n table_to_file(fid, query_pairs)\n query_pairs = [(i, j) for i, j, _ in query_pairs]\n query_img_kapture_gv = add_image_to_kapture(kdata_map_gv,\n kdata_sub_gv, img_query, query_pairs,\n keypoints_type, descriptors_type)\n # sanity check\n if len(query_pairs) != len(query_img_kapture_gv.matches[keypoints_type]):\n logger.info(f'not all query matches available')\n\n # localize in COLMAP map\n try:\n colmap_localize_from_loaded_data(\n query_img_kapture_gv,\n map_plus_query_gv_path,\n tar_handlers_map_gv,\n os.path.join(kdata_sub_colmap_path, 'registered'),\n os.path.join(kdata_sub_colmap_path, 'colmap.db'),\n os.path.join(kdata_sub_colmap_path, 'reconstruction'),\n colmap_binary,\n keypoints_type,\n False,\n ['--Mapper.ba_refine_focal_length', '0',\n '--Mapper.ba_refine_principal_point', '0',\n '--Mapper.ba_refine_extra_params', '0',\n '--Mapper.min_num_matches', '4',\n '--Mapper.init_min_num_inliers', '4',\n '--Mapper.abs_pose_min_num_inliers', '4',\n '--Mapper.abs_pose_min_inlier_ratio', '0.05',\n '--Mapper.ba_local_max_num_iterations', '50',\n '--Mapper.abs_pose_max_error', '20',\n '--Mapper.filter_max_reproj_error', '12'],\n [],\n True)\n except ValueError:\n logger.info(f'{img_query} was not localized')\n continue\n\n if not os.path.exists(os.path.join(os.path.join(kdata_sub_colmap_path, 'registered'),\n 'reconstruction/images.txt')):\n logger.info(f'colmap localization of {img_query} did not work, image was not localized')\n continue\n\n # add to results kapture\n kdata_reg_query = import_colmap(\n kdata_reg_query_path,\n os.path.join(os.path.join(kdata_sub_colmap_path, 'registered'), 'colmap.db'),\n os.path.join(os.path.join(kdata_sub_colmap_path, 'registered'),\n 'reconstruction'),\n None,\n None,\n True,\n True,\n True,\n TransferAction.skip)\n\n if add_pose_to_query_kapture(kdata_reg_query, kdata_query, img_query):\n logger.info('successfully localized')\n\n # write results (after each image to see the progress)\n kapture_to_dir(output_path, kdata_query)\n\n # clean up (e.g. remove temporal files and folders)\n safe_remove_any_path(kdata_sub_colmap_path, True)\n safe_remove_any_path(kdata_reg_query_path, True)\n safe_remove_file(sub_kapture_pairsfile_path, True)\n\n logger.info('all done')\n\n\ndef local_sfm_command_line():\n parser = argparse.ArgumentParser(\n description='local sfm localization with COLMAP: each query image has topk db images (defined in one pairsfile)'\n 'these db images are used to build a COLMAP model'\n 'then the query image is localized in this model')\n parser_verbosity = parser.add_mutually_exclusive_group()\n parser_verbosity.add_argument('-v', '--verbose', nargs='?', default=logging.WARNING, const=logging.INFO,\n action=kapture.utils.logging.VerbosityParser,\n help='verbosity level (debug, info, warning, critical, ... or int value) [warning]')\n parser_verbosity.add_argument('-q', '--silent', '--quiet', action='store_const',\n dest='verbose', const=logging.CRITICAL)\n parser.add_argument('-f', '-y', '--force', action='store_true', default=False,\n help='Force recomputation of existing data')\n parser.add_argument('--map_plus_query', required=True,\n help='input path to kapture containing mapping and query data (sensors and reconstruction)')\n parser.add_argument('--map_plus_query_gv', required=True,\n help='input path to kapture containing mapping and query data after geometric verification (sensors and reconstruction)')\n parser.add_argument('--query', required=True,\n help='path to kapture containing query data (sensors)')\n parser.add_argument('-o', '--output', required=True,\n help='output kapture directory')\n parser.add_argument('-colmap', '--colmap_binary', required=False,\n default=\"colmap\",\n help='full path to colmap binary '\n '(default is \"colmap\", i.e. assume the binary'\n ' is in the user PATH).')\n parser.add_argument('--pairsfile-path',\n default=None,\n type=str,\n help='text file containing the image pairs between query and mapping images')\n parser.add_argument('-desc', '--descriptors-type', default=None, help='kapture descriptors type.')\n\n args = parser.parse_args()\n\n logger.setLevel(args.verbose)\n logging.getLogger('colmap').setLevel(args.verbose)\n if args.verbose <= logging.DEBUG:\n # also let kapture express its logs\n kapture.utils.logging.getLogger().setLevel(args.verbose)\n\n if not os.path.exists(args.output):\n os.makedirs(args.output)\n\n local_sfm(args.map_plus_query, args.map_plus_query_gv, args.query,\n args.descriptors_type,\n args.pairsfile_path, args.output,\n args.colmap_binary, args.force)\n\n\nif __name__ == '__main__':\n local_sfm_command_line()\n", "repo_name": "naver/kapture-localization", "sub_path": "tools/kapture_colmap_localize_localsfm.py", "file_name": "kapture_colmap_localize_localsfm.py", "file_ext": "py", "file_size_in_byte": 25744, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 239, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "kapture.Trajectories", "line_number": 30, "usage_type": "call"}, {"api_name": "kapture.Sensors", "line_number": 31, "usage_type": "call"}, {"api_name": "kapture.RecordsCamera", "line_number": 32, "usage_type": "call"}, {"api_name": "kapture.Keypoints", "line_number": 33, "usage_type": "call"}, {"api_name": "kapture.Descriptors", "line_number": 37, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 44, "usage_type": "call"}, {"api_name": "kapture.flatten", "line_number": 47, "usage_type": "call"}, {"api_name": "kapture.Kapture", "line_number": 64, "usage_type": "call"}, {"api_name": "kapture.flatten", "line_number": 75, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 89, "usage_type": "call"}, {"api_name": "kapture.flatten", "line_number": 99, "usage_type": "call"}, {"api_name": "kapture.flatten", "line_number": 113, "usage_type": "call"}, {"api_name": "kapture.flatten", "line_number": 119, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 134, "usage_type": "call"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 135, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 135, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 156, "usage_type": "name"}, {"api_name": "kapture.io.csv.kapture_from_dir", "line_number": 173, "usage_type": "call"}, {"api_name": "kapture.io.csv.get_all_tar_handlers", "line_number": 174, "usage_type": "call"}, {"api_name": "kapture.Keypoints", "line_number": 175, "usage_type": "attribute"}, {"api_name": "kapture.Descriptors", "line_number": 176, "usage_type": "attribute"}, {"api_name": "kapture.GlobalFeatures", "line_number": 177, "usage_type": "attribute"}, {"api_name": "kapture.Matches", "line_number": 178, "usage_type": "attribute"}, {"api_name": "kapture.io.csv.kapture_from_dir", "line_number": 179, "usage_type": "call"}, {"api_name": "kapture.io.csv.get_all_tar_handlers", "line_number": 180, "usage_type": "call"}, {"api_name": "kapture.Keypoints", "line_number": 181, "usage_type": "attribute"}, {"api_name": "kapture.Descriptors", "line_number": 182, "usage_type": "attribute"}, {"api_name": "kapture.GlobalFeatures", "line_number": 183, "usage_type": "attribute"}, {"api_name": "kapture.Matches", "line_number": 184, "usage_type": "attribute"}, {"api_name": "kapture.io.csv.kapture_from_dir", "line_number": 185, "usage_type": "call"}, {"api_name": "kapture.Kapture", "line_number": 197, "usage_type": "attribute"}, {"api_name": "kapture.Kapture", "line_number": 198, "usage_type": "attribute"}, {"api_name": "kapture.Kapture", "line_number": 199, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 202, "usage_type": "name"}, {"api_name": "kapture.io.tar.TarCollection", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 203, "usage_type": "name"}, {"api_name": "kapture.io.tar.TarCollection", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 204, "usage_type": "name"}, {"api_name": "kapture.Trajectories", "line_number": 227, "usage_type": "call"}, {"api_name": "kapture.Trajectories", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 243, "usage_type": "call"}, {"api_name": "kapture.io.csv.kapture_from_dir", "line_number": 244, "usage_type": "call"}, {"api_name": "kapture.core.Trajectories.rigs_remove_inplace", "line_number": 250, "usage_type": "call"}, {"api_name": "kapture.core.Trajectories.rigs_remove_inplace", "line_number": 252, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_from_file", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "kapture.utils.Collections.try_get_only_key_from_collection", "line_number": 268, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 277, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 281, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_to_file", "line_number": 296, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_to_file", "line_number": 297, "usage_type": "call"}, {"api_name": "kapture_compute_matches.compute_matches_from_loaded_data", "line_number": 303, "usage_type": "call"}, {"api_name": "kapture.io.tar.retrieve_tar_handler_from_collection", "line_number": 310, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 310, "usage_type": "attribute"}, {"api_name": "kapture.io.tar.get_feature_tar_fullpath", "line_number": 313, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 313, "usage_type": "attribute"}, {"api_name": "kapture.io.tar.TarHandler", "line_number": 314, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_to_file", "line_number": 327, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_to_file", "line_number": 328, "usage_type": "call"}, {"api_name": "kapture_run_colmap_gv.run_colmap_gv_from_loaded_data", "line_number": 344, "usage_type": "call"}, {"api_name": "kapture.io.tar.retrieve_tar_handler_from_collection", "line_number": 358, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 358, "usage_type": "attribute"}, {"api_name": "kapture.io.tar.get_feature_tar_fullpath", "line_number": 362, "usage_type": "call"}, {"api_name": "kapture.Matches", "line_number": 362, "usage_type": "attribute"}, {"api_name": "kapture.io.tar.TarHandler", "line_number": 363, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_to_file", "line_number": 373, "usage_type": "call"}, {"api_name": "kapture_colmap_build_map.colmap_build_map_from_loaded_data", "line_number": 383, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 398, "usage_type": "call"}, {"api_name": "os.path", "line_number": 398, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 398, "usage_type": "call"}, {"api_name": "kapture.io.csv.table_to_file", "line_number": 404, "usage_type": "call"}, {"api_name": "kapture_colmap_localize.colmap_localize_from_loaded_data", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path", "line_number": 419, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 421, "usage_type": "call"}, {"api_name": "os.path", "line_number": 421, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 441, "usage_type": "call"}, {"api_name": "os.path", "line_number": 441, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 441, "usage_type": "call"}, {"api_name": "kapture.converter.colmap.import_colmap.import_colmap", "line_number": 447, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 449, "usage_type": "call"}, {"api_name": "os.path", "line_number": 449, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 450, "usage_type": "call"}, {"api_name": "os.path", "line_number": 450, "usage_type": "attribute"}, {"api_name": "kapture.io.records.TransferAction.skip", "line_number": 457, "usage_type": "attribute"}, {"api_name": "kapture.io.records.TransferAction", "line_number": 457, "usage_type": "name"}, {"api_name": "kapture.io.csv.kapture_to_dir", "line_number": 463, "usage_type": "call"}, {"api_name": "kapture.utils.paths.safe_remove_any_path", "line_number": 466, "usage_type": "call"}, {"api_name": "kapture.utils.paths.safe_remove_any_path", "line_number": 467, "usage_type": "call"}, {"api_name": "kapture.utils.paths.safe_remove_file", "line_number": 468, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 474, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 479, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 479, "usage_type": "attribute"}, {"api_name": "kapture.utils", "line_number": 480, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 483, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 508, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 509, "usage_type": "attribute"}, {"api_name": "kapture.utils.logging.getLogger", "line_number": 511, "usage_type": "call"}, {"api_name": "kapture.utils", "line_number": 511, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 513, "usage_type": "call"}, {"api_name": "os.path", "line_number": 513, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 514, "usage_type": "call"}]} +{"seq_id": "4344224039", "text": "import spacy\nimport pandas as pd\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nfrom textblob import TextBlob\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef news_scraper():\n \"\"\"Scrape a list of news articles from MarketWatch.\n\n The news is taken from: https://www.marketwatch.com/investing?mod=top_nav\n\n Returns:\n list: list of html tags of type bs4.element.Tag.\n Each element represents a single news article.\n \"\"\"\n news = []\n url = \"https://www.marketwatch.com/investing?mod=top_nav\"\n\n try:\n webpage = requests.get(url)\n except Exception as err:\n print(\"Error!\")\n print(f\"Message: {err}\")\n print(f\"Type: {type(err)}\")\n return None\n\n soup = BeautifulSoup(webpage.content, 'html.parser')\n articles = soup.find(attrs={'class': 'collection__elements j-scrollElement'})\n articles = articles.find_all(attrs={'class': 'element--article'})\n\n for article in articles:\n link = article.find(attrs={'class': 'link'})\n if link:\n news.append(link)\n\n return news\n\n\ndef article_scraper(url):\n \"\"\"Scrape the text from a MarketWatch article.\n\n The scraped article text is divided into title and body segments.\n\n Args:\n url (string): article URL\n Returns:\n tuple: tuple consisting of (title, body), where title and body are strings.\n \"\"\"\n try:\n webpage = requests.get(url)\n except requests.exceptions.MissingSchema:\n return None\n\n soup = BeautifulSoup(webpage.content, 'html.parser')\n try:\n title = soup.find(attrs={'itemprop': 'headline'}).get_text().strip()\n body = soup.find(attrs={'itemprop': 'articleBody'}).get_text().strip()\n except AttributeError:\n return None\n\n return title, body\n\n\ndef get_subjectivity(text):\n \"\"\"Get subjectivity score.\n\n The output range is [0.0, 1.0], where 0.0 is objective and 1.0 is subjective.\n\n Args:\n text (string): text to analyze\n Returns:\n float: subjectivity score\n \"\"\"\n return TextBlob(text).sentiment.subjectivity\n\n\ndef get_polarity(text):\n \"\"\"Get polarity scores.\n\n Output:\n pos: probability that the sentiment is positive\n neu: probability that the sentiment is neural\n neg: probability that the sentiment is negative\n compound: normalized compound score, range is [-1.0, 1.0],\n where -1.0 is negative and 1.0 is positive\n \n Args:\n text (string): text to analyze\n Returns:\n dict: dictionary of polarity scores.\n Keys are 'pos', 'neu', 'neg', and 'compound'.\n Values are floats representing the associated positive, neutral,\n negative, and compound polarity scores.\n \"\"\"\n return vader.polarity_scores(text)\n\n\ndef get_polarizing_sentences(text):\n \"\"\"Get the sentences rated as most negative and most positive.\n \n Args:\n text (string): text to analyze\n Returns:\n tuple: tuple consisting of (most negative sentence, most positive sentence),\n where each tuple element is a string.\n \"\"\"\n doc = nlp2(text)\n\n min_polarity = float('inf')\n max_polarity = float('-inf')\n min_sentence = None\n max_sentence = None\n\n for sent in doc.sents:\n # Skip empty sentences\n if not str(sent.text).strip():\n continue\n\n polarity = get_polarity(sent.text)['compound']\n if polarity < min_polarity:\n min_polarity = polarity\n min_sentence = sent\n if polarity > max_polarity:\n max_polarity = polarity\n max_sentence = sent\n\n min_sentence = str(min_sentence.text).strip()\n max_sentence = str(max_sentence.text).strip()\n\n return min_sentence, max_sentence\n\n\ndef get_counts_df(text):\n \"\"\"Count the number of times each stock or company is mentioned.\n\n Args:\n text (string): text to analyze\n Returns:\n DataFrame: DataFrame consisting of company, symbol, and counts columns.\n Each row is a unique company that is detected in the text.\n \"\"\"\n counts = []\n doc = nlp(text)\n\n # Iterate through doc entities and if the entity is a Stock, then we find\n # the corresponding CompanyName of that Stock and append it to our counts\n # list. If the entity is a CompanyName, then we simply append it to our counts list.\n for ent in doc.ents:\n if ent.label_ == 'Stock':\n counts.append(df_symbol.loc[ent.text]['CompanyName'])\n else:\n counts.append(ent.text)\n\n # Convert company counts list into a DataFrame. This gives us a DataFrame\n # with company and counts columns.\n counts_df = pd.DataFrame(counts).reset_index()\n try:\n counts_df = counts_df.groupby(0).index.count().reset_index().rename(\n columns={0: 'company', 'index': 'counts'})\n except KeyError:\n return None\n\n # Add a symbol column to the DataFrame and sort by counts.\n counts_df['symbol'] = counts_df.company\\\n .apply(lambda x: df_company_name.loc[x]['Symbol'])\n counts_df = counts_df[['company', 'symbol', 'counts']]\n counts_df = counts_df.sort_values(by='counts', ascending=False)\n\n return counts_df\n\n\n# Setup\n\n# Stop words\nstops = {'A', 'RBC', 'two', 'UK'}\n\n# stocks.tsv modified from spacy.pythonhumanities.com\ndf = pd.read_csv(\"static/data/stocks.tsv\", sep='\\t')\n\n# List of stock symbols and companies.\n# Used to create a patterns list for the entity ruler.\nsymbols = df.Symbol.tolist()\ncompanies = df.CompanyName.tolist()\n\n# DataFrame modified so that Symbol/CompanyName is the index to facilitate\n# efficient lookup. Used in get_counts_df()\ndf_symbol = df.set_index('Symbol')\ndf_company_name = df.set_index('CompanyName')\n\ndf_symbol = df_symbol[['CompanyName']]\n# Some companies have multiple stock symbols which results in rows with\n# duplicate indices. To handle this, we group by company name and set the\n# `symbol` column to equal all grouped stock symbols separated by commas\n# https://stackoverflow.com/questions/50422809/pandas-group-by-with-all-the-values-of-the-group-as-comma-separated\ndf_company_name = df_company_name.groupby(df_company_name.index).Symbol\\\n .agg([('Symbol', ', '.join)])\n\n# Set up spaCy with entity ruler to find stock symbols/companies\n# Adapted from https://spacy.pythonhumanities.com/03_01_stock_analysis.html\nnlp = spacy.blank('en')\nruler = nlp.add_pipe(\"entity_ruler\")\npatterns = []\nfor symbol in symbols:\n if symbol not in stops:\n patterns.append({'label': 'Stock', 'pattern': symbol})\nfor company in companies:\n if company not in stops:\n patterns.append({'label': 'Company', 'pattern': company})\nruler.add_patterns(patterns)\n\n# Set up spaCy for use with sentencizer\nnlp2 = spacy.load('en_core_web_sm')\n\n# Set up vader\nvader = SentimentIntensityAnalyzer()\n", "repo_name": "kaichang1/stock-article-analyzer", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6841, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 52, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 53, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 56, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 180, "usage_type": "call"}, {"api_name": "spacy.blank", "line_number": 202, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 214, "usage_type": "call"}, {"api_name": "vaderSentiment.vaderSentiment.SentimentIntensityAnalyzer", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "25777414867", "text": "import requests\r\n\r\n\r\ndef get_adr(proxy=\"\"):\r\n if proxy!=\"\":\r\n print(proxy)\r\n res = requests.get(\"https://post-shift.ru/api.php?action=new&type=json\",proxies=proxy,timeout=15)\r\n else:\r\n res = requests.get(\"https://post-shift.ru/api.php?action=new&type=json\")\r\n if(\"exceeded_the_limit_for_one_person\") in res.text:\r\n raise Exception(\"Too many requests wait 10 minutes\")\r\n print(res.json())\r\n try:\r\n return res.json()['email'], res.json()['key']\r\n except:\r\n return res.json()['error']\r\n\r\n\r\ndef get_message_by_text(key, text):\r\n res = requests.get(\"https://post-shift.ru/api.php?action=getlist&key=\" + key + \"&type=json\")\r\n if 'key_not_found' in res.text:\r\n raise Exception(\"InvalidKey\")\r\n if \"the_list_is_empty\" in res.text:\r\n return None\r\n\r\n num = 1\r\n for i in res.json():\r\n message = requests.get(\"https://post-shift.ru/api.php?action=getmail&key=\" + key + \"&type=json&id=\" + str(num))\r\n if text in message.json()['message']:\r\n return message.json()['message']\r\n num += 1\r\n return \"not_found\"\r\n\r\n\r\ndef get_messages_list(key):\r\n return requests.get(\"https://post-shift.ru/api.php?action=getlist&key=\" + key).text\r\n# def get_message_by_sender(key,message_sender):\r\n\r\n# email,key = get_adr()\r\n# print(email+ \" \"+key)\r\n", "repo_name": "PavlovPVz/twitch-autoreg", "sub_path": "disposablemail.py", "file_name": "disposablemail.py", "file_ext": "py", "file_size_in_byte": 1342, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "2", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "23532808929", "text": "from typing import Tuple\n\nimport einops\nimport torch\nimport torch.nn.functional as F\nfrom huggingface_hub import PyTorchModelHubMixin\nfrom torch.distributions import normal\nfrom torch.nn.modules.pixelshuffle import PixelUnshuffle\nfrom torch.nn.utils.parametrizations import spectral_norm\n\nfrom dgmr.layers import AttentionLayer\nfrom dgmr.layers.utils import get_conv_layer\n\n\nclass GBlock(torch.nn.Module):\n \"\"\"Residual generator block without upsampling\"\"\"\n\n def __init__(\n self,\n input_channels: int = 12,\n output_channels: int = 12,\n conv_type: str = \"standard\",\n spectral_normalized_eps=0.0001,\n ):\n \"\"\"\n G Block from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf\n Args:\n input_channels: Number of input channels\n output_channels: Number of output channels\n conv_type: Type of convolution desired, see satflow/models/utils.py for options\n \"\"\"\n super().__init__()\n self.output_channels = output_channels\n self.bn1 = torch.nn.BatchNorm2d(input_channels)\n self.bn2 = torch.nn.BatchNorm2d(input_channels)\n self.relu = torch.nn.ReLU()\n # Upsample in the 1x1\n conv2d = get_conv_layer(conv_type)\n self.conv_1x1 = spectral_norm(\n conv2d(\n in_channels=input_channels,\n out_channels=output_channels,\n kernel_size=1,\n ),\n eps=spectral_normalized_eps,\n )\n # Upsample 2D conv\n self.first_conv_3x3 = spectral_norm(\n conv2d(\n in_channels=input_channels,\n out_channels=input_channels,\n kernel_size=3,\n padding=1,\n ),\n eps=spectral_normalized_eps,\n )\n self.last_conv_3x3 = spectral_norm(\n conv2d(\n in_channels=input_channels, out_channels=output_channels, kernel_size=3, padding=1\n ),\n eps=spectral_normalized_eps,\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Optionally spectrally normalized 1x1 convolution\n if x.shape[1] != self.output_channels:\n sc = self.conv_1x1(x)\n else:\n sc = x\n\n x2 = self.bn1(x)\n x2 = self.relu(x2)\n x2 = self.first_conv_3x3(x2) # Make sure size is doubled\n x2 = self.bn2(x2)\n x2 = self.relu(x2)\n x2 = self.last_conv_3x3(x2)\n # Sum combine, residual connection\n x = x2 + sc\n return x\n\n\nclass UpsampleGBlock(torch.nn.Module):\n \"\"\"Residual generator block with upsampling\"\"\"\n\n def __init__(\n self,\n input_channels: int = 12,\n output_channels: int = 12,\n conv_type: str = \"standard\",\n spectral_normalized_eps=0.0001,\n ):\n \"\"\"\n G Block from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf\n Args:\n input_channels: Number of input channels\n output_channels: Number of output channels\n conv_type: Type of convolution desired, see satflow/models/utils.py for options\n \"\"\"\n super().__init__()\n self.output_channels = output_channels\n self.bn1 = torch.nn.BatchNorm2d(input_channels)\n self.bn2 = torch.nn.BatchNorm2d(input_channels)\n self.relu = torch.nn.ReLU()\n # Upsample in the 1x1\n conv2d = get_conv_layer(conv_type)\n self.conv_1x1 = spectral_norm(\n conv2d(\n in_channels=input_channels,\n out_channels=output_channels,\n kernel_size=1,\n ),\n eps=spectral_normalized_eps,\n )\n self.upsample = torch.nn.Upsample(scale_factor=2, mode=\"nearest\")\n # Upsample 2D conv\n self.first_conv_3x3 = spectral_norm(\n conv2d(\n in_channels=input_channels,\n out_channels=input_channels,\n kernel_size=3,\n padding=1,\n ),\n eps=spectral_normalized_eps,\n )\n self.last_conv_3x3 = spectral_norm(\n conv2d(\n in_channels=input_channels, out_channels=output_channels, kernel_size=3, padding=1\n ),\n eps=spectral_normalized_eps,\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Spectrally normalized 1x1 convolution\n sc = self.upsample(x)\n sc = self.conv_1x1(sc)\n\n x2 = self.bn1(x)\n x2 = self.relu(x2)\n # Upsample\n x2 = self.upsample(x2)\n x2 = self.first_conv_3x3(x2) # Make sure size is doubled\n x2 = self.bn2(x2)\n x2 = self.relu(x2)\n x2 = self.last_conv_3x3(x2)\n # Sum combine, residual connection\n x = x2 + sc\n return x\n\n\nclass DBlock(torch.nn.Module):\n def __init__(\n self,\n input_channels: int = 12,\n output_channels: int = 12,\n conv_type: str = \"standard\",\n first_relu: bool = True,\n keep_same_output: bool = False,\n ):\n \"\"\"\n D and 3D Block from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf\n Args:\n input_channels: Number of input channels\n output_channels: Number of output channels\n conv_type: Convolution type, see satflow/models/utils.py for options\n first_relu: Whether to have an ReLU before the first 3x3 convolution\n keep_same_output: Whether the output should have the same spatial dimensions as input, if False, downscales by 2\n \"\"\"\n super().__init__()\n self.input_channels = input_channels\n self.output_channels = output_channels\n self.first_relu = first_relu\n self.keep_same_output = keep_same_output\n self.conv_type = conv_type\n conv2d = get_conv_layer(conv_type)\n if conv_type == \"3d\":\n # 3D Average pooling\n self.pooling = torch.nn.AvgPool3d(kernel_size=2, stride=2)\n else:\n self.pooling = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n self.conv_1x1 = spectral_norm(\n conv2d(\n in_channels=input_channels,\n out_channels=output_channels,\n kernel_size=1,\n )\n )\n self.first_conv_3x3 = spectral_norm(\n conv2d(\n in_channels=input_channels,\n out_channels=output_channels,\n kernel_size=3,\n padding=1,\n )\n )\n self.last_conv_3x3 = spectral_norm(\n conv2d(\n in_channels=output_channels,\n out_channels=output_channels,\n kernel_size=3,\n padding=1,\n stride=1,\n )\n )\n # Downsample at end of 3x3\n self.relu = torch.nn.ReLU()\n # Concatenate to double final channels and keep reduced spatial extent\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n if self.input_channels != self.output_channels:\n x1 = self.conv_1x1(x)\n if not self.keep_same_output:\n x1 = self.pooling(x1)\n else:\n x1 = x\n\n if self.first_relu:\n x = self.relu(x)\n x = self.first_conv_3x3(x)\n x = self.relu(x)\n x = self.last_conv_3x3(x)\n\n if not self.keep_same_output:\n x = self.pooling(x)\n x = x1 + x # Sum the outputs should be half spatial and double channels\n return x\n\n\nclass LBlock(torch.nn.Module):\n \"\"\"Residual block for the Latent Stack.\"\"\"\n\n def __init__(\n self,\n input_channels: int = 12,\n output_channels: int = 12,\n kernel_size: int = 3,\n conv_type: str = \"standard\",\n ):\n \"\"\"\n L-Block for increasing the number of channels in the input\n from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf\n Args:\n input_channels: Number of input channels\n output_channels: Number of output channels\n conv_type: Which type of convolution desired, see satflow/models/utils.py for options\n \"\"\"\n super().__init__()\n # Output size should be channel_out - channel_in\n self.input_channels = input_channels\n self.output_channels = output_channels\n conv2d = get_conv_layer(conv_type)\n self.conv_1x1 = conv2d(\n in_channels=input_channels,\n out_channels=output_channels - input_channels,\n kernel_size=1,\n )\n\n self.first_conv_3x3 = conv2d(\n input_channels,\n out_channels=output_channels,\n kernel_size=kernel_size,\n padding=1,\n stride=1,\n )\n self.relu = torch.nn.ReLU()\n self.last_conv_3x3 = conv2d(\n in_channels=output_channels,\n out_channels=output_channels,\n kernel_size=kernel_size,\n padding=1,\n stride=1,\n )\n\n def forward(self, x) -> torch.Tensor:\n if self.input_channels < self.output_channels:\n sc = self.conv_1x1(x)\n sc = torch.cat([x, sc], dim=1)\n else:\n sc = x\n\n x2 = self.relu(x)\n x2 = self.first_conv_3x3(x2)\n x2 = self.relu(x2)\n x2 = self.last_conv_3x3(x2)\n return x2 + sc\n\n\nclass ContextConditioningStack(torch.nn.Module, PyTorchModelHubMixin):\n def __init__(\n self,\n input_channels: int = 1,\n output_channels: int = 768,\n num_context_steps: int = 4,\n conv_type: str = \"standard\",\n **kwargs\n ):\n \"\"\"\n Conditioning Stack using the context images from Skillful Nowcasting, , see https://arxiv.org/pdf/2104.00954.pdf\n\n Args:\n input_channels: Number of input channels per timestep\n output_channels: Number of output channels for the lowest block\n conv_type: Type of 2D convolution to use, see satflow/models/utils.py for options\n \"\"\"\n super().__init__()\n config = locals()\n config.pop(\"__class__\")\n config.pop(\"self\")\n self.config = kwargs.get(\"config\", config)\n input_channels = self.config[\"input_channels\"]\n output_channels = self.config[\"output_channels\"]\n num_context_steps = self.config[\"num_context_steps\"]\n conv_type = self.config[\"conv_type\"]\n\n conv2d = get_conv_layer(conv_type)\n self.space2depth = PixelUnshuffle(downscale_factor=2)\n # Process each observation processed separately with 4 downsample blocks\n # Concatenate across channel dimension, and for each output, 3x3 spectrally normalized convolution to reduce\n # number of channels by 2, followed by ReLU\n self.d1 = DBlock(\n input_channels=4 * input_channels,\n output_channels=((output_channels // 4) * input_channels) // num_context_steps,\n conv_type=conv_type,\n )\n self.d2 = DBlock(\n input_channels=((output_channels // 4) * input_channels) // num_context_steps,\n output_channels=((output_channels // 2) * input_channels) // num_context_steps,\n conv_type=conv_type,\n )\n self.d3 = DBlock(\n input_channels=((output_channels // 2) * input_channels) // num_context_steps,\n output_channels=(output_channels * input_channels) // num_context_steps,\n conv_type=conv_type,\n )\n self.d4 = DBlock(\n input_channels=(output_channels * input_channels) // num_context_steps,\n output_channels=(output_channels * 2 * input_channels) // num_context_steps,\n conv_type=conv_type,\n )\n self.conv1 = spectral_norm(\n conv2d(\n in_channels=(output_channels // 4) * input_channels,\n out_channels=(output_channels // 8) * input_channels,\n kernel_size=3,\n padding=1,\n )\n )\n\n self.conv2 = spectral_norm(\n conv2d(\n in_channels=(output_channels // 2) * input_channels,\n out_channels=(output_channels // 4) * input_channels,\n kernel_size=3,\n padding=1,\n )\n )\n\n self.conv3 = spectral_norm(\n conv2d(\n in_channels=output_channels * input_channels,\n out_channels=(output_channels // 2) * input_channels,\n kernel_size=3,\n padding=1,\n )\n )\n\n self.conv4 = spectral_norm(\n conv2d(\n in_channels=output_channels * 2 * input_channels,\n out_channels=output_channels * input_channels,\n kernel_size=3,\n padding=1,\n )\n )\n\n self.relu = torch.nn.ReLU()\n\n def forward(\n self, x: torch.Tensor\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n # Each timestep processed separately\n x = self.space2depth(x)\n steps = x.size(1) # Number of timesteps\n scale_1 = []\n scale_2 = []\n scale_3 = []\n scale_4 = []\n for i in range(steps):\n s1 = self.d1(x[:, i, :, :, :])\n s2 = self.d2(s1)\n s3 = self.d3(s2)\n s4 = self.d4(s3)\n scale_1.append(s1)\n scale_2.append(s2)\n scale_3.append(s3)\n scale_4.append(s4)\n scale_1 = torch.stack(scale_1, dim=1) # B, T, C, H, W and want along C dimension\n scale_2 = torch.stack(scale_2, dim=1) # B, T, C, H, W and want along C dimension\n scale_3 = torch.stack(scale_3, dim=1) # B, T, C, H, W and want along C dimension\n scale_4 = torch.stack(scale_4, dim=1) # B, T, C, H, W and want along C dimension\n # Mixing layer\n scale_1 = self._mixing_layer(scale_1, self.conv1)\n scale_2 = self._mixing_layer(scale_2, self.conv2)\n scale_3 = self._mixing_layer(scale_3, self.conv3)\n scale_4 = self._mixing_layer(scale_4, self.conv4)\n return scale_1, scale_2, scale_3, scale_4\n\n def _mixing_layer(self, inputs, conv_block):\n # Convert from [batch_size, time, h, w, c] -> [batch_size, h, w, c * time]\n # then perform convolution on the output while preserving number of c.\n stacked_inputs = einops.rearrange(inputs, \"b t c h w -> b (c t) h w\")\n return F.relu(conv_block(stacked_inputs))\n\n\nclass LatentConditioningStack(torch.nn.Module, PyTorchModelHubMixin):\n def __init__(\n self,\n shape: (int, int, int) = (8, 8, 8),\n output_channels: int = 768,\n use_attention: bool = True,\n **kwargs\n ):\n \"\"\"\n Latent conditioning stack from Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf\n\n Args:\n shape: Shape of the latent space, Should be (H/32,W/32,x) of the final image shape\n output_channels: Number of output channels for the conditioning stack\n use_attention: Whether to have a self-attention block or not\n \"\"\"\n super().__init__()\n config = locals()\n config.pop(\"__class__\")\n config.pop(\"self\")\n self.config = kwargs.get(\"config\", config)\n shape = self.config[\"shape\"]\n output_channels = self.config[\"output_channels\"]\n use_attention = self.config[\"use_attention\"]\n\n self.shape = shape\n self.use_attention = use_attention\n self.distribution = normal.Normal(loc=torch.Tensor([0.0]), scale=torch.Tensor([1.0]))\n\n self.conv_3x3 = spectral_norm(\n torch.nn.Conv2d(\n in_channels=shape[0], out_channels=shape[0], kernel_size=(3, 3), padding=1\n )\n )\n self.l_block1 = LBlock(input_channels=shape[0], output_channels=output_channels // 32)\n self.l_block2 = LBlock(\n input_channels=output_channels // 32, output_channels=output_channels // 16\n )\n self.l_block3 = LBlock(\n input_channels=output_channels // 16, output_channels=output_channels // 4\n )\n if self.use_attention:\n self.att_block = AttentionLayer(\n input_channels=output_channels // 4, output_channels=output_channels // 4\n )\n self.l_block4 = LBlock(input_channels=output_channels // 4, output_channels=output_channels)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n\n Args:\n x: tensor on the correct device, to move over the latent distribution\n\n Returns:\n\n \"\"\"\n\n # Independent draws from Norma ldistribution\n z = self.distribution.sample(self.shape)\n # Batch is at end for some reason, reshape\n z = torch.permute(z, (3, 0, 1, 2)).type_as(x)\n\n # 3x3 Convolution\n z = self.conv_3x3(z)\n\n # 3 L Blocks to increase number of channels\n z = self.l_block1(z)\n z = self.l_block2(z)\n z = self.l_block3(z)\n # Spatial attention module\n z = self.att_block(z)\n\n # L block to increase number of channel to 768\n z = self.l_block4(z)\n return z\n", "repo_name": "openclimatefix/skillful_nowcasting", "sub_path": "dgmr/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 17158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 172, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "dgmr.layers.utils.get_conv_layer", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "dgmr.layers.utils.get_conv_layer", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn.Upsample", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "attribute"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 132, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "attribute"}, {"api_name": "dgmr.layers.utils.get_conv_layer", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn.AvgPool3d", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "attribute"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 228, "usage_type": "attribute"}, {"api_name": "dgmr.layers.utils.get_conv_layer", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 273, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 287, "usage_type": "attribute"}, {"api_name": "huggingface_hub.PyTorchModelHubMixin", "line_number": 287, "usage_type": "name"}, {"api_name": "dgmr.layers.utils.get_conv_layer", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.nn.modules.pixelshuffle.PixelUnshuffle", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 339, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 357, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 366, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 375, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 375, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 378, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 396, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 397, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 398, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 399, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 379, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 379, "usage_type": "attribute"}, {"api_name": "einops.rearrange", "line_number": 410, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 411, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 414, "usage_type": "attribute"}, {"api_name": "huggingface_hub.PyTorchModelHubMixin", "line_number": 414, "usage_type": "name"}, {"api_name": "torch.distributions.normal.Normal", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.distributions.normal", "line_number": 441, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.nn.utils.parametrizations.spectral_norm", "line_number": 443, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 444, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 444, "usage_type": "attribute"}, {"api_name": "dgmr.layers.AttentionLayer", "line_number": 456, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 461, "usage_type": "attribute"}, {"api_name": "torch.permute", "line_number": 474, "usage_type": "call"}]} +{"seq_id": "27501863756", "text": "\"\"\" train a rnn network using pose sequence of Exam cheat dataset\"\"\"\r\n\r\n# python packages\r\nimport numpy as np\r\nimport os\r\nfrom keras import backend as K\r\nfrom keras.utils import to_categorical\r\nfrom keras.optimizers import Adam\r\nfrom keras import losses\r\nfrom keras.callbacks import LearningRateScheduler\r\n\r\n\r\n# project modules\r\nfrom . import my_models\r\nfrom . import model_utils\r\nfrom . import make_dataset_3dcd\r\nfrom .. import config\r\n\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \r\n\r\ndef lr_scheduler(epoch):\r\n if (epoch == 50):\r\n K.set_value(model.optimizer.lr, config.lr_1)\r\n\r\n elif (epoch == 100):\r\n K.set_value(model.optimizer.lr, config.lr_2)\r\n\r\n elif (epoch == 170):\r\n K.set_value(model.optimizer.lr, config.lr_3)\r\n \r\n print(\"learning rate: \", K.get_value(model.optimizer.lr))\r\n return K.get_value(model.optimizer.lr)\r\n\r\n\r\n### custom loss\r\ndef zero_loss(y_true, y_pred):\r\n return 0.5 * K.sum(y_pred, axis = 0)\r\n\r\n\r\n\r\nX_train, X_valid, y_train, y_valid = make_dataset_3dcd.get_train_data()\r\nchange_lr = LearningRateScheduler(lr_scheduler)\r\n\r\n\r\nprint(y_train.shape)\r\nprint(y_valid.shape)\r\n\r\n# constructing model\r\nmodel = my_models.get_temporal_model()\r\n\r\n# train model once again\r\n#model = model_utils.read_rnn_model(angle)\r\n\r\n\r\n### run model\r\nlambda_centerloss = 0.008\r\n\r\noptimizer = Adam(lr = config.learning_rate)\r\nmodel.compile(optimizer = optimizer,\r\n loss=[losses.categorical_crossentropy, zero_loss],\r\n loss_weights=[1, lambda_centerloss],\r\n metrics=['accuracy'])\r\n\r\n\r\n# training and evaluating model\r\nmodel_cp = model_utils.save_rnn_model_checkpoint()\r\nearly_stop = model_utils.set_early_stopping()\r\n\r\n\r\n# fit\r\n#y_train_value = np.argmax(y_train, axis = 2)\r\n#y_valid_value = np.argmax(y_valid, axis = 2)\r\n\r\nrandom_y_train = np.random.rand(X_train.shape[0], 1)\r\nrandom_y_valid = np.random.rand(X_valid.shape[0], 1)\r\n\r\n\r\nmodel.fit([X_train, y_train], [y_train, random_y_train], \r\n batch_size = config.batch_size,\r\n shuffle = True,\r\n epochs = config.nb_epochs,\r\n callbacks = [change_lr, model_cp],\r\n verbose = 2,\r\n validation_data=([X_valid, y_valid], [y_valid, random_y_valid]))", "repo_name": "Mahedi-61/Exam_Cheat_detection", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2245, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "keras.backend.set_value", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 23, "usage_type": "name"}, {"api_name": "keras.backend.set_value", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 26, "usage_type": "name"}, {"api_name": "keras.backend.set_value", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 29, "usage_type": "name"}, {"api_name": "keras.backend.get_value", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 31, "usage_type": "name"}, {"api_name": "keras.backend.get_value", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 32, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 37, "usage_type": "name"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.losses.categorical_crossentropy", "line_number": 60, "usage_type": "attribute"}, {"api_name": "keras.losses", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 75, "usage_type": "attribute"}]} +{"seq_id": "30852005802", "text": "from asyncio import gather\nfrom collections.abc import Awaitable\nfrom logging import Logger\n\nfrom asyncpg import create_pool\nfrom asyncpg import Pool\nfrom pgbelt.cmd.helpers import run_with_configs\nfrom pgbelt.config.models import DbupgradeConfig\nfrom pgbelt.util.dump import apply_target_constraints\nfrom pgbelt.util.dump import create_target_indexes\nfrom pgbelt.util.dump import dump_source_tables\nfrom pgbelt.util.dump import load_dumped_tables\nfrom pgbelt.util.logs import get_logger\nfrom pgbelt.util.postgres import analyze_table_pkeys\nfrom pgbelt.util.postgres import compare_100_rows\nfrom pgbelt.util.postgres import compare_latest_100_rows\nfrom pgbelt.util.postgres import dump_sequences\nfrom pgbelt.util.postgres import load_sequences\nfrom pgbelt.util.postgres import run_analyze\nfrom typer import Option\n\n\nasync def _sync_sequences(\n targeted_sequences: list[str],\n src_pool: Pool,\n dst_pool: Pool,\n src_logger: Logger,\n dst_logger: Logger,\n) -> None:\n seq_vals = await dump_sequences(src_pool, targeted_sequences, src_logger)\n await load_sequences(dst_pool, seq_vals, dst_logger)\n\n\n@run_with_configs\nasync def sync_sequences(config_future: Awaitable[DbupgradeConfig]) -> None:\n \"\"\"\n Retrieve the current value of all sequences in the source database and update\n the sequences in the target to match.\n \"\"\"\n conf = await config_future\n pools = await gather(\n create_pool(conf.src.pglogical_uri, min_size=1),\n create_pool(conf.dst.root_uri, min_size=1),\n )\n src_pool, dst_pool = pools\n try:\n src_logger = get_logger(conf.db, conf.dc, \"sync.src\")\n dst_logger = get_logger(conf.db, conf.dc, \"sync.dst\")\n await _sync_sequences(\n conf.sequences, src_pool, dst_pool, src_logger, dst_logger\n )\n finally:\n await gather(*[p.close() for p in pools])\n\n\n@run_with_configs(skip_dst=True)\nasync def dump_tables(\n config_future: Awaitable[DbupgradeConfig],\n tables: list[str] = Option([], help=\"Specific tables to dump\"),\n) -> None:\n \"\"\"\n Dump all tables without primary keys from the source database and save\n them to files locally.\n\n You may also provide a list of tables to dump with the\n --tables option and only these tables will be dumped.\n \"\"\"\n conf = await config_future\n logger = get_logger(conf.db, conf.dc, \"sync.src\")\n\n if tables:\n tables = tables.split(\",\")\n else:\n async with create_pool(conf.src.pglogical_uri, min_size=1) as src_pool:\n _, tables, _ = await analyze_table_pkeys(src_pool, logger)\n\n if conf.tables:\n tables = [t for t in tables if t in conf.tables]\n\n await dump_source_tables(conf, tables, logger)\n\n\n@run_with_configs(skip_src=True)\nasync def load_tables(\n config_future: Awaitable[DbupgradeConfig],\n tables: list[str] = Option([], help=\"Specific tables to load\"),\n):\n \"\"\"\n Load all locally saved table data files into the destination db. A table will\n only be loaded into the destination if it currently contains no rows.\n\n You may also provide a list of tables to load with the\n --tables option and only these files will be loaded.\n \"\"\"\n conf = await config_future\n logger = get_logger(conf.db, conf.dc, \"sync.dst\")\n\n if tables:\n tables = tables.split(\",\")\n else:\n if conf.tables:\n tables = [t for t in tables if t in conf.tables]\n else:\n tables = []\n\n await load_dumped_tables(conf, tables, logger)\n\n\n@run_with_configs\nasync def sync_tables(\n config_future: Awaitable[DbupgradeConfig],\n tables: list[str] = Option([], help=\"Specific tables to sync\"),\n):\n \"\"\"\n Dump and load all tables from the source database to the destination database.\n Equivalent to running dump-tables followed by load-tables. Table data will be\n saved locally in files.\n\n You may also provide a list of tables to sync with the\n --tables option and only these tables will be synced.\n \"\"\"\n conf = await config_future\n src_logger = get_logger(conf.db, conf.dc, \"sync.src\")\n dst_logger = get_logger(conf.db, conf.dc, \"sync.dst\")\n\n if tables:\n dump_tables = tables.split(\",\")\n else:\n async with create_pool(conf.src.pglogical_uri, min_size=1) as src_pool:\n _, dump_tables, _ = await analyze_table_pkeys(src_pool, src_logger)\n\n if conf.tables:\n dump_tables = [t for t in dump_tables if t in conf.tables]\n\n await dump_source_tables(conf, dump_tables)\n await load_dumped_tables(\n conf, [] if not tables and not conf.tables else dump_tables, dst_logger\n )\n\n\n@run_with_configs(skip_src=True)\nasync def analyze(config_future: Awaitable[DbupgradeConfig]) -> None:\n \"\"\"\n Run ANALYZE in the destination database. This should be run after data is\n completely replicated and before applications are allowed to use the new db.\n \"\"\"\n conf = await config_future\n logger = get_logger(conf.db, conf.dc, \"sync.dst\")\n async with create_pool(conf.dst.root_uri, min_size=1) as dst_pool:\n await run_analyze(dst_pool, logger)\n\n\n@run_with_configs\nasync def validate_data(config_future: Awaitable[DbupgradeConfig]) -> None:\n \"\"\"\n Compares data in the source and target databases. Both a random sample and a\n sample of the latest rows will be compared for each table. Does not validate\n the entire data set.\n \"\"\"\n conf = await config_future\n pools = await gather(\n create_pool(conf.src.pglogical_uri, min_size=1),\n create_pool(conf.dst.owner_uri, min_size=1),\n )\n src_pool, dst_pool = pools\n\n try:\n logger = get_logger(conf.db, conf.dc, \"sync\")\n await gather(\n compare_100_rows(src_pool, dst_pool, conf.tables, logger),\n compare_latest_100_rows(src_pool, dst_pool, conf.tables, logger),\n )\n finally:\n await gather(*[p.close() for p in pools])\n\n\nasync def _dump_and_load_all_tables(\n conf: DbupgradeConfig, src_pool: Pool, src_logger: Logger, dst_logger: Logger\n) -> None:\n _, tables, _ = await analyze_table_pkeys(src_pool, src_logger)\n if conf.tables:\n tables = [t for t in tables if t in conf.tables]\n await dump_source_tables(conf, tables, src_logger)\n await load_dumped_tables(conf, tables, dst_logger)\n\n\n@run_with_configs\nasync def sync(config_future: Awaitable[DbupgradeConfig]) -> None:\n \"\"\"\n Sync and validate all data that is not replicated with pglogical. This includes all\n tables without primary keys and all sequences. Also loads any previously omitted\n NOT VALID constraints into the destination db and runs ANALYZE in the destination.\n\n This command is equivalent to running the following commands in order:\n sync-sequences, sync-tables, validate-data, load-constraints, analyze.\n Though here they may run concurrently when possible.\n \"\"\"\n conf = await config_future\n pools = await gather(\n create_pool(conf.src.pglogical_uri, min_size=1),\n create_pool(conf.dst.root_uri, min_size=1),\n create_pool(conf.dst.owner_uri, min_size=1),\n )\n src_pool, dst_root_pool, dst_owner_pool = pools\n\n try:\n src_logger = get_logger(conf.db, conf.dc, \"sync.src\")\n dst_logger = get_logger(conf.db, conf.dc, \"sync.dst\")\n validation_logger = get_logger(conf.db, conf.dc, \"sync\")\n\n await gather(\n _sync_sequences(\n conf.sequences, src_pool, dst_root_pool, src_logger, dst_logger\n ),\n _dump_and_load_all_tables(conf, src_pool, src_logger, dst_logger),\n )\n\n # Creating indexes should run before validations and ANALYZE, but after all the data exists\n # in the destination database.\n\n await gather(\n apply_target_constraints(conf, dst_logger),\n create_target_indexes(conf, dst_logger, during_sync=True),\n )\n\n await gather(\n compare_100_rows(src_pool, dst_owner_pool, conf.tables, validation_logger),\n compare_latest_100_rows(\n src_pool, dst_owner_pool, conf.tables, validation_logger\n ),\n run_analyze(dst_owner_pool, dst_logger),\n )\n finally:\n await gather(*[p.close() for p in pools])\n\n\nCOMMANDS = [\n sync_sequences,\n dump_tables,\n load_tables,\n sync_tables,\n analyze,\n validate_data,\n sync,\n]\n", "repo_name": "Autodesk/pgbelt", "sub_path": "pgbelt/cmd/sync.py", "file_name": "sync.py", "file_ext": "py", "file_size_in_byte": 8375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "2", "api": [{"api_name": "asyncpg.Pool", "line_number": 25, "usage_type": "name"}, {"api_name": "asyncpg.Pool", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 27, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 28, "usage_type": "name"}, {"api_name": "pgbelt.util.postgres.dump_sequences", "line_number": 30, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.load_sequences", "line_number": 31, "usage_type": "call"}, {"api_name": "collections.abc.Awaitable", "line_number": 35, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 35, "usage_type": "name"}, {"api_name": "asyncio.gather", "line_number": 41, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 42, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 43, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 47, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 48, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 53, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 34, "usage_type": "name"}, {"api_name": "collections.abc.Awaitable", "line_number": 58, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 58, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 59, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 69, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 74, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.analyze_table_pkeys", "line_number": 75, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.dump_source_tables", "line_number": 80, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 56, "usage_type": "call"}, {"api_name": "collections.abc.Awaitable", "line_number": 85, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 85, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 86, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 96, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.load_dumped_tables", "line_number": 106, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 83, "usage_type": "call"}, {"api_name": "collections.abc.Awaitable", "line_number": 111, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 111, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 112, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 123, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 124, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 129, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.analyze_table_pkeys", "line_number": 130, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.dump_source_tables", "line_number": 135, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.load_dumped_tables", "line_number": 136, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 109, "usage_type": "name"}, {"api_name": "collections.abc.Awaitable", "line_number": 142, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 142, "usage_type": "name"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 148, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 149, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.run_analyze", "line_number": 150, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 141, "usage_type": "call"}, {"api_name": "collections.abc.Awaitable", "line_number": 154, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 154, "usage_type": "name"}, {"api_name": "asyncio.gather", "line_number": 161, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 162, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 163, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 168, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 169, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.compare_100_rows", "line_number": 170, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.compare_latest_100_rows", "line_number": 171, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 174, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 153, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 178, "usage_type": "name"}, {"api_name": "asyncpg.Pool", "line_number": 178, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 178, "usage_type": "name"}, {"api_name": "pgbelt.util.postgres.analyze_table_pkeys", "line_number": 180, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.dump_source_tables", "line_number": 183, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.load_dumped_tables", "line_number": 184, "usage_type": "call"}, {"api_name": "collections.abc.Awaitable", "line_number": 188, "usage_type": "name"}, {"api_name": "pgbelt.config.models.DbupgradeConfig", "line_number": 188, "usage_type": "name"}, {"api_name": "asyncio.gather", "line_number": 199, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 200, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 201, "usage_type": "call"}, {"api_name": "asyncpg.create_pool", "line_number": 202, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 207, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 208, "usage_type": "call"}, {"api_name": "pgbelt.util.logs.get_logger", "line_number": 209, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 211, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 221, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.apply_target_constraints", "line_number": 222, "usage_type": "call"}, {"api_name": "pgbelt.util.dump.create_target_indexes", "line_number": 223, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 226, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.compare_100_rows", "line_number": 227, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.compare_latest_100_rows", "line_number": 228, "usage_type": "call"}, {"api_name": "pgbelt.util.postgres.run_analyze", "line_number": 231, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 234, "usage_type": "call"}, {"api_name": "pgbelt.cmd.helpers.run_with_configs", "line_number": 187, "usage_type": "name"}]} +{"seq_id": "25644386305", "text": "import torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nimport math\n\n\nclass Attention(nn.Module):\n \"\"\"\n Compute 'Scaled Dot Product Attention\n \"\"\"\n\n def forward(self, query, key, value, mask=None, dropout=None, relem=None):\n if relem is not None:\n #print(query.size(), relem.size())\n relscore = torch.matmul(query.permute(0, 2, 1, 3), relem.transpose(-2, -1))\n scores = torch.matmul(query, key.transpose(-2, -1))#torch.matmul(query.unsqueeze(3), (key.unsqueeze(2) + relem).transpose(-2, -1)).squeeze(-2)\n scores = (relscore.permute(0, 2, 1, 3) + scores) / math.sqrt(query.size(-1))\n else:\n scores = torch.matmul(query, key.transpose(-2, -1)) \\\n / math.sqrt(query.size(-1))\n\n if mask is not None:\n if len(list(mask.size())) != 4:\n #print(mask.size())\n mask = mask.unsqueeze(1).repeat(1, query.size(2), 1).unsqueeze(1)\n #print(mask.shape)\n scores = scores.masked_fill(mask == 0, -1e9)\n p_attn = F.softmax(scores, dim=-1)\n\n if dropout is not None:\n p_attn = dropout(p_attn)\n if relem is not None:\n ans1 = torch.matmul(p_attn, value)\n ans2 = torch.matmul(p_attn.permute(0, 2, 1, 3), relem)\n ans = ans1 + ans2.permute(0, 2, 1, 3)#torch.matmul(p_attn.unsqueeze(3), (value.unsqueeze(2) + relem)).squeeze(-2)\n else:\n ans = torch.matmul(p_attn, value)\n return ans, p_attn", "repo_name": "pkuzqh/ICSE23Repair", "sub_path": "model/Attention.py", "file_name": "Attention.py", "file_ext": "py", "file_size_in_byte": 1531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "2", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 17, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 20, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "70050871098", "text": "import numpy as np\nimport scipy.integrate as spint\nimport scipy.special as sp\nimport scipy.optimize as so\nimport matplotlib.pyplot as plt\nimport sys\n\nimport funcs\n\n# define domain constants\nL = np.pi\nN = 50\ndz = 2*L/(2*N + 1)\n\n# define domain\n# z = np.arange(-L, L, dz) # 2N + 1 domain points\nz = np.linspace(-L, L, N) # N points (used for now because I'm not zero padding)\nprint(len(z))\n\n# define magnetic constants \nB = 1.5\nb = 0.1\n\nepsilon = 1 - B/2\n\n\n## define integral components (eq. 2.18)\n# bessel functions\nbess_first = sp.jv(1, z)\nbess_sec = sp.yn(1, z)\n\n\ndef mainIntegrand(S, z, N, L, b, gamma, rho, V, epsilon):\n\n # define S derivatives (spectral)\n S_z = funcs.fftDeriv(S, z, order=1)\n S_zz = funcs.fftDeriv(S, z, order=2)\n\n # define components in integrand (eqn 2.19)\n kappa = - (S_zz/np.power(1 + S_z**2, 3/2)) + (1/(S*np.sqrt(1 + S_z**2)))\n F = ((gamma*kappa)/rho) - V - epsilon\n\n # bessel functions \n def I(domain, order=1):\n # bessel of first kind\n return sp.jv(order, domain)\n \n def K(domain, order=1):\n # bessel of second kind\n return sp.yn(order, domain)\n\n\n integrand = np.empty((N,len(z))) # initialize array of N integrand equations \n\n # get k values (101 values but we discard the eq'n with k=0 in the for loop) \n k_values = np.arange(-N/2, N/2 + 1, 1)*(np.pi/L)\n i = 0\n\n for k in k_values:\n\n if k == 0.0:\n continue # we don't want to include the equation with k = 0 (trivial solution)\n\n # individual terms\n c = 1\n one = k*S*np.sqrt((1 + S_z**2)*(c**2 - 2*F))\n two = K(k*b)*I(k*S) - I(k*b)*K(k*S)\n three = np.cos(k*z)\n\n # add eq'n to integrand array (each row is one eq'n) \n integrand[i,:] = one*two*three\n i += 1\n\n return integrand\n\n\ndef mainIntegral(S, params):\n\n # define parameters \n parameters = [z, N, L, b, gamma, rho, V, epsilon]\n \n for i in range(0,7):\n parameters[i] = params[i]\n\n # get N integrand equations\n integrands = mainIntegrand(S, z, N, L, b, gamma, rho, V, epsilon)\n\n equations = np.empty(N) # initialize array of N integral equations\n\n n = 0 \n\n # define all N integral equations (with trapezium rule)\n for i in integrands:\n equations[n] = np.trapz(integrands[n,:], z)\n n += 1\n\n return equations\n\n\n# set parameter values\ngamma = 1\nrho = 1\nV = 1\nparams = [z, N, L, b, gamma, rho, V, epsilon]\n\n# set initial guess (simple cosine wave in real space for now)\ninitial_guess = np.cos(z)\n\n\nsolution = so.fsolve(mainIntegral, initial_guess, args = params)\n\nprint(f\"Solution computed.\")\nprint(f\"Solution length: {solution.size}\")\n\n# plotting \n\nplt.plot(z, solution, color='#00264D')\n\nplt.xlabel(\"z\", labelpad=10)\nplt.ylabel(\"S\", labelpad=10)\n\nplt.show()\n", "repo_name": "karnavr/CylindricalWaves", "sub_path": "cylindrical.py", "file_name": "cylindrical.py", "file_ext": "py", "file_size_in_byte": 2791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.pi", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.special.jv", "line_number": 29, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 29, "usage_type": "name"}, {"api_name": "scipy.special.yn", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 30, "usage_type": "name"}, {"api_name": "funcs.fftDeriv", "line_number": 36, "usage_type": "call"}, {"api_name": "funcs.fftDeriv", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.special.jv", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 46, "usage_type": "name"}, {"api_name": "scipy.special.yn", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 107, "usage_type": "call"}, {"api_name": "scipy.optimize.fsolve", "line_number": 110, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}]} +{"seq_id": "71050103086", "text": "# 剑指 Offer 03. 数组中重复的数字\n# https://leetcode-cn.com/problems/shu-zu-zhong-zhong-fu-de-shu-zi-lcof/\nfrom typing import List\nclass Solution:\n # 使用思想：hash表\n def findRepeatNumber(self, nums: List[int]) -> int:\n my_dict ={}\n for num in nums:\n if num in my_dict:\n return num\n else:\n my_dict[num]=1\n", "repo_name": "elfisworking/PY_Leet", "sub_path": "剑指offer/python/03.py", "file_name": "03.py", "file_ext": "py", "file_size_in_byte": 394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "72713812845", "text": "from .models import UserProfile, Post\nfrom django.contrib.auth.models import AnonymousUser\n\n\ndef get_avatar(request):\n if 'admin' in request.path:\n return {}\n if not request.user.is_authenticated or request.user is AnonymousUser:\n return dict(get_avatar=None)\n user = request.user\n userprofile = UserProfile.objects.filter(user=user).first()\n avatar = userprofile.profile_picture.url\n return dict(get_avatar=avatar)\n\n\ndef get_followed_posts(request):\n if 'admin' in request.path:\n return {}\n if not request.user.is_authenticated or request.user is AnonymousUser:\n return dict(followed_posts=None)\n user = request.user\n userprofile = UserProfile.objects.filter(user=user).first()\n followed_posts = userprofile.get_following_posts()\n return dict(get_followed_posts=followed_posts)\n\n\ndef get_liked_posts_by_user(request):\n if 'admin' in request.path:\n return {}\n if not request.user.is_authenticated or request.user is AnonymousUser:\n return dict(get_liked_posts_by_user=None)\n user = request.user\n userprofile = UserProfile.objects.filter(user=user).first()\n all_likes = userprofile.get_all_liked_posts()\n liked_posts_id = []\n for like in all_likes:\n liked_posts_id.append(like.pk)\n all_x = Post.objects.filter(id__in=liked_posts_id).all()\n return dict(get_liked_posts_by_user=all_x)\n\n\ndef get_current_user_profile(request):\n if 'admin' in request.path:\n return {}\n if not request.user.is_authenticated or request.user is AnonymousUser:\n return dict(get_current_user_profile=None)\n user = request.user\n userprofile = UserProfile.objects.filter(user=user).first()\n return dict(get_current_user_profile=userprofile)", "repo_name": "drutkoowski/django-social-media", "sub_path": "accounts/context_processors.py", "file_name": "context_processors.py", "file_ext": "py", "file_size_in_byte": 1752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 8, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 11, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 19, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 22, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 30, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 33, "usage_type": "name"}, {"api_name": "models.Post.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 38, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 45, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "40418756693", "text": "from collections import defaultdict\n\ndef solution(today, terms, privacies):\n sign = defaultdict()\n res = []\n \n for term in terms:\n inputs = term.split(\" \")\n sign[inputs[0]] = int(inputs[1])*28\n \n year = int(today.split(\".\")[0])*336\n mon = int(today.split(\".\")[1])*28\n day = int(today.split(\".\")[2])\n \n day = year + mon + day\n \n for i in range(len(privacies)):\n tmp = privacies[i].split(\" \")[0]\n term = privacies[i].split(\" \")[1]\n count = int(tmp.split(\".\")[0])*336+int(tmp.split(\".\")[1])*28+int(tmp.split(\".\")[2]) + sign[term]\n if (count <= day): res.append(i + 1)\n \n return res", "repo_name": "its-sky/algorithm", "sub_path": "프로그래머스/1/150370. 개인정보 수집 유효기간/개인정보 수집 유효기간.py", "file_name": "개인정보 수집 유효기간.py", "file_ext": "py", "file_size_in_byte": 662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "collections.defaultdict", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "23842662043", "text": "import csv\nimport os\n\nimport numpy as np\nimport pyspark.sql.functions as F\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import Window\nfrom tqdm import tqdm\n\nfilename = \"email-Eu-core-temporal-Dept3.txt\"\n# filename = \"email-Eu-core-temporal.txt\"\nsave_dir = \"results\"\nos.makedirs(save_dir, exist_ok=True)\n\nconf = SparkConf().setMaster(\"local\").setAppName(\"My App\")\nsc = SparkContext(conf = conf)\n\n# create a spark session\nspark = SparkSession.builder.appName('katz').getOrCreate()\n\n# read data as list\nwith open(filename) as csvfile:\n data = [list(map(int, row)) for row in csv.reader(csvfile, delimiter=' ')]\n\n# make a spark dataframe\ncolumns = [\"u\", \"v\", \"t\"]\ndf = spark.createDataFrame(data=data, schema=columns)\n\ndf.toPandas()\n\ntrain_df = df.filter(F.col(\"t\") < 20_000_000)\ntest_df = df.filter(F.col(\"t\") >= 20_000_000).filter(F.col(\"t\") < 22_000_000)\n\ntrain_df.toPandas(), test_df.toPandas()\n\n\n# u, v\nedges = train_df\\\n .drop(\"t\")\\\n .groupby(\"u\", \"v\")\\\n .count()\n\nnodes = train_df\\\n .select(\"u\")\\\n .union(edges.select(\"v\"))\\\n .withColumnRenamed(\"u\", \"node\")\\\n .distinct().orderBy(\"node\")\n\n# nodes.write.mode(\"ignore\").csv(os.path.join(save_dir, f\"{filename.split('.')[0]}_nodes.csv\"))\nnum_nodes = nodes.count()\n\n# _u, _v\n_adj = nodes.withColumnRenamed(\"node\", \"_u\")\\\n .join(nodes.withColumnRenamed(\"node\", \"_v\"))\\\n .orderBy(\"_u\", \"_v\")\n\n# __u, total\n_total = edges\\\n .groupBy(\"u\")\\\n .agg(F.sum(edges[\"count\"]))\\\n .withColumnRenamed(\"sum(count)\", \"total\")\\\n .withColumnRenamed(\"u\", \"__u\")\n\n\"\"\"\nT = [\n[0, 1, 0]\n[0.5, 0, 0.5]\n[0, 0, 0]\n]\n\n\"\"\"\n\n# u, value\n_edges = edges\\\n .join(_total, edges.u == _total.__u)\\\n .withColumn(\"value\", edges[\"count\"] / (_total[\"total\"]))\\\n .drop(\"count\")\\\n .drop(\"total\")\\\n .drop(\"__u\")\n\nprint(\"_edges\", _edges.toPandas())\n\n# u, v, value\nadj = _adj\\\n .join(\n _edges,\n (_adj._u == _edges.u)\n & (_adj._v == _edges.v),\n 'left'\n )\\\n .select(\"_u\", \"_v\", \"value\")\\\n .fillna(0)\\\n .withColumnRenamed(\"_u\", \"u\")\\\n .withColumnRenamed(\"_v\", \"v\")\n\nprint(\"adj\")\nprint(adj.orderBy(\"u\", \"v\").toPandas())\n\nw = Window.partitionBy(\"u\").orderBy(\"v\")\n_A = adj\\\n .withColumn(\"sorted_list\", F.collect_list(\"value\").over(w))\\\n .groupBy(\"u\")\\\n .agg(F.max(\"sorted_list\").alias('row'))\\\n .orderBy('u')\\\n .withColumn(\"id\", F.monotonically_increasing_id())\n\nprint(\"A\")\nprint(_A.toPandas())\n\n\nA = IndexedRowMatrix(_A.select(\"id\", \"row\").rdd.map(lambda row: IndexedRow(*row)))\\\n .toBlockMatrix(num_nodes, num_nodes)\nmatrix = IndexedRowMatrix(_A.select(\"id\", \"row\").rdd.map(lambda row: IndexedRow(*row)))\\\n .toBlockMatrix(num_nodes, num_nodes)\nstationary_distribution = IndexedRowMatrix(sc.parallelize([IndexedRow(_, [0] * num_nodes) for _ in range(num_nodes)]))\\\n .toBlockMatrix(num_nodes, num_nodes)\nA.toLocalMatrix().toArray()\n\nprint(\"A\")\nprint(A.toLocalMatrix().toArray())\n\nn = 100\n_n = int(n*0.9)\nfor i in tqdm(range(n), total=n):\n matrix = matrix.multiply(A)\n # print(matrix.toLocalMatrix().toArray())\n if i >= _n:\n stationary_distribution = stationary_distribution.add(matrix)\n\n\nnp.save(os.path.join(save_dir, f\"{filename.split('.')[0]}_stationary-distribution.npy\"),\n stationary_distribution.toLocalMatrix().toArray() / (n - _n))", "repo_name": "sreeja-g/link-prediction-pyspark", "sub_path": "based_on_path/stationary_distribution.py", "file_name": "stationary_distribution.py", "file_ext": "py", "file_size_in_byte": 3327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "os.makedirs", "line_number": 15, "usage_type": "call"}, {"api_name": "pyspark.SparkConf", "line_number": 17, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 18, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 21, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 21, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 25, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 33, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 33, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 34, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.sum", "line_number": 62, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 62, "usage_type": "name"}, {"api_name": "pyspark.sql.Window.partitionBy", "line_number": 101, "usage_type": "call"}, {"api_name": "pyspark.sql.Window", "line_number": 101, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.collect_list", "line_number": 103, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 103, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.max", "line_number": 105, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 105, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.monotonically_increasing_id", "line_number": 107, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 107, "usage_type": "name"}, {"api_name": "pyspark.mllib.linalg.distributed.IndexedRowMatrix", "line_number": 113, "usage_type": "call"}, {"api_name": "pyspark.mllib.linalg.distributed.IndexedRow", "line_number": 113, "usage_type": "call"}, {"api_name": "pyspark.mllib.linalg.distributed.IndexedRowMatrix", "line_number": 115, "usage_type": "call"}, {"api_name": "pyspark.mllib.linalg.distributed.IndexedRow", "line_number": 115, "usage_type": "call"}, {"api_name": "pyspark.mllib.linalg.distributed.IndexedRowMatrix", "line_number": 117, "usage_type": "call"}, {"api_name": "pyspark.mllib.linalg.distributed.IndexedRow", "line_number": 117, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}]} +{"seq_id": "2913754492", "text": "# -*- coding: utf-8 -*-\n#\n# base idea:\thttp://markkeller.me/2016-09-19-media_info_extractor/\n\n\nimport platform\nimport subprocess\nimport os\nimport sys\n\nfrom openpyxl import Workbook\nfrom openpyxl import __version__ as opx_v\n\n# Import necessary style classes\nfrom openpyxl.styles import Font, Alignment, Border, Side\n\nprint(\" 'Python' - Version : \" + platform.python_version() + \" on os: \" + sys.platform)\nprint(\" 'OpenPyXL' - Version : \" + opx_v, '\\n')\n\n# this is a list with items of the type 'string'\n# these are extensions of files (in the execution folder) which are 'no media files':\n# no_media_files = ['.evs', '.py', '.xml', '.pdf', '.docx', '.db', '.exe', '.hide', '.xls', '.xlsx', '.csv', '.log', '.txt']\n\n# here i read an external text file (again with the 'no media file' extensions)\n# i believe this is more flexible, but notice the wrong entry 'no media file.txt', because we have no entry 'txt' in our 'extensions' list\nwith open (\"extensions.txt\") as file:\n\tno_media_files = file.read()\n\tfile.close()\nprint(no_media_files, '\\n')\n\nno_media_files = no_media_files.split(\"\\n\")\nprint(no_media_files, '\\n')\n\nno_media_files = [\".\" + item for item in no_media_files]\nprint(no_media_files, '\\n')\n\n# test - in the following line i filter also 'avi' 'jpg' and 'mpg' files\n# no_media_files = ['.evs', '.py', '.xml', '.pdf', '.docx', '.db', '.exe', '.hide', '.xls', '.xlsx', '.csv', '.log', '.avi', '.jpg', '.mpg']\n\nif __name__ == '__main__':\n\n\tdir_work = os.getcwd() # Return a string representing the current working directory\n\tprint('\\n', \"current 'working directory' : \", dir_work)\n\n\t# path = directory + '/folder' # os: linux\n\t# path = directory + '\\\\folder' # os: windows\n\tdir_media = dir_work + '\\\\media'\n\tprint('\\n', \"current 'media directory' : \", dir_media)\n\tdir_log = dir_work + '\\\\log'\n\tprint('\\n', \"current 'log directory' : \", dir_log)\n\tdir_xlsx = dir_work + '\\\\xlsx'\n\tprint('\\n', \"current 'workbook directory' : \", dir_xlsx)\n\n\t# return a list containing the names of the entries in the directory given by 'path' ,\n\t# here our 'current working directory'\n\t# at this time, this 'list_of_all_files' contains all files, without any filter applied\n\tlist_of_all_files = os.listdir(dir_media)\n\tprint(\"\\n\", \"list of all files in our 'media directory' :\")\n\tfor file in list_of_all_files:\n\t\tprint(\" \", file)\n\n\tprint(\"\\n\")\n\n\tonly_media_files = [] # here we create an empty list\n\n\t# this for-loop starts with the keyword \"for\" followed by an arbitrary variable name ( in this source: 'file' ),\n\t# which will hold the values of the following sequence object, which is stepped through.\n\t# the items of the sequence object are assigned one after the other to the loop variable;\n\t# to be precise: the variable points to the items.\n\t# For each item the loop body is executed, \t(we apply the filter (.evs, .py, .xml ...)\n\t# to get a list only with our media-files)\n\n\tfor file in list_of_all_files:\n\n\t\t# os.path.isfile(path) : return 'true' if 'path' is an existing regular file, and 'false' for our 'sub_directory'\n\t\t# in this source, at the time of the first loop-step, 'path' is the first 'file' in our 'list_of_all_files'\n\t\t#\n\t\t# print(os.path.isfile(os.path.join(dir_media,file)))\t# prints 'True' or 'False'\n\n\t\t# 'if' is a 'conditional statement' , and in this for-loop we have another for-loop:\n\t\t# it starts with 'for' followed by the variable name 'filter'\n\t\t# if the item 'file' of 'list_of_files' has no extension listed in our 'filters'-list,\n\t\t# than append this 'file' ( = filename ) to our, until now empty, 'only_media_files'\n\n\t\t# if os.path.isfile(file) and all([filter not in file for filter in filters_no_media]):\n\t\t# pycharm says we can this above line split in \"two if's\"\n\n\t\tif os.path.isfile(os.path.join(dir_media,file)):\n\t\t\tif all([filter not in file for filter in no_media_files]):\n\t\t\t\tonly_media_files.append(file)\n\t\t\t\t# only_media_files.extend(file)\n\n\t\t\t\t# list_of_files = only_media_files # new reference to this list, and this list is 'filtered'\n\t\t\t\t# print('\\n', \"list of files (new referenced) : \", \"\\n\", \" \", list_of_files)\n\n\t# at this time we have applied our filter rule\n\tprint(\"filtered list (only media files):\")\n\tfor file in only_media_files:\n\t\tprint(\" \", file)\n\n\t# for-loop: for every media-file in our 'only_media_files'\n\tfor file in only_media_files:\n\n\t\t# os: linux\n\t\t# media_info[file] = subprocess.check_output(['mediainfo.exe %s'%file], shell=True, executable='/bin/bash').split('\\n')\n\n\t\t# os: windows\n\t\t# media_info[file] = subprocess.check_output(['mediainfo.exe', '%s', '%file'], shell=True, executable='/bin/bash').split('\\n')\n\n\t\t# if 'args' is a list, then the first item in this list is considered as the executable and the rest\n\t\t# of the items in the list are passed as command line arguments to the program:\n\t\t# mi_cmd = ['mediainfo.exe', '-s', (os.path.join(dir_media, file))]\n\n\t\t# in Python 3.6 + you can use the new f - strings:\n\t\t# https://docs.python.org/3/whatsnew/3.6.html#pep-498-formatted-string-literals\n\n\t\t# print('\\n', \"directory: \", directory, '\\n')\n\n\t\tmi_cmd = ['mediainfo', '--Language=raw', '--Full', (os.path.join(dir_media, file)),\n\t\t\tf\"--Logfile={dir_log}\\{file.replace('.', '_')}_raw.log\"]\n\n\t\tprint('\\n', \"'mi_cmd' is a list: \", mi_cmd)\n\n\t\t# media_info is a dictionary:\n\t\t# here we generate an empty dictionary\n\t\t# the difference between lists and dictionaries:\n\t\t# a list is an ordered sequence of objects, whereas dictionaries are unordered sets.\n\t\t# but the main difference is, that items in dictionaries are accessed via keys and not via their position.\n\t\tmedia_info = {}\n\n\t\tmedia_info[file] = subprocess.check_output(mi_cmd, universal_newlines=True, encoding='utf-8').split('\\n')\n\n\t\tprint (\"-\" * 100)\n\t\t# the answer of 'check_output' is a 'dictionary':\n\t\t# the 'media file name' as the key, and a list as the value to this key ,\n\t\t# this 'value'-list contains all 'categories' and 'elements' from mediainfo\n\n\t\t# print(\"-\" * 100)\n\t\t# print('media_info is : ', '\\n\\n\\n', media_info, '\\n\\n\\n')\n\t\t# !!! the output starts with { , followed by the name of our mediafile,\n\t\t# THAN a lowercase b , just before 'General .....\n\t\t# this b means in Python3 'byte string': it consists of sequences of 8-bit values, and is for storing to disk,\n\t\t# while 'str' consists of sequences of Unicode characters, and is for displaying to humans to read on a computer\n\n\t\t# print (\"-\" * 100)\n\t\t# print('class: ', type(dict.keys(media_info)),'\\n')\n\t\t# Get all keys\n\t\t# here we see, that 'key' in our 'key-value' pairs in this dictionary is the name of our 'file'\n\t\tprint(\"the 'key' : \", dict.keys(media_info))\n\t\t# print(\"the 'key' : \", media_info.keys())\n\n\t\tprint(\"-\" * 100)\n\t\t# Get all values\n\t\t# and 'value' is the answer of 'mediainfo.exe'\n\t\tprint(\"the 'value' : \", '\\n', dict.values(media_info), '\\n')\n\t\t# print(\"the 'value' : \", '\\n', media_info.values(), '\\n')\n\n\t\t# print(\"-\" * 100)\n\n\t\t# at this time, we have build up our 'media_info' dictionary for one media file\n\n\t\t# 'dict.items' iterates over the key-value pairs of our dictionary.\n\t\t# print(\"these are the 'items' : \", '\\n', dict.items(media_info), '\\n')\n\n\t\t# for-loop: for every 'mediafile' in our 'media_info' -dictionary,\n\t\t# we create 'category' - 'append_' and 'category_dictionary' :\n\t\t# depart the 'for loop' and 'if - else' with the debugger of your ide, and watch the content of all variables\n\n\t\tfor file in media_info.keys():\n\n\t\t\t# create Workbook object\n\t\t\twb = Workbook()\n\t\t\tws = wb.active\n\n\t\t\t# Create a few styles\texamples from realpython\n\t\t\tbold_font = Font(bold=True)\n\t\t\t# big_red_text = Font(color=colors.RED, size=20)\t\t\t# error\n\t\t\tcenter_aligned_text = Alignment(horizontal=\"center\")\n\t\t\tdouble_border_side = Side(border_style=\"double\")\n\t\t\tsquare_border = Border(top=double_border_side,\n right=double_border_side,\n bottom=double_border_side,\n left=double_border_side)\n\n\t\t\t# Style some cells!\n\t\t\t# sheet[\"A2\"].font = bold_font\n\t\t\t# sheet[\"A3\"].font = big_red_text\n\t\t\t# sheet[\"A4\"].alignment = center_aligned_text\n\t\t\t# sheet[\"A5\"].border = square_border\n\n\t\t\t# sheet_row = 2\n\t\t\tsheet_col = 1\t\t# counter for worksheet column\n\t\t\tws_cat = 0\t\t\t# counter for worksheet position\n\n\t\t\t# here starts a loop :\n\t\t\tfor line in media_info[file]:\n\n\t\t\t\tif line == '': # there are some empty lines, ignore , don't do anything, go on\n\t\t\t\t\tcontinue\n\t\t\t\t# print(\"Line is: \", line)\n\n\t\t\t\t# find the 'category' names (General, Audio, ...)\n\t\t\t\t# .strip() removes all whitespace at the start and end, including spaces, tabs, newlines and carriage returns\n\t\t\t\tif ':' not in line.strip(): # there is no colon ':' in the category-lines 'General' - 'Video' - 'Audio' - ....\n\t\t\t\t\tprint('\\n', \"No colon ':' in 'line': \", line)\n\n\t\t\t\t\tcategory = line.strip() # update the variable 'category' with the value of stripped 'line' , eg 'General'\n\n\t\t\t\t\t# != if values of two operands are not equal, then condition becomes 'true' ,\n\t\t\t\t\t# if category != '': # in the 1st round of this loop 'category' is empty\n\t\t\t\t\t# category_list.append(category)\n\t\t\t\t\tprint('\\n', \"category is : \", category, '\\n')\n\n\t\t\t\t\t# category_dict = {} # create a dictionary, or delete content of existing dictionary\n\n\t\t\t\t\tws_category = wb.create_sheet(category, ws_cat) # insert sheet with name 'category' at 'ws_cat' position\n\n\t\t\t\t\t# set Worksheet tab color.\n\t\t\t\t\tws_category.sheet_properties.tabColor = \"FF0000\"\n\n\t\t\t\t\tws_category['A1'] = 'MI Element'\t# write text to fixed cell 'A1'\n\t\t\t\t\tws_category['B1'] = 'MI Value'\n\n\t\t\t\t\tws_category[\"A1\"].font = bold_font\n\t\t\t\t\tws_category[\"B1\"].font = bold_font\n\n\t\t\t\t\tws_category[\"A1\"].alignment = center_aligned_text\n\t\t\t\t\tws_category[\"B1\"].alignment = center_aligned_text\n\n\t\t\t\t\t# sheet[\"A3\"].font = big_red_text\n\t\t\t\t\t# sheet[\"A4\"].alignment = center_aligned_text\n\n\t\t\t\t\tws_cat = ws_cat + 1\t\t\t\t\t# increase counter for worksheet position\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# next worksheet will be inserted to the next right position\n\n\t\t\t\t\tsheet_row = 2\t\t\t\t\t\t# on every worksheet the first row is filled with fixed text\n\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Line is: \", line)\n\n\t\t\t\t\t# some lines have a lot of colons ' : '\n\t\t\t\t\t# example for 'line' from the audio category ( two ' : ')\n\t\t\t\t\t#\t\t\tChannel positions : Front: L R\n\t\t\t\t\t# another example\n\t\t\t\t\t#\t\t\tTagged date : UTC 2013-12-13 15:39:16\n\t\t\t\t\t#\n\t\t\t\t\t# second parameter for 'split' is called 'maxsplits', meaning the number of times 'line.split' should do\n\t\t\t\t\tmi_element, mi_value = line.split(':', 1)\t# first part of splited line is assigned to variable\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 'mi_element', second part is variable 'mi_value'\n\t\t\t\t\tmi_element = mi_element.rstrip()\t\t\t# delete\n\t\t\t\t\tmi_value = mi_value.lstrip()\t\t\t\t# delete\n\n\t\t\t\t\t# wb = load_workbook(os.path.join(dir_xlsx, (file.replace('.', '_') + '.xlsx')))\n\t\t\t\t\tprint(wb.sheetnames)\n\n\t\t\t\t\tws_category.cell(row = sheet_row, column = sheet_col).value = mi_element\n\t\t\t\t\tsheet_col = sheet_col + 1\n\t\t\t\t\tws_category.cell(row = sheet_row, column = sheet_col).value = mi_value\n\t\t\t\t\tsheet_row = sheet_row + 1\n\t\t\t\t\tsheet_col = sheet_col - 1\n\n\t\t\t\t\t# wb.save(os.path.join(dir_xlsx, (file.replace('.', '_') + '.xlsx')))\n\n\t\t\t\twb.save(os.path.join(dir_xlsx, (file.replace('.', '_') + '.xlsx')))\n\n\n# https://adamj.eu/tech/2021/10/10/the-many-ways-to-exit-in-python/\t\t\t\t\nprint(\"\\n\\n!!! Done !!!\")\nraise SystemExit()", "repo_name": "Pullem/mi2xlsx", "sub_path": "mi2xlsx.py", "file_name": "mi2xlsx.py", "file_ext": "py", "file_size_in_byte": 11281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "2", "api": [{"api_name": "platform.python_version", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 17, "usage_type": "attribute"}, {"api_name": "openpyxl.__version__", "line_number": 18, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 42, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 131, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 172, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 176, "usage_type": "call"}, {"api_name": "openpyxl.styles.Alignment", "line_number": 178, "usage_type": "call"}, {"api_name": "openpyxl.styles.Side", "line_number": 179, "usage_type": "call"}, {"api_name": "openpyxl.styles.Border", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}]} +{"seq_id": "8712141131", "text": "import cv2\nimport os\nimport ConfigParser\n\n_PROP_FRAME_COUNT = cv2.CAP_PROP_FRAME_COUNT\n\n\nclass VideoReader:\n def __init__(self, video_path):\n self._capture = cv2.VideoCapture(video_path)\n self._video_file = video_path\n self._index = 0\n self._last_frame = None\n self._index = 0\n\n def read(self):\n self._index += 1\n ret_code, self._last_frame = self._capture.read()\n return self._last_frame\n\n def get_frame_count(self):\n return int(self._capture.get(_PROP_FRAME_COUNT))\n\n def get_submat(self, img, x, y, height, width):\n crop_img = img[x:x + width, y:y + height]\n return crop_img\n\n def get_specific_frame(self, frame_index):\n self._capture.set(1, frame_index)\n ret_code, specific_frame = self._capture.read()\n return specific_frame\n\n def get_time(self):\n return self._capture.get(cv2.CAP_PROP_POS_MSEC) / 1000\n\n\nif __name__ == \"__main__\":\n\n cf = ConfigParser.ConfigParser()\n cf.read('../config/video.conf')\n\n video_path = cf.get('video', 'video_path')\n dpm_neg_dir = cf.get('video', 'dpm_neg_dir')\n\n video_reader = VideoReader(video_path)\n for img_name in os.listdir(dpm_neg_dir):\n extension_name = os.path.splitext(img_name)[1]\n if extension_name == '.png':\n image_idx = float(os.path.splitext(img_name)[0].strip('0'))\n huge_img = video_reader.get_specific_frame(image_idx)\n cv2.imwrite('/Volumes/Bingbong/hoop_data/dpm_hard_neg_huge/' + str(image_idx) + '.png', huge_img)\n", "repo_name": "shaozhefeng/video_annotated", "sub_path": "tool/video_reader.py", "file_name": "video_reader.py", "file_ext": "py", "file_size_in_byte": 1560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_POS_MSEC", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ConfigParser.ConfigParser", "line_number": 39, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "17302688260", "text": "# coding: utf8\n\nimport datetime\nimport re\nimport events\n\n\nclass LanguageProcessing:\n def __init__(self):\n # Шаблон для проверки соответствия формального запроса\n self.pattern_add = '^(?P\\d{2})\\.(?P\\d{2})\\.(?P\\d{2}|\\d{4})' \\\n '\\s(?P\\d{2}):(?P\\d{2})' \\\n '(\\s(?P\\d{2})\\.(?P\\d{2})\\.(?P\\d{2}|\\d{4})' \\\n '\\s(?P\\d{2}):(?P\\d{2}))?' \\\n '(\\s(?P\\d+)\\s?мин)?(\\s#(?P\\w+))?' \\\n '\\s(?P.+)$'\n\n def analyse(self, chat_id, request):\n \"\"\"\n Функция для определения типа запроса (формальный или неформальный) и вызова функции-обработчика\n :param chat_id:\n :param request:\n :return:\n \"\"\"\n result = None\n if not re.match(self.pattern_add, request) is None:\n print('>> formal')\n result = [self.formal(chat_id, request)]\n print(result)\n else:\n print('>> informal')\n pass\n return result\n\n def formal(self, chat_id, request):\n \"\"\"\n Функция для преобразования формального запроса в ответ типа event\n :param chat_id:\n :param request:\n :return:\n \"\"\"\n exp = re.match(self.pattern_add, request)\n\n year_real = exp.group('year_real')\n if len(year_real) == 2:\n year_real = '20' + year_real\n year_real = int(year_real)\n\n date_real = datetime.datetime(year_real,\n int(exp.group('month_real')),\n int(exp.group('day_real')),\n int(exp.group('hour_real')),\n int(exp.group('minutes_real')))\n if exp.group('year_notify') is None or exp.group('month_notify') is None or exp.group('day_notify') is None:\n date_notify = date_real\n else:\n year_notify = exp.group('year_notify')\n if len(year_notify) == 2:\n year_notify = int('20' + year_notify)\n date_notify = datetime.datetime(year_notify,\n int(exp.group('month_notify')),\n int(exp.group('day_notify')),\n int(exp.group('hour_notify')),\n int(exp.group('minutes_notify')))\n duration = exp.group('duration')\n description = exp.group('description')\n category = exp.group('category')\n if category is not None:\n category = category.title()\n\n return events.Event(chat_id, date_real, date_notify, duration, description, category)\n", "repo_name": "tasktrack/TaskTrackLegacy", "sub_path": "language_processing.py", "file_name": "language_processing.py", "file_ext": "py", "file_size_in_byte": 3073, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "22", "api": [{"api_name": "re.match", "line_number": 26, "usage_type": "call"}, {"api_name": "re.match", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "call"}, {"api_name": "events.Event", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "26530413295", "text": "import yfinance as yf\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom datetime import date\nfrom matplotlib.colors import LinearSegmentedColormap\n\n\nclass StockPerformanceAnalyzer:\n def __init__(self, stock_symbol, start_date):\n self.stock_symbol = stock_symbol\n self.start_date = start_date\n self.performance_data = self._get_stock_performance_transposed()\n\n def _get_stock_performance_transposed(self):\n stock_data = yf.download(self.stock_symbol, start=self.start_date, end=date.today())\n stock_data['Month'] = stock_data.index.to_period('M')\n monthly_performance = stock_data.groupby('Month').apply(\n lambda x: (x['Adj Close'][-1] - x['Adj Close'][0]) / x['Adj Close'][0]\n )\n monthly_performance = monthly_performance.reset_index()\n monthly_performance['Year'] = monthly_performance['Month'].dt.year\n monthly_performance['Month'] = monthly_performance['Month'].dt.month\n performance_pivot = monthly_performance.pivot(\"Year\", \"Month\", 0)\n \n return performance_pivot\n\n def plot_heatmap(self):\n colored_data_with_nan = self._color_data_with_nan()\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n xticks = [month_names[month] for month in self.performance_data.columns]\n \n plt.figure(figsize=(17, 8))\n sns.heatmap(\n colored_data_with_nan, \n annot=self.performance_data, \n fmt=\".2%\", \n cmap=self._get_colormap(), \n cbar_kws={\n 'label': 'Monthly Performance',\n 'ticks': [-1, 0, 1, 2], \n 'boundaries': [-1.5, -0.5, 0.5, 1.5, 2.5]\n }, \n linewidths=0.5, \n xticklabels=xticks, \n yticklabels=self.performance_data.index\n )\n plt.title(f'Monthly Performance of {self.stock_symbol} from {self.start_date} to Today')\n plt.show()\n plt.subplots_adjust(hspace=0.8)\n\n def plot_monthly_histogram(self):\n self._plot_histogram(axis=0)\n\n def plot_yearly_histogram(self):\n self._plot_histogram(axis=1)\n\n def _plot_histogram(self, axis):\n positive_counts, negative_counts = self._get_positive_negative_counts(axis)\n xlabel = \"Month\" if axis == 0 else \"Year\"\n \n index = np.arange(len(positive_counts))\n bar_width = 0.35\n \n plt.figure(figsize=(10, 5))\n bar1 = plt.bar(index, positive_counts, bar_width, label='Positive', color='green')\n bar2 = plt.bar(index + bar_width, negative_counts, bar_width, label='Negative', color='red')\n\n # Add data values on top of the bars\n for bars in (bar1, bar2):\n for bar in bars:\n height = bar.get_height()\n plt.text(bar.get_x() + bar.get_width() / 2., height, '%d' % int(height), ha='center', va='bottom')\n\n plt.xlabel(xlabel)\n plt.ylabel('Counts')\n plt.title(f'Counts of Positive and Negative Performance by {xlabel}')\n plt.xticks(index + bar_width/2, positive_counts.index) \n plt.legend()\n plt.tight_layout()\n plt.show()\n plt.subplots_adjust(hspace=0.8)\n\n def _get_positive_negative_counts(self, axis):\n positive_counts = (self.performance_data > 0).sum(axis=axis)\n negative_counts = (self.performance_data <= 0).sum(axis=axis)\n return positive_counts, negative_counts\n\n def _color_data_with_nan(self):\n return np.vectorize(self._apply_color_with_nan)(self.performance_data)\n\n def _apply_color_with_nan(self, value):\n if np.isnan(value):\n return -1\n elif value > 0.1:\n return 2\n elif 0 <= value <= 0.1:\n return 1\n else:\n return 0\n\n def _get_colormap(self):\n colors_with_nan = [\"grey\", \"red\", \"yellow\", \"green\"]\n return LinearSegmentedColormap.from_list(\"\", colors_with_nan)\n\n\nif __name__ == \"__main__\":\n analyzer = StockPerformanceAnalyzer(\"TUPRS.IS\", \"2010-01-01\")\n analyzer.plot_heatmap()\n analyzer.plot_monthly_histogram()\n analyzer.plot_yearly_histogram()\n", "repo_name": "utkuyucel/Stock_Heatmap_Visualization", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "yfinance.download", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "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": "matplotlib.pyplot.subplots_adjust", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "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.pyplot.title", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.vectorize", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.colors.LinearSegmentedColormap.from_list", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.colors.LinearSegmentedColormap", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "30083289826", "text": "\nimport cv2\nfrom streamlit_webrtc import WebRtcMode,RTCConfiguration, VideoTransformerBase, webrtc_streamer, AudioProcessorBase\nfrom DistanceEstimation import *\nfrom typing import Awaitable, Callable, Generic, List, Optional, TypeVar\nimport streamlit as st\nfrom streamlit_autorefresh import st_autorefresh\nimport time\n\naudio_counter = 0\nnew_audio_file = open('audio.mp3', 'rb')\naudio_bytes = new_audio_file.read()\nst.audio(audio_bytes, format='audio/ogg')\nnew_audio_file.close() \nRTC_CONFIGURATION = RTCConfiguration(\n {\"iceServers\": [{\"urls\": [\"stun:stun.l.google.com:19302\"]}]}\n)\n\ncount = st_autorefresh(interval=2500, limit=1000000, key=\"fizzbuzzcounter\")\n\nimport av\nfrom tts import *\n\n\n\nclass VideoTransformer(VideoTransformerBase):\n def __init__(self) -> None:\n super().__init__()\n self.frame_count = 0\n\n\n def transform(self, frame):\n img = frame.to_ndarray(format=\"bgr24\")\n new_img = get_frame_output(img, self.frame_count)\n return new_img\n\n def recv(self, frame: av.VideoFrame) -> av.VideoFrame:\n new_image = self.transform(frame)\n \n return av.VideoFrame.from_ndarray(new_image, format=\"bgr24\")\n\nclass AudioProcessor(AudioProcessorBase):\n\n def reset_audio(self):\n # time.sleep(0.1)\n self.new_audio_file = open('audio.mp3', 'rb')\n self.audio_bytes = self.new_audio_file.read()\n print(len(self.audio_bytes))\n st.audio(self.audio_bytes, format='audio/ogg')\n self.new_audio_file.close() \n \n async def recv_queued(self, frames: List[av.AudioFrame]) -> List[av.AudioFrame]:\n get_audio()\n self.reset_audio()\n return []\n\nif __name__ == \"__main__\":\n # webrtc_streamer(key=\"example\", video_processor_factory=VideoTransformer)\n webrtc_streamer( key=\"WYH\",\n mode=WebRtcMode.SENDRECV,\n rtc_configuration=RTC_CONFIGURATION,\n media_stream_constraints={\"video\": True, \"audio\": False},\n video_processor_factory=VideoTransformer,\n audio_processor_factory=AudioProcessor,\n )", "repo_name": "subhailams/streamlitdistance", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "streamlit.audio", "line_number": 13, "usage_type": "call"}, {"api_name": "streamlit_webrtc.RTCConfiguration", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit_autorefresh.st_autorefresh", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit_webrtc.VideoTransformerBase", "line_number": 26, "usage_type": "name"}, {"api_name": "av.VideoFrame", "line_number": 37, "usage_type": "attribute"}, {"api_name": "av.VideoFrame.from_ndarray", "line_number": 40, "usage_type": "call"}, {"api_name": "av.VideoFrame", "line_number": 40, "usage_type": "attribute"}, {"api_name": "streamlit_webrtc.AudioProcessorBase", "line_number": 42, "usage_type": "name"}, {"api_name": "streamlit.audio", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 52, "usage_type": "name"}, {"api_name": "av.AudioFrame", "line_number": 52, "usage_type": "attribute"}, {"api_name": "streamlit_webrtc.webrtc_streamer", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlit_webrtc.WebRtcMode.SENDRECV", "line_number": 60, "usage_type": "attribute"}, {"api_name": "streamlit_webrtc.WebRtcMode", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "70843381177", "text": "# Imports\n# 3rd party:\nfrom django.db import models\nfrom django.contrib.auth.models import User\nfrom django.utils.translation import gettext_lazy as _\n\n# Internal:\n# Status of a inspo item, draft or published\nSTATUS = (\n (0, \"Draft\"),\n (1, \"Published\")\n)\n\n\nclass Inspo(models.Model):\n \"\"\"\n This model is for a inspo item\n \"\"\"\n class Meta:\n ordering = ['-create_date']\n\n title = models.CharField(\n verbose_name=_('Title'),\n max_length=250,\n unique=True\n )\n user = models.ForeignKey(\n User,\n on_delete=models.CASCADE,\n related_name='inspo_items'\n )\n inspo_item_text = models.TextField(\n max_length=500,\n )\n image = models.ImageField(\n null=True,\n blank=True\n )\n update_date = models.DateTimeField(\n auto_now=True\n )\n create_date = models.DateTimeField(\n auto_now_add=True\n )\n status = models.IntegerField(\n choices=STATUS,\n default=0\n )\n\n def __str__(self):\n \"\"\"\n Return new title string\n Args:\n self (object): self\n Returns:\n inspo title\n \"\"\"\n return self.title\n", "repo_name": "PillowFishSticks/Kassie-MS4", "sub_path": "inspo/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "django.db.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "21703974972", "text": "import serial\nimport time\nimport paho.mqtt.client as mqtt\n\ndef on_connect(client, userdata, flags, rc):\n if rc == 0:\n print(\"Connected to MQTT Broker!\")\n else:\n print('Failed to connect, return code {:d}'.format(rc))\n\ndef on_message(client, userdata, msg):\n cmd = msg.payload.decode()\n print('Received command from cloud: {}'.format(cmd))\n print('Send command to Micro:bit: {}'.format(cmd))\n sendCommand(cmd)\n \n \ndef sendCommand(command):\n command = command + '\\n'\n ser.write(str.encode(command))\n \ntry:\n broker = 'broker.emqx.io'\n port = 1883\n topic = '/sws3025/command'\n client = mqtt.Client()\n client.on_connect = on_connect\n client.connect(broker, port)\n \n print('Listening on /dev/ttyACM0.')\n ser = serial.Serial(port = '/dev/ttyACM0', baudrate = 115200, timeout = 1)\n\n client.subscribe(topic)\n client.on_message = on_message\n# client.loop_forever()\n\nexcept KeyboardInterrupt:\n if ser.is_open:\n ser.close()\n print('Program terminated!')\n", "repo_name": "TTangNingzhi/SWS3025-AIoT-Smart-Kitchen", "sub_path": "ServerControl/raspi_down.py", "file_name": "raspi_down.py", "file_ext": "py", "file_size_in_byte": 1038, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "paho.mqtt.client.Client", "line_number": 26, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 26, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "17675484681", "text": "import re\n\nfrom functools import partial\n\n\nclass UserInput:\n\n\tdef __init__(self, gflags):\n\t\tself.scale_source = [10**-9, 1, 100, 10**6]\n\t\tself.scale_det = [100, 1, 1, 1]\n\t\tself.def_values_source = [1, 0.1, 0, None]\n\t\tself.def_values_det = [1, 100, 1, 0.1]\n\t\tself.gflags = gflags\n\t\tself.data = self.gflags.DATA\n\t\tself.data_loc = self.gflags.LOADER.parser['LOC']\n\t\tself.regexp = re.compile('\\S+')\n\t\tself.detectors = []\n\t\tself.sources = []\n\t\tself.medium = 'DRY_AIR'\n\t\tself.c_isotope_table = partial(\n\t\t\tself.__namespace_search,\n\t\t\tself.data.names_isotopes()\n\t\t)\n\t\tself.c_scintillator_table = partial(\n\t\t\tself.__namespace_search,\n\t\t\tself.data.names_scintillators()\n\t\t)\n\t\tself.c_media_table = partial(\n\t\t\tself.__namespace_search,\n\t\t\tself.data.names_media()\n\t\t)\n\t\tself.c_materials_table = partial(\n\t\t\tself.__namespace_search,\n\t\t\tself.data.names_materials()\n\t\t)\n\n\t\tself.commands = {\n\t\t\t'quit': (\n\t\t\t\tself.c_quit,\n\t\t\t\tself.data_loc['NOGUI_STR_QUIT']\n\t\t\t),\n\t\t\t'is': (\n\t\t\t\tself.c_isotope_table,\n\t\t\t\tself.data_loc['NOGUI_STR_IS']\n\t\t\t),\n\t\t\t'sc': (\n\t\t\t\tself.c_scintillator_table,\n\t\t\t\tself.data_loc['NOGUI_STR_SC']\n\t\t\t),\n\t\t\t'ma': (\n\t\t\t\tself.c_materials_table,\n\t\t\t\tself.data_loc['NOGUI_STR_MA']\n\t\t\t),\n\t\t\t'me': (\n\t\t\t\tself.c_media_table,\n\t\t\t\tself.data_loc['NOGUI_STR_ME']\n\t\t\t),\n\t\t\t'help': (\n\t\t\t\tself.c_help,\n\t\t\t\tself.data_loc['NOGUI_STR_HELP']\n\t\t\t),\n\t\t\t'setm': (\n\t\t\t\tself.c_set_medium,\n\t\t\t\tself.data_loc['NOGUI_STR_SETM']\n\t\t\t),\n\t\t\t'adds': (\n\t\t\t\tself.c_add_source,\n\t\t\t\tself.data_loc['NOGUI_STR_ADDS']\n\t\t\t),\n\t\t\t'addd': (\n\t\t\t\tself.c_add_detector,\n\t\t\t\tself.data_loc['NOGUI_STR_ADDD']\n\t\t\t),\n\t\t\t'ls': (\n\t\t\t\tself.c_list,\n\t\t\t\tself.data_loc['NOGUI_STR_LS']\n\t\t\t),\n\t\t\t'sim': (\n\t\t\t\tself.c_simulate,\n\t\t\t\tself.data_loc['NOGUI_STR_SIM']\n\t\t\t),\n\t\t\t'rm': (\n\t\t\t\tself.c_rm,\n\t\t\t\tself.data_loc['NOGUI_STR_RM']\n\t\t\t),\n\t\t}\n\n\tdef __namespace_search(self, db, *args):\n\t\tif args[0]:\n\t\t\tkey = args[0].lower()\n\t\t\tfor name in db:\n\t\t\t\tif name.lower().startswith(key):\n\t\t\t\t\tprint(name)\n\t\telse:\n\t\t\tprint(db)\n\n\tdef run(self):\n\t\tself.__intro()\n\t\tself.__set_default_values()\n\t\twhile self.gflags.FLG_RUN:\n\t\t\tself.parse_user_input(input())\n\n\tdef parse_user_input(self, arg):\n\t\targs = self.regexp.findall(arg) + [None]\n\t\tf = self.commands.get(args[0], (self.__no_such_cmd,))[0]\n\t\treturn f(*args[1:])\n\n\tdef c_help(self, *args):\n\t\ts = self.data_loc['NOGUI_STR_HELP_OUTPUT'] + '\\n'\n\t\tfor c in self.commands:\n\t\t\tprint(s.format(c, self.commands[c][1]))\n\n\tdef c_quit(self, *args):\n\t\tself.gflags.FLG_RUN = False\n\n\tdef c_list(self, *args):\n\n\t\tdef __list_d():\n\t\t\t_s = self.data_loc['NOGUI_STR_LS_D']\n\t\t\tfor i, d in enumerate(self.detectors):\n\t\t\t\tname, pos, q, d, mat, d_cover, mat_cover = d\n\t\t\t\t_pos, _q, _d, _d_cover = self.scale_det\n\t\t\t\tprint(_s.format(\n\t\t\t\t\ti, mat.name, d / _d, d_cover / _d_cover, mat_cover.name,\n\t\t\t\t\tq / _q, pos[2] / _pos))\n\n\t\tdef __list_s():\n\t\t\t_s = self.data_loc['NOGUI_STR_LS_S']\n\t\t\tfor i, s in enumerate(self.sources):\n\t\t\t\tname, isotope, m, d_cover, mat_cover, pos, activity = s\n\t\t\t\t_m, _d_cover, _pos, _activity = self.scale_source\n\t\t\t\tif activity:\n\t\t\t\t\tunits = 'Mbk'\n\t\t\t\t\tm = activity / _activity\n\t\t\t\telse:\n\t\t\t\t\tunits = 'ng'\n\t\t\t\t\tm = m / _m\n\t\t\t\tprint(_s.format(\n\t\t\t\t\ti, isotope.name, m, units, d_cover / _d_cover,\n\t\t\t\t\tmat_cover.name, pos[2] / _pos))\n\n\t\tdef __list_other():\n\t\t\t_s = self.data_loc['NOGUI_STR_MEDIUM_SET']\n\t\t\tprint(_s.format(self.medium))\n\n\t\tif args[0] == 'd':\n\t\t\t__list_d()\n\t\telif args[0] == 's':\n\t\t\t__list_s()\n\t\telse:\n\t\t\t__list_other()\n\t\t\t__list_s()\n\t\t\t__list_d()\n\n\tdef c_rm(self, *args):\n\t\targs = (args + (None,) * 2)[:2]\n\t\tprint(args)\n\t\tif args[0] == 'd':\n\t\t\tdata = self.detectors\n\t\telif args[0] == 's':\n\t\t\tdata = self.sources\n\t\telse:\n\t\t\tdata = None\n\t\tif args[1]:\n\t\t\ttry:\n\t\t\t\tv = int(args[1])\n\t\t\t\tdel data[v]\n\t\t\texcept Exception as err:\n\t\t\t\tprint(self.data_loc['NOGUI_STR_INVALID_INPUT'].format(args[1]))\n\n\tdef c_add_detector(self, *args):\n\t\targs = (args[:-1] + ('*',) * 6)[:6]\n\t\tmat, q, d, d_cover, mat_cover, pos = args\n\t\tvalues = [pos, q, d, d_cover]\n\t\tfor i, (v, s) in enumerate(zip(values, self.scale_det)):\n\t\t\tif v == '*':\n\t\t\t\tv = self.def_values_det[i]\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tv = float(v)\n\t\t\t\texcept:\n\t\t\t\t\tprint(self.data_loc['NOGUI_STR_INVALID_INPUT'].format(v))\n\t\t\t\t\treturn\n\t\t\tvalues[i] = v * s\n\t\tpos, q, d, d_cover = values\n\t\tif mat and mat in self.data.names_scintillators():\n\t\t\tmat = self.data.find_scintillator(mat)\n\t\telse:\n\t\t\tmat = self.data.scintillators[0]\n\t\tif mat_cover and mat_cover in self.data.names_materials():\n\t\t\tmat_cover = self.data.find_material(mat_cover)\n\t\telse:\n\t\t\tmat_cover = self.data.materials[0]\n\t\tname = mat.name\n\t\td = [name, [0, 0, pos], q, d, mat, d_cover, mat_cover]\n\t\tself.detectors.append(d)\n\n\tdef c_add_source(self, *args):\n\t\targs = (args[:-1] + ('*',) * 6)[:6]\n\t\tisotope, m, d_cover, mat_cover, pos, activity = args\n\t\tvalues = [m, d_cover, pos, activity]\n\t\tfor i, (v, s) in enumerate(zip(values, self.scale_source)):\n\t\t\tif v == '*':\n\t\t\t\tv = self.def_values_source[i]\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tv = float(v)\n\t\t\t\texcept:\n\t\t\t\t\tprint(self.data_loc['NOGUI_STR_INVALID_INPUT'].format(v))\n\t\t\t\t\treturn\n\t\t\tif v:\n\t\t\t\tvalues[i] = v * s\n\t\t\telse:\n\t\t\t\tvalues[i] = v\n\t\tm, d_cover, pos, activity = values\n\t\tif activity is not None:\n\t\t\tm = None\n\t\tif isotope and isotope in self.data.names_isotopes():\n\t\t\tisotope = self.data.find_isotope(isotope)\n\t\telse:\n\t\t\tisotope = self.data.isotopes[0]\n\t\tif mat_cover and mat_cover in self.data.names_materials():\n\t\t\tmat_cover = self.data.find_material(mat_cover)\n\t\telse:\n\t\t\tmat_cover = self.data.materials[0]\n\t\tname = isotope.name\n\t\ts = [name, isotope, m, d_cover, mat_cover, [0, 0, pos], activity]\n\t\tself.sources.append(s)\n\n\tdef c_set_medium(self, *args):\n\t\tname = args[0]\n\t\tif name in self.data.names_media():\n\t\t\tself.medium = name\n\t\t\tprint(self.data_loc['NOGUI_STR_MEDIUM_SET'].format(name))\n\t\telse:\n\t\t\tprint(self.data_loc['NOGUI_STR_NO_MEDIUM'])\n\n\tdef c_simulate(self, *args):\n\t\texp = 300\n\t\tif args[0]:\n\t\t\ttry:\n\t\t\t\texp = float(args[0])\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.gflags.BUF_DETECTORS = self.detectors\n\t\tself.gflags.BUF_SOURCES = self.sources\n\t\tself.gflags.BUF_EXPOSITION = exp\n\t\tself.gflags.BUF_MEDIUM_TYPE = self.data.find_medium(self.medium)\n\t\tself.gflags.FLG_SIMULATE = True\n\n\tdef __set_default_values(self, *args):\n\t\tself.medium = self.data.names_media()[0]\n\n\tdef __intro(self, *args):\n\t\tprint(self.data_loc['NOGUI_STR_INTRO'])\n\n\tdef __no_such_cmd(self, *args):\n\t\tprint(self.data_loc['NOGUI_STR_NOCMDWARNING'])\n", "repo_name": "industrialsynthfreak/xr3", "sub_path": "userinput.py", "file_name": "userinput.py", "file_ext": "py", "file_size_in_byte": 6358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "re.compile", "line_number": 16, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 20, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 24, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 28, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "28874710640", "text": "'''\nThis file contains functions used to load embedding dictionaries,\nembed text onto an n-dimensional space using loaded dictionaries,\nand help validate an embedding.\n'''\n\nfrom enum import Enum\nfrom typing import Callable, Dict, Iterable\nimport numpy as np\nimport pandas as pd\nfrom tensorflow.keras.layers import TextVectorization\n\n\nclass PreTrainedEmbeddings(Enum):\n '''\n Defines supported pre-trained embeddings.\n The value of each enum variant should provide\n a way to access the pre-trained embeddings\n themselves.\n '''\n GLOVE = 'glove.6B.{}d.txt'\n\n @classmethod\n def map_string(cls, s: str):\n if s.lower() == 'glove':\n return PreTrainedEmbeddings.GLOVE\n else:\n raise ValueError(f'There is no implementation for: {s}.')\n\n def validate_embedding_dimension(self, embedding_dimension: int) -> bool: \n if (self is PreTrainedEmbeddings.GLOVE):\n return embedding_dimension in {50, 100, 200, 300}\n else:\n raise ValueError(f'There is no implementation for: {self}.')\n\ndef get_embedding_dictionary(\n embedding_rootpath: str,\n embedding_type: PreTrainedEmbeddings,\n embedding_dimension: int):\n '''\n Builds a pre-trained embedding dictionary. The file\n at `embedding_path` should contain the pre-trained embeddings\n for a specific dimension.\n '''\n emb_filepath = _build_pretrained_embedding_filepath(\n embedding_rootpath, \n embedding_type, \n embedding_dimension=embedding_dimension)\n\n emb_file = open(emb_filepath)\n emb_dict = _build_pretrained_embedding(emb_file)\n emb_file.close()\n return emb_dict\n\ndef get_embedding_index(\n embedding_rootpath: str,\n embedding_type: PreTrainedEmbeddings,\n embedding_dimension: int):\n '''\n Generates an embedding index for Keras Embedding layers.\n '''\n emb_filepath = _build_pretrained_embedding_filepath(\n embedding_rootpath, \n embedding_type, \n embedding_dimension=embedding_dimension)\n emb_file = open(emb_filepath)\n\n embeddings_index = {}\n for i, line in enumerate(emb_file):\n values = line.split()\n word = values[0]\n embeddings_index[word] = i\n emb_file.close()\n return embeddings_index\n\ndef embed_phrases(\n phrases: Iterable[str],\n embedding_rootpath: str,\n embedding_type: PreTrainedEmbeddings,\n embedding_dimension: int) -> pd.DataFrame:\n '''\n Generates embeddings for each entry of `phrases`.\n\n The returned `DataFrame` includes both tokenized phrases \n (column name: `phrases`) and the embedded versions of each \n phrase (column name: `embedded`).\n '''\n emb_filepath = _build_pretrained_embedding_filepath(\n embedding_rootpath, \n embedding_type, \n embedding_dimension=embedding_dimension)\n f = open(emb_filepath)\n emb_dict = _build_pretrained_embedding(f)\n\n embedded_phrases = []\n processed_phrases = []\n for phrase in phrases:\n if len(phrase.split(' ')) > 1:\n # Embed each token in `phrase`.\n subphrases = phrase.split(' ')\n embedded_phrases.extend([_embed_phrase(p, emb_dict, embedding_dimension) for p in subphrases])\n processed_phrases.extend(subphrases)\n else:\n # Only one token in the phrase, so embed only once.\n embedded_phrases.append(_embed_phrase(phrase, emb_dict, embedding_dimension))\n processed_phrases.append(phrase)\n\n f.close()\n return pd.DataFrame.from_dict({'phrases': processed_phrases, 'embedded': embedded_phrases})\n\ndef generate_ngram_matrix(\n texts: Iterable[str],\n max_doc_len: int,\n tokenizer: Callable[[str], int],\n embedding_rootpath: str,\n embedding_type: PreTrainedEmbeddings,\n embedding_dimension: int,\n pad_word='inv') -> np.ndarray:\n '''\n Builds a 2D matrix representation of the inputed `texts`\n using pretrained GloVe word embeddings.\n\n Returns a numpy matrix with shape `(n, emb_dim)`\n where `n == len(texts)`\n '''\n emb_filepath = _build_pretrained_embedding_filepath(\n embedding_rootpath, \n embedding_type, \n embedding_dimension=embedding_dimension)\n f = open(emb_filepath)\n emb_dict = _build_pretrained_embedding(f)\n\n line_vecs = []\n for line in texts:\n vecs = []\n words = tokenizer(line)\n for word in words:\n vec = _embed_phrase(\n word,\n emb_dict,\n embedding_dimension,\n pad_word\n )\n vecs.append(vec)\n\n doc_len = len(vecs)\n if (max_doc_len - doc_len) > 0:\n zero_arrs = [np.zeros(embedding_dimension)] * (max_doc_len - doc_len)\n vecs.extend(zero_arrs)\n\n line_vec = np.stack(vecs)\n line_vecs.append(line_vec)\n \n return np.stack(line_vecs)\n\ndef generate_vocabulary_matrix(\n trained_vectorizer: TextVectorization,\n num_tokens: int,\n embedding_type: PreTrainedEmbeddings,\n embedding_rootpath: str,\n embedding_dimension: int\n ):\n '''\n Given a `word_index`, which should come from `TextVectorizer.word_index`, returns a\n dictionary mapping each word to it's embedded representation.\n\n Docs: https://keras.io/examples/nlp/pretrained_word_embeddings/\n '''\n word_index = dict(zip(trained_vectorizer.get_vocabulary(), range(num_tokens-2)))\n\n emb_filepath = _build_pretrained_embedding_filepath(\n embedding_rootpath, \n embedding_type, \n embedding_dimension=embedding_dimension)\n f = open(emb_filepath)\n emb_dict = _build_pretrained_embedding(f)\n\n embedding_matrix = np.zeros((num_tokens, embedding_dimension))\n for word, i in word_index.items():\n embedding_vector = emb_dict.get(word)\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector\n\n return embedding_matrix\n\ndef flatten_sentence_vectors(word_matrix: np.ndarray) -> np.ndarray:\n '''\n Creates a flattened 1D vector per sentence, with \n length equal to number of words in the sentence and\n the embedding dimension.\n \n Requires a word matrix as input.\n \n in_shape: (?, x, y)\n out_shape: (?, x * y)\n '''\n new_vecs = []\n for sent_vec in word_matrix:\n r, c = sent_vec.shape\n new_vec = sent_vec.reshape((r*c))\n new_vecs.append(new_vec)\n \n return np.stack(new_vecs)\n\ndef check_embedding(text: str, text_embedding: np.ndarray, emb_dict: Dict[str, np.ndarray]) -> bool:\n '''\n Simple check to make sure an inputed sentence\n or text fragment correctly matches a matrix\n of word vectors.\n \n Parameters:\n - `text`: text fragment\n - `text_embedding`: appended word vectors to check\n - `emb_dict`: dictionary containing embeddings\n '''\n for word, word_emb in zip(text.split(), text_embedding):\n if (emb_dict[word] != word_emb).all(): return False\n return True\n\ndef create_keras_vectorizer(batched_texts: Iterable[Iterable[str]], max_document_length: int):\n '''\n Returns a `TextVectorizer` object with a vocabulary covering all unique words in `texts`.\n\n Currently, this function assumes `texts` contains batched data.\n \n `max_document_length` is the maximum number of words in a single document contained in `texts`.\n '''\n print('\\nCreating a Keras TextVectorization object...')\n unique_words = set()\n for batch in batched_texts:\n for e in batch:\n [unique_words.add(w) for w in e.split(' ')]\n\n return TextVectorization(output_sequence_length=max_document_length, vocabulary=list(unique_words))\n\ndef _embed_phrase(\n phrase: str, \n emb_dict: Dict[str, np.ndarray], \n emb_dim: int, \n pad_word: str = None) -> np.ndarray:\n '''\n Provides the embedding for a given phrase.\n\n If the given phrase cannot be found in the pre-trained embeddings,\n a zero-array of dimension `emb_dim` is returned.\n '''\n if pad_word is not None and phrase == pad_word:\n return np.zeros(emb_dim)\n elif phrase in emb_dict:\n return emb_dict[phrase] \n else:\n return np.zeros(emb_dim)\n\ndef _build_pretrained_embedding(f) -> Dict[str, np.ndarray]:\n '''\n Builds pretrained embedding dictionary from the inputed GloVe file `f`.\n\n The returned dictionary has the following schema:\n - key: word\n - value: associated word vector\n '''\n embeddings_index = {}\n for line in f:\n values = line.split()\n word = values[0]\n coefs = np.asarray(values[1:], dtype='float32')\n embeddings_index[word] = coefs\n return embeddings_index\n\ndef _build_pretrained_embedding_filepath(\n rootpath: str, \n embedding_type: PreTrainedEmbeddings,\n **kwargs):\n '''\n Builds the path to a pre-trained embedding file given a rootpath,\n the type of embedding being used, and any specific information required\n by that embedding type.\n\n For `GloVe` embeddings, `embedding_dimension` must be provided in `kwargs`.\n '''\n if embedding_type == PreTrainedEmbeddings.GLOVE:\n embedding_dimension = kwargs['embedding_dimension']\n \n # Make sure the provided embedding dimensions is valid.\n if not embedding_type.validate_embedding_dimension(embedding_dimension):\n raise ValueError(f'Invalid embedding dimension, {embedding_dimension}, provided.')\n \n return rootpath + embedding_type.value.format(embedding_dimension)\n else:\n raise NotImplementedError('Only GLOVE pre-trained vectors are supported right now.')", "repo_name": "jayantmadugula/absa_models", "sub_path": "data_handling/embedding_generation.py", "file_name": "embedding_generation.py", "file_ext": "py", "file_size_in_byte": 9583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "enum.Enum", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 77, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 80, "usage_type": "attribute"}, {"api_name": "typing.Iterable", "line_number": 112, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 114, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 118, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.TextVectorization", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 205, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 220, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.TextVectorization", "line_number": 234, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 238, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 240, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 266, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 254, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 254, "usage_type": "attribute"}]} +{"seq_id": "74112207736", "text": "\"\"\"\nAlignment models are given a sequence of events along with a piece of audio, and then return a\nsequence of timestamps, with one timestamp for each event, indicating the position of this event\nin the audio. The events are listed in order of occurrence in the audio, so that output\ntimestamps have to be monotonically increasing.\nEvaluation usually involves taking the series of predicted and ground truth timestamps and\ncomparing their distance, usually on a pair-wise basis, e.g. taking the median absolute error in\nseconds.\n\nConventions\n-----------\nTimestamps should be provided in the form of a 1-dimensional array of onset\ntimes in seconds in increasing order.\n\nMetrics\n-------\n* :func:`mir_eval.alignment.absolute_error`: Median absolute error and average absolute error\n* :func:`mir_eval.alignment.percentage_correct`: Percentage of correct timestamps,\nwhere a timestamp is counted\nas correct if it lies within a certain tolerance window around the ground truth timestamp\n* :func:`mir_eval.alignment.pcs`: Percentage of correct segments: Percentage of overlap between\npredicted segments and ground truth segments, where segments are defined by (start time,\nend time) pairs\n* :func:`mir_eval.alignment.perceptual_metric`: metric based on human synchronicity perception as\nmeasured in the paper \"User-centered evaluation of lyrics to audio alignment\",\nN. Lizé-Masclef, A. Vaglio, M. Moussallam, ISMIR 2021\n\nReferences\n----------\n .. [#lizemasclef2021] N. Lizé-Masclef, A. Vaglio, M. Moussallam.\n \"User-centered evaluation of lyrics to audio alignment\",\n International Society for Music Information Retrieval (ISMIR) conference,\n 2021.\n\n .. [#mauch2010] M. Mauch, F: Hiromasa, M. Goto.\n \"Lyrics-to-audio alignment and phrase-level segmentation using\n incomplete internet-style chord annotations\",\n Frontiers in Proceedings of the Sound Music Computing Conference (SMC), 2010.\n\n .. [#dzhambazov2017] G. Dzhambazov.\n \"Knowledge-Based Probabilistic Modeling For Tracking Lyrics In Music Audio Signals\",\n PhD Thesis, 2017.\n\n .. [#fujihara2011] H. Fujihara, M. Goto, J. Ogata, H. Okuno.\n \"LyricSynchronizer: Automatic synchronization system between musical audio signals and lyrics\",\n IEEE Journal of Selected Topics in Signal Processing, VOL. 5, NO. 6, 2011\n\n\"\"\"\n\nimport collections\nfrom typing import Optional\n\nimport numpy as np\nfrom scipy.stats import skewnorm\n\nfrom mir_eval.util import filter_kwargs\n\n\ndef validate(\n reference_timestamps: np.ndarray, estimated_timestamps: np.ndarray\n):\n \"\"\"Checks that the input annotations to a metric look like valid onset time\n arrays, and throws helpful errors if not.\n\n Parameters\n ----------\n reference_timestamps : np.ndarray\n reference timestamp locations, in seconds\n estimated_timestamps : np.ndarray\n estimated timestamp locations, in seconds\n \"\"\"\n # We need to have 1D numpy arrays\n if not isinstance(reference_timestamps, np.ndarray):\n raise ValueError(\n \"Reference timestamps need to be a numpy array, but got\"\n f\" {type(reference_timestamps)}\"\n )\n if not isinstance(estimated_timestamps, np.ndarray):\n raise ValueError(\n \"Estimated timestamps need to be a numpy array, but got\"\n f\" {type(estimated_timestamps)}\"\n )\n if reference_timestamps.ndim != 1:\n raise ValueError(\n \"Reference timestamps need to be a one-dimensional vector, but got\"\n f\" {reference_timestamps.ndim} dimensions\"\n )\n if estimated_timestamps.ndim != 1:\n raise ValueError(\n \"Estimated timestamps need to be a one-dimensional vector, but got\"\n f\" {estimated_timestamps.ndim} dimensions\"\n )\n\n # If reference or estimated timestamps are empty, cannot compute metric\n if reference_timestamps.size == 0:\n raise ValueError(\"Reference timestamps are empty.\")\n if estimated_timestamps.size != reference_timestamps.size:\n raise ValueError(\n \"Number of timestamps must be the same in prediction and ground\"\n f\" truth, but found {estimated_timestamps.size} in prediction and\"\n f\" {reference_timestamps.size} in ground truth\"\n )\n\n # Check monotonicity\n if not np.all(reference_timestamps[1:] - reference_timestamps[:-1] >= 0):\n raise ValueError(\n \"Reference timestamps are not monotonically increasing!\"\n )\n if not np.all(estimated_timestamps[1:] - estimated_timestamps[:-1] >= 0):\n raise ValueError(\n \"Estimated timestamps are not monotonically increasing!\"\n )\n\n # Check positivity (need for correct PCS metric calculation)\n if not np.all(reference_timestamps >= 0):\n raise ValueError(\"Reference timestamps can not be below 0!\")\n if not np.all(estimated_timestamps >= 0):\n raise ValueError(\"Estimated timestamps can not be below 0!\")\n\n\ndef absolute_error(reference_timestamps, estimated_timestamps):\n \"\"\"Compute the absolute deviations between estimated and reference timestamps,\n and then returns the median and average over all events\n\n Examples\n --------\n >>> reference_timestamps = mir_eval.io.load_events('reference.txt')\n >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt')\n >>> mae, aae = mir_eval.align.absolute_error(reference_onsets, estimated_timestamps)\n\n Parameters\n ----------\n reference_timestamps : np.ndarray\n reference timestamps, in seconds\n estimated_timestamps : np.ndarray\n estimated timestamps, in seconds\n\n Returns\n -------\n mae : float\n Median absolute error\n aae: float\n Average absolute error\n \"\"\"\n validate(reference_timestamps, estimated_timestamps)\n deviations = np.abs(reference_timestamps - estimated_timestamps)\n return np.median(deviations), np.mean(deviations)\n\n\ndef percentage_correct(reference_timestamps, estimated_timestamps, window=0.3):\n \"\"\"Compute the percentage of correctly predicted timestamps. A timestamp is predicted\n correctly if its position doesn't deviate more than the window parameter from the ground\n truth timestamp.\n\n Examples\n --------\n >>> reference_timestamps = mir_eval.io.load_events('reference.txt')\n >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt')\n >>> pc = mir_eval.align.percentage_correct(reference_onsets, estimated_timestamps, window=0.2)\n\n Parameters\n ----------\n reference_timestamps : np.ndarray\n reference timestamps, in seconds\n estimated_timestamps : np.ndarray\n estimated timestamps, in seconds\n window : float\n Window size, in seconds\n (Default value = .3)\n\n Returns\n -------\n pc : float\n Percentage of correct timestamps\n \"\"\"\n validate(reference_timestamps, estimated_timestamps)\n deviations = np.abs(reference_timestamps - estimated_timestamps)\n return np.mean(deviations <= window)\n\n\ndef percentage_correct_segments(\n reference_timestamps, estimated_timestamps, duration: Optional[float] = None\n):\n \"\"\"Calculates the percentage of correct segments (PCS) metric.\n\n It constructs segments out of predicted and estimated timestamps separately\n out of each given timestamp vector and calculates the percentage of overlap between correct\n segments compared to the total duration.\n\n WARNING: This metrics behaves differently depending on whether \"duration\" is given!\n\n If duration is not given (default case), the computation follows the MIREX lyrics alignment\n challenge 2020. For a timestamp vector with entries (t1,t2, ... tN), segments with\n the following (start, end) boundaries are created: (t1, t2), ... (tN-1, tN).\n After the segments are created, the overlap between the reference and estimated segments is\n determined and divided by the total duration, which is the distance between the\n first and last timestamp in the reference.\n\n If duration is given, the segment boundaries are instead (0, t1), (t1, t2), ... (tN, duration).\n The overlap is computed in the same way, but then divided by the duration parameter given to\n this function.\n This method follows the original paper [#fujihara2011] more closely, where the metric was\n proposed.\n As a result, this variant of the metrics punishes cases where the first estimated timestamp\n is too early or the last estimated timestamp is too late, whereas the MIREX variant does not.\n On the other hand, the MIREX metric is invariant to how long the eventless beginning and end\n parts of the audio are, which might be a desirable property.\n\n Examples\n --------\n >>> reference_timestamps = mir_eval.io.load_events('reference.txt')\n >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt')\n >>> pcs = mir_eval.align.percentage_correct_segments(reference_timestamps, estimated_timestamps)\n\n Parameters\n ----------\n reference_timestamps : np.ndarray\n reference timestamps, in seconds\n estimated_timestamps : np.ndarray\n estimated timestamps, in seconds\n duration : float\n Optional. Total duration of audio (seconds). WARNING: Metric is computed differently\n depending on whether this is provided or not - see documentation above!\n\n Returns\n -------\n pcs : float\n Percentage of time where ground truth and predicted segments overlap\n \"\"\"\n validate(reference_timestamps, estimated_timestamps)\n if duration is not None:\n duration = float(duration)\n if duration <= 0:\n raise ValueError(\n f\"Positive duration needs to be provided, but got {duration}\"\n )\n if np.max(reference_timestamps) > duration:\n raise ValueError(\n \"Expected largest reference timestamp\"\n f\"{np.max(reference_timestamps)} to not be \"\n f\"larger than duration {duration}\"\n )\n if np.max(estimated_timestamps) > duration:\n raise ValueError(\n \"Expected largest estimated timestamp \"\n f\"{np.max(estimated_timestamps)} to not be \"\n f\"larger than duration {duration}\"\n )\n\n ref_starts = np.concatenate([[0], reference_timestamps])\n ref_ends = np.concatenate([reference_timestamps, [duration]])\n est_starts = np.concatenate([[0], estimated_timestamps])\n est_ends = np.concatenate([estimated_timestamps, [duration]])\n else:\n # MIREX lyrics alignment 2020 style:\n # Ignore regions before start and after end reference timestamp\n duration = reference_timestamps[-1] - reference_timestamps[0]\n if duration <= 0:\n raise ValueError(\n f\"Reference timestamps are all identical, can not compute PCS\"\n f\" metric!\"\n )\n\n ref_starts = reference_timestamps[:-1]\n ref_ends = reference_timestamps[1:]\n est_starts = estimated_timestamps[:-1]\n est_ends = estimated_timestamps[1:]\n\n overlap_starts = np.maximum(ref_starts, est_starts)\n overlap_ends = np.minimum(ref_ends, est_ends)\n overlap_duration = np.sum(np.maximum(overlap_ends - overlap_starts, 0))\n return overlap_duration / duration\n\n\ndef karaoke_perceptual_metric(reference_timestamps, estimated_timestamps):\n \"\"\"Metric based on human synchronicity perception as measured in the paper\n \"User-centered evaluation of lyrics to audio alignment\" [#lizemasclef2021]\n\n The parameters of this function were tuned on data collected through a user Karaoke-like\n experiment\n It reflects human judgment of how \"synchronous\" lyrics and audio stimuli are perceived\n in that setup.\n Beware that this metric is non-symmetrical and by construction it is also not equal to 1 at 0.\n\n Examples\n --------\n >>> reference_timestamps = mir_eval.io.load_events('reference.txt')\n >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt')\n >>> score = mir_eval.align.karaoke_perceptual_metric(reference_onsets, estimated_timestamps)\n\n Parameters\n ----------\n reference_timestamps : np.ndarray\n reference timestamps, in seconds\n estimated_timestamps : np.ndarray\n estimated timestamps, in seconds\n\n Returns\n -------\n perceptual_score : float\n Perceptual score, averaged over all timestamps\n \"\"\"\n validate(reference_timestamps, estimated_timestamps)\n offsets = estimated_timestamps - reference_timestamps\n\n # Score offsets using a certain skewed normal distribution\n skewness = 1.12244251\n localisation = -0.22270315\n scale = 0.29779424\n normalisation_factor = 1.6857\n perceptual_scores = (1.0 / normalisation_factor) * skewnorm.pdf(\n offsets, skewness, loc=localisation, scale=scale\n )\n\n return np.mean(perceptual_scores)\n\n\ndef evaluate(reference_timestamps, estimated_timestamps, **kwargs):\n \"\"\"Compute all metrics for the given reference and estimated annotations.\n Examples\n --------\n >>> reference_timestamps = mir_eval.io.load_events('reference.txt')\n >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt')\n >>> duration = max(np.max(reference_timestamps), np.max(estimated_timestamps)) + 10\n >>> scores = mir_eval.align.evaluate(reference_onsets, estimated_timestamps, duration)\n\n Parameters\n ----------\n reference_timestamps : np.ndarray\n reference timestamp locations, in seconds\n estimated_timestamps : np.ndarray\n estimated timestamp locations, in seconds\n kwargs\n Additional keyword arguments which will be passed to the\n appropriate metric or preprocessing functions.\n\n Returns\n -------\n scores : dict\n Dictionary of scores, where the key is the metric name (str) and\n the value is the (float) score achieved.\n \"\"\"\n # Compute all metrics\n scores = collections.OrderedDict()\n\n scores[\"pc\"] = filter_kwargs(\n percentage_correct, reference_timestamps, estimated_timestamps, **kwargs\n )\n scores[\"mae\"], scores[\"aae\"] = absolute_error(\n reference_timestamps, estimated_timestamps\n )\n scores[\"pcs\"] = filter_kwargs(\n percentage_correct_segments,\n reference_timestamps,\n estimated_timestamps,\n **kwargs,\n )\n scores[\"perceptual\"] = karaoke_perceptual_metric(\n reference_timestamps, estimated_timestamps\n )\n\n return scores\n", "repo_name": "craffel/mir_eval", "sub_path": "mir_eval/alignment.py", "file_name": "alignment.py", "file_ext": "py", "file_size_in_byte": 14446, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 535, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.ndarray", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.all", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 178, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 182, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 271, "usage_type": "call"}, {"api_name": "scipy.stats.skewnorm.pdf", "line_number": 311, "usage_type": "call"}, {"api_name": "scipy.stats.skewnorm", "line_number": 311, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 315, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 344, "usage_type": "call"}, {"api_name": "mir_eval.util.filter_kwargs", "line_number": 346, "usage_type": "call"}, {"api_name": "mir_eval.util.filter_kwargs", "line_number": 352, "usage_type": "call"}]} +{"seq_id": "29982075884", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchtext as text\n\n\nclass SummationEmbeddingLayer(nn.Module):\n def __init__(self, input_dim, output_dim, dropout_rate, glove):\n super(SummationEmbeddingLayer, self).__init__()\n self.embedding = nn.Embedding(len(glove), input_dim)\n self.embedding.weight = nn.Parameter(glove.vectors, requires_grad=True)\n self.dropout_input = nn.Dropout(dropout_rate)\n self.bottleneck = TanhLayer(input_dim, output_dim)\n self.droptout_output = nn.Dropout(dropout_rate)\n\n def forward(self, x):\n h = self.embedding(x)\n h = torch.sum(h, 1)\n h = self.dropout_input(h)\n h = self.bottleneck(h)\n y = self.droptout_output(h)\n return y\n\n\nclass LSTMEmbeddingLayer(nn.Module):\n def __init__(self, input_dim, output_dim, num_rnn, dropout_rate, glove):\n super(LSTMEmbeddingLayer, self).__init__()\n self.embedding = nn.Embedding(len(glove), input_dim)\n self.embedding.weight = nn.Parameter(glove.vectors, requires_grad=True)\n self.dropout_input = nn.Dropout(dropout_rate)\n self.lstm = nn.LSTM(input_dim, input_dim, num_rnn, batch_first=True)\n self.bottleneck = TanhLayer(input_dim, output_dim)\n self.droptout_output = nn.Dropout(dropout_rate)\n\n def forward(self, x):\n h = self.embedding(x)\n h = self.dropout_input(h)\n output, other = self.lstm(h)\n h = output[:, -1]\n print(h.shape)\n print(self.bottleneck)\n h = self.bottleneck(h)\n y = self.droptout_output(h)\n return y\n\n\nclass TanhLayer(nn.Module):\n def __init__(self, input_dim, output_dim):\n super(TanhLayer, self).__init__()\n self.linear = nn.Linear(input_dim, output_dim)\n self.tanh = nn.Tanh()\n\n def forward(self, x):\n h = self.linear(x)\n y = self.tanh(h)\n return y\n\n\nclass ClassificationLayer(nn.Module):\n def __init__(self, input_dim, output_dim):\n super(ClassificationLayer, self).__init__()\n self.linear = nn.Linear(input_dim, output_dim)\n #self.softmax = nn.Softmax(dim=output_dim)\n\n def forward(self, x):\n h = self.linear(x)\n #y = self.softmax(h)\n return h\n\n\nclass OneLayerModel(nn.Module):\n def __init__(self, embedding_input_dim, embedding_output_dim, output_dim, dropout_rate, glove):\n super(OneLayerModel, self).__init__()\n self.embedding1 = SummationEmbeddingLayer(embedding_input_dim, embedding_output_dim, dropout_rate, glove)\n #self.embedding2 = SummationEmbeddingLayer(embedding_input_dim, embedding_output_dim, dropout_rate, glove)\n self.tanh1 = TanhLayer(embedding_output_dim * 2, embedding_output_dim * 2)\n self.tanh2 = TanhLayer(embedding_output_dim * 2, embedding_output_dim * 2)\n self.tanh3 = TanhLayer(embedding_output_dim * 2, embedding_output_dim * 2)\n self.classification_layer = ClassificationLayer(embedding_output_dim * 2, output_dim)\n\n def forward(self, x):\n print(x[0].shape, x[1].shape)\n h1 = self.embedding1(x[0])\n h2 = self.embedding1(x[1])\n print(h1.shape, h2.shape)\n h = torch.cat([h1, h2], dim=1)\n h = self.tanh1(h)\n h = self.tanh2(h)\n h = self.tanh3(h)\n y = self.classification_layer(h)\n return y\n\n\ndef test():\n from torchtext.vocab import GloVe\n glove = GloVe(name='42B', dim=300)\n model = OneLayerModel(embedding_input_dim=300, embedding_output_dim=100, output_dim=3, dropout_rate=0.5, glove=glove)\n examples = ['you', 'are', 'beautiful']\n example_ids = [glove.stoi[example] for example in examples]\n examples_2 = ['you', 'are', 'so', 'nice']\n example_ids_2 = [glove.stoi[example] for example in examples_2]\n example_tensor = torch.LongTensor([example_ids])\n example_tensor2 = torch.LongTensor([example_ids_2])\n input_example = [example_tensor, example_tensor2]\n model(input_example)\n print(model)\n\n\nif __name__ == '__main__':\n test()", "repo_name": "t-hoso/temporal_natural_language_inference", "sub_path": "tnli/src/tnli/train/models/one_layer_model.py", "file_name": "one_layer_model.py", "file_ext": "py", "file_size_in_byte": 4038, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 47, "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.Tanh", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 86, "usage_type": "call"}, {"api_name": "torchtext.vocab.GloVe", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "40193176011", "text": "# -*- coding:utf-8 -*-\r\nimport sys\r\nimport re\r\nimport jieba.posseg as pseg\r\nimport nltk\r\n\r\nif len(sys.argv) < 2:\r\n print('No Files!')\r\n sys.exit(1)\r\nelse:\r\n file_list = sys.argv[1:]\r\n\r\nname_reg = re.compile(u'''\r\n ^\r\n \\ *((姓名|Name|name){1}(:|-|：|\\ )*)?\r\n (([\\u4e00-\\u9fa5]{2,4}(?!\\·))|([\\u4e00-\\u9fa5]{2,4}(\\·{1}[\\u4e00-\\u9fa5]{1,4}){1,4}))?\r\n \\ *\r\n ([a-zA-Z\\ ,]{1,20})?\r\n \\ *\r\n $\r\n ''', re.X) # get group 3 7 index-0\r\nphone_reg = re.compile(u'''\r\n ^\r\n ((Tel|电话|手机|Mobile){1}(:|-|：|\\ )*)?\r\n ((?= free[free_idx][1]:\n # no busy or busy starts after end of this free - take all free\n start = free[free_idx][0]\n end = free[free_idx][1]\n free_idx += 1\n elif busy[busy_idx][0] <= free[free_idx][0]:\n if busy[busy_idx][1] >= free[free_idx][1]:\n # busy over all this free range, skip it\n free_idx += 1\n continue\n else:\n # busy starts before this free range, and ends in middle of it\n # update start of this free range, rounded up to nearest boundary\n new_free_start = ceil_dt(\n busy[busy_idx][1],\n free[free_idx][0],\n config[\"meeting_length_minutes\"],\n )\n if new_free_start < free[free_idx][1]:\n free[free_idx][0] = new_free_start\n else:\n free_idx += 1\n busy_idx += 1\n continue\n elif busy[busy_idx][1] >= free[free_idx][1]:\n # busy starts in this free range, and ends after it\n start = free[free_idx][0]\n end = busy[busy_idx][0]\n free_idx += 1\n else:\n # busy starts and ends within this free range\n start = free[free_idx][0]\n end = busy[busy_idx][0]\n # update start of this free range, rounded up to nearest boundary\n new_free_start = ceil_dt(\n busy[busy_idx][1], free[free_idx][0], config[\"meeting_length_minutes\"]\n )\n if new_free_start < free[free_idx][1]:\n free[free_idx][0] = new_free_start\n else:\n free_idx += 1\n busy_idx += 1\n\n length = (end - start).total_seconds() // 60\n if length >= config[\"meeting_length_minutes\"]:\n ranges.append([start, end])\n\n return ranges\n\n\ndef _to_weekday(t, config):\n return (t.weekday() + int(config[\"week_starts_on_sunday\"])) % 7\n\n\ndef print_ranges(config, ranges):\n tz = pytz.timezone(config[\"show_timezone\"])\n\n def format_time(t):\n if config[\"show_24hr\"]:\n return t.strftime(\"%-H:%M\")\n else:\n if t.minute != 0:\n return t.strftime(\"%-I:%M%p\").lower()\n else:\n return t.strftime(\"%-I%p\").lower()\n\n # https://stackoverflow.com/questions/5891555/display-the-date-like-may-5th-using-pythons-strftime\n def suffix(d):\n return \"th\" if 11 <= d <= 13 else {1: \"st\", 2: \"nd\", 3: \"rd\"}.get(d % 10, \"th\")\n\n def custom_strftime(t, format):\n return t.strftime(format).replace(\"{S}\", str(t.day) + suffix(t.day))\n\n day_ranges = []\n day = None\n for r0, r1 in ranges:\n r0 = r0.astimezone(tz)\n r1 = r1.astimezone(tz)\n\n if day == r0.weekday():\n day_ranges[-1].append([r0, r1])\n else:\n day_ranges.append([[r0, r1]])\n day = r0.weekday()\n last_weekday = _to_weekday(day_ranges[0][0][0], config)\n for day_list in day_ranges:\n range_str_list = [\n f\"{format_time(r0)} - {format_time(r1)}\" for r0, r1 in day_list\n ]\n if _to_weekday(day_list[0][0], config) < last_weekday:\n print(\"Next week:\")\n last_weekday = _to_weekday(day_list[0][0], config)\n day = custom_strftime(day_list[0][0], \"%a (%b {S}):\")\n print(f\" * {day:14s} {', '.join(range_str_list).lower()}\")\n\n\ndef get_args():\n parser = OptionParser()\n parser.add_option(\n \"-l\",\n \"--list\",\n dest=\"list\",\n help=\"list calendars\",\n action=\"store_true\",\n default=False,\n )\n parser.add_option(\n \"-c\",\n \"--calendar\",\n action=\"append\",\n dest=\"cal\",\n help=\"choose a calendar for busy times (multiple allowed)\",\n )\n parser.add_option(\n \"-t\",\n \"--time_config\",\n metavar=\"FILE\",\n dest=\"conf\",\n help=\"choose a configuration JSON file\",\n )\n parser.add_option(\n \"-o\",\n \"--opt\",\n action=\"append\",\n dest=\"opt\",\n metavar=\"OPTNAME=VALUE\",\n help=\"override a configuration option (multiple allowed), use OPT=VAL format. See -O for possible options\",\n )\n parser.add_option(\n \"-O\",\n \"--list-config-options\",\n dest=\"list_conf_options\",\n help=\"list possible configuration options\",\n action=\"store_true\",\n default=False,\n )\n\n (options, args) = parser.parse_args()\n\n if options.list_conf_options:\n print(\n tabulate(\n [[key, str(val)] for key, val in _CONFIG_DEFAULT_KEYS.items()],\n headers=[\"Option name\", \"Default value\"],\n maxcolwidths=[None, 50],\n )\n )\n return None\n\n conf_override = {}\n if options.opt:\n for opt_entry in options.opt:\n if \"=\" not in opt_entry:\n parser.error(\n \"Any configuration option should be provided as OPTNAME=VALUE\"\n )\n name, val_str = opt_entry.split(\"=\")\n if name not in _CONFIG_DEFAULT_KEYS:\n parser.error(f\"Unknown option {name}, use -O to see possible options\")\n try:\n if isinstance(_CONFIG_DEFAULT_KEYS[name], (dict, list)):\n val = json.loads(val_str)\n elif isinstance(_CONFIG_DEFAULT_KEYS[name], bool):\n try:\n val = int(val_str)\n except ValueError:\n try:\n val = {\"false\": False, \"true\": True}[val_str.lower()]\n except KeyError:\n parser.error(\n f\"Bad type for option {name}, should be like a boolean, but is '{val_str}'\"\n )\n val = bool(val)\n else:\n val = type(_CONFIG_DEFAULT_KEYS[name])(val_str)\n conf_override[name] = val\n except ValueError as e:\n parser.error(\n f\"Bad type for option {name}, should be like '{_CONFIG_DEFAULT_KEYS[name]}' but is '{val_str}'. Error is: {e}\"\n )\n\n if options.list and options.cal:\n parser.error(\"options -l and -c are mutually exclusive\")\n # if options.conf is None:\n # parser.error(\"must set time configuration with -t\")\n if options.cal is None and not options.list:\n parser.error(\"must set either -c or -l\")\n return options, conf_override\n\n\ndef _order_cal_list(cal):\n return (cal[\"accessRole\"] != \"owner\", -len(cal[\"defaultReminders\"]), cal[\"id\"])\n\n\ndef main():\n opts, conf_override = get_args()\n if opts is None:\n return\n\n service = get_calendar_service()\n calendar_list = service.calendarList().list().execute()\n\n chosen_cals = []\n not_found = []\n if opts.cal is not None:\n calendar_by_ids = {cal[\"id\"]: cal for cal in calendar_list[\"items\"]}\n for cal in opts.cal:\n if cal not in calendar_by_ids:\n not_found.append(cal)\n else:\n chosen_cals.append(calendar_by_ids[cal])\n if len(not_found) > 0:\n print(f\"Calendars {not_found} not found! Possible calendars are:\")\n\n if len(not_found) > 0 or opts.list:\n table = [\n (cal[\"id\"], cal[\"summary\"])\n for cal in sorted(calendar_list[\"items\"], key=_order_cal_list)\n ]\n print(tabulate(table, headers=[\"Id\", \"Name\"]))\n return\n\n if opts.conf:\n config = json.load(open(opts.conf, \"r\"))\n else:\n config = _CONFIG_DEFAULT_KEYS.copy()\n config.update(conf_override)\n\n timezone_str = (\n \"\"\n if config[\"show_timezone_name\"] is None\n else f\" (all {config['show_timezone_name']})\"\n )\n\n free = prep_work_ranges(config)\n for chosen_cal in chosen_cals:\n busy = get_busy_ranges(config, service, chosen_cal[\"id\"])\n free = combine_ranges(config, free, busy)\n\n print(f\"Availability for next few days{timezone_str}:\")\n print_ranges(config, free)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "krakov/calendar-availability", "sub_path": "src/get_availability.py", "file_name": "get_availability.py", "file_ext": "py", "file_size_in_byte": 11936, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "dateutil.parser.parser", "line_number": 36, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 67, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 97, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 105, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 171, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 213, "usage_type": "name"}, {"api_name": "optparse.OptionParser", "line_number": 213, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.add_option", "line_number": 214, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 214, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.add_option", "line_number": 222, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 222, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.add_option", "line_number": 229, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 229, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.add_option", "line_number": 236, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 236, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.add_option", "line_number": 244, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 244, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse_args", "line_number": 253, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 253, "usage_type": "name"}, {"api_name": "tabulate.tabulate", "line_number": 257, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.error", "line_number": 269, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 269, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.error", "line_number": 274, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 274, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 277, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.error", "line_number": 285, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 285, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.error", "line_number": 293, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 293, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.error", "line_number": 298, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 298, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.error", "line_number": 302, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 302, "usage_type": "name"}, {"api_name": "google_api.get_calendar_service", "line_number": 315, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 335, "usage_type": "call"}, {"api_name": "json.load", "line_number": 339, "usage_type": "call"}]} +{"seq_id": "18141747735", "text": "from amt import utils\nfrom amt.utils import estimate_tempo, estimate_piano_roll, estimate_onset_times, smooth_onsets, round_to_sixteenth, \\\n rotate, Instrument\nimport numpy as np\nimport scipy.stats\nimport librosa\nfrom enum import Enum\nfrom midiutil import MIDIFile\nimport midiutil\n\n\ndef estimate_key(notes):\n pitch_class_dict = dict.fromkeys(utils.PITCH_CLASSES, 0)\n for note in notes:\n pitch_class_dict[note.pitch.get_pitch_class()] += note.duration.total_beat\n song_distribution = list(pitch_class_dict.values())\n maj_scores = []\n min_scores = []\n for i in range(12):\n maj_scores.append(scipy.stats.pearsonr(song_distribution, rotate(utils.KRUMHANSL_MAJ, i))[0])\n min_scores.append(scipy.stats.pearsonr(song_distribution, rotate(utils.KRUMHANSL_MIN, i))[0])\n max_score = np.argmax([maj_scores, min_scores])\n if max_score > 11:\n key = Key(pitch_class=KeyName(max_score - 12), scale=Scale.MINOR)\n else:\n key = Key(pitch_class=KeyName(max_score), scale=Scale.MAJOR)\n return key\n\n\ndef notes_from_piano_roll(piano_roll, tempo):\n notes = []\n for i in range(np.shape(piano_roll)[0]):\n if np.sum(piano_roll[i, :]) != 0:\n one_mode = False\n start_frame = None\n for j in range(np.shape(piano_roll)[1]):\n if piano_roll[i, j] == 1 and not one_mode:\n one_mode = True\n start_frame = j\n if piano_roll[i, j] == 0 and one_mode:\n one_mode = False\n notes.append(\n Note(Pitch(name=utils.NOTE_NAME_LIST[i]),\n Duration(tempo=tempo, frames=(start_frame, j - start_frame))))\n start_frame = None\n if one_mode:\n notes.append(\n Note(Pitch(name=utils.NOTE_NAME_LIST[i]),\n Duration(tempo=tempo, frames=(start_frame, (np.shape(piano_roll)[1] - 1) - start_frame))))\n return notes\n\n\nclass Track:\n def __init__(self, tempo=None, key=None, notes=None):\n self.tempo = tempo\n self.key = key\n if notes is None:\n self.notes = []\n else:\n self.notes = notes\n self.cqt = None\n self.piano_roll = None\n self.onsets = []\n self.samples = None\n self.display_cqt = None\n self.instrument = None\n self.time_signature = None\n\n def transcribe(self,\n plca_threshold,\n note_length_threshold,\n onset_range,\n previous_note_range,\n pre_max,\n post_max,\n instrument,\n time_signature):\n self.instrument = instrument\n self.tempo = int(round(estimate_tempo(self.samples)))\n self.cqt, self.piano_roll, self.display_cqt = estimate_piano_roll(self.samples, self.tempo, plca_threshold,\n note_length_threshold, instrument)\n self.onsets = estimate_onset_times(self.samples, pre_max=pre_max, post_max=post_max)\n smooth_onsets(self.piano_roll, self.onsets, onset_range=onset_range, prev_note_range=previous_note_range)\n if instrument == Instrument.GUITAR:\n self.piano_roll = np.pad(self.piano_roll, ((4, 8), (0, 0)))\n self.notes = notes_from_piano_roll(self.piano_roll, self.tempo)\n self.key = estimate_key(self.notes)\n self.time_signature = time_signature\n\n def to_midi_file(self, file):\n midi = MIDIFile(1)\n midi.addTempo(0, 0, self.tempo)\n midi.addTimeSignature(0, 0, self.time_signature[0], int(np.sqrt(self.time_signature[1])), 24)\n if self.key.scale == Scale.MAJOR:\n mode = midiutil.MAJOR\n else:\n mode = midiutil.MINOR\n if self.key.get_circle_of_fifths()[1] == 'Sharps':\n accidental_type = midiutil.SHARPS\n else:\n accidental_type = midiutil.FLATS\n midi.addKeySignature(0, 0, self.key.get_circle_of_fifths()[0], accidental_type, mode)\n\n for note in self.notes:\n midi.addNote(0, 0, note.pitch.midi_number, note.duration.start_beat * 4, note.duration.total_beat * 4,\n utils.MIDI_VOLUME)\n with open(file, \"wb\") as output_file:\n midi.writeFile(output_file)\n\n\nclass Note:\n def __init__(self, pitch=None, duration=None):\n self.pitch = pitch\n self.duration = duration\n\n def __str__(self):\n return \"{} at time {} for {}(s)\".format(self.pitch.name, self.duration.start_time, self.duration.total_time)\n\n\nclass Pitch:\n def __init__(self, name=None, frequency=None, midi_number=None):\n if name is not None:\n self.name = name\n elif frequency is not None:\n self.frequency = frequency\n elif midi_number is not None:\n self.midi_number = midi_number\n\n def __str__(self):\n return \"Name: {}, Frequency: {:.2f}, Midi Number: {}\".format(self.name, self.frequency, self.midi_number)\n\n @property\n def name(self):\n return self.__name\n\n @property\n def frequency(self):\n return self.__frequency\n\n @property\n def midi_number(self):\n return self.__midi_number\n\n @name.setter\n def name(self, value):\n if value in utils.NOTE_NAME_LIST:\n self.__name = value\n index = utils.NOTE_NAME_LIST.index(value)\n self.__frequency = utils.NOTE_FREQ_LIST[index]\n self.__midi_number = utils.NOTE_MIDI_LIST[index]\n else:\n raise Exception(\"Note name {} does not exist\".format(value))\n\n @frequency.setter\n def frequency(self, value):\n if value in utils.NOTE_FREQ_LIST:\n self.__frequency = value\n index = utils.NOTE_FREQ_LIST.index(value)\n self.__name = utils.NOTE_NAME_LIST[index]\n self.__midi_number = utils.NOTE_MIDI_LIST[index]\n else:\n raise Exception(\"Note frequency {} does not exist\".format(value))\n\n @midi_number.setter\n def midi_number(self, value):\n if value in utils.NOTE_MIDI_LIST:\n self.__midi_number = value\n index = utils.NOTE_MIDI_LIST.index(value)\n self.__name = utils.NOTE_NAME_LIST[index]\n self.__frequency = utils.NOTE_FREQ_LIST[index]\n else:\n raise Exception(\"Note midi number {} does not exist\".format(value))\n\n def get_pitch_class(self):\n return self.name[:-1]\n\n\nclass Duration:\n def __init__(self, tempo, time=None, beat=None, frames=None):\n self.tempo = tempo\n self.quarter_note_time = 60 / self.tempo\n self.beat_time_dict = {\n 0.0625: self.quarter_note_time / 4,\n 0.125: self.quarter_note_time / 2,\n 0.25: self.quarter_note_time,\n 0.5: self.quarter_note_time * 2,\n 1.0: self.quarter_note_time * 4\n }\n if time is not None:\n self.start_time = time[0]\n self.total_time = time[1]\n elif beat is not None:\n self.start_beat = beat[0]\n self.total_beat = beat[1]\n elif frames is not None:\n self.start_frames = frames[0]\n self.total_frames = frames[1]\n\n def __str__(self):\n return \"Begins at time {:.2f} for a total of {:.2f}(s)\".format(self.start_time, self.total_time)\n\n @property\n def start_time(self):\n return self.__start_time\n\n @property\n def total_time(self):\n return self.__total_time\n\n @property\n def start_beat(self):\n return self.__start_beat\n\n @property\n def total_beat(self):\n return self.__total_beat\n\n @property\n def start_frames(self):\n return self.__start_frames\n\n @property\n def total_frames(self):\n return self.__total_frames\n\n @start_time.setter\n def start_time(self, value):\n self.__start_time = value\n # self.__start_beat = min(self.get_beat_time_dict().items(), key=lambda x: np.abs(x[1] - value))\n self.__start_beat = round_to_sixteenth(0.25 * (value / self.quarter_note_time))\n self.__start_frames = librosa.time_to_frames(value, sr=utils.SAMPLE_RATE)\n\n @total_time.setter\n def total_time(self, value):\n self.__total_time = value\n # self.__start_beat = min(self.get_beat_time_dict().items(), key=lambda x: np.abs(x[1] - value))\n self.__total_beat = round_to_sixteenth(0.25 * (value / self.quarter_note_time))\n if self.__total_beat == 0:\n self.__total_beat = 0.0625\n self.__total_frames = librosa.time_to_frames(value, sr=utils.SAMPLE_RATE)\n\n @start_beat.setter\n def start_beat(self, value):\n self.__start_beat = value\n self.__start_time = value / self.quarter_note_time\n self.__start_frames = librosa.time_to_frames(self.__start_time, sr=utils.SAMPLE_RATE)\n\n @total_beat.setter\n def total_beat(self, value):\n self.__total_beat = value\n self.__total_time = value / self.quarter_note_time\n self.__total_frames = librosa.time_to_frames(self.__total_time, sr=utils.SAMPLE_RATE)\n\n @start_frames.setter\n def start_frames(self, value):\n self.__start_frames = value\n self.__start_time = librosa.frames_to_time(value, sr=utils.SAMPLE_RATE)\n self.__start_beat = round_to_sixteenth(0.25 * (self.__start_time / self.quarter_note_time))\n\n @total_frames.setter\n def total_frames(self, value):\n self.__total_frames = value\n self.__total_time = librosa.frames_to_time(value, sr=utils.SAMPLE_RATE)\n self.__total_beat = round_to_sixteenth(0.25 * (self.__total_time / self.quarter_note_time))\n if self.__total_beat == 0:\n self.__total_beat = 0.0625\n\n\nclass Key:\n def __init__(self, pitch_class, scale):\n self.__pitch_class = pitch_class\n self.__scale = scale\n\n def __str__(self):\n return self.pitch_class.name.title() + \" \" + self.scale.name.title()\n\n @property\n def pitch_class(self):\n return self.__pitch_class\n\n @property\n def scale(self):\n return self.__scale\n\n @pitch_class.setter\n def pitch_class(self, value):\n self.__pitch_class = value\n self.name = self.pitch_class.name.title() + \" \" + self.scale.name.title()\n\n @scale.setter\n def scale(self, value):\n self.__scale = value\n self.name = self.pitch_class.name.title() + \" \" + self.scale.name.title()\n\n def to_display(self):\n return_str = str(self.pitch_class.name).split('_')\n if len(return_str) == 1:\n return return_str[0] + ' ' + self.scale.name.title()\n if return_str[1] == 'SHARP':\n return return_str[0] + '#' + ' ' + self.scale.name.title()\n else:\n return return_str[0] + '\\u266d' + ' ' + self.scale.name.title()\n\n def get_circle_of_fifths(self):\n return utils.CIRCLE_OF_FIFTHS[str(self)]\n\n\nclass KeyName(Enum):\n C = 0\n C_SHARP = 1\n D = 2\n E_FLAT = 3\n E = 4\n F = 5\n F_SHARP = 6\n G = 7\n A_FLAT = 8\n A = 9\n B_FLAT = 10\n B = 11\n\n\nclass Scale(Enum):\n MAJOR = 0\n MINOR = 1\n", "repo_name": "Lunarzewski/ELEC4840", "sub_path": "amt/entities.py", "file_name": "entities.py", "file_ext": "py", "file_size_in_byte": 11212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "amt.utils.PITCH_CLASSES", "line_number": 13, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 13, "usage_type": "name"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 20, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 20, "usage_type": "name"}, {"api_name": "amt.utils.rotate", "line_number": 20, "usage_type": "call"}, {"api_name": "amt.utils.KRUMHANSL_MAJ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 20, "usage_type": "name"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 21, "usage_type": "name"}, {"api_name": "amt.utils.rotate", "line_number": 21, "usage_type": "call"}, {"api_name": "amt.utils.KRUMHANSL_MIN", "line_number": 21, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 36, "usage_type": "call"}, {"api_name": "amt.utils.NOTE_NAME_LIST", "line_number": 43, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 43, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_NAME_LIST", "line_number": 48, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 49, "usage_type": "call"}, {"api_name": "amt.utils.estimate_tempo", "line_number": 79, "usage_type": "call"}, {"api_name": "amt.utils.estimate_piano_roll", "line_number": 80, "usage_type": "call"}, {"api_name": "amt.utils.estimate_onset_times", "line_number": 82, "usage_type": "call"}, {"api_name": "amt.utils.smooth_onsets", "line_number": 83, "usage_type": "call"}, {"api_name": "amt.utils.Instrument.GUITAR", "line_number": 84, "usage_type": "attribute"}, {"api_name": "amt.utils.Instrument", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.pad", "line_number": 85, "usage_type": "call"}, {"api_name": "midiutil.MIDIFile", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 93, "usage_type": "call"}, {"api_name": "midiutil.MAJOR", "line_number": 95, "usage_type": "attribute"}, {"api_name": "midiutil.MINOR", "line_number": 97, "usage_type": "attribute"}, {"api_name": "midiutil.SHARPS", "line_number": 99, "usage_type": "attribute"}, {"api_name": "midiutil.FLATS", "line_number": 101, "usage_type": "attribute"}, {"api_name": "amt.utils.MIDI_VOLUME", "line_number": 106, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 106, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_NAME_LIST", "line_number": 146, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 146, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_NAME_LIST.index", "line_number": 148, "usage_type": "call"}, {"api_name": "amt.utils.NOTE_NAME_LIST", "line_number": 148, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 148, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_FREQ_LIST", "line_number": 149, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 149, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_MIDI_LIST", "line_number": 150, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 150, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_FREQ_LIST", "line_number": 156, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 156, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_FREQ_LIST.index", "line_number": 158, "usage_type": "call"}, {"api_name": "amt.utils.NOTE_FREQ_LIST", "line_number": 158, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 158, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_NAME_LIST", "line_number": 159, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 159, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_MIDI_LIST", "line_number": 160, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 160, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_MIDI_LIST", "line_number": 166, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 166, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_MIDI_LIST.index", "line_number": 168, "usage_type": "call"}, {"api_name": "amt.utils.NOTE_MIDI_LIST", "line_number": 168, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 168, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_NAME_LIST", "line_number": 169, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 169, "usage_type": "name"}, {"api_name": "amt.utils.NOTE_FREQ_LIST", "line_number": 170, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 170, "usage_type": "name"}, {"api_name": "amt.utils.round_to_sixteenth", "line_number": 230, "usage_type": "call"}, {"api_name": "librosa.time_to_frames", "line_number": 231, "usage_type": "call"}, {"api_name": "amt.utils.SAMPLE_RATE", "line_number": 231, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 231, "usage_type": "name"}, {"api_name": "amt.utils.round_to_sixteenth", "line_number": 237, "usage_type": "call"}, {"api_name": "librosa.time_to_frames", "line_number": 240, "usage_type": "call"}, {"api_name": "amt.utils.SAMPLE_RATE", "line_number": 240, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 240, "usage_type": "name"}, {"api_name": "librosa.time_to_frames", "line_number": 246, "usage_type": "call"}, {"api_name": "amt.utils.SAMPLE_RATE", "line_number": 246, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 246, "usage_type": "name"}, {"api_name": "librosa.time_to_frames", "line_number": 252, "usage_type": "call"}, {"api_name": "amt.utils.SAMPLE_RATE", "line_number": 252, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 252, "usage_type": "name"}, {"api_name": "librosa.frames_to_time", "line_number": 257, "usage_type": "call"}, {"api_name": "amt.utils.SAMPLE_RATE", "line_number": 257, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 257, "usage_type": "name"}, {"api_name": "amt.utils.round_to_sixteenth", "line_number": 258, "usage_type": "call"}, {"api_name": "librosa.frames_to_time", "line_number": 263, "usage_type": "call"}, {"api_name": "amt.utils.SAMPLE_RATE", "line_number": 263, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 263, "usage_type": "name"}, {"api_name": "amt.utils.round_to_sixteenth", "line_number": 264, "usage_type": "call"}, {"api_name": "amt.utils.CIRCLE_OF_FIFTHS", "line_number": 305, "usage_type": "attribute"}, {"api_name": "amt.utils", "line_number": 305, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 308, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 323, "usage_type": "name"}]} +{"seq_id": "3431022662", "text": "#!/usr/bin/python\nimport os\nimport re\nimport vcf\nimport glob\nimport numpy as np\n\nimport json\nimport requests\nimport pickle\n\n#GENE FORMAT\n##chr start stop name\n#3 178866311 178952497 PIK3CA\n\nfrom optparse import OptionParser\n# -------------------------------------------------\nparser = OptionParser()\nparser.add_option(\"--vcfdir\", dest=\"vcfdir\", help=\"Path to directory containing VCF files\", default=False)\nparser.add_option(\"--outdir\", dest=\"outdir\", help=\"Path to directory to write output to\", default=\"./DriverProfile/\")\nparser.add_option(\"--genelist\", dest=\"genelist\", help=\"File containing Genes to test/plot)\", default=False)\nparser.add_option(\"--canon\", dest=\"canonical\", help=\"Only report Canonical effects\", default=False)\n\nparser.add_option(\"--bgzip\", dest=\"bgzip\", help=\"Path to bgzip binary\", default=\"bgzip\")\nparser.add_option(\"--tabix\", dest=\"tabix\", help=\"Path to tabix binary\", default=\"tabix\")\n\nparser.add_option(\"--t\", dest=\"nrcpus\", help=\"Number of CPUs to use per sample\", default=2)\n\nparser.add_option(\"--dp\", dest=\"mindepth\", help=\"Minimum read depth to consider reliable\", default=10)\nparser.add_option(\"--af\", dest=\"minvaf\", help=\"Minimum variant allele fraction\", default=0.25)\nparser.add_option(\"--pf\", dest=\"popfreq\", help=\"Maximum popultaion frequency\", default=0.05)\nparser.add_option(\"--cf\", dest=\"cohfreq\", help=\"Maximum cohort frequency\", default=0.10)\nparser.add_option(\"--me\", dest=\"mineff\", help=\"Minimum variant effect score\", default=1.50)\n\nparser.add_option(\"--debug\", dest=\"debug\", help=\"Flag for debug logging\", default=False)\nparser.add_option(\"--format\", dest=\"format\", help=\"VCF output format [GATK/FREEB/..]\", default=\"GATK\")\n(options, args) = parser.parse_args()\n# -------------------------------------------------\n\n# -------------------------------------------------\nvocabulary = {\n \"None\":-1, \"clean\":0,\n \"sequence_feature\":0, \"intron_variant\":0,\n \"3_prime_UTR_variant\":0, \"5_prime_UTR_variant\":0, \"non_coding_exon_variant\":0,\n \"TF_binding_site_variant\":0.5, \"splice_region_variant\":0.5,\n \"synonymous_variant\":1.0,\n \"missense_variant\":1.5,\n \"splice_donor_variant\":2, \"splice_acceptor_variant\":2,\n \"inframe_deletion\":2.1, \"inframe_insertion\":2.1,\n \"disruptive_inframe_deletion\":2.5, \"disruptive_inframe_insertion\":2.5,\n \"5_prime_UTR_premature_start_codon_gain_variant\":3,\n \"stop_gained\":4, \"nonsense_mediated_decay\":4, \"frameshift_variant\":4\n}\n\n# Mapping of SNEPeff effects to 'MAF' names for variation effects, enables later use in MAF tools\n# https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+%28MAF%29+Specification+-+v1.0\n# https://bioconductor.org/packages/3.7/bioc/vignettes/maftools/inst/doc/maftools.html\nmapping = {\n \"synonymous_variant\":\"Silent\", \"missense_variant\":\"Missense_Mutation\", \"disruptive_inframe_deletion\":\"Frame_Shift_Del\", \"disruptive_inframe_insertion\":\"Frame_Shift_Ins\",\n \"5_prime_UTR_premature_start_codon_gain_variant\":\"Nonsense_Mutation\", \"stop_gained\":\"Nonsense_Mutation\", \"nonsense_mediated_decay\":\"Nonsense_Mutation\", \"frameshift_variant\":\"Frame_Shift_???\"\n}\n# Data fields needed to make lollipop plots\nlollipop = [\"Hugo_Symbol\",\"Sample_ID\",\"Protein_Change\",\"Mutation_Type\",\"Chromosome\",\"Start_Position\",\"End_Position\",\"Reference_Allele\",\"Variant_Allele\",\"VAF\"]\n\n# Known fields with information on population frequency\nFREQ_FIELDS = [\"dbNSFP_ExAC_AF\", \"dbNSFP_ExAC_Adj_AF\", \"GoNLv5_Freq\", \"GoNLv5_AF\"]\n\nCANONICAL_TRANSCRIPTS = {}\n\n\n# -------------------------------------------------\n# DETERMINE which effects to report based on 'abribitrary' variant impact score\ntoselect = [k for k,v in vocabulary.items() if v >= float(options.mineff)]\n# -------------------------------------------------\n\n\n# -------------------------------------------------\ndebug = options.debug\nDEPTH_KEY=\"\"\nVAF_KEY=\"\"\n# -------------------------------------------------\ndef check_arguments():\n global DEPTH_KEY\n global VAF_KEY\n\n if not os.path.exists(options.vcfdir):\n print(\"Invalid VCF folder %s\"%(options.vcfdir))\n return False\n\n if not os.path.exists(options.outdir):\n print(\"Creating output folder %s\"%(options.outdir))\n try:\n os.mkdir(options.outdir)\n except OSError:\n print(\"Invalid / unable to create, output folder %s\"%(options.outdir))\n return(False)\n\n if options.format == \"GATK\":\n DEPTH_KEY=\"AD\"\n VAF_KEY=\"AD\"\n\n if options.format == \"FREEB\":\n DEPTH_KEY=\"DP\"\n VAF_KEY=\"DPR\"\n\n\n print(\"Running with the following settings:\")\n print(\"------------------------------------\")\n print(options)\n print(\"DEPTH FIELD:\"+DEPTH_KEY)\n print(\"ALLELE FIELD:\"+VAF_KEY)\n print(\"------------------------------------\")\n return(True)\n\n# -------------------------------------------------\n\n# Extract population frequency from VCF record\n# Annoation assumed to be in SNPeff formatting\ndef find_popfreq(vcf_record):\n popfreq=[0.0]\n for field in FREQ_FIELDS:\n if field in vcf_record.INFO:\n #if debug: print(vcf_record.INFO[field])\n for x in vcf_record.INFO[field]:\n if x is None:\n popfreq.append(0.0)\n else:\n popfreq.append(float(x))\n return(popfreq)\n\n# Determine the most damaging effect of the variant\ndef find_effects(vcf_record, sample_gt):\n maxeffect=\"None\"\n if debug: print(vcf_record.INFO)\n\n if \"ANN\" not in vcf_record.INFO:\n return maxeffect\n\n # TRAVERSE ALL ANNOTATIONS\n for pred in vcf_record.INFO[\"ANN\"]:\n # SPLIT THE SEPERATE FIELDS WITHIN THE ANNOTATION\n items = pred.split(\"|\")\n #if debug: print(\"~~~\\t\"+items[3]+\"\\t\"+items[4]+\"\\n\"+\"|\".join(items))\n\n # Skip if annotation ALT allele does not match sample ALT allele\n if str(items[0]) != str(sample_gt):\n if debug: print(\"SKIPPING DUE TO MISMATCHING GENOTYPE\\t|{}|\\t|{}|\".format(items[0], sample_gt))\n continue\n\n # IF Canonical only mode, skip all other transcripts\n if options.canonical:\n gene = items[4]\n if len(gene) <= 1:\n continue\n if gene not in CANONICAL_TRANSCRIPTS:\n CANONICAL_TRANSCRIPTS[gene] = get_canonical(gene)\n if debug: print(\"~~~\\t\"+items[6]+\" \"+gene+\" \"+CANONICAL_TRANSCRIPTS[gene])\n if items[6] != CANONICAL_TRANSCRIPTS[gene]:\n continue\n\n allele = items[0]\n effects = items[1].split(\"&\")\n for effect in effects:\n if debug: print(effect)\n if effect not in vocabulary:\n # A NEW MUTATION EFFECT WAS FOUND\n if debug:\n print(\"NEW Mutation effect identified:\")\n print(pred)\n print(effect)\n\n else:\n # STORE THE MOST DELETERIOUS EFFECT\n if vocabulary[effect] > vocabulary[maxeffect]:\n maxeffect = effect\n if debug: print(maxeffect)\n return(maxeffect)\n\n# ETRACT THE MOST DELETERIOUS MUTATIONS IN A GENE\ndef select_maximum_effect(effects):\n effectvalues = [vocabulary[eff] for eff in effects]\n if debug: print(effectvalues)\n indices = np.argmax(effectvalues)\n return(indices)\n\n# CHECK AND GENERATE GZ AND TBI\ndef zip_and_index(vcffile):\n if not os.path.exists(vcffile+\".gz\"):\n os.system(options.bgzip+\" -c \"+vcffile+\" > \"+vcffile+\".gz\")\n if not os.path.exists(vcffile+\".gz\"+\".tbi\"):\n os.system(options.tabix+\" \"+vcffile+\".gz\")\n\n# -------------------------------------------------\n# GENE FORMAT\n# Gene name + location + variants or not\n# VARIANT FORMAT\n# Variant + DEPTH + POP FREQ + MLEAF + EFFECT\n\ndef check_ad(sample_vcf):\n try:\n ad_item = sample_vcf[DEPTH_KEY]\n except AttributeError as e:\n return(False)\n if sample_vcf[DEPTH_KEY] is None:\n return(False)\n return(True)\n\n#sample_vcf == vcf_record.genotype(sample)\ndef check_depth(sample_vcf):\n #single depth field\n if isinstance(sample_vcf[DEPTH_KEY], int):\n # SKIP LOW DEPTH POSITIONS\n if sample_vcf[DEPTH_KEY] < int(options.mindepth):\n return(False)\n #multi depth field\n else:\n # SKIP LOW DEPTH POSITIONS\n if sum(sample_vcf[DEPTH_KEY]) < int(options.mindepth):\n return(False)\n return(True)\n\ndef check_vaf(sample_vcf):\n #single depth field\n if isinstance(sample_vcf[DEPTH_KEY], int):\n # CHECK VAF\n if (sum(sample_vcf[VAF_KEY][1:])*1.0/sample_vcf[DEPTH_KEY]) < float(options.minvaf):\n return(False)\n #multi depth field\n else:\n # CHECK VAF\n if (sum(sample_vcf[VAF_KEY][1:])*1.0/sum(sample_vcf[DEPTH_KEY])) < float(options.minvaf):\n return(False)\n return(True)\n\n# -------------------------------------------------\n# RESTfull functions\ndef generic_json_request_handler(server, ext):\n r = requests.get(server+ext, headers={ \"Content-Type\" : \"application/json\"})\n if not r.ok:\n r.raise_for_status()\n sys.exit()\n\n return(r.json())\n\n\ndef get_geneinfo(gene, idtype):\n server = \"https://grch37.rest.ensembl.org\"\n\n if idtype == \"symbol\":\n ext = \"/lookup/symbol/homo_sapiens/{}?content-type=application/json\".format(gene)\n else:\n ext = \"/lookup/id/{}?content-type=application/json\".format(gene)\n\n json = generic_json_request_handler(server, ext)\n genedef = {\"Chr\":json['seq_region_name'], \"Start\":json['start'], \"Stop\":json['end'], \"SYMBOL\":json['display_name'], \"ENSEMBLID\":json['id']}\n\n return(genedef)\n\n\ndef get_canonical(ensembleid):\n server = \"https://grch37.rest.ensembl.org\"\n ext = \"/lookup/id/{}?content-type=application/json;expand=1;db_type=core\".format(ensembleid)\n json = generic_json_request_handler(server, ext)\n\n for i in range(0,len(json[\"Transcript\"])):\n if json['Transcript'][i]['is_canonical'] == 1:\n return(json['Transcript'][i]['id'])\n\n # if there is no canonical just take the first\n print(\"[WARN] No cannonical transcript found for gene {}, taking the first transcript\".format(ensembleid))\n return(json['Transcript'][0]['id'])\n\n# -------------------------------------------------\n\ndef main():\n global DEPTH_KEY\n global VAF_KEY\n\n file_list = glob.glob(os.path.join(options.vcfdir, \"*.vcf\"))\n for vcf_file in file_list:\n zip_and_index(vcf_file)\n\n\n genelist=[]\n\n # We only want to run this once per genelist, faster and kinder\n if not os.path.isfile(options.genelist+\".pkl\"):\n if debug: print(\"GENERATING ENSEMBL GENELIST\")\n genecollection=[]\n with open(options.genelist, 'r') as infile:\n for line in infile:\n genesymbol = line.strip().split('\\t')[3]\n\n if genesymbol not in genecollection:\n genelist.append(get_geneinfo(genesymbol, 'symbol'))\n genecollection.append(genesymbol)\n\n f = open(options.genelist+\".pkl\",\"wb\")\n pickle.dump(genelist,f)\n f.close()\n else:\n with open(options.genelist+\".pkl\", 'rb') as handle:\n genelist = pickle.load(handle)\n\n if debug: print(\"GENES {}\".format(genelist))\n\n # DF to keep the mutation effcts per gene\n df = {}\n #VCF record df, for MAX effects only, used for lollipop data\n rdf= {}\n #Count data frame\n cdf = {}\n\n # FOR ALL VCF FILES\n for vcf_file in file_list:\n if (debug):\n print(\"------\")\n print(vcf_file)\n vcfread = vcf.Reader(open(vcf_file+\".gz\",'r'), compressed=\"gz\")\n\n if (debug): print(vcfread.samples)\n if (debug): print(options.format)\n\n # FOR EACH SAMPLE\n for i,sample in enumerate(vcfread.samples):\n samplename = False\n\n if options.format == \"GATK\":\n samplename = sample\n elif options.format == \"FREEB\":\n if (debug): print(\"++ \"+vcfread.samples[1])\n samplename = vcfread.samples[i+1]\n #samplename = vcf_file.split(\".\")[1].split(\"_\")[1]\n df[samplename] = {}\n rdf[samplename] = {}\n cdf[samplename] = {}\n\n if debug: print(df)\n\n # FOR EACH GENE OF INTREST\n for thisgene in genelist:\n nr_of_positions = 0\n if len(thisgene)<=0:\n continue\n\n #if debug: print(\")\n vcf_records=False\n try:\n vcf_records = vcfread.fetch(thisgene[\"Chr\"], int(thisgene[\"Start\"])-20, int(thisgene[\"Stop\"])+20)\n except ValueError as e:\n if debug: print(\"-- {}\\tNO RECORDS FOUND\".format(thisgene))\n for samplename in df:\n df[samplename][thisgene[\"SYMBOL\"]] = \"None\"\n continue\n\n # Prep containers\n effects = {}\n records = {}\n for samplename in df:\n effects[samplename] = []\n records[samplename] = []\n\n # For each variant position within gene\n for vcf_record in vcf_records:\n if debug: print(\"@@@\\t {}\".format(vcf_record.INFO))\n\n if not \"ANN\" in vcf_record.INFO:\n if debug: print(\"@@@\\t skipping record {} due to missing ANN field\".format(vcf_record))\n continue\n\n\n gencheck = [thisgene[\"SYMBOL\"] in a for a in vcf_record.INFO[\"ANN\"]]\n if sum(gencheck) <= 0:\n if debug: print(\"@@@\\t skipping record {} due to missing GENE SYMBOL {}\".format(vcf_record, thisgene[\"SYMBOL\"]))\n continue\n\n nr_of_positions += 1\n # For each sample\n for samplename in df:\n #CHECK IF SAMPLE GENOTYPE AVAILABLE\n sgenot = None\n try:\n sgenot = vcf_record.genotype(samplename)\n #if debug: print(\"-- {}\\t{}\\t{}\\tGT FOUND\".format(thisgene, samplename, sgenot))\n except AttributeError as e:\n #if debug: print(\"-- {}\\t{}\\tNO GT FOUND\".format(thisgene, samplename))\n continue\n\n # FILTER NON-QC RECORDS\n PASS = False\n log = \"++ {}\\t{}\\t{}\\t{}\".format(thisgene, samplename, vcf_record, vcf_record.genotype(samplename)['GT'])\n # CHEK IF AD FIELD PRESENT\n if check_ad(sgenot):\n log += \"\\tAD:PASS\"\n log += \"\\tDEPTH:{}\".format(vcf_record.genotype(samplename)[DEPTH_KEY])\n # CHECK TOTAL COVERAGE OF IDENTIFIED ALLELLES\n if check_depth(sgenot):\n log += \":PASS\"\n log += \"\\tVAF:{}\".format(sum(vcf_record.genotype(samplename)[VAF_KEY][1:])*1.0/sum(vcf_record.genotype(samplename)[DEPTH_KEY]))\n\n # add clean if sufficient depth is measured\n effects[samplename].append(\"clean\")\n records[samplename].append(None)\n\n # CHECK VARIANT ALLELE FREQUENCY\n if check_vaf(sgenot):\n log +=\":PASS\"\n log +=\"\\tPOP:{}\".format([vcf_record.INFO[rf] for rf in FREQ_FIELDS if rf in vcf_record.INFO])\n # CHECK POPULATION FREQUENCY\n if max(find_popfreq(vcf_record)) <= float(options.popfreq):\n log += \":PASS\"\n log += \"\\tMLEAF:{}\".format(vcf_record.INFO[\"MLEAF\"])\n # CHECK OCCURENCE IN TOTAL POOL\n if max(vcf_record.INFO[\"MLEAF\"]) <= float(options.cohfreq):\n log +=\":PASS\"\n PASS = True\n\n if debug: print(log)\n if PASS:\n # PARSE '0/1' into ALT[0] or '0/2' into ALT[1]\n sample_call = sgenot['GT'].replace(\"|\",\"\").split(\"/\")\n sample_gt = vcf_record.ALT[int(sample_call[-1])-1]\n #if debug: print(\"-- {}\\t{}\\tPARSED GT\\t{}\\t{}\\t{}\".format(thisgene, samplename, sgenot, sample_call, sample_gt))\n\n effects[samplename].append(find_effects(vcf_record, sample_gt))\n #print(\"SAMPLE: {} \\t\\t EFF: {}\".format(samplename,effects[samplename]))\n records[samplename].append(vcf_record)\n\n #exit(0)\n # ON GENE+SAMPLE LEVEL determine the number of mutations and the maximum mutation effect\n for samplename in df:\n # If no murtations/effects measured consider the gene as 'not assesed'\n if len(effects[samplename]) <= 0:\n df[samplename][thisgene[\"SYMBOL\"]] = \"None\"\n cdf[samplename][thisgene[\"SYMBOL\"]] = 0\n\n # Else determine the max effect\n else:\n cdf[samplename][thisgene[\"SYMBOL\"]] = sum([eff in toselect for eff in effects[samplename]])\n #len(effects[samplename]) - effects[samplename].count(\"clean\")\n loc = select_maximum_effect(effects[samplename])\n eff = effects[samplename][loc]\n\n # If a 'strong enough' effect is detected report it in the summary\n if eff in toselect:\n df[samplename][thisgene[\"SYMBOL\"]] = eff\n if eff in mapping:\n rdf[samplename][thisgene[\"SYMBOL\"]] = {}\n rdf[samplename][thisgene[\"SYMBOL\"]][\"REC\"] = records[samplename][loc]\n rdf[samplename][thisgene[\"SYMBOL\"]][\"EFF\"] = eff\n\n # Else check if gene was not observed 'None' or not mutated 'clean'\n else:\n # check number of 'clean' positions\n # if 50% of positions passes DP metric count as clean\n if effects[samplename].count(\"clean\") >= (nr_of_positions/2):\n df[samplename][thisgene[\"SYMBOL\"]] = \"clean\"\n else:\n df[samplename][thisgene[\"SYMBOL\"]] = \"None\"\n\n if debug: print(\"** {}\\t{}\\t{}\\t{}\\t{}\".format(thisgene, samplename, df[samplename][thisgene[\"SYMBOL\"]], cdf[samplename][thisgene[\"SYMBOL\"]], \",\".join(effects[samplename])))\n\n\n # Printing the mutation overview table\n outfile = open(options.outdir+\"/\"+\"MutationOverview.txt\",'w')\n # Print header with gene names\n if debug: print(df)\n firstsample = list(df.keys())[0]\n outfile.write(\"Sample\\t{}\\n\".format('\\t'.join(df[firstsample].keys()) ))\n if debug: print(\"##############################\")\n # Loop all samples\n for sp in df:\n if debug: print(\"{}\\t{}\\n\".format(sp, '\\t'.join(df[sp].values()) ))\n outfile.write(\"{}\\t{}\\n\".format(sp, '\\t'.join(df[sp].values()) ))\n\n if debug: print(\"##############################\")\n outfile.close()\n\n\n # Printing the mutation count table\n outfile = open(options.outdir+\"/\"+\"MutationCounts.txt\",'w')\n # Print header with gene names\n firstsample = list(cdf.keys())[0]\n outfile.write(\"Sample\\t{}\\tTotMutCount\\n\".format('\\t'.join(cdf[firstsample].keys()) ))\n if debug: print(\"##############################\")\n # Loop all samples\n for sp in cdf:\n if debug: print(\"{}\\t{}\\t{}\\n\".format(sp, '\\t'.join([str(i) for i in cdf[sp].values()]), sum(cdf[sp].values()) ))\n outfile.write(\"{}\\t{}\\t{}\\n\".format(sp, '\\t'.join([str(i) for i in cdf[sp].values()]), sum(cdf[sp].values()) ))\n\n if debug: print(\"##############################\")\n outfile.close()\n\n\n # Printing the mutation details chart/table\n outfile = open(options.outdir+\"/\"+\"MutationChart.txt\",'w')\n # Printing annotations header\n outfile.write(\"{}\\n\".format('\\t'.join(lollipop)))\n\n if debug: print(\"##############################\")\n for samplename in rdf:\n for gene in rdf[samplename]:\n thisrec = rdf[samplename][gene][\"REC\"]\n\n vaf=round((sum(thisrec.genotype(samplename)[VAF_KEY][1:])*1.0)/sum(thisrec.genotype(samplename)[DEPTH_KEY]),2)\n\n sample_call = thisrec.genotype(samplename)['GT'].replace(\"|\",\"\").split(\"/\")\n #print(sample_call)\n #print(sample_call[-1])\n #print(thisrec.ALT)\n sample_gt = thisrec.ALT[int(sample_call[-1])-1]\n\n proteffect=None\n for pred in thisrec.INFO[\"ANN\"]:\n # Look for the first transcript with this effect\n if rdf[samplename][gene][\"EFF\"] in pred.split(\"|\")[1].split(\"&\"):\n proteffect=pred.split(\"|\")[10]\n break\n\n if (debug): print(gene, samplename, proteffect, mapping[rdf[samplename][gene][\"EFF\"]], str(thisrec.CHROM), str(thisrec.POS), str(thisrec.POS+len(thisrec.ALT[0])), thisrec.REF, str(thisrec.ALT[0]), vaf)\n\n outfile.write(\"\\t\".join([gene, samplename, proteffect, mapping[rdf[samplename][gene][\"EFF\"]], str(thisrec.CHROM), str(thisrec.POS), str(thisrec.POS+len(sample_gt)), thisrec.REF, str(sample_gt), str(vaf)])+\"\\n\")\n if debug: print(\"##############################\")\n outfile.close()\n\n\n\n\n# -------------------------------------------------\n\nprint(\"Starting Analysis\")\n\nif __name__ == '__main__':\n if check_arguments():\n main()\n else:\n print(\"Error in provided arguments\")\n\nprint(\"DONE\")\n\n# -------------------------------------------------\n", "repo_name": "UMCUGenetics/SmallTools", "sub_path": "Make_Somatic_Mutation_Overview.py", "file_name": "Make_Somatic_Mutation_Overview.py", "file_ext": "py", "file_size_in_byte": 22045, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "2", "api": [{"api_name": "optparse.OptionParser", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 189, "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.system", "line_number": 191, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 238, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 299, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 303, "usage_type": "call"}, {"api_name": "vcf.Reader", "line_number": 319, "usage_type": "call"}]} +{"seq_id": "25153254195", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Group',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=50)),\n ('created', models.DateTimeField(auto_now=True)),\n ],\n ),\n migrations.CreateModel(\n name='GroupUserDetails',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('joined', models.DateTimeField(auto_now=True)),\n ('group', models.ForeignKey(to='meetup.Group')),\n ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n ),\n migrations.CreateModel(\n name='Meeting',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=250)),\n ('agenda', models.TextField(null=True, blank=True)),\n ('fees', models.CharField(max_length=5)),\n ('scheduled', models.DateTimeField(null=True, blank=True)),\n ('venue', models.CharField(max_length=200)),\n ('group', models.ForeignKey(to='meetup.Group')),\n ],\n ),\n ]\n", "repo_name": "jerinzam/my-website", "sub_path": "meetup/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1693, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.swappable_dependency", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "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": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "71731487736", "text": "from flask import Flask\nfrom flask import request, make_response\nimport os\nimport json\n\napp = Flask(__name__)\n\n@app.route(\"/slackbutton\", methods=['POST'])\ndef respond():\n \"\"\"\n This route listens for incoming message button actions from Slack.\n \"\"\" \n response_msg = json.loads(request.form[\"payload\"])\n\n parameters = {\"name\": response_msg['user']['id'], \"time\": response_msg['actions'][0]['value'], \"threadts\": response_msg['container']['message_ts']}\n \n user_file = open(\"user-data.txt\", \"a\")\n user_file.writelines(str(parameters) + \"\\n\")\n user_file.close()\n \n command = \"ansible-playbook send-message.yml --connection=local --extra-vars \\\"\" + str(parameters) + \"\\\"\"\n os.system(command)\n return make_response(\"OK\",200)\n \nif __name__ == \"__main__\":\n\tapp.run()\n", "repo_name": "humeyraucar/ngrok-flask-demo", "sub_path": "app-response.py", "file_name": "app-response.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "os.system", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "11163", "text": "import numpy as np\nimport json\nimport cv2\nimport pandas as pd\nimport copy\nimport random\n\nclass myCoCoObj():\n\n def __init__(self):\n self.img_path = []\n self.annotations = []\n self.meta = []\n self.ShapeList = []\n self.FinalJson = {}\n self.seg_list = []\n \n \n def AddOneLine(self, Points, file_path, SHAPE,seg, MetaData = None, check = True):\n if check:\n Points = self.CheckPoints(Points)\n if isinstance(Points[0],list):\n Points = Points[0]\n if len(Points) == 4:\n self.annotations.append(Points)\n self.img_path.append(file_path)\n self.meta.append(MetaData)\n self.ShapeList.append(SHAPE[:2])\n self.seg_list.append(seg)\n\n \n def Finalize(self, Category = None):\n image_path_list, image_idx_list = np.unique(np.array(self.img_path), return_inverse = True)\n Image_dict = []\n Annot_dict = []\n complete_img_idx = []\n for anno_idx, img_idx in enumerate(image_idx_list):\n \n if img_idx not in complete_img_idx:\n SingleImageDict = self.CreateSingleImageDict(image_idx = img_idx,\n file_path = self.img_path[anno_idx],\n SHAPE = self.ShapeList[anno_idx], \n MetaData = self.meta[anno_idx]\n )\n Image_dict.append(SingleImageDict)\n complete_img_idx.append(img_idx)\n \n \n SingleAnnotDict = self.CreateSingleAnnotDict(anno_idx = anno_idx,\n img_idx = img_idx,\n bbox = self.annotations[anno_idx],\n seg = self.seg_list[anno_idx])\n Annot_dict.append(SingleAnnotDict)\n \n if Category is None:\n Category = [{\n 'id': 1,\n 'name': 'Scaphoid',\n 'supercategory': ''\n }]\n self.FinalJson['images'] = Image_dict\n self.FinalJson['categories'] = Category\n self.FinalJson['annotations'] = Annot_dict\n print(f\"There are {len(Image_dict)} images\")\n print(f\"There are {len(Category)} categories\")\n print(f\"There are {len(Annot_dict)} annotations\")\n\n def SaveJson(self, SaveName):\n with open(SaveName,\"w\") as FF:\n json.dump(self.FinalJson,FF,indent = 4, separators = (',',': '))\n\n\n def Visualize(self):\n annotation = self.FinalJson['annotations'][-1]\n ii = annotation[\"image_id\"]\n \n imgid_to_posid = {}\n for i in range(len(self.FinalJson['images'])):\n imgid_to_posid[self.FinalJson['images'][i][\"id\"]] = i\n img_path = self.FinalJson['images'][imgid_to_posid[ii]][\"path\"]\n bbox = annotation[\"bbox\"]\n\n #cv2.namedWindow(img_path,cv2.WINDOW_NORMAL)\n #cv2.resizeWindow(img_path, (600,600))\n img = cv2.imread(img_path)\n cv2.rectangle(img, (bbox[0],bbox[1]), (bbox[0]+bbox[2],bbox[1]+bbox[3]), (0,0,255), 3)\n cv2.imwrite(\"Test.png\", img)\n #cv2.imshow(img_path, img)\n #cv2.waitKey()\n #cv2.destroyAllWindows()\n\n\n def FromJson(self, JsonFile):\n with open(JsonFile,\"r\") as FF:\n Json = json.loads(FF.read())\n\n imgid_to_posid = {}\n for i in range(len(Json['images'])):\n imgid_to_posid[Json['images'][i][\"id\"]] = i\n\n for k in range(len(Json['annotations'])):\n Points = Json[\"annotations\"][k][\"bbox\"]\n i = Json[\"annotations\"][k][\"image_id\"]\n seg = Json['annotations'][k][\"segmentation\"]\n file_path = Json['images'][imgid_to_posid[i]][\"path\"]\n width = Json['images'][imgid_to_posid[i]][\"width\"]\n height = Json['images'][imgid_to_posid[i]][\"height\"]\n metadata = Json['images'][imgid_to_posid[i]][\"metadata\"]\n self.AddOneLine(Points = Points, file_path = file_path, SHAPE = (height, width), MetaData = metadata, check = False,seg=seg)\n\n def MergeCoCoObj(self, COCO):\n print(f\"There are {len(self.annotations)} annotation in original coco\")\n print(f\"There are {len(COCO.annotations)} annotation in to-be-add coco\")\n self.annotations.extend(COCO.annotations)\n self.img_path.extend(COCO.img_path)\n self.meta.extend(COCO.meta)\n self.ShapeList.extend(COCO.ShapeList)\n self.seg_list.extend(COCO.seg_list)\n print(f\"There are {len(self.annotations)} annotation in merged coco\")\n\n\n def Extract(self, file_list):\n AA = pd.Series(self.img_path)\n \n target_idx = AA[AA.isin(file_list)].index\n old_annotations = self.annotations\n old_img_path = self.img_path\n old_meta = self.meta\n old_ShapeList = self.ShapeList\n old_seglist = self.seg_list\n\n self.img_path = []\n self.annotations = []\n self.meta = []\n self.ShapeList = []\n self.seg_list = []\n for i in target_idx:\n self.annotations.append(old_annotations[i])\n self.img_path.append(old_img_path[i])\n self.meta.append(old_meta[i])\n self.ShapeList.append(old_ShapeList[i])\n self.seg_list.append(old_seglist[i])\n print(f\"Extract {len(target_idx)} annotations\")\n \n\n @staticmethod\n def CreateSingleImageDict(image_idx, file_path, SHAPE, MetaData):\n DD = {}\n DD['id'] = int(image_idx)\n DD['path'] = file_path\n DD['width'] = int(SHAPE[1])\n DD['height']= int(SHAPE[0])\n DD['file_name'] = file_path\n DD['metadata'] = MetaData\n return DD\n\n\n @staticmethod\n def CreateSingleAnnotDict(anno_idx, img_idx, bbox, seg = [],class_idx = 1):\n DD = {}\n DD['id'] = int(anno_idx)\n DD['image_id'] = int(img_idx)\n DD['category_id'] = int(class_idx)\n DD[\"bbox\"] = bbox\n DD['iscrowd'] = False\n DD['segmentation'] = seg\n DD['area'] = int(bbox[2]*bbox[3])\n return DD\n\n\n @staticmethod\n def CheckPoints(Points):\n \n cX1 = int(Points[0]); cY1 = int(Points[1])\n cX2 = int(Points[2]); cY2 = int(Points[3])\n\n diff_x = cX2 - cX1\n diff_y = cY2 - cY1\n\n if diff_x > 0 and diff_y > 0:\n bbox = [ cX1, cY1, (cX2-cX1), (cY2-cY1)]\n elif diff_x <= 0 and diff_y <= 0:\n bbox = [ cX2, cY2, abs(diff_x), abs(diff_y)]\n elif diff_x <= 0 and diff_y > 0:\n cX3 = cX1 - abs(diff_x); cY3 = cY1\n bbox = [ cX3, cY3, abs(diff_x), abs(diff_y)]\n elif diff_x > 0 and diff_y <= 0:\n cX3 = cX2 - abs(diff_x); cY3 = cY2\n bbox = [ cX3, cY3, abs(diff_x), abs(diff_y)]\n\n return bbox\n\n\nif __name__ == \"__main__\":\n DA = pd.read_csv(\"Data_info_json\")\n uniq_id = DA.ID.unique()\n random.shuffle(uniq_id)\n ii = len(uniq_id) // 6\n DA_val = DA.loc[DA.ID.isin(uniq_id[:ii])]\n DA_test = DA.loc[DA.ID.isin(uniq_id[ii*5:])]\n DA_train = DA.loc[DA.ID.isin(uniq_id[ii:ii*5])]\n \n DA_val[\"divide\"] = \"val\"\n DA_test[\"divide\"] = \"test\"\n DA_train[\"divide\"] = \"train\"\n\n mycoco = myCoCoObj()\n mycoco.FromJson(\"Disc0909.json\")\n \n test_coco = copy.deepcopy(mycoco)\n test_coco.Extract(DA_test.Path)\n test_coco.Finalize()\n test_coco.SaveJson(\"Test0910.json\")\n\n test_coco = copy.deepcopy(mycoco)\n test_coco.Extract(DA_val.Path)\n test_coco.Finalize()\n test_coco.SaveJson(\"Val0910.json\")\n \n test_coco = copy.deepcopy(mycoco)\n test_coco.Extract(DA_train.Path)\n test_coco.Finalize()\n test_coco.SaveJson(\"Train0910.json\")\n\n DA = pd.concat([DA_val,DA_test,DA_train])\n \n DA.to_csv(\"Data_info_json0910\",index=False)\n ", "repo_name": "soxHenry433/TF2_U2Model", "sub_path": "Json/ConvertJson.py", "file_name": "ConvertJson.py", "file_ext": "py", "file_size_in_byte": 7948, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.unique", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 87, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 195, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 197, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 210, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 215, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 220, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 225, "usage_type": "call"}]} +{"seq_id": "25590274260", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Dec 20 00:37:49 2017\r\n\r\n@author: Yamini\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport alpha_vantage as av\r\nfrom alpha_vantage.timeseries import TimeSeries\r\nimport bt\r\n#%%\r\nstocks=pd.read_csv('symbols.txt',sep='|')\r\nts=TimeSeries(key='AIGN7DB0PZGS3C4X', output_format='pandas')\r\n\r\n#%%\r\ndata2,meta=ts.get_daily_adjusted(symbol='ACUR',outputsize='full')\r\n\r\ndata=bt.get('BTC',start='2017-10-01')\r\n\r\n#%%\r\nimport scraper\r\n\r\ndata=get_data()\r\n\r\n\r\n#%%\r\ndata=pd.DataFrame(data)\r\n#%%\r\n\r\nimport pypyodbc\r\n\r\ncnxn = pypyodbc.connect(\"DRIVER={SQL Server};SERVER=DESKTOP-38U09HS\\SQLEXPRESS;DATABASE=StockData\")\r\n\r\ncursor=cnxn.cursor()\r\n\r\n\r\n\r\n#%%\r\ndata=pd.read_sql(\" select distinct ticker from companynames except select distinct symbol from historicalprices\",cnxn)\r\nfor i in data.ticker:\r\n try:\r\n data2,meta=ts.get_daily_adjusted(symbol=i,outputsize='full')\r\n data2['symbol']=str(i)\r\n data2.reset_index(level=0,inplace=True)\r\n cols=['symbol','Date', 'low', 'open', 'high', 'close', 'volume', 'adjusted close', 'split coefficient', 'dividend amount' ]\r\n data2=data2[cols]\r\n cursor.executemany(\"\"\" insert into historicalprices values(?,?,?,?,?,?,?,?,?,?) \"\"\",[list(c) for c in data2[cols].values])\r\n cnxn.commit()\r\n except:\r\n continue\r\n\r\n#%%update daily\r\nimport numpy as np \r\ndata=pd.read_sql(\" select distinct symbol,max(pricedate) pricedate from historicalprices group by symbol\",cnxn)\r\nfor i in data.symbol:\r\n try:\r\n data2,meta=ts.get_daily_adjusted(symbol=i,outputsize=10)\r\n data2['symbol']=str(i)\r\n data2.reset_index(level=0,inplace=True)\r\n cols=['symbol','Date', 'low', 'open', 'high', 'close', 'volume', 'adjusted close', 'split coefficient', 'dividend amount' ]\r\n data2=data2[cols]\r\n data2=data2[[np.datetime64(j) for j in data2.loc[data2.symbol==i,'Date'].values]>data.loc[data.symbol==i,'pricedate'].values[0]]\r\n cursor.executemany(\"\"\" insert into historicalprices values(?,?,?,?,?,?,?,?,?,?) \"\"\",[list(c) for c in data2[cols].values])\r\n cnxn.commit()\r\n except:\r\n continue\r\n #%%\r\nx= ['Ticker', 'Company', 'Country', 'Industry', 'Market Cap', 'Sector', 'Volume']\r\n\r\ndata=data[x]\r\n\r\ndata.to_csv('companynames.csv',index=False)\r\n#%%\r\nsql=\"\"\"\r\n\r\nselect * from historicalprices where symbol='aapl' and pricedate between '2015-01-01' and '2016-08-01'\r\n\r\n\"\"\"\r\n\r\nmsft=pd.read_sql(sql,cnxn)\r\nmsft.index=msft.pricedate\r\n#%%\r\nfrom matplotlib import pyplot as plt\r\nfrom pandas import read_csv\r\nfrom pandas import datetime\r\nfrom matplotlib import pyplot\r\nfrom pandas.plotting import autocorrelation_plot\r\n\r\ndef parser(x):\r\n\treturn datetime.strptime('190'+x, '%Y-%m')\r\n\r\n#series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\r\n#msft.plot(x='pricedate',y=['closeprice','low','high'])\r\nautocorrelation_plot(msft.closeprice)\r\npyplot.show()\r\n\r\n#%%\r\nfrom pandas import read_csv\r\nfrom pandas import datetime\r\nfrom pandas import DataFrame\r\nfrom statsmodels.tsa.arima_model import ARIMA\r\nfrom matplotlib import pyplot\r\n\r\ndef parser(x):\r\n\treturn datetime.strptime('190'+x, '%Y-%m')\r\n\r\n#series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\r\n# fit model\r\nmodel = ARIMA(msft.closeprice, order=(2,1,0))\r\nmodel_fit = model.fit()\r\nprint(model_fit.summary())\r\n# plot residual errors\r\nresiduals = DataFrame(model_fit.resid)\r\nresiduals.plot()\r\npyplot.show()\r\nresiduals.plot(kind='kde')\r\npyplot.show()\r\nprint(residuals.describe())\r\n\r\n#%%\r\nfrom pandas import read_csv\r\nfrom pandas import datetime\r\nfrom matplotlib import pyplot\r\nfrom statsmodels.tsa.arima_model import ARIMA\r\nfrom sklearn.metrics import mean_squared_error\r\nimport numpy as np\r\n\r\ndef parser(x):\r\n\treturn datetime.strptime('190'+x, '%Y-%m')\r\n\r\n#series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\r\nmsft['loghigh']=np.log(msft.high)\r\nX =msft.high\r\nsize = int(len(X) * 0.66)\r\ntrain, test = X[0:size], X[size:len(X)]\r\nhistory = [x for x in train]\r\npredictions = list()\r\nci=[]\r\nfor t in range(len(test)):\r\n model = ARIMA(history, order=(3,1,0))\r\n model_fit = model.fit(disp=0)\r\n output = model_fit.forecast()\r\n yhat = output[0]\r\n ci.append(output[2])\r\n predictions.append(yhat)\r\n obs = test[t]\r\n history.append(obs)\r\n# print('predicted=%f, expected=%f' % (yhat, obs))\r\nerror = mean_squared_error(test, predictions)\r\nprint('Test MSE: %.3f' % error)\r\n# plot\r\n#test['predicted']=[x[0] for x in predictions]\r\npyplot.plot((test.values),color='blue')\r\npyplot.plot((predictions), color='red')\r\nci=np.reshape(ci,newshape=(136,2))\r\n#pyplot.fill_between(range(len(test)),ci[:,0],ci[:,1],color='grey')\r\npyplot.show()\r\n\r\n#%%\r\n\r\nfrom sklearn.tree import DecisionTreeRegressor\r\nfrom sklearn.ensemble import AdaBoostRegressor\r\n# Create the dataset\r\nrng = np.random.RandomState(1)\r\nregr_1 = DecisionTreeRegressor(max_depth=4)\r\nregr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=10), n_estimators=500, random_state=rng)\r\n\r\n\r\nhistory=[x for x in train]\r\n\r\nx1=history[:-5]\r\nx2=history[1:-4]\r\nx3=history[2:-3]\r\nx4=history[3:-2]\r\nx5=history[4:-1]\r\n\r\nhistory2=history[5:]\r\n\r\nX=np.vstack((x1,x2,x3,x4,x5))\r\nX=X.T\r\n\r\n\r\nregr_1.fit(np.log(X),history2)\r\n\r\nregr_2.fit(np.log(X),history2)\r\n\r\nplt.figure()\r\n#plt.scatter(np.arange(len(history2)), history2, c=\"k\", label=\"training samples\")\r\n\r\ntest=msft.high[size-5:]\r\n\r\nx1=test[:-5]\r\nx2=test[1:-4]\r\nx3=test[2:-3]\r\nx4=test[3:-2]\r\nx5=test[4:-1]\r\n\r\nhistory2=test[5:]\r\n\r\nX=np.vstack((x1,x2,x3,x4,x5))\r\nX=X.T\r\n\r\n\r\ny_1=regr_1.predict(np.log(X))\r\n\r\ny_2=regr_2.predict(np.log(X))\r\n\r\n\r\nplt.plot(np.arange(len(history2)), history2, c=\"b\", label=\"testing samples\")\r\n\r\n#plt.plot(np.arange(len(y_1)), y_1, c=\"g\", label=\"n_estimators=1\", linewidth=2)\r\nplt.plot(np.arange(len(y_1)), y_2, c=\"r\", label=\"n_estimators=300\", linewidth=2)\r\nplt.xlabel(\"data\")\r\nplt.ylabel(\"target\")\r\nplt.title(\"Boosted Decision Tree Regression\")\r\nplt.legend()\r\nplt.show()\r\n#%%\r\nimport numpy as np\r\n\r\nfrom sklearn.tree import DecisionTreeRegressor\r\nfrom sklearn.ensemble import AdaBoostRegressor\r\n# Create the dataset\r\nrng = np.random.RandomState(1)\r\nregr_1 = DecisionTreeRegressor(max_depth=4)\r\nregr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=10), n_estimators=500, random_state=rng)\r\n\r\n\r\nhistory=[x for x in train]\r\n\r\nx1=history[:-9]\r\nx2=history[1:-8]\r\nx3=history[2:-7]\r\nx4=history[3:-6]\r\nx5=history[4:-5]\r\nx6=history[5:-4]\r\nhistory2=history[9:]\r\n\r\nX=np.vstack((x1,x2,x3,x4,x5))\r\nX=X.T\r\n\r\n\r\nregr_1.fit(np.log(X),history2)\r\n\r\nregr_2.fit(np.log(X),history2)\r\n\r\nplt.figure()\r\n#plt.scatter(np.arange(len(history2)), history2, c=\"k\", label=\"training samples\")\r\n\r\ntest=msft.high[size-5:]\r\nx1=test[:-9]\r\nx2=test[1:-8]\r\nx3=test[2:-7]\r\nx4=test[3:-6]\r\nx5=test[4:-5]\r\nx6=test[5:-4]\r\nhistory2=test[5:-4]\r\n\r\nX=np.vstack((x1,x2,x3,x4,x5))\r\nX=X.T\r\n\r\n\r\ny_1=regr_1.predict(np.log(X))\r\n\r\ny_2=regr_2.predict(np.log(X))\r\n\r\n\r\nplt.plot(np.arange(len(history2)), history2, c=\"b\", label=\"testing samples\")\r\n\r\n#plt.plot(np.arange(len(y_1)), y_1, c=\"g\", label=\"n_estimators=1\", linewidth=2)\r\nplt.plot(np.arange(len(y_1)), y_2, c=\"r\", label=\"n_estimators=300\", linewidth=2)\r\nplt.xlabel(\"data\")\r\nplt.ylabel(\"target\")\r\nplt.title(\"Boosted Decision Tree Regression\")\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n\r\n\r\n", "repo_name": "bharathbunny/Stockdata", "sub_path": "tickervalues.py", "file_name": "tickervalues.py", "file_ext": "py", "file_size_in_byte": 7381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "alpha_vantage.timeseries.TimeSeries", "line_number": 14, "usage_type": "call"}, {"api_name": "bt.get", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "pypyodbc.connect", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.datetime64", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.datetime.strptime", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 91, "usage_type": "name"}, {"api_name": "pandas.plotting.autocorrelation_plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "pandas.datetime.strptime", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 106, "usage_type": "name"}, {"api_name": "statsmodels.tsa.arima_model.ARIMA", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "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": "pandas.datetime.strptime", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 133, "usage_type": "call"}, {"api_name": "statsmodels.tsa.arima_model.ARIMA", "line_number": 141, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostRegressor", "line_number": 167, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 225, "usage_type": "attribute"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 226, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostRegressor", "line_number": 227, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}]} +{"seq_id": "32836262785", "text": "import os, time, warnings\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\n\nfrom .IO import ThreadedVideoStream as TVS\n#\n# Copyright 2020- IBM Inc. All rights reserved\n# SPDX-License-Identifier: Apache-2.0\n#\nfrom .pipeline import Pipeline\nfrom .helpers import utils\nfrom .detectors_descriptors import detect as ddd\nfrom .matchers import match\nfrom .filters import match_filters as mfilters\nfrom .interpolation import TPS\n# from .interpolation import rigid\nfrom .reflections import glare_detection as glare\n\n\n#%%\ndef overlay_query_points(img, match_list, keypoints):\n img0 = img.copy()\n for m in match_list:\n cv2.drawMarker(img0, tuple(map(int,keypoints[m.queryIdx].pt)), (0,255,0), cv2.MARKER_SQUARE, 1, cv2.LINE_4, 1)\n return img0\n\ndef overlay_train_points(img, match_list, keypoints):\n img0 = img.copy()\n for m in match_list:\n cv2.drawMarker(img0, tuple(map(int,keypoints[m.trainIdx].pt)), (0,255,0), cv2.MARKER_SQUARE, 1, cv2.LINE_4, 1)\n cv2.putText(img0, f'#matches={len(match_list)}', (300,350), cv2.FONT_HERSHEY_COMPLEX_SMALL, .8, (0,255,0))\n return img0\n\ndef resize_image_to_max(img, max_height_width):\n mh, mw = max_height_width\n h,w = img.shape[:2]\n fy = 1 if h<=mh else mh/h\n fx = 1 if w<=mw else mw/w\n f = min(fy,fx)\n return img if f>=1 else cv2.resize(img, None, None, f,f)\n\n\ndef assemble_output_frame6(vis1, grey1, vis1_warped, grey1_warped,\n vis0, initial_keypoints, last_matches, last_keypoints):\n \"\"\"Note: all images are assumed to be 3-channel, so make sure they are before calling\"\"\"\n return np.vstack((\n np.hstack( (vis1_warped, vis1,\n overlay_query_points(vis0, last_matches, initial_keypoints)\n ) ),\n np.hstack( (grey1_warped, grey1,\n overlay_train_points(vis1, last_matches, last_keypoints)\n ) )\n ))\n\ndef assemble_output_frame9(vis1, grey1, vis1_warped, grey1_warped,\n vis0, initial_keypoints, last_matches, last_keypoints, img_warped_grid):\n \"\"\"Note: all images are assumed to be 3-channel, so make sure they are before calling\"\"\"\n h,w = vis1.shape[:2]\n return np.vstack((\n assemble_output_frame6(vis1, grey1, vis1_warped, grey1_warped,\n vis0, initial_keypoints, last_matches, last_keypoints),\n np.hstack( (np.zeros((h, 2*w, 3), dtype=np.uint8),\n img_warped_grid[:,:,::-1])\n )\n ))\n\ndef assemble_output_frame2h(vis1, vis1_warped ):\n \"\"\"Note: all images are assumed to be 3-channel, so make sure they are before calling\"\"\"\n return np.hstack( (vis1, vis1_warped))\n\ndef assemble_output_frame2v(vis1_warped, grey1_warped):\n \"\"\"Note: all images are assumed to be 3-channel, so make sure they are before calling\"\"\"\n return np.vstack((vis1_warped, grey1_warped))\n\ndef assemble_output_frame4(vis1, grey1, vis1_warped, grey1_warped):\n \"\"\"Note: all images are assumed to be 3-channel, so make sure they are before calling\"\"\"\n return np.vstack((\n np.hstack( (vis1_warped, vis1 ) ),\n np.hstack( (grey1_warped, grey1))\n ))\n\n# %% Video stabilizer\n\nclass VideoStabilizer:\n \"\"\"A class wrapping the tedious setup and loop of frame acquisition, deformation\n estimation, deformation, and storage of video and potentially data.\n Example usage:\n\n >> vs = vidstab.VideoStabilizer('sample_data/synthetic_example.mp4',\n end=10.0,\n FPS=5,\n panel=(0,0,389,256), panel2=(0,256,389,256,1),\n output_layout='9',\n stats=True)\n >> vs.loop()\n\n which stabilize the first 10 seconds of the video. A video will be stored as\n 'sample_data/synthetic_example.mp4' and the results will be available as numpy arrays\n in 'video_stabilized_data.npz'.\n\n See the Init docstring for more information.\n \"\"\"\n def __init__(self,\n video, start:float=0.0, end:float=None, skip_frames:int=0,\n max_frames=None, FPS=None,\n min_matches=8, panel=None, panel2=None,\n store_numpy=True, numpy_outfile=None,\n video_outfile=None, output_layout='2h', out_grid=None,\n pipeline:Pipeline=None, interpolator=None, TPS_alpha:float=.1,\n lowe_ratio:float=.8,\n cam_stream_sleep=.5, resize_input_to=None\n ) -> None:\n \"\"\"A summary\n\n Parameters\n ----------\n video : str or endeform.IO.ThreadedVideoStream\n The path to the video. May be relative and include '~'.\n start : float, optional\n The time, in seconds, at which to begin processing the video, by default 0.0\n end : float, optional\n The time, in seconds, at which to stop, by default None. If None, then the\n video will be processed until it ends. If both `max_frames` and `end` are given, `max_frames` takes precedence.\n skip_frames : int, optional\n Frames to skip when stabilizing, by default 0. This results in the FPS of the\n stabilized video being 1/(1+skip_frames) of the original video FPS, but will,\n of course, increase processing speed.\n max_frames : int, optional\n Maximum amout of frames to be processed, by default None. If both `max_frames` and `end` are given, `max_frames` takes precedence.\n FPS : float, optional\n It is difficult to obtain the FPS of a video from the video file directly, you\n can provide it here explicitly. It is used to convert `end` to a number of\n frames, and to set the FPS of the resulting video; by default None, in which \n case the FPS are read from the cv2.VideoCapture object.\n min_matches : int, optional\n Processing will stop, if the current frame has less than `min_matches` with the\n initial frame, by default 15\n panel : tuple or list of (left,top,width,height), optional\n If specified, the video will be cropped to the corresponding rectangle before\n processing. That is helpful if the video is a composite view, or there are\n timestamps or similar metadata around the margins. If None, the whole video\n is processed, by default None\n panel2 : tuple or list of (left, top, width, height, channel(s) ), optional\n If speficied, this panel is stabilized using the deformation computed on the\n first panel. The data of this panel, stabilized, is stored as numpy files. If\n None, it's taken to be the same as the first panel, and if False, it is not\n used at all (no numpy data will be stored in this case), by default None.\n store_numpy : bool, optional\n Whether to store the resulting stabilized panel2 as a numpy file, by default True\n numpy_outfile : str, optional\n The filename for the numpy file. By default, it is be the name of the video\n + `_stabilized_data.npz`.\n video_outfile : str, optional\n The name of the produced output video, by default the name of the video +\n `_stabilized.mp4`.\n output_layout : str in {1,2h,2v,4,6,9}, optional\n The layout of the produced video, by default '2h'.\n Consider these \"building blocks\":\n F[t] : the current frame (if `panel` is set, then only that section of it)\n cF[t] : the current frame, with applied estimated deformation\n F2[t], cF2[t]: same as above, but the 2nd panel (if `panel2` is set)\n kF0[t]: F[0] overlaid with the keypoints that were matched with keypoints in F[t]\n kF[t] : F[t] overlaid with the keypoints that were matched with keypoints in F[0]\n G[t] : Regular grid, deformed by the estimated deformation between F[0] and F[t]\n Then the layouts are:\n '1' : [ cF[t] ]\n\n '2h' : [ F[t] cF[t] ]\n\n '2v' : [ cF[t] ]\n [ cF2[t] ]\n\n '4' : [ cF[t] F[t] ]\n [ cF2[t] F2[t] ]\n\n '6' : [ cF[t] F[t] kF0[t] ]\n [ cF2[t] F2[t] kF[t] ]\n\n '9' : [ cF[t] F[t] kF0[t] ]\n [ cF2[t] F2[t] kF[t] ]\n [ G[t] ]\n TODO: Add option for deformed grid spacing if part of layout\n out_grid : _type_, optional\n Not yet in use, by default None\n pipeline : Pipeline, optional\n The pipeline object used for the deformation estimation. By default, a pipeline\n of AKAZE detector, LATCH descriptor, Lowe matcher, and TPS is used.\n interpolator : , optional\n Not yet in use, by default None. Supply a Pipeline object if you want more\n freedom in the pipeline.\n TPS_alpha : float, optional\n If the default Pipeline is used, this is the alpha parameter for the TPS\n interpolator , by default .1\n lowe_ratio : float, optional\n The ratio in the default Lowe ration matcher, by default .8\n cam_stream_sleep : float\n If you're using a stream from a camera, there needs to be some time between \n setting up and acquiring the first frame. If you experience problems with \n completely black initial frames, try increasing this value. Default: .5 [seconds].\n resize_input_to : None or (w,h) tuple of int\n If set, the _input_ video is resized to (w,h) before any further processing.\n That means that `panel` and `panel2` are take from the _resized_ video. This\n can be helpful/necessary when dealing with cams of varying resolution.\n \"\"\"\n if isinstance(video, str):\n self.video_path = os.path.abspath(os.path.expanduser(video))\n self.video_stream = self.cam_id = None\n elif isinstance(video, int) and video>=0:\n # camera id provided\n self.video_path = self.video_stream = None\n self.cam_id = video\n else:\n self.video_stream = video\n self.video_path = self.cam_id = None\n\n self.start = start\n self.end = end\n self.skip_frames = skip_frames\n self.max_frames = max_frames\n self.FPS = FPS\n self.panel = panel\n self.panel2 = panel2\n\n self.store_numpy = store_numpy\n self.numpy_outfile = numpy_outfile\n\n self.output_layout = output_layout\n self.video_outfile = video_outfile\n self.out_grid = out_grid # NOTE: Only None supported for now\n\n self.min_matches = min_matches\n self.pipeline = pipeline\n self.interpolator = interpolator\n self.TPS_alpha = TPS_alpha\n\n self.lowe_ratio = lowe_ratio\n\n self.extrapolation_color = [0,255,0]\n\n self._c2 = None # number of channels in \"measurement\" panel, panel2 (if it is specified)\n self._warped_grid = False\n \n self.__setup(cam_stream_sleep, resize_input_to)\n\n def __setup(self, cam_stream_sleep, resize_input_to):\n \"\"\" Default values where needed, and initialization steps.\"\"\"\n\n # -- Video Stream\n # Figuring out what transform is needed on the read frames\n if self.panel is None:\n if self.panel2 is None or self.panel2 is False:\n frame_transform1 = lambda f: (f, None)\n else:\n raise ValueError(\"If `panel` is None, `panel2` can be only None or False.\")\n else:\n x,y,w,h = self.panel\n if self.panel2 is None or self.panel2 is False:\n frame_transform1 = lambda f: (f[y:y+h,x:x+w,...], None)\n else:\n x2,y2,w2,h2,c2 = self.panel2\n if w2!=w or h2!=h:\n raise ValueError(\"Panels have to have same size, but panel 1 \"\n f\"is {(w,h)} while panel2 is {(w2,h2)}\")\n frame_transform1 = lambda f: (f[y:y+h,x:x+w,...], f[y2:y2+h,x2:x2+w,c2])\n\n if resize_input_to is None:\n frame_transform = frame_transform1\n else:\n frame_transform = lambda f: frame_transform1(cv2.resize(f, resize_input_to))\n\n if self.panel2 is None: self._c2 = 3\n elif self.panel2 is False: self._c2 = None\n else: self._c2 = getattr(self.panel2[4], '__len__', lambda: 1)()\n # this should return len() if it's available (i.e. a list or so was given) and\n # simply 1 if it's not (i.e. a scalar was supplied)\n \n # setup video stream\n CAM_STREAM = False\n if self.video_path is not None:\n self.video_stream = TVS.FileVideoStream(self.video_path, offset_ms=1000*self.start)\n elif self.video_stream is None:\n self.video_stream = TVS.CamVideoStream(self.cam_id, FPS=self.FPS)\n CAM_STREAM = True # so we can wait a beat after starting the stream\n # else we have a stream already\n\n if self.FPS is None:\n self.FPS = self.video_stream.get(cv2.CAP_PROP_FPS)\n if self.FPS is None:\n ## STILL TO DO. WITH CAMERA IT IS UNCLEAR WHAT THE REPORTED FPS MEAN IF WE\n ## DIDN'T SET THEM MANUALLY\n warnings.warn(\"Video Stream FPS not available, likely because you're using \"\n \"a camera and the backend doesn't provide the FPS. Setting FPS=30 to \" \n \"avoid exceptions in further processing, but bear in mind that time is \"\n \"now unreliable.\")\n self.FPS = 30\n\n if self.max_frames is None:\n if self.end is None:\n max_frames = None\n else:\n max_frames = int( (self.end - self.start)*self.FPS/(1+self.skip_frames) )\n else:\n max_frames = self.max_frames\n self.max_frames = max_frames\n\n self._cap = TVS.VideoStream(stream=self.video_stream,\n transform=frame_transform,\n skip_frames=self.skip_frames,\n max_frames=max_frames,\n default_if_empty=(None, None))\n if CAM_STREAM:\n time.sleep(cam_stream_sleep) \n self._cap.start()\n\n # acquire first frame\n vis00, grey00 = self._cap.read()\n if vis00 is None:\n raise RuntimeError(\"Acquisition of first frame failed. Did you provide \"\n f'a valid video? I have {self.video_path}')\n self.initial_frame = vis00\n self.initial_frame2 = grey00 # could be None, and is likely not needed\n h,w = vis00.shape[:-1]\n self.w, self.h = (w,h)\n\n # -- set up the output video\n if self.video_outfile is None:\n self.video_outfile = os.path.splitext(self.video_path)[0] + '_stabilized.mp4'\n\n if self.out_grid is None:\n yy, xx = np.meshgrid(np.arange(self.h), np.arange(self.w))\n XY = np.column_stack( (xx.flat, yy.flat ) )\n self.out_grid = XY\n\n # Check if layout is compatible with output layout\n if self.panel2 is False and self.output_layout not in ('1','2h'):\n warnings.warn(f'Layout {self.output_layout} is incompatible with '\n '`panel2==False`. Changing to \"2h\"')\n self.output_layout='2h'\n\n if self.output_layout=='2h':\n self.__assemble_output = self.__assemble_output_2h\n self._output_frame_size = (2*w,h)\n self._warped_grid = False\n elif self.output_layout=='2v':\n self.__assemble_output = self.__assemble_output_2v\n self._output_frame_size = (w, 2*h)\n self._warped_grid = False\n elif self.output_layout=='4':\n self.__assemble_output = self.__assemble_output_4\n self._output_frame_size = (2*w, 2*h)\n self._warped_grid = False\n elif self.output_layout=='6':\n self.__assemble_output = self.__assemble_output_6\n self._output_frame_size = (3*w, 2*h)\n self._warped_grid = False\n elif self.output_layout=='9':\n self.__assemble_output = self.__assemble_output_9\n self._output_frame_size = (3*w, 3*h)\n self._warped_grid = True\n elif self.output_layout=='1':\n self.__assemble_output = self.__assemble_output_1\n self._output_frame_size = (w,h)\n self._warped_grid = False\n else:\n raise ValueError(f\"Unknown output layout {self.output_layout}\")\n\n self._writer = cv2.VideoWriter( self.video_outfile,\n cv2.VideoWriter_fourcc(*'avc1'),\n self.FPS//(self.skip_frames+1), self._output_frame_size, True)\n\n # -- set up pipeline\n if self.pipeline is None:\n akaze = ddd.cv2AKAZE(threshold=.0001) # detector descriptor\n latch = ddd.cv2LATCH() # descriptor only\n lowe = match.LoweRatioMatcherBinary(ratio=self.lowe_ratio)\n avg_trans = mfilters.average_translation_filter(factor=1.1)\n if self.interpolator is None or self.interpolator.upper()=='TPS':\n interpol = TPS.TPS(self.TPS_alpha)\n else:\n raise NotImplementedError(\n \"Only implemented TPS so far. Manually specify and supply Pipeline for more freedom\")\n self.pipeline = Pipeline(\n detector=akaze,\n descriptor=latch,\n keypoint_mask=glare.green_glare_mask,\n matcher=lowe,\n match_filter=avg_trans,\n interpolator=interpol,\n )\n\n # initialize pipeline\n self.pipeline.init(self.initial_frame)\n\n # -- storing numpy?\n if self.max_frames is None and self.store_numpy:\n warnings.warn('Storing results on disk is only possible if the number of '\n 'frames is known. Either specify `max_frames` or `end`.')\n self.store_numpy = False\n if self.panel2 is False and self.store_numpy:\n warnings.warn('Storing results on disk is only possible if panel2 is '\n 'specified')\n self.store_numpy = False\n if self.store_numpy:\n if self._c2 == 1:\n self.G = np.empty((h,w,self.max_frames), dtype=np.uint8)\n self.G[:,:,0] = grey00\n else:\n self.G = np.empty((h,w,self.max_frames, self._c2), dtype=np.uint8)\n self.G[:,:,0,...] = vis00 if self.panel2 is None else grey00\n self.G_valid = np.full((h,w,self.max_frames), True, dtype=bool)\n if self.numpy_outfile is None:\n self.numpy_outfile = os.path.splitext(self.video_path)[0] + '_stabilized_data.npz'\n\n def loop(self, return_arrays=False, stats=False, stats_every: int=60,\n live_view=False, live_view_window_name=\"Live View\", live_update_every: int=1,\n live_view_max_height=9999, live_view_max_width=9999):\n \"\"\"Starts the stabilization loop. Optionally displays some stats, and returns the\n numpy arrays corresponding to the stabilized values in panel2\n\n Parameters\n ----------\n return_arrays : bool, optional\n If True, returns `G`, `G_valid`, see below, else no value is returned, by default False.\n NOTE: Currently only available if `store_numpy` is True, because we pre-allocate\n the arrays, and without specifying (at least an upper bound on) the maximum\n number of frames, that isn't possible.\n stats : bool, optional\n If True, prints an update with FPS every `stats_every` frames, by default False\n stats_every : int, optional\n By default 60\n live_view : bool, optional\n If True, shows the produced video frame (according to `output_layout`) in an\n OpenCV GUI window and updates it every `live_view_every` frames.\n NOTE: The window should be resizeable, but often isn't. \n live_view_every : int, optional\n By default 1\n live_view_window_name : str\n By default 'Live View'.\n live_view_max_height, live_view_max_width : int, optional.\n By default 9999. If given, resizes the image before updating the live view, \n so that the height/width doesn't exceed the given value.\n Returns\n -------\n G : (h,w,N,c) or (h,w,N) np.array of uint8\n The values extracted from the stabilized `panel2`. N is the number of frames\n processed, and c is the number of channels. If c==1, the singleton dimension\n is removed.\n G_valid : (h,w,N) np.array of bool\n G_valid[y,x,f] == False, if pixel (x,y) in frame f corresponded to a location\n outside the frame (after deformation).\n \"\"\"\n frame_ctr = 0; t00 = time.perf_counter(); t0 = t00\n num_matches = [0] if self.max_frames is None else np.zeros(self.max_frames, dtype=int)\n # matched_indices = [[]] if self.max_frames is None else \\\n # [ [] for i in range(self.max_frames) ]\n\n if return_arrays and not self.store_numpy:\n warnings.warn('You did not set `store_numpy` to True, so we are not keeping'\n ' track of the arrays and will not be able to return them.')\n\n if live_view:\n cv2.namedWindow(live_view_window_name, flags=cv2.WINDOW_NORMAL)\n\n if stats: print('[INFO] Starting loop')\n while self._cap.isNotDone():\n # acquire next frame\n vis, grey = self._cap.read()\n\n if self.panel2 is None: grey = vis\n # estimate next deformation\n self.pipeline.step(vis)\n if len(self.pipeline._last_matches)%(funcName)s():%(lineno)s]%(levelname)s: %(message)s\"\nlogging.basicConfig(format=FORMAT)\nlogger.setLevel(logging.DEBUG)\n\napp = FastAPI()\n\nconfig = {\n 'host': '172.17.0.4',\n 'port': 5672, \n 'exchange' : 'animais'\n}\n\n@app.get(\"/\")\nasync def root():\n return {\n \"status\": \"SUCESS\",\n \"data\": \"NO DATAS\"\n }\n\n#--- Animais\n\n@app.get(\"/animal\")\nasync def get_all_animal():\n animais_query = session.query(Animal)\n animais = animais_query.all()\n return {\n \"status\": \"SUCESS\",\n \"data\": animais\n }\n\n@app.put(\"/animais\")\nasync def alterar_animal(request_animal: Request_Animal):\n try: \n animal_json = request_animal\n animal_query = session.query(Animal).filter(\n Animal.id==animal_json.id\n )\n animal = animal_query.first()\n print(animal.nome)\n animal.nome = animal_json.nome\n animal.raca = animal_json.raca\n animal.sexo = animal_json.sexo\n animal.categoria = animal_json.categoria\n animal.idade = animal_json.idade\n animal.id_fazenda = animal_json.id_fazenda\n animal.dt_nascimento = animal_json.dt_nascimento\n\n session.add(animal)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": animal_json\n }\n \n except Exception as e:\n return {\n \"status\": \"SUCESS\",\n \"data\": \"ANIMAL NÃO ENCONTRADO\"\n }\n\n@app.post(\"/animais\")\nasync def criar_animal(request_animal: Request_Animal):\n animal_json = request_animal\n print(animal_json.nome)\n\n animal = Animal(\n nome = animal_json.nome,\n raca = animal_json.raca,\n sexo = animal_json.sexo,\n categoria = animal_json.categoria,\n idade = animal_json.idade,\n id_fazenda = animal_json.id_fazenda\n\n )\n session.add(animal)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": animal_json\n }\n\n\n\n\n\n\n@app.get(\"/enviar_animais\", status_code=200)\nasync def get_all_animais():\n animais_to_send = []\n logger.info('Coletando as informações dos animais no banco de dados')\n try:\n animais_query = session.query(Animal)\n animais = animais_query.all()\n for animal in animais:\n item = {\n \"id\": animal.id,\n \"nome\": animal.nome,\n \"raca\": animal.raca,\n \"sexo\": animal.sexo,\n \"dt_nascimento\": animal.dt_nascimento,\n \"categoria\" : animal.categoria,\n \"idade\" : animal.idade,\n \"id_fazenda\" : animal.id_fazenda\n }\n animal_serializer = Request_Animal(**item)\n animais_to_send.append(animal_serializer)\n \n publisher = Publisher(config) \n logger.info('Enviando mensagem para o RabbitMQ') \n publisher.publish('routing_key', animal_serializer.model_dump_json().encode())\n except Exception as e:\n logger.error(f'Erro na consulta dos animais -- get_all_animais() -- {e}')\n print(e)\n return {\n \"status\": \"SUCESS\",\n \"result\": \"OK\"\n }\n\n\n\n\n\n#----- Fazendeiro\n\n@app.get(\"/fazendeiros\")\nasync def get_all_fazendeiro():\n fazendeiros_query = session.query(Fazendeiro)\n fazendeiros = fazendeiros_query.all()\n return {\n \"status\": \"SUCESS\",\n \"data\": fazendeiros\n }\n\n@app.put(\"/fazendeiros\")\nasync def alterar_fazendeiro(request_fazendeiro: Request_Fazendeiro):\n try: \n fazendeiro_json = request_fazendeiro\n fazendeiro_query = session.query(Fazendeiro).filter(\n Fazendeiro.idFazendeiro ==fazendeiro_json.idFazendeiro\n )\n fazendeiro = fazendeiro_query.first()\n print(fazendeiro.nome)\n fazendeiro.nome = fazendeiro_json.nome\n fazendeiro.dt_nascimento = fazendeiro_json.dt_nascimento\n fazendeiro.sexo = fazendeiro_json.sexo\n fazendeiro.endereco = fazendeiro_json.endereco\n fazendeiro.contato = fazendeiro_json.contato\n fazendeiro.senha = fazendeiro_json.senha\n fazendeiro.email = fazendeiro_json.email\n \n \n \n session.add(fazendeiro)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": fazendeiro_json\n }\n \n except Exception as e:\n return {\n \"status\": \"SUCESS\",\n \"data\": \"FAZENDEIRO NÃO ENCONTRADO\"\n }\n\n\n@app.post(\"/fazendeiros\")\nasync def criar_fazendeiro(request_fazendeiro: Request_Fazendeiro):\n fazendeiro_json = request_fazendeiro\n print(fazendeiro_json.nome)\n\n fazendeiro = Fazendeiro(\n nome = fazendeiro_json.nome,\n dt_nascimento = fazendeiro_json.dt_nascimento,\n sexo = fazendeiro_json.sexo,\n endereco = fazendeiro_json.endereco,\n contato = fazendeiro_json.contato,\n senha = fazendeiro_json.senha,\n email = fazendeiro_json.email\n )\n session.add(fazendeiro)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": fazendeiro_json\n }\n\n@app.get(\"/enviar_fazendeiros\", status_code=200)\nasync def get_all_fazendeiros():\n fazendeiros_to_send = []\n logger.info('Coletando as informações dos fazendeiros no banco de dados')\n try:\n fazendeiros_query = session.query(Fazendeiro)\n fazendeiros = fazendeiros_query.all()\n for fazendeiro in fazendeiros:\n item = {\n \"idFazendeiro\":fazendeiro.idFazendeiro,\n \"nome\":fazendeiro.nome,\n \"dt_nascimento\":fazendeiro.dt_nascimento,\n \"sexo\":fazendeiro.sexo ,\n \"endereco\":fazendeiro.endereco ,\n \"contato\":fazendeiro.contato ,\n \"email\": fazendeiro.email,\n \"senha\":fazendeiro.senha\n }\n fazendeiro_serializer = Request_Fazendeiro(**item)\n fazendeiros_to_send.append(fazendeiro_serializer)\n \n publisher = Publisher(config) \n logger.info('Enviando mensagem para o RabbitMQ') \n publisher.publish('routing_key', fazendeiro_serializer.model_dump_json().encode())\n except Exception as e:\n logger.error(f'Erro na consulta dos fazendeiros -- get_all_fazendeiros() -- {e}')\n print(e)\n return {\n \"status\": \"SUCESS\",\n \"result\": \"OK\"\n }\n\n\n\n\n\n\n\n#--- Fazenda\n\n@app.get(\"/fazendas\")\nasync def get_all_fazendas():\n fazendas_query = session.query(Fazenda)\n fazendas = fazendas_query.all()\n return {\n \"status\": \"SUCESS\",\n \"data\": fazendas\n }\n\n@app.put(\"/fazendas\")\nasync def alterar_fazenda(request_fazenda: Request_Fazenda):\n try: \n fazenda_json = request_fazenda\n fazenda_query = session.query(Fazenda).filter(\n Fazenda.idFazenda==fazenda_json.idFazenda\n )\n fazenda = fazenda_query.first()\n print(fazenda.nome)\n fazenda.idFazenda = fazenda_json.idFazenda\n fazenda.nome = fazenda_json.nome \n fazenda.endereco = fazenda_json.endereco\n fazenda.idFazendeiro = fazenda_json.idFazendeiro\n\n session.add(fazenda)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": fazenda_json\n }\n \n except Exception as e:\n return {\n \"status\": \"SUCESS\",\n \"data\": \"FAZENDA NÃO ENCONTRADO\"\n }\n\n\n@app.post(\"/fazendas\")\nasync def criar_fazenda(request_fazenda: Request_Fazenda):\n fazenda_json = request_fazenda\n print(fazenda_json.nome)\n\n fazenda = Fazenda(\n nome = fazenda_json.nome,\n endereco = fazenda_json.endereco,\n idFazendeiro = fazenda_json.idFazendeiro\n )\n session.add(fazenda)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": fazenda_json\n }\n\n@app.get(\"/enviar_fazendas\", status_code=200)\nasync def get_all_fazendas():\n fazendas_to_send = []\n logger.info('Coletando as informações dos fazendas no banco de dados')\n try:\n fazendas_query = session.query(Fazenda)\n fazendas = fazendas_query.all()\n for fazenda in fazendas:\n item = {\n \"idFazenda\": fazenda.idFazenda,\n \"nome\": fazenda.nome,\n \"endereco\": fazenda.endereco,\n \"idFazendeiro\": fazenda.idFazendeiro\n }\n fazenda_serializer = Request_Fazenda(**item) \n fazendas_to_send.append(fazenda_serializer)\n \n publisher = Publisher(config) \n logger.info('Enviando mensagem para o RabbitMQ') \n publisher.publish('routing_key', fazenda_serializer.model_dump_json().encode())\n except Exception as e:\n logger.error(f'Erro na consulta das fazendas -- get_all_fazendas() -- {e}')\n print(e)\n return {\n \"status\": \"SUCESS\",\n \"result\": \"OK\"\n }\n\n\n \n \n# --- Ordenha\n\n@app.get(\"/ordenhas\")\nasync def get_all_ordenhas():\n ordenhas_query = session.query(Ordenha)\n ordenhas = ordenhas_query.all()\n return {\n \"status\": \"SUCESS\",\n \"data\": ordenhas\n }\n\n@app.put(\"/ordenhas\")\nasync def alterar_ordenha(request_ordenha: Request_Ordenha):\n try: \n ordenha_json = request_ordenha\n ordenha_query = session.query(Ordenha).filter(\n Ordenha.idOrdenha==ordenha_json.idOrdenha\n )\n ordenha = ordenha_query.first()\n print(ordenha.idOrdenha)\n ordenha.qtdLeite = ordenha_json.qtdLeite\n ordenha.dataOrdenha = ordenha_json.dataOrdenha\n ordenha.idAnimal = ordenha_json.idAnimal\n\n session.add(ordenha)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": ordenha_json\n }\n \n except Exception as e:\n return {\n \"status\": \"SUCESS\",\n \"data\": \"ORDENHA NÃO ENCONTRADA\"\n }\n\n\n@app.post(\"/ordenhas\")\nasync def criar_ordenha(request_ordenha: Request_Ordenha):\n ordenha_json = request_ordenha\n print(ordenha_json.idOrdenha)\n\n ordenha = Ordenha(\n qtdLeite = ordenha_json.qtdLeite,\n dataOrdenha = ordenha_json.dataOrdenha,\n idAnimal = ordenha_json.idAnimal\n )\n session.add(ordenha)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": ordenha_json\n }\n\n@app.get(\"/enviar_ordenhas\", status_code=200)\nasync def get_all_ordenhas():\n ordenhas_to_send = []\n logger.info('Coletando as informações das ordenhas no banco de dados')\n try:\n ordenhas_query = session.query(Ordenha)\n ordenhas = ordenhas_query.all()\n for ordenha in ordenhas:\n item = {\n \"idOrdenha\": ordenha.idOrdenha,\n \"qtdLeite\": ordenha.qtdLeite,\n \"dataOrdenha\": ordenha.dataOrdenha,\n \"idAnimal\": ordenha.idAnimal\n }\n ordenha_serializer = Request_Ordenha(**item) \n ordenhas_to_send.append(ordenha_serializer)\n \n publisher = Publisher(config) \n logger.info('Enviando mensagem para o RabbitMQ') \n publisher.publish('routing_key', ordenha_serializer.model_dump_json().encode())\n except Exception as e:\n logger.error(f'Erro na consulta dos ordenhas -- get_all_ordenhas() -- {e}')\n print(e)\n return {\n \"status\": \"SUCESS\",\n \"result\": \"OK\"\n }\n\n\n\n#----- PESAGEM\n\n@app.get(\"/pesagem\")\nasync def get_all_pesagem():\n pesagens_query = session.query(Pesagem)\n pesagens = pesagens_query.all()\n return {\n \"status\": \"SUCESS\",\n \"data\": pesagens\n }\n\n@app.put(\"/pesagens\")\nasync def alterar_pesagem(request_pesagem: Request_Pesagem):\n try: \n pesagem_json = request_pesagem\n pesagem_query = session.query(Pesagem).filter(\n Pesagem.idPesagem==pesagem_json.idPesagem\n )\n pesagem = pesagem_query.first()\n print(pesagem.idPesagem)\n pesagem.peso = pesagem_json.peso\n pesagem.dataPesagem = pesagem_json.dataPesagem\n pesagem.idAnimal = pesagem_json.idAnimal\n\n\n session.add(pesagem)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": pesagem_json\n }\n \n except Exception as e:\n return {\n \"status\": \"SUCESS\",\n \"data\": \"pesagem NÃO ENCONTRADO\"\n }\n\n@app.post(\"/pesagens\")\nasync def criar_pesagem(request_pesagem: Request_Pesagem):\n pesagem_json = request_pesagem\n print(pesagem_json.idPesagem)\n\n pesagem = Pesagem(\n peso = pesagem_json.peso,\n dataPesagem = pesagem_json.dataPesagem,\n idAnimal = pesagem_json.idAnimal\n\n )\n session.add(pesagem)\n session.commit()\n\n return {\n \"status\": \"SUCESS\",\n \"data\": pesagem_json\n }\n\n\n@app.get(\"/enviar_pesagens\", status_code=200)\nasync def get_all_pesagens():\n pesagens_to_send = []\n logger.info('Coletando as informações dos pesagens no banco de dados')\n try:\n pesagens_query = session.query(Pesagem)\n pesagens = pesagens_query.all()\n for pesagem in pesagens:\n item = {\n \"idPesagem\": pesagem.idPesagem,\n \"peso\": pesagem.peso,\n \"dataPesagem\": pesagem.dataPesagem,\n \"idAnimal\": pesagem.idAnimal\n }\n pesagem_serializer = Request_Pesagem(**item) \n pesagens_to_send.append(pesagem_serializer)\n \n publisher = Publisher(config) \n logger.info('Enviando mensagem para o RabbitMQ') \n publisher.publish('routing_key', pesagem_serializer.model_dump_json().encode())\n except Exception as e:\n logger.error(f'Erro na consulta dos pesagens -- get_all_pesagens() -- {e}')\n print(e)\n return {\n \"status\": \"SUCESS\",\n \"result\": \"OK\"\n }\n", "repo_name": "artB-Java/net_farm_asa", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 14276, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "fastapi.FastAPI", "line_number": 13, "usage_type": "call"}, {"api_name": "models.session.query", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Animal", "line_number": 32, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 32, "usage_type": "name"}, {"api_name": "classes.Request_Animal", "line_number": 40, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 43, "usage_type": "call"}, {"api_name": "models.Animal", "line_number": 43, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 43, "usage_type": "name"}, {"api_name": "models.Animal.id", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.Animal", "line_number": 44, "usage_type": "name"}, {"api_name": "models.session.add", "line_number": 56, "usage_type": "call"}, {"api_name": "models.session", "line_number": 56, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 57, "usage_type": "call"}, {"api_name": "models.session", "line_number": 57, "usage_type": "name"}, {"api_name": "classes.Request_Animal", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Animal", "line_number": 75, "usage_type": "call"}, {"api_name": "models.session.add", "line_number": 84, "usage_type": "call"}, {"api_name": "models.session", "line_number": 84, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 85, "usage_type": "call"}, {"api_name": "models.session", "line_number": 85, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 102, "usage_type": "call"}, {"api_name": "models.Animal", "line_number": 102, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 102, "usage_type": "name"}, {"api_name": "classes.Request_Animal", "line_number": 115, "usage_type": "call"}, {"api_name": "publisher.Publisher", "line_number": 118, "usage_type": "call"}, {"api_name": "publisher.publish", "line_number": 120, "usage_type": "call"}, {"api_name": "models.session.query", "line_number": 137, "usage_type": "call"}, {"api_name": "models.Fazendeiro", "line_number": 137, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 137, "usage_type": "name"}, {"api_name": "classes.Request_Fazendeiro", "line_number": 145, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 148, "usage_type": "call"}, {"api_name": "models.Fazendeiro", "line_number": 148, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 148, "usage_type": "name"}, {"api_name": "models.Fazendeiro.idFazendeiro", "line_number": 149, "usage_type": "attribute"}, {"api_name": "models.Fazendeiro", "line_number": 149, "usage_type": "name"}, {"api_name": "models.session.add", "line_number": 163, "usage_type": "call"}, {"api_name": "models.session", "line_number": 163, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 164, "usage_type": "call"}, {"api_name": "models.session", "line_number": 164, "usage_type": "name"}, {"api_name": "classes.Request_Fazendeiro", "line_number": 179, "usage_type": "name"}, {"api_name": "models.Fazendeiro", "line_number": 183, "usage_type": "call"}, {"api_name": "models.session.add", "line_number": 192, "usage_type": "call"}, {"api_name": "models.session", "line_number": 192, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 193, "usage_type": "call"}, {"api_name": "models.session", "line_number": 193, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 205, "usage_type": "call"}, {"api_name": "models.Fazendeiro", "line_number": 205, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 205, "usage_type": "name"}, {"api_name": "classes.Request_Fazendeiro", "line_number": 218, "usage_type": "call"}, {"api_name": "publisher.Publisher", "line_number": 221, "usage_type": "call"}, {"api_name": "publisher.publish", "line_number": 223, "usage_type": "call"}, {"api_name": "models.session.query", "line_number": 242, "usage_type": "call"}, {"api_name": "models.Fazenda", "line_number": 242, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 242, "usage_type": "name"}, {"api_name": "classes.Request_Fazenda", "line_number": 250, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 253, "usage_type": "call"}, {"api_name": "models.Fazenda", "line_number": 253, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 253, "usage_type": "name"}, {"api_name": "models.Fazenda.idFazenda", "line_number": 254, "usage_type": "attribute"}, {"api_name": "models.Fazenda", "line_number": 254, "usage_type": "name"}, {"api_name": "models.session.add", "line_number": 263, "usage_type": "call"}, {"api_name": "models.session", "line_number": 263, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 264, "usage_type": "call"}, {"api_name": "models.session", "line_number": 264, "usage_type": "name"}, {"api_name": "classes.Request_Fazenda", "line_number": 279, "usage_type": "name"}, {"api_name": "models.Fazenda", "line_number": 283, "usage_type": "call"}, {"api_name": "models.session.add", "line_number": 288, "usage_type": "call"}, {"api_name": "models.session", "line_number": 288, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 289, "usage_type": "call"}, {"api_name": "models.session", "line_number": 289, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 301, "usage_type": "call"}, {"api_name": "models.Fazenda", "line_number": 301, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 301, "usage_type": "name"}, {"api_name": "classes.Request_Fazenda", "line_number": 310, "usage_type": "call"}, {"api_name": "publisher.Publisher", "line_number": 313, "usage_type": "call"}, {"api_name": "publisher.publish", "line_number": 315, "usage_type": "call"}, {"api_name": "models.session.query", "line_number": 331, "usage_type": "call"}, {"api_name": "models.Ordenha", "line_number": 331, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 331, "usage_type": "name"}, {"api_name": "classes.Request_Ordenha", "line_number": 339, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 342, "usage_type": "call"}, {"api_name": "models.Ordenha", "line_number": 342, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 342, "usage_type": "name"}, {"api_name": "models.Ordenha.idOrdenha", "line_number": 343, "usage_type": "attribute"}, {"api_name": "models.Ordenha", "line_number": 343, "usage_type": "name"}, {"api_name": "models.session.add", "line_number": 351, "usage_type": "call"}, {"api_name": "models.session", "line_number": 351, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 352, "usage_type": "call"}, {"api_name": "models.session", "line_number": 352, "usage_type": "name"}, {"api_name": "classes.Request_Ordenha", "line_number": 367, "usage_type": "name"}, {"api_name": "models.Ordenha", "line_number": 371, "usage_type": "call"}, {"api_name": "models.session.add", "line_number": 376, "usage_type": "call"}, {"api_name": "models.session", "line_number": 376, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 377, "usage_type": "call"}, {"api_name": "models.session", "line_number": 377, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 389, "usage_type": "call"}, {"api_name": "models.Ordenha", "line_number": 389, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 389, "usage_type": "name"}, {"api_name": "classes.Request_Ordenha", "line_number": 398, "usage_type": "call"}, {"api_name": "publisher.Publisher", "line_number": 401, "usage_type": "call"}, {"api_name": "publisher.publish", "line_number": 403, "usage_type": "call"}, {"api_name": "models.session.query", "line_number": 418, "usage_type": "call"}, {"api_name": "models.Pesagem", "line_number": 418, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 418, "usage_type": "name"}, {"api_name": "classes.Request_Pesagem", "line_number": 426, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 429, "usage_type": "call"}, {"api_name": "models.Pesagem", "line_number": 429, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 429, "usage_type": "name"}, {"api_name": "models.Pesagem.idPesagem", "line_number": 430, "usage_type": "attribute"}, {"api_name": "models.Pesagem", "line_number": 430, "usage_type": "name"}, {"api_name": "models.session.add", "line_number": 439, "usage_type": "call"}, {"api_name": "models.session", "line_number": 439, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 440, "usage_type": "call"}, {"api_name": "models.session", "line_number": 440, "usage_type": "name"}, {"api_name": "classes.Request_Pesagem", "line_number": 454, "usage_type": "name"}, {"api_name": "models.Pesagem", "line_number": 458, "usage_type": "call"}, {"api_name": "models.session.add", "line_number": 464, "usage_type": "call"}, {"api_name": "models.session", "line_number": 464, "usage_type": "name"}, {"api_name": "models.session.commit", "line_number": 465, "usage_type": "call"}, {"api_name": "models.session", "line_number": 465, "usage_type": "name"}, {"api_name": "models.session.query", "line_number": 478, "usage_type": "call"}, {"api_name": "models.Pesagem", "line_number": 478, "usage_type": "argument"}, {"api_name": "models.session", "line_number": 478, "usage_type": "name"}, {"api_name": "classes.Request_Pesagem", "line_number": 487, "usage_type": "call"}, {"api_name": "publisher.Publisher", "line_number": 490, "usage_type": "call"}, {"api_name": "publisher.publish", "line_number": 492, "usage_type": "call"}]} +{"seq_id": "22120806614", "text": "\nimport os\nimport sys\n\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install\n\nVERSION = \"1.1.8\"\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\n\nclass VerifyVersionCommand(install):\n \"\"\"Custom command to verify that the git tag matches our version\"\"\"\n description = 'verify that the git tag matches our version'\n\n def run(self):\n tag = os.getenv('CIRCLE_TAG')\n\n if tag != VERSION:\n info = \"Git tag: {0} does not match the version of this app: {1}\".format(\n tag, VERSION\n )\n sys.exit(info)\n\nsetup(\n name=\"typetastic\",\n version=VERSION,\n author=\"Doug Bridgens\",\n author_email=\"typetastic@far-oeuf.com\",\n keywords='automation screencast videotut',\n description=\"Python tool for building great screencasts, presentations, video tutorials..\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/thisdougb/typetastic\",\n project_urls={\n \"Bug Tracker\": \"https://github.com/thisdougb/typetastic/issues\",\n \"Source Code\": \"https://github.com/thisdougb/typetastic\",\n },\n packages=find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Operating System :: POSIX :: Linux\",\n \"Topic :: Education :: Computer Aided Instruction (CAI)\"\n ],\n python_requires='>=3.6',\n install_requires=[\n 'getch>=1.0',\n 'pexpect>=4.8.0',\n 'PyYAML>=5.3.1'\n ],\n cmdclass={\n 'verify': VerifyVersionCommand,\n }\n)\n", "repo_name": "thisdougb/typetastic", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "setuptools.command.install.install", "line_number": 14, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 25, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 27, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "7793204868", "text": "import threading\nimport MyVehicle\nimport json\nimport time\n\nMODE_LAND = 'LAND'\nMODE_GUIDED = 'GUIDED'\nMODE_RTL = 'RTL'\nMODE_LOITER = 'LOITER'\n\nclass ControlStation:\n def __init__(self, from_dronology, to_dronology, connection, drone):\n self.connection = connection\n self.from_dronology = from_dronology\n self.to_dronology = to_dronology\n self.drone = drone\n self.keep_running = 1\n self.ready_to_start = 0\n self.vehicle = None\n \n def start(self):\n while self.keep_running:\n in_msgs = self.from_dronology.get_messages()\n for message in in_msgs:\n msg = json.loads(message)\n threading.Thread(target=self.handle_message, args=(msg,)).start()\n time.sleep(.1)\n \n in_msgs = self.to_dronology.get_messages()\n for msg in in_msgs:\n threading.Thread(target=self.to_dronology_thread, args=(msg,)).start()\n time.sleep(.1)\n time.sleep(1)\n self.vehicle.send_state_message()\n \n def to_dronology_thread(self, message):\n success = self.connection.send(str(message))\n if not success:\n self.to_dronology.put_message(message)\n \n def add_vehicle(self):\n vehicle = MyVehicle.Copter(self.drone, self.to_dronology)\n vehicle.connect_vehicle()\n self.vehicle = vehicle\n \n def handle_message(self, msg):\n if msg['command'] == \"gotoLocation\":\n self.vehicle.goto(msg['data']['x'], msg['data']['y'], msg['data']['z'])\n elif msg['command'] == \"setArmed\":\n self.vehicle.set_armed(msg['data']['armed'])\n elif msg['command'] == \"setGroundspeed\":\n self.vehicle.set_groundspeed(msg['data']['speed'])\n elif msg['command'] == \"setHome\":\n self.vehicle.set_home(msg['data']['x'], msg['data']['y'], msg['data']['z'])\n elif msg['command'] == \"setMode\":\n self.vehicle.set_mode(msg['data']['mode'])\n elif msg['command'] == \"takeoff\":\n self.vehicle.takeoff(msg['data']['altitude'])\n else:\n print(\"Error: no command {}\".format(msg['command']))\n \n \n \n", "repo_name": "jwalke17/uavclass", "sub_path": "hw3/controlstation.py", "file_name": "controlstation.py", "file_ext": "py", "file_size_in_byte": 2248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "MyVehicle.Copter", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "20365500826", "text": "from time import sleep\nimport requests\nimport json\n#Get data from OpenWeather for current values\n#Get data from weather.gov forecast\n#Get data from weather.gov alerts\nLATITUDE = 38.73121\nLONGITUDE = -96.74154\nAPI_KEY = '37fe7dced1adaf904d0ca7f5e66ff95b'\n\ndef get_current_conditions(latitude, longitude, api_key):\n open_weather_url = 'https://api.openweathermap.org/data/2.5/onecall?'\n url_params = 'lat=%d&lon=%d&exclude=daily,hourly,minutely&appid=%s&units=imperial'%(latitude, longitude, api_key)\n final_url = open_weather_url + url_params\n response = requests.get(final_url)\n json_response = response.json()\n return json_response\n\ndef get_forecast_url_and_county_code(latitude, longitude):\n weather_gov_url = 'https://api.weather.gov/points/%s,%s'%(latitude, longitude)\n response = requests.get(weather_gov_url)\n json_response = response.json()\n forecast_url = json_response['properties']['forecast']\n county_code = json_response['properties']['county'].split('/')[-1]\n return [forecast_url, county_code]\n\ndef get_forecast(forecast_url):\n response = requests.get(forecast_url)\n json_response = response.json()\n return json_response\n\ndef get_alerts(county_code):\n alerts_url = 'https://api.weather.gov/alerts/active/zone/%s'%county_code\n response = requests.get(alerts_url)\n json_response = response.json()\n print(json_response['features'])\n return json_response\n\n# Step 1. Get the forecast URL and county code.\nprint('... get forecast url and county code')\n[forecast_url, county_code] = get_forecast_url_and_county_code(LATITUDE, LONGITUDE)\nprint('forecast url:', forecast_url)\nprint('county code:', county_code)\nprint('')\n\n# Step 2. Get weather reports every n seconds.\nwhile True:\n print('... get current conditions')\n current_conditions = get_current_conditions(LATITUDE, LONGITUDE, API_KEY);\n print(json.dumps(current_conditions, indent=4))\n print('')\n \n print('... get forecast')\n forecast = get_forecast(forecast_url)\n print(json.dumps(forecast, indent=4))\n print('')\n\n print('... get alerts')\n alerts = get_alerts(county_code)\n print(json.dumps(alerts, indent=4))\n print('')\n \n print('')\n print('')\n \n sleep(15)", "repo_name": "FrancoRosa/weather-pi", "sub_path": "rpi/weather_pi.py", "file_name": "weather_pi.py", "file_ext": "py", "file_size_in_byte": 2158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "28472875680", "text": "# the json module to work with json files \r\nimport json\r\nimport tkinter\r\nfrom tkinter import *\r\nimport random\r\n\r\n\r\n# questions = [\r\n# \"How many Keywords are there in C Programming language ?\",\r\n# \"Which of the following functions takes A console Input in Python ?\",\r\n# \"In which language is Python written?\",\r\n# \"Which of The Following is must to Execute a Python Code ?\",\r\n# \"Which character is used in Python to make a single line comment?\",\r\n# \"The append Method adds value to the list at the ?\",\r\n# \"What do we use to define a block of code in Python language\",\r\n# \"Which of The following is executed in browser(client side) ?\",\r\n# \"Which of the following keyword is used to create a function in Python ?\",\r\n# \"To Declare a Global variable in python we use the keyword ?\",\r\n# ]\r\n\r\n# answers_choice = [\r\n# [\"23\",\"32\",\"33\",\"43\",],\r\n# [\"get()\",\"input()\",\"gets()\",\"scan()\",],\r\n# [\"English\",\"PHP\",\"C\",\"All of the above\",],\r\n# [\"TURBO C\",\"Py Interpreter\",\"Notepad\",\"IDE\",],\r\n# [\"//\",\"/\",\"#\",\"!\",],\r\n# [\"custom location\",\"end\",\"center\",\"beginning\",],\r\n# [\"Indentation\",\"Key\",\"Brackets\",\"Non of these\",],\r\n# [\"perl\",\"css\",\"python\",\"java\",],\r\n# [\"function\",\"void\",\"fun\",\"def\",],\r\n# [\"all\",\"var\",\"let\",\"global\",],\r\n# ] \r\n\r\n# load questions and answer choices from json file instead of the file\r\nwith open('./data.json', encoding=\"utf8\") as f:\r\n data = json.load(f)\r\n\r\n\r\nquestions = [v for v in data[0].values()]\r\nanswers_choice = [v for v in data[1].values()]\r\n\r\nanswers = [1,1,2,1,2,1,0,1,3,3] \r\n\r\nuser_answer = []\r\n\r\nindexes = []\r\ndef gen():\r\n global indexes\r\n while(len(indexes) < 5):\r\n x = random.randint(0,9)\r\n if x in indexes:\r\n continue\r\n else:\r\n indexes.append(x)\r\n\r\n\r\ndef showresult(score):\r\n lblQuestion.destroy()\r\n r1.destroy()\r\n r2.destroy()\r\n r3.destroy()\r\n r4.destroy()\r\n #labelimage = Label(\r\n # root,\r\n # background = \"#000000\",\r\n # border = 0,\r\n #)\r\n #labelimage.pack(pady=(50,30))\r\n labelresulttext = Label(\r\n root,\r\n font = (\"Calibri\",20),\r\n background = \"#86CED2\",\r\n foreground = \"#18252A\",\r\n )\r\n labelresulttext.pack(pady=(170,50))\r\n if score == 25:\r\n # img = PhotoImage(file=\"great.png\")\r\n # labelimage.configure(image=img)\r\n # labelimage.image = img\r\n labelresulttext.configure(text=\"Well done!!\\n\\nyou've got 25 marks out of 25.\")\r\n elif (score == 20):\r\n # img = PhotoImage(file=\"ok.png\")\r\n # labelimage.configure(image=img)\r\n # labelimage.image = img\r\n labelresulttext.configure(text=\"Excellent!!\\n\\n you've got 20 out of 25.\")\r\n elif (score == 15):\r\n #img = PhotoImage(file=\"ok.png\")\r\n # labelimage.configure(image=img)\r\n # labelimage.image = img\r\n labelresulttext.configure(text=\"Keep it up!!\\n\\nyou've got 15 marks out of 25.\")\r\n elif (score == 10):\r\n \r\n # img = PhotoImage(file=\"bad.png\")\r\n # labelimage.configure(image=img)\r\n # labelimage.image = img\r\n labelresulttext.configure(text=\"Not satifactory!!\\n\\nYou've got 10 marks out of 25.\")\r\n else:\r\n # img = PhotoImage(file=\"bad.png\")\r\n # labelimage.configure(image=img)\r\n # labelimage.image = img\r\n labelresulttext.configure(text=\"You are failed this quiz!!\\n\\nYou've got 5 marks out of 25.\")\r\n\r\n\r\ndef calc():\r\n global indexes,user_answer,answers\r\n x = 0\r\n score = 0\r\n for i in indexes:\r\n if user_answer[x] == answers[i]:\r\n score = score + 5\r\n x += 1\r\n print(score)\r\n showresult(score)\r\n\r\n\r\nques = 1\r\ndef selected():\r\n global radiovar,user_answer\r\n global lblQuestion,r1,r2,r3,r4\r\n global ques\r\n x = radiovar.get()\r\n user_answer.append(x)\r\n radiovar.set(-1)\r\n if ques < 5:\r\n lblQuestion.config(text= questions[indexes[ques]])\r\n r1['text'] = answers_choice[indexes[ques]][0]\r\n r2['text'] = answers_choice[indexes[ques]][1]\r\n r3['text'] = answers_choice[indexes[ques]][2]\r\n r4['text'] = answers_choice[indexes[ques]][3]\r\n ques += 1\r\n else:\r\n calc()\r\n \r\n\r\n\r\n\r\n\r\ndef startquiz():\r\n global lblQuestion,r1,r2,r3,r4\r\n lblQuestion = Label(\r\n root,\r\n text = questions[indexes[0]],\r\n font = (\"Times New Roman\", 18),\r\n background = \"#86CED2\",\r\n foreground = \"#18252A\",\r\n width = 500,\r\n justify = \"center\",\r\n wraplength = 400,\r\n #background = \"#ffffff\",\r\n )\r\n lblQuestion.pack(pady=(100,30))\r\n\r\n global radiovar\r\n radiovar = IntVar()\r\n radiovar.set(-1)\r\n\r\n r1 = Radiobutton(\r\n root,\r\n text = answers_choice[indexes[0]][0],\r\n font = (\"Calibri\", 18),\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n value = 0,\r\n variable = radiovar,\r\n justify = \"left\",\r\n command = selected,\r\n \r\n )\r\n r1.pack(pady=5)\r\n\r\n r2 = Radiobutton(\r\n root,\r\n text = answers_choice[indexes[0]][1],\r\n font = (\"Calibri\", 18),\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n value = 1,\r\n variable = radiovar,\r\n justify = \"left\",\r\n command = selected,\r\n \r\n )\r\n r2.pack(pady=5)\r\n\r\n r3 = Radiobutton(\r\n root,\r\n text = answers_choice[indexes[0]][2],\r\n font = (\"Calibri\", 18),\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n value = 2,\r\n variable = radiovar,\r\n justify = \"left\",\r\n command = selected,\r\n \r\n )\r\n r3.pack(pady=5)\r\n\r\n r4 = Radiobutton(\r\n root,\r\n text = answers_choice[indexes[0]][3],\r\n font = (\"Calibrir\", 18),\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n value = 3,\r\n variable = radiovar,\r\n justify = \"left\",\r\n command = selected,\r\n )\r\n r4.pack(pady=5)\r\n\r\n\r\ndef startIspressed():\r\n labeltext.destroy()\r\n lblinstruction.destroy()\r\n btnstart.destroy()\r\n gen()\r\n startquiz()\r\n\r\n\r\n\r\nroot = tkinter.Tk()\r\nroot.title(\"Quizstar\")\r\nroot.geometry(\"700x600\")\r\nimg0 = PhotoImage(file=\"wawa1.Png\")\r\nroot.resizable(0,0)\r\n#root.config(background=\"#ffffff\")\r\n\r\nbgimglabel = Label(\r\n root,\r\n image = img0,\r\n)\r\nbgimglabel.place(x=0, y=0, relwidth=1, relheight=1)\r\n\r\nlabeltext = Label(\r\n root,\r\n text = \"Quiz Star\",\r\n font = (\"Showcard Gothic\",24,\"bold\"),\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n justify = \"center\",\r\n \r\n)\r\nlabeltext.pack(pady=(100,60))\r\n\r\n\r\nimg2 = PhotoImage(file=\"Untitled-9.Png\")\r\n\r\nbtnstart = Button(\r\n root,\r\n image = img2,\r\n #background1 = \"#ffffff\", \r\n background = \"#FFEC00\",\r\n #foreground = \"#000000\",\r\n relief = FLAT,\r\n border = 0,\r\n command = startIspressed,\r\n justify = \"center\",\r\n \r\n)\r\nbtnstart.pack(pady = (30))\r\n\r\nlblinstruction = Label(\r\n root,\r\n text = \"Click start once you are ready\",\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n font = (\"Calibri\",16),\r\n justify = \"center\",\r\n)\r\nlblinstruction.pack(pady = (60,30))\r\n\r\nlbrules = Label(\r\n root,\r\n text = \"Good luck.\",\r\n width = 100,\r\n font = (\"Calibri\",14),\r\n background = \"#FFEC00\",\r\n foreground = \"#18252A\",\r\n)\r\nlbrules.pack(pady=(40,0))\r\n\r\nroot.mainloop()", "repo_name": "hibakanwal106/Quiz-Game-Project", "sub_path": "project/quizstar.py", "file_name": "quizstar.py", "file_ext": "py", "file_size_in_byte": 7392, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "json.load", "line_number": 36, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 222, "usage_type": "call"}]} +{"seq_id": "72376732848", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseNotFound, HttpResponseRedirect\nfrom django.urls import reverse\n\nmonthly_challenges = {\n \"january\": \"Eat no meat\",\n \"february\": \"Walk for at least 20 minutes every day!\",\n \"march\": \"Learn Data Structures and Algorithms\",\n \"april\": \"Learn Django\",\n \"may\": \"Prepare and apply for interview\",\n \"june\": \"Start internship\",\n \"july\": \"Work hard\",\n \"august\": \"Travel at least 3 cities\",\n \"september\": \"Back to university\",\n \"october\": \"start the last semester\",\n \"november\": \"prepare for CPT\",\n \"december\": \"apply for graduation\"\n}\n\n# Create your views here.\n\ndef index(request):\n list_items = \"\"\n months = list(monthly_challenges.keys())\n\n for month in months:\n capitalized_month = month.capitalize()\n month_path = reverse(\"month-challenge\", args=[month])\n list_items += f\"
  • {capitalized_month}
    • \"\n\n response_data = f\"
      {list_items}
    \"\n return HttpResponse(response_data)\n\ndef monthly_challenge_by_number(requet, month):\n months = list(monthly_challenges.keys())\n\n if month > len(months):\n return HttpResponseNotFound(\"Invalid month\")\n\n redirect_month = months[month - 1]\n redirect_path = reverse(\"month-challenge\", args=[redirect_month]) # /challange/january\n return HttpResponseRedirect(redirect_path)\n\ndef monthly_challenge(request, month):\n try:\n challenge_text = monthly_challenges[month]\n return render(request, \"challenges/challenge.html\")\n except:\n return HttpResponseNotFound(\"

    This month is not supported

    \")", "repo_name": "DrErkinbek/django", "sub_path": "monthly_challanges/challenges/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "2", "api": [{"api_name": "django.urls.reverse", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 41, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "37747008450", "text": "\"\"\"vulnerable URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\n\nfrom django.urls import path, include\nfrom camara import views\n\nfrom admisiones import views as viewsAdmisiones\n\nfrom usuarios import views as viewsUsuarios\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom clinico import views as viewsClinico\n#from mecanicosPacientes import views as viewsmecanicosPacientes\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('acceso/', views.acceso),\n\n # Acceso al Programa\n\n path('menu/', views.menu),\n # path('menuAcceso/validaAcceso/', views.validaAcceso),\n path('contrasena/', views.contrasena),\n # path('salir/validaAcceso/', views.validaAcceso),\n\n # HISTORIA CLINICA\n\n #path('accesoEspecialidadMedico/historiaView/', viewsClinico.nuevoView.as_view()),\n #path('historia1View/', viewsClinico.historia1View),\n #path('historiaExamenesView/', viewsClinico.historiaExamenesView),\n # path('consecutivo_folios/', viewsClinico.consecutivo_folios),\n # path('buscaExamenes/', viewsClinico.buscaExamenes),\n path('motivoSeñas/', viewsClinico.motivoSeñas),\n path('subjetivoSeñas/', viewsClinico.subjetivoSeñas),\n path('motivoInvidente/', viewsClinico.motivoInvidente),\n # path('resMotivoInvidente/', viewsClinico.s),\n path('reconocerAudio/', views.reconocerAudio),\n path('reproduceAudio/', views.reproduceAudio),\n path('accesoEspecialidadMedico/', views.accesoEspecialidadMedico),\n path('crearHistoriaClinica/', viewsClinico.crearHistoriaClinica),\n #path('crearHistoriaClinica1/', viewsClinico.crearHistoriaClinica1.as_view()),\n path('buscarAdmisionClinico/', viewsClinico.buscarAdmisionClinico),\n path('cargaPanelMedico/', viewsClinico.cargaPanelMedico),\n path('buscarAntecedentes/', viewsClinico.buscarAntecedentes),\n\n\n # Actividaes Mecanicas\n\n path('prueba/', viewsClinico.prueba),\n # path('manejoLuz/', viewsmecanicosPacientes.manejoLuz.as_view()),\n # path('ambienteMusical/', viewsmecanicosPacientes.ambienteMusical.as_view()),\n path('camara/', views.camara),\n path('leeAudio/', views.leeAudio),\n\n # Admisiones\n\n\n path('chaining/', include('smart_selects.urls')),\n path('menuAcceso/', viewsAdmisiones.menuAcceso),\n path('validaAcceso/', viewsAdmisiones.validaAcceso),\n\n path('retornarAdmision/, , , , ', viewsAdmisiones.retornarAdmision),\n\n path('salir/', viewsAdmisiones.salir),\n path('grabar1/,,,',\n viewsAdmisiones.validaPassword),\n path('findOne/ , , /', viewsAdmisiones.Modal),\n # path('buscarAdmision/,,,,,', viewsAdmisiones.buscarAdmision),\n path('buscarAdmision/', viewsAdmisiones.buscarAdmision),\n\n path('buscarEspecialidadesMedicos/', viewsAdmisiones.buscarEspecialidadesMedicos),\n path('buscarCiudades/', viewsAdmisiones.buscarCiudades),\n path('buscarHabitaciones/', viewsAdmisiones.buscarHabitaciones),\n path('buscarSubServicios/', viewsAdmisiones.buscarSubServicios),\n #path('crearAdmision/,, , ', viewsAdmisiones.crearAdmision.as_view()),\n path('crearAdmisionDef/', viewsAdmisiones.crearAdmisionDef),\n\n\n path('findOneUsuario/', viewsAdmisiones.UsuariosModal),\n path('guardarUsuariosModal/', viewsAdmisiones.guardarUsuariosModal),\n\n path('crearResponsables/', viewsAdmisiones.crearResponsables),\n\n # Facturacion\n\n # Citas Medicas\n\n # Usuarios\n\n\n path('crearUsuarios/', viewsUsuarios.crearUsuarios),\n\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root= settings.MEDIA_ROOT)", "repo_name": "albertobernalf/practica7", "sub_path": "vulner/vulner/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 4512, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "camara.views.acceso", "line_number": 33, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "camara.views.menu", "line_number": 37, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "camara.views.contrasena", "line_number": 39, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 39, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "clinico.views.motivoSeñas", "line_number": 49, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 49, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "clinico.views.subjetivoSeñas", "line_number": 50, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 50, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "clinico.views.motivoInvidente", "line_number": 51, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "camara.views.reconocerAudio", "line_number": 53, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 53, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "camara.views.reproduceAudio", "line_number": 54, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 54, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "camara.views.accesoEspecialidadMedico", "line_number": 55, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 55, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "clinico.views.crearHistoriaClinica", "line_number": 56, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 56, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 58, "usage_type": "call"}, {"api_name": "clinico.views.buscarAdmisionClinico", "line_number": 58, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 58, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 59, "usage_type": "call"}, {"api_name": "clinico.views.cargaPanelMedico", "line_number": 59, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 59, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "clinico.views.buscarAntecedentes", "line_number": 60, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 65, "usage_type": "call"}, {"api_name": "clinico.views.prueba", "line_number": 65, "usage_type": "attribute"}, {"api_name": "clinico.views", "line_number": 65, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 68, "usage_type": "call"}, {"api_name": "camara.views.camara", "line_number": 68, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 68, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 69, "usage_type": "call"}, {"api_name": "camara.views.leeAudio", "line_number": 69, "usage_type": "attribute"}, {"api_name": "camara.views", "line_number": 69, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 74, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 74, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 75, "usage_type": "call"}, {"api_name": "admisiones.views.menuAcceso", "line_number": 75, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 75, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 76, "usage_type": "call"}, {"api_name": "admisiones.views.validaAcceso", "line_number": 76, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 76, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 78, "usage_type": "call"}, {"api_name": "admisiones.views.retornarAdmision", "line_number": 78, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 78, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 80, "usage_type": "call"}, {"api_name": "admisiones.views.salir", "line_number": 80, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 80, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 81, "usage_type": "call"}, {"api_name": "admisiones.views.validaPassword", "line_number": 82, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 82, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 83, "usage_type": "call"}, {"api_name": "admisiones.views.Modal", "line_number": 83, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 83, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 85, "usage_type": "call"}, {"api_name": "admisiones.views.buscarAdmision", "line_number": 85, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 85, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 87, "usage_type": "call"}, {"api_name": "admisiones.views.buscarEspecialidadesMedicos", "line_number": 87, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 87, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 88, "usage_type": "call"}, {"api_name": "admisiones.views.buscarCiudades", "line_number": 88, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 88, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 89, "usage_type": "call"}, {"api_name": "admisiones.views.buscarHabitaciones", "line_number": 89, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 89, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 90, "usage_type": "call"}, {"api_name": "admisiones.views.buscarSubServicios", "line_number": 90, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 90, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 92, "usage_type": "call"}, {"api_name": "admisiones.views.crearAdmisionDef", "line_number": 92, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 92, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 95, "usage_type": "call"}, {"api_name": "admisiones.views.UsuariosModal", "line_number": 95, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 95, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 96, "usage_type": "call"}, {"api_name": "admisiones.views.guardarUsuariosModal", "line_number": 96, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 96, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 98, "usage_type": "call"}, {"api_name": "admisiones.views.crearResponsables", "line_number": 98, "usage_type": "attribute"}, {"api_name": "admisiones.views", "line_number": 98, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 107, "usage_type": "call"}, {"api_name": "usuarios.views.crearUsuarios", "line_number": 107, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 107, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 111, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 111, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 112, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 112, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 112, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 112, "usage_type": "attribute"}]} +{"seq_id": "2184164649", "text": "from pyramid.view import view_config\nfrom monkeyball.models.message import Message\n\n\n@view_config(route_name='api_messages',\n renderer='json',\n request_param='game_id')\ndef messages(request):\n db = request.db\n game_id = request.GET['game_id']\n\n messages = db.query(Message).filter_by(game_id=game_id).all()\n\n messages_json = []\n for message in messages:\n messages_json.append({\n 'id': message.id,\n 'game_id': message.game_id,\n 'player_id': message.player.id,\n 'player_name': message.player.name,\n 'body': message.body\n })\n\n return messages_json\n\n\n@view_config(route_name='api_messages',\n renderer='json',\n request_method='POST',\n request_param='game_id')\ndef create_message(request):\n db = request.db\n payload = request.json_body\n game_id = request.GET['game_id']\n\n message = Message(game_id=game_id,\n player=request.player,\n body=payload['body'])\n db.add(message)\n db.flush()\n return {\n 'id': message.id,\n 'game_id': message.game_id,\n 'player_id': message.player.id,\n 'player_name': message.player.name,\n 'body': message.body\n }\n", "repo_name": "jayd3e/MonkeyBall", "sub_path": "monkeyball/views/api/message.py", "file_name": "message.py", "file_ext": "py", "file_size_in_byte": 1278, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "monkeyball.models.message.Message", "line_number": 12, "usage_type": "argument"}, {"api_name": "pyramid.view.view_config", "line_number": 5, "usage_type": "call"}, {"api_name": "monkeyball.models.message.Message", "line_number": 36, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "20857230130", "text": "# from easyocr import Reader\n# import cv2\nimport modal.aio\nimport asyncio\n\n\nstub = modal.aio.AioStub(\n \"run-ocr\",\n image=modal.Image.debian_slim()\n .run_commands(\"apt update\", \"apt-get install -y libglib2.0-0 libsm6 libxrender1 libxext6\")\n .pip_install(\n \"easyocr\",\n \"opencv-python==4.1.2.30\",\n )\n)\n\nvolume = modal.SharedVolume().persist(\"ocr_model_vol\")\nCACHE_PATH = \"/root/model_cache\"\n\n@stub.function(gpu='any', shared_volumes={CACHE_PATH: volume})\nasync def get_text(image, rotated=0):\n from easyocr import Reader\n import cv2\n height, width, _ = image.shape\n if rotated == 1:\n image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)\n elif rotated == 3:\n image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)\n elif rotated == 2:\n image = cv2.rotate(image, cv2.ROTATE_180)\n reader = Reader(['en'],gpu=True, model_storage_directory=CACHE_PATH)\n\n results = reader.readtext(image)\n\n if rotated == 1:\n results = [(\n ([\n [width - result[0][3][1], result[0][3][0]],\n [width - result[0][0][1], result[0][0][0]],\n [width - result[0][1][1], result[0][1][0]], \n [width - result[0][2][1], result[0][2][0]],\n ]),\n result[1],\n result[2])\n for result in results]\n if rotated == 3:\n results = [(\n ([\n [result[0][1][1], height - result[0][1][0]], \n [result[0][2][1], height - result[0][2][0]],\n [result[0][3][1], height - result[0][3][0]],\n [result[0][0][1], height - result[0][0][0]],\n ]),\n result[1],\n result[2])\n for result in results]\n if rotated == 2:\n results = [(\n ([\n [width - result[0][2][0], height - result[0][2][1]],\n [width - result[0][3][0], height - result[0][3][1]],\n [width - result[0][0][0], height - result[0][0][1]],\n [width - result[0][1][0], height - result[0][1][1]],\n ]),\n result[1],\n result[2])\n for result in results]\n return results\n\n\nasync def process_image(image, rotation):\n print(f'Starting OCR for rotation {rotation}')\n results = await get_text.call(image, rotated=rotation)\n best_results = [result for result in results if len(result[1]) * result[2] ** 2 > 0.9]\n print(f'Finishing OCR for rotation {rotation}')\n return (best_results, sum([result[2] for result in best_results]) / len(results)) if results else ([], 0)\n\n\n@stub.function()\nasync def run_process_image(image):\n print(\"Starting OCR\")\n combined_results = {}\n scores = {}\n\n tasks = []\n for rotation in range(4):\n tasks.append(process_image(image, rotation))\n\n results = await asyncio.gather(*tasks)\n\n for rotation, (best_results, score) in enumerate(results):\n combined_results[rotation] = best_results\n scores[rotation] = score\n print(best_results)\n print(score)\n\n # scores[2] = sum([result[2] for result in best_results]) / len(results)\n # print(sum([result[2] for result in best_results]) / len(results))\n\n # Take the best scoring rotation and the best one adjacent to it\n highest_key = max(scores, key=scores.get)\n print(highest_key)\n if scores[(highest_key + 1) % 4] > scores[(highest_key + 3) % 4]:\n second_highest_key = (highest_key + 1) % 4\n else:\n second_highest_key = (highest_key + 3) % 4\n print(second_highest_key)\n results = combined_results[highest_key] + combined_results[second_highest_key]\n \n # This needs tidying up and making async!\n # Also, I think we should choose the rotations at the book level, since different books can be rotated differently\n\n print(\"Finished OCR\")\n return results\n\n@stub.function()\nasync def predict(image):\n results = await run_process_image.call(image)\n return results\n", "repo_name": "woodwardmw/bookshelf_rater", "sub_path": "ocr.py", "file_name": "ocr.py", "file_ext": "py", "file_size_in_byte": 4182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "modal.aio.aio.AioStub", "line_number": 7, "usage_type": "call"}, {"api_name": "modal.aio.aio", "line_number": 7, "usage_type": "attribute"}, {"api_name": "modal.aio", "line_number": 7, "usage_type": "name"}, {"api_name": "modal.aio.Image.debian_slim", "line_number": 9, "usage_type": "call"}, {"api_name": "modal.aio.Image", "line_number": 9, "usage_type": "attribute"}, {"api_name": "modal.aio", "line_number": 9, "usage_type": "name"}, {"api_name": "modal.aio.SharedVolume", "line_number": 17, "usage_type": "call"}, {"api_name": "modal.aio", "line_number": 17, "usage_type": "name"}, {"api_name": "cv2.rotate", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.ROTATE_90_COUNTERCLOCKWISE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.rotate", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.ROTATE_90_CLOCKWISE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.rotate", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.ROTATE_180", "line_number": 30, "usage_type": "attribute"}, {"api_name": "easyocr.Reader", "line_number": 31, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "5980405632", "text": "import yaml\nfrom oslo_concurrency import processutils\nfrom oslo_log import log as logging\nfrom stevedore import driver\n\nfrom __init__ import __version__\nfrom bm_instance_agent.common import utils as bm_utils\nfrom bm_instance_agent import exception\nfrom bm_instance_agent.objects import BmInstanceObj\nfrom bm_instance_agent.objects import NetworkObj\nfrom bm_instance_agent.objects import VolumeObj\n\n\nLOG = logging.getLogger(__name__)\n\n\nBM_INSTANCE_UUID = None\nDRIVER = None\nZWATCH_AGENT_CONF_PATH = \"/usr/local/zstack/zwatch-vm-agent/conf.yaml\"\n\n\nclass AgentManager(object):\n\n def __init__(self):\n global DRIVER\n if not DRIVER:\n DRIVER = self._load_driver()\n self.driver = DRIVER\n\n def _load_driver(self):\n return driver.DriverManager(\n namespace='bm_instance_agent.systems.driver',\n name=bm_utils.get_distro(),\n invoke_on_load=True).driver\n\n def _check_uuid_corrent(self, bm_uuid):\n global BM_INSTANCE_UUID\n if not BM_INSTANCE_UUID == bm_uuid:\n raise exception.BmInstanceUuidConflict(\n req_instance_uuid=bm_uuid,\n exist_instance_uuid=BM_INSTANCE_UUID)\n\n def _check_gateway_ip(self, instance_obj):\n push_gateway_url = \"http://%s:9092\" % instance_obj.gateway_ip\n with open(ZWATCH_AGENT_CONF_PATH) as f:\n doc = yaml.load(f)\n\n old_url = doc.get('pushGatewayUrl')\n old_uuid = doc.get('bm2InstanceUuid')\n if old_url is not None and old_url == push_gateway_url\\\n and old_uuid is not None and old_uuid == instance_obj.uuid:\n return\n\n LOG.info(\"pushGatewayUrl and bmInstanceUuid changed from %s to %s, %s to %s\" %\n (old_url, push_gateway_url, old_uuid, instance_obj.uuid))\n doc['pushGatewayUrl'] = push_gateway_url\n doc['bm2InstanceUuid'] = instance_obj.uuid\n\n with open(ZWATCH_AGENT_CONF_PATH, 'w') as f:\n yaml.safe_dump(doc, f, encoding='utf-8', allow_unicode=True)\n # f.write(\"\\npushGatewayUrl: %s\\nbm2InstanceUuid: %s\\n\" % (push_gateway_url, instance_obj.uuid))\n\n cmd = 'service zwatch-vm-agent restart'\n processutils.execute(cmd, shell=True)\n\n def ping(self, bm_instance):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n\n global BM_INSTANCE_UUID\n if not BM_INSTANCE_UUID:\n BM_INSTANCE_UUID = instance_obj.uuid\n self._check_uuid_corrent(instance_obj.uuid)\n self.driver.ping(instance_obj)\n self.driver.discovery_target(instance_obj)\n self._check_gateway_ip(instance_obj)\n return {'version': __version__, 'ping': {'bmInstanceUuid': BM_INSTANCE_UUID}}\n\n def reboot(self, bm_instance):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to reboot the system: '\n '{bm_uuid}').format(bm_uuid=instance_obj.uuid)\n LOG.info(msg)\n self.driver.reboot(instance_obj)\n\n def stop(self, bm_instance):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to stop the system: '\n '{bm_uuid}').format(bm_uuid=instance_obj.uuid)\n LOG.info(msg)\n self.driver.stop(instance_obj)\n\n def attach_volume(self, bm_instance, volume):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n volume_obj = VolumeObj.from_json(volume)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to attach the volume: {volume_uuid} '\n 'to the system: {bm_uuid}').format(\n volume_uuid=volume_obj.uuid, bm_uuid=instance_obj.uuid)\n LOG.info(msg)\n self.driver.attach_volume(instance_obj, volume_obj)\n\n def detach_volume(self, bm_instance, volume):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n volume_obj = VolumeObj.from_json(volume)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to detach the volume: {volume_uuid} '\n 'from the system: {bm_uuid}').format(\n volume_uuid=volume_obj.uuid, bm_uuid=instance_obj.uuid)\n LOG.info(msg)\n self.driver.detach_volume(instance_obj, volume_obj)\n\n def attach_port(self, bm_instance, port):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n network_obj = NetworkObj.from_json(port)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to attach port: {port_mac} '\n 'to the system: {bm_uuid}').format(\n bm_uuid=instance_obj.uuid,\n port_mac=[x.mac for x in network_obj.ports])\n LOG.info(msg)\n self.driver.attach_port(instance_obj, network_obj)\n\n def detach_port(self, bm_instance, port):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n network_obj = NetworkObj.from_json(port)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to detach port: {port_mac} '\n 'from the system: {bm_uuid}').format(\n bm_uuid=instance_obj.uuid,\n port_mac=[x.mac for x in network_obj.ports])\n LOG.info(msg)\n self.driver.detach_port(instance_obj, network_obj)\n\n def update_default_route(\n self, bm_instance, old_default_port, new_default_port):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n old_network_obj = NetworkObj.from_json(old_default_port)\n new_network_obj = NetworkObj.from_json(new_default_port)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to update the gateway from the system: '\n '{bm_uuid}').format(bm_uuid=instance_obj.uuid)\n LOG.info(msg)\n self.driver.update_default_route(\n instance_obj, old_network_obj, new_network_obj)\n\n def update_password(self, bm_instance, username, password):\n instance_obj = BmInstanceObj.from_json(bm_instance)\n\n self._check_uuid_corrent(instance_obj.uuid)\n msg = ('Call the driver to update user password')\n LOG.info(msg)\n self.driver.update_password(instance_obj, username, password)\n\n def console(self):\n msg = ('Call the driver to start console')\n LOG.info(msg)\n return self.driver.console()\n", "repo_name": "zstackio/zstack-utility", "sub_path": "bm-instance-agent/bm_instance_agent/manager.py", "file_name": "manager.py", "file_ext": "py", "file_size_in_byte": 6438, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 61, "dataset": "github-code", "pt": "22", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 14, "usage_type": "name"}, {"api_name": "stevedore.driver.DriverManager", "line_number": 31, "usage_type": "call"}, {"api_name": "stevedore.driver", "line_number": 31, "usage_type": "name"}, {"api_name": "bm_instance_agent.common.utils.get_distro", "line_number": 33, "usage_type": "call"}, {"api_name": "bm_instance_agent.common.utils", "line_number": 33, "usage_type": "name"}, {"api_name": "bm_instance_agent.exception.BmInstanceUuidConflict", "line_number": 39, "usage_type": "call"}, {"api_name": "bm_instance_agent.exception", "line_number": 39, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 46, "usage_type": "call"}, {"api_name": "yaml.safe_dump", "line_number": 60, "usage_type": "call"}, {"api_name": "oslo_concurrency.processutils.execute", "line_number": 64, "usage_type": "call"}, {"api_name": "oslo_concurrency.processutils", "line_number": 64, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 67, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 67, "usage_type": "name"}, {"api_name": "__init__.__version__", "line_number": 76, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 79, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 79, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 88, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 88, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 97, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 97, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.VolumeObj.from_json", "line_number": 98, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.VolumeObj", "line_number": 98, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 108, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 108, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.VolumeObj.from_json", "line_number": 109, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.VolumeObj", "line_number": 109, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 119, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 119, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.NetworkObj.from_json", "line_number": 120, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.NetworkObj", "line_number": 120, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 131, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 131, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.NetworkObj.from_json", "line_number": 132, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.NetworkObj", "line_number": 132, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 144, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 144, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.NetworkObj.from_json", "line_number": 145, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.NetworkObj", "line_number": 145, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.NetworkObj.from_json", "line_number": 146, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.NetworkObj", "line_number": 146, "usage_type": "name"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj.from_json", "line_number": 156, "usage_type": "call"}, {"api_name": "bm_instance_agent.objects.BmInstanceObj", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "35499163839", "text": "import numpy as np\nimport numpy.testing as npt\nimport scipy.signal\nfrom lenstronomy.ImSim.image_model import ImageModel\nfrom lenstronomy.Data.imaging_data import ImageData\nfrom lenstronomy.Data.psf import PSF\nfrom lenstronomy.LightModel.light_model import LightModel\nfrom lenstronomy.LensModel.lens_model import LensModel\nimport lenstronomy.Util.simulation_util as sim_util\nfrom lenstronomy.Util import kernel_util\nimport lenstronomy.Util.util as util\n\nfrom lenstronomy.ImSim.image_linear_solve import ImageLinearFit\n\n\"\"\"\nTest the linear solver for natwt (natural weighting) interferometric data.\nTest the _image_linear_solve function of ImageLinearFit class.\nThe idea is to define data, psf, source, lens, lens light classes respectively, and run the linear solving\ninside and outside of the _image_linear_solve function. Verify the 1st and 4th output of _image_linear_solve.\nThe test should be independent of the specific definitions of the light and lens profiles.\n\"\"\"\n\n\ndef test_image_linear_solve_with_primary_beam_and_interferometry_psf():\n background_rms = 0.05\n exp_time = np.inf\n numPix = 80\n deltaPix = 0.05\n psf_type = \"PIXEL\"\n kernel_size = 161\n\n # simulate a primary beam (pb)\n primary_beam = np.zeros((numPix, numPix))\n for i in range(numPix):\n for j in range(numPix):\n primary_beam[i, j] = np.exp(-1e-4 * ((i - 78) ** 2 + (j - 56) ** 2))\n primary_beam /= np.max(primary_beam)\n\n # simulate a spherical sinc function as psf, which contains negative pixels\n psf_test = np.zeros((221, 221))\n for i in range(221):\n for j in range(221):\n if i > j:\n psf_test[i, j] = psf_test[j, i]\n r = np.sqrt((i - 110) ** 2 + (j - 110) ** 2)\n if r == 0:\n psf_test[i, j] = 1\n else:\n psf_test[i, j] = np.sin(r * 0.5) / (r * 0.5)\n\n # note that the simulated noise here is not the interferometric noise. we just use it to test the numerics\n test_noise = scipy.signal.fftconvolve(\n np.random.normal(0, 1, (numPix, numPix)), psf_test, mode=\"same\"\n )\n\n kwargs_data = sim_util.data_configure_simple(\n numPix, deltaPix, exp_time, background_rms\n )\n kwargs_data[\"ra_at_xy_0\"] = -(40) * deltaPix\n kwargs_data[\"dec_at_xy_0\"] = -(40) * deltaPix\n kwargs_data[\"antenna_primary_beam\"] = primary_beam\n kwargs_data[\n \"likelihood_method\"\n ] = \"interferometry_natwt\" # testing just for interferometry natwt method\n data_class = ImageData(**kwargs_data)\n\n kernel_cut = kernel_util.cut_psf(psf_test, kernel_size, normalisation=False)\n kwargs_psf = {\n \"psf_type\": psf_type,\n \"pixel_size\": deltaPix,\n \"kernel_point_source\": kernel_cut,\n \"kernel_point_source_normalisation\": False,\n }\n psf_class = PSF(**kwargs_psf)\n\n # define lens model and source model\n kwargs_shear = {\"gamma1\": 0.01, \"gamma2\": 0.01}\n kwargs_spemd = {\n \"theta_E\": 1.0,\n \"gamma\": 1.8,\n \"center_x\": 0,\n \"center_y\": 0,\n \"e1\": 0.1,\n \"e2\": 0.04,\n }\n lens_model_list = [\"SPEP\", \"SHEAR\"]\n kwargs_lens = [kwargs_spemd, kwargs_shear]\n lens_model_class = LensModel(lens_model_list=lens_model_list)\n\n kwargs_sersic = {\n \"amp\": 25.0,\n \"R_sersic\": 0.3,\n \"n_sersic\": 2,\n \"center_x\": 0,\n \"center_y\": 0,\n }\n lens_light_model_list = [\"SERSIC\"]\n kwargs_lens_light = [kwargs_sersic]\n lens_light_model_class = LightModel(light_model_list=lens_light_model_list)\n\n kwargs_sersic_ellipse = {\n \"amp\": 10.0,\n \"R_sersic\": 0.6,\n \"n_sersic\": 7,\n \"center_x\": 0,\n \"center_y\": 0,\n \"e1\": 0.05,\n \"e2\": 0.02,\n }\n source_model_list = [\"SERSIC_ELLIPSE\"]\n kwargs_source = [kwargs_sersic_ellipse]\n source_model_class = LightModel(light_model_list=source_model_list)\n\n kwargs_numerics = {\"supersampling_factor\": 1, \"supersampling_convolution\": False}\n\n imageModel = ImageModel(\n data_class,\n psf_class,\n lens_model_class,\n source_model_class,\n lens_light_model_class,\n kwargs_numerics=kwargs_numerics,\n )\n image_sim = imageModel.image(kwargs_lens, kwargs_source, kwargs_lens_light)\n\n # normalize the noise to make it small compared to the model image\n test_noise *= 1e-2 * (np.max(image_sim) / np.std(test_noise))\n sim_data = image_sim + test_noise\n data_class.update_data(sim_data)\n\n # define the ImageLinearFit class using the materials defined above, run the _image_linear_solve function\n imageLinearFit = ImageLinearFit(\n data_class,\n psf_class,\n lens_model_class,\n source_model_class,\n lens_light_model_class,\n kwargs_numerics=kwargs_numerics,\n )\n model, _, _, amps = imageLinearFit._image_linear_solve(\n kwargs_lens, kwargs_source, kwargs_lens_light\n )\n\n # execute the same linear solving outside of the _image_linear_solve function\n A = imageLinearFit._linear_response_matrix(\n kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps=None, unconvolved=True\n )\n A0 = util.array2image(A[0])\n A1 = util.array2image(A[1])\n A0c = scipy.signal.fftconvolve(A0, psf_test, mode=\"same\")\n A1c = scipy.signal.fftconvolve(A1, psf_test, mode=\"same\")\n M = np.zeros((2, 2))\n b = np.zeros((2))\n M[0, 0] = np.sum(A0c * A0)\n M[0, 1] = np.sum(A0c * A1)\n M[1, 0] = np.sum(A1c * A0)\n M[1, 1] = np.sum(A1c * A1)\n b[0] = np.sum(A0 * sim_data)\n b[1] = np.sum(A1 * sim_data)\n\n amps0 = np.linalg.lstsq(M, b)[0]\n clean_model = amps0[0] * A0 + amps0[1] * A1\n dirty_model = amps0[0] * A0c + amps0[1] * A1c\n\n npt.assert_almost_equal([clean_model, dirty_model], model, decimal=8)\n npt.assert_almost_equal(amps0, amps, decimal=8)\n", "repo_name": "lenstronomy/lenstronomy", "sub_path": "test/test_ImSim/test_image_linear_solve_with_interferometric_changes.py", "file_name": "test_image_linear_solve_with_interferometric_changes.py", "file_ext": "py", "file_size_in_byte": 5827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 164, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.inf", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.signal.signal.fftconvolve", "line_number": 52, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 52, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.random.normal", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 53, "usage_type": "attribute"}, {"api_name": "lenstronomy.Util.simulation_util.data_configure_simple", "line_number": 56, "usage_type": "call"}, {"api_name": "lenstronomy.Util.simulation_util", "line_number": 56, "usage_type": "name"}, {"api_name": "lenstronomy.Data.imaging_data.ImageData", "line_number": 65, "usage_type": "call"}, {"api_name": "lenstronomy.Util.kernel_util.cut_psf", "line_number": 67, "usage_type": "call"}, {"api_name": "lenstronomy.Util.kernel_util", "line_number": 67, "usage_type": "name"}, {"api_name": "lenstronomy.Data.psf.PSF", "line_number": 74, "usage_type": "call"}, {"api_name": "lenstronomy.LensModel.lens_model.LensModel", "line_number": 88, "usage_type": "call"}, {"api_name": "lenstronomy.LightModel.light_model.LightModel", "line_number": 99, "usage_type": "call"}, {"api_name": "lenstronomy.LightModel.light_model.LightModel", "line_number": 112, "usage_type": "call"}, {"api_name": "lenstronomy.ImSim.image_model.ImageModel", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 127, "usage_type": "call"}, {"api_name": "lenstronomy.ImSim.image_linear_solve.ImageLinearFit", "line_number": 132, "usage_type": "call"}, {"api_name": "lenstronomy.Util.util.array2image", "line_number": 148, "usage_type": "call"}, {"api_name": "lenstronomy.Util.util", "line_number": 148, "usage_type": "name"}, {"api_name": "lenstronomy.Util.util.array2image", "line_number": 149, "usage_type": "call"}, {"api_name": "lenstronomy.Util.util", "line_number": 149, "usage_type": "name"}, {"api_name": "scipy.signal.signal.fftconvolve", "line_number": 150, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 150, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 150, "usage_type": "name"}, {"api_name": "scipy.signal.signal.fftconvolve", "line_number": 151, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 151, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 151, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 165, "usage_type": "name"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 166, "usage_type": "name"}]} +{"seq_id": "30143395273", "text": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nimport utils\n\n\ndef norm(T):\n row_sum = np.sum(T, 1)\n T_norm = T / row_sum\n return T_norm\n\n\ndef error(T, T_true):\n error = np.sum(np.abs(T-T_true)) / np.sum(np.abs(T_true))\n return error\n\n\ndef get_estimation_error(T, T_true):\n row_ind, col_ind = linear_sum_assignment(-np.dot(np.transpose(T), T_true))\n T = T[:, col_ind]\n\n return np.sum(np.abs(T - T_true)) / np.sum(np.abs(T_true))\n\n\n# flip clean labels to noisy labels\n# train set and val set split\ndef dataset_split(train_images, train_labels, noise_rate=0.5, percent_instance_noise=0.1, transform=[], split_per=0.9, random_seed=1, num_class=10, feature_size=28*28):\n\n noisy_labels, real_noise_rate, transition_matrix, flag_instance_dep_noise = utils.noisify(train_images,\n train_labels,\n random_seed,\n noise_rate=noise_rate,\n feature_size=feature_size,\n percent_instance_noise=percent_instance_noise,\n transform=transform,\n num_class=num_class)\n\n noisy_labels = np.array(noisy_labels)\n flag_instance_dep_noise = np.array(flag_instance_dep_noise)\n\n noisy_labels = noisy_labels.squeeze()\n flag_instance_dep_noise = flag_instance_dep_noise.squeeze()\n\n num_samples = int(noisy_labels.shape[0])\n np.random.seed(random_seed)\n train_set_index = np.random.choice(num_samples, int(num_samples * split_per), replace=False)\n index = np.arange(train_images.shape[0])\n val_set_index = np.delete(index, train_set_index)\n\n train_set, val_set = train_images[train_set_index, :], train_images[val_set_index, :]\n train_labels_noisy, val_labels_noisy = noisy_labels[train_set_index], noisy_labels[val_set_index]\n clean_train_labels, clean_val_labels = train_labels[train_set_index], train_labels[val_set_index]\n flag_train, flag_val = flag_instance_dep_noise[train_set_index], flag_instance_dep_noise[val_set_index]\n\n return train_set, val_set, train_labels_noisy, val_labels_noisy, transition_matrix, clean_train_labels, clean_val_labels, flag_train, flag_val\n", "repo_name": "GreyWolnick/RobVolMinNet", "sub_path": "tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 2682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.sum", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.optimize.linear_sum_assignment", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.noisify", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "28503533524", "text": "from __future__ import print_function\n\nimport sys\nimport numpy as np\nfrom numpy import ma\nimport warnings\nimport getopt\nimport os\n\npython3 = sys.version_info[0] > 2\nif python3:\n # no unicode type in python 3, use bytes instead when testing\n # for a string-like object\n unicode = str\nelse:\n range = xrange\ntry:\n bytes\nexcept NameError:\n # no bytes type in python < 2.6\n bytes = str\n\ndef _safecast(a,b):\n # check to see if array a can be safely cast\n # to array b. A little less picky than numpy.can_cast.\n try:\n is_safe = ((a == b) | (np.isnan(a) & np.isnan(b))).all()\n #is_safe = np.allclose(a, b, equal_nan=True) # numpy 1.10.0\n except:\n try:\n is_safe = (a == b).all() # string arrays.\n except:\n is_safe = False\n return is_safe\n\ndef _sortbylist(A,B):\n # sort one list (A) using the values from another list (B)\n return [A[i] for i in sorted(range(len(A)), key=B.__getitem__)]\n\ndef _find_dim(grp, dimname):\n # find Dimension instance given group and name.\n # look in current group, and parents.\n group = grp\n dim = None\n while 1:\n try:\n dim = group.dimensions[dimname]\n break\n except:\n try:\n group = group.parent\n except:\n raise ValueError(\"cannot find dimension %s in this group or parent groups\" % dimname)\n return dim\n\ndef _walk_grps(topgrp):\n \"\"\"Iterate through all (sub-) groups of topgrp, similar to os.walktree.\n\n \"\"\"\n grps = topgrp.groups.values()\n yield grps\n for grp in topgrp.groups.values():\n for children in _walk_grps(grp):\n yield children\n\ndef _quantize(data,least_significant_digit):\n \"\"\"\nquantize data to improve compression. data is quantized using\naround(scale*data)/scale, where scale is 2**bits, and bits is determined\nfrom the least_significant_digit. For example, if\nleast_significant_digit=1, bits will be 4.\n \"\"\"\n precision = pow(10.,-least_significant_digit)\n exp = np.log10(precision)\n if exp < 0:\n exp = int(np.floor(exp))\n else:\n exp = int(np.ceil(exp))\n bits = np.ceil(np.log2(pow(10.,-exp)))\n scale = pow(2.,bits)\n datout = np.around(scale*data)/scale\n if ma.isMA(datout):\n datout.set_fill_value(data.fill_value)\n return datout\n else:\n return datout\n\ndef _StartCountStride(elem, shape, dimensions=None, grp=None, datashape=None,\\\n put=False, no_get_vars = True):\n \"\"\"Return start, count, stride and indices needed to store/extract data\n into/from a netCDF variable.\n\n This function is used to convert a slicing expression into a form that is\n compatible with the nc_get_vars function. Specifically, it needs\n to interpret integers, slices, Ellipses, and 1-d sequences of integers\n and booleans.\n\n Numpy uses \"broadcasting indexing\" to handle array-valued indices.\n \"Broadcasting indexing\" (a.k.a \"fancy indexing\") treats all multi-valued\n indices together to allow arbitrary points to be extracted. The index\n arrays can be multidimensional, and more than one can be specified in a\n slice, as long as they can be \"broadcast\" against each other.\n This style of indexing can be very powerful, but it is very hard\n to understand, explain, and implement (and can lead to hard to find bugs).\n Most other python packages and array processing\n languages (such as netcdf4-python, xray, biggus, matlab and fortran)\n use \"orthogonal indexing\" which only allows for 1-d index arrays and\n treats these arrays of indices independently along each dimension.\n\n The implementation of \"orthogonal indexing\" used here requires that\n index arrays be 1-d boolean or integer. If integer arrays are used,\n the index values must be sorted and contain no duplicates.\n\n In summary, slicing netcdf4-python variable objects with 1-d integer or\n boolean arrays is allowed, but may give a different result than slicing a\n numpy array.\n\n Numpy also supports slicing an array with a boolean array of the same\n shape. For example x[x>0] returns a 1-d array with all the positive values of x.\n This is also not supported in netcdf4-python, if x.ndim > 1.\n\n Orthogonal indexing can be used in to select netcdf variable slices\n using the dimension variables. For example, you can use v[lat>60,lon<180]\n to fetch the elements of v obeying conditions on latitude and longitude.\n Allow for this sort of simple variable subsetting is the reason we decided to\n deviate from numpy's slicing rules.\n\n This function is used both by the __setitem__ and __getitem__ method of\n the Variable class.\n\n Parameters\n ----------\n elem : tuple of integer, slice, ellipsis or 1-d boolean or integer\n sequences used to slice the netCDF Variable (Variable[elem]).\n shape : tuple containing the current shape of the netCDF variable.\n dimensions : sequence\n The name of the dimensions.\n __setitem__.\n grp : netCDF Group\n The netCDF group to which the variable being set belongs to.\n datashape : sequence\n The shape of the data that is being stored. Only needed by __setitime__\n put : True|False (default False). If called from __setitem__, put is True.\n\n Returns\n -------\n start : ndarray (..., n)\n A starting indices array of dimension n+1. The first n\n dimensions identify different independent data chunks. The last dimension\n can be read as the starting indices.\n count : ndarray (..., n)\n An array of dimension (n+1) storing the number of elements to get.\n stride : ndarray (..., n)\n An array of dimension (n+1) storing the steps between each datum.\n indices : ndarray (..., n)\n An array storing the indices describing the location of the\n data chunk in the target/source array (__getitem__/__setitem__).\n\n Notes:\n\n netCDF data is accessed via the function:\n nc_get_vars(grpid, varid, start, count, stride, data)\n\n Assume that the variable has dimension n, then\n\n start is a n-tuple that contains the indices at the beginning of data chunk.\n count is a n-tuple that contains the number of elements to be accessed.\n stride is a n-tuple that contains the step length between each element.\n\n \"\"\"\n # Adapted from pycdf (http://pysclint.sourceforge.net/pycdf)\n # by Andre Gosselin..\n # Modified by David Huard to handle efficiently fancy indexing with\n # sequences of integers or booleans.\n\n nDims = len(shape)\n if nDims == 0:\n nDims = 1\n shape = (1,)\n\n # When a single array or (non-tuple) sequence of integers is given\n # as a slice, assume it applies to the first dimension,\n # and use ellipsis for remaining dimensions.\n if np.iterable(elem):\n if type(elem) == np.ndarray or (type(elem) != tuple and \\\n np.array([_is_int(e) for e in elem]).all()):\n elem = [elem]\n for n in range(len(elem)+1,nDims+1):\n elem.append(slice(None,None,None))\n else: # Convert single index to sequence\n elem = [elem]\n \n # ensure there is at most 1 ellipse\n # we cannot use elem.count(Ellipsis), as with fancy indexing would occur\n # np.array() == Ellipsis which gives ValueError: The truth value of an \n # array with more than one element is ambiguous. Use a.any() or a.all()\n if sum(1 for e in elem if e is Ellipsis) > 1:\n raise IndexError(\"At most one ellipsis allowed in a slicing expression\")\n \n # replace boolean arrays with sequences of integers.\n newElem = []\n IndexErrorMsg=\\\n \"only integers, slices (`:`), ellipsis (`...`), and 1-d integer or boolean arrays are valid indices\"\n i=0\n for e in elem:\n # string-like object try to cast to int\n # needs to be done first, since strings are iterable and\n # hard to distinguish from something castable to an iterable numpy array.\n if type(e) in [str,bytes,unicode]:\n try:\n e = int(e)\n except:\n raise IndexError(IndexErrorMsg)\n ea = np.asarray(e)\n # Raise error if multidimensional indexing is used.\n if ea.ndim > 1:\n raise IndexError(\"Index cannot be multidimensional\")\n # set unlim to True if dimension is unlimited and put==True\n # (called from __setitem__)\n if put and (dimensions is not None and grp is not None) and len(dimensions):\n try:\n dimname = dimensions[i]\n # is this dimension unlimited?\n # look in current group, and parents for dim.\n dim = _find_dim(grp, dimname)\n unlim = dim.isunlimited()\n except IndexError: # more slices than dimensions (issue 371)\n unlim = False\n else:\n unlim = False\n # convert boolean index to integer array.\n if np.iterable(ea) and ea.dtype.kind =='b':\n # check that boolen array not too long\n if not unlim and shape[i] != len(ea):\n msg=\"\"\"\nBoolean array must have the same shape as the data along this dimension.\"\"\"\n raise IndexError(msg)\n ea = np.flatnonzero(ea)\n # an iterable (non-scalar) integer array.\n if np.iterable(ea) and ea.dtype.kind == 'i':\n # convert negative indices in 1d array to positive ones.\n ea = np.where(ea < 0, ea + shape[i], ea)\n if np.any(ea < 0):\n raise IndexError(\"integer index out of range\")\n # if unlim, let integer index be longer than current dimension\n # length.\n if ea.shape != (0,):\n elen = shape[i]\n if unlim:\n elen = max(ea.max()+1,elen)\n if ea.max()+1 > elen:\n msg=\"integer index exceeds dimension size\"\n raise IndexError(msg)\n newElem.append(ea)\n # integer scalar\n elif ea.dtype.kind == 'i':\n newElem.append(e)\n # slice or ellipsis object\n elif type(e) == slice or type(e) == type(Ellipsis):\n if no_get_vars and type(e) == slice and e.step not in [None,-1,1] and\\\n dimensions is not None and grp is not None:\n # convert strided slice to integer sequence if possible\n # (this will avoid nc_get_vars, which is slow - issue #680).\n start = e.start if e.start is not None else 0\n step = e.step\n if e.stop is None and dimensions is not None and grp is not None:\n stop = len(_find_dim(grp, dimensions[i]))\n else:\n stop = e.stop\n if stop < 0:\n stop = len(_find_dim(grp, dimensions[i])) + stop\n try:\n ee = np.arange(start,stop,e.step)\n if len(ee) > 0:\n e = ee\n except:\n pass\n newElem.append(e)\n else: # castable to a scalar int, otherwise invalid\n try:\n e = int(e)\n newElem.append(e)\n except:\n raise IndexError(IndexErrorMsg)\n if type(e)==type(Ellipsis): \n i+=1+nDims-len(elem)\n else:\n i+=1\n elem = newElem\n\n # replace Ellipsis and integer arrays with slice objects, if possible.\n newElem = []\n for e in elem:\n ea = np.asarray(e)\n # Replace ellipsis with slices.\n if type(e) == type(Ellipsis):\n # The ellipsis stands for the missing dimensions.\n newElem.extend((slice(None, None, None),) * (nDims - len(elem) + 1))\n # Replace sequence of indices with slice object if possible.\n elif np.iterable(e) and len(e) > 1:\n start = e[0]\n stop = e[-1]+1\n step = e[1]-e[0]\n try:\n ee = range(start,stop,step)\n except ValueError: # start, stop or step is not valid for a range\n ee = False\n if no_get_vars and ee and len(e) == len(ee) and (e == np.arange(start,stop,step)).all():\n # don't convert to slice unless abs(stride) == 1\n # (nc_get_vars is very slow, issue #680)\n if step not in [1,-1]:\n newElem.append(e)\n else:\n newElem.append(slice(start,stop,step))\n else:\n newElem.append(e)\n elif np.iterable(e) and len(e) == 1:\n newElem.append(slice(e[0], e[0] + 1, 1))\n else:\n newElem.append(e)\n elem = newElem\n\n # If slice doesn't cover all dims, assume ellipsis for rest of dims.\n if len(elem) < nDims:\n for n in range(len(elem)+1,nDims+1):\n elem.append(slice(None,None,None))\n\n # make sure there are not too many dimensions in slice.\n if len(elem) > nDims:\n raise ValueError(\"slicing expression exceeds the number of dimensions of the variable\")\n\n # Compute the dimensions of the start, count, stride and indices arrays.\n # The number of elements in the first n dimensions corresponds to the\n # number of times the _get method will be called.\n sdim = []\n for i, e in enumerate(elem):\n # at this stage e is a slice, a scalar integer, or a 1d integer array.\n # integer array: _get call for each True value\n if np.iterable(e):\n sdim.append(np.alen(e))\n # Scalar int or slice, just a single _get call\n else:\n sdim.append(1)\n\n # Create the start, count, stride and indices arrays.\n\n sdim.append(max(nDims, 1))\n start = np.empty(sdim, dtype=int)\n count = np.empty(sdim, dtype=int)\n stride = np.empty(sdim, dtype=int)\n indices = np.empty(sdim, dtype=object)\n\n for i, e in enumerate(elem):\n\n ea = np.asarray(e)\n\n # set unlim to True if dimension is unlimited and put==True\n # (called from __setitem__). Note: grp and dimensions must be set.\n if put and (dimensions is not None and grp is not None) and len(dimensions):\n dimname = dimensions[i]\n # is this dimension unlimited?\n # look in current group, and parents for dim.\n dim = _find_dim(grp, dimname)\n unlim = dim.isunlimited()\n else:\n unlim = False\n\n # SLICE #\n if type(e) == slice:\n\n # determine length parameter for slice.indices.\n\n # shape[i] can be zero for unlim dim that hasn't been written to\n # yet.\n # length of slice may be longer than current shape\n # if dimension is unlimited (and we are writing, not reading).\n if unlim and e.stop is not None and e.stop > shape[i]:\n length = e.stop\n elif unlim and e.stop is None and datashape != ():\n if e.start is None:\n length = datashape[i]\n else:\n length = e.start+datashape[i]\n else:\n if unlim and datashape == () and len(dim) == 0:\n # writing scalar along unlimited dimension using slicing\n # syntax (var[:] = 1, when var.shape = ())\n length = 1\n else:\n length = shape[i]\n\n beg, end, inc = e.indices(length)\n n = len(range(beg,end,inc))\n\n start[...,i] = beg\n count[...,i] = n\n stride[...,i] = inc\n indices[...,i] = slice(None)\n\n # ITERABLE #\n elif np.iterable(e) and np.array(e).dtype.kind in 'i': # Sequence of integers\n start[...,i] = np.apply_along_axis(lambda x: e*x, i, np.ones(sdim[:-1]))\n indices[...,i] = np.apply_along_axis(lambda x: np.arange(sdim[i])*x, i, np.ones(sdim[:-1], int))\n\n count[...,i] = 1\n stride[...,i] = 1\n\n # all that's left is SCALAR INTEGER #\n else:\n if e >= 0:\n start[...,i] = e\n elif e < 0 and (-e <= shape[i]) :\n start[...,i] = e+shape[i]\n else:\n raise IndexError(\"Index out of range\")\n\n count[...,i] = 1\n stride[...,i] = 1\n indices[...,i] = -1 # Use -1 instead of 0 to indicate that\n # this dimension shall be squeezed.\n\n return start, count, stride, indices#, out_shape\n\ndef _out_array_shape(count):\n \"\"\"Return the output array shape given the count array created by getStartCountStride\"\"\"\n\n s = list(count.shape[:-1])\n out = []\n\n for i, n in enumerate(s):\n if n == 1:\n c = count[..., i].ravel()[0] # All elements should be identical.\n out.append(c)\n else:\n out.append(n)\n return out\n\ndef _is_container(a):\n # is object container-like? (can test for\n # membership with \"is in\", but not a string)\n try: 1 in a\n except: return False\n if type(a) == type(basestring): return False\n return True\n\ndef _is_int(a):\n try:\n return int(a) == a\n except:\n return False\n\ndef _tostr(s):\n try:\n ss = str(s)\n except:\n ss = s\n return ss\n\n\ndef _getgrp(g,p):\n import posixpath\n grps = p.split(\"/\")\n for gname in grps:\n if gname == \"\": continue\n g = g.groups[gname]\n return g\n\ndef ncinfo():\n\n from netCDF4 import Dataset\n\n\n usage = \"\"\"\n Print summary information about a netCDF file.\n\n usage: %s [-h] [-g grp or --group=grp] [-v var or --variable=var] [-d dim or --dimension=dim] filename\n\n -h -- Print usage message.\n -g or --group= -- Print info for this group\n (default is root group). Nested groups specified\n using posix paths (\"group1/group2/group3\").\n -v or --variable= -- Print info for this variable.\n -d or --dimension= -- Print info for this dimension.\n\n netcdf filename must be last argument.\n\\n\"\"\" % os.path.basename(sys.argv[0])\n\n try:\n opts, pargs = getopt.getopt(sys.argv[1:],'hv:g:d:',\n ['group=',\n 'variable=',\n 'dimension='])\n except:\n (type, value, traceback) = sys.exc_info()\n sys.stdout.write(\"Error parsing the options. The error was: %s\\n\" % value)\n sys.stderr.write(usage)\n sys.exit(0)\n\n # Get the options\n group = None; var = None; dim=None\n for option in opts:\n if option[0] == '-h':\n sys.stderr.write(usage)\n sys.exit(0)\n elif option[0] == '--group' or option[0] == '-g':\n group = option[1]\n elif option[0] == '--variable' or option[0] == '-v':\n var = option[1]\n elif option[0] == '--dimension' or option[0] == '-d':\n dim = option[1]\n else:\n sys.stdout.write(\"%s: Unrecognized option\\n\" % option[0])\n sys.stderr.write(usage)\n sys.exit(0)\n\n # filename passed as last argumenbt\n filename = pargs[-1]\n\n f = Dataset(filename)\n if group is None:\n if var is None and dim is None:\n print(f)\n else:\n if var is not None:\n print(f.variables[var])\n if dim is not None:\n print(f.dimensions[dim])\n else:\n if var is None and dim is None:\n print(_getgrp(f,group))\n else:\n g = _getgrp(f,group)\n if var is not None:\n print(g.variables[var])\n if dim is not None:\n print(g.dimensions[var])\n f.close()\n\ndef _nc4tonc3(filename4,filename3,clobber=False,nchunk=10,quiet=False,format='NETCDF3_64BIT'):\n \"\"\"convert a netcdf 4 file (filename4) in NETCDF4_CLASSIC format\n to a netcdf 3 file (filename3) in NETCDF3_64BIT format.\"\"\"\n\n from netCDF4 import Dataset\n\n ncfile4 = Dataset(filename4,'r')\n if ncfile4.file_format != 'NETCDF4_CLASSIC':\n raise IOError('input file must be in NETCDF4_CLASSIC format')\n ncfile3 = Dataset(filename3,'w',clobber=clobber,format=format)\n # create dimensions. Check for unlimited dim.\n unlimdimname = False\n unlimdim = None\n # create global attributes.\n if not quiet: sys.stdout.write('copying global attributes ..\\n')\n #for attname in ncfile4.ncattrs():\n # setattr(ncfile3,attname,getattr(ncfile4,attname))\n ncfile3.setncatts(ncfile4.__dict__)\n if not quiet: sys.stdout.write('copying dimensions ..\\n')\n for dimname,dim in ncfile4.dimensions.items():\n if dim.isunlimited():\n unlimdimname = dimname\n unlimdim = dim\n ncfile3.createDimension(dimname,None)\n else:\n ncfile3.createDimension(dimname,len(dim))\n # create variables.\n for varname,ncvar in ncfile4.variables.items():\n if not quiet:\n sys.stdout.write('copying variable %s\\n' % varname)\n # is there an unlimited dimension?\n if unlimdimname and unlimdimname in ncvar.dimensions:\n hasunlimdim = True\n else:\n hasunlimdim = False\n if hasattr(ncvar, '_FillValue'):\n FillValue = ncvar._FillValue\n else:\n FillValue = None\n var = ncfile3.createVariable(varname,ncvar.dtype,ncvar.dimensions,fill_value=FillValue)\n # fill variable attributes.\n attdict = ncvar.__dict__\n if '_FillValue' in attdict:\n del attdict['_FillValue']\n var.setncatts(attdict)\n #for attname in ncvar.ncattrs():\n # if attname == '_FillValue': continue\n # setattr(var,attname,getattr(ncvar,attname))\n # fill variables with data.\n if hasunlimdim: # has an unlim dim, loop over unlim dim index.\n # range to copy\n if nchunk:\n start = 0; stop = len(unlimdim); step = nchunk\n if step < 1:\n step = 1\n for n in range(start, stop, step):\n nmax = n+nchunk\n if nmax > len(unlimdim):\n nmax=len(unlimdim)\n var[n:nmax] = ncvar[n:nmax]\n else:\n var[0:len(unlimdim)] = ncvar[:]\n else: # no unlim dim or 1-d variable, just copy all data at once.\n var[:] = ncvar[:]\n ncfile3.sync() # flush data to disk\n # close files.\n ncfile3.close()\n ncfile4.close()\n\ndef nc4tonc3():\n usage = \"\"\"\n Convert a netCDF 4 file (in NETCDF4_CLASSIC format) to netCDF 3 format.\n\n usage: %s [-h] [-o] [--chunk] netcdf4filename netcdf3filename\n -h -- Print usage message.\n -o -- Overwrite destination file (default is to raise an error if output file already exists).\n --quiet=(0|1) -- if 1, don't print diagnostic information.\n --format -- netcdf3 format to use (NETCDF3_64BIT by default, can be set to NETCDF3_CLASSIC)\n --chunk=(integer) -- number of records along unlimited dimension to\n write at once. Default 10. Ignored if there is no unlimited\n dimension. chunk=0 means write all the data at once.\n\\n\"\"\" % os.path.basename(sys.argv[0])\n\n try:\n opts, pargs = getopt.getopt(sys.argv[1:], 'ho',\n ['format=','chunk=','quiet='])\n except:\n (type, value, traceback) = sys.exc_info()\n sys.stdout.write(\"Error parsing the options. The error was: %s\\n\" % value)\n sys.stderr.write(usage)\n sys.exit(0)\n\n # default options\n quiet = 0\n chunk = 1000\n format = 'NETCDF3_64BIT'\n overwritefile = 0\n\n # Get the options\n for option in opts:\n if option[0] == '-h':\n sys.stderr.write(usage)\n sys.exit(0)\n elif option[0] == '-o':\n overwritefile = 1\n elif option[0] == '--quiet':\n quiet = int(option[1])\n elif option[0] == '--format':\n format = option[1]\n elif option[0] == '--chunk':\n chunk = int(option[1])\n else:\n sys.stdout.write(\"%s : Unrecognized option\\n\" % options[0])\n sys.stderr.write(usage)\n sys.exit(0)\n\n # if we pass a number of files different from 2, abort\n if len(pargs) < 2 or len(pargs) > 2:\n sys.stdout.write(\"You need to pass both source and destination!\\n.\")\n sys.stderr.write(usage)\n sys.exit(0)\n\n # Catch the files passed as the last arguments\n filename4 = pargs[0]\n filename3 = pargs[1]\n\n # copy the data from filename4 to filename3.\n _nc4tonc3(filename4,filename3,clobber=overwritefile,quiet=quiet,format=format)\n\n\ndef _nc3tonc4(filename3,filename4,unpackshort=True,\n zlib=True,complevel=6,shuffle=True,fletcher32=False,\n clobber=False,lsd_dict=None,nchunk=10,quiet=False,classic=0,\n vars=None,istart=0,istop=-1):\n \"\"\"convert a netcdf 3 file (filename3) to a netcdf 4 file\n The default format is 'NETCDF4', but can be set\n to NETCDF4_CLASSIC if classic=1.\n If unpackshort=True, variables stored as short\n integers with a scale and offset are unpacked to floats.\n in the netcdf 4 file. If the lsd_dict is not None, variable names\n corresponding to the keys of the dict will be truncated to the decimal place\n specified by the values of the dict. This improves compression by\n making it 'lossy'..\n If vars is not None, only variable names in the list\n will be copied (plus all the dimension variables).\n The zlib, complevel and shuffle keywords control\n how the compression is done.\"\"\"\n\n from netCDF4 import Dataset\n\n ncfile3 = Dataset(filename3,'r')\n if classic:\n ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4_CLASSIC')\n else:\n ncfile4 = Dataset(filename4,'w',clobber=clobber,format='NETCDF4')\n mval = 1.e30 # missing value if unpackshort=True\n # create dimensions. Check for unlimited dim.\n unlimdimname = False\n unlimdim = None\n # create global attributes.\n if not quiet: sys.stdout.write('copying global attributes ..\\n')\n #for attname in ncfile3.ncattrs():\n # setattr(ncfile4,attname,getattr(ncfile3,attname))\n ncfile4.setncatts(ncfile3.__dict__)\n if not quiet: sys.stdout.write('copying dimensions ..\\n')\n for dimname,dim in ncfile3.dimensions.items():\n if dim.isunlimited():\n unlimdimname = dimname\n unlimdim = dim\n ncfile4.createDimension(dimname,None)\n if istop == -1: istop=len(unlimdim)\n else:\n ncfile4.createDimension(dimname,len(dim))\n # create variables.\n if vars is None:\n varnames = ncfile3.variables.keys()\n else:\n # variables to copy specified\n varnames = vars\n # add dimension variables\n for dimname in ncfile3.dimensions.keys():\n if dimname in ncfile3.variables.keys() and\\\n dimname not in varnames:\n varnames.append(dimname)\n for varname in varnames:\n ncvar = ncfile3.variables[varname]\n if not quiet: sys.stdout.write('copying variable %s\\n' % varname)\n # quantize data?\n if lsd_dict is not None and varname in lsd_dict:\n lsd = lsd_dict[varname]\n if not quiet: sys.stdout.write('truncating to least_significant_digit = %d\\n'%lsd)\n else:\n lsd = None # no quantization.\n # unpack short integers to floats?\n if unpackshort and hasattr(ncvar,'scale_factor') and hasattr(ncvar,'add_offset'):\n dounpackshort = True\n datatype = 'f4'\n else:\n dounpackshort = False\n datatype = ncvar.dtype\n # is there an unlimited dimension?\n if unlimdimname and unlimdimname in ncvar.dimensions:\n hasunlimdim = True\n else:\n hasunlimdim = False\n if dounpackshort:\n if not quiet: sys.stdout.write('unpacking short integers to floats ...\\n')\n sys.stdout.write('')\n # is there missing value?\n if hasattr(ncvar, '_FillValue'):\n fillvalue3 = ncvar._FillValue\n elif hasattr(ncvar, 'missing_value'):\n fillvalue3 = ncvar.missing_value\n else:\n fillvalue3 = None\n if fillvalue3 is not None:\n fillvalue4 = fillvalue3 if not dounpackshort else mval\n else:\n fillvalue4 = None\n var = ncfile4.createVariable(varname,datatype,ncvar.dimensions, fill_value=fillvalue4, least_significant_digit=lsd,zlib=zlib,complevel=complevel,shuffle=shuffle,fletcher32=fletcher32)\n # fill variable attributes.\n attdict = ncvar.__dict__\n if '_FillValue' in attdict: del attdict['_FillValue']\n if dounpackshort and 'add_offset' in attdict:\n del attdict['add_offset']\n if dounpackshort and 'scale_factor' in attdict:\n del attdict['scale_factor']\n if dounpackshort and 'missing_value' in attdict:\n attdict['missing_value'] = fillvalue4\n var.setncatts(attdict)\n # fill variables with data.\n if hasunlimdim: # has an unlim dim, loop over unlim dim index.\n # range to copy\n if nchunk:\n start = istart; stop = istop; step = nchunk\n if step < 1: step = 1\n for n in range(start, stop, step):\n nmax = n+nchunk\n if nmax > istop: nmax=istop\n var[n-istart:nmax-istart] = ncvar[n:nmax]\n else:\n var[0:len(unlimdim)] = ncvar[:]\n else: # no unlim dim or 1-d variable, just copy all data at once.\n var[:] = ncvar[:]\n ncfile4.sync() # flush data to disk\n # close files.\n ncfile3.close()\n ncfile4.close()\n\n\ndef nc3tonc4():\n usage = \"\"\"\n Convert a netCDF 3 file to netCDF 4 format, optionally\n unpacking variables packed as short integers (with scale_factor and add_offset)\n to floats, and adding zlib compression (with the HDF5 shuffle filter and fletcher32 checksum).\n Data may also be quantized (truncated) to a specified precision to improve compression.\n\n usage: %s [-h] [-o] [--vars=var1,var2,..] [--zlib=(0|1)] [--complevel=(1-9)] [--shuffle=(0|1)] [--fletcher32=(0|1)] [--unpackshort=(0|1)] [--quantize=var1=n1,var2=n2,..] netcdf3filename netcdf4filename\n -h -- Print usage message.\n -o -- Overwrite destination file (default is to raise an error if output file already exists).\n --vars -- comma separated list of variable names to copy (default is to copy\n all variables)\n --classic=(0|1) -- use NETCDF4_CLASSIC format instead of NETCDF4 (default 1)\n --zlib=(0|1) -- Activate (or disable) zlib compression (default is activate).\n --complevel=(1-9) -- Set zlib compression level (6 is default).\n --shuffle=(0|1) -- Activate (or disable) the shuffle filter (active by default).\n --fletcher32=(0|1) -- Activate (or disable) the fletcher32 checksum (not\n active by default).\n --unpackshort=(0|1) -- Unpack short integer variables to float variables\n using scale_factor and add_offset netCDF variable attributes (active by default).\n --quantize=(comma separated list of \"variable name=integer\" pairs) --\n Truncate the data in the specified variables to a given decimal precision.\n For example, 'speed=2, height=-2, temp=0' will cause the variable\n 'speed' to be truncated to a precision of 0.01, 'height' to a precision of 100\n and 'temp' to 1. This can significantly improve compression. The default\n is not to quantize any of the variables.\n --quiet=(0|1) -- if 1, don't print diagnostic information.\n --chunk=(integer) -- number of records along unlimited dimension to\n write at once. Default 10. Ignored if there is no unlimited\n dimension. chunk=0 means write all the data at once.\n --istart=(integer) -- number of record to start at along unlimited dimension.\n Default 0. Ignored if there is no unlimited dimension.\n --istop=(integer) -- number of record to stop at along unlimited dimension.\n Default -1. Ignored if there is no unlimited dimension.\n\\n\"\"\" % os.path.basename(sys.argv[0])\n\n try:\n opts, pargs = getopt.getopt(sys.argv[1:], 'ho',\n ['classic=',\n 'vars=',\n 'zlib=',\n 'quiet=',\n 'complevel=',\n 'shuffle=',\n 'fletcher32=',\n 'unpackshort=',\n 'quantize=',\n 'chunk=',\n 'istart=',\n 'istop='])\n except:\n (type, value, traceback) = sys.exc_info()\n sys.stdout.write(\"Error parsing the options. The error was: %s\\n\" % value)\n sys.stderr.write(usage)\n sys.exit(0)\n\n # default options\n overwritefile = 0\n complevel = 6\n classic = 1\n zlib = 1\n shuffle = 1\n fletcher32 = 0\n unpackshort = 1\n vars = None\n quantize = None\n quiet = 0\n chunk = 1000\n istart = 0\n istop = -1\n\n # Get the options\n for option in opts:\n if option[0] == '-h':\n sys.stderr.write(usage)\n sys.exit(0)\n elif option[0] == '-o':\n overwritefile = 1\n elif option[0] == '--classic':\n classic = int(option[1])\n elif option[0] == '--zlib':\n zlib = int(option[1])\n elif option[0] == '--quiet':\n quiet = int(option[1])\n elif option[0] == '--complevel':\n complevel = int(option[1])\n elif option[0] == '--shuffle':\n shuffle = int(option[1])\n elif option[0] == '--fletcher32':\n fletcher32 = int(option[1])\n elif option[0] == '--unpackshort':\n unpackshort = int(option[1])\n elif option[0] == '--chunk':\n chunk = int(option[1])\n elif option[0] == '--vars':\n vars = option[1]\n elif option[0] == '--quantize':\n quantize = option[1]\n elif option[0] == '--istart':\n istart = int(option[1])\n elif option[0] == '--istop':\n istop = int(option[1])\n else:\n sys.stdout.write(\"%s: Unrecognized option\\n\" % option[0])\n sys.stderr.write(usage)\n sys.exit(0)\n\n # if we pass a number of files different from 2, abort\n if len(pargs) < 2 or len(pargs) > 2:\n sys.stdout.write(\"You need to pass both source and destination!.\\n\")\n sys.stderr.write(usage)\n sys.exit(0)\n\n # Catch the files passed as the last arguments\n filename3 = pargs[0]\n filename4 = pargs[1]\n\n # Parse the quantize option, create a dictionary from key/value pairs.\n if quantize is not None:\n lsd_dict = {}\n for p in quantize.split(','):\n kv = p.split('=')\n lsd_dict[kv[0]] = int(kv[1])\n else:\n lsd_dict=None\n\n # Parse the vars option, create a list of variable names.\n if vars is not None:\n vars = vars.split(',')\n\n # copy the data from filename3 to filename4.\n _nc3tonc4(filename3,filename4,unpackshort=unpackshort,\n zlib=zlib,complevel=complevel,shuffle=shuffle,\n fletcher32=fletcher32,clobber=overwritefile,lsd_dict=lsd_dict,\n nchunk=chunk,quiet=quiet,vars=vars,classic=classic,\n istart=istart,istop=istop)\n", "repo_name": "ryfeus/lambda-packs", "sub_path": "HDF4_H5_NETCDF/source2.7/netCDF4/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 35547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1104, "dataset": "github-code", "pt": "22", "api": [{"api_name": "sys.version_info", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.ma.isMA", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.iterable", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.iterable", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.flatnonzero", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.iterable", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.iterable", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.iterable", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.iterable", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.alen", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.iterable", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 486, "usage_type": "call"}, {"api_name": "os.path", "line_number": 486, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 486, "usage_type": "attribute"}, {"api_name": "getopt.getopt", "line_number": 489, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 489, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 494, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 495, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 495, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 496, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 496, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 497, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 503, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 503, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 504, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 512, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 512, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 513, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 513, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 514, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 519, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 545, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 548, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 553, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 553, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 557, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 557, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 568, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 568, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 620, "usage_type": "call"}, {"api_name": "os.path", "line_number": 620, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 620, "usage_type": "attribute"}, {"api_name": "getopt.getopt", "line_number": 623, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 623, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 626, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 627, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 627, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 628, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 628, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 629, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 640, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 640, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 641, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 651, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 651, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 652, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 652, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 653, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 657, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 657, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 658, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 658, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 659, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 689, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 691, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 693, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 699, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 699, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 703, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 703, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 725, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 725, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 729, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 729, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 745, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 745, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 746, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 746, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 823, "usage_type": "call"}, {"api_name": "os.path", "line_number": 823, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 823, "usage_type": "attribute"}, {"api_name": "getopt.getopt", "line_number": 826, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 826, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 840, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 841, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 841, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 842, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 842, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 843, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 863, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 863, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 864, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 892, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 892, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 893, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 893, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 894, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 898, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 898, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 899, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 899, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 900, "usage_type": "call"}]} +{"seq_id": "4165250122", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n#from matplotlib2tikz import save as tikz_save\n\n\ndef polyinterp(u, x, y, w=None):\n if w == None:\n w = baryweights(x)\n\n ret = np.zeros(len(u))\n for i in range(len(ret)):\n if u[i] in x:\n ret[i] = y[np.where(x==u[i])]\n else:\n weights = w /(u[i] - x)\n ret[i] = weights.dot(y)/sum(weights)\n return ret\n \ndef baryweights(x):\n w = np.ones(len(x))\n for j, xj in enumerate(x):\n for xi in x[np.arange(len(x))!=j]: \n w[j] /= (xj - xi)\n return w\n\ndef p4_foo(x):\n return np.sqrt( x**2 + .1)\n\ndef p4b(tikz=False):\n samples = []\n labels = []\n x = np.linspace(-1,1,15)\n samples.append(x)\n labels.append('Equispaced points:')\n x = []\n for j in range(0,15):\n x.append( -1* np.cos( (2*j+1)/30 * np.pi))\n samples.append( np.sort( np.array(x) ) )\n labels.append('First kind Chebyshev points:')\n x = []\n for j in range(0,15):\n x.append( -1* np.cos( (j*np.pi)/14 ) )\n samples.append( np.sort( np.array(x) ) )\n labels.append('Second kind Chebyshev points:')\n x = [-0.987992518020485,\n -0.394151347077563,\n 0.570972172608539,\n -0.937273392400706,\n -0.201194093997435,\n 0.724417731360170,\n -0.848206583410427,\n 0,\n 0.848206583410427,\n -0.724417731360170,\n 0.201194093997435,\n 0.937273392400706,\n -0.570972172608539,\n 0.394151347077563,\n 0.987992518020485]\n samples.append( np.sort( np.array(x) ) )\n labels.append('Legendre points:')\n for x, l, file in zip(samples, labels, range(1,5)):\n y = p4_foo(x)\n u = np.linspace(-1,1,201)\n p = polyinterp(u, x, y)\n f = p4_foo(u)\n print(l)\n print('Eucidian norm: %f' % np.linalg.norm(f-p))\n print('Infinity norm: %f' % np.linalg.norm(f-p,np.inf))\n plt.subplot(2,1,1)\n plt.plot(u,p, 'r-')\n plt.plot(u,f, 'k-')\n plt.plot(x,y, 'bo')\n plt.xlim( (-1, 1) )\n plt.ylim( (.2, 1.2) )\n \n plt.subplot(2,1,2)\n plt.plot(u, p-f, 'k-')\n plt.xlim( (-1, 1) )\n y_range = np.max( np.abs(p-f) )\n plt.ylim( (-y_range, y_range) )\n #if tikz:\n #tikz_save('images/hw4_figure_' + str(file) + '_tikz.tex')\n plt.show()\n\ndef p5a():\n x = np.array( [0,2,4] )\n y = np.array( [0,1,2] )\n \n u = np.linspace(0,4, 200)\n f = polyinterp(u, x, y)\n \n plt.plot(u,f, 'r-')\n \n plt.plot( (0,2,4), (0,1,2), 'bo')\n plt.plot( (-10,10), (2,2), 'k-')\n plt.plot( (-10,10), (0,0), 'k-')\n \n plt.xlim( (-0.5, 4.5) )\n plt.ylim( (-.5, 2.5) )\n \n plt.show()\n \ndef p5b():\n \n x = np.array( [0,2,4] )\n y = np.array( [0,1,2] )\n \n u = np.linspace(0,4, 200)\n f = .25 * u**2 - 1/16 * u**2 * (u-2)\n \n plt.plot(u,f, 'r-')\n \n plt.plot( (0,2,4), (0,1,2), 'bo')\n plt.plot( (-10,10), (2,2), 'k-')\n plt.plot( (-10,10), (0,0), 'k-')\n \n plt.xlim( (-0.5, 4.5) )\n plt.ylim( (-.5, 2.5) )\n \n plt.show()\n\ndef p5c():\n x = np.array( [0,2,4] )\n y = np.array( [0,1,2] )\n \n u = np.linspace(0,2, 100)\n f = 1/8 * u**3 - 1/16 * u**3 * (u-2)\n plt.plot(u,f, 'r-')\n u = np.linspace(2,4, 100)\n f = 1 + 1 *(u-2) - 1/4 * (u-2)**2 - 1/16 * (u-2)**2 * (u-4)**2\n plt.plot(u,f, 'g-')\n \n plt.plot( (0,2,4), (0,1,2), 'bo')\n plt.plot( (-10,10), (2,2), 'k-')\n plt.plot( (-10,10), (0,0), 'k-')\n \n plt.xlim( (-0.5, 4.5) )\n plt.ylim( (-.5, 2.5) )\n \n plt.show()\n \n \n", "repo_name": "shawsa/m565", "sub_path": "hw4.py", "file_name": "hw4.py", "file_ext": "py", "file_size_in_byte": 3661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "22", "api": [{"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 69, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "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.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"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.plot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.linspace", "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": 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.xlim", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}]}