diff --git "a/1358.jsonl" "b/1358.jsonl" new file mode 100644--- /dev/null +++ "b/1358.jsonl" @@ -0,0 +1,362 @@ +{"seq_id": "8502085091", "text": "'''#list\r\nlist = [1,2 ,3 ,4 ]\r\ntouple = (1, 2, 3, 4)\r\nset = {1, 2, 3, 4}\r\ndictionary = {\"Name\": \"Kumail\", \"Age\":\"19\",\"Class\":\"BS Artificial Intelligence 4th M(B)\"}\r\n\r\nprint(list)\r\nprint(type(list))\r\nprint(touple)\r\nprint(type(touple))\r\nprint(set)\r\nprint(type(set))\r\nprint(dictionary)\r\nprint(type(dictionary))\r\n#print(dictionary(\"Name\"))\r\n\r\na= 12\r\nb = \"Hasilpur\"\r\nname = \"Kumail\"\r\n\r\n#import keyword\r\n\r\n#print(keyword.kwlist)\r\n\r\na = input(\"Enter Your First Number = \")\r\nb = input(\"Enter Your Sure Number = \")\r\n\r\nprint(int(a)+int(b)) '''\r\n\r\n\r\nimport random\r\nimport click\r\n\r\nanswer = random.randint(1,100)\r\ncounter = 1\r\nguess= int(input(\"Guess the Number = \"))\r\n\r\nwhile guess != answer:\r\n\r\n if guess< answer:\r\n print(\"Guess Higher!\")\r\n\r\n else:\r\n print(\"Guess Lower!\")\r\n\r\n click.clear()\r\n guess = int(input(\"Again Guess the Number = \"))\r\n counter += 1\r\n\r\n\r\nprint(\"Correct Answer!\")\r\nprint(counter,\"Times Guesses Attempted\")", "repo_name": "Syedkumailhaider512/Python", "sub_path": "Others/Test.py", "file_name": "Test.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "click.clear", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "40051193342", "text": "import re\nfrom django.core.exceptions import ValidationError\n\n\ndef sno_validator(value):\n if not (value.isdigit() and len(value) == 12):\n raise ValidationError('%s is not an correct number' % value)\n\n\ndef mac_address_validator(value):\n regex = r'^[A-F0-9]{2}(:[A-F0-9]{2}){5}$'\n regex = re.compile(regex)\n regex_matches = bool(regex.search(str(value)))\n if not regex_matches:\n raise ValidationError('%s is not a valid Mac Address' % value)\n\n\ndef phone_no_validator(value):\n regex = r'^[1][0-9]{10}$'\n regex = re.compile(regex)\n regex_matches = bool(regex.search(str(value)))\n if not regex_matches:\n raise ValidationError('%s is not a valid Phone Number' % value)\n\n", "repo_name": "RockMeroll/Wifi_Probe", "sub_path": "Wifi_Probe/tools/validators.py", "file_name": "validators.py", "file_ext": "py", "file_size_in_byte": 712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "django.core.exceptions.ValidationError", "line_number": 7, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 15, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 20, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "39780501189", "text": "# coding:utf-8\r\n\r\nfrom django.urls import path\r\nimport blog.views as blog_view\r\n\r\nurlpatterns=[\r\n path('',blog_view.blog_index,name='blog_index'),\r\n path('test/',blog_view.blog_test,name='blog_test'),\r\n path('articles//',blog_view.blog_articles,name='blog_articles'),\r\n path('article_detail//',blog_view.blog_article_detail,name='blog_article_detail'),\r\n path('article_detail//',blog_view.blog_article_detail,name='blog_article_detail'),\r\n path('add_comment/',blog_view.blog_add_comment_ajax,name='blog_add_comment_ajax'),\r\n]", "repo_name": "rhj231223/blog", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "blog.views.blog_index", "line_number": 7, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "blog.views.blog_test", "line_number": 8, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "blog.views.blog_articles", "line_number": 9, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "blog.views.blog_article_detail", "line_number": 10, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "blog.views.blog_article_detail", "line_number": 11, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "blog.views.blog_add_comment_ajax", "line_number": 12, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "86574719322", "text": "#!/usr/bin/env python\nfrom TrigEgammaDevelopments.plots.Efficiency import EfficiencyParser\nfrom RingerCore import Logger, LoggingLevel, BooleanStr\nimport argparse\n\nmainLogger = Logger.getModuleLogger(\"PlotTool\")\n\nparser = argparse.ArgumentParser(description = '',\n add_help = False)\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-i','--inputFiles', action='store', \n dest='inputFiles', required = True, nargs='+',\n help = \"The input files that will be used to generate the plots\")\n\nparser.add_argument('-o','--outputDir', action='store', \n dest='outputDir', required = False, default = 'plots',\n help = \"The output directory name.\")\n\nparser.add_argument('-k','--key', action='store', \n dest='key', required = False, default = None,\n help = \"key generated to reproduce plots\")\n\nparser.add_argument('-l','--atlaslabel', action='store', \n dest='atlaslabel', required = False, default = 'Internal',\n help = \"The Atlas label\")\n\nparser.add_argument('-b','--isBkg', action='store_true', \n dest='bkg', required = False, \n help = \"Use this to switch the scale to background mode\")\n\n\n\nimport sys,os\nif len(sys.argv)==1:\n parser.print_help()\n sys.exit(1)\n\nargs = parser.parse_args()\nefficiencyParser = EfficiencyParser( args.inputFiles ) \nefficiencyParser(key = args.key, atlaslabel = args.atlaslabel, outputdir=args.outputDir, isbackground=args.bkg)\n\n\n\n", "repo_name": "kaducovas/ringerLegacy", "sub_path": "root/rDev/scripts/standalone/plot_eff.py", "file_name": "plot_eff.py", "file_ext": "py", "file_size_in_byte": 1447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "RingerCore.Logger.getModuleLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "RingerCore.Logger", "line_number": 6, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}, {"api_name": "TrigEgammaDevelopments.plots.Efficiency.EfficiencyParser", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "16605419508", "text": "import pytest\nfrom pytest_django.asserts import assertNumQueries\n\nfrom reportcreator_api.pentests.models import PentestProject\nfrom .mock import create_project_type, create_project\n\n\n@pytest.mark.django_db\ndef test_model_diff():\n project = create_project()\n\n p = PentestProject.objects.get(id=project.id)\n p.name = 'changed'\n p.update_data({'title': 'changed'})\n\n assert p.has_changed\n assert set(p.changed_fields) == {'name', 'custom_fields'}\n assert p.get_field_diff('name') == (project.name, p.name)\n assert p.get_field_diff('custom_fields'), (project.custom_fields, p.custom_fields)\n\n\n@pytest.mark.django_db\ndef test_diff_related():\n project_type = create_project_type()\n project_type2 = create_project_type()\n project = create_project(project_type=project_type)\n\n p = PentestProject.objects.get(id=project.id)\n p.project_type = project_type2\n assert p.has_changed\n assert set(p.changed_fields) == {'project_type_id'}\n assert p.get_field_diff('project_type_id') == (project_type.id, project_type2.id)\n\n\n@pytest.mark.django_db\ndef test_diff_deferred_fields():\n project = create_project()\n\n # Deferred fields should not cause DB queries\n with assertNumQueries(1):\n p = PentestProject.objects.only('id', 'readonly').get(id=project.id)\n \n # Changes on deferred fields are not detected\n p.name = 'changed' # write deferred\n assert not p.has_changed\n # Changes on non-deferred fields are detected\n p.readonly = True\n assert p.has_changed\n", "repo_name": "Dotwut/Tools", "sub_path": "sysreptor/api/src/reportcreator_api/tests/test_model_diff.py", "file_name": "test_model_diff.py", "file_ext": "py", "file_size_in_byte": 1551, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "mock.create_project", "line_number": 10, "usage_type": "call"}, {"api_name": "reportcreator_api.pentests.models.PentestProject.objects.get", "line_number": 12, "usage_type": "call"}, {"api_name": "reportcreator_api.pentests.models.PentestProject.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "reportcreator_api.pentests.models.PentestProject", "line_number": 12, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mock.create_project_type", "line_number": 24, "usage_type": "call"}, {"api_name": "mock.create_project_type", "line_number": 25, "usage_type": "call"}, {"api_name": "mock.create_project", "line_number": 26, "usage_type": "call"}, {"api_name": "reportcreator_api.pentests.models.PentestProject.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "reportcreator_api.pentests.models.PentestProject.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "reportcreator_api.pentests.models.PentestProject", "line_number": 28, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mock.create_project", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest_django.asserts.assertNumQueries", "line_number": 40, "usage_type": "call"}, {"api_name": "reportcreator_api.pentests.models.PentestProject.objects.only", "line_number": 41, "usage_type": "call"}, {"api_name": "reportcreator_api.pentests.models.PentestProject.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "reportcreator_api.pentests.models.PentestProject", "line_number": 41, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "27710893523", "text": "# 보조 : stack\n\nfrom collections import deque\n\n\ndef solution(order):\n answer = 0\n\n sub = deque([])\n\n cnt = 0\n idx = 0\n box = 1\n while True:\n # print(\"Order : \", order[idx])\n if (len(sub)>0 and sub[-1] > order[idx]) or idx >= len(order):\n break\n if len(sub) > 0 and sub[-1] == order[idx]:\n sub.pop()\n answer += 1\n idx += 1\n box -= 1\n elif box == order[idx]:\n answer += 1\n idx += 1\n else:\n sub.append(box)\n box += 1\n if box > len(order):\n box = len(order)\n \n \n\n return answer\n\norder = [5,4,3,2,1]\n\nprint(solution(order))\n", "repo_name": "201710757/cpp_study", "sub_path": "0727/test3.py", "file_name": "test3.py", "file_ext": "py", "file_size_in_byte": 708, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "collections.deque", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "72277779343", "text": "import csv\nimport hashlib\nimport os\nimport sqlite3\n\ncon = sqlite3.connect('transactions.db')\ncur = con.cursor()\n\ndata = []\nexpected_columns = ['Date', 'Description', 'Original Description', 'Amount', 'Transaction Type', 'Category', 'Account Name', 'Labels', 'Notes']\nfilepaths = ['mint transactions/' + fn for fn in os.listdir('mint transactions') if fn.endswith('.csv')]\n\nfor filepath in filepaths:\n with open(filepath) as f:\n reader = csv.reader(f)\n \n columns = next(reader)\n if columns != expected_columns:\n raise Error(f'Columns do not equal expected columns in {filepath}!')\n \n for tx in reader:\n old_date = tx[0]\n month, day, year = [int(x) for x in old_date.split('/')]\n new_date = f'{year}-{month:02d}-{day:02d}'\n\n tx_id = hashlib.sha256((new_date + tx[1] + tx[3] + tx[4]).encode('utf-8')).hexdigest()\n data.append((tx_id, new_date, tx[1], tx[3], tx[4], tx[5], tx[6]))\n\n cur.executemany('INSERT OR IGNORE INTO transactions VALUES(?, ?, ?, ?, ?, ?, ?)', data)\n con.commit()", "repo_name": "jackdahms/biru", "sub_path": "load_mint_transactions.py", "file_name": "load_mint_transactions.py", "file_ext": "py", "file_size_in_byte": 1105, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 15, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "42930948554", "text": "from typing import Set, Tuple\n\n\ndef solve() -> int:\n segments: Set[int] = set([2, 3, 4, 7])\n digit_times = 0\n\n input = open(\"input.txt\", \"r\")\n for output_values in [line.strip().split(\" | \")[1] for line in input.readlines()]:\n for output_value in output_values.split(\" \"):\n if len(output_value) in segments:\n digit_times += 1\n\n return digit_times\n\n\ndef main() -> None:\n print(solve())\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "UnsafePointer/advent-of-code-2021", "sub_path": "08/P1.py", "file_name": "P1.py", "file_ext": "py", "file_size_in_byte": 476, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "typing.Set", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "74208808141", "text": "from datetime import timedelta, datetime\n\nfrom airflow import DAG\nfrom airflow.operators.trigger_dagrun import TriggerDagRunOperator\n\ndefault_args = {\n \"owner\": \"Fernando Palacios\",\n \"depends_on_past\": False,\n \"start_date\": datetime(2021, 10, 1),\n \"email\": [\"fpalacios.fm.gcp@gmail.com\"],\n \"email_on_failure\": False,\n \"email_on_retry\": False,\n \"retries\": 1,\n \"retry_delay\": timedelta(minutes=1),\n}\n\ndag = DAG(\n \"WDEB_CP_MovieAnalytics_ETL\",\n default_args=default_args,\n schedule_interval=\"@once\",\n catchup=False,\n max_active_runs=1,\n)\n\ntrigger_extract_log_review = TriggerDagRunOperator(\n dag=dag,\n task_id=\"extract_log_review\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_ExtractLogReview\",\n wait_for_completion=True,\n poke_interval=5,\n)\n\ntrigger_extract_movie_review = TriggerDagRunOperator(\n dag=dag,\n task_id=\"extract_movie_review\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_ExtractMovieReview\",\n wait_for_completion=True,\n poke_interval=5,\n)\n\ntrigger_extract_user_purchase = TriggerDagRunOperator(\n dag=dag,\n task_id=\"extract_user_purchase\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_ExtractUserPurchase\",\n wait_for_completion=True,\n poke_interval=5,\n)\n\ntrigger_transform_log_review = TriggerDagRunOperator(\n dag=dag,\n task_id=\"transform_log_review\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_TransformLogReview\",\n wait_for_completion=True,\n retries=8,\n)\n\ntrigger_transform_movie_review = TriggerDagRunOperator(\n dag=dag,\n task_id=\"transform_movie_review\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_TransformMovieReview\",\n wait_for_completion=True,\n retries=8,\n)\n\ntrigger_transform_user_purchase = TriggerDagRunOperator(\n dag=dag,\n task_id=\"transform_user_purchase\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_TransformUserPurchase\",\n wait_for_completion=True,\n retries=8,\n)\n\ntrigger_load_dim_tables = TriggerDagRunOperator(\n dag=dag,\n task_id=\"load_dim_tables\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_LoadDimTables\",\n wait_for_completion=True,\n poke_interval=5,\n)\n\ntrigger_load_fact_table = TriggerDagRunOperator(\n dag=dag,\n task_id=\"load_fact_table\",\n trigger_dag_id=\"WDEB_CP_MovieAnalytics_ETL_LoadFactTable\",\n wait_for_completion=True,\n poke_interval=5,\n)\n\n(\n trigger_extract_log_review\n >> trigger_transform_log_review\n >> trigger_load_dim_tables\n >> trigger_load_fact_table\n)\n(\n trigger_extract_movie_review\n >> trigger_transform_movie_review\n >> trigger_load_fact_table\n)\n(\n trigger_extract_user_purchase\n >> trigger_transform_user_purchase\n >> trigger_load_fact_table\n)\n", "repo_name": "fpalaciosFM/WizelineDEB_MovieAnalytics", "sub_path": "Airflow-DAGs/deliverable/WDEB_CP_MovieAnalytics_ETL.py", "file_name": "WDEB_CP_MovieAnalytics_ETL.py", "file_ext": "py", "file_size_in_byte": 2686, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "datetime.datetime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 14, "usage_type": "call"}, {"api_name": "airflow.DAG", "line_number": 17, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 25, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 33, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 41, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 49, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 57, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 65, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 73, "usage_type": "call"}, {"api_name": "airflow.operators.trigger_dagrun.TriggerDagRunOperator", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "30296021241", "text": "import daft\nimport matplotlib.pyplot as plt\n\n\nplt.rcParams[\"text.usetex\"] = True\n\nplt.style.use(\n [\n \"./visualization/stylesheets/fontsize/7pt.mplstyle\",\n \"./visualization/stylesheets/color/probnum_colors.mplstyle\",\n ]\n)\npgm = daft.PGM(dpi=300, aspect=1.0, grid_unit=1.41)\n\n# Bridge prior\npgm.add_plate(\n [-0.5, -1.5, 5.0, 2.0],\n rect_params={\"ec\": \"C1\", \"linewidth\": 2},\n)\n\n# Classic prior\npgm.add_plate(\n [-0.55, -0.55, 5.10, 1.10],\n rect_params={\"ec\": \"C0\", \"linewidth\": 2},\n)\n\n# Placeholder for space reasons\npgm.add_plate(\n [-0.55, -1.85, 5.0, 2.0],\n rect_params={\"ec\": \"None\", \"linewidth\": 2},\n)\n\n\npgm.add_node(\"y0\", r\"$Y(t_0)$\", 0, 0)\npgm.add_node(\"y1\", r\"...\", 1.0, 0, plot_params={\"ec\": \"None\"})\npgm.add_node(\"y2\", r\"$Y(t_n)$\", 2, 0)\npgm.add_node(\"y3\", r\"...\", 3, 0, plot_params={\"ec\": \"None\"})\npgm.add_node(\"y4\", r\"$Y(t_N)$\", 4, 0)\n\npgm.add_node(\"ell0\", r\"$\\ell_0$\", 0, 1.0, alternate=True)\npgm.add_node(\"ell2\", r\"$\\ell_n$\", 2, 1.0, alternate=True)\npgm.add_node(\"ell4\", r\"$\\ell_N$\", 4, 1.0, alternate=True)\n\npgm.add_node(\"ellL\", r\"$\\ell_L$\", 0, -1.0, alternate=True)\npgm.add_node(\"ellR\", r\"$\\ell_R$\", 4, -1.0, alternate=True)\n\npgm.add_edge(\"y0\", \"y1\")\npgm.add_edge(\"y1\", \"y2\")\npgm.add_edge(\"y2\", \"y3\")\npgm.add_edge(\"y3\", \"y4\")\n\npgm.add_edge(\"y0\", \"ell0\")\npgm.add_edge(\"y2\", \"ell2\")\npgm.add_edge(\"y4\", \"ell4\")\n\npgm.add_edge(\"y0\", \"ellL\")\npgm.add_edge(\"y4\", \"ellR\")\n\n\nax = pgm.render()\n# ax.set_title(\n# r\"$\\bf A$\" + \" \",\n# loc=\"left\",\n# fontweight=\"bold\",\n# ha=\"right\",\n# fontsize=\"x-large\",\n# pad=-20,\n# )\n\nplt.savefig(\"./figures/pgm.pdf\")\nplt.show()\n", "repo_name": "pnkraemer/probabilistic-bvp-solver", "sub_path": "visualization/pgm.py", "file_name": "pgm.py", "file_ext": "py", "file_size_in_byte": 1624, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "47", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 5, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "daft.PGM", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "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"}]} +{"seq_id": "1373889000", "text": "import time\nimport random\nimport paho.mqtt.client as paho\nfrom paho import mqtt\nfrom random import randint\nfrom time import sleep\nfrom datetime import datetime\n\n# MAC fixo\nmac = \"B0:A7:32:15:00:CC\"\n# Configurando callbacks para diferentes eventos para verificar se eles funcionam e imprimir a mensagem, etc.\ndef on_connect(client, userdata, flags, rc, properties=None):\n \"\"\"\n Imprime o resultado da conexao com um codigo de motivo no stdout (usado como callback para conectar)\n :param client: o proprio cliente\n :param userdata: userdata e definido ao iniciar o cliente, aqui e userdata=None\n :param flags: sao os flags de resposta enviados pelo broker\n :param rc: representa reasonCode, que e um codigo para o resultado da conexao\n :param properties: pode ser usado no MQTTv5, mas e opcional\n \"\"\"\n print(\"CONNACK recebido com codigo %s.\" % rc)\n\n\n# Com esse callback, voce pode ver se a publicacao foi bem-sucedida\ndef on_publish(client, userdata, mid, properties=None):\n \"\"\"\n Imprime mid no stdout para garantir uma publicacao bem-sucedida (usado como callback para publicar)\n :param client: o proprio cliente\n :param userdata: userdata e definido ao iniciar o cliente, aqui e userdata=None\n :param mid: variavel retornada da chamada correspondente a publish(), para permitir o rastreamento de mensagens enviadas\n :param properties: pode ser usado no MQTTv5, mas e opcional\n \"\"\"\n print(\"mid: \" + str(mid))\n\n\n# Imprime a qual topico foi inscrito\ndef on_subscribe(client, userdata, mid, granted_qos, properties=None):\n \"\"\"\n Imprime uma confirmacao de inscricao bem-sucedida\n :param client: o proprio cliente\n :param userdata: userdata e definido ao iniciar o cliente, aqui e userdata=None\n :param mid: variavel retornada da chamada correspondente a publish(), para permitir o rastreamento de mensagens enviadas\n :param granted_qos: este e o qos que voce declara ao se inscrever, use o mesmo ao publicar\n :param properties: pode ser usado no MQTTv5, mas e opcional\n \"\"\"\n print(\"Inscrito: \" + str(mid) + \" \" + str(granted_qos))\n\n\n# Imprime mensagem, util para verificar se foi bem-sucedido\ndef on_message(client, userdata, msg):\n \"\"\"\n Imprime uma mensagem MQTT no stdout (usado como callback para se inscrever)\n :param client: o proprio cliente\n :param userdata: userdata e definido ao iniciar o cliente, aqui e userdata=None\n :param msg: a mensagem com topico e carga util\n \"\"\"\n print(msg.topic + \" \" + str(msg.qos) + \" \" + str(msg.payload))\n\n\n# Usando a versao 5 do MQTT aqui, para 3.1.1: MQTTv311, 3.1: MQTTv31\n# userdata e um dado definido pelo usuario de qualquer tipo, atualizado por user_data_set()\n# client_id e o nome dado ao cliente\nclient = paho.Client(client_id=\"\", userdata=None, protocol=paho.MQTTv5)\nclient.on_connect = on_connect\n\n# Habilita TLS para conexao segura\n#client.tls_set(tls_version=mqtt.client.ssl.PROTOCOL_TLS)\n# Define nome de usuario e senha\nclient.username_pw_set(\"ClienteAPI\", \"#Api2023\")\n# Conecta ao HiveMQ Cloud na porta 8883 (padrao para MQTT)\nclient.connect('54.160.155.170', 1883)\n\n# Configurando callbacks, use funcoes separadas como acima para melhor visibilidade\nclient.on_subscribe = on_subscribe\nclient.on_message = on_message\nclient.on_publish = on_publish\n\n# Inscreve em todos os topicos do esp32 usando o wildcard \"#\"\n#client.subscribe(\"esp32/#\", qos=1)\n#client.subscribe(\"CONFIG/#\", qos=1)\n#client.subscribe(\"teste/topic\", qos=1)\n\nwhile True:\n # Obtém a data e hora do sistema\n current_time = datetime.now()\n date_time_str = current_time.strftime(\"%d-%m-%Y %H:%M:%S\")\n\n # Gera um valor de temperatura do ar aleatório\n rta = random.uniform(20, 25)\n ta_topic = f\"esp32/{mac}/{date_time_str}/RmTemperature\"\n payload = str(rta)\n client.publish(ta_topic, payload, qos=0)\n print(ta_topic + \"/\" + payload)\n\n # Gera um valor de umidade do ar aleatório\n rha = randint(80, 100)\n ha_topic = f\"esp32/{mac}/{date_time_str}/SilTemperature\"\n payload = str(rha)\n client.publish(ha_topic, payload, qos=0)\n print(ha_topic + \"/\" + payload)\n\n # Gera um valor de temperatura do solo aleatório\n rts = random.uniform(20, 25)\n ts_topic = f\"esp32/{mac}/{date_time_str}/SilHumidity\"\n payload = str(rts)\n client.publish(ts_topic, payload, qos=0)\n print(ts_topic + \"/\" + payload)\n\n # Gera um valor de umidade do solo aleatório\n rhs = randint(80, 100)\n hs_topic = f\"esp32/{mac}/{date_time_str}/AirHumidity\"\n payload = str(rhs)\n client.publish(hs_topic, payload, qos=0)\n print(hs_topic + \"/\" + payload)\n\n\n # Gera um valor de luminosidade aleatório\n rl = randint(0, 1023)\n l_topic = f\"esp32/{mac}/{date_time_str}/BatteryLevel\"\n payload = str(rl)\n client.publish(l_topic, payload, qos=0)\n print(l_topic + \"/\" + payload)\n\n # Gera um valor de pH aleatório\n rph = random.uniform(5, 9)\n ph_topic = f\"esp32/{mac}/{date_time_str}/PH\"\n payload = str(rph)\n client.publish(ph_topic, payload, qos=0)\n print(ph_topic + \"/\" + payload)\n\n sleep(60)\n\n# Uma unica publicacao, isso tambem pode ser feito em loops, etc.\n#client.publish(\"esp32/working\", payload=\"yes\", qos=1)\n\n# loop_forever para simplicidade, aqui voce precisa parar o loop manualmente\n# voce tambem pode usar loop_start e loop_stop\n#client.loop_forever()\n", "repo_name": "wagner-a-becker/ExemploMensageria", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5428, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "paho.mqtt.client.Client", "line_number": 63, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 63, "usage_type": "name"}, {"api_name": "paho.mqtt.client.MQTTv5", "line_number": 63, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "name"}, {"api_name": "random.uniform", "line_number": 89, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 96, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 103, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 110, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 118, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "74938903821", "text": "from __future__ import annotations\nimport argparse\nimport sys\nimport os\nimport json\nfrom typing import Sequence\n\nfrom .hapcut2_mec_solver import AlleleMatrix, MECSolver\n\n\ndef parse_cli_arguments(args: Sequence[str] | None = None) -> argparse.Namespace:\n parser = argparse.ArgumentParser(\n prog=\"HapCUT2 MEC solver\",\n description=\"A Python wrapper around HapCUT2 to solve general minimum error correction (MEC) problems.\",\n epilog=\"\",\n )\n parser.add_argument(\n \"matrix\",\n metavar=\"ALLELE_MATRIX\",\n default=\"\",\n help=\"The allele matrix file in json or npz format, with each row representing a fragment and each column representing a variant. Alternatively, the allele matrix can be supplied as a JSON string wrapped in quotes.\",\n )\n parser.add_argument(\n \"--latency-wait\",\n metavar=\"SECONDS\",\n dest=\"latency_wait\",\n default=5,\n type=int,\n help=\"Number of seconds to wait after HapCUT2 exits before parsing the results.\",\n )\n parser.add_argument(\n \"--verbose\", action=\"store_true\", help=\"Print debugging information to STDERR\"\n )\n\n parsed_args = parser.parse_args(args)\n return parsed_args\n\n\ndef load_allele_matrix(matrix_arg: str) -> AlleleMatrix:\n fragments: Sequence[Sequence[int]] = []\n try:\n return AlleleMatrix.from_json_string(matrix_arg)\n except Exception:\n pass\n\n extension: str = os.path.splitext(matrix_arg)[1]\n if extension == \".json\":\n return AlleleMatrix.from_json(matrix_arg)\n elif extension == \".npz\":\n return AlleleMatrix.from_npz(matrix_arg)\n else:\n raise ValueError(\n f\"Invalid file extension: {extension!r}. Expecting .json or .npz format.\"\n )\n\n\ndef main(args: Sequence[str] | None = None) -> None:\n parsed_args = parse_cli_arguments(args=args)\n if parsed_args.verbose:\n print(\"Loading allele matrix\", file=sys.stderr, flush=True)\n matrix = load_allele_matrix(parsed_args.matrix)\n if parsed_args.verbose:\n n_row, n_col = matrix.shape\n print(\n f\"Loaded allele matrix with {n_row} rows and {n_col} columns\",\n file=sys.stderr,\n flush=True,\n )\n print(\n f\"Initializing MEC solver\",\n file=sys.stderr,\n flush=True,\n )\n solver = MECSolver(matrix)\n result = solver.solve(\n verbose=parsed_args.verbose, latency_wait=parsed_args.latency_wait\n )\n print(result.to_json(), file=sys.stdout, flush=True)\n", "repo_name": "jzhang-dev/hapcut2-mec-solver", "sub_path": "src/hapcut2_mec_solver/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 2546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "47", "api": [{"api_name": "typing.Sequence", "line_number": 11, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 40, "usage_type": "name"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix.from_json_string", "line_number": 42, "usage_type": "call"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix.from_json", "line_number": 48, "usage_type": "call"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix", "line_number": 48, "usage_type": "name"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix.from_npz", "line_number": 50, "usage_type": "call"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix", "line_number": 50, "usage_type": "name"}, {"api_name": "hapcut2_mec_solver.AlleleMatrix", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 57, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 66, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 71, "usage_type": "attribute"}, {"api_name": "hapcut2_mec_solver.MECSolver", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 78, "usage_type": "attribute"}]} +{"seq_id": "17977456761", "text": "import xlsxwriter\n\n############ fazer leitura do arquivo e filtar #############\nworkbook = xlsxwriter.Workbook('./tabela_tempo.xlsx')\nworksheet = workbook.add_worksheet()\n\nnumero_agv = 3\nrow = 0\n\nfor agv in range(numero_agv):\n logs = open('./TrafficWarden.txt', 'r')\n nova_tarefa = f'[AgvNr:{agv+1}] : Encontrado tarefa:'\n\n tag_descarga = 0\n lt_info = []\n lt_escrever = []\n\n #1 '09:45:55,058' ,8 TARGET_POSITION:802, 9 TARGET_ACTION:NONE\n\n #percorrer linha por linha e pegar informaçoes necessárias\n for log in logs:\n if nova_tarefa in log:\n lista_reduzida = []\n lista_reduzida.append(log.split()[1].split(',')[0])\n lista_reduzida.append(log.split()[8].split(':')[1])\n lista_reduzida.append(':'+log.split()[9].split(':')[1])\n lt_info.append(lista_reduzida)\n logs.close()\n\n while len(lt_info) > 0:\n lt_final = []\n if len(lt_final) < 6:\n\n if len(lt_final) == 0 and len(lt_info) > 0:\n if ':UNLOAD' in lt_info[0][2] and len(lt_info) > 0:\n lt_info.remove(lt_info[0]) # remover linha\n elif ':NONE' in lt_info[0][2] and len(lt_info) > 0:\n lt_final.append(lt_info[0][1]) # adicionar tag\n lt_info.remove(lt_info[0]) # remover linha\n elif ':LOAD' in lt_info[0][2] and len(lt_final) == 0 and len(lt_info) > 0:\n lt_final.append(tag_descarga)\n lt_final.append(lt_info[0][0]) # adicionar horario\n lt_final.append(lt_info[0][1]) # adicionar tag\n lt_info.remove(lt_info[0])\n\n if len(lt_final) == 1 and len(lt_info) > 0:\n if ':LOAD' in lt_info[0][2]:\n lt_final.append(lt_info[0][0]) # adicionar horario\n lt_final.append(lt_info[0][1]) # adicionar tag\n lt_info.remove(lt_info[0])\n\n if len(lt_final) == 3 and len(lt_info) > 0:\n if ':UNLOAD' in lt_info[0][2]:\n lt_final.append(lt_info[0][0]) # adicionar horario\n lt_final.append(lt_info[0][1]) # adicionar tag\n lt_info.remove(lt_info[0])\n\n if len(lt_final) == 5 and len(lt_info) > 0:\n if ':NONE' in lt_info[0][2]:\n lt_final.append(lt_info[0][0]) # adicionar horario\n\n elif ':LOAD' in lt_info[0][2]:\n lt_final.append(lt_info[0][0]) # adicionar horario\n\n if len(lt_final) > 3:\n tag_descarga = lt_final[4]\n else:\n tag_descarga = 0\n\n if len(lt_final) == 6:\n lt_escrever.append(lt_final)\n\n ###### escrever arquivo ########\n column = 0\n worksheet.write(row, column, f'LGV{agv+1}')\n row += 1\n for a,b,c,d,e,f in lt_escrever:\n\n worksheet.write(row, column, a)\n worksheet.write(row, column +1, b)\n worksheet.write(row, column +2, c)\n worksheet.write(row, column +3, d)\n worksheet.write(row, column +4, e)\n worksheet.write(row, column +5, f)\n row += 1\nworkbook.close()", "repo_name": "WillCrystian/LerLog", "sub_path": "ler_log.py", "file_name": "ler_log.py", "file_ext": "py", "file_size_in_byte": 3152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "xlsxwriter.Workbook", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "3338990557", "text": "\n\nimport requests\nimport json\nimport os\nimport math, hashlib\nimport datetime, time\nimport PyV8\nimport sqlitedict\nfrom gevent.pool import Pool\n\nUA_ST = '''Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:62.0) Gecko/20100101 Firefox/62.0'''\nUA_js = '''var navigator = {};\nnavigator[\"userAgent\"] = \"%s\";\n''' % UA_ST\nSIG_JS_OBJ = False\nDATE_FORMAT = '%Y-%m-%d %H:%M:%S'\nHEADERS = {\"User-Agent\": UA_ST}\nCOOKIES = {'uuid': 'w:c1554d99c34047cc8e89fd', 'tt_webid': '6614290627788719619'}\n\n\ndef dump_json(jo, i=None, e=None):\n if e is None:\n e = 'gbk' if os.name == 'nt' else 'utf8'\n return json.dumps(jo, ensure_ascii=False, indent=i).encode(e, 'ignore')\n\n\ndef get_date(fmt=DATE_FORMAT, base=datetime.datetime.now(), isobj=False, **kwargs):\n i_str2date = lambda str_date, fmt: datetime.datetime.fromtimestamp(time.mktime(time.strptime(str_date, fmt)))\n if type(base) == str:\n dateobj = i_str2date(base, fmt) + datetime.timedelta(**kwargs)\n else:\n dateobj = base + datetime.timedelta(**kwargs)\n if isobj:\n return dateobj\n else:\n return dateobj.strftime(fmt)\n\n\ndef unix2ts(uxts, mask=DATE_FORMAT, base=10):\n return \\\n datetime.datetime.fromtimestamp(\n int(uxts, base)\n ).strftime(mask)\n\n\ndef ts2unix(str_date, mask=DATE_FORMAT):\n return \\\n int(time.mktime(\n time.strptime(str_date, mask)\n ))\n\n\ndef getASCP():\n t = int(math.floor(time.time()))\n e = hex(t).upper()[2:]\n m = hashlib.md5()\n m.update(str(t).encode(encoding='utf-8'))\n i = m.hexdigest().upper()\n\n if len(e) != 8:\n AS = '479BB4B7254C150'\n CP = '7E0AC8874BB0985'\n return AS, CP\n n = i[0:5]\n a = i[-5:]\n s = ''\n r = ''\n for o in range(5):\n s += n[o] + e[o]\n r += e[o + 3] + a[o]\n\n AS = 'A1' + s + e[-3:]\n CP = e[0:3] + r + 'E1'\n return AS, CP\n\n\ndef get_signature(uid, maxhot='0'):\n global UA_js\n global SIG_JS_OBJ\n if not SIG_JS_OBJ:\n js = open('toutiao.sig.js', 'rb').read().decode('utf8')\n js = UA_js + '\\n' + js\n ctxt = PyV8.JSContext()\n ctxt.enter()\n SIG_JS_OBJ = ctxt.eval(js)\n # ctxt.leave()\n return SIG_JS_OBJ(uid + '' + maxhot)\n\n\ndef test_encrypt():\n AS, CP = getASCP()\n print\n AS, CP\n sig = get_signature('5824952602')\n print\n type(sig), sig\n url = 'https://www.toutiao.com/c/user/article/?page_type=1&user_id=5824952602&max_behot_time=0&count=20&as=A1053B1C1833323&cp=5BC883239283EE1&_signature=-VnTmAAAoptwuOoCoL-Wb.lZ04'\n print\n url.replace('-VnTmAAAoptwuOoCoL-Wb.lZ04', sig)\n\n\ndef get_veri_data(uid, maxhot='0'):\n _as, cp = getASCP()\n sig = get_signature(uid, maxhot)\n return {'uid': uid, '_as': _as, 'cp': cp, 'sig': sig}\n\n\ndef get_index_page(cat='news_car', maxhot='1539912409'):\n global COOKIES\n global HEADERS\n data = get_veri_data('', maxhot=maxhot)\n data['cat'] = cat\n data['maxhot'] = maxhot\n url = 'https://www.toutiao.com/api/pc/feed/?category={cat}&utm_source=toutiao&widen=1&max_behot_time={maxhot}&max_behot_time_tmp={maxhot}&tadrequire=true&as={_as}&cp={cp}&_signature={sig}'.format(\n **data)\n print\n url\n rp = requests.get(url, headers=HEADERS, cookies=COOKIES)\n jo = rp.json()\n # open('debug.js','w').write(dump_json(jo))\n return jo\n\n\ndef extract_index_user_list(jo):\n ilist = []\n for d in jo['data']:\n # print d.keys()_url')})\n print('[%s][%s][%s]' % (d.get('title', ''), d.get('source'), d.get('chinese_tag'))).encode('gbk', 'ignore')\n return ilist # print dump_json()\n\n\ndef get_uid_page(uid):\n global HEADERS\n # print uj.keys()\n data = get_veri_data(uid)\n url = 'https://www.toutiao.com/c/user/article/?page_type=1&user_id={uid}' \\\n '&max_behot_time=0&count=200&as={_as}&cp={cp}&_signature={sig}'.format(\n **data)\n res = requests.get(url, headers=HEADERS)\n print\n url\n jo = res.json()\n jo.update({'uid': uid, 'sig_data': data})\n jst = dump_json(jo)\n print\n jo['data'][0].keys()\n return jo\n # break\n\n\ndef stat_feed():\n from collections import Counter\n cnt = Counter()\n idxd = sqlitedict.SqliteDict('./idx_db.db')\n upgd = sqlitedict.SqliteDict('./upg_db.db')\n for k, idx_js in upgd.items():\n print\n dump_json(k.decode('utf8')), idx_js.keys()\n for k, idx_js in idxd.items():\n uj = idx_js\n # cnt[dump_json([uj['source'],uj['api_meta'][0]])]+=1\n cnt[dump_json([uj['api_meta'][0]])] += 1\n # cnt[dump_json([uj['api_meta']])]+=1\n for k, v in cnt.most_common(3):\n print (k, v)\n print ('category_cnt', len(cnt), 'item_cnt', len(idxd))\n\n\ndef user_page_crawl():\n res = []\n pool = Pool(8)\n idxd = sqlitedict.SqliteDict('./idx_db.db')\n upgd = sqlitedict.SqliteDict('./upg_db.db', autocommit=True)\n\n def get_upg(uid, upgd):\n jo = get_uid_page(uid)\n upgd[source] = jo\n print\n dump_json(len(jo['data']), i=2)\n\n for k, idx_js in idxd.items():\n rj = idx_js\n source = rj['source']\n print\n k, dump_json(source)\n # print dump_json(rj,i=2)\n # print rj.keys()\n try:\n uid = rj['media_url'].split('/')[-2]\n except:\n continue\n pool.spawn(get_upg, uid, upgd)\n pool.join()\n\n\ndef index_crawl():\n pool = Pool(8)\n cols = 'news_finance,news_entertainment,news_tech,news_game,news_sports,news_travel,news_car,news_hot,news_military,news_fashion,news_history,news_world,news_discovery,news_regime,news_baby,news_essay'.split(\n ',')\n car_cols = 'car_new_arrival,SUV,car_guide,car_usage'\n idxd = sqlitedict.SqliteDict('./idx_db.db', autocommit=True)\n\n def fetch_one_col(col, i, idxd):\n try:\n maxhot = str(ts2unix(get_date()) - i * 2000)\n jo = get_index_page(col, maxhot)\n ilist = extract_index_user_list(jo)\n for art in jo['data']:\n key = 'idx_%s' % (art['item_id'])\n art['api_meta'] = [col, maxhot]\n idxd[key] = art\n except Exception as e:\n print\n e.message, col\n\n for col in cols[:]:\n for i in range(0, 90):\n pool.spawn(fetch_one_col, col, i, idxd)\n pool.join()\n idxd.close()\n\n\nif __name__ == '__main__':\n # index_crawl()\n stat_feed()\n # user_page_crawl()", "repo_name": "linannn/EEG", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 6390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "os.name", "line_number": 24, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 25, "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": "datetime.datetime.fromtimestamp", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.mktime", "line_number": 29, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "time.mktime", "line_number": 49, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 50, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 57, "usage_type": "call"}, {"api_name": "PyV8.JSContext", "line_number": 84, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 119, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 140, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 154, "usage_type": "call"}, {"api_name": "sqlitedict.SqliteDict", "line_number": 155, "usage_type": "call"}, {"api_name": "sqlitedict.SqliteDict", "line_number": 156, "usage_type": "call"}, {"api_name": "gevent.pool.Pool", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlitedict.SqliteDict", "line_number": 173, "usage_type": "call"}, {"api_name": "sqlitedict.SqliteDict", "line_number": 174, "usage_type": "call"}, {"api_name": "gevent.pool.Pool", "line_number": 198, "usage_type": "call"}, {"api_name": "sqlitedict.SqliteDict", "line_number": 202, "usage_type": "call"}]} +{"seq_id": "32868570548", "text": "import logging\nfrom django_tables2 import RequestConfig\n\nfrom django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.http import Http404\nfrom django.urls import reverse\n\nfrom .models import PveStats\nimport pvestats.tables as stats_tables\n\n\nPVESTATS_OPTIONS = ['updated', 'greatness', 'number_story_missions', 'number_strikes', 'number_nightfalls', 'number_raid_clears',\n 'seconds_played', 'longest_single_life', 'average_life', 'kills_pga', 'deaths_pga', 'kd', 'longest_spree',\n 'most_precision_kills', 'precision_kills_pga', 'longest_kill', 'favorite_weapon', 'assists_pga', 'suicides_pga', 'assists_pga',\n 'resurrections_received_pga', 'resurrections_performed_pga', 'orbs_dropped_pga', 'orbs_gathered_pga']\n\n\n\"\"\"\nSet up logger: for now just print everything to stdout.\n\"\"\"\nlogging.basicConfig(format = '%(asctime)s - %(levelname)s - %(message)s',\n datefmt =' %m/%d/%y %H:%M:%S')\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n# Create your views here.\ndef pvestats(request, stat = 'kd'):\n \"\"\"Controlling display of pve stats\"\"\" \n all_stats = PveStats.objects.all()\n if not all_stats:\n raise Http404(\"No pve stats yet.\")\n latest_update = all_stats.latest('updated').updated\n logger.debug(f\"stat: {stat}\")\n if stat == 'all':\n pvestats_table = stats_tables.PveStatsTable(all_stats, order_by = '-kd')\n else:\n logger.debug(f\"Making table for {stat}\")\n logger.debug(f\"set(pve stats): {set(PVESTATS_OPTIONS)}\")\n logger.debug(f\"set(stat.split()): {set(stat.split())}\")\n to_exclude = tuple(set(PVESTATS_OPTIONS) - set(stat.split()) )\n logger.debug(f\"Excluding {to_exclude}.\")\n pvestats_table = stats_tables.PveStatsTable(all_stats, order_by = '-'+stat, exclude = to_exclude)\n RequestConfig(request, paginate={'per_page':10}).configure(pvestats_table)\n context = {'pvestats_table': pvestats_table, 'updated': latest_update}\n return render(request, 'pvestats/pvestats.html', context)\n\n\ndef pve_redirect(request):\n \"\"\"\n Redirects to pve main landing page. Used information from this helpful site:\n https://overiq.com/django/1.10/redirecting-urls-in-django/\n \"\"\"\n return HttpResponseRedirect(reverse('pvestats:pvestats', kwargs = {'stat': 'number_nightfalls'}))\n", "repo_name": "cortical-iv/salted_vex_milk", "sub_path": "pvestats/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2329, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "47", "api": [{"api_name": "logging.basicConfig", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.PveStats.objects.all", "line_number": 30, "usage_type": "call"}, {"api_name": "models.PveStats.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.PveStats", "line_number": 30, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 32, "usage_type": "call"}, {"api_name": "pvestats.tables.PveStatsTable", "line_number": 36, "usage_type": "call"}, {"api_name": "pvestats.tables", "line_number": 36, "usage_type": "name"}, {"api_name": "pvestats.tables.PveStatsTable", "line_number": 43, "usage_type": "call"}, {"api_name": "pvestats.tables", "line_number": 43, "usage_type": "name"}, {"api_name": "django_tables2.RequestConfig", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 54, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "6488041747", "text": "import json\nimport logging\nimport pickle\nimport random\nfrom os.path import isfile\n\nfrom nltk.classify import NaiveBayesClassifier\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\nlogging.basicConfig(level=logging.INFO)\n\n\nclass Classifier:\n\n def __init__(self):\n \"\"\"\n Default constructor\n \"\"\"\n pass\n\n @staticmethod\n def get_corpus():\n \"\"\" get the corpus from Corpus.json and make a list\n Args:\n None\n Returns:\n corpus (list): list of corpus data\n \"\"\"\n\n with open(\"C:/Users/rahul/PycharmProjects/AmazonReviewsClassifier/AROM_Logic/Corpus.json\", \"r\") as jf:\n corpus = json.load(jf)\n random.shuffle(corpus)\n return corpus\n\n @staticmethod\n def word_features(words):\n \"\"\" Create a dictionary of features\n Args:\n words (string): sentence to be converted as features\n Returns:\n features (dict): dictionary of feature words\n \"\"\"\n\n stopWords = set(stopwords.words(\"english\"))\n features = dict([(word, True) for word in word_tokenize(words) if word not in stopWords])\n return features\n\n def get_trainingSet(self):\n \"\"\" Convert the corpus data into training data \n Args:\n None\n Returns:\n trainingSet (list): list of training data\n \"\"\"\n\n corpus = self.get_corpus()\n trainingSet = list()\n for item in corpus:\n trainingSet.append((self.word_features(item[\"text\"]), item[\"label\"]))\n return trainingSet\n\n def get_classifier(self):\n \"\"\" Trains and returns the classifier or a pickle\n Args:\n None\n Returns:\n classifier (object): negative and positive opinion count\n \"\"\"\n\n if isfile('AROM_Logic/classifier.pickle'):\n with open(\"AROM_Logic/classifier.pickle\", \"rb\") as pickleFile:\n classifier = pickle.load(pickleFile)\n return classifier\n trainingSet = self.get_trainingSet()\n classifier = NaiveBayesClassifier.train(trainingSet)\n with open(\"AROM_Logic/classifier.pickle\", \"wb\") as pickleFile:\n pickle.dump(classifier, pickleFile)\n return classifier\n\n def classify(self, reviews):\n \"\"\" Classify the text as postive or negative and sum the count of each\n Args:\n reviews (list): list of reviews to be classified\n Returns:\n opinions (list): negative and positive opinion count\n \"\"\"\n\n classifier = self.get_classifier()\n negative_reviews_count = 0\n positive_reviews_count = 0\n for review in reviews:\n try:\n opinion = classifier.classify(self.word_features(review))\n if opinion == \"negative\":\n negative_reviews_count += 1\n elif opinion == \"positive\":\n positive_reviews_count += 1\n except Exception as e:\n logging.info(e)\n return [negative_reviews_count, positive_reviews_count]\n", "repo_name": "DotOp3rator011/AmazonReviewsClassifier", "sub_path": "AROM_Logic/SentimentClassifier.py", "file_name": "SentimentClassifier.py", "file_ext": "py", "file_size_in_byte": 3118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 32, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 33, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 45, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 45, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 71, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 73, "usage_type": "call"}, {"api_name": "nltk.classify.NaiveBayesClassifier.train", "line_number": 76, "usage_type": "call"}, {"api_name": "nltk.classify.NaiveBayesClassifier", "line_number": 76, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "6890075627", "text": "\"\"\"\nThis file is to profile everything in that example using Python API for the profiler.\nI love you ALL.\nI wanna ask you something ?!\nLearning Tensorflow does it worth ?!\n\"\"\"\nimport sys\nfrom tqdm import tqdm\nimport tensorflow as tf\n\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n\ndef main(config):\n # clear the graph\n tf.reset_default_graph()\n\n # Import data\n mnist = input_data.read_data_sets(config.data_dir, one_hot=False)\n\n def feed_dict(train):\n if train:\n xs, ys = mnist.train.next_batch(config.batch_size)\n else:\n xs, ys = mnist.test.next_batch(config.batch_size)\n\n return {x: xs, y: ys}\n\n sess = tf.InteractiveSession()\n\n # Create the MNIST neural network graph.\n\n # Input placeholders.\n with tf.name_scope(\"input\"):\n x = tf.placeholder(tf.float32, [config.batch_size, config.image_size ** 2], name=\"x-input\")\n y = tf.placeholder(tf.int64, [config.batch_size], name=\"y-input\")\n\n # The Network\n hidden = tf.layers.dense(x, config.hidden_size, activation=tf.nn.relu,\n kernel_initializer=tf.initializers.truncated_normal(),\n name=\"hidden\")\n logits = tf.layers.dense(hidden, config.num_classes,\n kernel_initializer=tf.initializers.truncated_normal(),\n name=\"logits\")\n # probabilities\n probs = tf.nn.softmax(logits)\n\n # out\n out = tf.argmax(logits, axis=1)\n\n with tf.name_scope(\"loss_cross_entropy\"):\n print(y.shape)\n print(logits.shape)\n loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=logits)\n\n with tf.name_scope(\"train_Step\"):\n train_step = tf.train.AdamOptimizer(config.learning_rate).minimize(loss)\n\n with tf.name_scope(\"accuracy\"):\n # Talk with them about argmax for logits not softmax\n accuracy = tf.reduce_mean(tf.cast(tf.equal(out, y), tf.float32))\n\n # initialize the variables of the network YA LO2Y <3 ba7ebak.\n sess.run(tf.global_variables_initializer())\n\n # Say WELCOME TO OUR PROFILER!!! YAY\n profiler = tf.profiler.Profiler(sess.graph)\n options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n\n with tf.contrib.tfprof.ProfileContext('./profile_dir') as pctx:\n # train on some steps\n for i in tqdm(range(config.n_iterations)):\n # Enable tracing for next session.run.\n pctx.trace_next_step()\n # Dump the profile to '/tmp/train_dir' after the step.\n pctx.dump_next_step()\n # run the session\n sess.run(train_step,\n options=options,\n feed_dict=feed_dict(True))\n\n # Enable tracing for next session.run.\n pctx.trace_next_step()\n # Dump the profile to '/tmp/train_dir' after the step.\n pctx.dump_next_step()\n print(\"Accuracy on test_data {}\".format(sess.run(accuracy,\n options=options,\n feed_dict=feed_dict(False))))\n\n\n\n\nclass Config:\n # network design\n image_size = 28\n hidden_size = 500\n num_classes = 10\n\n # training design\n n_iterations = 500\n batch_size = 32\n learning_rate = 1e-3\n data_dir = '../data/mnist_data'\n\n # profiler config\n file_output = 'v1'\n\n\nif __name__ == \"__main__\":\n main(Config)\n", "repo_name": "moemen95/Tensorflow_Sessions_Template", "sub_path": "scripts/profiler_cmd_example.py", "file_name": "profiler_cmd_example.py", "file_ext": "py", "file_size_in_byte": 3438, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "47", "api": [{"api_name": "tensorflow.reset_default_graph", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.examples.tutorials.mnist.input_data.read_data_sets", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.examples.tutorials.mnist.input_data", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.InteractiveSession", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dense", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.initializers.truncated_normal", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.initializers", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dense", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.initializers.truncated_normal", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.initializers", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.softmax", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.argmax", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.losses.sparse_softmax_cross_entropy", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.profiler.Profiler", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.profiler", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.RunOptions", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.contrib.tfprof.ProfileContext", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "73139767503", "text": "import json\n\nfrom flask import Blueprint, request\nfrom pynamodb.exceptions import PynamoDBException\n\nfrom .utils import PynamoEncoder\nfrom .models import Routine\n\n\nbp = Blueprint('routines', __name__, url_prefix='/routines')\n\n\n@bp.route('/', methods=('GET',))\ndef index():\n return json.dumps({\n 'urls': [\n request.base_url + 'of',\n ]\n })\n\n\n@bp.route('/of', methods=('GET',))\ndef users():\n encoder = PynamoEncoder()\n try:\n results = dict(\n (r.user, request.base_url + '/' + r.user)\n for r in Routine.scan(attributes_to_get=['user'])\n )\n return encoder.encode({'results': results})\n except PynamoDBException as e:\n return encoder.encode({\n 'error': {\n 'code': e.cause_response_code,\n 'message': e.cause_reponse_message\n }\n })\n\n\n@bp.route('/of/', methods=('GET',))\ndef list(username):\n encoder = PynamoEncoder()\n try:\n results = [r for r in Routine.query(username)]\n return encoder.encode({'results': results})\n except PynamoDBException as e:\n return encoder.encode({\n 'error': {\n 'code': e.cause_response_code,\n 'message': e.cause_reponse_message\n }\n })\n\n\n@bp.route('/of//', methods=('GET',))\ndef detail(username, routine):\n encoder = PynamoEncoder()\n try:\n results = Routine.get(username, routine)\n return encoder.encode({'results': results})\n except Routine.DoesNotExist as e:\n return encoder.encode({\n 'error': {\n 'code': 'DoesNotEist',\n 'message': str(e)\n }\n })\n except PynamoDBException as e:\n return encoder.encode({\n 'error': {\n 'code': e.cause_response_code,\n 'message': e.cause_reponse_message\n }\n })\n", "repo_name": "danizen/routines-bakeoff", "sub_path": "flask-routines-api/routines/routines.py", "file_name": "routines.py", "file_ext": "py", "file_size_in_byte": 1933, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "flask.Blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.base_url", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "utils.PynamoEncoder", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.base_url", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Routine.scan", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Routine", "line_number": 28, "usage_type": "name"}, {"api_name": "pynamodb.exceptions.PynamoDBException", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.PynamoEncoder", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Routine.query", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Routine", "line_number": 44, "usage_type": "name"}, {"api_name": "pynamodb.exceptions.PynamoDBException", "line_number": 46, "usage_type": "name"}, {"api_name": "utils.PynamoEncoder", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Routine.get", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Routine", "line_number": 59, "usage_type": "name"}, {"api_name": "models.Routine.DoesNotExist", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.Routine", "line_number": 61, "usage_type": "name"}, {"api_name": "pynamodb.exceptions.PynamoDBException", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "11332904015", "text": "import unittest\nfrom io import StringIO\nfrom ...relationships import Relationships\n\n\nclass TestInitialisation(unittest.TestCase):\n \"\"\"\n Test initialisation of the Relationships class and call a method.\n\n \"\"\"\n\n def setUp(self):\n self.fh = StringIO()\n self.relationships = Relationships()\n self.relationships._set_filehandle(self.fh)\n\n def test_xml_declaration(self):\n \"\"\"Test Relationships xml_declaration()\"\"\"\n\n self.relationships._xml_declaration()\n\n exp = \"\"\"\\n\"\"\"\n got = self.fh.getvalue()\n\n self.assertEqual(got, exp)\n", "repo_name": "jmcnamara/XlsxWriter", "sub_path": "xlsxwriter/test/relationships/test_initialisation.py", "file_name": "test_initialisation.py", "file_ext": "py", "file_size_in_byte": 648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3366, "dataset": "github-code", "pt": "51", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 13, "usage_type": "call"}, {"api_name": "relationships.Relationships", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "23044744344", "text": "from container_insights.metric_query_generator import MetricQueryGenerator\nfrom jinja2 import BaseLoader, Environment\n\n\nclass PodMetricQueryGenerator(MetricQueryGenerator):\n \"\"\"Concrete implementation of the Pod specific Metric Query Generator class.\"\"\"\n\n QUERY_TEMPLATE = (\n \"fields {metric}, \"\n \"{% for pod_name in pod_names %}\"\n '(PodName = \\\\\"{{ pod_name }}\\\\\") as pod{{ loop.index }}{{ \", \" if not loop.last else \" \" }}'\n \"{% endfor %}\"\n '| filter (Type = \\\\\"Pod\\\\\" or Type = \\\\\"PodNet\\\\\") and Namespace = \\\\\"{{ namespace }}\\\\\" and ispresent({metric}) '\n \"| stats \"\n \"{% for pod_name in pod_names %}\"\n 'sum({metric} * pod{{ loop.index }}) / sum(pod{{ loop.index }}) as `{{ pod_name }}`{{ \", \" if not loop.last else \" \" }}'\n \"{% endfor %}\"\n \"by bin({{ period }})\"\n )\n\n def generate_lookup_query(self, event) -> str:\n \"\"\"The Pod lookup query is about retrieving all the pod names for a given namespace.\"\"\"\n\n return (\n 'fields PodName | filter Type = \"Pod\" and Namespace = \"{namespace}\" | stats count() by PodName'\n ).format(namespace=event[\"ResourceProperties\"][\"iNamespace\"])\n\n def generate_metric_query(self, event, response) -> str:\n \"\"\"\n Thanks to the results collected via the lookup query, we can render the Pod metric\n query.\n This query is not metric-specific but it is specific to pod metrics.\n The query will be formatted for a specific pod metric at a later stage thanks to\n the Custom::ContainerInsights-MetricQueryFormatter resource.\n \"\"\"\n pod_names = [\n field[\"value\"]\n for result in response[\"results\"]\n for field in result\n if field[\"field\"] == \"PodName\"\n ]\n\n query_template = Environment(loader=BaseLoader()).from_string(\n PodMetricQueryGenerator.QUERY_TEMPLATE\n )\n return query_template.render(\n namespace=event[\"ResourceProperties\"][\"iNamespace\"],\n pod_names=pod_names,\n period=\"1m\",\n )\n", "repo_name": "aws-samples/aws-eks-containerinsights-log-based-dashboards", "sub_path": "assets/serverless/code/logs_insights_handler/container_insights/metric_query_generator/pod/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2105, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "47", "api": [{"api_name": "container_insights.metric_query_generator.MetricQueryGenerator", "line_number": 5, "usage_type": "name"}, {"api_name": "jinja2.Environment", "line_number": 43, "usage_type": "call"}, {"api_name": "jinja2.BaseLoader", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "29337262140", "text": "import pygame, sys, random, math\nfrom pygame.locals import *\npygame.init()\nDISPLAYHEIGHT = 600\nDISPLAYLENGHT = 1200\nDISPLAYSURF = pygame.display.set_mode((DISPLAYLENGHT, DISPLAYHEIGHT))\npygame.display.set_caption('Endless mobrun')\nFPS = 30\nfpsClock = pygame.time.Clock()\npygame.key.set_repeat(300, 100)\nFONT = pygame.font.Font('freesansbold.ttf', 12)\n\nMOBXPOS = 800\nMOBYPOS = 200\nGUNSIZEX = 100\nGUNSIZEY = 50\nX = 300\nY = 200 - GUNSIZEY/2\nGUNCENTRX = X + GUNSIZEX/2\nGUNCENTRY = Y + GUNSIZEY/2\n\nGunImg = pygame.image.load('D:\\Puthon\\My lern progr\\BestIdleGameEver\\BigGun.gif')\nGunImg = pygame.transform.scale(GunImg, (GUNSIZEX,GUNSIZEY))\nLaserImg = pygame.image.load('D:\\Puthon\\My lern progr\\BestIdleGameEver\\LaserCan.png')\nLaserImg = pygame.transform.scale(LaserImg, (GUNSIZEX,GUNSIZEY))\nCoord = pygame.image.load('D:\\Puthon\\My lern progr\\BestIdleGameEver\\Cd.png')\nCoord = pygame.transform.scale(Coord, (GUNSIZEX,GUNSIZEY))\n\ny1=300\nx1=300\ny2=300\nx2=500\ny3=100\nx3=500\nDispCord= 0\nangle = 0\n## main game screen\nwhile True:\n DISPLAYSURF.fill((110, 100, 100))\n if DispCord == 1:\n COORDS = FONT.render(\"xmouse: \" + str(xmouse) + \" ymouse: \" + str(ymouse), True, (100, 100, 100), (10, 10, 10))\n DISPLAYSURF.blit(COORDS, (1000, 10))\n ##pygame.draw.line(DISPLAYSURF, (200, 0, 0), (x1, y1), (x2, y2), 2)\n ##pygame.draw.line(DISPLAYSURF, (200, 0, 0), (x2, y2), (x3, y3), 2)\n ##pygame.draw.line(DISPLAYSURF, (0, 200, 0, 50), (x1, y1), (x1+int(math.sqrt((x2-x1)**2 + (y2-y3)**2)*math.cos(math.fabs(math.atan2(x2-x1,y2-y3)))), y1-int(math.sqrt((x2-x1)**2 + (y2-y3)**2)*math.sin(math.fabs(math.atan2(x2-x1,y2-y3))))), 8)\n ##pygame.draw.line(DISPLAYSURF, (0, 200, 0), (x1, y1), (x3, y3), 4)\n angle = (math.atan2(Y + GUNSIZEY/2 - MOBYPOS, MOBXPOS - X - GUNSIZEX/2))*57.29\n print(angle)\n anglerad = math.fabs(math.atan2(Y + GUNSIZEY/2 - MOBYPOS, MOBXPOS - X - GUNSIZEX/2))\n a = GUNSIZEX*math.cos(anglerad)\n b = GUNSIZEX*math.sin(anglerad)\n c = GUNSIZEY*math.cos(anglerad)\n d = GUNSIZEY*math.sin(anglerad)\n if angle >= 0:\n Y2 = Y + b + c\n X2 = X + d\n Y1 = Y\n X1 = X + a\n Yc = int(Y1 + (Y2 - Y1)/2)\n Xc = int(X2 + (X1 - X2)/2)\n dY = Yc - Y - GUNSIZEY/2\n dX = Xc - X - GUNSIZEX/2\n else:\n Y2 = Y + b + c\n X2 = X + a\n Y1 = Y\n X1 = X + d\n Yc = int(Y1 + (Y2 - Y1)/2)\n Xc = int(X1 + (X2 - X1)/2)\n dY = Yc - Y - GUNSIZEY/2\n dX = Xc - X - GUNSIZEX/2\n ##DISPLAYSURF.blit(pygame.transform.rotate(Coord, angle), (X-dX , Y-dY))\n pygame.draw.line(DISPLAYSURF, (200, 0, 0), (X+GUNSIZEX/2, Y+GUNSIZEY/2), (MOBXPOS, MOBYPOS), 4)\n DISPLAYSURF.blit(pygame.transform.rotate(LaserImg, angle), (X-dX , Y-dY))\n pygame.draw.circle(DISPLAYSURF, (0, 200, 0), (X+GUNSIZEX/2, Y+GUNSIZEY/2), 4, 0)\n pygame.draw.line(DISPLAYSURF, (200, 0, 0), (X, Y), (X-dX, Y-dY), 4)\n pygame.draw.circle(DISPLAYSURF, (200, 0, 0), (MOBXPOS, MOBYPOS), 8, 0)\n \n \n## Control mechanic \n for event in pygame.event.get():\n if pygame.mouse.get_pressed()[0]:\n DispCord = 1\n xmouse = pygame.mouse.get_pos()[0]\n ymouse = pygame.mouse.get_pos()[1]\n if pygame.key.get_pressed()[pygame.K_UP]:\n MOBYPOS -=5\n if pygame.key.get_pressed()[pygame.K_DOWN]:\n MOBYPOS +=5\n if pygame.key.get_pressed()[pygame.K_LEFT]:\n MOBXPOS -=5\n if pygame.key.get_pressed()[pygame.K_RIGHT]:\n MOBXPOS +=5\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n \n pygame.display.update()\n fpsClock.tick(FPS)", "repo_name": "Azzaid/Py-Game", "sub_path": "Angler.py", "file_name": "Angler.py", "file_ext": "py", "file_size_in_byte": 3692, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "pygame.init", "line_number": 3, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.key.set_repeat", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.transform", "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.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": "math.atan2", "line_number": 47, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 49, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 49, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 50, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 51, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 52, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotate", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 98, "usage_type": "attribute"}]} +{"seq_id": "33019847993", "text": "import wx\nimport win32api\nimport sys\nimport os\nimport jj\nAPP_TITLE = u'加解密程序'\nAPP_ICON = 'res/Icon.ico'\nclass mainFrame(wx.Frame):\n '''程序主窗口类，继承自wx.Frame'''\n \n def __init__(self, parent):\n '''构造函数'''\n wx.Frame.__init__(self, parent, -1, APP_TITLE)\n self.SetBackgroundColour(wx.Colour(224, 224, 224))\n self.SetSize((520, 220))\n self.Center()\n if hasattr(sys, \"frozen\") and getattr(sys, \"frozen\") == \"windows_exe\":\n exeName = win32api.GetModuleFileName(win32api.GetModuleHandle(None))\n icon = wx.Icon(exeName, wx.BITMAP_TYPE_ICO)\n else :\n icon = wx.Icon(APP_ICON, wx.BITMAP_TYPE_ICO)\n self.SetIcon(icon)\n wx.StaticText(self, -1, u'加密 or 解密：', pos=(40, 50), size=(100, -1), style=wx.ALIGN_RIGHT)\n self.tip = wx.StaticText(self, -1, u'', pos=(145, 110), size=(150, -1), style=wx.ST_NO_AUTORESIZE)\n self.tc1 = wx.TextCtrl(self, -1, '', pos=(145, 50), size=(150, -1), name='TC01', style=wx.TE_CENTER)\n btn_mea = wx.Button(self, -1, u'加密', pos=(350, 50), size=(100, 25))\n btn_meb = wx.Button(self, -1, u'解密', pos=(350, 80), size=(100, 25))\n # 鼠标事件 \n btn_mea.Bind(wx.EVT_LEFT_DOWN, self.OnLeftDown)\n btn_mea.Bind(wx.EVT_LEFT_UP, self.OnLeftUp)\n btn_mea.Bind(wx.EVT_MOUSEWHEEL, self.OnMouseWheel)\n btn_meb.Bind(wx.EVT_LEFT_DOWN, self.OnLeftDown_2)\n btn_meb.Bind(wx.EVT_LEFT_UP, self.OnLeftUp_2)\n btn_meb.Bind(wx.EVT_MOUSEWHEEL, self.OnMouseWheel)\n # 键盘事件\n self.Bind(wx.EVT_KEY_DOWN, self.OnKeyDown)\n # 系统事件\n self.Bind(wx.EVT_CLOSE, self.OnClose)\n self.Bind(wx.EVT_SIZE, self.On_size)\n def On_size(self, evt):\n #改变窗口大小事件函数\n self.Refresh()\n evt.Skip() # 体会作用\n def OnClose(self, evt):\n #关闭窗口事件函数\n dlg = wx.MessageDialog(None, u'确定要关闭本窗口？', u'操作提示', wx.YES_NO | wx.ICON_QUESTION)\n if(dlg.ShowModal() == wx.ID_YES):\n self.Destroy()\n def OnLeftDown(self, evt):\n '''左键按下事件函数'''\n self.tip.SetLabel(\"\")\n def OnLeftUp(self, evt):\n '''左键弹起事件函数''' \n self.tip.SetLabel(jj.ei(self.tc1.GetValue()))\n def OnLeftDown_2(self, evt):\n '''左键按下事件函数'''\n self.tip.SetLabel(\"\")\n def OnLeftUp_2(self, evt):\n '''左键弹起事件函数''' \n self.tip.SetLabel(jj.di(self.tc1.GetValue()))\n def OnMouseWheel(self, evt):\n '''鼠标滚轮事件函数'''\n vector = evt.GetWheelRotation()\n self.tip.SetLabel(str(vector))\n def OnMouse(self, evt):\n '''鼠标事件函数'''\n self.tip.SetLabel(str(evt.EventType))\n \n def OnKeyDown(self, evt):\n '''键盘事件函数'''\n key = evt.GetKeyCode() \n self.tip.SetLabel(str(key))\nclass mainApp(wx.App):\n def OnInit(self):\n self.SetAppName(APP_TITLE)\n self.Frame = mainFrame(None)\n self.Frame.Show()\n return True\nif __name__ == \"__main__\":\n app = mainApp()\n app.MainLoop()\n\n", "repo_name": "Wzp-2008/-Fixed-group-activity_translator", "sub_path": "main.pyw", "file_name": "main.pyw", "file_ext": "pyw", "file_size_in_byte": 3218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "wx.Frame", "line_number": 8, "usage_type": "attribute"}, {"api_name": "wx.Frame.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 13, "usage_type": "attribute"}, {"api_name": "wx.Colour", "line_number": 14, "usage_type": "call"}, {"api_name": "win32api.GetModuleFileName", "line_number": 18, "usage_type": "call"}, {"api_name": "win32api.GetModuleHandle", "line_number": 18, "usage_type": "call"}, {"api_name": "wx.Icon", "line_number": 19, "usage_type": "call"}, {"api_name": "wx.BITMAP_TYPE_ICO", "line_number": 19, "usage_type": "attribute"}, {"api_name": "wx.Icon", "line_number": 21, "usage_type": "call"}, {"api_name": "wx.BITMAP_TYPE_ICO", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 23, "usage_type": "call"}, {"api_name": "wx.ALIGN_RIGHT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 24, "usage_type": "call"}, {"api_name": "wx.ST_NO_AUTORESIZE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 25, "usage_type": "call"}, {"api_name": "wx.TE_CENTER", "line_number": 25, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 26, "usage_type": "call"}, {"api_name": "wx.Button", "line_number": 27, "usage_type": "call"}, {"api_name": "wx.EVT_LEFT_DOWN", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.EVT_LEFT_UP", "line_number": 30, "usage_type": "attribute"}, {"api_name": "wx.EVT_MOUSEWHEEL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wx.EVT_LEFT_DOWN", "line_number": 32, "usage_type": "attribute"}, {"api_name": "wx.EVT_LEFT_UP", "line_number": 33, "usage_type": "attribute"}, {"api_name": "wx.EVT_MOUSEWHEEL", "line_number": 34, "usage_type": "attribute"}, {"api_name": "wx.EVT_KEY_DOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "wx.EVT_CLOSE", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wx.EVT_SIZE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "wx.MessageDialog", "line_number": 46, "usage_type": "call"}, {"api_name": "wx.YES_NO", "line_number": 46, "usage_type": "attribute"}, {"api_name": "wx.ICON_QUESTION", "line_number": 46, "usage_type": "attribute"}, {"api_name": "wx.ID_YES", "line_number": 47, "usage_type": "attribute"}, {"api_name": "jj.ei", "line_number": 54, "usage_type": "call"}, {"api_name": "jj.di", "line_number": 60, "usage_type": "call"}, {"api_name": "wx.App", "line_number": 73, "usage_type": "attribute"}]} +{"seq_id": "19823786839", "text": "# uncompyle6 version 3.2.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)]\n# Embedded file name: lib.coginvasion.shop.DistributedGagShop\nfrom direct.directnotify.DirectNotifyGlobal import directNotify\nfrom direct.actor.Actor import Actor\nfrom lib.coginvasion.shop.DistributedShop import DistributedShop\nfrom lib.coginvasion.globals import CIGlobals\nfrom lib.coginvasion.shop.GagShop import GagShop\nfrom lib.coginvasion.npc.Char import Char\n\nclass DistributedGagShop(DistributedShop):\n notify = directNotify.newCategory('DistributedGagShop')\n\n def __init__(self, cr):\n DistributedShop.__init__(self, cr)\n self.shop = GagShop(self, 'gagShopDone')\n self.barrel = None\n self.barrelGags = []\n return\n\n def setupClerk(self):\n DistributedShop.setupClerk(self)\n self.clerk = Char()\n self.clerk.generateChar(CIGlobals.Goofy)\n self.clerk.setName(CIGlobals.Goofy)\n self.clerk.setupNameTag()\n self.clerk.reparentTo(self)\n self.clerk.animFSM.request('neutral')\n self.barrel = loader.loadModel('phase_5.5/models/estate/wheelbarrel.bam')\n self.barrel.find('**/dirt').removeNode()\n self.barrel.reparentTo(self.clerk)\n self.barrel.setX(-3.5)\n self.barrel.setH(90)\n gags = {'tart': {'pos': (0, 0.65, 1), 'hpr': (0, 30.26, 0)}, 'tart': {'pos': (0, 0, 1.14)}, 'cps': {'pos': (0, -0.56, 1.42), 'hpr': (323.97, 37.87, 0)}, 'cps': {'pos': (0, 0, 1.49)}, 'cake': {'pos': (0, 0.94, 1.4), 'playrate': 0.3}, 'cake': {'pos': (0, -0.1, 1.4), 'scale': 0.5, 'playrate': -0.3}}\n for gag, info in gags.iteritems():\n mdl = None\n if gag == 'cake':\n mdl = Actor('phase_5/models/props/birthday-cake-mod.bam', {'chan': 'phase_5/models/props/birthday-cake-chan.bam'})\n if 'playrate' in info:\n mdl.setPlayRate(info.get('playrate'), 'chan')\n mdl.loop('chan')\n if not mdl:\n if gag == 'tart':\n mdl = loader.loadModel('phase_3.5/models/props/tart.bam')\n elif gag == 'cps':\n mdl = loader.loadModel('phase_5/models/props/cream-pie-slice.bam')\n if 'pos' in info:\n mdl.setPos(info.get('pos'))\n if 'hpr' in info:\n mdl.setHpr(info.get('hpr'))\n if 'scale' in info:\n mdl.setScale(info.get('scale'))\n else:\n mdl.setScale(0.6)\n mdl.reparentTo(self.barrel)\n\n return\n\n def deleteClerk(self):\n if hasattr(self, 'barrel'):\n for gag in self.barrel.getChildren():\n if isinstance(gag, Actor):\n gag.cleanup()\n else:\n gag.removeNode()\n\n self.barrel.removeNode()\n del self.barrel\n DistributedShop.deleteClerk(self)\n\n def enterAccepted(self):\n if not self.inShop:\n self.shop.load()\n self.shop.enter()\n self.acceptOnce(self.shop.doneEvent, self.handleShopDone)\n self.inShop = True\n\n def handleShopDone(self):\n self.shop.exit()\n self.shop.unload()\n self.d_requestExit()\n\n def disable(self):\n DistributedShop.disable(self)\n self.ignore(self.shop.doneEvent)\n if self.inShop:\n self.handleShopDone()\n\n def delete(self):\n DistributedShop.delete(self)\n self.shop = None\n return", "repo_name": "theclashingfritz/Cog-Invasion-Online-Dump", "sub_path": "lib/coginvasion/shop/DistributedGagShop.py", "file_name": "DistributedGagShop.py", "file_ext": "py", "file_size_in_byte": 3575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop", "line_number": 12, "usage_type": "name"}, {"api_name": "direct.directnotify.DirectNotifyGlobal.directNotify.newCategory", "line_number": 13, "usage_type": "call"}, {"api_name": "direct.directnotify.DirectNotifyGlobal.directNotify", "line_number": 13, "usage_type": "name"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop", "line_number": 16, "usage_type": "name"}, {"api_name": "lib.coginvasion.shop.GagShop.GagShop", "line_number": 17, "usage_type": "call"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop.setupClerk", "line_number": 23, "usage_type": "call"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop", "line_number": 23, "usage_type": "name"}, {"api_name": "lib.coginvasion.npc.Char.Char", "line_number": 24, "usage_type": "call"}, {"api_name": "lib.coginvasion.globals.CIGlobals.Goofy", "line_number": 25, "usage_type": "attribute"}, {"api_name": "lib.coginvasion.globals.CIGlobals", "line_number": 25, "usage_type": "name"}, {"api_name": "lib.coginvasion.globals.CIGlobals.Goofy", "line_number": 26, "usage_type": "attribute"}, {"api_name": "lib.coginvasion.globals.CIGlobals", "line_number": 26, "usage_type": "name"}, {"api_name": "direct.actor.Actor.Actor", "line_number": 39, "usage_type": "call"}, {"api_name": "direct.actor.Actor.Actor", "line_number": 63, "usage_type": "argument"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop.deleteClerk", "line_number": 70, "usage_type": "call"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop", "line_number": 70, "usage_type": "name"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop.disable", "line_number": 85, "usage_type": "call"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop", "line_number": 85, "usage_type": "name"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop.delete", "line_number": 91, "usage_type": "call"}, {"api_name": "lib.coginvasion.shop.DistributedShop.DistributedShop", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "33720480129", "text": "import os\n\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\nfrom apscheduler.triggers.cron import CronTrigger\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\nimport discord\n\nimport json_handler\nimport recipes\nimport mail_handler\n\nload_dotenv()\nTOKEN = os.getenv('DISCORD_TOKEN')\n\nbot = commands.Bot(command_prefix='!')\n\n\nasync def dm():\n if not json_handler.get_all_users():\n print(\"No emails sent because no users were defined.\")\n return\n else:\n for user in json_handler.get_all_users():\n recipe_config = recipes.RecipeConfiguration(user['diet'], user['exclude'],\n user['target_calories'])\n recipe_data = recipes.send_request(recipe_config)\n mail_handler.sendmail(recipe_data, user)\n user = await bot.fetch_user(int(user['user_id']))\n embedVar = discord.Embed(title=\"Recipes\", description=\"Todays recipes\")\n for meal in recipe_data['meals']:\n embedVar.add_field(name=meal['title'], value=meal['sourceUrl'])\n embedVar.add_field(name=\"Nutrition\", value=recipe_data['nutrients'])\n await user.send(embed=embedVar)\n\n\n@bot.event\nasync def on_ready():\n print(\"Logged in as\")\n print(bot.user.name)\n print(\"------\")\n scheduler = AsyncIOScheduler()\n scheduler.add_job(dm, CronTrigger(second=\"30\"))\n scheduler.start()\n\n\n@bot.event\nasync def on_command_error(ctx, error):\n if isinstance(error, commands.MissingRequiredArgument):\n arg = error.param.name\n await ctx.send(\"Missing argument: \" + arg)\n\n\n@bot.command(name='configure')\nasync def on_message(ctx, email, diet, exclude, target_calories: int):\n try:\n if ctx.author == bot.user:\n return\n if not ctx.guild:\n config = {'email': email, 'user_id': ctx.author.id, 'diet': diet, 'exclude': exclude,\n 'target_calories': target_calories}\n json_handler.update(config)\n await ctx.send(\"Successfully updated config.\")\n except discord.ext.commands.errors.MissingRequiredArgument as e:\n await ctx.send(\"Wrong configuration.\")\n\n\nbot.run(TOKEN)\n", "repo_name": "seankuendig/recipe-generator", "sub_path": "discord_handler.py", "file_name": "discord_handler.py", "file_ext": "py", "file_size_in_byte": 2214, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands.Bot", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}, {"api_name": "json_handler.get_all_users", "line_number": 20, "usage_type": "call"}, {"api_name": "json_handler.get_all_users", "line_number": 24, "usage_type": "call"}, {"api_name": "recipes.RecipeConfiguration", "line_number": 25, "usage_type": "call"}, {"api_name": "recipes.send_request", "line_number": 27, "usage_type": "call"}, {"api_name": "mail_handler.sendmail", "line_number": 28, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 30, "usage_type": "call"}, {"api_name": "apscheduler.schedulers.asyncio.AsyncIOScheduler", "line_number": 42, "usage_type": "call"}, {"api_name": "apscheduler.triggers.cron.CronTrigger", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.ext.commands.MissingRequiredArgument", "line_number": 49, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 49, "usage_type": "name"}, {"api_name": "json_handler.update", "line_number": 62, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 64, "usage_type": "attribute"}]} +{"seq_id": "21328322627", "text": "from .validators import ISBNValidator\nfrom django.db.models import CharField\nfrom django.utils.translation import gettext_lazy as _\nfrom django.core.validators import EMPTY_VALUES\n\nclass ISBNField(CharField):\n\n description = _(\"ISBN-10 or ISBN-13\")\n\n def __init__(self, clean_isbn=True, *args, **kwargs):\n self.clean_isbn = clean_isbn\n kwargs['max_length'] = kwargs['max_length'] if 'max_length' in kwargs else 28\n kwargs['verbose_name'] = kwargs['verbose_name'] if 'verbose_name' in kwargs else u'ISBN'\n kwargs['validators'] = [ISBNValidator]\n super(ISBNField, self).__init__(*args, **kwargs)\n\n def formfield(self, **kwargs):\n defaults = {\n 'min_length': 10,\n 'validators': [ISBNValidator],\n }\n defaults.update(kwargs)\n return super(ISBNField, self).formfield(**defaults)\n\n def deconstruct(self):\n name, path, args, kwargs = super(ISBNField, self).deconstruct()\n # Only include clean_isbn in kwarg if it's not the default value\n if not self.clean_isbn:\n kwargs['clean_isbn'] = self.clean_isbn\n return name, path, args, kwargs\n\n def pre_save(self, model_instance, add):\n \"\"\"Remove dashes, spaces, and convert isbn to uppercase before saving\n when clean_isbn is enabled\"\"\"\n value = getattr(model_instance, self.attname)\n if self.clean_isbn and value not in EMPTY_VALUES:\n cleaned_isbn = value.replace(' ', '').replace('-', '').upper()\n setattr(model_instance, self.attname, cleaned_isbn)\n return super(ISBNField, self).pre_save(model_instance, add)\n\n def __unicode__(self):\n return self.value\n", "repo_name": "secnot/django-isbn-field", "sub_path": "isbn_field/fields.py", "file_name": "fields.py", "file_ext": "py", "file_size_in_byte": 1698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 8, "usage_type": "call"}, {"api_name": "validators.ISBNValidator", "line_number": 14, "usage_type": "name"}, {"api_name": "validators.ISBNValidator", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.validators.EMPTY_VALUES", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "36618912090", "text": "#!/usr/bin/python3\n# Øystein Godøy, METNO/FOU, 2021-02-11 \n#\nimport sys\nimport os\nimport argparse\nimport threddsclient\nimport urllib.request\n\ndef traverse_thredds(mystart, dstdir, mydepth, createlist, printscreen):\n print('Traversing:', mystart)\n mylist = []\n for ds in threddsclient.crawl(mystart, depth=mydepth):\n mypath = (ds.url.split('?')[0].replace('catalog.xml','')).replace(mystart.replace('catalog.html',''),'')\n newdstdir = os.path.join(dstdir,mypath)\n if not os.path.exists(newdstdir):\n try:\n os.makedirs(newdstdir)\n except:\n print(\"Can't create directory for list file or download files\")\n return\n file2create = '/'.join([newdstdir,os.path.basename(ds.download_url())])\n if printscreen:\n print(ds.download_url(), file2create)\n mylist.append(ds.download_url())\n if not createlist:\n try:\n urllib.request.urlretrieve(ds.download_url(),file2create)\n except:\n print('Can\\'t retrieve:', ds.download_url())\n continue\n\n if createlist:\n mylistfile = '/'.join([dstdir,'files2download.txt'])\n try:\n print('Creating output file at:', mylistfile)\n myfile = open(mylistfile,'w')\n except:\n print(\"Can't open output file for list: \", mylistfile)\n return\n for line in mylist:\n print(line, file=myfile)\n\n myfile.close()\n\n return\n\nif __name__ == '__main__':\n # Parse command line arguments\n parser = argparse.ArgumentParser(\n description='Traverse THREDDS catalogues and extract '+\n 'discovery metadata to MMD where ACDD elements are present')\n parser.add_argument('starturl', type=str, \n help='URL to start traverse')\n parser.add_argument('dstdir', type=str, \n help='Directory where to put MMD files')\n parser.add_argument('-d', '--depth', type=int, default=3, \n help='How meny levels below the top level to evaluate')\n parser.add_argument('-l', '--createlist', action='store_true', \n help='Do not download, only create list of files to download')\n parser.add_argument('-p', '--printscreen', action='store_true', \n help='Print list of files to download to screen')\n try:\n args = parser.parse_args()\n except:\n parser.print_help()\n sys.exit()\n \n try:\n traverse_thredds(args.starturl, args.dstdir, args.depth, args.createlist, args.printscreen)\n except:\n print('Something went wrong', sys.exc_info()[0])\n sys.exit()\n\n", "repo_name": "steingod/downloadfromthredds", "sub_path": "script/downloadfromthredds.py", "file_name": "downloadfromthredds.py", "file_ext": "py", "file_size_in_byte": 2664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "threddsclient.crawl", "line_number": 13, "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": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 28, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 28, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "38420109156", "text": "import argparse\nimport logging\nimport os\nimport numpy as np\nimport torch\nos.environ['CUDA_VISIBLE_DEVICES'] = '0,1'\n\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader, SubsetRandomSampler\nimport torchio as tio\n\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nimport torch.nn.functional as F\nfrom dataset import ADNIdataset, OASISdataset\nfrom dataset import ADNIdataset_sp # split train and test\nfrom model import GFNet, UNet3d\nfrom utils import split_dataset, calc_loss, embedding_evaluation, AverageMeter, setup_seed, calc_eval\n\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\ndef run(args):\n\n path = args.result_path + '/SL_{}'.format(args.date)\n if not os.path.exists(path):\n os.mkdir(path)\n print(\"make the dir\")\n logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S', filename=path + '/%s.log'%(args.date), filemode='a')\n #logging_dirc = '/.....'log—dir\n #parser.add_argument('--', type=str, ....)\n #nohup python3 main.py --log-dir='/...'\n #log的文件夹目录\n logging.info(args)\n setup_seed(args.seed)\n#============================================ dataset ========================================#\n '''\n # Adding noise\n torchio.transforms.RandomNoise(mean: Union[float, Tuple[float, float]] = 0, std: Union[float, Tuple[float, float]] = (0, 0.25), **kwargs)\n # Random rotation\n transform = tio.RandomAffine(\n scales=(0.9, 1.2),\n # degree --> random rotate\n degrees=15,\n # center -- If 'image', rotations and scaling will be performed around the image center. If 'origin', rotations and scaling will be performed around the origin in world coordinates.\n )\n # Intensity adjustment/ normalization\n tio.RescaleIntensity( percentiles=(0.5, 99.5)\n out_min_max=(-1, 1), in_min_max=(ct_air, ct_bone))\n # Contrast stretching\n ### Randomly change contrast of an image by raising its values to the power gamma\n tio.RandomGamma(log_gamma=(-0.3, 0.3))\n '''\n #################################### using original data, no downsample ####################################\n '''\n # the transform sample from joint distribution, that is, each transform conduct six kinds of transformation \n # however, the original dataset are omitted.\n transform = tio.Compose([\n tio.RandomAffine(\n degrees=(-30,30,-30,30,-30,30),\n center='image', # rotations and scaling will be performed around the image center. If 'origin', rotations and scaling will be performed around the origin in world coordinates.\n ),\n tio.OneOf({\n tio.RandomFlip(axes=('L', )),\n tio.RandomFlip(axes=('R', )), \n tio.RandomFlip(axes=('P', )), \n }, p=0.5),\n tio.transforms.RandomNoise((0, 0.25)),\n tio.RandomElasticDeformation(p=0.5),\n tio.RandomGamma(log_gamma=(-0.3, 0.3)),\n # swap是mask掉，所以要等所有augmentation做完再加\n tio.RandomSwap(patch_size = (16,16,16), num_iterations=100, p=0.5)\n ], p = 0.5)\n '''\n transform = tio.Compose([\n tio.OneOf({\n tio.RandomAffine(\n degrees=(-30,30,-30,30,-30,30),\n center='image', # rotations and scaling will be performed around the image center. If 'origin', rotations and scaling will be performed around the origin in world coordinates.\n ),\n tio.OneOf({\n tio.RandomFlip(axes=('L', )),\n tio.RandomFlip(axes=('R', )), \n tio.RandomFlip(axes=('P', )), \n }),\n tio.transforms.RandomNoise((0, 0.25)),\n tio.RandomElasticDeformation(p=0.5),\n tio.RandomGamma(log_gamma=(-0.3, 0.3)),\n # swap是mask掉，所以要等所有augmentation做完再加\n tio.RandomSwap(patch_size = (16,16,16), num_iterations=100)})\n ], p = args.prob) # only half the probability to transform original pic\n \n\n if args.name == 'ADNI1':\n train_dataset = ADNIdataset(args.label_path, args.img_path, 'train', transform=transform)\n test_dataset = ADNIdataset(args.label_path, args.img_path, 'test', transform=None)\n else:\n train_dataset = OASISdataset(args.label_path, args.img_path, 'train', transform=transform)\n test_dataset = OASISdataset(args.label_path, args.img_path, 'test', transform=None) \n print('len(train_dataset):', len(train_dataset))\n print(\"any intersection:\", set(train_dataset.patients_list)&set(test_dataset.patients_list), len(train_dataset.patients_list), len(test_dataset.patients_list))\n train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=4, shuffle=True)\n test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=4, shuffle=False)\n '''\n dataset = ADNIdataset(args.label_path, args.img_path, transform=transform)\n train_indice, test_indice = split_dataset(args, dataset)\n train_sampler = SubsetRandomSampler(train_indice)\n test_sampler = SubsetRandomSampler(test_indice)\n train_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=4)\n test_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=test_sampler, num_workers=4)\n '''\n#========================================== model & optimizer ========================================#\n # gfnet 只要两层 learning rate = \n model = GFNet(depth=args.gf_depth, num_classes=args.gfopc, in_channels=args.out_channel)\n # model = GFNet_ds(img_size=[96, 112, 96], patch_size=[16,16,16], embed_dim=4096, feature_dim=args.bag_dim)\n print(\"model:\", model)\n print('#latent_encoder parameters:', sum(param.numel() for param in model.parameters())) \n \n if torch.cuda.device_count() > 1:\n print(\"Let's use\", torch.cuda.device_count(), \"GPUs!\")\n model = nn.DataParallel(model).to(device)\n\n if args.optim == 'sgd':\n opt_l = torch.optim.SGD(model.parameters(), lr=args.l_lr, momentum=0.9, weight_decay=1e-5)\n else:\n opt_l = torch.optim.Adam(model.parameters(), lr=args.l_lr, weight_decay=1e-5)\n\n\n criterion = nn.CrossEntropyLoss()\n max_val_acc = 0\n best_epoch = 0\n#========================================= load latent vectors =======================================\n for epoch in range(args.epochs):\n\n losses = AverageMeter()\n acc_s = AverageMeter()\n f1_s = AverageMeter()\n training_process = tqdm(train_loader, desc='training')\n # training_process = tqdm(train_loader, desc='training')\n model.train()\n for idx, batch in enumerate(training_process):\n _, img, label = batch\n label = label.to(device)\n img = img.to(device) \n # opt_l.zero_grad()\n model.zero_grad()\n y_pred = model(img)\n #y_pred = torch.argmax(y_pred, dim=1)\n loss = criterion(y_pred, label)\n dict = calc_eval(y_pred, label)\n acc_s.update(dict['acc'], img.size(0))\n f1_s.update(dict['f1'], img.size(0))\n loss.backward()\n losses.update(loss.item(), img.size(0))\n opt_l.step()\n\n # logging.info(\"acc: {} f1: {} spe: {} sen: {}\".format(dict['acc'], dict['f1'], dict['spe'], dict['sen']))\n logging.info(\"train: acc: {} f1: {} \".format(acc_s.avg, f1_s.avg))\n\n logging.info(\n 'Epoch {}, loss: {}'.format(epoch + 1, losses.avg))\n\n if epoch % 30 == 0:\n state = {'latent_encoder': model.state_dict(),\n 'opt_l': opt_l.state_dict()}\n torch.save(state, path + '/b{}_{}_{}.pth'.format(args.batch_size, epoch, args.reg))\n\n e_acc_s = AverageMeter()\n e_f1_s = AverageMeter()\n model.eval()\n testing_process = tqdm(test_loader, desc='testing')\n with torch.no_grad():\n for idx, batch in enumerate(testing_process):\n _, img, label = batch\n img = img.to(device)\n label = label.to(device) \n output = model(img)\n eval_dict = calc_eval(output, label)\n e_acc_s.update(eval_dict['acc'], img.size(0))\n e_f1_s.update(eval_dict['f1'], img.size(0))\n # 附上auc** area under curve: value; ROC: xxx curve\n\n logging.info(\"val: acc: {} f1: {}\".format(e_acc_s.avg, e_f1_s.avg))\n \n if max_val_acc <= e_acc_s.avg:\n max_val_acc = e_acc_s.avg\n best_epoch = epoch\n\n logging.info(\n 'best epoch: {}, max val acc: {}'. format(best_epoch, max_val_acc)\n )\n\n state = {'latent_encoder': model.state_dict(),\n 'opt_l': opt_l.state_dict()}\n torch.save(state, path + '/best_latent_encoder' + '_%s.pth'%args.reg)\n\n\ndef arg_parse():\n parser = argparse.ArgumentParser(description='ADNI classification')\n parser.add_argument('--name', type=str, default='ADNI1',\n help='name of dataset')\n parser.add_argument('--seed', type=int, default=123,\n help='random seed')\n parser.add_argument('--label_path', type=str, default='/home/zhang_istbi/zhangsj/ACGF/ADNI.csv',\n help='label path')\n parser.add_argument('--img_path', type=str, default='/home/zhang_istbi/zhangsj/ACGF/processed_ADNI',\n help='data path')\n parser.add_argument('--pretrain_path', type=str, default='/home/zhang_istbi/zhangsj/ACGF/result/best_latent_encoder_True.pth',\n help='data path')\n parser.add_argument('--pretrain', type=bool, default=False,\n help='pretrain or not')\n parser.add_argument('--size', type=tuple, default=(32,32,32),\n help='size of data')\n parser.add_argument('--shuffle_dataset', type=str,\n default='True', help='shuffle indice')\n parser.add_argument('--l_lr', type=float, default=0.0005,\n help='latent encoder Learning rate.')\n parser.add_argument('--prob', type=float, default=1.0,\n help='augmentation probability.')\n parser.add_argument('--batch_size', type=int, default=10,\n help='batch size')\n parser.add_argument('--reg', type=str, default='True',\n help='regularization')\n parser.add_argument('--epochs', type=int, default=1,\n help='Train Epochs')\n parser.add_argument('--out_channel', type=int, default=1,\n help='output channel of unet3d')\n parser.add_argument('--gf_depth', type=int, default=4,\n help='depth of gfnet')\n # sgd容易陷入local minimum\n parser.add_argument('--optim', type=str, default='Adam',\n help='type of optimizer')\n parser.add_argument('--result_path', type=str, default='/home/zhang_istbi/zhangsj/ACGF/result',\n help='path to save')\n parser.add_argument('--date', type=str, default='731',\n help='date and num ') \n parser.add_argument('--gfopc', type=int, default=2,\n help='output channels of gfnet')\n \n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = arg_parse()\n run(args)", "repo_name": "qbmizsj/GFNet", "sub_path": "main_sl.py", "file_name": "main_sl.py", "file_ext": "py", "file_size_in_byte": 11615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "47", "api": [{"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.setup_seed", "line_number": 37, "usage_type": "call"}, {"api_name": "torchio.Compose", "line_number": 77, "usage_type": "call"}, {"api_name": "torchio.OneOf", "line_number": 78, "usage_type": "call"}, {"api_name": "torchio.RandomAffine", "line_number": 79, "usage_type": "call"}, {"api_name": "torchio.OneOf", "line_number": 83, "usage_type": "call"}, {"api_name": "torchio.RandomFlip", "line_number": 84, "usage_type": "call"}, {"api_name": "torchio.RandomFlip", "line_number": 85, "usage_type": "call"}, {"api_name": "torchio.RandomFlip", "line_number": 86, "usage_type": "call"}, {"api_name": "torchio.transforms.RandomNoise", "line_number": 88, "usage_type": "call"}, {"api_name": "torchio.transforms", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torchio.RandomElasticDeformation", "line_number": 89, "usage_type": "call"}, {"api_name": "torchio.RandomGamma", "line_number": 90, "usage_type": "call"}, {"api_name": "torchio.RandomSwap", "line_number": 92, "usage_type": "call"}, {"api_name": "dataset.ADNIdataset", "line_number": 97, "usage_type": "call"}, {"api_name": "dataset.ADNIdataset", "line_number": 98, "usage_type": "call"}, {"api_name": "dataset.OASISdataset", "line_number": 100, "usage_type": "call"}, {"api_name": "dataset.OASISdataset", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 105, "usage_type": "call"}, {"api_name": "model.GFNet", "line_number": 116, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cuda.device_count", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 126, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 128, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "utils.AverageMeter", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.AverageMeter", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.AverageMeter", "line_number": 139, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 140, "usage_type": "call"}, {"api_name": "model.train", "line_number": 142, "usage_type": "call"}, {"api_name": "model.zero_grad", "line_number": 148, "usage_type": "call"}, {"api_name": "utils.calc_eval", "line_number": 152, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 160, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 162, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 168, "usage_type": "call"}, {"api_name": "utils.AverageMeter", "line_number": 170, "usage_type": "call"}, {"api_name": "utils.AverageMeter", "line_number": 171, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 172, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 174, "usage_type": "call"}, {"api_name": "utils.calc_eval", "line_number": 180, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 185, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 191, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 197, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "16586807575", "text": "#!/usr/bin/env python3\n\nfrom melo import Melo\nfrom ncaabb_games import games as g\nimport numpy as np\nfrom pyDOE import lhs\n\n\ndef design(bounds, samples):\n \"\"\"\n Latin hypercube experiment design\n\n \"\"\"\n xmin, xmax = map(np.array, zip(*bounds))\n ndim = len(bounds)\n\n return xmin + (xmax - xmin) * lhs(ndim, samples=samples)\n\n\ndef melo_wrapper(k, bias, smooth, regress):\n \"\"\"\n Wrapper to pass arguments to the Melo library.\n\n \"\"\"\n print(k, bias, smooth, regress)\n\n bias *= np.logical_not(g.neutral)\n\n return Melo(\n g.date, g.home_team, g.away_team, g.home_points - g.away_points,\n lines=np.arange(-70.5, 71.5), k=k, bias=bias, smooth=smooth,\n regress=lambda t: regress*(t > 3), regress_unit='month'\n )\n\n\ndef optimize(bounds, samples=50):\n \"\"\"\n Estimate optimal model parameters using cross entropy\n\n \"\"\"\n X = design(bounds, samples=samples)\n y = [melo_wrapper(*x).entropy for x in X]\n\n return X[np.argmin(y)]\n\n\nif __name__ == \"__main__\":\n bounds = [(0, 0.5), (0, 0.5), (0, 15), (0, 0.5)]\n args = optimize(bounds, samples=1000)\n print(args)\nelse:\n ncaabb_spreads = melo_wrapper(.286, .38, 4.0, 0.03)\n", "repo_name": "morelandjs/ncaa-basketball", "sub_path": "melo_ncaabb.py", "file_name": "melo_ncaabb.py", "file_ext": "py", "file_size_in_byte": 1185, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.array", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyDOE.lhs", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 27, "usage_type": "call"}, {"api_name": "ncaabb_games.games.neutral", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ncaabb_games.games", "line_number": 27, "usage_type": "name"}, {"api_name": "melo.Melo", "line_number": 29, "usage_type": "call"}, {"api_name": "ncaabb_games.games.date", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ncaabb_games.games", "line_number": 30, "usage_type": "name"}, {"api_name": "ncaabb_games.games.home_team", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ncaabb_games.games.away_team", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ncaabb_games.games.home_points", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ncaabb_games.games.away_points", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "38694050604", "text": "import csv\nfrom statistics import variance\nfrom math import sqrt\nimport matplotlib.pyplot as plt\nimport click\nimport calc\ndef parseCsv(path, delim=','):\n print(\"Using \\'\" + delim + \"\\' as the csv delimiter. Be careful!\")\n arr = []\n with open(path, \"r\") as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=delim)\n for lines in csv_reader:\n try:\n arr.append(float(lines[0]))\n except:\n print(\"Could not parse value: \" + str(lines[0]) + \", please check the data! (value skipped)\")\n continue\n return arr\n\n\ndef findAllDivisors(num):\n divList = []\n for i in range(2, num):\n if (num % i == 0):\n divList.append(i)\n return divList\n\n\ndef calculateBlockAverage(data: list, blockSize: int):\n averages = []\n count = 0\n sum = 0\n num = 0\n for i in range(0, len(data) + 1):\n if (i < len(data)):\n num = data[i]\n if (count == blockSize):\n averages.append(sum / blockSize)\n count = 1\n sum = num\n else:\n sum += num\n count += 1\n return averages\n\n\ndef blockAveragesPerDivisor(data: list, divisors=None):\n # returns in format avgPerDiv[blockSize] = [array of block averages corresponding to that divisor]\n if divisors is None:\n divisors = findAllDivisors(len(data))\n avgPerDiv = {}\n for divisor in divisors:\n blockSize = len(data) / divisor\n avgPerDiv[blockSize] = calculateBlockAverage(data, blockSize)\n return avgPerDiv\n\n\ndef calculateStatistics(avgPerSize: dict):\n stats = {} # in format (stats[blockSize] = (average, standard deviation, standart error), statsForPlotting = [[\n # block size], [se]])\n statsForPlotting = ([], []) # format [[x vals],[y vals]]\n for blockSize in avgPerSize:\n avg = sum(avgPerSize[blockSize]) / len(avgPerSize[blockSize])\n stdev = sqrt(variance(avgPerSize[blockSize]))\n se = stdev / sqrt(len(avgPerSize[blockSize]))\n stats[blockSize] = (avg, stdev, se)\n statsForPlotting[0].append(blockSize)\n statsForPlotting[1].append(se)\n return (stats,statsForPlotting)\n\n\ndef plotData(data: tuple, figureName=\"plot.png\"):\n forplot = data[1]\n plt.plot(forplot[0], forplot[1])\n plt.savefig(figureName)\n plt.show()\n\n\ndef generateOutputCsv(csvData: dict, filepath=\"\", filename='output.csv'):\n with open(filepath + filename, 'w', newline='') as csvfile:\n filewriter = csv.writer(csvfile, delimiter=',',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n filewriter.writerow(['Block Size', 'Average', 'Standart Deviation', 'Standart Error'])\n for blockSize in csvData:\n filewriter.writerow([str(blockSize),\n str(csvData[blockSize][0]),\n str(csvData[blockSize][1]),\n str(csvData[blockSize][2])])\n\n\n@click.command()\n@click.argument('path')\n@click.option(\n '--outputpath', '-op', default=\"\",\n help='Specify an output path. Default: \"\"'\n)\n@click.option(\n '--outputname', '-on', default=\"output\",\n help='Specify an output file name. Default: \"output\"'\n)\n@click.option(\n '--delimiter', '-d', default=\",\",\n help='Specify a delimiter. Default: \",\"'\n)\n@click.option('--plot/--no-plot', default=True,\n help='Generate (or not) the block size vs standart error plot. Default: --plot')\n@click.option(\n '--plotname', '-pn', default=\"plot\",\n help='Specify plot file name. Default: \"plot\"')\n@click.option('--highspeed/--no-highspeed', default=False,\n help='Uses pre-compiled C++ versions of the functions that do the calculations.Not completely memory safe! Default: --no-highSpeed')\ndef main(path, outputpath, plot, delimiter, plotname, outputname, highspeed):\n 'PATH should be a direct or a relative (to the script) path to the file (including the filename)!\\n\\nThis simple script deals with the problem of calculating block averages fast. Takes a single-column csv with numerical data and outputs another csv with calculated statistical parameters such as the average of the block averages, the standart deviation and the standart error of said blocks. Optinally can output a plot of the block size vs SE.'\n\n if(highspeed):\n data = calc.parseCsv(path, delimiter)\n statistics = calc.calculateStatistics(calc.blockAveragesPerDivisor(data,[]))\n calc.generateOutputCsv(statistics[0], outputpath, outputname + \".csv\")\n if (plot):\n\n calc.plotData(statistics, outputpath + plotname + \".png\")\n else:\n print(\"Plot generation turned off!\")\n else:\n data = parseCsv(path, delimiter)\n statistics = calculateStatistics(blockAveragesPerDivisor(data))\n generateOutputCsv(statistics[0], outputpath, outputname + \".csv\")\n if (plot):\n plotData(statistics, outputpath + plotname + \".png\")\n else:\n print(\"Plot generation turned off!\")\n\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "vasilvas99/Block-Averages", "sub_path": "blockavg.py", "file_name": "blockavg.py", "file_ext": "py", "file_size_in_byte": 5097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "csv.reader", "line_number": 11, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 64, "usage_type": "call"}, {"api_name": "statistics.variance", "line_number": 64, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 65, "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.savefig", "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": "csv.writer", "line_number": 81, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 82, "usage_type": "attribute"}, {"api_name": "calc.parseCsv", "line_number": 116, "usage_type": "call"}, {"api_name": "calc.calculateStatistics", "line_number": 117, "usage_type": "call"}, {"api_name": "calc.blockAveragesPerDivisor", "line_number": 117, "usage_type": "call"}, {"api_name": "calc.generateOutputCsv", "line_number": 118, "usage_type": "call"}, {"api_name": "calc.plotData", "line_number": 121, "usage_type": "call"}, {"api_name": "click.command", "line_number": 91, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 92, "usage_type": "call"}, {"api_name": "click.option", "line_number": 93, "usage_type": "call"}, {"api_name": "click.option", "line_number": 97, "usage_type": "call"}, {"api_name": "click.option", "line_number": 101, "usage_type": "call"}, {"api_name": "click.option", "line_number": 105, "usage_type": "call"}, {"api_name": "click.option", "line_number": 107, "usage_type": "call"}, {"api_name": "click.option", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "11977492547", "text": "from picongpu.pypicongpu.laser import GaussianLaser\n\nimport unittest\nimport logging\nimport copy\n\nimport typeguard\n\n\"\"\" @file we only test for types here, test for values errors is done in the\n custom picmi-objects\"\"\"\n\n\nclass TestGaussianLaser(unittest.TestCase):\n def setUp(self):\n self.laser = GaussianLaser()\n self.laser.wavelength = 1.2\n self.laser.waist = 3.4\n self.laser.duration = 5.6\n self.laser.focus_pos = [0, 7.8, 0]\n self.laser.phase = 2.9\n self.laser.E0 = 9.0\n self.laser.pulse_init = 1.3\n self.laser.propagation_direction = [0., 1., 0.]\n self.laser.polarization_type = GaussianLaser.PolarizationType.LINEAR\n self.laser.polarization_direction = [0., 1., 0.]\n self.laser.laguerre_modes = [1.0]\n self.laser.laguerre_phases = [0.0]\n self.laser.huygens_surface_positions = [[1, -1], [1, -1], [1, -1]]\n\n def test_types(self):\n \"\"\"invalid types are rejected\"\"\"\n laser = GaussianLaser()\n for not_float in [None, [], {}, \"1\"]:\n with self.assertRaises(typeguard.TypeCheckError):\n laser.wavelength = not_float\n with self.assertRaises(typeguard.TypeCheckError):\n laser.waist = not_float\n with self.assertRaises(typeguard.TypeCheckError):\n laser.duration = not_float\n with self.assertRaises(typeguard.TypeCheckError):\n laser.phase = not_float\n with self.assertRaises(typeguard.TypeCheckError):\n laser.E0 = not_float\n with self.assertRaises(typeguard.TypeCheckError):\n laser.pulse_init = not_float\n\n for not_position_vector in [1, 1., None, [\"string\"]]:\n with self.assertRaises(typeguard.TypeCheckError):\n laser.focus_pos = not_position_vector\n\n for not_polarization_type in [1, 1.3, None, \"\", []]:\n with self.assertRaises(typeguard.TypeCheckError):\n laser.polarization_type = not_polarization_type\n\n for not_direction_vector in [1, 1.3, None, \"\", [\"string\"]]:\n with self.assertRaises(typeguard.TypeCheckError):\n laser.polarization_direction = not_direction_vector\n with self.assertRaises(typeguard.TypeCheckError):\n laser.propagation_direction = not_direction_vector\n\n for invalid_list in [None, 1.2, \"1.2\", [\"string\"]]:\n with self.assertRaises(typeguard.TypeCheckError):\n laser.laguerre_modes = invalid_list\n with self.assertRaises(typeguard.TypeCheckError):\n laser.laguerre_phases = invalid_list\n with self.assertRaises(typeguard.TypeCheckError):\n laser.polarization_direction = invalid_list\n with self.assertRaises(typeguard.TypeCheckError):\n laser.propagation_direction = invalid_list\n with self.assertRaises(typeguard.TypeCheckError):\n laser.huygens_surface_positions = invalid_list\n\n def test_polarization_type(self):\n \"\"\"polarization type enum sanity checks\"\"\"\n lin = GaussianLaser.PolarizationType.LINEAR\n circular = GaussianLaser.PolarizationType.CIRCULAR\n\n self.assertNotEqual(lin, circular)\n\n self.assertNotEqual(lin.get_cpp_str(), circular.get_cpp_str())\n\n for polarization_type in [lin, circular]:\n self.assertEqual(str, type(polarization_type.get_cpp_str()))\n\n def test_invalid_huygens_surface_description_types(self):\n \"\"\"Huygens surfaces must be described as\n [[x_min:int, x_max:int], [y_min:int,y_max:int],\n [z_min:int, z_max:int]]\"\"\"\n laser = self.laser\n\n invalid_elements = [None, [], [1.2, 3.4]]\n valid_rump = [[5, 6], [7, 8]]\n\n invalid_descriptions = []\n for invalid_element in invalid_elements:\n for pos in range(3):\n base = copy.deepcopy(valid_rump)\n base.insert(pos, invalid_element)\n invalid_descriptions.append(base)\n\n for invalid_description in invalid_descriptions:\n with self.assertRaises(TypeError):\n laser.huygens_surface_positions(invalid_description)\n\n def test_invalid_laguerre_modes_empty(self):\n \"\"\"laguerre modes must be set non-empty\"\"\"\n laser = self.laser\n laser.laguerre_modes = []\n with self.assertRaisesRegex(ValueError, \".*mode.*empty.*\"):\n laser.get_rendering_context()\n laser.laguerre_modes = [1.0]\n laser.laguerre_phases = []\n with self.assertRaisesRegex(ValueError, \".*phase.*empty.*\"):\n laser.get_rendering_context()\n\n def test_invalid_laguerre_modes_invalid_length(self):\n \"\"\"num of laguerre modes/phases must be equal\"\"\"\n laser = self.laser\n laser.laguerre_modes = [1.0]\n laser.laguerre_phases = [2, 3]\n\n with self.assertRaisesRegex(ValueError, \".*[Ll]aguerre.*length.*\"):\n laser.get_rendering_context()\n\n laser.laguerre_modes = [1, 0]\n # no error anymore:\n self.assertNotEqual({}, laser.get_rendering_context())\n\n def test_positive_definite_laguerre_modes(self):\n \"\"\"test whether laguerre modes are positive definite\"\"\"\n laser = self.laser\n laser.laguerre_modes = [-1.0]\n with self.assertLogs(level=\"WARNING\") as caught_logs:\n # valid, but warns\n self.assertNotEqual({}, laser.get_rendering_context())\n self.assertEqual(1, len(caught_logs.output))\n self.assertTrue(\"positive\" in caught_logs.output[0])\n\n # reverse: no warning if >=0\n laser.laguerre_modes = [0]\n with self.assertLogs(level=\"WARNING\") as other_caught_logs:\n # no warning\n self.assertNotEqual({}, laser.get_rendering_context())\n # produce at least one warning, workaround for python <= 3.9\n logging.warning(\"TESTWARN\")\n self.assertEqual(1, len(other_caught_logs.output))\n self.assertTrue(\"TESTWARN\" in other_caught_logs.output[0])\n\n def test_translation(self):\n \"\"\"is translated to context object\"\"\"\n # note: implicitly checks against schema\n context = self.laser.get_rendering_context()\n self.assertEqual(context[\"wave_length_si\"], self.laser.wavelength)\n self.assertEqual(context[\"waist_si\"], self.laser.waist)\n self.assertEqual(context[\"pulse_duration_si\"], self.laser.duration)\n self.assertEqual(context[\"focus_pos_si\"], [\n {\"component\": self.laser.focus_pos[0]},\n {\"component\": self.laser.focus_pos[1]},\n {\"component\": self.laser.focus_pos[2]}])\n self.assertEqual(context[\"phase\"], self.laser.phase)\n self.assertEqual(context[\"E0_si\"], self.laser.E0)\n self.assertEqual(context[\"pulse_init\"], self.laser.pulse_init)\n self.assertEqual(context[\"propagation_direction\"], [\n {\"component\": self.laser.propagation_direction[0]},\n {\"component\": self.laser.propagation_direction[1]},\n {\"component\": self.laser.propagation_direction[2]}])\n self.assertEqual(context[\"polarization_type\"],\n self.laser.polarization_type.get_cpp_str())\n self.assertEqual(context[\"polarization_direction\"], [\n {\"component\": self.laser.polarization_direction[0]},\n {\"component\": self.laser.polarization_direction[1]},\n {\"component\": self.laser.polarization_direction[2]}])\n self.assertEqual(context[\"laguerre_modes\"],\n [{\"single_laguerre_mode\": 1.0}])\n self.assertEqual(context[\"laguerre_phases\"],\n [{\"single_laguerre_phase\": 0.0}])\n self.assertEqual(context[\"modenumber\"], 0)\n self.assertEqual(context[\"huygens_surface_positions\"], {\n \"row_x\": {\n \"negative\": self.laser.huygens_surface_positions[0][0],\n \"positive\": self.laser.huygens_surface_positions[0][1]},\n \"row_y\": {\n \"negative\": self.laser.huygens_surface_positions[1][0],\n \"positive\": self.laser.huygens_surface_positions[1][1]},\n \"row_z\": {\n \"negative\": self.laser.huygens_surface_positions[2][0],\n \"positive\": self.laser.huygens_surface_positions[2][1]},\n })\n", "repo_name": "ComputationalRadiationPhysics/picongpu", "sub_path": "test/python/picongpu/quick/pypicongpu/laser.py", "file_name": "laser.py", "file_ext": "py", "file_size_in_byte": 8379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 652, "dataset": "github-code", "pt": "47", "api": [{"api_name": "unittest.TestCase", "line_number": 13, "usage_type": "attribute"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser", "line_number": 15, "usage_type": "call"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser.PolarizationType", "line_number": 24, "usage_type": "attribute"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser", "line_number": 24, "usage_type": "name"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser", "line_number": 32, "usage_type": "call"}, {"api_name": "typeguard.TypeCheckError", "line_number": 34, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 36, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 40, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 42, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 44, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 48, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 52, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 56, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 58, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 62, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 64, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 66, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 68, "usage_type": "attribute"}, {"api_name": "typeguard.TypeCheckError", "line_number": 70, "usage_type": "attribute"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser.PolarizationType", "line_number": 75, "usage_type": "attribute"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser", "line_number": 75, "usage_type": "name"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser.PolarizationType", "line_number": 76, "usage_type": "attribute"}, {"api_name": "picongpu.pypicongpu.laser.GaussianLaser", "line_number": 76, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 97, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 145, "usage_type": "call"}]} +{"seq_id": "25305966490", "text": "import math\nimport bpy \nimport os\nimport random\n\n#c=lower southwest corner, s=length of a side, i=indent\ndef makeCube(c, s, i, mat):\n verts = [(c[0]+i, c[1]+i, c[2]+i),\n (c[0]+s-i, c[1]+i, c[2]+i),\n (c[0]+i, c[1]+s-i, c[2]+i),\n (c[0]+s-i, c[1]+s-i, c[2]+i),\n (c[0]+i, c[1]+i, c[2]+s-i),\n (c[0]+s-i, c[1]+i, c[2]+s-i),\n (c[0]+i, c[1]+s-i, c[2]+s-i),\n (c[0]+s-i, c[1]+s-i, c[2]+s-i)]\n\n faces = [(0, 1, 3, 2),\n (4, 5, 7, 6),\n (0, 1, 5, 4),\n (3 ,2 ,6 ,7 ),\n (0 ,2 ,6, 4 ),\n (1, 3, 7 ,5 ),] \n \n mesh_data = bpy.data.meshes.new(\"cube_mesh_data\") \n mesh_data.from_pydata(verts, [], faces) \n mesh_data.update() # (calc_edges=True) not needed here \n \n cube_object = bpy.data.objects.new(\"Cube_Object\", mesh_data) \n \n scene = bpy.context.scene\n scene.objects.link(cube_object)\n cube_object.select = True\n cube_object.data.materials.append(mat)\n bpy.context.scene.objects.active = cube_object\n return cube_object\n #image = bpy.data.images.load(randEmojiPath())\n #for uv_face in cube_object.data.uv_textures:\n # uv_face.image = image\n\n\ndef makePlane(v, f, mat):\n p = bpy.data.meshes.new(\"plane_data\")\n p.from_pydata(v, [], f)\n p.update()\n pob = bpy.data.objects.load(\"plane_object\", p)\n scene = bpy.context.scene\n scene.objects.link(pob)\n pob.select = True\n pob.data.materials.append(mat)\n \n\n#ts = total side length\n#ss = small side length\n#cps = cubesPerSide\n#i = indent\ndef makeCubeGrid(ts, cps, i, c):\n objs = []\n ss = ts/(1.0*cps)\n print('ss:'+str(ss))\n for x in range(cps):\n for y in range(cps):\n for z in range(cps):\n mat = makeMat(randEmojiPath('/home/brink/photo-mosaic-video-generator/win/'))\n cube = makeCube((c[0] + x*ss, c[1] + y*ss, c[2] + z*ss), ss, i, mat) \n objs.append(cube)\n return objs\n\n\ndef makeMat(imgName = None):\n tex = bpy.data.textures.new('tiny_icon', type = 'IMAGE')\n if (imgName != None):\n img = bpy.data.images.load(imgName)\n tex.image = img\n mat = bpy.data.materials.new('MatName')\n mtex = mat.texture_slots.add()\n mtex.texture = tex\n mtex.texture_coords = 'UV'\n mtex.use_map_color_diffuse = True \n mtex.diffuse_color_factor = 1.0\n mtex.blend_type = 'MULTIPLY'\n return mat\n\ndef randEmojiPath(path = '/home/brink/photo-mosaic-video-generator/emoji/'):\n return path + random.choice(os.listdir(path))\n\n\ndef stackCubeGrids(ts, cps, i, c, cubes, d = (0, 0, 1)):\n objs = []\n for n in range(cubes):\n nc = (c[0] + (ts*n)*d[0], c[1] + (ts*n)*d[1], c[2] + (ts*n)*d[2])\n grid = makeCubeGrid(ts, cps, i, nc)\n for cube in grid:\n objs.append(cube)\n return objs\n\ndef move(objs, d = (0, 0, 1)):\n for obj in objs:\n #obj = bpy.context.object\n obj.location[2] = 0.0\n obj.keyframe_insert(data_path=\"location\", frame=0.0, index=2)\n obj.location[2] = 40.0\n obj.keyframe_insert(data_path=\"location\", frame=3000.0, index=2)\n \ndef main():\n objs = stackCubeGrids(10, 12, .25, (-6, -4, -5), 5, d = (0, 0, -1))\n move(objs)\n \nmain()\n#file_path = StringProperty(name = \"/home/brink/Desktop/tiny_icon.png\", subtype = \"FILE_PATH\")\n#img = bpy.data.images.load(file_path) #most of the time you will have to do self.file_path\n#console.log(img)", "repo_name": "andrewhannebrink/blender", "sub_path": "sz.py", "file_name": "sz.py", "file_ext": "py", "file_size_in_byte": 3300, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "bpy.data.meshes.new", "line_number": 24, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 24, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 28, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 28, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 30, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 34, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 42, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 42, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.load", "line_number": 45, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 45, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 46, "usage_type": "attribute"}, {"api_name": "bpy.data.textures.new", "line_number": 70, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bpy.data.images.load", "line_number": 72, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 72, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.new", "line_number": 74, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 74, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 84, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "10826446781", "text": "from scipy.stats import entropy\nimport copy\nimport misc_utils as mu\nimport cv2\ncv2.ocl.setUseOpenCL(False)\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions.categorical import Categorical\nimport numpy as np\nfrom ppo_discrete import Scale, layer_init, Agent\n\n\n\"\"\" Explorer takes in observations and decides where to move next. it does not control termination \"\"\"\n\n\nclass Explorer:\n def get_move(self, obs):\n \"\"\"\n obs is of shape (1, height, width) or (n, 1, height, weight).\n obs is of numpy array.\n return\n move, int or (n, ) np array\n \"\"\"\n raise NotImplementedError\n\n def reset(self, obs):\n # reset the explorer using the initial observations\n pass\n\n\nclass RandomExplorer(Explorer):\n pattern = 'random'\n\n def __init__(self, move_dim):\n self.move_dim = move_dim\n\n def get_move(self, obs):\n if obs.ndim == 3:\n move = np.random.choice(range(self.move_dim))\n elif obs.ndim == 4:\n n = obs.shape[0]\n move = np.random.choice(range(self.move_dim), n)\n else:\n raise TypeError\n return move\n\n\nclass PPOExplorer(Explorer, nn.Module):\n pattern = 'PPO'\n\n def __init__(self, action_dim, device, model_path=None, frames=1, img_size=50):\n super(PPOExplorer, self).__init__()\n self.img_size = img_size\n self.agent = Agent(action_dim=action_dim, device=device, frames=frames, img_size=img_size)\n self.model_path = model_path\n if self.model_path is not None:\n self.agent.load_state_dict(torch.load(self.model_path))\n\n def get_move(self, obs):\n if obs.ndim == 3:\n move = self.agent.get_move_stochastic(obs[None, ...])[0]\n probs = self.agent.get_move_probabilities(obs[None, ...])[0]\n next_loc = mu.compute_next_loc(mu.get_current_loc(obs), move, height=self.img_size, width=self.img_size)\n index = 0\n while obs[0][next_loc] == mu.white:\n # move = mu.get_next_direction_clockwise(move)\n move = sorted(zip(probs, range(4)), reverse=True)[index][1]\n next_loc = mu.compute_next_loc(mu.get_current_loc(obs), move, height=self.img_size, width=self.img_size)\n index += 1\n # collision checking false, all neighbours are white\n if index == 4:\n return np.random.choice(4)\n return move\n elif obs.ndim == 4:\n return self.agent.get_move_stochastic(obs)\n\n def get_move_bkup(self, obs):\n if obs.ndim == 3:\n probs = self.agent.get_move_probabilities(obs[None, ...])[0]\n for prob, move in sorted(zip(probs, range(4)), reverse=True):\n next_loc = mu.compute_next_loc(mu.get_current_loc(obs), move, height=self.img_size, width=self.img_size)\n if obs[0][next_loc] != mu.white and obs[0][next_loc] != mu.black and obs[0][next_loc] != mu.current_black:\n return move\n for prob, move in sorted(zip(probs, range(4)), reverse=True):\n next_loc = mu.compute_next_loc(mu.get_current_loc(obs), move, height=self.img_size, width=self.img_size)\n if obs[0][next_loc] != mu.white and obs[0][next_loc] != mu.current_black:\n return move\n elif obs.ndim == 4:\n probs = self.agent.get_move_probabilities(obs)\n\n\nclass AllInONeExplorer(PPOExplorer, nn.Module):\n \"\"\" This is the same as PPO explorer except action dimension \"\"\"\n pattern = 'all_in_one'\n\n def get_move(self, obs):\n \"\"\" not considering moving into white pixels \"\"\"\n if obs.ndim == 3:\n move = self.agent.get_move_stochastic(obs[None, ...])[0]\n return move\n elif obs.ndim == 4:\n return self.agent.get_move_stochastic(obs)\n\n\nclass EdgeFollowExplorer:\n pattern = 'edge'\n\n # similar to a bug algorithm\n # This explorer does not handle parallel envs, because it has to track pre move and ob for each env\n def __init__(self, img_size=60):\n super(EdgeFollowExplorer, self).__init__()\n self.img_size = img_size\n\n self.pre_move = None\n self.old_obs = None\n\n def reset(self, obs):\n if obs.ndim == 3:\n dim_0, dim_1 = np.where(obs[0] == mu.white)\n current_loc = mu.get_current_loc(obs)\n first_white_loc = (dim_0[0], dim_1[0])\n self.pre_move = mu.get_direction(current_loc, first_white_loc)\n self.old_obs = copy.deepcopy(obs)\n self.old_obs[0][first_white_loc] = mu.unexplored\n else:\n raise NotImplementedError\n\n def get_move_single_ob(self, old_ob, ob):\n \"\"\" old_ob and ob are of shape (1, height, width) \"\"\"\n # This algorithm won't work if agent bumps into the border. It will follow the boarder as well.\n # A move that leads the agent into the walls won't exit the while loop\n move = None\n current_loc = mu.get_current_loc(ob)\n collision, collision_loc = mu.check_grid_collision(old_ob, ob)\n if collision:\n # if collision happens: turn clockwise until you can move forward (unexplored, or black), return the action\n move = mu.get_next_direction_clockwise(self.pre_move)\n else:\n # if no collision happens: starting from the first anti-clockwise move from pre move\n # turn clockwise until you can move forward (unexplored, or black), return the action\n move = mu.get_next_direction_anti_clockwise(self.pre_move)\n initial_move = move\n new_loc = mu.compute_next_loc(current_loc, move, height=self.img_size, width=self.img_size)\n while not (ob[0][new_loc] == mu.unexplored or ob[0][new_loc] == mu.black):\n move = mu.get_next_direction_clockwise(move)\n if move == initial_move:\n return np.random.choice(4)\n new_loc = mu.compute_next_loc(current_loc, move, height=self.img_size, width=self.img_size)\n return move\n\n def get_move(self, obs):\n if obs.ndim == 3:\n move = self.get_move_single_ob(self.old_obs, obs)\n # self.old_obs should be a separate copy instead of another name of the occupancy grid of the emv.\n # Otherwise, env.step will change self.old_obs immediately\n self.old_obs = copy.deepcopy(obs)\n self.pre_move = move\n else:\n raise NotImplementedError\n return move\n\n\nclass InfoGainExplorer(Explorer):\n pattern = 'info'\n\n \"\"\" Can only handle a single ob, so only works with a single env \"\"\"\n def __init__(self,\n discriminator):\n super(InfoGainExplorer, self).__init__()\n self.move_dim = 4\n self.discriminator = discriminator # info gain discriminator requires a\n\n def get_move_single_ob(self, ob):\n assert self.discriminator is not None, 'info_gain policy requires a discriminator'\n good_moves = mu.find_not_go_back_moves(ob)\n current_loc = mu.get_current_loc(ob)\n height, width = ob[0].shape\n\n if len(good_moves) == 0:\n # all explored\n move = np.random.choice(range(self.move_dim))\n else:\n prediction, max_prob, probs = self.discriminator.predict(ob)\n old_entropy = entropy(probs)\n\n info_gains = np.zeros(len(good_moves))\n for i, move in enumerate(good_moves):\n new_loc = mu.compute_next_loc(current_loc, move, height, width)\n masks = []\n\n # new pixel is white\n ob_w = copy.deepcopy(ob)\n ob_w[0][new_loc] = mu.white\n probs_w = self.discriminator.predict(ob_w)[2]\n if not any(probs_w):\n # if this particular color of the pixel makes the ob not belong to any class\n masks.append(0)\n entropy_w = 1\n else:\n masks.append(1)\n entropy_w = entropy(probs_w)\n\n # new pixel is black\n ob_b = copy.deepcopy(ob)\n ob_b[0][new_loc] = mu.black\n probs_b = self.discriminator.predict(ob_b)[2]\n if not any(probs_b):\n masks.append(0)\n entropy_b = 1\n else:\n masks.append(1)\n entropy_b = entropy(probs_b)\n\n weights = np.array(masks) / np.array(masks).sum()\n avg_entropy = weights[0] * entropy_w + weights[1] * entropy_b\n info_gains[i] = old_entropy - avg_entropy\n\n # print(info_gains)\n if np.all(info_gains == info_gains[0]):\n move = np.random.choice(good_moves)\n else:\n move_idx = np.argmax(info_gains)\n move = good_moves[move_idx]\n return move\n\n def get_move(self, obs):\n if obs.ndim == 3:\n return self.get_move_single_ob(obs)\n else:\n raise NotImplementedError\n\n\nclass NotGoBackExplorer(Explorer):\n pattern = 'not_go_back'\n\n def __init__(self):\n super(NotGoBackExplorer, self).__init__()\n self.move_dim = 4\n\n def get_move_single_ob(self, ob):\n good_moves = mu.find_not_go_back_moves(ob)\n if len(good_moves) == 0:\n # all explored\n move = np.random.choice(range(self.move_dim))\n else:\n move = np.random.choice(good_moves)\n return move\n\n def get_move(self, obs):\n if obs.ndim == 3:\n return self.get_move_single_ob(obs)\n else:\n raise NotImplementedError\n", "repo_name": "jingxixu/tandem-public", "sub_path": "explorer.py", "file_name": "explorer.py", "file_ext": "py", "file_size_in_byte": 9745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "47", "api": [{"api_name": "cv2.ocl.setUseOpenCL", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.ocl", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "ppo_discrete.Agent", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 58, "usage_type": "call"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 64, "usage_type": "call"}, {"api_name": "misc_utils.get_current_loc", "line_number": 64, "usage_type": "call"}, {"api_name": "misc_utils.white", "line_number": 66, "usage_type": "attribute"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 69, "usage_type": "call"}, {"api_name": "misc_utils.get_current_loc", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 73, "usage_type": "attribute"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 82, "usage_type": "call"}, {"api_name": "misc_utils.get_current_loc", "line_number": 82, "usage_type": "call"}, {"api_name": "misc_utils.white", "line_number": 83, "usage_type": "attribute"}, {"api_name": "misc_utils.black", "line_number": 83, "usage_type": "attribute"}, {"api_name": "misc_utils.current_black", "line_number": 83, "usage_type": "attribute"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 86, "usage_type": "call"}, {"api_name": "misc_utils.get_current_loc", "line_number": 86, "usage_type": "call"}, {"api_name": "misc_utils.white", "line_number": 87, "usage_type": "attribute"}, {"api_name": "misc_utils.current_black", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 120, "usage_type": "call"}, {"api_name": "misc_utils.white", "line_number": 120, "usage_type": "attribute"}, {"api_name": "misc_utils.get_current_loc", "line_number": 121, "usage_type": "call"}, {"api_name": "misc_utils.get_direction", "line_number": 123, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 124, "usage_type": "call"}, {"api_name": "misc_utils.unexplored", "line_number": 125, "usage_type": "attribute"}, {"api_name": "misc_utils.get_current_loc", "line_number": 134, "usage_type": "call"}, {"api_name": "misc_utils.check_grid_collision", "line_number": 135, "usage_type": "call"}, {"api_name": "misc_utils.get_next_direction_clockwise", "line_number": 138, "usage_type": "call"}, {"api_name": "misc_utils.get_next_direction_anti_clockwise", "line_number": 142, "usage_type": "call"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 144, "usage_type": "call"}, {"api_name": "misc_utils.unexplored", "line_number": 145, "usage_type": "attribute"}, {"api_name": "misc_utils.black", "line_number": 145, "usage_type": "attribute"}, {"api_name": "misc_utils.get_next_direction_clockwise", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 148, "usage_type": "attribute"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 149, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 157, "usage_type": "call"}, {"api_name": "misc_utils.find_not_go_back_moves", "line_number": 176, "usage_type": "call"}, {"api_name": "misc_utils.get_current_loc", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 182, "usage_type": "attribute"}, {"api_name": "scipy.stats.entropy", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "misc_utils.compute_next_loc", "line_number": 189, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 193, "usage_type": "call"}, {"api_name": "misc_utils.white", "line_number": 194, "usage_type": "attribute"}, {"api_name": "scipy.stats.entropy", "line_number": 202, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 205, "usage_type": "call"}, {"api_name": "misc_utils.black", "line_number": 206, "usage_type": "attribute"}, {"api_name": "scipy.stats.entropy", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 221, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 223, "usage_type": "call"}, {"api_name": "misc_utils.find_not_go_back_moves", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 245, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 247, "usage_type": "attribute"}]} +{"seq_id": "33518972153", "text": "import os\n# Imports the Google Cloud client library\nfrom google.cloud import speech\n\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r\"C:\\Users\\SOGANG\\Documents\\development\\unity\\STT\\Assets\\speech-to-text.json\"\n# Instantiates a client\nclient = speech.SpeechClient()\n\n# file_name = input(\"Enter a file name(with the extension name) : \")\n# The name of the audio file to transcribe\n# gcs_uri = f\"gs://speech_to_txt_bucket/{file_name}\"\ngcs_uri = f\"gs://speech_to_txt_bucket/sample_mono.wav\"\n\naudio = speech.RecognitionAudio(uri=gcs_uri)\n\n# Google의 기본 인코딩 타입\n# config = speech.RecognitionConfig(\n# encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,\n# sample_rate_hertz=44100,\n# language_code=\"en-US\",\n# )\n\n# 변경된 인코딩 타입\nconfig = speech.RecognitionConfig(\n encoding=speech.RecognitionConfig.AudioEncoding.ENCODING_UNSPECIFIED,\n sample_rate_hertz=44100,\n language_code=\"en-US\",\n)\n\n# Detects speech in the audio file\nresponse = client.recognize(config=config, audio=audio)\n\nfor result in response.results:\n print(\"Transcript: {}\".format(result.alternatives[0].transcript))", "repo_name": "SeongyeonOh/python-STT-in-Unity", "sub_path": "Assets/STT.py", "file_name": "STT.py", "file_ext": "py", "file_size_in_byte": 1125, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "google.cloud.speech.SpeechClient", "line_number": 7, "usage_type": "call"}, {"api_name": "google.cloud.speech", "line_number": 7, "usage_type": "name"}, {"api_name": "google.cloud.speech.RecognitionAudio", "line_number": 14, "usage_type": "call"}, {"api_name": "google.cloud.speech", "line_number": 14, "usage_type": "name"}, {"api_name": "google.cloud.speech.RecognitionConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "google.cloud.speech", "line_number": 24, "usage_type": "name"}, {"api_name": "google.cloud.speech.RecognitionConfig", "line_number": 25, "usage_type": "attribute"}, {"api_name": "google.cloud.speech", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "11333097695", "text": "import unittest\nfrom io import StringIO\nfrom ...vml import Vml\n\n\nclass TestWriteOidmap(unittest.TestCase):\n \"\"\"\n Test the Vml _write_idmap() method.\n\n \"\"\"\n\n def setUp(self):\n self.fh = StringIO()\n self.vml = Vml()\n self.vml._set_filehandle(self.fh)\n\n def test_write_idmap(self):\n \"\"\"Test the _write_idmap() method\"\"\"\n\n self.vml._write_idmap(1)\n\n exp = \"\"\"\"\"\"\n got = self.fh.getvalue()\n\n self.assertEqual(got, exp)\n", "repo_name": "jmcnamara/XlsxWriter", "sub_path": "xlsxwriter/test/vml/test_write_idmap.py", "file_name": "test_write_idmap.py", "file_ext": "py", "file_size_in_byte": 517, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3366, "dataset": "github-code", "pt": "51", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 13, "usage_type": "call"}, {"api_name": "vml.Vml", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "26748314904", "text": "import numpy as np\r\nfrom sklearn import datasets\r\nfrom itertools import combinations_with_replacement\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef dist(o1, o2):\r\n return abs(o1[0] - o2[0])\r\n\r\n\r\ndef static(data_set):\r\n D = np.zeros(shape=(1, 1))\r\n L = [data_set[0]]\r\n\r\n for ok in data_set[1:]:\r\n d = [dist(ok, op) for op in L]\r\n\r\n lamb = [dist(ok, L[0])]\r\n for p in range(1, len(L) - 1):\r\n lamb.append(dist(ok, L[p - 1]) + dist(ok, L[p]) - dist(L[p - 1], L[p]))\r\n if len(L) > 1:\r\n lamb.append(dist(ok, L[-1]))\r\n\r\n p_opt = np.argmin(lamb)\r\n L.insert(p_opt, ok)\r\n\r\n D = np.insert(D, p_opt, d, 0)\r\n d.insert(p_opt, 0)\r\n D = np.insert(D, p_opt, d, 1)\r\n\r\n return D\r\n\r\n\r\ndef dynamic(data_set):\r\n D_s_max = None\r\n s_max = 50\r\n D = np.zeros(shape=(1, 1))\r\n L = [(data_set[0], 0)]\r\n\r\n for k, ok in enumerate(data_set[1:], 1):\r\n\r\n if k >= s_max:\r\n o_to_delete, _ = min(enumerate(L), key=lambda x: x[1][1])\r\n del L[o_to_delete]\r\n\r\n D = np.delete(D, o_to_delete, axis=0)\r\n D = np.delete(D, o_to_delete, axis=1)\r\n\r\n if k == s_max:\r\n D_s_max = D.copy()\r\n\r\n d = [dist(ok, op[0]) for op in L]\r\n\r\n lamb = [dist(ok, L[0][0])]\r\n for p in range(1, len(L) - 1):\r\n lamb.append(dist(ok, L[p - 1][0]) + dist(ok, L[p][0]) - dist(L[p - 1][0], L[p][0]))\r\n if len(L) > 1:\r\n lamb.append(dist(ok, L[-1][0]))\r\n\r\n p_opt = np.argmin(lamb)\r\n L.insert(p_opt, (ok, k))\r\n\r\n D = np.insert(D, p_opt, d, 0)\r\n d.insert(p_opt, 0)\r\n D = np.insert(D, p_opt, d, 1)\r\n\r\n return D_s_max, D\r\n\r\n\r\ndef prepare_origin_order():\r\n result = np.zeros(shape=(150, 150))\r\n\r\n for i1, i2 in combinations_with_replacement(range(150), 2):\r\n distance = dist(iris['data'][i1], iris['data'][i2])\r\n result[i1][i2] = distance\r\n result[i2][i1] = distance\r\n\r\n f = plt.figure(1, figsize=(8, 6))\r\n plt.pcolor(result, figure=f)\r\n\r\n\r\ndef prepare_static_reordering():\r\n result = static(iris['data'])\r\n\r\n f = plt.figure(2, figsize=(8, 6))\r\n plt.pcolor(result, figure=f)\r\n\r\n\r\ndef prepare_dynamic_reordering():\r\n result_50, result_150 = dynamic(iris['data'])\r\n\r\n f = plt.figure(3, figsize=(8, 6))\r\n plt.pcolor(result_50, figure=f)\r\n\r\n f = plt.figure(4, figsize=(8, 6))\r\n plt.pcolor(result_150, figure=f)\r\n\r\n\r\nif __name__ == '__main__':\r\n iris = datasets.load_iris()\r\n\r\n prepare_origin_order()\r\n prepare_static_reordering()\r\n prepare_dynamic_reordering()\r\n\r\n plt.show()\r\n", "repo_name": "damirsib/Incremental-Matrix-Reordering-for-Similarity-Based-Dynamic-Data-Sets", "sub_path": "data_visualization.py", "file_name": "data_visualization.py", "file_ext": "py", "file_size_in_byte": 2641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "itertools.combinations_with_replacement", "line_number": 73, "usage_type": "call"}, {"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.pcolor", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 100, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}]} +{"seq_id": "20906065181", "text": "# 7 - You are given a list of projects and a list of dependencies\n# (which is a list of pairs of projects, where the second project is dependent on the first project)\n# All of a project's dependencies must be built before the project is. Find a build order that will allow the projects\n# to be built. If there is no valid build order, return an error.\nimport typing\nimport collections\nfrom utils.collections.graph import Graph, AbstractGraph\n\n\nclass Project(typing.NamedTuple):\n id: int\n name: str\n\n\nclass Dependency(typing.NamedTuple):\n father: Project\n child: Project\n\n\ndef projects_build(projects: typing.Sequence[Project], dependencies: typing.Sequence[Dependency]) -> typing.Sequence[Project] | None:\n dependency_graph: AbstractGraph[Project] = Graph(directed=True)\n\n for project in projects:\n dependency_graph.add_node(project)\n for dependency in dependencies:\n dependency_graph.add_edge(dependency.father, dependency.child)\n\n queue: typing.Deque[Project] = collections.deque()\n\n # can't remove edges during iterations!\n in_degrees: typing.Dict[Project, int] = {}\n for project in dependency_graph.nodes:\n current_in_degree = dependency_graph.in_degree(project)\n if current_in_degree == 0:\n queue.append(project)\n else:\n in_degrees[project] = current_in_degree\n\n build: typing.List[Project] = []\n while len(queue) > 0:\n project = queue.popleft()\n\n build.append(project)\n for child in dependency_graph.neighbors(project):\n in_degrees[child] -= 1\n if in_degrees[child] == 0:\n queue.append(child)\n del in_degrees[child]\n\n if len(build) != len(projects):\n return None\n\n return build\n\n\n", "repo_name": "Pentracchiano/cracking-the-code-interview", "sub_path": "Trees And Graphs/BuildOrder.py", "file_name": "BuildOrder.py", "file_ext": "py", "file_size_in_byte": 1768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.NamedTuple", "line_number": 10, "usage_type": "attribute"}, {"api_name": "typing.NamedTuple", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.collections.graph.AbstractGraph", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.collections.graph.Graph", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.Deque", "line_number": 28, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "37535077668", "text": "# -*- coding: utf-8 -*-\r\n\r\nfrom sklearn import preprocessing\r\nfrom sklearn.model_selection import GridSearchCV\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.model_selection import KFold\r\nimport seaborn as sns; sns.set()\r\n\r\n# We first need to standardize the data, and then separate it back into training/test/unlabeled (another\r\n# option would be to standardize the training data and then transform the test and initially unlabeled sets)\r\nunprocessed = np.ones((244,50))\r\nunprocessed[0:153] = projected_train[0:153,:]\r\nunprocessed[153:192] = projected_test[:,:]\r\nunprocessed[192:244] = projected_unknown[:,:]\r\nprocessed = preprocessing.scale(unprocessed)\r\nprocessed_train = processed[0:153,:]\r\nprocessed_test = processed[153:192,:]\r\nprocessed_unknown = processed[192:244,:]\r\n\r\n\r\n\r\n# Kernel/Hyper-parameter selection\r\n# Note that if there is a tie, linear kernels, and lower values of C and gamma will be used\r\n\r\ntuned_parameters = [{'kernel': ['linear'], 'C': [1, 10, 100, 1000,10000]},{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4,1e-2,1e-1],\r\n 'C': [1,10,100, 1000,10000]}]\r\n\r\nprint(\"# Tuning hyper-parameters for accuracy\")\r\nprint()\r\n\r\n\r\ncv = KFold(n_splits = 5,random_state=2018)\r\n\r\nclf = GridSearchCV(SVC(probability = True), tuned_parameters, cv=cv,\r\n scoring='accuracy' )\r\nclf.fit(processed_train, label_list_train)\r\n\r\nprint(\"Best parameters set found on development set:\")\r\nprint()\r\nprint(clf.best_params_)\r\nprint()\r\nprint(\"Grid scores on development set:\")\r\nprint()\r\nmeans = clf.cv_results_['mean_test_score']\r\nstds = clf.cv_results_['std_test_score']\r\nfor mean, std, params in zip(means, stds, clf.cv_results_['params']):\r\n print(\"%0.3f (+/-%0.03f) for %r\"\r\n % (mean, std * 2, params))\r\n\r\n# Test set confusion matrix\r\nbaseline_svm = clf.best_estimator_\r\npredicted_test_labels_baseline = baseline_svm.predict(processed_test)\r\nmat = confusion_matrix(label_list_test, predicted_test_labels_baseline)\r\nplt.figure(0)\r\nsns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,xticklabels=[-1,1],yticklabels=[-1,1])\r\nplt.xlabel('True Label')\r\nplt.ylabel('Predicted Label')\r\n\r\n# Training set confusion matrix\r\npredicted_train_labels_baseline = baseline_svm.predict(processed_train)\r\nmat = confusion_matrix(label_list_train, predicted_train_labels_baseline)\r\nplt.figure(1)\r\nsns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,xticklabels=[-1,1],yticklabels=[-1,1])\r\nplt.xlabel('True Label')\r\nplt.ylabel('Predicted Label')\r\n", "repo_name": "cjkunselman18/Class-Assignment-in-Ambiguous-Microstructures", "sub_path": "Python_Scripts/Train_Baseline_SVM.py", "file_name": "Train_Baseline_SVM.py", "file_ext": "py", "file_size_in_byte": 2537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "51", "api": [{"api_name": "seaborn.set", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 16, "usage_type": "name"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 54, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 62, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "14886098160", "text": "from typing import List\n\n\nclass Solution:\n def maxProfit(self, prices: List[int]) -> int:\n \"\"\"\n 如果冷冻期为多天，可以考虑扩大dp数组；\n :param prices:\n :return:\n \"\"\"\n dp_buy = [-prices[0], -prices[0]]\n dp_sell = [0, 0]\n for price in prices:\n dp_buy[0], dp_buy[1] = dp_buy[1], max(dp_buy[1], dp_sell[0]-price)\n dp_sell[0], dp_sell[1] = dp_sell[1], max(dp_sell[1], dp_buy[0]+price)\n return dp_sell[1]\n", "repo_name": "ABenxj/leetcode", "sub_path": "309 macProfit.py", "file_name": "309 macProfit.py", "file_ext": "py", "file_size_in_byte": 505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "19807480226", "text": "import sys\nimport os\nimport datetime\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nPLOT_LIMIT = 48 * 3600\n\nif len(sys.argv) < 3:\n print(\"Usage: python3 plotter.py \")\n exit(1)\n\noutput_path = sys.argv[1]\nnode_addr = int(sys.argv[2])\n\nbattery_ts = []\nbattery_values = []\n\nfriendless_ts = []\nfriendless_values = []\n\nonoff_ts = []\nonoff_values = []\n\nwith open(os.path.join(output_path, \"protocol_transcript\")) as f:\n for line in f:\n tokens = line.strip().split(\" \")\n\n if len(tokens) < 2:\n continue\n\n if tokens[1] != \"sta\" or (tokens[2] not in (\"health\", \"battery\", \"onoff\")):\n continue\n\n if int(tokens[3]) != node_addr:\n continue\n\n ts = datetime.datetime.fromtimestamp(float(tokens[0]))\n op = tokens[2]\n\n if op == \"battery\":\n battery_ts.append(ts)\n battery_values.append(float(tokens[6]) * 6.0 * 0.6 / float(1 << 14))\n elif op == \"health\":\n friendless_ts.append(ts)\n friendless_values.append(1 if \"01\" in tokens[6] else 0)\n elif op == \"onoff\":\n if int(tokens[6]) == 1:\n onoff_ts.append(ts)\n onoff_values.append(0)\n onoff_ts.append(ts)\n onoff_values.append(1)\n onoff_ts.append(ts)\n onoff_values.append(0)\n\n# Create DataFrames\ndf_battery = pd.DataFrame({\"timestamp\": battery_ts, \"voltage\": battery_values})\ndf_friendless = pd.DataFrame(\n {\"timestamp\": friendless_ts, \"friendless\": friendless_values}\n)\ndf_onoff = pd.DataFrame({\"timestamp\": onoff_ts, \"onoff\": onoff_values})\n\n# Plot only the last PLOT_LIMIT seconds\ndatetime_cutoff = df_battery.iloc[-1][\"timestamp\"] - datetime.timedelta(\n seconds=PLOT_LIMIT\n)\ndf_battery = df_battery[df_battery[\"timestamp\"] > datetime_cutoff]\ndf_friendless = df_friendless[df_friendless[\"timestamp\"] > datetime_cutoff]\ndf_onoff = df_onoff[df_onoff[\"timestamp\"] > datetime_cutoff]\n\n# Finally, let's plot it :)\nplt.plot(df_battery[\"timestamp\"], df_battery[\"voltage\"].rolling(10).mean(), 'b')\nplt.plot(df_friendless[\"timestamp\"], 0.5 * df_friendless[\"friendless\"] + 2, 'r')\nplt.plot(df_onoff[\"timestamp\"], 0.5 * df_onoff[\"onoff\"] + 2.5, 'g')\nplt.show()\n", "repo_name": "isundaylee/nRF5", "sub_path": "console/plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 2289, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "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": "datetime.datetime.fromtimestamp", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "16735319999", "text": "\"\"\"\n工具函数\n\"\"\"\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2019-11-01 17:07\n# @Site :\n# @File : tools.py\n# @Software: PyCharm\n\n\nfrom pydub import AudioSegment\n\n\ndef mp3_to_wav(mp3_path, wav_path):\n \"\"\"\n MP3转wav\n :param mp3_path:\n :param wav_path:\n :return:\n \"\"\"\n sound = AudioSegment.from_mp3(mp3_path)\n sound = sound.set_frame_rate(16000).set_channels(1)\n sound.export(wav_path, format='wav', codec='pcm_s16le')\n", "repo_name": "LongmaoTeamTf/audio_aligner_app", "sub_path": "modules/audio_aligner/models/tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pydub.AudioSegment.from_mp3", "line_number": 22, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "25253831559", "text": "import os\n\nimport boto3\n\nfrom books.use_cases.repositories import BaseS3Repository\n\n\nAUTHORS_BUCKET_NAME = os.environ['AWS_AUTHORS_BUCKET_NAME']\nBOOKS_BUCKET_NAME = os.environ['AWS_BOOKS_BUCKET_NAME']\n\n\nclass S3Repository(BaseS3Repository):\n def save_author_image(self, image: bytes, key: str) -> str:\n client = self._get_client()\n client.put_object(\n Body=image,\n Bucket=AUTHORS_BUCKET_NAME,\n Key=key,\n ContentType=image.mimetype\n )\n\n return key\n\n def save_book_image(self, image: bytes, key: str) -> str:\n client = self._get_client()\n client.put_object(\n Body=image,\n Bucket=BOOKS_BUCKET_NAME,\n Key=key,\n ContentType=image.mimetype\n )\n\n return key\n\n def _get_client(self):\n endpoint = os.environ.get('AWS_ENDPOINT_URL', None)\n\n s3 = boto3.resource('s3', endpoint_url=endpoint)\n return boto3.client('s3', endpoint_url=endpoint)\n", "repo_name": "OurBooks-Team-Yuml/Books", "sub_path": "books/repositories/s3.py", "file_name": "s3.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "books.use_cases.repositories.BaseS3Repository", "line_number": 12, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 38, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "20469523992", "text": "import logging as logger\nimport re\nimport time\nfrom datetime import datetime\n\nimport requests\nfrom adsingestp.parsers import arxiv\nfrom SciXPipelineUtils import utils\nfrom SciXPipelineUtils.scix_uuid import scix_uuid as uuid\n\nfrom harvester import db\nfrom harvester.base.OAIHarvester import OAIHarvester as OAI\n\nMAX_RETRIES = 5\n\n\ndef arxiv_harvesting(app, job_request, producer):\n \"\"\"\n Main harvesting routine for arxiv metadata.\n\n job_request: (json) task message passed to Harvester input topic.\n producer: The harvester kafka producer instance\n\n return: (str) The final state of the harvesting process.\n \"\"\"\n datestamp = datetime.now().strftime(\"%Y%m%d\")\n resumptionToken = job_request[\"task_args\"].get(\"resumptionToken\")\n daterange = job_request[\"task_args\"].get(\"daterange\")\n app.logger.info(\"{}, {}, {}\".format(daterange, resumptionToken, datestamp))\n harvester_output_schema = utils.get_schema(\n app, app.schema_client, app.config.get(\"HARVESTER_OUTPUT_SCHEMA\")\n )\n\n harvester = ArXiV_Harvester(\n app.config.get(\"ARXIV_OAI_URL\"), daterange=daterange, resumptionToken=resumptionToken\n )\n\n for record in harvester:\n # Assign ID to new record\n record_id = uuid.uuid7()\n # Generate filepath for S3\n file_path = \"/{}/{}\".format(datestamp, record_id)\n # write record to S3\n checksum = None\n for provider in app.s3Clients.keys():\n try:\n checksum = app.s3Clients[provider].write_object_s3(\n file_bytes=bytes(record, \"utf-8\"), object_name=file_path\n )\n except Exception as e:\n app.logger.error(\n \"Failed to write to S3 provider: {} with Exception: {}\".format(provider, e)\n )\n\n if checksum:\n app.logger.debug(\"AWS checksum for {} is: {}\".format(record_id, checksum))\n s3_key = file_path\n produce = db.write_harvester_record(\n app, record_id, datetime.now(), s3_key, checksum, job_request.get(\"task\")\n )\n if produce:\n producer_message = {\n \"record_id\": str(record_id),\n \"record_xml\": record,\n \"s3_path\": file_path,\n \"task\": job_request.get(\"task\"),\n \"datetime\": datetime.now(),\n }\n producer.produce(\n topic=app.config.get(\"HARVESTER_OUTPUT_TOPIC\"),\n value=producer_message,\n value_schema=harvester_output_schema,\n )\n else:\n app.logger.error(\"No checksums generated, All S3 uploads must have failed. Stopping.\")\n return \"Error\"\n\n return \"Success\"\n\n\nclass ArXiV_Harvester(OAI):\n def __init__(self, harvest_url, daterange, resumptionToken):\n \"\"\"\n Initialization of harvesting class.\n\n url: (str) The OAI harvesting url\n params: (json) The required requests parameters\n daterange: (str) A specified harvesting date range.\n parsed_records: (array) Calls harvest method to produce an array of records accessible from the __next__ method.\n \"\"\"\n self.url = harvest_url\n self.params = {\"metadataPrefix\": \"oai_dc\"}\n self.daterange = daterange\n self.raw_xml = None\n self.fullHarvest = False\n self.parsed_records = self.harvest_arxiv(resumptionToken)\n\n def harvest_arxiv(self, resumptionToken=None):\n \"\"\"\n daterange: (str) date with value given as YYYY-MM-DD\n resumptionToken: (str) value returned by previous API call for paging.\n\n return: (json) ArXiV API response\n \"\"\"\n\n success = False\n retries = 0\n\n try:\n while success is not True:\n \"\"\"\n This loop:\n 1. Sends the relevant request to the ArXiV API\n 2. Checks to make sure we aren't receiving any flow control responses.\n 3. If we are it waits the specified amount of time before proceeding.\n 4. If we repeatedly hit 503 or any other error, we stop.\n \"\"\"\n if not resumptionToken:\n self.params[\"from\"] = self.daterange\n\n else:\n # specifying any other query params besides the verb with the resumptionToken will result in an error.\n self.params = {\"resumptionToken\": resumptionToken}\n\n try:\n raw_response = self.ListRecords(self.url, self.params)\n self.raw_xml = raw_response.text\n \"\"\"\n Still need to write the code that extracts the retry-after time from the response.\n \"\"\"\n if not raw_response.ok:\n if raw_response.status_code == 503 and retries < MAX_RETRIES:\n logger.info(raw_response.text)\n retries += 1\n search = re.compile(r\"\\Retry after ([0-9]+) seconds\\\")\n sleep_time = int(search.search(raw_response.text)[1])\n time.sleep(sleep_time)\n else:\n logger.error(\n \"Failed to Harvest ArXiV records for daterange: {}\".format(\n self.daterange\n )\n )\n raise requests.exceptions.Timeout\n else:\n success = True\n\n except Exception as e:\n raise e\n except Exception as e:\n raise e\n\n arxivparser = arxiv.MultiArxivParser()\n return arxivparser.parse(self.raw_xml)\n\n def __iter__(self):\n \"\"\"\n Iterate through all parsed records.\n If next(self.parsed_records) fails, we attempt to extract a resumptionToken and then rerun the harvest process with the token.\n\n return:\n record: (str) XML of a single ArXiv record\n \"\"\"\n\n while not self.fullHarvest:\n for record in self.parsed_records:\n yield record\n try:\n resumptionToken = self.extract_resumptionToken(self.raw_xml)\n except Exception:\n resumptionToken = None\n logger.debug(\"No resumptionToken present\")\n if resumptionToken:\n self.parsed_records = self.harvest_arxiv(resumptionToken=resumptionToken)\n else:\n self.fullHarvest = True\n\n @staticmethod\n def extract_resumptionToken(raw_xml):\n \"\"\"\n extracts the resumptionToken from the raw response. (ArXiv uses these for paging, see OAI harvesting docs for details).\n\n raw_xml: (str) XML content from ArXiv\n\n return: (str) ArXiv resumptionToken\n \"\"\"\n arxiv_parser = arxiv.MultiArxivParser()\n token_text = next(\n arxiv_parser.get_chunks(raw_xml, r\"\")\n )\n pattern = re.compile(r\"[0-9]+\\|[0-9]+\")\n return pattern.search(token_text)[0]\n", "repo_name": "adsabs/SciXHarvesterPipeline", "sub_path": "SciXHarvester/harvester/metadata/arxiv_harvester.py", "file_name": "arxiv_harvester.py", "file_ext": "py", "file_size_in_byte": 7290, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "SciXPipelineUtils.utils.get_schema", "line_number": 30, "usage_type": "call"}, {"api_name": "SciXPipelineUtils.utils", "line_number": 30, "usage_type": "name"}, {"api_name": "SciXPipelineUtils.scix_uuid.scix_uuid.uuid7", "line_number": 40, "usage_type": "call"}, {"api_name": "SciXPipelineUtils.scix_uuid.scix_uuid", "line_number": 40, "usage_type": "name"}, {"api_name": "harvester.db.write_harvester_record", "line_number": 58, "usage_type": "call"}, {"api_name": "harvester.db", "line_number": 58, "usage_type": "name"}, {"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": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "name"}, {"api_name": "harvester.base.OAIHarvester.OAIHarvester", "line_number": 81, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 133, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 135, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 137, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 139, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 144, "usage_type": "attribute"}, {"api_name": "adsingestp.parsers.arxiv.MultiArxivParser", "line_number": 153, "usage_type": "call"}, {"api_name": "adsingestp.parsers.arxiv", "line_number": 153, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 172, "usage_type": "call"}, {"api_name": "adsingestp.parsers.arxiv.MultiArxivParser", "line_number": 187, "usage_type": "call"}, {"api_name": "adsingestp.parsers.arxiv", "line_number": 187, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "2260023645", "text": "# %%\n# DSO106 - Machine Learning and Modeling\n # Lesson 2 - Modeling with Logistic Regression\n\n# Page 6 - Logistic Regression in Python\n\n# Goal: Determine whether home runs predict a winning game score\n # IV (x axis, continuous): home runs\n # DV (y axis, categorical): winning game score\n # H0: Home runs do not predict a winning game score\n # H1: Home runs do predict a winning game score\n\n# Import packages\nimport pandas as pd\nimport statsmodels.api as sm\n\n# %%\n\n# Import and preview data\nbaseball = pd.read_csv('/Users/hannah/Library/CloudStorage/GoogleDrive-gracesnouveaux@gmail.com/My Drive/Bethel Tech/Data Science/DSO106 Machine Learning and Modeling/1: Modeling and Optimization – Lesson 2. Modeling with Logistic Regression/baseball.csv')\n\nbaseball\n\n# %%\n# 4860 rows × 10 columns\n\n# Wrangling\n\n # Recode DV\ndef recodeDV (series):\n if series == 'W':\n return 1\n if series == 'L':\n return 0\n\nbaseball['winsR'] = baseball['W/L'].apply(recodeDV)\n\nbaseball\n\n# %%\n# 4860 rows × 11 columns\n\n# Run analysis\n\n # Create logit\nbaseballLogit = sm.Logit(baseball.winsR, baseball['HR Count'])\n\n# %%\n \n # Run analysis\nbaseballLogitResults = baseballLogit.fit()\n\nprint(baseballLogitResults.summary2())\n\n# %%\n# Note: The p value for HR's is significant, which means they do predict wins;\n # reject the null and accept the alternative hypothesis\n # For every home run, the odds of winning increases by ~28%... this seems\n # more realistic than the ~66% results in R, but I'm curious about that big\n # difference!\n # Home runs account for ~4% of the variance in predicting a winning score\n \n# %%\n\n# Testing to see if I get different results if I store the IV and DV in their\n # own variables, as lesson dictates\nx = baseball['HR Count']\ny = baseball.winsR\n\nbaseballLogit2 = sm.Logit(y, x)\n\nbaseballLogitResults2 = baseballLogit2.fit()\n\nprint(baseballLogitResults2.summary2())\n\n# %%\n# Note: Same results\n # I had tested this in the prior lesson in this course, but quiz \n # specifically said on this page these variables are required.. which is\n # still not true...", "repo_name": "sh3laughs/Machine-Learning-and-Modeling", "sub_path": "DSO106-L02-Python.py", "file_name": "DSO106-L02-Python.py", "file_ext": "py", "file_size_in_byte": 2128, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "statsmodels.api.Logit", "line_number": 46, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 46, "usage_type": "name"}, {"api_name": "statsmodels.api.Logit", "line_number": 70, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "4930652878", "text": "from oslo_log import log as logging\nfrom oslo_utils import uuidutils\nimport pecan\nfrom pecan import hooks\nfrom pecan import rest\nfrom wsme import types as wtypes\nimport wsmeext.pecan as wsme_pecan\n\nfrom mistral.api import access_control as acl\nfrom mistral.api.controllers.v2 import member\nfrom mistral.api.controllers.v2 import resources\nfrom mistral.api.controllers.v2 import types\nfrom mistral.api.controllers.v2 import validation\nfrom mistral.api.hooks import content_type as ct_hook\nfrom mistral import context\nfrom mistral.db.v2 import api as db_api\nfrom mistral import exceptions as exc\nfrom mistral.lang import parser as spec_parser\nfrom mistral.services import workflows\nfrom mistral.utils import filter_utils\nfrom mistral.utils import rest_utils\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass WorkflowsController(rest.RestController, hooks.HookController):\n # TODO(nmakhotkin): Have a discussion with pecan/WSME folks in order\n # to have requests and response of different content types. Then\n # delete ContentTypeHook.\n __hooks__ = [ct_hook.ContentTypeHook(\"application/json\", ['POST', 'PUT'])]\n\n validate = validation.SpecValidationController(\n spec_parser.get_workflow_list_spec_from_yaml\n )\n\n @pecan.expose()\n def _lookup(self, identifier, sub_resource, *remainder):\n LOG.debug(\n \"Lookup subcontrollers of WorkflowsController, \"\n \"sub_resource: %s, remainder: %s.\",\n sub_resource,\n remainder\n )\n\n if sub_resource == 'members':\n if not uuidutils.is_uuid_like(identifier):\n raise exc.WorkflowException(\n \"Only support UUID as resource identifier in resource \"\n \"sharing feature.\"\n )\n\n # We don't check workflow's existence here, since a user may query\n # members of a workflow, which doesn't belong to him/her.\n return member.MembersController('workflow', identifier), remainder\n\n return super(WorkflowsController, self)._lookup(\n identifier,\n sub_resource,\n *remainder\n )\n\n @rest_utils.wrap_wsme_controller_exception\n @wsme_pecan.wsexpose(resources.Workflow, wtypes.text, wtypes.text,\n types.uniquelist)\n def get(self, identifier, namespace='', fields=''):\n \"\"\"Return the named workflow.\n\n :param identifier: Name or UUID of the workflow to retrieve.\n :param namespace: Optional. Namespace of the workflow to retrieve.\n :param fields: Optional. A specified list of fields of the resource to\n be returned. 'id' will be included automatically in\n fields if it's not provided.\n \"\"\"\n acl.enforce('workflows:get', context.ctx())\n\n LOG.debug(\"Fetch workflow [identifier=%s]\", identifier)\n\n if fields and 'id' not in fields:\n fields.insert(0, 'id')\n\n # Use retries to prevent possible failures.\n r = rest_utils.create_db_retry_object()\n db_model = r.call(\n db_api.get_workflow_definition,\n identifier,\n namespace=namespace,\n fields=fields,\n )\n\n if fields:\n return resources.Workflow.from_tuples(zip(fields, db_model))\n return resources.Workflow.from_db_model(db_model, fields=fields)\n\n @rest_utils.wrap_pecan_controller_exception\n @pecan.expose(content_type=\"text/plain\")\n def put(self, identifier=None, namespace=''):\n \"\"\"Update one or more workflows.\n\n :param identifier: Optional. If provided, it's UUID of a workflow.\n Only one workflow can be updated with identifier param.\n :param namespace: Optional. If provided, it's the namespace of the\n workflow/workflows. Currently, namespace cannot be\n changed.\n\n The text is allowed to have definitions of multiple workflows. In such\n case, they all will be updated.\n \"\"\"\n acl.enforce('workflows:update', context.ctx())\n\n # NOTE(rakhmerov): We can't use normal method arguments to access\n # request data because it will break dynamic sub controller lookup\n # functionality (see _lookup() above) so we have to get the data\n # directly from the request object.\n\n definition = pecan.request.text\n scope = pecan.request.GET.get('scope', 'private')\n\n # If \"skip_validation\" is present in the query string parameters\n # then workflow language validation will be disabled.\n skip_validation = 'skip_validation' in pecan.request.GET\n\n resources.Workflow.validate_scope(scope)\n\n if scope == 'public':\n acl.enforce('workflows:publicize', context.ctx())\n\n LOG.debug(\"Update workflow(s) [definition=%s]\", definition)\n\n db_wfs = rest_utils.rest_retry_on_db_error(workflows.update_workflows)(\n definition,\n scope=scope,\n identifier=identifier,\n namespace=namespace,\n validate=not skip_validation\n )\n\n workflow_list = [\n resources.Workflow.from_db_model(db_wf) for db_wf in db_wfs\n ]\n\n return (workflow_list[0].to_json() if identifier\n else resources.Workflows(workflows=workflow_list).to_json())\n\n @rest_utils.wrap_pecan_controller_exception\n @pecan.expose(content_type=\"text/plain\")\n def post(self, namespace=''):\n \"\"\"Create a new workflow.\n\n :param namespace: Optional. The namespace to create the workflow\n in. Workflows with the same name can be added to a given\n project if they are in two different namespaces.\n\n The text is allowed to have definitions of multiple workflows.\n In such case, they all will be created.\n \"\"\"\n acl.enforce('workflows:create', context.ctx())\n\n # NOTE(rakhmerov): We can't use normal method arguments to access\n # request data because it will break dynamic sub controller lookup\n # functionality (see _lookup() above) so we have to get the data\n # directly from the request object.\n\n definition = pecan.request.text\n scope = pecan.request.GET.get('scope', 'private')\n\n # If \"skip_validation\" is present in the query string parameters\n # then workflow language validation will be disabled.\n skip_validation = 'skip_validation' in pecan.request.GET\n\n pecan.response.status = 201\n\n resources.Workflow.validate_scope(scope)\n\n if scope == 'public':\n acl.enforce('workflows:publicize', context.ctx())\n\n LOG.debug(\"Create workflow(s) [definition=%s]\", definition)\n\n db_wfs = rest_utils.rest_retry_on_db_error(workflows.create_workflows)(\n definition,\n scope=scope,\n namespace=namespace,\n validate=not skip_validation\n )\n\n workflow_list = [\n resources.Workflow.from_db_model(db_wf) for db_wf in db_wfs\n ]\n\n return resources.Workflows(workflows=workflow_list).to_json()\n\n @rest_utils.wrap_wsme_controller_exception\n @wsme_pecan.wsexpose(None, wtypes.text, wtypes.text, status_code=204)\n def delete(self, identifier, namespace=''):\n \"\"\"Delete a workflow.\n\n :param identifier: Name or ID of workflow to delete.\n :param namespace: Optional. Namespace of the workflow to delete.\n \"\"\"\n acl.enforce('workflows:delete', context.ctx())\n\n LOG.debug(\"Delete workflow [identifier=%s, namespace=%s]\",\n identifier, namespace)\n\n @rest_utils.rest_retry_on_db_error\n def _delete_workflow_definition():\n with db_api.transaction():\n db_api.delete_workflow_definition(identifier, namespace)\n\n _delete_workflow_definition()\n\n @rest_utils.wrap_wsme_controller_exception\n @wsme_pecan.wsexpose(resources.Workflows, types.uuid, int,\n types.uniquelist, types.list, types.uniquelist,\n wtypes.text, wtypes.text, wtypes.text, wtypes.text,\n resources.SCOPE_TYPES, types.uuid, wtypes.text,\n wtypes.text, bool, wtypes.text)\n def get_all(self, marker=None, limit=None, sort_keys='created_at',\n sort_dirs='asc', fields='', name=None, input=None,\n definition=None, tags=None, scope=None,\n project_id=None, created_at=None, updated_at=None,\n all_projects=False, namespace=None):\n \"\"\"Return a list of workflows.\n\n :param marker: Optional. Pagination marker for large data sets.\n :param limit: Optional. Maximum number of resources to return in a\n single result. Default value is None for backward\n compatibility.\n :param sort_keys: Optional. Columns to sort results by.\n Default: created_at.\n :param sort_dirs: Optional. Directions to sort corresponding to\n sort_keys, \"asc\" or \"desc\" can be chosen.\n Default: asc.\n :param fields: Optional. A specified list of fields of the resource to\n be returned. 'id' will be included automatically in\n fields if it's not provided, since it will be used when\n constructing 'next' link.\n :param name: Optional. Keep only resources with a specific name.\n :param namespace: Optional. Keep only resources with a specific\n namespace\n :param input: Optional. Keep only resources with a specific input.\n :param definition: Optional. Keep only resources with a specific\n definition.\n :param tags: Optional. Keep only resources containing specific tags.\n :param scope: Optional. Keep only resources with a specific scope.\n :param project_id: Optional. The same as the requester project_id\n or different if the scope is public.\n :param created_at: Optional. Keep only resources created at a specific\n time and date.\n :param updated_at: Optional. Keep only resources with specific latest\n update time and date.\n :param all_projects: Optional. Get resources of all projects.\n \"\"\"\n acl.enforce('workflows:list', context.ctx())\n\n if all_projects:\n acl.enforce('workflows:list:all_projects', context.ctx())\n\n filters = filter_utils.create_filters_from_request_params(\n created_at=created_at,\n name=name,\n scope=scope,\n tags=tags,\n updated_at=updated_at,\n input=input,\n definition=definition,\n project_id=project_id,\n namespace=namespace\n )\n\n LOG.debug(\"Fetch workflows. marker=%s, limit=%s, sort_keys=%s, \"\n \"sort_dirs=%s, fields=%s, filters=%s, all_projects=%s\",\n marker, limit, sort_keys, sort_dirs, fields, filters,\n all_projects)\n\n return rest_utils.get_all(\n resources.Workflows,\n resources.Workflow,\n db_api.get_workflow_definitions,\n db_api.get_workflow_definition_by_id,\n marker=marker,\n limit=limit,\n sort_keys=sort_keys,\n sort_dirs=sort_dirs,\n fields=fields,\n all_projects=all_projects,\n **filters\n )\n", "repo_name": "openstack/mistral", "sub_path": "mistral/api/controllers/v2/workflow.py", "file_name": "workflow.py", "file_ext": "py", "file_size_in_byte": 11591, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 252, "dataset": "github-code", "pt": "47", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 24, "usage_type": "name"}, {"api_name": "pecan.rest.RestController", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pecan.rest", "line_number": 27, "usage_type": "name"}, {"api_name": "pecan.hooks.HookController", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pecan.hooks", "line_number": 27, "usage_type": "name"}, {"api_name": "mistral.api.hooks.content_type.ContentTypeHook", "line_number": 31, "usage_type": "call"}, {"api_name": "mistral.api.hooks.content_type", "line_number": 31, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.validation.SpecValidationController", "line_number": 33, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.validation", "line_number": 33, "usage_type": "name"}, {"api_name": "mistral.lang.parser.get_workflow_list_spec_from_yaml", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mistral.lang.parser", "line_number": 34, "usage_type": "name"}, {"api_name": "oslo_utils.uuidutils.is_uuid_like", "line_number": 47, "usage_type": "call"}, {"api_name": "oslo_utils.uuidutils", "line_number": 47, "usage_type": "name"}, {"api_name": "mistral.exceptions.WorkflowException", "line_number": 48, "usage_type": "call"}, {"api_name": "mistral.exceptions", "line_number": 48, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.member.MembersController", "line_number": 55, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.member", "line_number": 55, "usage_type": "name"}, {"api_name": "pecan.expose", "line_number": 37, "usage_type": "call"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 75, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 75, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 75, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 75, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.create_db_retry_object", "line_number": 83, "usage_type": "call"}, {"api_name": "mistral.utils.rest_utils", "line_number": 83, "usage_type": "name"}, {"api_name": "mistral.db.v2.api.get_workflow_definition", "line_number": 85, "usage_type": "attribute"}, {"api_name": "mistral.db.v2.api", "line_number": 85, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow.from_tuples", "line_number": 92, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 92, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 92, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow.from_db_model", "line_number": 93, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 93, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 93, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.wrap_wsme_controller_exception", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mistral.utils.rest_utils", "line_number": 63, "usage_type": "name"}, {"api_name": "wsmeext.pecan.wsexpose", "line_number": 64, "usage_type": "call"}, {"api_name": "wsmeext.pecan", "line_number": 64, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 64, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 64, "usage_type": "name"}, {"api_name": "wsme.types.text", "line_number": 64, "usage_type": "attribute"}, {"api_name": "wsme.types", "line_number": 64, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.types.uniquelist", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.types", "line_number": 65, "usage_type": "name"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 109, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 109, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 109, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 109, "usage_type": "name"}, {"api_name": "pecan.request", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pecan.request.GET.get", "line_number": 117, "usage_type": "call"}, {"api_name": "pecan.request", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pecan.request", "line_number": 121, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow.validate_scope", "line_number": 123, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 123, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 123, "usage_type": "name"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 126, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 126, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 126, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 126, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.rest_retry_on_db_error", "line_number": 130, "usage_type": "call"}, {"api_name": "mistral.utils.rest_utils", "line_number": 130, "usage_type": "name"}, {"api_name": "mistral.services.workflows.update_workflows", "line_number": 130, "usage_type": "attribute"}, {"api_name": "mistral.services.workflows", "line_number": 130, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow.from_db_model", "line_number": 139, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 139, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 139, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflows", "line_number": 143, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 143, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.wrap_pecan_controller_exception", "line_number": 95, "usage_type": "attribute"}, {"api_name": "mistral.utils.rest_utils", "line_number": 95, "usage_type": "name"}, {"api_name": "pecan.expose", "line_number": 96, "usage_type": "call"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 157, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 157, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 157, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 157, "usage_type": "name"}, {"api_name": "pecan.request", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pecan.request.GET.get", "line_number": 165, "usage_type": "call"}, {"api_name": "pecan.request", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pecan.request", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pecan.response", "line_number": 171, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow.validate_scope", "line_number": 173, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 173, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 173, "usage_type": "name"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 176, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 176, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 176, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 176, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.rest_retry_on_db_error", "line_number": 180, "usage_type": "call"}, {"api_name": "mistral.utils.rest_utils", "line_number": 180, "usage_type": "name"}, {"api_name": "mistral.services.workflows.create_workflows", "line_number": 180, "usage_type": "attribute"}, {"api_name": "mistral.services.workflows", "line_number": 180, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow.from_db_model", "line_number": 188, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 188, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 188, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflows", "line_number": 191, "usage_type": "call"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 191, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.wrap_pecan_controller_exception", "line_number": 145, "usage_type": "attribute"}, {"api_name": "mistral.utils.rest_utils", "line_number": 145, "usage_type": "name"}, {"api_name": "pecan.expose", "line_number": 146, "usage_type": "call"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 201, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 201, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 201, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 201, "usage_type": "name"}, {"api_name": "mistral.db.v2.api.transaction", "line_number": 208, "usage_type": "call"}, {"api_name": "mistral.db.v2.api", "line_number": 208, "usage_type": "name"}, {"api_name": "mistral.db.v2.api.delete_workflow_definition", "line_number": 209, "usage_type": "call"}, {"api_name": "mistral.db.v2.api", "line_number": 209, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.rest_retry_on_db_error", "line_number": 206, "usage_type": "attribute"}, {"api_name": "mistral.utils.rest_utils", "line_number": 206, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.wrap_wsme_controller_exception", "line_number": 193, "usage_type": "attribute"}, {"api_name": "mistral.utils.rest_utils", "line_number": 193, "usage_type": "name"}, {"api_name": "wsmeext.pecan.wsexpose", "line_number": 194, "usage_type": "call"}, {"api_name": "wsmeext.pecan", "line_number": 194, "usage_type": "name"}, {"api_name": "wsme.types.text", "line_number": 194, "usage_type": "attribute"}, {"api_name": "wsme.types", "line_number": 194, "usage_type": "name"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 255, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 255, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 255, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 255, "usage_type": "name"}, {"api_name": "mistral.api.access_control.enforce", "line_number": 258, "usage_type": "call"}, {"api_name": "mistral.api.access_control", "line_number": 258, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 258, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 258, "usage_type": "name"}, {"api_name": "mistral.utils.filter_utils.create_filters_from_request_params", "line_number": 260, "usage_type": "call"}, {"api_name": "mistral.utils.filter_utils", "line_number": 260, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.get_all", "line_number": 277, "usage_type": "call"}, {"api_name": "mistral.utils.rest_utils", "line_number": 277, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflows", "line_number": 278, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 278, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflow", "line_number": 279, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 279, "usage_type": "name"}, {"api_name": "mistral.db.v2.api.get_workflow_definitions", "line_number": 280, "usage_type": "attribute"}, {"api_name": "mistral.db.v2.api", "line_number": 280, "usage_type": "name"}, {"api_name": "mistral.db.v2.api.get_workflow_definition_by_id", "line_number": 281, "usage_type": "attribute"}, {"api_name": "mistral.db.v2.api", "line_number": 281, "usage_type": "name"}, {"api_name": "mistral.utils.rest_utils.wrap_wsme_controller_exception", "line_number": 213, "usage_type": "attribute"}, {"api_name": "mistral.utils.rest_utils", "line_number": 213, "usage_type": "name"}, {"api_name": "wsmeext.pecan.wsexpose", "line_number": 214, "usage_type": "call"}, {"api_name": "wsmeext.pecan", "line_number": 214, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.Workflows", "line_number": 214, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 214, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.types.uuid", "line_number": 214, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.types", "line_number": 214, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.types.uniquelist", "line_number": 215, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.types", "line_number": 215, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.types.list", "line_number": 215, "usage_type": "attribute"}, {"api_name": "wsme.types.text", "line_number": 216, "usage_type": "attribute"}, {"api_name": "wsme.types", "line_number": 216, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.resources.SCOPE_TYPES", "line_number": 217, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.resources", "line_number": 217, "usage_type": "name"}, {"api_name": "mistral.api.controllers.v2.types.uuid", "line_number": 217, "usage_type": "attribute"}, {"api_name": "mistral.api.controllers.v2.types", "line_number": 217, "usage_type": "name"}, {"api_name": "wsme.types.text", "line_number": 217, "usage_type": "attribute"}, {"api_name": "wsme.types", "line_number": 217, "usage_type": "name"}, {"api_name": "wsme.types.text", "line_number": 218, "usage_type": "attribute"}, {"api_name": "wsme.types", "line_number": 218, "usage_type": "name"}]} +{"seq_id": "39921255660", "text": "from __future__ import print_function\nfrom selenium import webdriver\nfrom requests import get, post\nfrom datetime import datetime\nfrom os.path import exists\nfrom time import sleep\n\nfrom selenium.common.exceptions import NoSuchElementException\n\nYEAR_CSS_SELECTOR = 'body > div > div.datepicker-years > table > tbody > tr > td > span.year'\nMONTH_CSS_SELECTOR = 'body > div > div.datepicker-months > table > tbody > tr > td > span'\nDATE_CSS_SELECTOR = 'body > div > div.datepicker-days > table > tbody > tr > td.day'\nCAPTCHA_SOLVER_API_KEY = '69e9ac9b83b9fa122a2e6843b6a7a922'\n\n\ndef captcha_solver(browser):\n \"\"\"\n Function should be invouced on the page with the recaptcha challenge \n :return: string with solution\n \"\"\"\n # Get the captcha img source\n recaptcha_image_url = browser.find_element_by_css_selector('#recaptcha_challenge_image').get_attribute('src')\n # download that img into the file captcha_image.jpeg\n response = get(recaptcha_image_url)\n with open('captcha_image.jpeg', 'w') as image_file:\n image_file.write(response.content)\n # solve the captcha with 2captcha.com API\n captcha = CaptchaUpload(key=CAPTCHA_SOLVER_API_KEY)\n return captcha.solve('captcha_image.jpeg')\n\n\ndef gnib_auto_submission():\n # TODO: implement both submissions.\n return True\n\n\ndef visa_auto_submission(date, slot_id, auto_submission_entity):\n \"\"\"\n Register visa appointment slot for the give auto_submission_entity\n :param date:[str] date of the appointment which would be registered by this func \n :param slot_id: [str] id of the specific time slot for registration\n :param auto_submission_entity: \n :return: reference_no, apt_date - tuple with to strings confirming the registration. \n If registration is not successful returns - False\n \"\"\"\n print ('in the visa_autosummission func')\n print (str(auto_submission_entity))\n try:\n browser = webdriver.PhantomJS()\n browser.get('https://reentryvisa.inis.gov.ie/website/INISOA/IOA.nsf/AppointmentSelection?OpenForm')\n # Click the agree btn\n browser.find_element_by_css_selector('#btCom').click()\n browser.find_element_by_css_selector('#GivenName').send_keys(auto_submission_entity['GivenName'])\n browser.find_element_by_css_selector('#Surname').send_keys(auto_submission_entity['SurName'])\n # Set Date of Birth:\n browser.execute_script('$(\"#DOB\").val(\"{}\")'.format(auto_submission_entity['DOB']))\n browser.find_element_by_css_selector('#Email').send_keys(auto_submission_entity['Email'])\n browser.find_element_by_css_selector('#EmailConfirm').send_keys(auto_submission_entity['Email'])\n # based on the FamAppYN answer figure out what should be passed ot the AppointmentType field\n browser.execute_script('$(\"#AppointType\").val(\"{}\")'.format(\n 'Individual' if auto_submission_entity['FamAppYN'] == 'No' else 'Family'))\n # Number of Applicants\n browser.execute_script('$(\"#AppsNum\").val(\"{}\")'.format(auto_submission_entity['FamAppNo']))\n browser.find_element_by_css_selector('#PassportNo').send_keys(auto_submission_entity['PassportNo'])\n browser.find_element_by_css_selector('#GNIBNo').send_keys(auto_submission_entity['GNIBNo'])\n # Nationality\n browser.execute_script('$(\"#Nationality\").val(\"{}\")'.format(auto_submission_entity['Nationality']))\n # Type of visa\n browser.execute_script('$(\"#VisaType\").val(\"{}\")'.format(auto_submission_entity['multi_or_single']))\n # Set the appointment date\n browser.execute_script('$(\"#Appdate\").val(\"{}\")'.format(date))\n # Call JS function bookit() passing the appointment ID as a parameter to invoke the booking.\n browser.execute_script('bookit(\"{}\")'.format(slot_id))\n # Solve captcha\n captcha_solution = captcha_solver(browser)\n # input the solution into the field\n browser.execute_script('$(\"#recaptcha_response_field\").val(\"{}\")'.format(captcha_solution))\n # Submit the form\n browser.find_element_by_css_selector('#SubmitButton_1_1_1').click()\n # wait for 8 sec to load:\n browser.implicitly_wait(10)\n\n # submission successful:\n try:\n browser.find_element_by_css_selector('#AppConfirmed')\n reference_no = browser.find_element_by_css_selector(\n '#AppConfirmed>div:nth-child(1)>h3:nth-child(1)').text[14:] # slice the ID reference no from the str\n apt_date = browser.find_element_by_css_selector('#AppConfirmed>div:nth-child(1)>h3:nth-child(2)').text\n return {'reference_no': reference_no,\n 'appointment_date': apt_date}\n except NoSuchElementException as e:\n # if there is an error on the page\n browser.save_screenshot('error_screenshot_{}.png'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))\n print('Error when submitting: \\n')\n print (e)\n return False\n except Exception as e:\n # something else is wrong:\n browser.save_screenshot('error_screenshot_{}.png'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))\n print('Error when submitting: \\n' + e)\n\n return False\n finally:\n print ('closing browser')\n browser.close()\n\n\nclass CaptchaUpload:\n def __init__(self, key, log=None, waittime=None):\n self.settings = {\"url_request\": \"http://2captcha.com/in.php\",\n \"url_response\": \"http://2captcha.com/res.php\",\n \"key\": key}\n if log:\n self.log = log\n self.logenabled = True\n else:\n self.logenabled = False\n\n if waittime:\n self.waittime = waittime\n else:\n self.waittime = 5\n\n def getbalance(self):\n \"\"\"\n This request need for get balance\n :return: OK | 1 ERROR!\n \"\"\"\n fullurl = \"%s?action=getbalance&key=%s\" % (\n self.settings['url_response'], self.settings['key'])\n request = get(fullurl)\n if \".\" in request.text:\n if self.logenabled:\n self.log.info(\"[2CaptchaUpload] Balance: %s\" % request.text)\n return request.text\n elif request.text == \"ERROR_KEY_DOES_NOT_EXIST\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] You used the wrong key in the query\")\n return 1\n elif request.text == \"ERROR_WRONG_ID_FORMAT\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] Wrong format ID CAPTCHA. \"\n \"ID must contain only numbers\")\n return 1\n\n def getresult(self, upload_id):\n \"\"\"\n This function return the captcha solved\n :param upload_id: id captcha returned by upload\n :return: OK | 1 ERROR!\n \"\"\"\n if self.logenabled:\n self.log.info(\"[2CaptchaUpload] Wait %s second..\" % self.waittime)\n sleep(self.waittime)\n fullurl = \"%s?key=%s&action=get&id=%s\" % (self.settings['url_response'],\n self.settings['key'], upload_id)\n if self.logenabled:\n self.log.info(\"[2CaptchaUpload] Get Captcha solved with id %s\"\n % upload_id)\n request = get(fullurl)\n if request.text.split('|')[0] == \"OK\":\n return request.text.split('|')[1]\n elif request.text == \"CAPCHA_NOT_READY\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] CAPTCHA is being solved, \"\n \"repeat the request several seconds later, wait \"\n \"another %s seconds\" % self.waittime)\n return self.getresult(upload_id)\n elif request.text == \"ERROR_KEY_DOES_NOT_EXIST\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] You used the wrong key in \"\n \"the query\")\n return 1\n elif request.text == \"ERROR_WRONG_ID_FORMAT\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] Wrong format ID CAPTCHA. \"\n \"ID must contain only numbers\")\n return 1\n elif request.text == \"ERROR_CAPTCHA_UNSOLVABLE\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] Captcha could not solve \"\n \"three different employee. Funds for this \"\n \"captcha not\")\n return 1\n\n def solve(self, pathfile):\n \"\"\"\n This function upload read, upload and is the wrapper for solve\n the captcha\n :param pathfile: path of image\n :return: OK | 1 ERROR!\n \"\"\"\n if exists(pathfile):\n files = {'file': open(pathfile, 'rb')}\n payload = {'key': self.settings['key'],\n 'method': 'post',\n 'regsense': '1',\n 'phrase': '1'}\n if self.logenabled:\n self.log.info(\"[2CaptchaUpload] Uploading to 2Captcha.com..\")\n request = post(self.settings['url_request'], files=files,\n data=payload)\n if request.ok:\n if request.text.split('|')[0] == \"OK\":\n if self.logenabled:\n self.log.info(\"[2CaptchaUpload] Upload Ok\")\n return self.getresult(request.text.split('|')[1])\n elif request.text == \"ERROR_WRONG_USER_KEY\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] Wrong 'key' parameter\"\n \" format, it should contain 32 symbols\")\n return 1\n elif request.text == \"ERROR_KEY_DOES_NOT_EXIST\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] The 'key' doesn't \"\n \"exist\")\n return 1\n elif request.text == \"ERROR_ZERO_BALANCE\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] You don't have money \"\n \"on your account\")\n return 1\n elif request.text == \"ERROR_NO_SLOT_AVAILABLE\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] The current bid is \"\n \"higher than the maximum bid set for \"\n \"your account.\")\n return 1\n elif request.text == \"ERROR_ZERO_CAPTCHA_FILESIZE\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] CAPTCHA size is less\"\n \" than 100 bites\")\n return 1\n elif request.text == \"ERROR_TOO_BIG_CAPTCHA_FILESIZE\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] CAPTCHA size is more\"\n \" than 100 Kbites\")\n return 1\n elif request.text == \"ERROR_WRONG_FILE_EXTENSION\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] The CAPTCHA has a \"\n \"wrong extension. Possible extensions \"\n \"are: jpg,jpeg,gif,png\")\n return 1\n elif request.text == \"ERROR_IMAGE_TYPE_NOT_SUPPORTED\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] The server cannot \"\n \"recognize the CAPTCHA file type.\")\n return 1\n elif request.text == \"ERROR_IP_NOT_ALLOWED\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] The request has sent \"\n \"from the IP that is not on the list of\"\n \" your IPs. Check the list of your IPs \"\n \"in the system.\")\n return 1\n elif request.text == \"IP_BANNED\":\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] The IP address you're\"\n \" trying to access our server with is \"\n \"banned due to many frequent attempts \"\n \"to access the server using wrong \"\n \"authorization keys. To lift the ban, \"\n \"please, contact our support team via \"\n \"email: support@2captcha.com\")\n return 1\n\n else:\n if self.logenabled:\n self.log.error(\"[2CaptchaUpload] File %s not exists\" % pathfile)\n return 1\n\n\n\n\n", "repo_name": "waffle-iron/visa-gnib-prod-kubectl", "sub_path": "app/src/selenium_phantomjs.py", "file_name": "selenium_phantomjs.py", "file_ext": "py", "file_size_in_byte": 13194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.PhantomJS", "line_number": 49, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 91, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 131, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 193, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "39491802502", "text": "import os\nimport requests\nfrom db.db import Database\n\nclass rebroproxy:\n def __init__(self,):\n self.db = Database()\n url = 'http://rebro.weebly.com/uploads/2/7/3/7/27378307/rebroproxy-all-113326062014.txt'\n r = requests.get(url,timeout=10)\n ips = list()\n for raw in r.text.split():\n ips.append(raw.split(':')[0])\n self.db.insert(ips)\n\nif __name__ == '__main__':\n rebroproxy()\n", "repo_name": "mthbernardes/ipChecker", "sub_path": "updater/plugins/rebroproxy.py", "file_name": "rebroproxy.py", "file_ext": "py", "file_size_in_byte": 435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 57, "dataset": "github-code", "pt": "51", "api": [{"api_name": "db.db.Database", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "36420693941", "text": "import numpy as np\nimport os\nimport tensorflow as tf\nimport py21cmfast as p21\nimport logging\n\n# Logging Config\nLOGGING_CONFIG = {}\nlogging_format = \"[%(asctime)s] %(process)d-%(levelname)s \"\nlogging_format += \"%(module)s::%(funcName)s():l%(lineno)d: \"\nlogging_format += \"%(message)s\"\nlogging.basicConfig(format=logging_format, level=logging.INFO)\nlog = logging.getLogger(\"21cmEMU\")\n\nclass p21cmEMU:\n r\"\"\"\n This class allows the use to load an emulator and use it to obtain 21cmFAST summaries.\n \"\"\"\n def __init__(self,\n io_options : dict = None,\n url : str = None,\n version : str = None\n ):\n \"\"\"\n Parameters\n ----------\n io_options : dict, optional\n Dict containing 'store' and 'cache_dir' keys with the keys of summaries to store and folder path\n where to store them, respectively. This must be provided only if you want to save the emulator output \n at each evaluation.\n \n \n \"\"\"\n log.debug('Init emulator...')\n try:\n emu = tf.keras.models.load_model('21cmEMU', compile = False)\n except OSError as e:\n log.warning('Emulator not found. Downloading...')\n from .get_emulator import Download21cmEMU\n status = Download21cmEMU(url = url, version = version).download_and_extract()\n emu = tf.keras.models.load_model('21cmEMU', compile = False)\n \n\n self.model = emu\n self.io_options = io_options\n\n all_emulator_numbers = np.load('emulator_constants.npz')\n\n self.zs = all_emulator_numbers['zs']\n self.limits = all_emulator_numbers['limits']\n self.zs_cut = self.zs[:60]\n self.ks_cut = all_emulator_numbers['ks'][1:-3]\n self.PS_mean = all_emulator_numbers['PS_mean']\n self.PS_std = all_emulator_numbers['PS_std']\n self.Tb_mean = all_emulator_numbers['Tb_mean']\n self.Tb_std = all_emulator_numbers['Tb_std']\n self.Ts_mean = all_emulator_numbers['Ts_mean']\n self.Ts_std = all_emulator_numbers['Ts_std']\n self.uv_lf_zs = np.array([6,7,8,10])\n\n self.PS_err = all_emulator_numbers[\"PS_err\"]\n self.Tb_err = all_emulator_numbers[\"Tb_err\"]\n self.Ts_err = all_emulator_numbers['Ts_err']\n self.xHI_err = all_emulator_numbers['xHI_err']\n self.tau_err = all_emulator_numbers['tau_err'] \n\n\n def predict(self, astro_params, cosmo_params = None, user_params = None, flag_options = None):\n r\"\"\"\n Call the emulator, evaluate it at the given parameters, restore dimensions.\n \n Parameters\n ----------\n astro_params : p21.AstroParams or np.ndarray or dict\n An array with the nine astro_params input all $\\in [0,1]$ OR in the p21.AstroParams input units.\n Dicts and p21.AstroParams are also accepted formats.\n Arrays of only dicts or AstroParams are accepted as well (for batch evaluation).\n \"\"\"\n self.check_params(cosmo_params, user_params, flag_options)\n astro_params, theta = self.format_theta(astro_params)\n \n emu_pred = self.model.predict(theta, verbose = False)\n\n Tb_pred_normed = emu_pred[:,:84] #First 84 numbers of emu prediction are Tb\n xHI_pred = emu_pred[:,84:84*2] # Next 84 numbers are xHI\n Ts_pred_normed = emu_pred[:,2*84:84*3] # Next 84 numbers are Ts\n Ts_undefined_pred = emu_pred[:,84*3] # Right after Ts is the redshift at which Ts becomes undefined\n PS_pred_normed = emu_pred[:,84*3+1:].reshape((theta.shape[0], 60,12)) # The rest is PS\n\n # Set the xHI < z(Ts undefined) to 0\n xHI_pred_fix = np.zeros(xHI_pred.shape)\n\n tau = np.zeros(theta.shape[0])\n uvlfs = np.zeros((theta.shape[0], 3, len(self.uv_lf_zs),100))\n for i in range(theta.shape[0]):\n zbin = np.argmin(abs(self.zs - Ts_undefined_pred[i]))\n if xHI_pred[i,zbin] < 1e-1:\n xHI_pred_fix[i,zbin:] = xHI_pred[i,zbin:]\n else:\n xHI_pred_fix[i,:] = xHI_pred[i,:]\n # Use py21cmFAST to analytically calculate UV LF and $\\tau_e$\n\n tau[i] = p21.wrapper.compute_tau(redshifts = self.zs, global_xHI = xHI_pred_fix[i,:], \n cosmo_params = self.cosmo_params, user_params = self.user_params)\n \n uvlfs[i,...] = np.array(p21.wrapper.compute_luminosity_function(redshifts = self.uv_lf_zs, \n astro_params=astro_params[i],\n cosmo_params = self.cosmo_params, \n user_params = self.user_params, \n flag_options = self.flag_options))\n if np.sum(np.isnan(uvlfs[i,-1,:,:])) > 200:\n log.warning('UV LF computation failed: mostly NaNs.')\n\n # Restore dimensions\n PS_pred = self.PS_mean + self.PS_std * PS_pred_normed # log10(PS[mK^2])\n Ts_pred = self.Ts_mean + self.Ts_std * Ts_pred_normed # log10(Ts[mK])\n Tb_pred = self.Tb_mean + self.Tb_std * Tb_pred_normed # Tb[mK]\n\n\n if theta.shape[0] == 1:\n\n summaries = {'delta': 10**PS_pred[0,...], 'k': self.ks_cut, 'brightness_temp': Tb_pred[0,...], \n 'spin_temp': 10**Ts_pred[0,...], 'tau_e': tau[0], 'Muv': uvlfs[0,0,:,:], 'lfunc': uvlfs[0,-1,:,:], 'uv_lfs_redshifts':self.uv_lf_zs,\n 'ps_redshifts':self.zs_cut, 'redshifts': self.zs, 'xHI': xHI_pred_fix[0,...]}\n else:\n summaries = {'delta': 10**PS_pred, 'k': self.ks_cut, 'brightness_temp': Tb_pred, \n 'spin_temp': 10**Ts_pred, 'tau_e': tau, 'Muv': uvlfs[:,0,:,:], 'lfunc': uvlfs[:,-1,:,:], 'uv_lfs_redshifts':self.uv_lf_zs,\n 'ps_redshifts':self.zs_cut, 'redshifts': self.zs, 'xHI': xHI_pred_fix}\n errors = self.get_errors(summaries, theta)\n # Put the summaries and errors in one single dict\n output = summaries.copy()\n for k in errors.keys():\n output[k] = errors[k]\n\n if self.io_options is not None and self.io_options['cache_dir'] is not None and len(self.io_options['store']) > 0:\n if isinstance(astro_params, p21.AstroParams) or isinstance(astro_params, dict):\n ap = astro_params.defining_dict\n fname = '_'.join([str(np.round(ap[i], 5)) for i in ap.keys()])\n else:\n fname = '_'.join([str(np.round(astro_params[i], 5)) for i in range(len(theta))])\n to_save = {}\n for i in self.io_options['store']:\n to_save[i] = output[i]\n np.savez(fname, to_save)\n\n return output\n\n def get_errors(self, summaries : dict, theta : np.ndarray = None):\n r\"\"\"\n Calculate the emulator error on its outputs.\n Returns the mean error on the test set (i.e. independent of theta).\n \n Parameters\n ----------\n summaries : dict\n Dict containing the emulator predictions, defined in p21cmEMU.predict\n theta : dict\n Dict containing the normalized parameters, also defined in p21cmEMU.predict\n \"\"\"\n\n # For now, we return the mean emulator error (obtained from the test set) for each summary.\n # Some errors are fractional => actual error = fractional error * value\n output = {'delta_err': self.PS_err/100. * summaries['delta'], 'brightness_temp_err': self.Tb_err, 'xHI_err': self.xHI_err,\n 'spin_temp_err': self.Ts_err, 'tau_e_err': self.tau_err/100. * summaries['tau_e']}\n return output\n \n def format_theta(self, astro_params):\n astro_param_keys = ['F_STAR10','ALPHA_STAR','F_ESC10','ALPHA_ESC','M_TURN', \n 't_STAR','L_X','NU_X_THRESH','X_RAY_SPEC_INDEX']\n is_astroparams = False\n if isinstance(astro_params, p21.AstroParams):\n is_astroparams = True\n theta = np.array([astro_params.defining_dict[key] for key in astro_param_keys])\n elif isinstance(astro_params, dict):\n theta = np.array([astro_params[key] for key in astro_param_keys])\n elif type(astro_params) == np.ndarray:\n if len(astro_params.shape) > 1 and astro_params.shape[0] > 1:\n #If we supply an array of p21.AstroParams / dict\n theta = np.zeros(astro_params.shape)\n if isinstance(astro_params[0], p21.AstroParams):\n is_astroparams = True\n for i in range(astro_params.shape[0]):\n theta[i,:] = np.array([astro_params[i].defining_dict[key] for key in astro_param_keys])\n elif isinstance(astro_params, dict):\n for i in range(astro_params.shape[0]):\n theta = np.array([astro_params[key] for key in astro_param_keys])\n elif type(astro_params[0]) == np.ndarray:\n theta = astro_params.copy()\n else:\n raise TypeError('theta is in the wrong format. Should be AstroParams object, dict of astro params or nine astrophysical parameters in same order as astro_param_keys. It can also be an array of either AstroParams objects, dicts, or arrays (not mixed together).')\n else:\n theta = astro_params.copy()\n else:\n raise TypeError('theta is in the wrong format. Should be AstroParams object, dict or nine astrophysical parameters in same order as astro_param_keys. It can also be an array of either AstroParams objects, dicts, or arrays (not mixed together).')\n if len(theta.shape) == 1:\n theta = theta.reshape([1,-1])\n normed = True\n # Check that theta is normalized, if not, normalise it.\n if is_astroparams or max(theta.ravel()) > 1 or min(theta.ravel()) < 0:\n normed = False # to indicate that input params was not normalised \n theta[:, [0,2,4,6]] = np.log10(theta[:, [0,2,4,6]]) \n theta[:, 7] /= 1000 \n theta -= self.limits[:, 0] \n theta /= (self.limits[:, 1] - self.limits[:, 0])\n # Restore dimensions i.e. undo the limits\n all_astro_params = self.undo_normalization(theta)\n \n return all_astro_params, theta\n else:\n if normed == True:\n return self.undo_normalization(theta), theta\n else:\n return np.array([astro_params]), theta\n \n def undo_normalization(self, theta):\n theta_wdims = theta.copy()\n theta_wdims *= (self.limits[:, 1] - self.limits[:, 0])\n theta_wdims += self.limits[:, 0] \n theta_wdims[:, 7] *= 1000 \n all_astro_params = []\n for i in range(theta.shape[0]):\n all_astro_params.append({'F_STAR10':theta_wdims[i,0], 'ALPHA_STAR': theta_wdims[i,1], 'F_ESC10': theta_wdims[i,2], \n 'ALPHA_ESC': theta_wdims[i,3], 'M_TURN': theta_wdims[i,4], 't_STAR': theta_wdims[i,5], \n 'L_X': theta_wdims[i,6], 'NU_X_THRESH': theta_wdims[i,7], 'X_RAY_SPEC_INDEX':theta_wdims[i,8]})\n return all_astro_params\n\n def check_params(self, cosmo_params, user_params, flag_options):\n training_cosmo_params = dict(SIGMA_8=0.82, hlittle=0.6774, OMm=0.3075, \n OMb=0.0486, POWER_INDEX=0.97)\n if cosmo_params is not None:\n if isinstance(cosmo_params, p21.CosmoParams):\n self.cosmo_params = cosmo_params\n else:\n self.cosmo_params = p21.CosmoParams(cosmo_params)\n \n ## Check that given cosmo params match emulator training data cosmo params\n ## if they do not, raise error and exit\n for key in emu_cosmo_params.keys():\n if self.cosmo_params.defining_dict[key] != training_cosmo_params[key]:\n raise ValueError('Input cosmo_params do not match the emulator cosmo_params. The emulator can only be used with a single set of cosmo params:', training_cosmo_params)\n else:\n self.cosmo_params = p21.CosmoParams(training_cosmo_params)\n \n training_flag_options = {\"USE_HALO_FIELD\": False, \"USE_MINI_HALOS\": False,\n \"USE_MASS_DEPENDENT_ZETA\": True, \"SUBCELL_RSD\": True,\n \"INHOMO_RECO\": True, \"USE_TS_FLUCT\": True,\n \"M_MIN_in_Mass\": False,\"PHOTON_CONS\": True,\n \"FIX_VCB_AVG\": False, \"EVOLVING_R_BUBBLE_MAX\": False}\n if flag_options is not None:\n if isinstance(flag_options, p21.FlagOptions):\n self.flag_options = flag_options\n else:\n self.flag_options = p21.FlagOptions(flag_options)\n \n ## Check that given flag options match emulator training data flag options\n ## if they do not, raise error and exit\n for key in training_flag_options.keys():\n if self.flag_options.defining_dict[key] != training_flag_options[key]:\n raise ValueError('Input flag options do not match the emulator flag options. The emulator can only be used with a single set of flag options:', training_flag_options)\n else:\n self.flag_options = training_flag_options\n \n training_user_params = {\"BOX_LEN\": 250, \"DIM\": 512,\n \"HII_DIM\": 128,\"USE_FFTW_WISDOM\": True,\n \"HMF\": 1,\"USE_RELATIVE_VELOCITIES\": False,\n \"POWER_SPECTRUM\": 0,\"N_THREADS\": 1,\n \"PERTURB_ON_HIGH_RES\": False,\"NO_RNG\": False,\n \"USE_INTERPOLATION_TABLES\": True,\"FAST_FCOLL_TABLES\": False,\n \"USE_2LPT\": True,\"MINIMIZE_MEMORY\": False}\n if user_params is not None:\n if isinstance(user_params, p21.UserParams):\n self.user_params = user_params\n else:\n self.user_params = p21.UserParams(user_params)\n \n ## Check that given flag options match emulator training data flag options\n ## if they do not, raise error and exit\n for key in training_user_params.keys():\n if self.user_params.defining_dict[key] != training_user_params[key]:\n raise ValueError('Input user params do not match the emulator user params. The emulator can only be used with a single set of user params:', training_user_params)\n else:\n self.user_params = training_user_params\n\n", "repo_name": "DanielaBreitman/Old21cmEMU", "sub_path": "py21cmemu/emulator.py", "file_name": "emulator.py", "file_ext": "py", "file_size_in_byte": 14949, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 36, "usage_type": "attribute"}, {"api_name": "get_emulator.Download21cmEMU", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 96, "usage_type": "call"}, {"api_name": "py21cmfast.wrapper.compute_tau", "line_number": 103, "usage_type": "call"}, {"api_name": "py21cmfast.wrapper", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "py21cmfast.wrapper.compute_luminosity_function", "line_number": 106, "usage_type": "call"}, {"api_name": "py21cmfast.wrapper", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 111, "usage_type": "call"}, {"api_name": "py21cmfast.AstroParams", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 148, "usage_type": "attribute"}, {"api_name": "py21cmfast.AstroParams", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "py21cmfast.AstroParams", "line_number": 180, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.log10", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 213, "usage_type": "call"}, {"api_name": "py21cmfast.CosmoParams", "line_number": 231, "usage_type": "attribute"}, {"api_name": "py21cmfast.CosmoParams", "line_number": 234, "usage_type": "call"}, {"api_name": "py21cmfast.CosmoParams", "line_number": 242, "usage_type": "call"}, {"api_name": "py21cmfast.FlagOptions", "line_number": 250, "usage_type": "attribute"}, {"api_name": "py21cmfast.FlagOptions", "line_number": 253, "usage_type": "call"}, {"api_name": "py21cmfast.UserParams", "line_number": 271, "usage_type": "attribute"}, {"api_name": "py21cmfast.UserParams", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "36555476880", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$',views.index,name='index'),\n url(r'^update$',views.update,name='update'),\n url(r'^show$',views.show,name='show'),\n url(r'^destroy$',views.destroy,name='destroy'),\n url(r'^user_timeline$',views.user_timeline,name='user_timeline')\n]\n", "repo_name": "dulabs/reactjs_test", "sub_path": "tweet/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "31521849348", "text": "api_key=\"AIzaSyBoETqNFSqKjTSyi6tlLpx3kjY_S8a05v4\"\nfrom apiclient.discovery import build\nyoutube=build('youtube','v3',developerKey=api_key)\n\nlist_videos=set()\nfor keyword in ['mt4','mt5','metatrader','algo trading','forex','expert advisor','ea mql5','ea mql4']:\n for order in [\"viewCount\",\"relevance\",\"rating\",\"date\"]:\n request = youtube.search().list(relevanceLanguage=\"en\",part=\"snippet\",maxResults=50,q=keyword,type=\"video\", order=order)\n response = request.execute()\n\n for item in response['items']:\n list_videos.add(item['id']['videoId'])\n\nfile = open('video_id.txt', 'w')\nfor i in list_videos:\n file.write(i+\"\\n\")\nfile.close()\n\n\n\nfile = open('video_id.txt', 'r')\nfor i in file:\n print(i)\nfile.close()\n\n", "repo_name": "Dorian-Ba/Post-Youtube-Comment", "sub_path": "get_link.py", "file_name": "get_link.py", "file_ext": "py", "file_size_in_byte": 748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "apiclient.discovery.build", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "1864278794", "text": "from flask import Blueprint, request\nfrom flask import jsonify\nfrom website.service_dir.dot_topic_attachments_service import createDotTopicAttachments, updateDotTopicAttachments\nfrom website.service_dir.dot_topic_attachments_service import findAll, findById, findBy\nfrom website.service_dir.dot_topic_attachments_service import importDotTopicAttachmentss, deleteById\nfrom website.oauth2 import require_oauth\nfrom website.model_dir.dot_topic_attachments import DotTopicAttachments\nfrom json import loads\nimport pandas as pd\n\nbp = Blueprint(__name__, 'dotTopicAttachments')\n\n\n@bp.route('', methods=['POST', 'PUT', 'GET'])\n@require_oauth()\ndef dotTopicAttachmentsOps():\n dotTopicAttachmentsObj = DotTopicAttachments()\n if request.method == 'POST':\n dotTopicAttachments = request.get_json(force=True)\n dotTopicAttachmentsObj = createDotTopicAttachments(dotTopicAttachments)\n\n elif request.method == 'PUT':\n dotTopicAttachments = request.get_json(force=True)\n dotTopicAttachmentsObj = updateDotTopicAttachments(dotTopicAttachments)\n\n elif request.method == 'GET':\n page = request.args.get('page', 1, type=int)\n per_page = request.args.get('per_page', 10, type=int)\n sort = request.args.get('sort', 'lastmodified_date,desc', type=str)\n dotTopicAttachmentsPages = findAll(page, per_page, sort)\n dotTopicAttachmentss = dotTopicAttachmentsPages.items\n dotTopicAttachmentsMaps = []\n for dotTopicAttachments in dotTopicAttachmentss:\n dotTopicAttachmentsMaps.append(dotTopicAttachments.as_dict())\n\n return jsonify(dotTopicAttachmentsMaps), 200, {'X-Total-Count': dotTopicAttachmentsPages.total}\n return jsonify(dotTopicAttachmentsObj.as_dict())\n\n\n@bp.route('/', methods=['GET'])\n@require_oauth()\ndef findbyId(id):\n dotTopicAttachments = findById(id)\n return jsonify(dotTopicAttachments.as_dict())\n\n\n@bp.route('/filter', methods=['POST'])\n@require_oauth()\ndef findbyFilter():\n filterDict = request.get_json(force=True)\n dotTopicAttachmentss = findBy(**filterDict)\n dotTopicAttachmentsMaps = []\n for dotTopicAttachments in dotTopicAttachmentss:\n dotTopicAttachmentsMaps.append(dotTopicAttachments.as_dict())\n return jsonify(dotTopicAttachmentsMaps), 200, {'X-Total-Count': len(dotTopicAttachmentss)}\n\n\n@bp.route('/import', methods=['POST'])\n@require_oauth()\ndef importDotTopicAttachmentssRoute():\n csv_file = pd.DataFrame(pd.read_csv(request.files.get('file'),skiprows=1, sep=\",\", names=[\"email\", \"language\", \"location\", \"mobile\", \"name\"], index_col=False))\n result = csv_file.to_json(orient=\"records\")\n parsed = loads(result)\n importDotTopicAttachmentss(parsed)\n return jsonify({\"success\": True})\n\n\n@bp.route('/', methods=['DELETE'])\n@require_oauth()\ndef deletebyId(id):\n deleteById(id)\n return jsonify({\"success\": True})\n", "repo_name": "abhinav1144/aws_angular_pipeline", "sub_path": "services/backend/website/routes_dir/dot_topic_attachments_routes.py", "file_name": "dot_topic_attachments_routes.py", "file_ext": "py", "file_size_in_byte": 2880, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Blueprint", "line_number": 11, "usage_type": "call"}, {"api_name": "website.model_dir.dot_topic_attachments.DotTopicAttachments", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "website.service_dir.dot_topic_attachments_service.createDotTopicAttachments", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "website.service_dir.dot_topic_attachments_service.updateDotTopicAttachments", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.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.get", "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.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "website.service_dir.dot_topic_attachments_service.findAll", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}, {"api_name": "website.oauth2.require_oauth", "line_number": 15, "usage_type": "call"}, {"api_name": "website.service_dir.dot_topic_attachments_service.findById", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 44, "usage_type": "call"}, {"api_name": "website.oauth2.require_oauth", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "website.service_dir.dot_topic_attachments_service.findBy", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 55, "usage_type": "call"}, {"api_name": "website.oauth2.require_oauth", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.files.get", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "website.service_dir.dot_topic_attachments_service.importDotTopicAttachmentss", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 65, "usage_type": "call"}, {"api_name": "website.oauth2.require_oauth", "line_number": 59, "usage_type": "call"}, {"api_name": "website.service_dir.dot_topic_attachments_service.deleteById", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 72, "usage_type": "call"}, {"api_name": "website.oauth2.require_oauth", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "36070494088", "text": "import argparse\nimport utils \n\ndef parse_args():\n '''Parse input arguments.'''\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--aml_config_path\", type=str, help=\"Path to resulting AML config file.\")\n\n args = parser.parse_args()\n\n return args\n\ndef main(args):\n aml_config = f\"\"\"\n {{\n \"subscription_id\": \"{utils.fs_config.get('subscription_id')}\",\n \"resource_group\": \"{utils.fs_config.get('resource_group')}\",\n \"workspace_name\":\"{utils.fs_config.get('workspace_name')}\"\n }}\n \"\"\"\n\n with open(args.config_path, \"w\") as file:\n file.write(aml_config)\n\nif __name__ == '__main__':\n args = parse_args()\n\n main(args)", "repo_name": "Rizo-R/aml-feathr-pipeline", "sub_path": "classical/aml-cli-v2/data-science/src/create_aml_config.py", "file_name": "create_aml_config.py", "file_ext": "py", "file_size_in_byte": 669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "utils.fs_config.get", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.fs_config", "line_number": 16, "usage_type": "attribute"}, {"api_name": "utils.fs_config.get", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.fs_config", "line_number": 17, "usage_type": "attribute"}, {"api_name": "utils.fs_config.get", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.fs_config", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "16519592916", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport logging\n\nimport six\n\nfrom cloud_tasks_deferred import deferred\n\nlogger = logging.getLogger(__name__)\n\n\ndef application(environ, start_response):\n \"\"\"A WSGI application that processes deferred invocations.\"\"\"\n\n def abort(status):\n start_response(status, [('Content-Type', 'text/plain')])\n return []\n\n if environ['REQUEST_METHOD'] != 'POST':\n return abort('405 Method Not Allowed')\n\n if environ.get('CONTENT_TYPE') != 'application/octet-stream':\n return abort('415 Unsupported Media Type')\n\n if not any(key.upper() == 'HTTP_X_APPENGINE_TASKNAME' for key in environ):\n logger.error(\n 'Detected an attempted XSRF attack. '\n 'The header \"X-AppEngine-Taskname\" was not set.'\n )\n return abort('403 Forbidden')\n\n headers = [\n k + ':' + v\n for k, v in six.iteritems(environ)\n if k.upper().startswith('HTTP_X_APPENGINE_')\n ]\n logger.log(deferred._DEFAULT_LOG_LEVEL, ', '.join(headers))\n\n content_length = int(environ.get('CONTENT_LENGTH', 0))\n data = environ['wsgi.input'].read(content_length)\n\n try:\n deferred.run(data)\n except deferred.SingularTaskFailure:\n logger.debug('Failure executing task, task retry forced')\n return abort('408 Request Timeout')\n except deferred.PermanentTaskFailure:\n logger.exception('Permanent failure attempting to execute task')\n except Exception:\n return abort('500 Internal Server Error')\n\n start_response('204 No Content', [])\n return []\n", "repo_name": "grktsh/python-cloud-tasks-deferred", "sub_path": "src/cloud_tasks_deferred/wsgi.py", "file_name": "wsgi.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 36, "usage_type": "call"}, {"api_name": "cloud_tasks_deferred.deferred._DEFAULT_LOG_LEVEL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cloud_tasks_deferred.deferred", "line_number": 39, "usage_type": "name"}, {"api_name": "cloud_tasks_deferred.deferred.run", "line_number": 45, "usage_type": "call"}, {"api_name": "cloud_tasks_deferred.deferred", "line_number": 45, "usage_type": "name"}, {"api_name": "cloud_tasks_deferred.deferred.SingularTaskFailure", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cloud_tasks_deferred.deferred", "line_number": 46, "usage_type": "name"}, {"api_name": "cloud_tasks_deferred.deferred.PermanentTaskFailure", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cloud_tasks_deferred.deferred", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "11215014978", "text": "# -*- coding: UTF-8 -*-\n\n\"\"\"PFE Component Tests - Proximity_Zones.\n\n* TC-43793 - Proximity_Zones GET:\n\n Verify that user is able to GET the details of proximities having id with 100 characters using request /proximities/{id}.\n\n\nEquivalent test CURL command:\n\n curl -H \"Host: \" -H \"Authorization: Bearer \"\n -X GET -H \"Content-Type: application/json\"\n \":///proximities/abcdefghijkl12345678abcdefghijkl12345678abcdefghijkl12345678abcdefghijkl12345678abcdefghijkl23456678\"\n\nSame, with test data:\n\n curl -H \"Host: \" -H \"Authorization: Bearer \"\n -X GET -H \"Content-Type: application/json\"\n \":///proximities/abcdefghijkl12345678abcdefghijkl12345678abcdefghijkl12345678abcdefghijkl12345678abcdefghijkl23456678\"\n\n\"\"\"\n\nimport pytest\n\nfrom qe_common import *\n\nlogger = init_logger()\n\n\n\n\n\n\n\n@pytest.mark.components\n@pytest.allure.story('Proximity_Zones')\n@pytest.allure.feature('GET')\nclass Test_PFE_Components(object):\n \"\"\"PFE Proximity_Zones test cases.\"\"\"\n\n @pytest.allure.link('https://jira.qumu.com/browse/TC-43793')\n @pytest.mark.Proximity_Zones\n @pytest.mark.GET\n def test_TC_43793_GET_Proximity_Zones_Able_To_Get_Details_Having_Id_With_100_Characters(self, context):\n \"\"\"TC-43793 - Proximity_Zones-GET\n Verify that user is able to GET the details of proximities having id with 100 characters using request /proximities/{id}.\"\"\"\n # Define a test step\n with pytest.allure.step('First Create Proximity Zone having id with 100 characters.'):\n proximityDetails = context.sc.ProximityDetails(\n cidr='0.0.0.0/0',\n metric=1,\n notes='')\n proximityZone = context.sc.ProximityZoneDetails(\n visibleInAllConfigurations=True,\n configAdminCanEdit=False,\n configurations=[],\n id='qwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiop',\n name='idwith100characters',\n proximityDetails=[proximityDetails])\n # POST the ProximityZone.\n # The `check` call validates return code and some of the swagger schema\n # (most schema checks are disabled)\n check(\n context.cl.Proximity_Zones.createEntity(\n body=proximityZone\n )\n )\n\n\n with pytest.allure.step(\"\"\"Verify that user is able to GET the details of proximities having id with 100 characters using request /proximities/{id}.\"\"\"):\n\n # listEntities the Proximity_Zones, having id with 100 characters\n # The `check` call validates return code\n # and some of the swagger schema.\n # Most schema checks are disabled.\n check(\n context.cl.Proximity_Zones.getEntity(\n id='qwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiopqwertyuiop')\n )\n", "repo_name": "muktabehera/QE", "sub_path": "functional/Components/ProximityZone/ProximityZone_GET_ID/test_TC_43793_Proximities_GET_Able_To_Get_Details_Having_Id_With_100_Characters.py", "file_name": "test_TC_43793_Proximities_GET_Able_To_Get_Details_Having_Id_With_100_Characters.py", "file_ext": "py", "file_size_in_byte": 3075, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pytest.allure.step", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pytest.allure.step", "line_number": 71, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pytest.allure.link", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pytest.allure.feature", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "28834929029", "text": "import re\n\nfrom peewee import Select\n\nfrom ramjet.data_interface.tess_ffi_light_curve_metadata_manager import TessFfiLightCurveMetadata\nfrom ramjet.photometric_database.derived.tess_ffi_light_curve_collection import TessFfiLightCurveCollection\n\ntry:\n # be ready for 3.10 when it drops\n from enum import StrEnum\nexcept ImportError:\n from backports.strenum import StrEnum\nfrom pathlib import Path\nfrom typing import Iterable, Union, List\n\nimport numpy as np\nimport pandas as pd\n\nfrom ramjet.photometric_database.light_curve_collection import LightCurveCollection\n\n\nclass ColumnName(StrEnum):\n TIME__DAYS = 'time__days'\n MAGNIFICATION = 'magnification'\n\n\nclass SiddhantSolankiHeartBeatSyntheticSignalsCollection(LightCurveCollection):\n def __init__(self):\n super().__init__()\n self.data_directory: Path = Path('data/siddhant_solanki_synthetic_signals')\n self.label = 1\n\n def get_paths(self) -> Iterable[Path]:\n all_synthetic_signal_paths = self.data_directory.glob('*.txt')\n heart_beat_synthetic_signals = [path for path in all_synthetic_signal_paths\n if re.match(r'generated_lc_\\d+.txt', path.name) is not None]\n return heart_beat_synthetic_signals\n\n def load_times_and_magnifications_from_path(self, path: Path) -> (np.ndarray, np.ndarray):\n synthetic_signal_data_frame = pd.read_csv(path, names=[ColumnName.MAGNIFICATION],\n skipinitialspace=True, delim_whitespace=True, skiprows=1)\n synthetic_signal_data_frame.dropna(inplace=True)\n magnifications = synthetic_signal_data_frame[ColumnName.MAGNIFICATION].values\n step_size__days = 0.0069444444\n times = np.arange(0, magnifications.shape[0] * step_size__days, step_size__days)\n assert times.shape[0] == magnifications.shape[0]\n return times, magnifications\n\n\nclass SiddhantSolankiNonHeartBeatSyntheticSignalsCollection(LightCurveCollection):\n def __init__(self):\n super().__init__()\n self.data_directory: Path = Path('data/siddhant_solanki_synthetic_signals')\n self.label = 0\n\n def get_paths(self) -> Iterable[Path]:\n all_synthetic_signal_paths = self.data_directory.glob('*.txt')\n non_heart_beat_synthetic_signals = [path for path in all_synthetic_signal_paths\n if re.match(r'generated_lc_fake_\\d+.txt', path.name) is not None]\n return non_heart_beat_synthetic_signals\n\n def load_times_and_magnifications_from_path(self, path: Path) -> (np.ndarray, np.ndarray):\n synthetic_signal_data_frame = pd.read_csv(path, names=[ColumnName.MAGNIFICATION],\n skipinitialspace=True, delim_whitespace=True, skiprows=1)\n synthetic_signal_data_frame.dropna(inplace=True)\n magnifications = synthetic_signal_data_frame[ColumnName.MAGNIFICATION].values\n step_size__days = 0.0069444444\n times = np.arange(0, magnifications.shape[0] * step_size__days, step_size__days)\n assert times.shape[0] == magnifications.shape[0]\n return times, magnifications\n\nclass TessFfiHeartBeatHardNegativeLightcurveCollection(TessFfiLightCurveCollection):\n \"\"\"\n A class representing the collection of TESS two minute cadence lightcurves containing eclipsing binaries.\n \"\"\"\n def __init__(self, dataset_splits: Union[List[int], None] = None,\n magnitude_range: (Union[float, None], Union[float, None]) = (None, None)):\n super().__init__(dataset_splits=dataset_splits, magnitude_range=magnitude_range)\n self.label = 0\n self.hard_negative_ids = list(pd.read_csv('data/heart_beat_hard_negatives.csv')['tic_id'].values)\n\n def get_sql_query(self) -> Select:\n \"\"\"\n Gets the SQL query for the database models for the lightcurve collection.\n :return: The SQL query.\n \"\"\"\n query = super().get_sql_query()\n query = query.where(TessFfiLightCurveMetadata.tic_id.in_(self.hard_negative_ids))\n return query\n", "repo_name": "golmschenk/ramjet", "sub_path": "src/ramjet/photometric_database/derived/siddhant_solanki_heart_beat_synthetic_signals_collection.py", "file_name": "siddhant_solanki_heart_beat_synthetic_signals_collection.py", "file_ext": "py", "file_size_in_byte": 4096, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "backports.strenum.StrEnum", "line_number": 22, "usage_type": "name"}, {"api_name": "ramjet.photometric_database.light_curve_collection.LightCurveCollection", "line_number": 27, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 30, "usage_type": "name"}, {"api_name": "re.match", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 33, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 33, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 39, "usage_type": "attribute"}, {"api_name": "ramjet.photometric_database.light_curve_collection.LightCurveCollection", "line_number": 50, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 53, "usage_type": "name"}, {"api_name": "re.match", "line_number": 59, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 56, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 56, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 62, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 62, "usage_type": "attribute"}, {"api_name": "ramjet.photometric_database.derived.tess_ffi_light_curve_collection.TessFfiLightCurveCollection", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 77, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 80, "usage_type": "call"}, {"api_name": "ramjet.data_interface.tess_ffi_light_curve_metadata_manager.TessFfiLightCurveMetadata.tic_id.in_", "line_number": 88, "usage_type": "call"}, {"api_name": "ramjet.data_interface.tess_ffi_light_curve_metadata_manager.TessFfiLightCurveMetadata.tic_id", "line_number": 88, "usage_type": "attribute"}, {"api_name": "ramjet.data_interface.tess_ffi_light_curve_metadata_manager.TessFfiLightCurveMetadata", "line_number": 88, "usage_type": "name"}, {"api_name": "peewee.Select", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "3753753740", "text": "import logging\nfrom sca.parsers import prf\n\nlogging.basicConfig(level=logging.INFO)\nlog = logging.getLogger(__name__)\n\nclass BadKindConversion(Exception):\n pass\n\nclass UnsupportedComplexConversion(Exception):\n pass\n\nclass UnsupportedStructConversion(Exception):\n pass\n\ndef convert(ossie_prf):\n '''\n Imports 'ossie_prf' (a REDHAWK ossie.parsers.prf.parse() object) into an\n SCA-compliant PRF format and returns it (the output of sca.parsers.prf.parse()).\n '''\n def do_try(fn, xml, p):\n try:\n fn(xml, p)\n except Exception as e:\n log.warning('Dropping property because: %s' % e.message)\n\n xml = prf.parseString('')\n [do_try(_add_rh_simple, xml, p) for p in ossie_prf.get_simple()]\n [do_try(_add_rh_simplesequence, xml, p) for p in ossie_prf.get_simplesequence()]\n [do_try(_add_rh_struct, xml, p) for p in ossie_prf.get_struct()]\n [do_try(_add_rh_structsequence, xml, p) for p in ossie_prf.get_structsequence()]\n return xml\n\n# Internal functions below.\n\ndef _resolve_kindtype(kindtype='configure', commandline='false'):\n '''\n Kind Conversions\n allocation -> allocation\n configure -> configure\n property - cmdline ---^\n property + cmdline ---v\n execparam -> execparam\n \n (un)Kind Conversions, which result in an exception\n message -> DROP\n\n Conversions that don't exist at all.\n N/A -> factory\n N/A -> test\n '''\n ALLOCATION_KIND = 'allocation'\n CONFIGURE_KIND = 'configure'\n EXECPARAM_KIND = 'execparam'\n PROPERTY_KIND = 'property'\n MAP_FALSE = {\n ALLOCATION_KIND : ALLOCATION_KIND,\n CONFIGURE_KIND : CONFIGURE_KIND,\n EXECPARAM_KIND : EXECPARAM_KIND,\n }\n MAP_TRUE = MAP_FALSE.copy()\n\n MAP_TRUE[PROPERTY_KIND] = EXECPARAM_KIND\n MAP_FALSE[PROPERTY_KIND] = CONFIGURE_KIND\n try:\n if 'true' == commandline:\n return MAP_TRUE[kindtype]\n elif 'false' == commandline:\n return MAP_FALSE[kindtype]\n else:\n raise BadKindConversion(\n 'Valid values of \"commandline\" are \"true\" and \"false\", strings.'\n )\n except:\n raise BadKindConversion(\n 'Non-translatable kindtype: %s' % kindtype\n )\n\ndef _map_kinds(rh_kinds, commandline='false'):\n '''\n Returns a list of SCA Kinds that map from REDHAWK kinds\n rh_kinds - list of REDHAWK kinds (usually prfobj.get_kind())\n commandline - if the parent property has this member, pass the value\n '''\n out_kinds = []\n for k in rh_kinds:\n out_kinds.append(\n prf.kind(_resolve_kindtype(k.kindtype, commandline))\n )\n return None if not len(out_kinds) else out_kinds\n\ndef _map_configurationkind(rh_configurationkinds):\n '''\n Returns a single configurationkind instance for the first\n configuration kind in rh_configurationkinds (get_configurationkind\n returns a list). This function will warn if multiple kinds are listed\n '''\n if not rh_configurationkinds:\n return None\n \n if len(rh_configurationkinds) > 1:\n log.warning('Using first configurationkind; multiple are listed for property')\n \n return prf.configurationkind(_resolve_kindtype(rh_configurationkinds[0].kindtype))\n\ndef _to_sca_enumerations(rh_enumerations):\n '''\n Returns an SCA 4 enumerations object using the REDHAWK enumerations data\n May return None if no enumerations were provided.\n '''\n sca_enums = None\n if rh_enumerations:\n sca_enums = prf.enumerations()\n for e in rh_enumerations.get_enumeration():\n sca_enums.add_enumeration(prf.enumeration(\n label = e.get_label(),\n value = e.get_value()\n ))\n return sca_enums\n\ndef _to_sca_action(rh_action):\n '''\n Returns an SCA 4 action object using the REDHAWK action data\n '''\n return prf.action(type_=rh_action.get_type())\n\ndef _to_sca_description(rh_description):\n '''\n Returns an SCA 4 description object using the REDHAWK description data\n '''\n if rh_description:\n return prf.description(valueOf_=rh_description)\n else:\n return None\n\ndef _to_sca_value(rh_value):\n '''\n Returns an SCA 4 value object using the REDHAWK value data\n '''\n return prf.value(valueOf_=rh_value)\n\ndef _to_sca_complex_value(str_value='0 + j0'):\n '''\n Returns a tuple of (real, imag) prf.value objects\n '''\n value_cplx = complex(str_value.strip().replace('j','') + 'j')\n return (\n prf.value(value_cplx.real),\n prf.value(value_cplx.imag)\n )\n\ndef _to_sca_struct(rh_struct):\n '''\n Creates an SCA 4.x representation of a REDHAWK struct.\n NOTE: SCA 4.x structs cannot have simplesequence members.\n NOTE: SCA 4.x structs cannot have simple complex members, since these\n are represented as structs, and structs cannot contain other\n structs in either framework at this time (SCA 4.x or REDHAWK 2.2)\n '''\n if rh_struct.get_simplesequence():\n raise UnsupportedStructConversion(\n 'SCA structs cannot contain simplesequences. (ID: %s)' % rh_struct.get_id()\n )\n \n configurationkind = _map_configurationkind(rh_struct.get_configurationkind())\n sca_struct = prf.struct(\n _id = rh_struct.get_id(),\n mode = rh_struct.get_mode(),\n name = rh_struct.get_name(),\n description = _to_sca_description(rh_struct.get_description()),\n configurationkind = configurationkind)\n\n for simple in rh_struct.get_simple():\n if simple.get_complex() == 'true':\n raise UnsupportedStructConversion(\n 'SCA structs cannot contain complex simples (becoming structs with struct members, ID: %s)' % rh_struct.get_id()\n )\n sca_struct.add_simple(prf.simple(\n _id = simple.get_id(),\n type_ = simple.get_type(),\n name = simple.get_name(),\n description = _to_sca_description(simple.get_description()),\n value = prf.value(simple.get_value()),\n units = simple.get_units(),\n _range = simple.get_range(),\n enumerations = _to_sca_enumerations(simple.get_enumerations())\n ))\n return sca_struct\n\ndef _sca_complex_struct(_id, name=None, type_=None, str_value=None):\n '''\n Creates an SCA 4.x struct with two simples:\n _id::real w/ 'real' portion of str_value\n _id::imag w/ 'imaginary' portion of str_value\n \n str_value should be a string, like '3 + j4'\n '''\n sca_struct = prf.struct(\n _id = _id,\n name = name or _id,\n type_ = type_\n )\n rvalue = ivalue = None\n if str_value:\n (rvalue, ivalue) = _to_sca_complex_value(str_value)\n\n real_id = _id + '::real'\n sca_struct.add_simple(prf.simple(\n _id = real_id,\n name = real_id,\n type_ = type_,\n value = rvalue\n ))\n imag_id = _id + '::imag'\n sca_struct.add_simple(prf.simple(\n _id = imag_id,\n name = imag_id,\n type_ = type_,\n value = ivalue\n ))\n return sca_struct\n\ndef _add_rh_simple(xml, rh_simple):\n '''\n Adds an SCA 4.x simple from a REDHAWK simple\n '''\n if rh_simple.get_optional() == 'true':\n log.warning(\n 'Ignoring \"optional\"; it has no translation to SCA 4.x (ID: %s)' % rh_simple.get_id()\n )\n\n kinds = _map_kinds(rh_simple.get_kind(), rh_simple.get_commandline())\n if rh_simple.get_complex() == 'false':\n sca_simple = prf.simple(\n _id = rh_simple.get_id(),\n type_ = rh_simple.get_type(),\n name = rh_simple.get_name(),\n mode = rh_simple.get_mode(),\n description = _to_sca_description(rh_simple.get_description()),\n value = _to_sca_value(rh_simple.get_value()),\n units = rh_simple.get_units(),\n _range = rh_simple.get_range(),\n enumerations = _to_sca_enumerations(rh_simple.get_enumerations()),\n kind = kinds,\n action = _to_sca_action(rh_simple.get_action())\n )\n xml.add_simple(sca_simple)\n else:\n if rh_simple.get_commandline() == 'true':\n raise UnsupportedComplexConversion(\n 'Conversion from complex simple becomes a struct, which cannot be an execparam (ID: %s)' % rh_simple.get_id()\n )\n log.warning('Converting a REDHAWK Complex Simple into a struct (ID: %s)' % rh_simple.get_id())\n sca_struct = _sca_complex_struct(\n _id = rh_simple.get_id(),\n type_ = rh_simple.get_type(),\n str_value = rh_simple.get_value()\n )\n sca_struct.set_mode(rh_simple.get_mode())\n sca_struct.set_description(_to_sca_description(rh_simple.get_description()))\n xml.add_struct(sca_struct)\n\ndef _add_rh_simplesequence(xml, rh_simpleseq):\n '''\n Adds SCA 4.x representation of a REDHAWK Simple Sequence\n NOTE: Complex simple sequences become struct sequences\n NOTE: \"optional\" is not recognized for SCA 4.x simple sequences\n '''\n if rh_simpleseq.get_optional() == 'true':\n log.warning('Ignoring \"optional\"; it has no translation to SCA 4.x (ID: %s)' % rh_simpleseq.get_id())\n\n if rh_simpleseq.get_complex() == 'false':\n kinds = _map_kinds(rh_simpleseq.get_kind())\n values = prf.values()\n for v in rh_simpleseq.get_values().get_value():\n values.add_value(_to_sca_value(v))\n\n sca_simpleseq = prf.simplesequence(\n _id = rh_simpleseq.get_id(),\n type_ = rh_simpleseq.get_type(),\n name = rh_simpleseq.get_name(),\n mode = rh_simpleseq.get_mode(),\n description = _to_sca_description(rh_simpleseq.get_description()),\n values = values,\n units = rh_simpleseq.get_units(),\n _range = rh_simpleseq.get_range(),\n kind = kinds,\n action = _to_sca_action(rh_simpleseq.get_action())\n )\n xml.add_simplesequence(sca_simpleseq)\n else:\n # Create a struct and struct sequence filled with struct values.\n log.warning('Converting a REDHAWK complex simple sequence into a struct sequence (ID: %s)' % rh_simpleseq.get_id())\n configurationkind = _map_configurationkind(rh_simpleseq.get_kind())\n structseq_value_id = rh_simpleseq.get_id() + '::value'\n real_value_id = structseq_value_id + '::real'\n imag_value_id = structseq_value_id + '::imag'\n xml.add_struct(\n _sca_complex_struct(\n _id = structseq_value_id,\n type_ = rh_simpleseq.get_type(),\n ))\n sca_structseq = prf.structsequence(\n _id = rh_simpleseq.get_id(),\n structrefid = structseq_value_id,\n name = rh_simpleseq.get_name(),\n mode = rh_simpleseq.get_mode(),\n description = _to_sca_description(rh_simpleseq.get_description()),\n configurationkind = configurationkind\n )\n for v in rh_simpleseq.get_values().get_value():\n # Returns \"value\" objects and simpleref needs the stored value.\n (r, i) = _to_sca_complex_value(v)\n r_ref = prf.simpleref(real_value_id, r.get_valueOf_())\n i_ref = prf.simpleref(imag_value_id, i.get_valueOf_())\n sca_structseq.add_structvalue(\n prf.structvalue([r_ref, i_ref])\n )\n xml.add_structsequence(sca_structseq)\n\ndef _add_rh_struct(xml, rh_struct):\n '''\n Adds an SCA 4.x representation of a REDHAWK struct.\n NOTE: See framework limitations of _to_sca_struct.\n '''\n xml.add_struct(_to_sca_struct(rh_struct))\n\ndef _add_rh_structsequence(xml, rh_structseq):\n '''\n Adds SCA 4.x representation of a REDHAWK struct sequence.\n NOTE: SCA 4.x struct sequences can only contain compliant struct\n definitions. See the limitations of _add_rh_struct.\n '''\n configurationkind = _map_configurationkind(rh_structseq.get_configurationkind())\n structseq_ref = _to_sca_struct(rh_structseq.get_struct())\n sca_structseq = prf.structsequence(\n _id = rh_structseq.get_id(),\n structrefid = structseq_ref.get_id(),\n name = rh_structseq.get_name(),\n mode = rh_structseq.get_mode(),\n description = _to_sca_description(rh_structseq.get_description()),\n configurationkind = configurationkind\n )\n for sv in rh_structseq.get_structvalue():\n if sv.get_simplesequenceref():\n raise UnsupportedStructConversion(\n 'Struct sequence cannot contain structs that have complex simples (ID: %s)' % rh_structseq.get_id()\n )\n sca_sv = prf.structvalue()\n for ref in sv.get_simpleref():\n sca_sv.add_simpleref(\n prf.simpleref(\n refid=ref.get_refid(),\n value=ref.get_value()\n )\n )\n sca_structseq.add_structvalue(sca_sv)\n xml.add_struct(structseq_ref)\n xml.add_structsequence(sca_structseq)\n", "repo_name": "Geontech/sca-jtnc", "sub_path": "base/framework/python/sca/utils/converters/prf.py", "file_name": "prf.py", "file_ext": "py", "file_size_in_byte": 13605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "51", "api": [{"api_name": "logging.basicConfig", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 4, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "sca.parsers.prf.parseString", "line_number": 27, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 27, "usage_type": "name"}, {"api_name": "sca.parsers.prf.kind", "line_number": 88, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 88, "usage_type": "name"}, {"api_name": "sca.parsers.prf.configurationkind", "line_number": 104, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 104, "usage_type": "name"}, {"api_name": "sca.parsers.prf.enumerations", "line_number": 113, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 113, "usage_type": "name"}, {"api_name": "sca.parsers.prf.enumeration", "line_number": 115, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 115, "usage_type": "name"}, {"api_name": "sca.parsers.prf.action", "line_number": 125, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 125, "usage_type": "name"}, {"api_name": "sca.parsers.prf.description", "line_number": 132, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 132, "usage_type": "name"}, {"api_name": "sca.parsers.prf.value", "line_number": 140, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 140, "usage_type": "name"}, {"api_name": "sca.parsers.prf.value", "line_number": 148, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 148, "usage_type": "name"}, {"api_name": "sca.parsers.prf.value", "line_number": 149, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 149, "usage_type": "name"}, {"api_name": "sca.parsers.prf.struct", "line_number": 166, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 166, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simple", "line_number": 178, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 178, "usage_type": "name"}, {"api_name": "sca.parsers.prf.value", "line_number": 183, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 183, "usage_type": "name"}, {"api_name": "sca.parsers.prf.struct", "line_number": 198, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 198, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simple", "line_number": 208, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 208, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simple", "line_number": 215, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 215, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simple", "line_number": 234, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 234, "usage_type": "name"}, {"api_name": "sca.parsers.prf.values", "line_number": 274, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 274, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simplesequence", "line_number": 278, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 278, "usage_type": "name"}, {"api_name": "sca.parsers.prf.structsequence", "line_number": 303, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 303, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simpleref", "line_number": 314, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 314, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simpleref", "line_number": 315, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 315, "usage_type": "name"}, {"api_name": "sca.parsers.prf.structvalue", "line_number": 317, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 317, "usage_type": "name"}, {"api_name": "sca.parsers.prf.structsequence", "line_number": 336, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 336, "usage_type": "name"}, {"api_name": "sca.parsers.prf.structvalue", "line_number": 349, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 349, "usage_type": "name"}, {"api_name": "sca.parsers.prf.simpleref", "line_number": 352, "usage_type": "call"}, {"api_name": "sca.parsers.prf", "line_number": 352, "usage_type": "name"}]} +{"seq_id": "71272562398", "text": "# main Script for loading the Text Images and fetching the text out of the scanned document\n\nimport cv2, numpy as np, os\nimport pickle\nfrom keras.models import load_model\n\n\nclass Document():\n def __init__(self):\n self.dfNames = ['FirstName', 'LastName', 'Email', 'Street', 'City', 'State', 'ZipCode', 'Phone', 'BirthDay']\n # self.modelPath = modelPath\n self.path = os.path.dirname(os.path.abspath('__file__')) + '/'\n self.path = self.path.replace('\\\\', '/')\n alphaNumericModel_path = self.path + 'Models/AlphabetNumeric_v3.h5'\n alphabetModel_path = self.path + 'Models/Alphabets_v3.h5'\n digitModel_path = self.path + 'Models/Digit_v2.h5'\n atr_Model_path = self.path + 'Models/@.h5'\n _modelpath = self.path + 'Models/_model.h5'\n\n \"\"\"#This is General model for everything(alpabets,digits and Special characters\"\"\"\n self.alphaNumericModel = load_model(alphaNumericModel_path)\n self.alphabetModel = load_model(alphabetModel_path)\n self.digitModel = load_model(digitModel_path)\n self.atrModel = load_model(atr_Model_path)\n self._model = load_model(_modelpath)\n\n self.model = None\n self.label = None\n self.checkForATR = False\n # getting the labels from pickled file #CNN_OCRModel_v1.h5\n with open(self.path + 'Models/AlphabetNumericLabels_v3.pkl', 'rb') as f:\n self.alphaNumericLabels = pickle.load(f)\n self.alphaNumericLabels = dict([(v, k) for k, v in self.alphaNumericLabels.items()])\n\n with open(self.path + 'Models/AlphabetsLabels_v3.pkl', 'rb') as f:\n self.alphabetLabel = pickle.load(f)\n self.alphabetLabel = dict([(v, k) for k, v in self.alphabetLabel.items()])\n\n with open(self.path + 'Models/Digit_v2.pkl', 'rb') as f:\n self.digitLabel = pickle.load(f)\n self.digitLabel = dict([(v, k) for k, v in self.digitLabel.items()])\n\n with open(self.path + 'Models/label2_1.pickle', 'rb') as f:\n self.atrLabel = pickle.load(f)\n self.atrLabel = dict([(v, k) for k, v in self.atrLabel.items()])\n\n # getting the labels from pickled file #CNN_OCRModel_v1.h5\n with open(self.path + 'Models/_Labels.pkl', 'rb') as f:\n self._label = pickle.load(f)\n self._label = dict([(v, k) for k, v in self._label.items()])\n\n self.list_Character_Positions = []\n self.count = 0\n\n def getCountours(self, input_Image):\n\n x, contours, hierarchy = cv2.findContours(input_Image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n return contours\n\n def sortCountours(self, cnts, method=\"left-to-right\"):\n\n # initializing the reverse flag and sorting index\n reverse = False\n i = 0\n # handling the flag if we need to sort in reverse\n if method == \"right-to-left\" or method == \"bottom-to-top\":\n reverse = True\n # handle if we are sorting against the y-coordinate rather than\n # the x-coordinate of the bounding box\n if method == \"top-to-bottom\" or method == \"bottom-to-top\":\n i = 1\n # construct the list of bounding boxes and sort them from top to\n # bottom\n boundingBoxes = [cv2.boundingRect(c) for c in cnts]\n (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),\n key=lambda b: b[1][i], reverse=reverse))\n\n # return the list of sorted contours and bounding boxes\n return (cnts)\n\n def getNewResizedImage(self, input_Image, image_Size):\n\n height, width = input_Image.shape\n # print (height, width)\n\n if width > height:\n aspect_Ratio = (float)(width / height)\n width = 22\n height = round(width / aspect_Ratio)\n else:\n aspect_Ratio = (float)(height / width)\n height = 22\n width = round(height / aspect_Ratio)\n\n input_Image = cv2.resize(input_Image, (width, height), interpolation=cv2.INTER_AREA)\n\n height, width = input_Image.shape\n\n number_Of_Column_To_Add = 30 - width\n temp_Column = np.zeros((height, int(number_Of_Column_To_Add / 2)), dtype=np.uint8)\n input_Image = np.append(temp_Column, input_Image, axis=1)\n input_Image = np.append(input_Image, temp_Column, axis=1)\n\n height, width = input_Image.shape\n number_Of_Row_To_Add = 30 - height\n temp_Row = np.zeros((int(number_Of_Row_To_Add / 2), width), dtype=np.uint8)\n input_Image = np.concatenate((temp_Row, input_Image))\n input_Image = np.concatenate((input_Image, temp_Row))\n\n return cv2.resize(input_Image, (image_Size, image_Size), interpolation=cv2.INTER_AREA)\n\n def getTextFromImage(self, flag, image):\n\n alphabetPrediction = ''\n img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n ret, thres = cv2.threshold(img.copy(), 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)\n\n # number : represents wideness of the dilation. i.e for line level or word level or character level.\n kernel = np.ones((3, 3), np.uint8)\n dil_image = cv2.dilate(thres, kernel, iterations=1)\n\n c = self.getCountours(dil_image)\n c = self.sortCountours(c, \"left-to-right\")\n for c1 in c:\n area = cv2.contourArea(c1)\n # print(\"area=\", area)\n if area < 110:\n continue\n x, y, w, h = cv2.boundingRect(c1)\n # print(w, h)\n if 290 < area < 360 and w < 60 and h < 20:\n alphabetPrediction = alphabetPrediction + '_'\n continue\n elif 150 < area < 220 and w < 20 and h < 20:\n alphabetPrediction = alphabetPrediction + '.'\n continue\n\n else:\n if len(dil_image[y - 1:y + h + 1, x - 1:x + w + 1]) == 0:\n continue\n resize_i = cv2.resize(dil_image[y - 1:y + h + 1, x - 1:x + w + 1], (32, 32))\n\n resize_img = cv2.resize(resize_i, (28, 28))\n resize_image = Document.getNewResizedImage(self, resize_i, 32)\n\n self.count += 1\n\n if flag == 'email':\n # print('email Model Selected..!!')\n self.model = load_model(\"Models/sp_char_model1.h5\")\n self.label = self.atrLabel\n\n resize = np.expand_dims(resize_img, axis=0)\n resize = np.expand_dims(resize, axis=-1)\n charProb = self.model.predict(resize)[0]\n index = np.argmax(charProb)\n char = self.label[index]\n\n if char == 'junk':\n self.model = self.alphaNumericModel\n self.label = self.alphaNumericLabels\n flag = 'alphanumeric'\n else:\n alphabetPrediction = alphabetPrediction + char\n\n if flag == 'alphabet':\n # print('Alphabet Model Selected..!')\n self.model = self.alphabetModel\n self.label = self.alphabetLabel\n\n elif flag == 'digit':\n # print('DIGIT Model Selected..!!')\n self.model = self.digitModel\n self.label = self.digitLabel\n\n else:\n # print('AlphaNumeric Model Selected..!!')\n self.model = self.alphaNumericModel\n self.label = self.alphaNumericLabels\n\n resize_image = resize_image.reshape(1, 32, 32, 1)\n charProb = self.model.predict(resize_image)\n index = int(np.argmax(charProb))\n char = self.label[index]\n # print(\"char=\", char)\n alphabetPrediction = alphabetPrediction + char\n\n return alphabetPrediction\n", "repo_name": "shahpriyanshi/Optical-Character-Recognition", "sub_path": "OCR_For_Form/src/ParseDocument.py", "file_name": "ParseDocument.py", "file_ext": "py", "file_size_in_byte": 7903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "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.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 25, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 32, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 36, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 44, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 115, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 116, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 125, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 183, "usage_type": "call"}]} +{"seq_id": "32052712612", "text": "from typing import Optional\nfrom datetime import datetime, timedelta\nimport os\nimport requests\nimport time\nimport webbrowser\n\nfrom .constants import CLIENT_ID, AUDIENCE\n\n# all API call headers\nHEADERS = {\n 'content-type': 'application/x-www-form-urlencoded',\n}\n\n# device code API call body\nDEVICE_BODY = {\n \"client_id\": os.environ[CLIENT_ID],\n \"audience\": os.environ[AUDIENCE]\n}\n\n# access token API call body\nACCESS_BODY = {\n \"grant_type\": \"urn:ietf:params:oauth:grant-type:device_code\",\n \"client_id\": os.environ[CLIENT_ID]\n}\n\n#Perform OAuth 2.0 Device auth procedure\ndef oauth() -> Optional[str]: \n res = requests.post(\"https://dev-1ky8c6vy.us.auth0.com/oauth/device/code\", headers=HEADERS, data=DEVICE_BODY)\n try:\n res.raise_for_status()\n except Exception as e:\n print(e)\n return None\n \n res_json = res.json()\n \n try:\n user_code = res_json[\"user_code\"]\n interval = res_json[\"interval\"]\n verification_url = res_json[\"verification_uri_complete\"]\n \n ACCESS_BODY[\"device_code\"] = res_json[\"device_code\"]\n except KeyError as ke:\n print(f\"Auth0 response invalid: {ke}\")\n return None\n \n print(f\"Please visit {verification_url} using a browser and login/sign up.\")\n print(f\"Confirmation code is {user_code}.\")\n \n # open link in browser if possible\n webbrowser.open_new(verification_url)\n \n # wait 5 minutes (interval = 5 seconds usually)\n for _ in range(60):\n time.sleep(interval)\n \n res = requests.post(\"https://dev-1ky8c6vy.us.auth0.com/oauth/token\", headers=HEADERS, data=ACCESS_BODY)\n res_json = res.json()\n \n if (res.status_code == 200):\n token = res_json[\"access_token\"]\n expiry = res_json[\"expires_in\"]\n \n with open(\"./token_cache.txt\", \"w\") as file:\n file.write(token + \"\\n\")\n file.write(str(datetime.now() + timedelta(seconds=expiry)))\n \n return token\n\n if (res_json[\"error\"] != \"authorization_pending\"):\n print(res_json[\"error_description\"])\n return None\n \n print(\"Time limit of 5 minutes exceeded, exiting.\")\n return None\n \nif (__name__ == \"__main__\"):\n print(oauth())\n ", "repo_name": "KTong821/shopify-challenge", "sub_path": "data_engineering/src/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 2309, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "constants.CLIENT_ID", "line_number": 17, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "constants.AUDIENCE", "line_number": 18, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "constants.CLIENT_ID", "line_number": 24, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 29, "usage_type": "call"}, {"api_name": "webbrowser.open_new", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 67, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "34977633168", "text": "#!/usr/bin/env python3\n\nimport argparse\nimport csv\nimport concurrent.futures\nimport subprocess as sp\nfrom pathlib import Path\n\ndef read_bed_regions(bed_filename):\n with bed_filename.open() as bed:\n bedreader = csv.reader(bed, delimiter='\\t')\n res = []\n for row in bedreader:\n res.append(row[0] + ':' + str(int(row[1]) + 1) + '-' + row[2])\n return res\n\ndef remove_vcf(vcf_filename, remove_index=True):\n vcf_filename.unlink()\n if remove_index:\n vcf_index_filename = vcf_filename.with_suffix(vcf_filename.suffix + '.tbi')\n if vcf_index_filename:\n vcf_index_filename.unlink()\n\ndef gatk_call_helper(reference, bam, region, output, ploidy):\n sp.call([\n \"gatk\", \\\n \"--java-options\", \"-Xmx4g -XX:ParallelGCThreads=1\", \\\n \"HaplotypeCaller\", \\\n \"-R\", reference, \\\n \"-I\", bam, \\\n \"-L\", region, \\\n \"-O\", output, \\\n \"-ploidy\", str(ploidy), \\\n \"--native-pair-hmm-threads\", \"1\", \\\n \"-stand-call-conf\", \"10\"\n ])\n\ndef main(args):\n regions = read_bed_regions(args.regions)\n tmp_dir = args.output.with_suffix('.tmp')\n tmp_dir.mkdir(exist_ok=True)\n tmp_vcfs = [tmp_dir / (region.replace(':', '_') + '.vcf.gz') for region in regions]\n with concurrent.futures.ThreadPoolExecutor(max_workers=args.threads) as executor:\n futures = []\n for region, tmp_vcf in zip(regions, tmp_vcfs):\n futures.append(executor.submit(gatk_call_helper, reference=args.reference, bam=args.bam, region=region, output=tmp_vcf, ploidy=args.sample_ploidy))\n for future in concurrent.futures.as_completed(futures):\n print(future.result())\n sp.call(['bcftools', 'concat', '-Oz', '-o', args.output] + tmp_vcfs)\n sp.call(['tabix', args.output])\n for tmp_vcf in tmp_vcfs:\n remove_vcf(tmp_vcf)\n tmp_dir.rmdir()\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-R', '--reference',\n type=Path,\n required=True,\n help='Reference FASTA')\n parser.add_argument('-I', '--bam',\n type=Path,\n required=True,\n help='Input BAM')\n parser.add_argument('-L', '--regions',\n type=Path,\n required=True,\n help='Regions BED')\n parser.add_argument('-O', '--output',\n type=Path,\n required=True,\n help='Output VCF')\n parser.add_argument('--sample-ploidy',\n type=int,\n default=2,\n help='Sample ploidy')\n parser.add_argument('--threads',\n type=int,\n default=1,\n help='Threads')\n parsed, unparsed = parser.parse_known_args()\n main(parsed)\n", "repo_name": "luntergroup/polyploid", "sub_path": "workflow/scripts/gatk_parallel.py", "file_name": "gatk_parallel.py", "file_ext": "py", "file_size_in_byte": 2960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "60", "api": [{"api_name": "csv.reader", "line_number": 11, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 25, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 43, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 43, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 43, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.as_completed", "line_number": 47, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 47, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 47, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 49, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 50, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 56, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 58, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 62, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 66, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "74468931712", "text": "import asyncio\n\nimport libdlt.protocol.ceph.rados.core as rados\nfrom libdlt.protocol.ceph.rados.core import Operation, ReadOperation, WriteOperation\n\nclass Ioctx:\n pass\n\nclass Cluster(rados.Cluster):\n def __init__(self, rados_id=None, name=None, clustername=None,\n conf_defaults=None, conffile=None, conf=None, flags=0):\n super().__init__(rados_id, name, clustername, conf_defaults, conffile, conf, flags)\n\n def open_aioctx(rados, pool_name: str, loop=None):\n ioctx = rados._open_ioctx_raw(pool_name)\n return Ioctx(pool_name, rados.librados, ioctx, loop=loop)\n\nclass _Completion(rados.Completion):\n def __init__(self, ioctx: Ioctx, loop, wait_on_safe=False):\n \"\"\"\n :param safe: can't be True for read operation\n \"\"\"\n self.loop = loop\n self.future = asyncio.Future(loop=loop)\n if wait_on_safe:\n super().__init__(ioctx, onsafe=self.__done)\n else:\n super().__init__(ioctx, oncomplete=self.__done)\n\n def __done(self, _):\n self.loop.call_soon_threadsafe(self.future.set_result, True)\n\n @asyncio.coroutine\n def complete(self):\n yield from self.future\n # after this point we can GC the _Completion object, because we are sure\n # the only CB has completed\n return self.get_return_value()\n\nclass Ioctx(rados.Ioctx):\n def __init__(self, name, librados, io, loop=None):\n self.loop = loop if loop else asyncio.get_event_loop()\n super().__init__(name, librados, io)\n\n @asyncio.coroutine\n def aio_read(self, oid: str, length=8192, offset=0):\n com = _Completion(self, self.loop)\n buffer = super().aio_read(oid, com, length=length, offset=offset)\n ret = yield from com.complete()\n if ret < 0:\n raise rados.make_ex(ret, \"Ioctx.aio_read(%s): failed to read %s\" % (self.name, oid))\n\n return buffer.read(ret)\n\n @asyncio.coroutine\n def aio_read_op_operate(self, oid: str, op: ReadOperation, flags=Operation.Flag.none):\n com = _Completion(self, self.loop)\n super().aio_read_op_operate(oid, op, com, flags)\n ret = yield from com.complete()\n if ret < 0:\n raise rados.make_ex(ret, \"Ioctx.aio_read_op_operate(%s): failed to read %s\" % (self.name, oid))\n\n @asyncio.coroutine\n def aio_write_op_operate(self, oid: str, op: WriteOperation, time=None, flags=Operation.Flag.none, wait_on_safe=False):\n com = _Completion(self, self.loop, wait_on_safe)\n super().aio_write_op_operate(oid, op, com, time, flags)\n ret = yield from com.complete()\n if ret < 0:\n raise rados.make_ex(ret, \"Ioctx.aio_write_op_operate(%s): failed to read %s\" % (self.name, oid))\n", "repo_name": "datalogistics/libdlt", "sub_path": "libdlt/protocol/ceph/rados/asyncio.py", "file_name": "asyncio.py", "file_ext": "py", "file_size_in_byte": 2743, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "libdlt.protocol.ceph.rados.core.Cluster", "line_number": 9, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 9, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core._open_ioctx_raw", "line_number": 15, "usage_type": "call"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 15, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core.librados", "line_number": 16, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 16, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core.Completion", "line_number": 18, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 18, "usage_type": "name"}, {"api_name": "asyncio.Future", "line_number": 24, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 33, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core.Ioctx", "line_number": 40, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 40, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 42, "usage_type": "call"}, {"api_name": "libdlt.protocol.ceph.rados.core.make_ex", "line_number": 51, "usage_type": "call"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 51, "usage_type": "name"}, {"api_name": "asyncio.coroutine", "line_number": 45, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core.ReadOperation", "line_number": 56, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core.Operation.Flag", "line_number": 56, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core.Operation", "line_number": 56, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core.make_ex", "line_number": 61, "usage_type": "call"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 61, "usage_type": "name"}, {"api_name": "asyncio.coroutine", "line_number": 55, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core.WriteOperation", "line_number": 64, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core.Operation.Flag", "line_number": 64, "usage_type": "attribute"}, {"api_name": "libdlt.protocol.ceph.rados.core.Operation", "line_number": 64, "usage_type": "name"}, {"api_name": "libdlt.protocol.ceph.rados.core.make_ex", "line_number": 69, "usage_type": "call"}, {"api_name": "libdlt.protocol.ceph.rados.core", "line_number": 69, "usage_type": "name"}, {"api_name": "asyncio.coroutine", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "28386632461", "text": "# coding: utf-8\nimport cherrypy\nfrom .database import Database_cl\nfrom .view import View_cl\n\n\n# ----------------------------------------------------------\nclass Application_cl(object):\n # ----------------------------------------------------------\n\n # -------------------------------------------------------\n def __init__(self):\n # -------------------------------------------------------\n # seperatie db_o objects to store employees and trainings\n self.db_employee = Database_cl(\"employee\")\n # change add,edit,and other route functions to handle seperate trainings db\n self.db_trainings = Database_cl(\"trainings\")\n self.db_trainingrelations = Database_cl(\"trainingRelations\")\n self.db_employeeparticipation = Database_cl(\"employeeParticipations\")\n self.db_certs = Database_cl(\"certs\")\n self.db_qualifications = Database_cl(\"quali\")\n testdata = self.db_qualifications.read_px()\n self.view_o = View_cl()\n @cherrypy.expose\n def Weiterbildungen(self, **params):\n form = params.get(\"nothing\", \"tabelle\")\n return self.createAuswertung_Weiterbildung()\n\n def createAuswertung_Weiterbildung(self):\n data_o = self.db_trainingrelations.read_px()\n # all trainings in data_o\n training_id = []\n sorted_array = []\n\n # Put all id´s in one array\n for training in data_o.values():\n training_id.append(training[0])\n\n # Sort the array\n training_id.sort()\n\n # Fill the new array with ordered entries\n for id_ in training_id:\n for training in data_o.values():\n if id_ == training[0]:\n sorted_array.append(training)\n break\n\n for counter, index in enumerate(data_o):\n data_o[index] = sorted_array[counter]\n\n return self.view_o.createAuswertungWeiterbildung(data_o)\n\n @cherrypy.expose\n def Zertifikate(self):\n return self.createAuswertung_Zertifikate()\n\n def createAuswertung_Zertifikate(self):\n data_o = self.db_certs.read_px()\n\n certificate_array = []\n sorted_array = []\n\n for certificate in data_o.values():\n certificate_array.append(certificate[0])\n\n certificate_array.sort()\n\n # Fill the new array with ordered entries\n for id_ in certificate_array:\n for training in data_o.values():\n if id_ == training[0]:\n sorted_array.append(training)\n break\n\n for counter, index in enumerate(data_o):\n data_o[index] = sorted_array[counter]\n\n return self.view_o.createAusertungCerts(data_o)\n\n @cherrypy.expose\n def Mitarbeiter(self, **params):\n form = params.get(\"nothing\", \"tabelle\")\n return self.createAuswertung_Mitarbeiter()\n\n def createAuswertung_Mitarbeiter(self, id_spl=None):\n # data_o = self.db_employee.read_px(id_spl)\n data_o = self.db_employeeparticipation.read_px()\n trainings = self.db_trainings.read_px()\n res = []\n temp_array = {}\n for counter, key in enumerate(data_o.values()):\n res.append(key[1]) # 1 weil 1 = Nachname\n res.sort()\n for name in res:\n for _ in data_o:\n if data_o[_][1] == name:\n temp_array[_] = data_o[_]\n\n for person in temp_array.values():\n if type(person[-1]) == list:\n for training in person[-1]:\n for trai in trainings.values():\n if training[0] == trai[0]:\n person[-1][person[-1].index(training)].append(trai[1])\n person[-1][person[-1].index(training)].append(trai[2])\n else:\n person.append([])\n\n # Sort the dates\n\n for person in temp_array.values():\n sorted_trainings = []\n dates = []\n for training in person[-1]:\n dates.append(training[3])\n dates.sort()\n for date in dates:\n for training in person[-1]:\n if training[3] == date:\n sorted_trainings.append(training)\n person[-1] = sorted_trainings\n return self.view_o.createFormauswertungMitarbeiter(temp_array)\n\n @cherrypy.expose\n # -------------------------------------------------------\n def index(self, **params):\n # -------------------------------------------------------\n form = params.get(\"index\", \"Startseite\")\n return self.createContent_p(form)\n\n @cherrypy.expose\n # -------------------------------------------------------\n def add(self, **params):\n # -------------------------------------------------------\n form = params.get(\"nothing\", \"tabelle\")\n #deliver htmlform to add employee\n return self.createForm_p(listform=form)\n\n @cherrypy.expose\n def addtrainings(self, **params):\n form = params.get(\"nothing\", \"tabelle\")\n return self.createForm_trainings(listform=form)\n\n @cherrypy.expose\n def showtrainingsdetail(self, id_spl, **params):\n form = params.get(\"index\", \"tabelle\")\n\n return self.createDetail(id_spl)\n\n @cherrypy.expose\n def showdetailt(self, id_t, **params):\n # get the training we need details for\n training = self.db_trainingrelations.read_px(id_t)\n # get list of participants( if there is no list we dont get a list)\n # so check if participants is list\n participants = training[len(training)-1]\n if isinstance(participants, list):\n # data present\n\n data_o = training\n data_p = participants\n return self.view_o.createDetailTrainings(data_o, data_p)\n else:\n data_o = training\n data_p = []\n return self.view_o.createDetailTrainings(data_o, data_p)\n\n\n @cherrypy.expose\n # -------------------------------------------------------\n def edit(self, id_spl, **params):\n # -------------------------------------------------------\n listform = params.get(\"listform\", \"tabelle\")\n return self.createForm_p(id_spl=id_spl, listform=listform)\n\n @cherrypy.expose\n def edittrainings(self, id_spl, **params):\n listform = params.get(\"listform\", \"tabelle\")\n return self.createForm_trainings(id_spl=id_spl, listform=listform)\n\n @cherrypy.expose\n # -------------------------------------------------------\n def save(self, id_spa, name_spa, vorname_spa, akademic_spa, tatigkeit_spa, **params):\n # -------------------------------------------------------\n id_s = id_spa\n data_a = [name_spa, vorname_spa, akademic_spa, tatigkeit_spa]\n if id_s != \"None\":\n self.db_employee.update_px(id_s, data_a)\n else:\n self.db_employee.create_px(data_a)\n listform = params.get(\"listform\", \"tabelle\")\n raise cherrypy.HTTPRedirect(\"/?index=Pflege_Mitarbeiterdaten\")\n #return self.createContent_p(listform)\n\n @cherrypy.expose\n # -------------------------------------------------------\n def default(self, *arguments, **kwargs):\n # -------------------------------------------------------\n msg_s = \"unbekannte Anforderung: \" + \\\n str(arguments) + \\\n ' ' + \\\n str(kwargs)\n raise cherrypy.HTTPError(404, msg_s)\n\n default.exposed = True\n\n @cherrypy.expose\n # -------------------------------------------------------\n def delete(self, id_spl, **params):\n # -------------------------------------------------------\n listform = params.get(\"listform\", \"tabelle\")\n if self.db_employee.delete_px(id_spl):\n raise cherrypy.HTTPRedirect(\"/?index=Pflege_Mitarbeiterdaten\")\n else:\n raise cherrypy.HTTPError(500, \"Existiert nicht\")\n\n # -------------------------------------------------------\n @cherrypy.expose\n def canceltraining(self, training, employee):\n if self.db_trainingrelations.delete_employee_px(training, employee):\n raise cherrypy.HTTPRedirect(\"/?index=Sichtweise_Weiterbildungen\")\n else:\n raise cherrypy.HTTPError(500, \"Existiert nicht\")\n\n @cherrypy.expose\n def deletetrainings(self, id_spl, **params):\n listform = params.get(\"listform\", \"tabelle\")\n if self.db_trainings.delete_px(id_spl):\n raise cherrypy.HTTPRedirect(\"/?index=Pflege_Weiterbildungen\")\n else:\n raise cherrypy.HTTPError(500, \"Existiert nicht\")\n\n def createList_p(self, listform):\n # -------------------------------------------------------\n data_o = self.db_employee.read_px()\n return self.view_o.createList_px(data_o, listform)\n\n # -------------------------------------------------------\n def createForm_p(self, listform, id_spl=None):\n # -------------------------------------------------------\n if id_spl != None:\n data_o = self.db_employee.read_px(id_spl)\n else:\n data_o = self.db_employee.getDefault_px()\n return self.view_o.createForm_px(id_spl=id_spl, data_opl=data_o, listform=listform)\n\n @cherrypy.expose\n def savetraining(self, id_spa, bezeichnung_spa, Von_spa, Bis_spa, beschreibung_spa, maxteilnehmer_spa, minteilnehmer_spa, **params):\n # -------------------------------------------------------\n id_s = id_spa\n data_a = [bezeichnung_spa, Von_spa, Bis_spa, beschreibung_spa, maxteilnehmer_spa, minteilnehmer_spa]\n if id_s != \"None\":\n self.db_trainings.update_px(id_s, data_a)\n else:\n self.db_trainings.create_px(data_a)\n self.db_trainingrelations.create_px(data_a)\n listform = params.get(\"listform\", \"tabelle\")\n raise cherrypy.HTTPRedirect(\"/?index=Pflege_Weiterbildungen\")\n # return self.createContent_p(listform)\n\n def createForm_trainings(self, listform, id_spl=None):\n if id_spl != None:\n data_o = self.db_trainings.read_px(id_spl)\n else:\n data_o = self.db_trainings.getDefault_px()\n return self.view_o.createForm_trainings(id_spl=id_spl, data_opl=data_o, listform=listform)\n\n @cherrypy.expose\n def showdetailpflegeemploy(self, id_spl):\n # get participations of employee\n participations = self.db_employeeparticipation.read_px(id_spl)\n employeeparticipations = participations[4]\n employee = self.db_employee.read_px(id_spl)\n # get certifications of employee\n certs = self.db_certs.read_px()\n certsofemployee = []\n for key_s in certs:\n for x in certs[key_s][3]:# x[0] is the name of employee, x[2] id\n if x[2] == id_spl:\n certsofemployee.append(certs[key_s][0])\n\n data_o = employeeparticipations\n data_c = certsofemployee\n data_p = employee\n return self.view_o.createDetailPflegeMitarbeiter(data_o, data_c, data_p)\n\n\n\n\n\n def createDetail(self, id_spl):\n # here we need to read all trainings from this employee(with the ID)\n\n # reading employee data\n data_o = self.db_employee.read_px(id_spl)\n data_o.append(id_spl)\n\n # reading training data for that employee\n data_p = self.db_trainingrelations.data_o\n\n # get name of employee\n name = data_o[0]\n sure_name = data_o[1]\n\n # arrays\n applied_trainings = []\n non_applied_trainings = []\n\n # for each training in database\n for training in data_p.values():\n # participants are set located in the last entry of each training\n participants = training[-1]\n\n participated_in_training = False\n\n # Check for every person whether he or she participated in the training\n for person in participants:\n if sure_name in person and name in person:\n participated_in_training = True\n break\n\n # Check the boolean and act accordingly\n if participated_in_training:\n applied_trainings.append(training)\n else:\n non_applied_trainings.append(training)\n\n return self.view_o.createDetail(data_o, applied_trainings, non_applied_trainings)\n\n def createStartSeite(self):\n # get maxID of employee\n Employe = self.db_employee.read_px()\n # get maxID of trainings\n Trainings = self.db_trainings.read_px()\n # get number of participations\n participations = self.db_employeeparticipation.read_px()\n num = 0\n for key_s in participations:\n num = num + len(participations[key_s][4])\n data_e = len(Employe)\n data_t = len(Trainings)\n data_p = num\n return self.view_o.createStartseite(data_e, data_t, data_p)\n\n def createContent_p(self, form):\n if form == \"Pflege_Weiterbildungen\":\n data_o = self.db_trainings.read_px()\n elif form == \"Pflege_Mitarbeiterdaten\":\n data_o = self.db_employee.read_px()\n elif form == \"Sichtweise_Mitarbeiter\":\n data_o = self.db_employee.read_px()\n elif form == \"Sichtweise_Weiterbildungen\":\n data_o = self.db_trainings.read_px()\n elif form == \"Startseite\":\n return self.createStartSeite()\n else:\n data_o = self.db_employee.getDefault_px()\n\n return self.view_o.createContent_px(data_o, form)\n# EOF\n", "repo_name": "hehoe006/WebP2", "sub_path": "app/application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 13581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "database.Database_cl", "line_number": 15, "usage_type": "call"}, {"api_name": "database.Database_cl", "line_number": 17, "usage_type": "call"}, {"api_name": "database.Database_cl", "line_number": 18, "usage_type": "call"}, {"api_name": "database.Database_cl", "line_number": 19, "usage_type": "call"}, {"api_name": "database.Database_cl", "line_number": 20, "usage_type": "call"}, {"api_name": "database.Database_cl", "line_number": 21, "usage_type": "call"}, {"api_name": "view.View_cl", "line_number": 23, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 81, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 125, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 132, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 140, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 145, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 151, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 170, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 177, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPRedirect", "line_number": 193, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 182, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPError", "line_number": 204, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 196, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPRedirect", "line_number": 214, "usage_type": "call"}, {"api_name": "cherrypy.HTTPError", "line_number": 216, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 208, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPRedirect", "line_number": 222, "usage_type": "call"}, {"api_name": "cherrypy.HTTPError", "line_number": 224, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 219, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPRedirect", "line_number": 230, "usage_type": "call"}, {"api_name": "cherrypy.HTTPError", "line_number": 232, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 226, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPRedirect", "line_number": 259, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 248, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 269, "usage_type": "attribute"}]} +{"seq_id": "34141400842", "text": "from django.urls import path\nfrom parent.views import (my_children,children_detail,results_home,student_results,course_mark_detail,children_report_card,\n contact_teacher,report_card_home)\nurlpatterns = [\n path('my_children/', my_children, name='my children'),\n path('children_detail/', children_detail, name='children detail'),\n path('results_home/', results_home, name='results home'),\n path('student_results/', student_results, name='student results'),\n path('course_mark_detail/',course_mark_detail,name='course mark detail'),\n path('children_report_card/',children_report_card,name='children report card'),\n path('contact_teacher/',contact_teacher,name='contact teacher'),\n path('report_card_home/',report_card_home,name='report card home')\n]\n", "repo_name": "ateba-creator/schoolmanagementsystem", "sub_path": "Mainproject/parent/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "parent.views.my_children", "line_number": 5, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "parent.views.children_detail", "line_number": 6, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "parent.views.results_home", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "parent.views.student_results", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "parent.views.course_mark_detail", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "parent.views.children_report_card", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "parent.views.contact_teacher", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "parent.views.report_card_home", "line_number": 12, "usage_type": "argument"}]} +{"seq_id": "13750184023", "text": "# Create your views here\nfrom django.http import HttpResponse\nfrom django.utils import simplejson\nfrom django.contrib.gis.geos import Point\n\n#http://localhost:8000/locations/nearest_to_point/?lat=4.679467&long=-74.050083\nfrom locations.models import Location\n\ndef get_nearest_to_point(request):\n latitude = request.GET.get('lat', 0)\n longitude = request.GET.get('long', 0)\n\n point = Point(float(latitude), float(longitude))\n locations = Location.objects.distance(point).order_by('distance')\n\n list = []\n for location in locations:\n list.append({'name':location.name,\n 'distance':location.distance.m,\n 'latitude':location.location.x,\n 'longitude':location.location.y,\n 'address':location.address,\n 'id':location.id,\n 'type':{\n 'id':location.type.id,\n 'name':location.type.name\n }\n })\n\n return HttpResponse(simplejson.dumps(list), mimetype='application/json')\n", "repo_name": "panchicore/andromorb", "sub_path": "locations/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.contrib.gis.geos.Point", "line_number": 13, "usage_type": "call"}, {"api_name": "locations.models", "line_number": 14, "usage_type": "name"}, {"api_name": "locations.models.Location.objects.distance", "line_number": 14, "usage_type": "call"}, {"api_name": "locations.models.Location.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "locations.models.Location", "line_number": 14, "usage_type": "name"}, {"api_name": "locations.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.simplejson.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.simplejson", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "7087549075", "text": "import pygame\nimport os\n\n\nclass Rocket():\n def __init__(self, game):\n self.game = game\n self.image = pygame.image.load(os.path.join(\"assets/sprites\", \"spaceship-a.svg\"))\n self.image = pygame.transform.rotate(self.image, -90)\n self.width, self.height = self.image.get_size()\n self.width, self.height = round(self.width * .4), round(self.height * .4)\n self.image = pygame.transform.scale(self.image, (self.width, self.height))\n self.x, self.y = 0, self.game.DISPLAY_H / 2 - self.height / 2\n self.vel = 1\n self.life = 10 # Initial amount of lives\n\n def starting_position(self, pos):\n if pos == 'CENTER':\n return self.game.DISPLAY_W / 2 - self.width / 2, self.game.DISPLAY_H / 2 - self.height / 2\n if pos == 'TOP':\n return self.game.DISPLAY_W / 2 - self.width / 2, 0\n if pos == 'BOTTOM':\n return self.game.DISPLAY_W / 2 - self.width / 2, self.game.DISPLAY_H - self.height\n if pos == 'LEFT':\n return 0, self.game.DISPLAY_H / 2 - self.height / 2\n if pos == 'RIGHT':\n return self.game.DISPLAY_W - self.width, self.game.DISPLAY_H / 2 - self.height / 2\n\n def blit_rocket(self): # Displaying rocket image\n self.game.display.blit(self.image, (self.x, self.y))\n\n def move_rocket(self): # Moving the rocket and checking the boundaries\n if self.game.LEFT_KEY:\n if self.x - self.vel > 0:\n self.x -= self.vel\n if self.game.RIGHT_KEY:\n if self.x + self.vel < self.game.DISPLAY_W - self.width:\n self.x += self.vel\n if self.game.UP_KEY:\n if self.y - self.vel > 0:\n self.y -= self.vel\n if self.game.DOWN_KEY:\n if self.y + self.vel < self.game.DISPLAY_H - self.height:\n self.y += self.vel\n\n def collision_rocket(self, asteroid):\n if asteroid.y + asteroid.height > self.y and asteroid.y < self.y + self.height \\\n and asteroid.x < self.x + self.width / 2 and asteroid.x + asteroid.width > self.x:\n return True\n elif asteroid.y + asteroid.height > self.y + self.height / self.width * (asteroid.x + asteroid.width / 2) \\\n and asteroid.y < self.y - self.height / self.width * (asteroid.x + asteroid.width / 2) \\\n and asteroid.x < self.x + self.width \\\n and asteroid.x + asteroid.width > self.x + self.width / 2:\n return True\n else:\n return False\n", "repo_name": "ThomasRochais/Astrododge", "sub_path": "rocket.py", "file_name": "rocket.py", "file_ext": "py", "file_size_in_byte": 2544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pygame.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotate", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 12, "usage_type": "attribute"}]} +{"seq_id": "32856433265", "text": "import ast\nimport inspect\nimport logging\nimport sys\nimport textwrap\nfrom collections import namedtuple\nfrom copy import deepcopy\nfrom dataclasses import is_dataclass\nfrom importlib import import_module\nfrom typing import Any, Dict, ForwardRef, FrozenSet, List, Optional, Set, Tuple, Type, Union, get_type_hints\n\nfrom ._optionals import typing_extensions_import\nfrom ._typehints import mapping_origin_types, sequence_origin_types, tuple_set_origin_types\nfrom ._util import get_typehint_origin\n\nvar_map = namedtuple(\"var_map\", \"name value\")\nnone_map = var_map(name=\"NoneType\", value=type(None))\nunion_map = var_map(name=\"Union\", value=Union)\npep585_map = {\n \"dict\": var_map(name=\"Dict\", value=Dict),\n \"frozenset\": var_map(name=\"FrozenSet\", value=FrozenSet),\n \"list\": var_map(name=\"List\", value=List),\n \"set\": var_map(name=\"Set\", value=Set),\n \"tuple\": var_map(name=\"Tuple\", value=Tuple),\n \"type\": var_map(name=\"Type\", value=Type),\n}\n\n\nclass BackportTypeHints(ast.NodeTransformer):\n def visit_Subscript(self, node: ast.Subscript) -> ast.Subscript:\n if isinstance(node.value, ast.Name) and node.value.id in pep585_map:\n value = self.new_name_load(pep585_map[node.value.id])\n else:\n value = node.value # type: ignore\n return ast.Subscript(\n value=value,\n slice=self.visit(node.slice),\n ctx=ast.Load(),\n )\n\n def visit_Constant(self, node: ast.Constant) -> Union[ast.Constant, ast.Name]:\n if node.value is None:\n return self.new_name_load(none_map)\n return node\n\n def visit_BinOp(self, node: ast.BinOp) -> Union[ast.BinOp, ast.Subscript]:\n out_node: Union[ast.BinOp, ast.Subscript] = node\n if isinstance(node.op, ast.BitOr):\n elts: list = []\n self.append_union_elts(node.left, elts)\n self.append_union_elts(node.right, elts)\n out_node = ast.Subscript(\n value=self.new_name_load(union_map),\n slice=ast.Index(\n value=ast.Tuple(elts=elts, ctx=ast.Load()),\n ctx=ast.Load(),\n ),\n ctx=ast.Load(),\n )\n return out_node\n\n def append_union_elts(self, node: ast.AST, elts: list) -> None:\n if isinstance(node, ast.BinOp) and isinstance(node.op, ast.BitOr):\n self.append_union_elts(node.left, elts)\n self.append_union_elts(node.right, elts)\n else:\n elts.append(self.visit(node))\n\n def new_name_load(self, var: var_map) -> ast.Name:\n name = f\"_{self.__class__.__name__}_{var.name}\"\n self.exec_vars[name] = var.value\n return ast.Name(id=name, ctx=ast.Load())\n\n def backport(self, input_ast: ast.AST, exec_vars: dict) -> ast.AST:\n typing = __import__(\"typing\")\n for key, value in exec_vars.items():\n if getattr(value, \"__module__\", \"\") == \"collections.abc\":\n if hasattr(typing, key):\n exec_vars[key] = getattr(typing, key)\n self.exec_vars = exec_vars\n backport_ast = self.visit(deepcopy(input_ast))\n return ast.fix_missing_locations(backport_ast)\n\n\nclass NamesVisitor(ast.NodeVisitor):\n def visit_Name(self, node: ast.Name) -> None:\n self.names_found.append(node.id)\n\n def find(self, node: ast.AST) -> list:\n from ._util import unique\n\n self.names_found: List[str] = []\n self.visit(node)\n self.names_found = unique(self.names_found)\n return self.names_found\n\n\nclass TypeCheckingVisitor(ast.NodeVisitor):\n type_checking_names: List[str] = []\n\n def visit_Import(self, node: ast.Import) -> None:\n for alias in node.names:\n if alias.name == \"typing\":\n name = ast.dump(\n ast.Attribute(\n value=ast.Name(id=alias.asname or \"typing\", ctx=ast.Load()),\n attr=\"TYPE_CHECKING\",\n ctx=ast.Load(),\n )\n )\n self.type_checking_names.append(name)\n break\n\n def visit_ImportFrom(self, node: ast.ImportFrom) -> None:\n if node.module == \"typing\":\n for alias in node.names:\n if alias.name == \"TYPE_CHECKING\":\n name = ast.dump(ast.Name(id=alias.asname or \"TYPE_CHECKING\", ctx=ast.Load()))\n self.type_checking_names.append(name)\n break\n\n def visit_If(self, node: ast.If) -> None:\n if (\n isinstance(node.test, (ast.Name, ast.Attribute))\n and any(ast.dump(node.test) == n for n in self.type_checking_names)\n ) or (\n isinstance(node.test, ast.BoolOp)\n and isinstance(node.test.op, (ast.And, ast.Or))\n and any(ast.dump(v) == n for n in self.type_checking_names for v in node.test.values)\n ):\n ast_exec = ast.parse(\"\")\n ast_exec.body = node.body\n try:\n exec(compile(ast_exec, filename=\"\", mode=\"exec\"), self.aliases, self.aliases)\n except Exception as ex:\n if self.logger:\n self.logger.debug(f\"Failed to execute 'TYPE_CHECKING' block in '{self.module}'\", exc_info=ex)\n\n def generic_visit(self, node: ast.AST) -> None:\n if isinstance(node, (ast.If, ast.Module)):\n super().generic_visit(node)\n\n def update_aliases(\n self, module_source: str, module: str, aliases: dict, logger: Optional[logging.Logger] = None\n ) -> None:\n self.module = module\n self.aliases = aliases\n self.logger = logger\n module_tree = ast.parse(module_source)\n self.visit(module_tree)\n\n\ndef get_arg_type(arg_ast, aliases):\n type_ast = ast.parse(\"___arg_type___ = 0\")\n type_ast.body[0].value = arg_ast\n exec_vars = {}\n bad_aliases = {}\n add_asts = False\n for name in NamesVisitor().find(arg_ast):\n value = aliases[name]\n if isinstance(value, tuple):\n value = value[1]\n if isinstance(value, Exception):\n bad_aliases[name] = value\n elif isinstance(value, ast.AST):\n add_asts = True\n else:\n exec_vars[name] = value\n if add_asts:\n body = []\n for name, (_, value) in aliases.items():\n if isinstance(value, ast.AST):\n body.append(ast.fix_missing_locations(value))\n elif not isinstance(value, Exception):\n exec_vars[name] = value\n type_ast.body = body + type_ast.body\n if \"TypeAlias\" not in exec_vars:\n type_alias = typing_extensions_import(\"TypeAlias\")\n if type_alias:\n exec_vars[\"TypeAlias\"] = type_alias\n if sys.version_info < (3, 10):\n backporter = BackportTypeHints()\n type_ast = backporter.backport(type_ast, exec_vars)\n try:\n exec(compile(type_ast, filename=\"\", mode=\"exec\"), exec_vars, exec_vars)\n except NameError as ex:\n ex_from = None\n for name, alias_exception in bad_aliases.items():\n if str(ex) == f\"name '{name}' is not defined\":\n ex_from = alias_exception\n break\n raise ex from ex_from\n return exec_vars[\"___arg_type___\"]\n\n\ndef getattr_recursive(obj, attr):\n if \".\" in attr:\n attr, *attrs = attr.split(\".\", 1)\n return getattr_recursive(getattr(obj, attr), attrs[0])\n return getattr(obj, attr)\n\n\ndef resolve_forward_refs(arg_type, aliases, logger):\n if isinstance(arg_type, str) and arg_type in aliases:\n arg_type = aliases[arg_type]\n\n def resolve_subtypes_forward_refs(typehint):\n if has_subtypes(typehint):\n try:\n subtypes = []\n for arg in typehint.__args__:\n if isinstance(arg, ForwardRef):\n forward_arg, *forward_args = arg.__forward_arg__.split(\".\", 1)\n if forward_arg in aliases:\n arg = aliases[forward_arg]\n if forward_args:\n arg = getattr_recursive(arg, forward_args[0])\n else:\n raise NameError(f\"Name '{forward_arg}' is not defined\")\n else:\n arg = resolve_subtypes_forward_refs(arg)\n subtypes.append(arg)\n if subtypes != list(typehint.__args__):\n typehint_origin = get_typehint_origin(typehint)\n if sys.version_info < (3, 10):\n if typehint_origin in sequence_origin_types:\n typehint_origin = List\n elif typehint_origin in tuple_set_origin_types:\n typehint_origin = Tuple\n elif typehint_origin in mapping_origin_types:\n typehint_origin = Dict\n typehint = typehint_origin[tuple(subtypes)]\n except Exception as ex:\n if logger:\n logger.debug(f\"Failed to resolve forward refs in {typehint}\", exc_info=ex)\n return typehint\n\n return resolve_subtypes_forward_refs(arg_type)\n\n\ndef has_subtypes(typehint):\n typehint_origin = get_typehint_origin(typehint)\n return (\n typehint_origin == Union\n or typehint_origin in sequence_origin_types\n or typehint_origin in tuple_set_origin_types\n or typehint_origin in mapping_origin_types\n )\n\n\ndef type_requires_eval(typehint):\n if has_subtypes(typehint):\n return any(type_requires_eval(a) for a in getattr(typehint, \"__args__\", []))\n return isinstance(typehint, (str, ForwardRef))\n\n\ndef get_types(obj: Any, logger: Optional[logging.Logger] = None) -> dict:\n global_vars = vars(import_module(obj.__module__))\n try:\n types = get_type_hints(obj, global_vars)\n except Exception as ex1:\n types = ex1 # type: ignore\n\n if isinstance(types, dict) and all(not type_requires_eval(t) for t in types.values()):\n return types\n\n try:\n source = textwrap.dedent(inspect.getsource(obj))\n tree = ast.parse(source)\n assert isinstance(tree, ast.Module) and len(tree.body) == 1\n node = tree.body[0]\n assert isinstance(node, (ast.FunctionDef, ast.ClassDef))\n except Exception as ex2:\n if isinstance(types, Exception):\n if logger:\n logger.debug(f\"Failed to parse to source code for {obj}\", exc_info=ex2)\n raise type(types)(f\"{repr(types)} + {repr(ex2)}\") from ex2 # type: ignore\n return types\n\n aliases = __builtins__.copy() # type: ignore\n aliases.update(global_vars)\n ex = None\n if isinstance(types, Exception):\n ex = types\n types = {}\n\n module_source = inspect.getsource(sys.modules[obj.__module__]) if obj.__module__ in sys.modules else \"\"\n if \"TYPE_CHECKING\" in module_source:\n TypeCheckingVisitor().update_aliases(module_source, obj.__module__, aliases, logger)\n\n if isinstance(node, ast.FunctionDef):\n arg_asts = [(a.arg, a.annotation) for a in node.args.args + node.args.kwonlyargs]\n else:\n arg_asts = [(a.target.id, a.annotation) for a in node.body if isinstance(a, ast.AnnAssign)] # type: ignore\n\n for name, annotation in arg_asts:\n if annotation and (name not in types or type_requires_eval(types[name])):\n try:\n if isinstance(annotation, ast.Constant) and annotation.value in aliases:\n types[name] = aliases[annotation.value]\n else:\n arg_type = get_arg_type(annotation, aliases)\n types[name] = resolve_forward_refs(arg_type, aliases, logger)\n except Exception as ex3:\n types[name] = ex3\n\n if all(isinstance(t, Exception) for t in types.values()):\n raise ex or next(iter(types.values()))\n\n return types\n\n\ndef evaluate_postponed_annotations(params, component, parent, logger):\n if not (params and any(type_requires_eval(p.annotation) for p in params)):\n return\n try:\n if (\n is_dataclass(parent)\n and component.__name__ == \"__init__\"\n and not component.__qualname__.startswith(parent.__name__ + \".\")\n ):\n types = get_types(parent, logger)\n else:\n types = get_types(component, logger)\n except Exception as ex:\n logger.debug(f\"Unable to evaluate types for {component}\", exc_info=ex)\n return\n for param in params:\n if param.name in types:\n param_type = types[param.name]\n if isinstance(param_type, Exception):\n logger.debug(f\"Unable to evaluate type of {param.name} from {component}\", exc_info=param_type)\n continue\n param.annotation = param_type\n", "repo_name": "omni-us/jsonargparse", "sub_path": "jsonargparse/_postponed_annotations.py", "file_name": "_postponed_annotations.py", "file_ext": "py", "file_size_in_byte": 12932, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 224, "dataset": "github-code", "pt": "47", "api": [{"api_name": "collections.namedtuple", "line_number": 16, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.FrozenSet", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 25, "usage_type": "name"}, {"api_name": "ast.NodeTransformer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "ast.Subscript", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ast.Name", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ast.Subscript", "line_number": 35, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 38, "usage_type": "call"}, {"api_name": "ast.Constant", "line_number": 41, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 41, "usage_type": "name"}, {"api_name": "ast.Name", "line_number": 41, "usage_type": "attribute"}, {"api_name": "ast.BinOp", "line_number": 46, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 47, "usage_type": "name"}, {"api_name": "ast.BinOp", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ast.Subscript", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ast.BitOr", "line_number": 48, "usage_type": "attribute"}, {"api_name": "ast.Subscript", "line_number": 52, "usage_type": "call"}, {"api_name": "ast.Index", "line_number": 54, "usage_type": "call"}, {"api_name": "ast.Tuple", "line_number": 55, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 55, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 56, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 58, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 46, "usage_type": "name"}, {"api_name": "ast.Subscript", "line_number": 46, "usage_type": "attribute"}, {"api_name": "ast.AST", "line_number": 62, "usage_type": "attribute"}, {"api_name": "ast.BinOp", "line_number": 63, "usage_type": "attribute"}, {"api_name": "ast.BitOr", "line_number": 63, "usage_type": "attribute"}, {"api_name": "ast.Name", "line_number": 72, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 72, "usage_type": "call"}, {"api_name": "ast.Name", "line_number": 69, "usage_type": "attribute"}, {"api_name": "ast.AST", "line_number": 74, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 81, "usage_type": "call"}, {"api_name": "ast.fix_missing_locations", "line_number": 82, "usage_type": "call"}, {"api_name": "ast.NodeVisitor", "line_number": 85, "usage_type": "attribute"}, {"api_name": "ast.Name", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ast.AST", "line_number": 89, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 92, "usage_type": "name"}, {"api_name": "_util.unique", "line_number": 94, "usage_type": "call"}, {"api_name": "ast.NodeVisitor", "line_number": 98, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 99, "usage_type": "name"}, {"api_name": "ast.Import", "line_number": 101, "usage_type": "attribute"}, {"api_name": "ast.dump", "line_number": 104, "usage_type": "call"}, {"api_name": "ast.Attribute", "line_number": 105, "usage_type": "call"}, {"api_name": "ast.Name", "line_number": 106, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 106, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 108, "usage_type": "call"}, {"api_name": "ast.ImportFrom", "line_number": 114, "usage_type": "attribute"}, {"api_name": "ast.dump", "line_number": 118, "usage_type": "call"}, {"api_name": "ast.Name", "line_number": 118, "usage_type": "call"}, {"api_name": "ast.Load", "line_number": 118, "usage_type": "call"}, {"api_name": "ast.If", "line_number": 122, "usage_type": "attribute"}, {"api_name": "ast.Name", "line_number": 124, "usage_type": "attribute"}, {"api_name": "ast.Attribute", "line_number": 124, "usage_type": "attribute"}, {"api_name": "ast.dump", "line_number": 125, "usage_type": "call"}, {"api_name": "ast.BoolOp", "line_number": 127, "usage_type": "attribute"}, {"api_name": "ast.And", "line_number": 128, "usage_type": "attribute"}, {"api_name": "ast.Or", "line_number": 128, "usage_type": "attribute"}, {"api_name": "ast.dump", "line_number": 129, "usage_type": "call"}, {"api_name": "ast.parse", "line_number": 131, "usage_type": "call"}, {"api_name": "ast.AST", "line_number": 139, "usage_type": "attribute"}, {"api_name": "ast.If", "line_number": 140, "usage_type": "attribute"}, {"api_name": "ast.Module", "line_number": 140, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 144, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 144, "usage_type": "attribute"}, {"api_name": "ast.parse", "line_number": 149, "usage_type": "call"}, {"api_name": "ast.parse", "line_number": 154, "usage_type": "call"}, {"api_name": "{'unique': '_util.unique'}", "line_number": 159, "usage_type": "call"}, {"api_name": "ast.AST", "line_number": 165, "usage_type": "attribute"}, {"api_name": "ast.AST", "line_number": 172, "usage_type": "attribute"}, {"api_name": "ast.fix_missing_locations", "line_number": 173, "usage_type": "call"}, {"api_name": "_optionals.typing_extensions_import", "line_number": 178, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 181, "usage_type": "attribute"}, {"api_name": "typing.ForwardRef", "line_number": 212, "usage_type": "argument"}, {"api_name": "_util.get_typehint_origin", "line_number": 224, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 225, "usage_type": "attribute"}, {"api_name": "_typehints.sequence_origin_types", "line_number": 226, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 227, "usage_type": "name"}, {"api_name": "_typehints.tuple_set_origin_types", "line_number": 228, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 229, "usage_type": "name"}, {"api_name": "_typehints.mapping_origin_types", "line_number": 230, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 231, "usage_type": "name"}, {"api_name": "_util.get_typehint_origin", "line_number": 242, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 244, "usage_type": "name"}, {"api_name": "_typehints.sequence_origin_types", "line_number": 245, "usage_type": "name"}, {"api_name": "_typehints.tuple_set_origin_types", "line_number": 246, "usage_type": "name"}, {"api_name": "_typehints.mapping_origin_types", "line_number": 247, "usage_type": "name"}, {"api_name": "typing.ForwardRef", "line_number": 254, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 257, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 257, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 257, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 258, "usage_type": "call"}, {"api_name": "typing.get_type_hints", "line_number": 260, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 268, "usage_type": "call"}, {"api_name": "inspect.getsource", "line_number": 268, "usage_type": "call"}, {"api_name": "ast.parse", "line_number": 269, "usage_type": "call"}, {"api_name": "ast.Module", "line_number": 270, "usage_type": "attribute"}, {"api_name": "ast.FunctionDef", "line_number": 272, "usage_type": "attribute"}, {"api_name": "ast.ClassDef", "line_number": 272, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 287, "usage_type": "attribute"}, {"api_name": "inspect.getsource", "line_number": 287, "usage_type": "call"}, {"api_name": "ast.FunctionDef", "line_number": 291, "usage_type": "attribute"}, {"api_name": "ast.AnnAssign", "line_number": 294, "usage_type": "attribute"}, {"api_name": "ast.Constant", "line_number": 299, "usage_type": "attribute"}, {"api_name": "dataclasses.is_dataclass", "line_number": 318, "usage_type": "call"}]} +{"seq_id": "30211383242", "text": "# standard library\nimport io\nfrom types import SimpleNamespace\nfrom typing import List, Dict\nfrom pathlib import Path\n\n# common numerical and scientific libraries\nimport numpy as np\nimport pandas as pd\n\n# pytorch\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nimport albumentations\nimport geffnet\nimport timm\nfrom resnest.torch import resnest101\nfrom pretrainedmodels import se_resnext101_32x4d\n\n# other common libraries\nfrom tqdm import tqdm\n\n# derm7pt feature training support\nimport derm7pt_support\n\ndef get_modelspec(model_n):\n \"\"\"return SimpleNamespace instance with attrs set\"\"\"\n data = pd.read_csv(io.StringIO(\"\"\"\nm kernel_type arxiv_m data imgsz init_lr\n01 9c_meta_b3_768_512_ext_18ep 1 768 512 3e-5\n02 9c_b4ns_2e_896_ext_15ep 5 1024 896 2e-5\n03 9c_b4ns_448_ext_15ep-newfold 6 512 448 3e-5\n04 9c_b4ns_768_640_ext_15ep 2 768 640 3e-5\n05 9c_b4ns_768_768_ext_15ep 3 768 768 3e-5\n06 9c_meta_b4ns_640_ext_15ep 4 768 640 3e-5\n07 4c_b5ns_1.5e_640_ext_15ep 9 768 640 1.5e-5\n08 9c_b5ns_1.5e_640_ext_15ep 8 768 640 1.5e-5\n09 9c_b5ns_448_ext_15ep-newfold 10 512 448 3e-5\n10 9c_meta128_32_b5ns_384_ext_15ep 7 512 384 3e-5\n11 9c_b6ns_448_ext_15ep-newfold 13 512 448 3e-5\n12 9c_b6ns_576_ext_15ep_oldfold 12 768 576 3e-5\n13 9c_b6ns_640_ext_15ep 11 768 640 3e-5\n14 9c_b7ns_1e_576_ext_15ep_oldfold 15 768 576 1e-5\n15 9c_b7ns_1e_640_ext_15ep 16 768 640 1e-5\n16 9c_meta_1.5e-5_b7ns_384_ext_15ep 14 512 384 3e-5\n17 9c_nest101_2e_640_ext_15ep 18 768 640 2e-5\n18 9c_se_x101_640_ext_15ep 17 768 640 3e-5\n21 9c_b4ns_380_ext_15ep 0 512 380 3e-5\n22 9c_b4ns_456_15ep 0 512 456 3e-5\n23 9c_b4ns_528_15ep 0 768 528 3e-5\n32 2c_b4ns_380_ext_15ep_feature_blue_whitish_veil 0 512 380 3e-5\n33 3c_b4ns_380_ext_15ep_feature_pigment_network 0 512 380 3e-5\n34 3c_b4ns_380_ext_15ep_feature_streaks 0 512 380 3e-5\n35 5c_b4ns_380_ext_15ep_feature_pigmentation 0 512 380 3e-5\n36 4c_b4ns_380_ext_15ep_feature_regression_structures 0 512 380 3e-5\n37 3c_b4ns_380_ext_15ep_feature_dots_and_globules 0 512 380 3e-5\n38 8c_b4ns_380_ext_15ep_feature_vascular_structures 0 512 380 3e-5\n40 9c_v2m_512_ext_15ep_test 0 512 512 2e-5\n41 9c_v2m_480_ext_15ep 0 512 480 2e-5\n\"\"\"), delim_whitespace=True, dtype={'m':str}).set_index('m')\n return SimpleNamespace(**data.loc[model_n])\n\n\nnet_type_dict = {\n '_b3_': 'efficientnet_b3',\n '_b4ns_': 'tf_efficientnet_b4_ns',\n '_b5ns_': 'tf_efficientnet_b5_ns',\n '_b6ns_': 'tf_efficientnet_b6_ns',\n '_b7ns_': 'tf_efficientnet_b7_ns',\n '_nest101_': 'resnest101',\n '_se_x101_': 'seresnext101',\n '_v2s_': 'tf_efficientnetv2_s_in21k',\n '_v2m_': 'tf_efficientnetv2_m_in21k',\n '_v2l_': 'tf_efficientnetv2_l_in21k',\n }\n\n######## dataset\n\nclass PredictDataset(Dataset):\n def __init__(self, images, metas, use_meta, image_size, data_folder):\n\n self.images = images\n self.metas = metas\n assert len(self.images) == len(self.metas)\n self.use_meta = use_meta\n self.data_folder = data_folder\n self.transform = albumentations.Compose([\n albumentations.Resize(image_size, image_size),\n albumentations.Normalize(),\n ])\n\n def __len__(self):\n return len(self.images)\n\n def __getitem__(self, index):\n\n image = self.images[index]\n\n res = self.transform(image=image)\n image = res['image'].astype(np.float32)\n\n image = image.transpose(2, 0, 1)\n\n if self.use_meta:\n inp_meta = self.metas[index]\n sex = {'male': 1, 'female': 0}.get(inp_meta['sex'], -1)\n if inp_meta['age'] is not None:\n age_approx = inp_meta['age'] / 90\n else:\n age_approx = 0\n n_images = inp_meta.get('n_images', 1)\n n_images = np.log1p(n_images)\n image_size = {\n 512: 10.989,\n 768: 11.590,\n 1024: 0, # no meta for data_folder==1024 model\n }[self.data_folder]\n # site, if present\n site_value = inp_meta.get('site')\n try:\n site_i = ['anterior torso', 'head/neck', 'lateral torso',\n 'lower extremity', 'oral/genital', 'palms/soles',\n 'posterior torso', 'torso', 'upper extremity',\n ].index(site_value)\n except ValueError:\n site_i = 9\n site = [0]*10\n site[site_i] = 1\n meta = [sex, age_approx, n_images, image_size] + site\n else:\n meta = 0.\n data = (torch.tensor(image).float(), torch.tensor(meta).float())\n\n return data\n\n\n######## models\n\nsigmoid = nn.Sigmoid()\n\nclass Swish(torch.autograd.Function):\n @staticmethod\n def forward(ctx, i):\n result = i * sigmoid(i)\n ctx.save_for_backward(i)\n return result\n @staticmethod\n def backward(ctx, grad_output):\n i = ctx.saved_variables[0]\n sigmoid_i = sigmoid(i)\n return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))\n\n\nclass Swish_Module(nn.Module):\n def forward(self, x):\n return Swish.apply(x)\n\n\nclass Effnet_Melanoma(nn.Module):\n def __init__(self, enet_type, out_dim, n_meta_features=0, n_meta_dim=[512, 128], pretrained=False, featurepred=False):\n super(Effnet_Melanoma, self).__init__()\n self.featurepred = featurepred\n self.n_meta_features = n_meta_features\n if 'efficientnetv2' in enet_type:\n self.enet = timm.create_model(enet_type, pretrained=pretrained)\n else:\n self.enet = geffnet.create_model(enet_type, pretrained=pretrained)\n self.dropouts = nn.ModuleList([\n nn.Dropout(0.5) for _ in range(5)\n ])\n in_ch = self.enet.classifier.in_features\n if n_meta_features > 0:\n self.meta = nn.Sequential(\n nn.Linear(n_meta_features, n_meta_dim[0]),\n nn.BatchNorm1d(n_meta_dim[0]),\n Swish_Module(),\n nn.Dropout(p=0.3),\n nn.Linear(n_meta_dim[0], n_meta_dim[1]),\n nn.BatchNorm1d(n_meta_dim[1]),\n Swish_Module(),\n )\n in_ch += n_meta_dim[1]\n self.myfc = nn.Linear(in_ch, out_dim)\n self.enet.classifier = nn.Identity()\n\n def extract(self, x):\n x = self.enet(x) \n return x \n\n def forward(self, x, x_meta=None):\n x = self.extract(x).squeeze(-1).squeeze(-1)\n if self.n_meta_features > 0:\n x_meta = self.meta(x_meta)\n x = torch.cat((x, x_meta), dim=1)\n if not self.featurepred:\n for i, dropout in enumerate(self.dropouts):\n if i == 0:\n out = self.myfc(dropout(x))\n else:\n out += self.myfc(dropout(x))\n out /= len(self.dropouts)\n else:\n out = x \n return out\n\n\nclass Resnest_Melanoma(nn.Module):\n def __init__(self, enet_type, out_dim, n_meta_features=0, n_meta_dim=[512, 128], pretrained=False, featurepred=False):\n super(Resnest_Melanoma, self).__init__()\n self.featurepred = featurepred\n self.n_meta_features = n_meta_features\n self.enet = resnest101(pretrained=pretrained)\n self.dropouts = nn.ModuleList([\n nn.Dropout(0.5) for _ in range(5)\n ])\n in_ch = self.enet.fc.in_features\n if n_meta_features > 0:\n self.meta = nn.Sequential(\n nn.Linear(n_meta_features, n_meta_dim[0]),\n nn.BatchNorm1d(n_meta_dim[0]),\n Swish_Module(),\n nn.Dropout(p=0.3),\n nn.Linear(n_meta_dim[0], n_meta_dim[1]),\n nn.BatchNorm1d(n_meta_dim[1]),\n Swish_Module(),\n )\n in_ch += n_meta_dim[1]\n self.myfc = nn.Linear(in_ch, out_dim)\n self.enet.fc = nn.Identity()\n\n def extract(self, x):\n x = self.enet(x)\n return x\n\n def forward(self, x, x_meta=None):\n x = self.extract(x).squeeze(-1).squeeze(-1)\n if self.n_meta_features > 0:\n x_meta = self.meta(x_meta)\n x = torch.cat((x, x_meta), dim=1)\n if not self.featurepred: # Predict Softmax\n for i, dropout in enumerate(self.dropouts):\n if i == 0:\n out = self.myfc(dropout(x))\n else:\n out += self.myfc(dropout(x))\n out /= len(self.dropouts)\n else:\n out = x\n return out\n\n\nclass Seresnext_Melanoma(nn.Module):\n def __init__(self, enet_type, out_dim, n_meta_features=0, n_meta_dim=[512, 128], pretrained=False, featurepred=False):\n super(Seresnext_Melanoma, self).__init__()\n self.featurepred = featurepred\n self.n_meta_features = n_meta_features\n if pretrained:\n self.enet = se_resnext101_32x4d(num_classes=1000, pretrained='imagenet')\n else:\n self.enet = se_resnext101_32x4d(num_classes=1000, pretrained=None)\n self.enet.avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n self.dropouts = nn.ModuleList([\n nn.Dropout(0.5) for _ in range(5)\n ])\n in_ch = self.enet.last_linear.in_features\n if n_meta_features > 0:\n self.meta = nn.Sequential(\n nn.Linear(n_meta_features, n_meta_dim[0]),\n nn.BatchNorm1d(n_meta_dim[0]),\n Swish_Module(),\n nn.Dropout(p=0.3),\n nn.Linear(n_meta_dim[0], n_meta_dim[1]),\n nn.BatchNorm1d(n_meta_dim[1]),\n Swish_Module(),\n )\n in_ch += n_meta_dim[1]\n self.myfc = nn.Linear(in_ch, out_dim)\n self.enet.last_linear = nn.Identity()\n\n def extract(self, x):\n x = self.enet(x)\n return x\n\n def forward(self, x, x_meta=None):\n x = self.extract(x).squeeze(-1).squeeze(-1)\n if self.n_meta_features > 0:\n x_meta = self.meta(x_meta)\n x = torch.cat((x, x_meta), dim=1)\n if not self.featurepred:\n for i, dropout in enumerate(self.dropouts):\n if i == 0:\n out = self.myfc(dropout(x))\n else:\n out += self.myfc(dropout(x))\n out /= len(self.dropouts)\n else:\n out = x\n return out\n\n########\n\n\nclass KWClassifier:\n \"\"\"Classifier with 5 cv-folds of the same deepnet\"\"\"\n\n def __init__(self,\n model_n, # str '01'..'18', or int 1 <= model_n <= 18\n weights_dir, # if str of Path: load models for this directory\n # if None: leave models uninitialized\n device = 'cpu', # 'cpu' or 'cuda'\n version = 'final',\n featurepred=False\n ):\n # process parameters\n if isinstance(model_n, int):\n model_n = '{:02d}'.format(model_n)\n self.model_n = model_n\n self.device = torch.device(device)\n self.version = version\n self.featurepred = featurepred\n # obtain further parameters from modelspec\n spec = get_modelspec(self.model_n)\n self.kernel_type = spec.kernel_type\n self.data_folder = spec.data\n self.image_size = spec.imgsz\n # self.net_type\n for key, value in net_type_dict.items():\n if key in self.kernel_type:\n self.net_type = value\n break\n else:\n raise ValueError(f'{self.kernel_type}: unknown net_type')\n self.init_lr = spec.init_lr\n # self.out_dim\n try:\n self.out_dim = int(self.kernel_type[0])\n except Exception:\n raise ValueError(f'{self.kernel_type}: unknown out_dim')\n # self.default_n_epochs\n if '_15ep' in self.kernel_type:\n self.default_n_epochs = 15\n elif '_18ep' in self.kernel_type:\n self.default_n_epochs = 18\n else:\n raise ValueError(f'{self.kernel_type}: unknown default_n_epochs')\n self.use_meta = ('meta' in self.kernel_type)\n self.n_meta_features = 14 if self.use_meta else 0\n self.n_meta_dim = (512, 128)\n if '_meta128_32_' in self.kernel_type:\n self.n_meta_dim = (128, 32)\n if '_feature_' in self.kernel_type:\n feature_name = self.kernel_type.partition('_feature_')[2]\n self.diagnosis2idx = derm7pt_support.diagnosis2idx[feature_name]\n self.mel_idx = np.array(list(derm7pt_support.is_positive[feature_name]), dtype=np.uint32)\n else:\n if self.out_dim == 9:\n self.diagnosis2idx = {\n 'AK': 0, 'BCC': 1, 'BKL': 2, 'DF': 3, 'SCC': 4, 'VASC': 5,\n 'MEL': 6, 'NV': 7, 'UNK': 8,\n }\n elif self.out_dim == 4:\n self.diagnosis2idx = {\n 'AK': 3, 'BCC': 3, 'BKL': 0, 'DF': 3, 'SCC': 3, 'VASC': 3,\n 'MEL': 1, 'NV': 2, 'UNK': 3,\n }\n self.mel_idx = np.array([self.diagnosis2idx['MEL']], dtype=np.uint32)\n\n self.models = None\n if weights_dir is not None:\n self.build_and_load(weights_dir,version=self.version)\n\n def _build_model(self, pretrained=False):\n \"\"\"build single model, return it\"\"\"\n if self.net_type == 'resnest101':\n ModelClass = Resnest_Melanoma\n elif self.net_type == 'seresnext101':\n ModelClass = Seresnext_Melanoma\n elif 'efficientnet' in self.net_type:\n ModelClass = Effnet_Melanoma\n else:\n raise NotImplementedError()\n\n model = ModelClass(\n self.net_type,\n n_meta_features = self.n_meta_features,\n n_meta_dim = self.n_meta_dim,\n out_dim = self.out_dim,\n pretrained = pretrained,\n featurepred = self.featurepred\n )\n return model\n\n def build_and_load(self, weights_dir, version='final'):\n \"\"\"build 5 models and load weights\"\"\"\n self.models = []\n for fold in range(5):\n model = self._build_model()\n model.to(self.device)\n path = (Path(weights_dir) \n / f'weights_m{self.model_n}_f{fold}_{version}.pth')\n if not path.exists():\n print(f'skip {path}')\n self.models.append(None)\n continue\n print(f'load {path} ...')\n try: # single GPU\n model.load_state_dict(torch.load(path, map_location=self.device), strict=True)\n #model.load_state_dict(torch.load(path), strict=True)\n except: # multi GPU\n state_dict = torch.load(path)\n # strip leading \"module.\" from keys\n state_dict = { k_[7:] if k_.startswith('module.') else k_: \n state_dict[k_] for k_ in state_dict.keys() }\n model.load_state_dict(state_dict, strict=True)\n self.models.append(model)\n\n def predict_softmax(self, list_of_images:List[np.ndarray],\n list_of_meta:List[Dict],\n batch_size=16,\n n_workers=1,\n ) -> np.ndarray:\n \"\"\"predict on a list of images+meta\n \n list_of_meta contains dicts per image, eg:\n {'sex': 'male', 'age':45} or {'sex':None, 'age':None}\n \"\"\"\n dataset = PredictDataset(list_of_images, list_of_meta,\n self.use_meta, self.image_size, self.data_folder)\n loader = torch.utils.data.DataLoader(dataset,\n batch_size=batch_size,\n num_workers=n_workers,\n )\n softmax = self._predict(loader)[:, self.mel_idx].sum(1)\n return softmax\n\n @classmethod\n def get_trans(cls, img, I):\n \"\"\"helper function: simple augmentation: flip/transpose\"\"\"\n if I >= 4:\n img = img.transpose(2, 3)\n if I % 4 == 0:\n return img\n elif I % 4 == 1:\n return img.flip(2)\n elif I % 4 == 2:\n return img.flip(3)\n elif I % 4 == 3:\n return img.flip(2).flip(3)\n\n def _predict(self,\n loader,\n models = None, # default: self.models\n n_tta = 8, # number of flip/transpose trials for test time augment\n use_tqdm = False,\n ):\n \"\"\"return prediction as np.array\"\"\"\n # params\n if models is None:\n models = self.models\n if use_tqdm:\n loader = tqdm(loader)\n #\n for model in models:\n try:\n model.eval()\n except Exception as e:\n print(e)\n raise RuntimeError('missing or illegal model')\n softmax = []\n with torch.no_grad():\n for data, meta in loader:\n data, meta = data.to(self.device), meta.to(self.device)\n #softmax_batch = torch.zeros((data.shape[0], self.out_dim))\n softmax_batch = None\n\n for model in models:\n for I in range(n_tta):\n logits = model(self.get_trans(data, I), meta)\n\n if softmax_batch== None:\n if self.featurepred:\n softmax_batch = torch.zeros((data.shape[0], logits.softmax(1).shape[1])) \n else:\n softmax_batch = torch.zeros((data.shape[0], self.out_dim))\n softmax_batch = softmax_batch.to(self.device)\n softmax_batch += logits.softmax(1) \n\n softmax_batch /= n_tta\n softmax_batch /= len(models)\n softmax.append(softmax_batch.detach().cpu())\n return torch.cat(softmax).numpy()\n\n# vim: set sw=4 sts=4 expandtab :\n", "repo_name": "semiquark1/skin", "sub_path": "src/kw_classifier.py", "file_name": "kw_classifier.py", "file_ext": "py", "file_size_in_byte": 18912, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 29, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 80, "usage_type": "name"}, {"api_name": "albumentations.Compose", "line_number": 88, "usage_type": "call"}, {"api_name": "albumentations.Resize", "line_number": 89, "usage_type": "call"}, {"api_name": "albumentations.Normalize", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.log1p", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.autograd", "line_number": 142, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "name"}, {"api_name": "timm.create_model", "line_number": 166, "usage_type": "call"}, {"api_name": "geffnet.create_model", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "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.BatchNorm1d", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 178, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 184, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 208, "usage_type": "name"}, {"api_name": "resnest.torch.resnest101", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 214, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 221, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 223, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 224, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 225, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 229, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 230, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 253, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 253, "usage_type": "name"}, {"api_name": "pretrainedmodels.se_resnext101_32x4d", "line_number": 259, "usage_type": "call"}, {"api_name": "pretrainedmodels.se_resnext101_32x4d", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 262, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 268, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 269, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 270, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 272, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 273, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 274, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 278, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 279, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 289, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 319, "usage_type": "call"}, {"api_name": "derm7pt_support.diagnosis2idx", "line_number": 354, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 355, "usage_type": "call"}, {"api_name": "derm7pt_support.is_positive", "line_number": 355, "usage_type": "attribute"}, {"api_name": "numpy.uint32", "line_number": 355, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 367, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 400, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 411, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 418, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 418, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 419, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 419, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 430, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 430, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 422, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 462, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 471, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 483, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 485, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 492, "usage_type": "call"}]} +{"seq_id": "7746133619", "text": "import jax\nimport jax.numpy as jnp\nimport jax.random as jr\nfrom jax import vmap\nfrom jax import lax\nimport chex\nfrom typing import Callable\n\n@chex.dataclass\nclass PendulumParams:\n m_0: chex.Array\n P_0: chex.Array\n f: Callable\n h: Callable\n Q: chex.Array\n R: chex.Array\n\n\n# 1-dimensional random walk simulation\ndef simulate_rw_1d(init, Q, R, num_steps, key=0):\n if isinstance(key, int):\n key = jr.PRNGKey(key)\n\n def _step(carry, rng):\n x_prev = carry\n key1, key2 = jr.split(rng)\n\n # Random walk and measurement\n x_post = x_prev + jr.normal(key1)*Q\n y = x_post + jr.normal(key2)\n return x_post, (x_post, y)\n\n carry = init\n rngs = jr.split(key, num_steps)\n _, (xs, ys) = lax.scan(\n _step, carry, rngs\n )\n return xs, ys\n\ndef simulate_rw_1d_with_default_params():\n return simulate_rw_1d(0, 1, 1, num_steps=100)\n\n# Car trajectory simulation (Example 3.6)\ndef simulate_trajectory(m_0, A, Q, H, R, num_steps, key=42):\n if isinstance(key, int):\n key = jr.PRNGKey(key)\n M, N = m_0.shape[-1], R.shape[-1]\n\n def _step(carry, rng):\n state = carry\n rng1, rng2 = jr.split(rng, 2)\n \n next_state = A @ state + jr.multivariate_normal(rng1, jnp.zeros(M), Q)\n observation = H @ state + jr.multivariate_normal(rng2, jnp.zeros(N), R)\n return next_state, (state, observation)\n\n rngs = jr.split(key, num_steps)\n _, (states, observations) = lax.scan(\n _step, m_0, rngs\n )\n return states, observations\n\ndef simulate_trajectory_with_default_params():\n m_0 = jnp.array([0., 0., 1., -1.])\n dt = 0.1\n q1, q2 = 1, 1\n rsig1, rsig2 = 0.5, 0.5\n A = jnp.array([[1, 0, dt, 0],\n [0, 1, 0, dt],\n [0, 0, 1, 0],\n [0, 0, 0, 1]])\n Q = jnp.array([[q1*dt**3/3, 0, q1*dt**2/2, 0],\n [ 0, q2*dt**3/3, 0, q2*dt**2/2],\n [q1*dt**2/2, 0, q1*dt, 0],\n [ 0, q2*dt**2/2, 0, q2*dt]])\n H = jnp.array([[1, 0, 0, 0],\n [0, 1, 0, 0]])\n R = jnp.array([[rsig1**2, 0],\n [ 0, rsig2**2]])\n return simulate_trajectory(m_0, A, Q, H, R, num_steps=100)\n\n# Pendulum simulation (Example 3.7)\ndef simulate_pendulum(m_0, f, h, Q, R, num_steps, key=0):\n if isinstance(key, int):\n key = jr.PRNGKey(key)\n M = m_0.shape[0]\n\n def _step(carry, rng):\n state = carry\n rng1, rng2 = jr.split(rng, 2)\n\n next_state = f(state) + jr.multivariate_normal(rng1, jnp.zeros(M), Q)\n obs = h(next_state) + jr.normal(rng2) * R\n return next_state, (next_state, obs)\n\n rngs = jr.split(key, num_steps)\n _, (states, observations) = lax.scan(\n _step, m_0, rngs\n )\n return states, observations\n\ndef pendulum_default_params(dt=0.0125):\n m_0 = jnp.array([jnp.pi/2, 0])\n P_0 = jnp.eye(2) * 0.1\n dt = dt\n q = 1\n g = 9.8\n Q = jnp.array([[q*dt**3/3, q*dt**2/2],\n [q*dt**2/2, q*dt]])\n R = 0.3\n f = lambda x: jnp.array([x[0] + x[1]*dt, x[1] - g*jnp.sin(x[0])*dt])\n h = lambda x: jnp.array([jnp.sin(x[0])])\n return PendulumParams(\n m_0 = m_0,\n P_0 = P_0,\n f = f,\n h = h,\n Q = Q,\n R = R\n )\n\ndef simulate_pendulum_with_default_params(dt=0.0125, num_steps=400):\n time_grid = jnp.arange(0.0, dt*num_steps, step=dt)\n m_0, _, f, h, Q, R = pendulum_default_params(dt=dt).to_tuple()\n return time_grid, simulate_pendulum(m_0, f, h, Q, R, num_steps)", "repo_name": "petergchang/sarkka-jax", "sub_path": "simulations.py", "file_name": "simulations.py", "file_ext": "py", "file_size_in_byte": 3647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "60", "api": [{"api_name": "chex.Array", "line_number": 11, "usage_type": "attribute"}, {"api_name": "chex.Array", "line_number": 12, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 14, "usage_type": "name"}, {"api_name": "chex.Array", "line_number": 15, "usage_type": "attribute"}, {"api_name": "chex.Array", "line_number": 16, "usage_type": "attribute"}, {"api_name": "chex.dataclass", "line_number": 9, "usage_type": "attribute"}, {"api_name": "jax.random.PRNGKey", "line_number": 22, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 22, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 26, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 26, "usage_type": "name"}, {"api_name": "jax.random.normal", "line_number": 29, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 29, "usage_type": "name"}, {"api_name": "jax.random.normal", "line_number": 30, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 30, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 34, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 34, "usage_type": "name"}, {"api_name": "jax.lax.scan", "line_number": 35, "usage_type": "call"}, {"api_name": "jax.lax", "line_number": 35, "usage_type": "name"}, {"api_name": "jax.random.PRNGKey", "line_number": 46, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 46, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 51, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 51, "usage_type": "name"}, {"api_name": "jax.random.multivariate_normal", "line_number": 53, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 53, "usage_type": "name"}, {"api_name": "jax.numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 53, "usage_type": "name"}, {"api_name": "jax.random.multivariate_normal", "line_number": 54, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 54, "usage_type": "name"}, {"api_name": "jax.numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 54, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 57, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 57, "usage_type": "name"}, {"api_name": "jax.lax.scan", "line_number": 58, "usage_type": "call"}, {"api_name": "jax.lax", "line_number": 58, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 64, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 68, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 72, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 76, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 78, "usage_type": "name"}, {"api_name": "jax.random.PRNGKey", "line_number": 85, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 85, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 90, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 90, "usage_type": "name"}, {"api_name": "jax.random.multivariate_normal", "line_number": 92, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 92, "usage_type": "name"}, {"api_name": "jax.numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 92, "usage_type": "name"}, {"api_name": "jax.random.normal", "line_number": 93, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 93, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 96, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 96, "usage_type": "name"}, {"api_name": "jax.lax.scan", "line_number": 97, "usage_type": "call"}, {"api_name": "jax.lax", "line_number": 97, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 103, "usage_type": "name"}, {"api_name": "jax.numpy.pi", "line_number": 103, "usage_type": "attribute"}, {"api_name": "jax.numpy.eye", "line_number": 104, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 104, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 108, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 111, "usage_type": "name"}, {"api_name": "jax.numpy.sin", "line_number": 111, "usage_type": "call"}, {"api_name": "jax.numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 112, "usage_type": "name"}, {"api_name": "jax.numpy.sin", "line_number": 112, "usage_type": "call"}, {"api_name": "jax.numpy.arange", "line_number": 123, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 123, "usage_type": "name"}]} +{"seq_id": "32863091012", "text": "import hashlib\nfrom time import time\nimport json\n\n\nclass Block:\n content= {}\n isFull= False\n def __init__(self, index,previousHash, blockSize=3):\n self.size=blockSize\n Block.content= {\n 'index':index,\n 'hash': '',\n 'transactions':[],\n 'nonce':0,\n 'previousHash': previousHash,\n 'timestamp': time()\n }\n \n def addTransaction(self):\n \"\"\"\n adds a transaction to the block\n \"\"\"\n transaction= {\n 'sender': input('Expéditeur: '),\n 'receiver': input('Destinataire: '),\n 'amount': int(input('Montant: '))\n }\n Block.content.transactions.append(transaction)\n\n def addTransaction(self,sender, receiver, amount):\n \"\"\"\n Overloaded Transaction method\n \"\"\"\n transaction= {\n 'sender': sender,\n 'receiver': receiver,\n 'amount': amount\n }\n Block.content['transactions'].append(transaction)\n \n def removeTransaction(self, idx):\n \"\"\"\n Removes the transaction at idx returns the removed transaction\n \"\"\"\n t= Block.content['transactions']\n del Block.content['transactions']\n return t\n \n def editTransaction(self, idx, sender, receiver, amount):\n \"\"\"\n edits a transaction\n \"\"\"\n Block.content['transactions'][idx]['sender']= sender\n Block.content['transactions'][idx]['receiver']= receiver\n Block.content['transactions'][idx]['amount']= amount\n\n def isFull(self):\n return len( Block.content['transactions']) == self.size \n\n\nBLOCK_SIZE=3\nBLOCKCHAIN=[]\nMENU_STRING=\"1- Continuer\\n2- Afficher la blockhain\\n0- Quittter\"\npendingBlock={}\nmakeDecision=1\ncurrentIndex=0\n\ndef init():\n global currentIndex\n global pendingBlock\n block_0= {\n 'index':currentIndex,\n 'hash': '',\n 'transactions':[],\n 'nonce':0,\n 'previousHash': 'NULL',\n 'timestamp': time()\n }\n block_0['hash']= hashlib.sha256(str(block_0).encode()).hexdigest()\n blockchain= [block_0]\n pendingBlock= block_0\n currentIndex+=1\n print(\"Genesis block initialised!\")\n newPendingBlock()\n\ninit()\n\n\ndef newTransaction():\n \"\"\"\n Adds a new transaction to the pending block\n \"\"\"\n global pendingBlock\n global makeDecision\n transaction= {\n 'sender': input('Expéditeur: '),\n 'receiver': input('Destinataire: '),\n 'amount': int(input('Montant: '))\n }\n pendingBlock['transactions'].append(transaction)\n print(\"Transaction ajouté!\")\n if( len(pendingBlock['transactions']) == BLOCK_SIZE ): \n print(\"Block complet\\n\")\n mining()\n makeDecision= int(input(MENU_STRING))\n \n return \n\ndef newPendingBlock():\n \"\"\"\n New block\n \"\"\"\n global currentIndex\n global pendingBlock\n block= {\n 'index':currentIndex,\n 'hash': '',\n 'transactions':[],\n 'nonce':0,\n 'previousHash': '',\n 'timestamp': time()\n }\n block['previousHash']= pendingBlock['hash']\n pendingBlock= block\n currentIndex+=1\n print(\"\\n\\nNouveau block initialisé!\")\n return\n\n\ndef mining():\n \"\"\"\n Mining Funciton \n \"\"\"\n print(\"Minage.... :P\")\n global BLOCKCHAIN\n validHash= computeHash(pendingBlock)\n pendingBlock['hash']= validHash\n BLOCKCHAIN.append(pendingBlock)\n print(\"Minage Terminé\\nBlock ajouté à la chaine:\")\n printty(pendingBlock)\n newPendingBlock()\n\n return\n\n\ndef computeHash(block):\n \"\"\"\n Hash Function\n \"\"\"\n b= str(block)\n while 1:\n data= str(block)+ str(block['nonce'])\n block['nonce']+=1\n h= hashlib.sha256(data.encode()).hexdigest()\n if(h[0]=='0' and h[1]=='0' and h[2]=='0'):\n print(\"Hash valide trouvé! avec un nonce de \"+ str(block['nonce']))\n break\n \n return h\n\ndef displayBlockchain():\n \"\"\"\n Displayin func\n \"\"\"\n printty(BLOCKCHAIN)\n return\n\ndef editBlock():\n idx= int(input(\"Entrez l'index du bloc à modifer: \"))\n block= BLOCKCHAIN[idx]\n printty(block)\n idx= int(input(\"Entrez l'index de la transaction à modifer: \"))\n printty(block['transactions'][idx])\n block['transactions'][idx]= {\n 'sender': input('Expéditeur: '),\n 'receiver': input('Destinataire: '),\n 'amount': int(input('Montant: '))\n }\n\n\ndef printty(obj):\n print (json.dumps(obj, indent=4))\n\n\nwhile 1:\n if(makeDecision==1):\n newTransaction()\n elif (makeDecision==2):\n displayBlockchain()\n makeDecision= int(input(MENU_STRING))\n else:\n print(\"Why so soon ??\\nbye!\")\n break", "repo_name": "Xsmael/blockchain-implementation", "sub_path": "blockchainOO.py", "file_name": "blockchainOO.py", "file_ext": "py", "file_size_in_byte": 4723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 155, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 183, "usage_type": "call"}]} +{"seq_id": "13981696120", "text": "import os\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nsys.path.append(os.getcwd())\nfrom module.data_handler import DataCleaner, DataTransformer, DataValidator\n\n\n@pytest.fixture\ndef setup_data():\n df = pd.DataFrame(\n {\n \"feature1\": np.random.randn(100),\n \"feature2\": np.random.choice([0, 1], 100),\n \"feature3\": np.random.randn(100),\n \"target\": np.random.randint(0, 2, 100),\n }\n )\n\n return DataTransformer(df)\n\n\n# TEST DATA TRANSFORMER CLASS\n\n\n# Test process_data method\ndef test_process_data(setup_data):\n X_train, X_test, y_train, y_test = setup_data.process_data(\n \"target\", \"classification\", 0.2, \"zscore\", [\"feature1\"]\n )\n assert len(X_train) == 80\n assert len(X_test) == 20\n assert len(y_train) == 80\n assert len(y_test) == 20\n\n\n# Test set_x_y_data method\ndef test_set_x_y(setup_data):\n setup_data.set_x_y_data(\"target\", [\"feature1\"])\n\n assert len(setup_data.X) == 100\n assert setup_data.y is not None\n assert \"feature1\" not in setup_data.X.columns\n\n\n# Test set_correct_data_type method_and_shape method\ndef test_set_correct_data_type_regressor(setup_data):\n setup_data.set_x_y_data(\"target\", [\"feature1\"])\n setup_data.set_correct_data_type_and_shape(False)\n assert setup_data.X.dtypes[0] == \"float32\"\n assert setup_data.X.shape == (100, 2)\n assert setup_data.y.dtype == \"float32\"\n assert setup_data.y.shape == (100, 1)\n\n\ndef test_set_correct_data_type_classifier(setup_data):\n setup_data.set_x_y_data(\"target\")\n setup_data.set_correct_data_type_and_shape(True)\n assert setup_data.X.dtypes[0] == \"float32\"\n assert setup_data.y.dtype == \"int64\"\n assert setup_data.y.shape == (100,)\n\n\n# Test split_scale_data method\n\n\n@pytest.fixture\ndef setup_values(setup_data):\n setup_data.set_x_y_data(\"target\")\n setup_data.X = setup_data.X.values\n return setup_data\n\n\ndef test_split_data_into_train_test(setup_values):\n split_size = 0.2\n (\n X_train,\n X_test,\n y_train,\n y_test,\n ) = setup_values.split_data_into_train_test(split_size, True)\n assert len(X_train) == len(setup_values.X) * (1 - split_size)\n assert len(X_test) == len(setup_values.X) * split_size\n assert len(y_train) == len(setup_values.X) * (1 - split_size)\n assert len(y_test) == len(setup_values.X) * split_size\n\n\n# Test scale_data_columns method\n\n\ndef test_scale_data_columns(setup_values):\n data = setup_values.split_data_into_train_test(0.2, True)\n X_train, *_ = setup_values.scale_data_columns(data, \"zscore\")\n\n assert np.isclose(X_train[:, 0].mean(), 0, atol=0.01)\n assert np.isclose(X_train[:, 0].std(), 1, atol=0.01)\n\n\ndef test_scale_data_columns_minmax(setup_values):\n data = setup_values.split_data_into_train_test(0.2, True)\n X_train, *_ = setup_values.scale_data_columns(data, \"minmax\")\n\n assert np.isclose(X_train[:, 0].min(), 0, atol=0.01)\n assert np.isclose(X_train[:, 0].max(), 1, atol=0.01)\n\n\ndef test_scale_data_columns_invalid_scaler(setup_values):\n with pytest.raises(ValueError):\n data = (0, 0, 0, 0)\n setup_values.scale_data_columns(data, \"invalid_scaler\")\n\n\n# Test get_feature_and_class_count method\ndef test_get_feature_and_class_count(setup_values):\n assert setup_values.get_feature_and_class_count() == (3, 2)\n\n\n# TEST DataValidator CLASS\ndef test_validate_data(setup_data):\n assert DataValidator.validate_data(setup_data.df, \"target\") is None\n\n\ndef test_validate_data_target(setup_data):\n assert (\n DataValidator.validate_target_column(setup_data.df, \"target\") is None\n )\n\n\ndef test_validate_data_invalid_target(setup_data):\n with pytest.raises(ValueError):\n DataValidator.validate_target_column(setup_data.df, \"invalid_target\")\n\n\ndef test_validate_data_drop_col(setup_data):\n assert (\n DataValidator.validate_drop_columns(setup_data.df, [\"feature1\"])\n is None\n )\n\n\ndef test_validate_data_invalid_drop_col(setup_data):\n with pytest.raises(ValueError):\n DataValidator.validate_drop_columns(setup_data.df, [\"invalid_col\"])\n\n\n# TEST DataCleaner CLASS\n\n\n# Test clean_data method\ndef test_clean_data_high_cardinality():\n high_cardinality_df = pd.DataFrame(\n {\n \"feature1\": list(map(str, range(21))),\n \"feature2\": np.random.choice([\"A\", \"B\"], 21),\n \"feature3\": np.random.randn(21),\n \"target\": np.random.randint(0, 2, 21),\n }\n )\n high_cardinality_df = DataCleaner.clean_data(high_cardinality_df)\n assert high_cardinality_df.shape[1] == 3\n\n\ndef test_clean_data_accepted_cardinality():\n high_cardinality_df = pd.DataFrame(\n {\n \"feature1\": list(map(str, range(20))),\n \"feature2\": np.random.choice([\"A\", \"B\"], 20),\n \"feature3\": np.random.randn(20),\n \"target\": np.random.randint(0, 2, 20),\n }\n )\n high_cardinality_df = DataCleaner.clean_data(high_cardinality_df)\n assert high_cardinality_df.shape[1] == 23\n\n\ndef test_clean_data_remove_nan():\n nan_df = pd.DataFrame(\n {\n \"feature1\": np.random.choice([\"A\", \"B\", None], 100),\n \"feature2\": list(np.random.randn(98)) + [None, None],\n \"target\": list(np.random.randint(0, 2, 98)) + [None, None],\n }\n )\n nan_df = DataCleaner.clean_data(nan_df)\n assert not np.isnan(nan_df).any()\n\n\n@pytest.fixture\ndef setup_df():\n return pd.DataFrame(\n {\n \"feature1\": np.random.choice([\"A\", \"B\", \"c\"], 100),\n \"feature2\": np.random.randn(100),\n \"target\": np.random.randint(0, 2, 100),\n }\n )\n\n\ndef test_clean_data_one_hot(setup_df):\n one_hot_df = DataCleaner.clean_data(setup_df)\n assert one_hot_df.shape[1] == 5\n\n\n# Test separate_features method\ndef test_separate_features(setup_df):\n num_idx, cat_idx = DataCleaner.separate_features(setup_df)\n assert len(num_idx) == 2\n assert len(cat_idx) == 1\n\n\n# Test get_low_cardinality_features method\ndef test_get_low_cardinality_features(setup_df):\n _, cat_idx = DataCleaner.separate_features(setup_df)\n low_cardinality_features = DataCleaner.get_low_cardinality_features(\n setup_df, cat_idx\n )\n assert len(low_cardinality_features) == 1\n\n\ndef test_get_low_cardinality_features_high_cardinality(setup_df):\n setup_df[\"feature1\"] = list(map(str, range(100)))\n _, cat_idx = DataCleaner.separate_features(setup_df)\n low_cardinality_features = DataCleaner.get_low_cardinality_features(\n setup_df, cat_idx\n )\n assert len(low_cardinality_features) == 0\n\n\nif __name__ == \"__main__\":\n pytest.main([__file__])\n", "repo_name": "v1kstrand/FastML", "sub_path": "tests/test_data_handler.py", "file_name": "test_data_handler.py", "file_ext": "py", "file_size_in_byte": 6665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "module.data_handler.DataTransformer", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.isclose", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 107, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 111, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator.validate_data", "line_number": 123, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator", "line_number": 123, "usage_type": "name"}, {"api_name": "module.data_handler.DataValidator.validate_target_column", "line_number": 128, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator", "line_number": 128, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 133, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator.validate_target_column", "line_number": 134, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator", "line_number": 134, "usage_type": "name"}, {"api_name": "module.data_handler.DataValidator.validate_drop_columns", "line_number": 139, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator", "line_number": 139, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 145, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator.validate_drop_columns", "line_number": 146, "usage_type": "call"}, {"api_name": "module.data_handler.DataValidator", "line_number": 146, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "module.data_handler.DataCleaner.clean_data", "line_number": 162, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 162, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 172, "usage_type": "attribute"}, {"api_name": "module.data_handler.DataCleaner.clean_data", "line_number": 175, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 175, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 182, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 184, "usage_type": "attribute"}, {"api_name": "module.data_handler.DataCleaner.clean_data", "line_number": 187, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 187, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 188, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 195, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 191, "usage_type": "attribute"}, {"api_name": "module.data_handler.DataCleaner.clean_data", "line_number": 203, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 203, "usage_type": "name"}, {"api_name": "module.data_handler.DataCleaner.separate_features", "line_number": 209, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 209, "usage_type": "name"}, {"api_name": "module.data_handler.DataCleaner.separate_features", "line_number": 216, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 216, "usage_type": "name"}, {"api_name": "module.data_handler.DataCleaner.get_low_cardinality_features", "line_number": 217, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 217, "usage_type": "name"}, {"api_name": "module.data_handler.DataCleaner.separate_features", "line_number": 225, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 225, "usage_type": "name"}, {"api_name": "module.data_handler.DataCleaner.get_low_cardinality_features", "line_number": 226, "usage_type": "call"}, {"api_name": "module.data_handler.DataCleaner", "line_number": 226, "usage_type": "name"}, {"api_name": "pytest.main", "line_number": 233, "usage_type": "call"}]} +{"seq_id": "22996539581", "text": "from django.shortcuts import render\nfrom rest_framework import viewsets\nfrom django.http.response import HttpResponse\nfrom rest_framework.response import Response\n\nfrom slowsql.esmodel import SlowQuery\nfrom elasticsearch_dsl import Q,A\n# Create your views here.\nfrom rest_framework.pagination import PageNumberPagination\nfrom rest_framework.decorators import action\n\n\nclass CustomPagination(PageNumberPagination):\n # 每页显示记录数，前端没有传入page_num，则默认显示此参数\n page_size = 10\n page_size_query_param = 'page_size'\n page_query_param = 'page_num'\n max_page_size = 500\n\n\ndef build_aggs(agg):\n for k in agg.keys():\n if k != \"aggs\":\n options = agg.get(k)\n return A(k, **options)\n\n\ndef get_aggs(agg, d):\n if 'aggs' not in d.keys():\n return\n\n aggs = d.get('aggs')\n if len(aggs.keys()) > 1:\n for metric_name in aggs.keys():\n agg = agg.metric(metric_name, build_aggs(aggs.get(metric_name)))\n elif len(aggs.keys()) == 1:\n k = list(aggs.keys())[0]\n agg = agg.bucket(k, build_aggs(aggs.get(k)))\n get_aggs(agg, aggs.get(k))\n\n\ndef get_results(agg_query, result):\n if 'aggs' not in agg_query.keys():\n return {}\n\n aggs = agg_query.get('aggs')\n if len(aggs.keys()) == 1:\n key_name = list(aggs.keys())[0]\n bucket_results = []\n for bucket in result[key_name]['buckets']:\n doc_count = 0\n key_val = ''\n if 'key_as_string' in bucket:\n key_val = bucket.key_as_string\n elif 'key' in bucket:\n key_val = bucket.key\n else:\n raise Exception('no key found in bucket')\n if 'doc_count' in bucket:\n doc_count = bucket.doc_count\n ret = get_results(aggs[key_name], bucket)\n if isinstance(ret, list):\n for r in ret:\n r[key_name + \"_count\"] = doc_count\n r[key_name] = key_val\n bucket_results.extend(ret)\n elif isinstance(ret, dict):\n ret[key_name] = key_val\n ret[key_name + \"_count\"] = doc_count\n bucket_results.append(ret)\n return bucket_results\n else:\n ret = {}\n for key_name in aggs.keys():\n if 'value' in result[key_name]:\n val = result[key_name]['value']\n ret[key_name] = val\n elif list(aggs[key_name].keys())[0] == 'top_hits':\n print(result[key_name])\n hits = result[key_name]['hits']['hits']\n if len(hits) > 0:\n for source_field in hits[0]['_source']:\n ret[source_field] = hits[0]['_source'][source_field]\n\n return ret\n\nclass SlowSqlViewSet(viewsets.ViewSet):\n\n @action(detail=False, methods=['get'])\n # 每个schema慢日志数量的饼状图\n def get_aggs_by_schema(self, request, *args, **kwargs):\n s = self.get_query_by_params(request)\n # 根据schema做聚合\n aggs = {\n \"aggs\":\n {\n \"schema\": {\n \"terms\": {\n \"field\": \"schema.keyword\"\n }\n }\n }\n }\n get_aggs(s.aggs, aggs)\n\n result = s.execute().aggregations\n\n rs = get_results(aggs, result)\n return Response(rs)\n\n @action(detail=False, methods=['get'])\n # 用来画每天的慢日志数据曲线图\n def get_aggs_by_date(self, request, *args, **kwargs):\n s = self.get_query_by_params(request, sorts=\"@timestamp\")\n aggs = {\n \"aggs\": {\n \"date\": {\n \"date_histogram\": {\n \"field\": \"@timestamp\",\n \"calendar_interval\": 'day'\n }\n }\n }\n }\n get_aggs(s.aggs, aggs)\n\n result = s.execute().aggregations\n\n rs = get_results(aggs, result)\n return Response(rs)\n\n # print(1)\n @action(detail=False, methods=['get'])\n # 慢日志top10\n def get_top10_sql(self, request, *args, **kwargs):\n s = self.get_query_by_params(request)\n composite = A('terms', script=\"doc['schema.keyword'].value+'#'+doc['hash.keyword'].value\", size=10)\n s.aggs.bucket('sql', composite).bucket('finger', A('top_hits', _source=[\"finger\"], size=1))\n aggs = s.execute().aggregations\n results = []\n for bucket in aggs.sql.buckets:\n k = bucket.key\n keys = k.split('#')\n data = {\n \"schema\": keys[0],\n \"hash\": keys[1],\n \"count\": bucket.doc_count,\n \"finger\": bucket.finger.hits.hits[0]['_source']['finger']\n }\n results.append(data)\n\n return Response(results)\n\n # 通用获取参数\n def get_query_by_params(self, request, sorts=None):\n start = request.query_params.get('start')\n end = request.query_params.get('end')\n s = SlowQuery.search()\n if start is None or not isinstance(start, str) or len(start.strip()) == 0:\n start = None\n if end is None or not isinstance(end, str) or len(end.strip()) == 0:\n end = None\n\n if start is not None and end is not None:\n options = {\n # greater or equal than -> gte 大于等于\n # greater than -> gt 大于\n # little or equal thant -> lte 小于或等于\n 'gte': start,\n 'lte': end\n }\n print(options)\n s = s.filter('range', **{'@timestamp': options})\n\n\n sorts = request.query_params.get('sorts', sorts)\n if isinstance(sorts, str) and len(sorts) > 0:\n sorts = [item.strip() for item in sorts.split(\",\") if len(item.strip()) > 0]\n s = s.sort(*sorts)\n else:\n s = s.sort('-@timestamp')\n return s\n\n def list(self, request, *args, **kwargs):\n\n # 入参： 开始时间、结束时间、库名、第几页、每页多少个\n start = request.query_params.get('start')\n end = request.query_params.get('end')\n schema = request.query_params.get('schema', None)\n hash = request.query_params.get('hash', None)\n\n is_aggr_by_hash = request.query_params.get('is_aggr_by_hash', False)\n if isinstance(is_aggr_by_hash, str) and is_aggr_by_hash.lower() == 'true':\n is_aggr_by_hash = True\n else:\n is_aggr_by_hash = False\n\n interval = request.query_params.get('interval', '1d')\n\n\n s = SlowQuery.search()\n if schema is not None and len(schema) > 0:\n s = s.filter('term', schema__keyword=schema)\n\n if hash is not None and len(hash) > 0:\n s = s.filter('term', hash=hash)\n\n if start is not None and end is not None:\n options = {\n 'gte': start,\n 'lte': end\n }\n s = s.filter('range', **{'@timestamp': options})\n s = s.sort('-@timestamp')\n\n paginator = CustomPagination()\n\n if is_aggr_by_hash:\n aggs = {\n \"aggs\": {\n \"date\": {\n \"date_histogram\": {\n \"field\": \"@timestamp\",\n \"calendar_interval\": interval\n },\n \"aggs\": {\n \"schema\": {\n \"terms\": {\n \"field\": \"schema.keyword\"\n },\n \"aggs\": {\n \"hash\": {\n \"terms\": {\n \"field\": \"hash.keyword\"\n },\n \"aggs\": {\n \"rowsSentAvg\": {\n \"avg\": {\n \"field\": \"slowlog_rows_sent\"\n }\n },\n \"rowsExamineAvg\": {\n \"avg\": {\n \"field\": \"slowlog_rows_examined\"\n }\n },\n \"queryTimeAvg\": {\n \"avg\": {\n \"field\": \"slowlog_query_time_sec\"\n }\n },\n \"queryTimeSum\": {\n \"sum\": {\n \"field\": \"slowlog_query_time_sec\"\n }\n },\n \"sql\": {\n \"top_hits\": {\n \"_source\": [\n \"finger\",\n \"@timestamp\"\n ],\n \"sort\": [\n {\n \"@timestamp\": {\n \"order\": \"desc\"\n }\n }\n ],\n \"size\": 1\n }\n }\n\n }\n }\n }\n }\n }\n }\n }\n }\n\n # 这一步要看看\n get_aggs(s.aggs, aggs)\n\n result = s.execute().aggregations\n\n # 这一步也要看看\n rs = get_results(aggs, result)\n\n if is_aggr_by_hash:\n data = paginator.paginate_queryset(rs, request)\n else:\n data = paginator.paginate_queryset(s, request)\n data = [q.to_dict() for q in data]\n\n # data = paginator.paginate_queryset(s, request)\n # data = [q.to_dict() for q in data]\n\n return paginator.get_paginated_response(data)\n\n # return Response('success')\n\n @action(detail=False, methods=['get'])\n def es_test(self, request):\n # print(1)\n # return HttpResponse(1)\n s = SlowQuery.search()\n\n # 查询所有的数据\n # result = s.scan()\n\n # 等值查询\n # result = s.filter('term', schema__keyword='Ena').scan()\n\n # 排序\n # result = s.sort('-@timestamp')\n\n # 指定范围\n # from_date = '2020-12-20T00:00:00'\n # to_date = '2020-12-21T00:00:00'\n # options = {\n # 'gte': from_date,\n # 'lte': to_date\n # }\n # result = s.filter('range', **{'@timestamp': options}).execute()\n\n #\n # result = s.filter('wildcard', schema__keyword='*An*').scan()\n\n # result = s.filter('prefix', schema__keyword='L').scan()\n\n # result = s.filter('match', query_sql='abc cda').scan()\n # result = s.filter('match', query_sql='abc urtaevhwcz').scan()\n\n # result = s.filter('match', query_sql='T_computer').filter('term', schema__keyword='Cna').scan()\n\n q = Q(\"match\", query_sql='T_computer') | Q(\"term\", schema__keyword=\"Cna\")\n\n result = s.filter(q).scan()\n\n # result = s.sort('-@timestamp', 'rows_examined')\n\n\n # 一层聚合数据: 简单的一个根据日期聚合的\n\n # 查询语句\n timeAggs = A('date_histogram', field='@timestamp', fixed_interval=\"1d\")\n\n # 'date' 为分组的名称\n s.aggs.bucket('date', timeAggs)\n\n # 获取数据\n aggs = s.execute().aggregations\n\n # 这里要 debugger 一下，然后才知道怎么取数据 \n print(aggs)\n\n for date_item in aggs['date'].buckets:\n print(date_item)\n\n\n\n # for i in result:\n # # print(i)\n # print(i.to_dict(include_meta=True))\n return Response('success')\n\n\n\n \"\"\"\n\n # 多层聚合数据\n s = SlowQuery.search()\n\n s = s.sort('-@timestamp')\n\n timeAggs = A('date_histogram', field='@timestamp',\n fixed_interval=\"1d\")\n\n schemaAggs = A('terms', field='schema.keyword')\n\n hashFingerAggs = A('terms', field='hash.keyword')\n\n rowsExaminedAvg = A('avg', field='rows_examined')\n queryTimeAvg = A('avg', field='query_time_sec')\n\n s.aggs.bucket('date', timeAggs)\\\n .bucket('schema', schemaAggs)\\\n .bucket('hash', hashFingerAggs)\n # .metric('rowsExaminedAvg',rowsExaminedAvg)\\\n # .metric('queryTimeAvg', queryTimeAvg)\n\n aggs = s.execute().aggregations\n\n print(aggs['date'])\n\n for date_buckets in aggs['date'].buckets:\n print(\"date_buckets: \", date_buckets)\n\n for schema_buckets in date_buckets['schema'].buckets:\n print(\"schema_buckets: \", schema_buckets)\n\n for hash_buckets in schema_buckets['hash'].buckets:\n print(\"hash_buckets: \", hash_buckets)\n \"\"\"\n\n\ndef send_with_matplotlib(request):\n\n s = SlowQuery.search()\n options = {\n # greater or equal than -> gte 大于等于\n # greater than -> gt 大于\n # little or equal thant -> lte 小于或等于\n 'gte': '2020-12-22T00:00:00.000Z',\n 'lte': '2021-01-21T00:00:00.000Z'\n }\n s = s.filter('range', **{'@timestamp': options})\n aggs = {\n \"aggs\": {\n \"date\": {\n \"date_histogram\": {\n \"field\": \"@timestamp\",\n \"calendar_interval\": \"day\"\n },\n \"aggs\": {\n \"avg_query_time\": {\n \"avg\": {\n \"field\": \"query_time\"\n }\n },\n \"avg_lock_time\": {\n \"avg\": {\n \"field\": \"lock_time\"\n }\n }\n }\n }\n }\n }\n get_aggs(s.aggs, aggs)\n result = s.execute().aggregations\n rs = get_results(aggs, result)\n dates = [r['date'][:10] for r in rs]\n counts = [r['date_count'] for r in rs]\n\n print(dates)\n print(counts)\n\n return Response(\"success\")", "repo_name": "mysqlbin/python_note", "sub_path": "2020-09-05-Python-ZST-4200-new/zst_mysql_1110/slowsql/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 15200, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "rest_framework.pagination.PageNumberPagination", "line_number": 13, "usage_type": "name"}, {"api_name": "elasticsearch_dsl.A", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 87, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 109, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 89, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 130, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 111, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.A", "line_number": 137, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.A", "line_number": 138, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 152, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 133, "usage_type": "call"}, {"api_name": "slowsql.esmodel.SlowQuery.search", "line_number": 158, "usage_type": "call"}, {"api_name": "slowsql.esmodel.SlowQuery", "line_number": 158, "usage_type": "name"}, {"api_name": "slowsql.esmodel.SlowQuery.search", "line_number": 201, "usage_type": "call"}, {"api_name": "slowsql.esmodel.SlowQuery", "line_number": 201, "usage_type": "name"}, {"api_name": "slowsql.esmodel.SlowQuery.search", "line_number": 308, "usage_type": "call"}, {"api_name": "slowsql.esmodel.SlowQuery", "line_number": 308, "usage_type": "name"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 338, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.A", "line_number": 348, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 367, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 304, "usage_type": "call"}, {"api_name": "slowsql.esmodel.SlowQuery.search", "line_number": 411, "usage_type": "call"}, {"api_name": "slowsql.esmodel.SlowQuery", "line_number": 411, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 451, "usage_type": "call"}]} +{"seq_id": "8835973594", "text": "import numpy as np\nfrom numpy.linalg import inv\nfrom numpy import matmul \nimport matplotlib.pyplot as plt\nnp.random.seed(2)\nfrom tqdm import tqdm\n\n\n'''\n1. Replicate the generation of random functions from Figure 2.2 . Use a regular (or random) \ngrid of scalar inputs and the covariance function from eq. (2.16). Hints on how to generate \nrandom samples from multi-variate Gaussian distributions are given in section A.2. Invent \nsome training data points, and make random draws from the resulting GP posterior using eq. (2.19).\n'''\n\ndef SquareKernel(xInput, error = None):#unsure if this is the right nomenclature\n #xp and xq some set of points in the input space, but for the exercise should be scalars\n Dim = len(xInput)\n xRowmat = np.tile(xInput,(Dim,1))\n\n DifferenceMat = xRowmat-xRowmat.T \n \n if error is None:\n return np.exp(-0.5*np.abs(DifferenceMat)**2)\n ErrorMat = error**2 * np.identity(Dim)\n return np.exp(-0.5*np.abs(DifferenceMat)**2) + ErrorMat\n\ndef RectKernel(xTraining, xQuery):\n\n #don't think this is needed? but constructs if needed\n\n #choose to compute k(X,X*), then tranpose to get other corner\n #X* are the points we want to query, X are the training data\n #offdiagonal submatrix \n DimQuery = len(xQuery)\n DimTraining = len(xTraining)\n\n xTrainingQueryDim = np.tile(xTraining,(DimQuery,1))\n xQueryTrainingDim = np.tile(xQuery, (DimTraining,1))\n\n # X-X*\n DifferenceMat = xTrainingQueryDim.T-xQueryTrainingDim\n\n return np.exp(-0.5*np.abs(DifferenceMat)**2)\n\n\ndef SamplePosterior(xTraining,xQuery,fTraining, error = None):\n\n #Training Data Kernel (top left with a small error)\n kXX = SquareKernel(xTraining, error)\n #Query Data Kernel (bottom right)\n kXStarXStar = SquareKernel(xQuery)\n #Off Diagonal (Top Right)\n kXXStar = RectKernel(xTraining,xQuery) #top right\n kXStarX = kXXStar.T #bottom left\n\n #construct the normal distribution to sample (equation 2.19)\n\n muConditioned = matmul(matmul(kXStarX,inv(kXX)),fTraining)\n covConditioned = kXStarXStar-matmul(kXStarX,matmul(inv(kXX), kXXStar))\n\n #sample the conditioned N dimensional Gaussian (MVG) distribution\n GaussianProcess = np.random.multivariate_normal(muConditioned,covConditioned)\n\n return GaussianProcess, muConditioned, covConditioned\n\n\n\n#use a regular grid of scalar inputs\nNPoints = 400\nxQuery = np.linspace(0,10,NPoints)\n\n#GP without training data\ncov = SquareKernel(xQuery)\nmu = np.zeros(NPoints)\n#sample the gaussian, and get the y values\n#plot GPs only given the kernel\nfig,ax = plt.subplots(1,1, figsize =(10,6))\n#generate and plot GPs\nfor i in range(3):\n fQuery = np.random.multivariate_normal(mu,cov)\n ax.plot(xQuery, fQuery, color = 'red', alpha = 0.4)\nfQuery = np.random.multivariate_normal(mu,cov) \nax.plot(xQuery, fQuery, color = 'red', label = 'GP', alpha = 0.3)\n#use marginalisation(?) to get the pm 2 sigma range\nMuPlusTwoSigma = mu+2*np.sqrt(np.diagonal(cov))\nMuMinusTwoSigma = mu-2*np.sqrt(np.diagonal(cov))\nax.fill_between(xQuery, MuPlusTwoSigma,MuMinusTwoSigma, color = 'gray', alpha = 0.3, label = '$\\mu \\pm 2 \\sigma$')\nax.legend()\nplt.show()\n\n\n####conditioned GP given training data#####\n\n#training data (don't think it is classed as part of the prior? uncertain)\nxTraining = np.array([2,3.5,5,8])\nfTraining = 3*np.array([0.3,0.5,0.4,1.2])\nmuTraining = fTraining\nerror = np.sqrt(10.0) #small uncertainty on the training data\n#plot\nfig, ax = plt.subplots(1,1, figsize =(10,6))\n#generate and plot *conditioned* GPs *given* the training data\nNSamples = 3\nfor i in tqdm(range(NSamples-1)):\n fQueryGivenTraining, muConditioned, covConditioned = SamplePosterior(xTraining,xQuery,fTraining, error)\n ax.plot(xQuery, fQueryGivenTraining, color = 'red', alpha = 0.4)\nfQueryGivenTraining, muConditioned, covConditioned = SamplePosterior(xTraining,xQuery,fTraining,error)\nax.plot(xQuery, fQueryGivenTraining, color = 'red', label = 'GP', alpha = 0.3)\n#use marginalisation(?) to get the pm 2 sigma range\nMuPlusTwoSigma = muConditioned+2*np.sqrt(np.diagonal(covConditioned))#+2*error\nMuMinusTwoSigma = muConditioned-2*np.sqrt(np.diagonal(covConditioned))#-2*error\nax.fill_between(xQuery, MuPlusTwoSigma,MuMinusTwoSigma, color = 'gray', alpha = 0.3, label = '$\\mu \\pm 2 \\sigma$')\n#plot the training data\nax.plot(xTraining, fTraining,'+' , ms = 20)\n\nax.plot(xQuery, muConditioned, color = 'gray', alpha =0.8)\n\n#ax.errorbar(xTraining, fTraining,yerr=2*error, marker = '+' , ls = 'none' ,color = 'black', label = 'Training Data', ms = 20)\nax.legend()\nplt.show()\n\n", "repo_name": "ivanshalashilin/Summer2023-Gaussian-Processes", "sub_path": "GP4MLExercises/exercise21.py", "file_name": "exercise21.py", "file_ext": "py", "file_size_in_byte": 4563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.random.seed", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.tile", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}]} +{"seq_id": "5054785922", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 29 10:38:51 2021\n\n@author: chengyou\n\"\"\"\n\nfrom ete3 import Tree\n\n## Start creating tables\nfin = open('ast.txt')\nast = fin.read() # read all ast tree\n\n# pre processing raw ANTLR output into ete3 tree format\n\npvt = 1\nwhile(pvt < len(ast)):\n if(ast[pvt]=='('):\n if(ast[pvt-1]==','):\n pvt += 1\n continue\n elif(ast[pvt-1]!='('):\n ast=ast[:pvt]+','+ast[pvt:]\n pvt += 2\n else:\n pvt += 1\n else:\n pvt += 1\nwhile('()' in ast):\n pvt = ast.find('()')\n ast = ast[:pvt+1] + 'None' +ast[pvt+1:]\n\n# start label estimating\nsubProg = {}\nt = Tree(ast+';', format = 1)\nt.show()\n\nprint('OK')", "repo_name": "ChengYouChang/v3_compiler", "sub_path": "plotTree.py", "file_name": "plotTree.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "ete3.Tree", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "4135725910", "text": "\"\"\"This program simulates a 1D cellular automaton. Visit\nhttps://en.wikipedia.org/wiki/Elementary_cellular_automaton for more info.\nUnfortunately, I failed to implement the isotropy condition from the paper,\nas I don't know what it means.\"\"\"\n\nimport numpy as np\nfrom pyics import Model\nfrom rule_func import *\nfrom copy import deepcopy\nimport time\n\n\nclass CASim(Model):\n def __init__(self):\n Model.__init__(self)\n\n self.t = 0\n self.rule_set = []\n self.config = None\n self.next = None\n self.changable = []\n self.new_changable = []\n self.rules = None\n self.k = 4\n self.done = False\n self.fraction = 0\n self.chance = [0] * 8\n\n\n self.make_param('width', 50)\n self.make_param('height', 50)\n self.make_param('spread', 0.5)\n self.make_param('density', 0.5)\n self.make_param('weather', 1)\n self.make_param('seed', 0)\n self.make_param('wind_angle', 0.0)\n self.make_param('wind_speed', 1.0)\n\n def build_rules(self):\n self.chance = wind_weights(self)\n return 0\n\n def check_rule(self, i, j):\n \"\"\"Returns the new state based on the input states.\n\n The input state will be an array of 2r+1 items between 0 and k, the\n neighbourhood which the state of the new cell depends on.\"\"\"\n\n \"\"\"This calculates the rule that should be used. It first calculates\n the decimal equivalent of the input and then checks what the next\n symbol should be.\"\"\"\n return fire(self, i, j)\n\n def setup_initial_state(self):\n \"\"\"Returns an array with the initial state for each of\n the cells in the first timestep. Values should be between 0 and k.\"\"\"\n np.random.seed(self.seed)\n\n one_amount = int((self.width * self.height) * self.density)\n self.config[:one_amount] = 1\n np.random.shuffle(self.config)\n self.config = self.config.reshape((self.height, self.width))\n\n for j in range(self.height):\n self.config[j][0] = 3\n self.changable.append([0,j])\n\n #self.config[self.height // 2][self.width // 2] = 3\n #self.changable.append([self.width // 2, self.height // 2])\n\n return self.config\n\n def reset(self):\n \"\"\"Initializes the configuration of the cells and converts the entered\n rule number to a rule set.\"\"\"\n\n self.t = 0\n self.config = np.zeros(self.height * self.width)\n self.changable = []\n self.config = self.setup_initial_state()\n self.build_rules()\n self.done = False\n self.fraction = 0\n\n\n def draw(self):\n \"\"\"Draws the current state of the grid.\"\"\"\n\n import matplotlib\n import matplotlib.pyplot as plt\n\n plt.cla()\n if not plt.gca().yaxis_inverted():\n plt.gca().invert_yaxis()\n plt.imshow(self.config, interpolation='none', vmin=0, vmax=self.k - 1,\n cmap=matplotlib.cm.binary)\n plt.axis('image')\n plt.title('t = %d' % self.t)\n\n def step(self):\n \"\"\"Performs a single step of the simulation by advancing time (and thus\n row) and applying the rule to determine the state of the cells.\"\"\"\n\n self.chance = wind_weights(self)\n\n self.t += 1\n self.new_changable = []\n same = True\n self.next = deepcopy(self.config)\n for k in range(len(self.changable)):\n i = self.changable[k][0]\n j = self.changable[k][1]\n self.next[j][i] = self.check_rule(i, j)\n if same == True and self.next[j][i] != self.config[j][i]:\n same = False\n if same == True:\n self.done = True\n calculate(self)\n return True\n self.config = self.next\n self.changable = self.new_changable\n\n\n\n\n\n\nif __name__ == '__main__':\n sim = CASim()\n from pyics import GUI\n cx = GUI(sim)\n cx.start()\n", "repo_name": "Lars-Janssen/Introduction-Computational-Science", "sub_path": "Assignment 4/Version 3.0/ca.py", "file_name": "ca.py", "file_ext": "py", "file_size_in_byte": 3955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pyics.Model", "line_number": 13, "usage_type": "name"}, {"api_name": "pyics.Model.__init__", "line_number": 15, "usage_type": "call"}, {"api_name": "pyics.Model", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.cm", "line_number": 96, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 109, "usage_type": "call"}, {"api_name": "{'matplotlib': 'matplotlib', 'plt': 'matplotlib.pyplot'}", "line_number": 129, "usage_type": "call"}, {"api_name": "pyics.GUI", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "17062695363", "text": "from typing import Type\n\nfrom autobahn.exception import Disconnected\nfrom channels.db import database_sync_to_async\nfrom channels.generic.websocket import AsyncJsonWebsocketConsumer\n\nfrom .utils import websocket_send_event\n\n\nclass GameConsumerMixin(AsyncJsonWebsocketConsumer):\n game_model: Type = NotImplemented\n player_model: Type = NotImplemented\n\n async def connect(self) -> None:\n self.scope_user = self.scope[\"user\"]\n self.scope_room_name = self.scope[\"url_route\"][\"kwargs\"][\"room_name\"]\n self.room_name = (\n f\"{self.game_model.__name__.lower()}_room_{self.scope_room_name}\"\n )\n try:\n await self.channel_layer.group_add(self.room_name, self.channel_name)\n except TypeError:\n print(f\"failed adding {self.game_model.__name__}\")\n await self.accept()\n\n async def disconnect(self, close_code: int) -> None:\n try:\n await self.channel_layer.group_discard(self.room_name, self.channel_name)\n except TypeError:\n print(\"failed disconnecting from game\")\n await super().disconnect(close_code)\n\n async def receive_json(self, content: dict, **kwargs) -> None:\n self.command = content.get(\"command\", None)\n self.message = content.get(\"message\", None)\n self.user = content.get(\"user\", None)\n self.value = content.get(\"value\", None)\n self.data = {\n \"type\": \"websocket_message\",\n \"command\": self.command,\n \"user\": self.user,\n \"message\": self.message,\n \"value\": self.value,\n }\n if self.command == \"join\":\n await self.set_player_inactive_or_active(True)\n elif self.command == \"leave\":\n await self.set_player_inactive_or_active(False)\n elif self.command == \"restart\":\n await self.restart_game()\n elif self.command == \"ready\":\n if await self.check_players_ready_state():\n await self.restart_game()\n self.data[\"command\"] = \"restart\"\n self.data[\"message\"] = \"The game has restarted\"\n if self.command == \"win\" or self.command == \"over\":\n await self.set_game_state_off()\n await self.set_users_ready_state_off()\n try:\n await self.channel_layer.group_send(self.room_name, self.data)\n except TypeError:\n print(f\"failed sending to {self.game_model.__name__}\")\n\n async def websocket_message(self, event: dict) -> None:\n field_names = [\"command\", \"user\", \"message\", \"value\"]\n try:\n await websocket_send_event(self, event, field_names)\n except Disconnected:\n pass\n\n @database_sync_to_async\n def set_player_inactive_or_active(self, status: bool) -> None:\n try:\n room = self.game_model.objects.get(room_name=self.scope_room_name)\n player = self.player_model.objects.get(username=self.user, room=room)\n player.is_active = status\n player.save()\n except (self.game_model.DoesNotExist, self.player_model.DoesNotExist):\n print(\n f\"{self.game_model.__name__} or \"\n f\"{self.player_model.__name__} doesn't exist\"\n )\n\n @database_sync_to_async\n def set_game_state_off(self) -> None:\n self.game_model.objects.filter(room_name=self.scope_room_name).update(\n game_state=False\n )\n\n @database_sync_to_async\n def set_users_ready_state_off(self) -> None:\n self.game_model.objects.get(\n room_name=self.scope_room_name\n ).players.all().update(is_ready=False)\n\n @database_sync_to_async\n def check_players_ready_state(self) -> bool:\n if not self.game_model.objects.filter(\n room_name=self.scope_room_name, game_state=False\n ).exists():\n return False\n for player in self.player_model.objects.filter(\n room__room_name=self.scope_room_name\n ):\n if not player.is_ready:\n return False\n return True\n\n\nclass OnlineRoomsConsumerMixin(AsyncJsonWebsocketConsumer):\n game: str = \"\"\n model: Type = NotImplemented\n\n async def connect(self) -> None:\n self.rooms = f\"online_{self.game}_rooms\"\n try:\n await self.channel_layer.group_add(self.rooms, self.channel_name)\n except TypeError:\n print(f\"failed adding {self.game} to group\")\n await self.get_rooms()\n await self.channel_layer.group_send(\n self.rooms,\n {\n \"type\": \"websocket_rooms\",\n \"command\": \"online_rooms\",\n \"online_rooms\": self.online_rooms,\n },\n )\n await self.accept()\n\n async def websocket_rooms(self, event: dict) -> None:\n field_names = [\"command\", \"online_rooms\"]\n await websocket_send_event(self, event, field_names)\n\n async def websocket_room_added_or_deleted(self, event: dict) -> None:\n field_names = [\"command\", \"room_name\", \"room_id\"]\n await websocket_send_event(self, event, field_names)\n\n async def disconnect(self, code: int) -> None:\n try:\n await self.channel_layer.group_discard(self.rooms, self.channel_name)\n except TypeError:\n print(\"failed disconnecting from online rooms\")\n await super().disconnect(code)\n\n @database_sync_to_async\n def get_rooms(self) -> None:\n self.online_rooms = [\n {\"room_name\": room.room_name, \"room_id\": room.id}\n for room in self.model.objects.all()\n ]\n\n\nclass OnlineUsersConsumerMixin(AsyncJsonWebsocketConsumer):\n app: str = \"\"\n model: Type = NotImplemented\n\n async def connect(self) -> None:\n self.users = f\"online_{self.app}_users\"\n try:\n await self.channel_layer.group_add(self.users, self.channel_name)\n except TypeError:\n print(f\"failed adding {self.users} to group\")\n await self.get_users()\n await self.channel_layer.group_send(\n self.users,\n {\n \"type\": \"websocket_online_users\",\n \"command\": \"online_users\",\n \"online_users\": self.online_users,\n },\n )\n await self.accept()\n\n async def websocket_online_users(self, event: dict) -> None:\n field_names = [\"command\", \"online_users\"]\n await websocket_send_event(self, event, field_names)\n\n async def websocket_user_added_or_deleted(self, event: dict) -> None:\n field_names = [\"command\", \"username\"]\n await websocket_send_event(self, event, field_names)\n\n async def disconnect(self, code: int) -> None:\n try:\n await self.channel_layer.group_discard(self.users, self.channel_name)\n except TypeError:\n print(\"failed disconnecting from online users\")\n await super().disconnect(code)\n\n @database_sync_to_async\n def get_users(self) -> None:\n self.online_users = [\n {\"username\": user.username} for user in self.model.objects.all()\n ]\n", "repo_name": "kamilkaminski01/monitoring-system", "sub_path": "backend/backend/mixins.py", "file_name": "mixins.py", "file_ext": "py", "file_size_in_byte": 7109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "channels.generic.websocket.AsyncJsonWebsocketConsumer", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.websocket_send_event", "line_number": 67, "usage_type": "call"}, {"api_name": "autobahn.exception.Disconnected", "line_number": 68, "usage_type": "name"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 71, "usage_type": "name"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 84, "usage_type": "name"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 90, "usage_type": "name"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 96, "usage_type": "name"}, {"api_name": "channels.generic.websocket.AsyncJsonWebsocketConsumer", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 112, "usage_type": "name"}, {"api_name": "utils.websocket_send_event", "line_number": 133, "usage_type": "call"}, {"api_name": "utils.websocket_send_event", "line_number": 137, "usage_type": "call"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 146, "usage_type": "name"}, {"api_name": "channels.generic.websocket.AsyncJsonWebsocketConsumer", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 156, "usage_type": "name"}, {"api_name": "utils.websocket_send_event", "line_number": 177, "usage_type": "call"}, {"api_name": "utils.websocket_send_event", "line_number": 181, "usage_type": "call"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 190, "usage_type": "name"}]} +{"seq_id": "41578467982", "text": "import datetime\nimport logging\nimport uuid\nfrom enum import Enum\n\nfrom blinker import signal\n\nBUSINESS = 0\nNON_BUSINESS = 1\n\nclass TripData:\n __type = NON_BUSINESS\n __odometer_start = 0\n __started_on = None\n __duration = datetime.timedelta(0)\n __dist = 0\n __avg_speed = 0\n\n __tripdata_changed = signal('tripdata_changed')\n\n __trip_type_changed = signal('trip_type_changed')\n __odometer_start_changed = signal('odometer_start_changed')\n __started_on_changed = signal('started_on_changed')\n __duration_changed = signal('duration_changed')\n __dist_changed = signal('dist_changed')\n __avg_speed_changed = signal('avg_speed_changed')\n\n def __init__(self, trip_type, odometer):\n self.trip_type = trip_type\n self.odometer_start = odometer\n self.__trip_id = uuid.uuid4().hex\n\n @property\n def trip_id(self):\n return self.__trip_id\n\n @property\n def trip_type(self):\n return self.__type\n\n @trip_type.setter\n def trip_type(self, value):\n if value != NON_BUSINESS and value != BUSINESS:\n raise ValueError(\"Trip type must be BUSINESS or NON_BUSINESS\")\n self.__type = value\n self.__trip_type_changed.send(newvalue=value)\n self.__tripdata_changed.send(self)\n\n @property\n def odometer_start(self):\n return self.__odometer_start\n\n @odometer_start.setter\n def odometer_start(self, value):\n if value < 0:\n raise ValueError(\"Odometer setting must be positive\")\n self.__odometer_start = value\n self.__odometer_start_changed.send(newvalue=value)\n self.__tripdata_changed.send(self)\n\n @property\n def started_on(self):\n return self.__started_on\n\n @started_on.setter\n def started_on(self, value):\n if value < 0:\n raise ValueError(\"Invalid time stampt for start time\")\n self.__started_on = value\n self.__started_on_changed.send(newvalue=value)\n self.__tripdata_changed.send(self)\n\n @property\n def duration(self):\n return self.__duration\n\n @duration.setter\n def duration(self, value):\n if value.total_seconds() < 0:\n raise ValueError(\"Duration must be positive\")\n self.__duration = value\n self.__duration_changed.send(newvalue=value)\n self.__tripdata_changed.send(self)\n\n @property\n def distance_covered(self):\n return self.__dist\n\n @distance_covered.setter\n def distance_covered(self, value):\n if value < 0:\n raise ValueError(\"Distance must be positive\")\n self.__dist = value\n self.__dist_changed.send(newvalue=value)\n self.__tripdata_changed.send(self)\n\n @property\n def average_speed(self):\n return self.__avg_speed\n\n @average_speed.setter\n def average_speed(self, value):\n if value < 0:\n raise ValueError(\"Average speed must be positive\")\n self.__avg_speed = value\n self.__avg_speed_changed.send(newvalue=value)\n self.__tripdata_changed.send(self)\n\n def __repr__(self, *args, **kwargs):\n return \"[TripData: Type = {0}, odometer_start={1}, started_on={2}, \" \\\n \"duration={3},distance_covered={4}, average_speed={5}\".format(self.trip_type, self.odometer_start,\n self.started_on, self.duration,\n self.distance_covered, self.average_speed)\n\n\nNO_FIX = 1\nTWOD_FIX = 2\nTHREED_FIX = 3\n\n\nclass CurrentData:\n logging.basicConfig(level=logging.DEBUG)\n logger = logging.getLogger(\"CurrentData\")\n time_changed = signal('current_time_changed')\n position_changed = signal('current_position_changed')\n speed_changed = signal('current_speed_changed')\n fixtype_changed = signal('current_fix_changed')\n\n __epoch_time = 0\n __position = (None, None)\n __speed_in_ms = 0\n __fixtype = None\n\n @property\n def epoch_time(self):\n return self.__epoch_time\n\n @epoch_time.setter\n def epoch_time(self, new_value):\n old_value = self.__epoch_time\n if new_value is not None and new_value < 0:\n raise ValueError(\"Epoch time cannot be negative\")\n\n self.__epoch_time = new_value\n self.time_changed.send(old_value=old_value, new_value=new_value)\n\n @property\n def fixtype(self):\n return self.__fixtype\n\n @fixtype.setter\n def fixtype(self, new_value):\n old_value = self.__fixtype\n\n if new_value is not None and (new_value < NO_FIX or new_value > THREED_FIX):\n raise ValueError(\"Fix type should be either NO_FIX (1), TWOD_FIX (2) or THREED_FIX (3)\")\n\n self.__fixtype = new_value\n self.fixtype_changed.send(old_value=old_value, new_value=new_value)\n\n @property\n def position(self):\n return self.__position\n\n @position.setter\n def position(self, new_value):\n old_value = self.__position\n\n new_lat = new_value[0]\n new_lon = new_value[1]\n\n if new_lat is not None and (new_lat < -90 or new_lat > 90):\n raise ValueError(\"Latitude should be between -90 and +90 degrees\")\n\n if new_lon is not None and (new_lon < -180 or new_lon > 180):\n raise ValueError(\"Latitude should be between -180 and +180 degrees\")\n\n self.__position = new_value\n self.position_changed.send(old_value=old_value, new_value=new_value)\n\n @property\n def speed_in_ms(self):\n return self.__speed_in_ms\n\n @speed_in_ms.setter\n def speed_in_ms(self, new_value):\n old_value = self.__speed_in_ms\n if new_value is not None and new_value < 0:\n raise ValueError(\"Speed cannot be negative\")\n\n self.__speed_in_ms = new_value\n self.speed_changed.send(old_value=old_value, new_value=new_value)\n\n\nclass LogEventType(Enum):\n NOP = 0\n TRIP_STARTED = 1\n TRIP_STOPPED = 2\n TRIP_PAUSED = 3\n TRIP_RESUMED = 4\n DATAPOINT = 5\n\nclass LogEvent:\n __timestamp = 0\n __event_type = LogEventType.NOP\n __data = dict()\n\n @property\n def event_type(self):\n return self.__event_type\n\n @event_type.setter\n def event_type(self, new_value):\n self.__event_type = new_value\n\n @property\n def timestamp(self):\n return self.timestamp\n\n @timestamp.setter\n def timestamp(self, new_value):\n if new_value is not None and new_value < 0:\n raise ValueError(\"Epoch time cannot be negative\")\n\n self.__timestamp = new_value\n\n @property\n def data(self):\n return self.__data\n\n @data.setter\n def data(self, new_value):\n self.__data = new_value\n\n", "repo_name": "frankforp/triptracker", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 6700, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "datetime.timedelta", "line_number": 15, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 19, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 21, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 22, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 23, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 24, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 25, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 26, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 122, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 123, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 124, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 125, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 126, "usage_type": "call"}, {"api_name": "blinker.signal", "line_number": 127, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 195, "usage_type": "name"}]} +{"seq_id": "30271816660", "text": "# -*- coding: utf-8 -*-\n\nimport numpy as np\n\nfrom keras.optimizers import SGD, Adagrad, Adadelta, RMSprop, Adam\nfrom keras.layers.recurrent import SimpleRNN, LSTM, GRU\nfrom keras.regularizers import l1, l2, l1l2\nfrom keras.initializations import glorot_uniform\n\nimport csv\nimport gensim\nimport logging\n\n\nclass TSVLogger(object):\n f = None\n writer = None\n\n def __init__(self, path):\n logging.info('TSVLogger(%s)' % path)\n self.path = path\n\n def write(self, entry):\n logging.info('TSVLogger.write(%s)' % entry)\n if self.writer is None:\n self.f = open(self.path, 'w')\n self.writer = csv.DictWriter(self.f, entry.keys())\n self.writer.writeheader()\n self.writer.writerow(entry)\n self.f.flush()\n\n def close(self):\n logging.info('TSVLogger.close()')\n self.f.close()\n\n\ndef compute_accuracy(true_labels, predicted_soft_labels):\n n_observations = true_labels.shape[0]\n predicted_labels = np.argmax(predicted_soft_labels, axis=1)\n assert n_observations == predicted_labels.shape[0]\n return np.sum(true_labels == predicted_labels) / n_observations\n\n\ndef get_optimizer(name, lr=None, momentum=None, decay=None, nesterov=False,\n epsilon=None, rho=None, beta_1=None, beta_2=None):\n optimizer = None\n if name == 'sgd':\n optimizer = SGD(lr=lr, momentum=momentum, decay=decay, nesterov=nesterov)\n elif name == 'adagrad':\n optimizer = Adagrad(lr=lr, epsilon=epsilon)\n elif name == 'adadelta':\n optimizer = Adadelta(lr=lr, rho=rho, epsilon=epsilon)\n elif name == 'rmsprop':\n optimizer = RMSprop(lr=lr, rho=rho, epsilon=epsilon)\n elif name == 'adam':\n optimizer = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon)\n if optimizer is None:\n raise ValueError('Unknown optimizer: %s' % name)\n return optimizer\n\n\ndef get_recurrent_layer(model_name, input_size, output_size, return_sequences=False):\n layer = None\n if model_name == 'rnn':\n layer = SimpleRNN(input_dim=input_size, output_dim=output_size, return_sequences=return_sequences)\n elif model_name == 'lstm':\n layer = LSTM(input_dim=input_size, output_dim=output_size, return_sequences=return_sequences)\n elif model_name == 'gru':\n layer = GRU(input_dim=input_size, output_dim=output_size, return_sequences=return_sequences)\n if layer is None:\n raise ValueError('Unknown recurrent layer: %s' % model_name)\n return layer\n\n\ndef get_regularizer(lambda_l1=None, lambda_l2=None):\n regularizer = None\n if lambda_l1 is None and lambda_l2 is not None:\n regularizer = l2(l=lambda_l2)\n elif lambda_l1 is not None and lambda_l2 is None:\n regularizer = l1(l=lambda_l1)\n elif lambda_l1 is not None and lambda_l2 is not None:\n regularizer = l1l2(l1=lambda_l1, l2=lambda_l2)\n return regularizer\n\n\ndef read_embeddings(path, token2idx, max_features):\n token2embedding, embedding_size = None, None\n\n if path.endswith('.txt'):\n with open(path, 'r') as fin:\n token2embedding = dict()\n for line in fin:\n elements = line.split()\n token = elements[0]\n if token in token2idx:\n embedding_vector = [float(e) for e in elements[1:]]\n token2embedding[token], embedding_size = embedding_vector, len(embedding_vector)\n\n elif path.endswith('.model'):\n model = gensim.models.Word2Vec.load(path)\n token2embedding = dict()\n for token in token2idx:\n if token in model:\n embedding_vector = model[token].tolist()\n token2embedding[token], embedding_size = embedding_vector, len(embedding_vector)\n\n else:\n raise ValueError('Unknown format: %s.' % path)\n\n weights = [get_weights(shape=(max_features, embedding_size), token2idx=token2idx, token2embedding=token2embedding)]\n return weights\n\n\ndef get_weights(shape, token2idx, token2embedding):\n weights = glorot_uniform(shape).get_value()\n if token2embedding is not None:\n vocabulary, tokens = set(token2idx.keys()), set(token2embedding.keys())\n for token_to_initialize in vocabulary.intersection(tokens):\n idx = token2idx[token_to_initialize]\n if idx < weights.shape[0]:\n weights[idx, :] = token2embedding[token_to_initialize]\n return weights\n", "repo_name": "arranger1044/allen-ai-science-qa", "sub_path": "code/mixture/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "logging.info", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.optimizers.Adagrad", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.optimizers.Adadelta", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.recurrent.SimpleRNN", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.recurrent.LSTM", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.recurrent.GRU", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.regularizers.l1", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.regularizers.l1l2", "line_number": 82, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec.load", "line_number": 100, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 100, "usage_type": "attribute"}, {"api_name": "keras.initializations.glorot_uniform", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "31824837334", "text": "import argparse\nimport json\nimport os\nfrom os import listdir\n\nfrom experiments.analysis.ast_parser import ASTParser\nfrom data_scripts.utils.constants import OVERNIGHT_DOMAINS\nfrom data_scripts.split_datasets import ANON_FN_MAP\nfrom experiments.analysis.utils.lf_utils import tokenize_lf\n\nif __name__ == \"__main__\":\n args = argparse.ArgumentParser()\n args.add_argument('--dataset', default='covr')\n args.add_argument('--no-anon', action='store_true')\n args = args.parse_args()\n\n if args.dataset == 'covr':\n rows = [json.loads(l) for l in open(\"../covr/train_9.jsonl\")][:5000]\n splits_dir_base = f\"../{args.dataset}/splits/cfg/seed_0\"\n splits_names = listdir(splits_dir_base)\n elif args.dataset == 'overnight':\n rows = []\n for domain in OVERNIGHT_DOMAINS:\n rows += [json.loads(l) for l in open(f\"../{args.dataset}/{domain}.all.jsonl\")]\n splits_dir_base = f\"../{args.dataset}/splits/template\"\n splits_names = [f\"split_{i}/{domain}.json\" for i in range(5) for domain in OVERNIGHT_DOMAINS]\n else:\n raise ValueError()\n\n ast_parser = ASTParser(config={})\n anon_fn = ANON_FN_MAP[args.dataset]\n target_to_tree = {}\n for ex in rows:\n try:\n if not args.no_anon:\n anonyimzed_target = anon_fn(ex['target']).replace('_', '') # remove underscores from anonymization since we don't want anonymization to affect the parsing\n else:\n anonyimzed_target = ex['target']\n target_to_tree[ex['qid']] = ast_parser.get_ast(tokenize_lf(anonyimzed_target))\n except Exception as e:\n print(ex['target'])\n print(e)\n\n dataset = args.dataset\n if args.no_anon:\n dataset += \"_no_anon\"\n\n out_dir_base = f\"../../../mcd-splitter/data/{dataset}\"\n os.makedirs(out_dir_base, exist_ok=True)\n json.dump(target_to_tree, open(f\"{out_dir_base}/all.json\", \"wt\"))\n\n splits_files_pointers = {}\n\n for split in splits_names:\n split_name = split.split('.json')[-2].replace('/', '_')\n with open(f\"{splits_dir_base}/{split}\") as f:\n split_info = json.load(f)\n\n train_file_name = f\"{split_name}_train.json\"\n test_file_name = f\"{split_name}_test.json\"\n with open(f\"{out_dir_base}/{train_file_name}\", \"wt\") as f_out:\n train_examples = split_info.get('train_examples') or split_info.get('train')\n json.dump({qid: target_to_tree[qid] for qid in train_examples if qid in target_to_tree}, f_out)\n with open(f\"{out_dir_base}/{test_file_name}\", \"wt\") as f_out:\n test_examples = split_info.get('test_examples') or split_info.get('test')\n json.dump({qid: target_to_tree[qid] for qid in test_examples if qid in target_to_tree}, f_out)\n\n splits_files_pointers[split_name] = {\n 'train': f'../data/{dataset}/' + train_file_name,\n 'test': f'../data/{dataset}/' + test_file_name\n }\n with open(f\"{out_dir_base}/splits.json\", \"wt\") as f_out:\n json.dump(splits_files_pointers, f_out)\n", "repo_name": "Shivanshu-Gupta/structural-diversity", "sub_path": "data_scripts/create_mcd_files.py", "file_name": "create_mcd_files.py", "file_ext": "py", "file_size_in_byte": 3080, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "data_scripts.utils.constants.OVERNIGHT_DOMAINS", "line_number": 23, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "data_scripts.utils.constants.OVERNIGHT_DOMAINS", "line_number": 26, "usage_type": "name"}, {"api_name": "experiments.analysis.ast_parser.ASTParser", "line_number": 30, "usage_type": "call"}, {"api_name": "data_scripts.split_datasets.ANON_FN_MAP", "line_number": 31, "usage_type": "name"}, {"api_name": "experiments.analysis.utils.lf_utils.tokenize_lf", "line_number": 39, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 50, "usage_type": "call"}, {"api_name": "json.load", "line_number": 57, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 63, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "70817199233", "text": "import torch\n# import numpy as np\n\nfrom lpsmap import TorchFactorGraph, DepTree\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n\ndef sparsemap_projection(arc_scores):\n fg = TorchFactorGraph()\n u = fg.variable_from(arc_scores.transpose(0,1).clone())\n fg.add(DepTree(u, packed=True, projective=False))\n fg.solve(max_iter=1, max_inner_iter=50, step_size=0)\n marg_u = u.value.transpose(0,1)\n return marg_u\n\n\ndef sparsemap_batched(arc_scores_batched):\n sparsemap_projections = torch.zeros(arc_scores_batched.shape)\n for i, arc_scores in enumerate(arc_scores_batched):\n sparsemap_proj = sparsemap_projection(arc_scores)\n sparsemap_proj = torch.tensor(sparsemap_proj)\n sparsemap_projections[i] = sparsemap_proj\n\n if device == 'cuda':\n sparsemap_projections = sparsemap_projections.cuda()\n\n return sparsemap_projections\n\n\ndef argmax_nonproj(arc_scores):\n fg = TorchFactorGraph()\n u = fg.variable_from(arc_scores.transpose(0,1).clone())\n fg.add(DepTree(u, packed=True, projective=False))\n fg.solve_map()\n marg_u = u.value.transpose(0,1)\n return marg_u\n\n\ndef argmax_batched(scores_batched):\n argmax_np = torch.zeros(scores_batched.shape)\n for i, scores in enumerate(scores_batched):\n argmax = torch.tensor(argmax_nonproj(scores))\n argmax_np[i] = argmax\n if device == 'cuda':\n argmax_np = argmax_np.cuda()\n return argmax_np\n\n\n\n", "repo_name": "deep-spin/understanding-spigot", "sub_path": "nli/sparsemap.py", "file_name": "sparsemap.py", "file_ext": "py", "file_size_in_byte": 1435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "60", "api": [{"api_name": "torch.cuda.is_available", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 6, "usage_type": "attribute"}, {"api_name": "lpsmap.TorchFactorGraph", "line_number": 10, "usage_type": "call"}, {"api_name": "lpsmap.DepTree", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 22, "usage_type": "call"}, {"api_name": "lpsmap.TorchFactorGraph", "line_number": 32, "usage_type": "call"}, {"api_name": "lpsmap.DepTree", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "71690701310", "text": "import requests\nfrom aiohttp import ClientSession\n\nfrom .exceptions import DarkSkyException\n\n\nclass BaseRequestManger:\n def __init__(self, gzip: bool):\n self.headers = {} if not gzip else {\"Accept-Encoding\": \"gzip\"}\n\n def make_request(self, url: str, **params):\n raise NotImplementedError\n\n\nclass RequestManger(BaseRequestManger):\n def __init__(self, gzip: bool):\n super().__init__(gzip)\n self.session = requests.Session()\n self.session.headers = self.headers\n\n def make_request(self, url: str, **params):\n response = self.session.get(url, params=params).json()\n if \"error\" in response:\n raise DarkSkyException(response[\"code\"], response[\"error\"])\n response[\"timezone\"] = params.get(\"timezone\") or response[\"timezone\"]\n return response\n\n\nclass RequestMangerAsync(BaseRequestManger):\n async def make_request(\n self,\n url: str,\n session: ClientSession,\n **params\n ):\n assert isinstance(session, ClientSession)\n\n for key in list(params.keys()):\n if params[key] is None:\n del params[key]\n elif isinstance(params[key], list):\n params[key] = \",\".join(params[key])\n\n async with session.get(\n url, params=params, headers=self.headers\n ) as resp:\n response = await resp.json()\n if \"error\" in response:\n raise DarkSkyException(response[\"code\"], response[\"error\"])\n response[\"timezone\"] = params.get(\"timezone\") or response[\"timezone\"]\n return response\n", "repo_name": "Detrous/darksky", "sub_path": "darksky/request_manager.py", "file_name": "request_manager.py", "file_ext": "py", "file_size_in_byte": 1604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 82, "dataset": "github-code", "pt": "60", "api": [{"api_name": "requests.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "exceptions.DarkSkyException", "line_number": 24, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 33, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 36, "usage_type": "argument"}, {"api_name": "exceptions.DarkSkyException", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "35665982165", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n'''\nThis is the script used to extract words from CHILDES and CELEX.\nThe script requires that the entire CHILDES English-NA corpus is placed in the same folder of the script.\nIt also requires having the CELEX file 'epl.cd' in the same folder.\nIf you don't have access to CELEX, you can use the interface at http://celex.mpi.nl to get the English lemma lexicon.\n\nauthor: Andrea Ceolin\ndate: February 2021\n'''\n\nfrom collections import Counter\nimport os\nimport cmudict\n\n'''\nCHILDES English-NA corpus\n'''\n\nfolders = ['Bates/Free20', 'Bates/Free28', 'Bates/Snack28', 'Bates/Story28', 'Bernstein/Children',\n 'Bernstein/Interview', 'Bliss', 'Bloom70/Eric', 'Bloom70/Gia', 'Bloom70/Peter', 'Bloom73',\n 'Bohannon/Bax', 'Bohannon/Nat', 'Braunwald', 'Braunwald/0diary', 'Brent/c1', 'Brent/d1',\n 'Brent/f1', 'Brent/f2', 'Brent/i1', 'Brent/j1', 'Brent/m1', 'Brent/m2', 'Brent/q1',\n 'Brent/s1', 'Brent/s2', 'Brent/s3', 'Brent/t1', 'Brent/v1', 'Brent/v2', 'Brent/w1',\n 'Brent/w3', 'Brown/Adam', 'Brown/Eve', 'Brown/Sarah', 'Carterette', 'Clark', 'ComptonPater/Julia',\n 'ComptonPater/Sean', 'ComptonPater/Trevor', 'Cornell', 'Davis/Aaron', 'Davis/Anthony', 'Davis/Ben',\n 'Davis/Cameron', 'Davis/Charlotte', 'Davis/Georgia', 'Davis/Hannah', 'Davis/Jodie', 'Davis/Kaeley',\n 'Davis/Kate', 'Davis/Martin', 'Davis/Micah', 'Davis/Nate', 'Davis/Nick', 'Davis/Paxton', 'Davis/Rachel',\n 'Davis/Rebecca', 'Davis/Rowan', 'Davis/Sadie', 'Davis/Sam', 'Davis/Willie', 'Demetras1',\n 'Demetras2/Jimmy', 'Demetras2/Michael', 'Demetras2/Tim', 'EllisWeismer/30ec', 'EllisWeismer/30pc',\n 'EllisWeismer/42ec', 'EllisWeismer/42pc', 'EllisWeismer/54ec', 'EllisWeismer/54int', 'EllisWeismer/66conv',\n 'Evans/CH1', 'Evans/CH2', 'Feldman', 'Garvey/CH1', 'Garvey/CH2', 'Gathercole', 'Gelman/1998-Books',\n 'Gelman/2004-Gender', 'Gelman/2014-IndDiff', 'Gleason/Dinner', 'Gleason/Father', 'Gleason/Mother',\n 'Goad/Julia', 'Goad/Sonya', 'Haggerty', 'Hall/BlackPro', 'Hall/BlackWork', 'Hall/WhitePro', 'Hall/WhiteWork',\n 'Higginson/April', 'Higginson/June', 'Higginson/May', 'HSLLD/HV1', 'HSLLD/HV2', 'HSLLD/HV3',\n 'HSLLD/HV5', 'HSLLD/HV7', 'Inkelas/E', 'Kuczaj', 'MacWhinney', 'MacWhinney/0notrans-late',\n 'McCune/Alice', 'McCune/Aurie', 'McCune/Danny', 'McCune/Jase', 'McCune/Kari', 'McCune/Nenni',\n 'McCune/Nenni', 'McCune/Rala', 'McCune/Rick', 'McCune/Ronny', 'McCune/Vito', 'McMillan',\n 'Morisset/Seattle', 'Morisset/Topeka', 'Morisset/UCLA', 'Nelson', 'NewEngland/14', 'NewEngland/20',\n 'NewEngland/32', 'NewEngland/60', 'NewmanRatner/07', 'NewmanRatner/10', 'NewmanRatner/11',\n 'NewmanRatner/18', 'NewmanRatner/24', 'NewmanRatner/interviews/07', 'NewmanRatner/interviews/10',\n 'NewmanRatner/interviews/11', 'NewmanRatner/interviews/18', 'NewmanRatner/interviews/24', 'NH',\n 'Normal-2', 'PaidoEnglish/2a', 'PaidoEnglish/2b', 'PaidoEnglish/3a', 'PaidoEnglish/3b',\n 'PaidoEnglish/4a', 'PaidoEnglish/4b', 'PaidoEnglish/5a', 'PaidoEnglish/5b', 'Peters', 'POLER/Chronic',\n 'POLER/Match', 'POLER/NewOnset', 'Post/Lew', 'Post/She', 'Post/Tow', 'Providence/Alex', 'Providence/Ethan',\n 'Providence/Lily', 'Providence/Naima', 'Providence/Violet', 'Providence/William', 'Rollins',\n 'Sachs', 'Sawyer', 'Snow', 'Soderstrom/Joe', 'Soderstrom/The', 'StanfordEnglish/deb', 'StanfordEnglish/emi',\n 'StanfordEnglish/mol', 'StanfordEnglish/sea', 'StanfordEnglish/tim', 'Suppes', 'Tardif', 'Valian',\n 'VanHouten/Threes', 'VanHouten/Twos', 'VanKleeck', 'Warren', 'Weist/Ben', 'Weist/Emily', 'Weist/Emma',\n 'Weist/Jillian', 'Weist/Matt', 'Weist/Roman']\n\n'''\nGet all the tokens you find in child-directed speech\n'''\n\ndef get_list():\n sentences = []\n for folder in folders:\n for file in os.listdir(folder):\n if file.endswith('.cha'):\n for line in open(folder+'/'+file, 'r'):\n if line[1:4] in {'MOT', 'FAT'}: #this filters out the speech of mothers and fathers\n sentences.extend(line[5:].strip().split())\n return sentences\n\n\n'''\nGet a dictionary of lemmas that appear at least twice in CELEX.\nThe dictionary has the lemma as a key, and its phonological form as its value\n'''\n\ncelex_lemmas = {line.split('\\\\')[1]:line.split('\\\\')[5].replace(\"'\", \"\").replace(\"-\", \"\") for line in open('epl.cd') if int(line.split('\\\\')[2]) > 1}\n\n'''\nIn order to obtain a North American English pronunciation, we need to use the CMU dictionary\n'''\ncmu_words = cmudict.dict()\nvarieties = ['', '(2)', '(3)', '(4)']\n\nlemma_dictionary = Counter()\n\ni=0\nfor word, count in Counter(get_list()).most_common():\n #in order to store the word, it must appear in CELEX, in CMU, and it has to start with a lowercase letter (to filter names out)\n if word in celex_lemmas and word in cmu_words and word[0].islower():\n i += 1\n #retrieve every pronunciation in the CMU dictionary\n for index, pronunciation in enumerate(cmu_words[word]):\n pron_standard = [phoneme[:2] for phoneme in pronunciation]\n lemma_dictionary[word + varieties[index] + ' ' + ' '.join(pron_standard)] += count\n if i == 5000:\n break\n\n'''\nSave the words in their phonological form, along with their CHILDES frequencies.\n'''\n\nlemmas = open('american_corpus.txt', 'w')\n\nfor word, count in lemma_dictionary.most_common():\n lemmas.write(word + '\\t' + str(count) + '\\n')\n\nlemmas.close()\n\n", "repo_name": "AndreaCeolin/Functionalism_Contrast_Change", "sub_path": "Chapter3/English (US varieties)/english_extractor_us.py", "file_name": "english_extractor_us.py", "file_ext": "py", "file_size_in_byte": 5656, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "cmudict.dict", "line_number": 79, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 82, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "29448765784", "text": "import click\nfrom nornir_napalm.plugins.tasks import napalm_ping\nfrom nornir_apps.commands.common import add_common_options\nfrom nornir_apps.hookspecs import hookimpl\n\n\n@hookimpl\ndef add_subcommand(nornir_apps):\n \"\"\"Add subcommand to nornir_apps\"\"\"\n\n @nornir_apps.command(name=\"napalm-ping\")\n @click.option(\n \"-d\", \"--dest\", help=\"Host or IP Address of the destination\", required=True\n )\n @click.option(\"-s\", \"--source\", help=\"Source address of echo request\")\n @click.option(\"--ttl\", default=255, help=\"Max number of hops\")\n @click.option(\n \"-t\",\n \"--timeout\",\n default=2,\n help=\"Max seconds to wait after sending final packet\",\n )\n @click.option(\"-z\", \"--size\", default=100, help=\"Size of request in bytes\")\n @click.option(\"-c\", \"--count\", default=5, help=\"Number of ping request to send\")\n @click.option(\"--vrf\", help=\"Name of vrf\")\n @add_common_options\n @click.pass_context\n def ping(\n ctx,\n dest,\n source,\n ttl,\n timeout,\n size,\n count,\n vrf,\n **cli_options,\n ):\n \"\"\"Ping device\"\"\"\n runner = ctx.obj.runner\n output = runner.run(\n task=napalm_ping,\n dest=dest,\n source=source or \"\",\n ttl=ttl,\n timeout=timeout,\n size=size,\n count=count,\n vrf=vrf,\n )\n if cli_options.get(\"output\") == \"text\":\n ctx.obj.console_printer(output)\n return output\n", "repo_name": "ttafsir/nornir-apps", "sub_path": "nornir_apps/commands/napalm_ping_cmd.py", "file_name": "napalm_ping_cmd.py", "file_ext": "py", "file_size_in_byte": 1526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "nornir_napalm.plugins.tasks.napalm_ping", "line_number": 42, "usage_type": "name"}, {"api_name": "nornir_apps.commands.common.command", "line_number": 11, "usage_type": "call"}, {"api_name": "nornir_apps.commands.common", "line_number": 11, "usage_type": "name"}, {"api_name": "click.option", "line_number": 12, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}, {"api_name": "click.option", "line_number": 16, "usage_type": "call"}, {"api_name": "click.option", "line_number": 17, "usage_type": "call"}, {"api_name": "click.option", "line_number": 23, "usage_type": "call"}, {"api_name": "click.option", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 25, "usage_type": "call"}, {"api_name": "nornir_apps.commands.common.add_common_options", "line_number": 26, "usage_type": "name"}, {"api_name": "click.pass_context", "line_number": 27, "usage_type": "attribute"}, {"api_name": "nornir_apps.hookspecs.hookimpl", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "22155115924", "text": "import cv2\nimport numpy as np\n\n#Captures all paths in OS\nimport os\n\n#PIL is Python Image Library\nfrom PIL import Image\n\nrecognizer = cv2.face.LBPHFaceRecognizer_create()\n#relative path of the samples\npath = 'dataset'\n\ndef getImagesWithId(path):\n #Concatenate root path with image name from dataset folder\n #List all directories (pictures in the folder) \n #Append it to path with the separator\n imagePaths = [os.path.join(path, f) for f in os.listdir(path)]\n faces = []\n IDs = []\n \n for imagePath in imagePaths: \n #Convert images to grayscale (if not already so)\n faceImg = Image.open(imagePath).convert('L')\n \n #Convert image to numpy array\n faceNp = np.array(faceImg, 'uint8') #OpenCV only works with NUMPY array\n \n #Extract ID from 'dataset\\\\User1.1.jpg'\n ID = int(os.path.split(imagePath)[-1].split('.')[1])\n faces.append(faceNp)\n IDs.append(ID)\n \n #Display frame\n cv2.imshow(\"Training\", faceNp)\n cv2.waitKey(10)\n \n return np.array(IDs), faces\n\n\nIDs, faces = getImagesWithId(path)\n\n#Train the recogniser\nrecognizer.train(faces, IDs)\nrecognizer.save('recognizer/trainingData.yml')\ncv2.destroyAllWindows()", "repo_name": "shreyasparbat/opencv-facial-recognition", "sub_path": "src/trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 1241, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "cv2.face.LBPHFaceRecognizer_create", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.face", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "10689555751", "text": "'''\r\nstable.py\r\nTest whether putative solution to stable roommates problem is, in fact, \r\na solution.\r\nInputs:\r\n 1. File of prefernce lists. First string on a line is the name of the person \r\n whose list this is. Remaining strings are the preference lists, in\r\n decreasing order of desirability.\r\n 2. File with same base name as prefrence file, but with .out.txt appended. \r\n Each line lists a person and his roommate.\r\n'''\r\n\r\nimport sys, os.path\r\nfrom itertools import takewhile\r\n\r\nif len(sys.argv) != 2:\r\n print(\"Usgae: python stable.py filename\")\r\n exit()\r\n\r\nprefs = dict()\r\nwith open(sys.argv[1]) as fin:\r\n for line in fin:\r\n line = line.split()\r\n prefs[line[0]] = line[1:]\r\n\r\nroommates = dict()\r\nwith open(os.path.splitext(sys.argv[1])[0]+'.out.txt') as fin:\r\n for line in fin:\r\n line = line.split()\r\n roommates[line[0]] = line[1]\r\n \r\noneOne = True \r\nfor r1 in roommates:\r\n r2 = roommates[r1]\r\n if roommates[r2] != r1:\r\n print(\"Error: %s's roommate is %s and %s's roommate is %s\" % r1, r2, r2, roommates[r2]) \r\n oneOne = False\r\nif not oneOne: exit()\r\n\r\nstable = True\r\nwish = dict()\r\nfor p in prefs:\r\n r = roommates[p]\r\n wish[p] = list(takewhile(lambda x: x != r, prefs[p]))\r\n \r\nfor w in wish:\r\n for r in wish[w]:\r\n if w in wish[r]:\r\n print(\"Blocking pair %s %s\" % (w, r))\r\n stable = False\r\n \r\nif stable: print(\"Matching is stable\")\r\n\r\n ", "repo_name": "rdasxy/stable-roommates-hs", "sub_path": "Stable Roommates Problem/stable.py", "file_name": "stable.py", "file_ext": "py", "file_size_in_byte": 1478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "60", "api": [{"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.path.splitext", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 27, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "itertools.takewhile", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "40343351792", "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\nimport json\nfrom functools import lru_cache\nfrom pathlib import Path\nfrom typing import Dict\n\nimport marshmallow.exceptions\nfrom inflection import underscore\nfrom marshmallow import EXCLUDE\n\nfrom ...model.metadata import MetadataSchema\nfrom ...utils.path import split_ext\n\n\nclass SidecarMetadataLoader:\n @staticmethod\n @lru_cache(maxsize=None)\n def load_json(file_path) -> Dict:\n stem, _ = split_ext(file_path)\n sidecar_file_path = Path(file_path).parent / f\"{stem}.json\"\n\n if not Path(sidecar_file_path).is_file():\n return dict()\n\n with open(sidecar_file_path, \"r\") as sidecar_file_handle:\n sidecar_file_contents = sidecar_file_handle.read()\n\n return json.loads(sidecar_file_contents)\n\n @classmethod\n @lru_cache(maxsize=None)\n def load(cls, file_path) -> Dict:\n try:\n in_data = cls.load_json(file_path)\n\n # data transformations\n\n try:\n from sdcflows.interfaces.fmap import get_ees\n\n # get effective echo spacing even if not explicitly specified\n in_data[\"EffectiveEchoSpacing\"] = get_ees(in_data, in_file=file_path)\n except Exception:\n pass\n\n if \"EchoTime1\" in in_data and \"EchoTime2\" in in_data:\n if \"EchoTimeDifference\" not in in_data:\n in_data[\"EchoTimeDifference\"] = abs(\n float(in_data[\"EchoTime1\"]) - float(in_data[\"EchoTime2\"])\n )\n\n # parse\n\n in_data = {underscore(k): v for k, v in in_data.items()}\n sidecar = MetadataSchema().load(in_data, unknown=EXCLUDE)\n\n except marshmallow.exceptions.ValidationError:\n return dict()\n\n return sidecar\n\n def fill(self, fileobj, key):\n sidecar = self.load(fileobj.path)\n\n value = sidecar.get(key)\n\n if value is None:\n return False\n\n fileobj.metadata[key] = value\n\n return True\n", "repo_name": "HALFpipe/HALFpipe", "sub_path": "src/halfpipe/ingest/metadata/sidecar.py", "file_name": "sidecar.py", "file_ext": "py", "file_size_in_byte": 2141, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 55, "dataset": "github-code", "pt": "60", "api": [{"api_name": "utils.path.split_ext", "line_number": 22, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "sdcflows.interfaces.fmap.get_ees", "line_number": 45, "usage_type": "call"}, {"api_name": "inflection.underscore", "line_number": 57, "usage_type": "call"}, {"api_name": "model.metadata.MetadataSchema", "line_number": 58, "usage_type": "call"}, {"api_name": "marshmallow.EXCLUDE", "line_number": 58, "usage_type": "name"}, {"api_name": "marshmallow.exceptions.exceptions", "line_number": 60, "usage_type": "attribute"}, {"api_name": "marshmallow.exceptions", "line_number": 60, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 34, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "13183346769", "text": "from django import forms\nfrom .models import Comment\nfrom django import forms\n\nfrom pagedown.widgets import PagedownWidget\n\nfrom .models import Post\nclass EmailPostForm(forms.Form):\n name = forms.CharField(max_length=25)\n email = forms.EmailField()\n to = forms.EmailField()\n comments = forms.CharField(required=False,widget=forms.Textarea)\n\n class Meta:\n widgets = {\n 'name': forms.TextInput(attrs={'class': 'form-control'}), # or whatever class you want to apply\n # and so on\n }\n\nclass CommentForm(forms.ModelForm):\n class Meta:\n model = Comment\n fields = ('name','email','body')\n", "repo_name": "Heart-Patient-Assistant/HPA-Web", "sub_path": "HPA_Apps/blogs/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "django.forms.Form", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.forms.TextInput", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Comment", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "41154553293", "text": "\nimport sys\nimport os\nimport pickle\nimport json\nimport time\nimport requests\nimport random\nimport logging\nimport logging.handlers\nimport config\nfrom datetime import datetime\nfrom concurrent.futures import ProcessPoolExecutor\n\n\n# LOG_FILENAME = 'jd_seckill_{}.log'.format(datetime.now().strftime(\"%Y_%m_%d\"))\nLOG_FILENAME = 'jd_seckill.log'\n\n\ncookies_dir_path = \"./cookies\"\nif not os.path.exists(cookies_dir_path):\n os.makedirs(cookies_dir_path)\n\n# 初始化日志\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s - %(process)d-%(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s')\nconsole_handler = logging.StreamHandler()\nconsole_handler.setFormatter(formatter)\nlogger.addHandler(console_handler)\nfile_handler = logging.handlers.RotatingFileHandler(LOG_FILENAME, maxBytes=10485760, backupCount=5, encoding=\"utf-8\")\nfile_handler.setFormatter(formatter)\nlogger.addHandler(file_handler)\n\n\ndef wait_some_time(random_range_min=10, random_range_max=100):\n time.sleep(random.randint(random_range_min, random_range_max) / 1000)\n\ndef parse_json(s):\n begin = s.find('{')\n end = s.rfind('}') + 1\n return json.loads(s[begin:end])\n\ndef open_image(image_file):\n if os.name == \"nt\":\n os.system('start ' + image_file) # for Windows\n else:\n if os.uname()[0] == \"Linux\":\n if \"deepin\" in os.uname()[2]:\n os.system(\"deepin-image-viewer \" + image_file) # for deepin\n else:\n os.system(\"eog \" + image_file) # for Linux\n else:\n os.system(\"open \" + image_file) # for Mac\n\ndef save_image(resp, image_file):\n with open(image_file, 'wb') as f:\n for chunk in resp.iter_content(chunk_size=1024):\n f.write(chunk)\n\nclass SKException(Exception):\n\n def __init__(self, message):\n super().__init__(message)\n\n return\n\nclass Timer(object):\n def __init__(self, sleep_interval_ms=50):\n # '2018-09-28 22:45:50.000'\n self.buy_time = datetime.strptime(config.GLOBAL_CONFIG['buy_time'], \"%Y-%m-%d %H:%M:%S.%f\")\n self.buy_time_ms = int(time.mktime(self.buy_time.timetuple()) * 1000.0 + self.buy_time.microsecond / 1000)\n self.ahead_ms = random.choice([0, 0, 50, 50, 100])\n self.script_buy_time_ms = self.buy_time_ms - self.ahead_ms\n self.sleep_interval_ms = sleep_interval_ms\n self.diff_time = self.local_jd_time_diff()\n\n def jd_time(self):\n \"\"\"\n 从京东服务器获取时间毫秒\n :return:\n \"\"\"\n url = 'https://a.jd.com//ajax/queryServerData.html'\n ret = requests.get(url).text\n js = json.loads(ret)\n return int(js[\"serverTime\"])\n\n def local_time(self):\n \"\"\"\n 获取本地毫秒时间\n :return:\n \"\"\"\n return int(round(time.time() * 1000))\n\n def local_jd_time_diff(self):\n \"\"\"\n 计算本地与京东服务器时间差\n :return:\n \"\"\"\n return self.local_time() - self.jd_time()\n\n def start(self):\n logger.info('正在等待到达抢购时间:{}，脚本提前{}毫秒, 检测本地时间与京东服务器时间误差为【{}】毫秒'.format(self.buy_time, self.ahead_ms, self.diff_time))\n while True:\n # 本地时间减去与京东的时间差，能够将时间误差提升到0.1秒附近\n # 具体精度依赖获取京东服务器时间的网络时间损耗\n if self.local_time() - self.diff_time >= self.script_buy_time_ms:\n logger.info('时间到达，开始执行……')\n break\n else:\n time.sleep(self.sleep_interval_ms/1000)\n\nclass SpiderSession(object):\n \"\"\"\n Session相关操作\n \"\"\"\n def __init__(self, account_info):\n self.account_info = account_info\n self.cookies_file_path = \"%s/%s_cookies\" % (cookies_dir_path, account_info['username'])\n self.user_agent = account_info['user_agent']\n self.session = self._init_session()\n self.load_cookies_from_local()\n\n def _init_session(self):\n session = requests.session()\n session.headers = {\n \"User-Agent\": self.user_agent,\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3\",\n \"Connection\": \"keep-alive\"\n }\n return session\n\n def get_user_agent(self):\n return self.user_agent\n\n def _set_cookies(self, cookies):\n return self.session.cookies.update(cookies)\n\n def load_cookies_from_local(self):\n \"\"\"\n 从本地加载Cookie\n :return:\n \"\"\"\n if not os.path.exists(self.cookies_file_path):\n return False\n # 如果cookie文件超过3个小时，要求重新登录\n if (time.time() - os.path.getctime(self.cookies_file_path)) > 60*60*3:\n return False\n with open(self.cookies_file_path, 'rb') as f:\n local_cookies = pickle.load(f)\n self._set_cookies(local_cookies)\n return\n\n def save_cookies_to_local(self):\n \"\"\"\n 保存Cookie到本地\n :param cookie_file_name: 存放Cookie的文件名称\n :return:\n \"\"\"\n with open(self.cookies_file_path, 'wb') as f:\n pickle.dump(self.session.cookies, f)\n return\n\nclass QrLogin(object):\n \"\"\"\n 扫码登录\n \"\"\"\n def __init__(self, account_info):\n \"\"\"\n 初始化扫码登录\n 大致流程：\n 1、访问登录二维码页面，获取Token\n 2、使用Token获取票据\n 3、校验票据\n \"\"\"\n self.account_info = account_info\n self.qrcode_img_file = '%s/%s_qr_code.png' % (cookies_dir_path, account_info['username'])\n self.spider_session = SpiderSession(account_info)\n self.is_login = False\n self.refresh_login_status()\n\n def refresh_login_status(self):\n \"\"\"\n 刷新是否登录状态\n :return:\n \"\"\"\n self.is_login = self._validate_cookies()\n\n def _validate_cookies(self):\n \"\"\"\n 验证cookies是否有效（是否登陆）\n 通过访问用户订单列表页进行判断：若未登录，将会重定向到登陆页面。\n :return: cookies是否有效 True/False\n \"\"\"\n url = 'https://order.jd.com/center/list.action'\n payload = {\n 'rid': str(int(time.time() * 1000)),\n }\n try:\n resp = self.spider_session.session.get(url=url, params=payload, allow_redirects=False)\n if resp.status_code == requests.codes.OK \\\n and \"https://passport.jd.com/uc/login?ReturnUrl\" not in resp.text:\n return True\n except Exception as e:\n logger.error(\"验证cookies是否有效发生异常\", e)\n return False\n\n def _get_login_page(self):\n \"\"\"\n 获取PC端登录页面\n :return:\n \"\"\"\n url = \"https://passport.jd.com/new/login.aspx\"\n headers = {\n \"User-Agent\": self.account_info['user_agent'],\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3\",\n \"Connection\": \"keep-alive\"\n }\n page = self.spider_session.session.get(url, headers=headers)\n return page\n\n def _get_qrcode(self):\n \"\"\"\n 缓存并展示登录二维码\n :return:\n \"\"\"\n url = 'https://qr.m.jd.com/show'\n payload = {\n 'appid': 133,\n 'size': 147,\n 't': str(int(time.time() * 1000)),\n }\n headers = {\n 'User-Agent': self.spider_session.get_user_agent(),\n 'Referer': 'https://passport.jd.com/new/login.aspx',\n }\n resp = self.spider_session.session.get(url=url, headers=headers, params=payload)\n\n if resp.status_code != requests.codes.OK:\n logger.info('获取二维码失败')\n return False\n\n # 保存图片\n save_image(resp, self.qrcode_img_file)\n # 打开二维码图片\n logger.info('二维码获取成功，请打开京东APP扫描')\n open_image(self.qrcode_img_file)\n return True\n\n def _get_qrcode_ticket(self):\n \"\"\"\n 通过 token 获取票据\n :return:\n \"\"\"\n url = 'https://qr.m.jd.com/check'\n payload = {\n 'appid': '133',\n 'callback': 'jQuery{}'.format(random.randint(1000000, 9999999)),\n 'token': self.spider_session.session.cookies.get('wlfstk_smdl'),\n '_': str(int(time.time() * 1000)),\n }\n headers = {\n 'User-Agent': self.spider_session.get_user_agent(),\n 'Referer': 'https://passport.jd.com/new/login.aspx',\n }\n resp = self.spider_session.session.get(url=url, headers=headers, params=payload)\n\n if resp.status_code != requests.codes.OK:\n logger.error('获取二维码扫描结果异常')\n return False\n\n resp_json = parse_json(resp.text)\n if resp_json['code'] != 200:\n logger.info('Code: %s, Message: %s', resp_json['code'], resp_json['msg'])\n return None\n else:\n logger.info('已完成手机客户端确认')\n return resp_json['ticket']\n\n def _validate_qrcode_ticket(self, ticket):\n \"\"\"\n 通过已获取的票据进行校验\n :param ticket: 已获取的票据\n :return:\n \"\"\"\n url = 'https://passport.jd.com/uc/qrCodeTicketValidation'\n headers = {\n 'User-Agent': self.spider_session.get_user_agent(),\n 'Referer': 'https://passport.jd.com/uc/login?ltype=logout',\n }\n\n resp = self.spider_session.session.get(url=url, headers=headers, params={'t': ticket})\n if resp.status_code != requests.codes.OK:\n return False\n\n resp_json = json.loads(resp.text)\n if resp_json['returnCode'] == 0:\n return True\n else:\n logger.info(resp_json)\n return False\n\n def login_by_qrcode(self):\n \"\"\"\n 二维码登陆\n :return:\n \"\"\"\n if self.is_login:\n logger.info('已经登录成功')\n return\n\n self._get_login_page()\n\n # download QR code\n if not self._get_qrcode():\n raise SKException('二维码下载失败')\n\n # get QR code ticket\n ticket = None\n retry_times = 85\n for _ in range(retry_times):\n ticket = self._get_qrcode_ticket()\n if ticket:\n break\n time.sleep(2)\n else:\n raise SKException('二维码过期，请重新获取扫描')\n\n # validate QR code ticket\n if not self._validate_qrcode_ticket(ticket):\n raise SKException('二维码信息校验失败')\n\n self.refresh_login_status()\n self.spider_session.save_cookies_to_local()\n\n logger.info('二维码登录成功')\n return\n\nclass JdSeckill(object):\n def __init__(self, account_info):\n self.account_info = account_info\n self.spider_session = SpiderSession(account_info)\n self.session = self.spider_session.session\n self.user_agent = self.spider_session.user_agent\n self.sku_id = config.GLOBAL_CONFIG['sku_id']\n self.seckill_num = account_info['seckill_num']\n return\n\n def reserve(self):\n make_reserve_result = False\n while True:\n try:\n make_reserve_result = self.make_reserve()\n except Exception as e:\n make_reserve_result = False\n logger.info('预约发生异常!', e)\n if make_reserve_result:\n break\n wait_some_time()\n\n return\n\n def make_reserve(self):\n make_reserve_result = False\n url = 'https://yushou.jd.com/youshouinfo.action?'\n payload = {\n 'callback': 'fetchJSON',\n 'sku': self.sku_id,\n '_': str(int(time.time() * 1000)),\n }\n headers = {\n 'User-Agent': self.user_agent,\n 'Referer': 'https://item.jd.com/{}.html'.format(self.sku_id),\n }\n resp = self.session.get(url=url, params=payload, headers=headers)\n resp_json = parse_json(resp.text)\n reserve_url = resp_json.get('url')\n while True:\n try:\n self.session.get(url='https:' + reserve_url)\n logger.info('预约成功，已获��抢购资格 / 您已成功预约过了，无需重复预约')\n make_reserve_result = True\n break\n except Exception as e:\n logger.error('预约失败正在重试...')\n return make_reserve_result\n\n def seckill_by_proc_pool(self):\n # with ProcessPoolExecutor(config.GLOBAL_CONFIG['work_count']) as pool:\n pool = ProcessPoolExecutor(config.GLOBAL_CONFIG['work_count'])\n for i in range(config.GLOBAL_CONFIG['work_count']):\n pool.submit(self.seckill)\n pool.shutdown(wait=False)\n return\n\n def seckill(self):\n Timer().start()\n while True:\n if config.GLOBAL_CONFIG['debug']:\n time.sleep(random.randint(1, 5))\n logger.info(self.account_info['username'] + '测试环境，抢购结束')\n break\n try:\n self.request_seckill_url()\n while True:\n self.request_seckill_checkout_page()\n self.submit_seckill_order()\n except Exception as e:\n logger.info('[非期望内异常] 抢购发生异常，稍后继续执行！', e)\n wait_some_time(0, 50)\n\n def request_seckill_url(self):\n \"\"\"获取商品的抢购链接\n 点击\"抢购\"按钮后，会有两次302跳转，最后到达订单结算页面\n 这里返回第一次跳转后的页面url，作为商品的抢购链接\n \"\"\"\n seckill_url = \"\"\n url = 'https://itemko.jd.com/itemShowBtn'\n payload = {\n 'callback': 'jQuery{}'.format(random.randint(1000000, 9999999)),\n 'skuId': self.sku_id,\n 'from': 'pc',\n '_': str(int(time.time() * 1000)),\n }\n headers = {\n 'User-Agent': self.user_agent,\n 'Host': 'itemko.jd.com',\n 'Referer': 'https://item.jd.com/{}.html'.format(self.sku_id),\n }\n while True:\n resp = self.session.get(url=url, headers=headers, params=payload)\n resp_json = parse_json(resp.text)\n if resp_json.get('url'):\n # https://divide.jd.com/user_routing?skuId=8654289&sn=c3f4ececd8461f0e4d7267e96a91e0e0&from=pc\n router_url = 'https:' + resp_json.get('url')\n # https://marathon.jd.com/captcha.html?skuId=8654289&sn=c3f4ececd8461f0e4d7267e96a91e0e0&from=pc\n seckill_url = router_url.replace('divide', 'marathon').replace('user_routing', 'captcha.html')\n logger.info(\"[获取抢购链接] 获取成功: %s\", seckill_url)\n break\n else:\n logger.info(\"[获取抢购链接] 获取失败，稍后自动重试\")\n wait_some_time(0, 50)\n\n logger.info('[获取抢购链接] 访问商品的抢购连接...')\n headers = {\n 'User-Agent': self.user_agent,\n 'Host': 'marathon.jd.com',\n 'Referer': 'https://item.jd.com/{}.html'.format(self.sku_id),\n }\n self.session.get(url=seckill_url, headers=headers, allow_redirects=False)\n return\n\n def request_seckill_checkout_page(self):\n \"\"\"访问抢购订单结算页面\"\"\"\n logger.info('[结算页面] 访问抢购订单结算页面...')\n url = 'https://marathon.jd.com/seckill/seckill.action'\n payload = {\n 'skuId': self.sku_id,\n 'num': self.seckill_num,\n 'rid': int(time.time())\n }\n headers = {\n 'User-Agent': self.user_agent,\n 'Host': 'marathon.jd.com',\n 'Referer': 'https://item.jd.com/{}.html'.format(self.sku_id),\n }\n self.session.get(url=url, params=payload, headers=headers, allow_redirects=False)\n\n return\n\n def _get_seckill_init_info(self):\n \"\"\"获取秒杀初始化信息（包括：地址，发票，token）\n :return: 初始化信息组成的dict\n \"\"\"\n logger.info('[抢购参数获取] 获取秒杀初始化信息...')\n url = 'https://marathon.jd.com/seckillnew/orderService/pc/init.action'\n data = {\n 'sku': self.sku_id,\n 'num': self.seckill_num,\n 'isModifyAddress': 'false',\n }\n headers = {\n 'User-Agent': self.user_agent,\n 'Host': 'marathon.jd.com',\n }\n resp = self.session.post(url=url, data=data, headers=headers)\n logger.info('[抢购参数获取] 参数日志:{}'.format(resp.text))\n resp_json = parse_json(resp.text)\n return resp_json\n\n def _get_seckill_order_data(self):\n \"\"\"生成提交抢购订单所需的请求体参数\n :return: 请求体参数组成的dict\n \"\"\"\n logger.info('[抢购参数拼接] 生成提交抢购订单所需参数...')\n # 获取用户秒杀初始化信息\n seckill_init_info = self._get_seckill_init_info()\n default_address = seckill_init_info['addressList'][0] # 默认地址dict\n invoice_info = seckill_init_info.get('invoiceInfo', {}) # 默认发票信息dict, 有可能不返回\n token = seckill_init_info['token']\n data = {\n 'skuId': self.sku_id,\n 'num': self.seckill_num,\n 'addressId': default_address['id'],\n 'yuShou': 'true',\n 'isModifyAddress': 'false',\n 'name': default_address['name'],\n 'provinceId': default_address['provinceId'],\n 'cityId': default_address['cityId'],\n 'countyId': default_address['countyId'],\n 'townId': default_address['townId'],\n 'addressDetail': default_address['addressDetail'],\n 'mobile': default_address['mobile'],\n 'mobileKey': default_address['mobileKey'],\n 'email': default_address.get('email', ''),\n 'postCode': '',\n 'invoiceTitle': invoice_info.get('invoiceTitle', -1),\n 'invoiceCompanyName': '',\n 'invoiceContent': invoice_info.get('invoiceContentType', 1),\n 'invoiceTaxpayerNO': '',\n 'invoiceEmail': '',\n 'invoicePhone': invoice_info.get('invoicePhone', ''),\n 'invoicePhoneKey': invoice_info.get('invoicePhoneKey', ''),\n 'invoice': 'true' if invoice_info else 'false',\n 'password': self.account_info['payment_pwd'],\n 'codTimeType': 3,\n 'paymentType': 4,\n 'areaCode': '',\n 'overseas': 0,\n 'phone': '',\n 'eid': self.account_info['eid'],\n 'fp': self.account_info['fp'],\n 'token': token,\n 'pru': ''\n }\n\n return data\n\n def submit_seckill_order(self):\n \"\"\"提交抢购（秒杀）订单\n :return: 抢购结果 True/False\n \"\"\"\n url = 'https://marathon.jd.com/seckillnew/orderService/pc/submitOrder.action'\n payload = {\n 'skuId': self.sku_id,\n }\n try:\n seckill_order_data = self._get_seckill_order_data()\n except Exception as e:\n logger.info('[提交抢购] 抢购失败，无法获取生成订单的基本信息，错误信息:【{}】'.format(str(e)))\n return False\n\n logger.info('[提交抢购] 提交抢购订单...')\n headers = {\n 'User-Agent': self.user_agent,\n 'Host': 'marathon.jd.com',\n 'Referer': 'https://marathon.jd.com/seckill/seckill.action?skuId={0}&num={1}&rid={2}'.format(self.sku_id, self.seckill_num, int(time.time())),\n }\n resp = self.session.post(\n url=url,\n params=payload,\n data=seckill_order_data,\n headers=headers\n )\n resp_json = None\n try:\n resp_json = parse_json(resp.text)\n except Exception as e:\n logger.info('[提交抢购] 抢购失败，返回信息:{}'.format(resp.text[0: 128]))\n return False\n # 返回信息\n # 抢购失败：\n # {'errorMessage': '很遗憾没有抢到，再接再厉哦。', 'orderId': 0, 'resultCode': 60074, 'skuId': 0, 'success': False}\n # {'errorMessage': '抱歉，您提交过快，请稍后再提交订单！', 'orderId': 0, 'resultCode': 60017, 'skuId': 0, 'success': False}\n # {'errorMessage': '系统正在开小差，请重试~~', 'orderId': 0, 'resultCode': 90013, 'skuId': 0, 'success': False}\n # 抢购成功：\n # {\"appUrl\":\"xxxxx\",\"orderId\":820227xxxxx,\"pcUrl\":\"xxxxx\",\"resultCode\":0,\"skuId\":0,\"success\":true,\"totalMoney\":\"xxxxx\"}\n if resp_json.get('success'):\n order_id = resp_json.get('orderId')\n total_money = resp_json.get('totalMoney')\n pay_url = 'https:' + resp_json.get('pcUrl')\n logger.info(\n \"\"\"\n ========================================================================\n [提交抢购] 抢购成功，\n 订单号:{},\n 总价:{},\n 电脑端付款链接:{}\n ============================================================================\n \"\"\".format(order_id, total_money, pay_url))\n return True\n else:\n logger.info('[提交抢购] 抢购失败，返回信息:{}'.format(resp_json))\n return False\n\ndef do_user_login():\n for account_info in config.GLOBAL_CONFIG['account_list']:\n QrLogin(account_info).login_by_qrcode()\n return\n\ndef do_user_reserve():\n for account_info in config.GLOBAL_CONFIG['account_list']:\n JdSeckill(account_info).reserve()\n return\n\ndef do_user_seckill():\n for account_info in config.GLOBAL_CONFIG['account_list']:\n JdSeckill(account_info).seckill_by_proc_pool()\n return\n\nif __name__ == '__main__':\n a = \"\"\"\n功能列表：\n 1.检查登录\n 2.预约商品\n 3.秒杀抢购商品\n \"\"\"\n print(a)\n\n choice_function = input('请选择:')\n # 执行功能\n if choice_function == '1':\n do_user_login()\n elif choice_function == '2':\n do_user_login()\n do_user_reserve()\n elif choice_function == '3':\n do_user_login()\n do_user_seckill()\n else:\n sys.exit(1)\n", "repo_name": "woxiqingxian/jd_seckill", "sub_path": "jd_seckill.py", "file_name": "jd_seckill.py", "file_ext": "py", "file_size_in_byte": 22903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 156, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 31, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "os.name", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 46, "usage_type": "call"}, {"api_name": "os.uname", "line_number": 48, "usage_type": "call"}, {"api_name": "os.uname", "line_number": 49, "usage_type": "call"}, {"api_name": "os.system", "line_number": 50, "usage_type": "call"}, {"api_name": "os.system", "line_number": 52, "usage_type": "call"}, {"api_name": "os.system", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "name"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 71, "usage_type": "attribute"}, {"api_name": "time.mktime", "line_number": 72, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path.getctime", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 150, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 161, "usage_type": "call"}, {"api_name": "time.time", "line_number": 197, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 201, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 231, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 239, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 258, "usage_type": "call"}, {"api_name": "time.time", "line_number": 260, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 268, "usage_type": "attribute"}, {"api_name": "requests.codes", "line_number": 293, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 296, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 325, "usage_type": "call"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 345, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 369, "usage_type": "call"}, {"api_name": "concurrent.futures.ProcessPoolExecutor", "line_number": 390, "usage_type": "call"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 390, "usage_type": "attribute"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 391, "usage_type": "attribute"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 399, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 400, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 400, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 420, "usage_type": "call"}, {"api_name": "time.time", "line_number": 423, "usage_type": "call"}, {"api_name": "time.time", "line_number": 460, "usage_type": "call"}, {"api_name": "time.time", "line_number": 557, "usage_type": "call"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 597, "usage_type": "attribute"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 602, "usage_type": "attribute"}, {"api_name": "config.GLOBAL_CONFIG", "line_number": 607, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 631, "usage_type": "call"}]} +{"seq_id": "17463892805", "text": "from typing import Dict, Any, List, Type\nimport json, urllib.request, urllib.error, urllib.parse, sys, math\n\nimport networkx as nx\n\nfrom exchange.rate import ExchangeRate\nfrom analysis.graph.bellman_ford import bellman_ford\n\n\nCONV_IDX = 'weight'\n\ndef normalize(value):\n return -1.0 * math.log(value)\n\ndef denormalize(value):\n return math.exp(-1.0 * value)\n\n# derived from algorith described here: https://math.stackexchange.com/questions/94414/an-algorithm-for-arbitrage-in-currency-exchange\nclass ArbitrageDetector(object):\n\n def __init__(self, exchange_rates: List[ExchangeRate]) -> None:\n self.success = False\n self.exchange_path, self.return_ratio = None, None\n self._build_graph(exchange_rates)\n\n\n def _build_graph(self, exchange_rates: List[ExchangeRate]):\n self.graph = nx.DiGraph()\n\n # {\"cur1_cur2\" : rate} , where rate is a multiplier of cur2 to cur1\n # converted into { (\"cur1\", \"cur2\") : rate }\n # Note: this is not directly used to search the graph, only for reference\n exchange_rate_lookup = { (er.origin, er.target): er.rate for er in exchange_rates }\n\n forward_log_exchange_rate = [ (start, end, normalize(rate)) for (start, end), rate in list(exchange_rate_lookup.items()) ]\n self.graph.add_weighted_edges_from(forward_log_exchange_rate)\n\n # prune leaf nodes (as these cannot be used in a cycle\n leafNodes = [node for node, degree in list(self.graph.degree()) if degree < 2]\n self.graph.remove_nodes_from(leafNodes)\n\n # mirror exchange rates between all currencies with edges\n currencySet = set( self.graph.edges() )\n rev_currency_set = set( [ (end, start) for start, end in self.graph.edges() ] )\n rev_currency_set -= currencySet\n\n reverse_log_exchange_rate = [ (start, end, normalize( 1.0/exchange_rate_lookup[end,start] )) for start, end in rev_currency_set ]\n self.graph.add_weighted_edges_from(reverse_log_exchange_rate)\n\n def discover(self, root_currency):\n exchange_graph, cost = bellman_ford(self.graph, source=root_currency, weight_index=CONV_IDX)\n\n # no path found! report no profit...\n if exchange_graph[root_currency] == None:\n return [], 0\n\n # there is at least a good path!...\n\n visited = [root_currency]\n profit = 1.0\n next_node = exchange_graph[root_currency]\n\n current_node = root_currency\n while next_node not in visited:\n visited.append(next_node)\n profit *= denormalize(self.graph[next_node][current_node][CONV_IDX])\n current_node = next_node\n next_node = exchange_graph[next_node]\n\n profit *= denormalize(self.graph[root_currency][current_node][CONV_IDX])\n visited.append(root_currency)\n\n return visited, profit\n", "repo_name": "wagoodman/coin-games", "sub_path": "analysis/arbitrage_detector.py", "file_name": "arbitrage_detector.py", "file_ext": "py", "file_size_in_byte": 2837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "math.log", "line_number": 13, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "exchange.rate.ExchangeRate", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "exchange.rate.ExchangeRate", "line_number": 27, "usage_type": "name"}, {"api_name": "networkx.DiGraph", "line_number": 28, "usage_type": "call"}, {"api_name": "analysis.graph.bellman_ford.bellman_ford", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "69991249150", "text": "from flask import Flask,request,url_for,redirect,render_template,session\nfrom pymongo import MongoClient\nimport time\nimport urllib2, json\nimport function\nimport re\n\nappid = \"65fb5a0e\"\nappkey = \"f8cbfed212e664b2f56f9e04a91c1bc9\" \t\n\napp=Flask(__name__)\n\nmongo = MongoClient()\ndbname = \"cynmiclawyummly\"\ndb = mongo[dbname]\nsearchdb = db['searches']\nmealdb = db['meals']\n\n@app.route(\"/test\")\ndef test():\n jsonname = \"cookietest.json\"\n jsonf = open(jsonname)\n content = jsonf.read()\n jsonf.close()\n load = json.loads(content)\n toprint = json.dumps(load, sort_keys=True, \n indent=4, separators=(',',':'))\n #load = json.loads(json.dumps(load[\"matches\"][1]))\n toprint = json.dumps(load, sort_keys=True, \n indent=4, separators=(',',':'))\n #return str(load)\n return \"
\"+toprint+\"
\"\n\n@app.route(\"/get\")\n@app.route(\"/get/\", methods=[\"GET\",\"POST\"])\ndef get(id=\"\"):\n if id:\n n = function.nutritionInfo(id) #returns a dict of nutritionalInfo or False\n d = function.moreInfo(id)\n if n and d:\n if request.method==\"GET\":\n return render_template(\"get.html\",d=d,n=n)\n else:\n button = request.form[\"button\"]\n if button==\"Add to Meal Plan\":\n n['id'] = id\n n['name'] = d['name']\n mealdb.insert(n)\n return redirect(\"/mealplan\")\n return render_template(\"get.html\",d=d,n=n)\n return \"Invalid\"\n\n@app.route(\"/mealplan\", methods=[\"GET\",\"POST\"])\ndef mealplan():\n if request.method==\"POST\":\n button = request.form[\"button\"]\n if button == \"Clear All Recipes\":\n mealdb.remove({})\n else: #the user must want to remove an individual recipe\n mealdb.remove({'id':button})\n d = {'recipes':{}}\n r = re.compile(\"[0-9]+\")\n for recipe in mealdb.find():\n for nutrition in recipe:\n d['recipes'][recipe['id']] = recipe['name']\n if not nutrition=='id' and not nutrition=='name':\n nutrition = nutrition.encode('ascii')\n value = str(recipe[nutrition])\n f = r.findall(value)\n number = f[0] #an int\n if nutrition in d.keys():\n d[nutrition] = int(number)+int(d[nutrition])\n else:\n d[nutrition] = number\n return render_template(\"mealplan.html\",d=d)\n\n@app.route(\"/\", methods=[\"GET\",\"POST\"])\ndef search():\n if request.method==\"GET\":\n return render_template(\"search.html\")\n else:\n button = request.form[\"button\"]\n if button==\"search\":\n keyword = request.form[\"keyword\"].strip().replace(\" \",\"+\")\n include = request.form[\"include\"].lower().split(\",\") #list of ingredients with spaces replaced by +, e.g. \"large eggs\" --> \"large+eggs\"\n exclude = request.form[\"exclude\"].lower().split(\",\") \n for i in include:\n include[include.index(i)] = i.strip().replace(\" \",\"+\") \n for i in exclude:\n exclude[exclude.index(i)] = i.strip().replace(\" \",\"+\")\n maketime = request.form[\"time\"] #seconds\n if maketime:\n try:\n maketime = str(int(request.form[\"time\"])*60) #seconds\n except:\n return \"Invalid\"\n course = request.form.getlist(\"course\")\n cuisine = request.form.getlist(\"cuisine\")\n tag = \"\"\n if keyword:\n tag = tag + \"&q=\" + keyword\n if include:\n for i in include:\n if len(i)>0:\n tag = tag + \"&allowedIngredient[]=\" + i\n if exclude:\n for i in exclude:\n if len(i)>0:\n tag = tag + \"&excludedIngredient[]=\" + i\n if maketime:\n tag = tag + \"&maxTotalTimeInSeconds=\" + maketime\n if cuisine:\n for i in cuisine:\n tag = tag + \"&allowedCuisine[]=cuisine^cuisine-\" + i.lower()\n if course:\n for i in course:\n tag = tag + \"&allowedCourse[]=course^course-\" + i\n #checks if the same search has been made in last 24hrs\n #if so, returns a saved copy of the search results\n #otherwise, make api request and save new search results\n #saves us a bunch of api calls so we don't go over limit\n srch = searchdb.find_one({\"tag\":tag})\n curtime = int(time.time())\n if srch:\n if curtime-srch[\"time\"] > 60*60*24:\n srch[\"time\"] = curtime\n srch[\"json\"] = function.findFoods(tag)\n searchdb.save(srch)\n else:\n srch = {\"tag\":tag,\n \"time\":curtime,\n \"json\":function.findFoods(tag)}\n searchdb.insert(srch)\n\n d = function.findFoods(tag)\n return render_template(\"findfoods.html\",\n tag=tag,\n d=d)\n # calc = cal)\n else:\n return redirect(\"/\")\n\nif __name__==\"__main__\":\n app.debug=True\n app.run()\n", "repo_name": "cyntzhou/api", "sub_path": "yummly.py", "file_name": "yummly.py", "file_ext": "py", "file_size_in_byte": 5371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "function.nutritionInfo", "line_number": 38, "usage_type": "call"}, {"api_name": "function.moreInfo", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "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.form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "function.findFoods", "line_number": 127, "usage_type": "call"}, {"api_name": "function.findFoods", "line_number": 132, "usage_type": "call"}, {"api_name": "function.findFoods", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "36088373966", "text": "\"\"\"\n Ce module permet de detecter des objets contenus dans une image.\n Ce module utilise le service cognitive d'Azure.\n\n Ce module contient 2 fonctions principales:\n - analyze_octet_stream_image : permet d'analyser une image type bytes\n - analize_url_image : permet d'annalyser une image type url.\n\n Les objets detectés sont:\n - car : les voitures en général\n - person : les personnes\n - motorcycle : Les motos\n - bicycle : Les vélos \n\n Plus d'infos ici : \n https://learn.microsoft.com/fr-fr/azure/cognitive-services/computer-vision/concept-object-detection?tabs=4-0\n\n https://learn.microsoft.com/fr-fr/python/api/azure-cognitiveservices-vision-computervision/azure.cognitiveservices.vision.computervision?view=azure-python\n\"\"\"\n\n\n# Azure Packages | Cfr les liens ci-haut.\nfrom azure.cognitiveservices.vision.computervision import ComputerVisionClient\nfrom azure.cognitiveservices.vision.computervision.models import OperationStatusCodes\nfrom azure.cognitiveservices.vision.computervision.models import VisualFeatureTypes\nfrom msrest.authentication import CognitiveServicesCredentials\n\n# Packages pour le traitement des données.\nimport json\nimport requests\nimport copy\n\n\n'''\nAuthentification\n\nAuthentifie vos informations d'identification et crée un client.\n'''\n\n# La clé azure pour se communiquer avec le service azure (ceci est une clé temporaire)\nsubscription_key = \"1c481c40dcef4d03a49d7858b54f1d89\"\n\n# Le point d'entrer du service azure\nendpoint = \"https://consta-tfe-cv.cognitiveservices.azure.com/\"\n\n# Adresse URL de l'api de detection d'objet\nANALYZE_URL = f'{endpoint}vision/v3.2/analyze'\n\n# Création du client azure computer vision.\ncomputervision_client = ComputerVisionClient(\n endpoint, CognitiveServicesCredentials(subscription_key))\n\n# Les images pour le test de la fonction -> analize_url_image\nDEFAULT_IMG_URL = \"https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/cognitive-services/Computer-vision/Images/readsample.jpg\"\nDEFAULT_IMG_URL = \"https://media.gettyimages.com/photos/cars-in-rush-hour-with-traffic-at-dawn-picture-id155287967?s=612x612\"\nDEFAULT_IMG_URL = \"https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/images/windows-kitchen.jpg\"\nDEFAULT_IMG_URL = \"https://lh4.googleusercontent.com/-UMwfTuruVrM/Tf8hhdLf-8I/AAAAAAAAKSA/IddvXSjfBug/IMG_4120.JPG\"\nDEFAULT_IMG_URL = \"https://www.francetvinfo.fr/pictures/EP91ws0bTR1ZJ5z71TAgQ4QZV7M/1200x1200/2022/09/20/phpEIBJEM.jpg\"\nDEFAULT_IMG_URL = \"https://cdn.who.int/media/images/default-source/imported/pedestrians-road-traffic-jpg.jpg?sfvrsn=132b8496_2\"\n\n\n# Le schema des données qui seront retournées par les 2 fonctions d'analyse\n\nDEFAULT_RESPONSE = {\n\n # Les objets detecté\n\n 'objects': {\n\n # La liste des voitures\n 'car': [\n # {\n # 'x': int, -> La position verticale de l'objet\n # 'y': int, -> La position horizontale de l'objet\n # 'w': int, -> La largeur de l'objet\n # 'h': int, -> la hauteur de l'objet\n # 'confidence': float, -> La précision de la detection en pourcentage\n # }\n ],\n\n # La liste des personnes\n 'person': [],\n\n # La liste des motos\n 'motorcycle': [],\n\n # La liste des vélos\n 'bicycle': [],\n },\n\n # Les données statistiques\n 'statistics':\n {\n # Nombre total d'objets detectés\n 'objects': 0,\n\n # Nombre total des voitures detectées\n 'car': 0,\n\n # Nombre total des personnes detectées\n 'person': 0,\n\n # Nombre total des motos detectées\n 'motorcycle': 0,\n\n # Nombre total des vélos detectés\n 'bicycle': 0\n }\n}\n\n# Les objets considérés comme des voitures\n# Pour éviter de gérer individuellement plusieurs objets d'une meme nature\nvehicule_objects = [\n 'Van',\n 'Land vehicle',\n 'taxi',\n]\n\n\ndef compute_statistics(data: dict) -> dict:\n \"\"\"\n Cette fonction permet de compiler les statistiques.\n\n return: dict\n - ce dictionnaire est semblable a DEFAULT_RESPONSE\n \"\"\"\n\n # On fait la copie profonde des données pour éviter de modifier le dictionnaire de depart.\n # en d'autre termes, on copie les données sans copier les références des données dans la memoire Ram.\n response = copy.deepcopy(data)\n\n # On calcule les statistiques\n\n # on parcours toutes les clés du sous dictionnaire statistique\n for key in response['statistics'].keys():\n\n # si la clé c'est objects on ne fait rien\n # car c'est la somme des autres clés.\n if key == 'objects':\n pass\n\n # si la clé est [car, person, motorcycle, bicycle]\n else:\n # On calcule la somme d'objet qui ont étaient detectés dans cette categorie\n response['statistics'][key] += len(response['objects'][key])\n\n # on met à jour le nombre total d'objets en ajoutant les objets de cette dite categorie.\n response['statistics']['objects'] += response['statistics'][key]\n\n # on retourne le dictionnaire de type DEFAULT_RESPONSE\n return response\n\n\ndef analyze_octet_stream_image(octet_stream_img: bytes = None) -> dict:\n \"\"\"\n Cette fonction permet d'analyser une image de type bytes.\n\n Params\n - octet_stream_img : image de type byte\n\n return: dict\n - ce dictionnaire est semblable a DEFAULT_RESPONSE\n \"\"\"\n\n # S'il n'y a aucune image à analyser\n if octet_stream_img == None:\n\n # on retourne le dictionnaire par defaut.\n return DEFAULT_RESPONSE\n\n # On fait la copie profonde des données pour éviter de modifier le dictionnaire de depart.\n # en d'autre termes, on copie les données sans copier les références des données dans la memoire Ram.\n response = copy.deepcopy(DEFAULT_RESPONSE)\n\n # Les en-tete HTTP (ou http headers)\n headers = {\n # La clé d'authentification Azure\n 'Ocp-Apim-Subscription-Key': subscription_key,\n\n # Le type de contenu (dans ce cas, c'est une image binaire)\n 'Content-Type': 'application/octet-stream'\n }\n\n # Les parametres http (ou http parameters)\n params = {\n # Les types de caracteristique visuel que l'on veut detecter\n # Dans ce cas, nous voulons detecter des objets\n\n 'visualFeatures': 'Objects'\n }\n\n # On fait une gestion d'erreur pour eviter que l'application plante\n try:\n # Inférence : on envoie la requete http POST au service azure de detection d'objet.\n http_response = requests.post(\n ANALYZE_URL, headers=headers, params=params, data=octet_stream_img)\n \n except requests.exceptions.SSLError as error:\n # S'il y a une erreur reseau (par exemple la machine n'est pas connectée)\n # (utile losque on fait des tests en localhost et qu'on a pas besoin de faire de detection en ligne)\n\n # on affiche ça dans les infos de logs\n print(f\"Network Error : {error}\")\n\n # on retourne les donnees par defaut.\n return DEFAULT_RESPONSE\n\n # Lève l'erreur HTTPError, s'il y en a une.\n http_response.raise_for_status()\n\n # On recupere les données d'analyse envoyées par le service Azure.\n http_response_json = http_response.json()\n\n \n # On construit le dictionnaire reponse\n\n # on iter sur les éléments du sous dictionnaire 'objects'\n for detected_object in http_response_json['objects']:\n\n # On recupere le tag (la categorie de l'objet detecté)\n object_tag = detected_object['object']\n\n # si le tag de l'objet se trouve dans la liste ['Van', 'Land vehicle','taxi'] \n # alors c'est une voiture, on change le tag, sinon on garde le tag\n object_tag = 'car' if object_tag in vehicule_objects else object_tag\n\n # si le tag se trouve dans le dictionnaire reponse par defaut\n if object_tag in response['objects'].keys():\n\n # On ajoute cet objet a la liste des objets similaires\n response['objects'][object_tag].append(\n {\n 'x': detected_object['rectangle']['x'], # -> La position verticale de l'objet \n 'y': detected_object['rectangle']['y'], # -> La position horizontale de l'objet\n 'w': detected_object['rectangle']['w'], # -> La largeur de l'objet\n 'h': detected_object['rectangle']['h'], # -> la hauteur de l'objet\n 'confidence': detected_object['confidence'], # -> La précision de la detection en pourcentage\n }\n )\n\n # si le tag ne se trouve dans le dictionnaire reponse par defaut\n else:\n # afficher un message dans le log pour notifier que le tag n'est pas parmi les objets à detecter.\n # exemple, les oiseaux n'entre pas dans les criteres pour changer l'état d'un feu de signalisation\n print(f\"Not found : {detected_object['object']}\")\n \n # On calcule (ajoute) les statistiques\n response = compute_statistics(response)\n\n # on retourne le dictionnaire de type DEFAULT_RESPONSE\n return response\n\n\ndef analize_url_image(read_image_url=DEFAULT_IMG_URL):\n \"\"\"\n Cette fonction permet d'analyser une image de type url.\n\n Params\n - read_image_url : url d'une image en ligne\n\n return: dict\n - ce dictionnaire est semblable a DEFAULT_RESPONSE\n \"\"\"\n\n # Inférence : on envoie la requete au service azure de detection d'objet.\n # visual_features -> Les types de caracteristique visuel que l'on veut detecter\n # Dans ce cas, nous voulons detecter des objets\n image_analysis = computervision_client.analyze_image(\n read_image_url, visual_features=[VisualFeatureTypes.objects])\n\n # On fait la copie profonde des données pour éviter de modifier le dictionnaire de depart.\n # en d'autre termes, on copie les données sans copier les références des données dans la memoire Ram.\n response = copy.deepcopy(DEFAULT_RESPONSE)\n\n # On construit le dictionnaire reponse\n # on iter sur les éléments de la propriete 'objects'\n for detected_object in image_analysis.objects:\n\n # On recupere le tag (la categorie de l'objet detecté)\n object_tag = detected_object.object_property\n\n # si le tag de l'objet se trouve dans la liste ['Van', 'Land vehicle','taxi'] \n # alors c'est une voiture, on change le tag, sinon on garde le tag\n object_tag = 'car' if object_tag in vehicule_objects else object_tag\n\n # si le tag se trouve dans le dictionnaire reponse par defaut\n if object_tag in response['objects'].keys():\n response['objects'][detected_object.object_property].append(\n {\n 'x': detected_object.rectangle.x, # -> La position verticale de l'objet \n 'y': detected_object.rectangle.y, # -> La position horizontale de l'objet\n 'w': detected_object.rectangle.w, # -> La largeur de l'objet \n 'h': detected_object.rectangle.h, # -> la hauteur de l'objet\n 'confidence': detected_object.confidence, # -> La précision de la detection en pourcentage\n }\n )\n\n # si le tag ne se trouve dans le dictionnaire reponse par defaut\n else:\n # afficher un message dans le log pour notifier que le tag n'est pas parmi les objets à detecter.\n # exemple, les oiseaux n'entre pas dans les criteres pour changer l'état d'un feu de signalisation\n print(f\"Not found : {detected_object.object_property}\")\n\n # On calcule (ajoute) les statistiques\n response = compute_statistics(response)\n\n # on retourne le dictionnaire de type DEFAULT_RESPONSE\n return response\n", "repo_name": "costa693/TFC1", "sub_path": "airoad/inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 12032, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "azure.cognitiveservices.vision.computervision.ComputerVisionClient", "line_number": 50, "usage_type": "call"}, {"api_name": "msrest.authentication.CognitiveServicesCredentials", "line_number": 51, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 130, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 173, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 195, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 198, "usage_type": "attribute"}, {"api_name": "azure.cognitiveservices.vision.computervision.models.VisualFeatureTypes.objects", "line_number": 269, "usage_type": "attribute"}, {"api_name": "azure.cognitiveservices.vision.computervision.models.VisualFeatureTypes", "line_number": 269, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 273, "usage_type": "call"}]} +{"seq_id": "38382042887", "text": "import pinecone\n\n\nclass PineconeService:\n def __init__(self):\n # connect to pinecone environment\n pinecone.init(\n api_key=\"45682637-3050-4490-b886-884db0f41035\",\n environment=\"us-west1-gcp-free\"\n )\n\n def get_index(self, retriever, index_name=\"extractive-question-answering\", dimension=512):\n # check if the extractive-question-answering index exists\n if index_name not in pinecone.list_indexes():\n # create the index if it does not exist\n pinecone.create_index(\n index_name,\n dimension=retriever.get_sentence_embedding_dimension() if retriever else dimension,\n metric=\"cosine\"\n )\n\n # connect to extractive-question-answering index we created\n index = pinecone.Index(index_name)\n return index, index_name\n\n def get_index_status(self, index):\n return index.describe_index_stats()\n\n def embed_and_store(self, index, retriever, chunks):\n embeds = retriever.encode(chunks).tolist()\n metadata = [{'id': i, 'context': chunk} for i, chunk in enumerate(chunks)]\n index_status = self.get_index_status(index)\n total_vectors = index_status['total_vector_count']\n ids = [f\"{idx}\" for idx in range(total_vectors, total_vectors+len(chunks))]\n to_upsert = list(zip(ids, embeds, metadata))\n # print(to_upsert)\n _ = index.upsert(vectors=to_upsert)\n\n # gets context passages from the pinecone index\n def get_context(self, index, retriever, question, top_k=1):\n # generate embeddings for the question\n xq = retriever.encode([question]).tolist()\n # search pinecone index for context passage with the answer\n xc = index.query(xq, top_k=top_k, include_metadata=True)\n # extract the context passage from pinecone search result\n c = [x[\"metadata\"][\"context\"] for x in xc[\"matches\"]]\n return c\n\n def delete_vectors(self, ids):\n index, _ = self.get_index(None)\n index.delete(ids)\n", "repo_name": "saitejadasari/QnAApp", "sub_path": "services/pineconeService.py", "file_name": "pineconeService.py", "file_ext": "py", "file_size_in_byte": 2056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "pinecone.init", "line_number": 7, "usage_type": "call"}, {"api_name": "pinecone.list_indexes", "line_number": 14, "usage_type": "call"}, {"api_name": "pinecone.create_index", "line_number": 16, "usage_type": "call"}, {"api_name": "pinecone.Index", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "28091682236", "text": "from typing import Optional, cast\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nimport escnn\nfrom escnn.nn import EquivariantModule, FieldType\n\nfrom ..encoders import Encoder, EncoderWithAction\nfrom .base import ContinuousQFunction, DiscreteQFunction\nfrom .utility import compute_huber_loss, compute_reduce, pick_value_by_action\nfrom scipy.spatial.transform import Rotation as R\n\n\ndef quaternion2rot(quaternion: torch.Tensor) -> torch.Tensor:\n r = R.from_quat(quaternion)\n rot = torch.tensor(r.as_matrix())\n flatten_rot = torch.flatten(rot, start_dim=1, end_dim=-1)\n return flatten_rot\n\n\n# Here we should design the process function in model forward call.\ndef process_trifinger_obs(batch_obs):\n transformed_ob_dim = 148 # to be confirmed\n batch_size = batch_obs.shape[0]\n transformed_obs = torch.zeros((batch_size, transformed_ob_dim), dtype=torch.float32)\n pos_ones = torch.ones((batch_size, 1))\n transformed_obs[:, :24] = batch_obs[:, :24]\n transformed_obs[:, 24:33] = batch_obs[:, 24:33]\n transformed_obs[:, 33: 57] = batch_obs[:, 33: 57]\n transformed_obs[:, 57: 58] = batch_obs[:, 57: 58]\n transformed_obs[:, 58: 59] = batch_obs[:, 58: 59]\n transformed_obs[:, 59: 83] = batch_obs[:, 59: 83]\n transformed_obs[:, 83: 92] = quaternion2rot(batch_obs[:, 83: 87])\n transformed_obs[:, 92: 96] = torch.cat([batch_obs[:, 87: 90], pos_ones], axis=-1)\n transformed_obs[:, 96: 99] = batch_obs[:, 90: 93]\n transformed_obs[:, 99: 103] = torch.cat([batch_obs[:, 93: 96], pos_ones], axis=-1)\n transformed_obs[:, 103: 107] = torch.cat([batch_obs[:, 96: 99], pos_ones], axis=-1)\n transformed_obs[:, 107: 111] = torch.cat([batch_obs[:, 99: 102], pos_ones], axis=-1)\n transformed_obs[:, 111: 120] = batch_obs[:, 102: 111]\n transformed_obs[:, 120: 129] = batch_obs[:, 111: 120]\n transformed_obs[:, 129:130] = batch_obs[:, 120: 121]\n transformed_obs[:, 130: 139] = batch_obs[:, 121: 130]\n transformed_obs[:, 139: 148] = batch_obs[:, 130: 139]\n\n return transformed_obs\n\n\n# This func is used to extract the invariant features of the outputs of equivariant encoder\ndef compute_invariant_features(x: torch.Tensor, field_type: FieldType) -> torch.Tensor:\n n_inv_features = len(field_type.irreps)\n # TODO: Ensure isotypic basis i.e irreps of the same type are consecutive to each other.\n inv_features = []\n for field_start, field_end, rep in zip(field_type.fields_start, field_type.fields_end,\n field_type.representations):\n # Each field here represents a representation of an Isotypic Subspace. This rep is only composed of a single\n # irrep type.\n x_field = x[..., field_start:field_end] # whether x is Tensor\n num_G_stable_spaces = len(rep.irreps) # Number of G-invariant features = multiplicity of irrep\n # Again this assumes we are already in an Isotypic basis\n assert len(np.unique(rep.irreps, axis=0)) == 1, \"This only works for now on the Isotypic Basis\"\n # This basis is useful because we can apply the norm in a vectorized way\n # Reshape features to [batch, num_G_stable_spaces, num_features_per_G_stable_space]\n x_field_p = torch.reshape(x_field, (x_field.shape[0], num_G_stable_spaces, -1))\n # Compute G-invariant measures as the norm of the features in each G-stable space\n inv_field_features = torch.norm(x_field_p, dim=-1)\n # Append to the list of inv features\n inv_features.append(inv_field_features)\n # Concatenate all the invariant features\n inv_features = torch.cat(inv_features, dim=-1)\n assert inv_features.shape[-1] == n_inv_features, f\"Expected {n_inv_features} got {inv_features.shape[-1]}\"\n return inv_features\n\n\nclass DiscreteMeanQFunction(DiscreteQFunction, nn.Module): # type: ignore\n _action_size: int\n _encoder: Encoder\n _fc: nn.Linear\n\n def __init__(self, encoder: Encoder, action_size: int):\n super().__init__()\n self._action_size = action_size\n self._encoder = encoder\n self._fc = nn.Linear(encoder.get_feature_size(), action_size)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return cast(torch.Tensor, self._fc(self._encoder(x)))\n\n def compute_error(\n self,\n observations: torch.Tensor,\n actions: torch.Tensor,\n rewards: torch.Tensor,\n target: torch.Tensor,\n terminals: torch.Tensor,\n gamma: float = 0.99,\n reduction: str = \"mean\",\n ) -> torch.Tensor:\n one_hot = F.one_hot(actions.view(-1), num_classes=self.action_size)\n value = (self.forward(observations) * one_hot.float()).sum(\n dim=1, keepdim=True\n )\n y = rewards + gamma * target * (1 - terminals)\n loss = compute_huber_loss(value, y)\n return compute_reduce(loss, reduction)\n\n def compute_target(\n self, x: torch.Tensor, action: Optional[torch.Tensor] = None\n ) -> torch.Tensor:\n if action is None:\n return self.forward(x)\n return pick_value_by_action(self.forward(x), action, keepdim=True)\n\n @property\n def action_size(self) -> int:\n return self._action_size\n\n @property\n def encoder(self) -> Encoder:\n return self._encoder\n\n\nclass ContinuousMeanQFunction(ContinuousQFunction, nn.Module): # type: ignore\n _encoder: EncoderWithAction\n _action_size: int\n _fc: nn.Linear\n\n def __init__(self, encoder: EncoderWithAction):\n super().__init__()\n self._encoder = encoder\n self._action_size = encoder.action_size\n self._fc = nn.Linear(encoder.get_feature_size(), 1)\n\n def forward(self, x: torch.Tensor, action: torch.Tensor) -> torch.Tensor:\n return cast(torch.Tensor, self._fc(self._encoder(x, action)))\n\n def compute_error(\n self,\n observations: torch.Tensor,\n actions: torch.Tensor,\n rewards: torch.Tensor,\n target: torch.Tensor,\n terminals: torch.Tensor,\n gamma: float = 0.99,\n reduction: str = \"mean\",\n ) -> torch.Tensor:\n value = self.forward(observations, actions)\n y = rewards + gamma * target * (1 - terminals)\n loss = F.mse_loss(value, y, reduction=\"none\")\n return compute_reduce(loss, reduction)\n\n def compute_target(\n self, x: torch.Tensor, action: torch.Tensor\n ) -> torch.Tensor:\n return self.forward(x, action)\n\n @property\n def action_size(self) -> int:\n return self._action_size\n\n @property\n def encoder(self) -> EncoderWithAction:\n return self._encoder\n\n\nclass EquivariantContinuousMeanQFunction(ContinuousQFunction, nn.Module): # type: ignore\n # _encoder: EncoderWithAction\n # _action_size: int\n _out_fc: nn.Linear\n\n def __init__(self,\n # encoder: EncoderWithAction,\n encoder,\n hidden_size: int,\n num_hidden_layers: int=2,\n ):\n\n super().__init__()\n self._encoder = encoder # equivariant encoder passing by critic_encoder_factory\n # self._action_size = encoder.action_size # Specify somewhere else\n self._head = nn.Sequential()\n self.activation = nn.ReLU()\n self._fc = nn.Linear(encoder.get_feature_size(), 1)\n for i in num_hidden_layers:\n self._head.add_module(f'head_hidden_layer_{i}', nn.Linear(hidden_size, hidden_size))\n self._head.add_module(f'head_hidden_activation_{i}', self.activation)\n\n self._out_fc = nn.Linear(hidden_size, 1)\n\n def forward(self, x: torch.Tensor, action: torch.Tensor) -> torch.Tensor:\n x = process_trifinger_obs(x)\n out = torch.cat([x, action], dim=-1)\n out = self._encoder(out).tensor\n out_type = self._encoder[-1].out_type\n inv_features = compute_invariant_features(out, out_type)\n out = self._head(inv_features)\n return cast(torch.Tensor, self._out_fc(self._encoder(x, action)))\n\n def compute_error(\n self,\n observations: torch.Tensor,\n actions: torch.Tensor,\n rewards: torch.Tensor,\n target: torch.Tensor,\n terminals: torch.Tensor,\n gamma: float = 0.99,\n reduction: str = \"mean\",\n ) -> torch.Tensor:\n value = self.forward(observations, actions)\n y = rewards + gamma * target * (1 - terminals)\n loss = F.mse_loss(value, y, reduction=\"none\")\n return compute_reduce(loss, reduction)\n\n def compute_target(\n self, x: torch.Tensor, action: torch.Tensor\n ) -> torch.Tensor:\n return self.forward(x, action)\n\n @property\n def action_size(self) -> int:\n return self._action_size\n\n @property\n def encoder(self) -> EncoderWithAction:\n return self._encoder\n", "repo_name": "splendidsummer/Trifinger_Offline_RL", "sub_path": "d3rlpy/models/torch/q_functions/mean_q_function.py", "file_name": "mean_q_function.py", "file_ext": "py", "file_size_in_byte": 8799, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "torch.Tensor", "line_number": 15, "usage_type": "attribute"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.flatten", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 50, "usage_type": "attribute"}, {"api_name": "escnn.nn.FieldType", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.reshape", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 70, "usage_type": "call"}, {"api_name": "base.DiscreteQFunction", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "encoders.Encoder", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "encoders.Encoder", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 86, "usage_type": "attribute"}, {"api_name": "typing.cast", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 99, "usage_type": "name"}, {"api_name": "utility.compute_huber_loss", "line_number": 104, "usage_type": "call"}, {"api_name": "utility.compute_reduce", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 108, "usage_type": "name"}, {"api_name": "utility.pick_value_by_action", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 109, "usage_type": "attribute"}, {"api_name": "encoders.Encoder", "line_number": 119, "usage_type": "name"}, {"api_name": "base.ContinuousQFunction", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 123, "usage_type": "name"}, {"api_name": "encoders.EncoderWithAction", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 126, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "encoders.EncoderWithAction", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 134, "usage_type": "attribute"}, {"api_name": "typing.cast", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 139, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 142, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 143, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 149, "usage_type": "name"}, {"api_name": "utility.compute_reduce", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 154, "usage_type": "attribute"}, {"api_name": "encoders.EncoderWithAction", "line_number": 162, "usage_type": "name"}, {"api_name": "base.ContinuousQFunction", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 166, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 169, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 188, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 190, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 192, "usage_type": "call"}, {"api_name": "typing.cast", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 197, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 201, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 202, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 203, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 205, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 211, "usage_type": "name"}, {"api_name": "utility.compute_reduce", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 215, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 216, "usage_type": "attribute"}, {"api_name": "encoders.EncoderWithAction", "line_number": 224, "usage_type": "name"}]} +{"seq_id": "19125384674", "text": "import activeUser\nimport cart\n\nfrom cart import *\n\nclass Book:\n def displayAll(self):\n while True:\n # readlines()\n file1 = open('classBooks.txt', 'r')\n Items = file1.readlines()\n\n # s to command line, not needed for final\n #Read first element from each item\n i = 0\n for j in Items:\n currItem = Items[i]\n currItemArray = currItem.split(\"|\")\n print(currItemArray[0] + \"\\n\")\n bookName = currItemArray[0]\n i += 1\n \n option = input(\"Which book would you like to look more at: \")\n bookName = option\n #compare to file\n file1 = open('classBooks.txt', 'r')\n Items = file1.readlines()\n i = 0\n exists = 0\n index = -1\n for j in Items:\n currItem = Items[i]\n currItemArray = currItem.split(\"|\")\n if currItemArray[0] == option:\n exists = 1\n index = i\n i += 1\n file1.close()\n if exists != 1:\n (\"Book does not exist\\n\")\n else:\n print(Items[index])\n print(\"What would you like to do next: \")\n print(\"1. Add book to cart\")\n print(\"2. Go Back\")\n option = input(\": \")\n if option == \"1\":\n cart.addItem(bookName, \"classBooks.txt\")\n break\n if option == \"2\":\n break\n", "repo_name": "htgordon/MethodToolsGroup1", "sub_path": "book.py", "file_name": "book.py", "file_ext": "py", "file_size_in_byte": 1495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "cart.addItem", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "9758710457", "text": "import numpy as np\nfrom keras.datasets import imdb\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras import layers\nfrom keras.optimizers import RMSprop\nimport matplotlib.pyplot as plt\nfrom reader import read_constant\nfrom my_generator import my_generator, get_apks_and_types, KFCV_index\nfrom tools import folders_set\nimport random\n\n\ndef deep_learning(TYPE, TYPE_list, type_map, word2vec_model,globals):\n # 训练集,测试集\n x_train, y_train, train_apk_count = get_apks_and_types(globals['L'],globals['K'],globals['train_path'], TYPE, TYPE_list, type_map, word2vec_model)\n x_test, y_test, test_apk_count = get_apks_and_types(globals['L'],globals['K'],globals['test_path'], TYPE, TYPE_list, type_map, word2vec_model)\n all_apk_count = train_apk_count + test_apk_count\n seed = random.random()\n\n random.seed(seed)\n all_x = np.vstack((x_train, x_test))\n random.shuffle(all_x)\n\n random.seed(seed)\n all_y = np.vstack((y_train, y_test))\n random.shuffle(all_y)\n\n if globals['KFCV']:\n test_count = int(all_apk_count * globals['test_split'])\n x_train, y_train = all_x[test_count:], all_y[test_count:]\n\n train_count = all_apk_count - test_count\n x_test, y_test = all_x[:test_count], all_y[:test_count]\n\n else:\n val_count = int(all_apk_count * globals['val_split'])\n x_val, y_val = all_x[:val_count], all_y[:val_count]\n\n test_count = int(all_apk_count * globals['test_split'])\n x_test, y_test = all_x[all_apk_count - test_count:all_apk_count], all_y[\n all_apk_count - test_count:all_apk_count]\n\n train_count = all_apk_count - val_count - test_count\n x_train, y_train = all_x[val_count: all_apk_count - test_count], all_y[val_count: all_apk_count - test_count]\n\n # 神经网络\n model = Sequential()\n # 卷积\n model.add(layers.Conv2D(filters=globals['filter_count'], kernel_size=globals['kernel_size'], activation='relu', input_shape=(globals['L'], globals['K'], 1)))\n model.summary()\n # 池化\n model.add(layers.MaxPooling2D(globals['maxpooling_size']))\n model.add(layers.Flatten())\n model.summary()\n # 第一个全连接\n model.add(layers.Dense(units=globals['first_neuron_count'], activation='relu'))\n model.summary()\n # 正则化\n model.add(layers.Dropout(globals['dropout']))\n # 第二个全连接\n model.add(layers.Dense(units=TYPE, activation='softmax'))\n model.summary()\n\n model.compile(optimizer=RMSprop(lr=1e-4),\n loss='binary_crossentropy',\n metrics=['acc'])\n\n # history = model.fit(x_train, y_train,\n # epochs=epochs_,\n # batch_size=batch_size,\n # validation_split=validation_split_)\n if globals['KFCV']:\n for index in KFCV_index(globals['KFCV_K'], train_count):\n history = model.fit_generator(my_generator(x_train, y_train, index[0], globals['batch_size']),\n steps_per_epoch=int(index[2] / globals['batch_size']),\n validation_data=my_generator(x_train, y_train, index[1], 1),\n validation_steps=int(index[3] / 1),\n epochs=globals['epochs'], verbose=2)\n else:\n history = model.fit_generator(my_generator(x_train, y_train, [[0, train_count]], globals['batch_size']),\n steps_per_epoch=int(train_count / globals['batch_size']),\n validation_data=my_generator(x_val, y_val, [[0, val_count]], 1),\n validation_steps=int(val_count / 1),\n epochs=globals['epochs'], verbose=2)\n model.save(globals['save_model_path'])\n\n # 根据结果画图\n acc = history.history['acc']\n val_acc = history.history['val_acc']\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n\n epochs = range(len(acc))\n\n plt.plot(epochs, acc, 'bo', label='Training acc')\n plt.plot(epochs, val_acc, 'b', label='Validation acc')\n plt.title('Training and validation accuracy')\n plt.legend()\n\n plt.figure()\n\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.legend()\n\n # 测试集\n # test_loss, test_acc = model.evaluate(x_test, y_test)\n test_loss, test_acc = model.evaluate_generator(my_generator(x_test, y_test, [[0, test_count]], globals['batch_size']),\n steps=int(test_count / globals['batch_size']))\n print('Testing and accuracy:', test_acc)\n\n plt.show()\n\n print(\"Having finished fourth step:deep learning!\")\n\n\nif __name__ == \"__main__\":\n from gensim.models import Word2Vec\n folders_set()\n globals = read_constant()\n\n word2vec_model = Word2Vec.load(globals['word2vec_model_path'])\n deep_learning(globals['TYPE'], globals['TYPE_list'], globals['type_map'], word2vec_model,globals)\n", "repo_name": "li0926/ThorDroid", "sub_path": "ml_src/four_deep_learning.py", "file_name": "four_deep_learning.py", "file_ext": "py", "file_size_in_byte": 5162, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "my_generator.get_apks_and_types", "line_number": 16, "usage_type": "call"}, {"api_name": "my_generator.get_apks_and_types", "line_number": 17, "usage_type": "call"}, {"api_name": "random.random", "line_number": 19, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 22, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 23, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 26, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 50, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 53, "usage_type": "name"}, {"api_name": "keras.layers.Flatten", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 54, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 57, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 60, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 62, "usage_type": "name"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 65, "usage_type": "call"}, {"api_name": "my_generator.KFCV_index", "line_number": 74, "usage_type": "call"}, {"api_name": "my_generator.my_generator", "line_number": 75, "usage_type": "call"}, {"api_name": "my_generator.my_generator", "line_number": 77, "usage_type": "call"}, {"api_name": "my_generator.my_generator", "line_number": 81, "usage_type": "call"}, {"api_name": "my_generator.my_generator", "line_number": 83, "usage_type": "call"}, {"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.title", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "my_generator.my_generator", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "tools.folders_set", "line_number": 121, "usage_type": "call"}, {"api_name": "reader.read_constant", "line_number": 122, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec.load", "line_number": 124, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "1705076643", "text": "import xarray as xr\nimport os\nimport numpy as np\nimport datetime\nfrom dateutil.relativedelta import relativedelta\nfrom sklearn.decomposition import PCA\nimport cftime\nfrom dateutil.relativedelta import relativedelta\nfrom sklearn.cluster import KMeans, MiniBatchKMeans\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport matplotlib.dates as mdates\nimport itertools\nfrom mpl_toolkits.basemap import Basemap\nimport matplotlib.cm as cm\nfrom sklearn.linear_model import LinearRegression\nimport pickle\nimport random as rm\nfrom functions import copulaSimulation\nfrom itertools import groupby\n\n\nclass climateIndices():\n '''\n Class containing camera data and functions'''\n\n def __init__(self, **kwargs):\n\n self.awtStart = kwargs.get('awtStart', 1880)\n self.awtEnd = kwargs.get('awtEnd',2022)\n self.ersstFolder = kwargs.get('ersstFolder', \"/users/dylananderson/Documents/data/ERSSTv5/\")\n self.latTop = kwargs.get('latTop', 50)\n self.resolution = kwargs.get('resolution',1)\n self.avgTime = kwargs.get('avgTime',24)\n self.startTime = kwargs.get('startTime',[1979,1,1])\n self.endTime = kwargs.get('endTime',[2020,12,31])\n self.slpMemory = kwargs.get('slpMemory',False)\n self.slpPath = kwargs.get('slpPath')\n # self.lonLeft = kwargs.get('cameraID', 'c1')\n # self.lonRight = kwargs.get('rawPath')\n # self.latBottom = kwargs.get('nFrames', 1)\n # self.latTop = kwargs.get('startFrame', 0)\n\n def atlanticAWT(self,loadPrevious=False,plotOutput=False):\n\n\n if loadPrevious == True:\n\n print('need to know what variables to load in this space')\n\n else:\n data_folder=\"/users/dylananderson/Documents/data/ERSSTv5/\"\n\n\n years = np.arange(self.awtStart,self.awtEnd)\n months = np.arange(1,13)\n ogTime = []\n for ii in years:\n for hh in months:\n if hh < 10:\n date = str(ii) + \"0\" + str(hh)\n else:\n date = str(ii) + str(hh)\n\n file = \"ersst.v5.\" + date + \".nc\"\n #print(file)\n if ii == self.awtStart and hh < 6:\n print(\"skipping {}/{}\".format(ii,hh))\n else:\n if ii == self.awtStart and hh == 6:\n with xr.open_dataset(os.path.join(data_folder, file)) as ds:\n temp = ds\n SSTvalues = ds['sst']\n ogTime.append(datetime.datetime(ii,hh,1))\n elif ii == (self.awtEnd-1) and hh > 5:\n print(\"skipping {}/{}\".format(ii,hh))\n else:\n with xr.open_dataset(os.path.join(data_folder,file)) as ds:\n SSTvalues = xr.concat([SSTvalues,ds['sst']],dim=\"time\")\n ogTime.append(datetime.datetime(ii,hh,1))\n\n\n\n dt = datetime.datetime(self.awtStart, 6, 1)\n end = datetime.datetime((self.awtEnd-1), 6, 1)\n step = relativedelta(years=1)\n sstTime = []\n while dt < end:\n sstTime.append(dt)\n dt += step\n\n data = SSTvalues.squeeze(\"lev\")\n\n # parse data to xr.Dataset\n xds_predictor = xr.Dataset(\n {\n 'SST': (('longitude','latitude','time'), data.data.T),\n },\n coords = {\n 'longitude': SSTvalues.lon.values,\n 'latitude': SSTvalues.lat.values,\n 'time': ogTime,\n }\n )\n\n\n var_name = \"SST\"\n y1 = self.awtStart\n y2 = self.awtEnd-1\n m1 = 6\n m2 = 5\n subset = xds_predictor.sel(longitude=slice(280,350),latitude=slice(0,65))\n\n\n d1 = datetime.datetime(self.awtStart, 6, 1)\n dt = datetime.datetime(self.awtStart, 6, 1)\n end = datetime.datetime((self.awtEnd-1), 6, 1)\n step = relativedelta(months=1)\n monthlyTime = []\n while dt < end:\n monthlyTime.append(dt)\n dt += step\n\n\n timeDelta = np.array([(d - d1).days/365.25 for d in monthlyTime])\n\n tempdata_runavg = np.nan*np.ones(subset[\"SST\"].shape)\n\n for lon in subset.longitude.values:\n for lat in subset.latitude.values:\n # indexes\n ix_lon = np.where(subset.longitude == lon)\n ix_lat = np.where(subset.latitude == lat)\n data_pnt = subset[\"SST\"].loc[lon, lat, :]\n if ~np.any(np.isnan(data_pnt.values)):\n model = LinearRegression()\n X = np.reshape(timeDelta, (len(timeDelta), 1))\n model.fit(X, data_pnt.values)\n trend = model.predict(X)\n detrended = [data_pnt.values[i] - trend[i] for i in range(0,len(data_pnt.values))]\n tempdata_runavg[ix_lon,ix_lat,:] = detrended\n\n\n d1 = datetime.datetime(self.awtStart, 6, 1)\n dt = datetime.datetime(self.awtStart, 6, 1)\n end = datetime.datetime((self.awtEnd-1), 6, 1)\n step = relativedelta(years=1)\n annualTime = []\n while dt < end:\n annualTime.append(dt)\n dt += step\n\n Xs = subset.longitude.values\n Ys = subset.latitude.values\n [XR, YR] = np.meshgrid(Xs, Ys)\n\n # if plotOutput:\n # plt.figure()\n # p1 = plt.subplot2grid((1, 1), (0, 0))\n # spatialField = tempdata_runavg[:,:,-1]#np.reshape(var_anom_mean.values,(33,36))\n # m = Basemap(projection='merc', llcrnrlat=-40, urcrnrlat=55, llcrnrlon=255, urcrnrlon=375, lat_ts=10, resolution='c')\n # m.drawcoastlines()\n # cx, cy = m(XR, YR)\n # CS = m.contour(cx, cy, spatialField.T),# np.arange(0,0.023,.003), cmap=cm.RdBu_r, shading='gouraud')\n\n\n nlon,nlat,ntime = np.shape(tempdata_runavg)\n\n collapsed = np.reshape(tempdata_runavg,(nlon*nlat, ntime))\n\n annual = np.nan*np.ones((int(nlon*nlat),int(ntime/12)))\n c = 0\n for hh in range(int(ntime/12)):\n annual[:,hh] = np.nanmean(collapsed[:,c:c+12],axis=1)\n c = c + 12\n\n index = ~np.isnan(annual[:,0])\n badIndex = np.isnan(annual[:,0])\n ocean = [i for i, x in enumerate(index) if x]\n land = [i for i, x in enumerate(badIndex) if x]\n realDataAnoms = annual[index,:]\n\n var_anom_mean = np.nanmean(realDataAnoms.T,axis=0)\n var_anom_std = np.nanstd(realDataAnoms.T,axis=0)\n timeSeries_mean = np.nanmean(realDataAnoms,axis=0)\n\n nk_m = np.kron(np.ones(((y2 - y1), 1)), var_anom_mean)\n nk_s = np.kron(np.ones(((y2 - y1), 1)), var_anom_std)\n var_anom_demean = (realDataAnoms.T - nk_m) / nk_s\n ipca = PCA()\n PCs = ipca.fit_transform(var_anom_demean)\n\n EOFs = ipca.components_\n variance = ipca.explained_variance_\n nPercent = variance / np.sum(variance)\n APEV = np.cumsum(variance) / np.sum(variance) * 100.0\n nterm = np.where(APEV <= 0.95 * 100)[0][-1]\n\n PC1 = PCs[:, 0]\n PC2 = PCs[:, 1]\n PC3 = PCs[:, 2]\n normPC1 = np.divide(PC1, np.nanmax(PC1)) * nPercent[0]\n normPC2 = np.divide(PC2, np.nanmax(PC2)) * nPercent[1]\n normPC3 = np.divide(PC3, np.nanmax(PC3)) * nPercent[2]\n\n n_components = 3 # !!!!\n pcAggregates = np.full((len(normPC1), n_components), np.nan)\n pcAggregates[:, 0] = normPC1\n pcAggregates[:, 1] = normPC2\n pcAggregates[:, 2] = normPC3\n\n n_clusters = 6\n kmeans = KMeans(n_clusters, init='k-means++', random_state=100) # 80\n data = pcAggregates\n data1 = data / np.std(data, axis=0)\n awt_bmus_og = kmeans.fit_predict(data1)\n # awt_bmus2 = awt_bmus\n awt_bmus2 = np.nan * np.ones((np.shape(awt_bmus_og)))\n\n avgSST = []\n for hh in np.unique(awt_bmus_og):\n indexAWT = np.where(awt_bmus_og == hh)\n avgSST.append(np.nanmean(annual[:, indexAWT[0]]))\n #print(np.nanmean(annual[:, indexAWT[0]]))\n\n order = np.argsort(np.asarray(avgSST))#[0, 4, 5, 3, 2, 1]\n\n print(order)\n\n for hh in np.arange(0, 6):\n indexOR = np.where(awt_bmus_og == order[hh])\n awt_bmus2[indexOR] = np.ones((len(indexOR[0], ))) * hh\n awt_bmus = awt_bmus2\n\n\n\n if plotOutput:\n plt.figure()\n gs2 = gridspec.GridSpec(2, 3)\n for hh in np.unique(awt_bmus):\n indexAWT = np.where(awt_bmus2 == hh)\n # rectField = np.nanmean(subset['SST'][:, :, indexAWT[0]], axis=2)\n # rectField = np.nanmean(tempdata_runavg[:, :, indexAWT[0]], axis=2)\n rectField = np.reshape(np.nanmean(annual[:, indexAWT[0]], axis=1), (36, 33))\n ax = plt.subplot(gs2[int(hh)])\n Xs = subset.longitude.values\n Ys = subset.latitude.values\n [XR, YR] = np.meshgrid(Xs, Ys)\n m = Basemap(projection='merc', llcrnrlat=0, urcrnrlat=55, llcrnrlon=255, urcrnrlon=375, lat_ts=10,\n resolution='c')\n m.drawcoastlines()\n cx, cy = m(XR, YR)\n CS = m.contour(cx, cy, rectField.T, np.arange(-0.8, 0.8, .05), cmap=cm.RdBu_r, shading='gouraud')\n ax.set_title('AWT #{} = {} years'.format(int(hh), len(indexAWT[0])))\n\n plt.colorbar(CS, ax=ax)\n\n\n\n d1 = datetime.datetime(1979, 6, 1)\n dt = datetime.datetime(1979, 6, 1)\n end = datetime.datetime(2022, 6, 2)\n step = relativedelta(days=1)\n dailyTime = []\n while dt < end:\n dailyTime.append(dt)\n dt += step\n\n DailyDatesMatrix = np.array([[r.year, r.month, r.day] for r in dailyTime])\n\n dailyAWT = np.ones((len(dailyTime),))\n dailyPC1 = np.ones((len(dailyTime),))\n dailyPC2 = np.ones((len(dailyTime),))\n dailyPC3 = np.ones((len(dailyTime),))\n\n anIndex = np.where(np.array(annualTime) >= datetime.datetime(1979, 5, 31))\n subsetAnnualTime = np.array(annualTime)[anIndex]\n # subsetAnnualTime = np.array(annualTime)\n subsetAWT = awt_bmus2[anIndex]\n # subsetPCs = pcAggregates[anIndex[0],:]#PCs[anIndex,:]\n subsetPCs = PCs[anIndex[0], :]\n\n for i in range(len(subsetAWT)):\n sSeason = np.where((DailyDatesMatrix[:, 0] == subsetAnnualTime[i].year) & (\n DailyDatesMatrix[:, 1] == subsetAnnualTime[i].month) & (DailyDatesMatrix[:, 2] == 1))\n ssSeason = np.where((DailyDatesMatrix[:, 0] == subsetAnnualTime[i].year + 1) & (\n DailyDatesMatrix[:, 1] == subsetAnnualTime[i].month) & (DailyDatesMatrix[:, 2] == 1))\n\n dailyAWT[sSeason[0][0]:ssSeason[0][0] + 1] = subsetAWT[i] * dailyAWT[sSeason[0][0]:ssSeason[0][0] + 1]\n dailyPC1[sSeason[0][0]:ssSeason[0][0] + 1] = subsetPCs[i, 0] * np.ones(\n len(dailyAWT[sSeason[0][0]:ssSeason[0][0] + 1]), )\n dailyPC2[sSeason[0][0]:ssSeason[0][0] + 1] = subsetPCs[i, 1] * np.ones(\n len(dailyAWT[sSeason[0][0]:ssSeason[0][0] + 1]), )\n dailyPC3[sSeason[0][0]:ssSeason[0][0] + 1] = subsetPCs[i, 2] * np.ones(\n len(dailyAWT[sSeason[0][0]:ssSeason[0][0] + 1]), )\n\n # make a markov chain of the AWT clusters\n\n chain = {}\n n_words = len(awt_bmus)\n for i, key1 in enumerate(awt_bmus):\n if n_words > i + 2:\n key2 = awt_bmus[i + 1]\n word = awt_bmus[i + 2]\n if (key1, key2) not in chain:\n chain[(key1, key2)] = [word]\n else:\n chain[(key1, key2)].append(word)\n\n print('Chain size: {0} distinct bmu pairs.'.format(len(chain)))\n #\n # chain3 = {}\n # n_words = len(awt_bmus)\n # for i, key1 in enumerate(awt_bmus):\n # if n_words > i + 3:\n # key2 = awt_bmus[i + 1]\n # key3 = awt_bmus[i + 2]\n # word = awt_bmus[i + 3]\n # if (key1, key2, key3) not in chain3:\n # chain3[(key1, key2, key3)] = [word]\n # else:\n # chain3[(key1, key2, key3)].append(word)\n # print('Chain size: {0} distinct bmu pairs.'.format(len(chain3)))\n print(chain)\n sim_num = 100\n sim_years = 500\n evbmus_sim = np.nan * np.ones((sim_num, (sim_years)))\n key = (awt_bmus[-2], awt_bmus[-1])\n for gg in range(sim_num):\n bmu_sim = [awt_bmus[-2], awt_bmus[-1]]\n c = 2\n while len(bmu_sim) < (sim_years):\n w = rm.choice(chain[key])\n bmu_sim.append(w)\n key = (key[1], w)\n c = c + 1\n evbmus_sim[gg, :] = bmu_sim\n\n # sim_num = 100\n bmus = awt_bmus # [1:]\n evbmus_sim = evbmus_sim # evbmus_simALR.T\n\n # Lets make a plot comparing probabilities in sim vs. historical\n probH = np.nan * np.ones((n_clusters,))\n probS = np.nan * np.ones((sim_num, n_clusters))\n for h in np.unique(bmus):\n findH = np.where((bmus == h))[0][:]\n probH[int(h - 1)] = len(findH) / len(bmus)\n\n for s in range(sim_num):\n findS = np.where((evbmus_sim[s, :] == h))[0][:]\n probS[s, int(h - 1)] = len(findS) / len(evbmus_sim[s, :])\n\n # from alrPlotting import colors_mjo\n # from alrPlotting import colors_awt\n etcolors = cm.jet(np.linspace(0, 1, 24)) # 70-20))\n tccolors = np.flipud(cm.autumn(np.linspace(0, 1, 2))) # 21)))\n dwtcolors = np.vstack((etcolors, tccolors[1:, :]))\n # dwtcolors = colors_mjo()\n\n if plotOutput:\n plt.figure()\n ax = plt.subplot2grid((1, 1), (0, 0), rowspan=1, colspan=1)\n tempPs = np.nan * np.ones((6,))\n for i in range(6):\n temp = probS[:, i]\n temp2 = probH[i]\n box1 = ax.boxplot(temp, positions=[temp2], widths=.01, notch=True, patch_artist=True, showfliers=False)\n plt.setp(box1['boxes'], color=dwtcolors[i])\n plt.setp(box1['means'], color=dwtcolors[i])\n plt.setp(box1['fliers'], color=dwtcolors[i])\n plt.setp(box1['whiskers'], color=dwtcolors[i])\n plt.setp(box1['caps'], color=dwtcolors[i])\n plt.setp(box1['medians'], color=dwtcolors[i], linewidth=0)\n tempPs[i] = np.mean(temp)\n # box1['boxes'].set(facecolor=dwtcolors[i])\n # plt.set(box1['fliers'],markeredgecolor=dwtcolors[i])\n ax.plot([0, 0.3], [0, 0.3], 'k--', zorder=10)\n plt.xlim([0, 0.3])\n plt.ylim([0, 0.3])\n plt.xticks([0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.3], ['0', '0.05', '0.10', '0.15', '0.20', '0.25', '0.3'])\n plt.xlabel('Historical Probability')\n plt.ylabel('Simulated Probability')\n plt.title('Validation of ALR SWT Simulations')\n\n\n a = list(bmus)\n seq = list()\n for i in np.arange(1, 7):\n temp = [len(list(v)) for k, v in groupby(a) if k == i - 1]\n seq.append(temp)\n\n simseqPers = list()\n for hhh in range(sim_num):\n b = list(evbmus_sim[hhh, :])\n seq_sim = list()\n for i in np.arange(1, 7):\n temp2 = [len(list(v)) for k, v in groupby(b) if k == i - 1]\n seq_sim.append(temp2)\n simseqPers.append(seq_sim)\n\n persistReal = np.nan * np.ones((6, 5))\n for dwt in np.arange(1, 7):\n sortDurs = np.sort(seq[dwt - 1])\n realPercent = np.nan * np.ones((5,))\n for qq in np.arange(1, 6):\n realInd = np.where((sortDurs <= qq))\n realPercent[qq - 1] = len(realInd[0]) / len(sortDurs)\n persistReal[dwt - 1, :] = realPercent\n\n persistSim = list()\n for dwt in np.arange(1, 7):\n persistDWT = np.nan * np.ones((sim_num, 5))\n for simInd in range(sim_num):\n\n sortDursSim = np.sort(simseqPers[simInd][dwt - 1])\n simPercent = np.nan * np.ones((5,))\n for qq in np.arange(1, 6):\n simIndex = np.where((sortDursSim <= qq))\n simPercent[qq - 1] = len(simIndex[0]) / len(sortDursSim)\n persistDWT[simInd, :] = simPercent\n persistSim.append(persistDWT)\n\n x = [0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5, 4.5, 4.5, 5.5]\n if plotOutput:\n plt.figure()\n gs2 = gridspec.GridSpec(2, 3)\n for xx in range(6):\n ax = plt.subplot(gs2[xx])\n ax.boxplot(persistSim[xx])\n y = [persistReal[xx, 0], persistReal[xx, 0], persistReal[xx, 1], persistReal[xx, 1], persistReal[xx, 2],\n persistReal[xx, 2], persistReal[xx, 3], persistReal[xx, 3], persistReal[xx, 4],\n persistReal[xx, 4], ]\n ax.plot(x, y, color=dwtcolors[xx])\n ax.set_ylim([0.25, 1.05])\n\n copulaData = list()\n for i in range(len(np.unique(bmus))):\n\n tempInd = np.where(((bmus) == i))\n dataCop = []\n for kk in range(len(tempInd[0])):\n dataCop.append(list([PC1[tempInd[0][kk]], PC2[tempInd[0][kk]], PC3[tempInd[0][kk]]]))\n copulaData.append(dataCop)\n\n gevCopulaSims = list()\n for i in range(len(np.unique(bmus))):\n tempCopula = np.asarray(copulaData[i])\n kernels = ['KDE', 'KDE', 'KDE']\n samples = copulaSimulation(tempCopula, kernels, 100000)\n print('generating samples for AWT {}'.format(i))\n gevCopulaSims.append(samples)\n\n # convert synthetic markovs to PC values\n # Fill in the Markov chain bmus with RMM vales\n pc1Sims = list()\n pc2Sims = list()\n pc3Sims = list()\n for kk in range(sim_num):\n tempSimulation = evbmus_sim[kk, :]\n tempPC1 = np.nan * np.ones((np.shape(tempSimulation)))\n tempPC2 = np.nan * np.ones((np.shape(tempSimulation)))\n tempPC3 = np.nan * np.ones((np.shape(tempSimulation)))\n\n groups = [list(j) for i, j in groupby(tempSimulation)]\n c = 0\n for gg in range(len(groups)):\n getInds = rm.sample(range(1, 100000), len(groups[gg]))\n tempPC1s = gevCopulaSims[int(groups[gg][0])][getInds[0], 0]\n tempPC2s = gevCopulaSims[int(groups[gg][0])][getInds[0], 1]\n tempPC3s = gevCopulaSims[int(groups[gg][0])][getInds[0], 2]\n tempPC1[c:c + len(groups[gg])] = tempPC1s\n tempPC2[c:c + len(groups[gg])] = tempPC2s\n tempPC3[c:c + len(groups[gg])] = tempPC3s\n c = c + len(groups[gg])\n pc1Sims.append(tempPC1)\n pc2Sims.append(tempPC2)\n pc3Sims.append(tempPC3)\n\n # sim_years = 100\n # start simulation at PCs available data\n d1 = datetime.datetime(2022, 6, 1)\n d2 = datetime.datetime(d1.year + sim_years, d1.month, d1.day)\n dates_sim2 = [d1 + datetime.timedelta(days=i) for i in range((d2 - d1).days + 1)]\n # dates_sim = dates_sim[0:-1]\n\n # sim_years = 100\n # start simulation at PCs available data\n d1 = datetime.datetime(2022, 6, 1) # x2d(xds_cov_fit.time[0])\n d2 = datetime.datetime(2022 + int(sim_years), 6, 1) # datetime(d1.year+sim_years, d1.month, d1.day)\n dt = datetime.date(2022, 6, 1)\n end = datetime.date(2022 + int(sim_years), 7, 1)\n # step = datetime.timedelta(months=1)\n step = relativedelta(months=1)\n dates_sim = []\n while dt < end:\n dates_sim.append(dt) # .strftime('%Y-%m-%d'))\n dt += step\n\n\n self.pc1Sims = pc1Sims\n self.pc2Sims = pc2Sims\n self.pc3Sims = pc3Sims\n self.evbmus_sim = evbmus_sim\n self.dates_sim = dates_sim\n\n # samplesPickle = 'awtSimulations.pickle'\n # outputSamples = {}\n # outputSamples['pc1Sims'] = pc1Sims\n # outputSamples['pc2Sims'] = pc2Sims\n # outputSamples['pc3Sims'] = pc3Sims\n # # outputSamples['pc4Sims'] = pc4Sims\n # outputSamples['evbmus_sim'] = evbmus_sim\n # outputSamples['dates_sim'] = dates_sim\n # with open(samplesPickle, 'wb') as f:\n # pickle.dump(outputSamples, f)\n\n # awtPickle = 'awtPCs.pickle'\n # outputMWTs = {}\n # outputMWTs['PC1'] = PC1\n # outputMWTs['PC2'] = PC2\n # outputMWTs['PC3'] = PC3\n # outputMWTs['normPC1'] = normPC1\n # outputMWTs['normPC2'] = normPC2\n # outputMWTs['normPC3'] = normPC3\n # outputMWTs['awt_bmus'] = awt_bmus\n # outputMWTs['annualTime'] = annualTime\n # outputMWTs['dailyAWT'] = dailyAWT\n # outputMWTs['dailyDates'] = DailyDatesMatrix\n # outputMWTs['dailyTime'] = dailyTime\n # outputMWTs['dailyPC1'] = dailyPC1\n # outputMWTs['dailyPC2'] = dailyPC2\n # outputMWTs['dailyPC3'] = dailyPC3\n # outputMWTs['nPercent'] = nPercent\n # with open(awtPickle, 'wb') as f:\n # pickle.dump(outputMWTs, f)\n\n #\n # mwtPickle = 'sstWTsPCsAndAllData.pickle'\n # outputMWTs = {}\n # outputMWTs['PCs'] = PCs\n # outputMWTs['EOFs'] = EOFs\n # outputMWTs['nPercent'] = nPercent\n # outputMWTs['awt_bmus'] = awt_bmus\n # outputMWTs['n_components'] = n_components\n # outputMWTs['variance'] = variance\n # outputMWTs['ocean'] = ocean\n # outputMWTs['land'] = land\n # outputMWTs['realDataAnoms'] = realDataAnoms\n # outputMWTs['tempdata_runavg'] = tempdata_runavg\n # outputMWTs['collapsed'] = collapsed\n # outputMWTs['annual'] = annual\n # outputMWTs['annualTime'] = annualTime\n # outputMWTs['subset'] = subset\n # outputMWTs['data'] = data\n #\n # with open(mwtPickle,'wb') as f:\n # pickle.dump(outputMWTs, f)\n\n\n def mjo(self,loadPrevious=False,plotOutput=False):\n\n\n if loadPrevious == True:\n\n print('need to know what variables to load in this space')\n\n else:\n data_folder=\"/users/dylananderson/Documents/data/ERSSTv5/\"", "repo_name": "anderdyl/gencadeClimate", "sub_path": "climateIndices.py", "file_name": "climateIndices.py", "file_ext": "py", "file_size_in_byte": 24264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 56, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 78, "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": "xarray.concat", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "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": "dateutil.relativedelta.relativedelta", "line_number": 86, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 137, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 144, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 146, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.nanstd", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 188, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 207, "usage_type": "attribute"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 218, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 239, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 248, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.cm.RdBu_r", "line_number": 253, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 260, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 262, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 327, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 327, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 344, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 345, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.cm.jet", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 356, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.cm.autumn", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 357, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 363, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 363, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 364, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 369, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 370, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 370, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 371, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 371, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 372, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 373, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 374, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 374, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 379, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 380, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 380, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 383, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 383, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 384, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 384, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 389, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 397, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 402, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 405, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 413, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 417, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 419, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 426, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 426, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 427, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 427, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 429, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 429, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 448, "usage_type": "call"}, {"api_name": "functions.copulaSimulation", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 461, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 462, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 463, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 463, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 465, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 468, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 482, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 483, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 484, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 489, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 490, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 491, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 492, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 494, "usage_type": "call"}]} +{"seq_id": "3365274480", "text": "# python 3 headers, required if submitting to Ansible\n\nfrom __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\n\nimport re\nimport os\nfrom ansible.utils.display import Display\n\ndisplay = Display()\n\n\nclass FilterModule(object):\n \"\"\"\n ansible filter\n \"\"\"\n\n def filters(self):\n return {\n 'release_version': self.release_version,\n 'checksum': self.checksum,\n 'glauth_plugins': self.plugins\n }\n\n def release_version(self, data, artefact, version, os, arch):\n \"\"\"\n \"\"\"\n # display.v(f\"release_version(self, data, {artefact}, {version}, {os}, {arch})\")\n download_url = None\n urls = []\n # display.v(f\" {type(data)}\")\n if isinstance(data, list):\n \"\"\"\n \"\"\"\n for d in data:\n \"\"\"\n \"\"\"\n assets = d.get(\"assets\", [])\n\n if assets and len(assets) > 0:\n for url in assets:\n urls.append(url.get(\"browser_download_url\"))\n\n display.v(f\" - {urls}\")\n\n # https://github.com/glauth/glauth/releases/download/v2.2.0-RC1/glauth-linux-amd64\n # https://github.com/glauth/glauth/releases/download/v2.1.0/darwinamd64.zip'\n download_url = [x for x in urls if re.search(rf\".*{version}.*{os}.*{arch}.*\", x)][0]\n\n display.v(f\"= download_url: {download_url}\")\n\n return download_url\n\n def checksum(self, data, artefact, os, arch):\n \"\"\"\n \"\"\"\n checksum = None\n\n if isinstance(data, list):\n # filter OS\n # linux = [x for x in data if re.search(r\".*prometheus-.*.{}.*.tar.gz\".format(os), x)]\n # filter OS and ARCH\n checksum = [x for x in data if re.search(r\".*{}-.*.{}-{}.tar.gz\".format(artefact, os, arch), x)][0]\n\n if isinstance(checksum, str):\n checksum = checksum.split(\" \")[0]\n\n # display.v(\"= checksum: {}\".format(checksum))\n\n return checksum\n\n def plugins(self, data):\n \"\"\"\n \"\"\"\n # display.v(\"plugins(self, data\")\n\n result = []\n\n for d in data:\n path = d.get(\"path\")\n\n if path:\n basename = os.path.basename(path)\n\n result.append(basename)\n\n return result\n", "repo_name": "bodsch/ansible-glauth", "sub_path": "filter_plugins/glauth.py", "file_name": "glauth.py", "file_ext": "py", "file_size_in_byte": 2345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "47", "api": [{"api_name": "ansible.utils.display.Display", "line_number": 10, "usage_type": "call"}, {"api_name": "re.search", "line_number": 48, "usage_type": "call"}, {"api_name": "re.search", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}]} +{"seq_id": "7038426508", "text": "import sqlite3\n\n\ndef check_table_exist(db_name, table_name):\n with sqlite3.connect('{}.db'.format(db_name)) as con:\n cur = con.cursor()\n sql = \"SELECT name FROM sqlite_master WHERE type='table' and name=:table_name\"\n cur.execute(sql, {\"table_name\": table_name})\n\n if len(cur.fetchall()) > 0:\n return True\n else:\n return False\n\n\ndef insert_df_to_db(db_name, table_name, df, option=\"replace\"):\n with sqlite3.connect('{}.db'.format(db_name)) as con:\n df.to_sql(table_name, con, if_exists=option)\n\n\ndef execute_sql(db_name, sql, param={}):\n with sqlite3.connect('{}.db'.format(db_name)) as con:\n cur = con.cursor()\n cur.execute(sql, param)\n return cur\n\n\nif __name__ == \"__main__\":\n pass", "repo_name": "papadaks/SystemTrading", "sub_path": "util/db_helper.py", "file_name": "db_helper.py", "file_ext": "py", "file_size_in_byte": 773, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "47", "api": [{"api_name": "sqlite3.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "9864430216", "text": "from urllib.request import urlopen\nimport pandas as pd\nimport requests\nimport json\nfrom bs4 import BeautifulSoup\n#from getdata import name2code\ndef detail(stock):\n result = list() #最終結果\n #chang2code\n #stockcode = str(name2code(stock)) not all_stock\n #爬取\n url = \"https://tw.stock.yahoo.com/q/q?s=\" + stock\n response = requests.get(url)\n soup = BeautifulSoup(response.text.replace(\"加到投資組合\", \"\"), \"lxml\")\n stock_date = soup.find(\"font\", {\"class\":\"tt\"}).getText().strip()[-9:] #資料日期\n tables = soup.find_all(\"table\")[2] #取得網頁中第三個表格(索引從0開始計算)\n tds = tables.find_all(\"td\")[0:11] #取得表格中0-10格 +11->6/8\n result.append((stock_date,) + tuple(td.getText().strip() for td in tds))\n scope = str(round((float(result[0][5]) - float(result[0][8])) / float(result[0][8]) * 100,2)) + '%'\n result[0] += (scope,)\n #json\n buyprice = '$' + result[0][4]\n sellprice = '$' + result[0][5]\n yesterdayprice = '$' + result[0][8]\n highestprice = '$' + result[0][10]\n lowestprice = '$' + result[0][11]\n time = result[0][0] + \" \" + result[0][2]\n targetjson = json.load(open('json/detail/alldetail.json', 'r', encoding='utf-8'))\n targetjson['body']['contents'][1]['text'] = result[0][1]\n targetjson['body']['contents'][2]['text'] = time\n targetjson['body']['contents'][4]['contents'][0]['contents'][1]['text'] = buyprice\n targetjson['body']['contents'][4]['contents'][1]['contents'][1]['text'] = sellprice\n targetjson['body']['contents'][4]['contents'][2]['contents'][1]['text'] = result[0][12] #scope\n targetjson['body']['contents'][4]['contents'][3]['contents'][1]['text'] = yesterdayprice\n targetjson['body']['contents'][4]['contents'][4]['contents'][1]['text'] = highestprice\n targetjson['body']['contents'][4]['contents'][5]['contents'][1]['text'] = lowestprice\n targetjson['body']['contents'][6]['action']['uri'] = url\n \n return targetjson\n\ndetail('0050')", "repo_name": "Hector151/linebot-stock", "sub_path": "stock02/getstock.py", "file_name": "getstock.py", "file_ext": "py", "file_size_in_byte": 2006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "39921535194", "text": "from typing import Optional\r\nimport torch\r\n\r\nfrom stacknn.superpos.functional.base import enforce_max_depth\r\n\r\n\r\ndef update_minimalist_stack(tapes: torch.FloatTensor,\r\n policies: torch.FloatTensor, # Distribution of shape [batch_size, 2].\r\n new_vecs: torch.FloatTensor, # Vectors of shape [batch_size, stack_dim].\r\n max_depth: Optional[int] = None,\r\n ) -> torch.FloatTensor:\r\n batch_size, length, stack_dim = tapes.size()\r\n device = tapes.device\r\n\r\n # Push operation.\r\n if length == 0:\r\n push_tapes = new_vecs.unsqueeze(dim=1)\r\n else:\r\n push_tapes = torch.empty(batch_size, length + 1, stack_dim, device=device)\r\n push_tapes[:, 0, :] = new_vecs\r\n push_tapes[:, 1:, :] = tapes\r\n\r\n # Merge operation.\r\n merge_tapes = torch.empty(batch_size, length + 1, stack_dim, device=device)\r\n merge_tapes[:, 0, :] = new_vecs\r\n if length > 2:\r\n merge_tapes[:, 1:-2, :] = tapes[:, 2:]\r\n merge_tapes[:, -2:, :] = 0.\r\n else:\r\n merge_tapes[:, 1:, :] = 0.\r\n\r\n\r\n policies = policies.unsqueeze(-1).unsqueeze(-1)\r\n tapes = policies[:, 0] * push_tapes + policies[:, 1] * merge_tapes\r\n return enforce_max_depth(tapes, max_depth)\r\n", "repo_name": "viking-sudo-rm/stacknn-core", "sub_path": "stacknn/superpos/functional/minimalist_stack.py", "file_name": "minimalist_stack.py", "file_ext": "py", "file_size_in_byte": 1382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 64, "dataset": "github-code", "pt": "47", "api": [{"api_name": "torch.FloatTensor", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 9, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.empty", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 24, "usage_type": "call"}, {"api_name": "stacknn.superpos.functional.base.enforce_max_depth", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 11, "usage_type": "attribute"}]} +{"seq_id": "30689726892", "text": "from django.utils.translation import ugettext_lazy as _\nfrom rest_framework.exceptions import ParseError\nfrom rest_framework.serializers import *\n\nfrom rest_framework_json_api.relations import ResourceRelatedField\nfrom rest_framework_json_api.utils import (\n get_resource_type_from_model, get_resource_type_from_instance,\n get_resource_type_from_serializer, get_included_serializers)\n\n\nclass ResourceIdentifierObjectSerializer(BaseSerializer):\n default_error_messages = {\n 'incorrect_model_type': _('Incorrect model type. Expected {model_type}, received {received_type}.'),\n 'does_not_exist': _('Invalid pk \"{pk_value}\" - object does not exist.'),\n 'incorrect_type': _('Incorrect type. Expected pk value, received {data_type}.'),\n }\n\n model_class = None\n\n def __init__(self, *args, **kwargs):\n self.model_class = kwargs.pop('model_class', self.model_class)\n if 'instance' not in kwargs and not self.model_class:\n raise RuntimeError('ResourceIdentifierObjectsSerializer must be initialized with a model class.')\n super(ResourceIdentifierObjectSerializer, self).__init__(*args, **kwargs)\n\n def to_representation(self, instance):\n return {\n 'type': get_resource_type_from_instance(instance),\n 'id': str(instance.pk)\n }\n\n def to_internal_value(self, data):\n if data['type'] != get_resource_type_from_model(self.model_class):\n self.fail('incorrect_model_type', model_type=self.model_class, received_type=data['type'])\n pk = data['id']\n try:\n return self.model_class.objects.get(pk=pk)\n except ObjectDoesNotExist:\n self.fail('does_not_exist', pk_value=pk)\n except (TypeError, ValueError):\n self.fail('incorrect_type', data_type=type(data['pk']).__name__)\n\n\nclass SparseFieldsetsMixin(object):\n def __init__(self, *args, **kwargs):\n context = kwargs.get('context')\n request = context.get('request') if context else None\n\n if request:\n sparse_fieldset_query_param = 'fields[{}]'.format(get_resource_type_from_serializer(self))\n try:\n param_name = next(key for key in request.query_params if sparse_fieldset_query_param in key)\n except StopIteration:\n pass\n else:\n fieldset = request.query_params.get(param_name).split(',')\n # iterate over a *copy* of self.fields' underlying OrderedDict, because we may modify the\n # original during the iteration. self.fields is a `rest_framework.utils.serializer_helpers.BindingDict`\n for field_name, field in self.fields.fields.copy().items():\n if field_name == api_settings.URL_FIELD_NAME: # leave self link there\n continue\n if field_name not in fieldset:\n self.fields.pop(field_name)\n\n super(SparseFieldsetsMixin, self).__init__(*args, **kwargs)\n\n\nclass IncludedResourcesValidationMixin(object):\n def __init__(self, *args, **kwargs):\n context = kwargs.get('context')\n request = context.get('request') if context else None\n view = context.get('view') if context else None\n\n def validate_path(serializer_class, field_path, path):\n serializers = get_included_serializers(serializer_class)\n if serializers is None:\n raise ParseError('This endpoint does not support the include parameter')\n this_field_name = field_path[0]\n this_included_serializer = serializers.get(this_field_name)\n if this_included_serializer is None:\n raise ParseError(\n 'This endpoint does not support the include parameter for path {}'.format(\n path\n )\n )\n if len(field_path) > 1:\n new_included_field_path = field_path[-1:]\n # We go down one level in the path\n validate_path(this_included_serializer, new_included_field_path, path)\n\n if request and view:\n include_resources_param = request.query_params.get('include') if request else None\n if include_resources_param:\n included_resources = include_resources_param.split(',')\n for included_field_name in included_resources:\n included_field_path = included_field_name.split('.')\n this_serializer_class = view.serializer_class\n # lets validate the current path\n validate_path(this_serializer_class, included_field_path, included_field_name)\n\n super(IncludedResourcesValidationMixin, self).__init__(*args, **kwargs)\n\n\nclass HyperlinkedModelSerializer(IncludedResourcesValidationMixin, SparseFieldsetsMixin, HyperlinkedModelSerializer):\n \"\"\"\n A type of `ModelSerializer` that uses hyperlinked relationships instead\n of primary key relationships. Specifically:\n\n * A 'url' field is included instead of the 'id' field.\n * Relationships to other instances are hyperlinks, instead of primary keys.\n\n Included Mixins:\n * A mixin class to enable sparse fieldsets is included\n * A mixin class to enable validation of included resources is included\n \"\"\"\n\n\nclass ModelSerializer(IncludedResourcesValidationMixin, SparseFieldsetsMixin, ModelSerializer):\n \"\"\"\n A `ModelSerializer` is just a regular `Serializer`, except that:\n\n * A set of default fields are automatically populated.\n * A set of default validators are automatically populated.\n * Default `.create()` and `.update()` implementations are provided.\n\n The process of automatically determining a set of serializer fields\n based on the model fields is reasonably complex, but you almost certainly\n don't need to dig into the implementation.\n\n If the `ModelSerializer` class *doesn't* generate the set of fields that\n you need you should either declare the extra/differing fields explicitly on\n the serializer class, or simply use a `Serializer` class.\n\n\n Included Mixins:\n * A mixin class to enable sparse fieldsets is included\n * A mixin class to enable validation of included resources is included\n \"\"\"\n serializer_related_field = ResourceRelatedField\n\n def get_field_names(self, declared_fields, info):\n \"\"\"\n We override the parent to omit explicity defined meta fields (such\n as SerializerMethodFields) from the list of declared fields\n \"\"\"\n meta_fields = getattr(self.Meta, 'meta_fields', [])\n\n declared = OrderedDict()\n for field_name in set(declared_fields.keys()):\n field = declared_fields[field_name]\n if field_name not in meta_fields:\n declared[field_name] = field\n fields = super(ModelSerializer, self).get_field_names(declared, info)\n return list(fields) + list(getattr(self.Meta, 'meta_fields', list()))\n", "repo_name": "bselliott/jobHunterBackEnd", "sub_path": "myvenv/lib/python3.6/site-packages/rest_framework_json_api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 7019, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "django.utils.translation.ugettext_lazy", "line_number": 13, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework_json_api.utils.get_resource_type_from_instance", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework_json_api.utils.get_resource_type_from_model", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework_json_api.utils.get_resource_type_from_serializer", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework_json_api.utils.get_included_serializers", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ParseError", "line_number": 77, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ParseError", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework_json_api.relations.ResourceRelatedField", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "38252551602", "text": "from django.shortcuts import render, redirect, HttpResponse\nfrom django.http import JsonResponse \nfrom django.db.models import Q\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth.decorators import login_required\nfrom inbox.models import Message\nfrom usuarios.models import Profile\nfrom .models import Post, SavePost, Comment, Histories, Notifications, Likes\nfrom .forms import PostCreation, HistoryCreation\nfrom django.contrib.auth.models import User\nfrom datetime import timedelta\nfrom django.utils import timezone\n\n\n# Create your views here.\n\n\n# PAGINA DE INICIO\ndef home(request):\n # print(profile.follow.all())\n histories = Histories.objects.all()\n for x in histories:\n if timezone.now() > x.expired and x.permanent == False:\n x.delete()\n \n try:\n followers = Profile.objects.get(user = request.user)\n followers = followers.follow.all()\n profile = Profile.objects.get(user = request.user)\n savedpost = SavePost.objects.filter(user = request.user)\n likepost = Likes.objects.filter(user = request.user)\n notifications = Message.objects.filter(received = profile, read = False).count()\n activity = Notifications.objects.filter(user = request.user, see = False).count()\n if not savedpost:\n postsaved = []\n else:\n postsaved = []\n for x in savedpost:\n postsaved.append(x.post)\n\n if not likepost:\n postliked = []\n else:\n postliked = []\n for x in likepost:\n postliked.append(x.post)\n \n except:\n postsaved = []\n postliked = []\n profile = []\n notifications = {}\n followers = []\n activity = 0\n\n return render(request, 'core/home.html', {\n 'notifications' : notifications, 'posts' :Post.objects.all().order_by('-created'),\n 'savedpost' : postsaved, 'followers' : followers, 'form' : PostCreation(),\n 'profile' : profile, 'histories' : histories,\n 'users' : Profile.objects.all(), 'formStory' : HistoryCreation(), \n 'notifi' : activity, 'likepost' : postliked,\n })\n\n\n\n\n@csrf_exempt\ndef save_post(request):\n if not request.user.is_authenticated:\n return JsonResponse({'type' : 400}, safe=False)\n id = request.POST.get('id')\n type = request.POST.get('type')\n post = Post.objects.get(id = id)\n if type == 'guardar':\n SavePost.objects.create(\n post = post,\n user = request.user\n )\n return JsonResponse({'type' : type, 'id' : id }, safe=False)\n else:\n SavePost.objects.get(post = post, user = request.user).delete()\n return JsonResponse({'type' : type, 'id' : id}, safe=False)\n\n\n@csrf_exempt\ndef like_post(request):\n if not request.user.is_authenticated:\n return JsonResponse({'type' : 400}, safe=False)\n id = request.POST.get('id')\n type = request.POST.get('type')\n post = Post.objects.get(id = id)\n if type == 'like':\n Likes.objects.create(\n post = post,\n user = request.user\n )\n post.like += 1\n post.save()\n return JsonResponse({'type' : type, 'id' : id }, safe=False)\n else:\n Likes.objects.get(post = post, user = request.user).delete()\n post.like -= 1\n post.save()\n return JsonResponse({'type' : type, 'id' : id}, safe=False)\n\n\n\n\n@login_required(login_url='login')\ndef createPost(request):\n profile = Profile.objects.get(user = request.user)\n if request.method == 'POST':\n imagen = request.FILES.get('imagen')\n taggeded = request.POST.getlist('tagged')\n description = request.POST.get('description')\n post = Post.objects.create(\n imagen = imagen,\n host = profile,\n description = description\n )\n for x in taggeded:\n user = User.objects.get(id = x)\n post.tagged.add(user)\n return redirect('home')\n else:\n return redirect('profile')\n\n\n# Crear historias\n@login_required(login_url='login')\ndef CreateHistory(request):\n profile = Profile.objects.get(user = request.user)\n formStory = HistoryCreation()\n if request.method == 'POST':\n form = HistoryCreation(request.POST, request.FILES)\n if form.is_valid():\n h = form.save(commit=False)\n h.user = profile\n h.expired = timezone.now() + timedelta(hours=21)\n h.save()\n\n return redirect('home')\n else:\n return redirect('createHistory')\n \n return render(request, 'core/create-histories.html', {'form': form})\n\n\n# Mostrar historias\ndef historyJson(request):\n pk = request.GET.get('id')\n profile = Profile.objects.get(id = pk)\n x = Histories.objects.filter(user = profile)\n historiesImg = []\n historiesDes = []\n historiesUser = []\n historiesTimes = []\n for y in x:\n historiesImg.append(y.history.url)\n historiesDes.append(y.description)\n historiesUser.append(y.user.name)\n historiesTimes.append(y.created.strftime(\"%a, %b a las %I:%M %p\"))\n\n return JsonResponse({\n 'historiesImg': historiesImg, 'historiesDes': historiesDes,\n 'historiesUser': historiesUser, 'historiesTimes' : historiesTimes,\n }, safe=False)\n\n\n# Mostrar la publicacion de un usuario\ndef postUser(request, pk):\n post = Post.objects.get(id = pk)\n comments = Comment.objects.filter(postcomments = post)\n try:\n save_post = SavePost.objects.filter(user = request.user, post = post)\n like_post = Likes.objects.filter(user = request.user, post = post)\n except:\n save_post = []\n like_post = []\n return render(request, 'core/post.html', {\n 'post': post, 'comments': comments,\n 'saved' : save_post, 'liked' : like_post,\n })\n\n\n\n@csrf_exempt\ndef createComment(request):\n if not request.user.is_authenticated:\n return JsonResponse({'status' : 'mal'}, safe = False)\n profile = Profile.objects.get(user = request.user)\n id = request.POST.get('id')\n post = Post.objects.get(id = id)\n comment = request.POST.get('comment')\n if not comment:\n return\n x = Comment.objects.create(\n user = profile,\n postcomments = post,\n name = comment\n )\n if profile.user != post.host.user:\n Notifications.objects.create(\n type = 2,\n post = post,\n sender = profile.user,\n user = post.host.user\n )\n return JsonResponse({\n 'comment' : comment, 'host': request.user.username, 'pk': x.id,\n 'avatar' : profile.avatar.url,\n }, safe=False)\n\ndef deleteComment(request):\n id = request.GET.get('id')\n comment = Comment.objects.get(id = id)\n if request.user == comment.user.user:\n comment.delete()\n return JsonResponse({}, safe=False)\n\n\n# Notificaciones de los usuarios\n@login_required(login_url='login')\ndef notifications_user(request, pk):\n user = User.objects.get(id = pk)\n if request.user != user:\n return redirect('home')\n profile = Profile.objects.get(user = user)\n notifications = Notifications.objects.filter(user = user).order_by('-created')\n for x in notifications:\n x.see = True\n x.save()\n return render(request, 'core/notifications.html', {'noti': notifications, 'profile' : profile})", "repo_name": "facuCogliati/instagram-copy", "sub_path": "core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "models.Histories.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Histories.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Histories", "line_number": 21, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 23, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 23, "usage_type": "name"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 27, "usage_type": "name"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 29, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 29, "usage_type": "name"}, {"api_name": "models.SavePost.objects.filter", "line_number": 30, "usage_type": "call"}, {"api_name": "models.SavePost.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.SavePost", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Likes.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Likes.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Likes", "line_number": 31, "usage_type": "name"}, {"api_name": "inbox.models.Message.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "inbox.models.Message.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "inbox.models.Message", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Notifications.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Notifications.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Notifications", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Post.objects.all", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 57, "usage_type": "name"}, {"api_name": "forms.PostCreation", "line_number": 58, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects.all", "line_number": 60, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 60, "usage_type": "name"}, {"api_name": "forms.HistoryCreation", "line_number": 60, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 73, "usage_type": "name"}, {"api_name": "models.SavePost.objects.create", "line_number": 75, "usage_type": "call"}, {"api_name": "models.SavePost.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.SavePost", "line_number": 75, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 79, "usage_type": "call"}, {"api_name": "models.SavePost.objects.get", "line_number": 81, "usage_type": "call"}, {"api_name": "models.SavePost.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.SavePost", "line_number": 81, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 82, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 67, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 91, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 91, "usage_type": "name"}, {"api_name": "models.Likes.objects.create", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Likes.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.Likes", "line_number": 93, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Likes.objects.get", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Likes.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.Likes", "line_number": 101, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 104, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 85, "usage_type": "name"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 111, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 111, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 111, "usage_type": "name"}, {"api_name": "models.Post.objects.create", "line_number": 116, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 116, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 122, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 122, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 124, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 126, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 109, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 132, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 132, "usage_type": "name"}, {"api_name": "forms.HistoryCreation", "line_number": 133, "usage_type": "call"}, {"api_name": "forms.HistoryCreation", "line_number": 135, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 139, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 139, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 139, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 142, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 144, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 146, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 130, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 152, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 152, "usage_type": "name"}, {"api_name": "models.Histories.objects.filter", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Histories.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Histories", "line_number": 153, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 164, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 172, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 172, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 172, "usage_type": "name"}, {"api_name": "models.Comment.objects.filter", "line_number": 173, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 173, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 173, "usage_type": "name"}, {"api_name": "models.SavePost.objects.filter", "line_number": 175, "usage_type": "call"}, {"api_name": "models.SavePost.objects", "line_number": 175, "usage_type": "attribute"}, {"api_name": "models.SavePost", "line_number": 175, "usage_type": "name"}, {"api_name": "models.Likes.objects.filter", "line_number": 176, "usage_type": "call"}, {"api_name": "models.Likes.objects", "line_number": 176, "usage_type": "attribute"}, {"api_name": "models.Likes", "line_number": 176, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 180, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 190, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 191, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 191, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 191, "usage_type": "name"}, {"api_name": "models.Post.objects.get", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 193, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 193, "usage_type": "name"}, {"api_name": "models.Comment.objects.create", "line_number": 197, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 197, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 197, "usage_type": "name"}, {"api_name": "models.Notifications.objects.create", "line_number": 203, "usage_type": "call"}, {"api_name": "models.Notifications.objects", "line_number": 203, "usage_type": "attribute"}, {"api_name": "models.Notifications", "line_number": 203, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 209, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 187, "usage_type": "name"}, {"api_name": "models.Comment.objects.get", "line_number": 216, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 216, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 216, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 219, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 225, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 225, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 225, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 227, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects.get", "line_number": 228, "usage_type": "call"}, {"api_name": "usuarios.models.Profile.objects", "line_number": 228, "usage_type": "attribute"}, {"api_name": "usuarios.models.Profile", "line_number": 228, "usage_type": "name"}, {"api_name": "models.Notifications.objects.filter", "line_number": 229, "usage_type": "call"}, {"api_name": "models.Notifications.objects", "line_number": 229, "usage_type": "attribute"}, {"api_name": "models.Notifications", "line_number": 229, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 233, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 223, "usage_type": "call"}]} +{"seq_id": "72548254542", "text": "__author__ = \"Simone Campagna\"\n\nfrom setuptools import setup\n#from setuptools.command.test import test as TestCommand\n\nimport glob\nimport os\nimport sys\n\n\nif __name__ == \"__main__\":\n DIRNAME = os.path.abspath(os.path.dirname(__file__))\n if DIRNAME:\n os.chdir(DIRNAME)\n try:\n py_dirname = DIRNAME\n sys.path.insert(0, py_dirname)\n \n import callerframe\n version = callerframe.__version__\n finally:\n del sys.path[0]\n \n def read_requirements(*filenames):\n requirements = []\n for filename in filenames:\n fpath = os.path.join(os.getcwd(), 'requirements', filename + '.txt')\n with open(fpath, \"r\") as f_in:\n for line in f_in:\n requirement = line.strip()\n if not requirement in requirements:\n requirements.append(requirement)\n return requirements\n \n # search executables\n scripts = []\n for filepath in glob.glob('bin/*'):\n if os.path.isfile(filepath) and os.access(filepath, os.X_OK):\n scripts.append(filepath)\n \n # search packages\n root_packages = []\n packages = []\n for package in root_packages:\n package_dirname = os.path.join(DIRNAME, package)\n for dirpath, dirnames, filenames in os.walk(package_dirname):\n if '__init__.py' in filenames:\n rdirpath = os.path.relpath(dirpath, DIRNAME)\n packages.append(os.path.normpath(rdirpath).replace(os.sep, '.'))\n \n# # search requirement files\n# data_files = []\n# for data_dirname, patterns in [('requirements', ('*.txt', )),\n# ('docs/source', ('conf.py', '*.rst')),\n# ('docs/source/examples', ('*.rst',)),\n# ('docs/source/img', ('*.png',)),\n# ('.', ('tox.ini', 'pylint.ini', 'flake8.ini',)),\n# ]:\n# files = []\n# for pattern in patterns:\n# for fpath in glob.glob(os.path.join(DIRNAME, data_dirname, pattern)):\n# files.append(os.path.relpath(fpath, DIRNAME))\n# data_files.append((data_dirname, files))\n \n setup(\n name=\"callerframe\",\n version=version,\n requires=[],\n description=\"Python library for configuration data\",\n author=\"Simone Campagna\",\n author_email=\"simone.campagna11@gmail.com\",\n install_requires=read_requirements('install'),\n package_data={},\n #data_files=data_files,\n url=\"https://github.com/simone-campagna/callerframe\",\n download_url = 'https://github.com/simone-campagna/callerframe/archive/{}.tar.gz'.format(version),\n packages=packages,\n scripts=scripts,\n py_modules=['callerframe'],\n classifiers=[\n # status:\n # 3 - Alpha\n # 4 - Beta\n # 5 - Production/Stable\n 'Development Status :: 4 - Beta',\n # audience:\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n # license:\n 'License :: OSI Approved :: Apache Software License',\n # language:\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 2.7',\n ],\n keywords='decorator caller frame',\n )\n\n", "repo_name": "simone-campagna/callerframe", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 3455, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "callerframe.__version__", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 22, "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.getcwd", "line_number": 27, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 38, "usage_type": "call"}, {"api_name": "os.X_OK", "line_number": 38, "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.walk", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 49, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "71955518222", "text": "from keras.models import Sequential, Model\nfrom keras.layers import Dense, Conv2D, MaxPool2D, Activation, BatchNormalization, Flatten, InputLayer\nfrom keras.utils import to_categorical\nfrom keras.optimizers import SGD\nimport os\nimport cv2\nimport numpy as np\n\n\ndef base_model():\n\n model = Sequential([\n InputLayer(input_shape=(160, 60, 3)),\n Conv2D(64, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(64, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n MaxPool2D(pool_size=(2,2), strides=(2,2)),\n\n Conv2D(128, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(128, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n MaxPool2D(pool_size=(2,2), strides=(2,2)),\n\n Conv2D(256, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(256, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(256, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n MaxPool2D(pool_size=(2,2), strides=(2,2)),\n\n Conv2D(512, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(512, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(512, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n MaxPool2D(pool_size=(2,2), strides=(2,2)),\n\n Conv2D(512, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(512, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n Conv2D(512, (3,3), padding='same'),\n BatchNormalization(axis=1, momentum=0.99, epsilon=1e-3),\n Activation('relu'),\n MaxPool2D(pool_size=(2,2), strides=(2,2)),\n\n Flatten(),\n Dense(4096, activation='relu'),\n Dense(4096, activation='relu'),\n Dense(972, activation='relu')\n ])\n# model.summary()\n\n return model\n\n\ndef load_data():\n\n filenames = os.listdir('./datasets/cuhk01/')\n x = np.array([cv2.imread(os.path.join(os.path.abspath('./datasets/cuhk01/'), filename)) for filename in filenames])\n labels = np.array([int(filename[:4]) for filename in filenames])\n return x, to_categorical(labels)\n\n\ndef get_feature_vec(model, x, labels):\n\n\tmodel.load_weights('model.h5py')\n\tnew_model = Model(model.layers[0].input, model.layers[-2].output)\n\tfeature_vec = new_model.predict(x)\n\n\treturn new_model, feature_vec\n\n\ndef cos_sim(model, feature_vec, labels, filename):\n\n image = np.array(cv2.imread(os.path.join(os.paht.abspath('./datasets/cuhk01/'), filename)))\n input_feature_vec = model.predict(np.expand_dims(image, axis=0))\n similarity = np.sum(feature_vec * input_feature_vec, axis=1)\n similarity = similarity / np.linalg.norm(feature_vec, axis=1)\n similarity = similarity / np.linalg.norm(input_feature_vec)\n\n return [np.argmax(labels[i]) for i in np.argsort(similarity)[:6]]\n\n\nif __name__ == '__main__':\n\n model = base_model()\n x, labels = load_data()\n print(x.shape)\n print(labels.shape)\n sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n model.compile(loss='categorical_crossentropy', optimizer=sgd)\n model.fit(x, labels, batch_size=50, epochs=500)\n model.save_weights('model.h5py')\n", "repo_name": "aup8497/Person-Re-identification-using-Siamese-networks", "sub_path": "Code/basic_model.py", "file_name": "basic_model.py", "file_ext": "py", "file_size_in_byte": 3908, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "47", "api": [{"api_name": "keras.models.Sequential", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.layers.InputLayer", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 66, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.imread", "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.abspath", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 92, "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.paht.abspath", "line_number": 92, "usage_type": "call"}, {"api_name": "os.paht", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "71932829582", "text": "import json\nimport requests\n\nclass Error_service:\n def __init__(self, error, thing_manager_endpoint, project_id, file_id=None):\n self.error = error\n self.project_id = project_id\n self.thing_manager_endpoint = thing_manager_endpoint\n self.file_id = file_id\n\n def handle_error(self):\n url = self.thing_manager_endpoint + \"/wrapper_error\"\n error_dict = {\n \"project_id\": self.project_id,\n \"file_id\": self.file_id,\n \"error\": self.error\n }\n headers = {\n 'Content-Type': 'application/json'\n }\n payload = json.dumps(error_dict)\n try:\n response = requests.request(\"POST\", url, headers=headers, data=payload)\n except:\n print(\"Error sending error to the Thing Manager\")\n", "repo_name": "oeg-upm/cogito_wrapper_module", "sub_path": "service/Error_service.py", "file_name": "Error_service.py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "70821703503", "text": "\"\"\"\r\n1. Goal of the Code: Able to download information by API and thereafter able to download in:\r\n- Pandas\r\n- Json\r\n-Excel\r\n-Check The Connection (Good and Bad Response etc)\r\n\r\nThis Code will work as a format for future Codes\r\n\r\n\"\"\"\r\nimport requests\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n#Function Files\r\ndef Json_File():\r\n # Save To Json File\r\n name = input('Name of the file? ')\r\n df.to_json(name)\r\ndef CSV_File():\r\n # Save to CSV File\r\n name = input('Name of the file? ')\r\n df.to_csv(name, index=False)\r\ndef Excel_File():\r\n # Save to Excel File\r\n name = input('Name of the file? ')\r\n df.to_excel(name, index=False)\r\n\r\n\r\n#Get the API key and then execute one of those\r\n# An example: url = \"https://api.publicapis.org/entries\"\r\n\r\n#Enter Your Api Key\r\nurl=\"\"\r\n\r\n\r\n# Getting the Response of the Link\r\nresponse = requests.get(url)\r\nprint('The Response of the website is', response)\r\n\r\n# Getting in to pandas\r\n\r\ndata = response.json()\r\n\r\ndf = pd.DataFrame(data)\r\n\r\nprint('Which file do you want to save in?')\r\nprint('Press 1 For Json-File')\r\nprint('Press 2 for CSV File')\r\nprint('Press 3 for Excel File')\r\n\r\nchoice = int(input('Choose your Number; '))\r\n\r\nif choice == 1:\r\n Json_File()\r\nelif choice == 2:\r\n CSV_File()\r\nelif choice == 3:\r\n Excel_File()\r\nelse:\r\n print('You Suck! ')\r\n", "repo_name": "mrimon93/My_Own_Projects", "sub_path": "Format_Code_For_Lazy_People/Download_Data_By_Api.py", "file_name": "Download_Data_By_Api.py", "file_ext": "py", "file_size_in_byte": 1324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "33458051683", "text": "# -*- coding: utf-8 -*-\nfrom action import app,db\nfrom flask import jsonify\nfrom action.model.user import User, Group, Group_discipline, Student,DisciplineType, Schedule,FirstWeek,ExceptionDays,ClassTime\nfrom.user import token_required\nfrom .allow_origin import crossdomain\nfrom .subgroup import get_sub\nfrom flask import render_template, request, redirect, url_for, jsonify, make_response\n\nimport calendar\nimport locale\n# from flask_cors import cross_origin\nimport datetime\n\n@app.route('/get_groups', methods=['GET'])\n@crossdomain(origin='*')\n# @token_required\ndef get_groups():\n current_user = User.query.filter_by(id=1).first()\n groups=current_user.rteacher[0].groups\n # teacher1 = Discipline.query.filter_by(id=1).all()\n #\n # print(teacher1)\n # groups = Teachers_group.query.filter_by(teacher_id=current_user.id).all()\n # # print(groups)\n output = []\n for gr in groups:\n\n group = Group.query.filter_by(id=gr.group_id).first()\n group_data = {}\n group_data['id'] = group.id\n group_data['name'] = group.name\n group_data['name_id'] = toLatin(group.name).replace(' ', '')\n group_data['major'] = group.major\n output.append(group_data)\n return jsonify(output)\n\n\n@app.route('/get_students')\n@crossdomain(origin='*')\ndef get_student():\n students = Student.query.all()\n output = []\n for student in students:\n student_data = {}\n student_data['id']=student.id\n student_data['full_name']= student.full_name\n student_data['group_id'] = student.group_id\n student_data['status_id'] = student.status_id\n student_data['phone'] = student.phone\n student_data['email'] = student.email\n output.append(student_data)\n return jsonify({'groups': output})\n\n\n@app.route('/get_group/id=&sub=&dis=', methods=['GET'])\n@crossdomain(origin='*')\n# @cross_origin()\n# @token_required\ndef get_group(id, sub, subject):\n\n # def get_group(current_user, token, id, sub, subject):\n if sub == '0':\n output = []\n group_list = Student.query.filter_by(group_id=id).all()\n for student in group_list:\n student_data = {}\n student_data['id'] = student.id\n student_data['full_name'] = student.full_name\n student_data['group_id'] = student.group_id\n student_data['status_id'] = student.status_id\n student_data['phone'] = student.phone\n student_data['email'] = student.email\n output.append(student_data)\n return jsonify(output)\n else:\n return get_sub(id, sub, 1, subject)\n\n\n@app.route('/get_disciplines/', methods=['GET'])\n@crossdomain(origin='*')\n# @cross_origin()\n# @token_required\ndef get_discipline(id):\n # current_user, token,\n current_user = User.query.filter_by(id=1).first()\n output = []\n print(id)\n # query = db.session.query(Group_discipline)\n disciplines = Group_discipline.query.filter_by(group_id=id, teacher_id=current_user.rteacher[0].id).group_by(Group_discipline.discipline_id)\n for discipline in disciplines:\n discipline_data = {}\n discipline_data['id'] = discipline.discipline.id\n discipline_data['group_id'] = int(id)\n discipline_data['sub_id'] = discipline.sub_id\n discipline_data['dis_type'] = discipline.dis_type\n discipline_data['dis_tn'] = DisciplineType.query.filter_by(id=discipline.dis_type).first().name\n if discipline.sub_id != 0:\n discipline_data['name'] = discipline.discipline.name + \"(\" + str(discipline.sub_id) + \")\"\n else:\n discipline_data['name'] = discipline.discipline.name\n discipline_data['name_id'] = str(toLatin(discipline.discipline.name) + str(discipline.sub_id)).replace(\" \", \"\")\n discipline_data['credit'] = discipline.discipline.credit\n discipline_data['academic_hours'] = discipline.discipline.academic_hours\n output.append(discipline_data)\n return jsonify({'list_of_discipline': output})\n\ndef toLatin(text):\n Rus = [\"Я\", \"я\", \"Ю\", \"ю\", \"Ч\", \"ч\", \"Ш\", \"ш\", \"Щ\", \"щ\", \"Ж\", \"ж\", \"А\", \"а\", \"Б\", \"б\", \"В\", \"в\", \"Г\", \"г\", \"Д\", \"д\",\n \"Е\", \"е\", \"Ё\", \"ё\", \"З\", \"з\", \"И\", \"и\", \"Й\", \"й\", \"К\", \"к\", \"Л\", \"л\", \"М\", \"м\", \"Н\", \"н\", \"О\",\n \"о\", \"П\", \"п\",\n \"Р\", \"р\", \"С\", \"с\", \"Т\", \"т\", \"У\", \"у\", \"Ф\", \"ф\", \"Х\", \"х\", \"Ц\", \"ц\", \"Ы\", \"ы\", \"Ь\", \"ь\", \"Ъ\", \"ъ\", \"Э\", \"э\"]\n\n Eng = [\"Ya\", \"ya\", \"Yu\", \"yu\", \"Ch\", \"ch\", \"Sh\", \"sh\", \"Sh\", \"sh\", \"Zh\", \"zh\", \"A\", \"a\", \"B\", \"b\", \"V\", \"v\", \"G\",\n \"g\", \"D\", \"d\", \"E\", \"e\", \"E\", \"e\", \"Z\", \"z\", \"I\", \"i\", \"J\", \"j\", \"K\", \"k\", \"L\", \"l\", \"M\", \"m\", \"N\",\n \"n\", \"O\",\n \"o\", \"P\", \"p\", \"R\", \"r\", \"S\", \"s\", \"T\", \"t\", \"U\", \"u\", \"F\", \"f\", \"H\", \"h\", \"C\", \"c\", \"Y\", \"y\", \"\", \"\", \"\\\"\",\n \"\\\"\", \"E\", \"e\"]\n translation = ''''''\n for i in range(len(text)):\n try:\n j = Rus.index(text[i])\n translation += Eng[j]\n except:\n translation += text[i]\n return str(translation)\n\n\n@app.route('/get_type/&dis=&sub=', methods=['GET'])\n@crossdomain(origin='*')\ndef get_type(id,dis,sub):\n # current_user, token,\n current_user = User.query.filter_by(id=1).first()\n types = Group_discipline.query.filter_by(group_id=id, teacher_id=current_user.rteacher[0].id, discipline_id=dis, sub_id=sub).all()\n output=[]\n for type in types:\n type_data={}\n type_data['dis_type'] = type.dis_type\n type_data['dis_tn'] = DisciplineType.query.filter_by(id=type.dis_type).first().name\n type_data['tn_eng']= toLatin(str(DisciplineType.query.filter_by(id=type.dis_type).first().name))\n output.append(type_data)\n return jsonify({'type_of_discipline': output})\n\n\n@app.route('/get_date/id=&dis=&sub=&type=', methods=['GET'])\n@crossdomain(origin='http://localhost:8000')\ndef get_date(id, dis, sub, dtype):\n current_user = User.query.filter_by(id=1).first()\n infos = Schedule.query.filter_by(group_id=id, teacher_id=current_user.rteacher[0].id, discipline_id=dis, sub_id=sub, dis_type=dtype).all()\n exceptions = ExceptionDays.query.all()\n print(infos)\n dates = []\n for info in infos:\n today = datetime.datetime.strptime(FirstWeek.query.filter_by(id=1).first().date, \"%Y-%m-%d\")\n daydif = (info.week_day-1)-today.weekday()\n interval = 2\n if info.week_type == 0:\n interval = 1\n elif info.week_type == 1:\n interval = 2\n else:\n today = today-datetime.timedelta(weeks=1)\n today = today + datetime.timedelta(days=daydif, weeks=-interval)\n for i in range(int(int(info.weeks)/len(infos))):\n next_monday = today + datetime.timedelta(weeks=interval)\n today=next_monday\n for exception in exceptions:\n if exception.date == str(next_monday.date()):\n dates.append(exception.todate)\n else:\n dates.append(str(next_monday.date()))\n dates = sorted(dates)\n # print(dates)\n months = ['Январь' , 'Февраль' , 'Март' , 'Апрель' , 'Май' , 'Июнь' , 'Июль' , 'Август' , 'Сентябрь' , 'Октябрь' , 'Ноябрь' , 'Декабрь'];\n # print(months[int(dates[0][5:-3])-1])\n output=[]\n m = []\n for i in range(len(dates)-1):\n dat = {}\n month = int(dates[i][5:-3])-1\n m.append(int(dates[i][8:]))\n\n if month !=int(dates[i+1][5:-3])-1:\n dat['month'] = months[month]\n dat['dates'] = m\n output.append(dat)\n m = []\n print(output)\n\n\n return jsonify(output)\n\n\n@app.route('/schedule', methods=[\"GET\"])\n@crossdomain(origin='*')\ndef schedule():\n date = request.args.get('date')\n date = datetime.date(int(date[:4]), int(date[5:7]), int(date[-2:])).weekday()\n schedule = Schedule.query.filter_by(teacher_id=1, week_day=date).all()\n\n lessons=[]\n for clas in schedule:\n lesson = {}\n group={}\n lesson[\"type\"] = clas.DisciplineType.name\n lesson[\"id\"] = clas.discipline_id\n lesson[\"name\"] = clas.Discipline.name\n lesson[\"group\"] = {\"id\": clas.group_id,\n \"name\": clas.group.name}\n lesson[\"description\"]=\"Плана нет пока!\"\n lesson[\"sub_group\"]=clas.sub_id\n lesson[\"beginning\"] =clas.Time.begining\n lesson[\"end\"] = clas.Time.end\n lesson[\"auditory\"] = clas.auditory\n lessons.append(lesson)\n\n rooms = set(d.get('auditory') for d in lessons)\n classrooms=[]\n for room in rooms:\n new_dict={}\n new_dict[\"name\"]=room\n disciplines=[]\n for lesson in lessons:\n if room == lesson.get('auditory'):\n disciplines.append(lesson)\n lesson.pop('auditory', None)\n new_dict[\"lessons\"]=disciplines\n classrooms.append(new_dict)\n\n\n # create a dictionary\n # new_dict = {}\n #\n # # iterate over the unique film names\n # for k in lessons:\n # # make a list of all the films that match the name we're on\n # filmswiththisname = [d for d in lessons if d.get('auditory') == k]\n # # add the list of films to the new dictionary with the film name as the key.\n # new_dict[k] = filmswiththisname\n\n return jsonify({'classRooms': classrooms})\n\n\n@app.route('/loggin', methods=['POST'])\ndef loggin():\n login=request.values['username']\n password=request.values['password']\n print(login,password)\n return '0'\n", "repo_name": "Nurlan1/Ej", "sub_path": "action/view/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 9642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "action.model.user.User.query.filter_by", "line_number": 19, "usage_type": "call"}, {"api_name": "action.model.user.User.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "action.model.user.User", "line_number": 19, "usage_type": "name"}, {"api_name": "action.model.user.Group.query.filter_by", "line_number": 29, "usage_type": "call"}, {"api_name": "action.model.user.Group.query", "line_number": 29, "usage_type": "attribute"}, {"api_name": "action.model.user.Group", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 15, "usage_type": "call"}, {"api_name": "action.app", "line_number": 15, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 16, "usage_type": "call"}, {"api_name": "action.model.user.Student.query.all", "line_number": 42, "usage_type": "call"}, {"api_name": "action.model.user.Student.query", "line_number": 42, "usage_type": "attribute"}, {"api_name": "action.model.user.Student", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 53, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 39, "usage_type": "call"}, {"api_name": "action.app", "line_number": 39, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 40, "usage_type": "call"}, {"api_name": "action.model.user.Student.query.filter_by", "line_number": 65, "usage_type": "call"}, {"api_name": "action.model.user.Student.query", "line_number": 65, "usage_type": "attribute"}, {"api_name": "action.model.user.Student", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "subgroup.get_sub", "line_number": 77, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 56, "usage_type": "call"}, {"api_name": "action.app", "line_number": 56, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 57, "usage_type": "call"}, {"api_name": "action.model.user.User.query.filter_by", "line_number": 86, "usage_type": "call"}, {"api_name": "action.model.user.User.query", "line_number": 86, "usage_type": "attribute"}, {"api_name": "action.model.user.User", "line_number": 86, "usage_type": "name"}, {"api_name": "action.model.user.Group_discipline.query.filter_by", "line_number": 90, "usage_type": "call"}, {"api_name": "action.model.user.Group_discipline.query", "line_number": 90, "usage_type": "attribute"}, {"api_name": "action.model.user.Group_discipline", "line_number": 90, "usage_type": "name"}, {"api_name": "action.model.user.Group_discipline.discipline_id", "line_number": 90, "usage_type": "attribute"}, {"api_name": "action.model.user.DisciplineType.query.filter_by", "line_number": 97, "usage_type": "call"}, {"api_name": "action.model.user.DisciplineType.query", "line_number": 97, "usage_type": "attribute"}, {"api_name": "action.model.user.DisciplineType", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 106, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 80, "usage_type": "call"}, {"api_name": "action.app", "line_number": 80, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 81, "usage_type": "call"}, {"api_name": "action.model.user.User.query.filter_by", "line_number": 133, "usage_type": "call"}, {"api_name": "action.model.user.User.query", "line_number": 133, "usage_type": "attribute"}, {"api_name": "action.model.user.User", "line_number": 133, "usage_type": "name"}, {"api_name": "action.model.user.Group_discipline.query.filter_by", "line_number": 134, "usage_type": "call"}, {"api_name": "action.model.user.Group_discipline.query", "line_number": 134, "usage_type": "attribute"}, {"api_name": "action.model.user.Group_discipline", "line_number": 134, "usage_type": "name"}, {"api_name": "action.model.user.DisciplineType.query.filter_by", "line_number": 139, "usage_type": "call"}, {"api_name": "action.model.user.DisciplineType.query", "line_number": 139, "usage_type": "attribute"}, {"api_name": "action.model.user.DisciplineType", "line_number": 139, "usage_type": "name"}, {"api_name": "action.model.user.DisciplineType.query.filter_by", "line_number": 140, "usage_type": "call"}, {"api_name": "action.model.user.DisciplineType.query", "line_number": 140, "usage_type": "attribute"}, {"api_name": "action.model.user.DisciplineType", "line_number": 140, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 142, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 129, "usage_type": "call"}, {"api_name": "action.app", "line_number": 129, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 130, "usage_type": "call"}, {"api_name": "action.model.user.User.query.filter_by", "line_number": 148, "usage_type": "call"}, {"api_name": "action.model.user.User.query", "line_number": 148, "usage_type": "attribute"}, {"api_name": "action.model.user.User", "line_number": 148, "usage_type": "name"}, {"api_name": "action.model.user.Schedule.query.filter_by", "line_number": 149, "usage_type": "call"}, {"api_name": "action.model.user.Schedule.query", "line_number": 149, "usage_type": "attribute"}, {"api_name": "action.model.user.Schedule", "line_number": 149, "usage_type": "name"}, {"api_name": "action.model.user.ExceptionDays.query.all", "line_number": 150, "usage_type": "call"}, {"api_name": "action.model.user.ExceptionDays.query", "line_number": 150, "usage_type": "attribute"}, {"api_name": "action.model.user.ExceptionDays", "line_number": 150, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "attribute"}, {"api_name": "action.model.user.FirstWeek.query.filter_by", "line_number": 154, "usage_type": "call"}, {"api_name": "action.model.user.FirstWeek.query", "line_number": 154, "usage_type": "attribute"}, {"api_name": "action.model.user.FirstWeek", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 162, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 191, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 145, "usage_type": "call"}, {"api_name": "action.app", "line_number": 145, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 197, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 197, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 197, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 198, "usage_type": "call"}, {"api_name": "action.model.user.Schedule.query.filter_by", "line_number": 199, "usage_type": "call"}, {"api_name": "action.model.user.Schedule.query", "line_number": 199, "usage_type": "attribute"}, {"api_name": "action.model.user.Schedule", "line_number": 199, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 241, "usage_type": "call"}, {"api_name": "action.app.route", "line_number": 194, "usage_type": "call"}, {"api_name": "action.app", "line_number": 194, "usage_type": "name"}, {"api_name": "allow_origin.crossdomain", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 246, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 246, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 247, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 247, "usage_type": "name"}, {"api_name": "action.app.route", "line_number": 244, "usage_type": "call"}, {"api_name": "action.app", "line_number": 244, "usage_type": "name"}]} +{"seq_id": "39446486581", "text": "#!/user/bin/env python\n# -*- coding: utf-8 -*-\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.parameter import Parameter\nfrom functools import reduce\nfrom models.model import HGAT, TextEncoder, EntityEncoder, Pooling, MatchingTransform, GatingMechanism\nimport pickle as pkl\n\nclass Classifier(nn.Module):\n def __init__(self, params, vocab_size, pte=None):\n super(Classifier, self).__init__()\n self.params = params\n self.vocab_size = vocab_size\n self.pte = False if pte is None else True\n\n self.text_encoder = TextEncoder(params)\n self.enti_encoder = EntityEncoder(params)\n # numOfEntity = 100000\n # self.enti_encoder = nn.Embedding(numOfEntity, params.hidden_dim)\n # nn.init.xavier_uniform_(self.enti_encoder.weight)\n self.topi_encoder = nn.Embedding(100, 100)\n self.topi_encoder.from_pretrained(torch.eye(100))\n self.match_encoder = MatchingTransform(params)\n # self.match_encoder = ConcatTransform(params) # 参数试验用的\n self.word_embeddings = nn.Embedding(vocab_size, params.emb_dim)\n if pte is None:\n nn.init.xavier_uniform_(self.word_embeddings.weight)\n else:\n self.word_embeddings.weight.data.copy_(torch.from_numpy(pte))\n # KB Field\n\n # with open(self.params.entity_tran, 'rb') as f:\n # transE_embedding = pkl.load(f)\n # self.enti_tran = nn.Embedding.from_pretrained(torch.from_numpy(transE_embedding))\n\n self.model = HGAT(params)\n self.pooling = Pooling(params)\n self.classifier_sen = nn.Linear(params.node_emb_dim, params.ntags)\n self.classifier_ent = nn.Linear(params.node_emb_dim, params.ntags)\n\n self.dropout = nn.Dropout(params.dropout, )\n\n # entity_num = transE_embedding.shape[0]\n # self.gating = GatingMechanism(params) # 这个要放在最后面，尽量少影响随机初始化\n\n # def forward(self, x_list, adj_list, sentPerDoc, entPerDoc=None):\n def forward(self, documents, ent_desc, doc_lens, ent_lens, adj_lists, feature_lists, sentPerDoc, entiPerDoc=None):\n x_list = []\n embeds_docu = self.word_embeddings(documents) # sents * max_seq_len * emb\n d = self.text_encoder(embeds_docu, doc_lens) # sents * max_seq_len * hidden\n d = self.dropout(F.relu_(d)) # Relu activation and dropout\n x_list.append(d)\n if self.params.node_type == 3 or self.params.node_type == 2:\n embeds_enti = self.word_embeddings(ent_desc) # sents * max_seq_len * emb\n e = self.enti_encoder(embeds_enti, ent_lens, feature_lists[1]) # sents * max_seq_len * hidden\n e = self.dropout(F.relu_(e)) # Relu activation and dropout\n x_list.append(e)\n if self.params.node_type == 3 or self.params.node_type == 1:\n t = self.topi_encoder(feature_lists[-1]) # tops * hidden\n x_list.append(t)\n\n X = self.model(x_list, adj_lists)\n\n X_s = self.pooling(X[0], sentPerDoc) # 选择句子的部分\n output = self.classifier_sen(X_s)\n\n if entiPerDoc is not None:\n # E_trans = self.enti_tran(feature_lists[1])\n E_GCN = X[1]\n # E_KB = self.gating(x_list[1], feature_lists[1])\n E_KB = x_list[1]\n X_e = self.match_encoder(E_GCN, E_KB) # 选择实体的部分\n X_e = self.pooling(X_e, entiPerDoc)\n X_e = self.classifier_ent(X_e)\n output += X_e\n output = F.softmax(output, dim=1) # 单分类\n # output = torch.sigmoid(output) # 多分类\n return output\n\n\nif __name__ == '__main__':\n pass", "repo_name": "BUPT-GAMMA/CompareNet_FakeNewsDetection", "sub_path": "models/classifier.py", "file_name": "classifier.py", "file_ext": "py", "file_size_in_byte": 3760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 81, "dataset": "github-code", "pt": "47", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "models.model.TextEncoder", "line_number": 19, "usage_type": "call"}, {"api_name": "models.model.EntityEncoder", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.Embedding", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.eye", "line_number": 25, "usage_type": "call"}, {"api_name": "models.model.MatchingTransform", "line_number": 26, "usage_type": "call"}, {"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.init.xavier_uniform_", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 32, "usage_type": "call"}, {"api_name": "models.model.HGAT", "line_number": 39, "usage_type": "call"}, {"api_name": "models.model.Pooling", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "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.Dropout", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu_", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu_", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "13799908286", "text": "from typing import Dict, Tuple\nimport operator\nimport torch\nimport torch.nn as nn\nimport hydra\nfrom omegaconf import OmegaConf\n\nCLS_MAP_PREFIX = 'cls_map_'\nPAST_LOGITS_PREFIX = 'past_'\n\n\nclass BaseModel(nn.Module):\n def __init__(self, model_cfg: OmegaConf, num_classes: Dict[str, int],\n class_mappings: Dict[Tuple[str, str], torch.FloatTensor]):\n super().__init__()\n # Takes as input (B, T, H, W, C) -> (B, T', H', W', C')\n _backbone_full = hydra.utils.instantiate(\n model_cfg.backbone,\n # Add dummy value for num_cls\n # will be removed next anyway\n num_classes=1)\n if model_cfg.backbone_last_n_modules_to_drop > 0:\n self.backbone = nn.Sequential()\n for name, child in list(_backbone_full.named_children(\n ))[:-model_cfg.backbone_last_n_modules_to_drop]:\n self.backbone.add_module(name, child)\n else:\n self.backbone = _backbone_full\n # Map the (B, T', H', W', C') -> (B, T', H', W', C*)\n # to the intermediate feature dimensions\n # IMP: this is only used if C' != C*\n if (model_cfg.backbone_last_n_modules_to_drop == 0\n and 'output_dim' in dir(self.backbone)):\n backbone_dim = self.backbone.output_dim\n else:\n backbone_dim = model_cfg.backbone_dim # TODO: Figure automatically\n self.mapper_to_inter = None\n if model_cfg.intermediate_featdim is None:\n model_cfg.intermediate_featdim = backbone_dim\n if backbone_dim != model_cfg.intermediate_featdim:\n self.mapper_to_inter = nn.Linear(backbone_dim,\n model_cfg.intermediate_featdim,\n bias=False)\n # Takes as input (B, T', H', W', C*) -> (B, C**)\n self.temporal_aggregator = hydra.utils.instantiate(\n model_cfg.temporal_aggregator,\n in_features=model_cfg.intermediate_featdim)\n self.reset_temp_agg_feat_dim = nn.Sequential()\n temp_agg_output_dim = self.temporal_aggregator.output_dim\n if model_cfg.same_temp_agg_dim and (temp_agg_output_dim !=\n model_cfg.intermediate_featdim):\n # Ideally want to maintain it so that the same project_mlp\n # can be used for the temporally aggregated features, or the\n # original features.\n self.reset_temp_agg_feat_dim = nn.Linear(\n temp_agg_output_dim, model_cfg.intermediate_featdim)\n temp_agg_output_dim = model_cfg.intermediate_featdim\n # Transforms the current features to future ones\n # (B, C**) -> (B, C**)\n self.future_predictor = hydra.utils.instantiate(\n model_cfg.future_predictor,\n in_features=temp_agg_output_dim,\n _recursive_=False)\n # Projection layer\n self.project_mlp = nn.Sequential()\n if model_cfg.project_dim_for_nce is not None:\n self.project_mlp = nn.Sequential(\n nn.Linear(temp_agg_output_dim, temp_agg_output_dim),\n nn.ReLU(inplace=True),\n nn.Linear(temp_agg_output_dim, model_cfg.project_dim_for_nce))\n # 2nd round of temporal aggregation, if needed\n self.temporal_aggregator_after_future_pred = hydra.utils.instantiate(\n model_cfg.temporal_aggregator_after_future_pred,\n self.future_predictor.output_dim)\n # Dropout\n self.dropout = nn.Dropout(model_cfg.dropout)\n # Takes as input (B, C**) -> (B, num_classes)\n cls_input_dim = self.temporal_aggregator_after_future_pred.output_dim\n # Make a separate classifier for each output\n self.classifiers = nn.ModuleDict()\n self.num_classes = num_classes\n for i, (cls_type, cls_dim) in enumerate(num_classes.items()):\n if model_cfg.use_cls_mappings and i > 0:\n # In this case, rely on the class mappings to generate the\n # other predictions, rather than creating a new linear layer\n break\n self.classifiers.update({\n cls_type:\n hydra.utils.instantiate(model_cfg.classifier,\n in_features=cls_input_dim,\n out_features=cls_dim)\n })\n # Store the class mappings as buffers\n for (src, dst), mapping in class_mappings.items():\n self.register_buffer(f'{CLS_MAP_PREFIX}{src}_{dst}', mapping)\n self.regression_head = None\n if model_cfg.add_regression_head:\n self.regression_head = nn.Linear(cls_input_dim, 1)\n # Init weights, as per the video resnets\n self._initialize_weights()\n # Set he BN momentum and eps here, Du uses a different value and its imp\n self._set_bn_params(model_cfg.bn.eps, model_cfg.bn.mom)\n self.cfg = model_cfg\n\n def _initialize_weights(self):\n # Copied over from\n # https://github.com/pytorch/vision/blob/75f5b57e680549d012b3fc01b356b2fb92658ea7/torchvision/models/video/resnet.py#L261\n # Making sure all layers get init to good video defaults\n for m in self.modules():\n if isinstance(m, nn.Conv3d):\n nn.init.kaiming_normal_(m.weight,\n mode='fan_out',\n nonlinearity='relu')\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.BatchNorm3d):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.Linear):\n nn.init.normal_(m.weight, 0, 0.01)\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n\n def _set_bn_params(self, bn_eps=1e-3, bn_mom=0.1):\n \"\"\"\n Set the BN parameters to the defaults: Du's models were trained\n with 1e-3 and 0.9 for eps and momentum resp.\n Ref: https://github.com/facebookresearch/VMZ/blob/f4089e2164f67a98bc5bed4f97dc722bdbcd268e/lib/models/r3d_model.py#L208\n \"\"\"\n for module in self.modules():\n if isinstance(module, nn.BatchNorm3d):\n module.eps = bn_eps\n module.momentum = bn_mom\n\n def forward_singlecrop(self, video, target_shape=None):\n \"\"\"\n Args:\n video (torch.Tensor, Bx#clipsxCxTxHxW)\n target_shape: The shape of the target. Some of these layers might\n be able to use this information.\n \"\"\"\n outputs = {}\n aux_losses = {}\n batch_size = video.size(0)\n num_clips = video.size(1)\n # Fold the clips dimension into the batch for feature extraction, upto\n # temporal aggregation\n video = video.flatten(0, 1)\n feats = self.backbone(video)\n outputs['backbone'] = feats\n # Spatial mean\n feats = torch.mean(feats, [-1, -2])\n # store temporal mean as well\n outputs['backbone_mean'] = torch.mean(feats, [-1])\n # If it's not sequential and can be applied here\n if len(self.project_mlp) > 0 and (outputs['backbone_mean'].size(-1) ==\n self.project_mlp[0].in_features):\n outputs['backbone_mean_projected'] = self.project_mlp(\n outputs['backbone_mean'])\n # Move the time dimension inside: B,C,T -> B,T,C\n feats = feats.permute((0, 2, 1))\n # Map the feats to intermediate dimension, that rest of the code\n # will operate on. Only if the original feature is not already\n if feats.shape[-1] != self.cfg.intermediate_featdim:\n assert self.mapper_to_inter is not None, (\n f'The backbone feat does not match intermediate {feats.shape} '\n f'and {self.cfg.intermediate_featdim}. Please set '\n f'model.backbone_dim correctly.')\n feats = self.mapper_to_inter(feats)\n feats_agg, agg_losses = self.temporal_aggregator(feats)\n aux_losses.update(agg_losses)\n feats_agg = self.reset_temp_agg_feat_dim(feats_agg)\n outputs['temp_agg'] = feats_agg\n # For the contrastive loss, I need a projected version of this feature\n outputs['temp_agg_projected'] = self.project_mlp(feats_agg)\n # Now before future prediction, need to unfold the clips back out,\n # and concat on the temporal dimension\n if num_clips > 1:\n assert (\n (feats_agg.ndim == 2)\n or (feats_agg.ndim == 3 and feats_agg.size(1) == 1)\n ), ('Should be using some temporal aggregation when using clips')\n feats_agg = feats_agg.reshape((batch_size, num_clips) +\n feats_agg.shape[1:])\n if feats_agg.ndim == 4:\n feats_agg = torch.flatten(feats_agg, 1, 2)\n # now feats_agg back to 3D (B, T, F)\n feats_past = feats_agg\n # Now the future prediction, also it might update the past features\n # like the GPT style models would\n (feats_past, feats_future, future_losses,\n endpoints) = self.future_predictor(feats_past, target_shape)\n aux_losses.update(future_losses)\n outputs.update(endpoints)\n outputs['future'] = feats_future\n outputs['past'] = feats_past\n # Apply a classifier on the past features, might be supervising that\n if self.cfg.classifier_on_past:\n feats_past_drop = self.dropout(feats_past)\n outputs.update(\n self._apply_classifier(feats_past_drop,\n outputs_prefix=PAST_LOGITS_PREFIX))\n # For the contrastive loss, I need a projected version of this feature\n outputs['future_projected'] = self.project_mlp(feats_agg)\n # Aggregate again, if asked for\n feats_future_agg, future_agg_losses = (\n self.temporal_aggregator_after_future_pred(feats_future))\n aux_losses.update(future_agg_losses)\n outputs['future_agg'] = feats_future_agg\n feats_future_agg_drop = self.dropout(feats_future_agg)\n outputs.update(self._apply_classifier(feats_future_agg_drop))\n if self.regression_head:\n outputs['logits_regression'] = self.regression_head(\n feats_future_agg_drop)\n return outputs, aux_losses\n\n def _apply_classifier(self, input_feat, outputs_prefix=''):\n outputs = {}\n for key in self.num_classes.keys():\n if key in self.classifiers:\n outputs[f'{outputs_prefix}logits/{key}'] = self.classifiers[\n key](input_feat)\n else:\n # A mapping must exist, in order to compute this, and must\n # have been computed already (so ordering in the config\n # matters)\n src_key = next(iter(self.classifiers.keys()))\n src_tensor = outputs[f'{outputs_prefix}logits/{src_key}']\n mapper = operator.attrgetter(\n f'{CLS_MAP_PREFIX}{key}_{src_key}')(self)\n outputs[f'{outputs_prefix}logits/{key}'] = torch.mm(\n src_tensor, mapper)\n return outputs\n\n def forward(self, video, *args, **kwargs):\n \"\"\"\n Args: video (torch.Tensor)\n Could be (B, #clips, C, T, H, W) or\n (B, #clips, #crops, C, T, H, W)\n Returns:\n Final features\n And any auxiliarly losses produced by the model\n \"\"\"\n if video.ndim == 6:\n video_crops = [video]\n elif video.ndim == 7 and video.size(2) == 1:\n video_crops = [video.squeeze(2)]\n elif video.ndim == 7:\n video_crops = torch.unbind(video, dim=2)\n else:\n raise NotImplementedError('Unsupported size %s' % video.shape)\n feats_losses = [\n self.forward_singlecrop(el, *args, **kwargs) for el in video_crops\n ]\n feats, losses = zip(*feats_losses)\n # Convert to dict of lists\n feats = {k: [dic[k] for dic in feats] for k in feats[0]}\n losses = {k: [dic[k] for dic in losses] for k in losses[0]}\n # Average over the crops\n feats = {\n k: torch.mean(torch.stack(el, dim=0), dim=0)\n for k, el in feats.items()\n }\n losses = {\n k: torch.mean(torch.stack(el, dim=0), dim=0)\n for k, el in losses.items()\n }\n return feats, losses\n", "repo_name": "facebookresearch/AVT", "sub_path": "models/base_model.py", "file_name": "base_model.py", "file_ext": "py", "file_size_in_byte": 12690, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 144, "dataset": "github-code", "pt": "47", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "omegaconf.OmegaConf", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 14, "usage_type": "attribute"}, {"api_name": "hydra.utils.instantiate", "line_number": 17, "usage_type": "call"}, {"api_name": "hydra.utils", "line_number": 17, "usage_type": "attribute"}, {"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.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "hydra.utils.instantiate", "line_number": 45, "usage_type": "call"}, {"api_name": "hydra.utils", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "hydra.utils.instantiate", "line_number": 60, "usage_type": "call"}, {"api_name": "hydra.utils", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "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": "hydra.utils.instantiate", "line_number": 72, "usage_type": "call"}, {"api_name": "hydra.utils", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "hydra.utils.instantiate", "line_number": 89, "usage_type": "call"}, {"api_name": "hydra.utils", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 110, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "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.init.constant_", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 115, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm3d", "line_number": 116, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 117, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm3d", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.flatten", "line_number": 186, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.unbind", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 265, "usage_type": "call"}]} +{"seq_id": "73690491981", "text": "import requests\nimport json\n\n\ndef get_access_token():\n \"\"\"\n 使用 API Key，Secret Key 获取access_token，替换下列示例中的应用API Key、应用Secret Key\n \"\"\"\n\n url = \"https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=[80bu03KBNz0XeWYOQIUzfW2k]&client_secret=[XIfESQz6HqsYjvuut3wcAEZKzpMZre8x]\"\n\n payload = json.dumps(\"\")\n headers = {\n 'Content-Type': 'application/json',\n 'Accept': 'application/json'\n }\n\n response = requests.request(\"POST\", url, headers=headers, data=payload)\n return response.json().get(\"access_token\")\n\n\ndef main():\n url = \"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions?access_token=\" + get_access_token()\n\n payload = json.dumps({\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下你自己\"\n }\n ]\n })\n headers = {\n 'Content-Type': 'application/json'\n }\n\n response = requests.request(\"POST\", url, headers=headers, data=payload)\n\n print(response.text)\n\n\nif __name__ == '__main__':\n main()", "repo_name": "zhangshuaibao922/guet_testforall", "sub_path": "al/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1131, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "36669217595", "text": "from tkinter import *\nfrom speedtest import Speedtest\n\ndef update_speed_details():\n speed_test = Speedtest()\n download = speed_test.download()\n upload = speed_test.upload()\n download_speed = round(download / (10**6), 2)\n upload_speed = round(upload / (10**6), 2)\n download_speed_label.config(text=\"Download Speed - \" + str(download_speed) + \"Mbps\")\n upload_speed_label.config(text=\"Upload Speed - \" + str(upload_speed) + \"Mbps\")\n\nwindow = Tk()\nwindow.title(\"Internet Speed Tracker\")\nwindow.geometry(\"300x100\")\nbutton = Button(window, text=\"Check Speed\", width=20, command=update_speed_details)\nbutton.pack()\n\ndownload_speed_label = Label(text=\"\")\ndownload_speed_label.pack()\nupload_speed_label = Label(text=\"\")\nupload_speed_label.pack()\nwindow.mainloop()", "repo_name": "achuthanandGit/Python-Learning", "sub_path": "Scripts/internet-speed-checker.py", "file_name": "internet-speed-checker.py", "file_ext": "py", "file_size_in_byte": 776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "speedtest.Speedtest", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "15813577658", "text": "import torch.nn as nn\nfrom typing import Dict, Any\n\nimport modeling.trainer as trainers\n\n\ndef get_trainers(config: Dict[str, Any], model, train_dataloader, val_dataloader):\n if config[\"project\"] == \"SimpleMCN\":\n trainer = getattr(trainers, \"SimpleMCNTrainer\")(\n config=config,\n model=model,\n train_dataloader=train_dataloader,\n valid_dataloader=val_dataloader,\n )\n\n elif config[\"arch\"][\"type\"] == \"NewMF\":\n trainer = getattr(trainers, \"newMFTrainer\")(\n config=config,\n model=model,\n train_data_loader=train_dataloader,\n valid_data_loader=val_dataloader,\n )\n elif config[\"arch\"][\"type\"] == \"MCN\":\n trainer = getattr(trainers, \"MCNTrainer\")(\n config=config,\n model=model,\n train_loader=train_dataloader,\n val_loader=val_dataloader,\n )\n else:\n raise NotImplementedError\n return trainer\n", "repo_name": "boostcampaitech4lv23recsys1/final-project-level3-recsys-01", "sub_path": "modeling/trainer/get_trainer.py", "file_name": "get_trainer.py", "file_ext": "py", "file_size_in_byte": 979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 77, "dataset": "github-code", "pt": "47", "api": [{"api_name": "typing.Dict", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 7, "usage_type": "name"}, {"api_name": "modeling.trainer", "line_number": 9, "usage_type": "argument"}, {"api_name": "modeling.trainer", "line_number": 17, "usage_type": "argument"}, {"api_name": "modeling.trainer", "line_number": 24, "usage_type": "argument"}]} +{"seq_id": "8583086372", "text": "import os, smbus, sched, time, datetime\nimport OPi.GPIO as GPIO\n\ndef sbyte (number):\n if number > 127: number -= 256\n return number\n\nPowerPIN=19\nOnPIN=21\nOffPIN=23\n\nlogfile = \"/var/log/heat\"\ndbglog = \"/var/log/bme\"\nonflag = \"/tmp/HEATON\"\noffflag = \"/tmp/HEATOFF\"\nnoauto = \"/tmp/AUTOOFF\"\nheating = \"/tmp/heating\"\n\nNight_Start = datetime.time(23,0,0)\nDay_Start = datetime.time(8,0,0)\n\n#Don't control heater on summer\n#MMDD\nSummer_Start = 610\nWinter_Start = 901\n\nMin_Cycle = 5 #minutes heat minimum\nForce_Cycle = 30 #Run heater when forced\n\nheat_counter = 0\nheat_op = 2 #1 - start, 0 - stop, 2 - not needed\n\n#Main Values, window - alowed deviation\nDay_Temp = 18.8\nNight_Temp = 17.8\nAway_Temp = 15.8\nTemp_Window = 0.1\n\n#FixMe\nAway_Level = 300 #Anything below considered nobody home\n\ndef log_console(data):\n global dbglog\n try:\n log_file = open(dbglog, \"a\")\n log_file.write(time.strftime(\"%Y-%m-%d %H:%M\")+\" \"+data+\"\\n\")\n log_file.close()\n except:\n print (\"Cannot write to log\")\n\ndef is_winter():\n global Summer_Start, Winter_Start\n Current_Date = datetime.datetime.now().month*100+datetime.datetime.now().day\n if Winter_Start <= Summer_Start:\n return Winter_Start <= Current_Date <= Summer_Start\n else:\n return Winter_Start <= Current_Date or Current_Date < Summer_Start\n\ndef is_night():\n global Night_Start\n global Day_Start\n Current_Time = datetime.datetime.now().time()\n if Night_Start <= Day_Start:\n return Night_Start <= Current_Time <= Day_Start\n else:\n return Night_Start <= Current_Time or Current_Time <= Day_Start\n\n#Global\nT1,T2,T3,P1,P2,P3,P4,P5,P6,P7,P8,P9,H1,H2,H3,H4,H5,H6 = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\nBME280 = 0x76\nI2CNUM = 0\ni2cdev = 0\nCO2LEVEL = 800\n\ndef init_bme280():\n\n global T1,T2,T3,P1,P2,P3,P4,P5,P6,P7,P8,P9,H1,H2,H3,H4,H5,H6\n global BME280\n global I2CNUM\n global i2cdev\n\n i2cdev = smbus.SMBus(I2CNUM)\n\n cdataTP = i2cdev.read_i2c_block_data(BME280, 0x88, 26)\n cdataH = i2cdev.read_i2c_block_data(BME280, 0xE1, 7)\n\n bc = 0\n #it would look much nicer in C\n T1 = cdataTP[bc]+cdataTP[bc+1]*256; bc+=2\n T2 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n T3 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P1 = cdataTP[bc]+cdataTP[bc+1]*256; bc+=2\n P2 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P3 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P4 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P5 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P6 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P7 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P8 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=2\n P9 = cdataTP[bc]+sbyte(cdataTP[bc+1])*256; bc+=3\n H1 = cdataTP[bc]\n\n bc = 0\n H2 = cdataH[bc]+sbyte(cdataH[bc+1])*256; bc+=2\n H3 = cdataH[bc]; bc+=1\n E5_L = cdataH[bc+1]&0x0f\n E5_H = (cdataH[bc+1]&0xf0)>>4\n H4 = (sbyte(cdataH[bc])*16) | E5_L\n H5 = (sbyte(cdataH[bc+2])*16) | E5_H\n H6 = sbyte(cdataH[bc+3])\n\n\ndef read_bme280():\n\n global T1,T2,T3,P1,P2,P3,P4,P5,P6,P7,P8,P9,H1,H2,H3,H4,H5,H6\n global BME280\n global I2CNUM\n global i2cdev\n\n #Oversampling\n OVS = 2\n #Set humidity oversampling (1,2,3,4,5)\n i2cdev.write_byte_data(BME280, 0xF2, OVS)\n #Set other oversampling and mode and apply humidity (3bits temp oversampling, 3bits pressure oversampling, 2bits mode)\n #forced mode\n i2cdev.write_byte_data(BME280, 0xF4, (OVS<<5)|(OVS<<2)|1 )\n #Set config (3bits standby ms, 3bits IIR, 1 skip, 1 SPI mode)\n #Not for forced mode\n #Calc delay\n DELAY = (1.25+2.3*(1<>4)\n temperature = (udata[3]<<12) | (udata[4]<<4) | (udata[5]>>4)\n humidity = (udata[6]<<8) | udata[7]\n\n #Deutsch eyebleed coding\n var1 = ((((temperature//8) - (T1*2))) * (T2)) // 2048\n var2 = (((((temperature//16) - (T1)) * ((temperature//16) - (T1))) // 4096) * (T3)) // 16384\n t_fine = var1 + var2\n T = (t_fine * 5 + 128) // 256\n ctemperature = T/100\n\n var1 = t_fine - 128000\n var2 = var1 * var1 * P6\n var2 = var2 + (var1 * P5 * 131072)\n var2 = var2 + (P4*34359738368)\n var1 = ((var1 * var1 * P3)//256) + ((var1 * P2)*4096)\n var1 = (140737488355328+var1)*(P1)//8589934592\n if (var1 != 0):\n p = 1048576 - pressure\n p = (((p*2147483648) - var2)*3125)/var1\n var1 = ((P9) * (p//8192) * (p//8192)) // 33554432\n var2 =((P8) * p) // 524288\n p = ((p + var1 + var2)//256) + ((P7)*16)\n cpressure = round(p/256/100,2)\n else:\n cpressure = 0\n\n v_x1_u32r = t_fine - 76800;\n v_x1_u32r = (((((humidity * 16384) - (H4 * 1048576) - (H5 * v_x1_u32r)) + 16384) // 32768) * (((((((v_x1_u32r * H6) // 1024) * (((v_x1_u32r * H3) // 2048) + 32768)) // 1024) + 2097152) * H2 + 8192) // 16384))\n v_x1_u32r = (v_x1_u32r - (((((v_x1_u32r // 32768) * (v_x1_u32r // 32768)) // 128) * H1) // 16 ));\n if (v_x1_u32r < 0): v_x1_u32r = 0\n if (v_x1_u32r > 419430400): v_x1_u32r = 419430400\n chumidity = round(v_x1_u32r/4096/1024,2);\n\n return ctemperature,cpressure,chumidity\n\nlast_op = -1\nop_count = 0\n\ndef heater(on):\n\n #Signal send rate limiter\n global last_op, op_count\n if (last_op == on):\n op_count+=1\n else:\n last_op=on\n op_count=0\n if ((op_count % 10) != 0):\n log_console(\"Skip send\")\n return\n\n #Flag for LCD\n if (on):\n if not (os.path.isfile(heating)):\n try:\n tmp_file=open(heating, \"w+\")\n tmp_file.write(\"1\")\n tmp_file.close()\n except:\n print (\"Cannot write to temp data file\")\n else:\n if (os.path.isfile(heating)):\n os.remove(heating)\n\n global GPIO\n\n #Power sender On\n GPIO.output(OnPIN, 1)\n GPIO.output(OffPIN, 1)\n GPIO.output(PowerPIN, 1)\n\n #Wake fixup\n time.sleep(3)\n\n if (on):\n SendPIN=OnPIN\n else:\n SendPIN=OffPIN\n\n GPIO.output(SendPIN, 0)\n time.sleep(0.5)\n GPIO.output(SendPIN, 1)\n time.sleep(1)\n\n GPIO.output(SendPIN, 0)\n time.sleep(0.5)\n GPIO.output(SendPIN, 1)\n time.sleep(1)\n\n GPIO.output(SendPIN, 0)\n time.sleep(0.5)\n GPIO.output(SendPIN, 1)\n time.sleep(1)\n\n #Wake fixup\n time.sleep(3)\n\n log_console(str(heat_op)+\" \"+str(SendPIN))\n\n #Power Off\n GPIO.output(PowerPIN, 0)\n GPIO.output(OnPIN, 0)\n GPIO.output(OffPIN, 0)\n\ndef log_data(t,p,h):\n global logfile\n try:\n log_file = open(logfile, \"a\")\n log_file.write(time.strftime(\"%Y-%m-%d %H:%M\")+\" \"+str(t)+\" \"+str(p)+\" \"+str(h)+\" \"+str (heat_op)+\"\\n\")\n log_file.close()\n except:\n print (\"Cannot write to log\")\n\n try:\n tmp_file=open(\"/tmp/tph\", \"w+\")\n tmp_file.write(str(datetime.datetime.timestamp(datetime.datetime.now()))+\",\"+str(t)+\",\"+str(p)+\",\"+str(h))\n tmp_file.close()\n except:\n print (\"Cannot write to temp data file\")\n\n try:\n tmp_file=open(\"/tmp/ht\", \"w+\")\n tmp_file.write(str(heat_op))\n tmp_file.close()\n except:\n print (\"Cannot write to temp data file\")\n\ndef time_func():\n#Once per minute\n global Min_Cycle, heat_counter, heat_op\n global Day_Temp, Night_Temp, Away_Temp, Away_Level, Temp_Window\n global last_op, op_count\n s.enter(60, 1, time_func, ())\n try:\n co2file=open(\"/tmp/co2level\",\"r\")\n CO2LEVEL=co2file.read()\n co2file.close()\n except:\n CO2LEVEL=800\n t,p,h=read_bme280()\n\n #Check for force operations\n if (os.path.isfile(onflag)):\n heat_counter = Force_Cycle\n heat_op = 1\n last_op = -1\n op_count = 0\n #Start the heater\n heater(heat_op)\n os.remove(onflag)\n log_console(\"Heater forced ON \"+str(Force_Cycle))\n log_data(t,p,h)\n return\n\n if (os.path.isfile(offflag)):\n heat_counter = Force_Cycle\n heat_op = 0\n last_op = -1\n op_count = 0\n #Stop the heater\n heater(heat_op)\n os.remove(offflag)\n log_console(\"Heater forced OFF \"+str(Force_Cycle))\n log_data(t,p,h)\n return\n\n#Let the active operation continue for set cycles\n if (heat_counter > 0):\n heat_counter -= 1\n heater(heat_op)\n log_console(\"Heater cycle \"+str(heat_counter))\n log_data(t,p,h)\n return\n\n#Exit if autocontrol disabled\n if (os.path.isfile(noauto)):\n #log_console(\"AutoControl is OFF\")\n log_data(t,p,h)\n return\n\n#Exit if summer time\n if (not is_winter()):\n #log_console(\"Summer Time: AutoControl is OFF\")\n log_data(t,p,h)\n return\n\n if (is_night()):\n t_min=Night_Temp-(Temp_Window/2)\n t_max=Night_Temp+(Temp_Window/2)\n else:\n t_min=Day_Temp-(Temp_Window/2)\n t_max=Day_Temp+(Temp_Window/2)\n if (int(CO2LEVEL) < Away_Level):\n t_min=Away_Temp-(Temp_Window/2)\n t_max=Away_Temp+(Temp_Window/2)\n #Now, let's start/stop the heater\n if (t < t_min):\n heat_counter = Min_Cycle\n heat_op = 1\n #Start the heater\n heater(heat_op)\n elif (t > t_max):\n heat_op = 0\n #Stop the heater\n heater(heat_op)\n else:\n heat_op = 2 #Nothing doing\n last_op = -1\n counter = 0\n log_data(t,p,h)\n\n#Let's start\ninit_bme280()\n\nGPIO.cleanup()\nGPIO.setboard(GPIO.ZEROPLUS2H5) # Orange Pi PC board\nGPIO.setmode(GPIO.BOARD) # set up BOARD BCM numbering\nGPIO.setup(PowerPIN, GPIO.OUT)\t#Pin 19 - power up transmitter\nGPIO.setup(OnPIN, GPIO.OUT)\t#Pin 21 - set to LOW to enable heater\nGPIO.setup(OffPIN, GPIO.OUT)\t#Pin 23 - set to LOW to disable heater\n\ns = sched.scheduler(time.time, time.sleep)\ns.enter(1, 1, time_func, ())\ns.run()\n\nwhile True:\n time.sleep(1)\n\nGPIO.cleanup()\n", "repo_name": "jinshin/Microclimate", "sub_path": "bme280.py", "file_name": "bme280.py", "file_ext": "py", "file_size_in_byte": 9322, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "47", "api": [{"api_name": "datetime.time", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 20, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "attribute"}, {"api_name": "smbus.SMBus", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 199, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 204, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 204, "usage_type": "name"}, {"api_name": "OPi.GPIO.output", "line_number": 205, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 205, "usage_type": "name"}, {"api_name": "OPi.GPIO.output", "line_number": 206, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 206, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 209, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 216, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 216, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 217, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 218, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 218, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 219, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 221, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 221, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 222, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 223, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 223, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 224, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 226, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 226, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 227, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 228, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 228, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 229, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 232, "usage_type": "call"}, {"api_name": "OPi.GPIO.output", "line_number": 237, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 237, "usage_type": "name"}, {"api_name": "OPi.GPIO.output", "line_number": 238, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 238, "usage_type": "name"}, {"api_name": "OPi.GPIO.output", "line_number": 239, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 239, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 245, "usage_type": "call"}, {"api_name": "datetime.datetime.timestamp", "line_number": 252, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 252, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 286, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path", "line_number": 291, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 312, "usage_type": "call"}, {"api_name": "os.path", "line_number": 312, "usage_type": "attribute"}, {"api_name": "OPi.GPIO.cleanup", "line_number": 351, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 351, "usage_type": "name"}, {"api_name": "OPi.GPIO.setboard", "line_number": 352, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 352, "usage_type": "name"}, {"api_name": "OPi.GPIO.ZEROPLUS2H5", "line_number": 352, "usage_type": "attribute"}, {"api_name": "OPi.GPIO.setmode", "line_number": 353, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 353, "usage_type": "name"}, {"api_name": "OPi.GPIO.BOARD", "line_number": 353, "usage_type": "attribute"}, {"api_name": "OPi.GPIO.setup", "line_number": 354, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 354, "usage_type": "name"}, {"api_name": "OPi.GPIO.OUT", "line_number": 354, "usage_type": "attribute"}, {"api_name": "OPi.GPIO.setup", "line_number": 355, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 355, "usage_type": "name"}, {"api_name": "OPi.GPIO.OUT", "line_number": 355, "usage_type": "attribute"}, {"api_name": "OPi.GPIO.setup", "line_number": 356, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 356, "usage_type": "name"}, {"api_name": "OPi.GPIO.OUT", "line_number": 356, "usage_type": "attribute"}, {"api_name": "sched.scheduler", "line_number": 358, "usage_type": "call"}, {"api_name": "time.time", "line_number": 358, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 358, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 363, "usage_type": "call"}, {"api_name": "OPi.GPIO.cleanup", "line_number": 365, "usage_type": "call"}, {"api_name": "OPi.GPIO", "line_number": 365, "usage_type": "name"}]} +{"seq_id": "73567051983", "text": "from dm_control.mujoco.wrapper import mjbindings\nfrom dm_control.viewer import renderer\nfrom dm_control.viewer import user_input\nfrom dm_control.viewer import util\nimport mujoco\n\nfunctions = mjbindings.functions\n\n_NUM_GROUP_KEYS = 10\n\n_PAN_CAMERA_VERTICAL_MOUSE = user_input.Exclusive(\n user_input.MOUSE_BUTTON_RIGHT)\n_PAN_CAMERA_HORIZONTAL_MOUSE = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_RIGHT, user_input.MOD_SHIFT))\n_ROTATE_OBJECT_MOUSE = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_LEFT, user_input.MOD_CONTROL))\n_MOVE_OBJECT_VERTICAL_MOUSE = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_RIGHT, user_input.MOD_CONTROL))\n_MOVE_OBJECT_HORIZONTAL_MOUSE = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_RIGHT, user_input.MOD_SHIFT_CONTROL))\n\n_PAN_CAMERA_VERTICAL_TOUCHPAD = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_LEFT, user_input.MOD_ALT))\n_PAN_CAMERA_HORIZONTAL_TOUCHPAD = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_RIGHT, user_input.MOD_ALT))\n_ROTATE_OBJECT_TOUCHPAD = user_input.Exclusive(\n user_input.MOUSE_BUTTON_RIGHT)\n_MOVE_OBJECT_VERTICAL_TOUCHPAD = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_LEFT, user_input.MOD_CONTROL))\n_MOVE_OBJECT_HORIZONTAL_TOUCHPAD = user_input.Exclusive(\n (user_input.MOUSE_BUTTON_LEFT, user_input.MOD_SHIFT_CONTROL))\n\n_ROTATE_CAMERA = user_input.Exclusive(user_input.MOUSE_BUTTON_LEFT)\n_CENTER_CAMERA = user_input.DoubleClick(user_input.MOUSE_BUTTON_RIGHT)\n_SELECT_OBJECT = user_input.DoubleClick(user_input.MOUSE_BUTTON_LEFT)\n_TRACK_OBJECT = user_input.DoubleClick(\n (user_input.MOUSE_BUTTON_RIGHT, user_input.MOD_CONTROL))\n_FREE_LOOK = user_input.KEY_ESCAPE\n_NEXT_CAMERA = user_input.KEY_RIGHT_BRACKET\n_PREVIOUS_CAMERA = user_input.KEY_LEFT_BRACKET\n_ZOOM_TO_SCENE = (user_input.KEY_A, user_input.MOD_CONTROL)\n_DOUBLE_BUFFERING = user_input.KEY_F5\n_PREV_RENDERING_MODE = (user_input.KEY_F6, user_input.MOD_SHIFT)\n_NEXT_RENDERING_MODE = user_input.KEY_F6\n_PREV_LABELING_MODE = (user_input.KEY_F7, user_input.MOD_SHIFT)\n_NEXT_LABELING_MODE = user_input.KEY_F7\n_PRINT_CAMERA = user_input.KEY_F11\n_VISUALIZATION_FLAGS = user_input.Range([\n ord(functions.mjVISSTRING[i][2]) if functions.mjVISSTRING[i][2] else 0\n for i in range(0, mujoco.mjtVisFlag.mjNVISFLAG)\n])\n_GEOM_GROUPS = user_input.Range(\n [i + ord('0') for i in range(min(_NUM_GROUP_KEYS, mujoco.mjNGROUP))])\n_SITE_GROUPS = user_input.Range([\n (i + ord('0'), user_input.MOD_SHIFT)\n for i in range(min(_NUM_GROUP_KEYS, mujoco.mjNGROUP))\n])\n_RENDERING_FLAGS = user_input.Range([\n ord(functions.mjRNDSTRING[i][2]) if functions.mjRNDSTRING[i][2] else 0\n for i in range(0, mujoco.mjtRndFlag.mjNRNDFLAG)\n])\n\n_CAMERA_MOVEMENT_ACTIONS = [\n mujoco.mjtMouse.mjMOUSE_MOVE_V, mujoco.mjtMouse.mjMOUSE_ROTATE_H\n]\n\n# Translates mouse wheel rotations to zoom speed.\n_SCROLL_SPEED_FACTOR = 0.05\n\n# Distance, in meters, at which to focus on the clicked object.\n_LOOK_AT_DISTANCE = 1.5\n\n# Zoom factor used when zooming in on the entire scene.\n_FULL_SCENE_ZOOM_FACTOR = 1.5\n\n\nclass Viewer:\n \"\"\"Viewport displaying the contents of a physics world.\"\"\"\n\n def __init__(self, viewport, mouse, keyboard, camera_settings=None,\n zoom_factor=_FULL_SCENE_ZOOM_FACTOR, scene_callback=None):\n \"\"\"Instance initializer.\n\n Args:\n viewport: Render viewport, instance of renderer.Viewport.\n mouse: A mouse device.\n keyboard: A keyboard device.\n camera_settings: Properties of the scene MjvCamera.\n zoom_factor: Initial scale factor for zooming into the scene.\n scene_callback: Scene callback.\n This is a callable of the form: `my_callable(MjModel, MjData, MjvScene)`\n that gets applied to every rendered scene.\n \"\"\"\n self._viewport = viewport\n self._mouse = mouse\n\n self._null_perturbation = renderer.NullPerturbation()\n self._render_settings = renderer.RenderSettings()\n self._input_map = user_input.InputMap(mouse, keyboard)\n\n self._camera = None\n self._camera_settings = camera_settings\n self._renderer = None\n self._manipulator = None\n self._free_camera = None\n self._camera_select = None\n self._zoom_factor = zoom_factor\n self._scene_callback = scene_callback\n\n def __del__(self):\n del self._camera\n del self._renderer\n del self._manipulator\n del self._free_camera\n del self._camera_select\n\n def initialize(self, physics, renderer_instance, touchpad):\n \"\"\"Initialize the viewer.\n\n Args:\n physics: Physics instance.\n renderer_instance: A renderer.Base instance.\n touchpad: A boolean, use input dedicated to touchpad.\n \"\"\"\n self._camera = renderer.SceneCamera(\n physics.model,\n physics.data,\n self._render_settings,\n settings=self._camera_settings,\n zoom_factor=self._zoom_factor,\n scene_callback=self._scene_callback)\n\n self._manipulator = ManipulationController(\n self._viewport, self._camera, self._mouse)\n\n self._free_camera = FreeCameraController(\n self._viewport, self._camera, self._mouse, self._manipulator)\n\n self._camera_select = CameraSelector(\n physics.model, self._camera, self._free_camera)\n\n self._renderer = renderer_instance\n\n self._input_map.clear_bindings()\n\n if touchpad:\n self._input_map.bind(\n self._manipulator.set_move_vertical_mode,\n _MOVE_OBJECT_VERTICAL_TOUCHPAD)\n self._input_map.bind(\n self._manipulator.set_move_horizontal_mode,\n _MOVE_OBJECT_HORIZONTAL_TOUCHPAD)\n self._input_map.bind(\n self._manipulator.set_rotate_mode, _ROTATE_OBJECT_TOUCHPAD)\n self._input_map.bind(\n self._free_camera.set_pan_vertical_mode,\n _PAN_CAMERA_VERTICAL_TOUCHPAD)\n self._input_map.bind(\n self._free_camera.set_pan_horizontal_mode,\n _PAN_CAMERA_HORIZONTAL_TOUCHPAD)\n else:\n self._input_map.bind(\n self._manipulator.set_move_vertical_mode, _MOVE_OBJECT_VERTICAL_MOUSE)\n self._input_map.bind(\n self._manipulator.set_move_horizontal_mode,\n _MOVE_OBJECT_HORIZONTAL_MOUSE)\n self._input_map.bind(\n self._manipulator.set_rotate_mode, _ROTATE_OBJECT_MOUSE)\n self._input_map.bind(\n self._free_camera.set_pan_vertical_mode, _PAN_CAMERA_VERTICAL_MOUSE)\n self._input_map.bind(\n self._free_camera.set_pan_horizontal_mode,\n _PAN_CAMERA_HORIZONTAL_MOUSE)\n\n self._input_map.bind(self._print_camera_transform, _PRINT_CAMERA)\n self._input_map.bind(\n self._render_settings.select_prev_rendering_mode, _PREV_RENDERING_MODE)\n self._input_map.bind(\n self._render_settings.select_next_rendering_mode, _NEXT_RENDERING_MODE)\n self._input_map.bind(\n self._render_settings.select_prev_labeling_mode, _PREV_LABELING_MODE)\n self._input_map.bind(\n self._render_settings.select_next_labeling_mode, _NEXT_LABELING_MODE)\n self._input_map.bind(\n self._render_settings.select_prev_labeling_mode, _PREV_LABELING_MODE)\n self._input_map.bind(\n self._render_settings.toggle_stereo_buffering, _DOUBLE_BUFFERING)\n self._input_map.bind(\n self._render_settings.toggle_visualization_flag, _VISUALIZATION_FLAGS)\n self._input_map.bind(\n self._render_settings.toggle_site_group, _SITE_GROUPS)\n self._input_map.bind(\n self._render_settings.toggle_geom_group, _GEOM_GROUPS)\n self._input_map.bind(\n self._render_settings.toggle_rendering_flag, _RENDERING_FLAGS)\n\n self._input_map.bind(self._camera.zoom_to_scene, _ZOOM_TO_SCENE)\n self._input_map.bind(self._camera_select.select_next, _NEXT_CAMERA)\n self._input_map.bind(self._camera_select.select_previous, _PREVIOUS_CAMERA)\n self._input_map.bind_z_axis(self._free_camera.zoom)\n self._input_map.bind_plane(self._free_camera.on_move)\n self._input_map.bind(self._free_camera.set_rotate_mode, _ROTATE_CAMERA)\n self._input_map.bind(self._free_camera.center, _CENTER_CAMERA)\n self._input_map.bind(self._free_camera.track, _TRACK_OBJECT)\n self._input_map.bind(self._camera_select.escape, _FREE_LOOK)\n self._input_map.bind(self._manipulator.select, _SELECT_OBJECT)\n self._input_map.bind_plane(self._manipulator.on_move)\n\n def deinitialize(self):\n \"\"\"Deinitializes the viewer instance.\"\"\"\n self._input_map.clear_bindings()\n self._camera_settings = self._camera.settings if self._camera else None\n del self._camera\n del self._renderer\n del self._manipulator\n del self._free_camera\n del self._camera_select\n self._camera = None\n self._renderer = None\n self._manipulator = None\n self._free_camera = None\n self._camera_select = None\n\n def render(self):\n \"\"\"Renders the visualized scene.\"\"\"\n if self._camera and self._renderer: # Can be None during env reload.\n scene = self._camera.render(self.perturbation)\n self._render_settings.apply_settings(scene)\n self._renderer.render(self._viewport, scene)\n\n def zoom_to_scene(self):\n \"\"\"Utility method that set the camera to embrace the entire scene.\"\"\"\n if self._camera:\n self._camera.zoom_to_scene()\n\n def _print_camera_transform(self):\n if self._camera:\n rotation_mtx, position = self._camera.transform\n right, up, _ = rotation_mtx\n print('' % (\n position[0], position[1], position[2], right[0], right[1],\n right[2], up[0], up[1], up[2]))\n\n @property\n def perturbation(self):\n \"\"\"Returns an active renderer.Perturbation object.\"\"\"\n if self._manipulator and self._manipulator.perturbation:\n return self._manipulator.perturbation\n else:\n return self._null_perturbation\n\n @property\n def camera(self):\n \"\"\"Returns an active renderer.SceneCamera instance.\"\"\"\n return self._camera\n\n @property\n def render_settings(self):\n \"\"\"Returns renderer.RenderSettings used by this viewer.\"\"\"\n return self._render_settings\n\n\nclass CameraSelector:\n \"\"\"Binds camera behavior to user input.\"\"\"\n\n def __init__(self, model, camera, free_camera, **unused):\n \"\"\"Instance initializer.\n\n Args:\n model: Instance of MjModel.\n camera: Instance of SceneCamera.\n free_camera: Instance of FreeCameraController.\n **unused: Other arguments, not used by this class.\n \"\"\"\n del unused # Unused.\n self._model = model\n self._camera = camera\n self._free_ctrl = free_camera\n\n self._camera_idx = -1\n self._active_ctrl = self._free_ctrl\n\n def select_previous(self):\n \"\"\"Cycles to the previous scene camera.\"\"\"\n self._camera_idx -= 1\n if not self._model.ncam or self._camera_idx < -1:\n self._camera_idx = self._model.ncam - 1\n self._commit_selection()\n\n def select_next(self):\n \"\"\"Cycles to the next scene camera.\"\"\"\n self._camera_idx += 1\n if not self._model.ncam or self._camera_idx >= self._model.ncam:\n self._camera_idx = -1\n self._commit_selection()\n\n def escape(self) -> None:\n \"\"\"Unconditionally switches to the free camera.\"\"\"\n self._camera_idx = -1\n self._commit_selection()\n\n def _commit_selection(self):\n \"\"\"Selects a controller that should go with the selected camera.\"\"\"\n if self._camera_idx < 0:\n self._activate(self._free_ctrl)\n else:\n self._camera.set_fixed_mode(self._camera_idx)\n self._activate(None)\n\n def _activate(self, controller):\n \"\"\"Activates a sub-controller.\"\"\"\n if controller == self._active_ctrl:\n return\n\n if self._active_ctrl is not None:\n self._active_ctrl.deactivate()\n self._active_ctrl = controller\n if self._active_ctrl is not None:\n self._active_ctrl.activate()\n\n\nclass FreeCameraController:\n \"\"\"Implements the free camera behavior.\"\"\"\n\n def __init__(self, viewport, camera, pointer, selection_service, **unused):\n \"\"\"Instance initializer.\n\n Args:\n viewport: Instance of mujoco_viewer.Viewport.\n camera: Instance of mujoco_viewer.SceneCamera.\n pointer: A pointer that moves around the screen and is used to point at\n bodies. Implements a single attribute - 'position' - that returns a\n 2-component vector of pointer's screen space position.\n selection_service: An instance of a class implementing a\n 'selected_body_id' property.\n **unused: Other optional parameters not used by this class.\n \"\"\"\n del unused # Unused.\n self._viewport = viewport\n self._camera = camera\n self._pointer = pointer\n self._selection_service = selection_service\n self._active = True\n self._tracked_body_idx = -1\n self._action = util.AtomicAction()\n\n def activate(self):\n \"\"\"Activates the controller.\"\"\"\n self._active = True\n self._update_camera_mode()\n\n def deactivate(self):\n \"\"\"Deactivates the controller.\"\"\"\n self._active = False\n self._action = util.AtomicAction()\n\n def set_pan_vertical_mode(self, enable):\n \"\"\"Starts/ends the camera panning action along the vertical plane.\n\n Args:\n enable: A boolean flag, True to start the action, False to end it.\n \"\"\"\n if self._active:\n if enable:\n self._action.begin(mujoco.mjtMouse.mjMOUSE_MOVE_V)\n else:\n self._action.end(mujoco.mjtMouse.mjMOUSE_MOVE_V)\n\n def set_pan_horizontal_mode(self, enable):\n \"\"\"Starts/ends the camera panning action along the horizontal plane.\n\n Args:\n enable: A boolean flag, True to start the action, False to end it.\n \"\"\"\n if self._active:\n if enable:\n self._action.begin(mujoco.mjtMouse.mjMOUSE_MOVE_H)\n else:\n self._action.end(mujoco.mjtMouse.mjMOUSE_MOVE_H)\n\n def set_rotate_mode(self, enable):\n \"\"\"Starts/ends the camera rotation action.\n\n Args:\n enable: A boolean flag, True to start the action, False to end it.\n \"\"\"\n if self._active:\n if enable:\n self._action.begin(mujoco.mjtMouse.mjMOUSE_ROTATE_H)\n else:\n self._action.end(mujoco.mjtMouse.mjMOUSE_ROTATE_H)\n\n def center(self):\n \"\"\"Focuses camera on the object the pointer is currently pointing at.\"\"\"\n if self._active:\n body_id, world_pos = self._camera.raycast(self._viewport,\n self._pointer.position)\n if body_id >= 0:\n self._camera.look_at(world_pos, _LOOK_AT_DISTANCE)\n\n def on_move(self, position, translation):\n \"\"\"Translates mouse moves onto camera movements.\"\"\"\n del position\n if self._action.in_progress:\n viewport_offset = self._viewport.screen_to_viewport(translation)\n self._camera.move(self._action.watermark, viewport_offset)\n\n def zoom(self, zoom_factor):\n \"\"\"Zooms the camera in/out.\n\n Args:\n zoom_factor: A floating point value, by how much to zoom the camera.\n Positive values zoom the camera in, negative values zoom it out.\n \"\"\"\n if self._active:\n offset = [0, _SCROLL_SPEED_FACTOR * zoom_factor * -1.]\n self._camera.move(mujoco.mjtMouse.mjMOUSE_ZOOM, offset)\n\n def track(self):\n \"\"\"Makes the camera track the currently selected object.\n\n The selection is managed by the selection service.\n \"\"\"\n if self._active and self._tracked_body_idx < 0:\n self._tracked_body_idx = self._selection_service.selected_body_id\n self._update_camera_mode()\n\n def free_look(self):\n \"\"\"Switches the camera to a free-look mode.\"\"\"\n if self._active:\n self._tracked_body_idx = -1\n self._update_camera_mode()\n\n def _update_camera_mode(self):\n \"\"\"Sets the camera into a tracking or a free-look mode.\"\"\"\n if self._tracked_body_idx >= 0:\n self._camera.set_tracking_mode(self._tracked_body_idx)\n else:\n self._camera.set_freelook_mode()\n\n\nclass ManipulationController:\n \"\"\"Binds control over scene objects to user input.\"\"\"\n\n def __init__(self, viewport, camera, pointer, **unused):\n \"\"\"Instance initializer.\n\n Args:\n viewport: Instance of mujoco_viewer.Viewport.\n camera: Instance of mujoco_viewer.SceneCamera.\n pointer: A pointer that moves around the screen and is used to point at\n bodies. Implements a single attribute - 'position' - that returns a\n 2-component vector of pointer's screen space position.\n **unused: Other arguments, unused by this class.\n \"\"\"\n del unused # Unused.\n self._viewport = viewport\n self._camera = camera\n self._pointer = pointer\n self._action = util.AtomicAction(self._update_action)\n self._perturb = None\n\n def select(self):\n \"\"\"Translates mouse double-clicks to object selection action.\"\"\"\n body_id, _ = self._camera.raycast(self._viewport, self._pointer.position)\n if body_id >= 0:\n self._perturb = self._camera.new_perturbation(body_id)\n else:\n self._perturb = None\n\n def set_move_vertical_mode(self, enable):\n \"\"\"Begins/ends an object translation action along the vertical plane.\n\n Args:\n enable: A boolean flag, True begins the action, False ends it.\n \"\"\"\n if enable:\n self._action.begin(mujoco.mjtMouse.mjMOUSE_MOVE_V)\n else:\n self._action.end(mujoco.mjtMouse.mjMOUSE_MOVE_V)\n\n def set_move_horizontal_mode(self, enable):\n \"\"\"Begins/ends an object translation action along the horizontal plane.\n\n Args:\n enable: A boolean flag, True begins the action, False ends it.\n \"\"\"\n if enable:\n self._action.begin(mujoco.mjtMouse.mjMOUSE_MOVE_H)\n else:\n self._action.end(mujoco.mjtMouse.mjMOUSE_MOVE_H)\n\n def set_rotate_mode(self, enable):\n \"\"\"Begins/ends an object rotation action.\n\n Args:\n enable: A boolean flag, True begins the action, False ends it.\n \"\"\"\n if enable:\n self._action.begin(mujoco.mjtMouse.mjMOUSE_ROTATE_H)\n else:\n self._action.end(mujoco.mjtMouse.mjMOUSE_ROTATE_H)\n\n def _update_action(self, action):\n if self._perturb is not None:\n if action is not None:\n _, grab_pos = self._camera.raycast(self._viewport,\n self._pointer.position)\n self._perturb.start_move(action, grab_pos)\n else:\n self._perturb.end_move()\n\n def on_move(self, position, translation):\n \"\"\"Translates mouse moves to selected object movements.\"\"\"\n del position\n if self._perturb is not None and self._action.in_progress:\n viewport_offset = self._viewport.screen_to_viewport(translation)\n self._perturb.tick_move(viewport_offset)\n\n @property\n def perturbation(self):\n \"\"\"Returns the Perturbation object that represents the manipulated body.\"\"\"\n return self._perturb\n\n @property\n def selected_body_id(self):\n \"\"\"Returns the id of the selected body, or -1 if none is selected.\"\"\"\n return self._perturb.body_id if self._perturb is not None else -1\n", "repo_name": "deepmind/dm_control", "sub_path": "dm_control/viewer/viewer.py", "file_name": "viewer.py", "file_ext": "py", "file_size_in_byte": 18714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3200, "dataset": "github-code", "pt": "47", "api": [{"api_name": "dm_control.mujoco.wrapper.mjbindings.functions", "line_number": 7, "usage_type": "attribute"}, {"api_name": "dm_control.mujoco.wrapper.mjbindings", "line_number": 7, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 11, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 11, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 12, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 13, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 13, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 14, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_SHIFT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 15, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 15, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_LEFT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 16, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_CONTROL", "line_number": 16, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 17, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 17, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 18, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 18, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_CONTROL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 19, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 19, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 20, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_SHIFT_CONTROL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 22, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 22, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_LEFT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 23, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_ALT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 24, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 24, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 25, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_ALT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 26, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 26, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 27, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 28, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 28, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_LEFT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 29, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_CONTROL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 30, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 30, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_LEFT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 31, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_SHIFT_CONTROL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Exclusive", "line_number": 33, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 33, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_LEFT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.DoubleClick", "line_number": 34, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 34, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.DoubleClick", "line_number": 35, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 35, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_LEFT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.DoubleClick", "line_number": 36, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 36, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOUSE_BUTTON_RIGHT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 37, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_CONTROL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.KEY_ESCAPE", "line_number": 38, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 38, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.KEY_RIGHT_BRACKET", "line_number": 39, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 39, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.KEY_LEFT_BRACKET", "line_number": 40, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 40, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.KEY_A", "line_number": 41, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 41, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_CONTROL", "line_number": 41, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.KEY_F5", "line_number": 42, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 42, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.KEY_F6", "line_number": 43, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 43, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_SHIFT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.KEY_F6", "line_number": 44, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 44, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.KEY_F7", "line_number": 45, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 45, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_SHIFT", "line_number": 45, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.KEY_F7", "line_number": 46, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 46, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.KEY_F11", "line_number": 47, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 47, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.Range", "line_number": 48, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 48, "usage_type": "name"}, {"api_name": "mujoco.mjtVisFlag", "line_number": 50, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Range", "line_number": 52, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 52, "usage_type": "name"}, {"api_name": "mujoco.mjNGROUP", "line_number": 53, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Range", "line_number": 54, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 54, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.MOD_SHIFT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input", "line_number": 55, "usage_type": "name"}, {"api_name": "mujoco.mjNGROUP", "line_number": 56, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.user_input.Range", "line_number": 58, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 58, "usage_type": "name"}, {"api_name": "mujoco.mjtRndFlag", "line_number": 60, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 64, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.renderer.NullPerturbation", "line_number": 97, "usage_type": "call"}, {"api_name": "dm_control.viewer.renderer", "line_number": 97, "usage_type": "name"}, {"api_name": "dm_control.viewer.renderer.RenderSettings", "line_number": 98, "usage_type": "call"}, {"api_name": "dm_control.viewer.renderer", "line_number": 98, "usage_type": "name"}, {"api_name": "dm_control.viewer.user_input.InputMap", "line_number": 99, "usage_type": "call"}, {"api_name": "dm_control.viewer.user_input", "line_number": 99, "usage_type": "name"}, {"api_name": "dm_control.viewer.renderer.SceneCamera", "line_number": 125, "usage_type": "call"}, {"api_name": "dm_control.viewer.renderer", "line_number": 125, "usage_type": "name"}, {"api_name": "dm_control.viewer.util.AtomicAction", "line_number": 346, "usage_type": "call"}, {"api_name": "dm_control.viewer.util", "line_number": 346, "usage_type": "name"}, {"api_name": "dm_control.viewer.util.AtomicAction", "line_number": 356, "usage_type": "call"}, {"api_name": "dm_control.viewer.util", "line_number": 356, "usage_type": "name"}, {"api_name": "mujoco.mjtMouse", "line_number": 366, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 368, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 378, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 380, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 390, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 392, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 418, "usage_type": "attribute"}, {"api_name": "dm_control.viewer.util.AtomicAction", "line_number": 461, "usage_type": "call"}, {"api_name": "dm_control.viewer.util", "line_number": 461, "usage_type": "name"}, {"api_name": "mujoco.mjtMouse", "line_number": 479, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 481, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 490, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 492, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 501, "usage_type": "attribute"}, {"api_name": "mujoco.mjtMouse", "line_number": 503, "usage_type": "attribute"}]} +{"seq_id": "31475747579", "text": "import os\nimport argparse\nfrom tqdm import tqdm\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-f', '--file', type=str, default='../../Data/MLM/withLIDtags/Hindi/combined/combined.txt', help='Input LID file switch LID tags')\nparser.add_argument('-o', '--outfile', type=str, default='invertedLID_EN_HI.txt', help='Output file for data with inverted LID tags')\nparser.add_argument('-l', '--lang', type=str, default=\"Hindi\", choices=[\"Hindi\", \"Spanish\"], help=\"Language CS with English\")\nargs = parser.parse_args()\n\ninput_file = args.file\noutput_file = args.outfile\n\nlang_ids = ['EN']\nif args.lang == 'Hindi':\n lang_ids.append('HI')\nelif args.lang == 'Spanish':\n lang_ids.append('ES')\nelse:\n raise Exception(\"Invalid input langauge\")\n\ntry:\n os.path.isfile(input_file)\n os.path.isfile(output_file)\nexcept:\n raise Exception(\"Invalid file path\")\n\ndef invert_lid(tag):\n if tag == 'OTHER':\n return tag\n elif tag == lang_ids[0]:\n return lang_ids[1]\n elif tag == lang_ids[1]:\n return lang_ids[0]\n else:\n raise Exception(f\"Invalid tag {tag}\")\n\nwith open(input_file, 'r') as fi:\n lines = fi.readlines()\n with open(output_file, 'w+') as fo:\n for line in tqdm(lines):\n if line.strip() == '':\n fo.write(line)\n continue\n \n word, lid = line.rstrip().split('\\t')\n newlid = invert_lid(lid)\n fo.write(word + '\\t' + newlid + '\\n')", "repo_name": "sahasrarjn/code-switched-mlm", "sub_path": "Code/experiments/invertLID/invertLIDtags.py", "file_name": "invertLIDtags.py", "file_ext": "py", "file_size_in_byte": 1475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "47", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "45090325019", "text": "import requests\nimport json\n# import pywhatkit\nimport flask\n\n\n# Rådsparken information\nlat_radsparken = 59.2409\nlon_radsparken = 17.9870\n\n# Stockholm information\nlat_stock = 59.329\nlon_stock = 18.069\n\nlat = lat_radsparken\nlon = lon_radsparken\n\n\n# Open Weather configuration\nAPI_key = \"4c0855da321c0d905dc62f6e3a6c8cff\"\now_endpoint = f\"https://api.openweathermap.org/data/3.0/onecall\"\now_param = {\n \"lat\": lat,\n \"lon\": lon,\n \"appid\": API_key\n}\n\n# yr configuration\nyr_endpoint = \"https://api.met.no/weatherapi/locationforecast/2.0/compact\"\nyr_param = {\n \"lat\": lat,\n \"lon\": lon,\n}\nyr_headers = {\n 'User-Agent': 'learning_python',\n 'From': 'nonworking@email.com',\n}\n\nendpoint = {\n \"endpoint\": yr_endpoint,\n \"params\": yr_param\n}\n\nanswer = requests.get(endpoint[\"endpoint\"], params=endpoint[\"params\"], headers=yr_headers)\n\ndata = answer.json()\n# Obtaining a list of dictionaries, hour by hour.\n# First key is \"time\" in format \"2022-05-21T13:00:00Z\"\n# Key \"data\" is a dictionary with dictionaries.\ndata = data[\"properties\"][\"timeseries\"]\nprint(json.dumps(data, indent=3))\n\n# Creating a new list of dictionaries with the information from the precipitation only.\n# This I might use later when attempting to forecast the solar panel productivity.\nnext_hour_rain_data = []\nfor idx, item in enumerate(data):\n try:\n next_hour_rain_data.append({\n \"time\": item[\"time\"],\n \"precipitation_amount\": item[\"data\"][\"next_1_hours\"][\"details\"][\"precipitation_amount\"],\n })\n except KeyError:\n pass\n\n# Going through the data and collecting when it is raining.\n# This is what it is actually required for the course.\nraining_at = []\nfor item in next_hour_rain_data:\n if item[\"precipitation_amount\"] > 0:\n raining_at.append({\n \"day\": item[\"time\"].split(\"T\")[0],\n \"hour\": item[\"time\"].split(\"T\")[1][0:2],\n \"amount\": item[\"precipitation_amount\"]\n })\n\n# print(json.dumps(raining_at, indent=3))\n\nif raining_at:\n print(\"It is going to rain.\")\nelse:\n print(\"It is not going to rain at all!\")\n\n# print(json.dumps(next_hour_rain_data, indent=2))\n# print(json.dumps(next_hour_rain_data, indent=2))\n", "repo_name": "mauricio-aljure-rey/Portfolio", "sub_path": "weather_report/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "6938369420", "text": "import urllib.request\nimport json\nimport datetime\nimport re\nimport pprint\nimport fileuploader as fup\n\n\ndef getOcupacaoGabinete():\n ocupacaoGabinete=\"http://splegisws.camara.sp.gov.br/ws/ws2.asmx/OcupacaoGabineteJSON\"\n resposta=urllib.request.urlopen(ocupacaoGabinete).read()\n fhand=open(\"gabinetes\",\"w\")\n\n jData=json.loads(resposta)\n fhand.write(str(jData))\n fhand.close()\n return jData\n\ndef getVereadorFile():\n url='http://www.camara.sp.gov.br/wp-content/uploads/dados_abertos/vereador/vereador.txt'\n arquivo=urllib.request.urlopen(url).read()\n texto=str(arquivo).split('\\\\r\\\\n')\n for i in texto:\n print (i)\n\ndef convertTimestampToDate(data):\n digitos = re.findall('[0-9]+',data)\n if len(digitos)==1:\n tstmp=int(digitos[0])\n dia=datetime.datetime.utcfromtimestamp(tstmp/1000)\n return dia\n #print(datetime.datetime.fromtimestamp(tstmp).strftime('%Y-%m-%d %H:%M:%S'))\n\ndef geraGabineteVereadorFile(lista_dict):\n for dicionario in lista_dict:\n return\n\ndef queryjData(jData):\n for d in jData:\n #if d['gabinete']==53 and d['legislatura']==17:\n # pprint.pprint(d)\n if d['vereador']=='CAIO MIRANDA CARNEIRO':\n pprint.pprint(d)\n\ndef getGastos(ano, mes):\n\turl=\"https://app-sisgvconsulta-prd.azurewebsites.net/ws/ws2.asmx/ObterDebitoVereadorJSON?ano=&mes=\"\n\tif ano.isdigit() and mes.isdigit():\n\t\tif int(ano)>datetime.datetime.today().year:\n\t\t\tprint(\"ano invalido! (\",ano,\")\")\n\t\telif int(mes) not in range(1,13) or int(mes)>datetime.datetime.today().month:\n\t\t\tprint(\"mes invalido! (\",mes,\")\")\n\t\telse:\n\t\t\turl=url.replace(\"\",ano)\n\t\t\turl=url.replace(\"\",mes)\n\t\t\tprint(url)\n\t\t\tresposta=urllib.request.urlopen(url).read()\n\t\t\tjData=json.loads(resposta)\n\t\t\treturn jData\n\telse:\n\t\tprint(\"Ano ou mes invalidos!\\nano: \",ano,\"\\tmes: \",mes)\n\t\t\t\n\t\t\t\n\ndef gastosToFile(gastosList):\n\tfhand=open(\"gastos.json\",\"w\")\n\tfor item in gastosList:\n\t\tfhand.write(str(item)+\"\\n\")\n\tfhand.close()\n\t\ndef testeGetGastos():\n\tgastosList=list()\n\tfor i in range(1,datetime.datetime.today().month):\n\t\td = getGastos(str(datetime.datetime.today().year),str(i))\n\t\tfor item in d:\n\t\t\tgastosList.append(item)\n\t\tprint(i,\" - \",len(gastosList))\n\t\n\t#gastosToFile(gastosList)\n\tfup.insertGastos(gastosList)\n\t\t\n\ntesteGetGastos()\n\t\t\t\n# funcoes de testes!\ndef testeOcupacaoGabinete():\n\tjData=getOcupacaoGabinete()\n\tqueryjData(jData)\n", "repo_name": "murilo-cerone/fup_vereadores", "sub_path": "crawlers/camara_restAPIs/camaraCrawler.py", "file_name": "camaraCrawler.py", "file_ext": "py", "file_size_in_byte": 2437, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 11, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 11, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"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": "re.findall", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlopen", "line_number": 56, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 56, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 56, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "attribute"}, {"api_name": "fileuploader.insertGastos", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "4916594968", "text": "import numpy as np\n\nfrom data_loader import n_identifier, g_identifier, l_identifier\nimport inspect\nfrom datetime import datetime\n\n\ndef load_default_identifiers(n, g, l):\n if n is None:\n n = n_identifier\n if g is None:\n g = g_identifier\n if l is None:\n l = l_identifier\n return n, g, l\n\n\ndef initialize_batch(entries, batch_size, shuffle=False):\n total = len(entries)\n indices = np.arange(0, total - 1, 1)\n if shuffle:\n np.random.shuffle(indices)\n batch_indices = []\n start = 0\n end = len(indices)\n curr = start\n while curr < end:\n c_end = curr + batch_size\n if c_end > end:\n c_end = end\n batch_indices.append(indices[curr:c_end])\n curr = c_end\n return batch_indices[::-1]\n\n\ndef tally_param(model):\n total = 0\n for param in model.parameters():\n total += param.data.nelement()\n return total\n\n\ndef debug(*msg, sep='\\t'):\n caller = inspect.stack()[1]\n file_name = caller.filename\n ln = caller.lineno\n now = datetime.now()\n time = now.strftime(\"%m/%d/%Y - %H:%M:%S\")\n print('[' + str(time) + '] File \\\"' + file_name + '\\\", line ' + str(ln) + ' ', end='\\t')\n for m in msg:\n print(m, end=sep)\n print('')\n", "repo_name": "saikat107/Devign", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 58, "dataset": "github-code", "pt": "51", "api": [{"api_name": "data_loader.n_identifier", "line_number": 10, "usage_type": "name"}, {"api_name": "data_loader.g_identifier", "line_number": 12, "usage_type": "name"}, {"api_name": "data_loader.l_identifier", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "inspect.stack", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "12268413276", "text": "import curses\ns = curses.initscr()\ncurses.curs_set(0)\ncurses.noecho()\n#curses.cbreak()\ns.timeout(-1)\ns.keypad(1)\ny,x = s.getmaxyx()\nchy, chx = int(y/2),int(x/2)\ncharacter = '∆'\ns.addch(chy,chx,character)\ns.refresh()\n\n\ndef move(i,j):\n s.delch(chy,chx)\n s.addch(i,j, character)\n\n#key = KEY_HOME\nwhile True:\n# next_key = s.getch()\n# key = key if next_key == -1 else next_key\n key = s.getch()\n if key == curses.KEY_RIGHT and chx <= x-2:\n move(chy,chx + 2)\n chx += 2\n if key == curses.KEY_LEFT and chx >= 1:\n move(chy,chx-2)\n chx-=2\n if key == curses.KEY_UP and chy >= 1:\n move(chy-1,chx)\n chy-=1\n if key== curses.KEY_DOWN and chy <= y-2:\n move(chy+1,chx)\n chy+=1\n if key == 113:\n curses.endwin()\n quit()\n \n\n\n\ncurses.napms(2000)\n\n\ncurses.endwin()\n", "repo_name": "xypage/cobra-crawler", "sub_path": "curse_test.py", "file_name": "curse_test.py", "file_ext": "py", "file_size_in_byte": 847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "curses.initscr", "line_number": 2, "usage_type": "call"}, {"api_name": "curses.curs_set", "line_number": 3, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 4, "usage_type": "call"}, {"api_name": "curses.KEY_RIGHT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "curses.KEY_LEFT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "curses.KEY_UP", "line_number": 30, "usage_type": "attribute"}, {"api_name": "curses.KEY_DOWN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "curses.endwin", "line_number": 37, "usage_type": "call"}, {"api_name": "curses.napms", "line_number": 43, "usage_type": "call"}, {"api_name": "curses.endwin", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "28690973495", "text": "from os import walk\nimport json\nimport boto3\nimport sys\n\nsession = boto3.Session(\n aws_access_key_id=\"AKIA2OVFE26QA6LMK46U\",\n aws_secret_access_key=\"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\",\n)\n\ndef checkBlogSSMLInS3():\n s3 = session.resource('s3')\n bucket= s3.Bucket(name=\"polly-blog-data\")\n pollyListFromS3 = []\n for obj in bucket.objects.filter():\n pollyListFromS3.append(obj.key.split(\".\")[0])\n\n pollyTextPath = \"./content/polly\"\n for (dirpath, dirnames, filenames) in walk(pollyTextPath):\n for dir in dirnames:\n if dir not in pollyListFromS3:\n print(dir)\n polly_handler({\"dirName\": dir, \"dirPrefix\": pollyTextPath})\n else:\n pass\n break\n\ndef polly_handler(event):\n try:\n polly = session.client(\"polly\")\n dirName = event['dirName']\n dirPrefix = event['dirPrefix']\n data = open(dirPrefix + \"/\" + dirName + \"/index.txt\", 'r').read()\n response = polly.start_speech_synthesis_task(\n Engine='standard',\n Text=data,\n TextType=\"text\",\n OutputFormat=\"mp3\", \n VoiceId=\"Joanna\", \n OutputS3BucketName=\"polly-blog-data\", \n OutputS3KeyPrefix=dirName, \n )\n taskId = response['SynthesisTask']['TaskId']\n task_status = polly.get_speech_synthesis_task(TaskId = taskId)\n while task_status['SynthesisTask']['TaskStatus'] == \"scheduled\" or task_status['SynthesisTask']['TaskStatus'] == \"inProgress\":\n task_status = polly.get_speech_synthesis_task(TaskId = taskId)\n\n task_status = polly.get_speech_synthesis_task(TaskId = taskId)\n if task_status['SynthesisTask']['TaskStatus'] == 'failed':\n return { 'status': \"Failed\" }\n elif task_status['SynthesisTask']['TaskStatus'] == 'completed':\n delete_s3(response['SynthesisTask']['OutputUri'])\n return { 'status': \"Success\" }\n else:\n return { 'status': \"Failed\" }\n except:\n print(sys.exc_info()[0])\n return {\n 'body': \"Error\"\n }\n\ndef delete_s3(pollyUri):\n s3 = session.resource('s3')\n filename = pollyUri.split(\"/\")[-1]\n s3.Object('polly-blog-data',filename.split(\".\")[0]+\".mp3\").copy_from(CopySource='polly-blog-data/{}'.format(filename))\n s3.Object('polly-blog-data',filename).delete()\n\n\nif __name__ == \"__main__\":\n checkBlogSSMLInS3()\n", "repo_name": "viral-sangani/blog.viralsangani.me", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2451, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "boto3.Session", "line_number": 6, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "29891321443", "text": "import uuid\n\nimport networkx as nx\nfrom PIL.Image import Image\n\nfrom hypergraphs.utils import get_node_id, Direction\n\n\n# hyp_b - B labeled hyperedge id\n# hyp_is - I labeled hyperedge_ids\n# hyp_f - F labeled hyperedge_id\n\ndef P3AutoDetect(graph, hyp_b, image):\n hyp_b_data = graph.node[hyp_b]\n b_neighbour_ids = list(graph.neighbors(hyp_b))\n\n graph.remove_node(hyp_b)\n\n path = nx.shortest_path(graph, b_neighbour_ids[0], b_neighbour_ids[1])\n\n if len(path) != 5:\n raise ValueError(f\"P3AutoDetect - wrong number of nodes in path {len(path)}\")\n\n graph.add_node(hyp_b, **hyp_b_data)\n graph.add_edge(hyp_b, b_neighbour_ids[0])\n graph.add_edge(hyp_b, b_neighbour_ids[1])\n\n hyp_is = [x for x in path if 'label' in graph.node[x] and graph.node[x]['label'] == 'I']\n\n common_i_node = list(set(graph.neighbors(hyp_is[0])) & set(graph.neighbors(hyp_is[1])))[0]\n\n common_i_node_neighbours = list(graph.neighbors(common_i_node))\n\n hyp_fs = []\n for x in common_i_node_neighbours:\n data = graph.node[x]\n if 'label' in data and data['label'] in map(lambda x: x.name, list(Direction)):\n hyp_fs.append(x)\n\n done = False\n for hyp_f in hyp_fs:\n try:\n P3(graph, hyp_b, hyp_is, hyp_f, image)\n done = True\n break\n except ValueError:\n print(\"P3AutoDetect - ValueError handled!\")\n\n if not done:\n raise ValueError('P3AutoDetect - ValueError not handled')\n\n\n\ndef P3(graph: nx.Graph, hyp_b, hyp_is, hyp_f, image: Image):\n __assert_hyper_edge(graph, [hyp_b], 'B')\n __assert_hyper_edge(graph, hyp_is, 'I')\n __assert_hyper_edge(graph, [hyp_f])\n\n hyp_f_data = graph.node[hyp_f]\n hyp_b_data = graph.node[hyp_b]\n\n hyp_f_neighbour_ids = list(graph.neighbors(hyp_f))\n\n if len(hyp_f_neighbour_ids) != 1:\n raise ValueError('F should have 1 neighbour')\n\n for hyp_i in hyp_is:\n if hyp_f_neighbour_ids[0] not in list(graph.neighbors(hyp_i)):\n raise ValueError('I is not connected with F1 via neighbour')\n\n hyp_f_neighbour_data = graph.node[hyp_f_neighbour_ids[0]]\n\n if hyp_f_data['label'] == Direction.N.name:\n if hyp_f_data['x'] != hyp_f_neighbour_data['x'] or hyp_f_data['y'] <= hyp_f_neighbour_data['y']:\n raise ValueError('F hyperedge has weird position')\n if hyp_f_data['x'] != hyp_b_data['x'] or hyp_f_data['y'] >= hyp_b_data['y']:\n raise ValueError('F hyperedge has weird position (B)')\n elif hyp_f_data['label'] == Direction.S.name:\n if hyp_f_data['x'] != hyp_f_neighbour_data['x'] or hyp_f_data['y'] >= hyp_f_neighbour_data['y']:\n raise ValueError('F hyperedge has weird position')\n if hyp_f_data['x'] != hyp_b_data['x'] or hyp_f_data['y'] <= hyp_b_data['y']:\n raise ValueError('F hyperedge has weird position (B)')\n elif hyp_f_data['label'] == Direction.E.name:\n if hyp_f_data['x'] <= hyp_f_neighbour_data['x'] or hyp_f_data['y'] != hyp_f_neighbour_data['y']:\n raise ValueError('F hyperedge has weird position')\n if hyp_f_data['x'] >= hyp_b_data['x'] or hyp_f_data['y'] != hyp_b_data['y']:\n raise ValueError('F hyperedge has weird position (B)')\n elif hyp_f_data['label'] == Direction.W.name:\n if hyp_f_data['x'] >= hyp_f_neighbour_data['x'] or hyp_f_data['y'] != hyp_f_neighbour_data['y']:\n raise ValueError('F hyperedge has weird position')\n if hyp_f_data['x'] <= hyp_b_data['x'] or hyp_f_data['y'] != hyp_b_data['y']:\n raise ValueError('F hyperedge has weird position (B)')\n else:\n raise ValueError('F hyperedge has weird label')\n\n\n new_node_position = (hyp_b_data['x'], hyp_b_data['y'])\n new_node_id = get_node_id(new_node_position)\n\n __add_new_node(graph, image, new_node_id, new_node_position) # add v\n __add_hyperedges_between_neighbour_nodes(graph, hyp_b, new_node_id, new_node_position) # add 1-b-v-b-2\n for hyp_i in hyp_is:\n __add_edges_between_nodes(graph, new_node_id, hyp_i) # add v-i and v-i\n __add_edges_between_nodes(graph, new_node_id, hyp_f) # add v-f1\n graph.remove_node(hyp_b)\n\n\ndef __assert_hyper_edge(graph, hyperedge_ids, label=None):\n for hyperedge_id in hyperedge_ids:\n if not hyperedge_id in graph.nodes:\n raise ValueError('Given node_id do not exists')\n\n if not graph.node[hyperedge_id]['is_hyperedge']:\n raise ValueError('Given node_id is not id of hyperedge')\n\n if label:\n if not graph.node[hyperedge_id]['label'] is label:\n raise ValueError(f\"Given node_id is not hyperedge type '{label}'\")\n\n\ndef __add_new_node(graph, image, new_node_id, new_node_position):\n new_node_rgb = image.getpixel(new_node_position)\n graph.add_node(\n new_node_id,\n x=new_node_position[0],\n y=new_node_position[1],\n is_hyperedge=False,\n r=new_node_rgb[0],\n g=new_node_rgb[1],\n b=new_node_rgb[2],\n )\n\n\ndef __add_hyperedges_between_neighbour_nodes(graph, hyperedge_id, new_node_id, new_node_position):\n hyperedge_neighbour_ids = graph.neighbors(hyperedge_id)\n for neighbour_id in hyperedge_neighbour_ids:\n neighbour = graph.node[neighbour_id]\n new_hyperedge_id = uuid.uuid4()\n graph.add_node(\n new_hyperedge_id,\n x=(neighbour['x'] + new_node_position[0]) // 2,\n y=(neighbour['y'] + new_node_position[1]) // 2,\n is_hyperedge=True,\n label='B',\n )\n graph.add_edge(new_hyperedge_id, neighbour_id)\n graph.add_edge(new_hyperedge_id, new_node_id)\n\n\ndef __add_edges_between_nodes(graph, node1, node2):\n graph.add_edge(node1, node2)\n", "repo_name": "mrapacz/hypergraphs", "sub_path": "hypergraphs/productions/p3.py", "file_name": "p3.py", "file_ext": "py", "file_size_in_byte": 5721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "networkx.shortest_path", "line_number": 19, "usage_type": "call"}, {"api_name": "hypergraphs.utils.Direction", "line_number": 37, "usage_type": "argument"}, {"api_name": "networkx.Graph", "line_number": 54, "usage_type": "attribute"}, {"api_name": "PIL.Image.Image", "line_number": 54, "usage_type": "name"}, {"api_name": "hypergraphs.utils.Direction.N", "line_number": 73, "usage_type": "attribute"}, {"api_name": "hypergraphs.utils.Direction", "line_number": 73, "usage_type": "name"}, {"api_name": "hypergraphs.utils.Direction.S", "line_number": 78, "usage_type": "attribute"}, {"api_name": "hypergraphs.utils.Direction", "line_number": 78, "usage_type": "name"}, {"api_name": "hypergraphs.utils.Direction.E", "line_number": 83, "usage_type": "attribute"}, {"api_name": "hypergraphs.utils.Direction", "line_number": 83, "usage_type": "name"}, {"api_name": "hypergraphs.utils.Direction.W", "line_number": 88, "usage_type": "attribute"}, {"api_name": "hypergraphs.utils.Direction", "line_number": 88, "usage_type": "name"}, {"api_name": "hypergraphs.utils.get_node_id", "line_number": 98, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "7579385582", "text": "import os\nimport random\nimport shutil\nimport xml\nimport xml.etree.ElementTree as ET\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n\ndef read_xml(annoPath):\n tree = ET.parse(annoPath)\n root = tree.getroot()\n # xmlContent = open(annoPath).read()\n # return tree, root, xmlContent\n return tree, root\n\n\ndef analyse(xmlRootPath):\n height = {}\n width = {}\n ratio = {}\n for file in os.listdir(xmlRootPath):\n tree, root = read_xml(xmlRootPath + file)\n objectList = root.findall('object')\n for obj in objectList:\n bndbox = obj.find('bndbox')\n xmin = float(bndbox.find('xmin').text)\n ymin = float(bndbox.find('ymin').text)\n xmax = float(bndbox.find('xmax').text)\n ymax = float(bndbox.find('ymax').text)\n anchorHeight = ymax - ymin\n anchorWidth = xmax - xmin\n # anchorRatio = anchorWidth / anchorHeight\n anchorRatio = anchorHeight / anchorWidth\n if anchorHeight in height:\n height[anchorHeight] = height[anchorHeight] + 1\n else:\n height[anchorHeight] = 1\n\n if anchorWidth in width:\n width[anchorWidth] = width[anchorWidth] + 1\n else:\n width[anchorWidth] = 1\n\n if anchorRatio in ratio:\n ratio[anchorRatio] = ratio[anchorRatio] + 1\n else:\n ratio[anchorRatio] = 1\n # print(height)\n # print(width)\n # print(ratio)\n # 设置matplotlib正常显示中文和负号\n matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 用黑体显示中文\n heightKeys = height.items()\n heightValue = height.values()\n widthKeys = width.items()\n widthValue = width.values()\n ratioKeys = ratio.items()\n ratioValue = ratio.values()\n\n print(heightValue)\n plt.figure(figsize=(8, 8), dpi=80)\n # 绘制第一个图\n plt.subplot(2, 2, 1)\n plt.hist(heightValue, bins=len(heightValue), facecolor=\"blue\", edgecolor=\"black\", alpha=0.7)\n plt.title(\"height分布直方图\")\n # 绘制第二个图\n plt.subplot(2, 2, 2)\n plt.hist(widthValue, bins=len(widthValue), facecolor=\"blue\", edgecolor=\"black\", alpha=0.7)\n plt.title(\"width分布直方图\")\n # 绘制第三个图\n plt.subplot(2, 2, 3)\n plt.hist(ratioValue, bins=len(ratioValue), facecolor=\"blue\", edgecolor=\"black\", alpha=0.7)\n plt.title(\"height/width分布直方图\")\n plt.savefig('anchorAnalyse')\n plt.show()\n\n\nif __name__ == '__main__':\n xmlRootPath = './ori_voc/annotations/'\n analyse(xmlRootPath)\n", "repo_name": "Sette-Tyx/ManageDataset", "sub_path": "AnchorAnalyse.py", "file_name": "AnchorAnalyse.py", "file_ext": "py", "file_size_in_byte": 2602, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "47", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 11, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 11, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 53, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "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.subplot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "15500219943", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.conf import settings\nimport network.netapi as netapi\nimport logging\nimport requests\nimport threading\nimport json\nimport os\n\n\nlogging.basicConfig(\n level=logging.DEBUG,\n format='%(asctime)s %(filename)s[line:%(lineno)d] \\\n %(levelname)s %(message)s',\n datefmt='%a, %d %b %Y %H:%M:%S',\n filename='views.log',\n filemode='w+'\n)\n\n# define a Handler which writes INFO messages or higher to the sys.stderr\nlogger = logging.getLogger('CyApi')\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.DEBUG)\n# add the handler to the root logger\nlogger.addHandler(console)\n\n\ndef ping(request):\n returnString = '233 - 怕是姿势不对噢.'\n if request.method == 'POST':\n r = json.loads(request.body.decode(encoding='utf-8'))\n if('address' in r):\n result = netapi.get_latency_of_address(r['address'], settings.TIMES, settings.TIME_OUT)\n returnString = json.dumps(result, ensure_ascii=False)\n else:\n logger.info('No address requests recived.')\n else:\n logger.info('Get request recived.')\n return HttpResponse(returnString.encode('utf-8'))\n\n\ndef latency(request):\n result = {'server': []}\n f = open(os.path.join(settings.BASE_DIR, 'server.json'), 'r')\n serverList = json.loads(f.read())\n if request.method == 'POST':\n address = json.loads(request.body.decode(encoding='UTF-8'))['address']\n logger.info('Latency request to %s recived.' % address)\n jobs = []\n for server in serverList['servers']:\n t = threading.Thread(\n target=netapi.send_distributed_ping_request,\n args=(server, address, result))\n t.start()\n jobs.append(t)\n logger.debug(' Ping request has been send to ' + server)\n for t in jobs:\n t.join()\n return HttpResponse(json.dumps(result, ensure_ascii=False).encode('utf-8'))\n", "repo_name": "bace1920/CyApi", "sub_path": "network/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "network.netapi.get_latency_of_address", "line_number": 34, "usage_type": "call"}, {"api_name": "network.netapi", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.settings.TIMES", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.settings.TIME_OUT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 40, "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": "django.conf.settings.BASE_DIR", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 45, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 52, "usage_type": "call"}, {"api_name": "network.netapi.send_distributed_ping_request", "line_number": 53, "usage_type": "attribute"}, {"api_name": "network.netapi", "line_number": 53, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 60, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "14729405441", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Sep 11 16:10:43 2015\n\n@author: cdondrup\n\"\"\"\n\nimport rospy\nfrom dynamic_reconfigure.client import Client as DynClient\nimport csv\nfrom geometry_msgs.msg import Pose, PoseStamped\nfrom bayes_people_tracker.msg import PeopleTracker\nimport numpy as np\nfrom hrsi_representation.msg import QTCArray\nimport json\nfrom scitos_teleop.msg import action_buttons\nfrom std_srvs.srv import Empty, EmptyResponse\nfrom test_cases.srv import Load, LoadRequest\nfrom fake_camera_effects.msg import CameraEffectsAction, CameraEffectsGoal\nfrom actionlib import SimpleActionClient\nimport yaml\nfrom roslib.packages import find_resource\nfrom collections import OrderedDict\nimport os\n\n\nPKG = \"hri16_experiment\"\n\n\nclass Test(object):\n __no_state__ = 9.\n\n __config_lookup = [\n {\"distance_threshold\": 4.0},\n {\"distance_threshold\": 4.0},\n {\"distance_threshold\": 4.0}\n ]\n\n __qtc_buffer = OrderedDict()\n\n\n def __init__(self, name):\n rospy.loginfo(\"Starting %s ...\" % name)\n self.out_dir = rospy.get_param(\"~out_dir\")\n self.par = str(rospy.get_param(\"~par\"))\n\n self.client = SimpleActionClient(\"/camera_effects\", CameraEffectsAction)\n self.client.wait_for_server()\n\n self.crea_dyn = DynClient(\"online_qtc_creator\")\n self.crea_dyn.update_configuration(self.__config_lookup[0])\n\n with open(find_resource(PKG, 'fake_goals.yaml')[0],'r') as f:\n conf = yaml.load(f)\n\n self.ppl_topic = rospy.get_param(\"~ppl_topic\", \"/people_tracker/positions\")\n self.robot_topic = rospy.get_param(\"~robot_topic\", \"/robot_pose\")\n self.qtc_topic = rospy.get_param(\"~qtc_topic\", \"/online_qtc_creator/qtc_array\")\n# self.goal_topic = rospy.get_param(\"~goal_topic\", \"/goal_pose_republisher/pose\")\n\n rospy.Service(\"~save\", Empty, self.write_file)\n\n self.robot_pose = None\n self.goal_pose = PoseStamped()\n self.goal_pose.pose.position.x = conf[\"point\"][\"x\"]\n self.goal_pose.pose.position.y = conf[\"point\"][\"y\"]\n\n self.pub = rospy.Publisher(\"/fake_goal\", PoseStamped, queue_size=1, latch=True)\n self.pub.publish(self.goal_pose)\n\n self.num_trial = 0\n self.scenario = \"record_robot\"\n\n self.trajectories = []\n self.traj_name = []\n self.ret = []\n\n rospy.loginfo(\"... loading scenario\")\n try:\n s = rospy.ServiceProxy(\"/scenario_server/load\", Load)\n rospy.loginfo(\"... waiting for service\")\n s.wait_for_service()\n rospy.loginfo(\"... loading scenario %s\" % self.scenario)\n l = LoadRequest(scenario=self.scenario)\n s(l)\n except (rospy.ServiceException, rospy.ROSInterruptException) as e:\n rospy.logfatal(e)\n\n rospy.Subscriber(\n \"/teleop_joystick/action_buttons\",\n action_buttons,\n self.button_callback,\n queue_size=1\n )\n\n rospy.loginfo(\"... done\")\n\n rospy.loginfo(\"... all done\")\n\n def ppl_callback(self, msg):\n if self.robot_pose == None or not msg.poses:\n return\n msgs = {\n \"agent1\": \"robot\",\n \"agent2\": \"human\",\n \"agent3\": \"goal\",\n \"x1\": self.robot_pose.position.x,\n \"y1\": self.robot_pose.position.y,\n \"x2\": msg.poses[0].position.x,\n \"y2\": msg.poses[0].position.y,\n \"x3\": self.goal_pose.pose.position.x,\n \"y3\": self.goal_pose.pose.position.y\n }\n self.trajectories[-1].append(msgs)\n if not msg.uuids[0] in self.traj_name: self.traj_name.append(msg.uuids[0])\n\n\n def pose_callback(self, msg):\n self.robot_pose = msg\n\n# def goal_callback(self, msg):\n# self.goal_pose = msg\n\n def get_new_states(self, key, qtc, buf):\n try:\n return qtc[np.where(np.all(qtc==buf[-1], axis=1))[0][-1]+1:]\n except IndexError:\n return qtc\n\n def qtc_callback(self, msg):\n for elem in msg.qtc:\n if elem.uuid not in self.__qtc_buffer.keys():\n self.__qtc_buffer[elem.uuid] = np.array([])\n\n qtc = np.array(json.loads(elem.qtc_goal_human))\n qtc[np.isnan(qtc)] = self.__no_state__\n\n self.__qtc_buffer[elem.uuid] = np.append(\n self.__qtc_buffer[elem.uuid],\n qtc[-1][[1,3]]\n )\n\n qtc = np.array(json.loads(elem.qtc_robot_human))\n qtc[np.isnan(qtc)] = self.__no_state__\n\n self.__qtc_buffer[elem.uuid] = np.append(\n self.__qtc_buffer[elem.uuid],\n qtc[-1][[1,3,0,2]]\n ).reshape(-1, 6)\n\n def button_callback(self, msg):\n rospy.loginfo(\"Button pressed\")\n if msg.A:\n rospy.loginfo(\"Starting run %s\" % self.num_trial)\n self.num_trial += 1\n self.trajectories.append([])\n self.crea_dyn.update_configuration({\"decay_time\":10.})\n rospy.loginfo(\"Creating services ...\")\n try:\n rospy.loginfo(\"Subscribing to human and robot pose\")\n self.ps = rospy.Subscriber(\n self.ppl_topic,\n PeopleTracker,\n callback=self.ppl_callback,\n queue_size=1\n )\n self.rs = rospy.Subscriber(\n self.robot_topic,\n Pose,\n callback=self.pose_callback,\n queue_size=1\n )\n self.qs = rospy.Subscriber(\n self.qtc_topic,\n QTCArray,\n callback=self.qtc_callback,\n queue_size=10\n )\n# gs = rospy.Subscriber(\n# self.goal_topic,\n# PoseStamped,\n# callback=self.goal_callback,\n# queue_size=1\n# )\n\n self.client.send_goal(CameraEffectsGoal())\n\n except (rospy.ServiceException, rospy.ROSInterruptException) as e:\n rospy.logfatal(e)\n finally:\n pass\n\n elif msg.B:\n rospy.loginfo(\"Unsubscribing\")\n try:\n self.ps.unregister()\n self.rs.unregister()\n self.qs.unregister()\n # gs.unregister()\n except UnboundLocalError as e:\n rospy.logwarn(e)\n self.ps = None; self.rs = None; self.qs = None; #gs = None\n self.crea_dyn.update_configuration({\"decay_time\":.1})\n\n try:\n r = rospy.ServiceProxy(\"/scenario_server/reset\", Empty)\n rospy.loginfo(\" ... waiting for %s\" % r.resolved_name)\n r.wait_for_service()\n rospy.loginfo(\" ... calling %s\" % r.resolved_name)\n r()\n rospy.loginfo(\" ... done\")\n except (rospy.ServiceException, rospy.ROSInterruptException) as e:\n rospy.logfatal(e)\n\n self.write_file(None)\n self.client.send_goal(CameraEffectsGoal())\n\n def write_file(self, req):\n trajectories = self.trajectories\n rospy.loginfo(\"Writing results to %s\" % self.out_dir)\n mydir = os.path.join(self.out_dir, \"p\"+self.par)\n try:\n os.makedirs(mydir)\n except OSError as e:\n rospy.logwarn(e)\n\n for i, t in enumerate(trajectories):\n name = \"p\"+self.par+\"_\"+str(i)+\"_\"+self.traj_name[i]+\".csv\"\n with open(mydir+\"/\"+name, 'w') as f:\n rospy.loginfo(\"Writing %s\" % name)\n try:\n writer = csv.DictWriter(f, t[0].keys())\n writer.writeheader()\n writer.writerows(t)\n except Exception as e:\n rospy.logwarn(e)\n\n for i, (k,v) in enumerate(self.__qtc_buffer.items()):\n name = \"p\"+self.par+\"_\"+str(i)+\"_\"+k+\"_qtc.txt\"\n with open(mydir+\"/\"+name, 'w') as f:\n rospy.loginfo(\"Writing %s\" % name)\n try:\n np.savetxt(f, v, fmt='%.0f')\n except Exception as e:\n rospy.logwarn(e)\n\n return EmptyResponse()\n\n\nif __name__ == \"__main__\":\n rospy.init_node(\"hri_record\")\n t = Test(rospy.get_name())\n rospy.spin()\n", "repo_name": "cdondrup/experiments", "sub_path": "hri16_experiment/scripts/hri16_robot_rec.py", "file_name": "hri16_robot_rec.py", "file_ext": "py", "file_size_in_byte": 8445, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "collections.OrderedDict", "line_number": 40, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 44, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 45, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 46, "usage_type": "call"}, {"api_name": "actionlib.SimpleActionClient", "line_number": 48, "usage_type": "call"}, {"api_name": "fake_camera_effects.msg.CameraEffectsAction", "line_number": 48, "usage_type": "argument"}, {"api_name": "dynamic_reconfigure.client.Client", "line_number": 51, "usage_type": "call"}, {"api_name": "roslib.packages.find_resource", "line_number": 54, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 55, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 57, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 58, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 59, "usage_type": "call"}, {"api_name": "rospy.Service", "line_number": 62, "usage_type": "call"}, {"api_name": "std_srvs.srv.Empty", "line_number": 62, "usage_type": "argument"}, {"api_name": "geometry_msgs.msg.PoseStamped", "line_number": 65, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 69, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PoseStamped", "line_number": 69, "usage_type": "argument"}, {"api_name": "rospy.loginfo", "line_number": 79, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 81, "usage_type": "call"}, {"api_name": "test_cases.srv.Load", "line_number": 81, "usage_type": "argument"}, {"api_name": "rospy.loginfo", "line_number": 82, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 84, "usage_type": "call"}, {"api_name": "test_cases.srv.LoadRequest", "line_number": 85, "usage_type": "call"}, {"api_name": "rospy.ServiceException", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rospy.ROSInterruptException", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rospy.logfatal", "line_number": 88, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 90, "usage_type": "call"}, {"api_name": "scitos_teleop.msg.action_buttons", "line_number": 92, "usage_type": "argument"}, {"api_name": "rospy.loginfo", "line_number": 97, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 147, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 153, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 155, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 159, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 161, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 162, "usage_type": "call"}, {"api_name": "bayes_people_tracker.msg.PeopleTracker", "line_number": 164, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 168, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Pose", "line_number": 170, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 174, "usage_type": "call"}, {"api_name": "hrsi_representation.msg.QTCArray", "line_number": 176, "usage_type": "argument"}, {"api_name": "fake_camera_effects.msg.CameraEffectsGoal", "line_number": 187, "usage_type": "call"}, {"api_name": "rospy.ServiceException", "line_number": 189, "usage_type": "attribute"}, {"api_name": "rospy.ROSInterruptException", "line_number": 189, "usage_type": "attribute"}, {"api_name": "rospy.logfatal", "line_number": 190, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 195, "usage_type": "call"}, {"api_name": "rospy.logwarn", "line_number": 202, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 207, "usage_type": "call"}, {"api_name": "std_srvs.srv.Empty", "line_number": 207, "usage_type": "argument"}, {"api_name": "rospy.loginfo", "line_number": 208, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 210, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 212, "usage_type": "call"}, {"api_name": "rospy.ServiceException", "line_number": 213, "usage_type": "attribute"}, {"api_name": "rospy.ROSInterruptException", "line_number": 213, "usage_type": "attribute"}, {"api_name": "rospy.logfatal", "line_number": 214, "usage_type": "call"}, {"api_name": "fake_camera_effects.msg.CameraEffectsGoal", "line_number": 217, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 224, "usage_type": "call"}, {"api_name": "rospy.logwarn", "line_number": 226, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 231, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 233, "usage_type": "call"}, {"api_name": "rospy.logwarn", "line_number": 237, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 244, "usage_type": "call"}, {"api_name": "rospy.logwarn", "line_number": 246, "usage_type": "call"}, {"api_name": "std_srvs.srv.EmptyResponse", "line_number": 248, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 252, "usage_type": "call"}, {"api_name": "rospy.get_name", "line_number": 253, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 254, "usage_type": "call"}]} +{"seq_id": "6242063159", "text": "import socket\r\nimport datetime\r\n\r\nprint(datetime.datetime.now())\r\n\r\nfrom local_machine_info import print_machine_info\r\n\r\nprint_machine_info()\r\n\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\ninputHost = input(\"Unesi adresu hosta: \")\r\nprint(\"Skeniranje porta za adresu: \", inputHost)\r\n\r\nprint(\"Unesite broj pocetnog i zavrsnog porta.\")\r\n\r\nstartPort = input(\"Start port=> \")\r\nendPort = input(\"End port => \")\r\n\r\nstartPort = int(startPort)\r\nendPort = int(endPort)\r\n\r\ndef scanner(port):\r\n try:\r\n sock.connect((inputHost,port))\r\n return True\r\n except:\r\n return False\r\n\r\nfor portNumber in range(startPort,endPort):\r\n print(\"Skeniranje: \", portNumber)\r\n if scanner(portNumber):\r\n print('Port: ',portNumber,'/tcp',' je otvoren')", "repo_name": "maricicmarin/Mrezno-Programiranje", "sub_path": "Lab5/scanner.py", "file_name": "scanner.py", "file_ext": "py", "file_size_in_byte": 773, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "datetime.datetime.now", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 4, "usage_type": "attribute"}, {"api_name": "local_machine_info.print_machine_info", "line_number": 8, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 10, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 10, "usage_type": "attribute"}]} +{"seq_id": "19117815499", "text": "from flask import Flask\nfrom pymongo import Connection\n\napp = Flask(__name__)\nconn = Connection('127.0.0.1', 27017)\n\n@app.route(\"/hello\")\ndef find():\n\tdbHello = conn['hello']\n\t\n\treturn dbHello['demo'].find()[0]['message'] + '\\n'\n\t\n@app.route('/')\ndef hello():\n\treturn \"Hello World!\\n\"\n\nif __name__ == \"__main__\":\n\tapp.run(host=\"0.0.0.0\", port=80)\n", "repo_name": "sfrnld/hello", "sub_path": "hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "pymongo.Connection", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "5403512148", "text": "import pandas as pd\nfrom nltk.metrics import agreement\n\n\ndf = pd.read_csv(\"final/survey_all.csv\", header=None)\n\n\ndata = []\nfor idx, row in df.iterrows():\n #print(str(row[3]) + \" - \" + str(row[4]))\n data.append((\"a1\", idx, row[3]))\n data.append((\"a2\", idx, row[4]))\n data.append((\"a3\", idx, row[5]))\n \natask = agreement.AnnotationTask(data=data)\n\nprint(\"Cohen's Kappa:\", atask.kappa())\nprint(\"Fleiss's Kappa:\", atask.multi_kappa())\nprint(\"Krippendorf's Alpha:\", atask.alpha())", "repo_name": "sven-h/gollum", "sub_path": "inter_rater_agreement.py", "file_name": "inter_rater_agreement.py", "file_ext": "py", "file_size_in_byte": 491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "nltk.metrics.agreement.AnnotationTask", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.metrics.agreement", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "41816108032", "text": "\"\"\" This script used to remove the **only files which are older \nthan N days.. you are required to use this program with \ncaution.\n\n\"\"\"\n\n\n\nfrom path import path\nimport time\n\n# Days which you need to remove files ...\n\nDAYS = 90\nrm_count = 0\n\n# You could replace with your directory\ndir_path = path(\"/tmp/testdir\")\n\n# convert days in seconds and subtract from actual time\nDAYS_IN_SEC = DAYS * 24 * 60 * 60\n\ntime_in_sec = time.time() - DAYS_IN_SEC\n\n#we could log all deleted files\n\nwith open(\"/var/tmp/delete_files_.txt\",'w') as fd:\n\n for file in dir_path.walk():\n if file.isfile():\n if file.mtime <= time_in_sec:\n fd.write(file + '\\n')\n file.remove()\n rm_count+=1\n\nprint(\"Total files older than {0} days: {1}\".format(DAYS,rm_count))\n", "repo_name": "samperay/pycodes", "sub_path": "sysadmin/deletefilesNdays.py", "file_name": "deletefilesNdays.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "path.path", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "14238932814", "text": "import torch\nimport torch.nn as nn\nfrom modules.patchmerger import PatchMerger\n\n\nclass HPMLP(nn.Module):\n def __init__(self, in_dim, hidden_dim, dropout):\n super(HPMLP, self).__init__()\n self.net = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.Dropout(dropout)\n )\n\n def forward(self, x):\n return self.net(x)\n\n\nclass HybridPooler(nn.Module):\n def __init__(self, hidden_size, dropout=0.2, f_patchmerger=-1):\n super(HybridPooler, self).__init__()\n self.hidden_size = hidden_size\n self.f_patchmerger = f_patchmerger\n self.pmp = PatchMerger(self.hidden_size, 2)\n self.mlp1 = HPMLP(3*self.hidden_size, self.hidden_size, dropout)\n self.mlp2 = HPMLP(3*self.hidden_size, self.hidden_size, dropout)\n\n def forward(self, tokens, lengths):\n tokens, clf_pooled = tokens[:, 1:], tokens[:, 0]\n # tokens = torch.fft.fft2(tokens, dim=(-1, -2)).real\n if self.f_patchmerger > 0:\n lengths = (lengths / self.f_patchmerger).long()\n\n mean_pooled = torch.cat([torch.mean(i[0:l], dim=0).view(1, -1)\n for i, l in zip(tokens, lengths)], dim=0)\n max_pooled = torch.cat([torch.max(i[0:l], dim=0)[0].view(1, -1)\n for i, l in zip(tokens, lengths)], dim=0)\n min_pooled = torch.cat([torch.min(i[0:l], dim=0)[0].view(1, -1)\n for i, l in zip(tokens, lengths)], dim=0)\n pmp_pooled = self.pmp(tokens, lengths)\n\n pooled_traditional = torch.cat([mean_pooled, max_pooled, min_pooled], -1)\n pooled_learned = torch.cat([pmp_pooled.flatten(1), clf_pooled], dim=-1)\n\n pooled = torch.cat([self.mlp1(pooled_traditional), self.mlp2(pooled_learned)], -1)\n return pooled\n", "repo_name": "romue404/deep_paralinguistic_audio_classification", "sub_path": "modules/hybrid_pooling.py", "file_name": "hybrid_pooling.py", "file_ext": "py", "file_size_in_byte": 1915, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.GELU", "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.Linear", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "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.nn.Module", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "modules.patchmerger.PatchMerger", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "15605084211", "text": "#!/usr/bin/env python3\n# SPDX-License-Identifier: Apache-2.0\n\"\"\"Integration test Vendor module.\"\"\"\nimport os\n\nimport pytest\n\nimport requests\n\nfrom onapsdk.sdc import SDC\nfrom onapsdk.sdc.vendor import Vendor\nfrom onapsdk.sdc.vsp import Vsp\nimport onapsdk.constants as const\n\n\n@pytest.mark.integration\ndef test_vsp_unknown():\n \"\"\"Integration tests for Vsp.\"\"\"\n response = requests.post(\"{}/reset\".format(Vendor.base_front_url))\n response.raise_for_status()\n vendor = Vendor(name=\"test\")\n vendor.onboard()\n vsp = Vsp(name=\"test\")\n vsp.vendor = vendor\n vsp.create()\n assert vsp.identifier is not None\n assert vsp.status == const.DRAFT\n vsp.upload_package(open(\"{}/ubuntu16.zip\".format(\n os.path.dirname(os.path.abspath(__file__))), 'rb'))\n assert vsp.status == const.UPLOADED\n vsp.validate()\n assert vsp.status == const.VALIDATED\n vsp.commit()\n assert vsp.status == const.COMMITED\n vsp.submit()\n assert vsp.status == const.CERTIFIED\n vsp.create_csar()\n assert vsp.csar_uuid is not None\n\n@pytest.mark.integration\ndef test_vsp_onboard_unknown():\n \"\"\"Integration tests for Vsp.\"\"\"\n response = requests.post(\"{}/reset\".format(Vendor.base_front_url))\n response.raise_for_status()\n vendor = Vendor(name=\"test\")\n vendor.onboard()\n vsp = Vsp(name=\"test\", package=open(\"{}/ubuntu16.zip\".format(\n os.path.dirname(os.path.abspath(__file__))), 'rb'))\n vsp.vendor = vendor\n vsp.onboard()\n assert vsp.status == const.CERTIFIED\n assert vsp.csar_uuid is not None\n", "repo_name": "Orange-OpenSource/python-onapsdk", "sub_path": "integration_tests/test_02_vsp.py", "file_name": "test_02_vsp.py", "file_ext": "py", "file_size_in_byte": 1548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "60", "api": [{"api_name": "requests.post", "line_number": 19, "usage_type": "call"}, {"api_name": "onapsdk.sdc.vendor.Vendor.base_front_url", "line_number": 19, "usage_type": "attribute"}, {"api_name": "onapsdk.sdc.vendor.Vendor", "line_number": 19, "usage_type": "name"}, {"api_name": "onapsdk.sdc.vendor.Vendor", "line_number": 21, "usage_type": "call"}, {"api_name": "onapsdk.sdc.vsp.Vsp", "line_number": 23, "usage_type": "call"}, {"api_name": "onapsdk.constants.DRAFT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "onapsdk.constants", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 29, "usage_type": "call"}, {"api_name": "onapsdk.constants.UPLOADED", "line_number": 30, "usage_type": "attribute"}, {"api_name": "onapsdk.constants", "line_number": 30, "usage_type": "name"}, {"api_name": "onapsdk.constants.VALIDATED", "line_number": 32, "usage_type": "attribute"}, {"api_name": "onapsdk.constants", "line_number": 32, "usage_type": "name"}, {"api_name": "onapsdk.constants.COMMITED", "line_number": 34, "usage_type": "attribute"}, {"api_name": "onapsdk.constants", "line_number": 34, "usage_type": "name"}, {"api_name": "onapsdk.constants.CERTIFIED", "line_number": 36, "usage_type": "attribute"}, {"api_name": "onapsdk.constants", "line_number": 36, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 16, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 43, "usage_type": "call"}, {"api_name": "onapsdk.sdc.vendor.Vendor.base_front_url", "line_number": 43, "usage_type": "attribute"}, {"api_name": "onapsdk.sdc.vendor.Vendor", "line_number": 43, "usage_type": "name"}, {"api_name": "onapsdk.sdc.vendor.Vendor", "line_number": 45, "usage_type": "call"}, {"api_name": "onapsdk.sdc.vsp.Vsp", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 48, "usage_type": "call"}, {"api_name": "onapsdk.constants.CERTIFIED", "line_number": 51, "usage_type": "attribute"}, {"api_name": "onapsdk.constants", "line_number": 51, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "38936880074", "text": "from django.core.exceptions import ObjectDoesNotExist\nfrom django.shortcuts import render\nfrom board.models import Board\nfrom django.shortcuts import redirect\nfrom django.utils import timezone\nfrom django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\n\n\ndef home(request):\n return render(request, \"home.html\")\n\n\ndef board(request):\n rsBoard=Board.objects.all()\n\n return render(request, \"board_list.html\", {\n 'rsBoard': rsBoard\n })\n\n\ndef board_write(request):\n return render(request, \"board_write.html\")\n\n\ndef board_insert(request):\n btitle = request.GET['b_title']\n bnote = request.GET['b_note']\n bwriter = request.GET['b_writer']\n bdate = timezone.now()\n\n if btitle != \"\":\n Board.objects.create(b_title=btitle, b_note=bnote, b_writer=bwriter, b_date=bdate)\n return redirect('/board_ajax')\n else:\n return redirect('/board_write')\n\n\ndef board_insert_ajax(request):\n btitle = request.GET['b_title']\n bnote = request.GET['b_note']\n bwriter = request.GET['b_writer']\n bdate = timezone.now()\n\n if btitle != \"\":\n Board.objects.create(b_title=btitle, b_note=bnote, b_writer=bwriter, b_date=bdate)\n return redirect('/board_ajax')\n else:\n return redirect('/board_write')\n\n\ndef board_view(request):\n bno = request.GET['b_no']\n rsData = Board.objects.get(b_no=bno)\n rsData.b_count += 1\n rsData.save()\n\n rsDetail = Board.objects.filter(b_no=bno)\n\n return render(request, \"board_view.html\", {\n 'rsDetail': rsDetail\n })\n\n\ndef board_edit(request):\n bno = request.GET['b_no']\n rsDetail = Board.objects.filter(b_no=bno)\n\n return render(request, \"board_edit.html\", {\n 'rsDetail': rsDetail\n })\n\n\ndef board_update(request):\n bno = request.GET['b_no']\n btitle = request.GET['b_title']\n bnote = request.GET['b_note']\n bwriter = request.GET['b_writer']\n bdate = timezone.now()\n\n try:\n board = Board.objects.get(b_no=bno)\n if btitle != \"\":\n board.b_title = btitle\n if bnote != \"\":\n board.b_note = bnote\n if bwriter != \"\":\n board.b_writer = bwriter\n board.b_date = bdate\n\n try:\n board.save()\n return redirect('/board')\n except ValueError:\n return Response({\"success\": False, \"msg\": \"에러입니다.\"})\n\n except ObjectDoesNotExist:\n return Response({\"success\": False, \"msg\": \" 게시글 없음.\"})\n\n\ndef board_delete(request):\n bno = request.GET['b_no']\n Board.objects.get(b_no=bno).delete()\n\n return redirect('/board')\n\n\ndef board_ajax(request):\n rsBoard = Board.objects.all()\n\n return render(request, \"board_ajax.html\", {\n 'rsBoard': rsBoard\n })\n\n\n@csrf_exempt\ndef board_deleteajax(request):\n bno = request.GET['b_no']\n Board.objects.get(b_no=bno).delete()\n\n data={}\n data['result_msg'] = '삭제되었습니다.'\n\n return JsonResponse(data, content_type=\"application/json\")\n\n\ndef portfolio(request):\n rsBoard = Board.objects.all()\n\n return render(request, \"portfolio.html\", {\n 'rsBoard': rsBoard\n })\n\n\ndef portfolio_detail(request):\n rsBoard = Board.objects.all()\n\n return render(request, \"portfolio_details.html\", {\n 'rsBoard': rsBoard\n })", "repo_name": "jihoon-jang/Django_board", "sub_path": "board/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3325, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "board.models.Board.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 30, "usage_type": "name"}, {"api_name": "board.models.Board.objects.create", "line_number": 33, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 43, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 43, "usage_type": "name"}, {"api_name": "board.models.Board.objects.create", "line_number": 46, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 46, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "board.models.Board.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 54, "usage_type": "name"}, {"api_name": "board.models.Board.objects.filter", "line_number": 58, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 60, "usage_type": "call"}, {"api_name": "board.models.Board.objects.filter", "line_number": 67, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 69, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 79, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 79, "usage_type": "name"}, {"api_name": "board.models", "line_number": 82, "usage_type": "name"}, {"api_name": "board.models.Board.objects.get", "line_number": 82, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 82, "usage_type": "name"}, {"api_name": "board.models.b_title", "line_number": 84, "usage_type": "attribute"}, {"api_name": "board.models", "line_number": 84, "usage_type": "name"}, {"api_name": "board.models.b_note", "line_number": 86, "usage_type": "attribute"}, {"api_name": "board.models", "line_number": 86, "usage_type": "name"}, {"api_name": "board.models.b_writer", "line_number": 88, "usage_type": "attribute"}, {"api_name": "board.models", "line_number": 88, "usage_type": "name"}, {"api_name": "board.models.b_date", "line_number": 89, "usage_type": "attribute"}, {"api_name": "board.models", "line_number": 89, "usage_type": "name"}, {"api_name": "board.models.save", "line_number": 92, "usage_type": "call"}, {"api_name": "board.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 93, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 97, "usage_type": "name"}, {"api_name": "board.models.Board.objects.get", "line_number": 103, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "board.models.Board.objects.all", "line_number": 109, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 109, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 111, "usage_type": "call"}, {"api_name": "board.models.Board.objects.get", "line_number": 119, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 119, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 124, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 116, "usage_type": "name"}, {"api_name": "board.models.Board.objects.all", "line_number": 128, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 128, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 130, "usage_type": "call"}, {"api_name": "board.models.Board.objects.all", "line_number": 136, "usage_type": "call"}, {"api_name": "board.models.Board.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "board.models.Board", "line_number": 136, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "44088924010", "text": "from selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nimport time\r\n\r\n\r\ndef check_is_available(driver, query):\r\n\tcount = 0\r\n\r\n\twhile True:\r\n\t\tif count > 3:\r\n\t\t\tbreak\r\n\r\n\t\ttry:\r\n\t\t\telement = WebDriverWait(driver, 10).until(EC.presence_of_element_located((query[0], query[1])))\r\n\r\n\t\texcept:\r\n\t\t\ttime.sleep(3)\r\n\t\t\tcontinue\r\n\r\n\t\telse:\r\n\t\t\tusable_element = driver.find_element(query[0], query[1])\r\n\t\t\tbreak\r\n\r\n\t\tcount += 1\r\n\r\n\tif count == 4:\r\n\t\tdriver.close()\r\n\r\n\treturn usable_element\r\n\r\n\r\ndef start_messaging(user_nme, pswd, message):\r\n\tdriver = webdriver.Firefox()\r\n\tdriver.get(\"https://www.instagram.com\")\r\n\r\n\tusername = check_is_available(driver, (By.NAME, \"username\"))\r\n\tpassword = check_is_available(driver, (By.NAME, \"password\"))\r\n\tlogin_button = check_is_available(driver, (By.XPATH, \"/html/body/div[1]/section/main/article/div[2]/div[1]/div/form/div/div[3]/button\"))\r\n\r\n\tusername.clear()\r\n\tpassword.clear()\r\n\r\n\tusername.send_keys(user_nme)\r\n\tpassword.send_keys(pswd)\r\n\tlogin_button.click()\r\n\r\n\tnot_now_button = check_is_available(driver, (By.XPATH, \"/html/body/div[6]/div/div/div/div[3]/button[2]\"))\r\n\tnot_now_button.click()\r\n\r\n\toff_notification_button = check_is_available(driver, (By.XPATH, \"/html/body/div[4]/div/div/div/div[3]/button[2]\"))\r\n\toff_notification_button.click()\r\n\r\n\tinbox_button = check_is_available(driver, (By.XPATH, \"/html/body/div[1]/section/nav/div[2]/div/div/div[3]/div/div[2]/a\"))\r\n\tinbox_button.click()\r\n\r\n\ttime.sleep(3)\r\n\r\n\tchat_count = driver.find_elements_by_class_name(\"_7UhW9.xLCgt.MMzan.KV-D4.fDxYl\")\r\n\tchat_id_list = []\r\n\tdone_id_list = []\r\n\r\n\tfor chat in chat_count:\r\n\t\tif chat.text not in chat_id_list:\r\n\t\t\tchat_id_list.append(chat.text)\r\n\r\n\tid_index = 0\r\n\r\n\twhile len(done_id_list) != len(chat_id_list):\r\n\r\n\t\tif chat_id_list[id_index] not in done_id_list:\r\n\t\t\tdone_id_list.append(chat_id_list[id_index])\r\n\r\n\t\t\tmessage_button = check_is_available(driver, (By.CLASS_NAME, \"wpO6b.ZQScA\"))\r\n\t\t\tmessage_button.click()\r\n\r\n\t\t\tchat_search_button = check_is_available(driver, (By.CLASS_NAME, \"j_2Hd.uMkC7.M5V28\"))\r\n\t\t\tchat_search_button.clear()\r\n\t\t\tchat_search_button.send_keys(done_id_list[-1])\r\n\r\n\t\t\ttime.sleep(3)\r\n\r\n\t\t\tfirst_result = check_is_available(driver, (By.CLASS_NAME, \"Igw0E.rBNOH.eGOV_.ybXk5._4EzTm.XfCBB.HVWg4\"))\r\n\t\t\tfirst_result.click()\r\n\r\n\t\t\ttime.sleep(2)\r\n\r\n\t\t\tnext_button = check_is_available(driver, (By.XPATH, \"/html/body/div[5]/div/div/div[1]/div/div[2]/div/button\"))\r\n\t\t\tnext_button.click()\r\n\r\n\t\t\ttime.sleep(2)\r\n\r\n\t\t\tmessage_box = check_is_available(driver, (By.TAG_NAME, \"textarea\"))\r\n\r\n\t\t\ttime.sleep(1)\r\n\r\n\t\t\tmessage_box.clear()\r\n\t\t\tmessage_box.send_keys(message)\r\n\r\n\t\t\ttime.sleep(1)\r\n\r\n\t\t\tmessage_box.send_keys(Keys.RETURN)\r\n\r\n\t\t\ttime.sleep(2)\r\n\r\n\r\n\t\tid_index += 1\r\n\r\n\t\tif id_index == len(chat_id_list):\r\n\t\t\tid_index = 0\r\n\r\n\ttime.sleep(10)\r\n\tdriver.close()\r\n\treturn 1\r\n", "repo_name": "SleepinNinja/Instagram-message-Automation", "sub_path": "message_automation.py", "file_name": "message_automation.py", "file_ext": "py", "file_size_in_byte": 3018, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 17, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 36, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.NAME", "line_number": 39, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 39, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.NAME", "line_number": 40, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 40, "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.XPATH", "line_number": 50, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 50, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 53, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 53, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 56, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 56, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 76, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 76, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 79, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 79, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 85, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 85, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 90, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 90, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 93, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 95, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 95, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 104, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 104, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "4550860154", "text": "\nfrom django.contrib.auth.models import User\nfrom django.utils.timezone import now\nimport datetime\nfrom students.models import *\nfrom django import forms\nfrom django.db import transaction, IntegrityError\nfrom datetime import date, timedelta\nfrom analyzer.utils import pin_generator\n#from scheduler import create_event, schedule_assignment\nfrom states.models import Country\nfrom django.contrib.admin.widgets import AdminDateWidget\nfrom django.forms.extras.widgets import SelectDateWidget\nfrom django.utils.text import slugify\nfrom core.models import Activation, StudentSetup\nimport uuid\nfrom .utils import create_user\n\n\n# class ScholarshipForm(forms.ModelForm):\n\n# class Meta:\n# model = Scholarhip\n# fields = (\n# 'title',\n# 'provider',\n# 'location',\n# 'website',\n# )\n\n\nclass DocumentForm(forms.ModelForm):\n\n class Meta:\n model = Document\n fields = (\n 'name',\n 'attached_file',\n 'description'\n )\n\n\n\nclass BasicProfileForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(BasicProfileForm, self).__init__(*args, **kwargs)\n self.fields['email'].widget.attrs = {'placeholder' : 'Email e.g. example@example.com', 'class': 'form-control'}\n self.fields['first_name'].widget.attrs = {'placeholder' : 'Student Surname', 'class': 'form-control'}\n self.fields['last_name'].widget.attrs = {'placeholder' : 'First Name', 'class': 'form-control'}\n self.fields['middle_name'].widget.attrs = {'placeholder' : 'Other Name', 'class': 'form-control'}\n self.fields['birth_date'].widget.attrs = {'class': 'form-control'}\n self.fields['phone_number'].widget.attrs = {'class': 'form-control'}\n\n class Meta:\n model = Student\n fields = (\n 'first_name',\n 'last_name',\n 'middle_name',\n 'email',\n 'birth_date',\n 'phone_number'\n )\n\n def save(self, commit=True):\n if self.instance.pk:\n user = self.instance.user\n user.first_name = self.cleaned_data['first_name']\n user.last_name = self.cleaned_data['last_name']\n user.email = self.cleaned_data['email']\n user.save()\n return super(BasicProfileForm, self).save(commit=commit)\n\n\nclass PersonalInformationForm(forms.ModelForm):\n \"\"\"Edit an student's personal information.\"\"\"\n def __init__(self, *args, **kwargs):\n super(PersonalInformationForm, self).__init__(*args, **kwargs)\n self.fields['sex'].widget.attrs = {'class': 'form-control'}\n self.fields['marital_status'].widget.attrs = {'class': 'form-control'}\n self.fields['address'].widget.attrs = {'placeholder' : 'Your location e.g #4 glo street, Ikeja ', 'class': 'form-control'}\n self.fields['state_of_residence'].widget.attrs = {'class': 'form-control'}\n self.fields['state_of_origin'].widget.attrs = {'class': 'form-control'}\n self.fields['country'].widget.attrs = {'class': 'form-control'}\n self.fields['religion'].widget.attrs = {'class': 'form-control'}\n\n class Meta:\n model = Student\n fields = (\n 'sex',\n 'marital_status',\n 'address',\n 'state_of_residence',\n 'state_of_origin',\n 'country',\n 'religion',\n )\n\n\nclass SchoolForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(SchoolForm, self).__init__(*args, **kwargs)\n self.fields['reg_number'].widget.attrs = {'placeholder' : 'Reg Number', 'class': 'form-control'}\n self.fields['institution'].widget.attrs = {'class': 'form-control'}\n self.fields['library_id_number'].widget.attrs = {'placeholder' : 'Enter Library Id', 'class': 'form-control'}\n self.fields['program_type'].widget.attrs = {'class': 'form-control'}\n self.fields['department'].widget.attrs = {'class': 'form-control'}\n self.fields['faculty'].widget.attrs = {'class': 'form-control'}\n self.fields['year_of_admission'].widget.attrs = {'class': 'form-control'}\n self.fields['level'].widget.attrs = {'class': 'form-control'}\n self.fields['course_duration'].widget.attrs = {'max_length': 1, 'class': 'form-control'}\n\n\n class Meta:\n model = Student\n fields = (\n 'reg_number',\n 'institution',\n 'library_id_number',\n 'program_type',\n 'department',\n 'faculty',\n 'year_of_admission',\n 'level',\n 'course_duration'\n )\n\nclass BankForm(forms.ModelForm):\n\n class Meta:\n model = Student\n fields = (\n 'bank',\n 'bank_account_number',\n )\n\n\nclass StudentCreationForm(forms.ModelForm):\n year_of_admission = forms.DateField(widget = SelectDateWidget(years=range(1990, datetime.date.today().year+50), attrs=({'class': 'form-control', 'style': 'width: 30%; display: inline-block;'})))\n\n def __init__(self, *args, **kwargs):\n super(StudentCreationForm, self).__init__(*args, **kwargs)\n self.fields['email'].widget.attrs = {'placeholder' : 'Email e.g. example@example.com', 'class': 'form-control'}\n self.fields['last_name'].widget.attrs = {'placeholder' : 'Student Surname', 'class': 'form-control'}\n self.fields['first_name'].widget.attrs = {'placeholder' : 'First Name', 'class': 'form-control'}\n self.fields['sex'].widget.attrs = {'class': 'form-control'}\n self.fields['reg_number'].widget.attrs = {'placeholder' : 'Reg Number', 'class': 'form-control'}\n self.fields['faculty'].widget.attrs = {'class': 'form-control'}\n self.fields['department'].widget.attrs = {'class': 'form-control'}\n self.fields['level'].widget.attrs = {'class': 'form-control'}\n self.fields['course_duration'].widget.attrs = {'max_length': 1, 'class': 'form-control'}\n\n class Meta:\n model = Student\n fields = (\n 'email',\n 'last_name',\n 'first_name',\n 'sex',\n 'reg_number',\n 'faculty',\n 'department',\n 'level',\n 'year_of_admission',\n 'course_duration'\n )\n\n\n @transaction.atomic\n def save(self, commit=True):\n user = create_user(self.cleaned_data['last_name'], self.cleaned_data['first_name'])\n # Set default password to this user's username and birth date (if provided):\n # password = pin_generator()\n user.username = self.cleaned_data['reg_number']\n user.set_password(self.cleaned_data['reg_number'])\n user.save()\n\n setup = StudentSetup(user=user)\n setup.save()\n\n activation = Activation(user=user)\n activation.save()\n\n instance = super(StudentCreationForm, self).save(commit=False)\n instance.user = user\n orig = slugify(instance.last_name)\n if Student.objects.filter(slug=instance.slug).exists():\n instance.slug = \"%s-%s\" % (orig, uuid.uuid4())\n else:\n instance.slug = \"%s-%s\" % (orig, uuid.uuid4())\n\n instance.save()\n return instance\n\n\nclass ScholarshipForm(forms.ModelForm):\n\n class Meta:\n model = Scholarship\n fields = {\n 'title',\n 'provider',\n 'location',\n }\n", "repo_name": "pastorenue/result_analytics", "sub_path": "students/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 7435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.forms.ModelForm", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 44, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 75, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 100, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 128, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 128, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 138, "usage_type": "name"}, {"api_name": "django.forms.DateField", "line_number": 139, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 139, "usage_type": "name"}, {"api_name": "django.forms.extras.widgets.SelectDateWidget", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 139, "usage_type": "attribute"}, {"api_name": "utils.create_user", "line_number": 171, "usage_type": "call"}, {"api_name": "core.models.StudentSetup", "line_number": 178, "usage_type": "call"}, {"api_name": "core.models.Activation", "line_number": 181, "usage_type": "call"}, {"api_name": "django.utils.text.slugify", "line_number": 186, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 188, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 190, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 169, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 169, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 196, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 196, "usage_type": "name"}]} +{"seq_id": "74342403712", "text": "import time\nimport logging\nimport numpy as np\nimport cv2\nimport go_vncdriver\nimport tensorflow as tf\nimport gym\nfrom gym import wrappers\nfrom gym.spaces.box import Box\nfrom universe import vectorized\nfrom universe.wrappers import Unvectorize, Vectorize\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\nNUM_EPISODES = 5\nCHECKPOINT_LOCATION = '/home/ubuntu/pacman/train'\nMONITOR_LOCATION = \"./pacman-1\"\n\n#\n# This was copy-pasted from openai/universe-starter-agent\n#\ndef DiagnosticsInfo(env, *args, **kwargs):\n return vectorized.VectorizeFilter(env, DiagnosticsInfoI, *args, **kwargs)\n\nclass DiagnosticsInfoI(vectorized.Filter):\n def __init__(self, log_interval=503):\n super(DiagnosticsInfoI, self).__init__()\n\n self._episode_time = time.time()\n self._last_time = time.time()\n self._local_t = 0\n self._log_interval = log_interval\n self._episode_reward = 0\n self._episode_length = 0\n self._all_rewards = []\n self._num_vnc_updates = 0\n self._last_episode_id = -1\n\n def _after_reset(self, observation):\n logger.info('Resetting environment')\n self._episode_reward = 0\n self._episode_length = 0\n self._all_rewards = []\n return observation\n\n def _after_step(self, observation, reward, done, info):\n to_log = {}\n if self._episode_length == 0:\n self._episode_time = time.time()\n\n self._local_t += 1\n if info.get(\"stats.vnc.updates.n\") is not None:\n self._num_vnc_updates += info.get(\"stats.vnc.updates.n\")\n\n if self._local_t % self._log_interval == 0:\n cur_time = time.time()\n elapsed = cur_time - self._last_time\n fps = self._log_interval / elapsed\n self._last_time = cur_time\n cur_episode_id = info.get('vectorized.episode_id', 0)\n to_log[\"diagnostics/fps\"] = fps\n if self._last_episode_id == cur_episode_id:\n to_log[\"diagnostics/fps_within_episode\"] = fps\n self._last_episode_id = cur_episode_id\n if info.get(\"stats.gauges.diagnostics.lag.action\") is not None:\n to_log[\"diagnostics/action_lag_lb\"] = info[\"stats.gauges.diagnostics.lag.action\"][0]\n to_log[\"diagnostics/action_lag_ub\"] = info[\"stats.gauges.diagnostics.lag.action\"][1]\n if info.get(\"reward.count\") is not None:\n to_log[\"diagnostics/reward_count\"] = info[\"reward.count\"]\n if info.get(\"stats.gauges.diagnostics.clock_skew\") is not None:\n to_log[\"diagnostics/clock_skew_lb\"] = info[\"stats.gauges.diagnostics.clock_skew\"][0]\n to_log[\"diagnostics/clock_skew_ub\"] = info[\"stats.gauges.diagnostics.clock_skew\"][1]\n if info.get(\"stats.gauges.diagnostics.lag.observation\") is not None:\n to_log[\"diagnostics/observation_lag_lb\"] = info[\"stats.gauges.diagnostics.lag.observation\"][0]\n to_log[\"diagnostics/observation_lag_ub\"] = info[\"stats.gauges.diagnostics.lag.observation\"][1]\n\n if info.get(\"stats.vnc.updates.n\") is not None:\n to_log[\"diagnostics/vnc_updates_n\"] = info[\"stats.vnc.updates.n\"]\n to_log[\"diagnostics/vnc_updates_n_ps\"] = self._num_vnc_updates / elapsed\n self._num_vnc_updates = 0\n if info.get(\"stats.vnc.updates.bytes\") is not None:\n to_log[\"diagnostics/vnc_updates_bytes\"] = info[\"stats.vnc.updates.bytes\"]\n if info.get(\"stats.vnc.updates.pixels\") is not None:\n to_log[\"diagnostics/vnc_updates_pixels\"] = info[\"stats.vnc.updates.pixels\"]\n if info.get(\"stats.vnc.updates.rectangles\") is not None:\n to_log[\"diagnostics/vnc_updates_rectangles\"] = info[\"stats.vnc.updates.rectangles\"]\n if info.get(\"env_status.state_id\") is not None:\n to_log[\"diagnostics/env_state_id\"] = info[\"env_status.state_id\"]\n\n if reward is not None:\n self._episode_reward += reward\n if observation is not None:\n self._episode_length += 1\n self._all_rewards.append(reward)\n\n if done:\n logger.info('Episode terminating: episode_reward=%s episode_length=%s', self._episode_reward, self._episode_length)\n total_time = time.time() - self._episode_time\n to_log[\"global/episode_reward\"] = self._episode_reward\n to_log[\"global/episode_length\"] = self._episode_length\n to_log[\"global/episode_time\"] = total_time\n to_log[\"global/reward_per_time\"] = self._episode_reward / total_time\n self._episode_reward = 0\n self._episode_length = 0\n self._all_rewards = []\n\n return observation, reward, done, to_log\n\ndef _process_frame42(frame):\n #frame = frame[34:34+160, :160]\n # Resize by half, then down to 42x42 (essentially mipmapping). If\n # we resize directly we lose pixels that, when mapped to 42x42,\n # aren't close enough to the pixel boundary.\n frame = cv2.resize(frame, (80, 80))\n frame = cv2.resize(frame, (42, 42))\n #frame = frame.mean(2)\n frame = frame.astype(np.float32)\n frame *= (1.0 / 255.0)\n frame = np.reshape(frame, [42, 42, 3])\n return frame\n\nclass AtariRescale42x42(vectorized.ObservationWrapper):\n def __init__(self, env=None):\n super(AtariRescale42x42, self).__init__(env)\n self.observation_space = Box(0.0, 1.0, [42, 42, 3])\n\n def _observation(self, observation_n):\n return [_process_frame42(observation) for observation in observation_n]\n\n\ndef create_atari_env(env_id):\n env = gym.make(env_id)\n env = Vectorize(env)\n env = AtariRescale42x42(env)\n env = DiagnosticsInfo(env)\n env = Unvectorize(env)\n return env\n#\n# copy-paste ends here\n#\n\n\nif __name__ == \"__main__\":\n env = wrappers.Monitor(create_atari_env(\"MsPacman-v0\"), MONITOR_LOCATION, force=True)\n chkpt = tf.train.latest_checkpoint(CHECKPOINT_LOCATION)\n logger.info(\"Loading checkpoint {}\".format(chkpt))\n\n with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess, sess.as_default():\n saver = tf.train.import_meta_graph(chkpt + \".meta\", clear_devices=True)\n g = tf.get_default_graph()\n saver.restore(sess, chkpt)\n state_out_0 = tf.get_collection(\"state_out_0\")[0]\n state_out_1 = tf.get_collection(\"state_out_1\")[0]\n get_sample_op = tf.get_collection(\"greedy_action\")[0]\n get_output_state = [state_out_0, state_out_1]\n\n inp = g.get_tensor_by_name(\"global/Placeholder:0\")\n c_in = g.get_tensor_by_name(\"global/Placeholder_1:0\")\n h_in = g.get_tensor_by_name(\"global/Placeholder_2:0\")\n\n # \n # print('Trainable vars in {}:'.format(tf.get_variable_scope().name))\n # for v in var_list:\n # print(' %s %s', v.name, v.get_shape())\n # for tensor in tf.get_default_graph().as_graph_def().node:\n # print(tensor.name)\n\n lengths = []\n rewards = []\n for ep in range(NUM_EPISODES):\n obs = env.reset()\n\n initial_state = np.zeros((1,256)).astype(float)\n last_state = [initial_state, initial_state]\n\n length = 0\n reward_sum = 0\n terminal = False\n while not terminal:\n feed_dict = {inp : obs[np.newaxis],\n c_in: last_state[0], \n h_in: last_state[1]}\n\n state, sampled_action = sess.run([get_output_state, get_sample_op], feed_dict = feed_dict)\n action = sampled_action #.argmax()\n obs, reward, terminal, info = env.step(action)\n\n last_state = state\n length += 1\n reward_sum += reward\n # print(\"Episode finished in {} reward: {}\".format(length, rewards))\n lengths.append(length)\n rewards.append(reward_sum)\n\n logger.info(\"Evaluated, reward: {} +/-{}\".format(np.mean(rewards), np.std(rewards)))\n #gym.upload(MONITOR_LOCATION, api_key=)\n", "repo_name": "libfun/deephack3", "sub_path": "universe-starter-agent/eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 8135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 14, "usage_type": "attribute"}, {"api_name": "universe.vectorized.VectorizeFilter", "line_number": 24, "usage_type": "call"}, {"api_name": "universe.vectorized", "line_number": 24, "usage_type": "name"}, {"api_name": "universe.vectorized.Filter", "line_number": 26, "usage_type": "attribute"}, {"api_name": "universe.vectorized", "line_number": 26, "usage_type": "name"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}, {"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 120, "usage_type": "call"}, {"api_name": "universe.vectorized.ObservationWrapper", "line_number": 123, "usage_type": "attribute"}, {"api_name": "universe.vectorized", "line_number": 123, "usage_type": "name"}, {"api_name": "gym.spaces.box.Box", "line_number": 126, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 133, "usage_type": "call"}, {"api_name": "universe.wrappers.Vectorize", "line_number": 134, "usage_type": "call"}, {"api_name": "universe.wrappers.Unvectorize", "line_number": 137, "usage_type": "call"}, {"api_name": "gym.wrappers.Monitor", "line_number": 145, "usage_type": "call"}, {"api_name": "gym.wrappers", "line_number": 145, "usage_type": "name"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 150, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 181, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "74332613312", "text": "from dataclasses import dataclass, field\nfrom datetime import datetime\nfrom typing import Optional\nimport math\n\n# Hard limits for parameters/configuration\n# Events\nUPDATE_RATE_MIN = 2.0\nUPDATE_RATE_MAX = 10.0\nINIT_TIMEOUT_MIN = 0.0\nINIT_TIMEOUT_MAX = 60.0\n# Winching\nWIRE_LENGTH_MIN = 2.0 # m\nWIRE_LENGTH_MAX = 100.0 # m\nDROP_LENGTH_MIN = 1.0 # m\nDROP_LENGTH_MAX = WIRE_LENGTH_MAX\nMAX_DROP_MARGIN = 1.0 # m\nSLOW_DROP_LENGTH_MIN = 0.0 # m\nSLOW_DROP_LENGTH_MAX = 0.5 # m\nFAST_VELOCITY_MIN = 0.25 # m/s\nFAST_VELOCITY_MAX = 5.00 # m/s\nSLOW_VELOCITY_MIN = 0.05 # m/s\nSLOW_VELOCITY_MAX = 0.50 # m/s\nFORCE_MIN = 0.0 # N\nFORCE_MAX = 120.0 # N\nFORCE_HOME_MIN = 1.0 # N\nFORCE_SENSE_MIN = 1.0 # N\nFORCE_TUG_DETECT_MIN = 10.0 # N\nLOAD_TIME_MIN = 1.0 # s\nLOAD_TIME_MAX = 10.0 # s\nLOAD_LENGTH_MIN = 0.05 # m\nLOAD_LENGTH_MAX = 1.00 # m\nHOMING_DISTANCE_MIN = 0.20 # m\nHOMING_DISTANCE_MAX = 1.00 # m\nLOCK_TIME_MIN = 0.0 # s\nLOCK_TIME_MAX = 10.0 # s\nTIMEOUT_MULTIPLIER_MIN = 1.0\nTIMEOUT_MULTIPLIER_MAX = 10.0\nTIMEOUT_OFFSET_MIN = 0.0\nTIMEOUT_OFFSET_MAX = 300.0\n# Drop height modifier (based on distance sensor)\nDISTANCE_TO_GROUND_MIN = -1.0 # m\nDISTANCE_TO_GROUND_MAX = 10.0 # m\nMAX_MODIFICATION_MIN = 0.5 # m\nMAX_MODIFICATION_MAX = 50.0 # m\n\n\n@dataclass\nclass EventConfiguration:\n update_frequency: int\n initialization_timeout: float\n\n def validate(self, conf_path: str) -> None:\n if self.update_frequency < UPDATE_RATE_MIN or self.update_frequency > UPDATE_RATE_MAX:\n raise ValueError(f'{conf_path}.update_frequency must be in range [{UPDATE_RATE_MIN}, {UPDATE_RATE_MAX}]')\n if self.initialization_timeout < INIT_TIMEOUT_MIN or self.initialization_timeout > INIT_TIMEOUT_MAX:\n raise ValueError(f'{conf_path}.initialization_timeout must be in range '\n f'[{INIT_TIMEOUT_MIN}, {INIT_TIMEOUT_MAX}]')\n\n\n@dataclass\nclass TimeoutModifier:\n multiplier: float\n offset: float\n\n def validate(self, conf_path: str) -> None:\n if self.multiplier < TIMEOUT_MULTIPLIER_MIN or self.multiplier > TIMEOUT_MULTIPLIER_MAX:\n raise ValueError(f'{conf_path}.multiplier must be in range '\n f'[{TIMEOUT_MULTIPLIER_MIN}, {TIMEOUT_MULTIPLIER_MAX}]')\n if self.offset < TIMEOUT_OFFSET_MIN or self.offset > TIMEOUT_OFFSET_MAX:\n raise ValueError(f'{conf_path}.offset must be in range [{TIMEOUT_OFFSET_MIN}, {TIMEOUT_OFFSET_MAX}]')\n\n\n@dataclass\nclass HeightModifier:\n distance_to_ground: float\n max_modification: float\n strict: bool\n\n def validate(self, conf_path: str) -> None:\n if self.distance_to_ground < DISTANCE_TO_GROUND_MIN or self.distance_to_ground > DISTANCE_TO_GROUND_MAX:\n raise ValueError(f'{conf_path}.distance_to_ground muse be in range '\n f'[{DISTANCE_TO_GROUND_MIN}, {DISTANCE_TO_GROUND_MAX}]')\n if self.max_modification < MAX_MODIFICATION_MIN or self.max_modification > MAX_MODIFICATION_MAX:\n raise ValueError(f'{conf_path}.max_modification muse be in range '\n f'[{MAX_MODIFICATION_MIN}, {MAX_MODIFICATION_MAX}]')\n\n\n@dataclass\nclass WinchingHoming:\n velocity: float\n force: float\n force_sense: float\n lock_time: float\n timeout_modifier: TimeoutModifier = field(default=TimeoutModifier(1.5, 5.0))\n\n def validate(self, conf_path: str) -> None:\n if self.velocity < SLOW_VELOCITY_MIN or self.velocity > SLOW_VELOCITY_MAX:\n raise ValueError(f'{conf_path}.velocity must be in range [{SLOW_VELOCITY_MIN}, {SLOW_VELOCITY_MAX}]')\n if self.force < FORCE_HOME_MIN or self.force > FORCE_MAX:\n raise ValueError(f'{conf_path}.force must be in range [{FORCE_HOME_MIN}, {FORCE_MAX}]')\n if self.force_sense < FORCE_SENSE_MIN or self.force_sense > self.force:\n raise ValueError(f'{conf_path}.force must be in range [{FORCE_SENSE_MIN}, {conf_path}.force]')\n if self.lock_time < LOCK_TIME_MIN or self.lock_time > LOCK_TIME_MAX:\n raise ValueError(f'{conf_path}.lock_time must be in range [{LOCK_TIME_MIN}, {LOCK_TIME_MAX}]')\n self.timeout_modifier.validate('timeout_modifier')\n\n\n@dataclass\nclass WinchingHome:\n velocity: float\n force: float\n\n def validate(self, conf_path: str) -> None:\n if self.velocity < SLOW_VELOCITY_MIN or self.velocity > SLOW_VELOCITY_MAX:\n raise ValueError(f'{conf_path}.velocity must be in range [{SLOW_VELOCITY_MIN}, {SLOW_VELOCITY_MAX}]')\n if self.force < FORCE_HOME_MIN or self.force > FORCE_MAX:\n raise ValueError(f'{conf_path}.force must be in range [{FORCE_HOME_MIN}, {FORCE_MAX}]')\n\n\n@dataclass\nclass WinchingLoad:\n time: float\n velocity: float\n force: float\n length: float\n timeout_modifier: TimeoutModifier = field(default=TimeoutModifier(1.5, 2.0))\n\n def validate(self, conf_path: str) -> None:\n if self.time < LOAD_TIME_MIN or self.time > LOAD_TIME_MAX:\n raise ValueError(f'{conf_path}.time must be in range [{LOAD_TIME_MIN}, {LOAD_TIME_MAX}]')\n if self.velocity < SLOW_VELOCITY_MIN or self.velocity > SLOW_VELOCITY_MAX:\n raise ValueError(f'{conf_path}.velocity must be in range [{SLOW_VELOCITY_MIN}, {SLOW_VELOCITY_MAX}]')\n if self.force < FORCE_MIN or self.force > FORCE_MAX:\n raise ValueError(f'{conf_path}.force must be in range [{FORCE_MIN}, {FORCE_MAX}]')\n if self.length < LOAD_LENGTH_MIN or self.length > LOAD_LENGTH_MAX:\n raise ValueError(f'{conf_path}.length must be in range [{LOAD_LENGTH_MIN}, {LOAD_LENGTH_MAX}]')\n self.timeout_modifier.validate('timeout_modifier')\n\n\n@dataclass\nclass WinchingDrop:\n velocity: float\n velocity_slow: float\n force: float\n force_brake: float\n force_hold: float\n allow_while_retracting: bool\n length: float\n length_slow: float = 0.0\n force_tug_detect: float = math.nan\n timeout_modifier: TimeoutModifier = field(default=TimeoutModifier(1.5, 5.0))\n height_modifier: Optional[HeightModifier] = None\n\n def validate(self, conf_path: str) -> None:\n if self.velocity < FAST_VELOCITY_MIN or self.velocity > FAST_VELOCITY_MAX:\n raise ValueError(f'{conf_path}.velocity must be in range [{FAST_VELOCITY_MIN}, {FAST_VELOCITY_MAX}]')\n if self.velocity_slow < SLOW_VELOCITY_MIN or self.velocity_slow > SLOW_VELOCITY_MAX:\n raise ValueError(f'{conf_path}.velocity_slow must be in range [{SLOW_VELOCITY_MIN}, {SLOW_VELOCITY_MAX}]')\n if self.force < FORCE_MIN or self.force > FORCE_MAX:\n raise ValueError(f'{conf_path}.force must be in range [{FORCE_MIN}, {FORCE_MAX}]')\n if self.force_brake < FORCE_MIN or self.force_brake > FORCE_MAX:\n raise ValueError(f'{conf_path}.force_brake must be in range [{FORCE_MIN}, {FORCE_MAX}]')\n if self.force_hold < FORCE_MIN or self.force_hold > FORCE_MAX:\n raise ValueError(f'{conf_path}.force_hold must be in range [{FORCE_MIN}, {FORCE_MAX}]')\n if math.isfinite(self.force_tug_detect):\n if self.force_tug_detect > self.force_hold:\n raise ValueError(f'{conf_path}.force_tug_detect can not be greater than {conf_path}.force_hold')\n if self.force_tug_detect < FORCE_TUG_DETECT_MIN:\n raise ValueError(f'{conf_path}.force_tug_detect can not be less than {FORCE_TUG_DETECT_MIN}')\n else:\n if not math.isnan(self.force_tug_detect):\n raise ValueError(f'{conf_path}.force_tug_detect must be a finite number or nan')\n if self.length < DROP_LENGTH_MIN or self.length > DROP_LENGTH_MAX:\n raise ValueError(f'{conf_path}.length must be in range [{DROP_LENGTH_MIN}, {DROP_LENGTH_MAX}]')\n if self.length_slow < SLOW_DROP_LENGTH_MIN or self.length_slow > SLOW_DROP_LENGTH_MAX:\n raise ValueError(f'{conf_path}.length_slow must be in range '\n f'[{SLOW_DROP_LENGTH_MIN}, {SLOW_DROP_LENGTH_MAX}]')\n if self.height_modifier is not None:\n self.height_modifier.validate(f'{conf_path}.height_modifier')\n self.timeout_modifier.validate('timeout_modifier')\n\n\n@dataclass\nclass WinchingRetract:\n velocity: float\n force: float\n homing_distance: float\n timeout_modifier: TimeoutModifier = field(default=TimeoutModifier(1.5, 5.0))\n\n def validate(self, conf_path: str) -> None:\n if self.velocity < FAST_VELOCITY_MIN or self.velocity > FAST_VELOCITY_MAX:\n raise ValueError(f'{conf_path}.velocity must be in range [{FAST_VELOCITY_MIN}, {FAST_VELOCITY_MAX}]')\n if self.force < FORCE_MIN or self.force > FORCE_MAX:\n raise ValueError(f'{conf_path}.force must be in range [{FORCE_MIN}, {FORCE_MAX}]')\n if self.homing_distance < HOMING_DISTANCE_MIN or self.homing_distance > HOMING_DISTANCE_MAX:\n raise ValueError(f'{conf_path}.homing_distance must be in range '\n f'[{HOMING_DISTANCE_MIN}, {HOMING_DISTANCE_MAX}]')\n self.timeout_modifier.validate('timeout_modifier')\n\n\n@dataclass\nclass WinchingConfiguration:\n wire_length: float\n homing: WinchingHoming\n home: WinchingHome\n load: WinchingLoad\n drop: WinchingDrop\n retract: WinchingRetract\n\n def validate(self, conf_path: str) -> None:\n self.homing.validate(conf_path + 'homing')\n self.home.validate(conf_path + 'home')\n self.load.validate(conf_path + 'load')\n self.drop.validate(conf_path + 'drop')\n self.retract.validate(conf_path + 'retract')\n if self.wire_length < WIRE_LENGTH_MIN or self.wire_length > WIRE_LENGTH_MAX:\n raise ValueError(f'{conf_path}.wire_length must be in range [{WIRE_LENGTH_MIN}, {WIRE_LENGTH_MAX}]')\n\n # Some additional checks that compares values from different sub components\n if self.wire_length < self.drop.length + MAX_DROP_MARGIN:\n raise ValueError(f'{conf_path}.wire_length must be greater than or equal to {conf_path}.drop.length '\n f'+ {MAX_DROP_MARGIN}')\n if self.drop.height_modifier is not None:\n if self.wire_length < self.drop.length + self.drop.height_modifier.max_modification + MAX_DROP_MARGIN:\n raise ValueError(f'{conf_path}.wire_length must be greater than or equal to '\n f'{conf_path}.drop.length + {conf_path}.drop.height_modifier.max_modification '\n f'+ {MAX_DROP_MARGIN}')\n if self.drop.length + self.drop.height_modifier.max_modification > DROP_LENGTH_MAX:\n raise ValueError(f'{conf_path}.drop.length + {conf_path}.drop.height_modifier.max_modification '\n f'must not exceed {DROP_LENGTH_MAX}')\n if self.drop.length - self.drop.height_modifier.max_modification < DROP_LENGTH_MIN:\n raise ValueError(f'{conf_path}.drop.length - {conf_path}.drop.height_modifier.max_modification '\n f'must not be at least {DROP_LENGTH_MIN}')\n\n\n@dataclass\nclass TelemetryLoggingConfiguration:\n path_format: str\n verbose: bool = False\n\n def validate(self, conf_path: str) -> None:\n try:\n sample_path = datetime.utcnow().strftime(self.path_format)\n if len(sample_path) < 1:\n # TODO Could add more checks to tests if the result is a valid path, and that we can create file\n raise ValueError(f'{conf_path}.path_format evaluates to an empty string')\n except Exception:\n raise ValueError(f'{conf_path}.path_format has a unknown error')\n", "repo_name": "satoki22ti/companioncomputer", "sub_path": "config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 11785, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "dataclasses.dataclass", "line_number": 48, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 61, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 74, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 95, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 89, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 109, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 127, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 121, "usage_type": "name"}, {"api_name": "math.nan", "line_number": 151, "usage_type": "attribute"}, {"api_name": "dataclasses.field", "line_number": 152, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 153, "usage_type": "name"}, {"api_name": "math.isfinite", "line_number": 166, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 172, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 141, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 189, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 184, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 202, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 244, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 244, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 237, "usage_type": "name"}]} +{"seq_id": "73536801792", "text": "import torch\nimport os\nfrom torch.utils.data import DataLoader\nimport torch.nn as nn\nfrom dataset import SpeechDataset, TDNN_SpeechDataset\nfrom tensorboardX import SummaryWriter\nimport pandas as pd\nfrom helper.utils import weights_init, print_eval\nfrom model import LSTM, DSCNN, TDNN\nimport numpy as np\nimport random\n\ndef setup_seed(seed):\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n torch.backends.cudnn.deterministic = True\n\ndef configuration():\n config = {}\n model_name = 'TDNN'\n config['epoch'] = 90\n config['batch_size'] = 100\n\n config['train_csv'] = './csv_linux/train_fulllabel.csv'\n config['val_csv'] = './csv_win/test_mfcc.csv'\n\n df = pd.read_csv(config['train_csv'])\n config['wanted_words'] = df['label'].unique()\n print(\"The number of wanted words is {}\".format(len(config[\"wanted_words\"])))\n config['device'] = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n config['output_path'] = './models_pt/' + model_name\n os.makedirs(config['output_path'], exist_ok=True)\n config['logdir'] = './models_pt/' + model_name + '/log'\n return config\n\ndef train():\n setup_seed(20)\n config = configuration()\n\n writer1 = SummaryWriter(config['logdir'] + '/Train')\n\n # dataset\n train_dataset = TDNN_SpeechDataset(config['train_csv'], config['wanted_words'])\n test_dataset = TDNN_SpeechDataset(config['val_csv'], config['wanted_words'])\n\n # model\n model = TDNN(n_labels=12)\n model = model.to(config['device'])\n model.apply(weights_init)\n\n # optimizer\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n criterion = nn.CrossEntropyLoss()\n\n # data_loader\n step = 0\n best_acc = 0\n train_loader = DataLoader(\n dataset=train_dataset,\n batch_size=config['batch_size'],\n shuffle=True,\n num_workers=4)\n\n test_loader = DataLoader(\n dataset=test_dataset,\n batch_size=config['batch_size'],\n shuffle=True,\n num_workers=4)\n\n for epoch in range(config['epoch']):\n for data, labels in train_loader:\n step += 1\n optimizer.zero_grad()\n data = data.to(config['device'])\n labels = labels.to(config['device'])\n\n scores, _ = model(data)\n loss = criterion(scores, labels)\n\n loss.backward()\n optimizer.step()\n writer1.add_scalar('Train/Loss', loss, step)\n print_eval(\"train step #{}\".format(step), scores, labels, loss)\n\n if step % 800 == 0:\n model.save(config['output_path'] + '/model_' + str(step) + '.pt')\n print('model saved !')\n model.eval()\n with torch.no_grad():\n accs = []\n for data, labels in test_loader:\n data = data.to(config['device'])\n labels = labels.to(config['device'])\n scores, _ = model(data)\n loss = criterion(scores, labels)\n accs.append(print_eval(\"dev\", scores, labels, loss))\n avg_acc = np.mean(accs)\n print(\"final dev accuracy: {}, best accuracy: {}\".format(avg_acc, best_acc))\n if avg_acc > best_acc:\n model.save(config['output_path'] + '/model_best.pt')\n best_acc = avg_acc\n writer1.add_scalar('Test/Acc', avg_acc, step)\n model.train()\n\ndef evaluate(model_file):\n config = configuration()\n model = TDNN(n_labels=12).cuda()\n model.load(model_file)\n test_dataset = TDNN_SpeechDataset(config[\"val_csv\"], config[\"wanted_words\"])\n test_loader = DataLoader(\n dataset=test_dataset,\n batch_size=1,\n shuffle=True,\n num_workers=8)\n model.eval()\n with torch.no_grad():\n positive_examples = 0\n for data, labels in test_loader:\n data = data.to(config['device'])\n labels = labels.to(config['device'])\n scores, _ = model(data)\n if torch.argmax(scores) == labels:\n positive_examples += 1\n avg_acc = positive_examples / len(test_dataset)\n print(\"final dev accuracy: {}\".format(avg_acc))\n\nif __name__ == '__main__':\n # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n train()\n # evaluate(\"./models_pt/TDNN/model_best.pt\")\n", "repo_name": "liuli1996/keyword-spotting-research", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 4479, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "60", "api": [{"api_name": "torch.manual_seed", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 32, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 42, "usage_type": "call"}, {"api_name": "dataset.TDNN_SpeechDataset", "line_number": 45, "usage_type": "call"}, {"api_name": "dataset.TDNN_SpeechDataset", "line_number": 46, "usage_type": "call"}, {"api_name": "model.TDNN", "line_number": 49, "usage_type": "call"}, {"api_name": "model.to", "line_number": 50, "usage_type": "call"}, {"api_name": "model.apply", "line_number": 51, "usage_type": "call"}, {"api_name": "helper.utils.weights_init", "line_number": 51, "usage_type": "argument"}, {"api_name": "torch.optim.Adam", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 54, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 66, "usage_type": "call"}, {"api_name": "helper.utils.print_eval", "line_number": 85, "usage_type": "call"}, {"api_name": "model.save", "line_number": 88, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 91, "usage_type": "call"}, {"api_name": "helper.utils.print_eval", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 99, "usage_type": "call"}, {"api_name": "model.save", "line_number": 102, "usage_type": "call"}, {"api_name": "model.train", "line_number": 105, "usage_type": "call"}, {"api_name": "model.TDNN", "line_number": 109, "usage_type": "call"}, {"api_name": "model.load", "line_number": 110, "usage_type": "call"}, {"api_name": "dataset.TDNN_SpeechDataset", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 112, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "71701560512", "text": "import shutil\nimport os\nimport sys\nimport re\n\nimport translators\n\ndef script_dir_with(*paths) -> str:\n return os.path.join(os.path.dirname(__file__), *paths)\n\n\ndef err(*msg: str, code=1, separator=\" \"):\n print(\"\\x1b[31;1merror\\x1b[0m: {}\".format(f\"{separator}\".join(msg)), file=sys.stderr)\n exit(code)\n\n\ndef ensure_cmd(cmd: str):\n if shutil.which(cmd) is None:\n err(f\"missing command '{cmd}', make sure it has been installed and added to PATH\")\n\n\ndef ensure_path(path: str, ext_msg=\"\"):\n if not os.path.exists(path):\n err(f\"path specified at '{path}' does not exist,\", ext_msg)\n\n\nclass Translator:\n def __init__(self, provider: str, lang: str, use_cache=False, whitelist={}):\n self.provider = provider\n self.lang = lang\n if use_cache:\n _ = translators.preaccelerate_and_speedtest()\n if type(whitelist) == set:\n self.whitelist = whitelist\n else:\n self.whitelist = set(whitelist)\n\n\n def translate(self, text: str) -> str:\n filtered = []\n # RIP performance\n for word in text.split(\" \"):\n if not word:\n continue\n if word in self.whitelist:\n filtered.append(\"[__{}]\".format(word))\n else:\n filtered.append(word)\n filtered_text = \" \".join(filtered)\n try:\n \n translated = translators.translate_text(\n filtered_text,\n translator=self.provider,\n from_language=\"en\",\n to_language=self.lang,\n if_ignore_limit_of_length=True,\n )\n return re.sub(r\"\\[__([^\\]]+)\\]\", r\"\\1\", translated)\n except KeyError:\n print(f\"failed to translate '{filtered_text}', returning the original string\")\n return text\n except Exception as e:\n print(f\"unknown exception caught when translating '{text}'\")\n raise e\n\n", "repo_name": "J-ZhengLi/lint-info-extractor", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 13, "usage_type": "attribute"}, {"api_name": "shutil.which", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "translators.preaccelerate_and_speedtest", "line_number": 32, "usage_type": "call"}, {"api_name": "translators.translate_text", "line_number": 52, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "10797551255", "text": "import typing\nimport functools\nimport numpy as np\nfrom decimal import Decimal\nfrom fractions import Fraction as fracs\nfrom sympy import *\n\n\n\ndef trackcalls(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n wrapper.has_been_called = True\n return func(*args, **kwargs)\n wrapper.has_been_called = False\n return wrapper\n\ndef using_floor(alpha, k=15):\n quotients = list()\n\n \n\n i=0\n\n while i= a:\n quotients.append(0)\n temp = a\n a=b\n b=temp\n\n while b != 0:\n q = a//b\n quotients.append(q)\n r = a%b\n if r == 0:gcd_val = b\n a = b\n b = r\n return quotients\n\n@trackcalls\ndef GCD(a:int,b:int)-> int:\n if (a%b == 0) and (a > b):\n return b\n return euclid_method(a, b)\n\ndef convergents(cf: list,k=None):\n p = [cf[0], cf[1]*cf[0]+1]\n q = [1, cf[1]]\n\n #Calculate p_k and q_k\n for i in range(2, len(cf)):\n p.append(cf[i]*p[i-1]+p[i-2])\n q.append(cf[i]*q[i-1]+q[i-2])\n\n #Writing all convergents\n convergent = [fracs(p[i],q[i]) for i in range(0, len(cf))]\n\n if k is not None:\n return convergent[k]\n return convergent\n\n\ndef continued_fraction(x, _rational=True, k=15):\n\n def infinite_continued_fraction(k):\n return using_floor(float(sympify(x)), k)\n\n def finite_continued_fraction():\n return euclid_method(fracs(x).numerator, fracs(x).denominator)\n\n \n if _rational:\n return finite_continued_fraction()\n else:\n return infinite_continued_fraction(k)\n \n ", "repo_name": "shamEiNew/pmath", "sub_path": "pymath/numtheory/_continued_fractions.py", "file_name": "_continued_fractions.py", "file_ext": "py", "file_size_in_byte": 1790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "functools.wraps", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 27, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 66, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "36432839615", "text": "# stdlib\nimport json\nimport pathlib\nimport shutil\nfrom typing import Dict, List\n\n# 3rd party\nimport pytest\nfrom domdf_python_tools.paths import PathPlus\n\n# this package\nfrom sphinxcontrib.extras_require.sources import requirements_from___pkginfo__\n\n\nclass MockBuildEnvironment:\n\n\tdef __init__(self, tmpdir: pathlib.Path):\n\t\tself.srcdir = tmpdir / \"docs\"\n\n\n@pytest.mark.parametrize(\n\t\t\"requirements, extra, expects\",\n\t\t[\n\t\t\t\t({\"extra_c\": [\"faker\", \"pytest\", \"tox\"]}, \"extra_c\", [\"faker\", \"pytest\", \"tox\"]),\n\t\t\t\t({\"extra_c\": [\"faker\", \"pytest\", \"tox; python<=3.6\"]},\n\t\t\t\t\t\"extra_c\", [\"faker\", \"pytest\", \"tox; python<=3.6\"]),\n\t\t\t\t]\n\t\t)\ndef test_from___pkginfo__(\n\t\ttmp_pathplus: PathPlus,\n\t\trequirements: Dict[str, List[str]],\n\t\textra: str,\n\t\texpects: List[str],\n\t\t) -> None:\n\tpkginfo_file = tmp_pathplus / \"__pkginfo__.py\"\n\tpkginfo_file.write_text(f\"extras_require = {json.dumps(requirements)}\")\n\n\tassert requirements_from___pkginfo__(\n\t\t\tpackage_root=tmp_pathplus,\n\t\t\toptions={},\n\t\t\tenv=MockBuildEnvironment(tmp_pathplus),\n\t\t\textra=extra,\n\t\t\t) == expects\n\n\ndef test_from___pkginfo___not_found(tmp_pathplus: PathPlus) -> None:\n\twith pytest.raises(FileNotFoundError, match=\"Cannot find __pkginfo__.py in\"):\n\t\trequirements_from___pkginfo__(\n\t\t\t\tpackage_root=tmp_pathplus,\n\t\t\t\toptions={},\n\t\t\t\tenv=MockBuildEnvironment(tmp_pathplus),\n\t\t\t\textra=\"extra\",\n\t\t\t\t)\n\n\ndef test_from___pkginfo___wrong_mime(tmp_pathplus: PathPlus) -> None:\n\tpkginfo_file = tmp_pathplus / \"__pkginfo__.py\"\n\tshutil.copy2(PathPlus(__file__).parent / \"Example.png\", pkginfo_file)\n\n\twith pytest.raises(ImportError, match=\"Could not import __pkginfo__.py\"):\n\t\trequirements_from___pkginfo__(\n\t\t\t\tpackage_root=tmp_pathplus,\n\t\t\t\toptions={},\n\t\t\t\tenv=MockBuildEnvironment(tmp_pathplus),\n\t\t\t\textra=\"extra\",\n\t\t\t\t)\n", "repo_name": "sphinx-toolbox/extras_require", "sub_path": "tests/test_from___pkginfo__.py", "file_name": "test_from___pkginfo__.py", "file_ext": "py", "file_size_in_byte": 1769, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pathlib.Path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "domdf_python_tools.paths.PathPlus", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "sphinxcontrib.extras_require.sources.requirements_from___pkginfo__", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 21, "usage_type": "attribute"}, {"api_name": "domdf_python_tools.paths.PathPlus", "line_number": 46, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "sphinxcontrib.extras_require.sources.requirements_from___pkginfo__", "line_number": 48, "usage_type": "call"}, {"api_name": "domdf_python_tools.paths.PathPlus", "line_number": 56, "usage_type": "name"}, {"api_name": "shutil.copy2", "line_number": 58, "usage_type": "call"}, {"api_name": "domdf_python_tools.paths.PathPlus", "line_number": 58, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 60, "usage_type": "call"}, {"api_name": "sphinxcontrib.extras_require.sources.requirements_from___pkginfo__", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "5525371841", "text": "import errno\nimport eventlet\nimport eventlet.greenio\nimport eventlet.wsgi\nimport os\nimport webob.dec\nimport webob.exc\n\nfrom eventlet.green import socket\nfrom eventlet.green import ssl\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\n\nfrom nova_api_proxy.common.exception import ProxyException\n\n\nLOG = logging.getLogger(__name__)\n\nURL_LENGTH_LIMIT = 50000\n\n\nserver_opts = [\n cfg.StrOpt('wsgi_log_format',\n default='%(client_ip)s \"%(request_line)s\" status: %('\n 'status_code)s len: %(body_length)s time: %(wall_'\n 'seconds).7f',\n help='A python format string that is used as the template to '\n 'generate log lines. The following values can be formatted'\n ' into it: client_ip, date_time, request_line, status_code'\n ', body_length, wall_seconds.'),\n cfg.StrOpt('ssl_ca_file',\n help=\"CA certificate file to use to verify \"\n \"connecting clients\"),\n cfg.StrOpt('ssl_cert_file',\n help=\"SSL certificate of API server\"),\n cfg.StrOpt('ssl_key_file',\n help=\"SSL private key of API server\"),\n cfg.IntOpt('tcp_keepidle',\n default=600,\n help=\"Sets the value of TCP_KEEPIDLE in seconds for each \"\n \"server socket. Not supported on OS X.\"),\n cfg.IntOpt('pool_size',\n default=1000,\n help=\"Size of the pool of greenthreads used by wsgi\"),\n cfg.IntOpt('max_header_line',\n default=16384,\n help=\"Maximum line size of message headers to be accepted. \"\n \"max_header_line may need to be increased when using \"\n \"large tokens (typically those generated by the \"\n \"Keystone v3 API with big service catalogs).\"),\n cfg.IntOpt('client_socket_timeout', default=900,\n help=\"Timeout for client connections' socket operations. \"\n \"If an incoming connection is idle for this number of \"\n \"seconds it will be closed. A value of '0' means \"\n \"wait forever.\"),\n]\n\nCONF = cfg.CONF\nCONF.register_opts(server_opts)\n\n\nclass Request(webob.Request):\n pass\n\n\nclass WritableLogger(object):\n \"\"\"A thin wrapper that responds to `write` and logs.\"\"\"\n\n def __init__(self, logger, level=logging.INFO):\n self.logger = logger\n self.level = level\n\n def write(self, msg):\n self.logger.debug(msg.rstrip())\n\n\nclass Server(object):\n \"\"\"Server class to manage multiple WSGI sockets and applications.\"\"\"\n\n def __init__(self, name, app, host='0.0.0.0', port=0,\n protocol=eventlet.wsgi.HttpProtocol, use_ssl=False,\n backlog=128, max_url_len=URL_LENGTH_LIMIT):\n \"\"\"Initialize, but do not start, a WSGI server.\n\n :param app: The name of WSGI application.\n :param app: The WSGI application to serve.\n :param host: IP address to serve the application.\n :param port: Port number to server the application.\n :returns: None\n :raises:\n \"\"\"\n eventlet.wsgi.MAX_HEADER_LINE = CONF.max_header_line\n self._server = None\n self.name = name\n self.app = app\n self._protocol = protocol\n self._pool = eventlet.greenpool.GreenPool(CONF.pool_size)\n self._use_ssl = use_ssl\n self._max_url_len = max_url_len\n self.client_socket_timeout = CONF.client_socket_timeout or None\n self._wsgi_logger = WritableLogger(LOG)\n\n if backlog < 1:\n raise ProxyException('The backlog must be more than 1')\n\n bind_addr = (host, port)\n try:\n info = socket.getaddrinfo(bind_addr[0],\n bind_addr[1],\n socket.AF_UNSPEC,\n socket.SOCK_STREAM)[0]\n family = info[0]\n bind_addr = info[-1]\n except Exception:\n family = socket.AF_INET\n\n self._socket = eventlet.listen(bind_addr, family, backlog=backlog)\n (self.host, self.port) = self._socket.getsockname()[0:2]\n LOG.info(\"%(name)s listening on %(host)s:%(port)s\" % self.__dict__)\n\n def _setup_ssl(self):\n LOG.info(\"%(name)s setup ssl\" % self.__dict__)\n try:\n ca_file = CONF.ssl_ca_file\n cert_file = CONF.ssl_cert_file\n key_file = CONF.ssl_key_file\n\n if cert_file and not os.path.exists(cert_file):\n raise RuntimeError(\"Unable to find cert_file : %s\" % cert_file)\n\n if ca_file and not os.path.exists(ca_file):\n raise RuntimeError(\"Unable to find ca_file : %s\" % ca_file)\n\n if key_file and not os.path.exists(key_file):\n raise RuntimeError(\"Unable to find key_file : %s\" % key_file)\n\n if self._use_ssl and (not cert_file or not key_file):\n raise RuntimeError(\"When running server in SSL mode, \"\n \"you must specify both a cert_file and key_\"\n \"file option value in your configuration \"\n \"file\")\n ssl_kwargs = {\n 'server_side': True,\n 'certfile': cert_file,\n 'keyfile': key_file,\n 'cert_reqs': ssl.CERT_NONE,\n }\n\n if CONF.ssl_ca_file:\n ssl_kwargs['ca_certs'] = ca_file\n ssl_kwargs['cert_reqs'] = ssl.CERT_REQUIRED\n\n self._socket = eventlet.wrap_ssl(self._socket, **ssl_kwargs)\n\n except socket.error:\n LOG.error(\"Failed to start %(name)s on %(host)s :%(port)s with SSL\"\n \" support\" % self.__dict__)\n\n def start(self):\n \"\"\"Start serving a WSGI application.\n\n :returns: None\n \"\"\"\n self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)\n if hasattr(socket, 'TCP_KEEPIDLE'):\n self._socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_KEEPIDLE,\n CONF.tcp_keepidle)\n\n if self._use_ssl:\n self._setup_ssl()\n\n try:\n eventlet.wsgi.server(self._socket, self.app,\n custom_pool=self._pool,\n url_length_limit=self._max_url_len,\n log=self._wsgi_logger,\n protocol=self._protocol,\n log_format=CONF.wsgi_log_format,\n debug=CONF.debug,\n socket_timeout=self.client_socket_timeout)\n except socket.error as err:\n if err[0] != errno.EINVAL:\n raise\n self._pool.waitall()\n\n def stop(self):\n \"\"\"Stop this server.\n\n This is not a very nice action, as currently the method by which a\n server is stopped is by killing its eventlet.\n\n :returns: None\n\n \"\"\"\n LOG.info(\"Stopping WSGI server.\")\n # Resize pool to stop new requests from being processed\n self._pool.resize(0)\n\n\nclass Application(object):\n\n @classmethod\n def factory(cls, global_config, **local_config):\n \"\"\"Used for paste app factories in paste.deploy config files.\n \"\"\"\n return cls(**local_config)\n\n def __call__(self, environ, start_response):\n raise NotImplementedError('You must implement __call__')\n\n\nclass Middleware(Application):\n \"\"\"\n Base WSGI middleware wrapper. These classes require an application to be\n initialized that will be called next. By default the middleware will\n simply call its wrapped app, or you can override __call__ to customize its\n behavior.\n \"\"\"\n @classmethod\n def factory(cls, global_config, **local_config):\n \"\"\"Used for paste app factories in paste.deploy config files.\n\n Any local configuration (that is, values under the [filter:APPNAME]\n section of the paste config) will be passed into the `__init__` method\n as kwargs.\n \"\"\"\n def _factory(app):\n return cls(app, global_config, **local_config)\n return _factory\n\n def __init__(self, application):\n self.application = application\n\n def process_request(self, req):\n \"\"\"\n Called on each request.\n\n If this returns None, the next application down the stack will be\n executed. If it returns a response then that response will be returned\n and execution will stop here.\n\n \"\"\"\n return None\n\n def process_response(self, response):\n \"\"\"Do whatever you'd like to the response.\"\"\"\n return response\n\n @webob.dec.wsgify\n def __call__(self, req):\n response = self.process_request(req)\n if response:\n return response\n response = req.get_response(self.application)\n return self.process_response(response)\n", "repo_name": "starlingx/nfv", "sub_path": "nova-api-proxy/nova-api-proxy/nova_api_proxy/common/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 9114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "60", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 18, "usage_type": "name"}, {"api_name": "oslo_config.cfg.StrOpt", "line_number": 24, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 24, "usage_type": "name"}, {"api_name": "oslo_config.cfg.StrOpt", "line_number": 32, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 32, "usage_type": "name"}, {"api_name": "oslo_config.cfg.StrOpt", "line_number": 35, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 35, "usage_type": "name"}, {"api_name": "oslo_config.cfg.StrOpt", "line_number": 37, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 37, "usage_type": "name"}, {"api_name": "oslo_config.cfg.IntOpt", "line_number": 39, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 39, "usage_type": "name"}, {"api_name": "oslo_config.cfg.IntOpt", "line_number": 43, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 43, "usage_type": "name"}, {"api_name": "oslo_config.cfg.IntOpt", "line_number": 46, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 46, "usage_type": "name"}, {"api_name": "oslo_config.cfg.IntOpt", "line_number": 52, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 52, "usage_type": "name"}, {"api_name": "oslo_config.cfg.CONF", "line_number": 59, "usage_type": "attribute"}, {"api_name": "oslo_config.cfg", "line_number": 59, "usage_type": "name"}, {"api_name": "webob.dec.Request", "line_number": 63, "usage_type": "attribute"}, {"api_name": "webob.dec", "line_number": 63, "usage_type": "name"}, {"api_name": "oslo_log.log.INFO", "line_number": 70, "usage_type": "attribute"}, {"api_name": "oslo_log.log", "line_number": 70, "usage_type": "name"}, {"api_name": "eventlet.wsgi", "line_number": 82, "usage_type": "attribute"}, {"api_name": "eventlet.wsgi", "line_number": 93, "usage_type": "attribute"}, {"api_name": "eventlet.greenpool.GreenPool", "line_number": 98, "usage_type": "call"}, {"api_name": "eventlet.greenpool", "line_number": 98, "usage_type": "attribute"}, {"api_name": "nova_api_proxy.common.exception.ProxyException", "line_number": 105, "usage_type": "call"}, {"api_name": "eventlet.green.socket.getaddrinfo", "line_number": 109, "usage_type": "call"}, {"api_name": "eventlet.green.socket", "line_number": 109, "usage_type": "name"}, {"api_name": "eventlet.green.socket.AF_UNSPEC", "line_number": 111, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 111, "usage_type": "name"}, {"api_name": "eventlet.green.socket.SOCK_STREAM", "line_number": 112, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 112, "usage_type": "name"}, {"api_name": "eventlet.green.socket.AF_INET", "line_number": 116, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 116, "usage_type": "name"}, {"api_name": "eventlet.listen", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "eventlet.green.ssl.CERT_NONE", "line_number": 147, "usage_type": "attribute"}, {"api_name": "eventlet.green.ssl", "line_number": 147, "usage_type": "name"}, {"api_name": "eventlet.green.ssl.CERT_REQUIRED", "line_number": 152, "usage_type": "attribute"}, {"api_name": "eventlet.green.ssl", "line_number": 152, "usage_type": "name"}, {"api_name": "eventlet.wrap_ssl", "line_number": 154, "usage_type": "call"}, {"api_name": "eventlet.green.socket.error", "line_number": 156, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 156, "usage_type": "name"}, {"api_name": "eventlet.green.socket.SOL_SOCKET", "line_number": 165, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 165, "usage_type": "name"}, {"api_name": "eventlet.green.socket.SO_REUSEADDR", "line_number": 165, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket.SOL_SOCKET", "line_number": 166, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 166, "usage_type": "name"}, {"api_name": "eventlet.green.socket.SO_KEEPALIVE", "line_number": 166, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 167, "usage_type": "argument"}, {"api_name": "eventlet.green.socket.IPPROTO_TCP", "line_number": 168, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 168, "usage_type": "name"}, {"api_name": "eventlet.green.socket.TCP_KEEPIDLE", "line_number": 168, "usage_type": "attribute"}, {"api_name": "eventlet.wsgi.server", "line_number": 175, "usage_type": "call"}, {"api_name": "eventlet.wsgi", "line_number": 175, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket.error", "line_number": 183, "usage_type": "attribute"}, {"api_name": "eventlet.green.socket", "line_number": 183, "usage_type": "name"}, {"api_name": "errno.EINVAL", "line_number": 184, "usage_type": "attribute"}, {"api_name": "webob.dec.dec", "line_number": 251, "usage_type": "attribute"}, {"api_name": "webob.dec", "line_number": 251, "usage_type": "name"}]} +{"seq_id": "24857387474", "text": "from abc import ABC, abstractmethod\nfrom stable_baselines.ddpg.ddpg import *\nfrom rl_with_teachers.utils import log_histogram, log_images\nimport logging\nimport time\nimport math\nimport random\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.contrib as tfc\nfrom stable_baselines import logger, deepq\nfrom stable_baselines.common import tf_util, SetVerbosity, TensorboardWriter\nfrom stable_baselines.common.mpi_adam import MpiAdam\nfrom stable_baselines.a2c.utils import find_trainable_variables, total_episode_reward_logger\nfrom stable_baselines.common import Dataset, explained_variance, fmt_row, zipsame\nfrom stable_baselines.common.schedules import LinearSchedule\nimport stable_baselines.common.tf_util as U\nfrom collections import defaultdict\nfrom stable_baselines.ddpg.policies import FeedForwardPolicy\nfrom stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy\nfrom tensorflow.python.framework import tensor_shape\nfrom tensorflow.python.framework.tensor_util import constant_value\nfrom gym.spaces import Box\nfrom scipy.stats import invgamma\nfrom gym.spaces import Discrete\n\nclass RLwTeachersLearner(ABC):\n \"\"\" An abstract class to represent an RL policy to be taught by policy teachers\n \"\"\"\n def __init__(self):\n pass\n\n @abstractmethod\n def select_action(self, obs, with_exploration=False):\n \"\"\"\n The policy act method.\n\n Should be able to return the action, and also return a Qvalue (as a 2-tuple).\n If compute_q is False, return None for q_value.\n\n :param obs: The environment observation, as a numpy array\n :param with_exploration: Whether to generate actions with exploration\n \"\"\"\n pass\n\n def step_learn(self, obs, new_obs, reward, done):\n \"\"\" Optional learn method \"\"\"\n pass\n\nclass DDPGwTeachers(DDPG, RLwTeachersLearner):\n \"\"\"\n Deep Deterministic Policy Gradient (DDPG) model.\n Largely same as Stable Baselines (https://github.com/hill-a/stable-baselines/blob/master/stable_baselines/ddpg/ddpg.py)\n implementation, main changes are noted with CHANGES.\n\n DDPG: https://arxiv.org/pdf/1509.02971.pdf\n :param policy: (DDPGPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)\n :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)\n :param teachers: (list) list of functions ; should be callable with one param, obs, to get action\n :param gamma: (float) the discount factor\n :param memory_policy: (Memory) the replay buffer (if None, default to baselines.ddpg.memory.Memory)\n :param eval_env: (Gym Environment) the evaluation environment (can be None)\n :param nb_train_steps: (int) the number of training steps\n :param nb_rollout_steps: (int) the number of rollout steps\n :param nb_eval_steps: (int) the number of evalutation steps\n :param param_noise: (AdaptiveParamNoiseSpec) the parameter noise type (can be None)\n :param action_noise: (ActionNoise) the action noise type (can be None)\n :param param_noise_adaption_interval: (int) apply param noise every N steps\n :param tau: (float) the soft update coefficient (keep old values, between 0 and 1)\n :param normalize_returns: (bool) should the critic output be normalized\n :param enable_popart: (bool) enable pop-art normalization of the critic output\n (https://arxiv.org/pdf/1602.07714.pdf)\n :param normalize_observations: (bool) should the observation be normalized\n :param batch_size: (int) the size of the batch for learning the policy\n :param observation_range: (tuple) the bounding values for the observation\n :param return_range: (tuple) the bounding values for the critic output\n :param critic_l2_reg: (float) l2 regularizer coefficient\n :param actor_lr: (float) the actor learning rate\n :param critic_lr: (float) the critic learning rate\n :param clip_norm: (float) clip the gradients (disabled if None)\n :param reward_scale: (float) the value the reward should be scaled by\n :param render: (bool) enable rendering of the environment\n :param render_eval: (bool) enable rendering of the evalution environment\n :param memory_limit: (int) the max number of transitions to store\n :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug\n :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)\n :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance\n :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation\n :param full_tensorboard_log: (bool) enable additional logging when using tensorboard\n WARNING: this logging can take a lot of space quickly\n \"\"\"\n def __init__(self, policy, env, gamma=0.99, memory_policy=None, eval_env=None, nb_train_steps=50,\n nb_rollout_steps=100, nb_eval_steps=100, param_noise=None, action_noise=None,\n normalize_observations=False, tau=0.001, batch_size=128, param_noise_adaption_interval=50,\n normalize_returns=False, enable_popart=False, observation_range=(-5., 5.), critic_l2_reg=0.,\n return_range=(-np.inf, np.inf), actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1., action_l2=0.0,\n render=False, render_eval=False, memory_limit=50000, verbose=0, tensorboard_log=None, use_meta_target=False,\n _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False):\n self.use_meta_target = use_meta_target\n self.action_l2 = action_l2\n\n # variables for model saving\n self.last_model_save_time = -1.\n self.hours_elapsed = 0\n\n super(DDPGwTeachers, self).__init__(policy, env, gamma, memory_policy, eval_env, nb_train_steps,\n nb_rollout_steps, nb_eval_steps, param_noise, action_noise,\n normalize_observations, tau, batch_size, param_noise_adaption_interval,\n normalize_returns, enable_popart, observation_range, critic_l2_reg,\n return_range, actor_lr, critic_lr, clip_norm, reward_scale,\n render, render_eval, memory_limit, verbose, tensorboard_log,\n _init_setup_model, policy_kwargs, full_tensorboard_log)\n\n\n def setup_model(self):\n super().setup_model() # See below in DropoutBayesianDDPG for full model set up\n\n # CHANGES - if running with meta target, also need to set up placeholders\n if self.use_meta_target:\n self.setup_meta_target()\n\n def setup_meta_target(self):\n \"\"\"\n New function to set up meta target TF ops\n \"\"\"\n with self.graph.as_default():\n clipped_obs1 = tf.clip_by_value(self.target_policy.processed_obs,\n self.observation_range[0], self.observation_range[1])\n\n with tf.variable_scope(\"meta_target\", reuse=False):\n critic_target = self.target_policy.make_critic(clipped_obs1, self.actions1)\n\n with tf.variable_scope(\"loss\", reuse=False):\n q_obs1 = critic_target\n self.target_q_no_actor = self.rewards + (1. - self.terminals1) * self.gamma * q_obs1\n\n with self.sess.as_default():\n self._initialize(self.sess)\n\n def _train_step(self, step, writer, log=False):\n \"\"\"\n run a step of training from batch\n :param step: (int) the current step iteration\n :param writer: (TensorFlow Summary.writer) the writer for tensorboard\n :param log: (bool) whether or not to log to metadata\n :return: (float, float) critic loss, actor loss\n \"\"\"\n if not self.use_meta_target:\n # Super class implements normal training step\n return super()._train_step(step, writer, log)\n\n batch = self.memory.sample(batch_size=self.batch_size)\n # CHANGES - choose action based on chosen policy\n actions1 = self.learn_behavior_policy.choose_actions_greedy(batch['obs1'])[1]\n\n target_q = self.sess.run(self.target_q, feed_dict={\n self.action_target: actions1,\n self.obs_target: batch['obs1'],\n self.rewards: batch['rewards'],\n self.terminals1: batch['terminals1'].astype('float32')\n })\n\n # Get all gradients and perform a synced update.\n ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]\n td_map = {\n self.obs_train: batch['obs0'],\n self.actions: batch['actions'],\n self.action_train_ph: batch['actions'],\n self.rewards: batch['rewards'],\n self.critic_target: target_q,\n self.param_noise_stddev: 0 if self.param_noise is None else self.param_noise.current_stddev\n }\n if writer is not None:\n # run loss backprop with summary if the step_id was not already logged (can happen with the right\n # parameters as the step value is only an estimate)\n if self.full_tensorboard_log and log and step not in self.tb_seen_steps:\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n summary, actor_grads, actor_loss, critic_grads, critic_loss = \\\n self.sess.run([self.summary] + ops, td_map, options=run_options, run_metadata=run_metadata)\n\n writer.add_run_metadata(run_metadata, 'step%d' % step)\n self.tb_seen_steps.append(step)\n else:\n summary, actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run([self.summary] + ops,\n td_map)\n writer.add_summary(summary, step)\n else:\n actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, td_map)\n\n self.actor_optimizer.update(actor_grads, learning_rate=self.actor_lr)\n self.critic_optimizer.update(critic_grads, learning_rate=self.critic_lr)\n\n return critic_loss, actor_loss\n\n def eval_q_value(self, obs, action=None):\n \"\"\"\n Evaluates a q value from the critic. If action is None, evaluates actor's action.\n \"\"\"\n if action is None:\n return self.sess.run(self.critic_with_actor_tf, {self.obs_train: obs})\n else:\n return self.sess.run(self.critic_tf, {self.obs_train: obs, self.actions: action})\n\n def eval_actor(self, obs):\n \"\"\"\n Computes the actions of the actor for given observation.\n \"\"\"\n feed_dict = {self.obs_train: obs}\n action = self.sess.run(self.actor_tf, feed_dict=feed_dict)\n return action\n\n def _policy(self, obs, apply_noise=True, compute_q=True):\n \"\"\"\n Get the agent actor and critic output, from a given observation.\n no change from stable baselines version, here for easy readability.\n :param obs: ([float] or [int]) the observation\n :param apply_noise: (bool) enable the noise\n :param compute_q: (bool) compute the critic output\n :return: ([float], float) the action and critic value\n \"\"\"\n obs = np.array(obs).reshape((-1,) + self.observation_space.shape)\n feed_dict = {self.obs_train: obs}\n if self.param_noise is not None and apply_noise:\n actor_tf = self.perturbed_actor_tf\n feed_dict[self.obs_noise] = obs\n else:\n actor_tf = self.actor_tf\n\n if compute_q:\n action, q_value = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)\n else:\n action = self.sess.run(actor_tf, feed_dict=feed_dict)\n q_value = None\n\n action = action.flatten()\n if self.action_noise is not None and apply_noise:\n noise = self.action_noise()\n assert noise.shape == action.shape\n action += noise\n action = np.clip(action, -1, 1)\n return action, q_value\n\n def select_action(self, obs, with_exploration=False):\n # Helper function that just calls _policy and returns action without q\n return self._policy(obs, apply_noise=with_exploration, compute_q=True)[0]\n\n def _setup_target_network_updates(self):\n \"\"\"\n set the target update operations.\n no change from stable baselines version, here for easy readability.\n \"\"\"\n init_updates, soft_updates = get_target_updates(tf_util.get_trainable_vars('model/'),\n tf_util.get_trainable_vars('target/'), self.tau,\n self.verbose)\n self.target_init_updates = init_updates\n self.target_soft_updates = soft_updates\n\n def _setup_critic_optimizer(self):\n \"\"\"\n setup the optimizer for the critic\n no change from stable baselines version, here for easy readability.\n \"\"\"\n if self.verbose >= 2:\n logger.info('setting up critic optimizer')\n normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms),\n self.return_range[0], self.return_range[1])\n self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))\n if self.critic_l2_reg > 0.:\n critic_reg_vars = [var for var in tf_util.get_trainable_vars('model/qf/')\n if 'bias' not in var.name and 'output' not in var.name and 'b' not in var.name]\n if self.verbose >= 2:\n for var in critic_reg_vars:\n logger.info(' regularizing: {}'.format(var.name))\n logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))\n critic_reg = tc.layers.apply_regularization(\n tc.layers.l2_regularizer(self.critic_l2_reg),\n weights_list=critic_reg_vars\n )\n self.critic_loss += critic_reg\n critic_shapes = [var.get_shape().as_list() for var in tf_util.get_trainable_vars('model/qf/')]\n critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])\n if self.verbose >= 2:\n logger.info(' critic shapes: {}'.format(critic_shapes))\n logger.info(' critic params: {}'.format(critic_nb_params))\n self.critic_grads = tf_util.flatgrad(self.critic_loss, tf_util.get_trainable_vars('model/qf/'),\n clip_norm=self.clip_norm)\n self.critic_optimizer = MpiAdam(var_list=tf_util.get_trainable_vars('model/qf/'), beta1=0.9, beta2=0.999,\nepsilon=1e-08)\n\n def _setup_actor_optimizer(self):\n \"\"\"\n Setup the optimizer for the actor\n \"\"\"\n if self.verbose >= 2:\n logger.info('setting up actor optimizer')\n self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)\n if self.action_l2:\n # CHANGES - add option to have l2 loss on actions\n self.actor_loss+= self.action_l2 * tf.reduce_mean(tf.square(self.actor_tf))\n\n actor_shapes = [var.get_shape().as_list() for var in tf_util.get_trainable_vars('model/pi/')]\n actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])\n if self.verbose >= 2:\n logger.info(' actor shapes: {}'.format(actor_shapes))\n logger.info(' actor params: {}'.format(actor_nb_params))\n self.actor_grads = tf_util.flatgrad(self.actor_loss, tf_util.get_trainable_vars('model/pi/'),\n clip_norm=self.clip_norm)\n self.actor_optimizer = MpiAdam(var_list=tf_util.get_trainable_vars('model/pi/'), beta1=0.9, beta2=0.999,\n epsilon=1e-08)\n\n def learn(self, total_timesteps, behavior_policy=None, callback=None, seed=None, log_interval=100, tb_log_name=\"DDPG\",\n reset_num_timesteps=True, tb_write_extra=False):\n \"\"\"\n The learning loop.\n Largely same as normal DDPG, but with addition of actions suggestions from teachers.\n \"\"\"\n self.learn_behavior_policy = behavior_policy\n new_tb_log = self._init_num_timesteps(reset_num_timesteps)\n if behavior_policy is None:\n num_teachers = 0\n else:\n num_teachers = len(behavior_policy.teachers)\n\n with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log)\\\n as writer:\n if not tb_write_extra:\n writer = None\n\n self._setup_learn(seed)\n\n # a list for tensorboard logging, to prevent logging with the same step number, if it already occured\n self.tb_seen_steps = []\n\n rank = MPI.COMM_WORLD.Get_rank()\n # we assume symmetric actions.\n assert np.all(np.abs(self.env.action_space.low) == self.env.action_space.high)\n if self.verbose >= 2:\n logger.log('Using agent with the following configuration:')\n logger.log(str(self.__dict__.items()))\n\n eval_episode_rewards_history = deque(maxlen=100)\n episode_rewards_history = deque(maxlen=100)\n self.episode_reward = np.zeros((1,))\n with self.sess.as_default(), self.graph.as_default():\n # Prepare everything.\n self._reset()\n obs = self.env.reset()\n eval_obs = None\n if self.eval_env is not None:\n eval_obs = self.eval_env.reset()\n episode_reward = 0.\n episode_step = 0\n episodes = 0\n step = 0\n total_steps = 0\n\n start_time = time.time()\n\n epoch_episode_rewards = []\n epoch_episode_steps = []\n epoch_actor_losses = []\n epoch_critic_losses = []\n epoch_adaptive_distances = []\n eval_episode_rewards = []\n eval_qs = []\n epoch_actions = []\n epoch_agent_choices = []\n epoch_agent_actions = [[] for i in range(1+num_teachers)]\n epoch_agent_q_vals = [[] for i in range(1+num_teachers)]\n epoch_qs = []\n epoch_episodes = 0\n epoch = 0\n\n logging.info('Starting DDPG training')\n while True:\n logging.info('----------------------------------------------')\n logging.info('Starting new episode')\n first_rollout = True\n first_rollout_eval = True\n for _ in range(log_interval):\n # Perform rollouts.\n discount = 1.0\n for _ in range(self.nb_rollout_steps):\n if total_steps >= total_timesteps:\n return self\n\n # Predict next action.\n if behavior_policy is not None:\n # CHANGES - this is where the behavioral policy is used to select action\n policy_choice, action, q_value = behavior_policy.choose_action(obs)\n else:\n policy_choice = 0\n action, q_value = self._policy(obs, apply_noise=True, compute_q=True)\n if q_value is None:\n q_value = 0.0\n assert action.shape == self.env.action_space.shape\n\n # Execute next action.\n new_obs, reward, done, _ = self.env.step(action * np.abs(self.action_space.low))\n logging.info('At state %s chose action %s from policy %d for reward of %f'%(str(obs), str(action), policy_choice, reward))\n\n if writer is not None:\n ep_rew = np.array([reward]).reshape((1, -1))\n ep_done = np.array([done]).reshape((1, -1))\n self.episode_reward = total_episode_reward_logger(self.episode_reward, ep_rew, ep_done,\n writer, self.num_timesteps)\n step += 1\n total_steps += 1\n self.num_timesteps += 1\n if self.render and first_rollout:\n self.env.render()\n episode_reward += discount*reward\n discount*=self.gamma\n episode_step += 1\n\n # Book-keeping.\n epoch_agent_choices.append(policy_choice)\n epoch_agent_actions[policy_choice].append(action)\n epoch_agent_q_vals[policy_choice].append(q_value)\n epoch_actions.append(action)\n epoch_qs.append(q_value)\n self._store_transition(obs, action, reward, new_obs, done)\n if behavior_policy is not None:\n behavior_policy.step_learn(obs, policy_choice, reward, new_obs, done)\n obs = new_obs\n if callback is not None:\n # Only stop training if return value is False, not when it is None.\n # This is for backwards compatibility with callbacks that have no return statement.\n if callback(locals(), globals()) is False:\n return self\n\n if done:\n logging.info('Episode finished! num steps=%d, final reward=%f'%(episode_step, episode_reward))\n #first_rollout = False\n\n # Episode done.\n epoch_episode_rewards.append(episode_reward)\n episode_rewards_history.append(episode_reward)\n epoch_episode_steps.append(episode_step)\n episode_reward = 0.\n episode_step = 0\n epoch_episodes += 1\n episodes += 1\n discount = 1.0\n\n self._reset()\n if not isinstance(self.env, VecEnv):\n obs = self.env.reset()\n\n if behavior_policy is not None:\n behavior_policy.reset()\n\n\n logging.info('----------------------------------------------')\n logging.info('Starting agent training!')\n # Train.\n epoch_actor_losses = []\n epoch_critic_losses = []\n epoch_adaptive_distances = []\n for t_train in range(self.nb_train_steps):\n if self.memory.nb_entries >= self.batch_size and \\\n t_train % self.param_noise_adaption_interval == 0:\n distance = self._adapt_param_noise()\n epoch_adaptive_distances.append(distance)\n\n # weird equation to deal with the fact the nb_train_steps will be different\n # to nb_rollout_steps\n step = (int(t_train * (self.nb_rollout_steps / self.nb_train_steps)) +\n self.num_timesteps - self.nb_rollout_steps)\n\n critic_loss, actor_loss = self._train_step(step, writer, log=t_train == 0)\n logging.info('Train step %d, critic mean loss=%f , actor mean loss=%f'%\\\n (t_train,np.mean(critic_loss),np.mean(actor_loss)))\n epoch_critic_losses.append(critic_loss)\n epoch_actor_losses.append(actor_loss)\n self._update_target_net()\n logging.info('Finished agent training!')\n\n # Evaluate.\n eval_episode_rewards = []\n eval_qs = []\n if self.eval_env is not None:\n eval_episode_reward = 0.0\n eval_steps = 0\n discount = 1.0\n self.eval_env.reset()\n for _ in range(self.nb_eval_steps):\n if total_steps >= total_timesteps:\n return self\n if self.render_eval and first_rollout_eval:\n self.eval_env.render()\n eval_action, eval_q = self._policy(eval_obs, apply_noise=False, compute_q=True)\n eval_obs, eval_r, eval_done, _ = self.eval_env.step(eval_action *\n np.abs(self.action_space.low))\n eval_steps+=1\n eval_episode_reward += discount*eval_r\n discount*=self.gamma\n\n eval_qs.append(eval_q)\n if eval_done:\n logging.info('Eval episode finished. num steps=%d, final reward=%f'%(eval_steps, eval_episode_reward ))\n if self.render_eval and first_rollout_eval:\n self.eval_env.render()\n eval_obs = self.eval_env.reset()\n first_rollout_eval = False\n yield eval_episode_reward\n eval_episode_rewards.append(eval_episode_reward)\n eval_episode_rewards_history.append(eval_episode_reward)\n eval_episode_reward = 0.\n discount = 1.0\n eval_steps=1\n\n # Save the model every hour.\n if time.time() - self.last_model_save_time > 3600:\n self.save(os.path.join(logger.get_dir(),'model_hour_{}'.format(self.hours_elapsed)))\n self.last_model_save_time = time.time()\n self.hours_elapsed += 1\n\n combined_stats = {}\n if self.nb_rollout_steps > 0:\n duration = time.time() - start_time\n stats = self._get_stats()\n for key in sorted(stats.keys()):\n if not ('Q_mc' in key):\n combined_stats[key] = stats[key]\n else:\n log_histogram(key, stats[key], step, 25)\n combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)\n if epoch_episode_rewards:\n log_histogram('rollout/return', epoch_episode_rewards, step, 25)\n combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)\n if episode_rewards_history:\n log_histogram('rollout/return', episode_rewards_history, step, 25)\n combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)\n combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)\n combined_stats['rollout/agent_choice_mean'] = np.mean(epoch_agent_choices)\n log_histogram('rollout/agent_choices', epoch_agent_choices, step, 25)\n combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)\n for i in range(num_teachers + 1):\n if len(epoch_agent_actions[i]) > 25:\n log_histogram('rollout/actions/%d'%i, epoch_agent_actions[i], step, 25)\n log_histogram('rollout/Q/%d'%i, epoch_agent_q_vals[i], step, 25)\n combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)\n combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)\n if len(epoch_adaptive_distances) != 0:\n combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)\n combined_stats['total/duration'] = duration\n combined_stats['total/steps_per_second'] = float(step) / float(duration)\n combined_stats['total/episodes'] = episodes\n combined_stats['rollout/episodes'] = epoch_episodes\n combined_stats['rollout/actions_std'] = np.std(epoch_actions)\n\n # Evaluation statistics.\n if self.eval_env is not None:\n combined_stats['eval/return'] = np.mean(eval_episode_rewards)\n combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)\n combined_stats['eval/Q'] = np.mean(eval_qs)\n combined_stats['eval/episodes'] = len(eval_episode_rewards)\n\n\n # Total statistics.\n combined_stats['total/epochs'] = epoch + 1\n combined_stats['total/steps'] = step\n\n for key in sorted(combined_stats.keys()):\n logger.record_tabular(key, combined_stats[key])\n logger.dump_tabular()\n logger.info('')\n\n logging.info('Finished DDPG training')\n\nclass DropoutBayesianDDPGModels(FeedForwardPolicy):\n \"\"\"\n Class that implements actor critic models for DDPG, using a MLP (2 layers of 64), with layer normalisation\n and dropout layers that allow for bayesian estimates of critic values\n\n :param sess: (TensorFlow session) The current TensorFlow session\n :param ob_space: (Gym Space) The observation space of the environment\n :param ac_space: (Gym Space) The action space of the environment\n :param n_env: (int) The number of environments to run\n :param n_steps: (int) The number of steps to run for each environment\n :param n_batch: (int) The number of batch to run (n_envs * n_steps)\n :param reuse: (bool) If the policy is reusable or not\n :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction\n \"\"\"\n def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, layers=None,\n cnn_extractor=nature_cnn, feature_extraction=\"mlp\", layer_norm=False, mc_samples=50,\n dropout_keep_prob=0.99, merge_layer = 1, **kwargs):\n FeedForwardPolicy.__init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, layers,\n cnn_extractor, feature_extraction, layer_norm)\n self.mc_samples = mc_samples\n self.nb_actions = self.ac_space.shape[0]\n self.keep_prob = dropout_keep_prob\n self.merge_layer = merge_layer\n self.hidden_sizes = self.layers\n\n def make_critic(self, obs=None, action=None, reuse=False, scope=\"critic\"):\n \"\"\"\n Builds the critic, given an op or a placeholder for observation and action\n \"\"\"\n self.num_qs = 0\n # Make the critic without dropout\n qf = self.make_qf(obs, action, reuse, scope, include_dropout=False)\n # Make N=mc_samples versions of critic with dropout layers, for bayesian estimation\n # Each instance has a dropout layer that masks output independently, so in effect we get N different outputs\n # These N monte carlo samples are then used to estimate mean and variance of the prediction\n # Having N ops with different outputs allows for computing N outputs at once, instead of in a loop\n qf_mc = [self.make_qf(obs, action, True, scope, include_dropout=True) for i in range(self.mc_samples)]\n\n return qf, qf_mc\n\n def make_qf(self, obs, action, reuse, scope, include_dropout=False):\n # Helper function to make multiple critics that share all weights but have independent dropout masks\n self.num_qs+=1\n with tf.variable_scope(scope, reuse=reuse):\n if self.feature_extraction == \"cnn\":\n qf_h = self.cnn_extractor(obs, **self.cnn_kwargs)\n else:\n qf_h = tf.layers.flatten(obs)\n for i, layer_size in enumerate(self.layers):\n qf_h = tf.layers.dense(qf_h, layer_size, name='fc' + str(i))\n if self.layer_norm:\n qf_h = tf.contrib.layers.layer_norm(qf_h, center=True, scale=True)\n qf_h = self.activ(qf_h)\n if include_dropout:\n # We do dropout after activation, at each layer\n qf_h = tf.layers.dropout(qf_h, rate=1-self.keep_prob, seed=self.num_qs, name='drop_%d'%self.num_qs,\n training=True)\n if i == 0:\n qf_h = tf.concat([qf_h, action], axis=-1)\n\n qf = tf.layers.dense(qf_h, 1,\n kernel_initializer=tf.random_uniform_initializer(minval=-3e-3,\n maxval=3e-3))\n return qf\n\ndef logsumexp(x, axis=None):\n # Helper func used for bayesian dropout estimation\n x_max = tf.reduce_max(x, axis=axis, keepdims=True)\n return tf.log(tf.reduce_sum(tf.exp(x - x_max), axis=axis, keepdims=True)) + x_max\n\ndef get_vars(scope, trainable=False):\n # Helper func for getting vars\n if trainable:\n return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)\n else:\n return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=scope)\n\nclass DropoutBayesianDDPG(DDPGwTeachers):\n \"\"\"\n Adapted from https://github.com/Breakend/BayesianPolicyGradients\n Deep Deterministic Policy Gradient (DDPG) model\n DDPG: https://arxiv.org/pdf/1509.02971.pdf\n :param policy: (DDPGPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)\n :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)\n :param gamma: (float) the discount factor\n :param memory_policy: (Memory) the replay buffer (if None, default to baselines.ddpg.memory.Memory)\n :param eval_env: (Gym Environment) the evaluation environment (can be None)\n :param nb_train_steps: (int) the number of training steps\n :param nb_rollout_steps: (int) the number of rollout steps\n :param nb_eval_steps: (int) the number of evalutation steps\n :param param_noise: (AdaptiveParamNoiseSpec) the parameter noise type (can be None)\n :param action_noise: (ActionNoise) the action noise type (can be None)\n :param param_noise_adaption_interval: (int) apply param noise every N steps\n :param tau: (float) the soft update coefficient (keep old values, between 0 and 1)\n :param normalize_returns: (bool) should the critic output be normalized\n :param enable_popart: (bool) enable pop-art normalization of the critic output\n (https://arxiv.org/pdf/1602.07714.pdf)\n :param batch_size: (int) the size of the batch for learning the policy\n :param observation_range: (tuple) the bounding values for the observation\n :param return_range: (tuple) the bounding values for the critic output\n :param critic_l2_reg: (float) l2 regularizer coefficient\n :param actor_lr: (float) the actor learning rate\n :param critic_lr: (float) the critic learning rate\n :param clip_norm: (float) clip the gradients (disabled if None)\n :param reward_scale: (float) the value the reward should be scaled by\n :param render: (bool) enable rendering of the environment\n :param render_eval: (bool) enable rendering of the evalution environment\n :param memory_limit: (int) the max number of transitions to store\n :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug\n :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)\n :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance\n :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation\n \"\"\"\n\n def __init__(self, env, name='agent', gamma=0.99, memory_policy=None, eval_env=None, nb_train_steps=50,\n nb_rollout_steps=100, nb_eval_steps=100, param_noise=None, action_noise=None,\n tau=0.001, batch_size=128, param_noise_adaption_interval=50,\n enable_popart=False, observation_range=(-5., 5.), critic_l2_reg=0.,\n return_range=(-np.inf, np.inf), actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1., action_l2=0.0,\n dropout_tau=0.85, num_timesteps_final=1e6, length_scale=0.01, is_teacher=False,\n render=False, render_eval=False, memory_limit=100, verbose=0, tensorboard_log=None, use_meta_target=False,\n include_mc_stats=True,_init_setup_model=True, policy_kwargs=None):\n\n self.dropout_tau = dropout_tau\n self.num_timesteps_final = num_timesteps_final\n self.length_scale = length_scale\n self.is_teacher = is_teacher\n self.name = name\n self.include_mc_stats = include_mc_stats\n super().__init__(DropoutBayesianDDPGModels, env, gamma, memory_policy, eval_env, nb_train_steps,\n nb_rollout_steps, nb_eval_steps, param_noise, action_noise,\n False, tau, batch_size, param_noise_adaption_interval,\n False, enable_popart, observation_range, critic_l2_reg,\n return_range, actor_lr, critic_lr, clip_norm, reward_scale, action_l2,\n render, render_eval, memory_limit, verbose, tensorboard_log, use_meta_target,\n _init_setup_model, policy_kwargs)\n\n def setup_model(self):\n logging.info('Starting DDPG model setup')\n # This is mostly same as in stable baselines, with a few changes marked with CHANGES\n with SetVerbosity(self.verbose):\n\n assert isinstance(self.action_space, gym.spaces.Box), \\\n \"Error: DDPG cannot output a {} action space, only spaces.Box is supported.\".format(self.action_space)\n assert issubclass(self.policy, DDPGPolicy), \"Error: the input policy for the DDPG model must be \" \\\n \"an instance of DDPGPolicy.\"\n self.graph = tf.get_default_graph()\n with self.graph.as_default():\n self.sess = tf_util.single_threaded_session(graph=self.graph)\n\n if not self.is_teacher:\n self.memory = self.memory_policy(limit=self.memory_limit,\n action_shape=self.action_space.shape,\n observation_shape=self.observation_space.shape)\n\n with tf.variable_scope(\"input\", reuse=tf.AUTO_REUSE):\n self.policy_tf = self.policy(self.sess, self.observation_space, self.action_space, 1, 1, None,\n **self.policy_kwargs)\n\n # Create target networks.\n self.target_policy = self.policy(self.sess, self.observation_space, self.action_space, 1, 1, None,\n **self.policy_kwargs)\n self.obs_target = self.target_policy.obs_ph\n self.action_target = self.target_policy.action_ph\n\n obs0 = tf.clip_by_value(self.policy_tf.processed_obs,\n self.observation_range[0], self.observation_range[1])\n obs1 = tf.clip_by_value(self.target_policy.processed_obs,\n self.observation_range[0], self.observation_range[1])\n\n if self.param_noise is not None:\n # Configure perturbed actor for better exploration\n # https://openai.com/blog/better-exploration-with-parameter-noise/\n self.param_noise_actor = self.policy(self.sess, self.observation_space, self.action_space, 1, 1,\n None, **self.policy_kwargs)\n self.obs_noise = self.param_noise_actor.obs_ph\n self.action_noise_ph = self.param_noise_actor.action_ph\n\n # Configure separate copy for stddev adoption.\n self.adaptive_param_noise_actor = self.policy(self.sess, self.observation_space,\n self.action_space, 1, 1, None,\n **self.policy_kwargs)\n self.obs_adapt_noise = self.adaptive_param_noise_actor.obs_ph\n self.action_adapt_noise = self.adaptive_param_noise_actor.action_ph\n\n # Inputs.\n self.obs_train = self.policy_tf.obs_ph\n self.obs_target = self.target_policy.obs_ph\n self.action_train_ph = self.policy_tf.action_ph\n self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')\n self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')\n self.actions = tf.placeholder(tf.float32, shape=(None,) + self.action_space.shape, name='actions')\n self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')\n self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')\n\n # Create networks and core TF parts that are shared across setup parts.\n self.model_scope = 'model' if not self.is_teacher else '%s_model'%self.name\n with tf.variable_scope(self.model_scope, reuse=False):\n # CHANGES - adding self.critic_tf_mc and self.critic_with_actor_tf_mc_avg\n # In plain DDPG don't have the _mc vars, since there are not multiple critic heads\n if not self.is_teacher:\n self.actor_tf = self.policy_tf.make_actor(obs0)\n self.critic_tf, \\\n self.critic_tf_mc = self.policy_tf.make_critic(obs0,\n self.actions,\n scope='critic')\n if not self.is_teacher:\n self.critic_with_actor_tf, \\\n self.critic_with_actor_tf_mc = self.policy_tf.make_critic(obs0,\n self.actor_tf,\n scope='critic',\n reuse=True)\n dropout_networks = self.critic_with_actor_tf_mc\n self.critic_with_actor_tf_mc_avg = tf.reduce_mean(dropout_networks, axis=0)\n\n # Noise setup\n if self.param_noise is not None and not self.is_teacher:\n self._setup_param_noise(obs0)\n\n self.target_scope = 'target' if not self.is_teacher else '%s_target'%self.name\n with tf.variable_scope(self.target_scope, reuse=False):\n if self.is_teacher or self.use_meta_target:\n self.target_actor = self.target_policy.make_actor(obs1)\n critic_target,_ = self.target_policy.make_critic(obs1,\n self.action_target)\n self.target_critic = critic_target\n else:\n # CHANGES - note '_', just ignore the mc version since not needed\n critic_target,_ = self.target_policy.make_critic(obs1,\n self.target_policy.make_actor(obs1))\n\n summaries = []\n with tf.variable_scope(\"loss\", reuse=False):\n self.critic_tf = tf.clip_by_value(self.critic_tf, self.return_range[0], self.return_range[1])\n\n # CHANGES - note use of self.critic_with_actor_tf_mc_avg\n # in plain DDPG this is just critic output but as per Henderson et al.\n # we optimized by avg of critics with dropout instead\n if not self.is_teacher:\n self.critic_with_actor_tf = tf.clip_by_value(self.critic_with_actor_tf_mc_avg,\n self.return_range[0], self.return_range[1])\n\n q_obs1 = critic_target\n self.target_q = self.rewards + (1. - self.terminals1) * self.gamma * q_obs1\n\n append = '_%s'%self.name if self.is_teacher else ''\n summaries.append(tf.summary.scalar('critic_target'+append, tf.reduce_mean(self.critic_target)))\n summaries.append(tf.summary.histogram('critic_target'+append, self.critic_target))\n\n self._setup_stats()\n self._setup_target_network_updates()\n\n if not self.is_teacher:\n with tf.variable_scope(\"input_info\", reuse=tf.AUTO_REUSE):\n summaries.append(tf.summary.scalar('rewards', tf.reduce_mean(self.rewards)))\n summaries.append(tf.summary.histogram('rewards', self.rewards))\n summaries.append(tf.summary.scalar('param_noise_stddev', tf.reduce_mean(self.param_noise_stddev)))\n summaries.append(tf.summary.histogram('param_noise_stddev', self.param_noise_stddev))\n if len(self.observation_space.shape) == 3 and self.observation_space.shape[0] in [1, 3, 4]:\n summaries.append(tf.summary.image('observation', self.obs_train))\n else:\n summaries.append(tf.summary.histogram('observation', self.obs_train))\n\n with tf.variable_scope(\"Adam_mpi\", reuse=False):\n if not self.is_teacher:\n self._setup_actor_optimizer()\n summaries.append(tf.summary.scalar('actor_loss', self.actor_loss))\n self._setup_critic_optimizer()\n summaries.append(tf.summary.scalar('critic_loss'+append, tf.reduce_mean(self.critic_loss)))\n\n self.params = find_trainable_variables(self.model_scope)\n self.target_params = find_trainable_variables(self.target_scope)\n\n self.obs_rms_params = [var for var in tf.global_variables()\n if \"obs_rms\" in var.name]\n self.ret_rms_params = [var for var in tf.global_variables()\n if \"ret_rms\" in var.name]\n\n with self.sess.as_default():\n self._initialize(self.sess)\n\n self.summary = tf.summary.merge(summaries)\n\n logging.info('Finished DDPG model setup')\n\n def _setup_target_network_updates(self):\n \"\"\"\n set the target update operations\n \"\"\"\n init_updates, soft_updates = get_target_updates(tf_util.get_trainable_vars(self.model_scope),\n tf_util.get_trainable_vars(self.target_scope), self.tau, self.verbose)\n self.target_init_updates = init_updates\n self.target_soft_updates = soft_updates\n\n def sample_q_value(self, obs, action=None):\n \"\"\"\n Samples a q value from one of the bootstrap critic heads. If action is None, evaluates actor's action.\n \"\"\"\n if action is None:\n to_eval = random.choice(self.critic_with_actor_tf_mc)\n return self.sess.run(to_eval, {self.obs_train: obs})\n else:\n to_eval = random.choice(self.critic_tf_mc)\n return self.sess.run(to_eval, {self.obs_train: obs, self.actions: action})\n\n def q_values(self, obs, action=None):\n \"\"\"\n Evaluates q values from all of the bootstrap critic heads. If action is None, evaluates actor's action.\n \"\"\"\n if action is None:\n return self.sess.run(self.critic_with_actor_tf_mc, {self.obs_train: obs})\n else:\n return self.sess.run(self.critic_tf_mc, {self.obs_train: obs, self.actions: action})\n\n def _setup_stats(self):\n \"\"\"\n setup the running means and std of the inputs and outputs of the model\n \"\"\"\n ops = []\n names = []\n\n ops += [tf.reduce_mean(self.critic_tf)]\n names += ['reference_Q_mean']\n ops += [reduce_std(self.critic_tf)]\n names += ['reference_Q_std']\n\n if self.include_mc_stats:\n ops += [self.critic_tf_mc]\n names += ['reference_Q_mc']\n ops += [tf.reduce_mean(self.critic_tf_mc, axis=0)]\n names += ['reference_Q_mc_mean']\n ops += [reduce_std(self.critic_tf_mc, axis=0)]\n names += ['reference_Q_mc_std']\n\n if not self.is_teacher:\n ops += [tf.reduce_mean(self.critic_with_actor_tf)]\n names += ['reference_actor_Q_mean']\n ops += [reduce_std(self.critic_with_actor_tf)]\n names += ['reference_actor_Q_std']\n\n ops += [tf.reduce_mean(self.actor_tf)]\n names += ['reference_action_mean']\n ops += [reduce_std(self.actor_tf)]\n names += ['reference_action_std']\n\n if self.param_noise:\n ops += [tf.reduce_mean(self.perturbed_actor_tf)]\n names += ['reference_perturbed_action_mean']\n ops += [reduce_std(self.perturbed_actor_tf)]\n names += ['reference_perturbed_action_std']\n\n for i in range(len(names)):\n names[i]+='_%s'%self.name\n\n self.stats_ops = ops\n self.stats_names = names\n\n def _setup_critic_optimizer(self):\n \"\"\"\n Setting up critic optimizer, but with additional loss for use of dropout\n \"\"\"\n if self.verbose >= 2:\n logger.info('setting up critic optimizer')\n critic_target_tf = tf.clip_by_value(self.critic_target, self.return_range[0], self.return_range[1])\n\n ### eq 10 of the alpha black box dropout\n self.alpha = 0.5\n x = critic_target_tf\n self.flat = self.critic_tf_mc\n flat_stacked = tf.stack(self.flat) # K x M x outsize\n sumsq = tf.reduce_sum(tf.square(x - flat_stacked), axis=-1, keepdims=False) # M x B X outsize\n sumsq *= (-.5 * self.alpha * self.dropout_tau)\n self.critic_loss = (-1.0 * self.alpha ** -1.0) * logsumexp(sumsq, 0)\n\n # eq 7 from https://arxiv.org/pdf/1506.02142.pdf\n N = self.num_timesteps_final # approx dataset size via constant value\n weight_decay = (self.policy_tf.keep_prob * self.length_scale**2)/(2*N*self.dropout_tau)\n\n critic_vars = get_vars(\"%s/critic\"%self.model_scope, trainable=True)\n critic_reg_vars = [var for var in critic_vars if \\\n 'kernel' in var.name and 'output' not in var.name]\n if self.verbose >= 2:\n for var in critic_reg_vars:\n logger.info(' regularizing: {}'.format(var.name))\n logger.info(' applying l2 regularization with weight decay {}'.format(weight_decay))\n critic_reg = tfc.layers.apply_regularization(\n tfc.layers.l2_regularizer(weight_decay),\n weights_list=critic_reg_vars\n )\n self.critic_loss += critic_reg\n\n critic_shapes = [var.get_shape().as_list() for var in critic_vars]\n critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])\n if self.verbose >= 2:\n logger.info(' critic shapes: {}'.format(critic_shapes))\n logger.info(' critic params: {}'.format(critic_nb_params))\n self.critic_grads = U.flatgrad(self.critic_loss, critic_vars, clip_norm=self.clip_norm)\n self.critic_optimizer = MpiAdam(var_list=critic_vars, beta1=0.9, beta2=0.999, epsilon=1e-08)\n\n def _initialize(self, sess):\n \"\"\"\n initialize the model parameters and optimizers\n :param sess: (TensorFlow Session) the current TensorFlow session\n \"\"\"\n self.sess = sess\n self.sess.run(tf.global_variables_initializer())\n if not self.is_teacher:\n self.actor_optimizer.sync()\n self.critic_optimizer.sync()\n self.sess.run(self.target_init_updates)\n\n def load(self, load_path):\n data, params = self._load_from_file(load_path)\n restores = []\n for param, loaded_p in zip(self.params + self.target_params, params):\n restores.append(param.assign(loaded_p))\n self.sess.run(restores)\n", "repo_name": "StanfordVL/ac-teach", "sub_path": "src/rl_with_teachers/learners.py", "file_name": "learners.py", "file_ext": "py", "file_size_in_byte": 54227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "51", "api": [{"api_name": "abc.ABC", "line_number": 27, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.RunOptions", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.RunMetadata", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 241, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 253, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 253, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 254, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 254, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 265, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 265, "usage_type": "name"}, {"api_name": "tensorflow.clip_by_value", "line_number": 266, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 268, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 268, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 270, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 270, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 274, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 274, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 275, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 275, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 281, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 281, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 284, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 284, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 285, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 285, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.flatgrad", "line_number": 286, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 286, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 286, "usage_type": "call"}, {"api_name": "stable_baselines.common.mpi_adam.MpiAdam", "line_number": 288, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 288, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 288, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 296, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 296, "usage_type": "name"}, {"api_name": "tensorflow.reduce_mean", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 300, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 300, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 302, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 302, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 305, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 305, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 306, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 306, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.flatgrad", "line_number": 307, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 307, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 307, "usage_type": "call"}, {"api_name": "stable_baselines.common.mpi_adam.MpiAdam", "line_number": 309, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 309, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 309, "usage_type": "name"}, {"api_name": "stable_baselines.common.SetVerbosity", "line_number": 325, "usage_type": "call"}, {"api_name": "stable_baselines.common.TensorboardWriter", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 337, "usage_type": "call"}, {"api_name": "stable_baselines.logger.log", "line_number": 339, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 339, "usage_type": "name"}, {"api_name": "stable_baselines.logger.log", "line_number": 340, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 340, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 344, "usage_type": "call"}, {"api_name": "time.time", "line_number": 358, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 375, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 377, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 400, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 405, "usage_type": "call"}, {"api_name": "stable_baselines.a2c.utils.total_episode_reward_logger", "line_number": 406, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 434, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 455, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 456, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 473, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 474, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 495, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 502, "usage_type": "call"}, {"api_name": "time.time", "line_number": 515, "usage_type": "call"}, {"api_name": "stable_baselines.logger.get_dir", "line_number": 516, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 516, "usage_type": "name"}, {"api_name": "time.time", "line_number": 517, "usage_type": "call"}, {"api_name": "time.time", "line_number": 522, "usage_type": "call"}, {"api_name": "rl_with_teachers.utils.log_histogram", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 529, "usage_type": "call"}, {"api_name": "rl_with_teachers.utils.log_histogram", "line_number": 531, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 532, "usage_type": "call"}, {"api_name": "rl_with_teachers.utils.log_histogram", "line_number": 534, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 536, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 537, "usage_type": "call"}, {"api_name": "rl_with_teachers.utils.log_histogram", "line_number": 538, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 539, "usage_type": "call"}, {"api_name": "rl_with_teachers.utils.log_histogram", "line_number": 542, "usage_type": "call"}, {"api_name": "rl_with_teachers.utils.log_histogram", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 544, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 547, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 552, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 556, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 557, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 558, "usage_type": "call"}, {"api_name": "stable_baselines.logger.record_tabular", "line_number": 567, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 567, "usage_type": "name"}, {"api_name": "stable_baselines.logger.dump_tabular", "line_number": 568, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 568, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 569, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 569, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 571, "usage_type": "call"}, {"api_name": "stable_baselines.ddpg.policies.FeedForwardPolicy", "line_number": 573, "usage_type": "name"}, {"api_name": "stable_baselines.common.policies.nature_cnn", "line_number": 588, "usage_type": "name"}, {"api_name": "stable_baselines.ddpg.policies.FeedForwardPolicy.__init__", "line_number": 590, "usage_type": "call"}, {"api_name": "stable_baselines.ddpg.policies.FeedForwardPolicy", "line_number": 590, "usage_type": "name"}, {"api_name": "tensorflow.variable_scope", "line_number": 616, "usage_type": "call"}, {"api_name": "tensorflow.layers.flatten", "line_number": 620, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 620, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dense", "line_number": 622, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 622, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.layer_norm", "line_number": 624, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 624, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dropout", "line_number": 628, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 628, "usage_type": "attribute"}, {"api_name": "tensorflow.concat", "line_number": 631, "usage_type": "call"}, {"api_name": "tensorflow.layers.dense", "line_number": 633, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 633, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform_initializer", "line_number": 634, "usage_type": "call"}, {"api_name": "tensorflow.reduce_max", "line_number": 640, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 641, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 641, "usage_type": "call"}, {"api_name": "tensorflow.exp", "line_number": 641, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 646, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 646, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 648, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 648, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 691, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 711, "usage_type": "call"}, {"api_name": "stable_baselines.common.SetVerbosity", "line_number": 713, "usage_type": "call"}, {"api_name": "gym.spaces.spaces", "line_number": 715, "usage_type": "attribute"}, {"api_name": "gym.spaces", "line_number": 715, "usage_type": "name"}, {"api_name": "tensorflow.get_default_graph", "line_number": 719, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.single_threaded_session", "line_number": 721, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 721, "usage_type": "name"}, {"api_name": "tensorflow.variable_scope", "line_number": 728, "usage_type": "call"}, {"api_name": "tensorflow.AUTO_REUSE", "line_number": 728, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 738, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 740, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 762, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 762, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 763, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 763, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 764, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 764, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 765, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 765, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 766, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 766, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 770, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 786, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 793, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 805, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 806, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 812, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 819, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 819, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 819, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 820, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 820, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 826, "usage_type": "call"}, {"api_name": "tensorflow.AUTO_REUSE", "line_number": 826, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 827, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 827, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 827, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 828, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 828, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 829, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 829, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 829, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 830, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 830, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 832, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 832, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.histogram", "line_number": 834, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 834, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 836, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 839, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 839, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 841, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 841, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 841, "usage_type": "call"}, {"api_name": "stable_baselines.a2c.utils.find_trainable_variables", "line_number": 843, "usage_type": "call"}, {"api_name": "stable_baselines.a2c.utils.find_trainable_variables", "line_number": 844, "usage_type": "call"}, {"api_name": "tensorflow.global_variables", "line_number": 846, "usage_type": "call"}, {"api_name": "tensorflow.global_variables", "line_number": 848, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge", "line_number": 854, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 854, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 856, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 862, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 862, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.get_trainable_vars", "line_number": 863, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 863, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 872, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 875, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 894, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 902, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 908, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 913, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 919, "usage_type": "call"}, {"api_name": "stable_baselines.logger.info", "line_number": 935, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 935, "usage_type": "name"}, {"api_name": "tensorflow.clip_by_value", "line_number": 936, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 942, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 943, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 943, "usage_type": "call"}, {"api_name": "stable_baselines.logger.info", "line_number": 956, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 956, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 957, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 957, "usage_type": "name"}, {"api_name": "tensorflow.contrib.layers.apply_regularization", "line_number": 958, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers", "line_number": 958, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib", "line_number": 958, "usage_type": "name"}, {"api_name": "tensorflow.contrib.layers.l2_regularizer", "line_number": 959, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers", "line_number": 959, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib", "line_number": 959, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 967, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 967, "usage_type": "name"}, {"api_name": "stable_baselines.logger.info", "line_number": 968, "usage_type": "call"}, {"api_name": "stable_baselines.logger", "line_number": 968, "usage_type": "name"}, {"api_name": "stable_baselines.common.tf_util.flatgrad", "line_number": 969, "usage_type": "call"}, {"api_name": "stable_baselines.common.tf_util", "line_number": 969, "usage_type": "name"}, {"api_name": "stable_baselines.common.mpi_adam.MpiAdam", "line_number": 970, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 978, "usage_type": "call"}]} +{"seq_id": "8780785555", "text": "import vk\n# import cProfile\n\nimport auth\nimport utils\nimport views\nimport handlers\n\nresult = auth.cookies_auth()\ntoken = result['access_token']\n\nsession = vk.Session(access_token=token)\napi = vk.API(session=session)\n\n\ndef messages_script():\n\n\tchat_id = 100\n\tnmessages = 3000\n\n\tmessages = utils.get_messages(\n\t\tapi,\n\t\tchat_id=chat_id,\n\t\tnmessages=nmessages\n\t)\n\n\tmsg_stats, = utils.data_to_dict(\n\t\thandler_list=[handlers.user_messages_count],\n\t\tdata=messages\n\t)\n\n\tusers = utils.get_chat_users(api, chat_id)\n\tutils.get_users(api, user_ids=msg_stats, user_dict=users)\n\n\tnames = utils.get_full_names(users)\n\n\tviews.dict_view(\n\t\tview_method=views.plotly_hist,\n\t\tkeys=sorted(\n\t\t\tlist(msg_stats.keys()),\n\t\t\tkey=lambda item: msg_stats[item]\n\t\t),\n\t\tlabel_dict=names,\n\t\tdata_dict=msg_stats,\n\t\trate=False\n\t)\n\n\ndef post_script():\n\n\tigm_id = -30602036\n\tplum_id = -50177168\n\n\tposts = utils.get_wall_posts(\n\t\tapi,\n\t\towner_id=igm_id,\n\t\tnposts=1000\n\t)\n\n\tdaily_likes, daily_posts = utils.data_to_dict(\n\t\tdata=posts,\n\t\thandler_list=[handlers.daily_likes, handlers.daily_posts]\n\t)\n\n\tviews.dict_view(\n\t\tview_method=views.plotly_hist,\n\t\tkeys=daily_likes.keys(),\n\t\tdata_dict={\n\t\t\tkey: daily_likes[key]/daily_posts[key] for key in daily_likes\n\t\t}\n\t)\n\npost_script()\n\n\n", "repo_name": "geoolekom/vk-scripts", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "auth.cookies_auth", "line_number": 9, "usage_type": "call"}, {"api_name": "vk.Session", "line_number": 12, "usage_type": "call"}, {"api_name": "vk.API", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.get_messages", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.data_to_dict", "line_number": 27, "usage_type": "call"}, {"api_name": "handlers.user_messages_count", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.get_chat_users", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.get_users", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.get_full_names", "line_number": 35, "usage_type": "call"}, {"api_name": "views.dict_view", "line_number": 37, "usage_type": "call"}, {"api_name": "views.plotly_hist", "line_number": 38, "usage_type": "attribute"}, {"api_name": "utils.get_wall_posts", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.data_to_dict", "line_number": 60, "usage_type": "call"}, {"api_name": "handlers.daily_likes", "line_number": 62, "usage_type": "attribute"}, {"api_name": "handlers.daily_posts", "line_number": 62, "usage_type": "attribute"}, {"api_name": "views.dict_view", "line_number": 65, "usage_type": "call"}, {"api_name": "views.plotly_hist", "line_number": 66, "usage_type": "attribute"}]} +{"seq_id": "14173504038", "text": "import pandas as pd\nimport tqdm\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nTARGET = 'HasDetections'\nTARGET_INDEX = 'MachineIdentifier'\ndata_base_path = '../Data/'\ndtypes = {\n 'MachineIdentifier': 'category',\n 'ProductName': 'category',\n 'EngineVersion': 'category',\n 'AppVersion': 'category',\n 'AvSigVersion': 'category',\n 'IsBeta': 'int8',\n 'RtpStateBitfield': 'float16',\n 'IsSxsPassiveMode': 'int8',\n 'DefaultBrowsersIdentifier': 'float16',\n 'AVProductStatesIdentifier': 'float32',\n 'AVProductsInstalled': 'float16',\n 'AVProductsEnabled': 'float16',\n 'HasTpm': 'int8',\n 'CountryIdentifier': 'int16',\n 'CityIdentifier': 'float32',\n 'OrganizationIdentifier': 'float16',\n 'GeoNameIdentifier': 'float16',\n 'LocaleEnglishNameIdentifier': 'int8',\n 'Platform': 'category',\n 'Processor': 'category',\n 'OsVer': 'category',\n 'OsBuild': 'int16',\n 'OsSuite': 'int16',\n 'OsPlatformSubRelease': 'category',\n 'OsBuildLab': 'category',\n 'SkuEdition': 'category',\n 'IsProtected': 'float16',\n 'AutoSampleOptIn': 'int8',\n 'PuaMode': 'category',\n 'SMode': 'float16',\n 'IeVerIdentifier': 'float16',\n 'SmartScreen': 'category',\n 'Firewall': 'float16',\n 'UacLuaenable': 'float32',\n 'Census_MDC2FormFactor': 'category',\n 'Census_DeviceFamily': 'category',\n 'Census_OEMNameIdentifier': 'float16',\n 'Census_OEMModelIdentifier': 'float32',\n 'Census_ProcessorCoreCount': 'float16',\n 'Census_ProcessorManufacturerIdentifier': 'float16',\n 'Census_ProcessorModelIdentifier': 'float16',\n 'Census_ProcessorClass': 'category',\n 'Census_PrimaryDiskTotalCapacity': 'float32',\n 'Census_PrimaryDiskTypeName': 'category',\n 'Census_SystemVolumeTotalCapacity': 'float32',\n 'Census_HasOpticalDiskDrive': 'int8',\n 'Census_TotalPhysicalRAM': 'float32',\n 'Census_ChassisTypeName': 'category',\n 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',\n 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',\n 'Census_InternalPrimaryDisplayResolutionVertical': 'float16',\n 'Census_PowerPlatformRoleName': 'category',\n 'Census_InternalBatteryType': 'category',\n 'Census_InternalBatteryNumberOfCharges': 'float32',\n 'Census_OSVersion': 'category',\n 'Census_OSArchitecture': 'category',\n 'Census_OSBranch': 'category',\n 'Census_OSBuildNumber': 'int16',\n 'Census_OSBuildRevision': 'int32',\n 'Census_OSEdition': 'category',\n 'Census_OSSkuName': 'category',\n 'Census_OSInstallTypeName': 'category',\n 'Census_OSInstallLanguageIdentifier': 'float16',\n 'Census_OSUILocaleIdentifier': 'int16',\n 'Census_OSWUAutoUpdateOptionsName': 'category',\n 'Census_IsPortableOperatingSystem': 'int8',\n 'Census_GenuineStateName': 'category',\n 'Census_ActivationChannel': 'category',\n 'Census_IsFlightingInternal': 'float16',\n 'Census_IsFlightsDisabled': 'float16',\n 'Census_FlightRing': 'category',\n 'Census_ThresholdOptIn': 'float16',\n 'Census_FirmwareManufacturerIdentifier': 'float16',\n 'Census_FirmwareVersionIdentifier': 'float32',\n 'Census_IsSecureBootEnabled': 'int8',\n 'Census_IsWIMBootEnabled': 'float16',\n 'Census_IsVirtualDevice': 'float16',\n 'Census_IsTouchEnabled': 'int8',\n 'Census_IsPenCapable': 'int8',\n 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',\n 'Wdft_IsGamer': 'float16',\n 'Wdft_RegionIdentifier': 'float16',\n 'HasDetections': 'int8'\n }\n\nnumerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']\nnumerical_columns = [c for c,v in dtypes.items() if v in numerics]\ncategorical_columns = [c for c,v in dtypes.items() if v not in numerics]\ndef generate_piece():\n test = pd.read_csv(data_base_path+'test.csv',nrows=10)\n test = test[0:1]\n train = pd.read_csv(data_base_path+'train.csv',nrows=10)\n train = train[0:3]\n columns = list(train.columns)\n\n file = open('piece_of_train.txt','a',encoding='utf-8')\n for i in columns:\n value = list(train[i])\n ready_to_write = i\n for j in value:\n ready_to_write += '\\t'+str(j)\n ready_to_write += '\\n'\n file.write(ready_to_write)\n file.close()\n train.to_csv('piece.csv',index=None)\n\n\ndef count(filename):\n i=0\n file = open(filename)\n while(file.readline()):\n i += 1\n print(i)\n\n print(i)\n\n\ndef values_of_columns_count():\n train = pd.read_csv('../Data/train.csv', dtype=dtypes)\n test = pd.read_csv('../Data/test.csv', dtype=dtypes)\n retained_columns = numerical_columns + categorical_columns\n retained_columns.remove('HasDetections')\n df_all = pd.concat((train, test), axis=0)\n columns = df_all.columns\n embed_cols = []\n len_embed_cols = []\n\n col_value_dict={'feature_name':[],'value':[]}\n for c in tqdm(columns[1:]):\n embed_cols.append(c)\n len_embed_cols.append(df_all[c].nunique())\n col_value_dict['feature_name'].append(c)\n col_value_dict['value'].append(df_all[c].nunique())\n print(c + ': %d values' % df_all[c].nunique()) # look at value counts to know the embedding dimensions\n print('\\n Number of embed features :', len(embed_cols))\n col_value_df = pd.DataFrame(col_value_dict)\n col_value_df.sort_values(by='value', inplace=True)\n col_value_df.to_csv('feature_value_num.csv', index=None, encoding='utf-8')\n\n\ndef distribution_of_feature(df, useful_feature,folder = 'jpg/'):\n for col in useful_feature:\n\n plt.figure(figsize=(20, 20))\n sns.distplot(df[col].values, bins=50, kde=False)\n plt.title(\"Histogram of yield\")\n plt.xlabel(col, fontsize=12)\n if '/' in col:\n col = col.replace('/','d')\n if '*' in col:\n col = col.replace('*', 'pd')\n plt.savefig(folder+col+'_count.jpg')\n plt.close()\n\n\nif __name__=='__main__':\n train = pd.read_csv('../Data/train.csv', dtype=dtypes)\n test = pd.read_csv('../Data/test.csv', dtype=dtypes)\n # train['data_flag'] = 1\n # test['data_flag'] = 0\n train_features = [f for f in train.columns if f != TARGET and f != 'Census_ProcessorClass']\n distribution_of_feature(train, train_features, folder='train_diagram/')\n distribution_of_feature(train, train_features, folder='test_diagram/')\n\n\n", "repo_name": "soapjk/Microsoft_midlleware_com", "sub_path": "DataAnalysis.py", "file_name": "DataAnalysis.py", "file_ext": "py", "file_size_in_byte": 9218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 127, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "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.close", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 165, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 166, "usage_type": "call"}]} +{"seq_id": "20737055525", "text": "from builtins import object\nimport argparse\n\nclass ArgParser(object):\n def __init__(self):\n def add_arg(*args, **kwargs):\n self.argparser.add_argument(*args, **kwargs)\n \n self.argparser = argparse.ArgumentParser(description=\"SCA service options\")\n \n add_arg('--cid', '-c', help='Course id')\n add_arg('--sid', '-s', type=int, help='Student id')\n add_arg('--grade', '-g', type=str, help='Grade. [\"A\",\"B\",\"C\",\"D\",\"E\"]')\n add_arg('--name', '-n', help='Assign student name')\n add_arg('--email', '-e', help='Assign student email')\n add_arg('--course_name', help='Assign course name')\n add_arg('--credits', help='Assign course credits')\n add_arg('--start_date', type=str, help='Course start date. Currently support format:YYYY-MM-DD')\n add_arg('--end_date', type=str, help='Course end date. Currently support format:YYYY-MM-DD')\n add_arg('--course_schedule', type=str, help='Course schedule info')\n add_arg('--timestamp', '-t', help='Timestamp for reset datebase')\n\n add_arg('--list_all_students', help='List all students profile', action='store_true')\n add_arg('--get_studentList', help='Get all students\\' ID and name only list', action='store_true')\n add_arg('--list_all_courses', help='List all courses\\' content', action='store_true')\n add_arg('--get_courseList', help='Get all courses\\' ID and name only list', action='store_true')\n add_arg('--get_student_by_id', help='Get student profile by id', action='store_true')\n add_arg('--get_course_by_id', help='Get course content by id', action='store_true')\n add_arg('--create_student_by_input', help='Create a student info without argument. Name and email input required.', action=\"store_true\")\n\n add_arg('--createStudent', help='Create a student info with argument', action=\"store_true\")\n add_arg('--updateStudent', help='Update a student info', action=\"store_true\")\n add_arg('--getCoursesOfStudent', help='Get Courses list of a specified student', action=\"store_true\")\n add_arg('--getStudentGradePointAverage', help='Calculate a specified student\\'s GPA', action=\"store_true\")\n add_arg('--createCourse', help='Create a course info', action=\"store_true\")\n add_arg('--updateCourse', help='Update a course info', action=\"store_true\")\n add_arg('--addStudentToCourse', help='Add a student to a course by id', action=\"store_true\")\n add_arg('--removeStudentFromCourse', help='Remove a student from a course by id', action=\"store_true\")\n add_arg('--calculateCourseAverage', help='Calculate the average of a specified course', action=\"store_true\")\n add_arg('--getStudentsOfCourse', help='Return a list of students\\'s id registered in a specified course', action=\"store_true\")\n add_arg('--setStudentGradeForCourse', help='Set grade to a specified student registered in a specified course', action=\"store_true\")\n add_arg('--getStudentGrade', help='Get grade to a specified student registered in a specified course', action=\"store_true\")\n add_arg('--resetDataStore', help='Reset data to the latest status up to the specified timestamp', action=\"store_true\")\n \n\n def parse(self, args=None):\n if args:\n return self.argparser.parse_args(args)\n return self.argparser.parse_args()", "repo_name": "henovation/StudentCourseApplication_rpc", "sub_path": "grpc_app/SCArgParser.py", "file_name": "SCArgParser.py", "file_ext": "py", "file_size_in_byte": 3399, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "builtins.object", "line_number": 4, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "19835254475", "text": "#! /usr/bin/env python\nfrom itertools import izip\nfrom pytbeaglehon.ccore.disc_state_cont_time_model import \\\n cpytbeaglehon_init, cpytbeaglehon_free, cget_num_comp_resources, \\\n cget_comp_resource_info, cget_model_list, cdsctm_set_q_mat, \\\n cdsctm_calc_eigens, cdsctm_calc_pr_mats, cdsctm_get_pr_mats, \\\n cdsctm_set_state_code, cdsctm_calc_partials, cdsctm_set_singleton_category_weights, \\\n cdsctm_set_state_freq, cdsctm_calc_root_likelihood\nfrom pytbeaglehon import DiscStateContTimeModel\nfrom pytbeaglehon import get_logger, CachingFacets\n_LOG = get_logger(__name__)\n_EMPTY_SET = set()\n_EMPTY_DICT = {}\n\n\nclass BeagleResourceFlags:\n PRECISION_SINGLE = 1 << 0 # Single precision computation \n PRECISION_DOUBLE = 1 << 1 # Double precision computation \n\n COMPUTATION_SYNCH = 1 << 2 # Synchronous computation (blocking) \n COMPUTATION_ASYNCH = 1 << 3 # Asynchronous computation (non-blocking) \n \n EIGEN_REAL = 1 << 4 # Real eigenvalue computation \n EIGEN_COMPLEX = 1 << 5 # Complex eigenvalue computation \n\n SCALING_MANUAL = 1 << 6 # Manual scaling \n SCALING_AUTO = 1 << 7 # Auto-scaling on \n SCALING_ALWAYS = 1 << 8 # Scale at every updatePartials \n SCALING_DYNAMIC = 1 << 19 # Manual scaling with dynamic checking \n \n SCALERS_RAW = 1 << 9 # Save raw scalers \n SCALERS_LOG = 1 << 10 # Save log scalers \n \n VECTOR_SSE = 1 << 11 # SSE computation \n VECTOR_NONE = 1 << 12 # No vector computation \n \n THREADING_OPENMP = 1 << 13 # OpenMP threading \n THREADING_NONE = 1 << 14 # No threading \n \n PROCESSOR_CPU = 1 << 15 # Use CPU as main processor \n PROCESSOR_GPU = 1 << 16 # Use GPU as main processor \n PROCESSOR_FPGA = 1 << 17 # Use FPGA as main processor \n PROCESSOR_CELL = 1 << 18 # Use Cell as main processor \n\n# add to_flag_name and to_flag_number dictionaries to BeagleResourceFlags\n_name_to_num = {}\n_num_to_name = {}\nfor _k, _v in BeagleResourceFlags.__dict__.items():\n if isinstance(_k, str) and (isinstance(_v, int) or isinstance(_v, long)):\n _name_to_num[_k] = _v\n _num_to_name[_v] = _k\nBeagleResourceFlags.to_flag_name = _num_to_name\nBeagleResourceFlags.to_flag_number = _name_to_num\ndel _num_to_name\ndel _name_to_num\n\n\n\n\ndef minimal_LCE(model_list, data):\n '''Simple constructor for a LikeCalcEnvironment that infers:\n data_type (num_states) and asrv from the models in model_list\n num_leaves, num_state_code_arrays, num_partials for len(data)\n num_patterns from len(data[0])\n \n Assumes that you will want a enough prob_matrix for every edge in a rooted,\n binary tree to have a set of matrices for each model/rate-category \n combination.\n Assumes that you want only one eigen solution per model.\n\n Assumes that you want one rescaling array for every 6 edges (every 4 leaves)\n '''\n asrv_list = []\n num_model_rate_cats = 0\n num_leaves = len(data)\n num_patterns = len(data[0])\n num_models = len(model_list) #TODO more generic for mixtures!\n for model in model_list:\n a = model.asrv\n if a is None:\n num_model_rate_cats += 1\n else:\n num_model_rate_cats += a.num_categories\n \n LCE = LikeCalcEnvironment(model_list=model_list,\n num_patterns=num_patterns,\n num_leaves=num_leaves,\n num_state_code_arrays=num_leaves,\n num_partials=(num_leaves - 1)*num_model_rate_cats,\n num_prob_matrices=num_model_rate_cats,\n num_eigen_storage_structs=num_models,\n num_rescalings_multipliers= 1 + num_leaves//4)\n for n, row in enumerate(data):\n LCE.set_state_code_array(n, row)\n return LCE\n\nNONE_HASH = ''\n\nclass BufferWrapper(object):\n '''Base class for Python objects that wrap a beagle buffer. Holds a\n reference to the LikeCalcEnvironment and the index of the struct within\n that context.\n '''\n def __init__(self, index, like_calc_env):\n self.index = index\n self.like_calc_env = like_calc_env\n self._is_calculated = False\n def set_calculated(self):\n self._is_calculated = True\n def get_is_calculated(self):\n return self._is_calculated\n def clear(self):\n self._is_calculated = False\n is_calculated = property(get_is_calculated)\n\nclass EigenSolutionWrapper(BufferWrapper):\n\n def __init__(self, index, like_calc_env, num_categ_slots):\n BufferWrapper.__init__(self, index=index, like_calc_env=like_calc_env)\n self._instance_hash_format = 'ES-%d-%d(%%s)' % (id(self), index)\n self.clear()\n self.num_categ_slots = num_categ_slots # because the number of category weight buffers in beagle is constrained to be the number of \n def clear(self):\n self._is_calculated = False\n self._model = None \n self._model_hash = None\n self._state_hash = None\n self._instance_hash = None\n self._prev_transmitted_weights = None\n self._prev_transmitted_weights_hash = None\n self._prev_transmitted_state_freq = None\n self._prev_transmitted_state_freq_hash = None\n\n def calc_hash(model_state_hash):\n return '%s' % model_state_hash\n calc_hash = staticmethod(calc_hash)\n\n def get_state_hash(self):\n if self._state_hash is None:\n if self._model_hash is None:\n raise ValueError('EigenSolutionWrapper with empty model is not hashable')\n self._state_hash = EigenSolutionWrapper.calc_hash(self._model_hash)\n return self._state_hash\n state_hash = property(get_state_hash)\n\n def get_instance_hash(self):\n if self._instance_hash is None:\n if self._model_hash is None:\n raise ValueError('EigenSolutionWrapper with empty model is not hashable')\n self._instance_hash = self._instance_hash_format % self._model_hash\n return self._state_hash\n instance_hash = property(get_instance_hash)\n \n def calculate(self, model, model_state_hash=None):\n cdsctm_set_q_mat(model.cmodel, model.q_mat)\n _LOG.debug(\"Calling cdsctm_calc_eigens(%d, %d)\" % (id(model), self.index))\n cdsctm_calc_eigens(model.cmodel, self.index)\n self._state_hash = None\n self._model = model\n if model_state_hash is None:\n self._model_hash = model.state_hash\n else:\n assert(model.state_hash == model_state_hash) # doublechecking\n self._model_hash = model_state_hash\n\n def get_category_weight_index_list(self, n):\n t = tuple([self.index + i for i in range(n)])\n _LOG.debug(\"%s.get_category_weight_index_list(%d) = %s\" % (str(self), n, str(t)))\n return t\n\n def transmit_category_weights(self, weights, weight_hash):\n assert(weight_hash is not None)\n if weight_hash == self._prev_transmitted_weights_hash:\n return\n w = tuple(weights)\n \n # TEMP this is a hack because we are always telling beagle that we have \n # one \n cw_indices = self.get_category_weight_index_list(len(w))\n cdsctm_set_singleton_category_weights(self.like_calc_env._handle, cw_indices, w)\n self._prev_transmitted_weights = w\n self._prev_transmitted_weights_hash = weight_hash\n\n def transmit_state_freq(self, state_freq, state_freq_hash):\n \"Intended for interal use only -- passes equilibrium state frequencies to likelihood calculator for integration of likelihood\"\n assert(state_freq_hash is not None)\n if state_freq_hash == self._prev_transmitted_state_freq_hash:\n return\n w = tuple(state_freq)\n cdsctm_set_state_freq(self.like_calc_env._handle, self.index, w)\n self._prev_transmitted_state_freq = w\n self._prev_transmitted_state_freq_hash = state_freq_hash\n\n\n\n def get_state_freq_hash(self):\n if self._state_freq_hash is None:\n assert(self._state_freq is not None)\n self._state_freq_hash = repr(self._state_freq)\n return self._state_freq_hash\n state_freq_hash = property(get_state_freq_hash)\n\n\n\n\nclass ProbMatWrapper(BufferWrapper):\n _hash_format = '%s-%s-%d-%s'\n def calc_hash(eigen_state_hash, asrv_hash, asrv_categ, edge_len_hash):\n return ProbMatWrapper._hash_format % (eigen_state_hash, \n asrv_hash,\n asrv_categ,\n edge_len_hash)\n calc_hash = staticmethod(calc_hash)\n def __init__(self, index, like_calc_env):\n BufferWrapper.__init__(self, index=index, like_calc_env=like_calc_env)\n self._instance_hash_format = ('PM-%d-%d' % (id(self), index)) + ProbMatWrapper._hash_format\n self.clear()\n def clear(self):\n self._is_calculated = False\n self._eigen_solution = None\n self._eigen_solution_hash = None\n self._asrv = None\n self._asrv_hash = None\n self._asrv_categ = None\n self._edge_length = None\n self._edge_length_hash = None\n self._state_hash = None\n self._instance_hash = None\n \n def get_state_hash(self):\n if self._state_hash is None:\n if self._eigen_solution_hash is None:\n raise ValueError('ProbMatWrapper without an eigen solution is not hashable')\n if self._asrv_hash is None:\n raise ValueError('ProbMatWrapper without an asrv object is not hashable')\n if self._asrv_categ is None:\n raise ValueError('ProbMatWrapper without an asrv category is not hashable')\n if self._edge_length_hash is None:\n raise ValueError('ProbMatWrapper without an edge_length is not hashable')\n self._state_hash = ProbMatWrapper._hash_format % (self._eigen_solution_hash,\n self._asrv_hash,\n self._asrv_categ,\n self._edge_length_hash)\n return self._state_hash\n state_hash = property(get_state_hash)\n\n def get_instance_hash(self):\n if self._instance_hash is None:\n if self._eigen_solution_hash is None:\n raise ValueError('ProbMatWrapper without an eigen solution is not hashable')\n if self._asrv_hash is None:\n raise ValueError('ProbMatWrapper without an asrv object is not hashable')\n if self._asrv_categ is None:\n raise ValueError('ProbMatWrapper without an asrv category is not hashable')\n if self._edge_length_hash is None:\n raise ValueError('ProbMatWrapper without an edge_length is not hashable')\n self._instance_hash = self._instance_hash_format % (self._eigen_solution_hash,\n self._asrv_hash,\n self._asrv_categ,\n self._edge_length_hash)\n return self._instance_hash\n instance_hash = property(get_instance_hash)\n def calculate_list(pr_wrap_list, eigen_soln, asrv, eff_edge_len_list, eigen_hash=None, asrv_hash=None, eff_edge_len_hash=None):\n lce = pr_wrap_list[0].like_calc_env\n ind_list = []\n for p in pr_wrap_list:\n assert(p.like_calc_env is lce)\n ind_list.append(p.index)\n\n _LOG.debug(\"Calling cdsctm_calc_pr_mats(%d, %d, %s, %s)\" % (lce._handle, eigen_soln.index, str(eff_edge_len_list), str(ind_list)))\n cdsctm_calc_pr_mats(lce._handle, eigen_soln.index, eff_edge_len_list, ind_list)\n \n for n, p in enumerate(pr_wrap_list):\n p._eigen_solution = eigen_soln\n p._eigen_solution_hash = eigen_hash\n p._asrv = asrv\n p._asrv_hash = asrv_hash\n p._asrv_categ = n\n eff_edge_len = eff_edge_len_list[n]\n p._edge_length = eff_edge_len\n if eff_edge_len_hash is None:\n p._edge_length_hash = repr(eff_edge_len)\n else:\n p._edge_length_hash = eff_edge_len_hash[n]\n p._state_hash = None\n p._instance_hash = None\n return pr_wrap_list\n calculate_list = staticmethod(calculate_list)\n\n\nclass PartialLikeWrapper(BufferWrapper):\n NO_LEAF_OP, ONE_LEAF_OP, TWO_LEAF_OP = 0, 1, 2\n def __init__(self, index, like_calc_env):\n BufferWrapper.__init__(self, index=index, like_calc_env=like_calc_env)\n self.beagle_buffer_index = index + like_calc_env.num_leaves # beagle starts numbering the partials for at num_leaves\n self.revision_index = None # stores the number of times that the wrapped object has changed -- but reversions are allowed\n self.next_revision_index = 0 # next unique identifier (will 1+self.revision_index if the current state is not a reversion to a previous state\n self._state_hash_format = 'PL-%d-%d-%%d' % (id(self), index)\n self.full_hash_format = 'PL-%d-%d-%%d(%%s-%%s+%%s-%%s)' % (id(self), index)\n self.clear()\n\n def clear(self):\n self._is_calculated = False\n self._left_data_hash = None\n self._left_prmat = None\n self._left_prmat_hash = None\n self._right_data_hash = None\n self._right_prmat = None\n self._right_prmat_hash = None\n self._state_hash = None\n self._full_state_hash = None\n self.revision_index = None\n self.rescaler_index = -1 # BEAGLE_OP_NONE flag (if we get to the root and this is still the rescaler, then no rescaling has been done)...\n\n def set_calculated(self):\n self.revision_index = self.next_revision_index\n self.next_revision_index += 1\n self._is_calculated = True\n def get_full_state_hash(self):\n if self._full_state_hash is None:\n if self.revision_index is None:\n raise ValueError('ProbMatWrapper that has not been calculated is not hashable')\n if self._left_data_hash is None:\n raise ValueError('ProbMatWrapper without an left child data is not hashable')\n if self._left_prmat_hash is None:\n raise ValueError('ProbMatWrapper without an left child probability matrix is not hashable')\n if self._right_data_hash is None:\n raise ValueError('ProbMatWrapper without an right child data is not hashable')\n if self._right_prmat_hash is None:\n raise ValueError('ProbMatWrapper without an right child probability matrix is not hashable')\n self._full_state_hash = self.full_hash_format % (self.revision_index,\n self._left_data_hash,\n self._left_prmat_hash,\n self._right_data_hash,\n self._right_prmat_hash)\n return self._full_state_hash\n full_state_hash = property(get_full_state_hash)\n def get_state_hash(self):\n if self._state_hash is None:\n if self.revision_index is None:\n raise ValueError('ProbMatWrapper that has not been calculated is not hashable')\n self._state_hash = self._state_hash_format % (self.revision_index)\n return self._state_hash\n state_hash = property(get_state_hash)\n\n def calc_partials_list(lce, operations_list, to_wait_for=tuple()):\n '''Elements of the list should be lists:\n [output PartialLikeWrapper,\n output RescalingMultiplier (or None),\n input RescalingMultiplier (or None),\n input left PartialLikeWrapper or StateCodeArrayWrapper,\n input left ProbMatWrapper,\n input right PartialLikeWrapper or StateCodeArrayWrapper,\n input right ProbMatWrapper,\n ]\n '''\n ind_list = []\n for dest_part, dest_resc, in_resc, left_data, left_pr, right_data, right_pr in operations_list:\n ind_list.append((dest_part.beagle_buffer_index,\n dest_resc is None and -1 or dest_resc.index,\n in_resc is None and -1 or in_resc.index,\n left_data.beagle_buffer_index,\n left_pr.index,\n right_data.beagle_buffer_index,\n right_pr.index,\n ))\n _LOG.debug(\"to_wait_for = \" + str(to_wait_for))\n twf = tuple([i.beagle_buffer_index for i in to_wait_for])\n if len(ind_list) > 0:\n _LOG.debug(\"Calling cdsctm_calc_partials(%d, %s, %s)\" % (lce._handle, repr(ind_list), repr(twf)))\n cdsctm_calc_partials(lce._handle, ind_list, twf)\n calc_partials_list = staticmethod(calc_partials_list)\n\nclass StateCodeArrayWrapper(BufferWrapper):\n def __init__(self, index, like_calc_env):\n BufferWrapper.__init__(self, index=index, like_calc_env=like_calc_env)\n self.beagle_buffer_index = index # beagle starts numbering the compact buffers at 0\n self._leaf_index = index\n self._state_hash_format = 'SC-%d-%d-%%d-%%d' % (id(self), index)\n self.next_revision_index = 0 # next unique identifier (will 1+self.revision_index if the current state is not a reversion to a previous state\n self.clear()\n\n def clear(self):\n self._is_calculated = False\n self._state_hash = None\n self.revision_index = None # stores the number of times that the wrapped object has changed -- but reversions are allowed\n\n def set_calculated(self):\n self.revision_index = self.next_revision_index\n self.next_revision_index += 1\n self._is_calculated = True\n\n def get_state_hash(self):\n if self._state_hash is None:\n if self.revision_index is None:\n raise ValueError('StateCodeArrayWrapper that has not been set is not hashable')\n self._state_hash = self._state_hash_format % (self._leaf_index, self.revision_index)\n return self._state_hash\n state_hash = property(get_state_hash)\n full_state_hash = property(get_state_hash)\n\n\nclass RescalingMultiplier(BufferWrapper):\n def __init__(self, index, like_calc_env):\n BufferWrapper.__init__(self, index=index, like_calc_env=like_calc_env)\n self.hash_format = 'RM-%d-%d(%%s)' % (id(self), index)\n self.clear()\n\n def clear(self):\n self._is_calculated = False\n self._state_hash = None\n self._partial_wrapper = None\n self._partial_wrapper_hash = None\n\n def get_state_hash(self):\n if self._state_hash is None:\n if self._partial_wrapper_hash is None:\n raise ValueError('RescalingMultiplier that has not been calculated is not hashable')\n self._state_hash = self.hash_format % (self._partial_wrapper_hash)\n return self._state_hash\n state_hash = property(get_state_hash)\n\nclass CalculatedCache(object):\n \n def __init__(self, wrappers, obj_name):\n self.obj_name = obj_name\n self._free = set(wrappers)\n self._saved = {}\n self._calculated = set()\n self._state_to_wrapper = {}\n self._queued = set()\n\n def get_from_cache(self, state_hash):\n return self._state_to_wrapper.get(state_hash)\n\n def save_obj(self, o):\n '''increments the reference count on o'''\n self._queued.discard(o)\n o.set_calculated()\n n = self._saved.setdefault(o, 0)\n self._saved[o] = (n + 1)\n try:\n self._state_to_wrapper[o.state_hash] = o\n except:\n pass\n\n def get_writable_object(self, o=None):\n '''Returns a free object, and \"tells\" the wrapper to clear itself.\n \n Following this call, the caller must call exactly one of the following:\n - `flag_as_calculated` (to signal the calculation was successful, but\n the object does not need to be stored long term).\n - `save_obj` to save the object, or\n - `release` to return the object (as uncalculated) to the free pool\n '''\n if o is None:\n try:\n o = self._free.pop()\n except KeyError:\n try:\n o = self._calculated.pop()\n except KeyError:\n raise ValueError(\"All %s instances are locked\" % self.obj_name)\n try:\n del self._state_to_wrapper[o.state_hash]\n except:\n pass\n o.clear()\n self._queued.add(o)\n else:\n self.make_writable(o)\n return o\n\n def flag_as_calculated(self, o):\n o.set_calculated()\n self._queued.discard(o)\n if o not in self._saved:\n self._calculated.add(o)\n try:\n self._state_to_wrapper[o.state_hash] = o\n except:\n pass\n\n def release(self, o):\n self._queued.discard(o)\n self._free.add(o)\n try:\n del self._state_to_wrapper[o.state_hash]\n except:\n pass\n\n def make_writable(self, o):\n r = self._saved.get(o)\n if r is None:\n if o in self._calculated:\n self._calculated.discard(o)\n else:\n assert(o in self._free)\n elif r == 1:\n del self._saved[o]\n else:\n return self.get_writable_object()\n try:\n del self._state_to_wrapper[o.state_hash]\n except:\n pass\n o.clear()\n self._queued.add(o)\n return o\n \n \n\nclass LikeCalcEnvironment(object):\n _CALC_ACTION = 0\n _FREE_ACTION = 1\n \n def get_num_comp_resources():\n return cget_num_comp_resources()\n get_num_comp_resources = staticmethod(get_num_comp_resources)\n\n def query_comp_resource_info(resourceIndex):\n return cget_comp_resource_info(resourceIndex)\n query_comp_resource_info = staticmethod(query_comp_resource_info)\n\n def get_comp_resource_info(self):\n if self._resource_index is None:\n raise ValueError(\"resource_index (the index of the computational resource) must be set before comp_resource_info can be accessed\")\n _LOG.debug(\"calling query_comp_resource_info for %d\" % self._resource_index)\n return LikeCalcEnvironment.query_comp_resource_info(self._resource_index)\n comp_resource_info = property(get_comp_resource_info)\n\n def __init__(self, **kwargs):\n \"\"\"Creates an new instance of a likelihood calculation context \n this is necessary because beagle does not support adding memory to an\n instance after initialization.\n \n keyword arguments (these can also be set as attributes of the object\n as long as they are set before the initialization of the beagle\n environment is triggered by requesting the model objects or some\n other calculation that requires the instance be running):\n `num_leaves`\n `num_patterns`\n `pattern_weight_list`\n `num_states`\n `num_state_code_arrays`\n `num_partials`\n `num_inst_rate_matrices`\n `asrv_list`\n `num_prob_matrices`\n `num_eigen_storage_structs`\n `num_rescalings_multipliers`\n `resource_index`\n `resource_preferences_flag`\n `resource_requirements_flag`\n \"\"\"\n self._handle = None\n self._incarnated = False\n self._pattern_weight_list = None\n self._model_list = None\n self._num_leaves = None\n self._num_patterns = None\n self._num_states = None\n self._num_state_code_arrays = None\n self._num_partials = None\n self._num_model_matrices = None\n self._asrv_list = None\n self._num_prob_matrices = None\n self._num_eigen_storage_structs = None\n self._num_rescalings_multipliers = None\n\n self._pattern_weight_list = kwargs.get(\"pattern_weight_list\")\n self._model_list = kwargs.get(\"model_list\")\n self._num_leaves = kwargs.get(\"num_leaves\")\n self.num_patterns = kwargs.get(\"num_patterns\")\n self.num_states = kwargs.get(\"num_states\")\n self._num_state_code_arrays = kwargs.get(\"num_state_code_arrays\", 0)\n self._num_partials = kwargs.get(\"num_partials\", 0)\n self.num_model_matrices = kwargs.get(\"num_model_matrices\")\n self._asrv_list = kwargs.get(\"asrv_list\")\n self._num_prob_matrices = kwargs.get(\"num_prob_matrices\", 1)\n self._num_eigen_storage_structs = kwargs.get(\"num_eigen_storage_structs\", 1)\n self._num_rescalings_multipliers = kwargs.get(\"num_rescalings_multipliers\", 0)\n self._resource_index = kwargs.get(\"resource_index\")\n self._resource_preferences_flag = kwargs.get(\"resource_preferences_flag\", 0)\n self._resource_requirements_flag = kwargs.get(\"resource_requirements_flag\", 0)\n\n def get_model_list(self):\n if self._model_list is not None:\n return tuple(self._model_list)\n nm = self.num_model_matrices\n return tuple([None]*nm)\n\n def set_model_list(self, v):\n if v is None:\n self._model_list = v\n else:\n if self._asrv_list is not None:\n if len(self._asrv_list) != len(self._model_list):\n raise ValueError(\"If asrv_list and model_list are both specified then they must be the same length\")\n for a, m in izip(self._asrv_list, v):\n m.asrv = a\n self._num_model_matrices = len(v)\n self._model_list = tuple(v)\n model_list = property(get_model_list, set_model_list)\n\n def get_num_eigen_storage_structs(self):\n return self._num_eigen_storage_structs\n def set_num_eigen_storage_structs(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < 0:\n raise ValueError(\"num_eigen_storage_structs cannot be less than 0\")\n self._num_eigen_storage_structs = v\n num_eigen_storage_structs = property(get_num_eigen_storage_structs, set_num_eigen_storage_structs)\n\n def get_num_rescalings_multipliers(self):\n return self._num_rescalings_multipliers\n def set_num_rescalings_multipliers(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < 0:\n raise ValueError(\"num_rescalings_multipliers cannot be less than 0\")\n self._num_rescalings_multipliers = v\n num_rescalings_multipliers = property(get_num_rescalings_multipliers, set_num_rescalings_multipliers)\n\n def get_num_prob_matrices(self):\n return self._num_prob_matrices\n def set_num_prob_matrices(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < 0:\n raise ValueError(\"num_prob_matrices cannot be less than 0\")\n self._num_prob_matrices = v\n num_prob_matrices = property(get_num_prob_matrices, set_num_prob_matrices)\n\n def get_num_patterns(self):\n np = self._num_patterns\n if (np is None) and (self._pattern_weight_list is not None):\n return len(self._pattern_weight_list)\n return np\n def set_num_patterns(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n if v is None:\n self._num_patterns = v\n return\n i = int(v)\n if i < 0:\n raise ValueError(\"num_patterns cannot be less than 0\")\n pw = self._pattern_weight_list\n if (pw is not None) and (len(pw) != i):\n raise ValueError(\"num_patterns must agree with the length of the list of pattern weights\")\n self._num_patterns = v\n num_patterns = property(get_num_patterns, set_num_patterns)\n\n def get_pattern_weight_list (self):\n if self._pattern_weight_list is None:\n return ()\n return self._pattern_weight_list\n def set_pattern_weight_list(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n if v is None:\n self._pattern_weight_list = None\n else:\n self._pattern_weight_list = tuple(v)\n self._num_patterns = len(self._pattern_weight_list)\n pattern_weight_list = property(get_pattern_weight_list, set_pattern_weight_list)\n\n def get_num_partials(self):\n return self._num_partials\n def set_num_partials(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < 0:\n raise ValueError(\"num_partials cannot be less than 0\")\n self._num_partials = v\n num_partials = property(get_num_partials, set_num_partials)\n\n def get_resource_index(self):\n return self._resource_index\n def set_resource_index(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < -1:\n raise ValueError(\"num_partials cannot be less than -1\")\n self._resource_index = i\n resource_index = property(get_resource_index, set_resource_index)\n\n\n def get_num_state_code_arrays(self):\n return self._num_state_code_arrays\n def set_num_state_code_arrays(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < 0:\n raise ValueError(\"num_state_code_arrays cannot be less than 0\")\n self._num_state_code_arrays = v\n num_state_code_arrays = property(get_num_state_code_arrays, set_num_state_code_arrays)\n\n def get_num_leaves(self):\n return self._num_leaves\n def set_num_leaves(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n i = int(v)\n if i < 0:\n raise ValueError(\"num_leaves cannot be less than 0\")\n self._num_leaves = v\n num_leaves = property(get_num_leaves, set_num_leaves)\n\n def get_num_states(self):\n return self._num_states\n def set_num_states(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n if v is None:\n self._num_states = v\n return\n i = int(v)\n if i < 2:\n raise ValueError(\"num_states cannot be less than 2\")\n if self._model_list:\n for m in self._model_list:\n ns = m.num_states\n if (ns is not None) and ns != i:\n raise ValueError(\"num_states must agree with num_states in all of the models is model_list\") # could be relaxed by using multiple beagle instances\n self._num_states = v\n num_states = property(get_num_states, set_num_states)\n\n def get_asrv_list(self):\n if bool(self._asrv_list):\n return tuple(self._asrv_list)\n nm = self.num_model_matrices\n if nm is None:\n return tuple()\n if self._model_list:\n return tuple(i.asrv for i in self._model_list)\n return tuple()\n\n def set_asrv_list(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n if v is None:\n self._asrv_list = None\n return\n if self._model_list:\n if len(v) != len(self._model_list):\n raise ValueError(\"len(asrv_list) must equal len(model_list)\") # could be relaxed by using multiple beagle instances\n for a, m in izip(v, self._model_list):\n m.asrv = a\n self._asrv_list = v\n asrv_list = property(get_asrv_list, set_asrv_list)\n\n def get_num_model_matrices(self):\n if self._num_model_matrices is None:\n if self._model_list is None:\n return None\n return len(self._model_list)\n return self._num_model_matrices\n def set_num_model_matrices(self, v):\n if self._incarnated:\n raise RuntimeError(\"configuration attributes cannot be altered while the instance is incarnated!\")\n if v is None:\n self._num_model_matrices = None\n else:\n i = int(v)\n if i < 0:\n raise ValueError(\"num_model_matrices cannot be negative\")\n if (self._model_list is None) or (i == len(self._model_list)):\n self._num_model_matrices = i\n else:\n raise ValueError(\"num_model_matrices and the length of the model list must agree (if both are used)\")\n num_model_matrices = property(get_num_model_matrices, set_num_model_matrices)\n \n\n def _do_beagle_init(self):\n if self._incarnated:\n raise ValueError(\"Calculation instance has already been initialized. Duplicate intialization is not allowed\")\n _LOG.debug(\"Calling cpytbeaglehon_init\")\n asrv = self.asrv_list\n if self._resource_index is None:\n self._resource_index = 0\n if self._resource_preferences_flag is None:\n self._resource_preferences_flag = 0\n if self._resource_requirements_flag is None:\n self._resource_requirements_flag = 0\n\n if (self._model_list is not None) and len(self._model_list) > 0: \n self.num_model_matrices = len(self._model_list)\n model_list = self._model_list\n self._model_list = list(model_list) # converting model_list from a tuple to a list\n models_wrappers_supplied = True\n self.num_states = model_list[0].num_states\n elif self.num_model_matrices is None:\n raise ValueError(\"A model list or num_model_matrices must be specified before initializing beagle\")\n else:\n models_wrappers_supplied = False\n model_list = []\n\n if self.num_patterns is None:\n raise ValueError(\"A num_patterns list or pattern_weight_list must be specified before initializing beagle\")\n resourceIndex = self._resource_index\n if resourceIndex is None:\n resourceIndex = -1\n max_num_rate_cats = 0\n for asrv_el in asrv:\n if asrv_el is None:\n nc = 1\n else:\n nc = asrv_el.num_categories\n if nc > max_num_rate_cats:\n max_num_rate_cats = nc\n self._num_eigen_allocated = self.num_eigen_storage_structs * max_num_rate_cats\n self._max_num_rate_cats = max_num_rate_cats\n arg_list = [self._num_leaves, \n self.num_patterns,\n self.pattern_weight_list,\n self.num_states,\n self.num_state_code_arrays,\n self.num_partials,\n self.num_model_matrices,\n asrv,\n self.num_prob_matrices,\n self.num_eigen_storage_structs,\n self.num_rescalings_multipliers,\n resourceIndex,\n self._resource_preferences_flag,\n self._resource_requirements_flag]\n _LOG.debug(\"Calling cpytbeaglehon_init with %s\" %str(arg_list))\n self._handle = cpytbeaglehon_init(*arg_list)\n raw_models = cget_model_list(self._handle)\n for n, cmodel in enumerate(raw_models):\n try:\n a = asrv[n]\n except:\n a = None\n if models_wrappers_supplied:\n model_list[n]._reassign_environ(self, n, cmodel=cmodel, asrv=a)\n else:\n wrapped = DiscStateContTimeModel(cmodel=cmodel, num_states=self._num_states, model_index=n, calc_env=self, asrv=a)\n model_list.append(wrapped)\n \n if not models_wrappers_supplied:\n self._model_list = tuple(model_list)\n \n self._incarnated = True\n \n self._wrap_eigen_soln_structs = [EigenSolutionWrapper(index=n*max_num_rate_cats, like_calc_env=self, num_categ_slots=max_num_rate_cats) for n in range(self.num_eigen_storage_structs)]\n self._wrap_prob_mat = [ProbMatWrapper(index=n, like_calc_env=self) for n in range(self.num_prob_matrices)]\n self._wrap_partial = [PartialLikeWrapper(index=n, like_calc_env=self) for n in range(self.num_partials)]\n self._wrap_state_code_array = [StateCodeArrayWrapper(index=n, like_calc_env=self) for n in range(self.num_state_code_arrays)]\n self._wrap_rescalers = [RescalingMultiplier(index=n, like_calc_env=self) for n in range(self.num_rescalings_multipliers)]\n \n # caching is implemented by keeping a free, saved and calculated pool.\n # if a new slot is needed, the free pool will be used, if it is empty\n # then the calculated pool will be used. If that is also empty, then \n # a ValueError will be raised.\n self._eigen_cache = CalculatedCache(self._wrap_eigen_soln_structs, \"EigenSolution\")\n self._prob_mat_cache = CalculatedCache(self._wrap_prob_mat, \"ProbMat\")\n self._partial_cache = CalculatedCache(self._wrap_partial, \"PartialLikelihood\")\n self._state_code_cache = CalculatedCache(self._wrap_state_code_array, \"StateCodeArray\")\n self._rescalers_cache = CalculatedCache(self._wrap_rescalers, \"Rescaler\")\n\n def __del__(self):\n self.release_resources()\n\n def release_resources(self):\n if self._incarnated:\n _LOG.debug(\"Calling cpytbeaglehon_free\")\n cpytbeaglehon_free(self._handle)\n self._model_list = ()\n self._handle = None\n self._incarnated = False\n \n del self._wrap_eigen_soln_structs\n del self._wrap_prob_mat\n del self._wrap_partial\n del self._wrap_state_code_array\n del self._wrap_rescalers\n del self._eigen_cache\n del self._prob_mat_cache\n del self._partial_cache\n del self._state_code_cache\n del self._rescalers_cache\n \n del self._free_eigen_storage_structs\n del self._saved_eigen_storage_structs\n del self._calculated_eigen_storage_structs\n del self._cached_eigen\n \n del self._free_prob_matrices\n del self._saved_prob_matrices\n del self._calculated_prob_matrices\n del self._cached_prob_matrices\n\n def calc_eigen_soln(self, model, model_state_hash, eigen_soln_caching=(CachingFacets.DO_NOT_SAVE,)):\n if not self._incarnated:\n self._do_beagle_init()\n _LOG.debug(\"LikeCalcEnvironment.calc_eigen_soln model=%d model_state_hash=%s eigen_soln_caching=%s\" % (id(model), str(model_state_hash), str(eigen_soln_caching)))\n cf = eigen_soln_caching[0]\n e_cache = self._eigen_cache\n e_hash = EigenSolutionWrapper.calc_hash(model_state_hash)\n if cf == CachingFacets.RELEASE_THEN_SAVE:\n es_wrap = self._wrap_eigen_soln_structs[ eigen_soln_caching[1] ]\n if es_wrap.state_hash == e_hash:\n return es_wrap\n es_wrap = e_cache.make_writable(es_wrap)\n else:\n es_wrap = e_cache.get_from_cache(e_hash)\n\n if es_wrap is not None:\n if cf == CachingFacets.SAVE_ANYWHERE:\n e_cache.incr_ref_count(es_wrap)\n return es_wrap\n \n es_wrap = e_cache.get_writable_object()\n\n try:\n es_wrap.calculate(model, model_state_hash)\n except:\n e_cache.release(es_wrap)\n raise\n \n if cf == CachingFacets.DO_NOT_SAVE:\n e_cache.flag_as_calculated(es_wrap)\n else:\n e_cache.save_obj(es_wrap)\n return es_wrap\n\n\n def calc_prob_from_eigen(self, edge_length, asrv, eigen_soln, prob_mat_caching=(CachingFacets.DO_NOT_SAVE,)):\n '''Returns a list of ProbMatWrapper objects.\"\"\"\n '''\n if not self._incarnated:\n self._do_beagle_init()\n _LOG.debug(\"LikeCalcEnvironment.calc_prob_from_eigen edge_length=%f asrv=%s eigen_soln_index=%s eigen_state_id=%s prob_mat_caching=%s\" % (float(edge_length), str(asrv), str(eigen_soln.index), str(eigen_soln.state_hash), str(prob_mat_caching)))\n cf = prob_mat_caching[0]\n\n if asrv is None:\n nc = 1\n rates = (1.0,)\n asrv_hash = NONE_HASH\n else:\n nc = asrv.num_categories\n rates = asrv.rates\n asrv_hash = asrv.state_hash\n p_cache = self._prob_mat_cache\n to_return = []\n eigen_state_hash = eigen_soln.state_hash\n eff_edge_len_list = []\n eff_edge_len_hash_list = []\n pr_wrap_list = []\n try:\n for asrv_categ, rate in enumerate(rates):\n eff_edge_len = rate*edge_length\n edge_len_hash = repr(eff_edge_len)\n eff_edge_len_list.append(eff_edge_len)\n eff_edge_len_hash_list.append(edge_len_hash)\n curr_hash = ProbMatWrapper.calc_hash(eigen_state_hash, \n asrv_hash,\n asrv_categ,\n edge_len_hash)\n pr_wrap = p_cache.get_from_cache(curr_hash)\n \n if pr_wrap is not None:\n _LOG.debug(\"Cache-hit on PrMat...\")\n if (cf == CachingFacets.RELEASE_THEN_SAVE) and (curr_hash != prob_mat_caching[1]):\n raise ValueError(\"Calculation of probability matrix: RELEASE_THEN_SAVE specified, but the cache returned an unexpected object\")\n else:\n pr_wrap = p_cache.get_writable_object()\n pr_wrap_list.append(pr_wrap)\n\n ProbMatWrapper.calculate_list(pr_wrap_list,\n eigen_soln,\n asrv,\n eff_edge_len_list, \n eigen_hash=eigen_state_hash,\n asrv_hash=asrv_hash, \n eff_edge_len_hash=eff_edge_len_hash_list)\n except:\n for p in pr_wrap_list:\n p_cache.release(p)\n raise\n if cf == CachingFacets.DO_NOT_SAVE:\n for p in pr_wrap_list:\n p_cache.flag_as_calculated(p)\n else:\n for p in pr_wrap_list:\n p_cache.save_obj(p)\n return pr_wrap_list\n\n\n def get_prob_matrices(self, pr_wrap_list):\n _LOG.debug(\"LikeCalcEnvironment.get_prob_matrices([%s])\" % (', '.join(['%d' % i.index for i in pr_wrap_list])))\n index_list = []\n for p in pr_wrap_list:\n assert(p.like_calc_env is self)\n assert(p.is_calculated)\n index_list.append(p.index)\n _LOG.debug(\"Calling cdsctm_get_pr_mats(%d, %s)\" % (self._handle, str(index_list)))\n return cdsctm_get_pr_mats(self._handle, index_list)\n\n def set_state_code_array(self, leaf_index, leaf_data):\n if not self._incarnated:\n self._do_beagle_init()\n if not isinstance(leaf_data, tuple):\n leaf_data = tuple(leaf_data)\n o = self._wrap_state_code_array[leaf_index]\n self._state_code_cache.get_writable_object(o)\n try:\n _LOG.debug(\"Calling cdsctm_set_state_code(%s, leaf_index=%s, leaf_data=%s,..))\" % (str(self._handle), str(leaf_index), str(leaf_data)))\n cdsctm_set_state_code(self._handle, leaf_index, leaf_data)\n except:\n self._state_code_cache.release(o)\n raise\n self._state_code_cache.save_obj(o)\n assert(o.is_calculated)\n\n def integrate_likelihood(self, model, root_partials):\n assert(self._incarnated)\n _LOG.debug(\"model.num_rate_categories == len(root_partials) ==> %d == %d\" % (model.num_rate_categories, len(root_partials)))\n assert(model.num_rate_categories == len(root_partials))\n num_categories = len(root_partials)\n asrv = model.asrv\n if asrv is None:\n assert(num_categories == 1)\n else:\n assert(num_categories == asrv.num_categories)\n es_wrapper = model.eigen_soln\n assert(es_wrapper is not None)\n cat_weight_index_tuple = es_wrapper.get_category_weight_index_list(num_categories)\n state_freq_index = es_wrapper.index\n state_freq_index_tuple = tuple([state_freq_index] * num_categories)\n rescalers = tuple([i.rescaler_index for i in root_partials])\n partial_ind_tuple = tuple([i.beagle_buffer_index for i in root_partials])\n _LOG.debug(\"calling cdsctm_calc_root_likelihood(%d, %s, %s, %s, %s)\" % (self._handle, \n repr(partial_ind_tuple),\n repr(cat_weight_index_tuple),\n repr(state_freq_index_tuple),\n repr(rescalers)))\n return cdsctm_calc_root_likelihood(self._handle, \n partial_ind_tuple,\n cat_weight_index_tuple,\n state_freq_index_tuple,\n rescalers)\n\n def tree_scorer(self, tree, tree_scorer_class):\n if not self._incarnated:\n self._do_beagle_init()\n from pytbeaglehon.tree_scorer import TreeScorer\n return tree_scorer_class(like_calc_env=self, tree=tree)\n\n def start_partial_calculations(self, model):\n self._freeable_partials = {}\n self._freeable_prob_mats = {}\n self._queue_nd_to_prob_mats = {}\n self._num_queued_prob_mats = 0\n self._queue_nd_to_partial = {}\n self._num_queued_partials = 0\n self._in_partial_calcs = True\n self._num_prob_mats_avail_for_current = self.num_prob_matrices - len(self._saved_prob_matrices)\n self._queue_nd_order = []\n esi = model.eigen_soln_index # this triggers calculation of eigen solution\n\n\n\ndef combine_state_id(*valist):\n return ' '.join([str(i) for i in valist])\n##############################################################################\n## pytbeaglehon phylogenetic likelihood caluclations using beaglelib.\n##\n## Copyright 2010 Mark T. Holder\n## All rights reserved.\n##\n## See \"LICENSE.txt\" for terms and conditions of usage.\n##\n##############################################################################\n", "repo_name": "mtholder/pytbeaglehon", "sub_path": "pytbeaglehon/like_calc_environ.py", "file_name": "like_calc_environ.py", "file_ext": "py", "file_size_in_byte": 47504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pytbeaglehon.get_logger", "line_number": 11, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_set_q_mat", "line_number": 155, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_calc_eigens", "line_number": 157, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_set_singleton_category_weights", "line_number": 180, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_set_state_freq", "line_number": 190, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_calc_pr_mats", "line_number": 271, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_calc_partials", "line_number": 371, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cget_num_comp_resources", "line_number": 518, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cget_comp_resource_info", "line_number": 522, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 600, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 756, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cpytbeaglehon_init", "line_number": 837, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cget_model_list", "line_number": 838, "usage_type": "call"}, {"api_name": "pytbeaglehon.DiscStateContTimeModel", "line_number": 847, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cpytbeaglehon_free", "line_number": 877, "usage_type": "call"}, {"api_name": "pytbeaglehon.CachingFacets.DO_NOT_SAVE", "line_number": 903, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 903, "usage_type": "name"}, {"api_name": "pytbeaglehon.CachingFacets.RELEASE_THEN_SAVE", "line_number": 910, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 910, "usage_type": "name"}, {"api_name": "pytbeaglehon.CachingFacets.SAVE_ANYWHERE", "line_number": 919, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 919, "usage_type": "name"}, {"api_name": "pytbeaglehon.CachingFacets.DO_NOT_SAVE", "line_number": 931, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 931, "usage_type": "name"}, {"api_name": "pytbeaglehon.CachingFacets.DO_NOT_SAVE", "line_number": 938, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 938, "usage_type": "name"}, {"api_name": "pytbeaglehon.CachingFacets.RELEASE_THEN_SAVE", "line_number": 974, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 974, "usage_type": "name"}, {"api_name": "pytbeaglehon.CachingFacets.DO_NOT_SAVE", "line_number": 991, "usage_type": "attribute"}, {"api_name": "pytbeaglehon.CachingFacets", "line_number": 991, "usage_type": "name"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_get_pr_mats", "line_number": 1008, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_set_state_code", "line_number": 1019, "usage_type": "call"}, {"api_name": "pytbeaglehon.ccore.disc_state_cont_time_model.cdsctm_calc_root_likelihood", "line_number": 1048, "usage_type": "call"}]} +{"seq_id": "35252948802", "text": "#!/usr/bin/python\n\n#Compares two trees using symmetric difference in the dendropy module. \n#Inputs are paths to both trees\n\nimport sys\nimport dendropy\nfrom dendropy.calculate import treecompare\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('reftree') \nparser.add_argument('tree')\nargs = parser.parse_args()\n\nInFile = open(args.reftree)\ntree = ''\n\nfor line in InFile:\n\ttree = line\n\t\ntns = dendropy.TaxonNamespace()\ntree1 = dendropy.Tree.get(data=tree,schema='newick',taxon_namespace=tns) #reference tree\n\nInFile =open(args.tree)\ntree = ''\n\nfor line in InFile:\n\ttree = line\n\ntree2 = dendropy.Tree.get(data=tree,schema='newick',taxon_namespace=tns) #original or noise reduced tree\n\nn = treecompare.symmetric_difference(tree1,tree2) #get a numerical value for symmetric difference..\n\nsys.stdout.write(str(n)+'\\n') #and write it to standard output\n\n", "repo_name": "laramk90/appbio", "sub_path": "src/compare_trees.py", "file_name": "compare_trees.py", "file_ext": "py", "file_size_in_byte": 870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "dendropy.TaxonNamespace", "line_number": 22, "usage_type": "call"}, {"api_name": "dendropy.Tree.get", "line_number": 23, "usage_type": "call"}, {"api_name": "dendropy.Tree", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dendropy.Tree.get", "line_number": 31, "usage_type": "call"}, {"api_name": "dendropy.Tree", "line_number": 31, "usage_type": "attribute"}, {"api_name": "dendropy.calculate.treecompare.symmetric_difference", "line_number": 33, "usage_type": "call"}, {"api_name": "dendropy.calculate.treecompare", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "2973938879", "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 ('bluesteel', '0004_bluesteelprojectentry_order'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='bluesteellayoutentry',\n name='active',\n field=models.BooleanField(default=False),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='bluesteellayoutentry',\n name='project_index_path',\n field=models.IntegerField(default=0),\n preserve_default=True,\n ),\n ]\n", "repo_name": "imvu/bluesteel", "sub_path": "app/logic/bluesteel/migrations/0005_auto_20150826_2351.py", "file_name": "0005_auto_20150826_2351.py", "file_ext": "py", "file_size_in_byte": 674, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "23180500884", "text": "# ファイル名：/app/Transformer-self-implementation/kakko/components/test_transformer.py\nimport logging\nlogging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n\nfrom tensorflow.keras.models import load_model\nimport numpy as np\nimport json\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # ERROR メッセージのみを表示\nimport tensorflow as tf\n\n\n# 元のトークンとIDの対応付け\ntokens = [\"(\", \")\", \"[\", \"]\", \"{\", \"}\"]\ntoken2id = {token: i for i, token in enumerate(tokens)}\nid2token = {i: token for i, token in enumerate(tokens)}\n\n# モデルの読み込み先\nmodel_path = \"/app/Transformer-self-implementation/kakko/models/mymodel0.h5\"\n\n# モデルの読み込み\nmodel = load_model(model_path)\n\n\n# 括弧の組み合わせが正しいかどうかを確認する関数\ndef is_valid_bracket_sequence(seq):\n bracket_pairs = {'(': ')', '[': ']', '{': '}'}\n stack = []\n for bracket in seq:\n if bracket in bracket_pairs: # 開始括弧\n stack.append(bracket)\n elif not stack or bracket_pairs[stack.pop()] != bracket: # 閉じ括弧\n return False\n return not stack \n\ndef predict_next_token(model, token_sequence, k):\n # 入力のトークンをIDに変換\n input_sequence = [token2id[token] for token in token_sequence]\n # モデルへの入力は (1, sequence_length) の形状である必要がある\n input_sequence = np.array(input_sequence)[np.newaxis, :]\n # モデルを使って予測を行う（verbose=0に設定して進捗ログを非表示にする）\n predictions = model.predict(input_sequence, verbose=0)\n # 予測されるIDがtokensリストの範囲内に収まるように修正\n predictions = predictions[0, :len(tokens)]\n # 最も確率の高いトークンのIDを取得\n top_k_ids = np.argsort(-predictions)[:k] # Top-kトークンIDを取得\n predicted_token_id = np.random.choice(top_k_ids) # Top-kの中からランダムに選択\n # IDをトークンに戻す\n predicted_token = id2token[predicted_token_id]\n return predicted_token\n\n\n\ndef generate_bracket_string(max_depth, length, bracket_types):\n assert max_depth <= len(bracket_types), \"Not enough bracket types for the given depth\"\n\n brackets = [bracket for bracket in bracket_types]\n stack = []\n string = ''\n \n for _ in range(length):\n # Reserve 1 space for closing bracket if necessary\n if len(string) + len(stack) >= length:\n break\n\n if not stack or (len(stack) < max_depth and np.random.choice([True, False])):\n depth = len(stack) # Determine the bracket type based on depth\n stack.append(brackets[depth][0]) # Opening bracket\n string += brackets[depth][0]\n elif stack: # Ensure stack is not empty before trying to pop\n # Find the depth of the last opened bracket\n depth = next(i for i, b in enumerate(brackets) if b[0] == stack[-1]) \n stack.pop() # Pop opening bracket\n string += brackets[depth][1] # Closing bracket\n \n # Close all remaining open brackets\n while stack and len(string) < length:\n # Find the depth of the last opened bracket\n depth = next(i for i, b in enumerate(brackets) if b[0] == stack[-1]) \n stack.pop()\n string += brackets[depth][1]\n \n return string\n\ndef test_model(model, num_samples, k):\n bracket_types = [('(', ')'), ('[', ']'), ('{', '}')]\n correct_predictions = 0\n total_predictions = 0\n \n # 出力ディレクトリの作成\n output_dir = \"/app/Transformer-self-implementation/kakko/output\"\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n \n # 結果を保存するファイルパスを生成\n existing_files = os.listdir(output_dir)\n existing_output_files = [f for f in existing_files if f.startswith(\"output\") and f.endswith(\".txt\")]\n new_file_id = len(existing_output_files) + 1\n file_path = f\"{output_dir}/output{new_file_id}.txt\"\n \n for sample_id in range(num_samples):\n string = generate_bracket_string(3, 30, bracket_types)\n prediction_output = \"\"\n correct_output = \"\"\n for i in range(len(string) - 1):\n token_sequence = list(string[:i+1])\n correct_next_token = string[i+1]\n predicted_token = predict_next_token(model, token_sequence, k) # トップ-k サンプリングを使用\n prediction_output += predicted_token\n correct_output += correct_next_token\n # 予測が正しい括弧の組み合わせならば、正解としてカウント\n if is_valid_bracket_sequence(prediction_output):\n correct_predictions += 1\n total_predictions += 1\n\n # 各テストの正答率を計算\n accuracy = correct_predictions / total_predictions\n\n # 結果をファイルに保存\n try:\n with open(file_path, \"a\") as f: # \"a\" モードで追記\n f.write(f\"Test{sample_id + 1} Accuracy: {accuracy * 100}%\\n\")\n f.write(f\"Input : {string}\\n\")\n f.write(f\"Output : {prediction_output}\\n\")\n f.write(f\"Correct: {correct_output}\\n\\n\") # 追加: 各テストの結果を区切るための空行\n except Exception as e:\n print(f\"Failed to write to file {file_path}. Error: {e}\")\n\n # 全体の正答率を表示\n overall_accuracy = correct_predictions / total_predictions\n print(f\"Model accuracy: {overall_accuracy * 100}%\")\n\n# test_model関数を呼び出す\ntest_model(model, 10, 5)\n", "repo_name": "neromehiro/wikigpt", "sub_path": "Transformer-self-implementation/kakko/components/test_transformer.py", "file_name": "test_transformer.py", "file_ext": "py", "file_size_in_byte": 5602, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 3, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 93, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "38609450140", "text": "## @example python_process_object.py\n# An example showing how to make a FAST process object in python.\n# A process object (PO) is a pipeline object which performs processing on zero or more input data\n# and generates zero or more output data.\n# @image html images/examples/python/python_process_object.jpg width=400px;\nimport fast\nimport numpy as np\n\n# Check if OpenCV is available\nuse_opencv = False\ntry:\n import cv2\n use_opencv = True\nexcept ImportError:\n pass\n\n\n\"\"\" Make a python process object which simply inverts image with numpy \"\"\"\nclass Inverter(fast.PythonProcessObject):\n def __init__(self):\n super().__init__()\n self.createInputPort(0)\n self.createOutputPort(0)\n\n def execute(self):\n # Get image and invert it with numpy\n image = self.getInputData()\n np_image = np.asarray(image)\n np_image = 255 - np_image # invert\n\n # If OpenCV is available, add some text using OpenCV\n if use_opencv:\n cv2.putText(np_image, 'OpenCV!', (40, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 2)\n\n # Create new fast image and add as output\n new_output_image = fast.Image.createFromArray(np_image)\n new_output_image.setSpacing(image.getSpacing())\n self.addOutputData(0, new_output_image)\n\n\n# Set up pipeline as normal\nimporter = fast.ImageFileStreamer.create(\n fast.Config.getTestDataPath() + 'US/Heart/ApicalFourChamber/US-2D_#.mhd',\n loop=True,\n framerate=40,\n)\n\ninverter = Inverter.create().connect(importer)\n\nrenderer = fast.ImageRenderer.create().connect(inverter)\n\nwindow = fast.SimpleWindow2D.create().connect(renderer).run()\n", "repo_name": "smistad/FAST", "sub_path": "source/FAST/Examples/Python/python_process_object.py", "file_name": "python_process_object.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 377, "dataset": "github-code", "pt": "51", "api": [{"api_name": "fast.PythonProcessObject", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 33, "usage_type": "attribute"}, {"api_name": "fast.Image.createFromArray", "line_number": 36, "usage_type": "call"}, {"api_name": "fast.Image", "line_number": 36, "usage_type": "attribute"}, {"api_name": "fast.ImageFileStreamer.create", "line_number": 42, "usage_type": "call"}, {"api_name": "fast.ImageFileStreamer", "line_number": 42, "usage_type": "attribute"}, {"api_name": "fast.Config.getTestDataPath", "line_number": 43, "usage_type": "call"}, {"api_name": "fast.Config", "line_number": 43, "usage_type": "attribute"}, {"api_name": "fast.ImageRenderer.create", "line_number": 50, "usage_type": "call"}, {"api_name": "fast.ImageRenderer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "fast.SimpleWindow2D.create", "line_number": 52, "usage_type": "call"}, {"api_name": "fast.SimpleWindow2D", "line_number": 52, "usage_type": "attribute"}]} +{"seq_id": "33834049243", "text": "# -*- coding: utf-8 -*\n# 求解非线性方程组2x1-x2^2=1,x1^2-x2=2\nfrom scipy.optimize import fsolve # 导入求解方程组的函数\n\ndef f(x): # 定义要求解的方程组\n x1 = x[0]\n x2 = x[1]\n return [2 * x1 - x2 ** 2 - 1, x1 ** 2 - x2 - 2]\n\nresult = fsolve(f, [1, 1]) # 输入初值[1, 1]并求解\nprint(result) # 输出结果，为array([ 1.91963957, 1.68501606])\n\n# 数值积分\nfrom scipy import integrate # 导入积分函数\n\ndef g(x): # 定义被积函数\n return (1 - x ** 2) ** 0.5\n\npi_2, err = integrate.quad(g, -1, 1) # 积分结果和误差\nprint(pi_2 * 2) # 由微积分知识知道积分结果为圆周率pi的一半\n", "repo_name": "keefecn/python_practice_of_data_analysis_and_mining", "sub_path": "chapter2/demo/code/2-2_Scipy.py", "file_name": "2-2_Scipy.py", "file_ext": "py", "file_size_in_byte": 660, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 253, "dataset": "github-code", "pt": "51", "api": [{"api_name": "scipy.optimize.fsolve", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.integrate", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "33195424411", "text": "#!/usr/bin/python3\nimport rospy\nimport rosbag\nimport rospkg\nimport pandas as pd\n\n\nrp = rospkg.RosPack()\n\n#Get the rospackage path\npackage_path = rp.get_path('gnss_navigation')\nbagfile_path_suffix = '/bag_files/output.bag'\ncsvfile_path_suffix = '/csv_files/output.csv'\n\nbagFilePath = package_path + bagfile_path_suffix\ncsvFilePath = package_path + csvfile_path_suffix\n\ndef main():\n\n #open ros bag file\n bag = rosbag.Bag(bagFilePath)\n\n\n topic = '/odometry/filtered'\n column_names = ['seq', 'x','y','orient_x','orient_y','orient_z','orient_w','linear_x','linear_y','angular_z']\n df = pd.DataFrame(columns=column_names)\n\n for topic, msg, t in bag.read_messages(topics=topic):\n \n\n seq = msg.header.seq\n x = msg.pose.pose.position.x\n y = msg.pose.pose.position.y\n orient_x = msg.pose.pose.orientation.x\n orient_y = msg.pose.pose.orientation.y\n orient_z = msg.pose.pose.orientation.z\n orient_w = msg.pose.pose.orientation.w\n linearx = msg.twist.twist.linear.x\n lineary = msg.twist.twist.linear.y\n angularz = msg.twist.twist.angular.z\n\n df = df.append({'seq': seq,\n 'x': x,\n 'y':y,\n 'orient_x':orient_x,\n 'orient_x':orient_x,\n 'orient_x':orient_x,\n 'orient_x':orient_x,\n 'linear_x':linearx,\n 'linear_y':lineary,\n 'angular_z':angularz},ignore_index=True)\n\n df.to_csv(csvFilePath)\n bag.close()\n print(\"[INFO]: The data has been successfully converted to Output.csv file\")\n \nif __name__ == '__main__':\n try:\n main()\n except rospy.ROSInterruptException:\n pass\n\n", "repo_name": "Abinav2695/sensor_fusion_basics", "sub_path": "src/gnss_navigation/src/rosbag2csv.py", "file_name": "rosbag2csv.py", "file_ext": "py", "file_size_in_byte": 1678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "rospkg.RosPack", "line_number": 8, "usage_type": "call"}, {"api_name": "rosbag.Bag", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "call"}, {"api_name": "rospy.ROSInterruptException", "line_number": 60, "usage_type": "attribute"}]} +{"seq_id": "71356254880", "text": "'''\nSave countries bb in a new shapefile\n'''\nimport os, os.path, shutil\n\nfrom osgeo import ogr\nfrom osgeo import osr\n\n# Open the source shapefile.\n\nsrcFile = ogr.Open(\"files/TM_WORLD_BORDERS-0.3.shp\")\nsrcLayer = srcFile.GetLayer(0)\n\n# Open the output shapefile.\n\nif os.path.exists(\"bounding-boxes\"):\n shutil.rmtree(\"bounding-boxes\")\nos.mkdir(\"bounding-boxes\")\n\nspatialReference = osr.SpatialReference()\nspatialReference.SetWellKnownGeogCS('WGS84')\n\ndriver = ogr.GetDriverByName(\"ESRI Shapefile\")\ndstPath = os.path.join(\"bounding-boxes\", \"boundingBoxes.shp\")\ndstFile = driver.CreateDataSource(dstPath)\ndstLayer = dstFile.CreateLayer(\"layer\", spatialReference)\n\nfieldDef = ogr.FieldDefn(\"NAME\", ogr.OFTString)\nfieldDef.SetWidth(50)\ndstLayer.CreateField(fieldDef)\n\nfieldDef = ogr.FieldDefn(\"ISO3\", ogr.OFTString)\nfieldDef.SetWidth(3)\ndstLayer.CreateField(fieldDef)\n\n# Read the country features from the source shapefile.\n\nfor i in range(srcLayer.GetFeatureCount()):\n feature = srcLayer.GetFeature(i)\n countryCode = feature.GetField(\"ISO3\")\n countryName = feature.GetField(\"NAME\")\n geometry = feature.GetGeometryRef()\n minLong,maxLong,minLat,maxLat = geometry.GetEnvelope()\n\n # Save the bounding box as a feature in the output\n # shapefile.\n\n linearRing = ogr.Geometry(ogr.wkbLinearRing)\n linearRing.AddPoint(minLong, minLat)\n linearRing.AddPoint(maxLong, minLat)\n linearRing.AddPoint(maxLong, maxLat)\n linearRing.AddPoint(minLong, maxLat)\n linearRing.AddPoint(minLong, minLat)\n\n polygon = ogr.Geometry(ogr.wkbPolygon)\n polygon.AddGeometry(linearRing)\n\n feature = ogr.Feature(dstLayer.GetLayerDefn())\n feature.SetGeometry(polygon)\n feature.SetField(\"NAME\", countryName)\n feature.SetField(\"ISO3\", countryCode)\n dstLayer.CreateFeature(feature)\n feature.Destroy()\n\n# All done.\n\nsrcFile.Destroy()\ndstFile.Destroy()\n\n", "repo_name": "fpl/geotutorial_basic", "sub_path": "11/02_boundingBoxesToShapefile.py", "file_name": "02_boundingBoxesToShapefile.py", "file_ext": "py", "file_size_in_byte": 1874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "51", "api": [{"api_name": "osgeo.ogr.Open", "line_number": 11, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 17, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 18, "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": "osgeo.ogr.GetDriverByName", "line_number": 23, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "osgeo.ogr.FieldDefn", "line_number": 28, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 28, "usage_type": "name"}, {"api_name": "osgeo.ogr.OFTString", "line_number": 28, "usage_type": "attribute"}, {"api_name": "osgeo.ogr.FieldDefn", "line_number": 32, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 32, "usage_type": "name"}, {"api_name": "osgeo.ogr.OFTString", "line_number": 32, "usage_type": "attribute"}, {"api_name": "osgeo.ogr.Geometry", "line_number": 48, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 48, "usage_type": "name"}, {"api_name": "osgeo.ogr.wkbLinearRing", "line_number": 48, "usage_type": "attribute"}, {"api_name": "osgeo.ogr.Geometry", "line_number": 55, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 55, "usage_type": "name"}, {"api_name": "osgeo.ogr.wkbPolygon", "line_number": 55, "usage_type": "attribute"}, {"api_name": "osgeo.ogr.Feature", "line_number": 58, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "26649441142", "text": "from cmath import log\nimport sys\nimport logging\nimport shutil\n\nimport click\nimport luigi\n\nfrom gear.utils.config import CONFIG_DIR, OUTPUT_DIR, PLUGIN_DIR, DATA_DIR, \\\n PLUGIN_REPOSITORY_DIR\nfrom gear.utils.utils import guess_filename, get_user\nfrom gear.utils.directorymanager import DirectoryManager\nfrom gear.tasks.starttask import StartTask\n\n# add plugin directory to path\nsys.path.append(PLUGIN_DIR)\n\n\n@click.group()\n@click.option(\"--debug/--no-debug\", default=False)\ndef cli(debug):\n if debug is True:\n # enable DEBUG log level\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.INFO)\n\n\n# add subcommands\nfrom gear.cmds.subcommands.config import config\nfrom gear.cmds.subcommands.plugin import plugin\ncli.add_command(config)\ncli.add_command(plugin)\n\n\n@cli.command()\n@click.argument(\"configname\")\n@click.option(\n \"--local/--no-local\", default=True, show_default=True,\n help=\"use local scheduler\"\n)\n@click.option(\n \"--workers\", default=1, show_default=True,\n help=\"number of workers\"\n)\ndef run(configname, local, workers):\n \"\"\"\n run given analysis\n \"\"\"\n try:\n # ensure that filename is a Path\n fn = guess_filename(\n name=configname,\n directories=CONFIG_DIR.joinpath(configname),\n default_extension=\".yml\"\n )\n\n # start task\n luigi.build(\n [StartTask(config_filename=fn, src_directory=\".\")],\n workers=workers,\n local_scheduler=local,\n log_level=logging.getLevelName(logging.INFO)\n )\n\n except FileNotFoundError as e:\n raise click.ClickException(e)\n\n\n@cli.command()\n@click.argument(\"configname\")\ndef reset(configname: str):\n \"\"\"\n reset the analysis' output directory\n\n :param configname: configuration name\n :type configname: str\n :raises click.ClickException: raised, if config does not exist\n \"\"\"\n try:\n # ensure that filename is a Path\n _ = guess_filename(\n name=configname,\n directories=CONFIG_DIR.joinpath(configname),\n default_extension=\".yml\"\n )\n\n directory_manager = DirectoryManager(\n output_directory=OUTPUT_DIR,\n config_directory=CONFIG_DIR,\n config_name=configname\n )\n if directory_manager.temp_directory.exists():\n # remove the output directory\n click.echo(f\"removing '{directory_manager.temp_directory}'...\")\n shutil.rmtree(directory_manager.temp_directory)\n\n except FileNotFoundError as e:\n raise click.ClickException(e)\n\n\n@cli.command()\ndef dumpenv():\n \"\"\"\n print the current config of the environment variables\n \"\"\"\n click.echo(f\"CONFIG_DIR='{CONFIG_DIR}'\")\n click.echo(f\"DATA_DIR='{DATA_DIR}'\")\n click.echo(f\"OUTPUT_DIR='{OUTPUT_DIR}'\")\n click.echo(f\"PLUGIN_DIR='{PLUGIN_DIR}'\")\n click.echo(f\"PLUGIN_REPOSITORY_DIR={PLUGIN_REPOSITORY_DIR}\")\n", "repo_name": "keans/gear", "sub_path": "gear/cmds/wrench.py", "file_name": "wrench.py", "file_ext": "py", "file_size_in_byte": 2971, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 16, "usage_type": "call"}, {"api_name": "gear.utils.config.PLUGIN_DIR", "line_number": 16, "usage_type": "argument"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "click.group", "line_number": 19, "usage_type": "call"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "gear.cmds.subcommands.config.config", "line_number": 32, "usage_type": "argument"}, {"api_name": "gear.cmds.subcommands.plugin.plugin", "line_number": 33, "usage_type": "argument"}, {"api_name": "gear.utils.utils.guess_filename", "line_number": 52, "usage_type": "call"}, {"api_name": "gear.utils.config.CONFIG_DIR.joinpath", "line_number": 54, "usage_type": "call"}, {"api_name": "gear.utils.config.CONFIG_DIR", "line_number": 54, "usage_type": "name"}, {"api_name": "luigi.build", "line_number": 59, "usage_type": "call"}, {"api_name": "gear.tasks.starttask.StartTask", "line_number": 60, "usage_type": "call"}, {"api_name": "logging.getLevelName", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 63, "usage_type": "attribute"}, {"api_name": "click.ClickException", "line_number": 67, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 37, "usage_type": "call"}, {"api_name": "click.option", "line_number": 38, "usage_type": "call"}, {"api_name": "click.option", "line_number": 42, "usage_type": "call"}, {"api_name": "gear.utils.utils.guess_filename", "line_number": 82, "usage_type": "call"}, {"api_name": "gear.utils.config.CONFIG_DIR.joinpath", "line_number": 84, "usage_type": "call"}, {"api_name": "gear.utils.config.CONFIG_DIR", "line_number": 84, "usage_type": "name"}, {"api_name": "gear.utils.directorymanager.DirectoryManager", "line_number": 88, "usage_type": "call"}, {"api_name": "gear.utils.config.OUTPUT_DIR", "line_number": 89, "usage_type": "name"}, {"api_name": "gear.utils.config.CONFIG_DIR", "line_number": 90, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 95, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 96, "usage_type": "call"}, {"api_name": "click.ClickException", "line_number": 99, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 71, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 107, "usage_type": "call"}, {"api_name": "gear.utils.config.CONFIG_DIR", "line_number": 107, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 108, "usage_type": "call"}, {"api_name": "gear.utils.config.DATA_DIR", "line_number": 108, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 109, "usage_type": "call"}, {"api_name": "gear.utils.config.OUTPUT_DIR", "line_number": 109, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 110, "usage_type": "call"}, {"api_name": "gear.utils.config.PLUGIN_DIR", "line_number": 110, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 111, "usage_type": "call"}, {"api_name": "gear.utils.config.PLUGIN_REPOSITORY_DIR", "line_number": 111, "usage_type": "name"}]} +{"seq_id": "13028806651", "text": "import tempfile\nimport os\n\nimport slate\nfrom PIL import Image\n\ntry:\n import ghostscript\n USE_GHOSTSCRIPT = True\nexcept RuntimeError:\n USE_GHOSTSCRIPT = False\n\nfrom mimetype.api import get_mimetype\n\nfrom converter.exceptions import UnknownFileFormat\nfrom converter.backends import ConverterBase\nfrom converter.literals import TRANSFORMATION_RESIZE, \\\n TRANSFORMATION_ROTATE, TRANSFORMATION_ZOOM\nfrom converter.literals import DEFAULT_PAGE_NUMBER, \\\n DEFAULT_FILE_FORMAT\nfrom converter.utils import cleanup\n\n\nclass ConverterClass(ConverterBase):\n def get_page_count(self, input_filepath):\n page_count = 1\n\n mimetype, encoding = get_mimetype(open(input_filepath, 'rb'), input_filepath, mimetype_only=True)\n if mimetype == 'application/pdf':\n # If file is a PDF open it with slate to determine the page\n # count\n with open(input_filepath) as fd:\n try:\n pages = slate.PDF(fd)\n except:\n return 1\n # TODO: Maybe return UnknownFileFormat to display proper unknwon file format message in document description\n return len(pages)\n \n try:\n im = Image.open(input_filepath)\n except IOError: # cannot identify image file\n raise UnknownFileFormat\n \n try:\n while 1:\n im.seek(im.tell() + 1)\n page_count += 1\n # do something to im\n except EOFError:\n pass # end of sequence\n \n return page_count\n\n def convert_file(self, input_filepath, output_filepath, transformations=None, page=DEFAULT_PAGE_NUMBER, file_format=DEFAULT_FILE_FORMAT, **kwargs):\n tmpfile = None\n mimetype = kwargs.get('mimetype', None)\n if not mimetype:\n mimetype, encoding = get_mimetype(open(input_filepath, 'rb'), input_filepath, mimetype_only=True)\n\n if mimetype == 'application/pdf' and USE_GHOSTSCRIPT:\n # If file is a PDF open it with ghostscript and convert it to\n # TIFF\n first_page_tmpl = '-dFirstPage=%d' % page\n last_page_tmpl = '-dLastPage=%d' % page\n fd, tmpfile = tempfile.mkstemp()\n os.close(fd)\n output_file_tmpl = '-sOutputFile=%s' % tmpfile\n input_file_tmpl = '-f%s' % input_filepath\n args = [\n 'gs', '-q', '-dQUIET', '-dSAFER', '-dBATCH',\n '-dNOPAUSE', '-dNOPROMPT', \n first_page_tmpl, last_page_tmpl,\n '-sDEVICE=jpeg', '-dJPEGQ=95',\n '-r150', output_file_tmpl,\n input_file_tmpl,\n '-c \"60000000 setvmthreshold\"', # use 30MB\n '-dNOGC', # No garbage collection\n '-dMaxBitmap=500000000',\n '-dAlignToPixels=0',\n '-dGridFitTT=0',\n '-dTextAlphaBits=4',\n '-dGraphicsAlphaBits=4', \n ] \n\n ghostscript.Ghostscript(*args)\n page = 1 # Don't execute the following while loop\n input_filepath = tmpfile \n\n try:\n im = Image.open(input_filepath)\n except Exception:\n # Python Imaging Library doesn't recognize it as an image\n raise UnknownFileFormat\n finally:\n if tmpfile:\n cleanup(tmpfile)\n \n current_page = 0\n try:\n while current_page == page - 1:\n im.seek(im.tell() + 1)\n current_page += 1\n # do something to im\n except EOFError:\n # end of sequence\n pass\n \n try:\n if transformations:\n aspect = 1.0 * im.size[0] / im.size[1]\n for transformation in transformations:\n arguments = transformation.get('arguments')\n if transformation['transformation'] == TRANSFORMATION_RESIZE:\n width = int(arguments.get('width', 0))\n height = int(arguments.get('height', 1.0 * width * aspect))\n im = self.resize(im, (width, height))\n elif transformation['transformation'] == TRANSFORMATION_ZOOM:\n decimal_value = float(arguments.get('percent', 100)) / 100\n im = im.transform((int(im.size[0] * decimal_value), int(im.size[1] * decimal_value)), Image.EXTENT, (0, 0, im.size[0], im.size[1])) \n elif transformation['transformation'] == TRANSFORMATION_ROTATE:\n # PIL counter degress counter-clockwise, reverse them\n im = im.rotate(360 - arguments.get('degrees', 0))\n except:\n # Ignore all transformation error\n pass\n\n if im.mode not in ('L', 'RGB'):\n im = im.convert('RGB')\n \n im.save(output_filepath, format=file_format)\n\n def get_format_list(self):\n \"\"\"\n Introspect PIL's internal registry to obtain a list of the\n supported file types\n \"\"\"\n formats = []\n for format_name in Image.ID:\n if format_name == 'GBR':\n formats.append('GBR_PIL')\n else:\n formats.append(format_name)\n \n if USE_GHOSTSCRIPT:\n formats.append('PDF')\n formats.append('PS')\n \n return formats\n\n def get_available_transformations(self):\n return [\n TRANSFORMATION_RESIZE, TRANSFORMATION_ROTATE, \\\n TRANSFORMATION_ZOOM\n ]\n\n # From: http://united-coders.com/christian-harms/image-resizing-tips-general-and-for-python\n def resize(self, img, box, fit=False, out=None):\n '''Downsample the image.\n @param img: Image - an Image-object\n @param box: tuple(x, y) - the bounding box of the result image\n @param fit: boolean - crop the image to fill the box\n @param out: file-like-object - save the image into the output stream\n '''\n #preresize image with factor 2, 4, 8 and fast algorithm\n factor = 1\n while img.size[0] / factor > 2 * box[0] and img.size[1] * 2 / factor > 2 * box[1]:\n factor *=2\n if factor > 1:\n img.thumbnail((img.size[0] / factor, img.size[1] / factor), Image.NEAREST)\n\n #calculate the cropping box and get the cropped part\n if fit:\n x1 = y1 = 0\n x2, y2 = img.size\n wRatio = 1.0 * x2 / box[0]\n hRatio = 1.0 * y2 / box[1]\n if hRatio > wRatio:\n y1 = y2 / 2 - box[1] * wRatio / 2\n y2 = y2 / 2 + box[1] * wRatio / 2\n else:\n x1 = x2 / 2 - box[0] * hRatio / 2\n x2 = x2 / 2 + box[0] * hRatio / 2\n img = img.crop((x1, y1, x2, y2))\n\n #Resize the image with best quality algorithm ANTI-ALIAS\n img.thumbnail(box, Image.ANTIALIAS)\n\n if out:\n #save it into a file-like object\n img.save(out, 'JPEG', quality=75)\n else:\n return img\n\n #if isinstance(self.regex, basestring):\n # self.regex = re.compile(regex)\n", "repo_name": "niksabogovac/mayan", "sub_path": "apps/converter/backends/python/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 7281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "converter.backends.ConverterBase", "line_number": 24, "usage_type": "name"}, {"api_name": "mimetype.api", "line_number": 28, "usage_type": "name"}, {"api_name": "mimetype.api.get_mimetype", "line_number": 28, "usage_type": "call"}, {"api_name": "mimetype.api", "line_number": 29, "usage_type": "name"}, {"api_name": "slate.PDF", "line_number": 34, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "converter.exceptions.UnknownFileFormat", "line_number": 43, "usage_type": "name"}, {"api_name": "converter.literals.DEFAULT_PAGE_NUMBER", "line_number": 55, "usage_type": "name"}, {"api_name": "converter.literals.DEFAULT_FILE_FORMAT", "line_number": 55, "usage_type": "name"}, {"api_name": "mimetype.api", "line_number": 57, "usage_type": "name"}, {"api_name": "mimetype.api", "line_number": 58, "usage_type": "name"}, {"api_name": "mimetype.api", "line_number": 59, "usage_type": "name"}, {"api_name": "mimetype.api.get_mimetype", "line_number": 59, "usage_type": "call"}, {"api_name": "mimetype.api", "line_number": 61, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 66, "usage_type": "call"}, {"api_name": "os.close", "line_number": 67, "usage_type": "call"}, {"api_name": "ghostscript.Ghostscript", "line_number": 86, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "converter.exceptions.UnknownFileFormat", "line_number": 94, "usage_type": "name"}, {"api_name": "converter.utils.cleanup", "line_number": 97, "usage_type": "call"}, {"api_name": "converter.literals.TRANSFORMATION_RESIZE", "line_number": 114, "usage_type": "name"}, {"api_name": "converter.literals.TRANSFORMATION_ZOOM", "line_number": 118, "usage_type": "name"}, {"api_name": "PIL.Image.EXTENT", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 120, "usage_type": "name"}, {"api_name": "converter.literals.TRANSFORMATION_ROTATE", "line_number": 121, "usage_type": "name"}, {"api_name": "PIL.Image.ID", "line_number": 139, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 139, "usage_type": "name"}, {"api_name": "converter.literals.TRANSFORMATION_RESIZE", "line_number": 153, "usage_type": "name"}, {"api_name": "converter.literals.TRANSFORMATION_ROTATE", "line_number": 153, "usage_type": "name"}, {"api_name": "converter.literals.TRANSFORMATION_ZOOM", "line_number": 154, "usage_type": "name"}, {"api_name": "PIL.Image.NEAREST", "line_number": 170, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 170, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 187, "usage_type": "name"}]} +{"seq_id": "27613539905", "text": "import tensorflow as tf\nimport numpy as np\nimport gym\nimport yaml\nimport os\n\nfrom helpers import discount_rewards\nfrom models import *\n\ndef train_model(num_episodes, model_version, discount_rate, learning_rate):\n \"\"\"\n Trains a policy model\n\n :param num_epochs: int. Determines how many epochs to train for.\n :param model_version: str. Provides the model architecture to use.\n :param discount_rate: float. Determines the impact of future actions on current reward.\n Valid entries in (0, 1) where a larger discount_rate forces model to consider current actions\n to have smaller effects on future rewards.\n :param learning_rate: float. Determines how far of a step is taken along gradient when training.\n :return: None\n \"\"\"\n\n # getting available models\n with open('model_architectures.yaml', 'r') as file:\n available_models = yaml.safe_load(stream = file)\n file.close()\n\n # input checks\n if type(num_episodes) != int:\n raise TypeError('num_episodes must be of type int')\n if num_episodes <= 0:\n raise ValueError('num_episodes must be greater than zero')\n if type(model_version) != str:\n raise TypeError('model_version must be of type string')\n if model_version not in available_models:\n raise ValueError('model_version not available')\n if type(discount_rate) != int and type(discount_rate) != float:\n raise TypeError('discount rate must be of type int or float')\n if discount_rate <= 0:\n raise ValueError('discount_rate must be greater than zero')\n if type(learning_rate) != float:\n raise TypeError('learning_rate must be of type float')\n if learning_rate <= 0 or learning_rate >= 1:\n raise ValueError('learning_rate must be within (0, 1)')\n\n # determining model sub-version\n model_sub_version_write = False\n sub_version = 0\n while not model_sub_version_write:\n if not os.path.exists(os.path.join('trained_models', model_version, model_version + '.' + str(sub_version) + '.0')):\n model_sub_version_write = True\n break\n sub_version += 1\n\n # building model\n model = eval(available_models[model_version])()\n optimizer = tf.keras.optimizers.Adam(learning_rate = learning_rate)\n compute_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True)\n\n # holding gradients\n gradient_holder = model.trainable_variables\n for i, gradient in enumerate(gradient_holder):\n gradient_holder[i] = gradient * 0\n\n # creating gym environment\n env = gym.make('CartPole-v1')\n env._max_episode_steps = 15000\n\n scores = []\n every_update = 5\n\n # training loop\n for episode in range(num_episodes + 1):\n observation = env.reset()\n\n episode_memory = []\n episode_score = 0\n done = False\n\n while not done:\n\n # creates vector of form [Position, Velocity, Angle, Angular Velocity]\n observation = observation.reshape([1, 4])\n\n with tf.GradientTape() as tape:\n\n # creating loss function and action\n logits = model(observation)\n a_dist = logits.numpy()\n action = np.random.choice(a = a_dist[0], p = a_dist[0])\n action = np.argmax(a_dist == action)\n loss = compute_loss([action], logits)\n\n # performing action and getting feedback from environment\n observation, reward, done, info = env.step(action)\n\n episode_score += reward\n\n # trick for quicker convergence\n if done:\n reward -= 10\n\n # getting gradients\n gradients = tape.gradient(target = loss, sources = model.trainable_variables)\n episode_memory.append([gradients, reward])\n\n scores.append(episode_score)\n\n # discounting rewards\n episode_memory = np.array(episode_memory)\n episode_memory[:, 1] = discount_rewards(rewards = episode_memory[:, 1], discount_rate = discount_rate)\n\n # applying rewards to corresponding gradients\n for grads, reward in episode_memory:\n for i, grad in enumerate(grads):\n gradient_holder[i] += grad * reward\n\n # back-propagating gradients, resetting gradients\n if episode % every_update == 0:\n optimizer.apply_gradients(zip(gradient_holder, model.trainable_variables))\n for i, grad in enumerate(gradient_holder):\n gradient_holder[i] = grad * 0\n\n if episode % 100 == 0:\n print('Episode {} Score {}'.format(episode, np.mean(scores[-20:])))\n tf.keras.models.save_model(model = model,\n filepath = os.path.join('trained_models', model_version, model_version + '.' +\n str(sub_version) + '.{}'.format(episode)))\n\n final_performance = int(round(np.mean(scores[-20:])))\n\n # dumping training results into yaml file\n yaml_dump = {}\n yaml_dump['Model Version'] = model_version\n yaml_dump['Model Sub-version'] = sub_version\n yaml_dump['Number of Training Episodes'] = num_episodes\n yaml_dump['Discount Rate'] = discount_rate\n yaml_dump['Learning Rate'] = learning_rate\n yaml_dump['Final Performance'] = final_performance\n\n with open(os.path.join('trained_models', model_version, model_version + '.' + str(sub_version) + '_training_details.yaml'), 'w') as file:\n yaml.dump(data = yaml_dump, stream = file)\n file.close()\n\n\nif __name__ == '__main__':\n\n num_episodes = 1000\n model_version = 'v3'\n discount_rate = 0.8\n learning_rate = 0.01\n\n train_model(num_episodes = num_episodes, model_version = model_version,\n discount_rate = discount_rate, learning_rate = learning_rate)\n", "repo_name": "MichaelNasello/CartPole_Balance", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 5828, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "yaml.safe_load", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 58, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.GradientTape", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "helpers.discount_rewards", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.save_model", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "73485105437", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.core.validators\nimport hoshimori.models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('hoshimori', '0003_auto_20170819_0742'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='student',\n name='_cache_total_senseis',\n field=models.PositiveIntegerField(null=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='student',\n name='mini_body',\n field=models.ImageField(default='', upload_to=hoshimori.models.uploadItem(b's'), verbose_name='Chibi'),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='student',\n name='mini_icon',\n field=models.ImageField(default='', upload_to=hoshimori.models.uploadItem(b's'), verbose_name='Chibi Icon'),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='account',\n name='device',\n field=models.CharField(max_length=150, null=True, verbose_name='Device'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='account',\n name='game_id',\n field=models.CharField(help_text='You can find it in-game. It is a series of 8 characters.', max_length=8, null=True, verbose_name='Game ID'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='account',\n name='i_player_type',\n field=models.PositiveIntegerField(default=0, help_text='Do you buy gems?', verbose_name='Player type', choices=[(0, b'Free-to-play'), (1, b'Pay-to-win'), (2, b'FTP PTW Hybrid')]),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='account',\n name='story_progress',\n field=models.PositiveIntegerField(help_text='Which episode have you cleared?', null=True, verbose_name='Story Progress', db_index=True, validators=[django.core.validators.MaxValueValidator(116)]),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='stagedifficulty',\n name='stage',\n field=models.ForeignKey(related_name='stage_with_difficulty', to='hoshimori.Stage', null=True),\n preserve_default=True,\n ),\n ]\n", "repo_name": "kokonguyen191/Hoshimori_Project", "sub_path": "hoshimori/migrations/0004_auto_20170820_1804.py", "file_name": "0004_auto_20170820_1804.py", "file_ext": "py", "file_size_in_byte": 2493, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "hoshimori.models.models.uploadItem", "line_number": 25, "usage_type": "call"}, {"api_name": "hoshimori.models.models", "line_number": 25, "usage_type": "attribute"}, {"api_name": "hoshimori.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "hoshimori.models.models.uploadItem", "line_number": 31, "usage_type": "call"}, {"api_name": "hoshimori.models.models", "line_number": 31, "usage_type": "attribute"}, {"api_name": "hoshimori.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.core.validators.MaxValueValidator", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.core", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 58, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 61, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "31666713550", "text": "import numpy as np\nimport argparse\nfrom datetime import datetime\nimport os\nimport sys\nimport time\n\nfrom model import Model\nfrom dataset import Dataset, custom_collate_fn, worker_init_fn\n\nimport torch\nimport torch.utils.data\n\nfrom tqdm import tqdm\n\nparser = argparse.ArgumentParser(description='Train a deep MIL model to predict sample-level tumor purity from WSIs')\n\nparser.add_argument('--init_model_file', default='', help='the path of initial model file', dest='init_model_file')\nparser.add_argument('--image_dir', default='../Images/all_cropped_patches_primary_solid_tumor__level1__stride512__size512', help='Image directory for tumor patches', dest='image_dir')\nparser.add_argument('--normal_image_dir', default='../Images/all_cropped_patches_solid_tissue_normal__level1__stride512__size512', help='Image directory for normal patches', dest='normal_image_dir')\nparser.add_argument('--dataset_dir', default='../dataset/all_patches__level1__stride512__size512', help='dataset info folder', dest='dataset_dir')\nparser.add_argument('--patch_size', default='299', type=int, help='patch size', dest='patch_size')\nparser.add_argument('--num_instances', default='200', type=int, help='number of instances (patches) in a bag', dest='num_instances')\nparser.add_argument('--num_features', default='128', type=int, help='number of features', dest='num_features')\nparser.add_argument('--num_bins', default='21', type=int, help='number of bins in distribution pooling filter', dest='num_bins')\nparser.add_argument('--sigma', default='0.05', type=float, help='sigma in distribution pooling filter', dest='sigma')\nparser.add_argument('--num_classes', default='1', type=int, help='number of classes', dest='num_classes')\nparser.add_argument('--batch_size', default='2', type=int, help='batch size', dest='batch_size')\nparser.add_argument('--learning_rate', default='1e-4', type=float, help='number of patches each patient has', dest='learning_rate')\nparser.add_argument('--num_epochs', default=10000, type=int, help='number of steps of execution (default: 1000000)', dest='num_epochs')\nparser.add_argument('--save_interval', default=10, type=int, help='model save interval (default: 1000)', dest='save_interval')\nparser.add_argument('--metrics_dir', default='loss_data', help='file to log training metrics (e.g. loss)', dest='metrics_dir')\nparser.add_argument('--models_dir', default='saved_models', help='directory to save models', dest='models_dir')\nparser.add_argument('--valid_fold', default=3, type=int, help='id of fold to be used as validation set', dest='valid_fold')\nparser.add_argument('--test_fold', default=4, type=int, help='id of fold to be used as test set', dest='test_fold')\n\nFLAGS = parser.parse_args()\nFLAGS_dict = vars(FLAGS)\n\n# create metrics_dir\nif not os.path.exists(FLAGS.metrics_dir):\n\tos.makedirs(FLAGS.metrics_dir)\n\n# create models_dir\nif not os.path.exists(FLAGS.models_dir):\n\tos.makedirs(FLAGS.models_dir)\n\n# get current time and use as model id\ncurrent_time = datetime.now().strftime(\"%Y_%m_%d__%H_%M_%S\")\nmetrics_file = '{}/loss_metrics__{}.txt'.format(FLAGS.metrics_dir,current_time)\n\ntrain_fold_list = np.arange(5)\ntrain_fold_list = np.delete(train_fold_list, [FLAGS.valid_fold,FLAGS.test_fold])\nprint('train_fold_list:{}'.format(train_fold_list))\n\nprint('Preparing training dataset ...')\ntrain_dataset = Dataset(image_dir=FLAGS.image_dir, normal_image_dir=FLAGS.normal_image_dir, dataset_dir=FLAGS.dataset_dir, dataset_type='train', patch_size=FLAGS.patch_size, fold_list=train_fold_list, num_instances=FLAGS.num_instances)\nnum_patients_train = train_dataset.num_patients\nprint(\"Training Data - num_patients: {}\".format(num_patients_train))\n\nprint('Preparing validation dataset ...')\nval_dataset = Dataset(image_dir=FLAGS.image_dir, normal_image_dir=FLAGS.normal_image_dir, dataset_dir=FLAGS.dataset_dir, dataset_type='valid', patch_size=FLAGS.patch_size, fold_list=[FLAGS.valid_fold], num_instances=FLAGS.num_instances)\nnum_patients_val = val_dataset.num_patients\nprint(\"Validation Data - num_patients: {}\".format(num_patients_val))\n\ntrain_data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=FLAGS.batch_size, shuffle=True, num_workers=4, collate_fn=custom_collate_fn, worker_init_fn=worker_init_fn)\nval_data_loader = torch.utils.data.DataLoader(val_dataset, batch_size=FLAGS.batch_size, shuffle=False, num_workers=4, collate_fn=custom_collate_fn, worker_init_fn=worker_init_fn)\n\n# construct model\ndevice=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = Model(num_classes=FLAGS.num_classes, num_instances=FLAGS.num_instances, num_features=FLAGS.num_features, num_bins=FLAGS.num_bins, sigma=FLAGS.sigma)\nmodel.to(device)\n\n# construct an optimizer\nparams = [p for p in model.parameters() if p.requires_grad]\noptimizer = torch.optim.Adam(params, lr=FLAGS.learning_rate, weight_decay=0.0005)\n\n# initialize weights from a file\nif FLAGS.init_model_file:\n\tif os.path.isfile(FLAGS.init_model_file):\n\t\tstate_dict = torch.load(FLAGS.init_model_file, map_location=device)\n\t\tmodel.load_state_dict(state_dict['model_state_dict'])\n\t\toptimizer.load_state_dict(state_dict['optimizer_state_dict'])\n\t\tprint('weights loaded successfully!!!\\n{}'.format(FLAGS.init_model_file))\n\n\n# print model parameters\nprint('# Model parameters:')\nfor key in FLAGS_dict.keys():\n\tprint('# {} = {}'.format(key, FLAGS_dict[key]))\n\nprint(\"# Training Data - num_samples: {}\".format(num_patients_train))\nprint(\"# Validation Data - num_samples: {}\".format(num_patients_val))\n\n\n# write model parameters into metrics file\nwith open(metrics_file,'w') as f_metrics_file:\n\tf_metrics_file.write('# Model parameters:\\n')\n\n\tfor key in FLAGS_dict.keys():\n\t\tf_metrics_file.write('# {} = {}\\n'.format(key, FLAGS_dict[key]))\n\n\tf_metrics_file.write(\"# Training Data - num_samples: {}\\n\".format(num_patients_train))\n\tf_metrics_file.write(\"# Validation Data - num_samples: {}\\n\".format(num_patients_val))\n\t\n\tf_metrics_file.write('# epoch\\ttraining_loss\\tvalidation_loss\\n')\n\n\n# define loss criterion\ncriterion = torch.nn.L1Loss()\n\nfor epoch in range(FLAGS.num_epochs):\n\tprint('############## EPOCH - {} ##############'.format(epoch+1))\n\ttraining_loss = 0\n\tvalidation_loss = 0\n\n\t# train for one epoch\n\tprint('******** training ********')\n\t\t\n\tnum_predictions = 0\n\n\tpbar = tqdm(total=len(train_data_loader))\n\t\n\tmodel.train()\n\tfor images, targets in train_data_loader:\n\t\timages = images.to(device)\n\t\ttargets = targets.to(device)\n\n\t\t# zero the parameter gradients\n\t\toptimizer.zero_grad()\n\n\t\t# forward + backward + optimize\n\t\ty_logits = model(images)\n\t\tloss = criterion(y_logits, targets)\n\t\tloss.backward()\n\t\toptimizer.step()\n\n\t\ttraining_loss += loss.item()*targets.size(0)\n\n\t\tnum_predictions += targets.size(0)\n\n\t\tpbar.update(1)\n\n\ttraining_loss /= num_predictions\n\n\tpbar.close()\n\n\n\t# evaluate on the validation dataset\n\tprint('******** validation ********')\n\n\tnum_predictions = 0\n\n\tpbar = tqdm(total=len(val_data_loader))\n\n\tmodel.eval()\n\twith torch.no_grad():\n\t\tfor images, targets in val_data_loader:\n\t\t\timages = images.to(device)\n\t\t\ttargets = targets.to(device)\n\n\t\t\t# forward\n\t\t\ty_logits = model(images)\n\t\t\tloss = criterion(y_logits, targets)\n\n\t\t\tvalidation_loss += loss.item()*targets.size(0)\n\n\t\t\tnum_predictions += targets.size(0)\n\n\t\t\tpbar.update(1)\n\n\tvalidation_loss /= num_predictions\n\n\tpbar.close()\n\n\tprint('Epoch=%d ### training_loss=%5.3f ### validation_loss=%5.3f' % (epoch+1, training_loss, validation_loss))\n\n\t# logging loss values into metrics file\n\twith open(metrics_file,'a') as f_metrics_file:\n\t\tf_metrics_file.write('%d\\t%5.3f\\t%5.3f\\n' % (epoch+1, training_loss, validation_loss))\n\n\t# save model\n\tif (epoch+1) % FLAGS.save_interval == 0:\n\t\tmodel_weights_filename = '{}/model_weights__{}__{}.pth'.format(FLAGS.models_dir,current_time,epoch+1)\n\t\tstate_dict = {\t'model_state_dict': model.state_dict(),\n\t\t\t\t\t\t'optimizer_state_dict': optimizer.state_dict()}\n\t\ttorch.save(state_dict, model_weights_filename)\n\t\tprint(\"Model weights saved in file: \", model_weights_filename)\n\n\nmodel_weights_filename = '{}/model_weights__{}__{}.pth'.format(FLAGS.models_dir,current_time,epoch+1)\nstate_dict = {\t'model_state_dict': model.state_dict(),\n\t\t\t\t'optimizer_state_dict': optimizer.state_dict()}\ntorch.save(state_dict, model_weights_filename)\nprint(\"Model weights saved in file: \", model_weights_filename)\n\nprint('Training finished!!!')\n\n", "repo_name": "onermustafaumit/SRTPMs", "sub_path": "LUAD/mil_dpf_regression/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 8335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 53, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 57, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 66, "usage_type": "attribute"}, {"api_name": "dataset.custom_collate_fn", "line_number": 66, "usage_type": "name"}, {"api_name": "dataset.worker_init_fn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 67, "usage_type": "attribute"}, {"api_name": "dataset.custom_collate_fn", "line_number": 67, "usage_type": "name"}, {"api_name": "dataset.worker_init_fn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 70, "usage_type": "attribute"}, {"api_name": "model.Model", "line_number": 71, "usage_type": "call"}, {"api_name": "model.to", "line_number": 72, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 75, "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": "os.path.isfile", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 81, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 122, "usage_type": "call"}, {"api_name": "model.train", "line_number": 124, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 154, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 157, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 187, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 194, "usage_type": "call"}]} +{"seq_id": "42767945639", "text": "'''\nCreated on 2020/07/23\n\n@author: CSM\n'''\n\nimport PySimpleGUI as sg\nimport subprocess\nimport platform\nimport pkg.csm.button_action as ba\n\nclass Window(object):\n '''\n classdocs\n '''\n\n\n def __init__(self, root):\n '''\n Constructor\n '''\n s = self\n s.__root = root\n\n #テーマ設定\n s._setWindowTheme('Dark Blue 3')\n s._setResourcesPath(s.__root +\"Resources\\\\\")\n s._setFilename(\"list\")\n s._setDefaultTargetPath()\n s._setInputRulePath(s.__resources+\"inputrule.json\")\n s._setOutputRulePath(s.__resources+\"outputrule.json\")\n s._setProgressBarMax(1000)\n s._setProgressPhase( [ 0.5, 0.49, 0.01 ] )\n s._setBuildedFilename()\n s._setLayout([\n [\n sg.Text('')\n ],[\n sg.Text(\"MOD Directory\", size=(15, 1)),\n sg.InputText(s.__defaultPath_Target,key=\"target\"),\n sg.FolderBrowse(key='browse_target'),\n sg.Submit(button_text='Default',key=\"btn_target\")\n ],[\n sg.Text('InputRule', size=(15, 1)),\n sg.InputText(s.__defaultPath_Input,key=\"inputrule\"),\n sg.FileBrowse(key='browse_input'),\n sg.Submit(button_text='Default',key=\"btn_iprl\")\n ],[\n sg.Text('OutputRule', size=(15, 1)),\n sg.InputText(s.__defaultPath_Output ,key=\"outputrule\"),\n sg.FileBrowse(key='browse_output'),\n sg.Submit(button_text='Default',key=\"btn_oprl\")\n ],[\n sg.Text('FileName', size=(15, 1)),\n sg.InputText(s.__defaultFilename ,key=\"filename\"),\n sg.Submit(button_text='Default',key=\"btn_filename\")\n ],[\n sg.Submit(button_text='Start',key=\"btn_run\"),\n sg.Submit(button_text='Open',key=\"btn_open\",disabled=True )\n ],[\n sg.ProgressBar(s.__progMax, orientation=\"h\", size=(60, 20), key=\"progbar\")\n ]\n ])\n\n s._bootSgWindow(\n 'Vomit Vortex',\n s.__layout,\n s.__resources + 'icon.ico'\n )\n return\n\n def _setWindowTheme(self,s): sg.theme(s)\n def _setResourcesPath(self,s): self.__resources = s\n def _setInputRulePath(self,s): self.__defaultPath_Input = s\n def _setOutputRulePath(self,s): self.__defaultPath_Output = s\n def _setFilename(self,s): self.__defaultFilename = s\n def _setProgressBarMax(self,n): self.__progMax = n\n def _setProgressPhase(self,ls): self.__progPhase = ls\n def _setBuildedFilename(self,s=''): self.__buildedFileName = s\n def _setLayout(self,layout): self.__layout = layout\n #実行環境に対応するMODディレクトリの標準的な値を設定\n def _setDefaultTargetPath(self):\n #32bitOSに対応したくないが対応\n is64bit = platform.machine().endswith(\"64\")\n fix = \" (x86)\" if is64bit else \"\"\n s = \"C:\\\\Program Files\"+fix+\"\\\\Steam\\\\steamapps\\\\workshop\\\\content\\\\294100\"\n self.__defaultPath_Target = s\n return\n #このウィンドウの全ボタンの有効状態を変更\n def _setAllButtonInvalid(self,valid):\n key = [\"target\",\"inputrule\",\"outputrule\",\"filename\",\"browse_target\",\"browse_input\",\"browse_output\",\"btn_target\",\"btn_iprl\",\"btn_oprl\",\"btn_filename\",\"btn_open\",\"btn_run\"]\n for k in key:self.__window[k].update(disabled=valid)\n\n def getResourcesPath(self):return self.__resources\n\n\n\n def _bootSgWindow(self,title, layout, ic):\n self.__window = sg.Window(title, layout, icon=ic)\n self._eventLoop()\n\n def _eventLoop(self):\n def getBuildFilePath(): return self.__root + self.__buildedFileName +'.html'\n w = self.__window\n live = True\n while live:\n e, v = w.read()\n if e == 'btn_target' : w[\"target\"].Update(self.__defaultPath_Target)\n if e == 'btn_iprl' : w[\"inputrule\"].Update(self.__defaultPath_Input)\n if e == 'btn_oprl' : w[\"outputrule\"].Update(self.__defaultPath_Output)\n if e == 'btn_filename': w[\"filename\"].Update(self.__defaultFilename)\n if e == 'btn_open' : subprocess.Popen(['start', getBuildFilePath()], shell=True)\n if e == 'btn_run' : self._btnRun(v)\n if e is None : live = False\n w.close()\n return\n\n def _btnRun(self,v):\n self._setAllButtonInvalid(True)\n self._setBuildedFilename(v[\"filename\"])\n ba.ButtonAction(self.__root, self, v[\"target\"], v[\"inputrule\"], v[\"outputrule\"], v[\"filename\"])\n return\n\n def btnEnd(self):\n self.updateProgressBar(2,1)\n self._setAllButtonInvalid(False)\n return\n\n #プログレスバーの長さを更新する\n #対象フェーズ・フェーズ進行度を指定\n def updateProgressBar(self, currentPhase, rate):\n totalRate = 0\n phaseList = self.__progPhase\n gaugeMax = self.__progMax\n if( currentPhase > len(phaseList) ) : raise\n for i, value in enumerate( phaseList ):\n if( i >= currentPhase ) : break\n totalRate += value\n totalRate += phaseList[ currentPhase ] * rate\n result = gaugeMax * totalRate\n self.__window[\"progbar\"].update_bar(result)\n\n\n", "repo_name": "gogonogoat/VomitVortex", "sub_path": "csm/window.py", "file_name": "window.py", "file_ext": "py", "file_size_in_byte": 5352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "PySimpleGUI.Text", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 39, "usage_type": "call"}, {"api_name": "PySimpleGUI.InputText", "line_number": 40, "usage_type": "call"}, {"api_name": "PySimpleGUI.FolderBrowse", "line_number": 41, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 42, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 44, "usage_type": "call"}, {"api_name": "PySimpleGUI.InputText", "line_number": 45, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 46, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 47, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 49, "usage_type": "call"}, {"api_name": "PySimpleGUI.InputText", "line_number": 50, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 51, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 52, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 54, "usage_type": "call"}, {"api_name": "PySimpleGUI.InputText", "line_number": 55, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 56, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 58, "usage_type": "call"}, {"api_name": "PySimpleGUI.Submit", "line_number": 59, "usage_type": "call"}, {"api_name": "PySimpleGUI.ProgressBar", "line_number": 61, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme", "line_number": 72, "usage_type": "call"}, {"api_name": "platform.machine", "line_number": 84, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 99, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 112, "usage_type": "call"}, {"api_name": "pkg.csm.button_action.ButtonAction", "line_number": 121, "usage_type": "call"}, {"api_name": "pkg.csm.button_action", "line_number": 121, "usage_type": "name"}]} +{"seq_id": "34929691615", "text": "from typing import Any, Dict, Iterable, List, Optional, Union\n\nfrom typing_extensions import Literal\n\nfrom aioqbt._paramdict import ParamDict\nfrom aioqbt.api.types import (\n SearchJobResults,\n SearchJobStart,\n SearchJobStatus,\n SearchPlugin,\n SearchPluginCategory,\n SearchResultEntry,\n)\nfrom aioqbt.client import APIGroup\nfrom aioqbt.version import APIVersion\n\n\nclass SearchAPI(APIGroup):\n \"\"\"\n Search APIs.\n\n .. note::\n\n Search API is experimental. Methods and results may change without notice.\n\n \"\"\"\n\n async def start(\n self,\n pattern: str,\n plugins: Union[List[str], Literal[\"all\"], Literal[\"enabled\"]],\n category: Union[str, Literal[\"all\"]],\n ) -> SearchJobStart:\n \"\"\"\n Start a search job.\n\n The result consists of only the search job :attr:`~.SearchJobStart.id`.\n\n :param pattern: Search pattern.\n :param plugins: Plugins used in search. Special values:\n ``all`` uses all plugins while ``enabled`` uses enabled plugins only.\n :param category: Search specific category or ``all``.\n\n :raises ~exc.ConflictError: if error.\n \"\"\"\n data = ParamDict()\n data.required_str(\"pattern\", pattern)\n data.required_list(\"plugins\", plugins, \"|\")\n data.required_str(\"category\", category)\n\n return await self._request_mapped_object(\n SearchJobStart,\n \"POST\",\n \"search/start\",\n data=data,\n )\n\n async def stop(self, id: int) -> None:\n \"\"\"\n Stop a search job.\n\n :raises ~exc.NotFoundError: if the search job is not found.\n \"\"\"\n data = ParamDict()\n data.required_int(\"id\", id)\n\n await self._request_text(\n \"POST\",\n \"search/stop\",\n data=data,\n )\n\n async def status(self, id: Optional[int] = None) -> List[SearchJobStatus]:\n \"\"\"\n Query search job statuses.\n\n :raises ~exc.NotFoundError: if the search job is specified but not found.\n \"\"\"\n params = ParamDict()\n params.optional_int(\"id\", id)\n\n return await self._request_mapped_list(\n SearchJobStatus,\n \"GET\",\n \"search/status\",\n params=params,\n )\n\n async def results(\n self,\n id: int,\n limit: Optional[int] = None,\n offset: Optional[int] = None,\n ) -> SearchJobResults:\n \"\"\"\n Get search job results.\n\n :raises ~exc.NotFoundError: if the search job is not found.\n :raises ~exc.ConflictError: if ``offset`` is out of range.\n \"\"\"\n\n params = ParamDict()\n params.required_int(\"id\", id)\n params.optional_int(\"limit\", limit)\n params.optional_int(\"offset\", offset)\n\n client = self._client()\n\n data = await client.request_json(\n \"GET\",\n \"search/results\",\n params=params,\n )\n\n mapper = client._mapper\n context = client._context\n ret = mapper.create_object(SearchJobResults, data, context)\n ret.results = mapper.create_list(SearchResultEntry, data[\"results\"], context)\n\n return ret\n\n async def delete(self, id: int) -> None:\n \"\"\"\n Delete a search job.\n\n :raises ~exc.NotFoundError: if the search job is not found.\n \"\"\"\n data = ParamDict()\n data.required_int(\"id\", id)\n\n await self._request_text(\n \"POST\",\n \"search/delete\",\n data=data,\n )\n\n async def plugins(self) -> List[SearchPlugin]:\n \"\"\"\n Get all plugins.\n \"\"\"\n client = self._client()\n\n data: List[Dict[str, object]] = await client.request_json(\n \"GET\",\n \"search/plugins\",\n )\n\n mapper = client._mapper\n context = client._context\n\n result: List[SearchPlugin] = mapper.create_list(SearchPlugin, data, context)\n\n plugin: SearchPlugin\n plugin_data: Dict[str, Any]\n\n dict_list = True\n\n if APIVersion.compare(client.api_version, (2, 5, 2)) < 0:\n # before API 2.5.2 ~= v4.3.0alpha1\n # supportedCategories is a list of localized category name strings\n # see commit 8e8cd59d90e63b992bc5c43c29d5aec001855a4e\n for plugin in result:\n if any(isinstance(s, str) for s in plugin.supportedCategories):\n dict_list = False\n break\n\n if dict_list:\n for plugin, plugin_data in zip(result, data):\n plugin.supportedCategories = mapper.create_list(\n SearchPluginCategory,\n plugin_data[\"supportedCategories\"], # type: ignore[arg-type]\n context,\n )\n\n return result\n\n async def install_plugin(self, sources: Iterable[str]) -> None:\n \"\"\"\n Install plugins.\n \"\"\"\n data = ParamDict()\n data.required_list(\"sources\", sources, \"|\")\n\n await self._request_text(\n \"POST\",\n \"search/installPlugin\",\n data=data,\n )\n\n async def uninstall_plugin(self, names: Iterable[str]) -> None:\n \"\"\"\n Uninstall plugins.\n \"\"\"\n data = ParamDict()\n data.required_list(\"names\", names, \"|\")\n\n await self._request_text(\n \"POST\",\n \"search/uninstallPlugin\",\n data=data,\n )\n\n async def enable_plugin(self, names: Iterable[str], enable: bool) -> None:\n \"\"\"\n Enable/disable plugins.\n\n :param names: a list of plugins to enable/disable.\n :param enable: ``True`` to enable or ``False`` to disable.\n \"\"\"\n data = ParamDict()\n data.required_list(\"names\", names, \"|\")\n data.required_bool(\"enable\", enable)\n\n await self._request_text(\n \"POST\",\n \"search/enablePlugin\",\n data=data,\n )\n\n async def update_plugins(self) -> None:\n \"\"\"\n Update plugins.\n \"\"\"\n await self._request_text( # pragma: no cover\n \"POST\",\n \"search/updatePlugins\",\n )\n", "repo_name": "tsangwpx/aioqbt", "sub_path": "src/aioqbt/api/search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 6197, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "aioqbt.client.APIGroup", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "typing_extensions.Literal", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 32, "usage_type": "name"}, {"api_name": "typing_extensions.Literal", "line_number": 32, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 46, "usage_type": "call"}, {"api_name": "aioqbt.api.types.SearchJobStart", "line_number": 52, "usage_type": "argument"}, {"api_name": "aioqbt.api.types.SearchJobStart", "line_number": 33, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 79, "usage_type": "call"}, {"api_name": "aioqbt.api.types.SearchJobStatus", "line_number": 83, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 73, "usage_type": "name"}, {"api_name": "aioqbt.api.types.SearchJobStatus", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 102, "usage_type": "call"}, {"api_name": "aioqbt.api.types.SearchJobResults", "line_number": 117, "usage_type": "argument"}, {"api_name": "aioqbt.api.types.SearchResultEntry", "line_number": 118, "usage_type": "argument"}, {"api_name": "aioqbt.api.types.SearchJobResults", "line_number": 94, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 128, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 151, "usage_type": "name"}, {"api_name": "aioqbt.api.types.SearchPlugin", "line_number": 151, "usage_type": "name"}, {"api_name": "aioqbt.api.types.SearchPlugin", "line_number": 153, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 154, "usage_type": "name"}, {"api_name": "aioqbt.version.APIVersion.compare", "line_number": 158, "usage_type": "call"}, {"api_name": "aioqbt.version.APIVersion", "line_number": 158, "usage_type": "name"}, {"api_name": "aioqbt.api.types.SearchPluginCategory", "line_number": 170, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 137, "usage_type": "name"}, {"api_name": "aioqbt.api.types.SearchPlugin", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 177, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 181, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 190, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 194, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 203, "usage_type": "name"}, {"api_name": "aioqbt._paramdict.ParamDict", "line_number": 210, "usage_type": "call"}]} +{"seq_id": "23281905582", "text": "#商周新聞\r\n#讀txt檔\r\n#進mongodb\r\n\r\nfrom pymongo import MongoClient\r\n\r\nclient = MongoClient('127.0.0.1:27017')\r\ndb = client[\"economic_news\"]\r\ncollect = db[\"news\"]\r\nf = open(r'C:\\Users\\Big data\\PycharmProjects\\FP\\news.txt','r',encoding=\"utf-8\")\r\n# print(f.read())\r\na = f.read().split(\"};{\")\r\n# print(a)\r\n# print(type(a))\r\n\r\nfor j in a:\r\n # print(post_data)\r\n\r\n post_data = {}\r\n\r\n for h in j.split(\"',\"):\r\n # print(h)\r\n\r\n p=h.replace(\"{\", \"\").replace(\"}\", \"\").replace(\"\\n\",\"\").replace(\"'\",\"\").replace(\"日期\",\"DATE\").replace(\"標題\",\"TITLE\").replace(\"內容\",\"TEXT\").replace(\"網址\",\"URL\").replace(\"預覽數\",\"BROWSE\")\r\n #print(p)\r\n\r\n key = p.split(\":\")[0]\r\n value = p.replace(p.split(\":\")[0] + ':', '').lstrip()\r\n print('key = {}, value = {}'.format(key, value))\r\n\r\n post_data[key]=value\r\n\r\n # print(post_data)\r\n db.news.insert_one(post_data)\r\n\r\n\r\n", "repo_name": "annann81/db106_g4", "sub_path": "read_txt.py", "file_name": "read_txt.py", "file_ext": "py", "file_size_in_byte": 922, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "51", "api": [{"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "12909179736", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport skimage.measure as ski_mea\nimport skimage.morphology as ski_mor\nfrom colorama import Fore, Style\n\n\nimport os_utils\n\n\nclass Cost:\n def __init__(self):\n pass\n\n def add(self, cost_dict):\n for k, v in cost_dict.items():\n if not hasattr(self, k):\n setattr(self, k, [])\n getattr(self, k).append(v)\n\n def get(self):\n return self.__dict__\n\n def show(self, ax=None):\n if ax is None:\n plt.ion()\n plt.figure()\n for k, v in self.get().items():\n plt.scatter(range(len(v)), v, s=3, label=k)\n\n plt.yscale(\"log\")\n plt.legend()\n plt.show()\n else:\n for k, v in self.get().items():\n ax.scatter(range(len(v)), v, s=3, label=k)\n ax.set_yscale(\"log\")\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n\n\nclass NucleiSegmentation:\n def __init__(self, filename_root, ch_id, overwrite=False, out_dir=None):\n self.filename_root = filename_root\n self.ch_id = ch_id\n self.overwrite = overwrite\n self.out_dir = out_dir\n self.costs = {}\n\n def _find_rows_cols(self, rng, aspect_ratio=1.4):\n rows = 1\n cols = 1\n while rows * cols < rng:\n if cols / rows > aspect_ratio:\n rows += 1\n else:\n cols += 1\n return rows, cols\n\n def plot_costs(self, aspect_ratio=1.4):\n rows, cols = self._find_rows_cols(len(self.costs), aspect_ratio)\n plt.ion()\n fig, axs = plt.subplots(rows, cols, sharex=True, sharey=True)\n for ax, (k, v) in zip(axs.flatten(), self.costs.items()):\n v.show(ax=ax)\n plt.subplots_adjust(wspace=0, hspace=0)\n plt.tight_layout()\n plt.show()\n\n def _cost(self, t, temp_region, temp_values, temp_cytoplasm=None, normalize=False):\n # Assumes nuclei to be brighter than background\n # --- Nuclei ---\n # Threshold volume within the Voronoi cell\n msk = np.zeros_like(temp_region, dtype=\"bool\")\n msk[temp_region] = temp_values[temp_region] >= t\n\n # Nucleus inside of volume\n in_idx = np.logical_and(msk, temp_region)\n in_variance = self._evaluate_variance_in_volume(\n in_idx, temp_values, normalize=normalize\n )\n\n # Nucleus outside of volume\n out_idx = np.logical_and(~msk, temp_region)\n out_variance = self._evaluate_variance_in_volume(\n out_idx, temp_values, normalize=normalize\n )\n\n # Cost if only nucleus is considered\n cost = out_variance + in_variance\n\n # --- Cytoplasm ---\n if temp_cytoplasm is not None:\n # # Threshold within the Voronoi cell\n # msk_cyto = np.zeros_like(temp_region, dtype=\"bool\")\n # msk_cyto[temp_region] = temp_cytoplasm[temp_region] >= t\n\n # Cytoplasm outside of volume (expected inside the cytoplasm)\n in_cyto_variance = self._evaluate_variance_in_volume(\n out_idx, temp_cytoplasm, normalize=normalize\n )\n # Cytoplasm inside of volume (expected outside the cytoplasm)\n out_cyto_variance = self._evaluate_variance_in_volume(\n in_idx, temp_cytoplasm, normalize=normalize\n )\n\n # Cost if also cytospasm is considered\n cost = out_variance + out_cyto_variance\n # cost += out_cyto_variance\n\n surface_idx = np.logical_xor(\n ski_mor.binary_dilation(msk, footprint=ski_mor.ball(1)), msk\n )\n surface = surface_idx.sum()\n\n # # Minimize surface as well\n cost -= surface\n\n # volume = in_idx.sum()\n # volume_val = temp_values[in_idx].sum()\n\n table = ski_mea.regionprops(ski_mea.label(msk))\n objects = len(table)\n\n # cost += 0 if (objects == 1) else np.inf\n\n # Store costs in dictionary\n cost_dict = {\n \"out_variance\": out_variance,\n \"in_variance\": in_variance,\n # \"in + out\": in_variance + out_variance,\n \"in_cyto_variance\": in_cyto_variance,\n \"out_cyto_variance\": out_cyto_variance,\n # \"out + out_cyto\": out_variance + out_cyto_variance,\n # \"in_cyto + out_cyto\": in_cyto_variance + out_cyto_variance,\n # \"in + out + out_cyto\": in_variance + out_variance + out_cyto_variance,\n # \"in + out_cyto\": in_variance + out_cyto_variance,\n # \"out + in_cyto\": out_variance + in_cyto_variance,\n \"surface\": surface,\n # \"volume\": volume,\n # \"volume_val\": volume_val,\n # \"objects\": objects,\n \"total_cost\": cost,\n }\n return (\n cost,\n objects,\n cost_dict,\n )\n\n def _evaluate_variance_in_volume(self, indexes, values, normalize=False):\n volume = indexes.sum()\n intensities = values[indexes]\n intensities_sum = intensities.sum()\n intensities_avg = 0 if volume == 0 else intensities_sum / volume\n intensities_var = np.square(intensities - intensities_avg)\n variance = intensities_var.sum()\n if normalize:\n variance = (variance / volume) if volume > 0 else 0\n\n return variance\n\n def _find_min_threshold(self, temp_region, temp_values, temp_cytoplasm=None):\n costs = Cost()\n min_cost = np.inf\n threshold_min = 0\n for threshold in range(1, 256):\n cost, objects, cost_dict = self._cost(\n threshold, temp_region, temp_values, temp_cytoplasm\n )\n costs.add(cost_dict)\n # Assumption: we are increasing the threshold so, if we have no\n # objects at a certain threshold, we will never have objects at\n # higher thresholds.\n if objects == 0:\n break\n if cost < min_cost:\n min_cost = cost\n threshold_min = threshold\n print(\n f\"\\rThreshold: {threshold:3d} => Cost: {cost:12.2f} Objects: {objects:3d} (Optimal: {threshold_min:3d} => {min_cost:12.2f})\",\n end=\"\",\n flush=True,\n )\n\n return threshold_min, costs\n\n def _find_region_limits(self, current_region):\n # Find extension of the region of interest within the whole volume\n z = np.any(current_region, axis=(1, 2))\n y = np.any(current_region, axis=(0, 2))\n x = np.any(current_region, axis=(0, 1))\n z_min, z_max = np.where(z)[0][[0, -1]]\n y_min, y_max = np.where(y)[0][[0, -1]]\n x_min, x_max = np.where(x)[0][[0, -1]]\n\n # Adjust z_max to include the last value excluded by slicing\n z_max += 1\n y_max += 1\n x_max += 1\n\n return (\n z_min,\n z_max,\n y_min,\n y_max,\n x_min,\n x_max,\n )\n\n def _get_centers_in_region(self, centers, z_min, z_max, y_min, y_max, x_min, x_max):\n return centers[\n np.logical_and.reduce(\n np.logical_and(\n np.logical_and(\n centers >= [z_min, y_min, x_min],\n centers < [z_max, y_max, x_max],\n ),\n True,\n ),\n axis=1,\n ),\n :,\n ].astype(\"uint16\") - [z_min, y_min, x_min]\n\n def _touch_edges(self, temp_mask_open):\n # Check that the region doesn't touch the border of the volume\n return (\n temp_mask_open[0, :, :].any()\n or temp_mask_open[-1, :, :].any()\n or temp_mask_open[:, 0, :].any()\n or temp_mask_open[:, -1, :].any()\n or temp_mask_open[:, :, 0].any()\n or temp_mask_open[:, :, -1].any()\n )\n\n def _check_mask(\n self, temp_mask_open, centers, z_min, z_max, y_min, y_max, x_min, x_max\n ):\n # Get the centers inside the region of interest\n centers_in_region = self._get_centers_in_region(\n centers, z_min, z_max, y_min, y_max, x_min, x_max\n )\n\n # Check if there is at least one center inside the region of interest\n check = temp_mask_open[\n centers_in_region[:, 0], centers_in_region[:, 1], centers_in_region[:, 2]\n ]\n if check.any() == False: # No center inside the region of interest\n return False, \"center out of region\"\n if check.sum() > 1: # More than one center inside the region of interest\n return False, \"multiple centers in region\"\n # if self._touch_edges(temp_mask_open): # Region touches the border of the volume\n # return False, \"region touches border\"\n return True, \"good\"\n\n def _segment(self, regions, values, centers, cytoplasm=None):\n labels = np.zeros_like(regions)\n start = 1\n for lbl in range(start, regions.max() + start):\n print(f\"Segment {self.ch_id}: {lbl:3d}/{regions.max():3d}\")\n # Create a mask that isolates just the region of interest (ones)\n # everything else is zero.\n current_region = regions == lbl\n # Find the limits of the smallest volumes that include the region of interest\n (\n z_min,\n z_max,\n y_min,\n y_max,\n x_min,\n x_max,\n ) = self._find_region_limits(current_region)\n\n # Create temporary masks for the smallest volume that includes\n # the region of interest\n temp_region = current_region[z_min:z_max, y_min:y_max, x_min:x_max]\n temp_values = values[z_min:z_max, y_min:y_max, x_min:x_max]\n temp_cytoplasm = (\n None\n if cytoplasm is None\n else cytoplasm[z_min:z_max, y_min:y_max, x_min:x_max]\n )\n\n # Optimize the threshold to identify the nucleus within the region\n threshold_min, costs = self._find_min_threshold(\n temp_region, temp_values, temp_cytoplasm\n )\n self.costs[lbl] = costs\n\n # Create a mask that isolates the nucleus within the region of interest\n temp_mask = np.zeros_like(temp_region, dtype=\"bool\")\n temp_mask[temp_region] = temp_values[temp_region] >= threshold_min\n\n # Open the mask. If this results in more than one component, keep the largest\n temp_mask_open = ski_mor.opening(temp_mask, footprint=ski_mor.ball(5)[2::3])\n temp_mask_open_labels = ski_mea.label(temp_mask_open)\n components = ski_mea.regionprops(temp_mask_open_labels)\n if len(components) > 1:\n area_max = np.max([c.area for c in components])\n for c in components:\n if c.area < area_max:\n temp_mask_open[temp_mask_open_labels == c.label] = 0\n\n # Verify that the mask can be added to the labels\n result, reason = self._check_mask(\n temp_mask_open, centers, z_min, z_max, y_min, y_max, x_min, x_max\n )\n if result:\n labels[\n z_min:z_max, y_min:y_max, x_min:x_max\n ] += lbl * temp_mask_open.astype(\"uint16\")\n print(f\"{Fore.GREEN} ✓ ({reason}){Style.RESET_ALL}\")\n else:\n print(f\"{Fore.RED} ✕ ({reason}){Style.RESET_ALL}\")\n\n return labels\n\n def segment(self, labels, values, centers, cytoplasm=None, write_to_tiff=False):\n print(f\"Segmenting {self.ch_id}\")\n labels = os_utils.store_to_npy(\n self._segment,\n filename_root=self.filename_root,\n ch_id=self.ch_id,\n suffix=\"labels\",\n func_args={\n \"regions\": labels,\n \"values\": values,\n \"centers\": centers,\n \"cytoplasm\": cytoplasm,\n },\n overwrite=self.overwrite,\n out_dir=self.out_dir,\n )\n # If we are writing to tiff\n if write_to_tiff:\n os_utils.write_to_tif(\n labels,\n filename_root=self.filename_root,\n ch_id=self.ch_id,\n suffix=\"labels\",\n out_dir=self.out_dir,\n )\n print(\"done!\")\n return labels\n", "repo_name": "avaccari/DrosophilaFISH", "sub_path": "segment.py", "file_name": "segment.py", "file_ext": "py", "file_size_in_byte": 12539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.ion", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "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.subplots_adjust", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.logical_xor", "line_number": 111, "usage_type": "call"}, {"api_name": "skimage.morphology.binary_dilation", "line_number": 112, "usage_type": "call"}, {"api_name": "skimage.morphology", "line_number": 112, "usage_type": "name"}, {"api_name": "skimage.morphology.ball", "line_number": 112, "usage_type": "call"}, {"api_name": "skimage.measure.regionprops", "line_number": 122, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 122, "usage_type": "name"}, {"api_name": "skimage.measure.label", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.logical_and.reduce", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 213, "usage_type": "attribute"}, {"api_name": "numpy.logical_and", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 292, "usage_type": "call"}, {"api_name": "skimage.morphology.opening", "line_number": 296, "usage_type": "call"}, {"api_name": "skimage.morphology", "line_number": 296, "usage_type": "name"}, {"api_name": "skimage.morphology.ball", "line_number": 296, "usage_type": "call"}, {"api_name": "skimage.measure.label", "line_number": 297, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 297, "usage_type": "name"}, {"api_name": "skimage.measure.regionprops", "line_number": 298, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 298, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 300, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 313, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 313, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 313, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 313, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 315, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 315, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 315, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 315, "usage_type": "name"}, {"api_name": "os_utils.store_to_npy", "line_number": 321, "usage_type": "call"}, {"api_name": "os_utils.write_to_tif", "line_number": 337, "usage_type": "call"}]} +{"seq_id": "38953509213", "text": "'''\n@author Tian Shi\nPlease contact tshi@vt.edu\n'''\nimport torch\nfrom torch.autograd import Variable\n\n\nclass natsEncoder(torch.nn.Module):\n '''\n RNN encoder for nats\n '''\n\n def __init__(self, emb_dim, hidden_size,\n rnn_network, device=torch.device(\"cpu\")):\n super(natsEncoder, self).__init__()\n self.hidden_size = hidden_size\n self.rnn_network = rnn_network\n self.device = device\n\n if rnn_network == 'lstm':\n self.encoder = torch.nn.LSTM(\n input_size=emb_dim,\n hidden_size=hidden_size,\n batch_first=True,\n bidirectional=True).to(device)\n elif rnn_network == 'gru':\n self.encoder = torch.nn.GRU(\n input_size=emb_dim,\n hidden_size=hidden_size,\n batch_first=True,\n bidirectional=True).to(device)\n\n def forward(self, input_):\n '''\n RNN encoder for nats\n '''\n batch_size = input_.size(0)\n\n h0_encoder = Variable(torch.zeros(\n 2, batch_size, self.hidden_size)).to(self.device)\n if self.rnn_network == 'lstm':\n c0_encoder = Variable(torch.zeros(\n 2, batch_size, self.hidden_size)).to(self.device)\n # encoding\n encoder_hy, (src_h_t, src_c_t) = self.encoder(\n input_, (h0_encoder, c0_encoder))\n\n return encoder_hy, (src_h_t, src_c_t)\n\n elif self.rnn_network == 'gru':\n # encoding\n encoder_hy, src_h_t = self.encoder(\n input_, h0_encoder)\n\n return encoder_hy, src_h_t\n", "repo_name": "wangpinggl/TREQS", "sub_path": "LeafNATS/modules/encoder/nats_encoder_rnn.py", "file_name": "nats_encoder_rnn.py", "file_ext": "py", "file_size_in_byte": 1655, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 109, "dataset": "github-code", "pt": "51", "api": [{"api_name": "torch.nn", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.LSTM", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn.GRU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "32289956880", "text": "import re\nimport nltk\nfrom nltk.corpus import wordnet as wn\nfrom nltk.corpus import sentiwordnet as swn\n\ndef tagger(sentence):\n text = sentence.split()\n taggedtext = nltk.pos_tag(text)\n\n print(taggedtext)\n return taggedtext\n\n\ndef sentiScores(word): #takes input as string, return bith +&- values\n word = swn.senti_synset(word)[0]\n values = []\n values.append(word.pos_score()) #positive value\n values.append(word.neg_score()) #negative value\n return values\n\n\ntext = \"he is a good boy\"\ntext = text.lower()\nTaggedtext = tagger(text)\n\n#converting tags into simpler ones as used in the paper\n# use this to see all possible tags nltk.help.upenn_tagset()\ncountV, countN, countA, countR = 0, 0, 0, 0 #for similarity measures\nlistV, listN, listA, listR = [], [], [], []\n\nfor i in range(len(Taggedtext)):\n old = Taggedtext[i][1]\n#for verb\n match = re.match(r'VB*', old)\n if match:\n Taggedtext[i] = (Taggedtext[i][0],'VB')\n listV.append(Taggedtext[i][0])\n countV += 1\n#for noun\n match = re.match(r'NN*', old)\n if match:\n Taggedtext[i] = (Taggedtext[i][0],'NN')\n countN += 1\n listN.append(Taggedtext[i][0])\n#for adjectives\n match = re.match(r'JJ*', old)\n if match:\n Taggedtext[i] = (Taggedtext[i][0],'JJ')\n countA += 1\n listA.append(Taggedtext[i][0])\n#for adverb\n match = re.match(r'RB*', old)\n if match:\n Taggedtext[i] = (Taggedtext[i][0],'RB')\n countR += 1\n listR.append(Taggedtext[i][0])\n\n\nprint(Taggedtext)\n", "repo_name": "prateek96/Exploratory-Project", "sub_path": "exploratoryProject-master/project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 1551, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "nltk.pos_tag", "line_number": 8, "usage_type": "call"}, {"api_name": "nltk.corpus.sentiwordnet.senti_synset", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.corpus.sentiwordnet", "line_number": 15, "usage_type": "name"}, {"api_name": "re.match", "line_number": 34, "usage_type": "call"}, {"api_name": "re.match", "line_number": 40, "usage_type": "call"}, {"api_name": "re.match", "line_number": 46, "usage_type": "call"}, {"api_name": "re.match", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "5965571149", "text": "#////////////////////////////////////////////////////////////////////////////\n# FILE: 2.1-Personal.py\n# AUTHOR: Fernanda De Andrade\n# CREATED: 20 september 2021\n# MODIFIED: 21 october(make use of combine table and linked family needed adjustment)\n# MODIFIED: 23 november 2021 (added print)\n# PURPOSE: Add pseudonimized personal information to geneticlines, sources; ADLAS and EPIC.\n# STATUS: in production\n# COMMENTS: more then 1000 geneticlines participants give trouble with maternal, paternal and linked familyids. Geslacht en geboortedatum moeten altijd gevuld,anders krijg je error(dit is wat afgesproken is met ADLAS team)\n#////////////////////////////////////////////////////////////////////////////\nimport molgenis.client as molgenis\nfrom datetime import datetime\nimport pprint\n\n# Save variables used through the entire script:\narguments = {\"entityType1\": \"adlasportal_adlasData\",\n \"entityType2\": \"epicportal_patients\",\n \"entityType3\": \"geneticlines_personal\",\n \"url\": \"http://localhost:8080/api/\",\n \"sort1\":\"GEN_numr\",\n \"sort2\": \"MRN\"\n }\n# server session\nsession = molgenis.Session(arguments[\"url\"],token=\"${molgenisToken}\")\n\n# Get a list with all adalasportaldata en tests\nadlaspatients = session.get(arguments[\"entityType1\"], batch_size=1000, sort_column=arguments[\"sort1\"])\nprint(\"\\nEntityType: {}\".format(arguments[\"entityType1\"]))\nepic = session.get(arguments[\"entityType2\"], batch_size=1000 , sort_column=arguments[\"sort2\"])\nprint(\"\\nEntityType: {}\".format(arguments[\"entityType2\"]))\n\n#functions used in this script\n#determine year of birth instead of date\ndef year(born):\n born = datetime.strptime(born, \"%Y-%m-%dT%H:%M\").date()\n return born.year\n#determine age of death\ndef age(born,death):\n born = datetime.strptime(born, \"%Y-%m-%dT%H:%M\").date()\n death= datetime.strptime(death, \"%Y-%m-%dT%H:%M\").date()\n return death.year - born.year - ((death.month,\n death.day) < (born.month,\n born.day))\n\n#get unique entries per gen_numr\npersonal = list({v['GEN_numr']:v for v in adlaspatients}.values())\n\n#add items to personal\nfor d in personal:\n d['is_deceased'] = 'true'\n d['patient_status'] = 'C28554'\n d['date_last_updated'] = datetime.now().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n d['year_birth'] = year(d['GEBOORTEDATUM'])##need geboortedatum, if not filled ==>ERROR\n d['biological_sex'] = d['GESLACHT'] ##need Geslacht, if not filled ==>ERROR\n d['biological_sex'] = d['biological_sex'].replace('Vrouw','383')\n d['biological_sex'] = d['biological_sex'].replace('Man','384')\n #not always filled fields\n try:\n d['age_at_death'] = age(d['GEBOORTEDATUM'],d['DATUM_OVERLEDEN'])\n d['date_deceased'] = d.pop('DATUM_OVERLEDEN')\n except KeyError:\n d['age_at_death'] = None\n d['date_deceased'] = None\n # 'twin_status': komt dit uit EPIC\nfor d in personal:\n if d['date_deceased'] == None:\n d['is_deceased'] = 'false'\n d['patient_status'] = 'C37987'\n del d['date_deceased']\n del d['age_at_death']\n\n#'///////////////////////////////////ADDING CONTENT from EPICportal//////////////////////////////////////////\n#add EPIC firt consultdate\nfor x in personal:\n for y in epic:\n if \"UMCGNR\" in x:\n if x['UMCGNR'] == y['MRN']:\n x['date_first_consult'] = y['Firstconsultdate']\n\n#change dateformat\nfor x in personal:\n if 'date_first_consult' in x:\n if \"T\" in x['date_first_consult']:\n x['date_first_consult'] = (x['date_first_consult']).replace (\"T00:00\",\"\")\n elif x['date_first_consult'] == \"NULL\":\n x['date_first_consult'] = None\n else:\n #print(type(x['date_first_consult']))\n x['date_first_consult'] = datetime.strptime((x['date_first_consult']),\"%m/%d/%Y\").strftime(\"%Y-%m-%d\")\n#///////////////////////////////////IMPORT PERSONALinfo/////////////////////////////////////////\n#add items to personal (if more then 1000, it is possible)\nsortpersonal = sorted(personal, key=lambda d: d['UMCGNR'])\n\nfor i in range(0, len(sortpersonal), 1000):\n session.add_all(arguments[\"entityType3\"], sortpersonal[i:i+1000])\nprint(\"Total of geneticlines participants: \", len(sortpersonal))\n#///////////////////////////////////IMPORT linked personaldata/////////////////////////////////////////\n#session.update_one(\"geneticlines_personal\", \"id\", \"paternal_id\", \"newValue\")\n#add paternal and maternalids, since 1000 add give problems, need to add referring personalIDs on later stage\nfor x in personal:\n if 'mama' in x:\n session.update_one(arguments[\"entityType3\"], x['GEN_numr'], \"maternal_id\", x['mama'])\n print(\"maternalid:\",x['mama'])\n if 'papa' in x:\n session.update_one(arguments[\"entityType3\"], x['GEN_numr'], \"paternal_id\", x['papa'])\n print(\"maternalid:\",x['papa'])\n if 'FAMILIELEDEN' in x:\n session.update_one(arguments[\"entityType3\"], x['GEN_numr'], \"linked_family_ids\", x['FAMILIELEDEN'])\n print(\"linked family:\",x['FAMILIELEDEN'])\n", "repo_name": "molgenis/molgenis-projects", "sub_path": "Geneticlines/Scripts_server/2.1-Personal.py", "file_name": "2.1-Personal.py", "file_ext": "py", "file_size_in_byte": 5134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "molgenis.client.Session", "line_number": 24, "usage_type": "call"}, {"api_name": "molgenis.client", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}]} +{"seq_id": "2226248003", "text": "from flask import Flask\nfrom expletives import badwords\napp = Flask(__name__)\n\n@app.route(\"/header_check/\")\ndef header_combinations(header):\n header_combination = []\n header_length = len(header)\n for i in range (header_length-2):\n header_combination.append(header[i:i+3])\n for i in range (header_length-3):\n header_combination.append(header[i:i+4])\n for i in range(header_length - 4):\n header_combination.append(header[i:i + 5])\n for i in range(header_length - 5):\n header_combination.append(header[i:i + 6])\n for i in range(header_length - 6):\n header_combination.append(header[i:i + 7])\n for i in range(header_length - 7):\n header_combination.append(header[i:i + 8])\n for i in range(header_length - 8):\n header_combination.append(header[i:i + 9])\n for i in range(header_length - 9):\n header_combination.append(header[i:i + 10])\n for i in range(header_length - 10):\n header_combination.append(header[i:i + 11])\n for header_word in header_combination:\n if badwords.__contains__(header_word):\n return '0'\n else:\n return '1'\n\n@app.route(\"/message_check/\")\ndef message_template_check(message):\n promotional_words = message.split()\n filename = ('promoWordDict.txt')\n f = open(filename)\n wordlist = f.readlines()\n wordlist = [w.strip() for w in wordlist if w]\n for promo_word in promotional_words:\n if wordlist.__contains__(promo_word):\n return '0'\n else:\n return '1'\n\n\napp.run()\n", "repo_name": "deven029/QTL_Modules", "sub_path": "Validator.py", "file_name": "Validator.py", "file_ext": "py", "file_size_in_byte": 1579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "expletives.badwords.__contains__", "line_number": 28, "usage_type": "call"}, {"api_name": "expletives.badwords", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "35295351307", "text": "from django.conf.urls import patterns, include, url\n\nfrom d1.views import *\nfrom comments.views import *\n\nfrom django.conf.urls import *\nfrom django.views.generic import TemplateView\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nadmin.autodiscover()\n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\nurlpatterns = patterns('',\n ('^hello/$', hello),\n ('^hi/$', hi),\n #('^$', hi),\n ('^time/$', time),\n ('^comment/((?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?)$', comment_board),\n (r'^time/plus/(\\d{1,2})/$', hours_ahead),\n (r'^write/(\\w{1,40})$', comment_board),\n (r'^test/(\\w+)$', get_comment_board_template),\n #(r'^test/', test_func),\n #url(r'^static/(?P.*)$', 'django.views.static.serve',\n #{'document_root': settings.STATIC_ROOT}),\n # Examples:\n # url(r'^$', 'd1.views.home', name='home'),\n # url(r'^d1/', include('d1.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n # url(r'^admin/', include(admin.site.urls)),\n)# + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n\nurlpatterns += patterns('',\n url(r'^articles/comments/', include('django.contrib.comments.urls')),\n)\n\nurlpatterns += patterns('',\n (r'^accounts/', include('registration.backends.default.urls')),\n url(r'^$', TemplateView.as_view(template_name='index.html'), name=\"index\"),\n)\n\nurlpatterns += patterns('',\n (r'^about/', TemplateView.as_view(template_name=\"about.html\")),\n)\n\nurlpatterns += patterns('',\n (r'^articles/(\\d{4})/$', 'news.views.year_archive'),\n (r'^articles/(\\d{4})/(\\d{2})/$', 'news.views.month_archive'),\n (r'^articles/(\\d{4})/(\\d{2})/(\\d+)/$', 'news.views.article_detail'),\n)\n\nfrom functools import wraps\nfrom django.conf import settings\nfrom django.contrib.staticfiles.views import serve as serve_static\nfrom django.conf.urls import patterns, url\n\n\nif settings.DEBUG:\n\n def custom_headers(view_func):\n\n @wraps(view_func)\n def wrapper(request, *args, **kwargs):\n response = view_func(request, *args, **kwargs)\n response['Access-Control-Allow-Origin'] = '*'\n response['Custom-header'] = 'Awesome'\n response['Another-header'] = 'Bad ass'\n return response\n\n return wrapper\n\n urlpatterns += patterns('',\n url(r'^static/(?P.*)$', custom_headers(serve_static)),\n )\n ", "repo_name": "whoislyc/vine", "sub_path": "server/d1/d1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.patterns", "line_number": 40, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 41, "usage_type": "call"}, {"api_name": "django.conf.urls.patterns", "line_number": 44, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 45, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 49, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 50, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 50, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 53, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 65, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 69, "usage_type": "call"}, {"api_name": "django.conf.urls.patterns", "line_number": 79, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 80, "usage_type": "call"}, {"api_name": "django.contrib.staticfiles.views.serve", "line_number": 80, "usage_type": "argument"}]} +{"seq_id": "33841568160", "text": "import Methods as meth\nimport auxiliary_functions as aux_fun\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\n\n\ndef domain_dispersion(dx,c):\n # domain dimensions and amount of points\n # a=4*c*(Nr+3)/f0\n # b=14*c/f0\n a=0.3\n b=0.2\n nx=int(round(a/dx))\n ny=int(round(b/dx))\n print('nx: ',nx)\n print('ny: ',ny)\n\n # spatial grid points\n x=np.linspace(dx,a,nx)\n y=np.linspace(b,dx,ny)\n X,Y=np.meshgrid(x,y)\n np.save('Dispersion_S/X.npy',X)\n np.save('Dispersion_S/Y.npy',Y)\n X=np.expand_dims(X.flatten('F'),1)\n Y=np.expand_dims(Y.flatten('F'),1)\n\n # velocity field depending on the example\n param=np.zeros((ny,nx))+c**2\n param=np.expand_dims(param.flatten('F'),1)\n\n # source term position\n # x0=4*c/f0\n # y0=7*c/f0\n x0=0.05\n y0=0.1\n\n dt=dx/np.max(np.sqrt(param))/8\n\n return a,b,nx,ny,X,Y,param,dt,x0,y0\n\n\ndef source_disperion(x0,y0,X,Y,nx,ny,rad=0.02):\n f=aux_fun.source_x_2D(x0=x0,y0=y0,rad=rad,X=X,Y=Y,nx=nx,ny=ny,equ='scalar_dx2',delta=0)\n var0=np.zeros((len(f[:,0]),1))\n source_type='Dispersion_S'\n\n return var0,f,source_type\n\n\ndef solution_dispersion(method,degree,Ndt,dx=0.005,dx_factor=1,Nr=np.array([0,1,2]),fig_ind=0):\n # Function to perform a dispersion analysis based in Fourier transform, and comparing the \"method\" with a reference\n # solution (RK9-7 with dx/2). For different receptors is computed the solution in a time interval where only a wavelet\n # is recorder, together with its fourier transform.\n\n # INPUT:\n # method: (string) the method to compute de dispersion\n # degree: degree used of the polynomial for the FA,HORK, and Krylov methods\n # Nr: (integer) number of receptors with a spacing of c/f0, the approximated wavelength of Ricker's wavelet, where c\n # is the velocity\n # dx: (float) spatial discretization grid space\n # dx_factor: this is to know the factor between the reference solution and the solutions of the methods\n # Nr: numer of receivers used in the simulations\n # fig_ind: indicator if an image of the wave propagation at the three time cuts (see below)is saved\n\n\n # OUTPUT:\n # 4 files .npy:\n # 1 - with the solution using \"method\" until time T=NS*c/f*1.1\n # 2 - with the solution using the reference method until time T=NS*c/f*1.1\n # 3 - with the estimated dissipation functions\n # 4 - with the estimated phase change functions\n\n # velocity of the homogeneous medium and central frequency of Ricker wavelet\n c=0.2\n f0=15\n t0=1.2/f0+0.1\n param_ricker=np.array([f0,t0])\n\n # parameters of the domain where the numerical dispersion is computed\n a,b,nx,ny,X,Y,param,dt,x0,y0=domain_dispersion(dx,c)\n\n # parameters of the source type (Ricker wavelet)\n var0,f,source_type=source_disperion(x0,y0,X,Y,nx,ny,0.02)\n\n # time cuts for the three reciever positions register the wave\n cuts_0=np.array([0.2710084,0.52209677,0.77277487])\n cuts=np.array([0.75,1,1.25])\n\n for i in Nr:\n # time steps given a smaller CFL condition\n T=cuts_0[i]\n dt*=Ndt\n print('dt_0: ',dt)\n NDt=np.ceil(T/dt).astype(int)\n Dt=T/NDt\n print('NDt[0]: ',NDt[0])\n\n # receivers positions\n points=np.array([np.argmin(pow(X-(i+2)*0.05,2)+pow(Y-0.1,2))])\n\n # code names of the methods and indicators of order\n meth_ind,meth_label=method_label(method)\n\n # solution with a larger time step\n var0=method_sol_call(method,var0,Ndt,NDt,Dt,dx,param,nx,ny,f,param_ricker,source_type,points,degree)\n\n # preparing the solution with shorter time step for frequency analysis\n T=cuts[i]-cuts_0[i]\n dt/=Ndt\n print('dt_1: ',dt)\n NDt=np.ceil(T/dt).astype(int)\n Dt=T/NDt\n print('NDt[0]: ',NDt[0])\n method_sol_call(method,var0,Ndt,NDt,Dt,dx,param,nx,ny,f,param_ricker,source_type,points,degree)\n\n # loading again the solution for calculating the transform\n sol=method_sol_load(meth_ind,meth_label,Ndt,dx,degree)[::dx_factor,:]\n\n if meth_ind<10:\n np.save('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(i)+'.npy',sol[:int((cuts[i]-cuts_0[i])/(dt*dx_factor*2))])\n transform=np.fft.fft(sol[:int((cuts[i]-cuts_0[i])/(dt*dx_factor*2)),0])\n np.save('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(i)+'_transform.npy',transform[:round(len(transform)/2)])\n else:\n np.save('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'_points_'+str(i)+'.npy',sol[:int((cuts[i]-cuts_0[i])/(dt*dx_factor*2))])\n transform=np.fft.fft(sol[:int((cuts[i]-cuts_0[i])/(dt*dx_factor*2)),0])\n np.save('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'_points_'+str(i)+'_transform.npy',transform[:round(len(transform)/2)])\n\n if fig_ind==1: # saving a figure of 2D wave propagation\n if meth_ind<10:\n sol=np.load('Dispersion_S/'+meth_label+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'.npy')\n else:\n sol=np.load('Dispersion_S/'+meth_label+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'.npy')\n plt.imshow(sol.reshape((ny,nx),order='F'),extent=[0,0.3,0,0.2], aspect='auto')\n plt.scatter(np.array([x0]),np.array([y0]),s=50,color='b')\n plt.scatter(np.expand_dims(X.flatten('F'),1)[points],np.expand_dims(Y.flatten('F'),1)[points],s=50,color='k',marker='s')\n plt.savefig('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'.pdf')\n plt.show()\n\n\ndef method_label(method):\n if method=='RK7':\n return 9,'RK_ref'\n elif method=='RK2':\n return 3,'sol_rk2'\n elif method=='RK4':\n return 4,'sol_rk4'\n elif method=='2MS':\n return 1,'sol_2MS'\n elif method=='FA':\n return 10,'sol_faber'\n elif method=='HORK':\n return 10,'sol_rk'\n elif method=='KRY':\n return 10,'sol_krylov'\n\n\ndef method_sol_call(method,var0,Ndt,NDt,Dt,dx,param,nx,ny,f,param_ricker,source_type,points,degree):\n example='Dispersion_S'\n equ='scalar_dx2'\n T_frac_snapshot=1 # this value is only to not save the velocity field\n free_surf=1\n delta=0\n ord='8'\n dim=2\n beta0=30\n ind_source='H_amplified'\n replace=1\n return meth.method_solver(method,var0,Ndt,NDt,Dt,T_frac_snapshot,equ,dim,free_surf,delta,beta0,ord,dx,param,nx,ny,f,param_ricker,source_type,points,example,degree,ind_source,replace)\n\n\ndef method_sol_load(meth_ind,meth_label,Ndt,dx,degree):\n if meth_ind<10:\n return np.load('Dispersion_S/'+meth_label+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points.npy')\n else:\n if meth_label=='sol_faber':\n return np.load('Dispersion_S/'+meth_label+'_equ_scalar_dx2_free_surf_1_ord_8_H_amplified_points_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'.npy')\n else:\n return np.load('Dispersion_S/'+meth_label+'_equ_scalar_dx2_free_surf_1_ord_8_points_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'.npy')\n\n\ndef graph_wave_disp_diss(method,degree,Ndt,dx=0.005,Nr=np.array([0,1,2])):\n\n meth_ind,meth_label=method_label(method)\n cuts=np.array([0.75,0.975,1.2])\n\n for i in range(len(Nr)):\n sol_ref=np.load('Dispersion_S/RK7_equ_scalar_dx2_free_surf_1_ord_8_Ndt_1_dx_'+str(dx/4)+'_points_'+str(Nr[i])+'.npy')[::Ndt[0]]\n if meth_ind<10:\n sol=np.load('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(Nr[i])+'.npy')\n else:\n sol=np.load('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'_points_'+str(Nr[i])+'.npy')\n a=np.linspace(0,cuts[i],len(sol))\n print('t-----------------------------',a[np.abs(sol_ref)<1e-10])\n plt.plot(np.linspace(0,cuts[i],len(sol)),sol_ref,label='Reference',linewidth=2)\n plt.plot(np.linspace(0,cuts[i],len(sol)),sol,label=method,linewidth=2)\n plt.legend()\n plt.savefig('Dispersion_S/'+method+'_dx_'+str(dx)+'_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_points_'+str(i)+'.pdf')\n plt.show()\n plt.clf()\n\n # for i in range(len(Nr)):\n # trans_ref=np.load('Dispersion_S/RK7_equ_scalar_dx2_free_surf_1_ord_8_Ndt_1_dx_'+str(dx/4)+'_points_'+str(Nr[i])+'_transform.npy')[::Ndt[0]]\n # if meth_ind<10:\n # trans=np.load('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(Nr[i])+'_transform.npy')\n # else:\n # trans=np.load('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'_points_'+str(Nr[i])+'_transform.npy')\n # print(trans_ref.shape,trans.shape)\n # a=trans_ref/trans\n #\n # plt.plot(np.arange(len(trans))/cuts[i],np.abs(a),label='Receiver_'+str(i),linewidth=2)\n #\n # plt.legend()\n # plt.savefig('Dispersion_S/'+method+'_dx_'+str(dx)+'_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_amplitude.pdf')\n # plt.show()\n # # plt.clf()\n\n for i in range(len(Nr)):\n trans_ref=np.load('Dispersion_S/RK7_equ_scalar_dx2_free_surf_1_ord_8_Ndt_1_dx_'+str(dx/4)+'_points_'+str(Nr[i])+'_transform.npy')[::Ndt[0]]\n if meth_ind<10:\n trans=np.load('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(Nr[i])+'_transform.npy')\n else:\n trans=np.load('Dispersion_S/'+method+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_dx_'+str(dx)+'_points_'+str(Nr[i])+'_transform.npy')\n\n a=trans_ref/trans\n # plt.plot(np.angle(trans_ref)*np.abs(trans_ref))\n # plt.plot(np.angle(trans)*np.abs(trans))\n # plt.show()\n # plt.plot(np.angle(trans_ref))\n # plt.plot(np.angle(trans))\n # plt.show()\n # print(np.max(np.array([np.max(np.abs(trans_ref)),np.max(np.abs(trans))])))\n plt.plot(np.angle(a))\n plt.show()\n a[(np.abs(trans_ref)+np.abs(trans))>0.01*(np.max(np.array([np.max(np.abs(trans_ref)),np.max(np.abs(trans))])))]=0\n plt.plot(np.angle(a))\n plt.show()\n\n # plt.plot(np.arange(len(trans))/cuts[i],np.angle(a),label='Receiver_'+str(i),linewidth=2)\n\n plt.legend()\n plt.savefig('Dispersion_S/'+method+'_dx_'+str(dx)+'_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[0])+'_phase.pdf')\n # plt.show()\n plt.clf()\n\n\ndef graph_estimate_diss_disp(methods,degree,Ndt,dx=0.005,Nr_ind=1,fig_ind='methods'):\n\n trans_ref=np.load('Dispersion_S/RK7_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx/4)+'_points_'+str(Nr_ind)+'_transform.npy')\n\n cuts=np.array([0.75,0.975,1.2])\n\n # dispersion\n for i in range(len(methods)):\n meth_ind,meth_label=method_label(methods[i])\n if meth_ind<10:\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dispersion_mes=np.trapz(np.abs(np.angle(a)),dx=1/cuts[Nr_ind])\n plt.scatter(np.array([meth_ind]),np.array([dispersion_mes]),label=methods[i],marker='s',s=40)\n else:\n dispersion_mes=np.zeros(len(degree))\n for j in range(len(degree)):\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[j])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n a[np.abs(trans)<1e-15]=0\n dispersion_mes[j]=np.trapz(np.abs(np.angle(a)),dx=1/cuts[Nr_ind])\n plt.plot(degree,dispersion_mes,label=methods[i],linewidth=2)\n plt.legend()\n plt.savefig('Dispersion_S/'+fig_ind+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_dispersion.pdf')\n plt.show()\n plt.clf()\n\n # dissipation\n for i in range(len(methods)):\n meth_ind,meth_label=method_label(methods[i])\n if meth_ind<10:\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dissipation_mes=np.trapz(np.abs(a),dx=1/cuts[Nr_ind])\n plt.scatter(np.array([meth_ind]),np.array([dissipation_mes]),label=methods[i],marker='s',s=40)\n else:\n dissipation_mes=np.zeros(len(degree))\n for j in range(len(degree)):\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_degree_'+str(degree[j])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dissipation_mes[j]=np.trapz(np.abs(a),dx=1/cuts[Nr_ind])\n plt.plot(degree,dissipation_mes,label=methods[i],linewidth=2)\n plt.legend()\n plt.savefig('Dispersion_S/'+fig_ind+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_dissipation.pdf')\n plt.show()\n\n\ndef graph_estimate_diss_disp_max_dt(methods,degree,Ndt,dx=0.005,Nr_ind=1,fig_ind='methods',tol=181,dt=0.003125):\n\n trans_ref=np.load('Dispersion_S/RK7_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx/4)+'_points_'+str(Nr_ind)+'_transform.npy')\n\n cuts=np.array([0.75,0.975,1.2])\n\n # dispersion max dt\n for i in range(len(methods)):\n meth_ind,meth_label=method_label(methods[i])\n if meth_ind<10:\n max_dt=0\n for j in range(len(Ndt)):\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[j])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dispersion_mes=np.trapz(np.abs(np.angle(a)),dx=1/cuts[Nr_ind])\n if dispersion_mes>tol:\n break\n else:\n max_dt=dt*Ndt[j]\n plt.scatter(np.array([meth_ind]),np.array([max_dt]),label=methods[i],marker='s',s=40)\n else:\n max_dt=np.zeros(len(degree))\n for j in range(len(degree)):\n for k in range(len(Ndt)):\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[k])+'_degree_'+str(degree[j])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dispersion_mes=np.trapz(np.abs(np.angle(a)),dx=1/cuts[Nr_ind])\n if dispersion_mes>tol:\n break\n else:\n max_dt[j]=dt*Ndt[k]\n plt.plot(degree,max_dt,label=methods[i],linewidth=2)\n plt.legend()\n plt.savefig('Dispersion_S/'+fig_ind+'_equ_scalar_dx2_free_surf_1_ord_8_dx_'+str(dx)+'_dispersion_max_dt.pdf')\n plt.show()\n plt.clf()\n\n # dissipation max dt\n for i in range(len(methods)):\n meth_ind,meth_label=method_label(methods[i])\n if meth_ind<10:\n max_dt=0\n for j in range(len(Ndt)):\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[j])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dissipation_mes=np.trapz(np.abs(a),dx=1/cuts[Nr_ind])\n if dissipation_mes>tol:\n break\n else:\n max_dt=dt*Ndt[j]\n plt.scatter(np.array([meth_ind]),np.array([max_dt]),label=methods[i],marker='s',s=40)\n else:\n max_dt=np.zeros(len(degree))\n for j in range(len(degree)):\n for k in range(len(Ndt)):\n trans=np.load('Dispersion_S/'+methods[i]+'_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[k])+'_degree_'+str(degree[j])+'_dx_'+str(dx)+'_points_'+str(Nr_ind)+'_transform.npy')\n a=trans_ref/trans\n dissipation_mes=np.trapz(np.abs(a),dx=1/cuts[Nr_ind])\n if dissipation_mes>tol:\n break\n else:\n max_dt[j]=dt*Ndt[k]\n plt.plot(degree,max_dt,label=methods[i],linewidth=2)\n plt.legend()\n plt.savefig('Dispersion_S/'+fig_ind+'_equ_scalar_dx2_free_surf_1_ord_8_dx_'+str(dx)+'_dissipation_max_dt.pdf')\n plt.show()\n\n\n\n\n\n\n\n\n\n # t=np.linspace(0,10,800)\n # fm=15\n # f=(1-2*(np.pi*fm*(t-5))**2)*np.exp(-(np.pi*fm*(t-5))**2)\n # print(np.argmax(np.abs(np.fft.fft(f))[:int(len(t)/2)])/t[-1])\n # plt.plot(np.arange(int(len(t)/2))/t[-1],np.abs(np.fft.fft(f))[:int(len(t)/2)])\n # # plt.plot(np.fft.fftfreq(1000),np.abs(np.fft.fft(f)))\n # # print(len(np.fft.fftfreq(1000)))\n # plt.show()\n # wert\n\n # for i in range(Nr):\n # trans_ref=np.load('Dispersion_S/RK_ref_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx/4)+'_points_'+str(i)+'_transform.npy')\n # trans=np.load('Dispersion_S/RK_ref_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(Ndt[0])+'_dx_'+str(dx)+'_points_'+str(i)+'_transform.npy')\n #\n # cuts=np.array([0.75,0.975,1.2])\n #\n # a=trans_ref/trans\n #\n # # plt.plot(trans_ref,label='ref')\n # # plt.plot(trans,label='orig')\n # # plt.plot(trans*a,label='sol')\n #\n # plt.plot(np.arange(len(trans))/cuts[i],np.abs(a),label='real')\n # plt.plot(np.arange(len(trans))/cuts[i],np.angle(a),label='imag')\n # plt.legend()\n # plt.show()\n\n #\n #\n # N=len(trans)\n # m=5\n # A=np.ones((N,m+1))\n # for j in range(N):\n # A[j,1:]=pow(j,np.arange(1,m+1))\n # # A[j,:]=np.cos(j*2*np.pi/N*np.arange(m+1))\n # print(np.transpose(A).dot(A))\n # b=np.log(trans_ref)-np.log(trans)\n # sol=np.linalg.solve(np.transpose(A).dot(A),np.transpose(A).dot(b))\n # print(np.max(np.abs(np.transpose(A).dot(A).dot(sol)-np.transpose(A).dot(b))))\n # # print(np.transpose(A).dot(b))\n #\n # # import matplotlib.pyplot as plt\n # # plt.plot(b,label='b')\n # # plt.plot(np.abs(np.log(trans_ref)-A.dot(sol)-np.log(trans)),label='sol')\n # # plt.plot(np.abs(np.log(trans_ref) - np.log(trans)), label='orig')\n #\n # plt.plot(np.abs(trans_ref-np.exp(A.dot(sol))*trans),label='sol')\n # plt.plot(np.abs(trans_ref - trans), label='orig')\n #\n # # plt.plot(trans_ref,label='ref')\n # # plt.plot(trans,label='orig')\n # # plt.plot(np.exp(A.dot(sol))*trans,label='sol')\n # plt.legend()\n #\n # plt.show()\n #\n # # plt.plot((A.dot(sol)).imag,label='imag')\n # # plt.plot((A.dot(sol)).real,label='real')\n # # plt.legend()\n # # plt.show()\n\n\n\n\n\n\n\n\n # sol1=np.load('Dispersion_S/RK_ref_equ_scalar_dx2_free_surf_1_ord_8_Ndt_'+str(1)+'_dx_'+str(0.005)+'_points.npy')\n # print(sol[::4,:].shape)\n # print(sol1.shape)\n #\n #\n # a=np.fft.fft(sol[:int(0.75/dt)*2:4,0])\n # a1 = np.fft.fft(sol1[:int(0.75 / dt) * 2, 0])\n # a=a[:round(len(a)/2)]\n # a1 = a1[:round(len(a1) / 2)]\n #\n # plt.plot(a.real,label='a')\n # plt.plot(a1.real,label='a1')\n # plt.legend()\n # plt.show()\n\n # b=np.log(a)\n # plt.plot(b.real, label='real')\n # plt.plot(b.imag, label='imag')\n # plt.legend()\n # plt.show()\n\n", "repo_name": "fernanvr/Faber_1D_2D", "sub_path": "dispersion_tfourier.py", "file_name": "dispersion_tfourier.py", "file_ext": "py", "file_size_in_byte": 19803, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.linspace", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "auxiliary_functions.source_x_2D", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 135, "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": "numpy.expand_dims", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "Methods.method_solver", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 195, "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": "numpy.linspace", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "numpy.angle", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "numpy.angle", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 267, "usage_type": "call"}, {"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.legend", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 313, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 339, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 344, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}]} +{"seq_id": "18606438102", "text": "\"\"\"Commands to interact with Yara rules stored in Yeti.\"\"\"\n\nimport os\n\nimport click\n\nfrom yeti_python_api.api import YetiAPI\nfrom cli.config import config\n\n@click.command()\n@click.option('--recurse', help='Recurse in directory', is_flag=True, default=False) # pylint: disable=line-too-long\n@click.option('--verbose', help='Display match details', is_flag=True, default=False) # pylint: disable=line-too-long\n@click.option('--name_filter', help='Filter indicators by name', type=click.STRING, default='') # pylint: disable=line-too-long\n@click.argument('path', type=click.STRING)\ndef yara_scan(path, name_filter, verbose, recurse):\n \"\"\"Scan a local file or directory using Yara rules from the Yeti server.\"\"\"\n if not os.path.exists(path):\n print('Error: {0:s} was not found'.format(path))\n exit(-1)\n api = YetiAPI(config.api_base, config.api_key)\n yara_rules = api.filter_indicators(name_filter, 'x-yara')\n\n paths = [path]\n max_path_len = 0\n if recurse:\n print('Recursing on directory {0:s}'.format(path))\n paths = []\n for root, _, files in os.walk(path):\n for filename in files:\n full_path = os.path.join(root, filename)\n paths.append(full_path)\n if len(full_path) > max_path_len:\n max_path_len = len(full_path)\n\n results = []\n max_ruleid_len = 0\n for rule in yara_rules:\n for filename in paths:\n matches = rule.compiled_pattern.match(filename)\n if matches:\n if len(rule.name) > max_ruleid_len:\n max_ruleid_len = len(rule.name + rule.id)\n results.append((filename, rule, matches))\n\n if not results:\n print('No matches found')\n exit()\n\n print('Found {0:d} matches!'.format(len(results)))\n print('{0:s} {1:s} {2:s}'.format(\n 'Filename'.ljust(max_path_len),\n 'ID'.ljust(max_ruleid_len + 3),\n 'Details' if verbose else ''))\n print('{0:s} {1:s} {2:s}'.format(\n '='.ljust(max_path_len, '='),\n '='.ljust(max_ruleid_len + 3, '='),\n '='*10 if verbose else ''))\n for filename, rule, result_list in results:\n for result in result_list:\n print('{0:s} {1:s} {2!s}'.format(\n filename.ljust(max_path_len),\n '{} ({})'.format(rule.name, rule.id).ljust(max_ruleid_len),\n result.strings if verbose else ''\n ))\n\n\n@click.command()\n@click.option('--name_filter', help='Filter indicators by name', type=click.STRING, default='') # pylint: disable=line-too-long\n@click.argument('path', type=click.STRING)\ndef dump_yara_rules(path, name_filter):\n \"\"\"Dump existing Yara rules to files in a local directory.\"\"\"\n if not os.path.exists(path):\n print('Error: {0:s} was not found'.format(path))\n exit(-1)\n api = YetiAPI(config.api_base, config.api_key)\n yara_rules = api.filter_indicators(name_filter, 'x-yara')\n choice = input('About to dump {0:d} Yara rules to \"{1:s}\"\\n'\n 'Continue? [Y/n] '.format(len(yara_rules), path))\n if not choice.lower() in ['y', 'yes', '']:\n exit()\n for rule in yara_rules:\n filename = (rule.name + '.yara').lower()\n with open(filename, 'w') as output:\n output.write(rule.pattern)\n print('[+] Wrote rule \"{0:s}\" to {1:s}'.format(rule.name, filename))\n", "repo_name": "yeti-platform/TibetanBrownBear", "sub_path": "cli/yara_commands.py", "file_name": "yara_commands.py", "file_ext": "py", "file_size_in_byte": 3419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "yeti_python_api.api.YetiAPI", "line_number": 20, "usage_type": "call"}, {"api_name": "cli.config.config.api_base", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cli.config.config", "line_number": 20, "usage_type": "name"}, {"api_name": "cli.config.config.api_key", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.walk", "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": "click.command", "line_number": 10, "usage_type": "call"}, {"api_name": "click.option", "line_number": 11, "usage_type": "call"}, {"api_name": "click.option", "line_number": 12, "usage_type": "call"}, {"api_name": "click.option", "line_number": 13, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 13, "usage_type": "attribute"}, {"api_name": "click.argument", "line_number": 14, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "yeti_python_api.api.YetiAPI", "line_number": 75, "usage_type": "call"}, {"api_name": "cli.config.config.api_base", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cli.config.config", "line_number": 75, "usage_type": "name"}, {"api_name": "cli.config.config.api_key", "line_number": 75, "usage_type": "attribute"}, {"api_name": "click.command", "line_number": 67, "usage_type": "call"}, {"api_name": "click.option", "line_number": 68, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 68, "usage_type": "attribute"}, {"api_name": "click.argument", "line_number": 69, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "10378265979", "text": "#!/usr/bin/env python3\n\nfrom imagesgl.command import Param, Command, NeedsEnv, NeedsInterpreter, NeedsBrowser, NeedsDirectory, NeedsEntry, Shortcut, ShortcutSet\nfrom imagesgl.command import MOD_CTRL, MOD_SHIFT, MOD_NONE, LETTER\nfrom imagesgl.browser import Browser, MODE_NORMAL, MODE_THUMBS, MODE_INPUT\nimport pygame\nfrom pygame.locals import *\n\nimport os\n\ndef __repr__(): return \"\"\n\n### Helpers\n\ndef create(directory, keys=None):\n b = Browser(directory, MODE_THUMBS)\n b.loader_callback = lambda: pygame.event.post(pygame.event.Event(pygame.USEREVENT, action='loaded'))\n keys = keys if not keys is None else sorted(directory.entries.keys())\n b.use_keys(keys, winsize=pygame.display.get_surface().get_size())\n return b\n\n### Image commands\n\n@Shortcut(MODE_THUMBS, MOD_SHIFT, K_PERIOD, 'rotate_marked', +90)\n@Shortcut(MODE_THUMBS, MOD_SHIFT, K_COMMA, 'rotate_marked', -90)\n@Shortcut(MODE_THUMBS, MOD_NONE, K_PAGEUP, 'scroll_up_page')\n@Shortcut(MODE_THUMBS, MOD_NONE, K_PAGEDOWN, 'scroll_down_page')\n@Shortcut(MODE_THUMBS, MOD_SHIFT, K_0, 'mark_none')\n@Shortcut(MODE_THUMBS, MOD_SHIFT, K_9, 'mark_all')\n@Shortcut(MODE_THUMBS, MOD_NONE, K_SPACE, 'toggle_mark')\n@Shortcut(MODE_THUMBS, MOD_SHIFT, K_SPACE, 'mark_from_last')\n@Shortcut(MODE_THUMBS, MOD_NONE, K_UP, 'browser_move', 0, -1)\n@Shortcut(MODE_THUMBS, MOD_NONE, K_DOWN, 'browser_move', 0, +1)\n@Shortcut(MODE_THUMBS, MOD_NONE, K_LEFT, 'browser_move', -1, 0)\n@Shortcut(MODE_THUMBS, MOD_NONE, K_RIGHT, 'browser_move', +1, 0)\n@Shortcut(MODE_NORMAL, MOD_NONE, K_SPACE, 'select', +1)\n@Shortcut(MODE_NORMAL, MOD_NONE, K_BACKSPACE, 'select', -1)\n@Shortcut(MODE_THUMBS, MOD_NONE, K_F3, 'create_thumbnails', False)\n@Shortcut(MODE_THUMBS, MOD_NONE, K_F4, 'filter_directory', 'Most items', '', 'X')\n@Shortcut(MODE_THUMBS, MOD_SHIFT, K_F4, 'filter_directory', 'All items', '', '')\n@Shortcut(MODE_THUMBS, MOD_CTRL, tuple(range(K_a, K_z+1)), 'filter_browser', LETTER, LETTER, '')\n@Shortcut(MODE_THUMBS, MOD_SHIFT, tuple(range(K_a, K_z+1)), 'toggle_category_marked', LETTER)\nclass image_shortcuts(ShortcutSet):\n pass\n\n@NeedsDirectory()\n@NeedsBrowser()\n@Param('angle', int, 0)\nclass rotate_marked(Command):\n def execute(self, directory, browser, angle):\n for entry in browser.marked:\n entry.rotate(angle)\n entry.create_thumbnail(\n basepath=directory.basepath,\n block_size = browser.block_size,\n border = browser.border,\n override = True\n )\n\n@NeedsBrowser()\nclass scroll_up_page(Command):\n def execute(self, browser):\n wbw, wbh = browser.get_block_dimensions(pygame.display.get_surface().get_size(), browser.block_size)\n browser.move((0, -wbh))\n\n@NeedsBrowser()\nclass scroll_down_page(Command):\n def execute(self, browser):\n wbw, wbh = browser.get_block_dimensions(pygame.display.get_surface().get_size(), browser.block_size)\n browser.move((0, +wbh))\n\n@NeedsBrowser()\nclass mark_none(Command):\n def execute(self, browser):\n browser.mark_none()\n\n@NeedsBrowser()\nclass mark_all(Command):\n def execute(self, browser):\n browser.mark_all()\n\n@NeedsEntry()\nclass toggle_mark(Command):\n def execute(self, entry):\n entry.toggle_marked()\n\n@NeedsBrowser()\nclass mark_from_last(Command):\n def execute(self, browser):\n browser.mark_from_last()\n\n@NeedsBrowser()\n@Param('delta', int, 1)\nclass select(Command):\n def execute(self, browser, delta):\n browser.select(delta, winsize=pygame.display.get_surface().get_size())\n\n@NeedsBrowser()\n@Param('x', int, 0)\n@Param('y', int, 0)\nclass browser_move(Command):\n def execute(self, browser, x, y):\n browser.move((x, y), winsize=pygame.display.get_surface().get_size())\n\n@NeedsEnv()\n@Param('name', str, 'untitled')\n@Param('include', str, '')\n@Param('exclude', str, '')\nclass filter_directory(Command):\n def execute(self, env, name, include, exclude):\n new_browser = create(env.directory, sorted(env.directory.get_filtered_keys(incl=include, excl=exclude)))\n new_browser.name = name\n env.browser = new_browser\n\n@NeedsEnv()\n@Param('name', str, 'untitled')\n@Param('include', str, '')\n@Param('exclude', str, '')\nclass filter_browser(Command):\n def execute(self, env, name, include, exclude):\n new_browser = create(env.directory, sorted(env.browser.get_filtered_keys(incl=include, excl=exclude)))\n new_browser.name = name\n env.browser = new_browser\n\n@NeedsBrowser()\n@Param('override', bool, False)\nclass create_thumbnails(Command):\n def execute(self, browser, override):\n browser.create_thumbs(override=override)\n\n@NeedsDirectory()\n@NeedsBrowser()\n@Param('category', str, 0)\nclass toggle_category_marked(Command):\n def execute(self, directory, browser, category):\n for entry in browser.marked:\n entry.toggle_category(category, directory)\n\n@NeedsBrowser()\n@Param('filename', str, 'untitled')\n@Param('name', str, 'untitled')\nclass save_browser(Command):\n def execute(self, browser, filename, name):\n browser.name = name\n browser.save(filename)\n\n@NeedsDirectory()\n@NeedsBrowser()\n@Param('sub_folder', str, None)\n@Param('longest_side', int, 800)\nclass export_browser_default(Command):\n def execute(self, directory, browser, sub_folder, longest_side):\n sub_folder = browser.name if sub_folder is None else sub_folder\n export_dir = os.path.expanduser(os.path.join(directory.settings.get('export_dir', ''), sub_folder))\n if os.path.exists(export_dir):\n print(\"PATH EXISTS! Aborting\")\n return\n os.mkdir(export_dir)\n name_c = 0\n for entry in browser.entries:\n entry.export(directory.basepath, longest_side, export_dir, \"img%.5i.jpg\" % name_c)\n name_c += 1\n\n def list_longest_side(self, interpreter, flt):\n return ['200', '400', '800', '1000', '1200', '1600']\n", "repo_name": "eblade/images", "sub_path": "imagesgl/browser_commands.py", "file_name": "browser_commands.py", "file_ext": "py", "file_size_in_byte": 6030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "imagesgl.browser.Browser", "line_number": 16, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 16, "usage_type": "argument"}, {"api_name": "pygame.event.post", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.event.Event", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.USEREVENT", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "imagesgl.command.ShortcutSet", "line_number": 43, "usage_type": "name"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 24, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 24, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 24, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 25, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 25, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 25, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 26, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 26, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 26, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 27, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 27, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 27, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 28, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 28, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 28, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 29, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 29, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 29, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 30, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 30, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 30, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 31, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 31, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 31, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 32, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 32, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 32, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 33, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 33, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 33, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 34, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 34, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 34, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 35, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 35, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 35, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 36, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_NORMAL", "line_number": 36, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 36, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 37, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_NORMAL", "line_number": 37, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 37, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 38, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 38, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 38, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 39, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 39, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_NONE", "line_number": 39, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 40, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 40, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 40, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 41, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 41, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_CTRL", "line_number": 41, "usage_type": "argument"}, {"api_name": "imagesgl.command.LETTER", "line_number": 41, "usage_type": "argument"}, {"api_name": "imagesgl.command.Shortcut", "line_number": 42, "usage_type": "call"}, {"api_name": "imagesgl.browser.MODE_THUMBS", "line_number": 42, "usage_type": "argument"}, {"api_name": "imagesgl.command.MOD_SHIFT", "line_number": 42, "usage_type": "argument"}, {"api_name": "imagesgl.command.LETTER", "line_number": 42, "usage_type": "argument"}, {"api_name": "imagesgl.command.Command", "line_number": 49, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsDirectory", "line_number": 46, "usage_type": "call"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 47, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 48, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 61, "usage_type": "name"}, {"api_name": "pygame.display.get_surface", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 63, "usage_type": "attribute"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 60, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 67, "usage_type": "name"}, {"api_name": "pygame.display.get_surface", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 69, "usage_type": "attribute"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 66, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 73, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 72, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 78, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 77, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 83, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsEntry", "line_number": 82, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 88, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 87, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 94, "usage_type": "name"}, {"api_name": "pygame.display.get_surface", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 96, "usage_type": "attribute"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 92, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 93, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 101, "usage_type": "name"}, {"api_name": "pygame.display.get_surface", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 103, "usage_type": "attribute"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 98, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 99, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 100, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 109, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsEnv", "line_number": 105, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 106, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 107, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 108, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 119, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsEnv", "line_number": 115, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 116, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 117, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 118, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 127, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 125, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 126, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 134, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsDirectory", "line_number": 131, "usage_type": "call"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 132, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 133, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 142, "usage_type": "name"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 139, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 140, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 141, "usage_type": "call"}, {"api_name": "imagesgl.command.Command", "line_number": 151, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 158, "usage_type": "call"}, {"api_name": "imagesgl.command.NeedsDirectory", "line_number": 147, "usage_type": "call"}, {"api_name": "imagesgl.command.NeedsBrowser", "line_number": 148, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 149, "usage_type": "call"}, {"api_name": "imagesgl.command.Param", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "26149133496", "text": "import pymysql\r\nfrom CONFIG import host, user, password, db_name\r\nfrom interface import ICourse, ILocalCourse, IOffsiteCourse, ICourseFactory, ITeacher\r\n\r\n\r\n# Сlass for Teacher's initialization and showing information about him\r\nclass Teacher(ITeacher):\r\n\r\n #Teacher's initialization\r\n def __init__(self, name):\r\n self.name = name\r\n self.course = None\r\n\r\n @property\r\n def name(self):\r\n return self.__name\r\n\r\n @name.setter\r\n def name(self, name):\r\n if not isinstance(name, str):\r\n raise TypeError\r\n if name == \" \":\r\n raise TypeError\r\n self.__name = name\r\n\r\n #Str method for teacher\r\n def __str__(self):\r\n return f'{self.name}: {self.course.name}'\r\n\r\n\r\n\r\n# Base class for Courses initialization and showing information about them\r\nclass Course(ICourse):\r\n\r\n #Course initialization\r\n def __init__(self, name, teacher, lessons):\r\n self.name = name\r\n self.teacher = teacher\r\n self.lessons = lessons\r\n\r\n @property\r\n def name(self):\r\n return self.__name\r\n\r\n @name.setter\r\n def name(self, name):\r\n if not isinstance(name, str) or name == \" \":\r\n raise TypeError\r\n self.__name = name\r\n\r\n @property\r\n def teacher(self):\r\n return self.__teacher\r\n\r\n @teacher.setter\r\n def teacher(self, name):\r\n if not isinstance(name, Teacher) :\r\n raise TypeError\r\n self.__teacher = name\r\n #Str method for teacher\r\n def __str__(self):\r\n return f'Corse name: {self.name}\\nTeachers name: {self.__teacher.name}' \\\r\n f'\\nLessons: {self.lessons}'\r\n\r\n\r\n\r\n# Class for LocalCourse initialization\r\nclass LocalCourse(Course, ILocalCourse):\r\n\r\n def __init__(self, name, teacher, lessons):\r\n super().__init__(name, teacher, lessons)\r\n self.type = \"Local\"\r\n\r\n def __str__(self):\r\n return super().__str__() + f\"\\nType: {self.type}\"\r\n\r\n\r\n# Class for OffsiteCourse initialization\r\nclass OffsiteCourse(Course,IOffsiteCourse):\r\n\r\n def __init__(self, name, teacher, lessons):\r\n super().__init__(name, teacher, lessons)\r\n self.type = \"Offsite\"\r\n\r\n def __str__(self):\r\n return super().__str__() + f\"\\nType: {self.type}\"\r\n\r\n\r\n# Class for Courses creation, adding info to database, showinf info from it\r\nclass CourseFactory(ICourseFactory):\r\n\r\n # Method which create connection to db\r\n def create_connection(self):\r\n try:\r\n connection = pymysql.connect(\r\n host = host,\r\n port = 3307,\r\n user = user,\r\n password=password,\r\n database=db_name,\r\n cursorclass=pymysql.cursors.DictCursor\r\n )\r\n return connection\r\n except Exception as ex:\r\n raise ValueError\r\n\r\n return connection\r\n\r\n # Method that insert info into db and create instance of Course class\r\n def create(self, name, teacher, lessons, type):\r\n if not isinstance(teacher, Teacher):\r\n raise ValueError\r\n\r\n connection = self.create_connection()\r\n with connection.cursor() as cursor:\r\n insert_data = \"INSERT INTO `course` (Name, Teachers_name, Course_type, Lessons_name)\" \\\r\n \"VALUES (%s, %s, %s, %s)\"\r\n val = [name, teacher.name, lessons, type]\r\n cursor.execute(insert_data, val)\r\n connection.commit()\r\n connection.close()\r\n if type == 'Local':\r\n return LocalCourse(name, teacher, lessons)\r\n elif type == \"Offset\":\r\n return OffsiteCourse(name, teacher, type)\r\n\r\n # Method that show info from db about Teacher and course\r\n def show_info(self, teacher):\r\n if not isinstance(teacher, Teacher):\r\n raise ValueError\r\n connection = self.create_connection()\r\n with connection.cursor() as cursor:\r\n select_info = \"SELECT Teachers_name, Name, Course_type, Lessons_name \" \\\r\n \"FROM `course`\" \\\r\n \"WHERE Teachers_name = %s\"\r\n val = [teacher.name]\r\n cursor.execute(select_info, val)\r\n rows = cursor.fetchall()\r\n if rows == \"\":\r\n print(\"Nothing found\")\r\n else:\r\n for row in rows:\r\n print(row)\r\n connection.close()\r\n\r\n # Delete course from db\r\n def delete_course(self, course_name):\r\n if isinstance(course_name, str):\r\n raise TypeError\r\n\r\n connection = self.create_connection()\r\n with connection.cursor() as cursor:\r\n delete_course = \"DELETE FROM `course` WHERE' Name = %s\"\r\n val = [course_name]\r\n try:\r\n cursor.execute(delete_course, val)\r\n except:\r\n print(\"No such course\")\r\n connection.commit()\r\n connection.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n teacher1 = Teacher(\"Gragham Itter\")\r\n course1 = CourseFactory()\r\n # course1.create(\"Math\", teacher1, \"Георгафия, математика, англ\", 'Local')\r\n course1.show_info(teacher1)\r\n", "repo_name": "bufebaa/python", "sub_path": "Lab4p2/courses.py", "file_name": "courses.py", "file_ext": "py", "file_size_in_byte": 5195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "interface.ITeacher", "line_number": 7, "usage_type": "name"}, {"api_name": "interface.ICourse", "line_number": 33, "usage_type": "name"}, {"api_name": "interface.ILocalCourse", "line_number": 68, "usage_type": "name"}, {"api_name": "interface.IOffsiteCourse", "line_number": 79, "usage_type": "name"}, {"api_name": "interface.ICourseFactory", "line_number": 90, "usage_type": "name"}, {"api_name": "pymysql.connect", "line_number": 95, "usage_type": "call"}, {"api_name": "CONFIG.host", "line_number": 96, "usage_type": "name"}, {"api_name": "CONFIG.user", "line_number": 98, "usage_type": "name"}, {"api_name": "CONFIG.password", "line_number": 99, "usage_type": "name"}, {"api_name": "CONFIG.db_name", "line_number": 100, "usage_type": "name"}, {"api_name": "pymysql.cursors", "line_number": 101, "usage_type": "attribute"}]} +{"seq_id": "1333302456", "text": "#!/usr/bin/env python3\n#\n# Note: put folio info into settings.ini in the same directory as this file.\n#\n\nimport sys\nfrom sys import exit as exit\nif sys.version_info <= (3, 8):\n print('foliage requires Python version 3.8 or higher,')\n print('but the current version of Python is ' +\n str(sys.version_info.major) + '.' + str(sys.version_info.minor) + '.')\n exit(1)\n\nfrom commonpy.exceptions import NoContent, ServiceFailure, RateLimitExceeded\nfrom commonpy.interrupt import wait\nfrom commonpy.string_utils import antiformat\nfrom commonpy.network_utils import net\nfrom decouple import config\nimport json\nimport os\nimport plac\nimport pywebio\nfrom pywebio.input import input\nfrom pywebio.output import put_text, put_markdown, put_buttons, put_row, put_html\nfrom pywebio.output import use_scope, set_scope, clear, remove\nfrom pywebio.output import toast, popup, close_popup\nfrom pywebio.pin import pin, pin_wait_change, put_input, put_actions\n# Default server is tornado, and tornado mucks with logging.\n# aiohttp server does not. Unfortunately, only tornado auto-reloads.\n# from pywebio.platform.aiohttp import start_server\nfrom pywebio import start_server\nfrom pywebio.session import run_js, eval_js\nimport signal\n\nif __debug__:\n from sidetrack import set_debug, log\n\n\f\n# Helper functions\n# .............................................................................\n\ndef folio(url, retry = 0):\n '''Do HTTP GET on \"url\" & return results of calling result_producer on it.'''\n headers = {\n \"x-okapi-token\": config('FOLIO_OKAPI_TOKEN'),\n \"x-okapi-tenant\": config('FOLIO_OKAPI_TENANT_ID'),\n \"content-type\": \"application/json\",\n }\n\n get_url = config('FOLIO_OKAPI_URL') + url\n (response, error) = net('get', get_url, headers = headers)\n if not error:\n if __debug__: log(f'got result from {url}')\n return json.loads(response.text)\n elif isinstance(error, NoContent):\n if __debug__: log(f'got empty content from {url}')\n return None\n elif isinstance(error, RateLimitExceeded):\n retry += 1\n if retry > 3:\n raise FolioError(f'Rate limit exceeded for {url}')\n else:\n # Wait and then call ourselves recursively.\n if __debug__: log(f'hit rate limit; pausing 2s')\n wait(2)\n return folio(url, retry = retry)\n else:\n raise RuntimeError(f'Problem contacting {url}: {antiformat(error)}')\n\n\ndef checker():\n put_markdown('## ISBN checker')\n put_text('Enter an ISBN number and click the \"Check\" button. This will'\n ' contact FOLIO to check if it\\'s a valid ISBN number.')\n put_html('
')\n put_row([\n # put_html('
'),\n put_input('isbn'),\n None, # Add space between input field & button\n put_actions('button', buttons = ['Check']),\n # put_html('
')\n ])\n put_row([\n put_buttons([dict(label = 'Quit', value = 'q', color = 'danger')],\n onclick = lambda _: quit_program()).style('margin-top: 5px')\n ])\n while True:\n clicked = pin_wait_change('button')\n url = f'/isbn/validator?isbn={pin.isbn}'\n if __debug__: log(f'asking Folio to check {pin.isbn}')\n result = folio(url)\n if 'isValid' in result:\n msg = (f'{pin.isbn} is ' +\n ('a valid' if result['isValid'] else 'not a valid')\n + ' ISBN number')\n popup(msg, put_buttons(['OK'], onclick = lambda _: close_popup()))\n pin.isbn = ''\n\n\ndef quit_program():\n if __debug__: log(f'user clicked the quit button')\n if eval_js('confirm_exit()'):\n if __debug__: log(f'user confirmed quitting')\n run_js('close_window()')\n wait(0.5)\n os._exit(0)\n\n\f\n# Main program.\n# .............................................................................\n\n# For more info about how plac works see https://plac.readthedocs.io/en/latest/\n@plac.annotations(\n version = ('print version info and exit', 'flag', 'V'),\n debug = ('log debug output to \"OUT\" (\"-\" is console)', 'option', '@'),\n)\n\ndef main(version = False, debug = 'OUT'):\n '''Foliage: FOLIo chAnGe Editor, a tool to do bulk changes in FOLIO.'''\n\n # Set up debug logging as soon as possible, if requested ------------------\n\n if debug != 'OUT':\n if __debug__: set_debug(True, debug)\n import faulthandler\n faulthandler.enable()\n if not sys.platform.startswith('win'):\n import signal\n from boltons.debugutils import pdb_on_signal\n pdb_on_signal(signal.SIGUSR1)\n else:\n debug = False\n\n # Preprocess arguments and handle early exits -----------------------------\n\n if version:\n from foliage import print_version\n print_version()\n exit()\n\n # Do the real work --------------------------------------------------------\n\n pywebio.config(js_code = '''\n function confirm_exit() { return confirm(\"Quit Foliage?\"); }\n function close_window() { window.close(); }\n ''')\n start_server(checker, port = 8080, auto_open_webbrowser = True, debug = debug)\n\n\f\n# Main entry point.\n# .............................................................................\n\n# The following allows users to invoke this using \"python3 -m handprint\".\nif __name__ == '__main__':\n plac.call(main)\n", "repo_name": "caltechlibrary/foliage", "sub_path": "dev/misc/experiments/isbn_checker.py", "file_name": "isbn_checker.py", "file_ext": "py", "file_size_in_byte": 5493, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.version_info", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 12, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 45, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 46, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 50, "usage_type": "call"}, {"api_name": "commonpy.network_utils.net", "line_number": 51, "usage_type": "call"}, {"api_name": "sidetrack.log", "line_number": 53, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "commonpy.exceptions.NoContent", "line_number": 55, "usage_type": "argument"}, {"api_name": "sidetrack.log", "line_number": 56, "usage_type": "call"}, {"api_name": "commonpy.exceptions.RateLimitExceeded", "line_number": 58, "usage_type": "argument"}, {"api_name": "sidetrack.log", "line_number": 64, "usage_type": "call"}, {"api_name": "commonpy.interrupt.wait", "line_number": 65, "usage_type": "call"}, {"api_name": "commonpy.string_utils.antiformat", "line_number": 68, "usage_type": "call"}, {"api_name": "pywebio.output.put_markdown", "line_number": 72, "usage_type": "call"}, {"api_name": "pywebio.output.put_text", "line_number": 73, "usage_type": "call"}, {"api_name": "pywebio.output.put_html", "line_number": 75, "usage_type": "call"}, {"api_name": "pywebio.output.put_row", "line_number": 76, "usage_type": "call"}, {"api_name": "pywebio.pin.put_input", "line_number": 78, "usage_type": "call"}, {"api_name": "pywebio.pin.put_actions", "line_number": 80, "usage_type": "call"}, {"api_name": "pywebio.output.put_row", "line_number": 83, "usage_type": "call"}, {"api_name": "pywebio.output.put_buttons", "line_number": 84, "usage_type": "call"}, {"api_name": "pywebio.pin.pin_wait_change", "line_number": 88, "usage_type": "call"}, {"api_name": "pywebio.pin.pin.isbn", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pywebio.pin.pin", "line_number": 89, "usage_type": "name"}, {"api_name": "sidetrack.log", "line_number": 90, "usage_type": "call"}, {"api_name": "pywebio.pin.pin.isbn", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pywebio.pin.pin", "line_number": 90, "usage_type": "name"}, {"api_name": "pywebio.pin.pin.isbn", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pywebio.pin.pin", "line_number": 93, "usage_type": "name"}, {"api_name": "pywebio.output.popup", "line_number": 96, "usage_type": "call"}, {"api_name": "pywebio.output.put_buttons", "line_number": 96, "usage_type": "call"}, {"api_name": "pywebio.output.close_popup", "line_number": 96, "usage_type": "call"}, {"api_name": "pywebio.pin.pin.isbn", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pywebio.pin.pin", "line_number": 97, "usage_type": "name"}, {"api_name": "sidetrack.log", "line_number": 101, "usage_type": "call"}, {"api_name": "pywebio.session.eval_js", "line_number": 102, "usage_type": "call"}, {"api_name": "sidetrack.log", "line_number": 103, "usage_type": "call"}, {"api_name": "pywebio.session.run_js", "line_number": 104, "usage_type": "call"}, {"api_name": "commonpy.interrupt.wait", "line_number": 105, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 106, "usage_type": "call"}, {"api_name": "sidetrack.set_debug", "line_number": 124, "usage_type": "call"}, {"api_name": "faulthandler.enable", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 127, "usage_type": "attribute"}, {"api_name": "boltons.debugutils.pdb_on_signal", "line_number": 130, "usage_type": "call"}, {"api_name": "signal.SIGUSR1", "line_number": 130, "usage_type": "attribute"}, {"api_name": "foliage.print_version", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 139, "usage_type": "call"}, {"api_name": "pywebio.config", "line_number": 143, "usage_type": "call"}, {"api_name": "pywebio.start_server", "line_number": 147, "usage_type": "call"}, {"api_name": "plac.annotations", "line_number": 113, "usage_type": "call"}, {"api_name": "plac.call", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "22186529676", "text": "import pymongo\nfrom pymongo import MongoClient\nfrom datetime import datetime\n\n\nclient=MongoClient(\"mongodb://localhost:27017/\")\nprint(client.list_database_names())\ndb=client[\"hi\"]\nmycol=db[\"1\"]\nmycol.insert_one({\"asd\":{\"asd\":\"qwe\"}})\nmycol=db[\"2\"]\nmycol.insert_one({\"asd\":{\"asd\":\"qwe\"}})\nmycol=db[\"3\"]\nmycol.insert_one({\"asd\":{\"asd\":\"qwe\"}})\nmycol=db[\"4\"]\nmycol.insert_one({\"asd\":{\"asd\":\"qwe\"}})\n\ncols=db.list_collection_names()\ncol_list=[]\nfor i in cols:\n col_list.append(i)\ncol_list.sort()\ndatetime=col_list[len(col_list)-1]\nprint(datetime)\n# def set_date():\n# # mongodb에 들어갈 날짜 형식 ex.20201111_1\n# today = datetime.today()\n# n = (today.hour * 60 + today.minute) // 10\n# date_key = f\"{today.year}{today.month}{today.day}_{n}\"\n# return date_key\n\n# def live_model(result):\n# date=str(set_date())\n# db = client['Real_time_Result']\n# mycol = db[date]\n# mycol.insert_one(result)\n\n# def day_model(date,result):\n# db = client['Day_Result']\n# mycol = db[date]\n# mycol.insert_one(result)", "repo_name": "Nanjangpan/NewsDashboard", "sub_path": "backend/mongotest.py", "file_name": "mongotest.py", "file_ext": "py", "file_size_in_byte": 1046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "pymongo.MongoClient", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "argument"}]} +{"seq_id": "40169082910", "text": "import json\nimport glob, os\n\ndef getPCs():\n '''\n Reads the files in this folder and returns a dictionary of PCs\n '''\n path = os.path.dirname(__file__)\n print(path)\n\n PCs = {} #PC dictionary\n \n for file in glob.glob(path+\"\\\\*.txt\"):\n print(file)\n json_data=open(file).read() \n PCs[file.split('\\\\')[-1][:-4]] = json.loads(json_data)\n return PCs\n\n\n#getPCs()\n", "repo_name": "JStuckner/DnD-GUI-2", "sub_path": "dnd_gui/Characters/Character.py", "file_name": "Character.py", "file_ext": "py", "file_size_in_byte": 410, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "75224218110", "text": "import os\nimport torch\nimport torchvision as vision\nfrom PIL import Image\nimport io\nimport numpy as np\n \ndef extract_feature(path, model, mode):\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n transform = vision.transforms.Compose([\n vision.transforms.Resize((160, 160)), \n vision.transforms.ToTensor(),\n vision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\n model.eval().to(device)\n if mode == 'api':\n img = Image.open(io.BytesIO(path))\n else:\n img = Image.open(path)\n img_transformed = transform(img).unsqueeze(0)\n img_embedding = model(img_transformed.to(device)).cpu().detach().numpy()\n return img_embedding\n\ndef Var(x:np.ndarray):\n n = len(x)\n x_hat = x.mean()\n return n * np.sum((x-x_hat)**2)\n\ndef NED(u,v):\n return 0.5 * Var(u-v) / (Var(u) + Var(v))\n\ndef embed2dict(data_path, model, mode='normal'):\n embeddings = {}\n for folder in os.listdir(data_path):\n for file in os.listdir(data_path + folder):\n img_path = data_path + folder + \"/\" + file\n img_embedding = extract_feature(img_path, model, mode)\n embeddings[img_path] = img_embedding\n return embeddings", "repo_name": "zogojogo/face-recognition-wii", "sub_path": "src/feature_extract.py", "file_name": "feature_extract.py", "file_ext": "py", "file_size_in_byte": 1255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "torch.device", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 10, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Resize", "line_number": 11, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 27, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "32577160555", "text": "from django.db import connection\nfrom django.db.migrations.executor import MigrationExecutor\nfrom django.test import TransactionTestCase\n\nfrom ohq.models import Semester\n\n\nclass MigrationTest(TransactionTestCase):\n \"\"\"\n Test applying a new migration on top of an old migration.\n Make sure that `self.migrate_from` and `self.migrate_to` are defined, and also implement\n `setUpBeforeMigration()` to setup the model before migrations are applied.\n \"\"\"\n\n migrate_from = None # need to be defined by subclasses\n migrate_to = None # need to be defined by subclasses\n\n @property\n def app(self):\n return \"ohq\"\n\n def setUp(self):\n super().setUp()\n assert (\n self.migrate_to and self.migrate_from\n ), f\"TestCase {type(self).__name} must define migrate_to and migrate_from properties\"\n\n self.migrate_from = [(self.app, self.migrate_from)]\n self.migrate_to = [(self.app, self.migrate_to)]\n self.executor = MigrationExecutor(connection)\n self.pre_migration = self.executor.loader.project_state(self.migrate_from).apps\n\n # revert to the original migration\n self.executor.migrate(self.migrate_from)\n\n # ensure return to the latest migration, even if the test fails\n self.addCleanup(self.force_migrate)\n\n # perform final migration setup\n self.setUpBeforeMigration(self.pre_migration)\n\n # Finally apply the migration\n self.executor.loader.build_graph()\n self.executor.migrate(self.migrate_to)\n self.post_migration = self.executor.loader.project_state(self.migrate_to).apps\n\n # Implement in subclasses to setup models before applying a migration\n def setUpBeforeMigration(self, apps):\n pass\n\n # forces a migration to the latest migration even if tests fail\n def force_migrate(self, migrate_to=None):\n self.executor.loader.build_graph() # reload.\n if migrate_to is None:\n # get latest migration of current app\n migrate_to = [\n key for key in self.executor.loader.graph.leaf_nodes() if key[0] == self.app\n ]\n\n self.executor.migrate(migrate_to)\n\n\nclass TestQuestionTemplatesMigration(MigrationTest):\n migrate_from = \"0014_question_student_descriptor\"\n migrate_to = \"0015_question_templates\"\n\n def setUpBeforeMigration(self, pre_migration):\n self.semester = pre_migration.get_model(self.app, \"Semester\").objects.create(\n year=2020, term=Semester.TERM_SUMMER\n )\n self.course = pre_migration.get_model(self.app, \"Course\").objects.create(\n course_code=\"000\", department=\"Penn Labs\", semester=self.semester\n )\n self.queue = pre_migration.get_model(self.app, \"Queue\").objects.create(\n name=\"Queue\", course=self.course\n )\n\n def test_question_templates_migrated(self):\n queue = self.post_migration.get_model(self.app, \"Queue\").objects.get(id=self.queue.id)\n\n # The queue created before migration now has a question_template attribute\n self.assertIs(hasattr(queue, \"question_template\"), True)\n\n # The question_template is \"\" by default\n self.assertIs(queue.question_template, \"\")\n", "repo_name": "pennlabs/office-hours-queue", "sub_path": "backend/tests/ohq/test_migrations.py", "file_name": "test_migrations.py", "file_ext": "py", "file_size_in_byte": 3238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.test.TransactionTestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.executor.MigrationExecutor", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 30, "usage_type": "argument"}, {"api_name": "ohq.models.Semester.TERM_SUMMER", "line_number": 69, "usage_type": "attribute"}, {"api_name": "ohq.models.Semester", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "70282189598", "text": "import pyrebase\r\nfrom flask import render_template, request, make_response, Flask, redirect\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nimport os\r\nimport shutil\r\nimport cv2\r\nimport face_recognition\r\nimport numpy as np\r\nimport pickle\r\n\r\n#pyrebase Setup\r\nconfig = {\r\n\t\"apiKey\": \"AIzaSyBGjfeNaWOzjNiK40UaIH9F0YUny9TXfzQ\",\r\n \"authDomain\": \"facecan-db.firebaseapp.com\",\r\n \"databaseURL\": \"https://facecan-db.firebaseio.com\",\r\n \"projectId\": \"facecan-db\",\r\n \"storageBucket\": \"facecan-db.appspot.com\",\r\n \"messagingSenderId\": \"442953815031\",\r\n \"appId\": \"1:442953815031:web:ff3d72dd0e31be9981d4f2\",\r\n \"measurementId\": \"G-9QGMQF8LD8\"\r\n}\r\n\r\n\r\nfireb = pyrebase.initialize_app(config)\r\nauth = fireb.auth()\r\n\r\napp = Flask(__name__)\r\n\r\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///facedatadb.db'\r\ndb = SQLAlchemy(app)\r\n\r\napp.config[\"ALLOWED_IMAGE_EXTENSIONS\"] = [\"JPEG\", \"JPG\", \"PNG\"]\r\n\r\n\r\nclass faces(db.Model):\r\n\tuserId = db.Column(db.String, nullable=False)\r\n\tuserName = db.Column(db.String(20), primary_key=True)\r\n\tname = db.Column(db.Text, default='N/A')\r\n\tupiId = db.Column(db.String, default='N/A')\r\n\tcontactNumber = db.Column(db.Integer, default='N/A')\r\n\temailId = db.Column(db.String, default='N/A')\r\n\tcompany = db.Column(db.String, default='N/A')\r\n\taddress = db.Column(db.Text, default='N/A')\r\n\tfb_url = db.Column(db.Text, default='N/A')\r\n\tinsta_handle = db.Column(db.String, default='N/A')\r\n\ttwitter = db.Column(db.String, default='N/A')\r\n\tfaceImage = db.Column(db.Boolean, default=False)\r\n\temailVerified = db.Column(db.Boolean, default=False)\r\n\r\n\tdef __repr__(self):\r\n\t\treturn 'faces ' + self.userName\r\n\r\n\r\n\r\n# def setCookie(Email,CUI):\r\n# \tcook = make_response('Setting Cookies')\r\n# \tcook.set_cookie('Email', Email, max_age=60*60*24*365*2)\r\n# \tcook.set_cookie('CUI', CUI, max_age=60*60*24*365*2)\r\n# \treturn cook\r\n\r\n\r\ncurrentUserId = ''\r\nfaceImage = ''\r\nfaceDataList = []\r\nfaceNameList = []\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html')\r\n\r\n\telse:\r\n\t\tfaceInfo = faces.query.filter_by(userId=CuI).first()\r\n\t\t\r\n\t\ttry:\r\n\t\t\tif (faceInfo.faceImage):\r\n\t\t\t\treturn render_template('home.html', CUI=CuI, faceNotUpdated=False, Email=Email)\r\n\t\texcept:\r\n\t\t\treturn render_template('home.html', CUI=CuI, faceNotUpdated=True, Email=Email)\r\n\t\t\r\n\treturn render_template('home.html')\r\n\r\n\r\n\r\n@app.route('/login', methods=['GET', 'POST'])\r\ndef basic():\r\n\r\n\tunsucc = 'Please check you credentials'\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\r\n\t\tif request.method == 'POST':\r\n\t\t\temail = request.form['name']\r\n\t\t\tpassword = request.form['pass']\r\n\r\n\t\t\t#for logging user using pyrebase for firebase authentication\r\n\t\t\ttry:\r\n\t\t\t\tuser = auth.sign_in_with_email_and_password(email, password)\r\n\t\t\t\tglobal currentUserId\r\n\t\t\t\tcurrentUserId = user['localId']\r\n\t\t\texcept:\r\n\t\t\t\treturn render_template('login.html', us=unsucc)\r\n\t\t\t\r\n\t\t\tfaceInfo = faces.query.filter_by(userId=str(currentUserId)).first()\r\n\r\n\t\t\t#for checking if user is having username or not\r\n\r\n\t\t\tif (faceInfo):\r\n\t\t\t\tprint(make_response('..').set_cookie('UN', faceInfo.userName))\r\n\t\t\telse:\r\n\t\t\t\treturn render_template('addUserName.html', CUI=currentUserId, Email=email)\t\t\t\r\n\r\n\r\n\t\t\t#for sending user to homepage depending upon if his facedata is uploaded or not\t\r\n\t\t\tfaceInfo = faces.query.filter_by(userId=str(currentUserId)).first()\r\n\t\t\tglobal faceImage\r\n\t\t\tfaceImage = faceInfo.faceImage\r\n\t\t\tif (faceImage):\r\n\t\t\t\treturn render_template('home.html', CUI=currentUserId, faceNotUpdated=False, Email=email, UN=faceInfo.userName)\r\n\t\t\telse:\r\n\t\t\t\treturn render_template('home.html', CUI=currentUserId, faceNotUpdated=True, Email=email, UN=faceInfo.userName)\r\n\t\t\t\t\r\n\r\n\telse:\r\n\r\n\t\tfaceInfo = faces.query.filter_by(userId=CuI).first()\r\n\r\n\t\tif (faceInfo):\r\n\t\t\t\treturn render_template('home.html', CUI=CuI, faceNotUpdated=False, Email=Email)\r\n\t\telse:\r\n\t\t\treturn render_template('home.html', CUI=CuI, faceNotUpdated=True, Email=Email)\r\n\r\n\r\n\treturn render_template('login.html')\r\n\r\n\r\n\r\n\r\n\r\n@app.route('/editprofile', methods=['GET', 'POST'])\r\ndef editprofile():\r\n\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html', msg='Login to continue...')\r\n\r\n\tif request.method == 'POST':\r\n\t\tf = faces.query.filter_by(userId=str(CuI)).first()\r\n\t\t# f.userName = request.form['userName']\r\n\t\t# f.name = request.form['name']\r\n\t\tf.upiId = request.form['upiId']\r\n\t\tf.contactNumber = request.form['contactNumber']\r\n\t\t# f.emailId = request.form['emailId']\r\n\t\tf.company = request.form['company']\r\n\t\tf.address = request.form['address']\r\n\t\tf.fb_url = request.form['fb_url']\r\n\t\tf.insta_handle = request.form['insta_handle']\r\n\t\tf.twitter = request.form['twitter']\r\n\t\tdb.session.commit()\r\n\t\treturn render_template('viewProfile.html', face=f)\r\n\tfaceInfo = faces.query.filter_by(userId= CuI).first()\r\n\treturn render_template('editProfile.html', face=faceInfo)\r\n\r\n\r\n\r\n\r\n@app.route('/viewprofile')\r\ndef viewprofile():\r\n\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html', msg='Login to continue...')\r\n\r\n\tfaceInfo = faces.query.filter_by(userId= CuI).first()\r\n\treturn render_template('viewProfile.html', face = faceInfo)\r\n\r\n\r\n\r\n\r\n\r\n@app.route('/logout', methods=[\"GET\", \"POST\"])\r\ndef logout():\r\n\tglobal currentUserId\r\n\tcurrentUserId = ''\r\n\treturn render_template('logout.html')\r\n\r\n\r\n\r\n\r\n@app.route('/signup', methods=['GET','POST'])\r\ndef signup():\r\n\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\tunsucc = 'Signup failed! Please try again'\r\n\t\tif request.method == 'POST':\r\n\t\t\temail = request.form['name']\r\n\t\t\tpassword = request.form['pass']\r\n\t\t\ttry:\r\n\t\t\t\tuser = auth.create_user_with_email_and_password(email, password)\r\n\t\t\texcept:\r\n\t\t\t\treturn render_template('signup.html', us=unsucc)\r\n\t\t\tsucMsg = 'Account created successfully. Please login to continue..'\r\n\t\t\treturn render_template('login.html', msg=sucMsg)\r\n\r\n\telse:\r\n\t\tfaceInfo = faces.query.filter_by(userId=CuI).first()\r\n\r\n\t\tif (faceInfo):\r\n\t\t\t\treturn render_template('home.html', CUI=currentUserId, faceNotUpdated=False, Email=Email, UN=faceInfo.userName)\r\n\t\telse:\r\n\t\t\treturn render_template('home.html', CUI=currentUserId, faceNotUpdated=True, Email=Email, UN=faceInfo.userName)\r\n\r\n\treturn render_template('signup.html')\r\n\r\n\r\n\r\n\r\n@app.route('/addUserName', methods=['GET','POST'])\r\ndef addUserName():\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html', msg='Login to continue...')\r\n\t\r\n\tif request.method == 'POST':\r\n\t\tnewUserName = request.form['userName']\r\n\t\tname = request.form['name']\r\n\r\n\t\tfaceInfo = faces.query.filter_by(userName=newUserName)\r\n\t\t#for checking if username already exists\r\n\t\tif faceInfo.count()>0:\r\n\t\t\treturn render_template('addUserName.html', msg='username already taken! try another', CUI=CuI, Email=Email)\r\n\t\telse:\r\n\t\t\tnewRecord = faces(userId=CuI,userName=newUserName,emailId=Email, name=name)\r\n\t\t\tdb.session.add(newRecord)\r\n\t\t\tdb.session.commit()\r\n\t\t\tfaceInfo = faces.query.filter_by(userId= CuI).first()\r\n\t\t\tif (faceImage):\r\n\t\t\t\treturn render_template('home.html', CUI=CuI, faceNotUpdated=False, Email=Email, UN=faceInfo.userName)\r\n\t\t\telse:\r\n\t\t\t\treturn render_template('home.html', CUI=CuI, faceNotUpdated=True, Email=Email, UN=faceInfo.userName)\r\n\r\n\treturn render_template('home.html', CUI=CuI, faceNotUpdated=True, Email=Email)\r\n\r\n\r\n\r\n\r\n\r\ndef allowed_image(filename):\r\n\r\n # We only want files with a . in the filename\r\n if not \".\" in filename:\r\n return False\r\n\r\n # Split the extension from the filename\r\n ext = filename.rsplit(\".\", 1)[1]\r\n\r\n # Check if the extension is in ALLOWED_IMAGE_EXTENSIONS\r\n if ext.upper() in app.config[\"ALLOWED_IMAGE_EXTENSIONS\"]:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\n\r\n\r\n@app.route('/addface', methods=['GET','POST'])\r\ndef addface():\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html', msg='Login to continue...')\r\n\r\n\r\n\tif request.method=='POST':\r\n\r\n\t\tif request.files:\r\n\t\t\timage = request.files[\"image\"]\r\n\r\n\t\t\tif image.filename == \"\":\r\n\t\t\t\tf = faces.query.filter_by(userName=str(user)).first()\r\n\t\t\t\tf.faceImage = False\r\n\t\t\t\tdb.session.commit()\r\n\t\t\t\treturn render_template('addface.html', msg='No Filename!')\r\n\r\n\t\t\tuser = request.cookies.get('UN')\r\n\t\t\tif allowed_image(image.filename):\r\n\t\t\t\timgName = 'static/imageSet/' + str(user) + '.' + image.filename.rsplit(\".\", 1)[1].lower()\r\n\t\t\t\timage.save(os.path.join(app.root_path, imgName))\r\n\r\n\t\t\t\t\r\n\t\t\t\t# #resize image\r\n\t\t\t\t# image = cv2.imread(imgName, cv2.IMREAD_UNCHANGED)\r\n\t\t\t\t# width = int(image.shape[1] * (20 / 100))\r\n\t\t\t\t# height = int(image.shape[0] * (20 / 100))\r\n\t\t\t\t# dim = (width, height)\r\n\t\t\t\t# resizedImage = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)\r\n\t\t\t\t# cv2.imwrite(imgName, resizedImage)\r\n\r\n\r\n\t\t\t\timage = face_recognition.load_image_file(imgName)\r\n\t\t\t\tface_locations = face_recognition.face_locations(image)\r\n\t\t\t\tif len(face_locations)!=1:\r\n\t\t\t\t\tos.remove(imgName)\r\n\t\t\t\t\treturn render_template('addface.html', msg='Image can not be added, it has either zero or more than one recognizable faces!')\r\n\t\t\t\telse:\r\n\t\t\t\t\tf = faces.query.filter_by(userName=str(user)).first()\r\n\t\t\t\t\tf.faceImage = True\r\n\t\t\t\t\tdb.session.commit()\r\n\r\n\t\t\t\t\t# global faceDataList\r\n\t\t\t\t\t# global faceNameList\r\n\r\n\t\t\t\t\t# data = pickle.loads(open(\"encodings.pickle\", \"rb\").read())\r\n\t\t\t\t\t# faceDataList = data[\"encodings\"]\r\n\t\t\t\t\t# faceNameList= data[\"names\"]\r\n\r\n\t\t\t\t\tfaceDataList = []\r\n\t\t\t\t\tfaceNameList = []\r\n\r\n\t\t\t\t\timUserFace = face_recognition.load_image_file(imgName)\r\n\t\t\t\t\timUserFace_encod = face_recognition.face_encodings(imUserFace)[0]\r\n\r\n\t\t\t\t\tfaceDataList.append(imUserFace_encod)\r\n\t\t\t\t\tfaceNameList.append(user)\r\n\r\n\t\t\t\t\tprint(faceDataList)\r\n\t\t\t\t\tprint(faceNameList)\r\n\r\n\t\t\t\t\tdata = {\"encodings\": faceDataList, \"names\": faceNameList}\r\n\t\t\t\t\tpickle.dump(data, open(\"encodings.pickle\", \"wb\"), protocol=0)\r\n\r\n\t\t\t\treturn redirect('/')\r\n\t\t\t\r\n\t\t\telse:\r\n\t\t\t\tf = faces.query.filter_by(userName=str(user)).first()\r\n\t\t\t\tf.faceImage = False\r\n\t\t\t\tdb.session.commit()\r\n\t\t\t\tprint(\"That file extension is not allowed\")\r\n\t\t\t\treturn render_template('addface.html', msg='That file extension is not allowed')\r\n\r\n\treturn render_template('addface.html')\r\n\r\n\r\n\r\n\r\n\r\n\r\n@app.route('/scan', methods=['GET','POST'])\r\ndef scanface():\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html', msg='Login to continue...')\r\n\r\n\r\n\tif request.method=='POST':\r\n\r\n\t\tif request.files:\r\n\t\t\timage = request.files[\"image\"]\r\n\r\n\t\t\tif image.filename == \"\":\r\n\t\t\t\treturn render_template('scanface.html', msg='No Filename!')\r\n\r\n\t\t\tuser = request.cookies.get('UN')\r\n\t\t\tif allowed_image(image.filename):\r\n\t\t\t\t\r\n\t\t\t\t#create directory for current user\r\n\t\t\t\tdirName = 'static/newImg/' + CuI + 'u/'\r\n\t\t\t\tdirToMake = os.path.join(app.root_path, dirName) \r\n\t\t\t\tos.mkdir(dirToMake)\r\n\r\n\t\t\t\timgName = 'static/newImg/' + CuI + 'u/' + image.filename\r\n\t\t\t\timage.save(os.path.join(app.root_path, imgName))\r\n\t\t\t\t\r\n\t\t\t\t# resize image\r\n\t\t\t\t# image = cv2.imread(imgName, cv2.IMREAD_UNCHANGED)\r\n\t\t\t\t# width = int(image.shape[1] * (30 / 100))\r\n\t\t\t\t# height = int(image.shape[0] * (30 / 100))\r\n\t\t\t\t# dim = (width, height)\r\n\t\t\t\t# resizedImage = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)\r\n\t\t\t\t# cv2.imwrite(imgName, resizedImage)\r\n\r\n\t\t\t\ttry:\r\n\t\t\t\t\timCheck = face_recognition.load_image_file(imgName)\r\n\t\t\t\t\timCheck_encod = face_recognition.face_encodings(imCheck)[0]\r\n\t\t\t\t\tdirName = 'static/newImg/' + CuI + 'u'\r\n\t\t\t\t\tshutil.rmtree(dirName)\r\n\r\n\t\t\t\t\tdata = pickle.loads(open(\"encodings.pickle\", \"rb\").read())\r\n\t\t\t\t\tfaceDataList = data[\"encodings\"]\r\n\t\t\t\t\tfaceNameList= data[\"names\"]\r\n\r\n\t\t\t\t\tresults = face_recognition.compare_faces(faceDataList, imCheck_encod)\r\n\t\t\t\t\tif len(results)==0:\r\n\t\t\t\t\t\treturn render_template('scanface.html', msg='User is not registered')\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tcounter = 0\r\n\t\t\t\t\t\tfor i in results:\r\n\t\t\t\t\t\t\tif i:\r\n\t\t\t\t\t\t\t\tresultRedirect = '/scanresult/' + faceNameList[counter]\r\n\t\t\t\t\t\t\t\treturn redirect(resultRedirect)\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tcounter+=1\r\n\t\t\t\t\t\r\n\t\t\t\t\treturn render_template('scanface.html', msg='User is not registered')\t\t\t\r\n\t\t\t\t\tprint(imCheck_encod)\r\n\r\n\t\t\t\texcept:\r\n\t\t\t\t\treturn render_template('scanface.html', msg='No face found')\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t\r\n\t\t\t\t# image = cv2.imread(imgName, cv2.IMREAD_UNCHANGED)\r\n\t\t\telse:\r\n\t\t\t\tprint(\"That file extension is not allowed\")\r\n\t\t\t\treturn render_template('scanface.html', msg='That file extension is not allowed')\r\n\r\n\treturn render_template('scanface.html')\r\n\r\n\r\n\r\n\r\n@app.route('/scanresult//')\r\ndef scanresult(user):\r\n\tCuI = str(request.cookies.get('CUI'))\r\n\tEmail = str(request.cookies.get('Email'))\r\n\r\n\tif (CuI == None or Email == None or Email == 'None' or CuI == 'None' or Email == '' or CuI == ''):\r\n\t\treturn render_template('login.html', msg='Login to continue...')\r\n\r\n\ttry:\r\n\t\tf = faces.query.filter_by(userName=user).first()\r\n\t\tprint(user)\r\n\t\treturn render_template('scanresult.html', face=f)\r\n\texcept:\r\n\t\treturn 'Some Error Happened'\r\n\r\n\r\n@app.route('/payment//', methods=['GET','POST'])\r\ndef payment(upiId, userName):\r\n\tamount = request.form[\"amount\"]\r\n\tpayUrl = 'upi://pay?pa='+upiId+'&pn='+userName+'&mc=null&tid=null&tr=test101&tn=This%20is%20test%20payment&am='+amount+'&mam=null&cu=INR&url=null'\r\n\treturn render_template(\"pay.html\", payUrl=payUrl)\r\n\r\nif __name__ == '__main__':\r\n\tapp.run(debug=True)", "repo_name": "cddharthsingh/facecan", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 14465, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pyrebase.initialize_app", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 27, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 148, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 148, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 154, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 158, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 159, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 159, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 161, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 162, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 163, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 163, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 164, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 164, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 165, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 165, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 178, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 178, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 181, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 202, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 203, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 208, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 208, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 209, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 209, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 213, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 215, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 221, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 225, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 232, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 232, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 232, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 233, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 233, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 233, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 238, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 238, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 239, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 239, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 240, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 245, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 252, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 254, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 256, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 282, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 282, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 282, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 283, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 283, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 283, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 286, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 289, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 289, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 291, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 291, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 292, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 298, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 300, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 300, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 300, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 303, "usage_type": "call"}, {"api_name": "os.path", "line_number": 303, "usage_type": "attribute"}, {"api_name": "face_recognition.load_image_file", "line_number": 315, "usage_type": "call"}, {"api_name": "face_recognition.face_locations", "line_number": 316, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 318, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 319, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 335, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 336, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 345, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 347, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 354, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 356, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 365, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 365, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 365, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 366, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 366, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 366, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 369, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 372, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 372, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 374, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 374, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 375, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 375, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 378, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 380, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 380, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 380, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 385, "usage_type": "call"}, {"api_name": "os.path", "line_number": 385, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 389, "usage_type": "call"}, {"api_name": "os.path", "line_number": 389, "usage_type": "attribute"}, {"api_name": "face_recognition.load_image_file", "line_number": 400, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 401, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 403, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 405, "usage_type": "call"}, {"api_name": "face_recognition.compare_faces", "line_number": 409, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 411, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 417, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 421, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 425, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 431, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 433, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 440, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 440, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 440, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 441, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 441, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 441, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 444, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 449, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 456, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 456, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 458, "usage_type": "call"}]} +{"seq_id": "34159934872", "text": "from functools import wraps\n\nfrom flask import request\n\nfrom app.main.service.auth_helper import Auth\nfrom itertools import groupby\n\n\ndef token_required(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n\n data, status = Auth.get_logged_in_user(request)\n token = data.get('data')\n\n if not token:\n return data, status\n\n return f(*args, **kwargs)\n\n return decorated\n\n\ndef admin_token_required(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n\n data, status = Auth.get_logged_in_user(request)\n token = data.get('data')\n\n if not token:\n return data, status\n\n admin = token.get('admin')\n if not admin:\n response_object = {\n 'status': 'fail',\n 'message': 'admin token required'\n }\n return response_object, 401\n\n return f(*args, **kwargs)\n\n return decorated\n\ndef print_decorator(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n\n result = {}\n\n output = f(*args, **kwargs)\n result[\"total\"] = len(output[\"data\"])\n result[\"categories\"] = { k: len(list(v)) for k,v in groupby(sorted(output[\"data\"], key=lambda x: x[\"category\"]), lambda x: x[\"category\"])}\n result[\"data\"] = output[\"data\"]\n\n\n \n\n return result\n\n return decorated", "repo_name": "mmussio85/flask-restplus-shop-cart-api", "sub_path": "app/main/util/decorator.py", "file_name": "decorator.py", "file_ext": "py", "file_size_in_byte": 1343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "app.main.service.auth_helper.Auth.get_logged_in_user", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "argument"}, {"api_name": "app.main.service.auth_helper.Auth", "line_number": 13, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 10, "usage_type": "call"}, {"api_name": "app.main.service.auth_helper.Auth.get_logged_in_user", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "argument"}, {"api_name": "app.main.service.auth_helper.Auth", "line_number": 28, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 25, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 54, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "3911089384", "text": "import numpy as np\nfrom numpy.linalg import inv\n\n# 1(a)\n# Initialize A as a 2D numpy array\na = np.array([[0., 2., 4.], [2., 4., 2.], [3., 3., 1.]])\n# Use numpy function inv() to compute the inverse\nprint(\"What is inv(A): \", inv(a))\n\n# 1(b)\n# Initialize b and c\nb = np.array([-2., -2., -4.]).T\nc = np.array([1., 1., 1.]).T\n# Compute the matrix products\nprint(\"What is inv(A)b: \", inv(a) @ b)\nprint(\"What is Ac: \", a @ c)\n\nimport matplotlib.pyplot as plt\n\n# 2(a)\nn = 40000 # 0.0025 = 1/sqrt(4n)\nZ = np.random.randn(n)\n\nplt.step(sorted(Z), np.arange(1,n+1)/float(n))\n\nplt.title(r'Empirical $\\widehat{F}_n(x)$ for the Standard Normal')\nplt.ylabel(r'$\\widehat{F}_n(x)$')\nplt.xlabel('x')\nplt.xlim([-3, 3])\nplt.show()\n\n# 2(b)\nplt.step(sorted(Z), np.arange(1,n+1)/float(n), label=r\"$\\widehat{F}_n(x)$\")\n\n# Plot for different k values\nk_set = [1, 8, 64, 512]\nfor k in k_set:\n Z = np.sum(np.sign(np.random.randn(n, k))*np.sqrt(1./k), axis=1)\n plt.step(sorted(Z), np.arange(1,n+1)/float(n), label=f\"k = {k}\")\n\nplt.title(r'Empirical CDFs for the Standard Normal')\nplt.ylabel('Empirical CDF')\nplt.xlabel('x')\nplt.legend(loc=\"upper left\")\nplt.xlim([-3, 3])\nplt.show()", "repo_name": "Kev-Y-Huang/cs184", "sub_path": "pset0/programming.py", "file_name": "programming.py", "file_ext": "py", "file_size_in_byte": 1159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.array", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.step", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.step", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.step", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "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.xlabel", "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.xlim", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "20797798416", "text": "from celery.task.schedules import crontab\nfrom celery.decorators import periodic_task\nfrom celery.utils.log import get_task_logger\n\nfrom django.utils import timezone\n\nfrom .models import Commits\nfrom urllib2 import urlopen\nfrom dateutil.parser import parse\n\nimport json\n\nlogger = get_task_logger(__name__)\n\n\n@periodic_task(ignore_result=True, run_every=(crontab(hour=\"*\", minute=timezone.now().minute+1, day_of_week=\"*\")))\ndef get_latest_commit(owner='nodejs', repo='node'):\n logger.info(\"Start task\")\n url = 'https://api.github.com/repos/{owner}/{repo}/commits'.format(owner=owner, repo=repo)\n response = urlopen(url).read()\n data = json.loads(response.decode('UTF-8'))\n list = data[:25]\n for el in list:\n sha = el['sha']\n name = el['commit']['author']['name']\n msg = el['commit']['message'].encode('ascii','ignore')\n date = parse(el['commit']['author']['date'])\n try:\n if not Commits.objects.filter(sha=sha):\n comment = Commits(author=name, text=msg, pub_date=date, read_status=False, sha=sha)\n comment.save()\n logger.info(\"Get!!!\")\n except Exception as ex:\n print('ex is: {}'.format(ex))\n logger.info(\"End task success!!!\")\n return list\n ", "repo_name": "fusion-tech-pro/work-with-github-api", "sub_path": "source/commits/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 1277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "celery.utils.log.get_task_logger", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 20, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Commits.objects.filter", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Commits.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Commits", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Commits", "line_number": 30, "usage_type": "call"}, {"api_name": "celery.decorators.periodic_task", "line_number": 16, "usage_type": "call"}, {"api_name": "celery.task.schedules.crontab", "line_number": 16, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 16, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "5671530228", "text": "#!/usr/bin/env python3\n\nimport qtrio\nimport trio\n\nfrom qtpy.QtWidgets import QApplication\n\nfrom .app import ContinuEDApp\n\n\nasync def loop(task_status) -> None:\n\n loop_completed = trio.Event()\n\n QApplication.setApplicationName('ContinuED')\n QApplication.setApplicationVersion('0.1')\n QApplication.setOrganizationName('ExtraArcam')\n QApplication.setApplicationDisplayName('ContinuED')\n\n widget = ContinuEDApp()\n\n async with qtrio.enter_emissions_channel(signals=[\n widget.closed,\n widget.update_clicked,\n ]) as emissions:\n\n widget.show()\n\n task_status.started(loop_completed)\n\n i = 0\n async for emission in emissions.channel:\n\n if emission.is_from(widget.closed):\n break\n\n if emission.is_from(widget.update_clicked):\n i += 1\n widget.set_message(f\"{i}\")\n\n loop_completed.set()\n", "repo_name": "blubberdiblub/ContinuED", "sub_path": "src/continued/gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "trio.Event", "line_number": 13, "usage_type": "call"}, {"api_name": "qtpy.QtWidgets.QApplication.setApplicationName", "line_number": 15, "usage_type": "call"}, {"api_name": "qtpy.QtWidgets.QApplication", "line_number": 15, "usage_type": "name"}, {"api_name": "qtpy.QtWidgets.QApplication.setApplicationVersion", "line_number": 16, "usage_type": "call"}, {"api_name": "qtpy.QtWidgets.QApplication", "line_number": 16, "usage_type": "name"}, {"api_name": "qtpy.QtWidgets.QApplication.setOrganizationName", "line_number": 17, "usage_type": "call"}, {"api_name": "qtpy.QtWidgets.QApplication", "line_number": 17, "usage_type": "name"}, {"api_name": "qtpy.QtWidgets.QApplication.setApplicationDisplayName", "line_number": 18, "usage_type": "call"}, {"api_name": "qtpy.QtWidgets.QApplication", "line_number": 18, "usage_type": "name"}, {"api_name": "app.ContinuEDApp", "line_number": 20, "usage_type": "call"}, {"api_name": "qtrio.enter_emissions_channel", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "12219496104", "text": "import re\nfrom typing import List\n\nfrom solutions.shared import SolutionABC\n\nCOLS = 1000\nROWS = 1000\n\nROW_RE = re.compile(r'(?Ptoggle|turn on|turn off) (?P\\d+),(?P\\d+) through (?P\\d+),(?P\\d+)')\n\nclass Solution(SolutionABC):\n def __init__(\n self,\n ):\n self.lights: List[List[bool]] = [[False] * COLS] * ROWS\n\n def parse_row(\n self,\n row: str,\n ):\n m = ROW_RE.match(row)\n assert m is not None\n for x in range(int(m['x_0']), int(m['x_1']) + 1):\n for y in range(int(m['y_0']), int(m['y_1']) + 1):\n if m['operation'] == 'turn on':\n self.lights[x][y] = True\n elif m['operation'] == 'turn off':\n self.lights[x][y] == False\n else:\n assert m['operation'] == 'toggle'\n self.lights[x][y] = not self.lights[x][y]\n \n def solve(\n self,\n ) -> int:\n return sum(sum(l for l in row) for row in self.lights)", "repo_name": "gregwhite90/Advent-of-Code-Python", "sub_path": "solutions/2015/06/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "re.compile", "line_number": 9, "usage_type": "call"}, {"api_name": "solutions.shared.SolutionABC", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "37222037196", "text": "import datetime, json, uuid\nfrom sca import format_flags as flags\nfrom sca.utils import hash_utils\nfrom dateutil import parser\n\n\nclass scadict(dict):\n\n def __init__(self, data=None, uuid4_id=None, def_url=None, def_text=None, software=None):\n self.uuid4 = str(uuid.uuid4() if uuid4_id is None else uuid4_id) \n self.def_url = def_url \n self.def_text = def_text \n self.software = software\n\n if data: self.update(data)\n \n self.updated = False\n\n \n def __setitem__(self, key, value):\n if self.get(key, None)!=value: self.updated = True\n super(scadict, self).__setitem__(key, value)\n\n @property\n def created_on(self): \n return self.get(flags.CREATED_ON_FLAG, None)\n @created_on.setter\n def created_on(self, value):\n if self.get(flags.CREATED_ON_FLAG, None)!=value: self.updated = True\n \n self[flags.CREATED_ON_FLAG] = value.isoformat(' ') if value else value\n\n @property\n def updated_on(self): \n return self.get(flags.UPDATED_ON_FLAG, None)\n @updated_on.setter\n def updated_on(self, value):\n if self.get(flags.UPDATED_ON_FLAG, None)!=value: self.updated = True\n \n self[flags.UPDATED_ON_FLAG] = value.isoformat(' ') if value else value\n\n @property\n def uuid4(self): \n return self.get(flags.UUID4_FLAG, None)\n @uuid4.setter\n def uuid4(self, value):\n if self.get(flags.UUID4_FLAG, None)!=str(value): self.updated = True\n \n self[flags.UUID4_FLAG] = str(value)\n\n @property\n def def_url(self): \n return self.get(flags.DEFINITION_URL_FLAG, None)\n @def_url.setter\n def def_url(self, value):\n if self.get(flags.DEFINITION_URL_FLAG, None)!=value: self.updated = True\n \n self[flags.DEFINITION_URL_FLAG] = value\n\n @property\n def def_text(self): \n return self.get(flags.DEFINITION_TEXT_FLAG, None)\n @def_text.setter\n def def_text(self, value):\n if self.get(flags.DEFINITION_TEXT_FLAG, None)!=value:\n self.updated = True\n self[flags.DEFINITION_TEXT_FLAG] = value\n\n @property\n def software(self): \n return self.get(flags.SOFTWARE_REF_FLAG, None)\n @software.setter\n def software(self, value):\n if self.get(flags.SOFTWARE_REF_FLAG, None)!=value:\n self.updated = True\n self[flags.SOFTWARE_REF_FLAG] = value\n\n\n def add_parent_ref(self, ref_uuid4): \n if flags.PARENT_REF_FLAG not in self:\n self[flags.PARENT_REF_FLAG] = []\n self.updated = True\n if str(ref_uuid4) not in self[flags.PARENT_REF_FLAG]:\n self[flags.PARENT_REF_FLAG].append(str(ref_uuid4))\n self.updated = True\n \n def add_external_ref(self, ref_uuid4):\n if flags.EXTERNAL_REF_FLAG not in self:\n self[flags.EXTERNAL_REF_FLAG] = []\n self.updated = True\n\n if str(ref_uuid4) not in self[flags.EXTERNAL_REF_FLAG]:\n self[flags.EXTERNAL_REF_FLAG].append(str(ref_uuid4)) \n self.updated = True\n \n def add_external_url(self, url): \n if flags.EXTERNAL_URL_FLAG not in self:\n self[flags.EXTERNAL_URL_FLAG] = [] \n self.updated = True\n\n if url not in self[flags.EXTERNAL_URL_FLAG]:\n self[flags.EXTERNAL_URL_FLAG].append(url) \n self.updated = True\n \n def add_external_file(self, filepath): \n if flags.EXTERNAL_FILE_FLAG not in self:\n self[flags.EXTERNAL_FILE_FLAG] = [] \n self.updated = True\n\n filename, hash_algorithm, hash_value = hash_utils.calculate_hash(filepath)\n\n found = False\n for filedata in self.get(flags.EXTERNAL_FILE_FLAG, []):\n f = filedata.get('filename', None)\n h = filedata.get('hash-algorithm', None)\n hv = filedata.get('hash-value', None)\n if f==filename and hash_algorithm==h and hv==hash_value:\n found = True\n break\n\n if not found:\n self[flags.EXTERNAL_FILE_FLAG].append({\n 'filename':os.path.basename(filepath),\n 'hash-algorithm': hash_algorithm,\n 'hash-value':hash_value\n })\n self.updated = True\n\n \n \n\ndef dump(data, filestream):\n if flags.UUID4_FLAG not in data: data[flags.UUID4_FLAG] = str(uuid.uuid4())\n\n now = datetime.datetime.now()\n if flags.CREATED_ON_FLAG not in data: data[flags.CREATED_ON_FLAG] = now.isoformat(' ') \n \n if data.updated:\n data[flags.UPDATED_ON_FLAG] = now.isoformat(' ') \n\n res = json.dump(data, filestream, indent=4, sort_keys=True)\n data.updated = False\n return res\n\ndef dumps(data, sort_keys=False):\n return json.dumps(data, sort_keys=sort_keys)\n\n\ndef load(filestream):\n data = json.load(filestream)\n return scadict(data=data)", "repo_name": "int-brain-lab/iblpybpod", "sub_path": "src/sca/formats/json.py", "file_name": "json.py", "file_ext": "py", "file_size_in_byte": 4891, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "uuid.uuid4", "line_number": 10, "usage_type": "call"}, {"api_name": "sca.format_flags.CREATED_ON_FLAG", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 26, "usage_type": "name"}, {"api_name": "sca.format_flags.CREATED_ON_FLAG", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 29, "usage_type": "name"}, {"api_name": "sca.format_flags.CREATED_ON_FLAG", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 31, "usage_type": "name"}, {"api_name": "sca.format_flags.UPDATED_ON_FLAG", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 35, "usage_type": "name"}, {"api_name": "sca.format_flags.UPDATED_ON_FLAG", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 38, "usage_type": "name"}, {"api_name": "sca.format_flags.UPDATED_ON_FLAG", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 40, "usage_type": "name"}, {"api_name": "sca.format_flags.UUID4_FLAG", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 44, "usage_type": "name"}, {"api_name": "sca.format_flags.UUID4_FLAG", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 47, "usage_type": "name"}, {"api_name": "sca.format_flags.UUID4_FLAG", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 49, "usage_type": "name"}, {"api_name": "sca.format_flags.DEFINITION_URL_FLAG", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 53, "usage_type": "name"}, {"api_name": "sca.format_flags.DEFINITION_URL_FLAG", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 56, "usage_type": "name"}, {"api_name": "sca.format_flags.DEFINITION_URL_FLAG", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 58, "usage_type": "name"}, {"api_name": "sca.format_flags.DEFINITION_TEXT_FLAG", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 62, "usage_type": "name"}, {"api_name": "sca.format_flags.DEFINITION_TEXT_FLAG", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 65, "usage_type": "name"}, {"api_name": "sca.format_flags.DEFINITION_TEXT_FLAG", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 67, "usage_type": "name"}, {"api_name": "sca.format_flags.SOFTWARE_REF_FLAG", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 71, "usage_type": "name"}, {"api_name": "sca.format_flags.SOFTWARE_REF_FLAG", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 74, "usage_type": "name"}, {"api_name": "sca.format_flags.SOFTWARE_REF_FLAG", "line_number": 76, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 76, "usage_type": "name"}, {"api_name": "sca.format_flags.PARENT_REF_FLAG", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 80, "usage_type": "name"}, {"api_name": "sca.format_flags.PARENT_REF_FLAG", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 81, "usage_type": "name"}, {"api_name": "sca.format_flags.PARENT_REF_FLAG", "line_number": 83, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 83, "usage_type": "name"}, {"api_name": "sca.format_flags.PARENT_REF_FLAG", "line_number": 84, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 84, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_REF_FLAG", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 88, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_REF_FLAG", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 89, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_REF_FLAG", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 92, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_REF_FLAG", "line_number": 93, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 93, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_URL_FLAG", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 97, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_URL_FLAG", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 98, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_URL_FLAG", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 101, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_URL_FLAG", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 102, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_FILE_FLAG", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 106, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_FILE_FLAG", "line_number": 107, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 107, "usage_type": "name"}, {"api_name": "sca.utils.hash_utils.calculate_hash", "line_number": 110, "usage_type": "call"}, {"api_name": "sca.utils.hash_utils", "line_number": 110, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_FILE_FLAG", "line_number": 113, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 113, "usage_type": "name"}, {"api_name": "sca.format_flags.EXTERNAL_FILE_FLAG", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 122, "usage_type": "name"}, {"api_name": "sca.format_flags.UUID4_FLAG", "line_number": 133, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 133, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 133, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sca.format_flags.CREATED_ON_FLAG", "line_number": 136, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 136, "usage_type": "name"}, {"api_name": "sca.format_flags.UPDATED_ON_FLAG", "line_number": 139, "usage_type": "attribute"}, {"api_name": "sca.format_flags", "line_number": 139, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 146, "usage_type": "call"}, {"api_name": "json.load", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "24702099475", "text": "import wmi\r\nimport time\r\nimport csv\r\n\r\nfrom datetime import datetime\r\n\r\nf = wmi.WMI()\r\n\r\ndef isMalicious(process):\r\n\tprocess = process.lower()\r\n\tif 'discord' in process or '.py' in process or 'whatsapp' in process:\r\n\t\treturn True\r\n\telse:\r\n\t\treturn False\r\n\r\ndef sendProcessInfoAcrossNetwork(process):\r\n\tprint(process)\r\n\r\ncurrent_running_processes = []\r\n\r\nwith open('log.csv', 'a', newline = '') as file:\r\n\twriter = csv.writer(file)\r\n\twriter.writerow(['S.No.','Process_Name', 'Time'])\r\n\r\ni = 1\r\n\r\nfor process in f.Win32_Process():\r\n\tproc_name = process.Name\r\n\tif proc_name not in current_running_processes:\r\n\t\tcurrent_running_processes.append(proc_name)\r\n\t\tif isMalicious(proc_name):\r\n\t\t\tnow = datetime.now()\r\n\t\t\tcurrent_time = now.strftime(\"%H:%M:%S\")\r\n\t\t\twith open('log.csv', 'a', newline = '') as file:\r\n\t\t\t\twriter = csv.writer(file)\r\n\t\t\t\twriter.writerow([str(i), proc_name, current_time])\r\n\t\t\t\ti += 1\r\n\r\nwhile True:\r\n\t#time.sleep(5)\r\n\tprint(\"Checking againn!\")\r\n\tfor process in f.Win32_Process():\r\n\t\tproc_name = process.Name\r\n\t\tif proc_name not in current_running_processes:\r\n\t\t\tnow = datetime.now()\r\n\t\t\tcurrent_time = now.strftime(\"%H:%M:%S\")\r\n\t\t\tcurrent_running_processes.append(proc_name)\r\n\t\t\twith open('log.csv', 'a', newline = '') as file:\r\n\t\t\t\twriter = csv.writer(file)\r\n\t\t\t\twriter.writerow([str(i), proc_name, current_time])\r\n\t\t\t\ti += 1\r\n", "repo_name": "a12dongithub/Exam-Proctor", "sub_path": "taskMan/process.py", "file_name": "process.py", "file_ext": "py", "file_size_in_byte": 1347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "wmi.WMI", "line_number": 7, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "7766986215", "text": "import turtle, click\r\nimport pandas as pd\r\n\r\n\r\n\"\"\"Args linea de comandos\"\"\"\r\n@click.command()\r\n@click.option('--i', default=1, prompt='Iteraciones',help='Numero de iteraciones.')\r\n@click.option('--v', default=0, help='Velocidad.')\r\n@click.option('--d', default=0, help='Retraso.')\r\ndef main_(i,v,d):\r\n \"\"\"inicio del programa\"\"\"\r\n click.echo(f\"inicio del programa!\")\r\n main(i,v,d)\r\n\r\n\"\"\"Pandas\"\"\"\r\ndef leer_libro(path): #Lee el archivo\r\n return pd.read_excel(path)\r\n\r\ndef hoja(libro): \r\n return round(libro[\"hoja\"].values[0])\r\n\r\ndef tronco(libro): \r\n return round(libro[\"tronco\"].values[0])\r\n\r\ndef velocidad(libro):\r\n return libro[\"velocidad\"].values[0]\r\n\r\ndef apilar(libro): \r\n return libro[\"apilar\"].values[0]\r\n\r\ndef desapilar(libro): \r\n return libro[\"desapilar\"].values[0]\r\n\"\"\"Pandas\"\"\"\r\n\r\n\"\"\"\r\nConfiguracion de turtle\r\n\"\"\"\r\ndef config_turtle(v,d):\r\n turtle.screensize(30000, 30000)\r\n turtle.speed(v)\r\n turtle.delay(d)\r\n turtle.hideturtle()\r\n\r\n\"\"\"\r\nCrea una cadena a partir de la produccion de la gramatica dada\r\n\"\"\"\r\ndef construir_cadena(hoja,tronco, iteraciones, libro):\r\n cadena = \"0\"\r\n # prod1=str(libro['produccion'].values[0])\r\n # prod2=str(libro['produccion'].values[1])\r\n # print(f'prod 1 {prod1} , prod 2 {prod2}')\r\n for i in range(iteraciones):\r\n cadena = cadena.replace(tronco, str(libro['produccion'].values[0]))\r\n cadena = cadena.replace(hoja, str(libro['produccion'].values[1]))\r\n return cadena\r\n\r\n\"\"\"\r\nFuncion principal\r\n\"\"\"\r\ndef main(iteraciones, velocidad, delay):\r\n\r\n \"\"\"\r\n No terminales: 0,1\r\n Terminales: [,]\r\n Cadena inicial: 0\r\n Reglas de produccion: (1 -> 11), (0 -> 1[0]0)\r\n Interpretacion:\r\n 0: Dibujar un segmento de linea hoja.\r\n 1: Dibujar un segmento de linea.\r\n [: Apilar (guardar) posicion y angulo actual, luego girar 45° a la izquierda.\r\n ]: Desapilar (restaurar) posicion y angulo guardados, luego girar 45° a la derecha.\r\n \"\"\"\r\n\r\n libro = leer_libro(\"./prueba.xlsx\")\r\n \r\n print(f'Lectura de datos del libro excel:\\n{libro}')\r\n \r\n tortuga=turtle.Turtle()\r\n\r\n config_turtle(velocidad, delay)\r\n\r\n posicion=[]\r\n angulo=[]\r\n tortuga.left(90)\r\n tamanio_hoja=30/iteraciones\r\n tamanio_tronco=25/iteraciones\r\n angle=45\r\n\r\n def giro_izq(angle):\r\n posicion.append(tortuga.pos())\r\n angulo.append(tortuga.heading())\r\n tortuga.left(angle)\r\n\r\n def giro_der(angle):\r\n posicion_guardada=posicion.pop()\r\n angulo_guardado=angulo.pop()\r\n tortuga.up()\r\n tortuga.goto(posicion_guardada,y=None)\r\n tortuga.seth(angulo_guardado)\r\n tortuga.down()\r\n tortuga.right(angle)\r\n\r\n def dibujar_hoja(tamanio_hoja):\r\n tortuga.color('green')\r\n tortuga.pensize(4)\r\n tortuga.forward(tamanio_hoja)\r\n\r\n def dibujar_tronco(tamanio_tronco):\r\n tortuga.color('brown')\r\n tortuga.pensize(4)\r\n tortuga.forward(tamanio_tronco)\r\n\r\n funciones = {str(hoja(libro)): lambda tamanio_hoja,tamanio_tronco,angle: dibujar_hoja(tamanio_hoja),\r\n str(tronco(libro)): lambda tamanio_hoja,tamanio_tronco,angle: dibujar_tronco(tamanio_tronco),\r\n apilar(libro) : lambda tamanio_hoja,tamanio_tronco,angle: giro_izq(angle),\r\n desapilar(libro) : lambda tamanio_hoja,tamanio_tronco,angle: giro_der(angle),\r\n }\r\n\r\n h = str(hoja(libro))\r\n t = str(tronco(libro))\r\n \r\n\r\n cadena = construir_cadena(h, t, iteraciones, libro)\r\n\r\n print(f'Cadena producida:\\n{cadena}\\ncon {iteraciones} iteraciones')\r\n\r\n for caracter in cadena:\r\n funciones[caracter](tamanio_hoja,tamanio_tronco,angle)\r\n\r\n turtle.done()\r\n turtle.exitonclick()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main_()", "repo_name": "rodrigo-arnaiz/arbol", "sub_path": "dibujo.py", "file_name": "dibujo.py", "file_ext": "py", "file_size_in_byte": 3798, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "click.echo", "line_number": 12, "usage_type": "call"}, {"api_name": "click.command", "line_number": 6, "usage_type": "call"}, {"api_name": "click.option", "line_number": 7, "usage_type": "call"}, {"api_name": "click.option", "line_number": 8, "usage_type": "call"}, {"api_name": "click.option", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 17, "usage_type": "call"}, {"api_name": "turtle.screensize", "line_number": 39, "usage_type": "call"}, {"api_name": "turtle.speed", "line_number": 40, "usage_type": "call"}, {"api_name": "turtle.delay", "line_number": 41, "usage_type": "call"}, {"api_name": "turtle.hideturtle", "line_number": 42, "usage_type": "call"}, {"api_name": "turtle.Turtle", "line_number": 78, "usage_type": "call"}, {"api_name": "turtle.done", "line_number": 130, "usage_type": "call"}, {"api_name": "turtle.exitonclick", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "3661871736", "text": "'''\r\nThis is the API for getting the answer from SRT data which is used by embedding.\r\nCreated by: Vikas Sharma\r\nDate: 17 June 2023\r\n\r\n'''\r\nimport os\r\nfrom fastapi import FastAPI\r\nfrom pydantic import BaseModel\r\nfrom typing import Optional\r\n\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain\r\nfrom langchain.llms import OpenAI\r\nimport uvicorn\r\n# For load the vector database to use.\r\nfrom langchain.embeddings import OpenAIEmbeddings\r\nfrom langchain.vectorstores import Chroma\r\nimport time\r\n\r\napp = FastAPI()\r\n#============= CORS Setting ================================\r\nfrom fastapi.middleware.cors import CORSMiddleware #For CORS\r\n# Configure CORS\r\norigins = [\r\n \"http://localhost:4201\",\r\n]\r\n\r\napp.add_middleware(\r\n CORSMiddleware,\r\n allow_origins=origins,\r\n allow_credentials=True,\r\n allow_methods=[\"GET\", \"POST\", \"PUT\", \"DELETE\"],\r\n allow_headers=[\"*\"],\r\n)\r\n#============= CORS Setting End================================\r\n\r\n\r\nopenai_embeddings = OpenAIEmbeddings()\r\n#Save the embeddings into the local vectore store.\r\n_persist_directory = 'E:/ChatGPT/MMC_Recipte_PDF/MMC_Data_db'\r\n_collection_name = 'MMC_coll'\r\n\r\n# vstore = Chroma.from_documents(srt_data, openai_embeddings, persist_directory=_persist_directory, collection_name=_collection_name)\r\n\r\n# ans = vstore.similarity_search(\"What is the Eligiblity of farmers in NY DBL?\", top_n=2)\r\n# print(ans)\r\n\r\n# Now we can load the persisted database from disk, and use it as normal. \r\n# perDir = 'D:/ChatGPT/API/SRT_Data_API/' + _persist_directory+'/'+_collection_name #AI server path\r\n\r\nperDir = _persist_directory+'/'+_collection_name #Loadl system path\r\n# print(perDir)\r\n# print(os.path.isdir(perDir))\r\nvectordb1 = Chroma(persist_directory=perDir, embedding_function=openai_embeddings) #, collection_name=_collection_name\r\n\r\n# ans1 = vectordb1.similarity_search(\"What is the Eligiblity of farmers in NY DBL?\", top_n=2)\r\n# print(ans1)\r\n\r\n #from load DB.\r\n# chain = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=vectordb1.as_retriever(search_kwargs={\"k\": 3}), input_key=\"question\")\r\n\r\nchain_with_source = RetrievalQAWithSourcesChain.from_llm(llm=ChatOpenAI(model_name='gpt-3.5-turbo'), retriever=vectordb1.as_retriever(search_kwargs={\"k\": 2}))\r\n\r\nclass QAResponse(BaseModel):\r\n question: str\r\n answer: Optional[str] = None\r\n sources: Optional[str] = None\r\n status: Optional[int] = None\r\n\r\n@app.post(\"/GetAnswer\", response_model=QAResponse)\r\ndef process_text(user_data: QAResponse):\r\n response = ''\r\n try:\r\n print('Calling API..')\r\n # print(user_question)\r\n start_time = time.time()\r\n response = chain_with_source({\"question\": user_data.question})\r\n end_time = time.time()\r\n execution_time = end_time - start_time\r\n print(f\"Execution time: {execution_time} seconds\")\r\n # print('Got response')\r\n # Perform processing on the input text\r\n if len(response)>=3:\r\n input_question = response.get('question')\r\n processed_answer = response.get('answer')\r\n sources_val = response.get('sources')\r\n # Create the response\r\n response = QAResponse(question=input_question,answer=processed_answer,sources=sources_val, status=1)\r\n # response = processed_answer\r\n else:\r\n response = \"Not able to process this request.\"\r\n return response\r\n except Exception as ee:\r\n print(ee)\r\n response =QAResponse(question=user_data.question,answer=ee.args[0],sources=\"\", status=0) \r\n return response\r\n\r\n@app.get(\"/\")\r\nasync def root():\r\n return {\"message\": \"Hello World\"}\r\n\r\nif __name__ == \"__main__\":\r\n uvicorn.run(app, host=\"127.0.0.1\", port=8004) #", "repo_name": "nakul1011/testProject", "sub_path": "API_MMC_Getting_Answers_V0.py", "file_name": "API_MMC_Getting_Answers_V0.py", "file_ext": "py", "file_size_in_byte": 3792, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "fastapi.FastAPI", "line_number": 21, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 30, "usage_type": "argument"}, {"api_name": "langchain.embeddings.OpenAIEmbeddings", "line_number": 39, "usage_type": "call"}, {"api_name": "langchain.vectorstores.Chroma", "line_number": 55, "usage_type": "call"}, {"api_name": "langchain.chains.RetrievalQAWithSourcesChain.from_llm", "line_number": 63, "usage_type": "call"}, {"api_name": "langchain.chains.RetrievalQAWithSourcesChain", "line_number": 63, "usage_type": "name"}, {"api_name": "langchain.chat_models.ChatOpenAI", "line_number": 63, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "name"}, {"api_name": "time.time", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}, {"api_name": "uvicorn.run", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "26408030134", "text": "import dash\nimport dash_cytoscape as cyto\nimport dash_html_components as html\nimport dash_core_components as dcc\nfrom pprint import pprint\nfrom dash.dependencies import Input, Output, State\n\napp = dash.Dash(__name__)\n\n\nnodes = [\n {\n 'data': {'id': short, 'label': label},\n 'position': {'x': 20*lat, 'y': -20*long}\n }\n for short, label, long, lat in (\n ('la', 'Los Angeles', 34.03, -118.25),\n ('nyc', 'New York', 40.71, -74),\n ('to', 'Toronto', 43.65, -79.38),\n ('mtl', 'Montreal', 45.50, -73.57),\n ('van', 'Vancouver', 49.28, -123.12),\n ('chi', 'Chicago', 41.88, -87.63),\n ('bos', 'Boston', 42.36, -71.06),\n ('hou', 'Houston', 29.76, -95.37)\n )\n]\n\nedges = [\n {'data': {'source': source, 'target': target}}\n for source, target in (\n ('van', 'la'),\n ('la', 'chi'),\n ('hou', 'chi'),\n ('to', 'mtl'),\n ('mtl', 'bos'),\n ('nyc', 'bos'),\n ('to', 'hou'),\n ('to', 'nyc'),\n ('la', 'nyc'),\n ('nyc', 'bos')\n )\n]\n\n\ndefault_stylesheet = [\n {\n 'selector': 'node',\n 'style': {\n 'background-color': '#BFD7B5',\n 'label': 'data(label)'\n }\n },\n {\n 'selector': 'edge',\n 'style': {\n 'line-color': '#A3C4BC'\n }\n }\n]\n\n\napp.layout = html.Div([\n html.Div([\n html.Button('Add Node', id='btn-add-node', n_clicks_timestamp=0),\n html.Button('Remove Node', id='btn-remove-node', n_clicks_timestamp=0)\n ]),\n\n cyto.Cytoscape(\n id='cytoscape-elements-callbacks',\n layout={'name': 'circle'},\n stylesheet=default_stylesheet,\n style={'width': '100%', 'height': '450px'},\n elements=edges+nodes\n )\n])\n\n\n@app.callback(Output('cytoscape-elements-callbacks', 'elements'),\n Input('btn-add-node', 'n_clicks_timestamp'),\n Input('btn-remove-node', 'n_clicks_timestamp'),\n State('cytoscape-elements-callbacks', 'elements'))\ndef update_elements(btn_add, btn_remove, elements):\n # If the add button was clicked most recently\n if int(btn_add) > int(btn_remove):\n next_node_idx = len(elements) - len(edges)\n\n # As long as we have not reached the max number of nodes, we add them\n # to the cytoscape elements\n if next_node_idx < len(nodes):\n return edges + nodes[:next_node_idx+1]\n\n # If the remove button was clicked most recently\n elif int(btn_remove) > int(btn_add):\n if len(elements) > len(edges):\n return elements[:-1]\n\n # Neither have been clicked yet (or fallback condition)\n return elements\n\n\nif __name__ == '__main__':\n app.run_server(debug=True)\n", "repo_name": "plotly/dash-docs", "sub_path": "dash_docs/chapters/dash_cytoscape/callbacks/examples/elements_callbacks.py", "file_name": "elements_callbacks.py", "file_ext": "py", "file_size_in_byte": 2729, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 370, "dataset": "github-code", "pt": "60", "api": [{"api_name": "dash.Dash", "line_number": 8, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 62, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 63, "usage_type": "call"}, {"api_name": "dash_html_components.Button", "line_number": 64, "usage_type": "call"}, {"api_name": "dash_html_components.Button", "line_number": 65, "usage_type": "call"}, {"api_name": "dash_cytoscape.Cytoscape", "line_number": 68, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 78, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 79, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 80, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "4607040897", "text": "#! /usr/bin/env python\n\n# helper functions\n# import os\n# import csv\n# import copy # copy.deepcopy()\n\nimport itertools # items()\n\nimport re\n\nfrom pathlib import Path # working with paths\n\nfrom pprint import pprint # giza a look\n\nimport urllib.request # for get file from server\n\n\n\ndef get_text_file_contents_from_asset_server(text_filename):\n file_text = 'FILE ACCESS ERROR: NO FILE or NO DATA IN FILE'\n \n print(\"----- get_text_file_contents_from_asset_server -------------------------------------------------\")\n base_url = 'http://127.0.0.1:8000/'\n url = f\"{base_url}{text_filename}\"\n print(url)\n\n # IN base_url/static/recipe/20190228_163410_monkfish and red pepper skewers.txt\n # url = url.replace(\" \", \"%20\") # WORKS \n # OUT base_url/static/recipe/20190228_163410_monkfish%20and%20red%20pepper%20skewers.txt\n\n # get recipe text from assest server\n url = urllib.parse.quote(url, safe='/:') # WORKS - likely more robust\n print(url)\n\n local_scratch_dir = Path(\"./scratch/\")\n\n # create directory (and subdirectory if NO exist) \n local_scratch_dir.mkdir(parents=True, exist_ok=True)\n \n #pathlib.Path('file_path').mkdir(parents=True, exist_ok=True) # create directory (and subdirectory if NO exist)\n #os.makedirs(local_scratch_dir, exist_ok=True) # create directory (and subdirectory if NO exist)\n \n file_path = local_scratch_dir.joinpath( Path(text_filename).name )\n \n print(\" * * * * * * \")\n print(Path(text_filename).name)\n print(file_path)\n print(\" * * * * * * \")\n \n try:\n ret_val = urllib.request.urlretrieve(url, file_path) \n #pprint(ret_val)\n \n if file_path.is_file():\n print(f\"File exists: {file_path}\")\n f = open(file_path, 'r') # load local file to work with\n else:\n print(f\"File NOT PRESENT: {file_path}\")\n return\n \n file_text = f.read()\n f.close()\n \n except Exception as e:\n msg = f\"File not present: {text_filename}\"\n log_exception(msg, e)\n file_text = f\"FILE-MISSING: {text_filename}\"\n \n finally:\n print(f\"RETRIEVED URL: finally segment\")\n \n return file_text\n\n\ndef log_exception(message, exception):\n print(\"------ caught exception rectrieving url - - - - - - < S\")\n print(f\"NOTE:{message}\\n\")\n print(exception)\n f = open('./scratch/error_log.txt', 'a')\n f.write(f\"\\n\\nNOTE: {message} <\\n{exception}\")\n f.close()\n print(\"------ caught exception rectrieving url - - - - - - < E\")\n return 0\n\ndef get_nutrinfo_vocab(text_data):\n \n #for line in text_data.splitlines():\n # print(line)\n \n vocab = re.findall(r'^-+ for the nutrition information (.*?) \\(', text_data, re.MULTILINE)\n \n #for word in vocab: #.items():\n # print(word)\n \n return vocab\n\n\ndef get_igd_vocab():\n \n nutrinfo_text = get_text_file_contents_from_asset_server('scratch/nutrinfo.txt')\n \n vocab = get_nutrinfo_vocab(nutrinfo_text)\n \n print(vocab) \n # vocab.sort()\n print(\"\\n*\\n*\\n*\\n*\\n*\")\n print(vocab)\n vocab = list( filter(None, vocab) ) # remove blanks\n print(\"\\n+\\n*\\n+\\n*\\n+\")\n print(vocab)\n\n \n print(f\"vocab: {len(vocab)} {vocab[0]}\")\n \n return vocab\n\n \nif __name__ == '__main__':\n # print(\"----- get CSV ------------------------------------S\")\n # fetch_file = 'base_url/static/sql_recipe_data.csv'\n # get_csv_from_server_as_disctionary(fetch_file)\n # print(\"----- get CSV ------------------------------------E\")\n\n file_path = 'scratch/nutrinfo.txt'\n #file_text = '20190109_143622_crabcakes.txt'\n #urllib.request = 'base_url/static/recipe/20190109_143622_crabcakes.txt'\n nutrinfo_text = get_text_file_contents_from_asset_server(file_path)\n\n print( get_nutrinfo_vocab(nutrinfo_text) )\n", "repo_name": "UnacceptableBehaviour/promise_await", "sub_path": "helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 3925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "urllib.request.parse.quote", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 33, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 33, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 44, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 52, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 52, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 52, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 91, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 91, "usage_type": "attribute"}]} +{"seq_id": "17093940036", "text": "import cv2\r\nimport numpy as np\r\nimport random\r\n\r\n# 初始化棋盘\r\nchessboard = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]\r\n\r\n# 初始化分数\r\nscore = 0\r\n\r\n# 创建窗口\r\ncv2.namedWindow('image')\r\n\r\n# 鼠标回调函数\r\ndef on_mouse(event, x, y, flags, param):\r\n global score\r\n if event == cv2.EVENT_LBUTTONDBLCLK:\r\n # 计算点击位置对应的棋盘坐标\r\n x = x // 100\r\n y = y // 100\r\n\r\n # 判断是否击��地鼠\r\n if chessboard[x][y] == 1:\r\n score += 10\r\n print('Hit! Score: ', score)\r\n else:\r\n score -= 2\r\n print('Miss! Score: ', score)\r\n\r\n# 设置鼠标回调函数\r\ncv2.setMouseCallback('image', on_mouse)\r\n\r\nwhile True:\r\n # 每秒更新一次地鼠位置\r\n cv2.waitKey(1000)\r\n\r\n # 随机生成地鼠位置\r\n for i in range(3):\r\n for j in range(3):\r\n chessboard[i][j] = random.choice([0, int(random.random() > 0.5)])\r\n\r\n # 显示棋盘\r\n img = np.zeros((300, 300), dtype=np.uint8)\r\n for i in range(3):\r\n for j in range(3):\r\n if chessboard[i][j] == 1:\r\n cv2.circle(img, (i * 100 + 50, j * 100 + 50), 30, (255), -1)\r\n cv2.imshow('image', img)\r\n\r\n # 判断是否达到100分\r\n if score >= 100:\r\n print('Success!')\r\n break\r\n\r\ncv2.destroyAllWindows()", "repo_name": "ophwsjtu18/ohw23s", "sub_path": "LZY/hw1/打地鼠.py", "file_name": "打地鼠.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "60", "api": [{"api_name": "cv2.namedWindow", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDBLCLK", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.setMouseCallback", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 35, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "random.random", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "5141075742", "text": "from bottle import route, run, request, template, post\nimport numpy as np\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import r2_score\n\ndef calculate_linear_regression(x, y):\n A = np.vstack([x, np.ones(len(x))]).T\n k, b = np.linalg.lstsq(A, y, rcond=None)[0]\n y_pred = k * x + b\n r2 = r2_score(y, y_pred)\n return k.round(), b.round(), r2.round()\n\ndef plot_graph(x, y, k, b):\n fig, ax = plt.subplots(figsize=(3, 3))\n ax.plot(x, y, 'o', label='Original data', markersize=5)\n ax.plot(x, k * x + b, 'r', label='Fitted line')\n ax.legend()\n plt.savefig('static/images/graph5.png', dpi=300, bbox_inches='tight')\n\n@post('/approx1', method='POST')\ndef approx1():\n req = request.forms.get('deg1_btn')\n try:\n x = np.array(request.forms.get('X').split(), dtype=float)\n y = np.array(request.forms.get('Y').split(), dtype=float)\n if not req:\n x = np.array(\"1 3 5 6 7\".split(), dtype=float)\n y = np.array(\"3 6 3 5 2\".split(), dtype=float)\n \n trigg = False\n for i in range(1, len(x)):\n if x[i] != x[0]:\n trigg = True\n else:\n trigg = False\n for i in range(1, len(y)):\n if y[i] != y[0]:\n trigg = True\n else:\n trigg = False\n\n if trigg:\n k, b, r2 = calculate_linear_regression(x, y)\n plot_graph(x, y, k, b)\n return template('approxim_1deg.tpl', title='linear', image_data='static\\images\\graph5.png', year=datetime.now().year, k=k, b=b, r=r2, ex='')\n else:\n return template('approxim_1deg.tpl', title='linear', image_data='static\\images\\graph5.png', year=datetime.now().year, k='err', b='err', r='err', ex='Ошибка входных данных!!')\n except:\n return template('approxim_1deg.tpl', title='linear', image_data='static\\images\\graph_.png', year=datetime.now().year, k='err', b='err', r='err', ex='Ошибка входных данных!!')\n\n", "repo_name": "bybLik04/UP02_group_3", "sub_path": "UP02_group_3/approx_1dg.py", "file_name": "approx_1dg.py", "file_ext": "py", "file_size_in_byte": 2053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.vstack", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 11, "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.savefig", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "bottle.request.forms.get", "line_number": 23, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 23, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "bottle.request.forms.get", "line_number": 25, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 25, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "bottle.request.forms.get", "line_number": 26, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 26, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "bottle.template", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "bottle.template", "line_number": 48, "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": "bottle.template", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}, {"api_name": "bottle.post", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "10687479240", "text": "import argparse\nimport os\nimport random\nfrom datetime import datetime\n\nimport numpy as np\nimport torch\n\nimport common.utils.logging as logging_utils\nimport common.utils.tensor as tensor_utils\nfrom datasets.tensor_completion_dataset import TensorCompletionDataset\n\n\ndef __set_initial_random_seed(random_seed: int):\n if random_seed != -1:\n np.random.seed(random_seed)\n torch.random.manual_seed(random_seed)\n random.seed(random_seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(random_seed)\n\n\ndef create_tensor_completion_dataset(target_tensor_cp_rank, target_fro_norm, mode_dim_size, order):\n target_tensor = tensor_utils.create_tensor_with_cp_rank([mode_dim_size] * order, target_tensor_cp_rank, fro_norm=target_fro_norm)\n train_indices_order = torch.randperm(target_tensor.numel()).tolist()\n dataset = TensorCompletionDataset(target_tensor, target_tensor_cp_rank, train_indices_order)\n return dataset\n\n\ndef create_and_save_dataset(args):\n dataset = create_tensor_completion_dataset(args.target_tensor_cp_rank, args.target_fro_norm, args.mode_dim_size, args.order)\n\n now_utc_str = datetime.utcnow().strftime(\"%Y_%m_%d-%H_%M_%S\")\n if args.custom_file_name:\n file_name = f\"{args.custom_file_name}_{now_utc_str}.pt\"\n else:\n file_name = f\"tensor_rank_{args.target_tensor_cp_rank}_fro_{int(args.target_fro_norm)}\" \\\n f\"_order_{args.order}_dim_{args.mode_dim_size}_{now_utc_str}.pt\"\n\n dataset.save(os.path.join(args.output_dir, file_name))\n\n\nif __name__ == \"__main__\":\n p = argparse.ArgumentParser()\n p.add_argument(\"-random_seed\", type=int, default=-1, help=\"Initial random seed\")\n p.add_argument(\"-output_dir\", type=str, default=\"data/tc\", help=\"Path to the directory to save the target matrix and dataset at.\")\n p.add_argument(\"-custom_file_name\", type=str, default=\"\", help=\"Custom file name prefix for the dataset.\")\n\n p.add_argument(\"-target_tensor_cp_rank\", type=int, default=1,\n help=\"CP rank of the target tensor. Use -1 for no rank constraint (tensor will be generated randomly)\")\n p.add_argument(\"-target_fro_norm\", type=float, default=1.0, help=\"Fro norm of the target tensor.\")\n\n p.add_argument(\"-mode_dim_size\", type=int, default=10, help=\"Number of dimensions per each mode.\")\n p.add_argument(\"-order\", type=int, default=3, help=\"Order of the tensor (number of modes).\")\n\n args = p.parse_args()\n\n logging_utils.init_console_logging()\n __set_initial_random_seed(args.random_seed)\n\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n\n create_and_save_dataset(args)\n", "repo_name": "noamrazin/imp_reg_dl_not_norms", "sub_path": "tc_data_generator.py", "file_name": "tc_data_generator.py", "file_ext": "py", "file_size_in_byte": 2664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.random.seed", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.random.manual_seed", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 18, "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.cuda.manual_seed_all", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 20, "usage_type": "attribute"}, {"api_name": "common.utils.tensor.create_tensor_with_cp_rank", "line_number": 24, "usage_type": "call"}, {"api_name": "common.utils.tensor", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.randperm", "line_number": 25, "usage_type": "call"}, {"api_name": "datasets.tensor_completion_dataset.TensorCompletionDataset", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 44, "usage_type": "call"}, {"api_name": "common.utils.logging.init_console_logging", "line_number": 58, "usage_type": "call"}, {"api_name": "common.utils.logging", "line_number": 58, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "4573526094", "text": "\"\"\"Dois triângulos, duas cores: exemplo de uniforms\"\"\"\nimport OpenGL.GL as GL\n\nfrom core.base import Base\nfrom core.utils import Utils\nfrom core.attribute import Attribute\nfrom core.uniform import Uniform\n\n\nclass Example(Base):\n \"\"\" Render two triangles with different positions and colors \"\"\"\n\n def initialize(self):\n print(\"Initializing program...\")\n # Initialize program #\n vs_code = \"\"\"\n in vec3 position;\n uniform vec3 translation;\n void main()\n {\n vec3 pos = position + translation;\n gl_Position = vec4(pos.x, pos.y, pos.z, 1.0);\n }\n \"\"\"\n fs_code = \"\"\"\n uniform vec3 baseColor;\n out vec4 fragColor;\n void main()\n {\n fragColor = vec4(baseColor.r, baseColor.g, baseColor.b, 1.0);\n }\n \"\"\"\n self.program_ref = Utils.initialize_program(vs_code, fs_code)\n # Set up vertex array object #\n vao_ref = GL.glGenVertexArrays(1)\n GL.glBindVertexArray(vao_ref)\n # Set up vertex attribute #\n position_data = [[ 0.0, 0.2, 0.0],\n [ 0.2, -0.2, 0.0],\n [-0.2, -0.2, 0.0]]\n self.vertex_count = len(position_data)\n position_attribute = Attribute('vec3', position_data)\n position_attribute.associate_variable(self.program_ref, 'position')\n # Set up uniforms #\n self.translation1 = Uniform('vec3', [-0.5, 0.0, 0.0])\n self.translation1.locate_variable(self.program_ref, 'translation')\n self.translation2 = Uniform('vec3', [0.5, 0.0, 0.0])\n self.translation2.locate_variable(self.program_ref, 'translation')\n self.base_color1 = Uniform('vec3', [1.0, 0.0, 0.0])\n self.base_color1.locate_variable(self.program_ref, 'baseColor')\n self.base_color2 = Uniform('vec3', [0.0, 0.0, 1.0])\n self.base_color2.locate_variable(self.program_ref, 'baseColor')\n\n def update(self):\n GL.glUseProgram(self.program_ref)\n # Draw the first triangle\n self.translation1.upload_data()\n self.base_color1.upload_data()\n GL.glDrawArrays(GL.GL_TRIANGLES, 0, self.vertex_count)\n # Draw the second triangle\n self.translation2.upload_data()\n self.base_color2.upload_data()\n GL.glDrawArrays(GL.GL_TRIANGLES, 0, self.vertex_count)\n\n\n# Instantiate this class and run the program\nExample().run()\n", "repo_name": "mouna21/CGr-P", "sub_path": "src4/onebuffer_tworenderings.py", "file_name": "onebuffer_tworenderings.py", "file_ext": "py", "file_size_in_byte": 2487, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "core.base.Base", "line_number": 10, "usage_type": "name"}, {"api_name": "core.utils.Utils.initialize_program", "line_number": 33, "usage_type": "call"}, {"api_name": "core.utils.Utils", "line_number": 33, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGenVertexArrays", "line_number": 35, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 35, "usage_type": "name"}, {"api_name": "OpenGL.GL.glBindVertexArray", "line_number": 36, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 36, "usage_type": "name"}, {"api_name": "core.attribute.Attribute", "line_number": 42, "usage_type": "call"}, {"api_name": "core.uniform.Uniform", "line_number": 45, "usage_type": "call"}, {"api_name": "core.uniform.Uniform", "line_number": 47, "usage_type": "call"}, {"api_name": "core.uniform.Uniform", "line_number": 49, "usage_type": "call"}, {"api_name": "core.uniform.Uniform", "line_number": 51, "usage_type": "call"}, {"api_name": "OpenGL.GL.glUseProgram", "line_number": 55, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 55, "usage_type": "name"}, {"api_name": "OpenGL.GL.glDrawArrays", "line_number": 59, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 59, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_TRIANGLES", "line_number": 59, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glDrawArrays", "line_number": 63, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 63, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_TRIANGLES", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "21494075049", "text": "#섬의 갯수\r\n#정사각형으로 이루어져 있는 섬과 바다 지도 => 인접행렬 섬의 개수를 세는 프로그램을 작성하시오.\r\n#너비 w와 높이 h\r\n#왼쪽 체크 시 = 현재 X - 1, 현재 Y + 0\r\n#오른쪽 체크 시 = 현재 X + 1, 현재 Y + 0\r\n#위쪽 체크 시 = 현재 X + 0, 현재 Y - 1\r\n#아랫쪽 체크 시 = 현재 X + 0, 현재 Y + 1\r\n# dx, dy에 상하좌우 + 대각선을 추가하여 자신 기준으로 총 8번을 탐색\r\n\r\nimport sys\r\nfrom collections import deque\r\nsys.setrecursionlimit(10000)\r\n\r\n\r\ndx = [-1, 1, 0, 0, -1, 1, -1, 1] #왼쪽은 -1 오른쪽은 +1 위,아래는 0, 각 대각선 -1,1\r\ndy = [0, 0, -1, 1, -1, 1, 1, -1]\r\ndef bfs(x, y):\r\n queue=deque()#탐색하려는 좌표를 담기\r\n queue.append((x,y))\r\n graph[x][y]=0\r\n\r\n while queue:\r\n now=queue.popleft()\r\n for i in range(8):\r\n nx = now[0] + dx[i]\r\n ny = now[1] + dy[i]\r\n if 0 <= nx < h and 0 <= ny < w and graph[nx][ny] == 1:\r\n graph[nx][ny] = 0\r\n queue.append((nx, ny))\r\n\r\nwhile(True):\r\n w,h= map(int,input().split())\r\n if w==0 and h==0:\r\n break\r\n graph = []\r\n cnt=0\r\n\r\n for _ in range(h):\r\n graph.append(list(map(int, input().split())))\r\n for i in range(h): #인접행렬 그래프의 인덱스만큼\r\n for j in range(w):\r\n if graph[i][j] == 1:\r\n cnt += 1\r\n bfs(i, j)\r\n print(cnt)", "repo_name": "ssamea/baekjoon", "sub_path": "4963.py", "file_name": "4963.py", "file_ext": "py", "file_size_in_byte": 1473, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "38890294175", "text": "import sys\nimport numpy as np\nimport json\nimport multiprocessing\nimport time\nfrom scipy import linalg\nimport matplotlib.pyplot as plt\n\nimport torch # テンソル計算など\nimport torch.nn as nn # ネットワーク構築用\nimport torch.optim as optim # 最適化関数\n\n\nclass TSSC(nn.Module):\n def __init__(self, M, N, W, md, ml, K, mod, Niter, AorF, v_com):\n super(TSSC, self).__init__()\n #SSC\n if v_com == 1:\n non0_c_ = -20*torch.ones([2*md, M*md])\n else:\n non0_c_ = -20*torch.ones([1*md, M*md])\n non0_c_[0, 0::2] = -2.2\n if md >= 2:\n for idx_md in range(1, md):\n non0_c_[idx_md, 1::2] = -2.2\n self.non0_c = nn.Parameter(non0_c_)\n\n self.M = M\n self.N = N\n self.W = W\n self.md = md\n self.ml = ml\n self.K = K\n\n #GaBP\n self.MM = 2*md*M\n if AorF == 1:\n self.NN = 2*N\n elif AorF == 0:\n self.NN = 2*N*M\n self.mod = mod\n\n v=4\n temp = -0.5 * np.ones(self.NN)\n for idx in range(0, int(self.NN/v)):\n temp[idx*v]=0.5\n\n temp_ = np.zeros((self.NN,Niter))\n for idx in range(0, Niter):\n temp_[:,idx] = np.roll(temp,idx)\n self.eta = nn.Parameter(torch.from_numpy(temp_))\n self.mu = nn.Parameter(torch.from_numpy(2*(np.arange(0,Niter,1)+1)/Niter))\n# self.eta = torch.from_numpy(temp_)\n# self.mu = torch.from_numpy(2*(np.arange(0,Niter,1)+1)/Niter)\n\n #SSC\n def sp_tanh(self, ele):\n return 10*torch.tanh(0.1*ele)\n \n def v_sigmoid(self,sp_ele):\n# return 1 + 1/(1 + torch.exp(sp_ele))\n return 10/(1 + torch.exp(-1.0*sp_ele))\n\n def SSC_code(self, x_com, non0_c, x, codebook, v_com, N0_, AorF):\n if v_com == 1:\n for idx_x in range(self.M):\n if idx_x == 0:\n c_mtx_re = self.v_sigmoid(non0_c[:self.md, self.md*idx_x:self.md*(idx_x+1)])\n c_mtx_im = self.v_sigmoid(non0_c[self.md:, self.md*idx_x:self.md*(idx_x+1)])\n\n if AorF == 1: # fading\n fad_re = torch.randn(self.M, self.N)/torch.sqrt(2*torch.ones(self.M, self.N))#*torch.sqrt([1/2])\n fad_im = torch.randn(self.M, self.N)/torch.sqrt(2*torch.ones(self.M, self.N))#*torch.sqrt([1/2])\n fading_re = torch.diag(fad_re[0, :])\n fading_im = torch.diag(fad_im[0, :])\n else:\n c_mtx_re = torch.block_diag(c_mtx_re, self.v_sigmoid(non0_c[:self.md, self.md*idx_x:self.md*(idx_x+1)]))\n c_mtx_im = torch.block_diag(c_mtx_im, self.v_sigmoid(non0_c[self.md:, self.md*idx_x:self.md*(idx_x+1)]))\n\n if AorF == 1: # fading\n fad_i_re = torch.diag(fad_re[idx_x, :])\n fad_i_im = torch.diag(fad_im[idx_x, :])\n fading_re = torch.cat((fading_re, fad_i_re), 1)\n fading_im = torch.cat((fading_im, fad_i_im), 1)\n\n c_mtx_row1 = torch.cat((c_mtx_re, -1*c_mtx_im), 1)\n c_mtx_row2 = torch.cat((c_mtx_im, c_mtx_re), 1)\n c_mtx = torch.cat((c_mtx_row1, c_mtx_row2), 0)\n codebook_ac = torch.mm(codebook, c_mtx)\n\n if AorF == 1: # fading\n fading_row1 = torch.cat((fading_re, -1*fading_im), 1)\n fading_row2 = torch.cat((fading_im, fading_re), 1)\n fading = torch.cat((fading_row1, fading_row2), 0) \n else:\n for idx_x in range(self.M):\n if idx_x == 0:\n c_mtx_re = self.v_sigmoid(non0_c[:, self.md*idx_x:self.md*(idx_x+1)])\n\n if AorF == 1: # fading\n fad_re = torch.randn(self.M, self.N)/torch.sqrt(2*torch.ones(self.M, self.N))#*torch.sqrt([1/2])\n fad_im = torch.randn(self.M, self.N)/torch.sqrt(2*torch.ones(self.M, self.N))#*torch.sqrt([1/2])\n fading_re = torch.diag(fad_re[0, :])\n fading_im = torch.diag(fad_im[0, :])\n\n else:\n c_mtx_re = torch.block_diag(c_mtx_re, self.v_sigmoid(non0_c[:, self.md*idx_x:self.md*(idx_x+1)]))\n\n if AorF == 1: # fading\n fad_i_re = torch.diag(fad_re[idx_x, :])\n fad_i_im = torch.diag(fad_im[idx_x, :])\n fading_re = torch.cat((fading_re, fad_i_re), 1)\n fading_im = torch.cat((fading_im, fad_i_im), 1)\n \n c_mtx = torch.block_diag(c_mtx_re, c_mtx_re)\n codebook_ac = torch.mm(codebook, c_mtx)\n\n if AorF == 1: # fading\n fading_row1 = torch.cat((fading_re, -1*fading_im), 1)\n fading_row2 = torch.cat((fading_im, fading_re), 1)\n fading = torch.cat((fading_row1, fading_row2), 0)\n \n x_code_ = torch.mm(codebook_ac, x)\n norm = torch.sqrt((x_code_.norm(dim=1)**2).sum()/self.K)\n x_code = x_code_/norm\n codebook = codebook_ac/norm\n\n x_ene = torch.sum(torch.sum(torch.abs(x_code)**2, 0)) / (self.K * self.M * self.md *self.ml)\n\n N0 = N0_ * x_ene.float()\n if AorF == 1:\n z_re = torch.randn(self.N, self.K)*torch.sqrt(N0/2)\n z_im = torch.randn(self.N, self.K)*torch.sqrt(N0/2)\n elif AorF == 0:\n z_re = torch.randn(self.N*self.M, self.K)*torch.sqrt(N0/2)\n z_im = torch.randn(self.N*self.M, self.K)*torch.sqrt(N0/2)\n\n # equivalent channel\n H = torch.mm(fading.float(), codebook.float())\n\n z = torch.cat((z_re, z_im), 0)\n\n y = torch.mm(H, x) + z\n\n return x.T.float(), y.T.float(), H.float(), N0\n\n def RX(self, x_code, codebook, x_ene, x, N0_, AorF):\n N0 = N0_ * x_ene\n\n if AorF == 1: # fading\n fad_re = torch.randn(self.M, self.N)/torch.sqrt(2*torch.ones(self.M, self.N))#*torch.sqrt([1/2])\n fad_im = torch.randn(self.M, self.N)/torch.sqrt(2*torch.ones(self.M, self.N))#*torch.sqrt([1/2])\n fading_re = torch.diag(fad_re[0, :])\n fading_im = torch.diag(fad_im[0, :])\n for fad_i in range(1, self.M):\n fad_i_re = torch.diag(fad_re[fad_i, :])\n fad_i_im = torch.diag(fad_im[fad_i, :])\n fading_re = torch.cat((fading_re, fad_i_re), 1)\n fading_im = torch.cat((fading_im, fad_i_im), 1)\n fading_row1 = torch.cat((fading_re, -1*fading_im), 1)\n fading_row2 = torch.cat((fading_im, fading_re), 1)\n fading = torch.cat((fading_row1, fading_row2), 0) \n\n z_re = torch.randn(self.N, self.K)*torch.sqrt(N0/2)\n z_im = torch.randn(self.N, self.K)*torch.sqrt(N0/2)\n\n elif AorF == 0: # AWGN\n fading = torch.eye(2*self.N*self.M)\n\n z_re = torch.randn(self.N*self.M, self.K)*torch.sqrt(N0/2)\n z_im = torch.randn(self.N*self.M, self.K)*torch.sqrt(N0/2)\n \n # equivalent channel\n H = torch.mm(fading.float(), codebook)\n# N0 = N0_ * x_ene\n# z_re = torch.randn(self.N, self.K)*torch.sqrt(N0/2)\n# z_im = torch.randn(self.N, self.K)*torch.sqrt(N0/2)\n z = torch.cat((z_re, z_im), 0)\n\n y = torch.mm(H, x) + z\n# y = torch.mm(fading.float(), x_code) + z\n\n return x.T.float(), y.T.float(), H.float(), N0 #, zz.float()\n\n #GaBP\n def sigmoid(self,eta):\n return 1/(1 + torch.exp(-4.0*eta))\n\n def SC(self, y, H, SR_mat, ER_mat): # Soft Canceller\n # SC\n Reconstruct_matrix = H.T * SR_mat #Reconstruct_matrix = c.mm(H.T, SR_mat)\n y_tilde = y - torch.sum(Reconstruct_matrix, axis=0) + Reconstruct_matrix\n delta = ER_mat - SR_mat ** 2\n return y_tilde, delta\n\n def TBG(self, H, HH, N0, y_tilde, delta, uu, vv, eta, x, ER_mat): # BG\n element = HH * delta\n\n psi = (torch.sum(element, axis=0).reshape(1, -1) - element) + N0 / 2.0\n if self.ml == 1:\n u = 2 * torch.sqrt(ER_mat) * H.T * y_tilde / psi\n v = 2 * torch.sqrt(ER_mat) * HH / psi\n else:\n u = H.T * y_tilde / psi\n v = HH / psi\n\n uu = self.sigmoid(eta) * u + (1 - self.sigmoid(eta)) * uu\n\n s = (torch.sum(uu, axis=1) - uu.transpose(0, 1)).transpose(0, 1)\n\n# v = 2 * torch.sqrt(ER_mat) * HH / psi\n vv = self.sigmoid(eta) * v + (1 - self.sigmoid(eta)) * vv\n\n omega = (torch.sum(vv, axis=1) - vv.transpose(0, 1)).transpose(0, 1)\n\n if self.ml == 1:\n gamma = s / (omega * torch.sqrt(ER_mat))\n else:\n gamma = s / omega\n gamma_post = torch.sum(u, axis=1) / torch.sum(v, axis=1)\n return gamma, gamma_post, uu, vv #, u_\n\n def BG(self, H, HH, N0, y_tilde, delta, uu, vv, eta, x, ER_mat, ns_num): # BG\n element = HH * delta\n\n psi = (torch.sum(element, axis=0).reshape(1, -1) - element) + N0 / 2.0\n if self.ml == 1:\n u = 2 * torch.sqrt(ER_mat) * H.T * y_tilde / psi\n v = 2 * torch.sqrt(ER_mat) * HH / psi\n else:\n u = H.T * y_tilde / psi\n v = HH / psi\n\n# uu = self.sigmoid(eta) * u + (1 - self.sigmoid(eta)) * uu\n uu[:, ns_num::4] = u[:, ns_num::4]\n\n s = (torch.sum(uu, axis=1) - uu.transpose(0, 1)).transpose(0, 1)\n# s = eta_n * s_ + (1 - eta_n) * s\n\n# v = 2 * torch.sqrt(ER_mat) * HH / psi\n# vv = self.sigmoid(eta) * v + (1 - self.sigmoid(eta)) * vv\n vv[:, ns_num::4] = v[:, ns_num::4]\n\n omega = (torch.sum(vv, axis=1) - vv.transpose(0, 1)).transpose(0, 1)\n# omega = eta_n * omega_ + (1 - eta_n) * omega\n\n if self.ml == 1:\n gamma = s / (omega * torch.sqrt(ER_mat))\n else:\n gamma = s / (omega + 1e-30)\n gamma_post = torch.sum(u, axis=1) / torch.sum(v, axis=1)\n return gamma, gamma_post, uu, vv #, u_\n\n def RG(self, gamma, mu, ER_mat): # RG\n if self.ml == 1:\n ER_mat_1 = torch.sqrt(ER_mat)\n# ER_mat_1[((self.MM+2-1)//2):, :] = 0 # ((self.M+2-1)//2)(self.M//2)\n ER_mat_1[(self.M*self.md):, :] = 0\n SR_mat = torch.tanh(mu * gamma) * ER_mat_1\n elif self.ml == 2:\n SR_mat = torch.tanh(mu * gamma) * self.mod.norm\n ER_mat = torch.ones((self.MM, self.NN)).float() / 2.0\n else:\n SR_mat = torch.zeros((self.MM, self.NN))\n ER_mat = torch.zeros((self.MM, self.NN))\n for gamma_ in self.mod.lay:\n temp = mu * (gamma - gamma_) / self.mod.norm\n SR_mat += torch.tanh(temp)\n ER_mat += gamma_ * torch.tanh(temp)\n SR_mat *= self.mod.norm\n ER_mat *= 2 * self.mod.norm\n ER_mat += self.mod.Esmax / 2\n return SR_mat, ER_mat\n\n def SD(self, gamma_post, mu, ER_mat):\n if self.ml == 1:\n# gamma_post[((self.MM+2-1)//2):] = 0\n gamma_post[(self.M*self.md):] = 0\n# SD_mat = gamma_post\n SD_mat = torch.tanh(mu * gamma_post) # * torch.t(ER_mat)\n elif self.ml == 2:\n SD_mat = torch.tanh(mu * gamma_post) * self.mod.norm\n else:\n SD_mat = torch.zeros(self.MM)\n for gamma_ in self.mod.lay:\n temp = mu*(gamma_post - gamma_) / self.mod.norm\n SD_mat += torch.tanh(temp)\n SD_mat *= self.mod.norm\n return SD_mat\n\n def forward(self, x_com, x, N0_, mod, Niter, AorF, TBPorBP, codebook, v_com): #x, y, H, N0, sp_mtx = model(dictionary_mtx, x_0, N0_)\n\n x_1, y, H, N0 = TSSC.SSC_code(self, x_com, self.non0_c, x, codebook, v_com, N0_, AorF)\n\n K, NN = y.shape\n HH = (H * H).T\n\n x_ = torch.zeros((K, self.MM))\n x_hat = torch.zeros((K, self.MM))\n# llr_ = np.zeros((K, self.MM*mod.ml))\n# x = x.float()\n# y = y.float()\n# H = H.float()\n\n lam = torch.zeros((K, self.MM))\n\n for idx_sym in range(0, K):\n SR_mat = torch.zeros((self.MM, self.NN)).float()\n# SR_mat = x.repeat(1, self.NN)\n if self.ml == 1:\n ER_mat = torch.ones((self.MM, self.NN)).float()\n else:\n ER_mat = torch.ones((self.MM, self.NN)).float() / 2.0\n\n # # Perfect priori\n # SR_mat = np.tile(x[idx_sym, :], (N, 1)).T\n # ER_mat = SR_mat ** 2\n uu = torch.zeros((self.MM, self.NN))\n vv = torch.zeros((self.MM, self.NN))\n if TBPorBP == 1:\n for idx_iter in range(0, Niter):\n # SC\n y_tilde, delta = TSSC.SC(self, y[idx_sym, :], H, SR_mat, ER_mat)\n # BG\n gamma, gamma_post, uu, vv = TSSC.TBG(self, H, HH, N0, y_tilde, delta, uu, vv, self.eta[:, idx_iter], x[:, idx_sym], ER_mat)\n # RG\n SR_mat, ER_mat = TSSC.RG(self, gamma, self.mu[idx_iter], ER_mat)\n else:\n for idx_iter in range(0, Niter):\n for ns_num in range(0, 4):\n # SC\n y_tilde, delta = TSSC.SC(self, y[idx_sym, :], H, SR_mat, ER_mat)\n # BG\n gamma, gamma_post, uu, vv = TSSC.BG(self, H, HH, N0, y_tilde, delta, uu, vv, self.eta[:, idx_iter], x[:, idx_sym], ER_mat, ns_num)\n # RG\n SR_mat, ER_mat = TSSC.RG(self, gamma, self.mu[idx_iter], ER_mat)\n # Output\n x_[idx_sym, :] = TSSC.SD(self, gamma_post, self.mu[idx_iter], ER_mat)\n x_hat[idx_sym, :] = gamma_post\n\n lam[idx_sym, :] = gamma_post\n\n# x_ = TSSC.GaBP_main(self, x, y, H, N0, mod, Niter)\n return x_, lam\n\nclass Customloss(nn.Module):\n def __init__(self):\n super(Customloss, self).__init__()\n self.k1 = 36/(8*np.sqrt(3)-9)\n self.k2 = 24/(16*np.sqrt(3)-27)\n\n def forward(self, x, x_hat, x_tch, loss_w):\n mse = torch.mean((x_tch - x) ** 2)\n \n x_bar = (x_hat - torch.mean(x_hat)) / torch.std(x_hat, unbiased=False)\n x1 = torch.mean(x_bar * torch.exp(-(x_bar**2)/2))\n x2 = torch.mean(torch.exp(-(x_bar**2)/2)) - 1/np.sqrt(2)\n\n neg = self.k1 * x1 ** 2 + self.k2 * x2 ** 2\n\n loss = loss_w * neg + (1 - loss_w) * mse\n\n return loss\n\n\nclass MOD():\n def __init__(self, ml, md):\n self.md = md\n self.ml = ml\n self.nsym = 2 ** ml\n\n def demodulation(self, y):\n b_tmp = np.empty((y.shape[1]*self.md, y.shape[0]), int)\n for idx_k in range(0, y.shape[0]):\n for idx_m in range(0, y.shape[1]):\n b_tmp[idx_m, idx_k] = np.signbit(y[idx_k, idx_m])\n return b_tmp\n\n\ndef gen_minibatch(M, md, K, mod, ml):\n if ml == 1:\n b = torch.randint(0, 2, (M * md, K))\n x_com = 2.0 * b - 1.0\n x = torch.cat((x_com, torch.zeros([M * md, K])), 0)\n elif ml == 2:\n b = torch.randint(0, 2, (M * md * ml, K))\n x_com = 2.0 * b - 1.0\n x = (2.0 * b - 1.0) / np.sqrt(2)\n else:\n b = np.random.randint(0, 2, (M * md * mod.ml, K))\n a = np.dot(np.kron(mod.lv, np.eye(M * md, dtype=int)), b)\n # TX symbol\n x_com = np.array(mod.val[mod.amap[a]])\n x = torch.from_numpy(np.concatenate([x.real, x.imag])).transpose(0, 1)\n return b.T, x_com, x\n\ndef Dictionary_hada(M, N, W, md):\n dic_mtx = np.zeros([N, M*md])\n hada_ = (1/np.sqrt(md))*linalg.hadamard(W*md)\n hada = hada_[1:, :]\n\n dic_idx_ = range(W*md-1)\n dic_idx_1_ = range(md, W*md)\n for idx_sec in range(M):\n dic_idx = np.random.permutation(dic_idx_)\n dic_idx_1 = np.random.permutation(dic_idx_1_)\n for idx_sec_2 in range(N):\n if idx_sec_2 == 0:\n dic_mtx_ = hada[dic_idx[idx_sec_2], dic_idx_1[idx_sec]-md:dic_idx_1[idx_sec]]\n else:\n dic_mtx_i = hada[dic_idx[idx_sec_2], dic_idx_1[idx_sec]-md:dic_idx_1[idx_sec]]\n dic_mtx_ = np.append(dic_mtx_, dic_mtx_i)\n dic_mtx[:, md*idx_sec:md*(idx_sec+1)] = dic_mtx_.reshape(N, md)\n return dic_mtx\n# return torch.from_numpy(dic_mtx)\n\n \ndef Dictionary_rand(M, N, W, md):\n dic_mtx_re = np.zeros([N*M, W*md])\n dic_mtx_im = np.zeros([N*M, W*md])\n for idx_sec in range(M):\n dic_mtx_re[idx_sec*N:(idx_sec+1)*N, :] = (np.random.randn((N, W*md)) + 1j * np.random.randn((N, W*md)))/np.sqrt(2*2)\n return linalg.block_diag(dic_mtx_re, dic_mtx_im)\n\n\ndef codebook_mtx_gen(M, N, md, dic_ri, codebook_):\n if dic_ri != 0:\n codebook_re__ = codebook_[:N, :]\n codebook_im__ = codebook_[N:, :]\n for idx_x in range(M):\n if idx_x == 0:\n if dic_ri == 0:\n codebook_re_ = codebook_[:, md*idx_x:md*(idx_x+1)]\n else:\n codebook_re_ = codebook_re__[:, md*idx_x:md*(idx_x+1)]\n codebook_im_ = codebook_im__[:, md*idx_x:md*(idx_x+1)]\n else:\n if dic_ri == 0:\n codebook_re_ = torch.block_diag(codebook_re_, codebook_[:, md*idx_x:md*(idx_x+1)])\n else:\n codebook_re_ = torch.block_diag(codebook_re_, codebook_re__[:, md*idx_x:md*(idx_x+1)])\n codebook_im_ = torch.block_diag(codebook_im_, codebook_im__[:, md*idx_x:md*(idx_x+1)])\n if dic_ri == 0:\n codebook_re = torch.block_diag(codebook_re_, codebook_re_)\n else:\n codebook_re_row1 = torch.cat((codebook_re_, -1*codebook_im_), 1)\n codebook_re_row2 = torch.cat((codebook_im_, codebook_re_), 1)\n codebook_re = torch.cat((codebook_re_row1, codebook_re_row2), 0)\n return codebook_re\n\n\ndef position_non0(B_, k, num_hot):\n if num_hot == 1:\n pos = []\n while len(pos) < k:\n n = np.random.randint(0, B_)\n if not n in pos:\n pos.append(n)\n else:\n pos = np.random.randint(0, B_, (num_hot, 1))\n while len(pos[0, :]) < k:\n n = np.random.randint(0, B_, (num_hot, 1))\n if not n in pos:\n pos = np.concatenate([pos, n], 1)\n return pos\n\ndef spread_mtx_gen(B_, M_, md, pos, num_hot):\n spread_mtx_re_ = np.zeros([B_*md, M_*md])\n for spm_row in range(M_*md):\n if num_hot == 1:\n spread_mtx_re_[B_*(spm_row % 2) + pos[spm_row], spm_row] = 1\n else:\n for spm_col in range(num_hot):\n spread_mtx_re_[B_*(spm_row % 2) + pos[spm_col, spm_row], spm_row] = 1\n return spread_mtx_re_\n\ndef train(params):\n rng = np.random.RandomState(params[0])\n torch.manual_seed(params[0])\n\n# Niter = 4 # GaBPの反復回数\n adam_lr = 0.005 # Adamの学習率\n\n loss_w = 0.6 # loss weight\n\n # train_or_test tx rx dic_size mbs md ml loop num_core En_st delta En_en\n method = params[1][1]\n EsN0 = range(int(params[1][10]),int(params[1][12])+int(params[1][11]),int(params[1][11]))\n M_ = int(params[1][2])\n N_ = int(params[1][3])\n B_ = int(params[1][4])\n K = int(params[1][5])\n md = int(params[1][6])\n ml = int(params[1][7])\n wloop = int(params[1][8])\n nloop = int(2*(10**wloop))\n AorF = int(params[1][13])\n TBPorBP = int(params[1][14]) # 1:TGaBP or 0:GaBP\n dic_i_h = 1 # 0:iid 1:hada\n num_hot = 1\n v_com = 0 # 0:real 1:complex\n dic_ri = 1 # dic 0:real only 1:complex\n\n if TBPorBP == 1:\n Niter = 32 # 反復回数\n else:\n Niter = 8\n\n mod = MOD(ml, md)\n model = TSSC(M_, N_, B_, md, ml, K, mod, Niter, AorF, v_com) # model = TGaBP(M_, N_, mod, Niter)\n opt = optim.Adam(model.parameters(), lr=adam_lr)\n # MSE\n loss_func = nn.MSELoss()\n # neg + MSE\n# loss_func = Customloss()\n\n N0_ = 10.0 ** (-EsN0[params[0]] / 10.0)\n # dictionary matrix\n if dic_ri == 0:\n if dic_i_h == 1:\n dic_mtx_1 = Dictionary_hada(M_, N_, B_, md)\n else:\n dic_mtx_1 = Dictionary_rand(M_, N_, B_, md)\n codebook_ = torch.from_numpy(dic_mtx_1).float()\n else:\n for idx_d in range(2):\n if dic_i_h == 1:\n dic_mtx_2_ = Dictionary_hada(M_, N_, B_, md)\n else:\n dic_mtx_2_ = Dictionary_rand(M_, N_, B_, md)\n if idx_d == 0:\n dic_mtx_1 = dic_mtx_2_\n else:\n dic_mtx_1 = np.concatenate([dic_mtx_1, dic_mtx_2_], 0)\n codebook_ = torch.from_numpy(dic_mtx_1).float()\n\n fn_dic = 'DATA/dic_'\n fn_dic += params[1][2] + '_'\n fn_dic += params[1][3] + '_'\n fn_dic += params[1][4] + '_'\n fn_dic += params[1][6] + '_'\n fn_dic += str(EsN0[params[0]])\n np.save(fn_dic, dic_mtx_1)\n fn_dic_csv = fn_dic + '.csv'\n np.savetxt(fn_dic_csv, dic_mtx_1, delimiter=',')\n\n codebook = codebook_mtx_gen(M_, N_, md, dic_ri, codebook_)\n\n for idx_loop in range(0, nloop):\n b, x_com, x = gen_minibatch(M_, md, K, mod, ml)\n opt.zero_grad()\n x_, lam = model(x_com, x, N0_, mod, Niter, AorF, TBPorBP, codebook, v_com)\n\n # MSE\n loss = loss_func(x_, x.T)\n loss.backward() # 誤差逆伝播法(後ろ向き計算の実行)\n# opt.step() # 学習可能パラメータの更新\n # print(gen, loss.item())\n if ((idx_loop % 100) == 0):\n print(idx_loop, EsN0[params[0]], loss.item())\n \n SIM_dict_loss = {'EsN0':EsN0[params[0]], 'loop':0, 'loss':1.0}\n SIM_dict_loss['loop'] = idx_loop\n SIM_dict_loss['loss'] = loss.item()\n fn_loss = 'DATA/TSSC_sp_loss_'\n if AorF == 1:\n fn_loss += 'fade_'\n else:\n fn_loss += 'AWGN_' \n if TBPorBP == 1:\n fn_loss += 'TGaBP_'\n else:\n fn_loss += 'GaBP_'\n fn_loss += str(nloop) + '.json'\n f_out_loss = open(fn_loss, 'a')\n json.dump(SIM_dict_loss, f_out_loss)\n f_out_loss.write(\"\\n\")\n f_out_loss.close()\n# if ((idx_loop % 200) == 0):\n# fn_TSSC = 'DATA/TSSC_sp_mtx'\n# fn_TSSC += str(EsN0[params[0]])\n# fn_TSSC += str(idx_loop) + '.csv'\n# np.savetxt(fn_TSSC, sp_mtx, delimiter=',')\n del loss\n torch.cuda.empty_cache()\n opt.step() # 学習可能パラメータの更新\n\n model_path = 'SSC_model/TSSC_sp_'\n model_path += params[1][2] + '_' # M\n model_path += params[1][3] + '_' # N\n model_path += params[1][4] + '_' # B\n# model_path += params[1][5] + '_' # K\n model_path += params[1][6] + '_' # md\n# model_path += params[1][8] + '_' # wloop\n model_path += params[1][13] + '_' # 1:fading 0:AWGN\n model_path += params[1][14] + '_' # 1:TGaBP 0:GaBP\n model_path += str(EsN0[params[0]]) + '.pth'\n torch.save(model.to('cpu').state_dict(), model_path)\n\n\n fn_tssc = 'DATA/c_mtx_'\n fn_tssc += params[1][2] + '_'\n fn_tssc += params[1][2] + '_'\n fn_tssc += params[1][3] + '_'\n fn_tssc += params[1][4] + '_'\n fn_tssc += params[1][6] + '_'\n fn_tssc += str(EsN0[params[0]]) + '.csv'\n np.savetxt(fn_tssc, model.v_sigmoid(model.non0_c).detach().numpy(), delimiter=',')\n c = plt.pcolor(model.v_sigmoid(model.non0_c).detach().numpy(), cmap='RdBu')\n\n plt.xlabel('TX')\n plt.ylabel(r'$section\\,size$')\n plt.colorbar(c)\n\n plt.savefig('FIG/TSSC_c_mtx_' + str(EsN0[params[0]]) + '_' + str(params[1][2]) + '_' + str(params[1][3]) + '_' + str(params[1][4]) + '_' + str(params[1][6]) + '_' + str(params[1][13]) + '.eps')\n\n\ndef main_task(params):\n rng = np.random.RandomState(params[0])\n torch.manual_seed(params[0])\n \n method = params[1][1]\n EsN0 = range(int(params[1][10]),int(params[1][12])+int(params[1][11]),int(params[1][11]))\n M_ = int(params[1][2])\n N_ = int(params[1][3])\n B_ = int(params[1][4])\n K = int(params[1][5])\n md = int(params[1][6])\n ml = int(params[1][7])\n wloop = int(params[1][8])\n nproc = int(params[1][9])\n nloop = int(np.ceil((5*(10**wloop))/nproc))\n AorF = int(params[1][13])\n TBPorBP = int(params[1][14]) # 1:TGaBP or 0:GaBP\n dic_i_h = 1 # 0:iid 1:hada\n noe = np.zeros((2,len(EsN0)),dtype = int)\n num_hot = 1\n v_com = 0 # 0:real 1:complex\n dic_ri = 1 # dic 0:real only 1:complex\n\n if TBPorBP == 1:\n Niter = 32 # 反復回数\n else:\n Niter = 8\n\n # load ssc codebook\n fn_dic = 'DATA/dic_'\n fn_dic += params[1][2] + '_'\n fn_dic += params[1][3] + '_'\n fn_dic += params[1][4] + '_'\n fn_dic += params[1][6] + '_'\n fn_dic += str(16) + '.npy'\n codebook_av = np.load(file=fn_dic)\n codebook_ = torch.from_numpy(codebook_av).float()\n codebook = codebook_mtx_gen(M_, N_, md, dic_ri, codebook_)\n\n\n mod = MOD(ml, md)\n model = TSSC(M_, N_, B_, md, ml, K, mod, Niter, AorF, v_com) # model = TGaBP(M_, N_, mod, Niter)\n\n model_path = 'SSC_model/TSSC_sp_'\n model_path += params[1][2] + '_'\n model_path += params[1][3] + '_'\n model_path += params[1][4] + '_'\n# model_path += params[1][5] + '_'\n model_path += params[1][6] + '_'\n# model_path += params[1][8] + '_'\n model_path += params[1][13] + '_' # 1:fading 0:AWGN\n model_path += params[1][14] + '_' # 1:TGaBP 0:GaBP\n model_path += str(16) + '.pth'\n model.load_state_dict(torch.load(model_path))\n\n\n with torch.no_grad():\n for idx_En in range (0, len(EsN0)):\n N0_ = 10.0 ** (-EsN0[idx_En] / 10.0)\n for idx_loop in range (0, nloop):\n # Fading generation\n b, x_com, x = gen_minibatch(M_, md, K, mod, ml)\n# if dic_i_h == 1:\n# dic_hada = dic_hada = Dictionary_mtx(M_, N_, B_, md)\n# else:\n# dic_hada = 1\n x_, lam = model(x_com, x, N0_, mod, Niter, AorF, TBPorBP, codebook, v_com)\n if ml == 1:\n b_1 = x_[:, :M_*md] > 0\n elif ml == 2:\n b_1 = x_ > 0\n b_r = b_1.to('cpu').detach().numpy().copy()\n b_t = b.to('cpu').detach().numpy().copy()\n tmp_ = np.abs(b_t - b_r)\n tmp = tmp_.sum()\n\n noe[0,idx_En] += tmp.sum()\n noe[1,idx_En] += (md*M_*ml*K)\n if noe[0,idx_En] > (md*ml*M_*K*nloop)*0.01:\n break\n print(params[0], EsN0[idx_En], noe[0,idx_En]/noe[1,idx_En])\n return noe\n\n\ndef resut2f(argvs,BER):\n EsN0 = range(int(argvs[10]),int(argvs[12])+int(argvs[11]),int(argvs[11]))\n for idx_En in range (0, len(EsN0)):\n SIM_dict = {'Method':argvs[1], 'M':argvs[2], 'N':argvs[3], 'B':argvs[4], 'K':argvs[5],'md':argvs[6], 'ml':argvs[7], 'wloop':argvs[8], 'AorF':argvs[13], 'TBPorBP':argvs[14], 'EbN0':0.0,'BER':0.0}\n SIM_dict['EbN0'] = EsN0[idx_En]\n SIM_dict['BER'] = BER[idx_En]\n fn = 'DATA/TSSC.json'\n f_out = open(fn, 'a')\n json.dump(SIM_dict, f_out)\n f_out.write(\"\\n\")\n f_out.close()\n\n\nif __name__ == '__main__':\n argvs = sys.argv # コマンドライン引数を格納したリストの取得\n argc = len(argvs) # 引数の個数\n if (argc != 15): # 引数が足りない場合\n print('Usage: # TSSC.py Method M N B K md ml wloop nproc En1 delta En2 0AWGNor1Fade TGaBP')\n quit() # プログラムの終了\n\n start = time.time()\n params = [(i, argvs) for i in range (0, int(argvs[9]))]\n\n if int(argvs[1]) == 0:\n # Training\n if int(argvs[9]) == 1:\n train((0, argvs))\n else:\n pool = multiprocessing.Pool(processes=int(argvs[9]))\n res_ = pool.map(train, params)\n pool.close()\n else:\n # Test\n if int(argvs[9])==1:\n res = main_task((0,argvs))\n else:\n pool = multiprocessing.Pool(processes=int(argvs[9]))\n res_ = pool.map(main_task, params)\n pool.close()\n res = sum(res_)\n BER = res[0]/res[1]\n\n elapsed_time = time.time() - start\n print (\"elapsed_time:{0}\".format(elapsed_time) + \"[sec]\")\n\n if int(argvs[1]) != 0:\n resut2f(argvs,BER)\n\n EsN0 = range(int(argvs[10]),int(argvs[12])+int(argvs[11]),int(argvs[11]))\n fig = plt.plot(EsN0, BER, 'bo', EsN0, BER, 'k')\n plt.axis([int(argvs[10]), int(argvs[12]), 1e-5, 1])\n plt.xticks(np.arange(int(argvs[10]), int(argvs[12]), 4))\n plt.xscale('linear')\n plt.yscale('log')\n plt.xlabel(r'$E_b/N_0$ [dB]')\n plt.ylabel('BER')\n plt.title('M='+ str(argvs[2]) + ', N=' + str(argvs[3])+ ', B=' + str(argvs[4]))\n plt.grid(True)\n plt.savefig('TSSC_' + argvs[1] + '_'+ str(argvs[2]) + '_' + str(argvs[3])+ '_' + str(argvs[4]) + '_' + str(argvs[5]) + '_' + str(argvs[6]) + '_' + str(argvs[13]) + '_' + str(argvs[14]) + '.eps', bbox_inches=\"tight\", pad_inches=0.05)\n plt.show()\n\n exit()\n\n exit()\n", "repo_name": "Ryu1-K/TSSC", "sub_path": "TSSC.py", "file_name": "TSSC.py", "file_ext": "py", "file_size_in_byte": 29096, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "47", "api": [{"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 21, "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": "numpy.ones", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.eye", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 202, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 256, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 282, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 308, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 311, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 319, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 346, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 346, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.std", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 356, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 356, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 357, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.signbit", "line_number": 376, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 382, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 390, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 393, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 399, "usage_type": "call"}, {"api_name": "scipy.linalg.hadamard", "line_number": 399, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 399, "usage_type": "name"}, {"api_name": "numpy.random.permutation", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 405, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 406, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 422, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 422, "usage_type": "call"}, {"api_name": "scipy.linalg.block_diag", "line_number": 423, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 423, "usage_type": "name"}, {"api_name": "torch.block_diag", "line_number": 439, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 442, "usage_type": "call"}, {"api_name": "torch.block_diag", "line_number": 444, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 446, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 447, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 456, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 456, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 460, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 462, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 468, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 478, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 511, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 511, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 513, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 513, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 524, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 534, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 545, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 576, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 585, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 585, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 598, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 608, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 609, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 609, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 611, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 611, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 612, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 612, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 613, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 613, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 615, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 615, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 619, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 619, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 620, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 632, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 636, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 653, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 654, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 671, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 674, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 691, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 710, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 716, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 722, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 730, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 738, "usage_type": "call"}, {"api_name": "time.time", "line_number": 744, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 751, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 751, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 752, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 752, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 753, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 753, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 753, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 754, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 754, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 755, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 755, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 756, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 756, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 757, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 757, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 758, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 758, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 759, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 759, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 760, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 760, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 761, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 761, "usage_type": "name"}]} +{"seq_id": "18867524284", "text": "import io\nfrom sklearn.metrics import silhouette_score\nimport numpy as np\nimport csv\nimport math\nfrom collections import Counter\nimport sys\nsys.path.append('..')\n\nfrom vdbscan import do_cluster\nfrom my_keywords import Keyword_Vocab\nfrom my_multithread import multithread_wrapper\n\ndef dbscan_cluster(vecs:np.ndarray, vocab:list, k:int=3):\n label = do_cluster(vecs, k)\n if all(label == -1):\n score = -1\n else:\n score = silhouette_score(vecs, label, metric='cosine')\n cluster_num = max(label) + 1\n clusters = []\n for i in range(cluster_num + 1):\n clusters.append(set())\n for word_idx, cluster_id in enumerate(label):\n clusters[cluster_id].add(vocab[word_idx])\n return score, clusters\n\ndef nmpi_analysis(counter:dict, output_file:str):\n print('Start NPMI analysis ...')\n Z = 0.\n word_freq = {}\n for pair, freq in counter.items():\n word0, word1 = pair.split('__')\n if word0 in word_freq.keys():\n word_freq[word0] += freq\n else:\n word_freq[word0] = freq\n if word1 in word_freq.keys():\n word_freq[word1] += freq\n else:\n word_freq[word1] = freq\n Z += 2 * freq\n\n with io.open(output_file, 'w', encoding='utf-8') as dump_file:\n csv_w = csv.writer(dump_file)\n for pair, freq in counter.items():\n word0, word1 = pair.split('__')\n npmi = -math.log((2 * Z * freq) / (word_freq[word0] * word_freq[word1])) / math.log(2 * freq / Z)\n csv_w.writerow([pair, freq, '%.2f' % npmi])\n print('NPMI analysis is done.')\n\nclass Co_Occur_Generator:\n def __init__(self, keyword_vocab:Keyword_Vocab):\n self.keyword_vocab = keyword_vocab\n\n def __extract_co_occur(self, line:str):\n if not line:\n return None\n words = line.strip().split()\n co_occur_set = set()\n for word in words:\n if word in self.keyword_vocab.stoi:\n co_occur_set.add(word)\n if len(co_occur_set) > 1:\n co_occur_list = list(co_occur_set)\n co_occur_list.sort()\n pair_list = []\n for i in range(len(co_occur_list)-1):\n for j in range(i + 1, len(co_occur_list)):\n pair_list.append('%s__%s' % (co_occur_list[i], co_occur_list[j]))\n return ' '.join(pair_list) + '\\n'\n else:\n return None\n\n def extract_co_occur(self, freq:int, input_file:str, output_file:str, thread_num:int=1):\n def count_pair(line_output_file, output_file):\n print('Start counting ...')\n pair_counter = Counter()\n with io.open(line_output_file, 'r', encoding='utf-8') as line_output:\n for line in line_output:\n pair_counter.update(Counter(line.strip().split()))\n print('Counting is done.')\n nmpi_analysis(pair_counter, output_file)\n multithread_wrapper(self.__extract_co_occur, freq=freq, input_file=input_file, output_file=output_file, thread_num=thread_num, post_operation=count_pair)\n\n def extract_semantic_related(self, dep_context_file:str, output_file:str):\n pair_dict = {}\n with io.open(dep_context_file, 'r', encoding='utf-8') as load_file:\n for idx, line in enumerate(load_file):\n if not line:\n continue\n word0, word1 = line.strip().split()\n word1 = word1.split('_', 1)[1]\n if word1 in self.keyword_vocab.stoi:\n pair = ('%s__%s' % (word0, word1)) if word0 < word1 else ('%s__%s' % (word1, word0))\n if pair not in pair_dict:\n pair_dict[pair] = 1\n else:\n pair_dict[pair] += 1\n\n if idx % 100000 == 0:\n print(idx)\n \n nmpi_analysis(pair_dict, output_file)\n \n def load_pairs(self, pair_file):\n self.pairs = {}\n self.related = {}\n print('Start loading pairs...')\n with io.open(pair_file, 'r', encoding='utf-8') as load_file:\n csv_r = csv.reader(load_file)\n for row in csv_r:\n pair = row[0]\n freq = int(row[1])\n npmi = float(row[2])\n self.pairs[pair] = {'freq':freq, 'npmi':npmi}\n word0, word1 = pair.split('__')\n if word0 not in self.related.keys():\n self.related[word0] = set()\n if word1 not in self.related.keys():\n self.related[word1] = set()\n self.related[word0].add(word1)\n self.related[word1].add(word0)\n\n self._pairs_save = self.pairs\n\n def get_related(self, keyword, min_count:int=1, min_npmi:float=-1.0):\n if keyword not in self.related:\n return None\n pairs = ((keyword + '__' + w if keyword < w else w + '__' + keyword) for w in self.related[keyword])\n return [w for pair, w in zip(pairs, self.related[keyword]) if self.pairs[pair]['freq'] >= min_count and self.pairs[pair]['npmi'] >= min_npmi]\n\nif __name__ == '__main__':\n kv = Keyword_Vocab()\n kv.load_vocab(sys.argv[1])\n cog = Co_Occur_Generator(keyword_vocab=kv)\n cog.extract_co_occur(80, sys.argv[2], sys.argv[3], thread_num=30)", "repo_name": "ZhuKerui/ECE397-Individual-Study", "sub_path": "word_embeddings/relation_similar/co_occur_generator.py", "file_name": "co_occur_generator.py", "file_ext": "py", "file_size_in_byte": 5349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 14, "usage_type": "attribute"}, {"api_name": "vdbscan.do_cluster", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 19, "usage_type": "call"}, {"api_name": "io.open", "line_number": 44, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 45, "usage_type": "call"}, {"api_name": "math.log", "line_number": 48, "usage_type": "call"}, {"api_name": "my_keywords.Keyword_Vocab", "line_number": 53, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 78, "usage_type": "call"}, {"api_name": "io.open", "line_number": 79, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 81, "usage_type": "call"}, {"api_name": "my_multithread.multithread_wrapper", "line_number": 84, "usage_type": "call"}, {"api_name": "io.open", "line_number": 88, "usage_type": "call"}, {"api_name": "io.open", "line_number": 110, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 111, "usage_type": "call"}, {"api_name": "my_keywords.Keyword_Vocab", "line_number": 134, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 137, "usage_type": "attribute"}]} +{"seq_id": "73736592799", "text": "from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\n\nfrom joints_detectors.Alphapose.gene_npz import handle_video\n\n\ndef remove_no_person_frames(video_path):\n results, name = handle_video(video_path)\n head, tail = 0, len(results)\n for result in results:\n if result['result']:\n break\n head += 1\n\n for result in reversed(results):\n if result['result']:\n break\n head += 1\n\n ffmpeg_extract_subclip(video_path, head, tail, video_path)\n\n\nif __name__ == '__main__':\n remove_no_person_frames('../outputs/gait_test/001-bg-01-090.avi')\n", "repo_name": "zh-plus/video-to-pose3D", "sub_path": "lab_processing/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 606, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 620, "dataset": "github-code", "pt": "51", "api": [{"api_name": "joints_detectors.Alphapose.gene_npz.handle_video", "line_number": 7, "usage_type": "call"}, {"api_name": "moviepy.video.io.ffmpeg_tools.ffmpeg_extract_subclip", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "40228819829", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 25 01:58:49 2021\n\n@author: riccelli\n\"\"\"\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import mannwhitneyu\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn import preprocessing\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import TSNE\nimport seaborn as sns \nfrom sklearn.ensemble import RandomForestClassifier\nimport time\nimport os\n\n\ndef Feature_selection(dataset,experiment,classes):\n \n if not os.path.exists(f'./FeatureSelection/{experiment}'):\n os.makedirs(f'./FeatureSelection/{experiment}')\n \n if experiment == 'video':\n features = [\"Anger Frames >= Threshold\",\"\tSadness Frames >= Threshold\",\"\tDisgust Frames >= Threshold\", \"Joy Frames >= Threshold\",\"Surprise Frames >= Threshold\",\"\tFear Frames >= Threshold\", \"Contempt Frames >= Threshold\",'gsr']\n # features_names = ['AGE',\"Anger\",\"Sadness\",\"Disgust\", \"Joy\",\"Surprise\",\"\tFear\", \"Contempt\",'et','gsr']\n features_names = [\"Anger\",\"Sadness\",\"Disgust\", \"Joy\",\"Surprise\",\"Fear\", \"Contempt\",'gsr']\n else:\n # features = ['AGE',\"Anger Frames >= Threshold\",\"\tSadness Frames >= Threshold\",\"\tDisgust Frames >= Threshold\", \"Joy Frames >= Threshold\",\"Surprise Frames >= Threshold\",\"\tFear Frames >= Threshold\", \"Contempt Frames >= Threshold\",'et','gsr']\n features = [\"Anger Frames >= Threshold\",\"\tSadness Frames >= Threshold\",\"\tDisgust Frames >= Threshold\", \"Joy Frames >= Threshold\",\"Surprise Frames >= Threshold\",\"\tFear Frames >= Threshold\", \"Contempt Frames >= Threshold\",'et','gsr']\n # features_names = ['AGE',\"Anger\",\"Sadness\",\"Disgust\", \"Joy\",\"Surprise\",\"\tFear\", \"Contempt\",'et','gsr']\n features_names = [\"Anger\",\"Sadness\",\"Disgust\", \"Joy\",\"Surprise\",\"Fear\", \"Contempt\",'et','gsr']\n \n x = dataset.loc[:, features].values\n # Separating out the target\n y = dataset.loc[:,['label']].values\n # le = preprocessing.LabelEncoder()\n # y = le.fit_transform(y)\n # Standardizing the features\n x = StandardScaler().fit_transform(x)\n \n ################################### Feature importance ########################\n pca = PCA(n_components=2)\n principalComponents = pca.fit_transform(x)\n print (pca.explained_variance_)\n print (pca.explained_variance_ratio_)\n print (pca.explained_variance_ratio_.cumsum())\n principalDf = pd.DataFrame(data = principalComponents\n , columns = ['principal component 1', 'principal component 2'])\n \n finalDf = pd.concat([principalDf, pd.DataFrame(y)], axis = 1)\n finalDf.columns = ['principal component 1', 'principal component 2', 'label']\n \n fig = plt.figure(figsize = (8,8))\n ax = fig.add_subplot(1,1,1) \n ax.set_xlabel('Principal Component 1', fontsize = 15)\n ax.set_ylabel('Principal Component 2', fontsize = 15)\n ax.set_title('2 component PCA', fontsize = 20)\n targets = ['look', 'pinarello', 'trek','specialized']\n colors = ['r', 'g', 'b','k']\n for target, color in zip(targets,colors):\n indicesToKeep = finalDf['label'] == target\n ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n , finalDf.loc[indicesToKeep, 'principal component 2']\n , c = color\n , s = 50)\n ax.legend(targets)\n ax.grid()\n plt.savefig(f'./FeatureSelection/{experiment}/pcaplot_variance_{pca.explained_variance_ratio_.cumsum()}.png')\n \n \n tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n tsne_results = tsne.fit_transform(x)\n \n df_subset = pd.DataFrame()\n df_subset['tsne-2d-one'] = tsne_results[:,0]\n df_subset['tsne-2d-two'] = tsne_results[:,1]\n df_subset['label'] = y\n \n plt.figure(figsize=(8,5))\n sns.scatterplot(\n x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n hue=\"label\",\n palette=sns.color_palette(\"dark\", classes),\n data=df_subset,\n legend=\"full\",\n sizes=(40, 40),\n alpha=1\n )\n plt.savefig(f'./FeatureSelection/{experiment}/tsneplot.png')\n ################################### Feature importance ########################\n forest = RandomForestClassifier(random_state=0)\n le = preprocessing.LabelEncoder()\n y = le.fit_transform(y)\n forest.fit(x, y)\n start_time = time.time()\n importances = forest.feature_importances_\n std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n elapsed_time = time.time() - start_time\n \n print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")\n \n forest_importances = pd.Series(importances, index=features_names)\n \n fig, ax = plt.subplots()\n forest_importances.plot.bar(yerr=std, ax=ax)\n ax.set_title(\"Feature importances using MDI\")\n ax.set_ylabel(\"Mean decrease in impurity\")\n fig.tight_layout() \n plt.savefig(f'./FeatureSelection/{experiment}/feature_importance.png')\n \ndef main():\n dataset = pd.read_csv(\"./Datasets/dataset_full.csv\" )\n '''\n LOGO\n '''\n # dataset = dataset.iloc[0:9,:]\n # experiment = 'logo'\n # n_classes = len(dataset['label'].value_counts())\n # Feature_selection(dataset,experiment,n_classes)\n '''\n PRODUCT\n '''\n # dataset = dataset.iloc[9:18,:]\n # experiment = 'product'\n # n_classes = len(dataset['label'].value_counts())\n # Feature_selection(dataset,experiment,n_classes)\n '''\n VIDEO\n '''\n # dataset = dataset.iloc[18:27,:]\n # n_classes = len(dataset['label'].value_counts())\n # experiment = 'video'\n # Feature_selection(dataset,experiment,n_classes)\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Riccellisp/Neuromarketing_USJ_BU_Bike", "sub_path": "Study-USJ-BU-P00-P08/pcaplot.py", "file_name": "pcaplot.py", "file_ext": "py", "file_size_in_byte": 5685, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "seaborn.scatterplot", "line_number": 84, "usage_type": "call"}, {"api_name": "seaborn.color_palette", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 96, "usage_type": "name"}, {"api_name": "time.time", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "8080126793", "text": "import abc\nfrom datetime import datetime\n\nimport numpy as np\nimport pandas as pd\n\nfrom core.datareader import ReadData\nfrom core.ticker import Ticker, KRW, BIL\n\n\nclass Strategy(abc.ABC):\n \"\"\"\n Abstract Strategy.\n \"\"\"\n\n def __init__(self, name, tickers):\n self.name = name\n self.tickers = tickers\n\n def __str__(self):\n return self.name\n\n def analyze(self, trading_day=\"end\", trading_price=\"Close\", start=None, end=None, in_krw=True,\n **kwargs) -> pd.DataFrame:\n \"\"\"\n Get daily profit of the strategy.\n :param trading_day: Rebalancing day. Possible values are: 1 ~ 31 or 'end', 'ending', 'begin', 'beginning'.\n :param trading_price: Price used when rebalancing assets.\n :param start: Start date of analyzing period.\n :param end: End date of analyzing period.\n :param in_krw: If true, convert the currency of USD asset in South Korean Won.\n :param kwargs:\n :return: Daily profit of the strategy.\n \"\"\"\n data, asset_weights = self.asset_weights_from_tickers(self.tickers,\n trading_day, trading_price, start, end, in_krw,\n **kwargs)\n profit = self.calculate_profit(data, asset_weights, in_krw, **kwargs)\n return profit\n\n def asset_weights_from_tickers(self, tickers: list[Ticker],\n trading_day=\"end\", trading_price=\"Close\", start=None, end=None, in_krw=True,\n **kwargs):\n data = self.read_data(tickers, trading_price, start, end, in_krw, **kwargs)\n trading_days = self.get_trading_days(data, trading_day, **kwargs)\n asset_weights = self.calculate_asset_weights(data, trading_days, **kwargs)\n return data, asset_weights\n\n @staticmethod\n def read_data(tickers: list[Ticker], trading_price=\"Close\", start=None, end=None, in_krw=True,\n **kwargs) -> pd.DataFrame:\n # read data\n tickers = tickers.copy()\n if in_krw:\n tickers.append(KRW)\n data = ReadData(tickers, start=start, end=end, **kwargs)\n data = data[trading_price]\n return data\n\n @staticmethod\n def get_trading_days(data: pd.DataFrame, trading_day, **kwargs) -> pd.Series:\n \"\"\"\n Get trading days.\n :param data: Daily assets data.\n :param trading_day: Rebalancing day. Possible values are: 1 ~ 31 or 'end', 'ending', 'begin', 'beginning'.\n :param kwargs:\n :return: Trading days.\n \"\"\"\n is_trading_day = pd.DataFrame()\n is_trading_day[\"year\"] = data.index.year\n is_trading_day[\"month\"] = data.index.month\n is_trading_day[\"day\"] = data.index.day\n\n if isinstance(trading_day, int):\n is_trading_day[\"day_err\"] = is_trading_day[\"day\"] - trading_day + 31 * (is_trading_day[\"day\"] < trading_day)\n\n group = []\n g = 0\n for i, row in enumerate(is_trading_day.itertuples()):\n if i == 0:\n group.append(g)\n elif is_trading_day.iloc[i-1][\"day_err\"] > is_trading_day.iloc[i][\"day_err\"]:\n g += 1\n group.append(g)\n else:\n group.append(g)\n\n is_trading_day[\"group\"] = group\n is_trading_day = is_trading_day.groupby([\"group\"]).apply(lambda x: x.iloc[x[\"day_err\"].argmin()])\n elif isinstance(trading_day, str):\n if trading_day.lower() in [\"end\", \"ending\"]:\n is_trading_day = is_trading_day.groupby([\"year\", \"month\"]).apply(lambda x: x.iloc[x[\"day\"].argmax()])\n elif trading_day.lower() in [\"begin\", \"beginning\"]:\n is_trading_day = is_trading_day.groupby([\"year\", \"month\"]).apply(lambda x: x.iloc[x[\"day\"].argmin()])\n else:\n raise NotImplementedError(f\"trading_day[{trading_day}] is not implemented\")\n\n trading_days = is_trading_day.apply(lambda x: datetime(x[\"year\"], x[\"month\"], x[\"day\"]), axis=1)\n return trading_days\n\n @abc.abstractmethod\n def calculate_asset_weights(self, data: pd.DataFrame, trading_days: pd.Series, **kwargs) -> pd.DataFrame:\n \"\"\"\n Calculate asset weights under the strategy.\n :param data: Daily assets data.\n :param trading_days: Trading days.\n :param kwargs:\n :return: Asset weights.\n \"\"\"\n ...\n\n @staticmethod\n def calculate_profit(data: pd.DataFrame, asset_weights: pd.DataFrame, in_krw=True,\n **kwargs) -> pd.DataFrame:\n \"\"\"\n Get daily profit.\n :param data: Daily assets data.\n :param asset_weights: Weights of assets trying to buy at each trading day.\n :param in_krw: If true, convert the currency of USD asset in South Korean Won.\n :param kwargs:\n :return: Daily profit.\n \"\"\"\n # sort by tickers\n tickers_trading = sorted(asset_weights.columns)\n asset_weights = asset_weights.loc[:, tickers_trading]\n\n trading_day = asset_weights.index\n start = trading_day[0]\n data_trading = data.loc[start:, tickers_trading]\n if in_krw:\n data_trading = data_trading.apply(lambda x: x * data[KRW] if x.name.currency == \"USD\" else x).dropna()\n full_day = data_trading.index\n asset_weights_at_trading_day = pd.DataFrame(data=asset_weights, index=full_day).fillna(method='ffill')\n\n change = data_trading.pct_change().fillna(0)\n change_cum = (1 + change).cumprod()\n change_cum_at_trading_day = pd.DataFrame(data=change_cum.loc[asset_weights.index], index=full_day)\\\n .fillna(method='ffill').fillna(1)\n change_cum_from_trading_day = change_cum / change_cum_at_trading_day\n asset_weights_daily = (asset_weights_at_trading_day * change_cum_from_trading_day)\\\n .apply(lambda x: x/x.sum(), axis=1) # normalize\n\n total_return = (1 + (asset_weights_daily.shift(1) * change).sum(axis=1)).cumprod()\n profit = pd.DataFrame()\n profit[tickers_trading] = asset_weights_daily.apply(lambda x: x * total_return)\n profit[\"total_return\"] = total_return\n profit[\"is_trading_day\"] = profit.apply(lambda x: x.name in asset_weights.index, axis=1)\n return profit\n\n\nclass SAA(Strategy):\n \"\"\"\n Static Asset Allocation.\n \"\"\"\n\n def __init__(self,\n name: str,\n tickers: list[Ticker],\n weights: list[float]):\n\n assert len(tickers) == len(weights), \"number of tickers and weights must be same\"\n weights = np.array(weights, dtype=np.float32) / np.sum(weights)\n super().__init__(name, tickers)\n self.weights = {t: w for t, w in zip(tickers, weights)}\n\n def calculate_asset_weights(self, data: pd.DataFrame, trading_days: pd.Series, **kwargs) -> pd.DataFrame:\n asset_weights = pd.DataFrame(index=trading_days, columns=self.tickers, data=[self.weights] * len(trading_days))\n return asset_weights\n\n\nclass BAA(Strategy):\n \"\"\"\n Bold Asset Allocation.\n \"\"\"\n\n def __init__(self,\n name: str,\n tickers_canary: list[Ticker],\n tickers_risk: list[Ticker],\n tickers_safe: list[Ticker],\n n_risk=1,\n n_safe=3):\n\n tickers = list(set(tickers_canary + tickers_risk + tickers_safe))\n super().__init__(name, tickers)\n self.tickers_canary = tickers_canary\n self.tickers_risk = tickers_risk\n self.tickers_safe = tickers_safe\n self.tickers_trading = list(set(self.tickers_risk + self.tickers_safe))\n self.n_risk = n_risk\n self.n_safe = n_safe\n\n def calculate_asset_weights(self, data: pd.DataFrame, trading_days: pd.Series, **kwargs) -> pd.DataFrame:\n # data for scoring - data of the day before trading days\n data_scoring = data.shift(periods=1).loc[trading_days]\n # data for scoring canary assets\n data_scoring_canary = data_scoring[self.tickers_canary]\n # data for scoring risk and safe assets\n data_scoring_trading = data_scoring[self.tickers_trading]\n\n # 13612W score for canary assets\n score_13612W = self._get_score_13612W(data_scoring_canary)\n score_SMA12 = self._get_score_SMA12(data_scoring_trading)\n\n # buy assets and calculate profit\n asset_weights = self._get_asset_weights(score_SMA12, score_13612W)\n return asset_weights\n\n def _get_score_13612W(self, data: pd.DataFrame) -> pd.DataFrame:\n m1 = data.pct_change(periods=1)\n m3 = data.pct_change(periods=3)\n m6 = data.pct_change(periods=6)\n m12 = data.pct_change(periods=12)\n score = (12 * m1 + 4 * m3 + 2 * m6 + 1 * m12).dropna(axis=0)\n return score\n\n def _get_score_SMA12(self, data: pd.DataFrame) -> pd.DataFrame:\n score = (data / data.rolling(window=13).mean() - 1).dropna(axis=0)\n return score\n\n def _get_asset_weights(self, score_SMA12: pd.DataFrame, score_13612W: pd.DataFrame) -> pd.DataFrame:\n run_to_safety = (score_13612W < 0).any(axis=1)\n\n asset_risk_score_threshold = score_SMA12[self.tickers_risk]\\\n .apply(lambda x: x.sort_values(ascending=False)[self.n_risk - 1], axis=1)\n asset_safe_score_threshold = score_SMA12[self.tickers_safe]\\\n .apply(lambda x: x.sort_values(ascending=False)[self.n_safe - 1], axis=1)\n\n asset_risk_top_n = score_SMA12[self.tickers_risk]\\\n .apply(lambda x: x >= asset_risk_score_threshold).applymap(int)\n asset_safe_top_n = score_SMA12[self.tickers_safe]\\\n .apply(lambda x: x >= asset_safe_score_threshold).applymap(int)\n\n # if a safe asset was worse than BIL, replace it by BIL.\n asset_safe_worse_than_bil = score_SMA12[self.tickers_safe]\\\n .apply(lambda x: x < x[BIL], axis=1).applymap(int) & asset_safe_top_n\n asset_safe_top_n -= asset_safe_worse_than_bil\n asset_safe_top_n[BIL] += asset_safe_worse_than_bil.sum(axis=1)\n\n # combine all together\n asset_risk_weight = asset_risk_top_n.apply(lambda x: x * (1 - run_to_safety) / self.n_risk)\n asset_safe_weight = asset_safe_top_n.apply(lambda x: x * run_to_safety / self.n_safe)\n asset_weights = pd.DataFrame(data=0, index=score_SMA12.index, columns=self.tickers_trading)\n asset_weights[self.tickers_risk] += asset_risk_weight\n asset_weights[self.tickers_safe] += asset_safe_weight\n return asset_weights\n\n\nclass Alternatives(Strategy):\n \"\"\"\n Asset Allocation with alternatives trading assets.\n \"\"\"\n\n def __init__(self,\n name: str,\n strategy: Strategy,\n alternatives: dict[Ticker, Ticker] = {}):\n tickers = list(set(strategy.tickers + list(alternatives.values())))\n super().__init__(name, tickers)\n self.strategy = strategy\n self.alternatives = alternatives\n\n def calculate_asset_weights(self, data: pd.DataFrame, trading_days: pd.Series, **kwargs) -> pd.DataFrame:\n # calculate asset weights by base strategy\n asset_weights = self.strategy.calculate_asset_weights(data, trading_days, **kwargs)\n # switch trading assets to alternatives\n asset_weights.rename(columns=self.alternatives, inplace=True)\n return asset_weights\n\n\nif __name__ == '__main__':\n from core.ticker import *\n\n pd.set_option('display.max_rows', 500)\n pd.set_option('display.max_columns', 20)\n pd.set_option('display.width', 1000)\n\n tickers_canary = [SPY, EFA, EEM, AGG]\n tickers_risk_g4 = [QQQ, EFA, EEM, AGG]\n tickers_risk_g12 = [SPY, QQQ, IWM, VGK, EWJ, EEM, VNQ, DBC, GLD, TLT, HYG, LQD]\n tickers_safe = [TLT, IEF, AGG, TIP, LQD, BIL, DBC]\n n_risk_g4 = 1\n n_risk_g12 = 6\n n_safe = 3\n\n baa_g4 = BAA(\"BAA_G4\",\n tickers_canary, tickers_risk_g4, tickers_safe,\n n_risk=n_risk_g4, n_safe=n_safe)\n baa_g4.analyze(in_krw=True)\n", "repo_name": "bds0822/quant", "sub_path": "core/strategy.py", "file_name": "strategy.py", "file_ext": "py", "file_size_in_byte": 12135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "abc.ABC", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "attribute"}, {"api_name": "core.ticker.Ticker", "line_number": 41, "usage_type": "name"}, {"api_name": "core.ticker.Ticker", "line_number": 50, "usage_type": "name"}, {"api_name": "core.ticker.KRW", "line_number": 55, "usage_type": "argument"}, {"api_name": "core.datareader.ReadData", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 102, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 113, "usage_type": "attribute"}, {"api_name": "core.ticker.KRW", "line_number": 131, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 137, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 114, "usage_type": "attribute"}, {"api_name": "core.ticker.Ticker", "line_number": 158, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "core.ticker.Ticker", "line_number": 178, "usage_type": "name"}, {"api_name": "core.ticker.Ticker", "line_number": 179, "usage_type": "name"}, {"api_name": "core.ticker.Ticker", "line_number": 180, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 209, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 221, "usage_type": "attribute"}, {"api_name": "core.ticker.BIL", "line_number": 236, "usage_type": "name"}, {"api_name": "core.ticker.BIL", "line_number": 238, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 243, "usage_type": "call"}, {"api_name": "core.ticker.Ticker", "line_number": 257, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pandas.set_option", "line_number": 274, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 275, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 276, "usage_type": "call"}, {"api_name": "core.ticker.BIL", "line_number": 281, "usage_type": "name"}]} +{"seq_id": "19628395956", "text": "from http.server import HTTPServer, CGIHTTPRequestHandler\n\nif __name__ == '__main__':\n try:\n CGIHTTPRequestHandler.cgi_directories = ['/cgi-bin']\n\n httpd = HTTPServer(('', 8000), # localhost:8000\n CGIHTTPRequestHandler) # CGI support.\n\n print(f\"Running server. Use [ctrl]-c to terminate.\")\n\n httpd.serve_forever()\n\n except KeyboardInterrupt:\n print(f\"\\nReceived keyboard interrupt. Shutting down server.\")\n httpd.socket.close()\n", "repo_name": "dtechspace/ml-exlab", "sub_path": "config_generator_gui/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 515, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "http.server.CGIHTTPRequestHandler.cgi_directories", "line_number": 5, "usage_type": "attribute"}, {"api_name": "http.server.CGIHTTPRequestHandler", "line_number": 5, "usage_type": "name"}, {"api_name": "http.server.HTTPServer", "line_number": 7, "usage_type": "call"}, {"api_name": "http.server.CGIHTTPRequestHandler", "line_number": 8, "usage_type": "argument"}]} +{"seq_id": "13347668208", "text": "from datetime import datetime as dt\n\nfrom django.core.exceptions import ValidationError\n\n\ndef validate_year(value: int) -> int:\n year_today = dt.today().year\n if value > year_today:\n raise ValidationError(\n 'Проверьте год выпуска!',\n )\n return value\n", "repo_name": "KatrinDevelopment/yamdb_final", "sub_path": "api_yamdb/reviews/validators.py", "file_name": "validators.py", "file_ext": "py", "file_size_in_byte": 303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "datetime.datetime.today", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "14029252461", "text": "import logging\nimport platform\nimport os\nimport re\n\nfrom .tools import which, to_bytes\nfrom .command import exec_command\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass Inventory:\n def __init__(self):\n pass\n\n\nclass SysInfo(Inventory):\n def __init__(self):\n self.os = platform.system()\n\n def cpu_info(self):\n if self.os == 'Linux':\n return self._cpu_info_linux()\n else:\n raise Exception(\"Unsupported OS.\")\n\n def mem_info(self):\n if self.os == 'Linux':\n return self._mem_info_linux()\n else:\n raise Exception(\"Unsupported OS.\")\n\n def ip_addresses(self):\n if self.os == 'Linux':\n return self._ip_addresses_linux()\n else:\n raise Exception(\"Unsupported OS.\")\n\n def file_systems(self):\n if self.os == 'Linux':\n return self._file_systems_linux()\n else:\n raise Exception(\"Unsupported OS.\")\n\n def find_mount_point(self, path, mount_points):\n if self.os == 'Linux':\n return self._find_mount_point_linux(path, mount_points)\n else:\n raise Exception(\"Unsupported OS.\")\n\n def _cpu_info_linux(self):\n cpus = []\n # TODO: implement for other OSes\n if self.os != 'Linux':\n return {}\n current_cpu = {}\n with open('/proc/cpuinfo') as f:\n for line in f.read().split(\"\\n\"):\n if ':' not in line:\n continue\n key, value = (part.strip() for part in line.split(':', 1))\n if key == 'processor':\n current_cpu = {}\n cpus.append(current_cpu)\n if key in ['core id', 'cpu MHz', 'model name',\n 'cache size', 'processor']:\n current_cpu[str(re.sub(r' +', '_', key)).lower()] = value\n if cpus:\n return {\n 'cpus': cpus,\n 'cpu_count': len(cpus)\n }\n return {}\n\n def _mem_info_linux(self):\n unit_re = re.compile(r'(\\d+) ?(\\wB)?')\n mem_values = {}\n with open('/proc/meminfo') as f:\n for line in f.read().split(\"\\n\"):\n if ':' not in line:\n continue\n key, value = (part.strip() for part in line.split(':', 1))\n size, unit = unit_re.match(value).groups()\n size = int(size)\n # convert everything to bytes, if a unit is specified\n if unit:\n size = to_bytes(size, unit[:-1])\n mem_values[key] = size\n return mem_values\n\n def _ip_addresses_linux(self):\n \"\"\"Find the host's IP addresses.\"\"\"\n addrs = []\n try:\n ip = which('ip')\n (rc, out, err) = exec_command([ip, \"addr\", \"show\"])\n if rc == 0:\n for line in out.decode('utf8').splitlines():\n m = re.match(r'^\\s+inet ([\\d\\.]+)/\\d+\\s', line)\n if m:\n addrs.append(m.group(1))\n\n m = re.match(r'^\\sinet6 ([\\dabcdef\\:]+)/\\d+ scope global',\n line)\n if m:\n addrs.append(m.group(1))\n return addrs\n except OSError:\n pass\n\n try:\n ifconfig = which('ifconfig', ['/sbin'])\n (rc, out, err) = exec_command([ifconfig, \"-a\"])\n if rc == 0:\n for line in out.decode('utf8').splitlines():\n m = re.match(r'^\\s+inet (addr:)?([\\d\\.]+)\\s', line)\n if m:\n addrs.append(m.group(2))\n\n m = re.match(r'^\\sinet6 (addr: )?([\\dabcdef\\:]+)(/\\d+)? '\n '.+[Gg]lobal$', line)\n if m:\n addrs.append(m.group(2))\n return addrs\n except OSError:\n pass\n\n return addrs\n\n def _file_systems_linux(self):\n logger.debug(\"Inspecting file systems.\")\n fs = []\n (rc, out, err) = exec_command([which('df'), '--local', '-k'])\n lines = out.splitlines()\n # Remove header\n del lines[0]\n dev = None\n for line in lines:\n cols = line.decode('utf-8').split()\n # Skip rootfs which is redundant on Debian\n if cols[0] == 'rootfs':\n logger.debug(\"Ignoring rootfs mount point.\")\n continue\n\n # Output of df can be multiline when the name of the\n # device is too large\n if len(cols) in (1, 6):\n dev = cols.pop(0)\n\n if not cols:\n # Multi-line output, skip to next line for next fields.\n continue\n\n # cols is now always [total, used, avail, use%, mount_point].\n total, used, _, _, mount_point = cols\n\n # Skip docker volumes.\n if dev in ('devtmpfs', 'overlay', 'shm', 'tmpfs'):\n logger.debug(\"Ignoring device %s as %s.\", dev, mount_point)\n continue\n\n if dev.startswith('/dev/loop'):\n logger.debug(\"Ignoring loopback device %s.\", dev)\n continue\n\n # Skip basic FHS directories.\n _, top_level_dir = mount_point.split('/', 2)[:2]\n if top_level_dir in ('dev', 'proc', 'run', 'sys'):\n logger.debug(\"Ignoring mount point %s.\", mount_point)\n continue\n\n logger.debug(\"Found filesystem %s at %s.\", dev, mount_point)\n fs.append({\n 'mount_point': mount_point,\n 'device': dev,\n 'total': int(total) * 1024,\n 'used': int(used) * 1024\n })\n dev = None\n return fs\n\n def mount_points(self):\n return [fs['mount_point'] for fs in self.file_systems()]\n\n def _find_mount_point_linux(self, path, mount_points):\n realpath = os.path.realpath(path)\n\n if not os.path.exists(realpath):\n return None\n\n # Get the parent dir when it is not a directory\n if not os.path.isdir(realpath):\n realpath = os.path.dirname(realpath)\n\n # Walk up parents directory\n while True:\n if realpath in mount_points or realpath == '/':\n return realpath\n\n realpath = os.path.dirname(realpath)\n\n\nclass PgInfo(Inventory):\n def __init__(self, db_conn):\n self.db_conn = db_conn\n\n def is_in_recovery(self):\n if self.db_conn.server_version >= 90000:\n return self.db_conn.queryscalar(\n \"SELECT pg_is_in_recovery() AS standby;\")\n return False\n\n def start_time(self):\n return self.db_conn.queryscalar(\"SELECT pg_postmaster_start_time();\")\n\n def tablespaces(self, data_directory):\n # Grab the list of tablespaces\n if self.db_conn.server_version < 90200:\n q = \"\"\"\\\n SELECT spcname, spclocation, pg_tablespace_size(oid) AS size\n FROM pg_tablespace;\n \"\"\"\n else:\n q = \"\"\"\\\n SELECT\n spcname,\n pg_tablespace_location(oid) AS spclocation,\n pg_tablespace_size(oid) AS size\n FROM pg_tablespace;\n \"\"\"\n\n tablespaces = []\n sysinfo = SysInfo()\n fs = sysinfo.mount_points()\n for row in self.db_conn.query(q):\n # when spclocation is empty, replace with data_directory\n if row['spclocation'] is None:\n path = data_directory\n else:\n path = row['spclocation']\n\n tablespaces.append({\n 'spcname': row['spcname'],\n 'path': path,\n 'mount_point': sysinfo.find_mount_point(path, fs),\n 'size': row['size']\n })\n\n def databases(self):\n q = \"\"\"\\\n SELECT\n datname AS dbname, pg_encoding_to_char(encoding) AS encoding,\n pg_database_size(datname) AS size\n FROM pg_database WHERE datallowconn;\n \"\"\"\n\n dbs = {}\n for r in self.db_conn.query(q):\n dbs[r['dbname']] = r\n return dbs\n", "repo_name": "dalibo/temboard", "sub_path": "agent/temboardagent/inventory.py", "file_name": "inventory.py", "file_ext": "py", "file_size_in_byte": 8279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 415, "dataset": "github-code", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 20, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 68, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 77, "usage_type": "call"}, {"api_name": "tools.to_bytes", "line_number": 88, "usage_type": "call"}, {"api_name": "tools.which", "line_number": 96, "usage_type": "call"}, {"api_name": "command.exec_command", "line_number": 97, "usage_type": "call"}, {"api_name": "re.match", "line_number": 100, "usage_type": "call"}, {"api_name": "re.match", "line_number": 104, "usage_type": "call"}, {"api_name": "tools.which", "line_number": 113, "usage_type": "call"}, {"api_name": "command.exec_command", "line_number": 114, "usage_type": "call"}, {"api_name": "re.match", "line_number": 117, "usage_type": "call"}, {"api_name": "re.match", "line_number": 121, "usage_type": "call"}, {"api_name": "command.exec_command", "line_number": 134, "usage_type": "call"}, {"api_name": "tools.which", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}]} +{"seq_id": "12660427458", "text": "from PySide2.QtWidgets import QLabel, QHBoxLayout, QSizePolicy\nfrom PySide2.QtGui import QCursor, QPainter, QColor\nfrom PySide2.QtCore import Qt, SIGNAL\n\nfrom angr.analyses.disassembly import Value\n\nfrom .qgraph_object import QGraphObject\nfrom .qoperand import QOperand\nfrom ...utils import should_display_string_label, get_string_for_display, get_comment_for_display\n\n\nclass QInstruction(QGraphObject):\n\n GRAPH_ADDR_SPACING = 20\n GRAPH_MNEMONIC_SPACING = 10\n GRAPH_OPERAND_SPACING = 2\n GRAPH_COMMENT_STRING_SPACING = 5\n\n LINEAR_INSTRUCTION_OFFSET = 120\n\n def __init__(self, workspace, func_addr, disasm_view, disasm, infodock, insn, out_branch, config, mode='graph'):\n super(QInstruction, self).__init__()\n\n # initialization\n self.workspace = workspace\n self.func_addr = func_addr\n self.disasm_view = disasm_view\n self.disasm = disasm\n self.infodock = infodock\n self.variable_manager = infodock.variable_manager\n self.insn = insn\n self.out_branch = out_branch\n self._config = config\n self.mode = mode\n\n self.selected = False\n\n # all \"widgets\"\n self._addr = None\n self._addr_width = None\n self._mnemonic = None\n self._mnemonic_width = None\n self._operands = [ ]\n self._string = None\n self._string_width = None\n self._comment = None\n self._comment_width = None\n\n self._init_widgets()\n\n #self.setContextMenuPolicy(Qt.CustomContextMenu)\n #self.connect(self, SIGNAL('customContextMenuRequested(QPoint)'), self._on_context_menu)\n\n def paint(self, painter):\n \"\"\"\n\n :param QPainter painter:\n :return:\n \"\"\"\n\n if self.mode == \"linear\":\n self._paint_linear(painter)\n else:\n self._paint_graph(painter)\n\n def refresh(self):\n super(QInstruction, self).refresh()\n\n for operand in self._operands:\n operand.refresh()\n\n self._update_size()\n\n def select(self):\n if not self.selected:\n self.toggle_select()\n\n def unselect(self):\n if self.selected:\n self.toggle_select()\n\n def toggle_select(self):\n self.selected = not self.selected\n\n def select_operand(self, operand_idx):\n\n if operand_idx < len(self._operands):\n self._operands[operand_idx].select()\n\n def unselect_operand(self, operand_idx):\n\n if operand_idx < len(self._operands):\n self._operands[operand_idx].unselect()\n\n def get_operand(self, operand_idx):\n if operand_idx < len(self._operands):\n return self._operands[operand_idx]\n return None\n\n def set_comment(self, new_text):\n self._comment = new_text\n self._comment_width = self._config.disasm_font_width * len(self._comment)\n self._update_size()\n\n #\n # Event handlers\n #\n\n def on_mouse_pressed(self, button, pos):\n if button == Qt.LeftButton:\n # left click\n\n # is it on one of the operands?\n for op in self._operands:\n if op.x <= pos.x() < op.x + op.width:\n op.on_mouse_pressed(button, pos)\n return\n\n self.disasm_view.toggle_instruction_selection(self.insn.addr)\n\n def on_mouse_released(self, button, pos):\n if button == Qt.RightButton:\n # right click\n # display the context menu\n self.disasm_view.instruction_context_menu(self.insn, QCursor.pos())\n\n def on_mouse_doubleclicked(self, button, pos):\n\n if button == Qt.LeftButton:\n # left double click\n\n # is it on one of the operands?\n for op in self._operands:\n if op.x <= pos.x() < op.x + op.width:\n op.on_mouse_doubleclicked(button, pos)\n return\n\n #\n # Private methods\n #\n\n @property\n def insn_backcolor(self):\n r, g, b = None, None, None\n\n # First we'll check for customizations\n if self.disasm_view.insn_backcolor_callback:\n r, g, b = self.disasm_view.insn_backcolor_callback(addr=self.insn.addr, selected=self.selected)\n\n # Fallback to defaults if we get Nones from the callback\n if r is None or g is None or b is None:\n if self.selected:\n r, g, b = 0xef, 0xbf, 0xba\n\n return r, g, b\n\n def _init_widgets(self):\n\n self._addr = \"%08x\" % self.insn.addr\n self._addr_width = self._config.disasm_font_width * len(self._addr)\n self._mnemonic = self.insn.mnemonic.render()[0]\n self._mnemonic_width = self._config.disasm_font_width * len(self._mnemonic)\n\n for i, operand in enumerate(self.insn.operands):\n is_branch_target = self.insn.type in ('branch', 'call') and i == self.insn.branch_target_operand\n is_indirect_branch = self.insn.branch_type == 'indirect'\n branch_targets = None\n if is_branch_target:\n if self.out_branch is not None:\n branch_targets = self.out_branch.targets\n else:\n # it does not create multiple branches. e.g., a call instruction\n if len(operand.children) == 1 and type(operand.children[0]) is Value:\n branch_targets = (operand.children[0].val,)\n qoperand = QOperand(self.workspace, self.func_addr, self.disasm_view, self.disasm, self.infodock,\n self.insn, operand, i, is_branch_target, is_indirect_branch, branch_targets, self._config\n )\n self._operands.append(qoperand)\n\n if should_display_string_label(self.workspace.instance.cfg, self.insn.addr):\n # yes we should display a string label\n self._string = get_string_for_display(self.workspace.instance.cfg, self.insn.addr)\n self._string_width = self._config.disasm_font_width * len(self._string)\n\n self._comment = get_comment_for_display(self.workspace.instance.cfg.kb, self.insn.addr)\n if self._comment is not None:\n self._comment_width = self._config.disasm_font_width * len(self._comment)\n\n self._update_size()\n\n def _update_size(self):\n\n self._height = self._config.disasm_font_height\n self._width = 0\n\n if self.disasm_view.show_address:\n self._width += self._addr_width + self.GRAPH_ADDR_SPACING\n\n self._width += self._mnemonic_width + self.GRAPH_MNEMONIC_SPACING + \\\n sum([ op.width for op in self._operands ]) + \\\n (len(self._operands) - 1) * (self._config.disasm_font_width + self.GRAPH_OPERAND_SPACING)\n\n # we only display string if there's no comment\n if self._comment is not None:\n self._width += self.GRAPH_COMMENT_STRING_SPACING + self._comment_width\n elif self._string is not None:\n self._width += self.GRAPH_COMMENT_STRING_SPACING + self._string_width\n\n def _paint_highlight(self, painter):\n r, g, b = self.insn_backcolor\n\n if r is not None and b is not None and g is not None:\n painter.setPen(QColor(r, g, b))\n painter.setBrush(QColor(r, g, b))\n painter.drawRect(self.x, self.y, self.width, self.height)\n\n def _paint_graph(self, painter):\n\n self._paint_highlight(painter)\n\n x = self.x\n\n # address\n if self.disasm_view.show_address:\n painter.setPen(Qt.black)\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._addr)\n\n x += self._addr_width + self.GRAPH_ADDR_SPACING\n\n # mnemonic\n painter.setPen(QColor(0, 0, 0x80))\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._mnemonic)\n\n x += self._mnemonic_width + self.GRAPH_MNEMONIC_SPACING\n\n # operands\n for i, op in enumerate(self._operands):\n op.x = x\n op.y = self.y\n op.paint(painter)\n\n x += op.width\n\n if i != len(self._operands) - 1:\n # draw the comma\n painter.drawText(x, self.y + self._config.disasm_font_ascent, \",\")\n x += self._config.disasm_font_width * 1\n\n x += self.GRAPH_OPERAND_SPACING\n\n # comment or string - comments have precedence\n if self._comment is not None:\n x += self.GRAPH_COMMENT_STRING_SPACING\n painter.setPen(Qt.blue)\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._comment)\n elif self._string is not None:\n x += self.GRAPH_COMMENT_STRING_SPACING\n painter.setPen(Qt.gray)\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._string)\n\n def _paint_linear(self, painter):\n\n self._paint_highlight(painter)\n\n x = self.x\n\n # address\n if self.disasm_view.show_address:\n painter.setPen(Qt.black)\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._addr)\n\n x += self._addr_width + self.LINEAR_INSTRUCTION_OFFSET\n\n # TODO: splitter\n #painter.setPen(Qt.black)\n #painter.drawLine()\n\n # mnemonic\n painter.setPen(QColor(0, 0, 0x80))\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._mnemonic)\n\n x += self._mnemonic_width + self.GRAPH_MNEMONIC_SPACING\n\n # operands\n for i, op in enumerate(self._operands):\n op.x = x\n op.y = self.y\n op.paint(painter)\n\n x += op.width\n\n if i != len(self._operands) - 1:\n # draw the comma\n painter.drawText(x, self.y + self._config.disasm_font_ascent, \",\")\n x += self._config.disasm_font_width * 1\n\n x += self.GRAPH_OPERAND_SPACING\n\n # comment or string - comments have precedence\n if self._comment is not None:\n x += self.GRAPH_COMMENT_STRING_SPACING\n painter.setPen(Qt.blue)\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._comment)\n elif self._string is not None:\n x += self.GRAPH_COMMENT_STRING_SPACING\n painter.setPen(Qt.gray)\n painter.drawText(x, self.y + self._config.disasm_font_ascent, self._string)\n", "repo_name": "0x6b7966/angr-management", "sub_path": "angrmanagement/ui/widgets/qinstruction.py", "file_name": "qinstruction.py", "file_ext": "py", "file_size_in_byte": 10403, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "60", "api": [{"api_name": "qgraph_object.QGraphObject", "line_number": 12, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.LeftButton", "line_number": 110, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 110, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.RightButton", "line_number": 122, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 122, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QCursor.pos", "line_number": 125, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QCursor", "line_number": 125, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.LeftButton", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 129, "usage_type": "name"}, {"api_name": "angr.analyses.disassembly.Value", "line_number": 173, "usage_type": "name"}, {"api_name": "qoperand.QOperand", "line_number": 175, "usage_type": "call"}, {"api_name": "utils.should_display_string_label", "line_number": 180, "usage_type": "call"}, {"api_name": "utils.get_string_for_display", "line_number": 182, "usage_type": "call"}, {"api_name": "utils.get_comment_for_display", "line_number": 185, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 213, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 214, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Qt.black", "line_number": 225, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 225, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 231, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Qt.blue", "line_number": 254, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 254, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.gray", "line_number": 258, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 258, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.black", "line_number": 269, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 269, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QColor", "line_number": 279, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Qt.blue", "line_number": 302, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 302, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.gray", "line_number": 306, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 306, "usage_type": "name"}]} +{"seq_id": "34020065432", "text": "# CRUMPET reaction class reaction.py\n# Separated from CRUMPET.py by holm10\n# Changelog\n# 200205 - Separated from CRUMPET.py #holm10\n# 200210 - Updated ADAS extrapolation, tidied up code #holm10\n \n\nclass Reaction:\n ''' Class containing reaction data\n\n This class contains data pertaining to a single defined reaction,\n such as energy transfer rates, reactants, products, and reaction\n rates.\n\n ...\n \n Attributes\n ----------\n name : string\n reaction handle/ID\n database : string\n database where reaction rates are defined\n rate(Te, Ti, ne=None, E=None, omegaj=1) : function\n returns the temperature- and density-dependent reaction rate\n coefficients\n Te : float\n electron temperature in eV\n Ti : float \n ion temperature in eV\n ne : float, optional (default: None)\n electron temperature in m**-3, only used for type='UE'\n E : float, optional (default: None)\n molecular energy in eV, only used for type='RATE'\n omegaj : float, optional (default: 1)\n statistical weight for ADAS reaction rates\n type : string\n identifier for the kind of reaction being defined:\n 'RATE' : EIRENE-type interpolation in one or two dimensions,\n depending on the shape of coeffs\n 'COEFFICIENT' : Spontaneous transition rate, s**-1\n 'ADAS' : ADAS-type interpolation\n 'SIGMA' : Sawada 1995-type cross-section fit\n 'APID' : APID-type ionization rate\n 'UE' : UEDGE-type interpolation\n reactants : list of strings\n a list of the reactant species handles\n products : list of strings\n a list of the fragment species handles\n Vp : float\n the total potential of the products\n Vr : float\n the total potential of the reactants\n K : float/string\n the kinetic energy transferred to the procucts in the reaction:\n if a species is born in the reaction, they are assigned the \n local temperature of that species, which is evaluated upon\n calls to getS\n Kdep : boolean\n marker determining wheter K is dependent on the species \n temperature, i.e. whether a new species is born\n S_V(*args) : function\n Returns the change in potential for the reaction calculated\n from the potential of the reactions and products\n S_e(Te,*args) : function\n Returns the electron energy increase for the reaction. Only\n relevant for ionization reactions. Te is the electron \n temperature in eV\n S_g(*args) : function\n Returns the gamma energy for the reaction calculated from the\n potential difference between the reactants and products. Only\n nonzero if the reaction is radiative decay\n S_r(Te,Ti,Tm) : function\n Returns the background reactant energy loss, calculated from\n the net energy balance\n Smat : 2D list\n Smat has len=8 along axis=0 and len=2 along axis=1. The first 4\n entries along axis=0 are: reactant energy loss, potential energy\n change, radiated energy in atomic lines, and radiated energy in \n molecular lines, all for electron-impact processes. The next 4 \n entries are the corresponding energy terms but for proton-impact\n processes. The first entries along axis=1 are the internal terms\n and the second due to external sinks and sources (currently 0)\n Tarr : ndarray\n A vector containing the electron temperature points of ADAS fits\n only relevant for ADAS rates, otherwise not used\n absorption : boolean\n marker determining if the reaction is an electron-absorption\n reaction\n coeffs : ndarray\n the reaction rate coefficients. The shape and values depend on\n the type of reaction. Used by rate() to calculate the reaction\n rates\n decay : boolean\n marker whether the reaction is non-radiative decay, e.g. decay\n from an unstable state into dissociation products\n radrelax : boolean\n marker whether the reaction is radiative relaxation\n prode : boolean\n marker whether an electron is created in the reaction, i.e. the\n reaction does not conserve electron energy\n e : boolean\n marker whether the reaction is an electron-impact reaction or\n not\n p : boolean \n marker whether the reaction is and proton-impact reaction or not\n p_mult : int\n list of fragment mutipliers, same length as products\n e_mult : int\n list of product mutipliers, same length as products\n \n Methods\n ------- \n '''\n\n\n def __init__(self, database, rtype, name, data, coeffs=None, bg=None,\n species=None, isotope='H', mass=1, Tarr=0, scale=1,\n sourcefile=None):\n ''' \n Parameters \n ----------\n name : string\n reaction handle/ID\n database : string\n database where reaction rates are defined\n rtype : string\n database fit type according to EIRENE definitions\n coeffs : ndarray\n the reaction rate coefficients. The shape and values depend\n on the type of reaction. Used by rate() to calculate the\n reaction rates\n type : string\n identifier for the kind of reaction being defined:\n 'RATE' : EIRENE-type interpolation in one or two dimensions,\n depending on the shape of coeffs\n 'COEFFICIENT' : Spontaneous transition rate, s**-1\n 'ADAS' : ADAS-type interpolation\n 'SIGMA' : Sawada 1995-type cross-section fit\n 'APID' : APID-type ionization rate\n 'UE' : UEDGE-type interpolation\n S : dict\n dict containing the reaction information, requiring the \n following keys:\n 'reactants' : list of strings\n reactant handles\n 'products' : list of strings\n fragment handles\n 'K' : string\n string of kinetic energy information\n bg : dict\n Crm.bg, dict of background species and potentials\n species : dict\n Crm.species, dict of CRM species and potentials\n Tarr : list, optional (default: 0)\n Array of ADAS temperature point. Only used when typ=ADAS\n isotope : string\n The handle for the isotope being used. Used to identify \n atomic and molecular species\n mass : float\n The isotope mass in AMU, used for calculations in Crm \n '''\n from numpy import array, log10\n\n self.database = database.upper()\n self.isotope = isotope.upper()\n self.name = name\n self.type = rtype.upper()\n self.scale = scale\n self.mass = mass\n self.sourcefile = sourcefile\n self.K = '0'\n self.tag = '{} {} {}'.format(self.database, self.type, self.name)\n\n reaction = data.pop(0)\n self.educts = [x.strip() for x in \\\n reaction.split(' > ')[0].split(' + ')]\n self.products = [x.strip() for x in \\\n reaction.split(' > ')[1].split(' + ')]\n self.e_mult, self.p_mult = [],[]\n\n for i in range(len(self.educts)):\n if '*' in self.educts[i]:\n m, s = self.educts[i].split('*')\n self.educts[i] = s.strip()\n self.e_mult.append(float(m))\n else:\n self.e_mult.append(1)\n\n for i in range(len(self.products)):\n if '*' in self.products[i]:\n m, s = self.products[i].split('*')\n self.products[i] = s.strip()\n self.p_mult.append(float(m))\n else:\n self.p_mult.append(1)\n\n self.e_mult = array(self.e_mult)\n self.p_mult = array(self.p_mult)\n # print(database, rtype, name, data, coeffs)\n # Make indices array if the reaction is a direct EIRENE rate taken \n # from a database\n # Read user input rates\n self.coeffs = coeffs\n if self.database == 'USER':\n self.coeffs = []\n if self.type == 'H.2':\n self.coeffs = list(map(float, data.pop(0).split()))\n elif self.type in ['H.3', 'H.4']:\n for i in range(9):\n self.coeffs.append(list(map(int, data.pop(0).split())))\n self.coeffs = array(self.coeffs)\n elif self.type.upper() == 'RELAXATION':\n self.coeffs = float(data.pop(0))\n elif self.type.upper() == 'COEFFICIENT':\n self.coeffs = float(data[0])\n elif self.type.upper() == 'SIGMA':\n self.coeffs = [float(x) for x in data.pop(0).split()]\n elif self.type.upper() == 'INTERPOLATION':\n pass\n else:\n print('Reaction database \"{}\" and type \"{}\"'\n ' not recognized!'.format(self.database, self.type))\n\n if self.type.upper() == 'INTERPOLATION':\n # Read the number of data points defined\n self.coeffs = []\n for i in range(int(data.pop(0))):\n self.coeffs.append([float(x) for x in data.pop(0).strip().split()])\n self.coeffs=log10(array(self.coeffs))\n\n # If user-defined, get it from data\n# if database.upper() == 'USER':\n# self.coeffs = data.pop(0) \n\n for s in data:\n if ('K=' in s) or ('K =' in s):\n self.K = s.split('=')[1].strip()\n# try:\n# self.K = eval(self.K)\n# except:\n# pass\n\n\n\n\n\n self.Tarr = array(Tarr)\n self.parseS(bg, species, isotope)\n self.rate = self.pick_rate()\n\n '''\n print('==============')\n print(self.database, self.type, self.name)\n print(self.coeffs)\n print(self.rate(1,1,ne=1e12,E=0.1))\n print(self.rate(10,10,ne=1e13,E=1))'''\n\n\n def parseS(self, bg, species, isotope):\n ''' Derives the reaction information\n \n Assigns the majority of the object attributes, including\n the energy sinks/sources for the different components \n\n Parameters\n ----------\n r : list of strings\n reactant handles\n p : list of strings\n fragment handles\n K : string\n string of kinetic energy information\n bg : dict\n Crm.bg, dict of background species and potentials\n species : dict\n Crm.species, dict of CRM species and potentials\n\n Returns\n -------\n None\n '''\n from numpy import ones\n\n self.e = ('e' in self.educts)\n self.p = ('p' in self.educts)\n self.absorption = False # Electron absorption\n self.prode = False # Electron production (not conserving Ee)\n self.radrelax = False # Radiative relaxation\n self.decay = False # Non-radiative decay: energy goes into \n # K of prod \n # TODO: how to treat ionization reactions? Included in K? Or not?\n # Check if reaction is an electron absorption reaction\n if self.e:\n try:\n for x in self.educts:\n if x in list(bg): \n b = x\n if b not in self.products:\n self.absorption = True\n # Account for two-step processes where electron is absorbed\n # creating a non-stable particle that decays\n if len(self.p_mult) > len(self.e_mult): \n self.decay = True\n except:\n pass\n # Do not consider e-impact ionization impact on e-balance:\n # all energy supplied to the created electron supplied by\n # the reactant electron\n else:\n # Proton-impact ionization\n if 'e' in self.products:\n self.prode = True\n # Radiative relaxation when one particle changes state\n if sum(self.e_mult) == 1 and sum(self.p_mult == 1): \n self.radrelax=True\n # Non-radiative decay when one particle becomes many\n if sum(self.e_mult) == 1 and sum(self.p_mult) > 1: \n self.decay = True\n # TODO: catches for UEDGE ionization and relaxation\n # Total potential of reactants and products\n self.Vr = 0\n self.Vp = 0\n for i in range(len(self.educts)):\n try:\n self.Vr += self.e_mult[i]*species[self.educts[i]]['V']\n except:\n self.Vr += self.e_mult[i]*bg[self.educts[i]]['V']\n for i in range(len(self.products)):\n try:\n self.Vp += self.p_mult[i]*species[self.products[i]]['V']\n except:\n self.Vp += self.p_mult[i]*bg[self.products[i]]['V']\n self.S_r = self.ret0\n self.S_V = self.ret0\n self.S_g = self.ret0\n self.S_e = self.ret0\n # Reactant energy loss\n if self.radrelax is False:\n if self.decay is False:\n self.S_r = self.Sr\n else: # Spontaneous decay\n self.S_r = self.decaySr\n self.S_V = self.fSV\n else:\n self.S_V = self.fSV\n self.S_g = self.fSg\n if self.prode is True:\n self.S_e = self.depSe\n else:\n self.S_e = self.ret0\n # Create energy matrix in order to only evaluate conditionals once\n\n # TODO: Verify external energy change - when is it relevant\n Sl = [self.S_r, self.S_V, self.S_g]\n self.Smat = []\n if self.p:\n if not self.e:\n Sl[0] = self.S_e\n for i in range(4):\n self.Smat.append([self.ret0, self.ret0])\n for i in range(3):\n ext = self.ret0\n if (i != 2):\n self.Smat.append([Sl[i], ext])\n elif (i == 2)*('{}2'.format(isotope) in ''.join(self.educts)):\n self.Smat.append([self.ret0, self.ret0])\n self.Smat.append([Sl[i], ext])\n else:\n self.Smat.append([Sl[i], ext])\n self.Smat.append([self.ret0, self.ret0])\n while len(self.Smat) < 8:\n for i in range(4):\n self.Smat.append([self.ret0, self.ret0])\n \n\n def getS(self, Te, Ti, Tm, E, marker=False):\n ''' Evaluates Smat at given Te, Ti, Tm, and E\n \n Parameters\n ----------\n Te : float\n electron temperature in eV\n Ti : float\n ion temperature in eV\n Tm : float\n molecular temperature in eV\n E : float\n assumed molecular rest energy in eV\n\n Returns\n -------\n ndarray\n Evaluated ndarray of Smat\n '''\n from numpy import array,sum\n if Tm == 0:\n Tm=E\n S=array([[column(Te, Ti, Tm) for column in row] for row in self.Smat])\n ret = array([\n [S[0] + S[4]],\n [-sum(S, axis=0)],\n [S[1] + S[5]],\n [S[2] + S[6]],\n [S[3] + S[7]] ],\n )[:,0,:]\n if marker is True:\n ret[ret<0]=0\n ret[ret!=0]=1\n # Only return positive values\n return ret\n \n\n def ret0(self, *args):\n ''' Zero-function'''\n return 0\n\n\n def fSV(self, *args):\n ''' Potential difference '''\n return self.Vp - self.Vr\n\n\n def fSg(self, *args):\n ''' Gamma energy'''\n return self.Vr - self.Vp\n\n\n def Sr(self, Te=0, Ti=0, Tm=0):\n ''' Temperature-dependent reactant sink \n try:\n ret = self.Vr - self.Vp - eval(self.K)\n except:\n ret = self.Vr - self.Vp - self.K\n return ret'''\n return self.Vr - self.Vp - eval(self.K)\n \n def decaySr(self, Te=0, Ti=0, Tm=0):\n ''' Temperature-dependent fragmentation \n # TODO: IS SIGN MISMATCH WARRANTED??!\n try:\n ret = -eval(self.K)\n except:\n ret = self.K\n return ret'''\n return -eval(self.K)\n\n def depSe(self, Te, *args):\n ''' Electron energy gain '''\n return Te\n\n def get_nsum(self, ne, ni):\n return max(1,self.e*ne + self.p*ni)\n \n def get_n(self, ne, ni):\n# if (self.e is True) and (self.p is True):\n# print(self.name)\n# return max(self.e*ne * self.p*ni,1)\n if self.e is True: # Electron impact: assume to use for rec. as well\n return ne\n elif self.p is True: # Proton impact\n return ni\n else: # Neither electron or ion impact: assume spontaneous\n return 1\n\n def interp1d_loglog(self,T):\n from numpy import log10\n return 10**self.interp(log10(T))\n \n \n \n def print_reaction(self):\n ''' Returns formatted a string with the reaction '''\n ret = '{}_{}_{}: '.format(self.database, self.type, self.name) # Append reaction ID\n for i in range(len(self.educts)): # Loop through all reactants\n ret += (self.e_mult[i] != 1)*'{}*'.format(self.e_mult[i])\\\n + '{}'.format(self.educts[i])\\\n + (i+1 != len(self.educts))*'+ '\n ret += ' => ' # Add reactants to string\n for i in range(len(self.products)): # Loop through all fragmetns\n ret += (self.p_mult[i] != 1)*'{}*'.format(self.p_mult[i])\\\n + '{}'.format(self.products[i])\\\n + (i+1 != len(self.products))*'+ ' \n # Add products to string\n \n return ret \n\n\n def pick_rate(self):\n ''' Initialization of the rate attribute '''\n # TODO: Dynamically choose between ni and ne in reaction\n from scipy.interpolate import interp1d\n from numpy import pi, log10\n if self.database == 'ADAS': \n # TODO: verify ADAS\n print('TBD')\n '''\n if ID == 'EXCITATION': # Electron impact excitation\n # Excitation only possible between current state and nmax\n rn = range(x + 1, self.nmax + 1) \n fit = 'ADAS' # ADAS-type fit\n Tarr = rdata[database]['T'] # Temperature array for interpolation\n elif ID=='RELAXATION': # Radiative relaxation\n rn = range(1, x) # Relaxation only possible to lower states\n fit = 'COEFFICIENT' # Coefficient-like decay\n Tarr = None # Not T-dependent\n # Loop through each of the available final states\n for y in rn:\n try:\n _name = '{}_{}-{}'.format(ID, x, y)\n #Get coefficients\n _ratecoeff = rdata['ADAS']['{}-{}'.format(x, y)] \n self.reactions['ADAS'][_name] = Reaction('ADAS',\n _ratecoeff, fit, data, bg, species, \n self.isotope, self.mass, Tarr)\n except:\n pass\n # Create 1D interpolation function\n self.interpolation = interp1d(\n self.Tarr, self.coeffs, kind='slinear') \n return self.ADAS_rate'''\n elif self.database == 'UE': \n # TODO: verify UE\n print('TBD')\n# t = [i for i in range(self.coeffs.shape[0])]\n# n = [i for i in range(self.coeffs.shape[1])]\n# # Create 2D interpolation function\n# self.interpolation = interp2d(n, t, self.coeffs, kind='linear') \n# return self.UE_rate\n elif self.database == 'APID': \n x = self.coeffs\n self.apidA = 0.14784*(x == 2) + 0.058463*(x == 3) \n self.apidB = [\n 0.0080871*(x == 2) - 0.051272*(x == 3), \n -0.06227*(x == 2) + 0.85310*(x == 3), \n 1.9414*(x == 2) - 0.57014*(x == 3), \n -2.198*(x == 2) + 0.76684*(x == 3), \n 0.95894*(x == 2),\n 1.133*(x > 3),\n -0.4059*(x > 3),\n 0.0714*(x > 3)\n ]\n return self.APID_rate\n elif self.database == 'JOHNSON':\n ''' L.C.JOHNSON, ASTROPHYS. J. 174, 227 (1972). '''\n # TODO: verify against Eirene's COLRAD!\n (i, f) = self.coeffs\n def g(i, f):\n g = [\n 1.133*(f == 1) + 1.0785*(f == 2) + (0.9935 + 0.2328/f\n - 0.1296/f**2)*(f > 2),\n -0.4059*(f == 1) - 0.2319*(f==2) - ((0.6282 - 0.5598/f\n + 0.5299/f**2)/f)*(f > 2),\n 0.07014*(f == 1) + 0.02947*(f == 2) + ((0.3887 - 1.181/f\n + 1.470/f**2)/f**2)*(f > 2) ]\n x=1 - (f/i)**2\n return g[0] + g[1]/x + g[2]/x**2\n\n h = 1.054571596e-27\n c = 2.99792458e10\n me = 9.10938188e-28\n e = 4.80274e-10\n I = (me*e**4)/(2*h**2)\n res = (2**6 * e**2 * I**2)/(3**1.5*pi*me*c**3 * h**2)\n freq = (1/f**2 - 1/i**2)\n Afac = (res*g(i, f))/(freq*(i**5)*(f**3))\n self.coeffs = Afac\n return self.coeff_constant\n\n elif self.database == 'MCCCDB':\n self.interp = interp1d(log10(self.coeffs[:,0]),\n log10(self.coeffs[:,1]))\n self.xsec = self.interp1d_loglog\n return self.integrated\n\n elif 'H.' in self.type.upper(): \n if self.type.upper() == 'H.0':\n print('Potential: TBD')\n return\n elif self.type.upper() == 'H.1':\n self.xsec = self.polyfit_H1\n return self.integrated\n elif self.type.upper() == 'H.2':\n return self.polyfit_H2\n elif self.type.upper() == 'H.3':\n return self.polyfit_H3\n elif self.type.upper() == 'H.4':\n return self.polyfit_H4\n elif self.type.upper() == 'H.5':\n print('Momentum-weighted rates vs. temperature, not in use')\n return\n elif self.type.upper() == 'H.6':\n print('Momentum-weighted rates vs. temperaturei and energy: TBD')\n return\n elif self.type.upper() == 'H.7':\n print('Momentum-weighted rates vs. temperature and density, not in use')\n return\n elif self.type.upper() == 'H.8':\n print('Energy-weighted rates vs. temperature: TBD')\n return\n elif self.type.upper() == 'H.9':\n print('Energy-weighted rates vs. temperature and energy, not in use')\n return\n elif self.type.upper() == 'H.10':\n print('Energy-weighted rates vs. temperature and density: TBD')\n return\n elif self.type.upper() == 'H.11':\n print('Other data: TBD')\n return\n elif self.type.upper() == 'H.12':\n print('Other data: TBD')\n return\n else:\n print('Unknown fit: {}, {}, {}'.format(\n self.database,self.type,self.name))\n elif self.type.upper() in ['COEFFICIENT', 'RELAXATION']: \n if ('e' in self.educts) or ('p' in self.educts):\n return self.coeff_rate\n else:\n return self.coeff_constant\n elif self.type.upper() == 'INTERPOLATION': \n self.Tl, self.Tu = 10**self.coeffs[0,0], 10**self.coeffs[-1,0]\n self.coeffs = interp1d(self.coeffs[:,0], self.coeffs[:,1])\n return self.loglog_interp\n elif self.type.upper() == 'SIGMA': \n # TODO: verify SIGMA\n print('Sigma rates to be verified!')\n # NOTE: To be verified\n return self.SAWADASIGMA_rate\n else: \n print('Unknown type \"{} {}\"'.format(self.database, self.type))\n\n\n def ADAS_rate(self, Te, Ti, omegaj=1, **kwargs):\n ''' Function returning the ADAS rate for T '''\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n if T < self.Tarr[0]:\n Tuse = self.Tarr[0]\n coeff = T/Tuse\n else:\n Tuse = T\n coeff = 1\n Tuse = min(Tuse, self.Tarr[-1])\n# T=max(T,self.Tarr[0])\n # TODO: How to deal with extrapolation?\n # TODO: figure out what is implied by the statistical weight omegaj?\n # set =1 for now\n # Return the rate as calculated from the ADAS fit, per the ADAS manual\n return 2.1716e-8*(1/omegaj)*sqrt(13.6048/Tuse)*self.interpolation(Tuse)\n\n\n def extrapolate_polyfit(self, T, fittype, E, ne, ni, frequency = False):\n from numpy import log, exp, log10\n x = log10(T)\n dx=1e-1\n x1 = 0.5\n y1 = log10(fittype(Te=x1,Ti=x1,E=E,ne=ne,ni=ni))\n dy = log10(fittype(Te=x1+dx,Ti=x1+dx,E=E,ne=ne,ni=ni))\n k = -(y1-dy)/(log10(x1+dx)-log10(x1))\n b = y1 - k*log10(x1)\n return self.get_n(ne, ni)**frequency*(10**(k*x+b))\n\n\n def polyfit_H1(self, Te, Ti=0, E=0, ne=0, ni=0, extrapolate=True):\n ''' Function returning the EIRENE rate for T '''\n from numpy import log, exp, log10\n ep = ((self.e + self.p) ==2)\n ret = 0\n if (Te < 0.5) and (extrapolate is True): \n return self.extrapolate_polyfit(Te, self.polyfit_H1, 0, 0, 0, False)\n # Rate coefficient vs temperature\n for i in range(9):\n ret += self.coeffs[i]*(log(Te)**i)\n return self.scale*selg.get_n(ne,ni)**frequency*exp(ret)\n\n def polyfit_H2(self, Te, Ti, E=0, ne=0, ni=0, frequency=False, extrapolate = True, **kwargs):\n ''' Function returning the EIRENE rate for T '''\n from numpy import log, exp, log10\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n ret = 0\n if (T < 0.5) and (extrapolate is True): \n return self.extrapolate_polyfit(T, self.polyfit_H2, E, ne, ni, frequency)\n # Rate coefficient vs temperature\n for i in range(9):\n ret += self.coeffs[i]*(log(T)**i) \n return self.scale*self.get_n(ne,ni)**frequency*exp(ret)\n \n def polyfit_H3(self, Te, Ti, E=None, ne=0, ni=0, frequency=False, extrapolate=True, **kwargs):\n ''' Function returning the EIRENE rate for T '''\n from numpy import log, exp, log10\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n ret = 0\n if (T < 0.5) and (extrapolate is True): \n return self.scale*self.extrapolate_polyfit(T, self.polyfit_H3, E, ne, ni, frequency)\n # Rate coefficient vs temperature and energy\n for i in range(9):\n for j in range(9):\n ret += self.coeffs[i,j]*(log(T)**i)*(log(E)**j)\n return self.scale*self.get_n(ne,ni)**frequency*exp(ret)\n\n def polyfit_H4(self, Te, Ti, E=0, ne=None, ni=None, frequency=False, extrapolate=True, **kwargs):\n ''' Function returning the EIRENE rate for T '''\n from numpy import log, exp, log10\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n ret = 0\n if (T < 0.5) and (extrapolate is True): \n return self.scale*self.extrapolate_polyfit(T, self.polyfit_H4, E, ne, ni, frequency)\n # Rate coefficient vs temperature and density\n for i in range(9):\n for j in range(9):\n ret += self.coeffs[i,j]*(log(T)**i)*(log(ne*1e-8)**j)\n return self.scale*self.get_n(ne,ni)**frequency*exp(ret)\n\n\n\n def loglog_interp(self, Te, Ti=0, E=None, ne=None, ni=None, frequency=False, **kwargs):\n from numpy import log10\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n if (T >= self.Tl) and (T <= self.Tu):\n fit = self.coeffs(log10(T))\n elif T < self.Tl:\n fit = self.coeffs(log10(self.Tl))\n elif T > self.Tu:\n fit = self.coeffs(log10(self.Tu))\n return self.scale*(self.get_n(ne,ni)**frequency)*10**fit\n\n def coeff_constant(self, *args, frequency=False, ne=None, ni=None, **kwargs):\n # NOTE: These are always assumed to be in 1/s \n return self.coeffs*self.scale\n \n def coeff_rate(self, *args, frequency=False, ne=None, ni=None, **kwargs):\n # NOTE: These are always assumed to be in 1/s \n return self.scale*self.coeffs*self.get_n(ne,ni)**frequency\n \n\n def UE_rate(self, Te, *args, E=None, ne=None, **kwargs):\n ''' Function returning the UEDGE rate for T and n '''\n from numpy import log10\n # Turn the temperature and density into log-log variables, \n # bounded to the limits\n jt = max(0,min(10*(log10(Te + 1e-99)+1.2), 60))\n jn = max(0,min(2*(log10(ne) - 10), 15))\n # Interpolate jt\n \n # If the density is being extrapolated, scale accordingly!\n # c=1\n # if self.name in ['RECRAD','IONIZRAD']: c=6.242e11\n return self.interpolation(jn, jt)[0]#*\\\n # (6.242e11**(self.name in ['RECRAD', 'IONIZRAD']))\n\n\n def SAWADASIGMA_rate(self, Te, Ti, **kwargs):\n ''' Function returning the Sawada-Sigma rate for T '''\n from numpy import sqrt,pi,inf,exp\n from scipy.integrate import quad\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n # TODO Extend to general species?\n mm = self.mass*2*1.6735575e-27 \n me = 9.10938356e-31 # Assume electron is reactant 2\n ev = 1.602e-19 # Helper\n #VH2=sqrt((2*E*ev)/mm) # Get the H2 velocity\n Ve = sqrt((2*T*ev)/me) # Get the e- velocity\n mr = (me*mm)/(me+mm) # Reduced mass\n vth = sqrt((2*self.coeffs[0]*ev)/mr) # Thermal CM speed\n \n def sigma(E, Eth, q0, A, Omega, W, gamma, nu): \n # Calculate sigma from the Sawada fit based on E\n Psi = (nu != 0)*(1-W/E)**nu+(gamma != 0)*(1 - (W/E)**gamma) \n # Get triplet/singlet Psi\n return (E >= Eth)*q0*(A/W**2)*((W/Eth)**Omega)*Psi \n # Perform fit\n \n def R(x, T, coeffs):\n # Integrand function as described in JUEL-3858\n return x*sigma(x*T, *self.coeffs)*exp(-x)\n\n # Perform integration over velocity space according to JUEL-3858\n return self.scale*(4/sqrt(pi))*sqrt((T*ev)/(2*me))*\\\n quad(R, 0, inf, args=(T, self.coeffs))[0]\n \n\n def APID_rate(self, Te, Ti, ne=None, ni=None, frequency=False, **kwargs):\n ''' Function returning the APID rate for T '''\n from numpy import exp, sqrt, log, pi\n ep = ((self.e + self.p) ==2)\n T = (Te*self.e + Ti*(self.p - ep))/(self.e + self.p - ep)\n ''' The APID-4 rates are taken from Janev's 1993 IAEA paper, the \n analytic solutions from Stotler's svlib routine used in DEGAS2 '''\n I = 13.6/self.coeffs**2\n n = self.get_n(ne, ni)**frequency\n\n def expint(k, p):\n a = [-0.57721566, 0.99999193, -0.24991055, 0.05519968, -0.00976004,\n 0.00107857]\n ap = [8.5733287401, 18.0590169730, 8.6347608925, 0.2677737343]\n b = [9.5733223454, 25.6329561486, 21.0996530827, 3.9584969228]\n\n def en(zn, z):\n return exp(-p)*( 1 + kp/(kp+p)**2 + kp*(kp - 2*p)/(kp + p)**4 +\n kp*(6*p**2 - 8*kp*p+kp**2)/(kp + p)**6 )/(kp + p)\n\n if p < 0 or (p == 0 and k == 1): \n return None\n elif k*p == 0: \n return self.scale*(k == 0)*exp(-p)/p + (p == 0)*(1/(k-1))\n\n elif p < 8 or k == 1:\n if p < 1:\n ze1 = a[5]\n for i in [4,3,2,1,0]:\n ze1 = a[i] + ze1*p\n ze1 -= log(p)\n\n else:\n znum = 1\n zden = 1\n for i in range(4):\n znum = ap[i] + znum*p\n zden = b[i] + zden*p\n ze1 = exp(-p)*znum/(zden*p)\n\n if k == 1:\n return self.scale*ze1\n else:\n zek = ze1\n for i in range(2, k+1):\n zek = (exp(-p) - p*zek)/(i - 1)\n return self.scale*zek\n \n else:\n kp = int(p + 0.5)\n if k < kp and k <= 10:\n if kp > 10: \n kp = 10\n zek = en(kp, p)\n for i in range(kp - 1, k - 1, -1): \n zek=(exp(-p) - i*zek)/p\n return self.scale*zek\n \n else: return self.scale*en(k, p)\n return 'Fell through'\n\n # Use set constants to eval sigma\n if self.coeffs <= 3: \n zarg = I/T\n zeint = []\n for i in range(1, 7): \n zeint.append(expint(i, zarg))\n zmul = []\n zmul.append(self.apidB[0] + 2*self.apidB[1] + 3*self.apidB[2] + \n 4*self.apidB[3] + 5*self.apidB[4])\n zmul.append(-2*(self.apidB[1] + 3*self.apidB[2] + 6*self.apidB[3]\n + 10*self.apidB[4] ))\n zmul.append(3*(self.apidB[2] + 4*self.apidB[3] \n + 10*self.apidB[4] ))\n zmul.append(-4*(self.apidB[3] + 5*self.apidB[4] ))\n zmul.append(5*self.apidB[4])\n zi1 = 0\n for i in range(5):\n zi1 += zmul[i]*zeint[i + 1]\n zi2 = self.apidA*zeint[0]\n zi3 = 1e-13/(I*T)\n return n*6.692e7*sqrt(T)*zi3*(zi1 + zi2)\n\n # Higher n, evaluate from formulae\n else:\n zn=self.coeffs\n zg0 = 0.9935 + 0.2328/zn - 0.1296/zn**2\n zg1 = -(0.6282 - 0.5598/zn + 0.5299/zn**2) / zn\n zg2 = -(0.3887 - 1.181/zn + 1.470/zn**2) / zn**2\n zmul = 32. / (3. * sqrt(3.) * pi)\n zan = zmul * zn * (zg0/3. + zg1/4. + zg2/5.) \n zrn = 1.94 * zn**(-1.57)\n zb = (4.0 - 18.63/zn + 36.24/zn**2 - 28.09/zn**3) / zn\n zbn = 2. * zn**2 * (5. + zb) / 3\n zyn = I / T\n zzn = zrn + zyn\n ze1y = expint(1, zyn)\n ze1z = expint(1, zzn)\n zint1 = zan * (ze1y/zyn - ze1z/zzn) \n zxiy = expint(0, zyn) - 2.*ze1y + expint(2, zyn)\n zxiz = expint(0, zzn) - 2.*ze1z + expint(2, zzn)\n zint2 = (zbn - zan * log(2.*zn**2)) * (zxiy - zxiz)\n zint3 = 1.76e-16 * zn**2 * zyn**2\n ret = 6.692e7 * sqrt(T) * zint3 * (zint1 + zint2)\n\n return n*ret\n \n\n '''\n def integrand(self, x, T):\n from numpy import exp, log\n return x*self.xsec(x*T)*exp(-x)\n\n def integrated(self, Te, Ti, ne=0, ni=0,E=None, frequency=False, **kwargs):\n from scipy.integrate import quad\n from numpy import inf, pi\n T = (self.e*Te+self.p*Ti)\n return self.scale*100*self.get_n(ne,ni)**frequency\\\n *(4/pi**0.5)*((1.602e-19*T)/\\\n (2*9.1093837e-31))**0.5*\\\n quad(self.integrand, 0, inf, args=(T))[0]\n\n\n '''\n\n def integrand(self, x, T):\n from numpy import exp, log\n ret = x*self.xsec(x*T)*exp(-x)\n ret[ret==0] = 1e-99\n return log(ret)\n\n def integrated(self, Te, Ti, ne=0, ni=0, E=None, frequency=False, **kwargs):\n \"\"\" Integration in log-log space using trapezoidal rule \"\"\"\n from numpy import logspace,exp,log, pi, diff\n from scipy.special import logsumexp\n\n T = (self.e*Te+self.p*Ti)\n x = logspace(-10,4,1000)\n y = self.integrand(x, T)\n deltas = log(diff(x))\n integrand = exp(-log(2.) + logsumexp([logsumexp(y[:-1]+deltas), logsumexp(y[1:]+deltas)]))\n\n return self.scale*100*self.get_n(ne,ni)**frequency\\\n *(4/pi**0.5)*((1.602e-19*T)/\\\n (2*9.1093837e-31))**0.5*integrand\n", "repo_name": "holm10/CRUMPET", "sub_path": "reactions.py", "file_name": "reactions.py", "file_ext": "py", "file_size_in_byte": 36648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.array", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 467, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 563, "usage_type": "name"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 570, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 570, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 571, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 622, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 654, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 657, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 658, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 659, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 660, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 673, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 674, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 687, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 700, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 701, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 714, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 715, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 724, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 726, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 728, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 745, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 746, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 769, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 780, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 783, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 783, "usage_type": "name"}, {"api_name": "scipy.integrate.quad", "line_number": 784, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 784, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 804, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 810, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 817, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 825, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 832, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 842, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 868, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 876, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 876, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 888, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 890, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 914, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 916, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 924, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 926, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 926, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 927, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 927, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 927, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 930, "usage_type": "name"}]} +{"seq_id": "10212161289", "text": "import sys\n\nfrom PySide2.QtUiTools import QUiLoader\nfrom PySide2.QtWidgets import QApplication, QPushButton, QLineEdit, QTextBrowser, QLabel\nfrom PySide2.QtCore import QObject, Signal, Slot\nfrom PySide2.QtGui import QPixmap\n\n\nclass MainScreen(QObject):\n\n submit_id = Signal(str)\n open_story = Signal()\n\n def __init__(self, ui_file, parent=None):\n super(MainScreen, self).__init__(parent)\n\n self.window = QUiLoader().load(ui_file)\n self.extract_items()\n self.connect_signals()\n self.inStory = False\n\n image = QPixmap(\"images/main.jpg\")\n self.base_image.setPixmap(image)\n\n self.window.show()\n\n def extract_items(self):\n self.submit_button = self.window.findChild(QPushButton, \"submit_button\")\n self.input_line = self.window.findChild(QLineEdit, \"input_line\")\n self.base_image = self.window.findChild(QLabel, \"base_image\")\n\n def connect_signals(self):\n self.submit_button.clicked.connect(self.submit_id_number)\n self.submit_button.clicked.connect(self.input_line.clear)\n\n def open_window(self):\n self.window.show()\n\n def close_window(self):\n self.window.hide()\n\n @Slot()\n def set_story_false(self):\n self.inStory = False\n self.window.show()\n\n def submit_id_number(self):\n if self.inStory:\n return\n id = self.input_line.text()\n self.submit_id.emit(id)\n self.open_story.emit()\n self.inStory = True\n self.window.hide()\n\n\nclass StoryScreen(QObject):\n\n story_finish = Signal()\n\n def __init__(self, ui_file, parent=None):\n super(StoryScreen, self).__init__(parent)\n self.window = QUiLoader().load(ui_file)\n\n self.extract_items()\n self.connect_signals()\n self.scene = 0\n self.storyText = []\n self.storyBegin = False\n\n image = QPixmap(\"images/cern_color.jpg\")\n self.bottom_image.setPixmap(image)\n\n self.window.hide()\n\n def extract_items(self):\n self.next_button = self.window.findChild(QPushButton, \"next_button\")\n self.story_box = self.window.findChild(QTextBrowser, \"story_box\")\n self.story_image = self.window.findChild(QLabel, \"story_image\")\n self.bottom_image = self.window.findChild(QLabel, \"bottom_image\")\n\n def connect_signals(self):\n self.next_button.clicked.connect(self.go_next_screen)\n\n @Slot()\n def open_window(self):\n self.window.show()\n\n @Slot()\n def close_window(self):\n self.window.hide()\n\n def set_story_start(self):\n self.storyBegin = True\n self.scene += 1\n self.update_screen()\n\n def go_next_screen(self):\n if not self.storyBegin:\n return\n if self.scene == 6:\n self.window.hide()\n self.scene = 0\n self.update_screen()\n self.storyBegin = False\n self.story_box.clear()\n self.story_finish.emit()\n else:\n self.scene += 1\n self.update_screen()\n\n def update_screen(self):\n self.story_box.setText(self.storyText[self.scene])\n\n image = QPixmap(\"images/image_10{}.jpg\".format(self.scene))\n self.story_image.setPixmap(image)\n", "repo_name": "gluoNNet/MyLastDayAtCERN", "sub_path": "Webfest QT/windows/Screens.py", "file_name": "Screens.py", "file_ext": "py", "file_size_in_byte": 3240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "PySide2.QtCore.QObject", "line_number": 9, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 11, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 12, "usage_type": "call"}, {"api_name": "PySide2.QtUiTools.QUiLoader", "line_number": 17, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QPixmap", "line_number": 22, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QPushButton", "line_number": 28, "usage_type": "argument"}, {"api_name": "PySide2.QtWidgets.QLineEdit", "line_number": 29, "usage_type": "argument"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 30, "usage_type": "argument"}, {"api_name": "PySide2.QtCore.Slot", "line_number": 42, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QObject", "line_number": 57, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Signal", "line_number": 59, "usage_type": "call"}, {"api_name": "PySide2.QtUiTools.QUiLoader", "line_number": 63, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QPixmap", "line_number": 71, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QPushButton", "line_number": 77, "usage_type": "argument"}, {"api_name": "PySide2.QtWidgets.QTextBrowser", "line_number": 78, "usage_type": "argument"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 79, "usage_type": "argument"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 80, "usage_type": "argument"}, {"api_name": "PySide2.QtCore.Slot", "line_number": 85, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Slot", "line_number": 89, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QPixmap", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "19322319732", "text": "import sys\nfrom datetime import datetime\nimport matplotlib\nimport matplotlib.pyplot as plt\n#plt.style.use(\"thesis\")#\n# plt.xkcd()\nimport pandas as pd\n\nthesis_meetings = [] # 2 august 2019\n\ndef main(filein):\n df = pd.read_csv(filein, header=None,\n delim_whitespace=True,\n names=[\"unix_time\", \"wc\"])\n dates = pd.to_datetime(df[\"unix_time\"], unit=\"s\")\n #plt.xkcd()\n fig, ax = plt.subplots()\n ax.plot_date(dates, df[\"wc\"], fmt='', linestyle=\"-\", label=\"Word count\")\n\n ax.set_ylabel(\"Word count\")\n ax.set_xlabel(\"Date\")\n ax.axvline(datetime(2020, 3, 23), color=\"orange\",linestyle=\"dotted\", label=\"Lockdown Starts\") # Started chapter 2\n #ax.axvline(datetime(2020, 4, 01), color=\"orange\") # Finished chapter 4\n\n # ax.axvline(datetime(2020, 3, 20), color=\"black\", label=\"Lewis's submission\")\n ax.axvline(datetime(2020, 4, 29), color=\"green\", linestyle=\"dotted\", label=\"Lewis's viva\")\n ax.axvline(datetime(2020, 4, 27), color=\"red\", linestyle=\"dotted\", label=\"2nd circulation\")\n ylim = ax.get_ylim()\n ax.axvspan(datetime(2019, 12, 20), datetime(2020, 3, 1),color=\"red\", alpha=0.3, linewidth=0, label=\"Analysis push\")\n\n # ax.axvline(datetime(2019, 11, 8), color=\"orange\", label=\"Deadline\")\n\n #ax.axvspan(datetime(2019, 7, 19), datetime(2019, 7, 21),\n # color=\"blue\", alpha=0.4, linewidth=0,\n # label=\"Helena's wedding\")\n\n ax.legend(loc=\"upper left\",frameon=0)\n # from IPython import embed; embed()\n\n # ax.set_yscale(\"log\")\n # ax.axvline(datetime(2019, 11, 8), color=\"orange\")\n\n plt.show()\n\nif __name__ == '__main__':\n main(\"timestamped_wordcounts.txt\")\n# main(\"timestamped_figurecounts.txt\")\n", "repo_name": "desh93/Thesis", "sub_path": "latex-wc/plot_wc.py", "file_name": "plot_wc.py", "file_ext": "py", "file_size_in_byte": 1717, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "73430498750", "text": "import argparse\nimport json\nimport os\nimport shutil\n\nfrom support.run import run\n\nNAME = \"setup_states\"\nDESCRIPTION = \"\"\nVALIDATOR_STATES = \"validator-states\"\nVALIDATOR_STATE = \"validator%s\"\nROOT_DIR = os.path.abspath(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\n\n# Retrieve bridge_eth_addresses.json and keys.json\n# arb-bridge-eth must be have been built first\ndef setup_validator_states_ethbridge(contract, n_validators, sudo=False):\n keys = \"keys.json\"\n ethaddrs = \"bridge_eth_addresses.json\"\n\n layer = run(\n \"docker create arb-bridge-eth\", capture_stdout=True, quiet=True, sudo=sudo\n ).strip()\n if layer == \"\":\n print(\"Docker image arb-bridge-eth does not exist\")\n return\n run(\n \"docker cp %s:/home/user/bridge_eth_addresses.json %s\" % (layer, ethaddrs),\n sudo=sudo,\n )\n run(\"docker cp %s:/home/user/keys.json %s\" % (layer, keys), sudo=sudo)\n run(\"docker rm %s\" % layer, quiet=True, sudo=sudo)\n\n setup_validator_states(contract, n_validators, keys, ethaddrs)\n\n os.remove(keys)\n os.remove(ethaddrs)\n\n\ndef setup_validator_states(contract, n_validators, acct_keys, ethaddrs):\n ARB_VALIDATOR = os.path.join(ROOT_DIR, \"packages\", \"arb-validator\")\n\n # Check for validator_states in cwd\n if os.path.isdir(VALIDATOR_STATES):\n exit(\"Error:\", VALIDATOR_STATES, \"exists in the current working directory\")\n\n # Extract keys from acct_keys\n with open(acct_keys, \"r\") as f:\n data = json.loads(f.read())[\"addresses\"]\n addresses = [addr for addr in list(data.keys())][-n_validators:]\n privates = []\n for key in [data[addr][\"secretKey\"][\"data\"] for addr in list(data.keys())]:\n privates.append(\"\".join([hex(byte)[2:].zfill(2) for byte in key]))\n privates = privates[-n_validators:]\n\n # Create VALIDATOR_STATES\n os.mkdir(VALIDATOR_STATES)\n for i in range(n_validators):\n state = os.path.join(VALIDATOR_STATES, VALIDATOR_STATE % i)\n os.mkdir(state)\n # contract.ao\n shutil.copyfile(contract, os.path.join(state, \"contract.ao\"))\n # bridge_eth_addresses.json\n shutil.copyfile(ethaddrs, os.path.join(state, \"bridge_eth_addresses.json\"))\n # server.crt and server.key\n shutil.copy(os.path.join(ARB_VALIDATOR, \"server.crt\"), state)\n shutil.copy(os.path.join(ARB_VALIDATOR, \"server.key\"), state)\n # validator_addresses.txt\n with open(os.path.join(state, \"validator_addresses.txt\"), \"w\") as f:\n f.write(\"\\n\".join(addresses))\n # private_key.txt\n with open(os.path.join(state, \"private_key.txt\"), \"w\") as f:\n f.write(privates[i])\n\n\ndef check_file(name):\n if not os.path.isfile(name):\n raise argparse.ArgumentTypeError(\"%s is not a valid file\" % name)\n return name\n\n\ndef check_json(name):\n if not os.path.isfile(name):\n raise argparse.ArgumentTypeError(\"%s is not a valid file\" % name)\n try:\n with open(name, \"r\") as f:\n json.loads(f.read())\n except ValueError:\n raise argparse.ArgumentTypeError(\"%s is not valid json\" % name)\n return name\n\n\ndef main():\n parser = argparse.ArgumentParser(prog=NAME, description=DESCRIPTION)\n parser.add_argument(\n \"contract\", type=check_file, help=\"The Arbitrum bytecode contract to deploy\"\n )\n parser.add_argument(\n \"n_validators\",\n choices=range(2, 101),\n metavar=\"[2-100]\",\n type=int,\n help=\"The number of validators to deploy\",\n )\n group = parser.add_mutually_exclusive_group(required=True)\n group.add_argument(\n \"--docker\",\n action=\"store_true\",\n dest=\"is_ethbridge\",\n help=\"Generate states based on arb-bridge-eth docker images\",\n )\n group.add_argument(\n \"--local\",\n action=\"store_false\",\n dest=\"is_ethbridge\",\n help=\"Generate states based on local inputs\",\n )\n parser.add_argument(\n \"-a\",\n \"--acctKeys\",\n type=check_json,\n required=False,\n help='Generate with: ganache-cli --acctKeys keys.json -m \"$MNEMONIC\" -a \"$NUM_WALLETS\"',\n )\n parser.add_argument(\n \"-b\",\n \"--bridge_eth_addresses\",\n type=check_json,\n help=\"EthBridge contract addresses\",\n )\n parser.add_argument(\n \"-p\",\n \"--port\",\n type=int,\n default=7545,\n help=\"Port number to search for Ganache on\",\n )\n\n args = parser.parse_args()\n\n print(\"is_ethbridge\", args.is_ethbridge)\n\n if args.is_ethbridge:\n setup_validator_states_ethbridge(args.contract, args.n_validators)\n else:\n setup_validator_states(\n args.contract, args.n_validators, args.acctKeys, args.bridge_eth_addresses\n )\n\n\nif __name__ == \"__main__\":\n try:\n main()\n except KeyboardInterrupt:\n exit(1)\n", "repo_name": "Fooooooooooox/run-avm-locally", "sub_path": "arbitrum-0.2.0/scripts/setup_states.py", "file_name": "setup_states.py", "file_ext": "py", "file_size_in_byte": 4851, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "support.run.run", "line_number": 21, "usage_type": "call"}, {"api_name": "support.run.run", "line_number": 27, "usage_type": "call"}, {"api_name": "support.run.run", "line_number": 31, "usage_type": "call"}, {"api_name": "support.run.run", "line_number": 32, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 36, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 49, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 60, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 87, "usage_type": "call"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 89, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "32574513149", "text": "import pandas as pd\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\n# filename\r\nfilepath = '../../datasets/'\r\nfilename = '2022-01-24 Synch Emin.xlsx'\r\n# read Excel file\r\ndf = pd.read_excel(filepath+filename)\r\n\r\n# Split Params to columns\r\ndf['Params'] = df['Params'].str.replace(' ','', regex=True)\r\ndf[['m1', 'c1', 'd1','m2','c2','d2','cc','dc']] = df['Params'].str.split(',', expand=True)\r\ndf = df.drop(columns='Params')\r\n#convert them to numeric\r\ndf['m1'] = df['m1'].str.replace('(', '', regex=True)\r\ndf['dc'] = df['dc'].str.replace(')', '', regex=True)\r\nfor column in df.columns[1:]:\r\n\tdf[column] = pd.to_numeric(df[column], downcast=\"float\")\r\n\r\nprint(df.columns)\r\n\r\ndf_mask = df['Guess+Best Count'] < 1.0\r\nbadResults_df = df[df_mask]\r\n\r\ncorr = badResults_df.corr()\r\nax = sns.heatmap(\r\n corr,\r\n vmin=-1, vmax=1, center=0,\r\n cmap=sns.diverging_palette(20, 220, n=200),\r\n square=True\r\n)\r\nax.set_xticklabels(\r\n ax.get_xticklabels(),\r\n rotation=45,\r\n horizontalalignment='right'\r\n);\r\n\r\nplt.tight_layout()\r\n\r\nplt.show()", "repo_name": "clagms/2023.ANNSIM.ErrorEstimators.Reproducibility", "sub_path": "src/adaptive_cosim/analyze_frequency_results.py", "file_name": "analyze_frequency_results.py", "file_ext": "py", "file_size_in_byte": 1069, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.read_excel", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 20, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 28, "usage_type": "call"}, {"api_name": "seaborn.diverging_palette", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "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": "14272654300", "text": "'''\nUnseal vault servers trouth ssh tunnel\nGet keys store in passwordstore as yaml\n'''\nimport os\nimport yaml\nimport sys\n\nfrom .op import get_config_op\nfrom .op_legacy import get_config_op_legacy\n\n\ndef get_config(config_name, yaml_file='~/.unseal_vault.yml'):\n '''\n Get config wrapper\n\n *config_type* need to be defined as 'passtore' or 'yaml'\n\n *path* the pass to get yaml configuration\n '''\n\n full_path = os.path.expanduser(yaml_file)\n if not os.path.exists(full_path):\n print('Missing {} file. Please create it.'.format(full_path))\n sys.exit(1)\n else:\n with open(full_path, 'r') as stream:\n try:\n return yaml.load(stream, Loader=yaml.FullLoader)[config_name]\n except KeyError as k:\n print(f'Missing config: {k}')\n sys.exit(1)\n except yaml.YAMLError as exc:\n print(exc)\n sys.exit(1)\n\n\ndef handle_config(config):\n if config['type'] == 'op':\n return get_config_op(config)\n if config['type'] == 'op_legacy':\n return get_config_op_legacy(config)\n", "repo_name": "nledez/vault_python_unseal", "sub_path": "unseal_vault/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.expanduser", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "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": "sys.exit", "line_number": 25, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 29, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 32, "usage_type": "call"}, {"api_name": "yaml.YAMLError", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "op.get_config_op", "line_number": 40, "usage_type": "call"}, {"api_name": "op_legacy.get_config_op_legacy", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "75130230877", "text": "from enum import Enum\nimport math\nfrom collections import namedtuple\nfrom dataclasses import dataclass\nfrom heapq import heappush, heappop\nfrom day_05 import IntcodeComputer, test_intcode_computer\n\nclass Direction(Enum):\n\tNORTH = 1\n\tSOUTH = 2\n\tWEST = 3\n\tEAST = 4\n\n\tdef opposite(self):\n\t\tif self == Direction.NORTH:\n\t\t\treturn Direction.SOUTH\n\t\tif self == Direction.SOUTH:\n\t\t\treturn Direction.NORTH\n\t\tif self == Direction.WEST:\n\t\t\treturn Direction.EAST\n\t\tif self == Direction.EAST:\n\t\t\treturn Direction.WEST\n\n\tdef all_forward(self):\n\t\tfor direction in list(Direction):\n\t\t\tif direction != self.opposite():\n\t\t\t\tyield direction\n\n@dataclass(frozen=True)\nclass Position:\n\tx: int\n\ty: int\n\n\tdef __hash__(self):\n\t\treturn hash((self.x, self.y))\n\n\tdef step(self, direction):\n\t\tif direction == Direction.NORTH:\n\t\t\treturn Position(self.x, self.y - 1)\n\t\telif direction == Direction.SOUTH:\n\t\t\treturn Position(self.x, self.y + 1)\n\t\telif direction == Direction.WEST:\n\t\t\treturn Position(self.x - 1, self.y)\n\t\telif direction == Direction.EAST:\n\t\t\treturn Position(self.x + 1, self.y)\n\n\ndef find_oxygen_system(program_file):\n\tpositions = {}\n\toxygen_position = None\n\toxygen_steps = None\n\n\tprogram = IntcodeComputer.init_from_file(program_file)\n\tstart_position = Position(0, 0)\n\tpositions[start_position] = 0\n\tsteps_taken = []\n\n\t# depth first search\n\tdef step(position, direction):\n\t\tnonlocal oxygen_position\n\t\tnonlocal oxygen_steps\n\t\tnonlocal program\n\t\tnonlocal steps_taken\n\t\tnonlocal positions\n\n\t\tsteps_taken.append(direction)\n\t\tnew_position = position.step(direction)\n\n\t\t# if this position is already in positions{} and the distance there is less than this,\n\t\t# we don't need to re-explore here\n\t\tprev_num_steps = positions.get(new_position, math.inf)\n\t\tif prev_num_steps <= len(steps_taken):\n\t\t\t# step back\n\t\t\tsteps_taken = steps_taken[:-1]\n\t\t\treturn\n\n\t\tpositions[new_position] = len(steps_taken)\n\t\tprogram.pass_in(direction.value)\n\t\tstate = program.parse_and_get_next_value()\n\n\t\t# hit oxygen system\n\t\tif state == 2:\n\t\t\toxygen_position = new_position\n\t\t\tif oxygen_steps is None or len(oxygen_steps) > len(steps_taken):\n\t\t\t\toxygen_steps = [s for s in steps_taken]\n\n\t\t\t# go backwards\n\t\t\tprogram.pass_in(direction.opposite().value)\n\t\t\tprogram.parse_and_get_next_value()\n\t\t\tsteps_taken = steps_taken[:-1]\n\n\t\t# hit a wall\n\t\telif state == 0:\n\t\t\tsteps_taken = steps_taken[:-1]\n\n\t\t# clear space\n\t\telif state == 1:\n\t\t\tfor new_direction in direction.all_forward():\n\t\t\t\tstep(new_position, new_direction)\n\n\t\t\t# go backwards\n\t\t\tprogram.pass_in(direction.opposite().value)\n\t\t\tprogram.parse_and_get_next_value()\n\t\t\tsteps_taken = steps_taken[:-1]\n\n\n\toxygen_steps = None\n\tfor first_direction in list(Direction):\n\t\tprogram = IntcodeComputer.init_from_file(program_file)\n\t\tstart_position = Position(0, 0)\n\t\tpositions[start_position] = 0\n\t\tsteps_taken = []\n\t\tstep(start_position, first_direction)\n\n\treturn oxygen_steps\n\ndef time_for_oxygen_to_fill_space(program_file, steps_to_oxygen_system):\n\tstarting_program = IntcodeComputer.init_from_file(program_file)\n\tstarting_position = Position(0, 0)\n\tfor direction in steps_to_oxygen_system:\n\t\tstarting_program.pass_in(direction.value)\n\t\tstarting_program.parse_and_get_next_value()\n\t\tstarting_position = starting_position.step(direction)\n\n\tpositions = {}\n\tpositions[starting_position] = 0\n\tsteps_taken = []\n\n\t# depth first search\n\tdef step(position, direction):\n\t\tnonlocal program\n\t\tnonlocal steps_taken\n\t\tnonlocal positions\n\n\t\tsteps_taken.append(direction)\n\t\tnew_position = position.step(direction)\n\n\t\t# if this position is already in positions{} and the distance there is less than this,\n\t\t# we don't need to re-explore here\n\t\tprev_num_steps = positions.get(new_position, math.inf)\n\t\tif prev_num_steps <= len(steps_taken):\n\t\t\t# step back\n\t\t\tsteps_taken = steps_taken[:-1]\n\t\t\treturn\n\n\t\tpositions[new_position] = len(steps_taken)\n\t\tprogram.pass_in(direction.value)\n\t\tstate = program.parse_and_get_next_value()\n\n\t\t# hit a wall\n\t\tif state == 0:\n\t\t\tsteps_taken = steps_taken[:-1]\n\t\telse:\n\t\t\t# step everywhere except backwards\n\t\t\tfor new_direction in direction.all_forward():\n\t\t\t\tstep(new_position, new_direction)\n\n\t\t\t# go backwards\n\t\t\tprogram.pass_in(direction.opposite().value)\n\t\t\tprogram.parse_and_get_next_value()\n\t\t\tsteps_taken = steps_taken[:-1]\n\n\n\tfor first_direction in list(Direction):\n\t\tsteps_taken = []\n\t\tprogram = starting_program\n\t\tstep(starting_position, first_direction)\n\n\treturn max(positions.values())\n\n\n\ntest_intcode_computer()\n\nsteps_to_oxygen_system = find_oxygen_system(\"data/15.txt\")\nassert(len(steps_to_oxygen_system) == 232)\nassert(time_for_oxygen_to_fill_space(\"data/15.txt\", steps_to_oxygen_system) == 320)\n\n", "repo_name": "carodewig/advent-of-code", "sub_path": "advent-py/2019/day_15.py", "file_name": "day_15.py", "file_ext": "py", "file_size_in_byte": 4633, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "enum.Enum", "line_number": 8, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 29, "usage_type": "call"}, {"api_name": "day_05.IntcodeComputer.init_from_file", "line_number": 53, "usage_type": "call"}, {"api_name": "day_05.IntcodeComputer", "line_number": 53, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 71, "usage_type": "attribute"}, {"api_name": "day_05.IntcodeComputer.init_from_file", "line_number": 109, "usage_type": "call"}, {"api_name": "day_05.IntcodeComputer", "line_number": 109, "usage_type": "name"}, {"api_name": "day_05.IntcodeComputer.init_from_file", "line_number": 118, "usage_type": "call"}, {"api_name": "day_05.IntcodeComputer", "line_number": 118, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 140, "usage_type": "attribute"}, {"api_name": "day_05.test_intcode_computer", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "14829134680", "text": "from typing import List, Tuple\n\n\nclass TimeMap:\n\n def __init__(self):\n self.hashMap = dict()\n\n def set(self, key: str, value: str, timestamp: int) -> None:\n if key not in self.hashMap:\n self.hashMap[key] = list()\n self.hashMap[key].append((value, timestamp))\n\n def get(self, key: str, timestamp: int) -> str:\n if key not in self.hashMap:\n return \"\"\n pairs = self.hashMap[key]\n i = self.binarySearch(0, len(pairs) - 1, pairs, timestamp)\n if i == -1:\n return \"\"\n return self.hashMap[key][i][0]\n\n def binarySearch(self, lo: int, hi: int, nums: List[Tuple[str, int]], target: int) -> int:\n if lo > hi:\n return hi\n mid = (lo + hi) // 2\n if nums[mid][1] == target:\n return mid\n elif nums[mid][1] < target:\n return self.binarySearch(mid + 1, hi, nums, target)\n else:\n return self.binarySearch(lo, mid - 1, nums, target)\n", "repo_name": "xinyi-han/leetcode", "sub_path": "algorithms/981-Time-Based-Key-Value-Store.py", "file_name": "981-Time-Based-Key-Value-Store.py", "file_ext": "py", "file_size_in_byte": 993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "26068438926", "text": "import requests\nfrom pprint import pprint as pp\nfrom bs4 import BeautifulSoup\nfrom measure import call_n_times\n\n\ndef run_program():\n pp(\"Running blocket.py.\")\n url = \"http://www.blocket.se/skaraborg/Bmw_X1_25D_65431664.htm?aw=1\"\n pp(\"Calling {}\".format(url))\n response = requests.get(url)\n lower = response.content.decode('utf-8').lower()\n pp(\"The word '{}' occurs {} times.\".format(\"bmw\", lower.count('bmw')))\n\n summary_of_object(url)\n call_n_times(5, requests.get, url)\n\n\n\ndef summary_of_object(url):\n response = requests.get(url)\n soup = BeautifulSoup(response.content, 'html.parser')\n pp(\"## Summary of {}\".format(url))\n pp(soup.title.string)\n\n\nif __name__ == '__main__':\n run_program()\n\n", "repo_name": "jiimaho/BlackHatPython", "sub_path": "blocket.py", "file_name": "blocket.py", "file_ext": "py", "file_size_in_byte": 731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pprint.pprint", "line_number": 8, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 13, "usage_type": "call"}, {"api_name": "measure.call_n_times", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 16, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 23, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "42467095748", "text": "from django.urls import path\nfrom .views import ListaProducto, ProductoDetalle, CategoriaURL\n\nurlpatterns=[\n path('v1/productos/',ListaProducto.as_view(), name= \"Lista de productos\"),\n path('v1/productos/',ProductoDetalle.as_view(),name=\"Detalle de Producto\"),\n path('v1/productos/add',ProductoDetalle.as_view(),name=\"Agregar un producto\"),\n path('v1/categoria/', CategoriaURL.as_view(),name=\"Categoria por PK\")\n\n]", "repo_name": "jorgegarba/CodiGo8", "sub_path": "BackEnd/Semana10/Dia4/DjangoRestFramework/productosdb/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 442, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "views.ListaProducto.as_view", "line_number": 5, "usage_type": "call"}, {"api_name": "views.ListaProducto", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "views.ProductoDetalle.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "views.ProductoDetalle", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "views.ProductoDetalle.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "views.ProductoDetalle", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.CategoriaURL.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.CategoriaURL", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "71225685598", "text": "import pandas as pd\r\nfrom tabulate import tabulate\r\n\r\n\r\ndef currency(x):\r\n return f\"${x:.2f}\"\r\n\r\n\r\nitem_dict = {\r\n \"Item\": ['Sea Salt Crackers', 'Griffins Snax', 'Pizza shapes', 'Arnotts Cheds ', 'Rosemary Wheat', 'Original Rice '\r\n 'Crackers'],\r\n \"Amount\": ['185.0g', '250.0g', '190.0g', '250.0g', '170.0g', '100.0g'],\r\n \"Converted amount\": ['0.185KG', '0.250KG', '0.190KG', '0.250KG', '0.170KG', '0.100KG'],\r\n \"Cost\": [2.0, 2.5, 3.3, 3.99, 2.0, 1.5],\r\n \"Unit Price\": ['$10.81/KG', '$10.00/KG', '$17.37/KG', '$15.96/KG', '$11.76/KG', '$15.00/KG'],\r\n \"num_unit_price\": [10.81081081081081, 10.0, 17.36842105263158, 15.96, 11.76470588235294, 15.0]\r\n}\r\nunit_types = [\"kg\"]\r\nuser_budget = 2.50\r\nprice_frame = pd.DataFrame(item_dict)\r\nprice_frame = price_frame.set_index('Item')\r\n\r\n# Sort the DataFrame by 'Unit Price' in ascending order\r\nprice_frame = price_frame.sort_values(by='num_unit_price', ascending=True)\r\n\r\n# Remove the Unit Price Numeric column\r\nprice_frame = price_frame.drop(columns=['num_unit_price'])\r\n\r\n# Create a new dataframe and filter items that are within the user's budget\r\naffordable_items = price_frame[price_frame['Cost'] <= user_budget]\r\n\r\n# Format the 'Cost' column in as currency\r\nprice_frame[['Cost']] = price_frame[['Cost']].applymap(currency)\r\n\r\n# Display the price information as a table\r\ntable = tabulate(price_frame, headers='keys', tablefmt='fancy_grid')\r\n\r\nif not affordable_items.empty:\r\n # Get the best option (lowest unit price) within the user's budget\r\n best_option = affordable_items.iloc[0]\r\n best_option_name = best_option.name\r\n conclusion = f\"The best option within your budget (${user_budget:.2f}) is: {best_option_name}\"\r\nelse:\r\n conclusion = \"There are no affordable options\"\r\n# Add disclaimer if user enter items with different unit types.\r\nif \"kg\" in unit_types and \"l\" in unit_types:\r\n important_note = \"Disclaimer - Since you have compared items that have different unit types, the \" \\\r\n \"result may differ from what \" \\\r\n \"you were expecting.\"\r\nelse:\r\n important_note = \"\"\r\nprint(f\"{table}\\n\\n{conclusion}\")", "repo_name": "Talwarh1010/Programming-Assessment_Lvl2", "sub_path": "recommend_best_item.py", "file_name": "recommend_best_item.py", "file_ext": "py", "file_size_in_byte": 2239, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "29365055319", "text": "from typing import Dict\n\nfrom web3 import Web3, eth # Noqa.\n\nweb3_api_new_to_old: Dict[str, str] = {\n \"to_checksum_address\": \"toChecksumAddress\",\n \"is_checksum_address\": \"isChecksumAddress\",\n \"is_connected\": \"isConnected\",\n \"solidity_keccak\": \"solidityKeccak\",\n \"client_version\": \"clientVersion\",\n \"toJSON\": \"to_json\",\n \"toWei\": \"to_wei\",\n}\n\n\ndef web3_type_fix():\n web3_type_fix_over_version6_generic()\n\n\ndef web3_type_fix_over_version6_generic():\n for api in web3_api_new_to_old.keys():\n if not hasattr(Web3, api):\n setattr(Web3, api, getattr(Web3, web3_api_new_to_old[api]))\n\n\ndef web3_contract_event_fix(event):\n if not hasattr(event, \"create_filter\"):\n event.create_filter = event.createFilter\n if not hasattr(event, \"process_receipt\"):\n event.process_receipt = event.processReceipt\n\n\nweb3_type_fix()\n", "repo_name": "starkware-libs/cairo-lang", "sub_path": "src/starkware/eth/web3_wrapper.py", "file_name": "web3_wrapper.py", "file_ext": "py", "file_size_in_byte": 871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1280, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.Dict", "line_number": 5, "usage_type": "name"}, {"api_name": "web3.Web3", "line_number": 22, "usage_type": "argument"}, {"api_name": "web3.Web3", "line_number": 23, "usage_type": "argument"}]} +{"seq_id": "18407784294", "text": "import csv\n\nfrom bs4 import BeautifulSoup\nimport requests\n\nBASE_URL = 'http://blog.pyq.jp/archive'\nARCHIVE_DICTS = {\n # TODO: 現在の日付から2017年度以降を生成\n '2019': (1, 4),\n '2018': (1, 12),\n '2017': (7, 12)\n}\n\n\nclass Entry:\n def __init__(self, title, link, posted_date, categories):\n self.title = title\n self.link = link\n self.posted_date = posted_date\n self.categories = categories\n\n def export_csv(self):\n for main_category in self.categories:\n csv_row_list = [self.title, self.link, self.posted_date]\n csv_row_list.append(main_category)\n\n # 紐付いてるカテゴリからメインカテゴリとなるものを除いた差分\n diff = list(set(self.categories) - set([main_category]))\n csv_row_list = csv_row_list + diff\n\n with open('blog_entry_bp.csv', 'a') as f:\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerow(csv_row_list)\n\n\ndef get_url_lists():\n \"\"\"対象urlのリストを生成する関数\n Returns\n ----------\n url_lists: list(str, str)\n URLのリスト\n\n Notes\n ------------\n 返り値のリストに含まれるURLは\n\n http://blog.pyq.jp/archive//形式\n \"\"\"\n url_lists = []\n for key, values in ARCHIVE_DICTS.items():\n start, end = values\n list_ = [f\"{BASE_URL}/{key}/{month}\" for month in range(start, end)]\n url_lists = url_lists + list_\n\n return url_lists\n\n\ndef fetch_entry_section(url):\n \"\"\"月ごとのブログ記事一覧ページからsectionタグを全て取得\"\"\"\n response = requests.get(url)\n soup = BeautifulSoup(response.text, 'html.parser')\n\n # 各記事のかたまりはsectionタグで囲われている\n sections = soup.find_all('section', class_='archive-entry')\n return sections\n\n\ndef generate_entry_instance(sec):\n \"\"\"entryインスタンスを生成する\"\"\"\n title = sec.find('a', class_='entry-title-link').text.strip()\n link = sec.find('a', class_='entry-title-link').get('href').strip()\n posted_date = sec.find('time').text.strip()\n categories_atags = sec.find_all('a', class_='archive-category-link')\n categories = [category.text for category in categories_atags]\n entry = Entry(\n title,\n link,\n posted_date,\n categories,\n )\n return entry\n\n\ndef main():\n \"\"\"\n Notes\n ---------------------\n 実行方法: python scrape.pyを実行\n \"\"\"\n\n with open('blog_entry_bp.csv', 'a') as f:\n # ファイルを上書き(ファイルがなければ新規作成)し、ヘッダーを追加\n header = [\n 'タイトル', 'リンク', '投稿日時',\n 'カテゴリ1', 'カテゴリ2', 'カテゴリ3', 'カテゴリ4'\n ]\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerow(header)\n\n # urlのリストを生成\n url_lists = get_url_lists()\n\n for url in url_lists:\n sections = fetch_entry_section(url) # 1ヶ月分\n\n for sec in sections:\n entry = generate_entry_instance(sec)\n entry.export_csv()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "raguna2/scrape_bpblog", "sub_path": "scrape.py", "file_name": "scrape.py", "file_ext": "py", "file_size_in_byte": 3223, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "csv.writer", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 60, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 61, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "38657030980", "text": "from django.urls import include, path\n\nfrom .views import ContentDetailAPIView, \\\n ContentListAPIView, \\\n DeleteContentAPIView, \\\n GetContentTypesAPIView, \\\n UpdateContentAPIView, \\\n create_story, \\\n create_story_form, delete_story, \\\n detail_story, \\\n update_story, \\\n delete_story\n\nurlpatterns = [\n path('all/', ContentListAPIView.as_view(), name='content-list'),\n path('detail//', ContentDetailAPIView.as_view(), name='content-detail'),\n path('delete//', DeleteContentAPIView.as_view(), name='content-delete'),\n path('update//', UpdateContentAPIView.as_view(), name='content-update'),\n path('content-types/', GetContentTypesAPIView.as_view()),\n path('story/create//', create_story, name=\"create-story\"),\n path('htmx/story//udpate/', update_story, name=\"story-update\"),\n path('htmx/story//delete/', delete_story, name=\"story-delete\"),\n path('htmx/story//', detail_story, name=\"story-detail\"),\n path('htmx/create-story-form/', create_story_form, name=\"create-story-form\")\n]\n", "repo_name": "sensorseverywhere/wl-do-backend", "sub_path": "content/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1207, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.ContentListAPIView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.ContentListAPIView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "views.ContentDetailAPIView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "views.ContentDetailAPIView", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "views.DeleteContentAPIView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "views.DeleteContentAPIView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "views.UpdateContentAPIView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "views.UpdateContentAPIView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "views.GetContentTypesAPIView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "views.GetContentTypesAPIView", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "views.create_story", "line_number": 20, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "views.update_story", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "views.delete_story", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "views.detail_story", "line_number": 23, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "views.create_story_form", "line_number": 24, "usage_type": "argument"}]} +{"seq_id": "38282249298", "text": "#Goal is to crop the unwanted erroneous area of the fits image. \n\n#code begins!\n\nfrom astropy.utils.data import get_pkg_data_filename\nfrom astropy.io import fits\nfrom decimal import Decimal\n\n#generic function to get the cropped images \n#cobbler and checksum is used to tell the python to ignore any header data are that not standard and create the output image.\ndef croppedimage(ipPath,noOfFiles,opPath,opFileName):\n for i in range(noOfFiles):\n raw_image_files = get_pkg_data_filename( ipPath +str(i+1)+'.fits') # getting the images from the input file.\n raw_image_data,header= fits.getdata(raw_image_files,header=True,ext=0, clobber=True) # getting image info and header info of one image. \n#This helps in not getting the verification error, as the input image contains 'e' for exponential but python understands only 'E' for exponentials.\n#This converts the 'e' to 'E'. \n header['CD1_1'] = \"{:.6E}\".format(Decimal(header['CD1_1']))\n header['CD1_2'] = \"{:.6E}\".format(Decimal(header['CD1_2']))\n header['CD2_1'] = \"{:.6E}\".format(Decimal(header['CD2_1']))\n header['CD2_2'] = \"{:.6E}\".format(Decimal(header['CD2_2']))\n print(header)\n#xmin and xmax crops the top and bottom of the image while ymin and ymax crops the left and right side of the image. \n croppednewimage = raw_image_data[1:-1,13:-64] #cropping parameter. [xmin:-xmax,ymin:-ymax] and (minus symbol - thats what the equation says!) \n#Path to save the output image \n fits.writeto(opPath + opFileName + str(i+1)+'.fits',croppednewimage , header, checksum=True) \n\n#Giving path for input and output image for Bias, Flat and Science images. \n \nipPath = 'D:/archiveunziped/ipopfiles/bias/Bias(I)(18.06.14)/b'\nnoOfFiles = 10\nopPath = 'D:/archiveunziped/ipopfiles/croppedbias/cb(10.05.18)/'\nopFileName = 'b'\n\ncroppedbiasimages= croppedimage(ipPath, noOfFiles,opPath,opFileName) #Calling the function for getting cropped bias images.\n\nipPath = 'D:/archiveunziped/ipopfiles/flats/flat(V)(18.06.14)/f'\nnoOfFiles = 3\nopPath = 'D:/archiveunziped/ipopfiles/croppedflats/cf(10.05.18)/'\nopFileName = 'f'\n\ncroppedflatimages= croppedimage(ipPath, noOfFiles,opPath,opFileName) #Calling the function for getting cropped flat images.\n\n\nipPath = 'D:/archiveunziped/ipopfiles/science/science(V)(18.06.14)/s'\nnoOfFiles = 3\nopPath = 'D:/archiveunziped/ipopfiles/croppedscience/cs(10.05.18)/'\nopFileName = 's'\n\ncroppedscienceimages= croppedimage(ipPath, noOfFiles,opPath,opFileName) #Calling the function for getting cropped science images. \n\n#hurray! end of code!!:)", "repo_name": "PriyadarshiniGokuldass/GRBlightCurve", "sub_path": "croppedimagecode-functionformat.py", "file_name": "croppedimagecode-functionformat.py", "file_ext": "py", "file_size_in_byte": 2597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "51", "api": [{"api_name": "astropy.utils.data.get_pkg_data_filename", "line_number": 13, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 14, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 14, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 17, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 18, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 19, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 20, "usage_type": "call"}, {"api_name": "astropy.io.fits.writeto", "line_number": 25, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "26303716958", "text": "import hashlib\nimport os\nimport os.path\nimport tempfile\nfrom collections import defaultdict, deque\n\nfrom horizons.constants import MAP, PATHS\nfrom horizons.savegamemanager import SavegameManager\nfrom horizons.util.dbreader import DbReader\nfrom horizons.util.random_map import create_random_island\nfrom horizons.util.savegameupgrader import SavegameUpgrader\n\n\nclass MapFileNotFound(Exception):\n\tdef __init__(self, msg=None):\n\t\tif msg is None:\n\t\t\tmsg = \"Map file not found.\"\n\t\tsuper().__init__(msg)\n\n\nclass SavegameAccessor(DbReader):\n\t\"\"\"\n\tSavegameAccessor is the class used for loading saved games.\n\n\tFrequent select queries are preloaded for faster access.\n\t\"\"\"\n\n\tdef __init__(self, game_identifier, is_map, options=None):\n\t\tis_random_map = False\n\t\tif is_map:\n\t\t\tself.upgrader = None\n\t\t\thandle, self._temp_path = tempfile.mkstemp()\n\t\t\tos.close(handle)\n\t\t\tsuper().__init__(dbfile=self._temp_path)\n\t\t\twith open('content/savegame_template.sql') as savegame_template:\n\t\t\t\tself.execute_script(savegame_template.read())\n\n\t\t\tif isinstance(game_identifier, list):\n\t\t\t\tis_random_map = True\n\t\t\t\trandom_island_sequence = game_identifier\n\t\t\telse:\n\t\t\t\tself._map_path = game_identifier\n\t\telse:\n\t\t\tself.upgrader = SavegameUpgrader(game_identifier)\n\t\t\tself._temp_path = None\n\t\t\tgame_identifier = self.upgrader.get_path()\n\t\t\tsuper().__init__(dbfile=game_identifier)\n\n\t\t\tmap_name_data = self('SELECT value FROM metadata WHERE name = ?', 'map_name')\n\t\t\tif not map_name_data:\n\t\t\t\tis_random_map = True\n\t\t\t\trandom_island_sequence = self('SELECT value FROM metadata WHERE name = ?', 'random_island_sequence')[0][0].split(' ')\n\t\t\telse:\n\t\t\t\tmap_name = map_name_data[0][0]\n\t\t\t\tif map_name.startswith('USER_MAPS_DIR:'):\n\t\t\t\t\tself._map_path = PATHS.USER_MAPS_DIR + map_name[len('USER_MAPS_DIR:'):]\n\t\t\t\telif os.path.isabs(map_name):\n\t\t\t\t\tself._map_path = map_name\n\t\t\t\telse:\n\t\t\t\t\tself._map_path = SavegameManager.get_filename_from_map_name(map_name)\n\n\t\tif is_random_map:\n\t\t\thandle, self._temp_path2 = tempfile.mkstemp()\n\t\t\tos.close(handle)\n\t\t\trandom_map_db = DbReader(self._temp_path2)\n\t\t\twith open('content/map-template.sql') as map_template:\n\t\t\t\trandom_map_db.execute_script(map_template.read())\n\t\t\tfor island_id, island_string in enumerate(random_island_sequence):\n\t\t\t\tcreate_random_island(random_map_db, island_id, island_string)\n\t\t\trandom_map_db.close()\n\t\t\tself._map_path = self._temp_path2\n\n\t\t\tself('INSERT INTO metadata VALUES(?, ?)', 'random_island_sequence',\n\t\t\t\t' '.join(random_island_sequence))\n\n\t\tif options is not None:\n\t\t\tif options.map_padding is not None:\n\t\t\t\tself(\"INSERT INTO map_properties VALUES(?, ?)\", 'padding', options.map_padding)\n\n\t\tif not os.path.exists(self._map_path):\n\t\t\traise MapFileNotFound(\"Map file \" + str(self._map_path) + \" not found!\")\n\n\t\tself('ATTACH ? AS map_file', self._map_path)\n\t\tif is_random_map:\n\t\t\tself.map_name = random_island_sequence\n\t\telif os.path.isabs(self._map_path):\n\t\t\tself.map_name = self._map_path\n\t\telse:\n\t\t\tself.map_name = SavegameManager.get_savegamename_from_filename(self._map_path)\n\n\t\tmap_padding = self(\"SELECT value FROM map_properties WHERE name = 'padding'\")\n\t\tself.map_padding = int(map_padding[0][0]) if map_padding else MAP.PADDING\n\n\t\tself._load_building()\n\t\tself._load_settlement()\n\t\tself._load_concrete_object()\n\t\tself._load_production()\n\t\tself._load_storage()\n\t\tself._load_wildanimal()\n\t\tself._load_unit()\n\t\tself._load_building_collector()\n\t\tself._load_production_line()\n\t\tself._load_unit_path()\n\t\tself._load_storage_global_limit()\n\t\tself._load_health()\n\t\tself._load_fish_data()\n\t\tself._hash = None\n\n\tdef close(self):\n\t\tsuper().close()\n\t\tif self.upgrader is not None:\n\t\t\tself.upgrader.close()\n\t\tif self._temp_path is not None:\n\t\t\tos.unlink(self._temp_path)\n\t\tif hasattr(self, '_temp_path2'):\n\t\t\tos.unlink(self._temp_path2)\n\n\tdef _load_building(self):\n\t\tself._building = {}\n\t\tfor row in self(\"SELECT rowid, x, y, location, rotation, level FROM building\"):\n\t\t\tself._building[int(row[0])] = row[1:]\n\n\tdef get_building_row(self, worldid):\n\t\t\"\"\"Returns (x, y, location, rotation, level)\"\"\"\n\t\treturn self._building[int(worldid)]\n\n\tdef get_building_location(self, worldid):\n\t\treturn self._building[int(worldid)][2]\n\n\tdef _load_settlement(self):\n\t\tself._settlement = {}\n\t\tfor row in self(\"SELECT rowid, owner, island FROM settlement\"):\n\t\t\tself._settlement[int(row[0])] = row[1:]\n\n\tdef get_settlement_owner(self, worldid):\n\t\t\"\"\"Returns the id of the owner of the settlement or None otherwise\"\"\"\n\t\treturn self._settlement.get(int(worldid), [None])[0]\n\n\tdef get_settlement_island(self, worldid):\n\t\treturn self._settlement[int(worldid)][1]\n\n\tdef _load_concrete_object(self):\n\t\tself._concrete_object = {}\n\t\tfor row in self(\"SELECT id, action_runtime, action_set_id FROM concrete_object\"):\n\t\t\tself._concrete_object[int(row[0])] = int(row[1]), row[2]\n\n\tdef get_concrete_object_data(self, worldid):\n\t\treturn self._concrete_object[int(worldid)]\n\n\tdef _load_production(self):\n\t\tself._productions_by_worldid = {}\n\t\tself._production_lines_by_owner = {}\n\t\tself._productions_by_id_and_owner = {}\n\t\tdb_data = self(\"SELECT rowid, state, owner, prod_line_id, remaining_ticks, _pause_old_state, creation_tick FROM production\")\n\t\tfor row in db_data:\n\t\t\trowid = int(row[0])\n\t\t\tdata = row[1:]\n\t\t\tself._productions_by_worldid[rowid] = data\n\t\t\towner = int(row[2])\n\t\t\tline = int(row[3])\n\t\t\tif line not in self._productions_by_id_and_owner:\n\t\t\t\tself._productions_by_id_and_owner[line] = {}\n\t\t\t# in the line dict, the owners are unique\n\t\t\tself._productions_by_id_and_owner[line][owner] = data\n\n\t\t\tif owner not in self._production_lines_by_owner:\n\t\t\t\tself._production_lines_by_owner[owner] = [line]\n\t\t\telse:\n\t\t\t\tself._production_lines_by_owner[owner].append(line)\n\n\t\t\tself._production_lines_by_owner[owner].append\n\n\t\tself._production_state_history = defaultdict(deque)\n\t\tfor object_id, production_id, tick, state in self(\"SELECT object_id, production, tick, state FROM production_state_history ORDER BY object_id, production, tick\"):\n\t\t\tself._production_state_history[int(object_id), int(production_id)].append((tick, state))\n\n\tdef get_production_by_id_and_owner(self, id, ownerid):\n\t\t# owner means worldid of entity\n\t\treturn self._productions_by_id_and_owner[id][ownerid]\n\n\tdef get_production_line_id(self, production_worldid):\n\t\t\"\"\"Returns the prod_line_id of the given production\"\"\"\n\t\treturn self._productions_by_worldid[int(production_worldid)][2]\n\n\tdef get_production_lines_by_owner(self, owner):\n\t\t\"\"\"Returns the prod_line_id of the given production\"\"\"\n\t\treturn self._production_lines_by_owner.get(owner, [])\n\n\tdef get_production_state_history(self, worldid, prod_id):\n\t\treturn self._production_state_history[int(worldid), int(prod_id)]\n\n\tdef _load_storage(self):\n\t\tself._storage = {}\n\t\tfor row in self(\"SELECT object, resource, amount FROM storage\"):\n\t\t\townerid = int(row[0])\n\t\t\tif ownerid in self._storage:\n\t\t\t\tself._storage[ownerid].append(row[1:])\n\t\t\telse:\n\t\t\t\tself._storage[ownerid] = [row[1:]]\n\n\tdef get_storage_rowids_by_ownerid(self, ownerid):\n\t\t\"\"\"Returns potentially empty list of worldids referencing storages\"\"\"\n\t\treturn self._storage.get(int(ownerid), [])\n\n\tdef _load_wildanimal(self):\n\t\tself._wildanimal = {}\n\t\tfor row in self(\"SELECT rowid, health, can_reproduce FROM wildanimal\"):\n\t\t\tself._wildanimal[int(row[0])] = row[1:]\n\n\tdef get_wildanimal_row(self, worldid):\n\t\t\"\"\"Returns (health, can_reproduce)\"\"\"\n\t\treturn self._wildanimal[int(worldid)]\n\n\tdef _load_unit(self):\n\t\tself._unit = {}\n\t\tfor row in self(\"SELECT rowid, owner FROM unit\"):\n\t\t\tself._unit[int(row[0])] = int(row[1])\n\n\tdef get_unit_owner(self, worldid):\n\t\treturn self._unit[int(worldid)]\n\n\tdef _load_building_collector(self):\n\t\tself._building_collector = {}\n\t\tfor row in self(\"SELECT rowid, home_building, creation_tick FROM building_collector\"):\n\t\t\tself._building_collector[int(row[0])] = (int(row[1]) if row[1] is not None else None, row[2])\n\n\t\tself._building_collector_job_history = defaultdict(deque)\n\t\tfor collector_id, tick, utilization in self(\"SELECT collector, tick, utilisation FROM building_collector_job_history ORDER BY collector, tick\"):\n\t\t\tself._building_collector_job_history[int(collector_id)].append((tick, utilization))\n\n\tdef get_building_collectors_data(self, worldid):\n\t\t\"\"\"Returns (id of the building collector's home or None otherwise, creation_tick)\"\"\"\n\t\treturn self._building_collector.get(int(worldid))\n\n\tdef get_building_collector_job_history(self, worldid):\n\t\treturn self._building_collector_job_history[int(worldid)]\n\n\tdef _load_production_line(self):\n\t\tself._production_line = {}\n\t\tfor row in self(\"SELECT for_worldid, type, res, amount FROM production_line\"):\n\t\t\tid = int(row[0])\n\t\t\tif id not in self._production_line:\n\t\t\t\tself._production_line[id] = []\n\t\t\tself._production_line[id].append(row[1:])\n\n\tdef get_production_line_row(self, for_worldid):\n\t\treturn self._production_line[int(for_worldid)]\n\n\tdef _load_unit_path(self):\n\t\tself._unit_path = {}\n\t\tfor row in self(\"SELECT unit, x, y FROM unit_path ORDER BY 'index'\"):\n\t\t\tid = int(row[0])\n\t\t\tif id not in self._unit_path:\n\t\t\t\tself._unit_path[id] = []\n\t\t\tself._unit_path[id].append(row[1:])\n\n\tdef get_unit_path(self, worldid):\n\t\treturn self._unit_path.get(int(worldid))\n\n\tdef _load_storage_global_limit(self):\n\t\tself._storage_global_limit = {}\n\t\tfor row in self(\"SELECT object, value FROM storage_global_limit\"):\n\t\t\tself._storage_global_limit[(int(row[0]))] = int(row[1])\n\n\tdef get_storage_global_limit(self, worldid):\n\t\treturn self._storage_global_limit[int(worldid)]\n\n\tdef _load_health(self):\n\t\tself._health = dict(self(\"SELECT owner_id, health FROM unit_health\"))\n\n\tdef get_health(self, owner):\n\t\treturn self._health[owner]\n\n\tdef _load_fish_data(self):\n\t\tself._fish_data = {}\n\t\tfor row in self(\"SELECT rowid, last_usage_tick FROM fish_data\"):\n\t\t\tself._fish_data[int(row[0])] = int(row[1])\n\n\tdef get_last_fish_usage_tick(self, worldid):\n\t\treturn self._fish_data[worldid]\n\n\t# Random savegamefile related utility that i didn't know where to put\n\n\t@classmethod\n\tdef get_players_num(cls, savegamefile):\n\t\t\"\"\"Return number of regular human and ai players\"\"\"\n\t\treturn DbReader(savegamefile)(\"SELECT count(rowid) FROM player WHERE is_trader = 0 AND is_pirate = 0\")[0][0]\n\n\t@classmethod\n\tdef get_hash(cls, savegamefile):\n\t\tif not os.path.exists(savegamefile):\n\t\t\treturn False\n\t\twith open(savegamefile, \"rb\") as f:\n\t\t\th = hashlib.sha1()\n\t\t\th.update(f.read())\n\t\t\tfilehash = h.hexdigest()\n\t\treturn filehash\n", "repo_name": "unknown-horizons/unknown-horizons", "sub_path": "horizons/util/savegameaccessor.py", "file_name": "savegameaccessor.py", "file_ext": "py", "file_size_in_byte": 10376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1376, "dataset": "github-code", "pt": "51", "api": [{"api_name": "horizons.util.dbreader.DbReader", "line_number": 21, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 32, "usage_type": "call"}, {"api_name": "os.close", "line_number": 33, "usage_type": "call"}, {"api_name": "horizons.util.savegameupgrader.SavegameUpgrader", "line_number": 44, "usage_type": "call"}, {"api_name": "horizons.constants.PATHS.USER_MAPS_DIR", "line_number": 56, "usage_type": "attribute"}, {"api_name": "horizons.constants.PATHS", "line_number": 56, "usage_type": "name"}, {"api_name": "os.path.isabs", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "horizons.savegamemanager.SavegameManager.get_filename_from_map_name", "line_number": 60, "usage_type": "call"}, {"api_name": "horizons.savegamemanager.SavegameManager", "line_number": 60, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 63, "usage_type": "call"}, {"api_name": "os.close", "line_number": 64, "usage_type": "call"}, {"api_name": "horizons.util.dbreader.DbReader", "line_number": 65, "usage_type": "call"}, {"api_name": "horizons.util.random_map.create_random_island", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "horizons.savegamemanager.SavegameManager.get_savegamename_from_filename", "line_number": 89, "usage_type": "call"}, {"api_name": "horizons.savegamemanager.SavegameManager", "line_number": 89, "usage_type": "name"}, {"api_name": "horizons.constants.MAP.PADDING", "line_number": 92, "usage_type": "attribute"}, {"api_name": "horizons.constants.MAP", "line_number": 92, "usage_type": "name"}, {"api_name": "os.unlink", "line_number": 114, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 116, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 173, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 173, "usage_type": "argument"}, {"api_name": "collections.defaultdict", "line_number": 227, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 227, "usage_type": "argument"}, {"api_name": "horizons.util.dbreader.DbReader", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path", "line_number": 291, "usage_type": "attribute"}, {"api_name": "hashlib.sha1", "line_number": 294, "usage_type": "call"}]} +{"seq_id": "21150887582", "text": "import unittest\nimport uuid\n\n\ndef addUUID(*arg):\n for c in arg:\n c.update(Id=uuid.uuid4().hex)\n\n\nclass TestDB(unittest.TestCase):\n \"\"\"\n 数据库操作测试用例\n \"\"\"\n\n def setUp(self):\n \"\"\"\n 测试前准备环境的搭建\n \"\"\"\n\n def test_insertrow(self):\n \"\"\"测试批量插入 - 初始化培训点分类、类别字典\"\"\"\n print(self.test_insertrow.__doc__.strip())\n from app_api.common.db import insert_rows, add,get_row,db_session\n from app_api.entity.examination.training_institution import TrainingInstitutionCategory,\\\n TrainingInstitutionType,TrainingInstitution,TrainingInstitutionAccount\n from app_api.entity.manytomany import TrainingInstitutionAndCategory\n categorylist = [\n {\"Name\": \"公共科目培训\"},\n {\"Name\": \"专业协会\"}\n ]\n addUUID(*categorylist)\n rowcount_category = insert_rows(TrainingInstitutionCategory, categorylist)\n assert rowcount_category >= 0\n\n typelist = [\n {\"Name\": \"专业技术人员\"},\n {\"Name\": \"公务员\"}\n ]\n addUUID(*typelist)\n rowcount_type = insert_rows(TrainingInstitutionType, typelist)\n assert rowcount_type >= 0\n\n librarycategory = {\"Name\": \"2017年继续教育试题\"}\n from app_api.entity.examination.testlibrary import LibraryCategory\n\n addkey_librarycategory = add(LibraryCategory, librarycategory)\n assert len(addkey_librarycategory) == 32\n\n trainginstitution_category = get_row(TrainingInstitutionCategory, Name=\"公共科目培训\")\n if not trainginstitution_category:\n raise Exception(\"未找到培训点\")\n trainginstitutionlist = [\n {\"Name\": \"市直·继续教育·淄博市人事培训中心\"},\n {\"Name\": \"市直·继续教育·淄博市卫生人才中心\"},\n {\"Name\": \"张店区·继续教育·淄博职业学院\"},\n {\"Name\": \"淄川区·继续教育·淄博理工学校\"},\n {\"Name\": \"博山区·继续教育·博山一职专\"},\n {\"Name\": \"临淄区·继续教育·淄博工业学校\"},\n {\"Name\": \"周村区·继续教育·周村职业中等专业学校\"},\n {\"Name\": \"桓台县·继续教育·桓台县人社局\"},\n {\"Name\": \"高青县·继续教育·高青县人社局\"},\n {\"Name\": \"沂源县·继续教育·淄博职业学院\"},\n {\"Name\": \"高新区组织人事部\"},\n {\"Name\": \"市直·继续教育·淄博市教师教育办公室\"},\n {\"Name\": \"张店区教育局培训点\"},\n {\"Name\": \"淄川区教育中心培训点\"},\n {\"Name\": \"博山区教研室培训点\"},\n {\"Name\": \"周村区教体局培训点\"},\n {\"Name\": \"临淄区教育局培训点\"},\n {\"Name\": \"桓台县教体局培训点\"},\n {\"Name\": \"沂源县教体局培训点\"},\n {\"Name\": \"高青县教育局报名点\"},\n {\"Name\": \"高新区教研室报名点\"},\n {\"Name\": \"测试培训点\"},\n {\"Name\": \"全市·公务��培训·系统测试点\"},\n {\"Name\": \"全市·继续教育·系统测试点\"},\n {\"Name\": \"农业系统竞赛\"},\n {\"Name\": \"市直·公务员培训·淄博市人社局\"},\n {\"Name\": \"张店区·公务员培训·张店区人社局\"},\n {\"Name\": \"淄川区·公务员培训·淄川区人社局\"},\n {\"Name\": \"博山区·公务员培训·博山区人社局\"},\n {\"Name\": \"临淄区·公务员培训·临淄区人社局\"},\n {\"Name\": \"周村区·公务员培训·周村区人社局\"},\n {\"Name\": \"桓台县·公务员培训·桓台县人社局\"},\n {\"Name\": \"高青县·公务员培训·高青县人社局\"},\n {\"Name\": \"沂源县·公务员培训·沂源县人社局\"},\n {\"Name\": \"高新区·公务员培训·高新区人社局\"},\n {\"Name\": \"文昌湖·继续教育·文昌湖人社局\"},\n {\"Name\": \"市直·继续教育·淄博职业学院\"},\n {\"Name\": \"淄博市人才市场\"},\n {\"Name\": \"文昌湖·公务员培训·文昌湖人社局\"},\n {\"Name\": \"市直·继续教育·淄博师范高等专科学校\"}\n ]\n addUUID(*trainginstitutionlist)\n traing_category = []\n\n for c in trainginstitutionlist:\n d={}\n d.update(TrainingInstitutionId=c[\"Id\"],CategoryId=trainginstitution_category.Id)\n traing_category.append(d)\n\n db_session.begin_nested()\n rowcount_traing = insert_rows(TrainingInstitution,trainginstitutionlist)\n rowcount_training_and_category = insert_rows(TrainingInstitutionAndCategory,traing_category)\n db_session.commit()\n\n assert rowcount_training_and_category == len(traing_category)\n assert rowcount_traing == len(trainginstitutionlist)\n\n training = get_row(TrainingInstitution, Name=\"市直·继续教育·淄博市人事培训中心\")\n\n training_account = {\n \"Name\":\"SZJJ_ZBRSPXZX\",\n \"PassWordHash\":\"123456\",\n \"UsbKey\":\"123123\",\n \"TrainingInstitutionId\":training.Id\n }\n\n\n addkey = add(TrainingInstitutionAccount, training_account)\n # addkey = db_session.add(training)\n # db_session.commit()\n\n def test_Add(self):\n pass\n\n\n def tearDown(self):\n \"\"\"\n 测试后环境的还原\n \"\"\"\n from app_api.common.db import db_session\n db_session.remove()\n pass\n\n if __name__ == \"__main__\":\n unittest.main()\n", "repo_name": "iqiangzi/WsCme", "sub_path": "app_api/common/db_test.py", "file_name": "db_test.py", "file_ext": "py", "file_size_in_byte": 5697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "uuid.uuid4", "line_number": 7, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "app_api.common.db.insert_rows", "line_number": 32, "usage_type": "call"}, {"api_name": "app_api.entity.examination.training_institution.TrainingInstitutionCategory", "line_number": 32, "usage_type": "name"}, {"api_name": "app_api.common.db.insert_rows", "line_number": 40, "usage_type": "call"}, {"api_name": "app_api.entity.examination.training_institution.TrainingInstitutionType", "line_number": 40, "usage_type": "name"}, {"api_name": "app_api.common.db.add", "line_number": 46, "usage_type": "call"}, {"api_name": "app_api.entity.examination.testlibrary.LibraryCategory", "line_number": 46, "usage_type": "name"}, {"api_name": "app_api.common.db.get_row", "line_number": 49, "usage_type": "call"}, {"api_name": "app_api.entity.examination.training_institution.TrainingInstitutionCategory", "line_number": 49, "usage_type": "name"}, {"api_name": "app_api.common.db.db_session.begin_nested", "line_number": 102, "usage_type": "call"}, {"api_name": "app_api.common.db.db_session", "line_number": 102, "usage_type": "name"}, {"api_name": "app_api.common.db.insert_rows", "line_number": 103, "usage_type": "call"}, {"api_name": "app_api.entity.examination.training_institution.TrainingInstitution", "line_number": 103, "usage_type": "name"}, {"api_name": "app_api.common.db.insert_rows", "line_number": 104, "usage_type": "call"}, {"api_name": "app_api.entity.manytomany.TrainingInstitutionAndCategory", "line_number": 104, "usage_type": "name"}, {"api_name": "app_api.common.db.db_session.commit", "line_number": 105, "usage_type": "call"}, {"api_name": "app_api.common.db.db_session", "line_number": 105, "usage_type": "name"}, {"api_name": "app_api.common.db.get_row", "line_number": 110, "usage_type": "call"}, {"api_name": "app_api.entity.examination.training_institution.TrainingInstitution", "line_number": 110, "usage_type": "name"}, {"api_name": "app_api.common.db.add", "line_number": 120, "usage_type": "call"}, {"api_name": "app_api.entity.examination.training_institution.TrainingInstitutionAccount", "line_number": 120, "usage_type": "name"}, {"api_name": "app_api.common.db.db_session.remove", "line_number": 133, "usage_type": "call"}, {"api_name": "app_api.common.db.db_session", "line_number": 133, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "18237321845", "text": "import typing as tp\nfrom treeflow.tree.topology.numpy_tree_topology import NumpyTreeTopology\n\n\ndef get_common_ancestors(\n topology: NumpyTreeTopology, indices: tp.Iterable[int]\n) -> tp.Set[int]:\n ancestors: tp.Set[int] = set()\n node_count = topology.node_count\n for base_index in indices:\n ancestors_remaining = True\n index = base_index\n while ancestors_remaining and index < (node_count - 1):\n parent = topology.parent_indices[index]\n if parent in ancestors:\n ancestors_remaining = False\n ancestors = set([x for x in ancestors if x >= parent])\n else:\n ancestors.add(parent)\n index = parent\n return ancestors\n\n\ndef get_mrca_index(topology: NumpyTreeTopology, taxa: tp.Iterable[str]) -> int:\n assert topology.taxon_set is not None\n all_taxa = list(topology.taxon_set)\n indices = [all_taxa.index(taxon) for taxon in taxa]\n ancestors = get_common_ancestors(topology, indices)\n return min(ancestors)\n", "repo_name": "christiaanjs/treeflow", "sub_path": "treeflow/evolution/calibration/mrca.py", "file_name": "mrca.py", "file_ext": "py", "file_size_in_byte": 1040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "60", "api": [{"api_name": "treeflow.tree.topology.numpy_tree_topology.NumpyTreeTopology", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 6, "usage_type": "attribute"}, {"api_name": "typing.Set", "line_number": 8, "usage_type": "attribute"}, {"api_name": "typing.Set", "line_number": 7, "usage_type": "attribute"}, {"api_name": "treeflow.tree.topology.numpy_tree_topology.NumpyTreeTopology", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "4802025221", "text": "\"\"\"\nQuicksort app implements Quicksort Active Sampling Algorithm\nauthor: Sumeet Katariya, sumeetsk@gmail.com\nlast updated: 09/20/2016\n\"\"\"\n\nimport numpy as np\nfrom datetime import datetime\nimport dateutil.parser\nimport next.utils as utils\nimport random\nimport time\n\n#def waitUntilDBClear(butler):\n# while butler.algorithms.get(key='wait'):\n# time.sleep(1e-3)\n# butler.algorithms.set(key='wait', value=True)\n\nclass Quicksort:\n app_id = 'ActiveRanking'\n def initExp(self, butler, n=None, params=None):\n nquicksorts = 8\n butler.algorithms.set(key='nquicksorts', value=nquicksorts)\n butler.algorithms.set(key='n', value=n)\n\n arrlist = []\n for _ in range(nquicksorts):\n arrlist.append(list(np.random.permutation(range(n))))\n butler.algorithms.set(key='arrlist', value=arrlist)\n\n queryqueuesallqs = [] #list of queryqueues for all the quicksorts\n for _ in range(nquicksorts):\n queryqueuesallqs.append([])\n\n stackparametersallqs = []\n for _ in range(nquicksorts):\n stackparametersallqs.append({})\n\n for c1 in range(nquicksorts):\n arr = arrlist[c1]\n l = 0\n h = n\n pivot = arr[n-1]\n smallerthanpivot = []\n largerthanpivot = []\n stackvalue = {'l':l, 'h':n, 'pivot':pivot, 'smallerthanpivot':smallerthanpivot, 'largerthanpivot':largerthanpivot, 'count':0}\n stackkey = utils.getNewUID()\n stacks = {stackkey: stackvalue}\n stackparametersallqs[c1] = stacks\n queryqueue = []\n for c2 in range(len(arr)-1):\n queryqueue.append([arr[c2], pivot, [c1,stackkey,'0']])\n #each query maintains a quicksort_id, a stack index (which is stackkey in the beginning), and a time when it was sent out, which is added when it is sent. That is why the sent time is '0' for now. It is also set to '0' if the query was removed from waitingforresponse because the response did not arrive within the prescribed limit.\n queryqueuesallqs[c1] = queryqueue\n\n butler.algorithms.set(key='stackparametersallqs', value= stackparametersallqs)\n butler.algorithms.set(key='queryqueuesallqs', value=queryqueuesallqs)\n waitingforresponse = []\n for _ in range(nquicksorts):\n waitingforresponse.append({})\n butler.algorithms.set(key='waitingforresponse', value=waitingforresponse)\n\n ranking = np.zeros(n)\n butler.algorithms.set(key='ranking', value=ranking)\n #butler.algorithms.set(key='wait', value=False)\n\n return True\n\n def getQuery(self, butler, participant_uid):\n #waitUntilDBClear(butler)\n lock = butler.memory.lock('lock')\n lock.acquire()\n\n nquicksorts = butler.algorithms.get(key='nquicksorts')\n n = butler.algorithms.get(key='n')\n arrlist = butler.algorithms.get(key='arrlist')\n queryqueuesallqs = butler.algorithms.get(key='queryqueuesallqs')\n waitingforresponse = butler.algorithms.get(key='waitingforresponse')\n stackparametersallqs = butler.algorithms.get(key='stackparametersallqs')\n\n #for all quicksort_ids, check if there are any queries that have been lying around in waitingforresponse for a long time\n cur_time = datetime.now()\n for qsid in range(nquicksorts): \n for key in waitingforresponse[qsid]:\n senttimeiniso = waitingforresponse[qsid][key][2][2]\n if senttimeiniso == '0':\n continue #this query has been added to the queue already\n else:\n senttime = dateutil.parser.parse(senttimeiniso)\n\n timepassedsincesent = cur_time - senttime\n timepassedsincesentinsecs = timepassedsincesent.total_seconds()\n if timepassedsincesentinsecs > 50:\n query = waitingforresponse[qsid][key]\n query[2][2] = '0'\n queryqueuesallqs[qsid].append(query)\n #utils.debug_print('time exceeded query: ' + str(query))\n waitingforresponse[qsid][key] = query #setting time to '0' indicates that the query has been added to the queue, avoid repeat additions.\n\n if queryqueuesallqs == [[]]*nquicksorts:\n #all quicksort queues empty: fork a new quicksort\n nquicksorts = nquicksorts + 1\n arr = np.random.permutation(range(n))\n arrlist.append(list(arr))\n stackvalue = {'l':0, 'h':n, 'pivot':arr[-1], 'smallerthanpivot':[], 'largerthanpivot':[], 'count':0}\n stackkey = utils.getNewUID()\n stackparametersallqs.append({stackkey: stackvalue})\n quicksort_id = nquicksorts-1\n queryqueue = []\n for c1 in range(len(arr)-1):\n queryqueue.append([arr[c1], arr[-1], [quicksort_id, stackkey, '0']])\n queryqueuesallqs.append(queryqueue)\n waitingforresponse.append({})\n butler.algorithms.set(key='nquicksorts', value=nquicksorts)\n butler.algorithms.set(key='stackparametersallqs', value= stackparametersallqs)\n butler.algorithms.set(key='arrlist', value=arrlist)\n\n #if any item from the previous query is repeated, sample a new quicksort_id\n #last_query = butler.participants.get(key='last_query')\n #if last_query == None:\n # butler.participants.set(key='last_query', value=(-1,-1))\n # last_query = butler.participants.get(key='last_query')\n\n ##utils.debug_print('last_query='+str(last_query))\n\n #item_repeated_last_query_count = 0\n #while item_repeated_last_query_count<10:\n # quicksort_id = np.random.randint(nquicksorts)\n\n # while queryqueuesallqs[quicksort_id] == []:\n # #current queue empty, switch to a different one\n # quicksort_id = np.random.randint(nquicksorts)\n\n # query_index = np.random.randint(len(queryqueuesallqs[quicksort_id]))\n # potential_query = queryqueuesallqs[quicksort_id][query_index]\n # query_tuple = (potential_query[0], potential_query[1])\n\n # if not any(x in query_tuple for x in last_query): #no repetition\n # break\n # else:\n # f = open('Repeats.log', 'a')\n # f.write(str(query_tuple)+'\\n')\n # f.write('Query item repeated\\n')\n # f.close()\n # item_repeated_last_query_count += 1\n\n #pop the query\n quicksort_id = np.random.randint(nquicksorts)\n\n while queryqueuesallqs[quicksort_id] == []:\n #current queue empty, switch to a different one\n quicksort_id = np.random.randint(nquicksorts)\n query_index = np.random.randint(len(queryqueuesallqs[quicksort_id])) #removed last_query business\n query = queryqueuesallqs[quicksort_id].pop(query_index)\n #flip with 50% chance\n if random.choice([True,False]):\n query[0],query[1] = query[1],query[0]\n\n #butler.participants.set(key='last_query', value=(query[0], query[1]))\n\n\n #add timestamp to query\n query[2][2] = datetime.now().isoformat()\n smallerindexitem = min(query[0], query[1])\n largerindexitem = max(query[0], query[1])\n waitingforresponse[quicksort_id][str(smallerindexitem)+','+str(largerindexitem)] = query\n\n f = open('Quicksort.log','a')\n f.write('In getQuery\\n')\n #f.write('Quicksort_id: ' + str(quicksort_id)+'\\n')\n f.write('Query being shown: ' + str(query)+'\\n')\n\n f.write('arrlist:\\n')\n for x in arrlist:\n f.write(str(x)+'\\n')\n\n f.write('Query queues:\\n')\n for l1 in queryqueuesallqs:\n for l2 in l1:\n f.write(str([l2[0],l2[1]])+', ')\n f.write('\\n')\n\n f.write('waitingforresponse:\\n')\n cd = 0\n for d in waitingforresponse:\n f.write(str(cd)+'\\n')\n cd = cd+1\n if d=={}:\n continue\n for k in d.keys():\n f.write('('+k+'), ')\n f.write('\\n')\n\n f.write('Stack:\\n')\n cd = 0\n for l in stackparametersallqs:\n f.write(str(cd)+'\\n')\n cd = cd+1\n for k in l.keys():\n v = l[k]\n f.write('[l:'+str(v['l'])+',h:'+str(v['h'])+',count:'+str(v['count'])+',smaller:'+str(v['smallerthanpivot'])+',larger:'+str(v['largerthanpivot'])+',pivot:'+str(v['pivot'])+']\\n')\n\n #utils.debug_print('quicksort_id: ' + str(quicksort_id))\n #utils.debug_print('queryqueuesallqs')\n #for x in queryqueuesallqs:\n # utils.debug_print(str(x))\n\n #utils.debug_print('stackparametersallqs')\n #for x in stackparametersallqs:\n # utils.debug_print(str(x))\n\n #utils.debug_print('waitingforresponse')\n #for x in waitingforresponse:\n # utils.debug_print(str(x))\n\n #utils.debug_print('new arr:')\n #for x in arrlist:\n # utils.debug_print(str(x))\n\n butler.algorithms.set(key='waitingforresponse', value=waitingforresponse)\n butler.algorithms.set(key='queryqueuesallqs', value=queryqueuesallqs)\n butler.algorithms.set(key='stackparametersallqs', value=stackparametersallqs)\n #butler.algorithms.set(key='wait', value=False)\n\n f.write('\\n')\n f.close()\n utils.debug_print('In Quicksort getQuery: Current Query ' + str(query))\n lock.release()\n return query\n\n def processAnswer(self, butler, left_id=0, right_id=0, winner_id=0, quicksort_data=0):\n#left_id is actually left item, similarly right_id, winner_id\n #waitUntilDBClear(butler)\n lock = butler.memory.lock('lock')\n lock.acquire()\n \n quicksort_id = quicksort_data[0]\n f = open('Quicksort.log','a')\n bugfile = open('Bugs.log', 'a')\n\n f.write('In processAnswer\\n')\n f.write(str([quicksort_id, left_id, right_id, winner_id]) + '\\n')\n utils.debug_print('In Quicksort processAnswer: Winner id ' + str([quicksort_id, left_id, right_id, winner_id]))\n\n nquicksorts = butler.algorithms.get(key='nquicksorts')\n n = butler.algorithms.get(key='n')\n arrlist = butler.algorithms.get(key='arrlist')\n queryqueuesallqs = butler.algorithms.get(key='queryqueuesallqs')\n stackparametersallqs = butler.algorithms.get(key='stackparametersallqs')\n waitingforresponse = butler.algorithms.get(key='waitingforresponse')\n\n arr = np.array(arrlist[quicksort_id])\n\n stackkey = quicksort_data[1]\n\n stacks = stackparametersallqs[quicksort_id] #dictionary of stacks for current quicksort_id\n\n smallerindexitem = min(left_id, right_id)\n largerindexitem = max(left_id, right_id)\n try:\n query = waitingforresponse[quicksort_id][str(smallerindexitem)+','+str(largerindexitem)]\n except KeyError:\n #this means that the query response has been received from a different user maybe, and this response should be ignored. This shouldn't happen too often.\n f.write('Query not found\\n\\n')\n bugfile.write(str([quicksort_id, left_id, right_id, winner_id]) + '\\n')\n bugfile.write('Query not found\\n\\n')\n #utils.debug_print('Query not found')\n f.write('\\n')\n f.close()\n bugfile.close()\n lock.release()\n #butler.algorithms.set(key='wait', value=False)\n return True\n \n del waitingforresponse[quicksort_id][str(smallerindexitem)+','+str(largerindexitem)]\n #if waitingforresponse is empty, it means there might be queries that have not been sent out to users so far.\n\n #if this query was added to the queue again to be resent because the first response wasn't received soon, delete it from the queue - the response has been received.\n for q in queryqueuesallqs[quicksort_id]:\n if ((q[0]==left_id and q[1]==right_id) or (q[0]==right_id and q[1]==left_id)):\n queryqueuesallqs[quicksort_id].remove(q)\n break\n\n curquerystackvalue = stacks[stackkey]\n if winner_id==left_id:\n loser = right_id\n else:\n loser = left_id\n\n #second check to make sure this response hasn't been recorded already. Check that the non-pivot id is not in the smallerthanpivot or largerthanpivot list\n nonpivot_id = (left_id==curquerystackvalue['pivot'])*right_id + (right_id==curquerystackvalue['pivot'])*left_id\n if nonpivot_id in curquerystackvalue['smallerthanpivot'] or nonpivot_id in curquerystackvalue['largerthanpivot']:\n bugfile.write(str([quicksort_id, left_id, right_id, winner_id]) + '\\n')\n bugfile.write(str(curquerystackvalue)+'\\n')\n bugfile.write('Response for this query has already been recorded\\n\\n')\n f.write('Response for this query has already been recorded\\n\\n')\n f.write('\\n')\n f.close()\n bugfile.close()\n lock.release()\n #butler.algorithms.set(key='wait', value=False)\n return True\n\n\n if winner_id==curquerystackvalue['pivot']:\n curquerystackvalue['smallerthanpivot'].append(loser)\n else:\n curquerystackvalue['largerthanpivot'].append(winner_id)\n\n curquerystackvalue['count'] = curquerystackvalue['count']+1\n if curquerystackvalue['count'] == curquerystackvalue['h']-curquerystackvalue['l']-1:\n del stackparametersallqs[quicksort_id][stackkey]\n l = curquerystackvalue['l']\n h = curquerystackvalue['h']\n smallerthanpivot = curquerystackvalue['smallerthanpivot']\n largerthanpivot = curquerystackvalue['largerthanpivot']\n pivot = curquerystackvalue['pivot']\n\n #update array\n arr[l:h] = smallerthanpivot + [pivot] + largerthanpivot\n arrlist[quicksort_id] = list(arr)\n butler.algorithms.set(key='arrlist', value=arrlist)\n\n #create two new stacks\n if len(smallerthanpivot) > 1:\n newstackvalue = {'l':l, 'h':l+len(smallerthanpivot), 'pivot':smallerthanpivot[-1], 'smallerthanpivot':[], 'largerthanpivot':[], 'count':0}\n newstackkey = utils.getNewUID()\n stackparametersallqs[quicksort_id][newstackkey] = newstackvalue\n for c3 in range(len(smallerthanpivot)-1):\n queryqueuesallqs[quicksort_id].append([smallerthanpivot[c3], smallerthanpivot[-1], [quicksort_id, newstackkey, '0']])\n if len(largerthanpivot) > 1:\n newstackvalue = {'l': l+len(smallerthanpivot)+1, 'h':h, 'pivot':largerthanpivot[-1], 'smallerthanpivot':[], 'largerthanpivot':[], 'count':0}\n newstackkey = utils.getNewUID()\n stackparametersallqs[quicksort_id][newstackkey] = newstackvalue\n for c3 in range(len(largerthanpivot)-1):\n queryqueuesallqs[quicksort_id].append([largerthanpivot[c3], largerthanpivot[-1], [quicksort_id, newstackkey, '0']])\n\n if stackparametersallqs[quicksort_id] == {}:\n #if stack is empty\n\n #1) update ranking\n ranking = np.array(butler.algorithms.get(key='ranking'))\n ranking = ranking + arr\n g = open('QSranking.log','a')\n g.write(str(arr)+'\\n')\n g.close()\n butler.algorithms.set(key='ranking', value=ranking)\n f.write('ranking = '+str(ranking)+'\\n')\n \n #2) create a new permutation\n arr = np.random.permutation(range(n))\n arrlist[quicksort_id] = arr\n butler.algorithms.set(key='arrlist', value=arrlist)\n \n #3) add queries to queue, and stack parameters to stack\n stackvalue = {'l':0, 'h':len(arr), 'pivot':arr[-1], 'smallerthanpivot':[], 'largerthanpivot':[], 'count':0}\n stackkey = utils.getNewUID()\n stackparametersallqs[quicksort_id] = {stackkey: stackvalue}\n for c4 in range(len(arr)-1):\n queryqueuesallqs[quicksort_id].append([arr[c4], arr[-1], [quicksort_id, stackkey, '0']])\n\n #write everything back\n butler.algorithms.set(key='stackparametersallqs', value=stackparametersallqs)\n butler.algorithms.set(key='queryqueuesallqs', value=queryqueuesallqs)\n butler.algorithms.set(key='waitingforresponse', value=waitingforresponse)\n #butler.algorithms.set(key='wait', value=False)\n\n f.write('arrlist:\\n')\n for x in arrlist:\n f.write(str(x)+'\\n')\n\n f.write('Query queues:\\n')\n for l1 in queryqueuesallqs:\n for l2 in l1:\n f.write(str([l2[0],l2[1]])+', ')\n f.write('\\n')\n\n f.write('waitingforresponse:\\n')\n cd = 0\n for d in waitingforresponse:\n f.write(str(cd)+'\\n')\n cd = cd+1\n if d=={}:\n continue\n for k in d.keys():\n f.write('('+k+'), ')\n f.write('\\n')\n\n f.write('Stack:\\n')\n cd = 0\n for l in stackparametersallqs:\n f.write(str(cd)+'\\n')\n cd = cd+1\n for k in l.keys():\n v = l[k]\n f.write('[l:'+str(v['l'])+',h:'+str(v['h'])+',count:'+str(v['count'])+',smaller:'+str(v['smallerthanpivot'])+',larger:'+str(v['largerthanpivot'])+',pivot:'+str(v['pivot'])+']\\n')\n \n f.write('\\n')\n f.close()\n bugfile.close()\n\n f = open('Queries.log','a')\n f.write('QS ' + str([quicksort_data[0],left_id,right_id,winner_id])+'\\n')\n f.close()\n\n f = open('QuicksortArraysAnalysis.log', 'a')\n f.write(str([quicksort_id, left_id, right_id, winner_id]) + '\\n')\n f.write('arrlist:\\n')\n for x in arrlist:\n f.write(str(x)+'\\n')\n f.write('\\n')\n f.close()\n lock.release()\n return True\n\n def getModel(self,butler):\n return range(5), range(5)\n", "repo_name": "sumeetsk/NEXT", "sub_path": "apps/ActiveRanking/algs/Quicksort/Quicksort.py", "file_name": "Quicksort.py", "file_ext": "py", "file_size_in_byte": 18315, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.random.permutation", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "next.utils.getNewUID", "line_number": 47, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 89, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 89, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.random.permutation", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 103, "usage_type": "attribute"}, {"api_name": "next.utils.getNewUID", "line_number": 106, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 153, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 163, "usage_type": "name"}, {"api_name": "next.utils.debug_print", "line_number": 227, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 227, "usage_type": "name"}, {"api_name": "next.utils.debug_print", "line_number": 243, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 243, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "next.utils.getNewUID", "line_number": 327, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 327, "usage_type": "name"}, {"api_name": "next.utils.getNewUID", "line_number": 333, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 333, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 351, "usage_type": "attribute"}, {"api_name": "next.utils.getNewUID", "line_number": 357, "usage_type": "call"}, {"api_name": "next.utils", "line_number": 357, "usage_type": "name"}]} +{"seq_id": "18486967133", "text": "import math\n# Sturges\nm = math.ceil(1 + math.log2(n))\nprint(f'm = {m}')\n\nfrom statistics import stdev\n\n# Scott\nb = 3.5 * stdev(diskspace) / (n ** (1 / 3))\nm = math.ceil((diskspace.max() - diskspace.min()) / b)\nprint(f'm = {m}')\n\n# Excel\nm = math.ceil(math.sqrt(n))\nprint(f'm = {m}')\n\n", "repo_name": "snakiertech4978/DataScienceP3", "sub_path": "Aantal klassen bepalen.py", "file_name": "Aantal klassen bepalen.py", "file_ext": "py", "file_size_in_byte": 284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "math.ceil", "line_number": 3, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 3, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 9, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 10, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 14, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "23110049428", "text": "from django.db.models import query\nfrom django.shortcuts import render\nfrom api import querysets, serializers\nfrom api.models import (\n Booking,\n CarBrand,\n CarModel,\n CarRegistrationNumber,\n City\n)\nfrom rest_framework.authtoken.models import Token\nfrom api.serializers import (\n BookingSerializer,\n CarBrandSerializer,\n CarModelSerializer,\n CarRegistrationNumberSerializer,\n CitySerializer,\n UserSerializer,\n LoginSerializer\n)\nfrom api.signal import (\n cancelled_booking,\n save_booking,\n update_booking,\n)\nfrom rest_framework.decorators import action, api_view, permission_classes\nfrom api.filters import CarCityFilter\nfrom django.contrib.auth import get_user_model\nfrom api.permission_helper import GenericObjectPermissions\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework.viewsets import ModelViewSet\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.pagination import PageNumberPagination\nfrom rest_framework.mixins import (\n CreateModelMixin,\n)\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import (\n GenericViewSet,\n)\nfrom rest_framework import status\nfrom zoom.settings import AUTH_PASSWORD_VALIDATORS\n\nUser = get_user_model()\n\n\nclass UserCreateView(CreateModelMixin, GenericViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n def perform_create(self, serializer):\n obj = serializer.save()\n obj.set_password(serializer.validated_data.get('password'))\n obj.save()\n\n\nclass CarBrandViewSet(ModelViewSet):\n queryset = CarBrand.objects.all()\n serializer_class = CarBrandSerializer\n permission_classes = (GenericObjectPermissions, )\n pagination_class = None\n\n perms_map = {\n 'POST': ('panel.add_carbrand',),\n 'PATCH': ('panel.change_carbrand',),\n 'PUT': ('panel.change_carbrand',),\n 'DELETE': ('panel.delete_carbrand',),\n }\n\n\n\nclass CarModelViewSet(ModelViewSet):\n queryset = CarModel.objects.all()\n serializer_class = CarModelSerializer\n permission_classes = (GenericObjectPermissions, )\n pagination_class = None\n filter_backends = (DjangoFilterBackend, )\n filter_fields = ('type', 'brand', )\n perms_map = {\n 'POST': ('panel.add_carmodel',),\n 'PATCH': ('panel.change_carmodel',),\n 'PUT': ('panel.change_carmodel',),\n 'DELETE': ('panel.delete_carmodel',),\n }\n\n\nclass CarRegistrationNumberViewSet(ModelViewSet):\n queryset = CarRegistrationNumber.objects.all()\n serializer_class = CarRegistrationNumberSerializer\n permission_classes = (GenericObjectPermissions, )\n perms_map = {\n 'POST': ('panel.add_carregistrationnumber',),\n 'PATCH': ('panel.change_carregistrationnumber',),\n 'PUT': ('panel.change_carregistrationnumber',),\n 'DELETE': ('panel.delete_carregistrationnumber',),\n }\n\n def paginate_queryset(self, queryset):\n if self.request.query_params.get('no_page'):\n return None\n return super().paginate_queryset(queryset)\n\n\nclass CityViewSet(ModelViewSet):\n queryset = City.objects.all()\n serializer_class = CitySerializer\n permission_classes = (GenericObjectPermissions, )\n perms_map = {\n 'POST': ('panel.add_city',),\n 'PATCH': ('panel.change_city',),\n 'PUT': ('panel.change_city',),\n 'DELETE': ('panel.delete_city',),\n }\n\n def paginate_queryset(self, queryset):\n if self.request.query_params.get('no_page'):\n return None\n return super().paginate_queryset(queryset)\n\n\nclass CarCityViewSet(ModelViewSet):\n queryset = City.objects.all()\n serializer_class = CitySerializer\n permission_classes = (GenericObjectPermissions, )\n pagination_class = PageNumberPagination\n filter_backends = (DjangoFilterBackend, )\n filter_class = CarCityFilter\n perms_map = {\n 'POST': ('panel.add_carcity',),\n 'PATCH': ('panel.change_carcity',),\n 'PUT': ('panel.change_carcity',),\n 'DELETE': ('panel.delete_carcity',),\n }\n\n\nclass BookingViewSet(ModelViewSet):\n queryset = Booking.objects.all()\n serializer_class = BookingSerializer\n permission_classes = (GenericObjectPermissions, )\n pagination_class = PageNumberPagination\n filter_backends = (DjangoFilterBackend, )\n filter_fields = ('available', 'since', 'upto', 'car')\n perms_map = {\n 'GET': ('panel.view_booking'),\n 'POST': ('panel.add_booking',),\n 'PATCH': ('panel.change_booking',),\n 'PUT': ('panel.change_booking',),\n 'DELETE': ('panel.delete_booking',),\n }\n\n def get_queryset(self):\n queryset = self.queryset\n args = self.request.query_params\n since = args.get('since')\n upto = args.get('upto')\n car = args.get('car')\n if car:\n queryset = queryset.filter(car=car)\n if self.action == \"list\":\n queryset = queryset.exclude(available=False,since__gte=since, upto__lte=upto)\n return queryset\n\n def create(self, request, *args, **kwargs):\n save_booking.delay(self, request, *args, **kwargs)\n return Response({'flag': True, 'msg': \"Booking Created Successfully!\"}, status=status.HTTP_201_CREATED)\n\n def update(self, request, *args, **kwargs):\n update_booking.delay(self, request, *args, **kwargs)\n return Response({'flag':True, 'msg': 'Booking Updated Successfully'})\n\n def destroy(self, request, *args, **kwargs):\n cancelled_booking.delay(self, request)\n return Response({'flag':True, 'msg': 'Booking cancelled Successfully!'}, status=status.HTTP_204_NO_CONTENT)\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated,])\ndef GetBookingHistory(request):\n user = request.user\n return Response(BookingSerializer(Booking.objects.filter(created_by=user.id)).data, status=200)\n\n\nclass LoginAPIView(APIView):\n def post(self, request, *args, **kwargs):\n serializer = LoginSerializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n req_data = serializer.validated_data\n login_key = req_data.get('login_key')\n password = req_data.get('password')\n user = get_user_model().objects.get(username=login_key)\n error = {}\n\n if not user.check_password(password):\n error.update({'password': 'Invalid Password'})\n\n if error:\n raise serializers.ValidationError(error)\n \n Token.objects.filter(user=user).delete()\n token_obj = Token.objects.create(user=user)\n access_token = token_obj.key\n response = {\n \"user\": user.id,\n \"Name\": user.first_name,\n \"username\": user.username,\n \"user_token\": access_token,\n }\n return Response(response,status=200)", "repo_name": "tausiftj/BIS", "sub_path": "api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6872, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 48, "usage_type": "name"}, {"api_name": "api.serializers.UserSerializer", "line_number": 50, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 58, "usage_type": "name"}, {"api_name": "api.models.CarBrand.objects.all", "line_number": 59, "usage_type": "call"}, {"api_name": "api.models.CarBrand.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "api.models.CarBrand", "line_number": 59, "usage_type": "name"}, {"api_name": "api.serializers.CarBrandSerializer", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 61, "usage_type": "name"}, {"api_name": "api.permission_helper.GenericObjectPermissions", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 73, "usage_type": "name"}, {"api_name": "api.models.CarModel.objects.all", "line_number": 74, "usage_type": "call"}, {"api_name": "api.models.CarModel.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "api.models.CarModel", "line_number": 74, "usage_type": "name"}, {"api_name": "api.serializers.CarModelSerializer", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 76, "usage_type": "name"}, {"api_name": "api.permission_helper.GenericObjectPermissions", "line_number": 76, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 78, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 88, "usage_type": "name"}, {"api_name": "api.models.CarRegistrationNumber.objects.all", "line_number": 89, "usage_type": "call"}, {"api_name": "api.models.CarRegistrationNumber.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "api.models.CarRegistrationNumber", "line_number": 89, "usage_type": "name"}, {"api_name": "api.serializers.CarRegistrationNumberSerializer", "line_number": 90, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 91, "usage_type": "name"}, {"api_name": "api.permission_helper.GenericObjectPermissions", "line_number": 91, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 105, "usage_type": "name"}, {"api_name": "api.models.City.objects.all", "line_number": 106, "usage_type": "call"}, {"api_name": "api.models.City.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "api.models.City", "line_number": 106, "usage_type": "name"}, {"api_name": "api.serializers.CitySerializer", "line_number": 107, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 108, "usage_type": "name"}, {"api_name": "api.permission_helper.GenericObjectPermissions", "line_number": 108, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 122, "usage_type": "name"}, {"api_name": "api.models.City.objects.all", "line_number": 123, "usage_type": "call"}, {"api_name": "api.models.City.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "api.models.City", "line_number": 123, "usage_type": "name"}, {"api_name": "api.serializers.CitySerializer", "line_number": 124, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 125, "usage_type": "name"}, {"api_name": "api.permission_helper.GenericObjectPermissions", "line_number": 125, "usage_type": "name"}, {"api_name": "rest_framework.pagination.PageNumberPagination", "line_number": 126, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 127, "usage_type": "name"}, {"api_name": "api.filters.CarCityFilter", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 137, "usage_type": "name"}, {"api_name": "api.models.Booking.objects.all", "line_number": 138, "usage_type": "call"}, {"api_name": "api.models.Booking.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "api.models.Booking", "line_number": 138, "usage_type": "name"}, {"api_name": "api.serializers.BookingSerializer", "line_number": 139, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 140, "usage_type": "name"}, {"api_name": "api.permission_helper.GenericObjectPermissions", "line_number": 140, "usage_type": "name"}, {"api_name": "rest_framework.pagination.PageNumberPagination", "line_number": 141, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 142, "usage_type": "name"}, {"api_name": "api.signal.save_booking.delay", "line_number": 165, "usage_type": "call"}, {"api_name": "api.signal.save_booking", "line_number": 165, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 166, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 166, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 166, "usage_type": "name"}, {"api_name": "api.signal.update_booking.delay", "line_number": 169, "usage_type": "call"}, {"api_name": "api.signal.update_booking", "line_number": 169, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 170, "usage_type": "call"}, {"api_name": "api.signal.cancelled_booking.delay", "line_number": 173, "usage_type": "call"}, {"api_name": "api.signal.cancelled_booking", "line_number": 173, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 174, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 174, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 174, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 180, "usage_type": "call"}, {"api_name": "api.serializers.BookingSerializer", "line_number": 180, "usage_type": "call"}, {"api_name": "api.models.Booking.objects.filter", "line_number": 180, "usage_type": "call"}, {"api_name": "api.models.Booking.objects", "line_number": 180, "usage_type": "attribute"}, {"api_name": "api.models.Booking", "line_number": 180, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 176, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 177, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 177, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 183, "usage_type": "name"}, {"api_name": "api.serializers.LoginSerializer", "line_number": 185, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 190, "usage_type": "call"}, {"api_name": "api.serializers.ValidationError", "line_number": 197, "usage_type": "call"}, {"api_name": "api.serializers", "line_number": 197, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.objects.filter", "line_number": 199, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 199, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.objects.create", "line_number": 200, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 200, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 200, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 208, "usage_type": "call"}]} +{"seq_id": "8668324563", "text": "#!/usr/bin/env python\n\"\"\"\\\nUsage: %prog [-r] RULES\n\nPreprocess RULES, an diazo rules file\n\"\"\"\nusage = __doc__\n\nimport logging\nimport re\n\nfrom optparse import OptionParser\nfrom lxml import etree\nfrom urlparse import urljoin\n\nfrom diazo.cssrules import convert_css_selectors\nfrom diazo.utils import namespaces, fullname, AC_READ_NET, AC_READ_FILE, pkg_xsl, _createOptionParser\n\nlogger = logging.getLogger('diazo')\n\nIMPORT_STYLESHEET = re.compile(r'''(?P@import[ \\t]+(?Purl\\([ \\t]?)?(?P['\"]?))(?P\\S+)(?P(?P=quote)(?(paren)\\)))''', re.IGNORECASE)\nCONDITIONAL_SRC= re.compile(r'''(?P<[^>]*?(src|href)=(?P['\"]?))(?P[^ \\t\\n\\r\\f\\v>]+)(?P(?P=quote)[^>]*?>)''', re.IGNORECASE)\n\n\nupdate_transform = pkg_xsl('update-namespace.xsl')\nnormalize_rules = pkg_xsl('normalize-rules.xsl')\napply_conditions = pkg_xsl('apply-conditions.xsl')\nmerge_conditions = pkg_xsl('merge-conditions.xsl')\nannotate_themes = pkg_xsl('annotate-themes.xsl')\nannotate_rules = pkg_xsl('annotate-rules.xsl')\napply_rules = pkg_xsl('apply-rules.xsl')\nfixup_themes = pkg_xsl('fixup-themes.xsl')\n\n\ndef update_namespace(rules_doc):\n \"\"\"Convert old namespace to new namespace in place\n \"\"\"\n update = False\n for ns in (namespaces['old1'], namespaces['old2']):\n if rules_doc.xpath(\"//*[namespace-uri()='%s']\" % ns):\n logger.warning('The %s namespace is deprecated, use %s instead.' % (ns, namespaces['diazo']))\n update = True\n for ns in (namespaces['oldcss1'], namespaces['oldcss2']):\n if rules_doc.xpath(\"//@*[namespace-uri()='%s']\" % ns):\n logger.warning('The %s namespace is deprecated, use %s instead.' % (ns, namespaces['css']))\n update = True\n if update:\n return update_transform(rules_doc)\n else:\n return rules_doc\n\ndef expand_themes(rules_doc, parser=None, absolute_prefix=None, read_network=False):\n \"\"\"Expand nodes with the theme html.\n \"\"\"\n if absolute_prefix is None:\n absolute_prefix = ''\n base = rules_doc.docinfo.URL\n if parser is None:\n parser = etree.HTMLParser()\n for element in rules_doc.xpath('//diazo:theme[@href]', namespaces=namespaces):\n url = urljoin(base, element.get('href'))\n if url[:6] in ('ftp://', 'http:/', 'https:'):\n raise ValueError(\"Supplied theme '%s', but network access denied.\" % url)\n theme_doc = etree.parse(url, parser=parser)\n prefix = urljoin(absolute_prefix, element.get('prefix', ''))\n apply_absolute_prefix(theme_doc, prefix)\n element.append(theme_doc.getroot())\n return rules_doc\n\ndef apply_absolute_prefix(theme_doc, absolute_prefix):\n if not absolute_prefix:\n return\n if not absolute_prefix.endswith('/'):\n absolute_prefix = absolute_prefix + '/'\n for node in theme_doc.xpath('//*[@src]'):\n url = urljoin(absolute_prefix, node.get('src'))\n node.set('src', url)\n for node in theme_doc.xpath('//*[@href]'):\n url = urljoin(absolute_prefix, node.get('href'))\n node.set('href', url)\n for node in theme_doc.xpath('//style'):\n node.text = IMPORT_STYLESHEET.sub(\n lambda match: match.group('before') + urljoin(absolute_prefix, match.group('url')) + match.group('after'),\n node.text)\n for node in theme_doc.xpath('//comment()[starts-with(., \"[if\")]'):\n node.text = IMPORT_STYLESHEET.sub(\n lambda match: match.group('before') + urljoin(absolute_prefix, match.group('url')) + match.group('after'),\n node.text)\n node.text = CONDITIONAL_SRC.sub(\n lambda match: match.group('before') + urljoin(absolute_prefix, match.group('url')) + match.group('after'),\n node.text)\n\ndef add_extra(rules_doc, extra):\n root = rules_doc.getroot()\n extra_elements = extra.xpath('/xsl:stylesheet/xsl:*', namespaces=namespaces)\n root.extend(extra_elements)\n return rules_doc\n\ndef add_theme(rules_doc, theme, parser=None, absolute_prefix=None, read_network=False):\n if isinstance(theme, basestring) and theme[:6] in ('ftp://', 'http:/', 'https:'):\n raise ValueError(\"Supplied theme '%s', but network access denied.\" % theme)\n if absolute_prefix is None:\n absolute_prefix = ''\n if parser is None:\n parser = etree.HTMLParser()\n root = rules_doc.getroot()\n element = root.makeelement(fullname(namespaces['diazo'], 'theme'))\n theme_doc = etree.parse(theme, parser=parser)\n prefix = urljoin(absolute_prefix, element.get('prefix', ''))\n apply_absolute_prefix(theme_doc, prefix)\n element.append(theme_doc.getroot()) \n root.append(element)\n return rules_doc\n\ndef fixup_theme_comment_selectors(rules):\n \"\"\"Comments must be converted to to be output, doing it early\n allows them to get an xml:id so they can be matched in the theme. The theme\n selector needs rewriting to replace comment() with xsl:comment\n \"\"\"\n for element in rules.xpath(\"//@theme[contains(., 'comment()')]/..\"):\n element.attrib['theme'] = element.attrib['theme'].replace('comment()', 'xsl:comment')\n return rules\n\ndef process_rules(rules, theme=None, extra=None, trace=None, css=True, xinclude=True, absolute_prefix=None,\n includemode=None, update=True, parser=None, rules_parser=None, read_network=False, stop=None):\n if trace:\n trace = '1'\n else:\n trace = '0'\n if rules_parser is None:\n rules_parser = etree.XMLParser(recover=False)\n rules_doc = etree.parse(rules, parser=rules_parser)\n if stop == 0: return rules_doc\n if parser is None:\n parser = etree.HTMLParser()\n if xinclude:\n rules_doc.xinclude() # XXX read_network limitation not yet supported for xinclude\n if stop == 1: return rules_doc\n if update:\n rules_doc = update_namespace(rules_doc)\n if stop == 2: return rules_doc\n if css:\n rules_doc = convert_css_selectors(rules_doc)\n if stop == 3: return rules_doc\n rules_doc = fixup_theme_comment_selectors(rules_doc)\n if stop == 4: return rules_doc\n rules_doc = expand_themes(rules_doc, parser, absolute_prefix, read_network)\n if theme is not None:\n rules_doc = add_theme(rules_doc, theme, parser, absolute_prefix, read_network)\n if stop == 5: return rules_doc\n if includemode is None:\n includemode = 'document'\n includemode = \"'%s'\" % includemode\n rules_doc = normalize_rules(rules_doc, includemode=includemode)\n if stop == 6: return rules_doc\n rules_doc = apply_conditions(rules_doc)\n if stop == 7: return rules_doc\n rules_doc = merge_conditions(rules_doc)\n if stop == 8: return rules_doc\n rules_doc = fixup_themes(rules_doc)\n if stop == 9: return rules_doc\n rules_doc = annotate_themes(rules_doc)\n if stop == 10: return rules_doc\n rules_doc = annotate_rules(rules_doc)\n if stop == 11: return rules_doc\n rules_doc = apply_rules(rules_doc, trace=trace)\n return rules_doc\n\n\ndef main():\n \"\"\"Called from console script\n \"\"\"\n parser = _createOptionParser(usage=usage)\n parser.add_option(\"-s\", \"--stop\", metavar=\"n\", type=\"int\",\n help=\"Stop preprocessing at stage n\", \n dest=\"stop\", default=None)\n (options, args) = parser.parse_args()\n\n if options.rules is None:\n if len(args) == 2 and options.theme is None:\n options.rules, options.theme = args\n elif len(args) == 1:\n options.rules, = args\n else:\n parser.error(\"Wrong number of arguments.\")\n elif args:\n parser.error(\"Wrong number of arguments.\")\n\n if options.trace:\n logger.setLevel(logging.DEBUG)\n\n rules_doc = process_rules(\n options.rules,\n theme=options.theme,\n extra=options.extra,\n trace=options.trace,\n absolute_prefix=options.absolute_prefix,\n includemode=options.includemode,\n read_network=options.read_network,\n stop=options.stop,\n )\n rules_doc.write(options.output, pretty_print=options.pretty_print)\n\nif __name__ == '__main__':\n main()\n", "repo_name": "nerdfiles/diazo-test0", "sub_path": "eggs/diazo-1.0b1-py2.7.egg/diazo/rules.py", "file_name": "rules.py", "file_ext": "py", "file_size_in_byte": 8144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 21, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 25, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 26, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 27, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 28, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 29, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 30, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 31, "usage_type": "call"}, {"api_name": "diazo.utils.pkg_xsl", "line_number": 32, "usage_type": "call"}, {"api_name": "diazo.utils.namespaces", "line_number": 39, "usage_type": "name"}, {"api_name": "diazo.utils.namespaces", "line_number": 41, "usage_type": "name"}, {"api_name": "diazo.utils.namespaces", "line_number": 43, "usage_type": "name"}, {"api_name": "diazo.utils.namespaces", "line_number": 45, "usage_type": "name"}, {"api_name": "lxml.etree.HTMLParser", "line_number": 59, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 59, "usage_type": "name"}, {"api_name": "diazo.utils.namespaces", "line_number": 60, "usage_type": "name"}, {"api_name": "urlparse.urljoin", "line_number": 61, "usage_type": "call"}, {"api_name": "lxml.etree.parse", "line_number": 64, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 64, "usage_type": "name"}, {"api_name": "urlparse.urljoin", "line_number": 65, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 76, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 79, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 83, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 87, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 90, "usage_type": "call"}, {"api_name": "diazo.utils.namespaces", "line_number": 95, "usage_type": "name"}, {"api_name": "lxml.etree.HTMLParser", "line_number": 105, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 105, "usage_type": "name"}, {"api_name": "diazo.utils.fullname", "line_number": 107, "usage_type": "call"}, {"api_name": "diazo.utils.namespaces", "line_number": 107, "usage_type": "name"}, {"api_name": "lxml.etree.parse", "line_number": 108, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 108, "usage_type": "name"}, {"api_name": "urlparse.urljoin", "line_number": 109, "usage_type": "call"}, {"api_name": "lxml.etree.XMLParser", "line_number": 131, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 131, "usage_type": "name"}, {"api_name": "lxml.etree.parse", "line_number": 132, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 132, "usage_type": "name"}, {"api_name": "lxml.etree.HTMLParser", "line_number": 135, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 135, "usage_type": "name"}, {"api_name": "diazo.cssrules.convert_css_selectors", "line_number": 143, "usage_type": "call"}, {"api_name": "diazo.utils._createOptionParser", "line_number": 173, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 190, "usage_type": "attribute"}]} +{"seq_id": "41817815909", "text": "from . import main\nfrom flask import render_template, flash, redirect, url_for, session, request\nfrom .forms import GroupForm, NameEntryForm, reg_name\nfrom ..models import Group, User\nfrom .. import db\nfrom ..utilities import permutation_without_fixed_points\n \n\n@main.route(\"/\", methods=[\"GET\"])\ndef index_get(group_name):\n group = Group.query.filter_by(name=group_name).first()\n\n if group is None:\n return redirect(url_for(\".create_group\"))\n\n if \"user\" in session:\n user = User.query.get(session[\"user\"])\n if user:\n if user.group_id != group.id:\n session[\"user\"] = \"\"\n\n return render_template(\"index.html\", group=group)\n\n\n@main.route(\"/\", methods=[\"POST\"])\ndef index_post(group_name):\n form = NameEntryForm()\n form.name.data = request.form[\"name\"] or \"\"\n if \"pin\" in request.form:\n form.pin.data = request.form[\"pin\"]\n\n group = Group.query.filter_by(name=group_name).first()\n\n if form.validate():\n name = form.name.data.strip().lower().capitalize()\n\n if group.secure:\n pin = form.pin.data\n user = User.query \\\n .filter_by(group=group) \\\n .filter_by(name=name) \\\n .filter_by(pin=pin) \\\n .first()\n else:\n user = User.query \\\n .filter_by(group=group) \\\n .filter_by(name=name) \\\n .first()\n\n if user is not None:\n session[\"user\"] = user.id\n\n return redirect( url_for(\".index_get\", group_name=group_name) )\n\n\n@main.route(\"/draw/\")\ndef draw(group_name):\n group = Group.query.filter_by(name=group_name).first()\n\n if group is None:\n return \"Group not found\", 400\n \n if session[\"user\"] is None:\n return \"User not logged\", 400\n\n user = User.query.get(session[\"user\"])\n\n if user is None:\n return \"User not logged (try clearing cache)\", 400\n\n return User.query.get(user.drafted_person_id).name, 200\n \n\n@main.route(\"/\", methods=[\"GET\", \"POST\"])\ndef create_group():\n form = GroupForm()\n \n if form.validate_on_submit():\n group_by_name = Group.query.filter_by(name=form.group_name.data).first()\n \n if group_by_name:\n flash(\"Group already exists!\")\n return redirect( url_for(\".create_group\"))\n\n names = list(set(map(lambda e : e[\"name\"].lower().capitalize() , form.names.data)))\n users = [ User(name=name) for name in names]\n\n for user in users:\n db.session.add(user)\n \n db.session.flush()\n\n perm = permutation_without_fixed_points( len(users) )\n\n for i, u in enumerate(perm):\n users[i].drafted_person_id = users[u].id\n\n group = Group(\n name = form.group_name.data,\n secure = form.secure.data,\n users = users,\n )\n\n db.session.add(group)\n db.session.commit()\n\n if group.secure:\n return redirect( url_for(\".group_overview\", group_hash=group.name_hash))\n\n return redirect( url_for(\".index_get\", group_name=group.name))\n\n elif len(form.errors) > 0:\n for field, err in form.errors.items():\n if field == \"names\":\n flash(reg_name.message)\n else:\n flash(err[0])\n\n return render_template(\"create_group.html\", form=form)\n\n\n@main.route(\"/overview/\")\ndef group_overview(group_hash):\n group = Group.query.filter_by(name_hash=group_hash).first()\n\n if group is None:\n return redirect(url_for(\".create_group\"))\n\n return render_template(\"group_overview.html\", group=group)", "repo_name": "RafalKornel/santa2020", "sub_path": "app/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3741, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "models.Group.query.filter_by", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Group.query", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 16, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 17, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "forms.NameEntryForm", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Group.query.filter_by", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Group.query", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 32, "usage_type": "name"}, {"api_name": "models.User.query.filter_by", "line_number": 39, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 39, "usage_type": "name"}, {"api_name": "models.User.query.filter_by", "line_number": 45, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Group.query.filter_by", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Group.query", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 66, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 66, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 71, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 71, "usage_type": "name"}, {"api_name": "forms.GroupForm", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Group.query.filter_by", "line_number": 79, "usage_type": "call"}, {"api_name": "models.Group.query", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 79, "usage_type": "name"}, {"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": "models.User", "line_number": 86, "usage_type": "call"}, {"api_name": "utilities.permutation_without_fixed_points", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 115, "usage_type": "call"}, {"api_name": "forms.reg_name.message", "line_number": 115, "usage_type": "attribute"}, {"api_name": "forms.reg_name", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 119, "usage_type": "call"}, {"api_name": "models.Group.query.filter_by", "line_number": 124, "usage_type": "call"}, {"api_name": "models.Group.query", "line_number": 124, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "26651426479", "text": "import json\nimport sqlite3\n\nimport numpy as np\nfrom scipy.stats import pearsonr\nfrom skimage.metrics import structural_similarity as calc_ssim\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom tqdm import tqdm\n\nfrom utils import utils\nfrom utils.utils import crop_center\n\n\ndef prepare_data(rows, img_size, crop_size, do_crop):\n \"\"\"\n Prepares the data retrieved from the db file to be used for the linear attacks.\n\n :param rows: array of CRPs from the db\n :param img_size: size of the responses\n :param crop_size: size to which the responses will be cropped if decided to\n :param do_crop: whether to crop the responses\n :return: tuple of prepared challenges and responses as numpy arrays\n \"\"\"\n challenges, responses = [], []\n\n for row in tqdm(rows, leave=True, position=0):\n challenge = np.array([int(bit) for bit in row[0]]).astype(np.uint8)\n response = np.array(json.loads(row[1])).astype(np.float32)\n response = response.flatten()\n\n if do_crop:\n response = crop_center(response, crop_size, img_size).flatten()\n\n challenges.append(challenge)\n responses.append(response)\n return np.array(challenges), np.array(responses)\n\n\ndef get_training_data(cursor, img_size, crop_size, do_crop):\n \"\"\"\n Retrieves and prepares the data that will be used for the training of the linear attacks.\n\n :param cursor: cursor object from sqlite to access the data\n :param img_size: size of the responses\n :param crop_size: size to which the responses will be cropped if decided to\n :param do_crop: whether to crop the responses\n :return: prepared data as tuple of challenges and responses\n \"\"\"\n table_name = cursor.execute(\"select name from sqlite_master where type = 'table';\").fetchone()[0]\n size = cursor.execute(f\"SELECT COUNT(*) FROM {table_name}\").fetchone()[0]\n threshold = int(size * 0.9)\n rows = cursor.execute(\n f\"SELECT DISTINCT challenge, response FROM {table_name} WHERE id <= {threshold}\").fetchall()\n return prepare_data(rows, img_size, crop_size, do_crop)\n\n\ndef get_test_data(cursor, img_size, crop_size, do_crop):\n \"\"\"\n Retrieves and prepares the data that will be used for the testing of the linear attacks.\n\n :param cursor: cursor object from sqlite to access the data\n :param img_size: size of the responses\n :param crop_size: size to which the responses will be cropped if decided to\n :param do_crop: whether to crop the responses\n :return: prepared data as tuple of challenges and responses\n \"\"\"\n table_name = cursor.execute(\"select name from sqlite_master where type = 'table';\").fetchone()[0]\n size = cursor.execute(f\"SELECT COUNT(*) FROM {table_name}\").fetchone()[0]\n threshold = int(size * 0.9)\n rows = cursor.execute(\n f\"SELECT DISTINCT challenge, response FROM {table_name} WHERE id > {threshold} \").fetchall()\n return prepare_data(rows, img_size, crop_size, do_crop)\n\n\ndef run_lr(c_train, r_train, *args):\n \"\"\"\n Fits the LR model and returns the results of the attack.\n\n :param c_train: training challenges\n :param r_train: training responses\n :param args: further arguments for the testing and evaluation of the attack\n :return: results of the attack for the test data\n \"\"\"\n print(\"Starting linear regression...\")\n reg = LinearRegression().fit(c_train, r_train)\n print(\"Fitting finished!\")\n return evaluate_linear_attack(reg, *args)\n\n\ndef run_ridge(c_train, r_train, *args, alpha):\n \"\"\"\n Fits the ridge model and returns the results of the attack.\n\n :param c_train: training challenges\n :param r_train: training responses\n :param args: further arguments for the testing and evaluation of the attack\n :param alpha: penalty term used for ridge\n :return: results of the attack for the test data\n \"\"\"\n print(f\"Starting Ridge...\")\n reg = Ridge(alpha=alpha).fit(c_train, r_train)\n print(\"Fitting finished!\")\n return evaluate_linear_attack(reg, *args)\n\n\ndef run_opr_lr(c_bits, c_train, r_train, c_test, r_test, *args):\n \"\"\"\n Fits the OPR LR model using the quadratic challenge transformation and returns the results of the attack.\n\n :param c_bits: number of bits of a challenge\n :param c_train: training challenges\n :param r_train: training responses\n :param c_test: test challenges\n :param r_test: test responses\n :param args: further arguments for the testing and evaluation of the attack\n :return: results of the attack for the test data\n \"\"\"\n c_train_quadratic = np.empty((c_train.shape[0], c_bits * (c_bits + 1) // 2))\n idx = 0\n for i in range(c_bits):\n for j in range(i + 1):\n c_train_quadratic[:, idx] = c_train[:, i] * c_train[:, j]\n idx += 1\n c_test_quadratic = np.empty((c_test.shape[0], c_bits * (c_bits + 1) // 2))\n idx = 0\n for i in range(c_bits):\n for j in range(i + 1):\n c_test_quadratic[:, idx] = c_test[:, i] * c_test[:, j]\n idx += 1\n\n print(\"Starting OPR LR...\")\n reg = LinearRegression().fit(c_train_quadratic, r_train)\n print(\"Fitting finished!\")\n return evaluate_linear_attack(reg, c_test_quadratic, r_test, *args)\n\n\ndef run_opr_ridge(c_bits, c_train, r_train, c_test, r_test, *args, alpha):\n \"\"\"\n Fits the OPR ridge model using the quadratic challenge transformation and returns the results of the attack.\n\n :param c_bits: number of bits of a challenge\n :param c_train: training challenges\n :param r_train: training responses\n :param c_test: test challenges\n :param r_test: test responses\n :param args: further arguments for the testing and evaluation of the attack\n :param alpha: penalty term used for ridge\n :return: results of the attack for the test data\n \"\"\"\n c_train_and = np.empty((c_train.shape[0], c_bits * (c_bits + 1) // 2))\n idx = 0\n for i in range(c_bits):\n for j in range(i + 1):\n c_train_and[:, idx] = c_train[:, i] * c_train[:, j]\n idx += 1\n c_test_and = np.empty((c_test.shape[0], c_bits * (c_bits + 1) // 2))\n idx = 0\n for i in range(c_bits):\n for j in range(i + 1):\n c_test_and[:, idx] = c_test[:, i] * c_test[:, j]\n idx += 1\n\n print(f\"Starting OPR ridge...\")\n reg = Ridge(alpha=alpha).fit(c_train_and, r_train)\n print(\"Fitting finished!\")\n return evaluate_linear_attack(reg, c_test_and, r_test, *args)\n\n\ndef evaluate_linear_attack(reg, c_test, r_test, img_size, crop_size, do_crop, custom_gabor):\n \"\"\"\n Evaluates the fitted linear attack for the test data and returns the absolute difference, pearson correlation \n coefficient, structural similarity index and fractional hamming distance for each real and predicted response pair.\n \n :param reg: fitted linear model\n :param c_test: test challenges\n :param r_test: test responses\n :param img_size: size of the responses\n :param crop_size: size to which the responses will be cropped if decided to\n :param do_crop: whether to crop the responses\n :param custom_gabor: whether to use the second gabor transformation\n :return: list containing the evaluated metrics for the test data\n \"\"\"\n r_preds = reg.predict(c_test)\n if do_crop:\n img_size = crop_size\n r_test = r_test.reshape(-1, img_size, img_size)\n r_preds = r_preds.reshape(-1, img_size, img_size)\n\n abs_diffs = []\n pcs = []\n ssims = []\n fhds = []\n\n for r_idx, r in enumerate(tqdm(r_test, leave=True, position=0)):\n r_pred = r_preds[r_idx]\n\n fhd = utils.calc_gabor_fhd(r, r_pred, not do_crop, img_size, crop_size, use_custom=custom_gabor)\n abs_diff = np.mean(np.absolute((r - r_pred))) * 255\n pc, _ = pearsonr(r.flatten(), r_pred.flatten().astype(r.dtype))\n ssim = calc_ssim(r, r_pred.astype(r.dtype))\n\n fhds.append(fhd)\n pcs.append(pc)\n ssims.append(ssim)\n abs_diffs.append(abs_diff)\n\n return [abs_diffs, pcs, ssims, fhds]\n\n\ndef run_linear(db_name, img_size, crop_size, do_crop, db_folder, root, log_folder, custom_gabor, only_lr):\n '''\n Runs all linear attacks on the provided dataset.\n \n :param db_name: name of the db file that contains the data\n :param img_size: size of the responses\n :param crop_size: size to which the responses will be cropped if decided to\n :param do_crop: whether to crop the responses\n :param db_folder: name of the folder that contains the db file\n :param root: root folder directory where the folder for the datasets is stored\n :param log_folder: folder where the results of the attacks will be stored\n :param custom_gabor: whether to use the second gabor transformation\n :param only_lr: whether to only run the linear regression attack\n :return: results of all linear attacks\n '''\n data = {}\n\n tmp_path = f'{log_folder}/tmp/{db_name}_linear_results{\"_Crop\" if do_crop else \"\"}.json'\n db_file = f\"{root}/{db_folder}/{db_name}.db\"\n c_bits = int(db_name.split('b')[0])\n conn = sqlite3.connect(db_file)\n cursor = conn.cursor()\n\n c_train, r_train = get_training_data(cursor, img_size, crop_size, do_crop)\n c_test, r_test = get_test_data(cursor, img_size, crop_size, do_crop)\n\n with open(\"linear/hparams.json\", \"r\") as file:\n hparams = json.load(file)\n try:\n ridge_alpha = hparams[db_folder][\"Normal\"][db_name]\n ridge_opr_alpha = hparams[db_folder][\"OPR\"][db_name]\n except Exception:\n print(\"Missing alpha value on db\", db_name, \"- falling back to alpha=1\")\n ridge_alpha = 1\n ridge_opr_alpha = 1\n\n if do_crop:\n img_size = img_size // 4\n\n columns = ['Abs. diff.', 'PC', 'SSIM', 'FHD']\n\n args = [c_train, r_train, c_test, r_test, img_size, crop_size, do_crop, custom_gabor]\n\n lr_results = run_lr(*args)\n data[\"LR\"] = dict(zip(columns, lr_results))\n\n if not only_lr:\n ridge_results = run_ridge(*args, alpha=ridge_alpha)\n data[\"Ridge\"] = dict(zip(columns, ridge_results))\n\n opr_lr_results = run_opr_lr(c_bits, *args)\n data[\"OPR\"] = dict(zip(columns, opr_lr_results))\n\n opr_ridge_results = run_opr_ridge(c_bits, *args, alpha=ridge_opr_alpha)\n data[\"OPR_Ridge\"] = dict(zip(columns, opr_ridge_results))\n\n conn.close()\n\n with open(tmp_path, 'w') as file:\n json.dump(data, file)\n\n return data\n", "repo_name": "lachnerm/bachelor_thesis", "sub_path": "linear/linear_attacks.py", "file_name": "linear_attacks.py", "file_ext": "py", "file_size_in_byte": 10419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "tqdm.tqdm", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.utils.crop_center", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 156, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 164, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 194, "usage_type": "call"}, {"api_name": "utils.utils.calc_gabor_fhd", "line_number": 197, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 197, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 198, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 199, "usage_type": "call"}, {"api_name": "skimage.metrics.structural_similarity", "line_number": 200, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 230, "usage_type": "call"}, {"api_name": "json.load", "line_number": 237, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "27033766821", "text": "#Import moduels \r\nfrom __future__ import unicode_literals\r\nimport os\r\nimport youtube_dl\r\nuserchosenCodec = 'm4a'\r\n\r\n#colours the terminal [CREDIT: G4G]\r\ndef prRed(skk): print(\"\\033[91m {}\\033[00m\" .format(skk))\r\n\r\n\r\n#start message\r\nprint(\"Welcome to the Youtube to Mp3 python file, You MUST have FFMPEG, and Youtube_dl installed.\")\r\nnumOfVids = input(\"How many videos do you want to download?: \")\r\narrayOfLink = []\r\n#this will allow the user to input a video URL into the first box and it will automaticaly start downloading it \r\nif numOfVids[0] == (\"h\"):\r\n prRed(\"QUICK DOWNLOAD: ENABLED\")\r\n quickMenu = True\r\n arrayOfLink.append(numOfVids)\r\n\r\n numOfVids = 1\r\nelse:\r\n int(numOfVids)\r\n quickMenu = False\r\n\r\nnumOfVids = int(numOfVids)\r\n\r\n\r\n#The user will enter the url here the entered URLS will be added to an array that will then be passed to the API moduel unless quickmenu is enabled\r\ndef vidUrllocater():\r\n for state in range(0,numOfVids):\r\n print(\"Enter video URL for video number\", state + 1)\r\n videoURL = input(\"URL: \")\r\n arrayOfLink.append(videoURL)\r\n \r\ndef codecLocator():\r\n fish = input(\"Do you want to change your format from m4a to a new format?: Y/N: \")\r\n fish = fish.lower()\r\n if fish[0] == \"y\":\r\n userchosenCodec = input(\"Enter your chosen Codec: \")\r\n print(\"Your Codec has been updated to\",userchosenCodec) \r\n return userchosenCodec\r\n \r\n\r\n \r\n#this is the modual that will contact the youtube website and rip the audio there is no user input in this sub\r\ndef vidyoinker2000():\r\n \r\n print(\"Starting Download Process: Codec\",userchosenCodec)\r\n for i in range (0,len(arrayOfLink)): \r\n ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'postprocessors': [{\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': userchosenCodec,\r\n 'preferredquality': '320'\r\n }],\r\n 'postprocessor_args': [\r\n '-ar', '16000'\r\n ],\r\n 'prefer_ffmpeg': True,\r\n 'keepvideo': True\r\n }\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n ydl.download([arrayOfLink[i]])\r\n#Gonna start calling subprograms here\r\nif quickMenu == False:\r\n vidUrllocater()\r\n userchosenCodec = codecLocator()\r\n vidyoinker2000()\r\nelif quickMenu == True:\r\n vidyoinker2000()\r\nelse:\r\n print(\"ERROR | Subprogram never called | Open an issue on Github!\")\r\n\r\ndef endmessage():\r\n print(\" ⠀ \")\r\n print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\r\n print(\"- -\")\r\n print(\"- Thanks for using this script! :D -\")\r\n print(\"- -\")\r\n print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\r\n print(\"- -\")\r\n print(\"- -\")\r\n print(\"- If you have any errors open an issue on github -\")\r\n print(\"- https://github.com/Cameron-Programer/YoutubeDownloaderPY -\")\r\n print(\"- -\")\r\n print(\"- o(*≧▽≦)ツ -\")#this need to be 1 further out than the rest proably an ususal char.\r\n print(\"- -\")\r\n print(\"============================================================\")\r\nendmessage()", "repo_name": "Cameron-Programer/YoutubeDownloaderPY", "sub_path": "ytdl.py", "file_name": "ytdl.py", "file_ext": "py", "file_size_in_byte": 3539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "youtube_dl.YoutubeDL", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "5237885800", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # Feature selection\n\n# The following content is mainly based on scikit learn documentations:\n# \n# - [Feature selection](https://scikit-learn.org/stable/modules/feature_selection.html)\n# - [Model-based and sequential feature selection](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py) from Manoj Kumar, Maria Telenczuk and Nicolas Hug.\n# - [Common pitfalls in the interpretation of coefficients of linear models](https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py)\n\n# ## Prepara data\n# \n# We use a data frame of major league baseball players to predict their salaries from some career statistics ([more information about data](https://rdrr.io/cran/ISLR/man/Hitters.html)). Note that the data is already preprocessed. \n# \n# *To get an overview about the data preparation, visit [this tutorial](https://kirenz.github.io/regression/docs/lasso.html#data).*\n\n# In[1]:\n\n\nimport pandas as pd\n\n# import data\ndf = pd.read_csv(\"https://raw.githubusercontent.com/kirenz/datasets/master/hitters-clean.csv\")\ndf.info()\n\n\n# In[2]:\n\n\n# create label\ny = df['Salary']\n\n# create features\nX = df.drop(['Salary'], axis=1).astype(float)\n\n# create list of feature names\nfeature_names = X.columns\n\n\n# In[3]:\n\n\nfrom sklearn.model_selection import train_test_split\n\n# data split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)\n\n\n# In[4]:\n\n\nfrom sklearn.preprocessing import StandardScaler\n\n# make list of numerical features (League_N, Division_W and NewLeague_N are categorcial) \nlist_numerical = X.drop(['League_N', 'Division_W', 'NewLeague_N'], axis=1).columns\n\n# standardize numerical features\nscaler = StandardScaler().fit(X_train[list_numerical]) \nX_train[list_numerical] = scaler.transform(X_train[list_numerical])\nX_test[list_numerical] = scaler.transform(X_test[list_numerical])\n\n\n# ## Model\n\n# We fit a lasso regression with 5-fold cross validation to choose the best regularization parameter based on the mean squared error:\n\n# In[5]:\n\n\nfrom sklearn.linear_model import LassoCV\n\nreg = LassoCV(cv=5, random_state=10, max_iter=10000).fit(X_train, y_train)\n\n\n# In[6]:\n\n\n# show best alpha parameter\nreg.alpha_\n\n\n# Show feature importance:\n\n# In[7]:\n\n\nimport seaborn as sns\nimport numpy as np\n\n# get absolute values of coefficients\nimportance = np.abs(reg.coef_)\n\nsns.barplot(x=importance, \n y=feature_names);\n\n\n# ## Feature selection\n\n# ### Filter method \n# \n# In this example, we use feature importance as a filter to select our features. In particular, we want to select the two features which are the most important according to the coefficients. The function `SelectFromModel` is meant just for that. `SelectFromModel` accepts a threshold parameter and will select the features whose importance (defined by the coefficients) are above this threshold.\n# \n# In our case, we want to select only 2 features. Hence, we will set the threshold slightly above the coefficient of the third most important feature. We also record the time the algorithm takes to obtain the results.\n\n# In[8]:\n\n\nfrom sklearn.feature_selection import SelectFromModel\nfrom time import time\n\n# set threshold\nthreshold = np.sort(importance)[-3] + 1\n\n# obtain time\ntic = time()\n\n# fit model\nsfm = SelectFromModel(reg, threshold=threshold).fit(X_train, y_train)\n\n# obtain time\ntoc = time()\n\n# print results\nprint(f\"Features selected by SelectFromModel: {feature_names[sfm.get_support()]}\")\nprint(f\"Done in {toc - tic:.3f}s\")\n\n\n# ### Wrapper method \n# \n# Another way of selecting features is to use a (greedy) wrapper method with scikit learn's `SequentialFeatureSelector` (SFS). SFS is a greedy procedure where, at each iteration, we choose the best new feature to add to our selected features based a cross-validation score: \n# \n# - `Forward-Selection`: That is, we start with 0 features and choose the best single feature with the highest score. The procedure is repeated until we reach the desired number of selected features.\n# \n# - `Backward selection`: We can also go in the reverse direction (backward SFS), i.e. start with all the features and greedily choose features to remove one by one. We illustrate both approaches here.\n\n# #### Forward selection\n\n# In[9]:\n\n\nfrom sklearn.feature_selection import SequentialFeatureSelector\n\ntic_fwd = time()\n\nsfs_forward = SequentialFeatureSelector(\n reg, n_features_to_select=2, \n direction=\"forward\").fit(X_train, y_train)\n\ntoc_fwd = time()\n\n\n# In[10]:\n\n\nprint(\n \"Features selected by forward sequential selection: \"\n f\"{feature_names[sfs_forward.get_support()]}\"\n)\nprint(f\"Done in {toc_fwd - tic_fwd:.3f}s\")\n\n\n# #### Backward selection\n# \n\n# In[11]:\n\n\ntic_bwd = time()\n\nsfs_backward = SequentialFeatureSelector(\n reg, n_features_to_select=2, \n direction=\"backward\").fit(X_train, y_train)\n\ntoc_bwd = time()\n\n\n# In[12]:\n\n\nprint(\n \"Features selected by backward sequential selection: \"\n f\"{feature_names[sfs_backward.get_support()]}\"\n)\nprint(f\"Done in {toc_bwd - tic_bwd:.3f}s\")\n\n\n# ## Discussion\n# \n# Note that: \n# \n# - `SelectFromModel` is significantly faster than SFS since `SelectFromModel` only needs to fit a model once, while SFS needs to cross-validate many different models for each of the iterations.\n# \n# - SFS however works with any model, while `SelectFromModel` requires the underlying estimator to expose a `coef_` attribute or a `feature_importances_` attribute. \n# \n# - Forward selection is much faster than backward selection because it only needs to perform `n_features_to_select = 2` iterations, while the backward selection needs to perform `n_features` - `n_features_to_select`.\n", "repo_name": "kirenz/regression", "sub_path": "_build/jupyter_execute/docs/feature-selection.py", "file_name": "feature-selection.py", "file_ext": "py", "file_size_in_byte": 5884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LassoCV", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 92, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectFromModel", "line_number": 119, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SequentialFeatureSelector", "line_number": 146, "usage_type": "call"}, {"api_name": "time.time", "line_number": 150, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SequentialFeatureSelector", "line_number": 171, "usage_type": "call"}, {"api_name": "time.time", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "26527925889", "text": "import os\nimport re\nfrom typing import Dict, Optional, Union\n\nfrom bs4 import BeautifulSoup, Tag\n\nfrom .options import Options\n\n\ndef get_pdf_metadata(metadata: Dict) -> Dict:\n \"\"\"\n Extracts PDF metadata from a given metadata dictionary.\n\n :param metadata: The metadata dictionary containing PDF-related information.\n :return: A dictionary containing PDF metadata. If no PDF metadata is found,\n we return the default PDF_LOCAL_OPTIONS dictionary.\n \"\"\"\n pdf_local_options = {\n \"build\": True,\n \"title\": None,\n \"subtitle\": None,\n \"type\": None,\n \"filename\": None,\n \"revision\": None,\n \"csv_name\": None,\n \"toc_txt\": None,\n \"legal_terms\": None,\n \"cover_image\": None,\n }\n if \"pdf\" in metadata and metadata[\"pdf\"] is not None:\n pdf_local_options.update(metadata.get(\"pdf\"))\n return pdf_local_options\n\n\ndef secure_filename(filename: str) -> str:\n r\"\"\"Pass it a filename, and it will return a secure version of it. This\n filename can then safely be stored on a regular file system and passed\n to :func:`os.path.join`. The filename returned is an ASCII only string\n for maximum portability.\n\n On Windows systems the function also makes sure that the file is not\n named after one of the special device files.\n\n >>> secure_filename(\"My cool movie.mov\")\n 'My_cool_movie.mov'\n >>> secure_filename(\"../../../etc/passwd\")\n 'etc_passwd'\n >>> secure_filename(u'i contain cool \\xfcml\\xe4uts.txt')\n 'i_contain_cool_umlauts.txt'\n\n The function might return an empty filename. It's your responsibility\n to ensure that the filename is unique and that you generate random\n filename if the function returned an empty one.\n\n :param filename: the filename to secure\n \"\"\"\n _filename_ascii_strip_re = re.compile(r\"[^A-Za-z0-9_.-]\")\n _windows_device_files = (\n \"CON\",\n \"AUX\",\n \"COM1\",\n \"COM2\",\n \"COM3\",\n \"COM4\",\n \"LPT1\",\n \"LPT2\",\n \"LPT3\",\n \"PRN\",\n \"NUL\",\n )\n\n for sep in os.path.sep, os.path.altsep:\n if sep:\n filename = filename.replace(sep, \" \")\n filename = str(_filename_ascii_strip_re.sub(\"\", \"_\".join(filename.split()))).strip(\"._\")\n\n # on nt a couple of special files are present in each folder. We\n # have to ensure that the target file is not such a filename. In\n # this case we prepend an underline\n if os.name == \"nt\" and filename and filename.split(\".\")[0].upper() in _windows_device_files:\n filename = \"_\" + filename\n\n return filename\n\n\ndef extract_h1_title(content: Union[str, Tag], page_metadata: Dict) -> Optional[str]:\n \"\"\"\n Extracts and returns the H1 title from the given HTML content or returns the\n page metadata title if no H1 title is found.\n\n :param content: HTML content as a string or BeautifulSoup PageElement.\n :param page_metadata: Metadata dictionary containing page information.\n\n :return: Extracted H1 title or page metadata title if H1 title is not found.\n \"\"\"\n soup = content\n if isinstance(soup, str):\n soup = BeautifulSoup(soup, \"html.parser\")\n\n title_element = soup.find(\"h1\", attrs={\"id\": re.compile(r\"[\\w_\\-]+\")})\n if title_element is None:\n return page_metadata.get(\"title\")\n\n title_text = title_element.text\n title_text = re.sub(r\"^[\\d.]+ \", \"\", title_text)\n return title_text\n\n\ndef enable_legal_terms(soup: BeautifulSoup, options: Options, pdf_metadata: Dict) -> BeautifulSoup:\n \"\"\"\n Enable and add legal_terms section to the PDF document content.\n\n .. note::\n\n This function modifies the input BeautifulSoup object in-place by inserting a legal_terms page.\n\n :param soup: The BeautifulSoup object representing the document's content.\n :param options: The options for the PDF generation process.\n :param pdf_metadata: The metadata associated with the PDF.\n\n :return: The modified BeautifulSoup object with added legal_terms content\n or the BeautifulSoup object without any changes.\n \"\"\"\n try:\n content = soup.find(\"article\", attrs={\"class\": \"md-content__inner\"})\n # Set legal_terms to document's legal_terms local option\n document_legal_terms: str = pdf_metadata.get(\"legal_terms\") or \"legal_terms\"\n # Select legal_terms template\n legal_terms_template_files = [document_legal_terms.lower()]\n template = options.template.select(legal_terms_template_files)\n\n options.logger.info(f'Add legal_terms content to PDF document using \"{template.name}\" template.')\n legal_terms_template = str(template.render())\n\n def format_legal_terms_html(legal_terms_html: str) -> Tag:\n \"\"\"\n Format legal_terms HTML to a BeautifulSoup Tag.\n\n :param legal_terms_html: The HTML content of the legal_terms.\n :return: The BeautifulSoup Tag with the added legal_terms content.\n \"\"\"\n html_soup = BeautifulSoup(legal_terms_html, \"html.parser\")\n headings = html_soup.find_all([\"h2\", \"h3\", \"h4\", \"h5\", \"h6\"])\n for h in headings:\n ref = h.get(\"id\")\n if ref is None:\n h[\"id\"] = generate_heading_id(h.string)\n return html_soup\n\n # Create legal_terms div wrapper\n legal_terms_div = soup.new_tag(\n \"div\",\n attrs={\n \"id\": \"mkdocs-pdf-gen-legal-terms\",\n \"class\": \"page-break\",\n },\n )\n legal_terms_div.append(format_legal_terms_html(legal_terms_template))\n\n content.append(legal_terms_div)\n return soup\n except Exception as e:\n options.logger.error(f\"Failed to add legal_terms: {str(e)}\")\n return soup\n\n\ndef generate_heading_id(input_string: str) -> str:\n \"\"\"\n Generate MkDocs appropriate ids for heading tags.\n\n :param input_string: The input string that needs to be processed.\n :return: The modified string with spaces replaced by hyphens and symbols removed.\n \"\"\"\n # Replace spaces with hyphens\n modified_string = re.sub(r\"\\s+\", \"-\", input_string)\n\n # Remove symbols using regex pattern [^\\w\\s-]\n modified_string = re.sub(r\"[^\\w\\s-]\", \"\", modified_string)\n\n return modified_string.replace(\"--\", \"-\").lower()\n", "repo_name": "iSOLveIT/mkdocs-pdf-generate", "sub_path": "mkdocs_pdf_generate/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.Dict", "line_number": 10, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 80, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 86, "usage_type": "name"}, {"api_name": "bs4.Tag", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 86, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 98, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 100, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 105, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 86, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 109, "usage_type": "name"}, {"api_name": "options.Options", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 109, "usage_type": "name"}, {"api_name": "options.template.select", "line_number": 130, "usage_type": "call"}, {"api_name": "options.template", "line_number": 130, "usage_type": "attribute"}, {"api_name": "options.logger.info", "line_number": 132, "usage_type": "call"}, {"api_name": "options.logger", "line_number": 132, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 142, "usage_type": "call"}, {"api_name": "bs4.Tag", "line_number": 135, "usage_type": "name"}, {"api_name": "options.logger.error", "line_number": 163, "usage_type": "call"}, {"api_name": "options.logger", "line_number": 163, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 175, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "17123306555", "text": "import random\nimport re\nfrom discord.ext import commands\n\nfrom shittybot.session import SessionManager\n\n\nclass HotOrColdCog(object):\n min_guess = 0\n max_guess = 100\n num_guesses = 7\n\n def __init__(self, bot: commands.Bot):\n self._bot = bot\n self._sessions = SessionManager(lambda: HotOrCold(self.min_guess, self.max_guess, self.num_guesses))\n\n @property\n def _usage(self):\n return ('**!hotcold start**: start a new Hot Or Cold game\\n'\n '**!hotcold stop**: stop current Hot Or Cold game\\n'\n '**!hotcold **: guess number')\n\n @commands.group(pass_context=True)\n async def hotcold(self, ctx):\n if ctx.invoked_subcommand is None:\n channel = ctx.message.channel\n if not self._sessions.session_exists_for_channel(channel):\n await self._bot.say(self._usage)\n else:\n guess = int(await self._try_parse_guess('^.hotcold\\s+(?P\\d+)\\s*$', ctx.message.content))\n await self._make_guess(ctx.message.author, channel, guess)\n\n @hotcold.command()\n async def help(self):\n await self._bot.say(self._usage)\n\n @hotcold.command(pass_context=True)\n async def start(self, ctx):\n channel = ctx.message.channel\n if self._sessions.session_exists_for_channel(channel):\n return await self._bot.say('Hot Or Cold session already in progress')\n\n self._sessions.create_session(channel)\n await self._bot.say('Started new Hot Or Cold session. Guess a number between {} and {}. You have {} guesses!'\n .format(self.min_guess, self.max_guess, self.num_guesses))\n\n @hotcold.command(pass_context=True)\n async def stop(self, ctx):\n channel = ctx.message.channel\n if not self._sessions.session_exists_for_channel(channel):\n return await self._bot.say('No Hot Or Cold session in progress')\n\n await self._bot.say('Hot Or Cold session ended')\n self._sessions.destroy_session(channel)\n\n async def _try_parse_guess(self, pattern, content):\n try:\n return re.match(pattern, content).group('guess')\n except AttributeError:\n await self._bot.say('Invalid guess: \"{}\"'.format(content))\n\n async def _make_guess(self, user, channel, number):\n try:\n session = self._sessions.get_session(channel)\n except ValueError:\n return await self._bot.say('No Hot Or Cold session in progress')\n\n answer = session.answer\n\n session.guess(number)\n\n if session.is_game_won:\n await self._bot.say('Congratulations {}! The number was {}'.format(user.name, answer))\n self._sessions.destroy_session(channel)\n\n elif session.is_game_lost:\n await self._bot.say('Unlucky! The number was {}'.format(answer))\n self._sessions.destroy_session(channel)\n\n else:\n await self._bot.say('```{}```'.format(session))\n\n\nclass HotOrCold(object):\n def __init__(self, min_guess: int, max_guess: int, allowed_guesses: int):\n if min_guess > max_guess or min(min_guess, max_guess, allowed_guesses) < 0:\n raise ValueError()\n\n self.answer = random.randint(min_guess, max_guess)\n self._allowed_guesses = allowed_guesses\n self._guesses = []\n\n guess_range = max_guess - min_guess\n self._bad_guess_diff = guess_range * 0.5\n self._okay_guess_diff = guess_range * 0.1\n self._good_guess_diff = guess_range * 0.05\n\n @property\n def is_game_over(self):\n return self.is_game_lost or self.is_game_won\n\n @property\n def is_game_won(self):\n return self.answer in self._guesses\n\n @property\n def is_game_lost(self):\n return len(self._guesses) >= self._allowed_guesses\n\n @property\n def _guesses_left(self):\n return self._allowed_guesses - len(self._guesses)\n\n def guess(self, number: int):\n if self.is_game_over:\n raise Exception('Hot Or Cold game is over')\n\n self._guesses.append(number)\n\n def __repr__(self):\n if not self._guesses:\n return '{No guesses made}'\n\n def diff(x):\n return abs(x - self.answer)\n\n diffs = [diff(x) for x in self._guesses]\n last_diff = diffs[-1]\n getting_hotter = len(diffs) >= 2 and diffs[-1] < diffs[-2]\n getting_colder = len(diffs) >= 2 and diffs[-1] > diffs[-2]\n\n output = ''\n\n if last_diff <= self._good_guess_diff:\n output += 'Very hot'\n if getting_hotter:\n output += ' and getting hotter'\n elif getting_colder:\n output += ' but getting colder'\n\n elif last_diff <= self._okay_guess_diff:\n output += 'Hot'\n if getting_hotter:\n output += ' and getting hotter'\n elif getting_colder:\n output += ' but getting colder'\n\n elif last_diff <= self._bad_guess_diff:\n output += 'Cold'\n if getting_hotter:\n output += ' but getting hotter'\n elif getting_colder:\n output += ' and getting colder'\n\n else: # Terrible guess\n output += 'Very cold'\n if getting_hotter:\n output += ' but getting hotter'\n elif getting_colder:\n output += ' and getting colder'\n\n output += ' ({} guesses left)'.format(self._guesses_left)\n return output\n", "repo_name": "TAGC/ShittyBot3000", "sub_path": "shittybot/hotorcold.py", "file_name": "hotorcold.py", "file_ext": "py", "file_size_in_byte": 5514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 13, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 13, "usage_type": "name"}, {"api_name": "shittybot.session.SessionManager", "line_number": 15, "usage_type": "call"}, {"api_name": "discord.ext.commands.group", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 23, "usage_type": "name"}, {"api_name": "re.match", "line_number": 58, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "74194685950", "text": "import pytesseract as pts\nimport cv2\n\nimg = cv2.imread(\"C:\\\\users\\\\HP\\\\DOcuments\\\\my folder\\\\yourmom.png\")\n\n# Print the text of the image\n\ndef text_image(text) -> str:\n print(text)\n\nget_text = pts.image_to_string(img) # Find the text\nprint(get_text)\n\ncv2.imshow('Your Mom',img)\ncv2.waitKey(0)\n", "repo_name": "Tho100/Find_Text_In_Image_Python", "sub_path": "SourceCode.py", "file_name": "SourceCode.py", "file_ext": "py", "file_size_in_byte": 296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "pytesseract.image_to_string", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "10628987549", "text": "from fpdf import FPDF\r\nimport numpy as np\r\nfrom nltk.stem import WordNetLemmatizer\r\nimport re\r\n\r\nfrom explainability import (get_ti_feature_contributions_for_instance_i,\r\n run_tree_interpreter)\r\n\r\n\r\nclass InterpretablePDF:\r\n \"\"\"\r\n Class to produce formatted vignettes that indicate which text elements are contributing to\r\n any given classification.\r\n\r\n Currently this class only works with the CAP Prostate Cancer data, because the formatting\r\n corresponds to the unique structure of this text data. However, it could easily be adapted to\r\n work with other textual data (such as LeDeR).\r\n \"\"\"\r\n\r\n def __init__(self,\r\n classifier,\r\n x_data,\r\n y_data,\r\n feature_columns,\r\n base_font_size=12, line_height=8,\r\n header_col_width=100, legend_offset=47.5,\r\n legend_offset_2=63,\r\n top_n_features=None,\r\n contributions=None):\r\n\r\n self.font_size = base_font_size\r\n self.line_height = line_height\r\n self.header_col_width = header_col_width\r\n self.legend_offset = legend_offset\r\n self.legend_offset_2 = legend_offset_2\r\n\r\n self.top_n = top_n_features\r\n\r\n self.pdf = None\r\n self.original_data = None\r\n\r\n self.feature_columns = feature_columns\r\n self.X = x_data\r\n self.y = y_data\r\n self.clf = classifier\r\n _, _, self.contributions = (run_tree_interpreter(self.clf,\r\n self.X)\r\n if contributions is None else (\r\n None, None, contributions))\r\n\r\n self.stemmer = WordNetLemmatizer()\r\n\r\n self.section_headers = ['Clinical features at diagnosis',\r\n 'Treatments received',\r\n 'Prostate cancer progression',\r\n 'Progression of co-morbidities',\r\n 'End of life']\r\n\r\n self.vignette_column_names = [\r\n 'Gleason Score at diagnosis (with dates)',\r\n 'Clinical stage (TNM)',\r\n 'Pathological stage (TNM)',\r\n 'Co-morbidities with dates of diagnosis',\r\n 'Other primary cancers with dates of diagnosis',\r\n 'PSA level at diagnosis with dates',\r\n 'Radiological evidence of local spread at diagnosis',\r\n 'Radiological evidence of metastases at diagnosis',\r\n 'Initial treatments (dates)',\r\n 'Hormone therapy (start date)',\r\n 'Maximum androgen blockade (start date)',\r\n 'Orchidectomy (date)',\r\n 'Chemotherapy (start date)',\r\n 'Treatment for complications of treating prostate cancer with dates (if available)',\r\n 'Serial PSA levels (dates)',\r\n 'Serum testosterone',\r\n 'Radiological evidence of metastases',\r\n 'Other indications or complications of disease progression',\r\n 'Date of recurrence following radical surgery or radiotherapy',\r\n 'Palliative care referrals and treatments',\r\n 'Treatment/ admission for co-morbidity with dates (if available)',\r\n 'Symptoms in last 3-6 months (i.e. bone pain, weight loss, cachexia,\\\r\n loss of appetite, obstructive uraemia)',\r\n 'Last consultation: speciality & date',\r\n 'Was a DS1500 report issued?',\r\n 'Post mortem findings']\r\n\r\n def create_pdf(self, case_id, original_data, filename):\r\n\r\n self.original_data = original_data\r\n\r\n self.pdf = FPDF()\r\n self.pdf.add_page()\r\n self.pdf.set_font('Arial', 'B', self.font_size)\r\n\r\n self.pdf.cell(w=0,\r\n h=self.line_height,\r\n txt='Interpretable Vignette Classification for Cause of Death Review',\r\n border=0,\r\n ln=0,\r\n align='C', fill=False, link='')\r\n\r\n self.pdf.set_font('')\r\n self.pdf.ln()\r\n self.pdf.ln()\r\n\r\n y = self.pdf.get_y()\r\n self.pdf.multi_cell(w=self.header_col_width,\r\n h=self.line_height,\r\n txt='Study ID number : %s\\nDate of death : %s\\nDate of diagnosis : %s' % (\r\n original_data['cp1random_id_5_char'],\r\n original_data['cnr19datedeath'].date(),\r\n original_data['cnr_date_pca_diag'].date()),\r\n border=0,\r\n align='L',\r\n fill=False)\r\n self.pdf.y = y\r\n self.pdf.x = self.header_col_width\r\n self.pdf.multi_cell(w=self.header_col_width,\r\n h=self.line_height,\r\n txt='Predicted death code: %d (%.2f)\\nActual death code: %d\\nCOD route: %d' % (\r\n self.clf.best_estimator_.predict(self.X)[case_id],\r\n self.clf.best_estimator_.predict_proba(self.X)[case_id][1],\r\n original_data.pca_death_code,\r\n original_data.cp1do_cod_route),\r\n border=0,\r\n align='R', fill=False)\r\n\r\n self.pdf.set_line_width(0.5)\r\n self.pdf.line(10, self.pdf.get_y(), 210 - 10, self.pdf.get_y())\r\n self.pdf.set_line_width(0.2)\r\n\r\n self.write_legend(case_id)\r\n\r\n for ci, col in enumerate(self.feature_columns):\r\n\r\n self.pdf.set_text_color(0, 0, 0)\r\n self.pdf.set_font_size(self.font_size)\r\n\r\n if ci in [0, 8, 14, 20, 21]:\r\n self.print_section_header(0)\r\n\r\n self.pdf.line(10, self.pdf.get_y(), 210 - 10, self.pdf.get_y())\r\n self.pdf.write(self.line_height, self.vignette_column_names[ci] + ': ')\r\n\r\n if 'palliative' not in col:\r\n text = str(original_data[col])\r\n self.print_paragraph(text, case_id)\r\n\r\n self.pdf.output(filename)\r\n\r\n def get_font_size_and_color(self,\r\n contribution,\r\n min_contribution,\r\n max_contribution,\r\n shrink=0.5):\r\n c = (255, 160, 0) if contribution < 0 else (0, 0, 255)\r\n s = (\r\n (self.font_size * shrink) + 1.5 * self.font_size\r\n * (np.absolute(contribution) - min_contribution)\r\n / float(max_contribution - min_contribution)\r\n )\r\n\r\n return s, c\r\n\r\n def legend_entry(self, fimps, fimp, align):\r\n\r\n size, color = self.get_font_size_and_color(fimp.contribution,\r\n fimps.magnitude.min(),\r\n fimps.magnitude.max())\r\n self.pdf.set_text_color(*color)\r\n self.pdf.set_font_size(size)\r\n self.pdf.cell(w=self.legend_offset,\r\n h=self.line_height,\r\n txt=fimp.feature,\r\n border=0, ln=0,\r\n align=align, fill=False, link='')\r\n\r\n def legend_label(self, text, align):\r\n\r\n self.pdf.cell(w=self.legend_offset_2,\r\n h=self.line_height,\r\n txt=text, border=0, ln=0,\r\n align=align, fill=False, link='')\r\n\r\n def write_legend(self, case_id):\r\n\r\n self.pdf.cell(w=0,\r\n h=self.line_height,\r\n txt='Feature contribution legend',\r\n border=0, ln=0,\r\n align='C', fill=False, link='')\r\n self.pdf.ln()\r\n\r\n fimps = get_ti_feature_contributions_for_instance_i(case_id,\r\n self.contributions,\r\n self.clf).sort_values(by='magnitude',\r\n ascending=False)\r\n fimps = fimps.head(self.top_n) if self.top_n is not None else fimps\r\n\r\n fimp = fimps.loc[fimps.contribution.idxmin()]\r\n self.legend_entry(fimps, fimp, align='L')\r\n\r\n fimp = fimps.loc[fimps.contribution < 0]\r\n fimp = fimp.loc[fimp.contribution.idxmax()]\r\n self.legend_entry(fimps, fimp, align='R')\r\n\r\n fimp = fimps.loc[fimps.contribution > 0]\r\n fimp = fimp.loc[fimp.contribution.idxmin()]\r\n self.legend_entry(fimps, fimp, align='L')\r\n\r\n fimp = fimps.loc[fimps.contribution.idxmax()]\r\n self.legend_entry(fimps, fimp, align='R')\r\n\r\n self.pdf.ln()\r\n self.pdf.set_text_color(0, 0, 0)\r\n self.pdf.set_font('Arial', '', self.font_size * .6)\r\n\r\n self.legend_label('Largest negative contribution', 'L')\r\n self.legend_label('Smallest contributions', 'C')\r\n self.legend_label('Largest positive contribution', 'R')\r\n\r\n self.pdf.set_font('Arial', '', self.font_size)\r\n\r\n def print_section_header(self, section):\r\n\r\n self.pdf.ln()\r\n self.pdf.set_line_width(0.5)\r\n self.pdf.line(10, self.pdf.get_y(), 210 - 10, self.pdf.get_y())\r\n self.pdf.set_line_width(0.2)\r\n self.pdf.set_font('Arial', 'B', self.font_size)\r\n self.pdf.write(self.line_height, self.section_headers[section] + '\\n')\r\n self.pdf.set_font('')\r\n\r\n # REFACTOR!\r\n def print_paragraph(self, text, i,\r\n base_color=(128, 128, 128)):\r\n\r\n fimps = get_ti_feature_contributions_for_instance_i(i,\r\n self.contributions,\r\n self.clf)\r\n fimps.sort_values(by='magnitude', inplace=True, ascending=False)\r\n fimps = fimps.head(self.top_n)\r\n\r\n old_word = ''\r\n old_tr_word = ''\r\n old_bigram = ''\r\n old_bigram_contribution = None\r\n\r\n old_color = base_color\r\n old_size = self.font_size\r\n\r\n words = text.split(' ')\r\n words.append(' .')\r\n\r\n for word in words:\r\n tr_word = self.transform_text(word)\r\n\r\n feat_tr = old_tr_word + ' ' + tr_word\r\n contribution_bi = (fimps.loc[fimps.feature == feat_tr]\r\n .iloc[0].contribution\r\n if feat_tr in list(fimps.feature)\r\n else None)\r\n magnitude_bi = np.absolute(contribution_bi) if contribution_bi is not None else 0\r\n\r\n feat_tr = old_tr_word\r\n contribution_uni = (fimps.loc[fimps.feature == feat_tr]\r\n .iloc[0].contribution\r\n if feat_tr in list(fimps.feature)\r\n else None)\r\n magnitude_uni = np.absolute(contribution_uni) if contribution_uni is not None else 0\r\n\r\n if contribution_bi and magnitude_bi > magnitude_uni:\r\n # print('bigram: ', old_tr_word)\r\n feat_tr = old_tr_word + ' ' + tr_word\r\n feat = old_word + ' ' + word\r\n\r\n contribution = fimps.loc[fimps.feature == feat_tr].iloc[0].contribution\r\n size, color = self.get_font_size_and_color(contribution,\r\n fimps.magnitude.min(),\r\n fimps.magnitude.max())\r\n self.pdf.set_text_color(*color)\r\n self.pdf.set_font_size(size)\r\n feat = feat.encode('latin-1', 'replace').decode('latin-1')\r\n self.pdf.write(self.line_height, feat + ' ')\r\n\r\n old_word = ''\r\n old_tr_word = ''\r\n old_color = base_color\r\n old_size = self.font_size\r\n\r\n elif contribution_uni and magnitude_uni > magnitude_bi:\r\n # print('unigram: ', old_tr_word)\r\n feat_tr = old_tr_word\r\n feat = old_word\r\n\r\n contribution = fimps.loc[fimps.feature == feat_tr].iloc[0].contribution\r\n size, color = self.get_font_size_and_color(contribution,\r\n fimps.magnitude.min(),\r\n fimps.magnitude.max())\r\n self.pdf.set_text_color(*color)\r\n self.pdf.set_font_size(size)\r\n feat = feat.encode('latin-1', 'replace').decode('latin-1')\r\n self.pdf.write(self.line_height, feat + ' ')\r\n\r\n old_word = word\r\n old_tr_word = tr_word\r\n old_color = base_color\r\n old_size = self.font_size\r\n\r\n else:\r\n self.pdf.set_text_color(*old_color)\r\n self.pdf.set_font_size(old_size)\r\n w = old_word.encode('latin-1', 'replace').decode('latin-1')\r\n self.pdf.write(self.line_height, w + ' ')\r\n\r\n old_word = word\r\n old_tr_word = tr_word\r\n old_color = base_color\r\n old_size = self.font_size\r\n\r\n self.pdf.ln()\r\n\r\n def transform_text(self, text):\r\n\r\n # Remove all the special characters\r\n document = re.sub(r'\\W', ' ', str(text))\r\n\r\n # remove all single characters\r\n document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\r\n # document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', str(X[sen]))\r\n\r\n # Remove single characters from the start\r\n document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Substituting multiple spaces with single space\r\n document = re.sub(r'\\s+', ' ', document, flags=re.I)\r\n\r\n # Removing prefixed 'b'\r\n document = re.sub(r'^b\\s+', '', document)\r\n\r\n # Converting to Lowercase\r\n document = document.lower()\r\n\r\n # Lemmatization\r\n document = document.split()\r\n\r\n document = [self.stemmer.lemmatize(word) for word in document]\r\n document = ' '.join(document)\r\n\r\n return document\r\n", "repo_name": "UHBristolDataScience/CAP-classification", "sub_path": "interpretable_pdf.py", "file_name": "interpretable_pdf.py", "file_ext": "py", "file_size_in_byte": 14297, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "explainability.run_tree_interpreter", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 51, "usage_type": "call"}, {"api_name": "fpdf.FPDF", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 159, "usage_type": "call"}, {"api_name": "explainability.get_ti_feature_contributions_for_instance_i", "line_number": 194, "usage_type": "call"}, {"api_name": "explainability.get_ti_feature_contributions_for_instance_i", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 270, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 326, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 329, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 333, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 336, "usage_type": "call"}, {"api_name": "re.I", "line_number": 336, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 339, "usage_type": "call"}]} +{"seq_id": "23291339643", "text": "import os,sys,PIL\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport cv2\ndef main():\n # Sample Image for Testing\n #imagepath = \"Sample/1.jpg\"\n imagepath = raw_input(\"enter image path\")\n\n savepath = \"Sample/2.jpg\"\n\n #Accept other imputs\n #pivot=[\"0.0\",\"0.0\"]\n pivot=raw_input(\"Enter centre point for zooming X,Y\").split(\",\")\n pivot[0]=float(pivot[0])\n pivot[1]=float(pivot[1])\n\n\n #scale=1.5\n scale=float(raw_input(\"Enter zoom value>1\"))\n\n\n image = cv2.imread(imagepath,1)\n\n\n height,width,z = image.shape\n x,y,_=image.shape\n\n x=x/(scale*2)\n y=y/(scale*2)\n xmin=0;ymin=0;xmax=width;ymax=height\n\n #pivot[0]=width/2\n #pivot[1]=height/2\n\n tempx1=pivot[0]-x\n tempx2=pivot[0]+x\n tempy1 = pivot[1] - y\n tempy2 = pivot[1] + y\n\n if tempx1<=0:\n xmin=0\n else:\n xmin=tempx1\n if tempx2>=width:\n xmax=width\n else:\n xmax=tempx2\n\n if tempy1<=0:\n ymin=0\n else:\n ymin=tempy1\n if tempy2>=height:\n ymax=height\n else:\n ymax=tempy2\n\n\n cropped = image[int(xmin):int(xmax),int(ymin):int(ymax)]\n\n\n\n cv2.imwrite(savepath,cropped)\nif __name__ == '__main__':\n main()", "repo_name": "mittalsuraj18/ImageProcessing", "sub_path": "ImageProcessing/ImageScalingWithoutLibrary.py", "file_name": "ImageScalingWithoutLibrary.py", "file_ext": "py", "file_size_in_byte": 1224, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "74599637597", "text": "import sys\nimport json\nimport re\nimport requests\nfrom elasticsearch import Elasticsearch\nfrom textblob import TextBlob\nimport numpy as np\nfrom multiprocessing import Pool\nfrom fuzzywuzzy import fuzz\n\npostags = [\"CC\",\"CD\",\"DT\",\"EX\",\"FW\",\"IN\",\"JJ\",\"JJR\",\"JJS\",\"LS\",\"MD\",\"NN\",\"NNS\",\"NNP\",\"NNPS\",\"PDT\",\"POS\",\"PRP\",\"PRP$\",\"RB\",\"RBR\",\"RBS\",\"RP\",\"SYM\",\"TO\",\"UH\",\"VB\",\"VBD\",\"VBG\",\"VBN\",\"VBP\",\"VBZ\",\"WDT\",\"WP\",\"WP$\",\"WRB\"]\n\nes = Elasticsearch(host=\"134.100.15.203\",port=32816)\n\n\nentembedcache = {}\nlabelembedcache = {}\nrelembedcache = {}\n\ndef getkgembedding(enturl):\n if enturl in entembedcache:\n return entembedcache[enturl]\n entityurl = ''\n res = es.search(index=\"wikidataembedsindex01\", body={\"query\":{\"term\":{\"key\":{\"value\":entityurl}}}})\n try:\n embedding = [float(x) for x in res['hits']['hits'][0]['_source']['embedding']]\n entembedcache[enturl] = embedding\n return embedding\n except Exception as e:\n print(enturl,' entity embedding not found')\n return 200*[0.0]\n return 200*[0.0]\n\ndef gettextmatchmetric(label,word):\n return [fuzz.ratio(label,word)/100.0,fuzz.partial_ratio(label,word)/100.0,fuzz.token_sort_ratio(label,word)/100.0]\n\ndef getlabelembedding(entid):\n if entid in labelembedcache:\n return labelembedcache[entid]\n res = es.search(index=\"wikidataentitylabelindex01\", body={\"query\":{\"term\":{\"uri\":{\"value\":'http://wikidata.dbpedia.org/resource/'+entid}}}})\n if len(res['hits']['hits']) == 0:\n return [0]*300\n try:\n description = res['hits']['hits'][0]['_source']['wikidataLabel']\n r = requests.post(\"http://134.100.15.203:8887/ftwv\",json={'chunks': [description]},headers={'Connection':'close'})\n labelembedding = r.json()[0]\n labelembedcache[entid] = labelembedding\n return labelembedding\n except Exception as e:\n print(\"getlabelembedding err: \",e)\n return [0.0]*300\n return [0.0]*300\n \ndef getrellabelembedding(rel,props):\n if rel in relembedcache:\n return relembedcache[rel]\n try:\n desc = props[rel]\n r = requests.post(\"http://134.100.15.203:8887/ftwv\",json={'chunks': [desc]},headers={'Connection':'close'})\n descembedding = r.json()[0]\n relembedcache[rel] = descembedding\n return descembedding\n except Exception as e:\n print(\"getrellabelembedding err: \",e)\n return [0.0]*300\n return [0.0]*300\n \n \n\n\nclass Vectoriser():\n def __init__(self, proppath):\n print(\"Initialising Vectoriser\")\n self.pool = Pool(4)\n self.props = {}\n for item in json.loads(open(proppath).read()):\n self.props[item['id']] = item['desc']\n print(\"Initialised Vectoriser\")\n \n\n def vectorise(self, nlquery, sparql):\n if not nlquery:\n return []\n q = re.sub(\"\\s*\\?\", \"\", nlquery.strip())\n ents = []\n rels = []\n ents = re.findall( r'wd:(.*?) ', sparql)\n rels = re.findall( r'wdt:(.*?) ',sparql)\n rels += re.findall( r'ps:(.*?) ',sparql)\n rels += re.findall( r'pq:(.*?) ',sparql)\n rels += re.findall( r'p:(.*?) ',sparql)\n# print(\"question: \",nlquery)\n# print(\"sparql: \", sparql)\n# print(\"entities: \",ents)\n# print(\"relations: \",rels)\n candidatetokens = []\n candidatevectors = []\n #questionembedding\n tokens = [token for token in q.split(\" \") if token != \"\"]\n r = requests.post(\"http://134.100.15.203:8887/ftwv\",json={'chunks': tokens},headers={'Connection':'close'})\n #print(\"r: \",r)\n questionembeddings = r.json()\n candidatevectors = [embedding+200*[0.0] for embedding in questionembeddings]#list(map(lambda x: sum(x)/len(x), zip(*questionembeddings)))\n candidatetokens += tokens\n candidatevectors.append(500*[-2.0]) #SEParator\n candidatetokens.append('[SEP]')\n for ent in ents:\n entityembedding = getkgembedding(ent)\n labelembedding = getlabelembedding(ent)\n# print(\"ent: \",ent)\n# print(\"embed: \",entityembedding)\n# print(\"labelembedding: \",labelembedding)\n candidatevectors.append(labelembedding+entityembedding) \n candidatetokens.append(ent)\n candidatevectors.append(500*[-2.0]) #SEParator\n candidatetokens.append('[SEP]')\n for rel in rels:\n relembedding = getkgembedding(rel)\n labelembedding = getrellabelembedding(rel, self.props)\n# print(\"rel: \",rel)\n# print(\"embed: \",relembedding)\n# print(\"labelembedding: \",labelembedding)\n candidatevectors.append(labelembedding+relembedding)\n candidatetokens.append(rel)\n return candidatetokens,candidatevectors,ents,rels\n \n \n \nif __name__ == '__main__':\n v = Vectoriser('wikidataprops.json')\n# print(v.vectorise(\"who is the president of India ?\"))\n print(json.dumps(v.vectorise(\"What is Park Geun-hye real name, who wrote in Hanja?\", \"SELECT ?obj WHERE { wd:Q138048 p:P1559 ?s . ?s ps:P1559 ?obj . ?s pq:P282 wd:Q485619 }\")))\n", "repo_name": "debayan/kgqadatasets", "sub_path": "lcquad2/vectorisergold.py", "file_name": "vectorisergold.py", "file_ext": "py", "file_size_in_byte": 5163, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 13, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz.ratio", "line_number": 35, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz", "line_number": 35, "usage_type": "name"}, {"api_name": "fuzzywuzzy.fuzz.partial_ratio", "line_number": 35, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz.token_sort_ratio", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 59, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 74, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 76, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 84, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 87, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 88, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 89, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 90, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "34482306885", "text": "import streamlit as st\nimport time\n\n\nst.title('Layouts and Containers')\n\n\n'-------------------------------------------------------------'\n\n\n# https://docs.streamlit.io/library/api-reference/layout/st.sidebar\n\nwith st.sidebar:\n st.header('Sidebar')\n with st.echo():\n st.write(\"This code will be printed to the sidebar.\")\n\n with st.spinner(\"Loading...\"):\n time.sleep(2)\n st.success(\"Done!\")\n\n '-------------------------------------------------------------'\n\n# Here's an example of how you'd add a selectbox and a radio button to your sidebar:\n\n# observe que mesmo nao estando no mesmo with do sidebar inicial eles foram incluidos no sidebar, importante notar que para organizacao melhor inclui-los todos abaixo do mesmo with\n\n# Using object notation\nadd_selectbox = st.sidebar.selectbox(\n \"How would you like to be contacted?\",\n (\"Email\", \"Home phone\", \"Mobile phone\")\n)\n\n# Using \"with\" notation\nwith st.sidebar:\n add_radio = st.radio(\n \"Choose a shipping method\",\n (\"Standard (5-15 days)\", \"Express (2-5 days)\")\n )\n\n st.warning(\"The only elements that aren't supported using object notation are st.echo and st.spinner. To use these elements, you must use with notation.\")\n\n\n\n#'-------------------------------------------------------------'\n\n\n\n# https://docs.streamlit.io/library/api-reference/layout/st.columns\nst.header('Columns')\n\nst.warning('Columns cannot be placed inside other columns in the sidebar. This is only possible in the main area of the app.')\n\ncol1, col2, col3 = st.columns(3)\n\nwith col1:\n st.header(\"A cat\")\n st.image(\"https://static.streamlit.io/examples/cat.jpg\")\n\nwith col2:\n st.header(\"A dog\")\n st.image(\"https://static.streamlit.io/examples/dog.jpg\")\n\nwith col3:\n st.header(\"An owl\")\n st.image(\"https://static.streamlit.io/examples/owl.jpg\")\n\n\n'-------------------------------------------------------------'\n\n\nimport numpy as np\n\n# observe que estamos setando a proporcao da largura entre as colunas\ncol1, col2 = st.columns([3, 1])\ndata = np.random.randn(10, 1)\n\ncol1.subheader(\"A wide column with a chart\")\ncol1.line_chart(data)\n\ncol2.subheader(\"A narrow column with the data\")\ncol2.write(data)\n\n\n\n'-------------------------------------------------------------'\n\n\n\n# https://docs.streamlit.io/library/api-reference/layout/st.tabs\nst.title('Tabs') # utilizando abas\n\ntab1, tab2, tab3 = st.tabs([\"Cat\", \"Dog\", \"Owl\"])\n\nwith tab1:\n st.header(\"A cat\")\n st.image(\"https://static.streamlit.io/examples/cat.jpg\", width=200)\n\nwith tab2:\n st.header(\"A dog\")\n st.image(\"https://static.streamlit.io/examples/dog.jpg\", width=200)\n\nwith tab3:\n st.header(\"An owl\")\n st.image(\"https://static.streamlit.io/examples/owl.jpg\", width=200)\n\n\n\n'-------------------------------------------------------------'\n\n\n\n# https://docs.streamlit.io/library/api-reference/layout/st.expander\nst.header('Expander')\n\nst.warning('Currently, you may not put expanders inside another expander.')\n\nst.bar_chart({\"data\": [1, 5, 2, 6, 2, 1]})\n\n# o expander gera uma nota explicativa para um grafico ou outra situacao qualquer, aqui o expander explica sobre os dados serem aleatorios\n\nst.write('Expander usando o with')\n\n# You can use with notation to insert any element into an expander\nwith st.expander(\"See explanation\"):\n st.write(\"\"\"\n The chart above shows some numbers I picked for you.\n I rolled actual dice for these, so they're *guaranteed* to\n be random.\n \"\"\")\n st.image(\"https://static.streamlit.io/examples/dice.jpg\")\n\n\n'-------------------------------------------------------------'\n\n\n# Or you can just call methods directly in the returned objects:\n\n\nst.bar_chart({\"data\": [1, 5, 2, 6, 2, 1]})\n\nst.write('Expander sem usar o with')\n\nexpander = st.expander(\"See explanation\")\nexpander.write(\"\"\"\n The chart above shows some numbers I picked for you.\n I rolled actual dice for these, so they're *guaranteed* to\n be random.\n\"\"\")\nexpander.image(\"https://static.streamlit.io/examples/dice.jpg\")\n\n\n\n'-------------------------------------------------------------'\n\n\n\n# https://docs.streamlit.io/library/api-reference/layout/st.container\nst.header('Container')\n\nst.markdown('Inserting elements using **\"with\"** notation:')\n\nwith st.container():\n st.write(\"This is inside the container\")\n\n # You can call any Streamlit command, including custom components:\n st.bar_chart(np.random.randn(50, 3))\n\nst.write(\"This is outside the container\")\n\n\n'-------------------------------------------------------------'\n\n\n# observe que o output estara ordenado de maneira diferente do input feito aqui no codigo\nst.markdown('Inserting elements out of order:')\n\ncontainer = st.container()\ncontainer.write(\"This is inside the container\")\nst.write(\"This is outside the container\")\n\n# Now insert some more in the container\ncontainer.write(\"This is inside too\")\n\n\n\n'-------------------------------------------------------------'\n\n\n\n# https://docs.streamlit.io/library/api-reference/layout/st.empty\nst.header('Empty')\n\n# uma boa opção para elementos que devem aparecer e desaparecer com o tempo, ficando sempre no mesmo container\n\n# Insert a single-element container.\n\n# Inserts a container into your app that can be used to hold a single element. This allows you to, for example, remove elements at any point, or replace several elements at once (using a child multi-element container).\n\nwith st.empty():\n for seconds in range(5):\n st.write(f\"⏳ {seconds} seconds have passed\")\n time.sleep(1)\n st.write(\"✔️ 1 minute over!\")\n\n\n'-------------------------------------------------------------'\n\n\nst.write('Replacing several elements, then clearing them:')\n\nplaceholder=st.empty()\n\ntime.sleep(3)\n# Replace the placeholder with some text:\nplaceholder.text(\"Hello\")\n\ntime.sleep(3)\n# Replace the text with a chart:\nplaceholder.line_chart({\"data\": [1, 5, 2, 6]})\n\ntime.sleep(3)\n# Replace the chart with several elements:\nwith placeholder.container():\n st.write(\"This is one element\")\n st.write(\"This is another\")\n\ntime.sleep(3)\n# Clear all those elements:\nplaceholder.empty()", "repo_name": "jharbes/dataApps_streamlit", "sub_path": "02-fundamentos_streamlit/17-layouts.py", "file_name": "17-layouts.py", "file_ext": "py", "file_size_in_byte": 6090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "streamlit.title", "line_number": 5, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 13, "usage_type": "attribute"}, {"api_name": "streamlit.header", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.echo", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.spinner", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 20, "usage_type": "call"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 29, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar", "line_number": 35, "usage_type": "attribute"}, {"api_name": "streamlit.radio", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 57, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 65, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 91, "usage_type": "call"}, {"api_name": "streamlit.tabs", "line_number": 93, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 97, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 100, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 101, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 104, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 105, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 114, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 116, "usage_type": "call"}, {"api_name": "streamlit.bar_chart", "line_number": 118, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 122, "usage_type": "call"}, {"api_name": "streamlit.expander", "line_number": 125, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 126, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 131, "usage_type": "call"}, {"api_name": "streamlit.bar_chart", "line_number": 140, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 142, "usage_type": "call"}, {"api_name": "streamlit.expander", "line_number": 144, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 159, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 161, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 163, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 164, "usage_type": "call"}, {"api_name": "streamlit.bar_chart", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 167, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 169, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 176, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 178, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 180, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 192, "usage_type": "call"}, {"api_name": "streamlit.empty", "line_number": 200, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 202, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 203, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 204, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 210, "usage_type": "call"}, {"api_name": "streamlit.empty", "line_number": 212, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 214, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 218, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 222, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 225, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 226, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 228, "usage_type": "call"}]} +{"seq_id": "34349684503", "text": "\"\"\"\nRun a genetic algorithm to find an appropriate architecture for some image\nclassification task with Keras+TF.\n\nTo use, define a `GenomeHandler` defined in genomehandler.py. Then pass it, with\ntraining data, to a DEvol instance to run the genetic algorithm. See the readme\nfor more detailed instructions.\n\"\"\"\n\nfrom __future__ import print_function\nimport random as rand\nimport csv\nimport operator\nimport gc\nimport os\nimport time\nfrom datetime import datetime\nfrom keras.callbacks import Callback\nfrom keras.models import load_model\nimport keras.backend as K\nfrom sklearn.metrics import log_loss, auc\n\nimport numpy as np\nfrom keras.backend.tensorflow_backend import set_session\n#from pyDOE import *\n\nif K.backend() == 'tensorflow':\n import tensorflow as tf\n\n__all__ = ['DEvol']\n\nMETRIC_OPS = [operator.__lt__, operator.__gt__]\nMETRIC_OBJECTIVES = [min, max]\n\n\nlocs_apk = np.genfromtxt('coords_app_020818.csv',delimiter=\",\");\n\ndef get_loc(ponto):\n return locs_apk[locs_apk[:,0]==ponto, 1:]\n\ndef get_cdf(pct, sorted_dist):\n return sorted_dist[int(np.round(pct*len(sorted_dist)/100))];\n \n \n \nclass calc_dist_test(Callback):\n\n def __init__(self, patience, x_test, y_test):\n self.patience = patience;\n self.history_auc = [];\n self.history_acc = []\n self.cont_distance = 0;\n self.min_acc = 0;\n self.x_test = x_test;\n self.y_test = y_test;\n \n def on_epoch_end(self, batch, logs={}):\n score_results = self.model.predict(data_lt)\n points_test = loc_real\n points_result = np.dot(score_results, locs_pts)\n #print(points_test)\n #print(points_result)\n distance = np.sqrt((points_test[:,0]-points_result[:,0])**2 + (points_test[:,1]-points_result[:,1])**2 + (points_test[:,2]-points_result[:,2])**2);\n #sorted_dist = np.sort(distance);\n #print(\"25% Dist: \", get_cdf(25, sorted_dist));\n #print(\"50% Dist: \", get_cdf(50, sorted_dist));\n #print(\"75% Dist: \", get_cdf(75, sorted_dist));\n #print(\"95% Dist: \", get_cdf(95, sorted_dist));\n \n sorted_ = np.sort(distance)\n yvals = np.arange(len(sorted_))/float(len(sorted_))\n sorted_ = np.append(sorted_, 100);\n yvals = np.append(yvals, 1);\n \n auc_value = auc(sorted_, yvals);\n print(\"Dist. Media: \", np.mean(distance));\n print(\"AUC (test): \", auc_value);\n \n if (self.history_auc and (auc_value <= max(self.history_auc))):\n self.cont_distance = self.cont_distance + 1;\n else:\n self.cont_distance = 0;\n\n self.history_auc.append(auc_value);\n self.history_acc.append(self.model.evaluate(self.x_test, self.y_test, verbose=0)[1]);\n index = np.argmax(self.history_auc);\n self.max_acc = self.history_acc[index];\n \n if (self.cont_distance>self.patience):\n self.model.stop_training = True\n print('Early Stopping');\n\n\nnum_rows = 10;#\nnum_cols = 15;#\nmin_val = -95;\nmax_val = -20;\nn_bins = 25;#\nnum_classes = 71;\n\nfilename_test = 'hists_new_val_050718_15aps_25bins_10rows.txt' #\ndata_final_test = np.genfromtxt(filename_test,delimiter=\",\");#\ndata_lt = data_final_test[:,:-1];\n\nclasses_lt = data_final_test[:,-1];\n\ncont = 0;\nloc_real = np.empty([classes_lt.shape[0], 3]);\nfor i in classes_lt:\n loc_real[cont] = get_loc(i);\n cont = cont + 1;\n\n#loc_real = data_final_test[:,-3:];\ndata_lt = data_lt/num_rows;\ndata_lt = data_lt.reshape(data_lt.shape[0], num_cols, n_bins, 1);\n\ncont = 0;\nlocs_pts = np.empty([71, 3]);\nfor i in range(71):\n locs_pts[cont] = get_loc(i+1);\n cont = cont + 1;\n\n \n\nclass DEvol:\n \"\"\"\n Object which carries out genetic search and returns top performing model\n upon completion.\n \"\"\"\n def __init__(self, genome_handler, data_path=\"\"):\n \"\"\"\n Initialize a DEvol object which carries out the training and evaluation\n of a genetic search.\n\n Args:\n genome_handler (GenomeHandler): the genome handler object defining\n the restrictions for the architecture search space\n data_path (str): the file which the genome encodings and metric data\n will be stored in\n \"\"\"\n self.genome_handler = genome_handler\n timestr = time.strftime(\"%Y%m%d_%H%M%S\")\n self.datafile = data_path or (timestr + '.csv')\n self._bssf = -1\n\n if os.path.isfile(data_path) and os.stat(data_path).st_size > 1:\n raise ValueError(('Non-empty file %s already exists. Please change'\n 'file path to prevent overwritten genome data.'\n % data_path))\n\n print(\"Genome encoding and metric data stored at\", self.datafile, \"\\n\")\n with open(self.datafile, 'a') as csvfile:\n writer = csv.writer(csvfile, delimiter=',', quotechar='\"',\n quoting=csv.QUOTE_MINIMAL)\n metric_cols = [\"Val Loss\", \"Val Accuracy\"]\n genome = genome_handler.genome_representation() + metric_cols\n writer.writerow(genome)\n\n def set_objective(self, metric):\n \"\"\"\n Set the metric for optimization. Can also be done by passing to\n `run`.\n\n Args:\n metric (str): either 'acc' to maximize classification accuracy, or\n else 'loss' to minimize the loss function\n \"\"\"\n if metric == 'acc':\n metric = 'accuracy'\n if metric not in ['loss', 'accuracy']:\n raise ValueError(('Invalid metric name {} provided - should be'\n '\"accuracy\" or \"loss\"').format(metric))\n self._metric = metric\n self._objective = \"max\" if self._metric == \"accuracy\" else \"min\"\n self._metric_index = 1 if self._metric == 'loss' else -1\n self._metric_op = METRIC_OPS[self._objective == 'max']\n self._metric_objective = METRIC_OBJECTIVES[self._objective == 'max']\n\n def run(self, dataset, num_generations, pop_size, epochs, fitness=None,\n metric='accuracy'):\n \"\"\"\n Run genetic search on dataset given number of generations and\n population size\n\n Args:\n dataset : tuple or list of numpy arrays in form ((train_data,\n train_labels), (validation_data, validation_labels))\n num_generations (int): number of generations to search\n pop_size (int): initial population size\n epochs (int): epochs for each model eval, passed to keras model.fit\n fitness (None, optional): scoring function to be applied to\n population scores, will be called on a numpy array which is\n a min/max scaled version of evaluated model metrics, so It\n should accept a real number including 0. If left as default\n just the min/max scaled values will be used.\n metric (str, optional): must be \"accuracy\" or \"loss\" , defines what\n to optimize during search\n\n Returns:\n keras model: best model found with weights\n \"\"\"\n self.set_objective(metric)\n (self.x_train, self.y_train), (self.x_test, self.y_test) = dataset\n\n # generate and evaluate initial population\n members = self._generate_random_population(pop_size)\n pop = self._evaluate_population(members,\n epochs,\n fitness,\n 0,\n num_generations)\n\n # evolve\n for gen in range(1, num_generations):\n members = self._reproduce(pop, gen)\n pop = self._evaluate_population(members,\n epochs,\n fitness,\n gen,\n num_generations)\n ##### GARBAGE COLLECTOR #####\n K.clear_session()\n tf.reset_default_graph()\n config = tf.ConfigProto()\n config.gpu_options.per_process_gpu_memory_fraction = 0.6\n config.gpu_options.visible_device_list = \"0\"\n set_session(tf.Session(config=config))\n #############################\n\n return load_model('best-model-cat.h5')\n\n def _reproduce(self, pop, gen):\n members = []\n perct_cross = 0.50\n # 95% of population from crossover\n for _ in range(int(len(pop) * perct_cross)):\n members.append(self._crossover(pop.select(), pop.select()))\n\n # best models survive automatically\n members += pop.get_best(len(pop) - int(len(pop) * perct_cross))\n\n # randomly mutate\n for imem, mem in enumerate(members):\n members[imem] = self._mutate(mem, gen)\n return members\n\n def _evaluate(self, genome, epochs):\n model = self.genome_handler.decode(genome)\n print(model.summary());\n loss, accuracy = None, None\n cback_dist = calc_dist_test(patience=5, x_test = self.x_test, y_test = self.y_test);\n try:\n model.fit(self.x_train, self.y_train,\n validation_data=(self.x_test, self.y_test),\n epochs=epochs,\n verbose=2,\n callbacks=[cback_dist])\n #_, accuracy = model.evaluate(self.x_test, self.y_test, verbose=0)\n #score_results = model.predict(data_lt)\n #points_test = loc_real\n #points_result = np.dot(score_results, locs_pts)\n #distance = np.sqrt((points_test[:,0]-points_result[:,0])**2 + (points_test[:,1]-points_result[:,1])**2 + (points_test[:,2]-points_result[:,2])**2);\n #print(\"Dist. Media (test): \", np.mean(distance));\n #print(\"Dist. Max (test): \", np.max(distance));\n \n max_auc = max(cback_dist.history_auc);\n max_acc = cback_dist.max_acc;\n accuracy = max_auc;\n print(\"AUC: {} - Val_Acc: {}\".format(max_auc, max_acc));\n \n #print(\"AUC: {}\".format(loss)); \n except Exception as e:\n loss, accuracy = self._handle_broken_model(model, e)\n\n self._record_stats(model, genome, loss, accuracy)\n\n return model, loss, accuracy\n\n def _record_stats(self, model, genome, loss, accuracy):\n with open(self.datafile, 'a') as csvfile:\n writer = csv.writer(csvfile, delimiter=',',\n quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n row = list(genome) + [loss, accuracy]\n writer.writerow(row)\n\n met = loss if self._metric == 'loss' else accuracy\n if (self._bssf is -1 or\n self._metric_op(met, self._bssf) and\n accuracy is not 0):\n try:\n os.remove('best-model-cat.h5')\n except OSError:\n pass\n self._bssf = met\n model.save('best-model-cat.h5')\n\n def _handle_broken_model(self, model, error):\n del model\n\n n = self.genome_handler.n_classes\n loss = log_loss(np.concatenate(([1], np.zeros(n - 1))), np.ones(n) / n)\n accuracy = 1 / n\n gc.collect()\n\n if K.backend() == 'tensorflow':\n K.clear_session()\n tf.reset_default_graph()\n\n print('An error occurred and the model could not train:')\n print(error)\n print(('Model assigned poor score. Please ensure that your model'\n 'constraints live within your computational resources.'))\n return loss, accuracy\n\n def _evaluate_population(self, members, epochs, fitness, igen, ngen):\n fit = []\n for imem, mem in enumerate(members):\n self._print_evaluation(imem, len(members), igen, ngen)\n res = self._evaluate(mem, epochs)\n v = res[self._metric_index]\n del res\n fit.append(v)\n\n fit = np.array(fit)\n self._print_result(fit, igen)\n return _Population(members, fit, fitness, obj=self._objective)\n\n def _print_evaluation(self, imod, nmod, igen, ngen):\n fstr = '\\nmodel {0}/{1} - generation {2}/{3}:\\n'\n print(fstr.format(imod + 1, nmod, igen + 1, ngen))\n\n def _generate_random_population(self, size):\n #return self.genome_handler.generate2(size)\n return [self.genome_handler.generate() for _ in range(size)]\n\n\n def _print_result(self, fitness, generation):\n result_str = ('Generation {3}:\\t\\tbest {4}: {0:0.4f}\\t\\taverage:'\n '{1:0.4f}\\t\\tstd: {2:0.4f}')\n print(result_str.format(self._metric_objective(fitness),\n np.mean(fitness),\n np.std(fitness),\n generation + 1, self._metric))\n\n def _crossover(self, genome1, genome2):\n cross_ind = rand.randint(0, len(genome1))\n child = genome1[:cross_ind] + genome2[cross_ind:]\n return child\n\n def _mutate(self, genome, generation):\n # increase mutations as program continues\n num_mutations = max(3, generation // 4)\n return self.genome_handler.mutate(genome, num_mutations)\n\n\nclass _Population(object):\n\n def __len__(self):\n return len(self.members)\n\n def __init__(self, members, fitnesses, score, obj='max'):\n self.members = members\n scores = fitnesses - fitnesses.min()\n if scores.max() > 0:\n scores /= scores.max()\n if obj == 'min':\n scores = 1 - scores\n if score:\n self.scores = score(scores)\n else:\n self.scores = scores\n self.s_fit = sum(self.scores)\n\n def get_best(self, n):\n combined = [(self.members[i], self.scores[i])\n for i in range(len(self.members))]\n sorted(combined, key=(lambda x: x[1]), reverse=True)\n return [x[0] for x in combined[:n]]\n\n def select(self):\n dart = rand.uniform(0, self.s_fit)\n sum_fits = 0\n for i in range(len(self.members)):\n sum_fits += self.scores[i]\n if sum_fits >= dart:\n return self.members[i]\n", "repo_name": "dizidio/devol-locindoor", "sub_path": "devol/devol.py", "file_name": "devol.py", "file_ext": "py", "file_size_in_byte": 14205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "keras.backend.backend", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 27, "usage_type": "name"}, {"api_name": "operator.__lt__", "line_number": 32, "usage_type": "attribute"}, {"api_name": "operator.__gt__", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.callbacks.Callback", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.dot", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 118, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 146, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 153, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 154, "usage_type": "attribute"}, {"api_name": "keras.backend.clear_session", "line_number": 222, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 222, "usage_type": "name"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 223, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 224, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.set_session", "line_number": 227, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 227, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 230, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 281, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 282, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 291, "usage_type": "call"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 301, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 303, "usage_type": "call"}, {"api_name": "keras.backend.backend", "line_number": 305, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 305, "usage_type": "name"}, {"api_name": "keras.backend.clear_session", "line_number": 306, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 306, "usage_type": "name"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 342, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 346, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 381, "usage_type": "call"}]} +{"seq_id": "7411252828", "text": "from flask import request, jsonify, Blueprint, Flask\nfrom datetime import datetime\nfrom flask_jwt_extended import jwt_required, get_jwt_identity, JWTManager\nimport stripe\nfrom models import *\n\napp = Flask(__name__)\napp.secret_key = 'your_secret_key'\njwt = JWTManager(app)\n\nstripe.api_key = \"pk_test_51MJWptKo6hjiMLcCn4CA6v4TEGkLzRzZ4r2rr3b93wLsPZ35YV0suqbcnQ3\" \\\n \"LZKMsQZtuOC8gPQNj4ejE5ZzB7zql00RjNbHXD4\"\n\n\npayments = Blueprint('payments', __name__)\n\n\ndef is_valid_card_number(card_number):\n # Add implementation to validate card number using luhn algorithm\n if not card_number.isdigit():\n return False\n if not len(card_number) in (13, 15, 16):\n return False\n if not luhn(card_number):\n return False\n return True\n\n\ndef luhn(card_number):\n def digits_of(n):\n return [int(d) for d in str(n)]\n digits = digits_of(card_number)\n odd_digits = digits[-1::-2]\n even_digits = digits[-2::-2]\n checksum = 0\n checksum += sum(odd_digits)\n for d in even_digits:\n for digit in digits_of(d*2):\n checksum += digit\n return checksum % 10 == 0 and len(digits) >= 13\n\n\ndef is_valid_expiration_date(expiration_date):\n # Add implementation to validate expiration date\n import datetime\n try:\n if datetime.datetime.strptime(expiration_date, '%m/%y') <= datetime.datetime.now():\n return False\n except ValueError:\n return False\n return True\n\n\ndef is_valid_cvv(cvv):\n # Add implementation to validate CVV\n if not cvv.isdigit():\n return False\n if not len(cvv) in (3, 4):\n return False\n return True\n\n\n@payments.route(\"/add-card\", methods=[\"POST\"])\n@jwt_required()\ndef charge():\n user_id = get_jwt_identity()\n errors = {}\n required_fields = ['card_holder_name', 'expiration_date', 'cvv', 'card_number']\n for field in required_fields:\n if not request.get_json().get(field):\n errors[field] = 'This field is required'\n\n if errors:\n return jsonify(errors), 302\n\n # Get the payment details from the form\n card_number = str(request.get_json().get(\"card_number\"))\n card_holder_name = request.get_json().get(\"card_holder_name\")\n expiration_date = request.get_json().get(\"expiration_date\")\n cvv = str(request.get_json().get(\"cvv\"))\n created_at = datetime.now()\n\n if not is_valid_card_number(card_number):\n errors['card_number'] = 'Invalid card number'\n\n # check if the expiration date is valid\n if not is_valid_expiration_date(expiration_date):\n errors['expiration_date'] = 'Invalid expiration date'\n\n # check if the cvv is valid\n if not is_valid_cvv(cvv):\n errors['cvv'] = 'Invalid CVV'\n\n if errors:\n return jsonify(errors), 302\n\n new_card = Payments(\n user_id=user_id,\n # username=username,\n card_number=card_number,\n card_holder_name=card_holder_name,\n expiration_date=expiration_date,\n cvv=cvv,\n created_at=datetime.now()\n )\n session.add(new_card)\n session.commit()\n\n card = new_card\n card = {\n 'id': card.id,\n 'card_number': card.card_number,\n 'card_holder_name': card.card_holder_name,\n 'expiration_date': card.expiration_date,\n 'cvv': card.cvv,\n 'created_at': card.created_at,\n }\n\n return jsonify(card=card)\n\n", "repo_name": "Eddy1-kivs/test-backend", "sub_path": "views/subscriptions/payments.py", "file_name": "payments.py", "file_ext": "py", "file_size_in_byte": 3371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_jwt_extended.JWTManager", "line_number": 9, "usage_type": "call"}, {"api_name": "stripe.api_key", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.Blueprint", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.now", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 119, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "31319647104", "text": "import os\nimport time\n\nimport pandas\nimport numpy as np\nimport skimage.io as io\nfrom PIL import Image\n\nimport tensorflow as tf\nfrom tensorflow.contrib.slim.nets import vgg\nslim = tf.contrib.slim\n\ntfrecords_filename='invasive-train.tfrecords'\nim_width=224\nim_height=224\n\n#Hyper Parameter to play with\nbatch_size=32\nnum_epochs=10\n\nlr = 0.001\ndecay_rate=0.1\ndecay_per=40 #epoch\n\ntrain_labels = pandas.read_csv('../data/dogs-breed/labels.csv')\ntest_labels = pandas.read_csv('../data/dogs-breed/sample_submission.csv')\n\ntrain_imgdir = '../data/dogs-breed/train/'\ntest_imgdir = '../data/dogs-breed/test/'\ntrain_images = os.listdir(train_imgdir)\ntest_images = os.listdir(test_imgdir)\n\nnum_iter = len(train_labels)/batch_size\n\nstart_time = time.time()\n\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\nif os.path.exists(tfrecords_filename):\n print(tfrecords_filename, \"already exists\")\nelse:\n writer = tf.python_io.TFRecordWriter(tfrecords_filename)\n print(\"Saving prepocessed file to '{}'\".format(tfrecords_filename))\n for img_path in train_images:\n idx = int(img_path.split('.')[0]) - 1\n label = train_labels.invasive[idx]\n img = Image.open(os.path.join(train_imgdir, img_path))\n img = np.array(img.resize((im_width,im_height), Image.ANTIALIAS))\n\n example = tf.train.Example(features=tf.train.Features(feature={\n 'image_raw': _bytes_feature(img.tostring()),\n 'label': _int64_feature(label)\n }))\n writer.write(example.SerializeToString())\n writer.close()\n print(\"Preprocessing done in %s seconds\" % (time.time() - start_time))\n\n\ndef read_and_decode(filename_queue):\n reader = tf.TFRecordReader()\n\n _, serialized_example = reader.read(filename_queue)\n\n features = tf.parse_single_example(\n serialized_example,\n features={\n 'image_raw': tf.FixedLenFeature([], tf.string),\n 'label': tf.FixedLenFeature([], tf.int64)\n })\n image = tf.decode_raw(features['image_raw'], tf.uint8)\n image = tf.reshape(image, [im_height, im_width, 3])\n label = tf.cast(features['label'], tf.int32)\n images, labels = tf.train.shuffle_batch([image, label],\n batch_size=batch_size, capacity=256, num_threads=2, min_after_dequeue=32)\n return images, labels\n\n\ndef infer(inputs, is_training=True):\n inputs = tf.cast(inputs, tf.float32)\n inputs = ((inputs / 255.0) - 0.5) * 2\n # Use Pretrained Base Model\n with tf.variable_scope(\"vgg_16\"):\n with slim.arg_scope(vgg.vgg_arg_scope()):\n net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')\n net = slim.max_pool2d(net, [2, 2], scope='pool1')\n net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')\n net = slim.max_pool2d(net, [2, 2], scope='pool2')\n net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')\n net = slim.max_pool2d(net, [2, 2], scope='pool3')\n net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')\n net = slim.max_pool2d(net, [2, 2], scope='pool4')\n net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')\n net = slim.max_pool2d(net, [2, 2], scope='pool5')\n # Append fully connected layer\n net = slim.flatten(net)\n net = slim.fully_connected(net, 512,\n weights_initializer=tf.contrib.layers.xavier_initializer(),\n weights_regularizer=slim.l2_regularizer(0.0005),\n scope='finetune/fc1')\n net = slim.fully_connected(net, 2,\n activation_fn=None,\n weights_initializer=tf.contrib.layers.xavier_initializer(),\n weights_regularizer=slim.l2_regularizer(0.0005),\n scope='finetune/fc2')\n return net\n\n\ndef losses(logits, labels):\n loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n return loss\n\n\ndef optimize(losses):\n global_step = tf.contrib.framework.get_or_create_global_step()\n learning_rate = tf.train.exponential_decay(lr, global_step,\n num_iter * decay_per, decay_rate, staircase=True)\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n train_op = optimizer.minimize(losses, global_step=global_step) # ,\n # var_list=slim.get_model_variables(\"finetune\"))\n return train_op\n\n\ntf.reset_default_graph()\n\n# Create the training graph\nfilename_queue = tf.train.string_input_producer([tfrecords_filename], num_epochs=num_epochs)\nimage, label = read_and_decode(filename_queue)\nprediction = infer(image)\nloss = losses(prediction, label)\ntrain_op = optimize(loss)\n\nprint(\"Training started\")\nwith tf.Session() as sess:\n init_op = tf.group(tf.global_variables_initializer(),\n tf.local_variables_initializer())\n restore = slim.assign_from_checkpoint_fn(\n 'vgg_16.ckpt',\n slim.get_model_variables(\"vgg_16\"))\n sess.run(init_op)\n restore(sess)\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\n for e in range(num_epochs):\n avg_loss, acc = 0, 0\n for i in range(num_iter):\n _, l = sess.run([train_op, loss])\n avg_loss += l / num_iter\n print(\"Epoch{:3d} avg_loss: {:f}\".format(e + 1, avg_loss))\n\n coord.request_stop()\n coord.join(threads)\n print('Training Done')\n saver = tf.train.Saver(slim.get_model_variables())\n saver.save(sess, 'model.ckpt')\n sess.close()", "repo_name": "owen8877/Fa17-statistic-learning", "sub_path": "tf/test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 5850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "tensorflow.contrib", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.train.BytesList", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Int64List", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 52, "usage_type": "name"}, {"api_name": "tensorflow.train.Example", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 54, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.TFRecordReader", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.parse_single_example", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.decode_raw", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.train.shuffle_batch", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim.nets.vgg.vgg_arg_scope", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim.nets.vgg", "line_number": 87, "usage_type": "name"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 101, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.nn.sparse_softmax_cross_entropy_with_logits", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.framework.get_or_create_global_step", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 118, "usage_type": "attribute"}, {"api_name": "tensorflow.train.exponential_decay", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.train.GradientDescentOptimizer", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 121, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.train.string_input_producer", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.local_variables_initializer", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.train.Coordinator", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tensorflow.train.start_queue_runners", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 158, "usage_type": "attribute"}]} +{"seq_id": "40185220144", "text": "import phonenumbers\n\nfrom phonenumbers import timezone,geocoder,carrier\n\nnumber = input(\"Enter Your Number with +__ : \")\npho = phonenumbers.parse(number)\ntime = timezone.time_zones_for_number(pho)\ncar = carrier.name_for_number(pho,\"en\")\ngec = geocoder.description_for_number(pho,'en')\nprint(pho)\nprint(time)\nprint(car)\nprint(gec)\n", "repo_name": "pranavg2901/Phone_Number_Details", "sub_path": "file01.py", "file_name": "file01.py", "file_ext": "py", "file_size_in_byte": 330, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "phonenumbers.parse", "line_number": 6, "usage_type": "call"}, {"api_name": "phonenumbers.timezone.time_zones_for_number", "line_number": 7, "usage_type": "call"}, {"api_name": "phonenumbers.timezone", "line_number": 7, "usage_type": "name"}, {"api_name": "phonenumbers.carrier.name_for_number", "line_number": 8, "usage_type": "call"}, {"api_name": "phonenumbers.carrier", "line_number": 8, "usage_type": "name"}, {"api_name": "phonenumbers.geocoder.description_for_number", "line_number": 9, "usage_type": "call"}, {"api_name": "phonenumbers.geocoder", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "12121711822", "text": "import sys\nfrom PyQt5 import uic\nfrom PyQt5.QtWidgets import QApplication, QMainWindow\nfrom PyQt5.QtGui import QPainter, QColor\nfrom random import randint\n\n\nclass Example(QMainWindow):\n def __init__(self):\n super().__init__()\n uic.loadUi('UI.ui', self)\n self.pushButton.clicked.connect(self.f)\n self.flag = False\n\n def f(self): # Обновление флага\n self.flag = True\n self.update()\n\n def paintEvent(self, event): # Функция рисования\n x = randint(30, 750)\n y = randint(30, 550)\n size = randint(50, 350)\n if self.flag:\n painter = QPainter(self)\n painter.setBrush(QColor(255, 255, 0))\n painter.drawEllipse(x, y, size, size) # Рисование окружности\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = Example()\n ex.show()\n sys.exit(app.exec())\n", "repo_name": "Fantastic-pro/Yellow_circle", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 11, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "39678695550", "text": "from dataclasses import dataclass, field\nfrom typing import Dict, Optional, Tuple, Type\n\nfrom rich.console import Console\nfrom torchtyping import TensorType\nfrom typing_extensions import Literal\n\ntry:\n import faiss\nexcept ImportError as err:\n pass\nimport numpy as np\nimport torch\nfrom torch.nn import Parameter\nfrom torch.nn import functional as F\n\nfrom nerfstudio.cameras.rays import RaySamples\nfrom nerfstudio.field_components.field_heads import FieldHeadNames\nfrom nerfstudio.field_components.spatial_distortions import SpatialDistortion\nfrom nerfstudio.fields.base_field import Field, FieldConfig\nfrom nerfstudio.utils.pointclouds import BasicPointClouds\n\ntry:\n import tinycudann as tcnn\nexcept ImportError:\n # tinycudann module doesn't exist\n pass\n\nCONSOLE = Console(width=128)\n\n\n@dataclass\nclass FeatureFieldConfig(FieldConfig):\n \"\"\"Feature Field Config\"\"\"\n\n _target: Type = field(default_factory=lambda: FeatureField)\n\n nc_position: int = 3\n \"\"\"Number of channels for positional inputs\"\"\"\n nc_feature: int = 384\n nc_hidden: int = 128\n n_hidden_layers: int = 2\n use_viewdirs: bool = False\n use_grid_feature: bool = True\n \"\"\"Whether to use multi-resolution feature grids (only support hash encoding for now)\"\"\"\n hash_grid_max_res: int = 128\n \"\"\"Maximum resolution of the feature grid (use a smaller default value since ViT features is of lower frequency)\"\"\"\n hash_grid_log2_hashmap_size: int = 19\n # hash_grid_precision: Literal[\"float32\", \"float16\"] = \"float32\"\n use_position_encoding: bool = True\n \"\"\"Whether to include positional encodings beyond the grid features (for better smoothness)\"\"\"\n use_fused_encoding_mlp: bool = True\n \"\"\"Whether to use TCNN fused encoding & mlp\"\"\"\n\n\nclass FeatureField(Field):\n \"\"\"A indivisual feature field without feature sharing with the main field.\"\"\"\n\n config: FeatureFieldConfig\n\n def __init__(self, config: FeatureFieldConfig, spatial_distortion: Optional[SpatialDistortion] = None) -> None:\n super().__init__()\n self.config = config\n\n self.spatial_distortion = spatial_distortion\n self.use_grid_feature = self.config.use_grid_feature\n self.use_viewdirs = self.config.use_viewdirs\n\n # feature field hash grid\n if self.config.use_viewdirs:\n raise NotImplementedError\n\n # if self.config.use_position_encoding:\n # raise NotImplementedError # tcnn nested positional encoding not working\n\n if self.config.use_grid_feature:\n self.build_ngp()\n else:\n self.build()\n\n def build_ngp(self):\n L, F, N_min = 16, 2, 16\n log2_T = self.config.hash_grid_log2_hashmap_size\n N_max = self.config.hash_grid_max_res\n b = np.exp(np.log(N_max / N_min) / (L - 1)) # feature is of lower-frequency -> N_max=128\n nc_pos = self.config.nc_position\n\n hash_encoding_config = {\n \"otype\": \"Grid\",\n \"type\": \"Hash\",\n \"n_levels\": L,\n \"n_features_per_level\": F,\n \"log2_hashmap_size\": log2_T,\n \"base_resolution\": N_min,\n \"per_level_scale\": b,\n \"n_dims_to_encode\": nc_pos,\n \"interpolation\": \"Linear\", # TODO: support \"Smoothstep\"\n }\n\n if self.config.use_fused_encoding_mlp: # tcnn encoding + mlp\n encoding_config = {\n \"otype\": \"Composite\",\n # Hash for the first nc_pos dims; Frequency for the later nc_pos dims\n \"nested\": [\n hash_encoding_config,\n {\n \"otype\": \"Frequency\",\n \"n_frequencies\": 8,\n \"n_dims_to_encode\": nc_pos,\n },\n ],\n }\n\n # when tinycudann is built w/ float16 support, it will use float16 automatically\n self.feature_encoder = tcnn.NetworkWithInputEncoding(\n n_input_dims=nc_pos * 2\n if self.config.use_position_encoding\n else nc_pos, # 2 * nc_pos for smooth encoding\n n_output_dims=self.config.nc_feature,\n encoding_config=encoding_config,\n network_config={\n \"otype\": \"FullyFusedMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"None\",\n \"n_neurons\": self.config.nc_hidden,\n \"n_hidden_layers\": self.config.n_hidden_layers,\n },\n # dtype=torch.float32 if self.config.hash_grid_precision == \"float32\" else torch.float16\n )\n else: # tcnn encoding + torch MLP\n raise NotImplementedError\n # self.pos_encoder = tcnn.Encoding(n_input_dims=nc_pos,\n # encoding_config=hash_encoding_config)\n # pos_dim = self.pos_encoder.n_output_dims\n\n # self.mlp = nn.Sequential(\n # fc_block(pos_dim, self.config.nc_hidden),\n # *[fc_block(self.config.nc_hidden, self.config.nc_hidden) for _ in range(self.config.n_hidden_layers)],\n # nn.Linear(self.config.nc_hidden, self.config.nc_feature) # TODO: support output activation\n # )\n\n def build(self):\n raise NotImplementedError\n\n def query_features(\n self, positions: TensorType[\"n_rays\", \"d_pos\"], viewdirs: Optional[TensorType[\"n_rays\", \"d_dir\"]] = None\n ) -> TensorType[\"n_rays\", \"d_feature\"]:\n \"\"\"Qeury the feature field\"\"\"\n x = self._get_input_positions(positions)\n\n if self.config.use_position_encoding:\n # FIXME: raise runtime_error after loading checkpoints to other fields.\n features = self.feature_encoder(torch.cat([x, x], dim=-1))\n else:\n features = self.feature_encoder(x)\n return features\n\n def get_outputs(self, ray_samples: RaySamples) -> Dict[str, torch.Tensor]:\n outputs = {}\n\n inputs = ray_samples.frustums.get_start_positions()\n inputs = inputs.view(-1, 3)\n\n directions = ray_samples.frustums.directions\n directions_flat = directions.reshape(-1, 3)\n features = self.query_features(inputs, directions_flat) # NOTE: feautures.dtype = torch.float16\n\n features = features.view(*ray_samples.frustums.directions.shape[:-1], -1)\n\n outputs = {FieldHeadNames.FEATURE: features}\n return outputs\n\n def forward(self, ray_samples: RaySamples):\n \"\"\"Evaluates the field at the given ray samples\"\"\"\n field_outputs = self.get_outputs(ray_samples)\n return field_outputs\n\n def _get_input_positions(self, positions: torch.Tensor):\n \"\"\"compute input positions (for positional encoding / feature encoding)\n from raw ray sample positions.\n\n NOTE: assume the whole scene is represented by a single feature field\n \"\"\"\n positions = self.spatial_distortion(positions) # [[-2, -1], [-1, 1], [1, 2]]\n positions = (positions + 2.0) / 4.0 # Full normalization: [-2, 2] -> [0, 1]\n # FIXME: handle positions out of [0, 1] after spatial_distortion & normalization\n return positions\n\n\n@dataclass\nclass FeatureSegFieldConfig(FieldConfig):\n \"\"\"FeatureSegField Config\"\"\"\n\n _target: Type = field(default_factory=lambda: FeatureSegField)\n\n knn: int = 10 # TOOD: seg fg & bg knn separately?\n \"\"\"number of reference points to retrieve for each point to be segmented\"\"\"\n distance_weighted_average: bool = False\n \"\"\"compute distance-weighted average of cosine similairties of the knn retrieved points\"\"\"\n in_fg_aabb_only: bool = True\n \"\"\"if a point is not in the fg aabb, it is directly considered as background\"\"\"\n contrast_fg_bg: bool = True\n \"\"\"whether to derive segmentation field by contrast between foreground and background points\"\"\"\n separate_bg_plane: bool = False\n \"\"\"when computing segmentation probs, treat background and plane as two different classes\"\"\"\n\n\nclass FeatureSegField(Field):\n \"\"\"calculate segmentation field from feature field\"\"\"\n\n config: FeatureSegFieldConfig\n\n def __init__(self, config: FeatureFieldConfig, ptcd_data: BasicPointClouds, aabb: TensorType[2, 3]) -> None:\n super().__init__()\n self.config = config\n self.ptcd_data = ptcd_data\n self.aabb = Parameter(aabb, requires_grad=False)\n self.ref_feats_fg = ptcd_data.fg_feats\n self.ref_feats_bg = torch.cat([ptcd_data.bg_feats, ptcd_data.plane_feats], 0)\n self.ref_pts_fg = ptcd_data.fg_pts\n self.ref_pts_bg = torch.cat([ptcd_data.bg_pts, ptcd_data.plane_pts], 0)\n self.fg_nn_index = ptcd_data.fg_nn_index\n self.bg_nn_index = ptcd_data.bg_nn_index\n\n if self.config.separate_bg_plane:\n self.plane_nn_index = ptcd_data.plane_nn_index\n self.ref_feats_bg, self.ref_pts_bg = ptcd_data.bg_feats, ptcd_data.bg_pts\n self.ref_feats_plane, self.ref_pts_plane = ptcd_data.plane_feats, ptcd_data.plane_pts\n\n self._register_all_tensors_as_buffers()\n\n @torch.no_grad()\n def compute_seg_prob(\n self,\n points: Optional[TensorType[\"num_samples\", 3]], # unnormalized, raw positions\n features: TensorType[\"num_samples\", \"nc_features\"],\n ptcd_data: Optional[BasicPointClouds] = None,\n ) -> TensorType[\"num_samples\", 1]:\n if ptcd_data is not None: # Override self.ptcd_data\n raise NotImplementedError\n\n features = features.detach()\n if points is not None:\n points = points.detach()\n if self.config.in_fg_aabb_only:\n assert points is not None\n in_fg_aabb_mask = ((points >= self.aabb[0]) & (points <= self.aabb[1])).all(-1) # (n, )\n # TODO: for pts not in fg aabb, omit the seg_probs computation directly.\n\n feats_l2_normalized = F.normalize(features, p=2, dim=-1)\n\n knn_inds_fg, knn_feats_fg, knn_pts_fg = self.search_knn_fg(feats_l2_normalized)\n knn_cos_sims_avg_fg = self.compute_cos_sim(points, feats_l2_normalized, knn_feats_fg, knn_pts_fg)\n\n if not self.config.contrast_fg_bg:\n seg_probs = knn_cos_sims_avg_fg * 0.5 + 0.5\n else:\n knn_inds_bg, knn_feats_bg, knn_pts_bg = self.search_knn_bg(features)\n knn_cos_sims_avg_bg = self.compute_cos_sim(points, feats_l2_normalized, knn_feats_bg, knn_pts_bg)\n\n # contrastive seg_probs\n if self.config.separate_bg_plane:\n knn_inds_plane, knn_feats_plane, knn_pts_plane = self.search_knn_plane(features)\n knn_cos_sims_avg_plane = self.compute_cos_sim(\n points, feats_l2_normalized, knn_feats_plane, knn_pts_plane\n ) # (n, 1)\n knn_cos_sims_avg_bg = torch.maximum(knn_cos_sims_avg_bg, knn_cos_sims_avg_plane)\n\n # TODO: tune temperature and offset cos_sims before applying softmax (since most cos_sims > -0.1)\n seg_probs = torch.softmax(torch.cat([knn_cos_sims_avg_fg, knn_cos_sims_avg_bg], -1), dim=-1)\n seg_probs = seg_probs[..., [0]]\n\n if self.config.in_fg_aabb_only:\n seg_probs[~in_fg_aabb_mask] = 0.0\n return seg_probs\n\n def search_knn_fg(self, feats: TensorType[\"n\", \"c\"]):\n return self._search_knn(feats, self.ref_feats_fg, self.ref_pts_fg, self.fg_nn_index)\n\n def search_knn_bg(self, feats: TensorType[\"n\", \"c\"]):\n return self._search_knn(feats, self.ref_feats_bg, self.ref_pts_bg, self.bg_nn_index)\n\n def search_knn_plane(self, feats: TensorType[\"n\", \"c\"]):\n return self._search_knn(feats, self.ref_feats_plane, self.ref_pts_plane, self.plane_nn_index)\n\n def _search_knn(\n self,\n feats: TensorType[\"n\", \"c\"],\n ref_feats: TensorType[\"m\", \"c\"],\n ref_pts: TensorType[\"m\", 3],\n nn_index: faiss.Index,\n ) -> Tuple[TensorType[\"n\", \"k\"], TensorType[\"n\", \"k\", \"c\"], TensorType[\"n\", \"k\", 3]]:\n feats = feats.to(torch.float32)\n knn_dists, knn_inds = nn_index.search(feats, self.config.knn) # (n, k)\n knn_feats = torch.index_select(ref_feats, 0, knn_inds.reshape(-1)).reshape(*knn_inds.shape, -1) # (n, k, c)\n knn_pts = torch.index_select(ref_pts, 0, knn_inds.reshape(-1)).reshape(*knn_inds.shape, -1) # (n, k, c)\n return knn_inds, knn_feats, knn_pts\n\n def compute_cos_sim(\n self,\n points: Optional[TensorType[\"n\", 3]],\n feats: TensorType[\"n\", \"c\"],\n knn_feats: TensorType[\"n\", \"k\", \"c\"],\n knn_pts: TensorType[\"n\", \"k\", 3],\n ) -> TensorType[\"n\", 1]:\n knn_cos_sims = F.cosine_similarity(feats[:, None], knn_feats, dim=-1) # (n, k)\n if not self.config.distance_weighted_average:\n return knn_cos_sims.mean(-1, keepdim=True)\n\n knn_pts_dists = torch.linalg.norm(points[:, None] - knn_pts, ord=2, dim=-1)\n knn_pts_weights = torch.exp(-knn_pts_dists) # TODO: tune temperature\n knn_pts_weights = knn_pts_weights / knn_pts_weights.sum(-1, keepdim=True) # normalized weights\n # use unnormalized weights would down weight bg_cos_sims too much\n knn_weighed_cos_sims = (knn_cos_sims * knn_pts_weights).sum(-1, keepdim=True)\n return knn_weighed_cos_sims\n\n def _register_all_tensors_as_buffers(self):\n \"\"\"register all Tensor attributes as buffers to automatically move them to the target device\"\"\"\n _attr_names = list(vars(self).keys())\n for _name in _attr_names:\n _attr = getattr(self, _name)\n if not isinstance(_attr, torch.Tensor):\n continue\n\n delattr(self, _name)\n self.register_buffer(_name, _attr, persistent=False)\n\n @torch.no_grad()\n def forward(\n self,\n ray_samples: Optional[RaySamples],\n features: TensorType[\"bs\":..., \"num_samples\", \"nc_features\"],\n ptcd_data: Optional[BasicPointClouds] = None,\n ) -> Dict[str, TensorType[\"bs\":..., 1]]:\n \"\"\"Calculate segmentation along the ray.\"\"\"\n prefix_dims, nc_feat = features.shape[:-1], features.shape[-1]\n features = features.view(-1, nc_feat)\n\n raw_positions = ray_samples.frustums.get_start_positions().view(-1, 3) if ray_samples is not None else None\n\n seg_probs = self.compute_seg_prob(raw_positions, features, ptcd_data=ptcd_data).reshape(*prefix_dims, 1)\n\n outputs = {FieldHeadNames.FG_SEG: seg_probs}\n return outputs\n", "repo_name": "zju3dv/AutoRecon", "sub_path": "nerfstudio/fields/feature_field.py", "file_name": "feature_field.py", "file_ext": "py", "file_size_in_byte": 14489, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 315, "dataset": "github-code", "pt": "51", "api": [{"api_name": "rich.console.Console", "line_number": 29, "usage_type": "call"}, {"api_name": "nerfstudio.fields.base_field.FieldConfig", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 36, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 36, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 32, "usage_type": "name"}, {"api_name": "nerfstudio.fields.base_field.Field", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "nerfstudio.field_components.spatial_distortions.SpatialDistortion", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.functional", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 92, "usage_type": "name"}, {"api_name": "tinycudann.NetworkWithInputEncoding", "line_number": 115, "usage_type": "call"}, {"api_name": "torchtyping.TensorType", "line_number": 146, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 153, "usage_type": "call"}, {"api_name": "torchtyping.TensorType", "line_number": 147, "usage_type": "name"}, {"api_name": "nerfstudio.cameras.rays.RaySamples", "line_number": 158, "usage_type": "name"}, {"api_name": "nerfstudio.field_components.field_heads.FieldHeadNames.FEATURE", "line_number": 170, "usage_type": "attribute"}, {"api_name": "nerfstudio.field_components.field_heads.FieldHeadNames", "line_number": 170, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 158, "usage_type": "attribute"}, {"api_name": "nerfstudio.cameras.rays.RaySamples", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 178, "usage_type": "attribute"}, {"api_name": "nerfstudio.fields.base_field.FieldConfig", "line_number": 191, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 194, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 194, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 190, "usage_type": "name"}, {"api_name": "nerfstudio.fields.base_field.Field", "line_number": 208, "usage_type": "name"}, {"api_name": "nerfstudio.utils.pointclouds.BasicPointClouds", "line_number": 213, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 213, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 221, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 235, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 235, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 236, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 237, "usage_type": "name"}, {"api_name": "nerfstudio.utils.pointclouds.BasicPointClouds", "line_number": 237, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 250, "usage_type": "name"}, {"api_name": "torch.maximum", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 232, "usage_type": "call"}, {"api_name": "torchtyping.TensorType", "line_number": 238, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 277, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 280, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 283, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 288, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 289, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 290, "usage_type": "name"}, {"api_name": "faiss.Index", "line_number": 291, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 293, "usage_type": "attribute"}, {"api_name": "torch.index_select", "line_number": 295, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 296, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 292, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 292, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 301, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 301, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 302, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 303, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 304, "usage_type": "name"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 306, "usage_type": "name"}, {"api_name": "torch.linalg.norm", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.linalg", "line_number": 310, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 311, "usage_type": "call"}, {"api_name": "torchtyping.TensorType", "line_number": 305, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 322, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 331, "usage_type": "name"}, {"api_name": "nerfstudio.cameras.rays.RaySamples", "line_number": 331, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 332, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 333, "usage_type": "name"}, {"api_name": "nerfstudio.utils.pointclouds.BasicPointClouds", "line_number": 333, "usage_type": "name"}, {"api_name": "nerfstudio.field_components.field_heads.FieldHeadNames.FG_SEG", "line_number": 343, "usage_type": "attribute"}, {"api_name": "nerfstudio.field_components.field_heads.FieldHeadNames", "line_number": 343, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 328, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 334, "usage_type": "name"}, {"api_name": "torchtyping.TensorType", "line_number": 334, "usage_type": "name"}]} +{"seq_id": "8257994614", "text": "from prometheus_client import Gauge\nimport requests\nimport time\nimport urllib3\nimport sys\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) \n\nmetric_map = {}\ncreated = False\n\n\nmetric_map[\"status\"] = -1\n\nos_ssd_count = \"os_count\"\ndata_ssd_count = \"data_count\"\nhealth = \"drive_health\"\navg_data = \"avg_data_used\"\navg_os = \"avg_os_used\"\n\n\n\ndef init():\n \n \"\"\"\n Init function to initialize the module,\n Initialize Prometheus metrics that would be later used in the module\n \n Args:\n None\n \n Returns:\n None\n \n \"\"\"\n \n # Check if metric already present in the metric_map\n if os_ssd_count not in metric_map:\n # Create metric and add it to metric_map\n metric_map[os_ssd_count] = Gauge(os_ssd_count, \"Number of OS Drives\")\n \n if data_ssd_count not in metric_map:\n metric_map[data_ssd_count] = Gauge(data_ssd_count, \"Number of Data Drives\")\n \n if health not in metric_map:\n metric_map[health] = Gauge(health, \"Drive Health\")\n \n if avg_data not in metric_map:\n metric_map[avg_data] = Gauge(avg_data, \"Average Percent used Data Drives\")\n \n if avg_os not in metric_map:\n metric_map[avg_os] = Gauge(avg_os, \"Average Percent Used OS Drives\")\n \n print(\"Initialized Storage Exporter...\")\n\n\ndef ExportMetric(ip=\"localhost\", port= \"273\"):\n \"\"\"\n ExportMetric: This function requests from NVSM-APIs using URL. Upon gettin valid JSON data traverses the data and create and set values to metrics.\n The metrics include:\n 1. Number of OS Drives\n 2. Number of Data Drives\n 3. Overall Drive Health\n 4. Average Percent used for Data Drives\n 5. Average Percent use for OS Drives\n 6. Per Drive Disk Capacity\n 7. Per Drive Percent Used\n \n Args:\n ip : IP address of the NVSM server\n port: Port number of the NVSM server\n \n Returns:\n None\n \"\"\"\n \n global metric_map\n \n avg_os_used = 0\n avg_data_used = 0\n os_count = 0\n data_count = 0\n \n # Read JWT token for NVSM-APIs\n with open ('/etc/nvsm-apis/nvsm-apis-perpetual.jwt', 'r') as jwt_file:\n tokenstring = jwt_file.read()\n\n # Request to URL to get the data\n r = requests.get('https://' + str(ip) + ':' + str(port) + '/redfish/v1/Systems/1/Storage', timeout=120, verify=False, headers={'Authorization': 'Bearer '+tokenstring})\n \n # Read data returned by URL\n storage_collection = r.json()\n\n # Iterate over the storage collection to get the storage information\n for data_id in storage_collection[\"Members\"]:\n \n # Request to URL to get the data\n r = requests.get('https://' + str(ip) + ':' + str(port) + '/' + data_id[\"@odata.id\"], timeout=120, verify=False, headers={'Authorization': 'Bearer '+tokenstring})\n\n # Read data returned by URL\n try:\n nvme_storage_subsys = r.json()\n except:\n continue\n \n # Iterate over the storage information to get the drive information\n for nvme_id in nvme_storage_subsys[\"Drives\"]:\n \n # Request to URL to get the data for each drive\n r = requests.get('https://' + str(ip) + ':' + str(port) +nvme_id[\"@odata.id\"], timeout=120, verify=False, headers={'Authorization': 'Bearer '+tokenstring})\n \n try:\n drive = r.json()\n except:\n continue\n \n if \"nvme0n1\" in drive[\"@odata.id\"] or \"nvme1n1\" in drive[\"@odata.id\"]:\n name = \"os_\" + drive[\"@odata.id\"][-7:] + \"_capacity\"\n else:\n name = \"data_\" + drive[\"@odata.id\"][-7:] + \"_capacity\"\n if name not in metric_map:\n metric_map[name] = Gauge(name, \"Disk Capacity\")\n c = metric_map[name]\n c.set(float(drive[\"Capacity\"]))\n\n if \"nvme0n1\" in drive[\"@odata.id\"] or \"nvme1n1\" in drive[\"@odata.id\"]:\n usage_name = \"os_\" + drive[\"@odata.id\"][-7:] + \"_percent_used\"\n avg_os_used += float(drive[\"Oem\"][\"Nvidia_HM\"][\"Metrics\"][\"PercentUsed\"])\n else:\n usage_name = \"data_\" + drive[\"@odata.id\"][-7:] + \"_percent_used\" \n avg_data_used += float(drive[\"Oem\"][\"Nvidia_HM\"][\"Metrics\"][\"PercentUsed\"])\n if usage_name not in metric_map:\n metric_map[usage_name] = Gauge(usage_name, \"Percent Used\")\n g = metric_map[usage_name]\n g.set(float(drive[\"Oem\"][\"Nvidia_HM\"][\"Metrics\"][\"PercentUsed\"])) \n\n if \"nvme0n1\" in drive[\"@odata.id\"] or \"nvme1n1\" in drive[\"@odata.id\"]:\n error_name = \"os_\" + drive[\"@odata.id\"][-7:] + \"_media_errors\"\n else:\n error_name = \"data_\" + drive[\"@odata.id\"][-7:] + \"_media_errors\"\n if error_name not in metric_map:\n metric_map[error_name] = Gauge(error_name, \"Media Errors\")\n d = metric_map[error_name]\n d.set(int(drive[\"Oem\"][\"Nvidia_HM\"][\"Errors\"][\"Media\"][\"Count\"]))\n \n h = drive[\"Status\"][\"Health\"] \n \n if \"nvme0n1\" in drive[\"@odata.id\"] or \"nvme1n1\" in drive[\"@odata.id\"]:\n os_count += 1\n else:\n data_count += 1\n status = metric_map[\"status\"]\n if(h == \"OK\"):\n temp = 0\n elif(h == \"Warning\"):\n temp = 1\n elif(h == \"Critical\"):\n temp = 2\n if(status < temp):\n status = temp\n metric_map[\"status\"] = status\n \n # Set values to metrics\n status = metric_map[\"status\"]\n health_metric = metric_map[health]\n health_metric.set(status)\n \n os_ssd_count_metric = metric_map[os_ssd_count]\n os_ssd_count_metric.set(os_count)\n \n data_ssd_count_metric = metric_map[data_ssd_count]\n data_ssd_count_metric.set(data_count)\n \n avg_os_metric = metric_map[avg_os]\n avg_os_metric.set(avg_os_used/os_count)\n \n avg_data_metric = metric_map[avg_data]\n avg_data_metric.set(avg_data_used/data_count)\n", "repo_name": "NVIDIA/NVSM", "sub_path": "NVSM-Prometheus/nvsm_exporters/storage_exporter.py", "file_name": "storage_exporter.py", "file_ext": "py", "file_size_in_byte": 6166, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "51", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 7, "usage_type": "attribute"}, {"api_name": "prometheus_client.Gauge", "line_number": 40, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 43, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 46, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 49, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 52, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 89, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 98, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 110, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 122, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 133, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "34972535452", "text": "\"\"\"Updated service model\n\nRevision ID: 4cffaa7ed046\nRevises: ec3b32585c1b\nCreate Date: 2023-11-29 20:20:31.303979\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '4cffaa7ed046'\ndown_revision = 'ec3b32585c1b'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n with op.batch_alter_table('service', schema=None) as batch_op:\n batch_op.add_column(sa.Column('status', sa.String(length=128), nullable=True))\n batch_op.add_column(sa.Column('cv', sa.String(length=300), nullable=True))\n batch_op.add_column(sa.Column('sop', sa.String(length=300), nullable=True))\n batch_op.drop_column('completed_status')\n\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n with op.batch_alter_table('service', schema=None) as batch_op:\n batch_op.add_column(sa.Column('completed_status', sa.VARCHAR(length=128), nullable=True))\n batch_op.drop_column('sop')\n batch_op.drop_column('cv')\n batch_op.drop_column('status')\n\n # ### end Alembic commands ###\n", "repo_name": "OkpePhillips/Scholarshub", "sub_path": "migrations/versions/4cffaa7ed046_updated_service_model.py", "file_name": "4cffaa7ed046_updated_service_model.py", "file_ext": "py", "file_size_in_byte": 1190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "alembic.op.batch_alter_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.String", "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": "alembic.op.batch_alter_table", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "28119967654", "text": "# Theo ví dụ mẫu của https://machinelearningcoban.com/2018/07/06/deeplearning/\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport pandas as pd\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\nprint(tf.__version__)\n\n# 1. create pseudo data y = 2*x0 + 3*x1 + 4\nX = np.random.rand(200, 2) # Tạo 200 hàng, 2 cột số ngẫu n 0..1.0\n\n\ndef generate_output(input):\n ''' return 4 * np.square(input[:, 0]) + 2 * np.square(input[:, 1]) + \\\n 2 * input[:, 0] + 3 * input[:, 1] + 4 + \\\n .2 * np.random.randn(np.shape(input)[0]) # noise added\n '''\n return 2 * input[:, 0] + 3 * input[:, 1] + 4 + .2 * np.random.randn(np.shape(input)[0]) # noise added\n\n\ny = generate_output(X)\n\n\n# 2. Build model\ndef build_model(num_features):\n model = keras.Sequential([\n layers.Dense(5, activation='linear', input_shape=(num_features,)),\n layers.Dense(1)\n ])\n\n optimizer = tf.keras.optimizers.RMSprop(0.001)\n\n model.compile(loss='mse',\n optimizer=optimizer,\n metrics=['mae', 'mse'])\n return model\n\n\nmodel = build_model(np.shape(X)[1])\n\nprint(model.summary())\n\n# The patience parameter is the amount of epochs to check for improvement\nearly_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n\n# 4. Train the model\nhistory = model.fit(X, y, epochs=1000, batch_size=2,\n validation_split=0.2, verbose=2, callbacks=[early_stop])\n\nfig = plt.figure(figsize=(16, 8))\n\n# 5. Xuất ra màn hình dữ liệu train đầu vào\ndef plot_train_data(X, y):\n ax1 = fig.add_subplot(121, projection='3d')\n ax1.scatter3D(X[:, 0], X[:, 1], y, c=['b'], marker='o')\n ax1.set_title('Train Dataset: blue, Predict: red')\n ax1.set_xlabel('x0')\n ax1.set_ylabel('x1')\n ax1.set_zlabel('y')\n return ax1\n\n\ndef plot_history(history):\n hist = pd.DataFrame(history.history)\n hist['epoch'] = history.epoch\n ax2 = fig.add_subplot(122)\n ax2.set_title('Train Result')\n ax2.set_xlabel('Epoch')\n ax2.set_ylabel('Mean Square Error')\n ax2.plot(hist['epoch'], hist['mse'],\n label='Train Error')\n ax2.plot(hist['epoch'], hist['val_mse'],\n label='Val Error')\n\n ax2.legend()\n\n\naxes1 = plot_train_data(X, y)\n\nplot_history(history)\n\n\ndef test_model():\n test_input = np.random.rand(20, 2)\n test_predict = model.predict(test_input)\n axes1.scatter3D(test_input[:, 0], test_input[:, 1], test_predict, c=['r'], marker='^')\n\n\ntest_model()\n\nplt.show()\n", "repo_name": "TechMaster/LearnAI", "sub_path": "LinearRegression/Keras/keras_linear2.py", "file_name": "keras_linear2.py", "file_ext": "py", "file_size_in_byte": 2584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "tensorflow.__version__", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 29, "usage_type": "name"}, {"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.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.RMSprop", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}]} +{"seq_id": "28520312283", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nVz = 0.8;\nV0 = 0;\nV1 = -0.25;\nV2 = 0.25;\nepsilon = 0.05;\n\ny0 = np.loadtxt('G_TG_Vz=' + str(Vz) + '_V=' + str(V0) + '.txt', delimiter=',',dtype='str')\ny1 = np.loadtxt('G_TG_Vz=' + str(Vz) + '_V=' + str(V1) + '.txt', delimiter=',',dtype='str')\ny2 = np.loadtxt('G_TG_Vz=' + str(Vz) + '_V=' + str(V2) + '.txt', delimiter=',',dtype='str')\n\nTG_Min = 0; TG_Max = 200; TG_Number = 2001;\nTG_Range = np.linspace(TG_Min, TG_Max, TG_Number);\n\nG_z = [];\nG_N = [];\nG_N1 = [];\nG_N2 = [];\ntemp = [];\nfor i in range(TG_Number):\n G_z.append(float(y0[i])/2); # Change the conductance to be in unit of (2e^2/h)\n G_N.append((float(y1[i]) + float(y2[i]))/4) # Average and Change the conductance to be in unit of (2e^2/h)\n G_N1.append(float(y1[i])/2);\n G_N2.append(float(y2[i])/2);\n temp.append(np.absolute(float(y0[i])/2 - 1) - epsilon);\n\nsign_change_index = []\nfor idx in range(0, len(temp)-1):\n # Checking for successive opposite index\n if (temp[idx] > 0 and temp[idx+1] < 0) or (temp[idx] < 0 and temp[idx+1] > 0):\n sign_change_index.append(idx)\n\nGN_min = np.minimum(G_N[sign_change_index[0]],G_N[sign_change_index[-1]])\nGN_max = np.maximum(G_N[sign_change_index[0]],G_N[sign_change_index[-1]])\n\nF = GN_max/GN_min;\n\n# ======== G as a function of Tunneling Barrier (Linecuts together)============== \nplt.figure(dpi = 600)\nplt.plot(TG_Range, G_z, label = \"$V_{bias}=$\"+str(V0))\nplt.plot(TG_Range, G_N1, label = \"$V_{bias}=$\"+str(V1))\nplt.plot(TG_Range, G_N2, label = \"$V_{bias}=$\"+str(V2))\nplt.legend()\nplt.ylim(0,1.1)\nplt.xlim(200,0)\nplt.xlabel('$E_{barrier}(meV)$')\nplt.ylabel('$G(2e^2/h)$')\nplt.title('$V_z=$'+str(Vz)+' meV, $T=0$.')\nplt.savefig('/Users/laiyihua/Google Drive/UMD/Research/Majorana/merit of Quantization/TG_good_2_Vz='+str(Vz)+'_linecuts.png')\nplt.show()\n\n# ======== G as a function of Tunneling Barrier (average G_N)============== \nplt.figure(dpi = 600)\nplt.plot(G_N,G_z)\nplt.ylim(0,1.1)\nplt.xlabel('$G_N(2e^2/h)$')\nplt.ylabel('$G_z(2e^2/h)$')\nplt.title('$V_z=$'+str(Vz)+' meV, $T=0$.')\nplt.savefig('/Users/laiyihua/Google Drive/UMD/Research/Majorana/merit of Quantization/TG_ugly_11_Vz='+str(Vz)+'_GzGN.png')\nplt.show()\n", "repo_name": "pauline610697/Quality-Factor-of-ZBCP", "sub_path": "meritF.py", "file_name": "meritF.py", "file_ext": "py", "file_size_in_byte": 2248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.loadtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"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.legend", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "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.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"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.ylim", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "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.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "31265954657", "text": "import json\nimport requests\nimport time\n\nfrom domain.Player import Player\nfrom domain.Team import Team\n\n\ndef main():\n page = 1\n\n while page is not None:\n response = requests.get('https://www.balldontlie.io/api/v1/players?page=' + str(page) + '&per_page=100')\n\n data = response.json()['data']\n meta = response.json()['meta']\n\n meta_dump = json.dumps(meta)\n\n print(page)\n\n for e in data:\n dump = json.dumps(e)\n\n t = json.loads(dump)\n\n team_dict = t.get('team')\n\n team = Team(team_dict.get('id'), team_dict.get('abbreviation'), team_dict.get('city'),\n team_dict.get('conference'), team_dict.get('division'), team_dict.get('full_name'),\n team_dict.get('name'))\n\n pl = Player(t.get('id'), t.get('first_name'), t.get('last_name'), t.get('height_feet'),\n t.get('height_inches'), t.get('position'), team, t.get('weight_pounds'))\n # print(pl.first_name)\n\n # we can only do 60 requests per minute, so sleep for a second after each request\n time.sleep(1)\n page = json.loads(meta_dump).get('next_page')\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "jimpeeters97/NBA-python", "sub_path": "api/ApiRequestApplication.py", "file_name": "ApiRequestApplication.py", "file_ext": "py", "file_size_in_byte": 1235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "domain.Team.Team", "line_number": 29, "usage_type": "call"}, {"api_name": "domain.Player.Player", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "22896875281", "text": "\"\"\"REST client handling, including KlaviyoStream base class.\"\"\"\n\nfrom __future__ import annotations\n\nimport typing as t\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom urllib.parse import parse_qsl\n\nfrom singer_sdk.authenticators import APIKeyAuthenticator\nfrom singer_sdk.pagination import BaseHATEOASPaginator\nfrom singer_sdk.streams import RESTStream\n\nif t.TYPE_CHECKING:\n from urllib.parse import ParseResult\n\n import requests\n\nSCHEMAS_DIR = Path(__file__).parent / Path(\"./schemas\")\nUTC = timezone.utc\nDEFAULT_START_DATE = datetime(2000, 1, 1, tzinfo=UTC).isoformat()\n\n\ndef _isodate_from_date_string(date_string: str) -> str:\n \"\"\"Convert a date string to an ISO date string.\n\n Args:\n date_string: The date string to convert.\n\n Returns:\n An ISO date string.\n \"\"\"\n return datetime.strptime(date_string, \"%Y-%m-%d\").replace(tzinfo=UTC).isoformat()\n\n\nclass KlaviyoPaginator(BaseHATEOASPaginator):\n \"\"\"HATEOAS paginator for the Klaviyo API.\"\"\"\n\n def get_next_url(self, response: requests.Response) -> str:\n data = response.json()\n return data.get(\"links\").get(\"next\")\n\n\nclass KlaviyoStream(RESTStream):\n \"\"\"Klaviyo stream class.\"\"\"\n\n url_base = \"https://a.klaviyo.com/api\"\n records_jsonpath = \"$[data][*]\"\n\n @property\n def authenticator(self) -> APIKeyAuthenticator:\n \"\"\"Return a new authenticator object.\n\n Returns:\n An authenticator instance.\n \"\"\"\n return APIKeyAuthenticator.create_for_stream(\n self,\n key=\"Authorization\",\n value=f'Klaviyo-API-Key {self.config.get(\"auth_token\", \"\")}',\n location=\"header\",\n )\n\n @property\n def http_headers(self) -> dict:\n \"\"\"Return the http headers needed.\n\n Returns:\n A dictionary of HTTP headers.\n \"\"\"\n headers = {}\n if \"user_agent\" in self.config:\n headers[\"User-Agent\"] = self.config.get(\"user_agent\")\n if \"revision\" in self.config:\n headers[\"revision\"] = self.config.get(\"revision\")\n return headers\n\n def get_new_paginator(self) -> BaseHATEOASPaginator:\n return KlaviyoPaginator()\n\n def get_url_params(\n self,\n context: dict | None,\n next_page_token: ParseResult | None,\n ) -> dict[str, t.Any]:\n params: dict[str, t.Any] = {}\n\n if next_page_token:\n params.update(parse_qsl(next_page_token.query))\n\n if self.replication_key:\n if self.get_starting_timestamp(context):\n filter_timestamp = self.get_starting_timestamp(context)\n elif self.config.get(\"start_date\"):\n filter_timestamp = _isodate_from_date_string(self.config(\"start_date\"))\n else:\n filter_timestamp = DEFAULT_START_DATE\n\n params[\n \"filter\"\n ] = f\"greater-than({self.replication_key},{filter_timestamp})\"\n\n return params\n", "repo_name": "MeltanoLabs/tap-klaviyo", "sub_path": "tap_klaviyo/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2981, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "singer_sdk.pagination.BaseHATEOASPaginator", "line_number": 36, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 39, "usage_type": "attribute"}, {"api_name": "singer_sdk.streams.RESTStream", "line_number": 44, "usage_type": "name"}, {"api_name": "singer_sdk.authenticators.APIKeyAuthenticator.create_for_stream", "line_number": 57, "usage_type": "call"}, {"api_name": "singer_sdk.authenticators.APIKeyAuthenticator", "line_number": 57, "usage_type": "name"}, {"api_name": "singer_sdk.authenticators.APIKeyAuthenticator", "line_number": 51, "usage_type": "name"}, {"api_name": "singer_sdk.pagination.BaseHATEOASPaginator", "line_number": 78, "usage_type": "name"}, {"api_name": "urllib.parse.ParseResult", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 86, "usage_type": "attribute"}, {"api_name": "urllib.parse.parse_qsl", "line_number": 89, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 85, "usage_type": "attribute"}]} +{"seq_id": "940108131", "text": "from context import karkadann\nfrom karkadann.prodigal import *\nfrom karkadann.database import data_location\nimport unittest as ut\n\n\nclass TestOverlap(ut.TestCase):\n\tdef test_overlap(self):\n\t\tfrom Bio.SeqFeature import FeatureLocation\n\n\t\tdef overlap_exercise(one, two, result):\n\t\t\tself.assertEqual(overlap(one, two), overlap(two, one))\n\t\t\tself.assertEqual(overlap(one, two), result)\n\n\t\treference_feature = FeatureLocation(100, 200, strand=+1)\n\t\tinside_feature = FeatureLocation(150, 175, strand=+1)\n\t\toverlap_exercise(reference_feature, inside_feature, True)\n\n\t\tleft_feature = FeatureLocation(75, 150, strand=+1)\n\t\toverlap_exercise(left_feature, reference_feature, True)\n\n\t\tleft_touchy = FeatureLocation(50, 100, strand=+1)\n\t\toverlap_exercise(left_touchy, reference_feature, False)\n\n\t\tleft_inside = FeatureLocation(100, 125, strand=+1)\n\t\toverlap_exercise(left_inside, reference_feature, True)\n\n\t\tdefinitely_outside = FeatureLocation(500, 1000, strand=-1)\n\t\toverlap_exercise(definitely_outside, reference_feature, False)\n\n\n# best bikeshed EU.\nclass FloatingOverlapTest(ut.TestCase):\n\tdef test_left(self):\n\t\tfrom Bio.SeqFeature import FeatureLocation\n\n\t\tdef overlap_exercise(one, two, result):\n\t\t\tself.assertEqual(floverlap(one, two), floverlap(two, one))\n\t\t\tself.assertEqual(floverlap(one, two), result)\n\t\t\tif one.strand == 1:\n\t\t\t\tnewstrand = -1\n\t\t\telif one.strand == 0 or one.strand is None:\n\t\t\t\tnewstrand = 0\n\t\t\telse:\n\t\t\t\tnewstrand = 1\n\t\t\tneg = FeatureLocation(one.start, one.end, newstrand)\n\t\t\tself.assertEqual(floverlap(neg, two), floverlap(one, two))\n\t\t\tself.assertEqual(floverlap(neg, two), result)\n\n\t\tref = FeatureLocation(100, 200, +1)\n\n\t\tleft = FeatureLocation(50, 75, -1)\n\n\t\toverlap_exercise(ref, left, 0)\n\n\t\ton_left = FeatureLocation(50, 150, +1)\n\n\t\toverlap_exercise(ref, on_left, .5)\n\n\t\tleft_touchy = FeatureLocation(50, 100, +1)\n\n\t\toverlap_exercise(left_touchy, ref, 0)\n\n\t\tinset_left = FeatureLocation(100, 150, +1)\n\n\t\toverlap_exercise(inset_left, ref, .5)\n\n\t\tinside_left = FeatureLocation(125, 175, +1)\n\n\t\toverlap_exercise(inside_left, ref, .5)\n\n\tdef test_right(self):\n\t\tfrom Bio.SeqFeature import FeatureLocation\n\n\t\tdef overlap_exercise(one, two, result):\n\t\t\tself.assertEqual(floverlap(one, two), floverlap(two, one))\n\t\t\tself.assertEqual(floverlap(one, two), result)\n\t\t\tif one.strand == 1:\n\t\t\t\tnewstrand = -1\n\t\t\telif one.strand == 0 or one.strand is None:\n\t\t\t\tnewstrand = 0\n\t\t\telse:\n\t\t\t\tnewstrand = 1\n\t\t\tneg = FeatureLocation(one.start, one.end, newstrand)\n\t\t\tself.assertEqual(floverlap(neg, two), floverlap(one, two))\n\t\t\tself.assertEqual(floverlap(neg, two), result)\n\n\t\tref = FeatureLocation(100, 200, -1)\n\n\t\tright = FeatureLocation(250, 300, +1)\n\n\t\toverlap_exercise(ref, right, 0)\n\n\t\ton_right = FeatureLocation(200, 300, -1)\n\n\t\toverlap_exercise(ref, on_right, 0)\n\n\tdef test_weird(self):\n\t\tfrom Bio.SeqFeature import FeatureLocation\n\n\t\tdef overlap_exercise(one, two, result):\n\t\t\tself.assertEqual(floverlap(one, two), floverlap(two, one))\n\t\t\tself.assertEqual(floverlap(one, two), result)\n\t\t\tif one.strand == 1:\n\t\t\t\tnewstrand = -1\n\t\t\telif one.strand == 0 or one.strand is None:\n\t\t\t\tnewstrand = 0\n\t\t\telse:\n\t\t\t\tnewstrand = 1\n\t\t\tneg = FeatureLocation(one.start, one.end, newstrand)\n\t\t\tself.assertEqual(floverlap(neg, two), floverlap(one, two))\n\t\t\tself.assertEqual(floverlap(neg, two), result)\n\n\t\tref = FeatureLocation(0, 1000)\n\n\t\todd = FeatureLocation(100, 200)\n\n\t\toverlap_exercise(ref, odd, .1)\n\n\t\tvery_long = FeatureLocation(500, 2000)\n\n\t\toverlap_exercise(very_long, odd, 0)\n\n\t\toverlap_exercise(ref, very_long, 1 / 3.0)\n\n\t\tsnug = FeatureLocation(1, 1000)\n\n\t\toverlap_exercise(ref, snug, 999 / 1000.0)\n\n\nclass TestAnnotate(ut.TestCase):\n\t# TODO use resources properly, like for the config file. :/\n\ttestgb = os.path.join(data_location, \"test/testassem.gb\")\n\ttestrec = SeqIO.parse(testgb, 'genbank')\n\ttestrec = list(testrec)\n\tpreserve_testrec = annotate(testrec, preserve_anno=True)\n\tspoiled_testrec = annotate(testrec, preserve_anno=False)\n\n\tdef test_annotate(self):\n\t\t# annotation should not delete contigs\n\t\tself.assertEqual(len(self.spoiled_testrec), len(self.preserve_testrec))\n\t\tpreserved_contig = self.preserve_testrec[0]\n\t\traw_contig = self.spoiled_testrec[0]\n\t\t# or change their order or sequence\n\t\tself.assertEqual(len(preserved_contig.seq), len(raw_contig.seq))\n\n\tdef test_merge(self):\n\t\tfor i in range(len(self.testrec)):\n\n\t\t\ttestcontig = self.testrec[i]\n\t\t\tpreservecontig = self.preserve_testrec[i]\n\t\t\tspoiledcontig = self.spoiled_testrec[i]\n\t\t\ttestprots = filter(lambda iscds: iscds.type == \"CDS\", testcontig.features)\n\t\t\tpreserveprots = filter(lambda iscds: iscds.type == \"CDS\", preservecontig.features)\n\t\t\tspoiledprots = filter(lambda iscds: iscds.type == \"CDS\", spoiledcontig.features)\n\t\t\t# preservation of features should only add features\n\t\t\tself.assertGreaterEqual(len(preserveprots), len(testprots))\n\t\t\t# there should be more preserved features than prodigal features\n\t\t\tself.assertGreaterEqual(len(preserveprots), len(spoiledprots))\n\t\t\tif testcontig.id == \"NZ_KK070022.1\":\n\t\t\t\tself.assertEqual(len(spoiledprots), 3)\n\t\t\t\tself.assertEqual(len(preserveprots), 3)\n\t\t\t\tself.assertEqual(len(testprots), 2)\n\n\nif __name__ == \"__main__\":\n\ttestgb = os.path.join(data_location, \"test/testassem.gb\")\n\ttestrec = SeqIO.parse(testgb, 'genbank')\n\tbetteranno = annotate(testrec)\n\tSeqIO.write(betteranno, 'test1.gb', 'genbank')\n\ttestrec = SeqIO.parse(testgb, 'genbank')\n\tpreserved_anno = annotate(testrec, preserve_anno=True)\n\tSeqIO.write(preserved_anno, 'test2.gb', 'genbank')\n\tut.main()\n", "repo_name": "kemball/karkadann", "sub_path": "unit_test/prodigal_test.py", "file_name": "prodigal_test.py", "file_ext": "py", "file_size_in_byte": 5501, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "attribute"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 15, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 16, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 19, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 22, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 25, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 28, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 46, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 50, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 52, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 56, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 60, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 64, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 68, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 84, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 88, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 90, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 94, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 110, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 114, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 116, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 120, "usage_type": "call"}, {"api_name": "Bio.SeqFeature.FeatureLocation", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 131, "usage_type": "attribute"}, {"api_name": "karkadann.database.data_location", "line_number": 133, "usage_type": "argument"}, {"api_name": "karkadann.database.data_location", "line_number": 167, "usage_type": "argument"}, {"api_name": "unittest.main", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "1758277095", "text": "import argparse\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\n \"-o\", \"--Output\", help=\"The Output File Path\", required=True)\nparser.add_argument(\n \"-i\", \"--Input\", help=\"The Input File Path\", required=True)\nparser.add_argument(\"-e\", \"--ErrorLocation\",\n help=\"The error BEL location which triggers the error in Vivado\", required=True)\n\n\nargs = parser.parse_args()\n\ninputFile = open(args.Input, 'r')\noutputFile = open(args.Output, 'w')\ntargetBELStr = args.ErrorLocation\n\nlines = inputFile.readlines()\n\ntargetLineId = 0\nfor i, line in enumerate(lines):\n if (line.find(targetBELStr) >= 0):\n targetLineId = i\n\nstartPrintOut = False\nfor line in lines[targetLineId:]:\n if (line.find(\"set result \") >= 0):\n startPrintOut = True\n if (startPrintOut):\n print(line, file=outputFile, end='')\n\ninputFile.close()\noutputFile.close()\n", "repo_name": "zslwyuan/AMF-Placer", "sub_path": "benchmarks/helperPythonScripts/removeFailurePartFromTcl.py", "file_name": "removeFailurePartFromTcl.py", "file_ext": "py", "file_size_in_byte": 885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 74, "dataset": "github-code", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "18791819612", "text": "import numpy as np\nimport json\n\nimport Utils\n\n\nclass NeuralNet:\n\t\"\"\"Class representing a set of neural networks,\n\tcontaining the weights and biases of one or more individual networks\"\"\"\n\tdef __init__(self, topography, trainer):\n\t\t# List of on or more neural networks with their weights and biases stored inside numpy arrays\n\t\tself.population = {\"pop\": []}\n\t\tself.topography = topography\n\t\tself.trainer = trainer\n\t\t# Initialize trainer\n\t\tself.trainer.prime(self.population, self.topography, self.loss)\n\n\tdef train(self, sampleList, classList, epochs: int = 1000, displayUpdate: int = 10, verbosity: int = 0, showPlots: bool = False):\n\t\t\"\"\"Trains the neural network with the given parameters along with progress reports for logging\"\"\"\n\t\t# displayUpdate indicates after how many epochs a progress update will be displayed,\n\t\t# showPlots tells whether to show the loss and accuracy graphs\n\t\tself.trainer.train(sampleList, classList, epochs, displayUpdate, verbosity, showPlots)\n\n\tdef test(self, sample, memberId):\n\t\t\"\"\"Returns the output of this neural network\"\"\"\n\t\toutput = sample\n\t\ttry:\n\t\t\t# Calculate output for each layer\n\t\t\tfor layer in range(0, len(self.population[\"pop\"][memberId])):\n\t\t\t\toutput = Utils.sigmoid(np.dot(output, self.population[\"pop\"][memberId][layer]))\n\t\texcept Exception as e:\n\t\t\tprint(\"ERROE: \"+str(e))\n\t\t# Return final output of network\n\t\treturn output\n\n\tdef loss(self, samples, memberId=-1, verbosity=0, displaySamples=None):\n\t\t\"\"\"Calculates the loss of the network over the given sample set and will output useful logging information\"\"\"\n\t\t# memberId is the id of the individual to test, only useful for genetic algorithms,\n\t\t# displaySamples is the numpy array to show during testing. It is useful to have it\n\t\t# be the original data set before any normalization for better viewing\n\t\t# samples[0] is a list of samples with their attributes, samples[1] is a list of the corresponding classes\n\t\tif memberId == -1:\n\t\t\t# For genetic algorithm, set fittnesses of individuals\n\t\t\tself.trainer.setAllFitness(samples)\n\t\t\tmemberId = self.trainer.selection(1)[0]\n\t\t# Get output of network for all samples\n\t\toutput = self.test(samples[0], memberId)\n\t\tcorrect = -1\n\t\t# Verbose logging\n\t\tif verbosity > 0:\n\t\t\t# Number of correct guesses\n\t\t\tcorrect = 0\n\t\t\toutCopy = np.copy(output)\n\t\t\tfor i in range(0, len(output)):\n\t\t\t\t# Guesses class\n\t\t\t\toutChoice = [round(outCopy[i][j]) for j in range(0, len(outCopy[i]))]\n\t\t\t\t# Correct class\n\t\t\t\tcorrectChoice = samples[1][i]\n\t\t\t\tcorrectGuess = True\n\t\t\t\tfor j in range(0, len(outCopy[i])):\n\t\t\t\t\tif outChoice[j] != correctChoice[j]:\n\t\t\t\t\t\tcorrectGuess = False\n\t\t\t\t\t\tbreak\n\t\t\t\tif verbosity > 1:\n\t\t\t\t\t# For linux Green color: \\033[32m, Yellow Color: \\033[93m, Default: \\033[38m\n\t\t\t\t\t# Print text indicating a correct choice or not\n\t\t\t\t\tprint((\"[=======CORRECT=======]\" if correctGuess == 1 else \"[xxxxxxxINCORRECTxxxxxxx]\") +\n\t\t\t\t\t\t \", Correct choice: \" + str(correctChoice))\n\t\t\t\t\tprint(f\"Output: {outCopy[i]}\")\n\t\t\t\t\tif verbosity > 2:\n\t\t\t\t\t\tif not displaySamples:\n\t\t\t\t\t\t\tprint(\"Sample Attributes: \"+str(samples[0][i].tolist()[:-1]))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Sample Attributes: \" + str(displaySamples[0][i].tolist()))\n\t\t\t\tcorrect += correctGuess\n\t\t# Calculate sum of squared errors for loss function\n\t\tloss = 0.5 * np.sum((output - samples[1]) ** 2) / samples[1].shape[0]\n\t\treturn loss, correct / len(output)\n\n\tdef saveWeights(self, fileName):\n\t\t\"\"\"Saves state of the network to a file\"\"\"\n\t\tflatWeights = []\n\t\tfor memberId in range(0, len(self.population[\"pop\"])):\n\t\t\tfor layer in range(0, len(self.population[\"pop\"][memberId])):\n\t\t\t\tfor weight in range(0, len(self.population[\"pop\"][memberId][layer])):\n\t\t\t\t\tflatWeights.append(list(self.population[\"pop\"][memberId][layer][weight]))\n\n\t\twith open(fileName, \"w\") as file:\n\t\t\tfile.write(str(flatWeights))\n\n\tdef loadWeights(self, fileName):\n\t\t\"\"\"Restores state of network from file\"\"\"\n\t\twith open(fileName, \"r\") as file:\n\t\t\tflatWeights = json.loads(file.read())\n\t\tfor memberId in range(0, len(self.population[\"pop\"])):\n\t\t\tfor layer in range(0, len(self.population[\"pop\"][memberId])):\n\t\t\t\tfor weight in range(0, len(self.population[\"pop\"][memberId][layer])):\n\t\t\t\t\tself.population[\"pop\"][memberId][layer][weight] = np.array(flatWeights.pop(0))\n\n\tdef __repr__(self):\n\t\treturn \"NeuralNetwork (\"+str(self.topography)+\")\"\n", "repo_name": "matthew-pisano/ChipFiring", "sub_path": "src/NeuralNet.py", "file_name": "NeuralNet.py", "file_ext": "py", "file_size_in_byte": 4308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "60", "api": [{"api_name": "Utils.sigmoid", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 77, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "14284964022", "text": "import copy\nimport logging\nimport warnings\nfrom typing import Dict, Sequence, Optional, Set, Union, Any, List\n\nfrom slack_sdk.models import show_unknown_key_warning\nfrom slack_sdk.models.basic_objects import (\n JsonObject,\n JsonValidator,\n)\nfrom .basic_components import MarkdownTextObject\nfrom .basic_components import PlainTextObject\nfrom .basic_components import TextObject\nfrom .block_elements import BlockElement\nfrom .block_elements import InputInteractiveElement\nfrom .block_elements import InteractiveElement\n\n\n# -------------------------------------------------\n# Base Classes\n# -------------------------------------------------\n\n\nclass Block(JsonObject):\n \"\"\"Blocks are a series of components that can be combined\n to create visually rich and compellingly interactive messages.\n https://api.slack.com/reference/block-kit/blocks\n \"\"\"\n\n attributes = {\"block_id\", \"type\"}\n block_id_max_length = 255\n logger = logging.getLogger(__name__)\n\n def _subtype_warning(self): # skipcq: PYL-R0201\n warnings.warn(\n \"subtype is deprecated since slackclient 2.6.0, use type instead\",\n DeprecationWarning,\n )\n\n @property\n def subtype(self) -> Optional[str]:\n return self.type\n\n def __init__(\n self,\n *,\n type: Optional[str] = None, # skipcq: PYL-W0622\n subtype: Optional[str] = None, # deprecated\n block_id: Optional[str] = None,\n ):\n if subtype:\n self._subtype_warning()\n self.type = type if type else subtype\n self.block_id = block_id\n self.color = None\n\n @JsonValidator(f\"block_id cannot exceed {block_id_max_length} characters\")\n def _validate_block_id_length(self):\n return self.block_id is None or len(self.block_id) <= self.block_id_max_length\n\n @classmethod\n def parse(cls, block: Union[dict, \"Block\"]) -> Optional[\"Block\"]:\n if block is None: # skipcq: PYL-R1705\n return None\n elif isinstance(block, Block):\n return block\n else:\n if \"type\" in block:\n type = block[\"type\"] # skipcq: PYL-W0622\n if type == SectionBlock.type: # skipcq: PYL-R1705\n return SectionBlock(**block)\n elif type == DividerBlock.type:\n return DividerBlock(**block)\n elif type == ImageBlock.type:\n return ImageBlock(**block)\n elif type == ActionsBlock.type:\n return ActionsBlock(**block)\n elif type == ContextBlock.type:\n return ContextBlock(**block)\n elif type == InputBlock.type:\n return InputBlock(**block)\n elif type == FileBlock.type:\n return FileBlock(**block)\n elif type == CallBlock.type:\n return CallBlock(**block)\n elif type == HeaderBlock.type:\n return HeaderBlock(**block)\n else:\n cls.logger.warning(f\"Unknown block detected and skipped ({block})\")\n return None\n else:\n cls.logger.warning(f\"Unknown block detected and skipped ({block})\")\n return None\n\n @classmethod\n def parse_all(\n cls, blocks: Optional[Sequence[Union[dict, \"Block\"]]]\n ) -> List[\"Block\"]:\n return [cls.parse(b) for b in blocks or []]\n\n\n# -------------------------------------------------\n# Block Classes\n# -------------------------------------------------\n\n\nclass SectionBlock(Block):\n type = \"section\"\n fields_max_length = 10\n text_max_length = 3000\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"text\", \"fields\", \"accessory\"})\n\n def __init__(\n self,\n *,\n block_id: Optional[str] = None,\n text: Union[str, dict, TextObject] = None,\n fields: Sequence[Union[str, dict, TextObject]] = None,\n accessory: Optional[Union[dict, BlockElement]] = None,\n **others: dict,\n ):\n \"\"\"A section is one of the most flexible blocks available.\n https://api.slack.com/reference/block-kit/blocks#section\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.text = TextObject.parse(text)\n field_objects = []\n for f in fields or []:\n if isinstance(f, str):\n field_objects.append(MarkdownTextObject.from_str(f))\n elif isinstance(f, TextObject):\n field_objects.append(f)\n elif isinstance(f, dict) and \"type\" in f:\n d = copy.copy(f)\n t = d.pop(\"type\")\n if t == MarkdownTextObject.type:\n field_objects.append(MarkdownTextObject(**d))\n else:\n field_objects.append(PlainTextObject(**d))\n else:\n self.logger.warning(f\"Unsupported filed detected and skipped {f}\")\n self.fields = field_objects\n self.accessory = BlockElement.parse(accessory)\n\n @JsonValidator(\"text or fields attribute must be specified\")\n def _validate_text_or_fields_populated(self):\n return self.text is not None or self.fields\n\n @JsonValidator(f\"fields attribute cannot exceed {fields_max_length} items\")\n def _validate_fields_length(self):\n return self.fields is None or len(self.fields) <= self.fields_max_length\n\n @JsonValidator(f\"text attribute cannot exceed {text_max_length} characters\")\n def _validate_alt_text_length(self):\n return self.text is None or len(self.text.text) <= self.text_max_length\n\n\nclass DividerBlock(Block):\n type = \"divider\"\n\n def __init__(\n self,\n *,\n block_id: Optional[str] = None,\n **others: dict,\n ):\n \"\"\"A content divider, like an
, to split up different blocks inside of a message.\n https://api.slack.com/reference/block-kit/blocks#divider\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n\nclass ImageBlock(Block):\n type = \"image\"\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"alt_text\", \"image_url\", \"title\"})\n\n image_url_max_length = 3000\n alt_text_max_length = 2000\n title_max_length = 2000\n\n def __init__(\n self,\n *,\n image_url: str,\n alt_text: str,\n title: Optional[Union[str, dict, TextObject]] = None,\n block_id: Optional[str] = None,\n **others: dict,\n ):\n \"\"\"A simple image block, designed to make those cat photos really pop.\n https://api.slack.com/reference/block-kit/blocks#image\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.image_url = image_url\n self.alt_text = alt_text\n self.title = TextObject.parse(title)\n\n @JsonValidator(\n f\"image_url attribute cannot exceed {image_url_max_length} characters\"\n )\n def _validate_image_url_length(self):\n return len(self.image_url) <= self.image_url_max_length\n\n @JsonValidator(f\"alt_text attribute cannot exceed {alt_text_max_length} characters\")\n def _validate_alt_text_length(self):\n return len(self.alt_text) <= self.alt_text_max_length\n\n @JsonValidator(f\"title attribute cannot exceed {title_max_length} characters\")\n def _validate_title_length(self):\n return (\n self.title is None\n or self.title.text is None\n or len(self.title.text) <= self.title_max_length\n )\n\n\nclass ActionsBlock(Block):\n type = \"actions\"\n elements_max_length = 5\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"elements\"})\n\n def __init__(\n self,\n *,\n elements: Sequence[Union[dict, InteractiveElement]],\n block_id: Optional[str] = None,\n **others: dict,\n ):\n \"\"\"A block that is used to hold interactive elements.\n https://api.slack.com/reference/block-kit/blocks#actions\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.elements = BlockElement.parse_all(elements)\n\n @JsonValidator(f\"elements attribute cannot exceed {elements_max_length} elements\")\n def _validate_elements_length(self):\n return self.elements is None or len(self.elements) <= self.elements_max_length\n\n\nclass ContextBlock(Block):\n type = \"context\"\n elements_max_length = 10\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"elements\"})\n\n def __init__(\n self,\n *,\n elements: Sequence[Union[dict, ImageBlock, TextObject]],\n block_id: Optional[str] = None,\n **others: dict,\n ):\n \"\"\"Displays message context, which can include both images and text.\n https://api.slack.com/reference/block-kit/blocks#context\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.elements = BlockElement.parse_all(elements)\n\n @JsonValidator(f\"elements attribute cannot exceed {elements_max_length} elements\")\n def _validate_elements_length(self):\n return self.elements is None or len(self.elements) <= self.elements_max_length\n\n\nclass InputBlock(Block):\n type = \"input\"\n label_max_length = 2000\n hint_max_length = 2000\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union(\n {\"label\", \"hint\", \"element\", \"optional\", \"dispatch_action\"}\n )\n\n def __init__(\n self,\n *,\n label: Union[str, dict, PlainTextObject],\n element: Union[str, dict, InputInteractiveElement],\n block_id: Optional[str] = None,\n hint: Optional[Union[str, dict, PlainTextObject]] = None,\n dispatch_action: Optional[bool] = None,\n optional: Optional[bool] = None,\n **others: dict,\n ):\n \"\"\"A block that collects information from users - it can hold a plain-text input element,\n a select menu element, a multi-select menu element, or a datepicker.\n Important Note: Input blocks are only available in modals.\n https://api.slack.com/reference/block-kit/blocks#input\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.label = TextObject.parse(label, default_type=PlainTextObject.type)\n self.element = BlockElement.parse(element)\n self.hint = TextObject.parse(hint, default_type=PlainTextObject.type)\n self.dispatch_action = dispatch_action\n self.optional = optional\n\n @JsonValidator(f\"label attribute cannot exceed {label_max_length} characters\")\n def _validate_label_length(self):\n return (\n self.label is None\n or self.label.text is None\n or len(self.label.text) <= self.label_max_length\n )\n\n @JsonValidator(f\"hint attribute cannot exceed {hint_max_length} characters\")\n def _validate_hint_length(self):\n return (\n self.hint is None\n or self.hint.text is None\n or len(self.hint.text) <= self.label_max_length\n )\n\n @JsonValidator(\n (\n \"element attribute must be a string, select element, multi-select element, \"\n \"or a datepicker. (Sub-classes of InputInteractiveElement)\"\n )\n )\n def _validate_element_type(self):\n return self.element is None or isinstance(\n self.element, (str, InputInteractiveElement)\n )\n\n\nclass FileBlock(Block):\n type = \"file\"\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"external_id\", \"source\"})\n\n def __init__(\n self,\n *,\n external_id: str,\n source: str = \"remote\",\n block_id: Optional[str] = None,\n **others: dict,\n ):\n \"\"\"Displays a remote file.\n https://api.slack.com/reference/block-kit/blocks#file\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.external_id = external_id\n self.source = source\n\n\nclass CallBlock(Block):\n type = \"call\"\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"call_id\", \"api_decoration_available\", \"call\"})\n\n def __init__(\n self,\n *,\n call_id: str,\n api_decoration_available: Optional[bool] = None,\n call: Optional[Dict[str, Dict[str, Any]]] = None,\n block_id: Optional[str] = None,\n **others: dict,\n ):\n \"\"\"Displays a call information\n https://api.slack.com/reference/block-kit/blocks#call\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.call_id = call_id\n self.api_decoration_available = api_decoration_available\n self.call = call\n\n\nclass HeaderBlock(Block):\n type = \"header\"\n text_max_length = 150\n\n @property\n def attributes(self) -> Set[str]:\n return super().attributes.union({\"text\"})\n\n def __init__(\n self,\n *,\n block_id: Optional[str] = None,\n text: Union[str, dict, TextObject] = None,\n **others: dict,\n ):\n \"\"\"A header is a plain-text block that displays in a larger, bold font.\n https://api.slack.com/reference/block-kit/blocks#header\n \"\"\"\n super().__init__(type=self.type, block_id=block_id)\n show_unknown_key_warning(self, others)\n\n self.text = TextObject.parse(text, default_type=PlainTextObject.type)\n\n @JsonValidator(\"text attribute must be specified\")\n def _validate_text(self):\n return self.text is not None\n\n @JsonValidator(f\"text attribute cannot exceed {text_max_length} characters\")\n def _validate_alt_text_length(self):\n return self.text is None or len(self.text.text) <= self.text_max_length\n", "repo_name": "cloud-sniper/cloud-sniper", "sub_path": "terraform/stacks/bot/lambdas/python/slack_automation_bot/slack_sdk copy/models/blocks/blocks.py", "file_name": "blocks.py", "file_ext": "py", "file_size_in_byte": 14211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 176, "dataset": "github-code", "pt": "60", "api": [{"api_name": "slack_sdk.models.basic_objects.JsonObject", "line_number": 24, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 41, "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": "typing.Optional", "line_number": 49, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 120, "usage_type": "name"}, {"api_name": "basic_components.TextObject", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 121, "usage_type": "name"}, {"api_name": "basic_components.TextObject", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 122, "usage_type": "name"}, {"api_name": "block_elements.BlockElement", "line_number": 122, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 129, "usage_type": "call"}, {"api_name": "basic_components.TextObject.parse", "line_number": 131, "usage_type": "call"}, {"api_name": "basic_components.TextObject", "line_number": 131, "usage_type": "name"}, {"api_name": "basic_components.MarkdownTextObject.from_str", "line_number": 135, "usage_type": "call"}, {"api_name": "basic_components.MarkdownTextObject", "line_number": 135, "usage_type": "name"}, {"api_name": "basic_components.TextObject", "line_number": 136, "usage_type": "argument"}, {"api_name": "copy.copy", "line_number": 139, "usage_type": "call"}, {"api_name": "basic_components.MarkdownTextObject.type", "line_number": 141, "usage_type": "attribute"}, {"api_name": "basic_components.MarkdownTextObject", "line_number": 141, "usage_type": "name"}, {"api_name": "basic_components.MarkdownTextObject", "line_number": 142, "usage_type": "call"}, {"api_name": "basic_components.PlainTextObject", "line_number": 144, "usage_type": "call"}, {"api_name": "block_elements.BlockElement.parse", "line_number": 148, "usage_type": "call"}, {"api_name": "block_elements.BlockElement", "line_number": 148, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 150, "usage_type": "call"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 154, "usage_type": "call"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 158, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 169, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 176, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 183, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 195, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 195, "usage_type": "name"}, {"api_name": "basic_components.TextObject", "line_number": 195, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 196, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 203, "usage_type": "call"}, {"api_name": "basic_components.TextObject.parse", "line_number": 207, "usage_type": "call"}, {"api_name": "basic_components.TextObject", "line_number": 207, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 209, "usage_type": "call"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 215, "usage_type": "call"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 219, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 233, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 239, "usage_type": "name"}, {"api_name": "block_elements.InteractiveElement", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 240, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 247, "usage_type": "call"}, {"api_name": "block_elements.BlockElement.parse_all", "line_number": 249, "usage_type": "call"}, {"api_name": "block_elements.BlockElement", "line_number": 249, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 251, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 261, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 267, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 267, "usage_type": "name"}, {"api_name": "basic_components.TextObject", "line_number": 267, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 268, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 275, "usage_type": "call"}, {"api_name": "block_elements.BlockElement.parse_all", "line_number": 277, "usage_type": "call"}, {"api_name": "block_elements.BlockElement", "line_number": 277, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 279, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 290, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 298, "usage_type": "name"}, {"api_name": "basic_components.PlainTextObject", "line_number": 298, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 299, "usage_type": "name"}, {"api_name": "block_elements.InputInteractiveElement", "line_number": 299, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 300, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 301, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 301, "usage_type": "name"}, {"api_name": "basic_components.PlainTextObject", "line_number": 301, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 302, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 303, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 312, "usage_type": "call"}, {"api_name": "basic_components.TextObject.parse", "line_number": 314, "usage_type": "call"}, {"api_name": "basic_components.TextObject", "line_number": 314, "usage_type": "name"}, {"api_name": "basic_components.PlainTextObject.type", "line_number": 314, "usage_type": "attribute"}, {"api_name": "basic_components.PlainTextObject", "line_number": 314, "usage_type": "name"}, {"api_name": "block_elements.BlockElement.parse", "line_number": 315, "usage_type": "call"}, {"api_name": "block_elements.BlockElement", "line_number": 315, "usage_type": "name"}, {"api_name": "basic_components.TextObject.parse", "line_number": 316, "usage_type": "call"}, {"api_name": "basic_components.TextObject", "line_number": 316, "usage_type": "name"}, {"api_name": "basic_components.PlainTextObject.type", "line_number": 316, "usage_type": "attribute"}, {"api_name": "basic_components.PlainTextObject", "line_number": 316, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 320, "usage_type": "call"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 328, "usage_type": "call"}, {"api_name": "block_elements.InputInteractiveElement", "line_number": 344, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 336, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 352, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 360, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 367, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 377, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 384, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 385, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 385, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 385, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 386, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 393, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 405, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 411, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 412, "usage_type": "name"}, {"api_name": "basic_components.TextObject", "line_number": 412, "usage_type": "name"}, {"api_name": "slack_sdk.models.show_unknown_key_warning", "line_number": 419, "usage_type": "call"}, {"api_name": "basic_components.TextObject.parse", "line_number": 421, "usage_type": "call"}, {"api_name": "basic_components.TextObject", "line_number": 421, "usage_type": "name"}, {"api_name": "basic_components.PlainTextObject.type", "line_number": 421, "usage_type": "attribute"}, {"api_name": "basic_components.PlainTextObject", "line_number": 421, "usage_type": "name"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 423, "usage_type": "call"}, {"api_name": "slack_sdk.models.basic_objects.JsonValidator", "line_number": 427, "usage_type": "call"}]} +{"seq_id": "27971105473", "text": "# Version 3.6.1\nfrom tia.log import *\nfrom datetime import datetime, timedelta\nimport timesale as timesale\nimport tickprocessor as tickprocessor\n#import strategies.s_momentum as strat\nimport strategies.s_momentum_short_sell_profit_or_loss as strat\nimport trade as tradeUtil\nimport sys\n\nOFFSET_IN_DAYS = int(sys.argv[2]) # How many days back from current day\nBAR_INTERVAL = 0.5 # In minutes\nQTY = 10\n\nconfig = {\n 'trade_quantity' : 50,\n 'bar_interval_in_mins' : 5/60,\n 'use_own_date' : False,\n 'is_backtest' : True,\n 'backtest_offset_days' : OFFSET_IN_DAYS,\n 'max_spread' : 0.10,\n }\n\n# CURRENT LOG LEVEL\nlog().setLevel(\"INFO\")\nstrategy = strat.MOMENTUM_SHORT_SELL_PROFIT_OR_LOSS(config)\n\n\n\n\n###########################################################################################################################################\n##################################### DO NOT TOUCH BELOW THE FOLLOWING ##########################################################################\n###########################################################################################################################################\ndt = datetime.today() - timedelta(days=OFFSET_IN_DAYS)\nstart = datetime.now().replace(month=dt.month, day=dt.day, hour=9, minute=30,second=0,microsecond=0)\nend = datetime.now().replace(month=dt.month, day=dt.day, hour=16, minute=00,second=0,microsecond=0)\nprint(start)\nprint(end)\n\nstart_input = start.strftime(\"%Y-%m-%d %H:%M\")\nend_input = end.strftime(\"%Y-%m-%d %H:%M\")\ntradeUtil.DO_NOT_TRADE = True\n\n\n\ndef run_backtest(symbol):\n # Critical for ANY TEST RUN\n global start_input,end_input, end\n tradeUtil.DO_NOT_TRADE = True\n dt = []\n unrealizedPnL = []\n realizedPnL = []\n price = []\n maxLoss = 0\n maxCost = 0\n for endHour in range(10,17):\n end = end.replace(hour=endHour)\n end_input = end.strftime(\"%Y-%m-%d %H:%M\")\n print(\"end_input\", end_input)\n timesales = timesale.getTimeSale(symbol, start_input, end_input)['series']['data']\n for i in timesales:\n # Convert it to actual timesale format\n tick = {'symbol' : symbol, 'type' : 'timesale', 'last' : i['price'], 'size' : i['volume'], 'date' : i['timestamp'], 'bid' : i['price'], 'ask' : i['price']}\n # handle the tick here\n strategy.hndl([tick])\n\n # From here on, get the corresponding broker and find out running pnL etc\n ac = strategy.getBroker().getAC(symbol)\n d = datetime.fromtimestamp(int(i['timestamp'])/1000)\n dt.append(d)\n unrealizedPnL.append(ac.positions * i['price'] - ac.cost)\n maxLoss = max(ac.cost - ac.positions * i['price'], maxLoss)\n maxCost = max(ac.cost, maxCost)\n realizedPnL.append(ac.realizedPnL)\n price.append(i['price'])\n start_input = end_input\n strategy.getProcessor().getTickStore(symbol).getTickBars().printBars()\n file1 = open('BACKTEST_RESULTS', 'a',)\n res = symbol + \"OFFSET :\" + str(OFFSET_IN_DAYS) + \", UNREAL :\" + str(unrealizedPnL[-1]) + \", Real :\" + str(realizedPnL[-1]) + \", MAX drawdown :\" + str(maxLoss) + \", maxCost: \" + str(maxCost) + \"\\n\"\n print(res)\n file1.write(res)\n file1.close()\n strategy.getProcessor().getTickStore(symbol).getTickBars().plotBars()\n\n '''\n import plotly.graph_objects as go\n import pandas as pd\n import plotly.express as px\n\n plot = px.scatter(x=dt, y=realizedPnL)\n plot.show()\n pd.options.plotting.backend = \"plotly\"\n df = pd.DataFrame(dict(unrealPnL=unrealizedPnL,realizedPnL=realizedPnL))\n fig = df.plot()\n fig.show()\n '''\n\n\n\nrun_backtest(sys.argv[1])\n\n'''\n# USAGE\nfor d in 2 3 4; do for sym in \"AMC\" \"TAL\" \"WKHS\" \"NKLA\" \"RIDE\"; do echo $sym $d; python3 backtest.py $sym $d; done; done;\npython3 backtest.py FB 3\n'''\n", "repo_name": "perabloom/tradStrategier", "sub_path": "backtest.py", "file_name": "backtest.py", "file_ext": "py", "file_size_in_byte": 3843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "strategies.s_momentum_short_sell_profit_or_loss.MOMENTUM_SHORT_SELL_PROFIT_OR_LOSS", "line_number": 26, "usage_type": "call"}, {"api_name": "strategies.s_momentum_short_sell_profit_or_loss", "line_number": 26, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "trade.DO_NOT_TRADE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "trade.DO_NOT_TRADE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "timesale.getTimeSale", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 100, "usage_type": "attribute"}]} +{"seq_id": "25170492942", "text": "\"\"\"\nimapintingにより修正された画像と、修正された領域を示すmask画像のペアを作成するプログラム\n\"\"\"\nimport cv2\nimport neuralgym as ng\nimport numpy as np\nimport tensorflow as tf\nfrom inpaint_model import InpaintCAModel\n\nclass MyDataSet():\n \"\"\"\n PATHを受取り、画像へのパスのリストを作成\n get__item__()された際にパスのリストから画像を読み取り、returnする\n\n つまり、指定されたindexの画像さえ返せば良いので、\n ディレクトリ構造に基づいて改造されたし\n \"\"\"\n def __init__(self, INPUT_IMAGE_DIR_PATH, INPUT_IMAGE_LIST_FILE, SIZE_N):\n \"\"\"IMAGE_PATH_LISTに画像へのパスを格納\"\"\"\n self.SIZE_N = SIZE_N\n with open(INPUT_IMAGE_DIR_PATH + INPUT_IMAGE_LIST_FILE) as f:\n self.IMAGE_PATH_LIST = f.read().splitlines()\n \n self.num = len(self.IMAGE_PATH_LIST)\n for i in range(self.num):\n self.IMAGE_PATH_LIST[i] = INPUT_IMAGE_DIR_PATH + \"train/\" + self.IMAGE_PATH_LIST[i].partition(' ')[0]\n\n def __len__(self):\n \"\"\"画像の枚数を返す\"\"\"\n return self.num\n\n def __getitem__(self, idx):\n \"\"\"idx番目の画像を読み込み、returnする\"\"\"\n image = cv2.imread(str(self.IMAGE_PATH_LIST[idx]))\n image = cv2.resize(image, dsize=(self.SIZE_N, self.SIZE_N))\n return image\n \n\ndef make_mask(SIZE_N, DIV = 2):\n \"\"\"\n maskを作成\n imageと同じ大きさの真っ黒の画像を作り、\n ランダムな領域を白くしてmaskを作り返す\n \"\"\"\n mask = np.zeros((SIZE_N, SIZE_N, 3)) \n\n new_height = SIZE_N//DIV\n new_width = SIZE_N//DIV\n\n w = np.random.randint(0, SIZE_N-new_width)\n h = np.random.randint(0, SIZE_N-new_height)\n\n cv2.rectangle(mask, (w, h), (w+new_width, h+new_height), (255, 255, 255), -1)\n\n return mask\n\n\ndef impainting(image, mask):\n \"\"\"\n imageinpaintingにより、imageからmaskの領域を削除・補完する\n 以上の修正がされたimpainting_imgを返す\n \"\"\"\n FLAGS = ng.Config('inpaint.yml')\n # ng.get_gpus(1)\n model = InpaintCAModel()\n #imgとmaskをmodelに適用してimageinpainting画像を作成\n mask = cv2.resize(mask, dsize=(image.shape[1], image.shape[0]))\n print(image.shape)\n print(mask.shape)\n assert image.shape == mask.shape\n\n h, w, _ = image.shape\n grid = 8\n image = image[:h//grid*grid, :w//grid*grid, :]\n mask = mask[:h//grid*grid, :w//grid*grid, :]\n mask2 = mask\n print('Shape of image: {}'.format(image.shape))\n\n image = np.expand_dims(image, 0)\n mask = np.expand_dims(mask, 0)\n input_image = np.concatenate([image, mask], axis=2)\n\n tf.reset_default_graph()\n sess_config = tf.ConfigProto()\n sess_config.gpu_options.allow_growth = True\n with tf.Session(config=sess_config) as sess:\n input_image = tf.constant(input_image, dtype=tf.float32)\n output = model.build_server_graph(FLAGS, input_image)\n output = (output + 1.) * 127.5\n output = tf.reverse(output, [-1])\n output = tf.saturate_cast(output, tf.uint8)\n # load pretrained model\n vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\n assign_ops = []\n for var in vars_list:\n vname = var.name\n from_name = vname\n var_value = tf.contrib.framework.load_variable(\"model_logs\", from_name)\n assign_ops.append(tf.assign(var, var_value))\n sess.run(assign_ops)\n print('Model loaded.')\n result = sess.run(output)\n result = result[0][:, :, ::-1]\n \n impainting_img = result\n return impainting_img\n\n\ndef true_mask(impainting_img, image):\n \"\"\"\n impainting_imgとimageの差分をとり、大きく修正された領域を求める\n 結果を白黒化し、newmaskとして返す\n \"\"\"\n N = 3\n image_blur = cv2.blur(image, (N, N))\n impaiting_img_blur = cv2.blur(impainting_img, (N, N))\n\n newmask =np.abs(image_blur - impaiting_img_blur)\n newmask = cv2.cvtColor(newmask, cv2.COLOR_BGR2GRAY)\n shikiiti, newmask = cv2.threshold(newmask, 20, 255, cv2.THRESH_BINARY)\n\n newmask = cv2.merge((newmask, newmask, newmask))\n return newmask\n\nif __name__ == \"__main__\":\n \n #データへのPATH\n INPUT_IMAGE_DIR_PATH = \"\"\n INPUT_IMAGE_LIST_FILE = \"train.txt\"\n\n #画像の大きさ\n SIZE_N = 256\n\n #保存先のPATH\n DESTINATION_DIR_PATH = \"\"\n\n #データセットの作成:datastet[i]でi番目の画像を取得できる\n dataset = MyDataSet(INPUT_IMAGE_DIR_PATH, INPUT_IMAGE_LIST_FILE, SIZE_N)\n\n #欲しい画像の枚数分ループ\n for i in range(10):\n \n #i番目の画像と、ランダムなmaskを取得\n image = dataset[i]\n mask = make_mask(SIZE_N)\n \n #imageとmaskを使ってimageinpaintingを行う\n impainting_img = impainting(image, mask)\n\n #ture_maskのためにデータ型をfloatに変換\n image = image.astype(np.float32)\n impainting_img = impainting_img.astype(np.float32)\n\n #impaintingにより大きく修正された領域を示すnewmaskを作成\n newmask = true_mask(impainting_img, image)\n \n #impainting_imgとtrue_maskを横に結合\n data = cv2.hconcat([impainting_img, newmask])\n \n #DESTINATION_DIR_PATHに\"data_i.png\"として保存\n cv2.imwrite(DESTINATION_DIR_PATH +\"data_\"+str(i)+\".png\", data)", "repo_name": "forexpts/Make_Impainting_Data", "sub_path": "make_impainting_data.py", "file_name": "make_impainting_data.py", "file_ext": "py", "file_size_in_byte": 5507, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "cv2.imread", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 53, "usage_type": "call"}, {"api_name": "neuralgym.Config", "line_number": 63, "usage_type": "call"}, {"api_name": "inpaint_model.InpaintCAModel", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.reverse", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.saturate_cast", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.framework.load_variable", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.assign", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.blur", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.blur", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.merge", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 151, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 152, "usage_type": "attribute"}, {"api_name": "cv2.hconcat", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "866506803", "text": "#!/usr/bin/python3\n\nfrom ctypes import util\nimport sys, subprocess, os, pathlib, argparse, shutil, glob\n\nimport utils\n\n# Run cmake command. the args is list of arguments.\ndef cmake(build_dir, cmdline):\n if not isinstance(cmdline, list):\n cmdline = str(cmdline).split()\n cmd = [\"cmake\"] + cmdline\n print(' '.join(cmd))\n try:\n subprocess.check_call(cmd, cwd=build_dir)\n except subprocess.CalledProcessError as err:\n print(f\"[ERROR] cmake failed. ErrorCode = {err.returncode}\")\n sys.exit(err.returncode)\n\ndef git(cmdline):\n if not isinstance(cmdline, list):\n cmdline = str(cmdline).split()\n cmd = [\"git\"] + cmdline\n print(' '.join(cmd))\n try:\n subprocess.check_call(cmd, cwd=sdk_root_dir)\n except:\n print(\"[ERROR] GIT process exits with non-zero exit code.\")\n sys.exit(1)\n\ndef update_submodules():\n submodules = [\n # list all submodules here to automatically fetch them as part of the build process.\n ]\n for s in submodules:\n dir = sdk_root_dir / s\n if not dir.is_dir():\n utils.rip(f\"{dir} not found. your working directory might be corrupted. Please consider re-cloning.\")\n items = dir.iterdir()\n if len(list(items)) == 0 :\n git(\"submodule update --init\")\n break\n\ndef cmake_config(args, build_dir, build_type):\n update_submodules()\n os.makedirs(build_dir, exist_ok=True)\n config = f\"-S {sdk_root_dir} -B {build_dir} -DCMAKE_BUILD_TYPE={build_type}\"\n if args.android_build:\n # Support only arm64 for now\n sdk = pathlib.Path(os.getenv('ANDROID_SDK_ROOT'))\n ndk = sdk / \"ndk/23.1.7779620\"\n if 'nt' == os.name:\n ninja = sdk / \"cmake/3.18.1/bin/ninja.exe\"\n if not ninja.exists(): utils.rip(f\"{ninja} not found. Please install cmake 3.18+ via Android SDK Manager.\" )\n else:\n ninja = \"ninja\"\n if not ndk.is_dir(): utils.rip(f\"{ndk} folder not found.\")\n toolchain = ndk / \"build/cmake/android.toolchain.cmake\"\n config += f\" \\\n -GNinja \\\n -DCMAKE_MAKE_PROGRAM={ninja} \\\n -DCMAKE_SYSTEM_NAME=Android \\\n -DANDROID_NDK={ndk} \\\n -DCMAKE_ANDROID_NDK={ndk} \\\n -DCMAKE_TOOLCHAIN_FILE={toolchain} \\\n -DANDROID_NATIVE_API_LEVEL=29 \\\n -DCMAKE_SYSTEM_VERSION=29 \\\n -DANDROID_PLATFORM=android-29 \\\n -DANDROID_ABI=arm64-v8a \\\n -DCMAKE_ANDROID_ARCH_ABI=arm64-v8a \\\n \"\n elif ('nt' != os.name) and (not args.use_makefile) :\n config += \" -GNinja\"\n cmake(build_dir, config)\n\n# ==========\n# main\n# ==========\n\n# parse command line arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-a\", dest=\"android_build\", action=\"store_true\", help=\"Build for Android\")\nap.add_argument(\"-b\", dest=\"build_dir\", default=\"build\", help=\"Build output folder.\")\nap.add_argument(\"-c\", dest=\"config_only\", action=\"store_true\", help=\"Run CMake config only. Skip cmake build.\")\nap.add_argument(\"-C\", dest=\"skip_config\", action=\"store_true\", help=\"Skip CMake config. Run build process only.\")\nap.add_argument(\"-m\", dest=\"use_makefile\", action=\"store_true\", help=\"Use OS's default makefile instead of Ninja\")\nap.add_argument(\"variant\", help=\"Specify build variant. Acceptable values are: d(ebug)/p(rofile)/r(elease)/c(lean). \"\n \"Note that all parameters alert this one will be considered \\\"extra\\\" and passed to CMake directly.\")\nap.add_argument(\"extra\", nargs=argparse.REMAINDER, help=\"Extra arguments passing to cmake.\")\nargs = ap.parse_args()\n#print(args.extra)\n\n# get the root directory of the code base\nsdk_root_dir = utils.get_sdk_root_folder()\n# print(f\"PhysRay-SDK root folder = {sdk_root_dir}\")\n\n# get cmake build variant and build folder\nbuild_type, build_dir = utils.get_cmake_build_type(args.variant, args.build_dir, args.android_build)\n\nif build_type is None:\n if os.name == \"nt\":\n os.system('taskkill /f /im java.exe 2>nul')\n else:\n # TODO: kill java process the Linux way.\n pass\n folders = glob.glob(str(sdk_root_dir / \"build*\"))\n for x in folders:\n print(f\"rm {x}\")\n shutil.rmtree(x)\nelse:\n if not args.skip_config:\n cmake_config(args, build_dir, build_type)\n if not args.config_only:\n jobs = [\"-j8\"] # limit to 8 cores.\n cmake(build_dir, [\"--build\", \".\"] + jobs + [\"--config\", build_type] + args.extra)\n # if args.install_destination_folder:\n # inst_dir = str(pathlib.Path(args.install_destination_folder).absolute())\n # cmake(build_dir, [\"--install\", \".\", \"--config\", build_type, \"--prefix\", inst_dir] + args.extra)\n", "repo_name": "randomgraphics/garnet", "sub_path": "env/bin/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 4737, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "subprocess.check_call", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 29, "usage_type": "call"}, {"api_name": "utils.rip", "line_number": 38, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 46, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 50, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 50, "usage_type": "call"}, {"api_name": "os.name", "line_number": 52, "usage_type": "attribute"}, {"api_name": "utils.rip", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.rip", "line_number": 57, "usage_type": "call"}, {"api_name": "os.name", "line_number": 72, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 81, "usage_type": "call"}, {"api_name": "argparse.REMAINDER", "line_number": 89, "usage_type": "attribute"}, {"api_name": "utils.get_sdk_root_folder", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.get_cmake_build_type", "line_number": 98, "usage_type": "call"}, {"api_name": "os.name", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 102, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 106, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "7174379142", "text": "from utils import create_dataframe\nfrom bs4 import BeautifulSoup\nimport requests\nfrom datetime import datetime\nimport logging\n\n\ndef fenicio(webdriver_path=None, table_id=None):\n\n logging.getLogger().setLevel(logging.INFO)\n\n stores = [\n # {\"store\": \"Alien Store\",\n # \"link\": \"https://alienstore.uy/catalogo\",\n # \"link_scroll\": \"https://alienstore.uy/catalogo?js=1&pag={}\",\n # \"pages\": 32},\n {\"store\": \"Divino Fenicio\",\n \"link\": \"https://www.divino.com.uy/catalogo\",\n \"link_scroll\": \"https://www.divino.com.uy/catalogo?ord=prd&js=1&pag={}\",\n \"pages\": 10},\n {\"store\": \"Route 66\",\n \"link\": \"https://route66.com.uy/catalogo\",\n \"link_scroll\": \"https://route66.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 20},\n {\"store\": \"FORUS\",\n \"link\": \"https://www.forus.uy/catalogo\",\n \"link_scroll\": \"https://www.forus.uy/catalogo?js=1&pag={}\",\n \"pages\": 34},\n {\"store\": \"El Emporio del Hogar\",\n \"link\": \"https://www.elemporiodelhogar.com.uy/catalogo\",\n \"link_scroll\": \"https://www.elemporiodelhogar.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 15},\n # {\"store\": \"La Dolfina\",\n # \"link\": \"https://www.ladolfinapolo.com.uy/catalogo\",\n # \"link_scroll\": \"https://www.ladolfinapolo.com.uy/catalogo?js=1&pag={}\",\n # \"pages\": 12},\n {\"store\": \"Le Blanc\",\n \"link\": \"https://www.leblanc.com.uy/catalogo\",\n \"link_scroll\": \"https://www.leblanc.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 20},\n # {\"store\": \"Legacy\",\n # \"link\": \"https://www.legacy.com.uy/catalogo\",\n # \"link_scroll\": \"https://www.legacy.com.uy/catalogo?js=1&pag={}\",\n # \"pages\": 94},\n {\"store\": \"Piece Of Cake\",\n \"link\": \"https://www.pieceofcake.com.uy/catalogo\",\n \"link_scroll\": \"https://www.pieceofcake.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 19},\n {\"store\": \"Mini So\",\n \"link\": \"https://www.minisouruguay.com.uy/catalogo\",\n \"link_scroll\": \"https://www.minisouruguay.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 10},\n {\"store\": \"Symphorine\",\n \"link\": \"https://symphorine.com.uy/productos\",\n \"link_scroll\": \"https://symphorine.com.uy/productos?js=1&pag={}\",\n \"pages\": 10},\n # {\"store\": \"Zurra Natural Leather\",\n # \"link\": \"https://www.zurraleather.com./catalogo\",\n # \"link_scroll\": \"https://www.zurraleather.com./catalogo?js=1&pag={}\",\n # \"pages\": 4},\n {\"store\": \"Nstore\",\n \"link\": \"https://nstore.com.uy/catalogo\",\n \"link_scroll\": \"https://nstore.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 15},\n {\"store\": \"Universo Binario\",\n \"link\": \"https://universobinario.com/catalogo\",\n \"link_scroll\": \"https://universobinario.com/catalogo?js=1&pag={}\",\n \"pages\": 10},\n # \"pages\": 217},\n {\"store\": \"Tienda We Play\",\n \"link\": \"https://tiendaweplay.com.uy/catalogo\",\n \"link_scroll\": \"https://tiendaweplay.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 15},\n {\"store\": \"name it\",\n \"link\": \"https://nameit.com.uy/catalogo\",\n \"link_scroll\": \"https://nameit.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 38},\n {\"store\": \"Veroca Joyas\",\n \"link\": \"https://www.verocajoyas.com.uy/catalogo\",\n \"link_scroll\": \"https://www.verocajoyas.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 47},\n {\"store\": \"Deco Hogar\",\n \"link\": \"https://www.decohogar.com.uy/catalogo\",\n \"link_scroll\": \"https://www.decohogar.com.uy/catalogo?js=1&pag={}\",\n # \"pages\": 379\n \"pages\": 100},\n {\"store\": \"Daniel Cassin\",\n \"link\": \"https://www.danielcassin.com.uy/catalogo\",\n \"link_scroll\": \"https://www.danielcassin.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 37},\n {\"store\": \"Da Pie\",\n \"link\": \"https://dapie.com.uy/catalogo\",\n \"link_scroll\": \"https://dapie.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 13},\n {\"store\": \"Boutique Erotica\",\n \"link\": \"https://www.boutiqueerotica.com.uy/catalogo\",\n \"link_scroll\": \"https://www.boutiqueerotica.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 54},\n {\"store\": \"Basefield\",\n \"link\": \"https://www.bsf.com.uy/catalogo\",\n \"link_scroll\": \"https://www.bsf.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 12},\n {\"store\": \"Adam Tailor\",\n \"link\": \"https://www.adamtailor.com.uy/catalogo\",\n \"link_scroll\": \"https://www.adamtailor.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 23},\n {\"store\": \"CUATROASES\",\n \"link\": \"https://cuatroases.com.uy/catalogo\",\n \"link_scroll\": \"https://cuatroases.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 10},\n {\"store\": \"Club House\",\n \"link\": \"https://www.clubhouse.com.uy/catalogo\",\n \"link_scroll\": \"https://www.clubhouse.com.uy/catalogo?js=1&pag={}\",\n \"pages\": 10},\n ]\n\n timestamp = []\n URL = []\n price = []\n price_lista = []\n store_name = []\n product_ID = []\n currency = []\n\n logging.info('Scraping set to Start for Fenicio Stores at {}'.format(datetime.now()))\n\n for store in stores:\n store_ID = store[\"store\"]\n\n for page in range(1, store[\"pages\"] + 1):\n link = store[\"link_scroll\"].format(page)\n content = requests.get(link)\n if content.reason == 'Not Found':\n logging.error('Error for store {} with status code {}'.format(store, content.status_code))\n\n soup = BeautifulSoup(content.text, 'html.parser')\n all_products = soup.find_all('div', {'data-disp': \"1\"})\n\n for product in all_products:\n exectution_date = datetime.utcnow()\n execution_date = exectution_date.strftime(\"%m/%d/%Y %H:%M:%S\")\n\n product_price_venta = product.find('strong', {'class': \"precio venta\"})\n product_price = float(\n product_price_venta.find('span', {'class': 'monto'}).get_text().replace('.', '').replace(',', '.'))\n product_currency = product_price_venta.find('span', {'class': 'sim'})\\\n .get_text()\\\n .replace('USD', 'U$S')\\\n .replace('UYU', '$')\n\n product_price_lista = product.find('del', {'class': \"precio lista\"})\n\n if product_price_lista is not None:\n price_lista_ = float(\n product_price_lista.find('span',\n {'class': 'monto'}).get_text().replace('.', '').replace(',', '.'))\n else:\n price_lista_ = product_price\n\n product_name = product.find('a', {'class': 'tit'}).get_text()\n product_link = product.find('a', {'class': 'tit'})['href']\n\n timestamp.append(execution_date)\n URL.append(product_link)\n price.append(product_price)\n store_name.append(store_ID)\n product_ID.append(product_name)\n currency.append(product_currency)\n price_lista.append(price_lista_)\n\n logging.info(' Completed store {} at {}'.format(store_ID, datetime.now()))\n\n logging.info('Scraping completed for Fenicio Stores at {}'.format(datetime.now()))\n\n return create_dataframe(\n timestamp=timestamp,\n URL=URL,\n price=price,\n store_name=store_name,\n product_ID=product_ID,\n currency=currency,\n price_lista=price_lista\n )\n\n\n\n", "repo_name": "AlphaLabsUY/price-monitor-alphalabs", "sub_path": "individual-stores-scraping/scraping_modules/fenicio_scraping.py", "file_name": "fenicio_scraping.py", "file_ext": "py", "file_size_in_byte": 7794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 125, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 125, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 125, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 134, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 136, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 140, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 171, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 171, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 171, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 173, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 173, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 173, "usage_type": "name"}, {"api_name": "utils.create_dataframe", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "19781497518", "text": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom surprise import Reader\nfrom surprise import Dataset\nfrom surprise import SVD\nfrom surprise import KNNBasic\nfrom surprise.model_selection import cross_validate\nimport matplotlib.pyplot as plt\n\nreader = Reader(line_format='user item rating timestamp', sep=',', \n rating_scale=(0.5, 5.0), skip_lines=1)\n\nratings = Dataset.load_from_file(\"/Users/dorymauretour/Hw5ML/ratings_small.csv\", \n reader=reader)\n\nPMF = SVD(biased=False) # probabalistic matrix factorization\nUCF = KNNBasic(sim_options={'user_based': True}) # user-based collaborative filtering\nICF = KNNBasic(sim_options={'user_based': False}) # item-based collaborative filtering\n\nPMF_results = cross_validate(PMF, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(PMF_results)\n\nUCF_results = cross_validate(UCF, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(UCF_results)\n\nICF_results = cross_validate(ICF, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(ICF_results)\n\ndata = [[np.mean(UCF_results['test_rmse']), np.mean(UCF_results['test_mae'])],\n [np.mean(ICF_results['test_rmse']), np.mean(ICF_results['test_mae'])],\n [np.mean(PMF_results['test_rmse']), np.mean(PMF_results['test_mae'])]]\n\ndf = pd.DataFrame(data, columns=['RMSE', 'MAE'], index=['UCF', 'ICF', 'PMF'])\n\nprint(df)\nfig = df.plot.bar(title='RMSE and MAE Comparison of UCF, ICF, and PMF', ylim=(0.5, 1.05))\nplt.show()\n\nUCF_cosine = KNNBasic(sim_options={'name': 'cosine', 'user_based': True}) # user-based collaborative filtering using cosine similarity\nUCF_msd = KNNBasic(sim_options={'name': 'MSD', 'user_based': True}) # user-based collaborative filtering using mean squared difference\nUCF_pearson = KNNBasic(sim_options={'name': 'pearson', 'user_based': True}) # user-based collaborative filtering using Pearson correlation coefficient\n\nICF_cosine = KNNBasic(sim_options={'name': 'cosine', 'user_based': False}) # item-based collaborative filtering using cosine similarity\nICF_msd = KNNBasic(sim_options={'name': 'MSD', 'user_based': False}) # item-based collaborative filtering using mean squared difference\nICF_pearson = KNNBasic(sim_options={'name': 'pearson', 'user_based': False}) # item-based collaborative filtering using Pearson correlation coefficient\n\nUCF_cosine_results = cross_validate(UCF_cosine, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(UCF_cosine_results)\n\nUCF_msd_results = cross_validate(UCF_msd, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(UCF_msd_results)\n\nUCF_pearson_results = cross_validate(UCF_pearson, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(UCF_pearson_results)\n\nICF_cosine_results = cross_validate(ICF_cosine, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(ICF_cosine_results)\n\nICF_msd_results = cross_validate(ICF_msd, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(ICF_msd_results)\n\nICF_pearson_results = cross_validate(ICF_pearson, ratings, measures=['RMSE', 'MAE'], cv=5, verbose=True)\nprint(ICF_pearson_results)\n\nUCF_sim_data = [[np.mean(UCF_cosine_results['test_rmse']), np.mean(UCF_cosine_results['test_mae'])],\n [np.mean(UCF_msd_results['test_rmse']), np.mean(UCF_msd_results['test_mae'])],\n [np.mean(UCF_pearson_results['test_rmse']), np.mean(UCF_pearson_results['test_mae'])]]\n\nUCF_sim_df = pd.DataFrame(UCF_sim_data, columns=['RMSE', 'MAE'], index=['Cosine', 'MSD', 'Pearson'])\n\nICF_sim_data = [[np.mean(ICF_cosine_results['test_rmse']), np.mean(ICF_cosine_results['test_mae'])],\n [np.mean(ICF_msd_results['test_rmse']), np.mean(ICF_msd_results['test_mae'])],\n [np.mean(ICF_pearson_results['test_rmse']), np.mean(ICF_pearson_results['test_mae'])]]\n\nICF_sim_df = pd.DataFrame(ICF_sim_data, columns=['RMSE', 'MAE'], index=['Cosine', 'MSD', 'Pearson'])\n\nfig, (ax1, ax2) = plt.subplots(1,2, figsize=(12, 4))\nfig.suptitle('Similarity Measures Comparison')\nplt.show()\n\nprint('UCF sim data\\n', UCF_sim_df, '\\n')\nUCF_sim_df.plot.bar(ax=ax1, ylim=(0.5, 1.05))\nax1.title.set_text('User-Based Collaborative Filtering')\nplt.show()\n\nprint('ICF sim data\\n', ICF_sim_df, '\\n')\nICF_sim_df.plot.bar(ax=ax2, ylim=(0.5, 1.05))\nax2.title.set_text('Item-Based Collaborative Filtering')\nplt.show()\n\nUCF_cosine = KNNBasic(sim_options={'name': 'cosine', 'user_based': True}) # user-based collaborative filtering using cosine similarity\nUCF_msd = KNNBasic(sim_options={'name': 'MSD', 'user_based': True}) # user-based collaborative filtering using mean squared difference\nUCF_pearson = KNNBasic(sim_options={'name': 'pearson', 'user_based': True}) # user-based collaborative filtering using Pearson correlation coefficient\n\nICF_cosine = KNNBasic(sim_options={'name': 'cosine', 'user_based': False}) # item-based collaborative filtering using cosine similarity\nICF_msd = KNNBasic(sim_options={'name': 'MSD', 'user_based': False}) # item-based collaborative filtering using mean squared difference\nICF_pearson = KNNBasic(sim_options={'name': 'pearson', 'user_based': False}) # item-based collaborative filtering using Pearson correlation coefficient\n\nUCF_k_results = []\n\nfor k in range(10, 101, 10):\n UCF_k = KNNBasic(k=k, sim_options={'user_based': True})\n result = cross_validate(UCF_k, ratings, measures=['RMSE', 'MAE'], cv=5)\n UCF_k_results.append([np.mean(result['test_rmse']), np.mean(result['test_mae'])])\n \nICF_k_results = []\n\nfor k in range(10, 101, 10):\n ICF_k = KNNBasic(k=k, sim_options={'user_based': False})\n result = cross_validate(ICF_k, ratings, measures=['RMSE', 'MAE'], cv=5)\n ICF_k_results.append([np.mean(result['test_rmse']), np.mean(result['test_mae'])])\n\nUCF_k_df = pd.DataFrame(UCF_k_results, columns=['RMSE', 'MAE'], index=['10', '20', '30', '40', '50', '60', '70', '80', '90', '100'])\nICF_k_df = pd.DataFrame(ICF_k_results, columns=['RMSE', 'MAE'], index=['10', '20', '30', '40', '50', '60', '70', '80', '90', '100'])\n\nfig, (ax1, ax2) = plt.subplots(1,2, figsize=(20, 4))\nfig.suptitle('Number of Neighbors Comparison')\nplt.show()\n\nprint('UCF k data\\n', UCF_k_df, '\\n')\nUCF_k_df.plot.bar(ax=ax1, ylim=(0.5, 1.05))\nax1.set_xlabel('number of neighbors')\nax1.title.set_text('User-Based Collaborative Filtering')\nplt.show()\n\nprint('ICF k data\\n', ICF_k_df, '\\n')\nICF_k_df.plot.bar(ax=ax2, ylim=(0.5, 1.05))\nax2.set_xlabel('number of neighbors')\nax2.title.set_text('Item-Based Collaborative Filtering')\nplt.show()\n\n\n\n\n", "repo_name": "dmauretour/hw5ML", "sub_path": "hw5.py", "file_name": "hw5.py", "file_ext": "py", "file_size_in_byte": 6464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "surprise.Reader", "line_number": 11, "usage_type": "call"}, {"api_name": "surprise.Dataset.load_from_file", "line_number": 14, "usage_type": "call"}, {"api_name": "surprise.Dataset", "line_number": 14, "usage_type": "name"}, {"api_name": "surprise.SVD", "line_number": 17, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 18, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 19, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 21, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 24, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "surprise.KNNBasic", "line_number": 40, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 41, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 42, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 44, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 45, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 46, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 48, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 51, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 54, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 57, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 60, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "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.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "surprise.KNNBasic", "line_number": 92, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 93, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 94, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 96, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 97, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 98, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 103, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 105, "usage_type": "call"}, {"api_name": "surprise.KNNBasic", "line_number": 110, "usage_type": "call"}, {"api_name": "surprise.model_selection.cross_validate", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 115, "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": "matplotlib.pyplot.show", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}]} +{"seq_id": "9243438446", "text": "from langchain.chat_models import ChatOpenAI\r\nfrom langchain.chains import ConversationChain\r\nfrom langchain.chains.conversation.memory import ConversationBufferWindowMemory\r\nfrom langchain.prompts import (\r\n SystemMessagePromptTemplate,\r\n HumanMessagePromptTemplate,\r\n ChatPromptTemplate,\r\n MessagesPlaceholder\r\n)\r\nimport streamlit as st\r\nfrom streamlit_chat import message\r\nimport pinecone\r\nfrom langchain.vectorstores import Pinecone\r\nfrom langchain.embeddings import OpenAIEmbeddings\r\n\r\n\r\n\r\nOPENAI_API = st.secrets[\"OPENAI_API\"]\r\nPINECONE_API = st.secrets[\"PINECONE_API\"]\r\nPINECONE_ENV = st.secrets[\"PINECONE_ENV\"]\r\n\r\nembeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API, model='text-embedding-ada-002')\r\nindex_name = \"for-langchain-index\"\r\npinecone.init(api_key=PINECONE_API , environment=PINECONE_ENV )\r\nindex = pinecone.Index(index_name)\r\nembeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API, model='text-embedding-ada-002')\r\n\r\ndef get_conversation_string():\r\n conversation_string = \"\"\r\n for i in range(len(st.session_state['responses']) - 1):\r\n conversation_string += \"Human: \" + st.session_state['requests'][i] + \"\\n\"\r\n conversation_string += \"Sadhguru: \" + st.session_state['responses'][i + 1] + \"\\n\"\r\n return conversation_string\r\n\r\ndef find_match(input):\r\n vectorstore = Pinecone(index, embeddings.embed_query, \"text\")\r\n docs = vectorstore.similarity_search(input, k=2)\r\n result = [doc.page_content for doc in docs]\r\n return result\r\n\r\n\r\n\r\nst.markdown(\"

SadhguruGPT 🧘

\", unsafe_allow_html=True)\r\nst.markdown(\"

AI-Chatbot trained on the wisdom of Sadhguru. It provides responses inspired by his books and discourses.

\", unsafe_allow_html=True)\r\n\r\nif 'responses' not in st.session_state:\r\n st.session_state['responses'] = [\"Namaskaram🙏 Please ask your question\"]\r\n\r\nif 'requests' not in st.session_state:\r\n st.session_state['requests'] = []\r\n\r\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", openai_api_key=OPENAI_API)\r\n\r\nif 'buffer_memory' not in st.session_state:\r\n st.session_state.buffer_memory = ConversationBufferWindowMemory(k=2, return_messages=True)\r\n\r\nsystem_msg_template = SystemMessagePromptTemplate.from_template(template=\"\"\"You are Sadhguru, a yogi, mystic and a spiritual guru. Now answer the question with only the context provided and as truthful as possible, and if the answer is not contained within the text below then respond 'I don't know'. If asked 'how are you' or something similar then reply \"I'm doing good, Please ask your question\". If you are greeted then greet them back. And do not mention about the context anywhere in the answers. Context: \"\"\")\r\n\r\nhuman_msg_template = HumanMessagePromptTemplate.from_template(template=\"{input}\")\r\n\r\nprompt_template = ChatPromptTemplate.from_messages(\r\n [system_msg_template, MessagesPlaceholder(variable_name=\"history\"), human_msg_template])\r\n\r\nconversation = ConversationChain(memory=st.session_state.buffer_memory, prompt=prompt_template, llm=llm, verbose=True)\r\n\r\n# container for chat history\r\nresponse_container = st.container()\r\n# container for text box\r\ntextcontainer = st.container()\r\n\r\nwith textcontainer:\r\n query = st.text_input(\"Query: \", key=\"input\")\r\n if query:\r\n with st.spinner(\"Loading...\"):\r\n conversation_string = get_conversation_string()\r\n context = find_match(query)\r\n response = conversation.predict(input=f\"Context:\\n {context} \\n\\n Query:\\n{query}\")\r\n st.session_state.requests.append(query)\r\n st.session_state.responses.append(response)\r\nwith response_container:\r\n if st.session_state['responses']:\r\n\r\n for i in range(len(st.session_state['responses'])):\r\n message(st.session_state['responses'][i], key=str(i))\r\n if i < len(st.session_state['requests']):\r\n message(st.session_state[\"requests\"][i], is_user=True, key=str(i) + '_user')\r\n", "repo_name": "akashpatil1996/SadhguruGPT", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "streamlit.secrets", "line_number": 18, "usage_type": "attribute"}, {"api_name": "streamlit.secrets", "line_number": 19, "usage_type": "attribute"}, {"api_name": "streamlit.secrets", "line_number": 20, "usage_type": "attribute"}, {"api_name": "langchain.embeddings.OpenAIEmbeddings", "line_number": 22, "usage_type": "call"}, {"api_name": "pinecone.init", "line_number": 24, "usage_type": "call"}, {"api_name": "pinecone.Index", "line_number": 25, "usage_type": "call"}, {"api_name": "langchain.embeddings.OpenAIEmbeddings", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 30, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 31, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 32, "usage_type": "attribute"}, {"api_name": "langchain.vectorstores.Pinecone", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 43, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 44, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 46, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 47, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 49, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 50, "usage_type": "attribute"}, {"api_name": "langchain.chat_models.ChatOpenAI", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 54, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 55, "usage_type": "attribute"}, {"api_name": "langchain.chains.conversation.memory.ConversationBufferWindowMemory", "line_number": 55, "usage_type": "call"}, {"api_name": "langchain.prompts.SystemMessagePromptTemplate.from_template", "line_number": 57, "usage_type": "call"}, {"api_name": "langchain.prompts.SystemMessagePromptTemplate", "line_number": 57, "usage_type": "name"}, {"api_name": "langchain.prompts.HumanMessagePromptTemplate.from_template", "line_number": 59, "usage_type": "call"}, {"api_name": "langchain.prompts.HumanMessagePromptTemplate", "line_number": 59, "usage_type": "name"}, {"api_name": "langchain.prompts.ChatPromptTemplate.from_messages", "line_number": 61, "usage_type": "call"}, {"api_name": "langchain.prompts.ChatPromptTemplate", "line_number": 61, "usage_type": "name"}, {"api_name": "langchain.prompts.MessagesPlaceholder", "line_number": 62, "usage_type": "call"}, {"api_name": "langchain.chains.ConversationChain", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 64, "usage_type": "attribute"}, {"api_name": "streamlit.container", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.spinner", "line_number": 74, "usage_type": "call"}, {"api_name": "streamlit.session_state.requests.append", "line_number": 78, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 78, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.responses.append", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 79, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 81, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 83, "usage_type": "attribute"}, {"api_name": "streamlit_chat.message", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 84, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 85, "usage_type": "attribute"}, {"api_name": "streamlit_chat.message", "line_number": 86, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 86, "usage_type": "attribute"}]} +{"seq_id": "4643821108", "text": "import glob\r\nimport os\r\nimport sys\r\nimport time\r\nimport random\r\nimport numpy as np\r\nimport math as math\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n\r\ntry:\r\n sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (\r\n sys.version_info.major,\r\n sys.version_info.minor,\r\n 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])\r\nexcept IndexError:\r\n pass\r\n\r\nimport carla\r\n\r\nN = 6 # Number of vehicles in the Platoon\r\nVel_max = 55 # Maximum Velocity of of leader vehicle in KMPH\r\nSpacing = 8 # Desired spacing between vehicles\r\nAttVeh = 0\r\nK_p = 70\r\nk_d = 20\r\nK_dAtt = -9\r\nworld = None\r\nIter = 1\r\n\r\nrel_vel_list = []\r\nvehicle_vel = []\r\nvehicle_data = []\r\npos_gap_list = []\r\nvehicle2_list = []\r\nacc_list = [17]\r\nclass CarEnv:\r\n\r\n def __init__(self): \r\n self.client = carla.Client('localhost', 2000)\r\n self.client.set_timeout(5.0) \r\n \r\n # Get the CARLA world and pick the town map\r\n self.world = self.client.get_world()\r\n global world\r\n world = self.world \r\n self.world = self.client.load_world('Town06')\r\n self.spectator = self.world.get_spectator()\r\n\r\n # Pick if you want rendering enabled or not. Uncomment if you wish for a no rendering mode\r\n self.settings = self.world.get_settings()\r\n self.settings.no_rendering_mode = True\r\n self.world.apply_settings(self.settings)\r\n\r\n # Get the blueprint Library\r\n self.blueprint_library = self.world.get_blueprint_library()\r\n self.model_3 = self.blueprint_library.filter(\"model3\")[0] \r\n \r\n self.N = N\r\n self.Vel_max = Vel_max\r\n\r\n \r\n\r\n def reset(self): \r\n self.actor_list = []\r\n self.sensor_list = []\r\n self.collision_hist = [] \r\n self.vehicle_data = vehicle_data\r\n self.vehicle_data = [] \r\n self.vehicle_vel = vehicle_vel\r\n self.vehicle_vel = [] \r\n self.pos_gap_list = pos_gap_list\r\n self.pos_gap_list = []\r\n step = 0\r\n\r\n # Adding randomness to the attack\r\n self.ranVar = random.randint(0,2)\r\n print(self.ranVar) \r\n\r\n for i in range(self.N):\r\n # self.transform = carla.Transform(carla.Location(x=380+step, y= -20.0, z= 1.5), carla.Rotation(yaw = -180))\r\n self.transform = carla.Transform(carla.Location(x=-50-step, y= -20.0, z= 1.5))\r\n\r\n self.actor_role_name = \"hero\"+str(i)\r\n self.model_3.set_attribute('role_name', self.actor_role_name)\r\n \r\n # Spawn the desired number of vehicles with desired spacing\r\n self.vehicle = self.world.spawn_actor(self.model_3, self.transform) \r\n self.actor_list.append(self.vehicle)\r\n # self.spectator.set_transform(self.transform)\r\n step += int(Spacing)\r\n self.vehicle.apply_control(carla.VehicleControl(throttle= 0, steer=0))\r\n\r\n # attach collision sensors to each of the vehicles\r\n colsensor = self.blueprint_library.find(\"sensor.other.collision\")\r\n self.colsensor = self.world.spawn_actor(colsensor, self.transform, attach_to = self.vehicle)\r\n self.sensor_list.append(self.colsensor)\r\n self.colsensor.listen(lambda event: self.collision_data(event)) \r\n \r\n \r\n \r\n def collision_data(self, event):\r\n # self.collision_hist.append(event)\r\n impulse = event.normal_impulse \r\n intensity = math.sqrt(impulse.x**2 + impulse.y**2 + impulse.z**2)\r\n self.collision_hist.append(intensity) \r\n # print(\"Collision when leader vehicle at %d\" % self.P)\r\n\r\n def speed(self, actor):\r\n v = actor.get_velocity()\r\n speed = 3.6*math.sqrt(v.x**2 + v.y**2 + v.z**2)\r\n return speed\r\n\r\n def NormalizeData(self, vel, data):\r\n acc = (vel - np.min(data)) / (np.max(data) - np.min(data))\r\n return acc\r\n\r\n def action(self, attacker):\r\n count = 0 \r\n \r\n for idx,vehicle in enumerate(self.actor_list):\r\n # print(idx,vehicle, \"to check iterator\",self.actor_list[idx])\r\n if vehicle.attributes.get('role_name') == \"hero0\":\r\n if count == 0: \r\n vehicle.apply_control(carla.VehicleControl(throttle = 0.65, steer = 0)) \r\n count = count+1\r\n # vehicle.set_velocity(carla.Vector3D(x = -15, y = 0, z = 0))\r\n self.P = vehicle.get_location().x\r\n # print(self.P)\r\n self.vehicle_data.append(self.P) \r\n self.v1 = self.speed(vehicle)\r\n self.vehicle_vel.append(self.v1)\r\n # print(vehicle.get_acceleration().x )\r\n else:\r\n \r\n if attacker == 0:\r\n self.P_i = self.actor_list[idx].get_location().x\r\n self.vehicle_data.append(self.P_i)\r\n \r\n self.v_i = self.speed(self.actor_list[idx])\r\n \r\n self.vehicle_vel.append(self.v_i)\r\n\r\n self.P_n = self.actor_list[idx-1].get_location().x \r\n self.v_n = self.speed(self.actor_list[idx-1])\r\n\r\n self.pos_gap_list.append(abs(self.P_i - self.P_n)-Spacing)\r\n # Condition to check if the vehicle is one at the end\r\n if idx != np.amax(idx): \r\n self.P_p = self.actor_list[idx+1].get_location().x \r\n self.v_p = self.speed(self.actor_list[idx+1])\r\n \r\n # Apply control\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + k_d*(self.v_n - self.v_i) + K_p*(self.P_p - self.P_i + Spacing) + k_d*(self.v_p - self.v_i)\r\n \r\n else:\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + k_d*(self.v_n - self.v_i)\r\n # self.spectator.set_transform(carla.Transform(carla.Location(x = self.P_i-10, y = self.actor_list[idx].get_location().y, z = self.actor_list[idx].get_location().z+10)))\r\n acc_list.append(acc_com)\r\n acc_com_norm = self.NormalizeData(acc_com, acc_list)\r\n if math.isnan(acc_com_norm):\r\n acc_com_norm = 0.3\r\n # print(acc_com_norm)\r\n self.actor_list[idx].apply_control(carla.VehicleControl(throttle = acc_com_norm, steer = 0))\r\n\r\n else:\r\n \r\n if self.P < (100 + self.ranVar):\r\n self.P_i = self.actor_list[idx].get_location().x\r\n self.vehicle_data.append(self.P_i)\r\n \r\n self.v_i = self.speed(self.actor_list[idx])\r\n \r\n self.vehicle_vel.append(self.v_i)\r\n\r\n self.P_n = self.actor_list[idx-1].get_location().x \r\n self.v_n = self.speed(self.actor_list[idx-1])\r\n\r\n self.pos_gap_list.append(abs(self.P_i - self.P_n)-Spacing)\r\n # Condition to check if the vehicle is one at the end\r\n if idx != np.amax(idx): \r\n self.P_p = self.actor_list[idx+1].get_location().x \r\n self.v_p = self.speed(self.actor_list[idx+1])\r\n \r\n # Apply control\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + k_d*(self.v_n - self.v_i) + K_p*(self.P_p - self.P_i + Spacing) + k_d*(self.v_p - self.v_i)\r\n \r\n else:\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + k_d*(self.v_n - self.v_i)\r\n # self.spectator.set_transform(carla.Transform(carla.Location(x = self.P_i-10, y = self.actor_list[idx].get_location().y, z = self.actor_list[idx].get_location().z+10)))\r\n acc_list.append(acc_com)\r\n acc_com_norm = self.NormalizeData(acc_com, acc_list)\r\n if math.isnan(acc_com_norm):\r\n acc_com_norm = 0.3\r\n # print(acc_com_norm)\r\n self.actor_list[idx].apply_control(carla.VehicleControl(throttle = acc_com_norm, steer = 0))\r\n \r\n # Inducing Attack \r\n else:\r\n if idx != attacker:\r\n self.P_i = self.actor_list[idx].get_location().x\r\n self.vehicle_data.append(self.P_i)\r\n \r\n self.v_i = self.speed(self.actor_list[idx])\r\n \r\n self.vehicle_vel.append(self.v_i)\r\n\r\n if idx == 1:\r\n vehicle2_list.append(self.P_i)\r\n\r\n self.P_n = self.actor_list[idx-1].get_location().x \r\n self.v_n = self.speed(self.actor_list[idx-1])\r\n\r\n self.pos_gap_list.append(abs(self.P_i - self.P_n)-Spacing)\r\n # Condition to check if the vehicle is one at the end\r\n if idx != np.amax(idx): \r\n self.P_p = self.actor_list[idx+1].get_location().x \r\n self.v_p = self.speed(self.actor_list[idx+1])\r\n \r\n # Apply control\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + k_d*(self.v_n - self.v_i) + K_p*(self.P_p - self.P_i + Spacing) + k_d*(self.v_p - self.v_i)\r\n \r\n else:\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + k_d*(self.v_n - self.v_i)\r\n # self.spectator.set_transform(carla.Transform(carla.Location(x = self.P_i-10, y = self.actor_list[idx].get_location().y, z = self.actor_list[idx].get_location().z+10)))\r\n acc_list.append(acc_com)\r\n acc_com_norm = self.NormalizeData(acc_com, acc_list)\r\n if math.isnan(acc_com_norm):\r\n acc_com_norm = 0.3\r\n # print(acc_com_norm)\r\n self.actor_list[idx].apply_control(carla.VehicleControl(throttle = acc_com_norm, steer = 0))\r\n elif idx == attacker:\r\n # print(idx,attacker)\r\n self.P_i = self.actor_list[attacker].get_location().x\r\n self.vehicle_data.append(self.P_i)\r\n \r\n self.v_i = self.speed(self.actor_list[attacker])\r\n \r\n self.vehicle_vel.append(self.v_i)\r\n\r\n self.P_n = self.actor_list[attacker-1].get_location().x \r\n self.v_n = self.speed(self.actor_list[attacker-1])\r\n\r\n self.pos_gap_list.append(abs(self.P_i - self.P_n)-Spacing)\r\n # Condition to check if the vehicle is one at the end\r\n if attacker != np.amax(idx): \r\n self.P_p = self.actor_list[attacker+1].get_location().x \r\n self.v_p = self.speed(self.actor_list[attacker+1])\r\n \r\n # Apply control\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + K_dAtt*(self.v_n - self.v_i) + K_p*(self.P_p - self.P_i + Spacing) + K_dAtt*(self.v_p - self.v_i)\r\n \r\n else:\r\n acc_com = K_p*(self.P_n - self.P_i - Spacing) + K_dAtt*(self.v_n - self.v_i)\r\n # self.spectator.set_transform(carla.Transform(carla.Location(x = self.P_i-10, y = self.actor_list[idx].get_location().y, z = self.actor_list[idx].get_location().z+10)))\r\n acc_list.append(acc_com)\r\n acc_com_norm = self.NormalizeData(acc_com, acc_list)\r\n if math.isnan(acc_com_norm):\r\n acc_com_norm = 0.3\r\n # print(acc_com_norm)\r\n self.actor_list[attacker].apply_control(carla.VehicleControl(throttle = acc_com_norm, steer = 0))\r\n \r\n \r\n \r\n \r\n\r\ndef main():\r\n data1 = []\r\n env = CarEnv()\r\n # collide = False\r\n try:\r\n for i in range(Iter): \r\n data2 = [] \r\n env.reset()\r\n time.sleep(5)\r\n\r\n while True:\r\n env.action(AttVeh)\r\n print(len(env.vehicle_vel))\r\n if len(env.vehicle_vel) > 8000:\r\n break\r\n for actor in env.actor_list:\r\n actor.destroy()\r\n for sensor in env.sensor_list:\r\n sensor.destroy() \r\n print(\"All actors destroyed!\")\r\n\r\n # if len(env.collision_hist) == 0:\r\n # print(\"No collisions! Hurray!\")\r\n # # collide = False\r\n rem = len(env.vehicle_data) % N\r\n env.vehicle_data = env.vehicle_data[: len(env.vehicle_data) - rem]\r\n env.vehicle_data = np.array(env.vehicle_data)\r\n vehicle_data_new = env.vehicle_data.reshape(-1,N)\r\n\r\n env.vehicle_vel = env.vehicle_vel[: len(env.vehicle_vel) - rem]\r\n env.vehicle_vel = np.array(env.vehicle_vel)\r\n vehicle_vel_new = env.vehicle_vel.reshape(-1,N) \r\n\r\n for j in range(np.size(vehicle_data_new,1)):\r\n data2.append(vehicle_vel_new[3200:3700,j]) \r\n # print(np.size(data2,1)) \r\n data1.append(data2) \r\n \r\n\r\n rem2 = len(env.pos_gap_list) % (N-1)\r\n env.pos_gap_list = env.pos_gap_list[: len(env.pos_gap_list) - rem2]\r\n env.pos_gap_list = np.array(env.pos_gap_list)\r\n pos_gap_new = env.pos_gap_list.reshape(-1,(N-1))\r\n\r\n \r\n # plt.figure(1)\r\n # plt.subplot(311) # the first subplot in the first figure\r\n # plt.plot(vehicle_data_new, label = \"Vehicle position\") \r\n # plt.title('Distance')\r\n # plt.ylabel('Distance')\r\n \r\n # plt.subplot(312) # the first subplot in the first figure\r\n # plt.plot(vehicle_vel_new, label = \"Vehicle velocities\") \r\n # # plt.title('Speed')\r\n # plt.ylabel('Vehicle Speed')\r\n \r\n # plt.subplot(313) # the first subplot in the first figure\r\n # plt.plot(pos_gap_new) \r\n # # plt.title('Error')\r\n # plt.ylabel('Error')\r\n # plt.show()\r\n\r\n # plt.figure(2)\r\n # plt.plot(env.collision_hist)\r\n # plt.title(\"Collision History\")\r\n # plt.show() \r\n \r\n # data1 = np.array(data1) \r\n # str1 = \"AbsoluteVelDataAtt\" + str(AttVeh)\r\n # np.save(str1, data1)\r\n\r\n\r\n except KeyboardInterrupt:\r\n pass \r\n\r\n finally:\r\n \r\n plt.figure(1)\r\n plt.subplot(311) # the first subplot in the first figure\r\n plt.plot(vehicle_data_new, label = \"Vehicle position\") \r\n plt.title('Distance')\r\n plt.ylabel('Distance')\r\n \r\n plt.subplot(312) # the first subplot in the first figure\r\n plt.plot(vehicle_vel_new, label = \"Vehicle velocities\") \r\n # plt.title('Speed')\r\n plt.ylabel('Vehicle Speed')\r\n \r\n plt.subplot(313) # the first subplot in the first figure\r\n plt.plot(pos_gap_new) \r\n # plt.title('Error')\r\n plt.ylabel('Error')\r\n plt.show()\r\n\r\n # plt.figure(2)\r\n # plt.plot(env.collision_hist)\r\n # plt.title(\"Collision History\")\r\n # plt.show() \r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n", "repo_name": "tarunkartik/Platooning-Threat-Detection-and-Mitigation", "sub_path": "LongitudinalAttackData.py", "file_name": "LongitudinalAttackData.py", "file_ext": "py", "file_size_in_byte": 17311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 16, "usage_type": "attribute"}, {"api_name": "carla.Client", "line_number": 41, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 78, "usage_type": "call"}, {"api_name": "carla.Transform", "line_number": 83, "usage_type": "call"}, {"api_name": "carla.Location", "line_number": 83, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 93, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 106, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 116, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 150, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 162, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 182, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 194, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 217, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 229, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 247, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 259, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 262, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 345, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 345, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 346, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 346, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 347, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 347, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 348, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 348, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 351, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 352, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 352, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 354, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 360, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 360, "usage_type": "name"}]} +{"seq_id": "39045394512", "text": "import numpy as np\nimport datetime as dt\n\n# Global Constants\nColdCod = 1\nHotCod = 2\nShiftBits = 5\nC2K = 273.15\n\ndef Calibrations(b):\n \n if (b.MetaData['SSTType'] != 'Auxiliary'):\n print ('--------------------------------------')\n print (': :')\n print (': Only for Auxiliary (BI) files :')\n print (': :')\n print ('--------------------------------------')\n return []\n\n b.CorrectAuxiliary()\n\n xhot,chot=getCalPos(b,HotCod)\n xcold,ccold=getCalPos(b,ColdCod)\n\n Ncalibs=np.min([ccold.shape[0],chot.shape[0]])\n\n Cal={'time' : np.array(np.zeros(Ncalibs,dtype=dt.datetime)),\n 'adc_h' : np.array(np.zeros([Ncalibs,6],dtype=np.float)),\n 'adc_c' : np.array(np.zeros([Ncalibs,6],dtype=np.float)),\n 'Trec' : np.array(np.zeros([Ncalibs,6],dtype=np.float)),\n 'ADC2K' : np.array(np.zeros([Ncalibs,6],dtype=np.float)),\n 'Thot' : np.array(np.zeros(Ncalibs,dtype=np.float)),\n 'Tcold' : np.array(np.zeros(Ncalibs,dtype=np.float)),\n 'IF_T' : np.array(np.zeros(Ncalibs,dtype=np.float)),\n 'Opt_T' : np.array(np.zeros(Ncalibs,dtype=np.float)),\n 'Rad_T' : np.array(np.zeros(Ncalibs,dtype=np.float))\n }\n\n y = int(b.MetaData['ISODate'][0:4])\n m = int(b.MetaData['ISODate'][5:7])\n d = int(b.MetaData['ISODate'][8:])\n\n # Get the Mean time of the Calibrations\n for i in np.arange(Ncalibs):\n ms = np.mean(b.Data['time'][xcold[ccold[i,0]:chot[i,1]]])\n Cal['time'][i] = ms2dt(y,m,d,ms)\n Cal['Thot'][i] = np.mean(b.Data['hot_temp'][xhot[chot[i,0]:chot[i,1]]])+C2K\n Cal['Tcold'][i] = np.mean(b.Data['amb_temp'][xcold[ccold[i,0]:ccold[i,1]]])+C2K\n Cal['IF_T'][i] = np.mean(b.Data['if_board'][xcold[ccold[i,0]:chot[i,1]]])\n Cal['Opt_T'][i] = np.mean(b.Data['opt_temp'][xcold[ccold[i,0]:chot[i,1]]])\n Cal['Rad_T'][i] = np.mean(b.Data['radome_temp'][xcold[ccold[i,0]:chot[i,1]]])\n for ch in np.arange(6):\n Cal['adc_c'][i,ch] = np.mean(b.Data['adc'][xcold[ccold[i,0]:ccold[i,1]],ch])\n Cal['adc_h'][i,ch] = np.mean(b.Data['adc'][xhot[chot[i,0]:chot[i,1]],ch])\n Cal['ADC2K'][i,ch] = (Cal['Thot'][i] - Cal['Tcold'][i]) / (Cal['adc_h'][i,ch]-Cal['adc_c'][i,ch])\n Cal['Trec'][i,ch] = Cal['adc_h'][i,ch] * Cal['ADC2K'][i,ch] - Cal['Thot'][i]\n\n return Cal\n\ndef getCalPos(b,Cod):\n x=np.where((b.Data['target'] >> ShiftBits) == Cod)\n x=np.asarray(x)\n x=x[0]\n return x,cntgs(x)\n\ndef ms2dt(y,m,d,ms):\n \n import datetime as dt\n\n ms = int(ms)\n hours = ms // 36000000\n minutes = (ms % 36000000) // 600000\n seconds = ((ms % 36000000) % 600000) / 1.0E+04\n seconds_int = int(seconds)\n seconds_frac = seconds - int(seconds)\n useconds = int(seconds_frac * 1.0E+06)\n\n return dt.datetime(y,m,d,hours,minutes,seconds_int,useconds)\n\ndef cntgs(s):\n\n sDim=s.shape[0]\n if (sDim <= 2):\n return np.array([])\n\n sDiff = s[1:] - s[0:-1]\n tDisc = np.where(sDiff != 1)\n xDisc = np.asarray(tDisc)\n xDisc = xDisc[0]\n nDisc = xDisc.shape[0]+1\n c = np.zeros( (nDisc,2), dtype=np.int)\n for i in np.arange(nDisc):\n if (i==0):\n c[0,0] = 0\n c[0,1] = xDisc[0]\n elif (i==nDisc-1):\n c[i,0] = xDisc[i-1]+1\n c[i,1] = s.shape[0]-1\n else:\n c[i,0] = xDisc[i-1]+1\n c[i,1] = xDisc[i]\n\n return c\n \n", "repo_name": "guigue/CRAAM-Instruments", "sub_path": "SST/pySST.py", "file_name": "pySST.py", "file_ext": "py", "file_size_in_byte": 3533, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "60", "api": [{"api_name": "numpy.min", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "37525350554", "text": "import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"ippanel\",\n version=\"2.0.7\",\n author=\"Asghar Dadashzadeh\",\n author_email=\"dev@ippanel.com\",\n description=\"ippanel sdk\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/ippanel/python-rest-sdk\",\n packages=setuptools.find_packages(),\n install_requires=['requests>=2.28.1'],\n license='BSD-2-Clause',\n classifiers=[\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n ]\n)\n", "repo_name": "ippanel/python-rest-sdk", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "51", "api": [{"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "4917262135", "text": "from functools import reduce\n\ndef gcd(x, y):\n x, y = max(x, y), min(x, y)\n while True:\n r = x % y\n x, y = y, r\n if r == 0:\n g = x\n break\n return g\n\ndef lcm(a, b):\n return a*b//gcd(a, b)\n\nfor _ in range(int(input())):\n n = int(input())\n print(reduce(lcm, range(1, n + 1)))\n", "repo_name": "PROxZIMA/Competitive-Coding", "sub_path": "Hackerrank/Contests/Project Euler/euler005.py", "file_name": "euler005.py", "file_ext": "py", "file_size_in_byte": 333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "functools.reduce", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "27950223042", "text": "'''\nModified from https://github.com/pytorch/vision.git\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom ..layers import *\nfrom ..data import sph_v2\nfrom ..layers.functions.sph_prior_box import SphPriorBox\nimport math\nfrom spherenet import SphereConv2D, SphereMaxPool2D\n\nclass VGG(nn.Module):\n def __init__(self, base, num_classes):\n super(VGG, self).__init__()\n\n self.vgg = nn.Sequential(*base)\n\n self.classifier = nn.Sequential(\n nn.Linear(1024*4*7, num_classes),\n )\n # Initialize weights\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n m.bias.data.zero_()\n\n def forward(self, x):\n\n x = self.vgg(x)\n x = x.view(x.size(0), -1)\n x = self.classifier(x)\n return x\n\n# This function is derived from torchvision VGG make_layers()\n# https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py\ndef vgg(cfg, i, batch_norm=False):\n layers = []\n in_channels = i\n for v in cfg:\n if v == 'M':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n elif v == 'C':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]\n else:\n conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n if batch_norm:\n layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n else:\n layers += [conv2d, nn.ReLU(inplace=True)]\n in_channels = v\n pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)\n conv7 = nn.Conv2d(1024, 1024, kernel_size=1)\n layers += [pool5, conv6,\n nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]\n return layers\n\ndef sph_vgg(cfg, i, batch_norm=False):\n layers = []\n in_channels = i\n for v in cfg:\n if v == 'M':\n layers += [SphereMaxPool2D(stride=2)]\n else:\n conv2d = SphereConv2D(in_channels, v, stride=1)\n if batch_norm:\n layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n else:\n layers += [conv2d, nn.ReLU(inplace=True)]\n in_channels = v\n pool5 = SphereMaxPool2D(stride=1)\n conv6 = SphereConv2D(512, 1024, stride=1)\n conv7 = SphereConv2D(1024, 1024, stride=1)\n layers += [pool5, conv6,\n nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]\n return layers\n\n\n\n\nbase = {\n '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'C',\n 512, 512, 512],\n '512': [],\n}\n\ndef vgg4ssd():\n return VGG(vgg(base['300'], 3),10)\n\ndef sph_vgg4ssd():\n return VGG(sph_vgg(base['300'], 3),10)", "repo_name": "MONET-IOBT/Spatio-temporal-Action-Localization-in-360-Videos", "sub_path": "model/vgg.py", "file_name": "vgg.py", "file_ext": "py", "file_size_in_byte": 2930, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 50, "usage_type": "call"}, {"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.MaxPool2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "spherenet.SphereMaxPool2D", "line_number": 66, "usage_type": "call"}, {"api_name": "spherenet.SphereConv2D", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "spherenet.SphereMaxPool2D", "line_number": 74, "usage_type": "call"}, {"api_name": "spherenet.SphereConv2D", "line_number": 75, "usage_type": "call"}, {"api_name": "spherenet.SphereConv2D", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "21039107326", "text": "# -*- coding: utf8 -*-\n\n\"\"\"\nGiven a binary tree, return the inorder traversal of its nodes' values.\n\nExample:\n\nInput: [1,null,2,3]\n 1\n \\\n 2\n /\n 3\n\nOutput: [1,3,2]\n\"\"\"\n\nfrom typing import List\n\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nBUFFER = []\n\n\ndef in_order_traversal_recursive(root: TreeNode):\n if root is not None:\n in_order_traversal_recursive(root.left)\n BUFFER.append(root.val)\n in_order_traversal_recursive(root.right)\n\n\ndef in_order_traversal_iterative(root: TreeNode) -> List[int]:\n ret = []\n stack = [(root, False)]\n while stack:\n root, is_visited = stack.pop()\n if root is None:\n continue\n if is_visited:\n ret.append(root.val)\n else:\n stack.append((root.right, False))\n stack.append((root, True))\n stack.append((root.left, False))\n\n return ret\n\n\ndef test_in_order_traversal():\n root = TreeNode(1)\n root.right = TreeNode(2)\n root.right.left = TreeNode(3)\n in_order_traversal_recursive(root)\n assert BUFFER == [1, 3, 2]\n\n assert in_order_traversal_iterative(root) == [1, 3, 2]\n\n\nif __name__ == '__main__':\n test_in_order_traversal()\n", "repo_name": "Sisyphus235/tech_lab", "sub_path": "algorithm/tree/lc94_in_order_traversal.py", "file_name": "lc94_in_order_traversal.py", "file_ext": "py", "file_size_in_byte": 1275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.List", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "37826873097", "text": "import numpy as np\nimport sklearn\nfrom sklearn.model_selection import cross_validate\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.linear_model import SGDRegressor\nimport pandas as pd\nimport gensim\nimport scipy\nfrom sklearn.metrics import make_scorer\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.preprocessing import StandardScaler\nimport sys\nimport seaborn as sns\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nfrom statsmodels.graphics.gofplots import ProbPlot\n\nplt.style.use('seaborn') # pretty matplotlib plots\nplt.rc('font', size=14)\nplt.rc('figure', titlesize=18)\nplt.rc('axes', labelsize=15)\nplt.rc('axes', titlesize=18)\n\ndef graph(formula, x_range, label=None):\n \"\"\"\n Helper function for plotting cook's distance lines\n \"\"\"\n x = x_range\n y = formula(x)\n plt.plot(x, y, label=label, lw=1, ls='--', color='red')\n\n\ndef diagnostic_plots(X, y, model_fit=None):\n \"\"\"\n Function to reproduce the 4 base plots of an OLS model in R.\n\n ---\n Inputs:\n\n X: A numpy array or pandas dataframe of the features to use in building the linear regression model\n\n y: A numpy array or pandas series/dataframe of the target variable of the linear regression model\n\n model_fit [optional]: a statsmodel.api.OLS model after regressing y on X. If not provided, will be\n generated from X, y\n \"\"\"\n\n if not model_fit:\n model_fit = sm.OLS(y, sm.add_constant(X)).fit()\n\n print(model_fit.summary())\n # create dataframe from X, y for easier plot handling\n #dataframe = pd.concat([X, y], axis=1)\n\n # model values\n model_fitted_y = model_fit.fittedvalues\n # model residuals\n model_residuals = model_fit.resid\n # normalized residuals\n model_norm_residuals = model_fit.get_influence().resid_studentized_internal\n # absolute squared normalized residuals\n model_norm_residuals_abs_sqrt = np.sqrt(np.abs(model_norm_residuals))\n # absolute residuals\n model_abs_resid = np.abs(model_residuals)\n # leverage, from statsmodels internals\n model_leverage = model_fit.get_influence().hat_matrix_diag\n # cook's distance, from statsmodels internals\n model_cooks = model_fit.get_influence().cooks_distance[0]\n\n plot_lm_1 = plt.figure()\n plot_lm_1.axes[0] = sns.residplot(model_fitted_y, y, data=None,\n lowess=True,\n scatter_kws={'alpha': 0.5},\n line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})\n\n plot_lm_1.axes[0].set_title('Residuals vs Fitted')\n plot_lm_1.axes[0].set_xlabel('Fitted values')\n plot_lm_1.axes[0].set_ylabel('Residuals');\n\n # annotations\n abs_resid = model_abs_resid.sort_values(ascending=False)\n abs_resid_top_3 = abs_resid[:3]\n for i in abs_resid_top_3.index:\n plot_lm_1.axes[0].annotate(i,\n xy=(model_fitted_y[i],\n model_residuals[i]));\n\n QQ = ProbPlot(model_norm_residuals)\n plot_lm_2 = QQ.qqplot(line='45', alpha=0.5, color='#4C72B0', lw=1)\n plot_lm_2.axes[0].set_title('Normal Q-Q')\n plot_lm_2.axes[0].set_xlabel('Theoretical Quantiles')\n plot_lm_2.axes[0].set_ylabel('Standardized Residuals');\n # annotations\n abs_norm_resid = np.flip(np.argsort(np.abs(model_norm_residuals)), 0)\n abs_norm_resid_top_3 = abs_norm_resid[:3]\n for r, i in enumerate(abs_norm_resid_top_3):\n plot_lm_2.axes[0].annotate(i,\n xy=(np.flip(QQ.theoretical_quantiles, 0)[r],\n model_norm_residuals[i]));\n\n plot_lm_3 = plt.figure()\n plt.scatter(model_fitted_y, model_norm_residuals_abs_sqrt, alpha=0.5);\n sns.regplot(model_fitted_y, model_norm_residuals_abs_sqrt,\n scatter=False,\n ci=False,\n lowess=True,\n line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8});\n plot_lm_3.axes[0].set_title('Scale-Location')\n plot_lm_3.axes[0].set_xlabel('Fitted values')\n plot_lm_3.axes[0].set_ylabel('$\\sqrt{|Standardized Residuals|}$');\n\n # annotations\n abs_sq_norm_resid = np.flip(np.argsort(model_norm_residuals_abs_sqrt), 0)\n abs_sq_norm_resid_top_3 = abs_sq_norm_resid[:3]\n for i in abs_norm_resid_top_3:\n try:\n plot_lm_3.axes[0].annotate(i,\n xy=(model_fitted_y[i],\n model_norm_residuals_abs_sqrt[i]));\n except:\n pass\n\n plot_lm_4 = plt.figure();\n plt.scatter(model_leverage, model_norm_residuals, alpha=0.5);\n sns.regplot(model_leverage, model_norm_residuals,\n scatter=False,\n ci=False,\n lowess=True,\n line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8});\n plot_lm_4.axes[0].set_xlim(0, max(model_leverage)+0.01)\n plot_lm_4.axes[0].set_ylim(-3, 5)\n plot_lm_4.axes[0].set_title('Residuals vs Leverage')\n plot_lm_4.axes[0].set_xlabel('Leverage')\n plot_lm_4.axes[0].set_ylabel('Standardized Residuals');\n\n # annotations\n leverage_top_3 = np.flip(np.argsort(model_cooks), 0)[:3]\n for i in leverage_top_3:\n plot_lm_4.axes[0].annotate(i,\n xy=(model_leverage[i],\n model_norm_residuals[i]));\n\n p = len(model_fit.params) # number of model parameters\n graph(lambda x: np.sqrt((0.5 * p * (1 - x)) / x),\n np.linspace(0.001, max(model_leverage), 50),\n 'Cook\\'s distance') # 0.5 line\n graph(lambda x: np.sqrt((1 * p * (1 - x)) / x),\n np.linspace(0.001, max(model_leverage), 50)) # 1 line\n plot_lm_4.legend(loc='upper right');\n\n\n\ndef rmsle(y, y0):\n y = np.array(y)\n y0 = np.array(y0)\n #return np.sqrt(np.mean(np.square(np.log1p(y) - np.log1p(y0))))\n return np.sqrt(np.square(np.log1p(y) - np.log1p(y0)).mean())\n\n# This version is to be used with the log transformed target\ndef rmsle_v2(y, y0):\n #return np.sqrt(np.mean(np.square(np.log1p(y) - np.log1p(y0))))\n return np.sqrt(np.square(y - y0 ).mean())\n\ndef mse(pred,y):\n pred = np.expm1(pred)\n y = np.expm1(y)\n return np.square(pred - y).mean()\n\ndef mse_(pred,y):\n return np.square(pred - y).mean()\n\nscore = make_scorer(mse_, greater_is_better=False)\n\n#train total\ntrain = pd.read_csv('train.csv', sep=',')\n#train = pd.read_csv('./hackathon_data/train.csv', sep=',')\ntrain['description'].fillna(' ',inplace=True)\ntrain = train[(train['num_votes'] < 50)]\n#train = train[train['created_time'] > '2013-01-01 00:00:00']\n#train = train.dropna(subset=['source'])\ntrain.reset_index(inplace=True)\nprint(train.info())\n\n#train reduced\n#train = train[(train['num_votes'] > 1) & (train['num_votes'] < 50)]\n#print(train.info())\n#print(train.head())\n\n#train baseline\ntrain_baseline = train[~train['tag_type'].isna()]\nprint(train_baseline.info())\ntrain = train_baseline\n\n\n#train['location'] = [[x1,x2] for x1,x2 in zip(train['latitude'].apply(lambda x:round(x,3)),train['longitude'].apply(lambda x:round(x,3)))]\n\n#################################### Baseline 1 ########################################\n# Strategy: for the 24% of the data that has issue_type populated, predict the average num_votes by neighborhood\n\n# train_baseline['lat_neighbor'] = train_baseline['latitude'].apply(lambda x: round(x,3))\n# train_baseline['lon_neighbor'] = train_baseline['longitude'].apply(lambda x: round(x,3))\n# votes_avg_issue_type = train_baseline[['tag_type','lat_neighbor','lon_neighbor','num_votes']].groupby(['tag_type','lat_neighbor','lon_neighbor']).mean()\n# pred = list()\n#\n# for index,sample in train_baseline.iterrows():\n# pred.append(votes_avg_issue_type.loc[(sample['tag_type'],sample['lat_neighbor'],sample['lon_neighbor']),['num_votes']][0])\n#\n# #print(\"\\n Baseline RMSLE: %f \" % rmsle(pred, train_baseline['num_votes']))\n# print(\"\\n Baseline MSE: %f \" % np.mean(np.square(pred - train_baseline['num_votes'])))\n# print(\"\\n\")\n\n########################################################################################\n\ntrain['hour'] = [str(pd.to_datetime(x).hour) for x in train['created_time']]\ntrain['dayofweek'] = [str(pd.to_datetime(x).weekday()) for x in train['created_time']]\ntrain['year'] = [str(pd.to_datetime(x).year) for x in train['created_time']]\n#print(train.info())\n#print(train.loc[:,['hour','dayofweek','year']])\n\ndescription = train['description']\nsummary = train['summary']\ndescription = description.apply(lambda x: ' '.join(gensim.utils.simple_preprocess(x)))\nsummary = summary.apply(lambda x: ' '.join(gensim.utils.simple_preprocess(x)))\nbow = CountVectorizer(max_features=5000, binary=True, max_df=0.5,ngram_range=(1,2))\n#bow = bow.fit(description+summary)\ndescription = bow.fit_transform(description)\nbow.vocabulary_ = None\nsummary = bow.fit_transform(summary)\n\n#source = dummies.fit_transform(train[['source','num_votes']]) #this should be wrong but is issuing a smaller error\n\n# Decimal places Object that can be unambiguously recognized at this scale\n# 0\t country or large region\n# 1\t \t large city or district\n# 2\t \t town or village\n# 3 \t neighborhood, street\n# 4 individual street, land parcel\n# 5 individual trees, door entrance\n# 6 individual humans\n# 7 practical limit of commercial surveying\n# 8 specialized surveying (e.g. tectonic plate mapping)\n\nlatitude = train['latitude'].apply(lambda x: np.round(x,3))\nlongitude = train['longitude'].apply(lambda x: np.round(x,3))\n#Location not rounded\n#location = train[['latitude','longitude']]\n#Location not scaled\n#location = np.stack((latitude,longitude),axis=-1)\n\n#################################### Baseline 2 ########################################\n\n# train.loc[:,'latitude'] = train['latitude'].apply(lambda x: round(x,3))\n# train.loc[:,'longitude'] = train['longitude'].apply(lambda x: round(x,3))\n# # MSE 0.62\n# votes_avg = train[['latitude','longitude','num_votes']].groupby(['latitude','longitude']).mean() # 0.62\n#\n# pred = list()\n#\n# for index,sample in train.iterrows():\n# pred.append(votes_avg.loc[(sample['latitude'],sample['longitude']),['num_votes']][0])\n#\n# #print(\"\\n Baseline RMSLE: %f \" % rmsle(pred, train_baseline['num_votes']))\n# print(\"\\n Baseline MSE: %f \" % np.mean(np.square(pred - train['num_votes'])))\n# print(\"\\n\")\n\n########################################################################################\n\n\n#onehot = np.stack((train['tag_type'],latitude,longitude),axis=-1)\n#onehot = np.stack((latitude,longitude),axis=-1)\n\nonehot = np.stack((train['hour'],train['dayofweek'],train['year'],latitude,longitude),axis=-1)\nfrom sklearn.preprocessing import OneHotEncoder\ndummies = OneHotEncoder(categories='auto')\nonehot = dummies.fit_transform(onehot)\nonehot = scipy.sparse.csr_matrix(onehot)\n\n\n#text_fields = scipy.sparse.hstack([description,summary],format='csr')\n#text_fields = onehot\ntext_fields = scipy.sparse.hstack([description,summary,onehot],format='csr')\nprint(\"Dimension of text_fields: %s\" % str(text_fields.get_shape()))\n\n#y_train = train['num_votes'].apply(lambda x: np.log1p(x))\ny_train = train['num_votes']\n\n\n\n# regressor = SGDRegressor(loss='squared_loss', penalty='l2', alpha=0.001, l1_ratio=0.15, fit_intercept=True,\n# max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, random_state=999,\n# learning_rate='invscaling', eta0=0.01, power_t=0.25, early_stopping=False,\n# validation_fraction=0.1, n_iter_no_change=5, warm_start=False, average=False)\n\n# regressor = sklearn.linear_model.ElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True,\n# normalize=False, precompute='auto', max_iter=1000, tol=0.0001, cv=5, copy_X=True,\n# verbose=0, n_jobs=1, positive=False, random_state=666, selection='cyclic')\n\n#regressor = sklearn.linear_model.RidgeCV(alphas=(40.0,45.0,50.0,60.0,70.0), fit_intercept=True, normalize=False, scoring=None, cv=5, gcv_mode=None, store_cv_values=False)\n#regressor.fit(text_fields,y_train)\n#alpha = regressor.alpha_\n#print(alpha)#10.0\n#regressor = sklearn.linear_model.Ridge(alpha=40.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=999)\n# Complete data\n# Without log : Part A: C = 40, MSE = 0.90, R2 = 0.34 Part B: C = 40, MSE = 0.88, R2 = 0.35\n# With log : Part A: C = 10.0, MSE = 0.86, R2 = 0.59 Part B: C = 10.0, MSE = 0.82, R2 = 0.60\n# Baseline\n# Without log : Part A: C = 40, MSE = 2.87, R2 = 0.13 Part B: C = 40, MSE = 2.70, R2 = 0.18\n# With log : Part A: C = 10.0, MSE = 3.05, R2 = 0.08 Part B: C = 10.0, MSE = 2.80, R2 = 0.15\n\n\n#Lasso regression: is also known as L1 regularization. The penalty it applies is a sum of the absolute values of the weights.\n# This leads to a different effect compared to the Ridge method as the weights can be set to zero if they are not relevant.\n# Therefore, Lasso also acts as a feature selection mechanism.\n# regressor = sklearn.linear_model.LassoCV(eps=0.001, n_alphas=10, alphas=None, fit_intercept=True, normalize=False,\n# precompute='auto', max_iter=1000, tol=0.0001, copy_X=True, cv='warn',\n# verbose=False, n_jobs=None, positive=False, random_state=999,\n# selection='cyclic')\n# regressor.fit(text_fields,y_train)\n# alpha = regressor.alpha_\n# print(alpha)#\n# regressor = sklearn.linear_model.Lasso(alpha=alpha, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=999, selection='cyclic')\n\n\n#### SVR\n# regressor = sklearn.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=0.01, loss='epsilon_insensitive',\n# fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0,\n# random_state=None, max_iter=5000)\n# param_grid = {'C':[0.0001,0.001,0.1, 1, 10, 100]}\n# gridSearch = sklearn.model_selection.GridSearchCV(regressor, param_grid, scoring=None, n_jobs=2, iid='warn', refit=False, cv='warn', verbose=0, pre_dispatch='2*n_jobs', error_score='raise-deprecating', return_train_score=False)\n# gridSearch.fit(text_fields,y_train)\n# C_ = gridSearch.best_params_['C']\n# print(C_) #0.001\nregressor = sklearn.svm.LinearSVR(epsilon=0.0, tol=0.1, C=0.1, loss='epsilon_insensitive',\n fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0,\n random_state=999, max_iter=5000)\n# Complete data\n# Without log : Part A: C = 0.1, MSE = 0.87, R2 = 0.40 Part B: C = 0.1, MSE = 0.85, R2 = 0.40\n# With log : Part A: C = 0.001, MSE = 0.94, R2 = 0.51 Part B: C = 0.001, MSE = 0.93, R2 = 0.53\n# Baseline\n# Without log : Part A: C = 0.1, MSE = 3.01, R2 = 0.1 Part B: C = 0.1, MSE = 2.81, R2 = 0.16\n# With log : Part A: C = 0.001, MSE = 3.14, R2 = 0.1 Part B: C = 0.001, MSE = 3.04, R2 = 0.14\n\n\n############################################################################\n\n\nresult = cross_validate(regressor,X=text_fields,y=y_train,cv=5,scoring={'mse':score,'r2':'r2','explained_variance':'explained_variance'}, return_estimator=True)\n\nprint(\"Overall MSE: %f\" % np.mean(result['test_mse']))\nprint(\"R2: %f\" % np.mean(result['test_r2']))\nprint(result['test_r2'])\n\n\n# from prettytable import PrettyTable\n# print(\"\\n BOW features\")\n# x = PrettyTable()\n# #x.field_names = [\" \",\"Fold 1\", \"Fold 2\", \"Fold 3\", \"Fold 4\", \"Fold 5\"]\n# x.field_names = [\" \",\"Fold 1\", \"Fold 2\"]\n# x.add_row([\"MSE: \"] + [str(v) for v in result['test_mse']])\n# print(x)\n\n\n############################### StatsModels ###################################\n######### Generating Diagnostic Plots for the data #################\n\n#y_train = train['num_votes'].apply(lambda x: np.log(x))\n# y_train = train['num_votes']\n#\n# rows = int(0.01 * (text_fields.get_shape()[0]))\n# text_fields = text_fields[0:rows,:]\n# text_fields = text_fields.toarray()\n# diagnostic_plots(text_fields,y_train[0:rows])\n# plt.show()\n\n\n", "repo_name": "masdeval/seeclickfix", "sub_path": "baseline_model.py", "file_name": "baseline_model.py", "file_ext": "py", "file_size_in_byte": 16067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "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": "statsmodels.api.OLS", "line_number": 49, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 49, "usage_type": "name"}, {"api_name": "statsmodels.api.add_constant", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "seaborn.residplot", "line_number": 71, "usage_type": "call"}, {"api_name": "statsmodels.graphics.gofplots.ProbPlot", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "seaborn.regplot", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "seaborn.regplot", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.log1p", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.expm1", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 172, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 175, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 214, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 215, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 216, "usage_type": "call"}, {"api_name": "gensim.utils.simple_preprocess", "line_number": 222, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 222, "usage_type": "attribute"}, {"api_name": "gensim.utils.simple_preprocess", "line_number": 223, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 223, "usage_type": "attribute"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 272, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 274, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 276, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 276, "usage_type": "attribute"}, {"api_name": "scipy.sparse.hstack", "line_number": 281, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 281, "usage_type": "attribute"}, {"api_name": "sklearn.svm.LinearSVR", "line_number": 333, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 333, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.cross_validate", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 350, "usage_type": "call"}]} +{"seq_id": "72836074717", "text": "import time\nimport uuid\nimport flask\nimport PIL\nfrom io import BytesIO\n\nfrom api import InputImage\nfrom api import get_all_deconv_results\nfrom api import get_deconv_images\n\nclass VisNetWebApp(flask.Flask):\n def __init__(self):\n super().__init__('VisNet')\n self.use_cpu = False\n\n def load_image_from_flask(self, file_storage):\n image_id = str(uuid.uuid4())\n # TODO: Check image type.\n with open('deconv_results/{}.jpg'.format(image_id), 'wb') as fp:\n file_storage.save(fp)\n return image_id\n\n\ndef get_web_app(\n use_cpu=False,\n full_deconv=True, \n):\n web_app = VisNetWebApp()\n web_app.config.update(\n DEBUG=True,\n SECRET_KEY='visnet secret key',\n )\n\n web_app.use_cpu = use_cpu\n web_app.full_deconv = full_deconv\n\n web_app.add_url_rule(\n '/', 'index', index, methods=['GET'],\n )\n web_app.add_url_rule(\n '/config', 'config', config, methods=['GET', 'POST'],\n )\n web_app.add_url_rule(\n '/input_image/', 'input_image', input_image, methods=['GET'],\n )\n web_app.add_url_rule(\n '/deconv_image/', 'deconv_image', deconv_image,\n methods=['GET'],\n )\n web_app.add_url_rule(\n '/results', 'show_results', show_results, methods=['POST'],\n )\n\n return web_app\n\n\ndef index():\n #return flask.render_template('index.html')\n return flask.redirect(flask.url_for('config'))\n\n\ndef config():\n app = flask.current_app\n image_file = None\n if flask.request.method == 'POST':\n try:\n image_file = flask.request.files['image_file']\n except KeyError:\n pass\n\n if image_file is not None and image_file.filename != '':\n image_id = app.load_image_from_flask(image_file) \n return flask.render_template(\n 'config.html',\n image_id=image_id,\n )\n else:\n # TODO: Load a default image file.\n return flask.render_template('config.html')\n\n\ndef show_results():\n app = flask.current_app\n image_id = flask.request.form['image_id']\n num_top_features=3\n input_image_path = get_image_path(image_id, 'jpg')\n input_image = InputImage(input_image_path)\n input_image = input_image.get_resized_image(224)\n input_image.image.save(input_image_path)\n\n rd = get_all_deconv_results(\n input_image,\n log_device_placement=False,\n num_top_features=num_top_features,\n use_cpu=app.use_cpu,\n full_deconv=app.full_deconv,\n ) \n\n get_deconv_images(\n input_image,\n save_path=get_image_path(image_id, 'svg'),\n num_top_features=num_top_features,\n deconv_layers=rd['deconv_layers'],\n )\n \n return flask.render_template(\n 'deconv_result.html',\n image_id=image_id,\n )\n\ndef input_image(image_id):\n try:\n rv = flask.send_file(\n get_image_path(image_id, 'jpg'),\n mimetype='image/jpg',\n cache_timeout=0,\n as_attachment=False,\n attachment_filename='input_image.jpg',\n )\n rv.set_etag(str(time.time()))\n return rv\n except:\n # TODO: Check exception type & gracefully return.\n raise RuntimeError('No input image file.')\n\n\ndef deconv_image(image_id):\n try:\n rv = flask.send_file(\n get_image_path(image_id, 'svg'),\n mimetype='image/svg+xml',\n cache_timeout=0,\n as_attachment=False,\n attachment_filename='deconv_image.svg',\n )\n rv.set_etag(str(time.time()))\n return rv\n except:\n # TODO: Check exception type & gracefully return.\n raise RuntimeError('No deconv result.')\n\n\ndef get_image_path(image_id, ext='jpg'):\n return 'deconv_results/{}.{}'.format(image_id, ext)\n", "repo_name": "chan-y-park/visnet", "sub_path": "web_api.py", "file_name": "web_api.py", "file_ext": "py", "file_size_in_byte": 3809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "attribute"}, {"api_name": "api.InputImage", "line_number": 87, "usage_type": "call"}, {"api_name": "api.get_all_deconv_results", "line_number": 91, "usage_type": "call"}, {"api_name": "api.get_deconv_images", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "71062491039", "text": "import sys\nimport time\nimport contextlib\nimport tempfile\n\nif sys.version_info >= (2, 7):\n import unittest\nelse:\n import unittest2 as unittest\n\nfrom ..configuration import IrodsConfig\nfrom ..controller import IrodsController\nfrom .resource_suite import ResourceBase\n\nfrom . import session\n\nfrom .. import paths\nfrom .. import lib\n\n@contextlib.contextmanager\ndef filesystem_usage_configured(arg=None):\n filename = paths.server_config_path()\n with lib.file_backed_up(filename):\n irods_config = IrodsConfig()\n irods_config.server_config['advanced_settings']['rule_engine_server_sleep_time_in_seconds'] = 1\n\n irods_config.server_config['plugin_configuration']['rule_engines'].insert(0,\n {\n \"instance_name\": \"irods_rule_engine_plugin-policy_engine-verify_checksum-instance\",\n \"plugin_name\": \"irods_rule_engine_plugin-policy_engine-verify_checksum\",\n \"plugin_specific_configuration\": {\n \"log_errors\" : \"true\"\n }\n }\n )\n\n irods_config.commit(irods_config.server_config, irods_config.server_config_path)\n\n IrodsController().restart()\n\n try:\n yield\n finally:\n pass\n\n\nclass TestPolicyEngineVerifyChecksum(ResourceBase, unittest.TestCase):\n def setUp(self):\n super(TestPolicyEngineVerifyChecksum, self).setUp()\n\n def tearDown(self):\n super(TestPolicyEngineVerifyChecksum, self).tearDown()\n\n def test_verify_checksum_success(self):\n with session.make_session_for_existing_admin() as admin_session:\n value = \"\"\n\n try:\n rule = \"\"\"\n{\n \"policy_to_invoke\" : \"irods_policy_execute_rule\",\n \"parameters\" : {\n \"policy_to_invoke\" : \"irods_policy_verify_checksum\",\n \"parameters\" : {\n \"logical_path\" : \"/tempZone/home/rods/file0\",\n \"source_resource\" : \"demoResc\"\n }\n }\n}\nINPUT null\nOUTPUT ruleExecOut\n\"\"\"\n\n rule_file = tempfile.NamedTemporaryFile(mode='wt', dir='/tmp', delete=False).name + '.r'\n with open(rule_file, 'w') as f:\n f.write(rule)\n\n admin_session.assert_icommand(['iput', '-fK', rule_file, 'file0'])\n\n out = 'need more scope'\n with filesystem_usage_configured():\n admin_session.assert_icommand(['irule', '-r', 'irods_rule_engine_plugin-cpp_default_policy-instance', '-F', rule_file], 'STDOUT_SINGLELINE', 'usage')\n\n finally:\n print('annnnd... were done\\n')\n\n\n def test_verify_checksum_failure(self):\n with session.make_session_for_existing_admin() as admin_session:\n value = \"\"\n\n try:\n rule = \"\"\"\n{\n \"policy_to_invoke\" : \"irods_policy_execute_rule\",\n \"parameters\" : {\n \"policy_to_invoke\" : \"irods_policy_verify_checksum\",\n \"parameters\" : {\n \"logical_path\" : \"/tempZone/home/rods/file0\",\n \"source_resource\" : \"demoResc\"\n }\n }\n}\nINPUT null\nOUTPUT ruleExecOut\n\"\"\"\n\n rule_file = tempfile.NamedTemporaryFile(mode='wt', dir='/tmp', delete=False).name + '.r'\n with open(rule_file, 'w') as f:\n f.write(rule)\n\n admin_session.assert_icommand(['iput', '-fK', rule_file, 'file0'])\n\n with open('/var/lib/irods/Vault/home/rods/file0', 'w') as f:\n f.write('X')\n\n out = 'need more scope'\n with filesystem_usage_configured():\n admin_session.assert_icommand(['irule', '-r', 'irods_rule_engine_plugin-cpp_default_policy-instance', '-F', rule_file], 'STDOUT_SINGLELINE', 'failed')\n\n finally:\n print('annnnd... were done\\n')\n\n", "repo_name": "irods/irods_rule_engine_plugins_policy", "sub_path": "packaging/test_plugin_policy_engine-verify_checksum.py", "file_name": "test_plugin_policy_engine-verify_checksum.py", "file_ext": "py", "file_size_in_byte": 3817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.version_info", "line_number": 6, "usage_type": "attribute"}, {"api_name": "configuration.IrodsConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "controller.IrodsController", "line_number": 39, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 20, "usage_type": "attribute"}, {"api_name": "resource_suite.ResourceBase", "line_number": 47, "usage_type": "name"}, {"api_name": "unittest2.TestCase", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 74, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "8535508499", "text": "from flask_wtf import FlaskForm\nfrom wtforms import StringField, BooleanField, DateField, DateTimeField, FileField, SubmitField, Label\nfrom wtforms.validators import DataRequired, regexp\n\nclass AddFriend(FlaskForm):\n student_name = StringField('Enter a name for this friend', validators=[DataRequired()])\n file = FileField(\"Upload an iCalender file\", validators=regexp(u'^[^/\\\\]\\.ics$'))\n selected_file_txt = Label(\"Selected file: f{file}\")\n\nclass HomeForm(FlaskForm):\n question_1 = Label(\"1. Who would you like to hang out with?\")\n question_2 = Label(\"1. Who would you like to hang out with?\")\n\n def create_checkboxes(self, students):\n checkboxes = []\n for student in students:\n checkboxes.append(BooleanField(\"f{student.name}\"))\n\n date = DateField('When would you like to hang out?')\n show_time = BooleanField(\"Specify time\")\n datetime = DateTimeField(\"When would you like to hang out?\")", "repo_name": "anishalatchman/down2hang", "sub_path": "forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "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.validators.DataRequired", "line_number": 6, "usage_type": "call"}, {"api_name": "wtforms.FileField", "line_number": 7, "usage_type": "call"}, {"api_name": "wtforms.validators.regexp", "line_number": 7, "usage_type": "call"}, {"api_name": "wtforms.Label", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 10, "usage_type": "name"}, {"api_name": "wtforms.Label", "line_number": 11, "usage_type": "call"}, {"api_name": "wtforms.Label", "line_number": 12, "usage_type": "call"}, {"api_name": "wtforms.BooleanField", "line_number": 17, "usage_type": "call"}, {"api_name": "wtforms.DateField", "line_number": 19, "usage_type": "call"}, {"api_name": "wtforms.BooleanField", "line_number": 20, "usage_type": "call"}, {"api_name": "wtforms.DateTimeField", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "21624201152", "text": "from keras.models import model_from_json\nfrom keras.preprocessing import image\nimport json\nimport cv2\nimport numpy as np\nimport os\n\n'''\n{'ed':0, 'miojo':1}\n\n'''\ndef load_images(img):\n\n img = image.load_img(img, target_size = (150,150), grayscale = True)\n x = image.img_to_array(img)\n x = x / 255\n x = np.expand_dims(x, axis=0)\n\n return x\n\ndef batch_predict():\n\n for fileName in os.listdir(\"./img/validation/miojo/\"):\n \n aux = dict()\n img = load_images(\"./img/validation/miojo/\"+fileName)\n y_prob = model.predict(img).tolist()\n\n aux['ed'] = y_prob[0][0]\n aux['miojo'] = y_prob[0][1]\n \n #print(json.dumps(aux))\n print(aux)\n\nif __name__ == \"__main__\":\n\n # load json and create model\n json_file = open('gray_model.json', 'r')\n loaded_model_json = json_file.read()\n json_file.close()\n model = model_from_json(loaded_model_json)\n \n # load weights into new model\n model.load_weights(\"model_weights_gray_model.h5\")\n print(\"Loaded model from disk\")\n\n model.compile(loss = 'categorical_crossentropy',\n optimizer = 'Adam',\n metrics = ['accuracy'])\n\n batch_predict()\n\n #pred = model.predict_classes(load_images(\"out.png\"))\n #print(pred)\n ", "repo_name": "henriqueluzz/keras_flask_webservice", "sub_path": "predictions.py", "file_name": "predictions.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "keras.preprocessing.image.load_img", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 14, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.models.model_from_json", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "21577903721", "text": "import numpy as np\nimport sto\nimport gto\nimport time\n\nverbose = False\nMAXITER = 40 # Maximum SCF iterations\nE_conv = 1.0e-6 # Energy convergence criterion\n\n\ndef run_hf(fs, Z):\n \"\"\"\n Run restricted hartree fock for a single atom.\n\n INPUT:\n fs: basis functions\n Z: nuclear charge of the atom\n \"\"\"\n # num of electron = nuclear charege (since it's atom)\n N = Z\n start = time.time()\n\n # initialization\n H = sto.H_matrix(fs, Z)\n S = sto.S_matrix(fs)\n e, Co = sto.secular_eqn(H, S)\n P = sto.P_matrix(Co, N)\n hf_e = sto.energy_tot(e, P, H)\n\n stop = time.time()\n print('------------------------------', \"Initialization\", '------------------------------')\n print('-------------------------', \"Ignore repulsion integral\", '------------------------')\n sto.print_info(e, Co, hf_e, start, stop, verbose=verbose)\n print('-----------', \"Caculating Electron Repulsion Integral (takes time)\", '------------')\n R = sto.R_matrix(fs)\n delta_e = 1\n ITER = 0\n previous_e = hf_e\n\n # Iterations\n while(delta_e > E_conv and ITER < MAXITER):\n print('------------------------------', \"Iteration\", ITER + 1, '------------------------------')\n start = time.time()\n\n # important scf steps\n G = sto.G_matrix(P, R)\n F = H + G\n e, Co = sto.secular_eqn(F, S)\n P = sto.P_matrix(Co, N)\n hf_e = sto.energy_tot(e, P, H)\n\n delta_e = np.abs(hf_e - previous_e)\n previous_e = hf_e\n ITER += 1\n stop = time.time()\n sto.print_info(e, Co, hf_e, start, stop, delta_e, verbose)\n\n return hf_e\n\n\ndef test1():\n \"\"\"\n Test of He (1s)\n \"\"\"\n # Use 2 Slator Type ourbital to represent Helium 1s orbital.\n # The final Helium 1s orbital is a linear combination of these two STO.\n f1s_1 = sto.STO(zeta=1.45363, n=1)\n f1s_2 = sto.STO(zeta=2.91093, n=1)\n\n # all basis functions\n fs = [f1s_1, f1s_2]\n\n # nuclear charge of He\n Z = 2\n\n # run hartree fock\n hf_e = run_hf(fs, Z)\n\n # compare result with reference\n ref_hf_e = -2.8616726\n sto.compare(hf_e, ref_hf_e)\n\n\ndef test2():\n \"\"\"\n Test of Be (1s, 2s)\n \"\"\"\n # Use 2 STO to represent Be 1s orbital and another 2 STO for 2s orbital\n # The final 1s orbital is a linear combination of these 4 STO.\n # Same for 2s orbital.\n f1s_1 = sto.STO(zeta=5.59108, n=1)\n f1s_2 = sto.STO(zeta=3.35538, n=1)\n f2s_1 = sto.STO(zeta=1.01122, n=2)\n f2s_2 = sto.STO(zeta=0.61000, n=2)\n\n # all basis functions\n fs = [f1s_1, f1s_2, f2s_1, f2s_2]\n\n # nuclear charge of Be\n Z = 4\n\n # run hartree fock\n hf_e = run_hf(fs, Z)\n\n # compare result with reference\n ref_hf_e = -14.572369\n sto.compare(hf_e, ref_hf_e)\n\n\ndef test3():\n \"\"\"\n Test of He (1s)\n \"\"\"\n # Use 2 Slator Type ourbital to represent Helium 1s orbital.\n # The final Helium 1s orbital is a linear combination of these two STO.\n f1s_1 = gto.CGF(zeta=1.45363, n=1, coordinates=[0, 0, 0]).cgf\n f1s_2 = gto.CGF(zeta=2.91093, n=1, coordinates=[0, 0, 0]).cgf\n\n # all basis functions\n fs = [f1s_1, f1s_2]\n\n # nuclear charge of He\n Z = 2\n\n # run hartree fock\n hf_e = run_hf(fs, Z)\n\n # compare result with reference\n ref_hf_e = -2.8616726\n sto.compare(hf_e, ref_hf_e)\n\n\nif __name__ == \"__main__\":\n test1()\n # test2()\n test3()\n", "repo_name": "yueyericardo/simuc", "sub_path": "notebooks/pchem/hartree-fock/hf/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "60", "api": [{"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "sto.H_matrix", "line_number": 24, "usage_type": "call"}, {"api_name": "sto.S_matrix", "line_number": 25, "usage_type": "call"}, {"api_name": "sto.secular_eqn", "line_number": 26, "usage_type": "call"}, {"api_name": "sto.P_matrix", "line_number": 27, "usage_type": "call"}, {"api_name": "sto.energy_tot", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "sto.print_info", "line_number": 33, "usage_type": "call"}, {"api_name": "sto.R_matrix", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "sto.G_matrix", "line_number": 46, "usage_type": "call"}, {"api_name": "sto.secular_eqn", "line_number": 48, "usage_type": "call"}, {"api_name": "sto.P_matrix", "line_number": 49, "usage_type": "call"}, {"api_name": "sto.energy_tot", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "sto.print_info", "line_number": 56, "usage_type": "call"}, {"api_name": "sto.STO", "line_number": 67, "usage_type": "call"}, {"api_name": "sto.STO", "line_number": 68, "usage_type": "call"}, {"api_name": "sto.compare", "line_number": 81, "usage_type": "call"}, {"api_name": "sto.STO", "line_number": 91, "usage_type": "call"}, {"api_name": "sto.STO", "line_number": 92, "usage_type": "call"}, {"api_name": "sto.STO", "line_number": 93, "usage_type": "call"}, {"api_name": "sto.STO", "line_number": 94, "usage_type": "call"}, {"api_name": "sto.compare", "line_number": 107, "usage_type": "call"}, {"api_name": "gto.CGF", "line_number": 116, "usage_type": "call"}, {"api_name": "gto.CGF", "line_number": 117, "usage_type": "call"}, {"api_name": "sto.compare", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "29139151932", "text": "\"\"\"Several supporting functions for the KL simulation\"\"\"\nimport numpy as np\nfrom scipy.integrate import solve_ivp\n\n\ndef build_model(num_env=10, num_phen=10, mean_fit=0, std_fit=1, range_switch=(0.01, 1), k_mu_dep=0.1, seed=False):\n \"\"\"\n Function which creates a fitness matrix with random growth rates on the diagonal and a switching matrix with a\n constant away-switching rate for all phenotypes in all environments (for now). These matrices are combined and\n returned as one matrix. An intial distribution x_0 with 1 for every phenotype is created and returned.\n :param num_env: number of phenotypes\n :param num_phen: number of environments\n :param mean_fit: mean growth rate (for sampling)\n :param std_fit: standard deviation of the growth rate (for sampling)\n :param range_switch: range from witch to draw the switching rate\n :param k_mu_dep: constant which determines sensitivity to mu\n :param seed: seed for PRNG\n :return a: growth/switching matrices\n :return x_0: vector x(0) filled with ones\n :return b: matrix of transition probabilities between environments\n \"\"\"\n if seed:\n np.random.seed(seed)\n\n # Initialise\n mat_fit = np.zeros((num_env, num_phen, num_phen))\n mat_switch_const = np.ones((num_env, num_phen, num_phen))\n mat_switch_dep = np.ones((num_env, num_phen, num_phen))\n\n # Fill diagonal of fitness matrix\n for i in range(num_env):\n np.fill_diagonal(mat_fit[i], np.random.normal(loc=mean_fit, scale=std_fit, size=num_phen))\n\n # First set all away-switching rates, then sum them in diagonal\n offset = mat_fit.min()\n for i in range(num_env):\n for j in range(num_phen):\n switch_rate = np.random.uniform(range_switch[0], range_switch[1])\n mat_switch_const[i, :, j] = switch_rate\n mat_switch_dep[i, :, j] = switch_rate / (mat_fit[i, j, j] - offset + k_mu_dep)\n mat_switch_dep[i, j, j] = -(num_phen - 1) * mat_switch_dep[i, j, j]\n mat_switch_const[i, j, j] = -(num_phen - 1) * mat_switch_const[i, j, j]\n\n # Create random transition probability matrix, switching to the same environment is not allowed\n b = np.random.uniform(0, 1, (num_env, num_env))\n np.fill_diagonal(b, 0)\n norm_factors = np.sum(b, 0)\n for i in range(num_env):\n b[:, i] = b[:, i] / norm_factors[i]\n\n # Return growth/switching matrix, starting distribution and transition probability matrix\n return [mat_fit + mat_switch_const, mat_fit + mat_switch_dep], np.ones(num_phen) / num_phen, b\n\n\ndef grow(eig_vecs, eig_vals, c_scale, t, extinction=False):\n \"\"\"\n calculate x(t)\n :param eig_vecs: eigenvectors\n :param eig_vals: eigenvalues\n :param c_scale: rescaling constants\n :param t: time\n :param extinction: flag to check whether population is extinct\n :return: x_t/total: x(t) in fractions per phenotype\n :return: mu: average growth rate in this environment\n \"\"\"\n max_eig = eig_vals.max()\n x_t = np.dot(np.multiply(c_scale, eig_vecs), np.exp((eig_vals - max_eig) * t))\n total = np.sum(x_t)\n\n if total <= 10 ** -6:\n extinction = True\n\n mu = np.log(total) / t + max_eig\n\n return np.real(x_t / total), np.real(mu), extinction\n\n\ndef grow_reportlag(eig_vecs, eig_vals, c_scale, t, extinction=False):\n \"\"\"\n calculate x(t)\n :param eig_vecs: eigenvectors\n :param eig_vals: eigenvalues\n :param c_scale: rescaling constants\n :param t: time\n :param extinction: flag to check whether population is extinct\n :return: x_t/total: x(t) in fractions per phenotype\n :return: mu: average growth rate in this environment\n \"\"\"\n max_eig = eig_vals.max()\n x_t = np.dot(np.multiply(c_scale, eig_vecs), np.exp((eig_vals - max_eig) * t))\n total = np.sum(x_t)\n\n if total <= 10 ** -6:\n extinction = True\n\n mu = np.log(total) / t + max_eig\n x_t_norm = x_t/total\n lag = t - (mu * t) / max_eig\n if (np.imag(mu) < 1e-10) and (np.max(np.abs(np.imag(x_t_norm))) < 1e-10) and (np.imag(lag) < 1e-10):\n mu = np.real(mu)\n x_t_norm = np.real(x_t_norm)\n lag = np.real(lag)\n else:\n pass\n return x_t_norm, mu, extinction, lag\n\n\ndef grow_ode(a_mat, x0, t_end, extinction=False):\n \"\"\"\n calculate x(t)\n\n \"\"\"\n\n def ode_fun(t, x):\n return np.dot(a_mat, x)\n\n t_span = (0, t_end)\n t_eval = [t_end]\n ode_sol = solve_ivp(ode_fun, t_span, x0, t_eval=t_eval, vectorized=True)\n x_t = ode_sol.y[:, -1]\n total = np.sum(x_t)\n\n if total <= 10 ** -6:\n extinction = True\n\n mu = np.log(total) / t_end\n x_t_norm = x_t/total\n if (np.imag(mu) < 1e-10) and (np.max(np.abs(np.imag(x_t_norm))) < 1e-10):\n mu = np.real(mu)\n x_t_norm = np.real(x_t_norm)\n else:\n pass\n return x_t_norm, mu, extinction\n\n\ndef grow_reportpdf(eig_vecs, eig_vals, c_scale, t, extinction=False, timestep=0.1):\n \"\"\"\n calculate x(t)\n :param eig_vecs: eigenvectors\n :param eig_vals: eigenvalues\n :param c_scale: rescaling constants\n :param t: time\n :param extinction: flag to check whether population is extinct\n :return: x_t/total: x(t) in fractions per phenotype\n :return: mu: average growth rate in this environment\n \"\"\"\n num_steps = int(np.ceil(t / timestep))\n times = np.linspace(0, t, num_steps)\n x_pdf = np.zeros(len(c_scale))\n t_cur = 0\n\n max_eig = eig_vals.max()\n for ind_time in range(1,num_steps):\n new_time = times[ind_time]\n new_pdf = np.dot(np.multiply(c_scale, eig_vecs), np.exp((eig_vals - max_eig) * t_cur))\n new_pdf = new_pdf/np.sum(new_pdf)\n\n x_pdf = x_pdf*(t_cur/new_time) + new_pdf*((new_time-t_cur)/new_time)\n t_cur = new_time\n\n x_t = np.dot(np.multiply(c_scale, eig_vecs), np.exp((eig_vals - max_eig) * t))\n total = np.sum(x_t)\n\n if total <= 10 ** -6:\n extinction = True\n\n mu = np.log(total) / t + max_eig\n x_t_norm = x_t/total\n lag = t - (mu * t) / max_eig\n if (np.imag(mu) < 1e-10) and (np.max(np.abs(np.imag(x_t_norm))) < 1e-10) and (np.imag(lag) < 1e-10) and (np.max(np.abs(np.imag(x_pdf))) < 1e-10):\n mu = np.real(mu)\n x_t_norm = np.real(x_t_norm)\n lag = np.real(lag)\n x_pdf = np.real(x_pdf)\n else:\n pass\n return x_t_norm, mu, extinction, x_pdf, t_cur\n\n\ndef grow_reporttrace(eig_vecs, eig_vals, c_scale, t, extinction=False, timestep=0.1):\n \"\"\"\n calculate x(t)\n :param eig_vecs: eigenvectors\n :param eig_vals: eigenvalues\n :param c_scale: rescaling constants\n :param t: time\n :param extinction: flag to check whether population is extinct\n :return: x_t/total: x(t) in fractions per phenotype\n :return: mu: average growth rate in this environment\n \"\"\"\n num_steps = max(int(np.ceil(t / timestep)), 10)\n times = np.linspace(0, t, num_steps)\n t_diff = times[1]-times[0]\n\n t_trace = times\n mu_trace = np.zeros(len(times))\n x_trace = np.zeros((num_steps, len(c_scale)))\n x_trace[0, :] = np.sum(np.multiply(c_scale, eig_vecs), axis=1)\n\n max_eig = eig_vals.max()\n for ind_time in range(1, num_steps):\n new_time = times[ind_time]\n x_trace[ind_time, :] = np.dot(np.multiply(c_scale, eig_vecs), np.exp((eig_vals - max_eig) * new_time))\n\n x_t = np.dot(np.multiply(c_scale, eig_vecs), np.exp((eig_vals - max_eig) * t))\n total = np.sum(x_t)\n\n if total <= 10 ** -6:\n extinction = True\n\n frac_trace = x_trace/np.tile(np.sum(x_trace, axis=1)[:, np.newaxis], (1, len(c_scale)))\n mu_trace[1:] = (np.diff(np.log(np.sum(x_trace, axis=1)))/t_diff)+max_eig\n\n t_trace = t_trace[1:]\n mu_trace = mu_trace[1:]\n frac_trace = frac_trace[1:]\n\n return x_t / total, t_trace, mu_trace, frac_trace\n\n\ndef grow_reportlogOD(eig_vecs, eig_vals, c_scale, times):\n \"\"\"\n calculate x(t)\n :param eig_vecs: eigenvectors\n :param eig_vals: eigenvalues\n :param c_scale: rescaling constants\n \"\"\"\n logOD_trace = np.zeros(len(times))\n\n for ind_time, time_curr in enumerate(times):\n logOD_trace[ind_time] = np.log(np.sum(np.dot(np.multiply(c_scale, eig_vecs), np.exp(eig_vals * time_curr))))\n\n return logOD_trace\n\n\ndef generate_env_seq(b, min_sim_time=100, avg_env_length=[10] * 10, seed=False, random_times=True):\n \"\"\"\n generates a sequence of environments based on transition probabilities and a sequence of exponentially distributed\n environment times\n :param b:\n :param min_sim_time:\n :param avg_env_length:\n :param seed:\n :return: environment sequence and times\n \"\"\"\n if seed:\n np.random.seed(seed)\n\n eig_val_b, eig_vec_b = np.linalg.eig(b)\n eig_vec_p = eig_vec_b[:, np.isclose(1, eig_val_b.real)].real\n p = (eig_vec_p / np.sum(eig_vec_p)).flatten()\n\n env_seq = [np.random.choice(np.shape(b)[0], p=p)]\n if random_times:\n env_times = [np.random.exponential(avg_env_length[env_seq[-1]])]\n else:\n env_times = [avg_env_length[env_seq[-1]]]\n sum_env_times = env_times[0]\n ind = 0\n\n while sum_env_times < min_sim_time:\n env_seq.append(np.random.choice(np.shape(b)[0], p=b[:, env_seq[ind]]))\n if random_times:\n env_times.append(np.random.exponential(avg_env_length[env_seq[-1]]))\n else:\n env_times.append(avg_env_length[env_seq[-1]])\n sum_env_times += env_times[-1]\n ind += 1\n\n return env_seq, env_times\n\n\ndef calc_eig_vals_vecs_approximations(mat, fit_mat):\n num_phen = mat.shape[0]\n mus = np.diag(fit_mat)\n zero_vals = mus.copy()\n zero_vecs = np.zeros(mat.shape)\n np.fill_diagonal(zero_vecs, 1)\n\n delta_h = mat.copy() - fit_mat\n h_mat = mat.copy()\n np.fill_diagonal(h_mat, 0)\n\n mu_diffs = np.tile(mus, (num_phen, 1)) - np.transpose(np.tile(mus, (num_phen, 1)))\n np.fill_diagonal(mu_diffs, 1)\n h_mat_rescale = np.divide(h_mat, mu_diffs)\n\n # Calculate first order approximation of eigenvalues\n first_val_corr = delta_h.diagonal()\n first_vals = zero_vals + first_val_corr\n\n # Calculate first order approximation of eigenvectors\n first_vecs_corr = h_mat_rescale.copy()\n first_vecs = zero_vecs + first_vecs_corr\n\n # Calculate second order approximation of eigenvalues\n second_val_corr = np.diagonal(np.dot(h_mat, h_mat_rescale))\n second_vals = first_vals + second_val_corr\n\n # Calculate second order approximation of eigenvectors\n switching_backforth = np.matmul(h_mat, np.divide(h_mat, mu_diffs))\n HiiminusHkk = np.transpose(np.tile(delta_h.diagonal(), (num_phen, 1))) - np.tile(delta_h.diagonal(),\n (num_phen, 1))\n switched_i_to_k = np.divide(h_mat, mu_diffs)\n second_vecs_corr = np.divide(switching_backforth + np.multiply(HiiminusHkk, switched_i_to_k), mu_diffs)\n\n np.fill_diagonal(second_vecs_corr, - 0.5 * np.sum(np.multiply(h_mat_rescale, h_mat_rescale), axis=0))\n second_vecs = first_vecs + second_vecs_corr\n\n # Do some tests:\n zero_vals_matrix = np.zeros(fit_mat.shape)\n np.fill_diagonal(zero_vals_matrix, zero_vals)\n # Does the zeroth order perturbation equation work out?\n test_zero = np.dot(fit_mat, zero_vecs) - np.dot(zero_vals_matrix, zero_vecs)\n\n # Does the first order perturbation equation work out?\n first_vals_matrix = np.zeros(fit_mat.shape)\n np.fill_diagonal(first_vals_matrix, first_val_corr)\n test_first = np.matmul(fit_mat, first_vecs_corr) + np.matmul(delta_h, zero_vecs) - np.matmul(first_vecs_corr,\n zero_vals_matrix) - np.matmul(\n first_vals_matrix, zero_vecs)\n\n # Does the second order perturbation equation work out?\n second_vals_matrix = np.zeros(fit_mat.shape)\n np.fill_diagonal(second_vals_matrix, second_val_corr)\n test_second = np.matmul(fit_mat, second_vecs_corr) + np.matmul(delta_h, first_vecs_corr) - np.matmul(\n second_vecs_corr, zero_vals_matrix) - np.matmul(first_vecs_corr, first_vals_matrix) - \\\n np.matmul(zero_vecs, second_vals_matrix)\n\n # Calculate inverses\n # Zeroth order\n zero_inv = np.zeros(mat.shape)\n np.fill_diagonal(zero_inv, 1)\n\n # First order\n first_inv_corr = - np.divide(h_mat, mu_diffs)\n first_inv = zero_inv + first_inv_corr\n\n # Second order\n # First fill the off-diagonal elements\n switching_ik_via_l = np.matmul(np.divide(h_mat, mu_diffs), np.divide(h_mat, mu_diffs))\n switching_il_lk = np.divide(np.matmul(h_mat, np.divide(h_mat, mu_diffs)), mu_diffs)\n switching_away_inv = np.multiply(HiiminusHkk, np.divide(h_mat, np.multiply(mu_diffs, mu_diffs)))\n second_inv_corr = switching_ik_via_l - switching_il_lk - switching_away_inv\n\n np.fill_diagonal(second_inv_corr, np.sum(\n np.divide(np.multiply(h_mat, 0.5 * h_mat - np.transpose(h_mat)), np.multiply(mu_diffs, mu_diffs)), axis=0))\n second_inv = first_inv + second_inv_corr\n\n # Test inverses\n test_inv_zero = np.matmul(zero_vecs, zero_inv)\n test_inv_first = np.matmul(first_vecs_corr, zero_inv) + np.matmul(zero_vecs, first_inv_corr)\n test_inv_second = np.matmul(zero_vecs, second_inv_corr) + np.matmul(first_vecs_corr, first_inv_corr) + np.matmul(\n second_vecs_corr, zero_inv)\n\n return zero_vals, first_vals, second_vals, zero_vecs, first_vecs, second_vecs, zero_inv, first_inv, second_inv\n\n\ndef calc_q_ij(mats, fit_mats, dominant_eigs_indices, order='second'):\n num_env = mats.shape[0]\n num_phen = mats.shape[1]\n q_ij = np.zeros((num_env, num_env))\n\n for env_i in range(num_env):\n # Collect all necessary matrices\n fit_mat_i = fit_mats[env_i]\n mus_i = np.diag(fit_mat_i)\n h_mat_i = mats[env_i].copy()\n np.fill_diagonal(h_mat_i, 0)\n mu_diffs_i = np.tile(mus_i, (num_phen, 1)) - np.transpose(np.tile(mus_i, (num_phen, 1)))\n np.fill_diagonal(mu_diffs_i, 1)\n alpha_i = dominant_eigs_indices[env_i]\n HcolcolminusHrowrow_i = np.tile(np.sum(h_mat_i,axis=0),(num_phen,1)) - np.transpose(np.tile(np.sum(h_mat_i,axis=0), (num_phen, 1)))\n\n for env_j in range(num_env):\n # Collect all necessary matrices\n fit_mat_j = fit_mats[env_j]\n mus_j = np.diag(fit_mat_j)\n h_mat_j = mats[env_j].copy()\n np.fill_diagonal(h_mat_j, 0)\n mu_diffs_j = np.tile(mus_j, (num_phen, 1)) - np.transpose(np.tile(mus_j, (num_phen, 1)))\n np.fill_diagonal(mu_diffs_j, 1)\n alpha_j = dominant_eigs_indices[env_j]\n HcolcolminusHrowrow_j = np.tile(np.sum(h_mat_j, axis=0), (num_phen, 1)) - np.transpose(\n np.tile(np.sum(h_mat_j, axis=0), (num_phen, 1)))\n\n if alpha_i == alpha_j: # We have a different formula if the dominant eigenvector remains at same index\n q_curr = 1\n\n q_first_corr = 0\n q_curr = q_curr + q_first_corr\n if order == 'second':\n # first order inverse times first order vectors\n first_first_corr = - np.matmul(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_j, mu_diffs_j))[alpha_i, alpha_j]\n\n # second order inverse times zeroth order vectors\n second_zero_corr = 0.5 * np.sum(np.multiply(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_i, mu_diffs_i))[:, alpha_i])\n second_zero_corr = second_zero_corr - np.matmul(h_mat_i, np.divide(h_mat_i, np.multiply(mu_diffs_i, mu_diffs_i)))[alpha_i, alpha_i]\n\n # zeroth order inverse times second order vectors\n zero_second_corr = - 0.5 * np.sum(np.multiply(np.divide(h_mat_j, mu_diffs_j), np.divide(h_mat_j, mu_diffs_j))[:, alpha_j])\n q_second_corr = first_first_corr + second_zero_corr + zero_second_corr\n q_curr = q_curr + q_second_corr\n\n else: # If the index of dominant eigenvector changes\n q_curr = 0\n\n q_first_corr = (np.divide(h_mat_j, mu_diffs_j) - np.divide(h_mat_i, mu_diffs_i))[alpha_i,alpha_j]\n q_curr = q_curr + q_first_corr\n if order == 'second':\n # first order inverse times first order vectors\n first_first_corr = -np.matmul(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_j, mu_diffs_j))[alpha_i, alpha_j]\n\n # second order inverse times zeroth order vectors\n second_zero_corr = np.matmul(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_i, mu_diffs_i))[alpha_i, alpha_j]\n second_zero_corr = second_zero_corr - np.divide(np.matmul(h_mat_i, np.divide(h_mat_i, mu_diffs_i)),mu_diffs_i)[alpha_i, alpha_j]\n second_zero_corr = second_zero_corr - np.multiply(HcolcolminusHrowrow_i, np.divide(h_mat_i,np.multiply(mu_diffs_i, mu_diffs_i)))[alpha_i, alpha_j]\n\n # zeroth order inverse times second order vectors\n zero_second_corr = np.divide(np.matmul(h_mat_j, np.divide(h_mat_j, mu_diffs_j)), mu_diffs_j)[alpha_i, alpha_j]\n zero_second_corr = zero_second_corr + np.multiply(HcolcolminusHrowrow_j,np.divide(h_mat_j, np.multiply(mu_diffs_j, mu_diffs_j)))[alpha_i, alpha_j]\n q_second_corr = second_zero_corr + first_first_corr + zero_second_corr\n q_curr = q_curr + q_second_corr\n\n q_ij[env_i, env_j] = q_curr\n\n return q_ij\n\n\ndef test_q_ij(mats, fit_mats, dominant_eigs_indices, zero_vecs, zero_inv, first_vecs, first_inv, second_vecs, second_inv):\n order = 'second'\n\n first_vecs_corr = first_vecs-zero_vecs\n second_vecs_corr = second_vecs - first_vecs\n first_inv_corr = first_inv - zero_inv\n second_inv_corr = second_inv - first_inv\n\n num_env = mats.shape[0]\n num_phen = mats.shape[1]\n q_ij = np.zeros((num_env, num_env))\n q_ij_test_first = np.zeros((num_env, num_env))\n q_ij_test_second = np.zeros((num_env, num_env))\n\n for env_i in range(num_env):\n # Collect all necessary matrices\n fit_mat_i = fit_mats[env_i]\n mus_i = np.diag(fit_mat_i)\n h_mat_i = mats[env_i].copy()\n np.fill_diagonal(h_mat_i, 0)\n mu_diffs_i = np.tile(mus_i, (num_phen, 1)) - np.transpose(np.tile(mus_i, (num_phen, 1)))\n np.fill_diagonal(mu_diffs_i, 1)\n alpha_i = dominant_eigs_indices[env_i]\n HcolcolminusHrowrow_i = np.tile(np.sum(h_mat_i,axis=0),(num_phen,1)) - np.transpose(np.tile(np.sum(h_mat_i,axis=0), (num_phen, 1)))\n\n for env_j in range(num_env):\n # Collect all necessary matrices\n fit_mat_j = fit_mats[env_j]\n mus_j = np.diag(fit_mat_j)\n h_mat_j = mats[env_j].copy()\n np.fill_diagonal(h_mat_j, 0)\n mu_diffs_j = np.tile(mus_j, (num_phen, 1)) - np.transpose(np.tile(mus_j, (num_phen, 1)))\n np.fill_diagonal(mu_diffs_j, 1)\n alpha_j = dominant_eigs_indices[env_j]\n HcolcolminusHrowrow_j = np.tile(np.sum(h_mat_j, axis=0), (num_phen, 1)) - np.transpose(\n np.tile(np.sum(h_mat_j, axis=0), (num_phen, 1)))\n\n q_ij_supposed_first = (np.matmul(zero_inv[env_i], first_vecs_corr[env_j]) + np.matmul(first_inv_corr[env_i], zero_vecs[env_j]))[alpha_i, alpha_j]\n q_ij_supposed_second = (np.matmul(zero_inv[env_i], second_vecs_corr[env_j]) + np.matmul(first_inv_corr[env_i], first_vecs_corr[env_j]) + np.matmul(second_inv_corr[env_i], zero_vecs[env_j]))[alpha_i,alpha_j]\n\n if alpha_i == alpha_j: # We have a different formula if the dominant eigenvector remains at same index\n q_curr = 1\n\n supposed_firstcorr = (np.matmul(zero_inv[env_i], first_vecs_corr[env_j]) + np.matmul(first_inv_corr[env_i], zero_vecs[env_j]))[alpha_i, alpha_j]\n\n q_first_corr = 0\n q_curr = q_curr + q_first_corr\n if order == 'second':\n # first order inverse times first order vectors\n supposed_firstfirst_corr = np.matmul(first_inv_corr[env_i], first_vecs_corr[env_j])[alpha_i, alpha_j]\n\n first_first_corr = - np.matmul(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_j, mu_diffs_j))[alpha_i, alpha_j]\n\n # second order inverse times zeroth order vectors\n supposed_secondzero_corr = np.matmul(second_inv_corr[env_i], zero_vecs[env_j])[alpha_i, alpha_j]\n\n second_zero_corr = 0.5 * np.sum(np.multiply(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_i, mu_diffs_i))[:, alpha_i])\n second_zero_corr = second_zero_corr - np.matmul(h_mat_i, np.divide(h_mat_i, np.multiply(mu_diffs_i, mu_diffs_i)))[alpha_i, alpha_i]\n\n # zeroth order inverse times second order vectors\n supposed_zerosecond_corr = np.matmul(zero_inv[env_i], second_vecs_corr[env_j])[alpha_i, alpha_j]\n\n zero_second_corr = - 0.5 * np.sum(np.multiply(np.divide(h_mat_j, mu_diffs_j), np.divide(h_mat_j, mu_diffs_j))[:, alpha_j])\n q_second_corr = first_first_corr + second_zero_corr + zero_second_corr\n q_curr = q_curr + q_second_corr\n\n else: # If the index of dominant eigenvector changes\n q_curr = 0\n supposed_firstcorr = (np.matmul(zero_inv[env_i], first_vecs_corr[env_j]) + np.matmul(first_inv_corr[env_i], zero_vecs[env_j]))[alpha_i, alpha_j]\n\n q_first_corr = (np.divide(h_mat_j, mu_diffs_j) - np.divide(h_mat_i, mu_diffs_i))[alpha_i,alpha_j]\n q_curr = q_curr + q_first_corr\n if order == 'second':\n # first order inverse times first order vectors\n supposed_firstfirst_corr = np.matmul(first_inv_corr[env_i], first_vecs_corr[env_j])[alpha_i, alpha_j]\n\n first_first_corr = -np.matmul(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_j, mu_diffs_j))[alpha_i, alpha_j]\n\n # second order inverse times zeroth order vectors\n supposed_secondzero_corr = np.matmul(second_inv_corr[env_i], zero_vecs[env_j])[alpha_i, alpha_j]\n\n second_zero_corr = np.matmul(np.divide(h_mat_i, mu_diffs_i), np.divide(h_mat_i, mu_diffs_i))[alpha_i, alpha_j]\n second_zero_corr = second_zero_corr - np.divide(np.matmul(h_mat_i, np.divide(h_mat_i, mu_diffs_i)),mu_diffs_i)[alpha_i, alpha_j]\n second_zero_corr = second_zero_corr - np.multiply(HcolcolminusHrowrow_i, np.divide(h_mat_i,np.multiply(mu_diffs_i, mu_diffs_i)))[alpha_i, alpha_j]\n\n # zeroth order inverse times second order vectors\n supposed_zerosecond_corr = np.matmul(zero_inv[env_i], second_vecs_corr[env_j])[alpha_i, alpha_j]\n\n zero_second_corr = np.divide(np.matmul(h_mat_j, np.divide(h_mat_j, mu_diffs_j)), mu_diffs_j)[alpha_i, alpha_j]\n zero_second_corr = zero_second_corr + np.multiply(HcolcolminusHrowrow_j,np.divide(h_mat_j, np.multiply(mu_diffs_j, mu_diffs_j)))[alpha_i, alpha_j]\n q_second_corr = second_zero_corr + first_first_corr + zero_second_corr\n q_curr = q_curr + q_second_corr\n\n q_ij[env_i, env_j] = q_curr\n q_ij_test_first[env_i, env_j] = q_ij_supposed_first - q_first_corr\n q_ij_test_second[env_i,env_j] = q_ij_supposed_second - q_second_corr\n\n return q_ij_test_first, q_ij_test_second\n\n\ndef calc_approx_fitness(mats, fit_mats, avg_wait_times, trans_mat, order='second'):\n num_env = mats.shape[0]\n num_phen = mats.shape[1]\n\n if np.isscalar(avg_wait_times): # In this case, all environments have the same avg length\n avg_wait_times = np.ones(num_env)*avg_wait_times\n\n dominant_eig_inds = np.zeros(num_env)\n dominant_eig_vals = np.zeros(num_env)\n\n # Store eigenvalues and eigenvector indices (alpha_i) for all environments\n for env_i in range(num_env):\n zero_vals, first_vals, second_vals, zero_vecs, first_vecs, second_vecs, zero_inv, first_inv, second_inv \\\n = calc_eig_vals_vecs_approximations(mats[env_i], fit_mats[env_i])\n if order == 'first':\n eig_vals = first_vals\n elif order == 'second':\n eig_vals = second_vals\n\n dominant_eig_vals[env_i] = np.max(eig_vals)\n dominant_eig_inds[env_i] = np.argmax(eig_vals)\n\n dominant_eig_inds = dominant_eig_inds.astype(int)\n\n # Get transition losses (q_ij)\n q_ij = calc_q_ij(mats, fit_mats, dominant_eig_inds, order=order)\n\n # Get probabilities of environments as right eigenvector with eigenvalue 1\n eig_val_b, eig_vec_b = np.linalg.eig(trans_mat)\n eig_vec_p = eig_vec_b[:, np.isclose(1, eig_val_b.real)].real\n p = (eig_vec_p / np.sum(eig_vec_p)).flatten()\n\n # Calculate average duration of environments\n tau = np.dot(p, avg_wait_times)\n\n # Add everything up to get average fitness\n # First the optimal growth (in the limiting case that population is always in dominant eigenvector immediately)\n max_growth = np.sum(np.multiply(np.multiply(p, avg_wait_times), dominant_eig_vals))\n # Then account for the cells that are lost during transitions\n transition_loss = np.sum(np.matmul(np.multiply(trans_mat, np.log(q_ij)), p))\n\n avg_fitness = (max_growth + transition_loss)/tau\n\n return avg_fitness\n\n\n\n", "repo_name": "dhdegroot/GRDS-code-repository", "sub_path": "python files general model/kl_simulation_support.py", "file_name": "kl_simulation_support.py", "file_ext": "py", "file_size_in_byte": 25430, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.fill_diagonal", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.integrate.solve_ivp", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 211, "usage_type": "attribute"}, {"api_name": "numpy.diff", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 247, "usage_type": "attribute"}, {"api_name": "numpy.linalg.eig", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 249, "usage_type": "attribute"}, {"api_name": "numpy.isclose", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.random.exponential", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 262, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.random.exponential", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 264, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 421, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 421, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 451, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 465, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 465, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 467, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 468, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 473, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 479, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 500, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 504, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 506, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 506, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.isscalar", "line_number": 534, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 538, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 550, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 558, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 558, "usage_type": "attribute"}, {"api_name": "numpy.isclose", "line_number": 559, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 560, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 563, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 569, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 569, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 569, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 569, "usage_type": "call"}]} +{"seq_id": "430713236", "text": "from rest_framework import serializers\nfrom rest_framework_simplejwt.serializers import TokenObtainPairSerializer\nfrom rest_framework_simplejwt.tokens import RefreshToken\nfrom .models import User,Patient,Doctor\n\n\nclass UserSerializerToken(serializers.ModelSerializer):\n slug = serializers.SerializerMethodField(read_only=True)\n class Meta:\n model = User\n fields = ('id','role','email','slug')\n \n def get_slug(self,obj):\n slug = f'{obj.first_name}-{obj.last_name}'\n return slug\n\n\nclass MyTokenObtainPairSerializer(TokenObtainPairSerializer):\n class Meta:\n fields = ('role','email','password')\n model = User\n \n @classmethod\n def get_token(cls, user):\n token = super().get_token(user)\n\n token[\"role\"] = user.role\n\n return token\n\n # def validate(self, attrs):\n # data = super().validate(attrs)\n\n # serializer = UserSerializerToken(self.user).data\n # for k, v in serializer.items():\n # data[k] = v\n # return data\n\nclass UserSignUpSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ('email', 'first_name', 'last_name', 'password','role')\n extra_kwargs = {'password': {'write_only': True}}\n\n def create(self, validated_data):\n user = User.objects.create_user(**validated_data)\n if user.role == 'Doctor':\n user.is_staff = True\n user.save()\n return user\n return user\n\n\nclass UserLoginSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ('email', 'password','role')\n extra_kwargs = {'password': {'write_only': True}}\n \n\n\nclass UserProfileSeriliazer(serializers.ModelSerializer):\n class Meta:\n model=User\n fields=('id','image','role','email','first_name','last_name','phone_number','gender','state','country') \n \n \nclass PatientSerializer(serializers.ModelSerializer):\n \n class Meta:\n model = Patient\n fields=('blood_group','age','weight','genotype','marital_status','medical_history')\n \n def update(self,instance,validated_data):\n print('validated_data', validated_data)\n return instance\n \n def to_representation(self, instance):\n print('att',instance)\n data = super().to_representation(instance)\n user = self.context['user']\n serializer = UserProfileSeriliazer(user).data\n print(serializer)\n for k, v in serializer.items():\n data[k] = v\n return data\n \nclass PatientProfileSerializer(serializers.ModelSerializer):\n uid = serializers.CharField(read_only=True,source='user.id')\n image = serializers.ImageField(source='user.image')\n email = serializers.EmailField(read_only=True,source='user.email')\n firstname = serializers.CharField(source='user.first_name')\n lastname = serializers.CharField(source='user.last_name')\n phonenumber = serializers.CharField(source='user.phone_number')\n gender = serializers.CharField(source='user.gender')\n state = serializers.CharField(source='user.state') \n country = serializers.CharField(source='user.country') \n class Meta:\n model=Patient\n fields=('uid','image','email','firstname','lastname','phonenumber','gender','state','country','blood_group','age','weight','genotype','marital_status','medical_history')\n \n def update(self,instance,validated_data):\n user_data = validated_data.pop('user')\n user = instance.user\n user.first_name = user_data.get('first_name',user.first_name)\n user.image = user_data.get('image',user.image)\n user.last_name = user_data.get('last_name',user.last_name)\n user.phone_number = user_data.get('phone_number',user.phone_number)\n user.gender = user_data.get('gender',user.gender)\n user.state = user_data.get('state',user.state)\n user.country = user_data.get('country',user.country)\n user.save()\n instance.blood_group = validated_data.get('blood_group',instance.blood_group)\n instance.age = validated_data.get('age',instance.age)\n instance.weight = validated_data.get('weight',instance.weight)\n instance.genotype = validated_data.get('genotype',instance.genotype)\n instance.marital_status = validated_data.get('marital_status',instance.marital_status)\n instance.medical_history = validated_data.get('medical_history',instance.medical_history)\n instance.save()\n return instance\n \n \n \n \n \n \nclass DoctorProfileSerializer(serializers.ModelSerializer):\n uid = serializers.CharField(read_only=True,source='user.id')\n image = serializers.ImageField(source='user.image')\n email = serializers.EmailField(read_only=True,source='user.email')\n firstname = serializers.CharField(source='user.first_name')\n lastname = serializers.CharField(source='user.last_name')\n phonenumber = serializers.CharField(source='user.phone_number')\n gender = serializers.CharField(source='user.gender')\n state = serializers.CharField(source='user.state') \n country = serializers.CharField(source='user.country') \n class Meta:\n model=Doctor\n fields=('uid','image','email','firstname','lastname','phonenumber','gender','state','country','hospital','experience','field','bio','qualification','location')\n\n def update(self,instance,validated_data):\n user_data = validated_data.pop('user')\n user = instance.user\n user.first_name = user_data.get('first_name',user.first_name)\n user.image = user_data.get('image',user.image)\n user.last_name = user_data.get('last_name',user.last_name)\n user.phone_number = user_data.get('phone_number',user.phone_number)\n user.gender = user_data.get('gender',user.gender)\n user.state = user_data.get('state',user.state)\n user.country = user_data.get('country',user.country)\n user.save()\n instance.hospital = validated_data.get('hospital',instance.hospital) \n instance.experience = validated_data.get('experience',instance.experience) \n instance.field = validated_data.get('field',instance.field) \n instance.bio = validated_data.get('bio',instance.bio) \n instance.qualification = validated_data.get('qualification',instance.qualification) \n instance.location = validated_data.get('location',instance.location) \n instance.save()\n return instance \n \n \n \n \n ", "repo_name": "Sosaristic/health-connect", "sub_path": "backend/Healthconnect/hcb/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 6588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "models.User", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework_simplejwt.serializers.TokenObtainPairSerializer", "line_number": 18, "usage_type": "name"}, {"api_name": "models.User", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 39, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 39, "usage_type": "name"}, {"api_name": "models.User", "line_number": 41, "usage_type": "name"}, {"api_name": "models.User.objects.create_user", "line_number": 46, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 54, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 54, "usage_type": "name"}, {"api_name": "models.User", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 62, "usage_type": "name"}, {"api_name": "models.User", "line_number": 64, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 68, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 68, "usage_type": "name"}, {"api_name": "models.Patient", "line_number": 71, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 88, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 89, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ImageField", "line_number": 90, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 90, "usage_type": "name"}, {"api_name": "rest_framework.serializers.EmailField", "line_number": 91, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 91, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 92, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 92, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 93, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 94, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 94, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 96, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 97, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 97, "usage_type": "name"}, {"api_name": "models.Patient", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 127, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 127, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ImageField", "line_number": 129, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 129, "usage_type": "name"}, {"api_name": "rest_framework.serializers.EmailField", "line_number": 130, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 130, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 131, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 131, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 133, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 134, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 134, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 135, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 135, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 136, "usage_type": "name"}, {"api_name": "models.Doctor", "line_number": 138, "usage_type": "name"}]} +{"seq_id": "28375770235", "text": "from flask import Flask, render_template, request\nimport mlab\nfrom bike import Bike\n\napp = Flask(__name__)\nmlab.connect()\n\n@app.route(\"/new_bike\", methods = [\"GET\", \"POST\"])\ndef new_bike():\n if request.method == \"GET\":\n return render_template(\"bike.html\")\n if request.method == \"POST\":\n form = request.form\n m = form[\"model\"]\n f = form[\"fee\"]\n img = form[\"image\"]\n y = form[\"year\"]\n # print(m, f, img, y)\n b = Bike(model=m, fee=f, image=img, year=y)\n b.save()\n return \"OK\"\n\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n", "repo_name": "khains/nguyen-sy-khai-web", "sub_path": "Web3/Homework/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "mlab.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "bike.Bike", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "19076921668", "text": "# Resize ground truth segmentation masks\n# for training U-net\n\nimport tensorflow as tf\nimport glob\nfrom keras.preprocessing.image import load_img, img_to_array, save_img\n\nprint('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))\n\nDIR = 'dataset/mask-big'\nFILES = glob.glob(f'{DIR}/*.png')\nTARGET_SIZE = (256, 256)\nSAVE_DIR = 'dataset/mask'\n\nwith tf.device('/GPU:0'):\n for f in FILES:\n img = load_img(f, target_size = TARGET_SIZE)\n img = img_to_array(img)\n filename = f.split('/')[2]\n print(f'Resize image {filename}')\n save_img(f'{SAVE_DIR}/{filename}', img)", "repo_name": "yuuiwqy622/isic2018melanoma-segmentation", "sub_path": "resize-masks.py", "file_name": "resize-masks.py", "file_ext": "py", "file_size_in_byte": 613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "tensorflow.config.list_physical_devices", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.save_img", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "73242109599", "text": "\n#!/home/pi/robotEnv/bin python \n\nimport os\nimport os.path\n\nfrom selenium import webdriver\nfrom time import sleep\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\n\nchrome_options = webdriver.ChromeOptions()\nchrome_options.add_argument('--headless')\nchrome_options.add_argument('--no-sandbox')\nchrome_options.add_argument('--disable-dev-shm-usage')\nchrome_options.add_argument('window-size=1920x1480')\n\nPathofDriver = Service(\"/usr/bin/chromedriver\")\ndriver = webdriver.Chrome(service=PathofDriver,options=chrome_options)\ndriver.maximize_window()\n\n\nWebsite = \"https://ttsreader.com/\"\ndriver.get(Website)\n\nsleep(0.5)\ndriver.find_element(by=By.XPATH, value='//*[@id=\"select_language\"]').click()\ndriver.find_element(by=By.XPATH, value='//*[@id=\"select_language\"]/option[1]').click()\n\n\ndef say(Text):\n try:\n driver.find_element(by=By.XPATH, value='//*[@id=\"clearBtn\"]').click()\n sleep(0.5)\n\n except:\n pass\n\n Data = str(Text)\n xpathtec = '//*[@id=\"text_box\"]'\n driver.find_element(by=By.XPATH, value=xpathtec).click()\n driver.find_element(by=By.XPATH, value=xpathtec).send_keys(Data)\n sleep(0.5)\n driver.find_element(by=By.XPATH, value='//*[@id=\"play_button\"]').click()\n\n print(\"\")\n print(f\" Robot-Niko Answer : {Text}.\")\n print(\"\")\n\nsay(\"Hello, how are you\")\n", "repo_name": "devjewel01/Robot-Niko", "sub_path": "src/talk4.py", "file_name": "talk4.py", "file_ext": "py", "file_size_in_byte": 1357, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "51", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 12, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 27, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 27, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 28, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 33, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"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.XPATH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 42, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "70971017792", "text": "# https://www.hackerrank.com/challenges/balanced-brackets/\n\nfrom collections import deque\nfrom sys import stdin, stdout\n\nn = int(stdin.readline().rstrip())\nb = {\"}\": \"{\", \")\": \"(\", \"]\": \"[\"}\nfor i in range(n):\n s = stdin.readline().rstrip()\n q = deque()\n result = \"YES\"\n for c in s:\n if c in b.values():\n q.append(c)\n continue\n\n if not q or q.pop() != b[c]:\n result = \"NO\"\n break\n if result == \"YES\" and q:\n result = \"NO\"\n stdout.write(f\"{result}\\n\")\n", "repo_name": "luanleonardo/algorithmic-problem-solving", "sub_path": "HackerRank/balanced-brackets.py", "file_name": "balanced-brackets.py", "file_ext": "py", "file_size_in_byte": 534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "60", "api": [{"api_name": "sys.stdin.readline", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 6, "usage_type": "name"}, {"api_name": "sys.stdin.readline", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 9, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "24659761123", "text": "from __future__ import annotations\nfrom pathlib import Path\nfrom robomeshcat import Robot\nimport numpy as np\nimport pinocchio as pin\n\nfrom robotics_toolbox.core import SE3, SO3, SE2\nfrom robotics_toolbox.robots.robot_base import RobotBase\n\n\nclass SpatialManipulator(RobotBase):\n def __init__(\n self,\n robot_name: str | None = None,\n urdf_path: str | Path | None = None,\n mesh_folder_path: Path | str | list[Path] | list[str] | None = None,\n srdf_path: Path | str | None = None,\n base_pose: SE3 | None = None,\n **kwargs,\n ) -> None:\n \"\"\"\n base_pose: where is the robot base placed\n robot_name: needs to be from the list: None, Panda, Talos, Tiago\n urdf_path and mesh_folder_path needs to be specified in robot_name is None\n srdf_path: path to srdf that disable collisions\n \"\"\"\n super().__init__()\n self.robot_name = robot_name\n if isinstance(robot_name, str):\n if robot_name.lower() == \"panda\":\n from example_robot_data.robots_loader import PandaLoader as RLoader\n elif robot_name.lower() == \"talos\":\n from example_robot_data.robots_loader import TalosLoader as RLoader\n elif robot_name.lower() == \"tiago\":\n from example_robot_data.robots_loader import TiagoDualLoader as RLoader\n else:\n raise NotImplementedError(\"Unknown robot.\")\n\n urdf_path = RLoader().df_path\n mesh_folder_path = Path(RLoader().model_path).parent.parent\n srdf_path = RLoader().srdf_path\n\n self.meshcat_robot = Robot(\n urdf_path=urdf_path, mesh_folder_path=mesh_folder_path, **kwargs\n )\n\n (\n self._model,\n self._data,\n self._geom_model,\n self._geom_data,\n ) = self.meshcat_robot._build_model_from_urdf(urdf_path, mesh_folder_path, True)\n self._geom_model.addAllCollisionPairs()\n if srdf_path is not None:\n pin.removeCollisionPairs(self._model, self._geom_model, srdf_path)\n self._geom_data = self._geom_model.createData()\n\n self.base_pose = base_pose if base_pose is not None else SE3()\n self.q = np.zeros(len(self.meshcat_robot._q))\n\n @property\n def dof(self) -> int:\n \"\"\"Return number of degrees of freedom for the robot.\"\"\"\n return len(self.meshcat_robot._q)\n\n def flange_pose(self, flange_link_name: str | None = None) -> SE3:\n \"\"\"Return a flange pose defined by the link name. Flange link name can be\n empty for Panda robot.\"\"\"\n flange_link_name = self._resolve_flange_link_name(flange_link_name)\n self.meshcat_robot[:] = self.q\n pin.updateFramePlacements(self._model, self._data)\n frame_id = self._model.getFrameId(flange_link_name)\n m = self._data.oMf[frame_id].homogeneous\n return SE3(m[:3, 3], SO3(rotation_matrix=m[:3, :3]))\n\n def jacobian(self, flange_link_name: str | None = None) -> np.ndarray:\n \"\"\"Computes jacobian of the manipulator for the given structure and\n configuration.\"\"\"\n flange_link_name = self._resolve_flange_link_name(flange_link_name)\n fid = self._model.getFrameId(flange_link_name)\n return pin.computeFrameJacobian(\n self._model,\n self._data,\n self.q,\n fid,\n pin.ReferenceFrame.LOCAL_WORLD_ALIGNED,\n )\n\n def _resolve_flange_link_name(self, flange_link_name: str | None = None) -> str:\n \"\"\"Resolve flange link name. Use known for known robots if None, raise error\n otherwise.\"\"\"\n if flange_link_name is None:\n assert self.robot_name is not None, \"You need to specify flange_link_name\"\n if self.robot_name.lower() == \"panda\":\n return \"panda_link8\"\n else:\n assert False, \"You need to specify flange_link_name\"\n assert self._model.existFrame(flange_link_name)\n return flange_link_name\n\n def sample_configuration(self) -> np.ndarray | SE2 | SE3:\n return pin.randomConfiguration(self._model)\n\n def set_configuration(self, configuration: np.ndarray | SE2 | SE3):\n self.q = configuration\n return self\n\n def in_collision(self) -> bool:\n return pin.computeCollisions(\n self._model,\n self._data,\n self._geom_model,\n self._geom_data,\n self.configuration(),\n True,\n )\n\n def configuration(self) -> np.ndarray | SE2 | SE3:\n return self.q\n", "repo_name": "CTURobotics/robotics_labs", "sub_path": "robotics_toolbox/robots/spatial_manipulator.py", "file_name": "spatial_manipulator.py", "file_ext": "py", "file_size_in_byte": 4607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "robotics_toolbox.robots.robot_base.RobotBase", "line_number": 11, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "name"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 18, "usage_type": "name"}, {"api_name": "example_robot_data.robots_loader.TiagoDualLoader", "line_number": 39, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "call"}, {"api_name": "example_robot_data.robots_loader.TiagoDualLoader", "line_number": 40, "usage_type": "call"}, {"api_name": "example_robot_data.robots_loader.TiagoDualLoader", "line_number": 41, "usage_type": "call"}, {"api_name": "robomeshcat.Robot", "line_number": 43, "usage_type": "call"}, {"api_name": "pinocchio.removeCollisionPairs", "line_number": 55, "usage_type": "call"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "pinocchio.updateFramePlacements", "line_number": 71, "usage_type": "call"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 74, "usage_type": "call"}, {"api_name": "robotics_toolbox.core.SO3", "line_number": 74, "usage_type": "call"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 66, "usage_type": "name"}, {"api_name": "pinocchio.computeFrameJacobian", "line_number": 81, "usage_type": "call"}, {"api_name": "pinocchio.ReferenceFrame", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pinocchio.randomConfiguration", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 101, "usage_type": "attribute"}, {"api_name": "robotics_toolbox.core.SE2", "line_number": 101, "usage_type": "name"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 104, "usage_type": "attribute"}, {"api_name": "robotics_toolbox.core.SE2", "line_number": 104, "usage_type": "name"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 104, "usage_type": "name"}, {"api_name": "pinocchio.computeCollisions", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 118, "usage_type": "attribute"}, {"api_name": "robotics_toolbox.core.SE2", "line_number": 118, "usage_type": "name"}, {"api_name": "robotics_toolbox.core.SE3", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "32082471923", "text": "import discord as nextcord\r\nfrom discord.ext import commands\r\n\r\nadmins = [878726683195240468,685180177419993102,899722893603274793]\r\n\r\nclass Root(commands.Cog):\r\n def __init__(self, bot):\r\n self.bot = bot\r\n\r\n @commands.command()\r\n async def view_guilds(self,ctx):\r\n if ctx.author.id in admins:\r\n embed = nextcord.Embed(title=\"Midna Guilds\")\r\n for guild in self.bot.guilds:\r\n embed.add_field(name=guild.name,value=f'Guild ID: {guild.id}\\nGuild Members: {guild.member_count}\\nOwner:{guild.owner}\\nRegion:{guild.region}')\r\n await ctx.send(embed=embed)\r\n\r\n @commands.command()\r\n async def unload(self,ctx,cog:str):\r\n if ctx.author.id in admins:\r\n self.bot.unload_extension(cog)\r\n await ctx.send(\"Unloaded {}\".format(cog))\r\n\r\n @commands.command()\r\n async def load(self,ctx,cog:str):\r\n if ctx.author.id in admins:\r\n self.bot.load_extension(cog)\r\n await ctx.send(\"Loaded {}\".format(cog))\r\n\r\n\r\ndef setup(bot):\r\n bot.add_cog(Root(bot))\r\n", "repo_name": "ZeroTwo36/midna", "sub_path": "cogs/root.py", "file_name": "root.py", "file_ext": "py", "file_size_in_byte": 1072, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 6, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 6, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 18, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 18, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 24, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "10593261620", "text": "import time\nimport os\n#import tensorflow as tf\n#from keras import backend as K\nimport numpy as np\nfrom PIL import Image\nimport pandas as pd\nimport glob\nimport matplotlib.pyplot as plt\nimport csv\n# from sklearn.model_selection import GridSearchCV\nfrom tune_sklearn import TuneSearchCV\nfrom tune_sklearn import TuneGridSearchCV\nfrom sklearn.linear_model import SGDClassifier\n#from ray import tune\n\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.experimental import enable_halving_search_cv\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom skimage.feature import local_binary_pattern\nfrom skimage.color import rgb2gray\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer\n#pip install imbalanced-learn\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom collections import Counter\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import RandomizedSearchCV, GridSearchCV\nimport subprocess\n#from ray.tune.suggest.bayesopt import BayesOptSearch\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import auc, roc_curve, roc_auc_score, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\nimport datetime\n\n\n\n\n\n\n\nfrom sklearn.model_selection import RepeatedStratifiedKFold\nfrom sklearn import metrics\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import svm\n\n# tf.config.list_physical_devices('GPU')\n\n# import os\n# os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n# os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n\n##Variaveis globais\nsave_metrics_path = \"../../../../data/bone/paper/Ivar/newexperiments/Metrics/\"\n#base_path_parts = \"../../../../data/bone/paper/Jonathan/PartitionsFeatures/\"\nbase_path_parts = \"../../../../data/bone/paper/Ivar/newexperiments/PartitionsFeatures/\"\nfiles_parts = os.listdir(base_path_parts)\ninput_size = (80,80)\nruntimeTrain = 0.0\nruntimeTest = 0.0\n\n\n\ndef specificity(tn, fp):\n return tn / (tn + fp)\n\n# Negative Predictive Error\ndef npv(tn, fn):\n return tn / (tn + fn + 1e-7)\n\n# Matthews Correlation_Coefficient\ndef mcc(tp, tn, fp, fn):\n num = tp * tn - fp * fn\n den = (tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)\n return num / np.sqrt(den + 1e-7)\n\n\n#Funções importantes\ndef calculateMeasures(y_pred, y_true, scores, folder, save_net_name):\n metricsTrain = pd.DataFrame()\n \n tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0,1]).ravel()\n #fpr, tpr, _ = roc_curve(y_true, scores, pos_label=2)\n auc_val = roc_auc_score(y_true, scores)\n\n # TRAIN RESULTS\n metricsTrain['folder'] = [folder]\n metricsTrain['accuracy'] = [accuracy_score(y_true, y_pred)]\n metricsTrain['precision'] = [precision_score(y_true, y_pred)]\n metricsTrain['sensitivity'] = [recall_score(y_true, y_pred)]\n metricsTrain['specificity'] = [specificity(tn,fp)]\n metricsTrain['fmeasure'] = [f1_score(y_true, y_pred)]\n metricsTrain['npv'] = [npv(tn, fn)]\n metricsTrain['mcc'] = [mcc(tp, tn, fp, fn)]\n metricsTrain['auc'] = [auc_val]\n metricsTrain['tn'] = [tn]\n metricsTrain['fp'] = [fp]\n metricsTrain['fn'] = [fn]\n metricsTrain['tp'] = [tp]\n metricsTrain['runtime'] = [runtimeTrain]\n\n print(metricsTrain)\n\n if os.path.exists(os.path.join(save_metrics_path, save_net_name)):\n metricsTrain.to_csv(os.path.join(save_metrics_path, save_net_name), sep=',', mode='a', index=False, header=False)\n else:\n metricsTrain.to_csv(os.path.join(save_metrics_path, save_net_name), sep=',', index=False) \n\n\ndef load_dataset(base_path):\n imagens, labels = list(), list()\n classes = os.listdir(base_path)\n for c in classes:\n for p in glob.glob(os.path.join(base_path, c, '.csv')):\n imagens.append(p)\n labels.append(c)\n \n return np.asarray(imagens), labels\n\ndef load_dataset_part(step):\n #train_Y, test_Y = list(), list()\n\n\n trainFrac = pd.read_csv(os.path.join(base_path_parts, \"%.2d-train-fractures.csv\"%(step)) )\n trainNoFrac = pd.read_csv(os.path.join(base_path_parts, \"%.2d-train-nofractures.csv\"%(step)) )\n #cc = list(trainFrac.columns)+list(trainNoFrac.columns)\n #print(cc)\n df_train = pd.concat([trainFrac,trainNoFrac], axis=0, ignore_index=True, sort=False)\n #print(\"df_train\", df_train)\n #df_train.columns = cc\n columns = df_train.columns.tolist()\n #print(\"columns\", columns)\n columns.remove(\"image\")\n columns.remove(\"target\")\n train_X = df_train[columns]\n train_Y = df_train.target.astype('category').cat.codes \n #Xo = Xo.loc[:,Xo.apply(pd.Series.nunique) != 1]\n\n #classes = list(enumerate(df_train.target.astype('category').cat.categories))\n #classes = [dd[1] for dd in classes]\n #print(\"datcat train\", classes)\n\n testFrac = pd.read_csv(os.path.join(base_path_parts, \"%.2d-test-fractures.csv\"%(step)))\n testNoFrac = pd.read_csv(os.path.join(base_path_parts, \"%.2d-test-nofractures.csv\"%(step)))\n df_test = pd.concat([testFrac,testNoFrac], axis=0, ignore_index=True, sort=False)\n columns = df_test.columns.tolist()\n #print(\"columnsx\", columns)\n columns.remove(\"image\")\n columns.remove(\"target\") \n test_X = df_test[columns]\n test_Y = df_test.target.astype('category').cat.codes \n\n #classes = list(enumerate(df_test.target.astype('category').cat.categories))\n #classes = [dd[1] for dd in classes]\n #print(\"datcat test\", classes)\n \n #print(\"train_X, test_X\", train_X, test_X, train_Y, test_Y)\n\n return train_X, test_X, train_Y, test_Y\n\ndef load_balance_class_parts(mdl,step):\n train_X, test_X, train_Y, test_Y = load_dataset_part(step)\n #exit()\n #lb = LabelBinarizer()\n #train_Y = lb.fit_transform(train_Y)\n #test_Y = lb.fit_transform(test_Y)\n\n #sc = preprocessing.MinMaxScaler()\n \n ##Balanceameto dos dados de treino\n undersample = RandomUnderSampler(sampling_strategy='majority', random_state=7)\n train_X, train_Y = undersample.fit_resample(train_X, train_Y)\n\n #sc = preprocessing.MinMaxScaler()\n #sc = preprocessing.StandardScaler()\n #train_X = sc.fit_transform(train_X)\n \n\n ##Balanceamento dos dados de test\n undersample = RandomUnderSampler(sampling_strategy='majority', random_state=7)\n test_X, test_Y = undersample.fit_resample(test_X, test_Y)\n \n #sc = preprocessing.MinMaxScaler()\n #sc = preprocessing.StandardScaler()\n #test_X = sc.fit_transform(test_X)\n\n \n if mdl[\"norm\"] == \"std\":\n sc = preprocessing.StandardScaler()\n train_X = sc.fit_transform(train_X)\n sc = preprocessing.StandardScaler()\n test_X = sc.fit_transform(test_X)\n \n\n return train_X, train_Y, test_X, test_Y \n\n\ndef save_parts_proc(part):\n with open(os.path.join(save_metrics_path, \"bayes/parts.txt\"), mode=\"a\") as f:\n f.write(f\"{part}\\n\")\n \ndef load_parts_proc():\n parts = []\n with open(os.path.join(save_metrics_path, \"bayes/parts.txt\"), mode=\"r\") as f:\n parts = f.readlines()\n\n parts = [p.replace(\"\\n\", \"\") for p in parts]\n \n return parts\n\n\n\ndef classifiers():\n cv = RepeatedStratifiedKFold(n_splits=3, n_repeats=1, random_state=7)\n rf_parameters = {\n \"n_estimators\" : [10, 100, 1000],\n \"max_features\" : ['sqrt', 'log2']\n }\n\n\n\n \"\"\"\n learning_rate=0.1,\n feature_fraction=0.7, \n scale_pos_weight=1.5,\n eval_metric='mlogloss', use_label_encoder=False \"\"\"\n\n clfs = { \n \"RFC\":{\n \"model\":RandomForestClassifier(n_estimators=400, random_state=7, n_jobs=-1),\n \"norm\":\"none\"\n },\n \"XGBC\":{\n \"model\":XGBClassifier(eval_metric='logloss', use_label_encoder=False),\n \"norm\":\"none\"\n },\n \"GNBC\":{\n \"model\":GaussianNB(),\n \"norm\":\"std\"\n },\n \"DTC\":{\n \"model\":DecisionTreeClassifier(random_state=7),\n \"norm\":\"none\"\n },\n \"ADBC\":{\n \"model\":AdaBoostClassifier(random_state=7),\n \"norm\":\"std\"\n },\n \"KNNC\":{\n \"model\":KNeighborsClassifier(n_neighbors = 2),\n \"norm\":\"std\"\n },\n \"SVMC\":{\n \"model\":svm.SVC(kernel=\"rbf\", probability=True, C=10, gamma=0.001),\n \"norm\":\"std\"\n },\n\n # ########## GridSearchCV\n \"RFC_GRID\":{\n \"model\":GridSearchCV( \n estimator = RandomForestClassifier(n_estimators=100, random_state=7, n_jobs=-1),\n param_grid = rf_parameters,\n #cv = 10,\n cv = cv,\n verbose=2,\n #scoring=\"roc_auc\",\n scoring='accuracy',\n error_score=0,\n n_jobs=-1\n ),\n \"norm\":\"none\"\n },\n }\n return clfs\n\n\ndef evaluation(scores, y_true, y_pred):\n\n y_true, y_pred = y_true.tolist(), y_pred.tolist()\n\n acc = metrics.accuracy_score(y_true, y_pred, normalize=True)\n f1 = metrics.f1_score(y_true, y_pred)\n #roc_curve = metrics.roc_curve(y_true, y_pred)\n #roc_auc_score = [metrics.roc_auc_score(y_true, y_pred)]\n jac = metrics.jaccard_score(y_true, y_pred)\n pre = metrics.precision_score(y_true, y_pred)\n rec = metrics.recall_score(y_true, y_pred)\n \n scores[\"acc\"].append(acc)\n scores[\"f1\"].append(f1)\n #scores[\"roc_auc_score\"].append(acc)\n scores[\"jac\"].append(jac)\n scores[\"pre\"].append(pre)\n scores[\"rec\"].append(rec)\n\n\n\nif __name__ == '__main__':\n\n # Simple\n modelnames = [\"RFC\",\"XGBC\",\"KNNC\",\"SVMC\"]\n\n ## GridSearchCV\n #modelnames = [\"RFC_GRID\"]\n \n\n # para cada modelo\n for mdname in modelnames:\n for step in range(1,101,1):\n\n cls = classifiers()\n mdl = cls[mdname]\n save_name_train = mdname+\"_train.csv\"\n save_name_test = mdname+\"_test.csv\"\n\n print(f\"Step: {step}\")\n \n print(\"Load features\")\n X_feat_train, train_under_Y, X_feat_test, test_under_Y = load_balance_class_parts(mdl, step)\n \n #print(\"X_feat_train.shape\", X_feat_train.shape)\n \n print(\"Trainning %s\"%(mdname))\n start_train = time.time()\n \n \n initClassifier = mdl[\"model\"].fit(X_feat_train, train_under_Y.ravel())\n y_pred_train = initClassifier.predict(X_feat_train)\n y_proba_train = initClassifier.predict_proba(X_feat_train)[:, 1]\n \n runtimeTrain = time.time() - start_train\n print(\"%s Trained in %2.2f seconds\"%(mdname, runtimeTrain))\n\n\n print(\"Testing %s\"%(mdname))\n start_test = time.time()\n y_pred_test = initClassifier.predict(X_feat_test)\n y_proba_test = initClassifier.predict_proba(X_feat_test)[:, 1]\n \n runtimeTest = time.time() - start_test\n print(\"%s Tested in %2.2f seconds\"%(mdname, runtimeTest)) \n\n #scores = {\"acc\":[],\"f1\":[],\"jac\":[],\"pre\":[],\"rec\":[]}\n #evaluation(scores, test_under_Y.ravel(), y_pred_test.ravel())\n #print (\"scores\", scores)\n \n calculateMeasures(y_pred_train, train_under_Y, y_proba_train, step, save_name_train)\n calculateMeasures(y_pred_test, test_under_Y, y_proba_test, step, save_name_test)\n #break\n\n \n", "repo_name": "ivarvb/BONE", "sub_path": "sourcecode/src/vx/bone/Experiments.py", "file_name": "Experiments.py", "file_ext": "py", "file_size_in_byte": 11486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.listdir", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 91, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 114, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 120, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 127, "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": "pandas.concat", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 145, "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": "pandas.read_csv", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 147, "usage_type": "call"}, {"api_name": "imblearn.under_sampling.RandomUnderSampler", "line_number": 173, "usage_type": "call"}, {"api_name": "imblearn.under_sampling.RandomUnderSampler", "line_number": 182, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 191, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 191, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 193, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 193, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.RepeatedStratifiedKFold", "line_number": 216, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 232, "usage_type": "call"}, {"api_name": "xgboost.XGBClassifier", "line_number": 236, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 240, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 244, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostClassifier", "line_number": 248, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 252, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 256, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 256, "usage_type": "name"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 262, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 263, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 283, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 283, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 284, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 284, "usage_type": "name"}, {"api_name": "sklearn.metrics.jaccard_score", "line_number": 287, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 287, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 288, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 288, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 289, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 289, "usage_type": "name"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}, {"api_name": "time.time", "line_number": 333, "usage_type": "call"}, {"api_name": "time.time", "line_number": 338, "usage_type": "call"}, {"api_name": "time.time", "line_number": 342, "usage_type": "call"}]} +{"seq_id": "18546669160", "text": "#!/usr/bin/env python3\n\n\"\"\"\nPrint the total size of files contained in the playlist.\n\"\"\"\n\nfrom sys import stdin\nimport argparse\nfrom playlisttools import get_playlist_filesize, humanize_number\n\n\ndef main(playlistfile):\n ntot, nf, bytesize = get_playlist_filesize(playlistfile)\n readablesize = humanize_number(bytesize)\n print(\"%d/%d files: %s (%s)\" %(ntot - nf, ntot, bytesize, readablesize))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(__doc__)\n parser.add_argument('playlistfile', nargs='?', type=argparse.FileType('r'),\n default=stdin)\n args = parser.parse_args()\n main(args.playlistfile)\n", "repo_name": "Gullumluvl/tracklistsync", "sub_path": "playlistsize.py", "file_name": "playlistsize.py", "file_ext": "py", "file_size_in_byte": 655, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "playlisttools.get_playlist_filesize", "line_number": 13, "usage_type": "call"}, {"api_name": "playlisttools.humanize_number", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "71651331999", "text": "import xml.sax\nfrom datetime import date\nfrom xml.sax import make_parser, handler\n\n\nclass DataHandler(handler.ContentHandler):\n def __init__(self, contentList: list):\n super().__init__()\n self.__contentList = contentList\n self.__currentRecord = dict()\n self.__name = False\n self.__year = False\n self.__group = False\n self.__total = False\n self.__done = False\n self.__language = False\n\n def startElement(self, tag, attrs):\n if tag == \"Record\":\n self.__currentRecord = dict()\n if tag == \"name\":\n self.__name = True\n if tag == \"year\":\n self.__year = True\n if tag == \"group\":\n self.__group = True\n if tag == \"total\":\n self.__total = True\n if tag == \"done\":\n self.__done = True\n if tag == \"language\":\n self.__language = True\n\n def endElement(self, tag):\n if tag == \"Record\":\n self.__contentList.append(self.__currentRecord)\n if tag == \"name\":\n self.__name = False\n if tag == \"year\":\n self.__year = False\n if tag == \"group\":\n self.__group = False\n if tag == \"total\":\n self.__total = False\n if tag == \"done\":\n self.__done = False\n if tag == \"language\":\n self.__language = False\n\n def characters(self, content):\n if self.__name:\n self.__currentRecord.update({\"name\": content})\n if self.__year:\n self.__currentRecord.update({\"year\": content})\n if self.__group:\n self.__currentRecord.update({\"group\": content})\n if self.__total:\n self.__currentRecord.update({\"total\": content})\n if self.__done:\n self.__currentRecord.update({\"done\": content})\n if self.__language:\n self.__currentRecord.update({\"language\": content})\n\n\ndef parse(filepath: str) -> list:\n out: list = list()\n parser = make_parser()\n parser.setContentHandler(DataHandler(out))\n parser.parse(filepath)\n return out\n\n\nif __name__ == \"__main__\":\n data: list = list()\n xml.sax.parse(\"../data/database.xml\", DataHandler(data))\n print(data)\n", "repo_name": "CAT-i0n/PPVIS_labs", "sub_path": "PPVIS_lab2/model/parserSAX.py", "file_name": "parserSAX.py", "file_ext": "py", "file_size_in_byte": 2246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "xml.sax.handler.ContentHandler", "line_number": 6, "usage_type": "attribute"}, {"api_name": "xml.sax.handler", "line_number": 6, "usage_type": "name"}, {"api_name": "xml.sax.make_parser", "line_number": 67, "usage_type": "call"}, {"api_name": "xml.sax.sax.parse", "line_number": 75, "usage_type": "call"}, {"api_name": "xml.sax.sax", "line_number": 75, "usage_type": "attribute"}, {"api_name": "xml.sax", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "4542472380", "text": "#!/usr/bin/python3\nimport first\nimport follow\nimport csv\n\ndef exportDictToCSV(dict1, filename):\n a_file = open(filename+\".csv\", \"w\")\n writer = csv.writer(a_file)\n for key, value in dict1.items():\n writer.writerow([key, value])\n\n a_file.close()\n\ngramatica = {}\nf = open(\"input2.txt\", \"r\")\n\nfor x in f:\n a,b = x.split('->')\n b = b.rstrip('\\n')\n gramatica[a] = b.split('|')\n\n\nfirstSet = first.run(gramatica.copy())\nfollowSet = follow.run(firstSet, gramatica.copy())\nprint(\"Conjunto First:\", firstSet)\nprint(\"Conjunto Follow:\", followSet)\n# exportDictToCSV(firstSet, \"item2_conjunto_first\")\n# exportDictToCSV(followSet, \"item2_conjunto_follow\")", "repo_name": "rwnicholas/fluffy-potato", "sub_path": "6P/CMP/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "csv.writer", "line_number": 8, "usage_type": "call"}, {"api_name": "first.run", "line_number": 23, "usage_type": "call"}, {"api_name": "follow.run", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "26970116118", "text": "# make a general def to plot stuff in matplotlib\n\nimport matplotlib.pyplot as plt\ndef plot_something(x, y, **kwargs):\n title = kwargs.pop( 'title' )\n xlabel = kwargs.pop( 'xlabel' )\n ylabel = kwargs.pop( 'ylabel' )\n plt.figure()\n plt.plot(x, y, **kwargs)\n fig = plt.gcf()\n for axis in fig.axes:\n axis.set_title( title )\n axis.xaxis.set_label_text( xlabel )\n axis.yaxis.set_label_text( ylabel )\n return axis\n\n\nplot_conf = {'title': 'Blabla', 'xlabel':'Time (s)', 'ylabel': 'Speed (m/s)'}\nx = [1.,2.,3.]\ny = [1.,4.,9.]\naxis = plot_something(x=x,y=y, **plot_conf)\n", "repo_name": "triphysics/Plotting_tools", "sub_path": "plot_test.py", "file_name": "plot_test.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"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.plot", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "32195026779", "text": "from flask import Flask, current_app\n\napp = Flask(__name__)\nwith app.app_context():\n a = current_app\n d = current_app.config['DEBUG']\n\n\nclass MyResource:\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_tb:\n print('process exception')\n else:\n print('no exception')\n print('close connection')\n", "repo_name": "sxycxwb/fisher", "sub_path": "test/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 5, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 6, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "70486563358", "text": "import os\nimport numpy as np\nimport cv2\nfrom hdf5storage import loadmat\nfrom tqdm import tqdm\nfrom opts import args\nimport pdb\nimport ujson as js\nfrom collections import defaultdict\n\ndomain = args.domain\n# alias syncds=\"rsync -av -e ssh fating@172.18.167.17:/data/fating/HighlightDataset/proDataset/ /home/share/Highlight/proDataset/\" \ndef generateGT_from_youtube(path):\n count = 0\n category = []\n for root,dirs,files in os.walk(path+'/video'):\n for dr in dirs:\n category.append(dr)\n ctg_dict = defaultdict(defaultdict)\n for ctg in category:\n video_dir = os.path.join(path+'/video', ctg)\n ret = defaultdict(list)\n # with open('/home/share/Highlight/code/instagram_dataset/video_list/{}_youtube'.format(domain), 'r') as file:\n for root,dirs,files in os.walk(video_dir):\n for video in files:\n count+=1\n prefix = video.split('.')[0]\n if os.path.exists(os.path.join(path+'/label', '{}.json'.format(prefix))):\n print(ctg+ ' processing: '+video)\n with open(os.path.join(path+'/label', '{}.json'.format(prefix))) as label_file:\n data = js.load(label_file)\n flag = data[-1]\n cap = cv2.VideoCapture(os.path.join(video_dir, video))\n n_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n frames = np.zeros(int(n_frames)+1, dtype=np.int16)\n # pdb.set_trace()\n for idx, pair in enumerate(data[0]):\n if flag[idx] == 1:\n frames[int(pair[0]): int(pair[1])] = 1\n ret[prefix] = frames[np.newaxis]\n # else:\n # os.remove(os.path.join(root,video))\n ctg_dict[ctg] = ret\n # pdb.set_trace()\n np.save(path+'/gt_youtube.npy',ctg_dict)\n # return \n\ndef generateGT_from_youtube_shot(path):\n count = 0\n category = []\n for root,dirs,files in os.walk(path+'/video'):\n for dr in dirs:\n category.append(dr)\n ctg_dict = defaultdict(defaultdict)\n for ctg in category:\n video_dir = os.path.join(path+'/video', ctg)\n ret = defaultdict(list)\n # with open('/home/share/Highlight/code/instagram_dataset/video_list/{}_youtube'.format(domain), 'r') as file:\n for root,dirs,files in os.walk(video_dir):\n for video in files:\n user_annot = defaultdict(list)\n count+=1\n prefix = video.split('.')[0]\n if os.path.exists(os.path.join(path+'/label', '{}.json'.format(prefix))):\n print(ctg+ ' processing: '+video)\n with open(os.path.join(path+'/label', '{}.json'.format(prefix))) as label_file:\n data = js.load(label_file)\n user_annot['shots'] = data[0]\n new_score = []\n for i in range(len(data[1])):\n if data[1][i] == 1:\n new_score.append(1)\n else:\n new_score.append(0)\n print(data[1])\n print(new_score)\n user_annot['scores'] = new_score\n ret[prefix].append(user_annot)\n ctg_dict[ctg] = ret\n # pdb.set_trace()\n np.save(path+'/gt_youtube.npy',ctg_dict)\n # return \n\n\ndef generateGT_from_tvsum(path):\n src = os.path.join('/home/share/Highlight/orgDataset/ydata-tvsum50-v1_1/matlab/ydata-tvsum50.mat')\n mat = loadmat(src)['tvsum50'][0] # ndarray (50, )\n ret = defaultdict(defaultdict)\n idx = 1\n for element in mat:\n ctg = element[1][0][0]\n if ctg in list(ret.keys()):\n pass\n else:\n ret[ctg] = defaultdict(list)\n video_name = element[0][0][0]+'.mp4'\n print(idx,video_name)\n idx+=1\n scores = element[5].transpose().tolist() \n \n pro_scores = []\n for sc in scores:\n sc = np.array(sc)\n \n idxs = np.argsort(-sc)\n sc[idxs[:int(len(idxs)/2)]]=1\n sc[idxs[int(len(idxs)/2):]]=0\n pro_scores.append(sc)\n scores = np.array(pro_scores)\n # pdb.set_trace()\n ret[ctg][video_name] = scores # user_annotations, (user_nums x n_frames)\n \n np.save(path+'/gt_tvsum1.npy',ret)\n\n\ndef generateGT_from_tvsum1(path):\n src = os.path.join('/home/share/Highlight/orgDataset/ydata-tvsum50-v1_1/matlab/ydata-tvsum50.mat')\n mat = loadmat(src)['tvsum50'][0] # ndarray (50, )\n ret = defaultdict(defaultdict)\n idx = 1\n for element in mat:\n ctg = element[1][0][0]\n if ctg in list(ret.keys()):\n pass\n else:\n ret[ctg] = defaultdict(list)\n video_name = element[0][0][0]+'.mp4'\n video = '/home/share/Highlight/proDataset/TVSum/video/'+ctg+'/'+video_name\n cap = cv2.VideoCapture(video)\n fps = cap.get(cv2.CAP_PROP_FPS)\n print(\"Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}\".format(fps))\n frame_num = cap.get(7)\n print(frame_num)\n print(ctg,video_name)\n # idx+=1\n scores = element[5].transpose().tolist() \n fps = round(fps)*2\n pro_scores = []\n for sc in scores:\n s_idxs = []\n start = -1\n user_annot = defaultdict(list)\n sc = np.array(sc)\n for i in range(len(sc)):\n if sc[i]!=start:\n start=sc[i]\n s_idxs.append(i)\n s_idxs.append(len(sc)-1)\n shot_idxs = []\n shot_values = []\n #划分segment\n for i in range(len(s_idxs)):\n if i==0:\n continue\n shot_idxs.append(s_idxs[i-1])\n if s_idxs[i]-s_idxs[i-1]>fps:\n sub_shot_num = int((s_idxs[i]-s_idxs[i-1])/fps)\n #最后一项不要加了\n if (s_idxs[i]-s_idxs[i-1])%fps==0:\n sub_shot_num-=1\n for ssn in range(sub_shot_num):\n shot_idxs.append(s_idxs[i-1]+(ssn+1)*fps)\n shot_idxs.append(s_idxs[-1])\n for i in range(len(shot_idxs)-1):\n shot_values.append(sc[shot_idxs[i]])\n shot_values = np.array(shot_values)\n idxs = np.argsort(-shot_values)\n shot_values[idxs[:int(len(idxs)/2)]]=1\n shot_values[idxs[int(len(idxs)/2):]]=0\n user_annot['shots'] = shot_idxs\n user_annot['scores'] = shot_values\n print(len(shot_values))\n pdb.set_trace()\n ret[ctg][video_name].append(user_annot)\n np.save(path+'/gt_tvsum.npy',ret)\n\ndef generateGT_from_tvsum2(path):\n src = os.path.join('/home/share/Highlight/orgDataset/ydata-tvsum50-v1_1/data/ydata-tvsum50-anno.tsv')\n mat = loadmat(src) # ndarray (50, )\n pdb.set_trace()\n mat = loadmat(src)['tvsum50'][0] # ndarray (50, )\n\n ret = defaultdict(defaultdict)\n idx = 1\n for element in mat:\n ctg = element[1][0][0]\n if ctg in list(ret.keys()):\n pass\n else:\n ret[ctg] = defaultdict(list)\n video_name = element[0][0][0]+'.mp4'\n print(idx,video_name)\n idx+=1\n scores = element[5].transpose().tolist() \n pro_scores = []\n start = -1\n idxs = []\n values = []\n for sc in scores:\n sc = np.array(sc)\n for i in range(len(sc)):\n if sc[i]!=start:\n print(sc[i])\n start=sc[i]\n print(value)\n idxs.append(i)\n # pdb.set_trace()\n ret[ctg][video_name] = scores # user_annotations, (user_nums x n_frames)\n \n np.save(path+'/gt_tvsum1.npy',ret)\n\n\ndef generateGT_from_CoSum(path,save):\n category = ['01_base_jump','02_bike_polo','03_eiffel_tower','04_excavators_river_xing','05_kids_playing_in_leaves','06_mlb','07_nfl','08_notre_dame_cathedral','09_statue_of_liberty','10_surfing']\n annotation_path = path+'/annotation/'\n shots_path = path+'/shots/'\n ret = defaultdict(defaultdict)\n users = ['__kk.mat','__vv.mat','__dualplus.mat']\n for ctg in category:\n ctg_ = ctg[3:]\n ret[ctg_] = defaultdict(list)\n ctg_annotation_path = annotation_path+ctg\n ctg_shorts_path = shots_path+ctg\n fileList = os.listdir(ctg_shorts_path)\n for shot in fileList:\n user_annot = defaultdict(list)\n idx = shot.split('_')[0][-1]\n shot_path = ctg_shorts_path+'/'+shot\n file = open(shot_path)\n shot_num = []\n # shot_num.append(0)\n begin=True\n for line in file.readlines():\n line=line.strip('\\n')\n num = int(line)\n if begin:\n begin=False\n if num!=1:\n shot_num.append(0)\n else:\n shot_num.append(num-1)\n else:\n shot_num.append(num-1)\n\n all_shot_idx = []\n for user in users:\n annot = ctg_annotation_path+'/'+idx+user\n shot_idx = loadmat(annot)['labels'][:,0]\n all_shot_idx.append(shot_idx)\n if ctg=='10_surfing':\n pdb.set_trace()\n print('aaa')\n inter1 = list(set(all_shot_idx[0]).intersection(set(all_shot_idx[1])))\n inter2 = list(set(all_shot_idx[1]).intersection(set(all_shot_idx[2])))\n inter3 = list(set(all_shot_idx[2]).intersection(set(all_shot_idx[0])))\n gt_shot = set(inter1).union(set(inter2))\n gt_shot = list(gt_shot.union(set(inter3)))\n shot_values = []\n for i in range(len(shot_num)-1):\n if (i+1) in gt_shot:\n shot_values.append(1)\n else:\n shot_values.append(0)\n user_annot['shots'] = shot_num\n user_annot['scores'] = shot_values\n # pdb.set_trace()\n ret[ctg_][idx].append(user_annot)\n np.save(save+'/gt_cosum.npy',ret)\n \n\n\n\n \n\nif __name__ == '__main__':\n # generateGT_from_CoSum('/home/share/Highlight/orgDataset/cosum','/home/share/Highlight/proDataset/CoSum')\n # generateGT_from_tvsum1('/home/share/Highlight/proDataset/TVSum')\n generateGT_from_youtube_shot('/home/share/Highlight/proDataset/DomainSpecific')\n # create_label\n", "repo_name": "Huangxt57/MINI-Net", "sub_path": "src/GT.py", "file_name": "GT.py", "file_ext": "py", "file_size_in_byte": 10750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "51", "api": [{"api_name": "opts.args.domain", "line_number": 11, "usage_type": "attribute"}, {"api_name": "opts.args", "line_number": 11, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 16, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 22, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "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": "ujson.load", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 33, "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": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 45, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 51, "usage_type": "call"}, {"api_name": "collections.defaultdict", "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": "collections.defaultdict", "line_number": 57, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 59, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "ujson.load", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "hdf5storage.loadmat", "line_number": 87, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 88, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "hdf5storage.loadmat", "line_number": 118, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 119, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 130, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 167, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "hdf5storage.loadmat", "line_number": 179, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 180, "usage_type": "call"}, {"api_name": "hdf5storage.loadmat", "line_number": 181, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 183, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 210, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 217, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 221, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 224, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 226, "usage_type": "call"}, {"api_name": "hdf5storage.loadmat", "line_number": 248, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 268, "usage_type": "call"}]} +{"seq_id": "28342088751", "text": "from .ordereddict import odict\n\n\n# A list of all keywords introduced in the\n# CIB language.\nkeywords = {\n \"node\": \"element\",\n \"primitive\": \"element\",\n \"resource\": \"element\",\n \"group\": \"element\",\n \"clone\": \"element\",\n \"ms\": \"element\",\n \"master\": \"element\",\n \"location\": \"element\",\n \"colocation\": \"element\",\n \"collocation\": \"element\",\n \"order\": \"element\",\n \"rsc_ticket\": \"element\",\n \"rsc_template\": \"element\",\n \"property\": \"element\",\n \"rsc_defaults\": \"element\",\n \"op_defaults\": \"element\",\n \"acl_target\": \"element\",\n \"acl_group\": \"element\",\n \"user\": \"element\",\n \"role\": \"element\",\n \"fencing_topology\": \"element\",\n \"fencing-topology\": \"element\",\n \"tag\": \"element\",\n \"monitor\": \"element\",\n \"params\": \"subelement\",\n \"meta\": \"subelement\",\n \"attributes\": \"subelement\",\n \"utilization\": \"subelement\",\n \"operations\": \"subelement\",\n \"op\": \"subelement\",\n \"rule\": \"subelement\",\n \"inf\": \"value\",\n \"INFINITY\": \"value\",\n \"and\": \"op\",\n \"or\": \"op\",\n \"lt\": \"op\",\n \"gt\": \"op\",\n \"lte\": \"op\",\n \"gte\": \"op\",\n \"eq\": \"op\",\n \"ne\": \"op\",\n \"defined\": \"op\",\n \"not_defined\": \"op\",\n \"in_range\": \"op\",\n \"in\": \"op\",\n \"date_spec\": \"op\",\n \"spec\": \"op\",\n \"date\": \"value\",\n \"yes\": \"value\",\n \"no\": \"value\",\n \"true\": \"value\",\n \"false\": \"value\",\n \"on\": \"value\",\n \"off\": \"value\",\n \"normal\": \"value\",\n \"member\": \"value\",\n \"ping\": \"value\",\n \"remote\": \"value\",\n \"start\": \"value\",\n \"stop\": \"value\",\n \"Mandatory\": \"value\",\n \"Optional\": \"value\",\n \"Serialize\": \"value\",\n \"ref\": \"value\",\n \"xpath\": \"value\",\n \"xml\": \"element\",\n}\n\ncib_cli_map = {\n \"node\": \"node\",\n \"primitive\": \"primitive\",\n \"group\": \"group\",\n \"clone\": \"clone\",\n \"master\": \"ms\",\n \"rsc_location\": \"location\",\n \"rsc_colocation\": \"colocation\",\n \"rsc_order\": \"order\",\n \"rsc_ticket\": \"rsc_ticket\",\n \"template\": \"rsc_template\",\n \"cluster_property_set\": \"property\",\n \"rsc_defaults\": \"rsc_defaults\",\n \"op_defaults\": \"op_defaults\",\n \"acl_target\": \"acl_target\",\n \"acl_group\": \"acl_group\",\n \"acl_user\": \"user\",\n \"acl_role\": \"role\",\n \"fencing-topology\": \"fencing_topology\",\n \"tag\": \"tag\"\n}\ncontainer_tags = (\"group\", \"clone\", \"ms\", \"master\")\nclonems_tags = (\"clone\", \"ms\", \"master\")\nresource_tags = (\"primitive\", \"group\", \"clone\", \"ms\", \"master\", \"template\")\nconstraint_tags = (\"rsc_location\", \"rsc_colocation\", \"rsc_order\", \"rsc_ticket\")\nconstraint_rsc_refs = (\"rsc\", \"with-rsc\", \"first\", \"then\")\nchildren_tags = (\"group\", \"primitive\")\nnvpairs_tags = (\"meta_attributes\", \"instance_attributes\", \"utilization\")\ndefaults_tags = (\"rsc_defaults\", \"op_defaults\")\nresource_cli_names = (\"primitive\", \"group\", \"clone\", \"ms\", \"master\", \"rsc_template\")\nconstraint_cli_names = (\"location\", \"colocation\", \"collocation\", \"order\", \"rsc_ticket\")\nnvset_cli_names = (\"property\", \"rsc_defaults\", \"op_defaults\")\nop_cli_names = (\"monitor\",\n \"start\",\n \"stop\",\n \"migrate_to\",\n \"migrate_from\",\n \"promote\",\n \"demote\",\n \"notify\")\nra_operations = (\"probe\", \"monitor\", \"start\", \"stop\",\n \"promote\", \"demote\", \"notify\", \"migrate_to\", \"migrate_from\")\nsubpfx_list = {\n \"instance_attributes\": \"instance_attributes\",\n \"meta_attributes\": \"meta_attributes\",\n \"utilization\": \"utilization\",\n \"operations\": \"ops\",\n \"rule\": \"rule\",\n \"expression\": \"expression\",\n \"date_expression\": \"expression\",\n \"duration\": \"duration\",\n \"date_spec\": \"date_spec\",\n \"read\": \"read\",\n \"write\": \"write\",\n \"deny\": \"deny\",\n}\nacl_rule_names = (\"read\", \"write\", \"deny\")\nacl_spec_map = odict({\n \"xpath\": \"xpath\",\n \"ref\": \"ref\",\n \"tag\": \"tag\",\n \"attribute\": \"attribute\",\n})\n# ACLs were rewritten in pacemaker 1.1.12\n# this is the new acl syntax\nacl_spec_map_2 = odict({\n \"xpath\": \"xpath\",\n \"ref\": \"reference\",\n \"reference\": \"reference\",\n \"tag\": \"object-type\",\n \"type\": \"object-type\",\n \"attr\": \"attribute\",\n \"attribute\": \"attribute\"\n})\n\nacl_spec_map_2_rev = (('xpath', 'xpath'),\n ('reference', 'ref'),\n ('attribute', 'attr'),\n ('object-type', 'type'))\n\nacl_shortcuts = {\n \"meta\":\n (r\"//primitive\\[@id='@@'\\]/meta_attributes\", r\"/nvpair\\[@name='@@'\\]\"),\n \"params\":\n (r\"//primitive\\[@id='@@'\\]/instance_attributes\", r\"/nvpair\\[@name='@@'\\]\"),\n \"utilization\":\n (r\"//primitive\\[@id='@@'\\]/utilization\",),\n \"location\":\n (r\"//rsc_location\\[@id='cli-prefer-@@' and @rsc='@@'\\]\",),\n \"property\":\n (r\"//crm_config/cluster_property_set\", r\"/nvpair\\[@name='@@'\\]\"),\n \"nodeattr\":\n (r\"//nodes/node/instance_attributes\", r\"/nvpair\\[@name='@@'\\]\"),\n \"nodeutil\":\n (r\"//nodes/node/utilization\", r\"\\[@uname='@@'\\]\"),\n \"node\":\n (r\"//nodes/node\", r\"\\[@uname='@@'\\]\"),\n \"status\":\n (r\"/cib/status\",),\n \"cib\":\n (r\"/cib\",),\n}\nlrm_exit_codes = {\n \"success\": \"0\",\n \"unknown\": \"1\",\n \"args\": \"2\",\n \"unimplemented\": \"3\",\n \"perm\": \"4\",\n \"installed\": \"5\",\n \"configured\": \"6\",\n \"not_running\": \"7\",\n \"master\": \"8\",\n \"failed_master\": \"9\",\n}\nlrm_status_codes = {\n \"pending\": \"-1\",\n \"done\": \"0\",\n \"cancelled\": \"1\",\n \"timeout\": \"2\",\n \"notsupported\": \"3\",\n \"error\": \"4\",\n}\ncib_user_attrs = (\"validate-with\",)\nnode_states = (\"online\", \"offline\", \"unclean\")\nprecious_attrs = (\"id-ref\",)\nop_extra_attrs = (\"interval\",)\nrsc_meta_attributes = (\n \"allow-migrate\", \"is-managed\", \"interval-origin\",\n \"migration-threshold\", \"priority\", \"multiple-active\",\n \"failure-timeout\", \"resource-stickiness\", \"target-role\",\n \"restart-type\", \"description\", \"remote-node\", \"requires\",\n \"provides\", \"remote-port\", \"remote-addr\", \"remote-connect-timeout\"\n)\ngroup_meta_attributes = (\"container\", )\nclone_meta_attributes = (\n \"ordered\", \"notify\", \"interleave\", \"globally-unique\",\n \"clone-max\", \"clone-node-max\", \"clone-state\", \"description\",\n)\nms_meta_attributes = (\n \"master-max\", \"master-node-max\", \"description\",\n)\ntrace_ra_attr = \"trace_ra\"\nscore_types = {'advisory': '0', 'mandatory': 'INFINITY'}\nboolean_ops = ('or', 'and')\nbinary_ops = ('lt', 'gt', 'lte', 'gte', 'eq', 'ne')\nbinary_types = ('string', 'version', 'number')\nunary_ops = ('defined', 'not_defined')\nsimple_date_ops = ('lt', 'gt')\ndate_ops = ('lt', 'gt', 'in_range', 'date_spec')\ndate_spec_names = '''hours monthdays weekdays yearsdays months \\\nweeks years weekyears moon'''.split()\nin_range_attrs = ('start', 'end')\nroles_names = ('Stopped', 'Started', 'Master', 'Slave')\nactions_names = ('start', 'promote', 'demote', 'stop')\nnode_default_type = \"normal\"\nnode_attributes_keyw = (\"attributes\", \"utilization\")\nshadow_envvar = \"CIB_shadow\"\nattr_defaults = {\n \"node\": {\"type\": \"normal\"},\n \"resource_set\": {\"sequential\": \"true\", \"require-all\": \"true\"},\n \"rule\": {\"boolean-op\": \"and\"},\n}\ncib_no_section_rc = 6\n# Graphviz attributes for various CIB elements.\n# Shared for edge and node and graph attributes.\n# Keys are graphviz attributes, values are dicts where keys\n# are CIB element names and values graphviz values.\n# - element \".\" refers to the whole graph\n# - element \"class:\" refers to primitives of a\n# specific RA class\n# - optional_set is a resource_set with require-all set to\n# false\n# - group and optional_set are subgraphs (boxes)\ngraph = {\n \".\": {\n \"compound\": \"true\",\n },\n \"*\": {\n \"fontname\": \"Helvetica\",\n \"fontsize\": \"11\",\n },\n \"node\": {\n \"style\": \"bold\",\n \"shape\": \"box\",\n \"color\": \"#7ac142\",\n },\n \"primitive\": {\n \"fillcolor\": \"#e4e5e6\",\n \"color\": \"#b9b9b9\",\n \"shape\": \"box\",\n \"style\": \"rounded,filled\",\n },\n \"rsc_template\": {\n \"fillcolor\": \"#ffd457\",\n \"color\": \"#b9b9b9\",\n \"shape\": \"box\",\n \"style\": \"rounded,filled,dashed\",\n },\n \"class:stonith\": {\n \"shape\": \"box\",\n \"style\": \"dashed\",\n },\n \"location\": {\n \"style\": \"dashed\",\n \"dir\": \"none\",\n },\n \"clone\": {\n \"color\": \"#ec008c\",\n },\n \"ms\": {\n \"color\": \"#f8981d\",\n },\n \"group\": {\n \"color\": \"#00aeef\",\n \"group\": \"#00aeef\",\n \"labelloc\": \"b\",\n \"labeljust\": \"r\",\n \"labelfontsize\": \"12\",\n },\n \"optional_set\": {\n \"style\": \"dotted\",\n },\n \"template:edge\": {\n \"color\": \"#b9b9b9\",\n \"style\": \"dotted\",\n \"arrowtail\": \"open\",\n \"dir\": \"back\",\n },\n}\n\nprompt = ''\ntmp_cib = False\ntmp_cib_prompt = \"@tmp@\"\nlive_cib_prompt = \"live\"\n\nsimulate_programs = {\n \"ptest\": \"ptest\",\n \"simulate\": \"crm_simulate\",\n}\n\nmeta_progs = (\"crmd\", \"pengine\", \"stonithd\", \"cib\")\n# elide these properties from tab completion\ncrmd_metadata_do_not_complete = (\"dc-version\",\n \"cluster-infrastructure\",\n \"crmd-integration-timeout\",\n \"crmd-finalization-timeout\",\n \"expected-quorum-votes\")\nextra_cluster_properties = (\"dc-version\",\n \"cluster-infrastructure\",\n \"last-lrm-refresh\",\n \"cluster-name\")\npcmk_version = \"\" # set later\n\n# vim:ts=4:sw=4:et:\n", "repo_name": "ingted/crmsh", "sub_path": "modules/constants.py", "file_name": "constants.py", "file_ext": "py", "file_size_in_byte": 9364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "ordereddict.odict", "line_number": 132, "usage_type": "call"}, {"api_name": "ordereddict.odict", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "81556690", "text": "import os\nimport logging\nimport asyncio\nimport functools\nfrom . import __main__\nfrom telethon.tl.types import PeerUser, PeerChat, PeerChannel\n\n\ndef get_args(message):\n try:\n message = message.message\n except AttributeError:\n pass\n if not message:\n return False\n return list(filter(lambda x: len(x) > 0, message.split(' ')))[1:]\n\n\ndef get_args_raw(message):\n try:\n message = message.message\n except AttributeError:\n pass\n if not message:\n return False\n args = message.split(' ', 1)\n if len(args) > 1:\n return args[1]\n else:\n return \"\"\n\n\ndef get_args_split_by(message, s):\n m = get_args_raw(message)\n mess = m.split(s)\n return [st.strip() for st in mess]\n\n\ndef get_chat_id(message):\n chat = message.to_id\n attrs = chat.__dict__\n if len(attrs) != 1:\n return None\n return next(iter(attrs.values()))\n\n\ndef escape_html(text):\n return str(text).replace(\"<\", \"<\").replace(\">\", \">\").replace(\"&\", \"&\")\n\n\ndef escape_quotes(text):\n return str(text).replace(\"<\", \"<\").replace(\">\", \">\").replace(\"&\", \"&\").replace('\"', \""\")\n\n\ndef get_base_dir():\n return os.path.abspath(os.path.dirname(os.path.abspath(__main__.__file__)))\n\n\nasync def get_user(message):\n try:\n return await message.client.get_entity(message.from_id)\n except ValueError: # Not in database. Lets go looking for them.\n logging.debug(\"user not in session cache. searching...\")\n if isinstance(message.to_id, PeerUser):\n await message.client.get_dialogs()\n return await message.client.get_entity(message.from_id)\n elif isinstance(message.to_id, PeerChat) or isinstance(message.to_id, PeerChannel):\n async for user in message.client.iter_participants(message.to_id, aggressive=True):\n if user.id == message.from_id:\n return user\n logging.error(\"WTF! user isn't in the group where they sent the message\")\n return None\n else:\n logging.error(\"WTF! to_id is not a user, chat or channel\")\n return None\n\n\ndef run_sync(func, *args, **kwargs):\n # Returning a coro\n return asyncio.get_event_loop().run_in_executor(None, functools.partial(func, *args, **kwargs))\n\n\ndef run_async(loop, coro):\n # When we bump minimum support to 3.7, use run()\n return asyncio.run_coroutine_threadsafe(coro, loop).result()\n\n\ndef censor(obj, to_censor=[\"phone\"], replace_with=\"redacted_{count}_chars\"):\n \"\"\"May modify the original object, but don't rely on it\"\"\"\n for k, v in obj.__dict__.items():\n if k in to_censor:\n setattr(obj, k, replace_with.format(count=len(v)))\n elif k[0] != \"_\" and hasattr(v, \"__dict__\"):\n setattr(obj, k, censor(v, to_censor, replace_with))\n return obj\n\n\nasync def answer(message, answer, **kwargs):\n CONT_MSG = \"[continued]\\n\"\n ret = [message]\n if isinstance(answer, str) and not kwargs.get(\"asfile\", False):\n await message.edit(answer)\n answer = answer[4096:]\n while len(answer) > 0:\n answer = CONT_MSG + answer\n message.message = answer[:4096]\n answer = answer[4096:]\n ret.append(await message.respond(message, **kwargs))\n else:\n if message.media is not None:\n await message.edit(file=answer, **kwargs)\n else:\n await message.edit(\"Loading media...\")\n ret = [await message.client.send_file(message.to_id, answer, reply_to=message.reply_to_msg_id, **kwargs)]\n await message.delete()\n", "repo_name": "penn5/friendly-telegram", "sub_path": "friendly-telegram/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.abspath", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 63, "usage_type": "call"}, {"api_name": "telethon.tl.types.PeerUser", "line_number": 64, "usage_type": "argument"}, {"api_name": "telethon.tl.types.PeerChat", "line_number": 67, "usage_type": "argument"}, {"api_name": "telethon.tl.types.PeerChannel", "line_number": 67, "usage_type": "argument"}, {"api_name": "logging.error", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 74, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 80, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 80, "usage_type": "call"}, {"api_name": "asyncio.run_coroutine_threadsafe", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "37214408145", "text": "from pyrogram import Client, enums\nimport asyncio\nimport random\nfrom rich.console import Console, Theme\n\nfrom settings.config import color_number\nfrom settings.function import SettingsFunction\n\nconsole = Console(theme=Theme({\"repr.number\": color_number}))\n\nclass InviteUsers(SettingsFunction):\n \"\"\"Inviting users to chat\"\"\"\n\n def __init__(self, sessions):\n self.sessions = sessions\n self.users = []\n\n link = self.invitation(console.input(\"[bold red]Where to get users from> \"))\n self.link_add_group = self.invitation(console.input(\"[bold red]Where to invite users> \"))\n\n self.account_count()\n self.parse_users(random.choice(self.sessions), link)\n\n def chunks(self, count_sessions):\n return [self.users[i::count_sessions] for i in range(count_sessions)]\n\n async def invite(self, users, session, invite_chat):\n await self.launch(session)\n\n count = 0\n for user in users:\n try:\n await session.add_chat_members(invite_chat.id, user)\n except Exception as error:\n console.print(\"Not invited. Error : {}\".format(error), style=\"bold red\")\n else:\n count += 1\n console.print(\"Invited: {} users\".format(count), style=\"bold green\")\n\n def parse_users(self, session, link):\n asyncio.get_event_loop().run_until_complete(\n self.launch(session)\n )\n\n try:\n chat = session.get_chat(link)\n invite_chat = session.get_chat(self.link_add_group)\n\n except Exception as error:\n console.print(error, style=\"bold\")\n\n else:\n for member in session.get_chat_members(chat.id):\n if not member.user.is_bot:\n if not member.user.username:\n self.users.append(member.user.id)\n else: \n self.users.append(member.user.username)\n\n console.print(\n \"[bold yellow][*] Got %d users.\" % len(self.users)\n )\n users = self.chunks(len(self.sessions))\n\n asyncio.get_event_loop().run_until_complete(\n asyncio.gather(*[\n self.invite(users, session, invite_chat)\n for session, users_chunk in zip(self.sessions, users)\n ])\n )", "repo_name": "Madara225/telegram-raid-botnet-pyrogram", "sub_path": "functions/invitation.py", "file_name": "invitation.py", "file_ext": "py", "file_size_in_byte": 2375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "51", "api": [{"api_name": "rich.console.Console", "line_number": 9, "usage_type": "call"}, {"api_name": "rich.console.Theme", "line_number": 9, "usage_type": "call"}, {"api_name": "settings.config.color_number", "line_number": 9, "usage_type": "name"}, {"api_name": "settings.function.SettingsFunction", "line_number": 11, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 22, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 41, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 65, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "41380771528", "text": "from random import randint as rd\r\nfrom bs4 import BeautifulSoup as bs\r\nfrom requests import get as req_get\r\nfrom my_storage import StorageOther\r\nfrom other_things import Port\r\nfrom typing import List\r\n\r\nschedule_db = StorageOther(password=Port.password, database='schedule', table='download', port=Port.port,\r\n host=Port.host)\r\n\r\nd_anek = {-2:'На чужой хентай свои тентакли не распускай!\\n\\n(А. Н. Павленко, aka Лысый)', -1:'Нынешние студенты - лучшая борьба с утечкой мозгов'}\r\n\r\ndef anekparse() -> str:\r\n try:\r\n sr = req_get('https://www.anekdot.ru/release/anekdot/day/').text\r\n s = bs(sr, features='html.parser')\r\n data = s.find_all('div', class_='text')\r\n x = rd(-2, len(data) - 1)\r\n if x != -1:\r\n return f'{data[x].text}\\n\\nАнек шлак, взят с кое-какого сайта, я над этим работаю'\r\n else:\r\n return d_anek.get(x)\r\n except:\r\n return 'Это временно не работает'\r\n\r\n\r\ndef schedparse(dates: list, link: str) -> List[str]:\r\n kolvo_par = '12345678'\r\n vremya_par = ('0', '08:00-9:30', '09:40-11:10', '11:20-12:50', '13:20-14:50', '15:00-16:30', '16:40-18:10',\r\n '18:20-19:50', '20:00-21:30')\r\n wdayd = {0: 'Понедельник', 1: 'Вторник', 2: 'Среда', 3: 'Четверг', 4: 'Пятница', 5: 'Суббота', 6: 'Воскресенье'}\r\n sr = req_get(link).text\r\n s = bs(sr, features='html.parser')\r\n data = s.find_all('tr')\r\n parys = []\r\n for date in dates:\r\n\r\n wday = date.date().weekday()\r\n date = str(date.date()).replace('-', '.')\r\n date = f'{date[-2:]}.{date[5:7]}.{date[:4]}'\r\n if wday == 6:\r\n parys.append(f'{date}\\nЭто воскресенье, гений')\r\n continue\r\n pary = ''\r\n for i in data:\r\n if i.find(string=date) is not None:\r\n pary = ''\r\n for n in kolvo_par:\r\n pary = f'{pary}✅{n} {vremya_par[(int(n))]}\\n'\r\n if len(i.find_all(pare_id=n)) > 1:\r\n for i1 in i.find_all(pare_id=n):\r\n try:\r\n int(i1.text[0])\r\n pary = f'{pary}{i1.text[10:]}\\n'\r\n except:\r\n pary = f'{pary}{i1.text}\\n'\r\n else:\r\n try:\r\n try:\r\n int(i.find(pare_id=n).text[0])\r\n pary = f'{pary}{i.find(pare_id=n).text[10:]}\\n'\r\n except:\r\n pary = f'{pary}{i.find(pare_id=n).text}\\n'\r\n except:\r\n pary = f'{pary}Нет пары\\n'\r\n break\r\n if pary != '':\r\n pary = f'{date} {wdayd.get(wday)}\\n{pary}'\r\n parys.append(pary)\r\n else:\r\n pary = f'{date} {wdayd.get(wday)}\\nНа эту дату расписания нет'\r\n parys.append(pary)\r\n return parys\r\n\r\n\r\nasync def from_db(key: str, day: int) -> List[str]:\r\n row = await schedule_db.get_row(column_name='shargroup1', key=key)\r\n if row is not None:\r\n if day == 1:\r\n return row.get('d1')\r\n elif day == 2:\r\n return row.get('d2')\r\n elif day == 3:\r\n return row.get('d3')\r\n elif day == 7:\r\n return [row.get(f'd{i}') for i in range(1, 8)]\r\n else:\r\n return ['Расписание временно недоступно']\r\n", "repo_name": "wqfcacfagv/bot", "sub_path": "my_functions.py", "file_name": "my_functions.py", "file_ext": "py", "file_size_in_byte": 3785, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "my_storage.StorageOther", "line_number": 8, "usage_type": "call"}, {"api_name": "other_things.Port.password", "line_number": 8, "usage_type": "attribute"}, {"api_name": "other_things.Port", "line_number": 8, "usage_type": "name"}, {"api_name": "other_things.Port.port", "line_number": 8, "usage_type": "attribute"}, {"api_name": "other_things.Port.host", "line_number": 9, "usage_type": "attribute"}, {"api_name": "other_things.Port", "line_number": 9, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 18, "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": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "2620666522", "text": "import numpy as np \r\nimport pandas as pd\r\nimport plotly\r\nimport plotly.graph_objects as go\r\nimport pymongo\r\n\r\nclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\r\ndb = client[\"armageddon\"]\r\ntoken = \"pk.eyJ1IjoibmVlaGFyaWthayIsImEiOiJjazJsOTlsYWcwNHFtM2JudHRpY2Y1ZnUzIn0.-9h08W2-BvmvhGfTO8Mobg\"\r\nmapbox_style = \"mapbox://styles/neeharikak/ck3yt0g624vwr1clm0itq8t5r\"\r\ncrash = db[\"plane_crashes\"]\r\nlats = []\r\nlons = []\r\nredlat300 = []\r\nredlon300 = []\r\norangelat200 = []\r\norangelon200 = []\r\nyellowlat100 = []\r\nyellowlon100 = []\r\nbluelat = []\r\nbluelon = []\r\n\r\ncrashArray = [0, 0, 0 ,0]\r\n\r\nfor obj in crash.find():\r\n \r\n lats.append(obj[\"Latitude\"])\r\n lons.append(obj[\"Longitude\"])\r\n if obj[\"TotalFatalInjuries\"] != ' ' and obj[\"TotalFatalInjuries\"] is not None:\r\n if (int(obj[\"TotalFatalInjuries\"]) > 300):\r\n redlat300.append(obj[\"Latitude\"])\r\n redlon300.append(obj[\"Longitude\"])\r\n elif (int(obj[\"TotalFatalInjuries\"]) > 200):\r\n orangelat200.append(obj[\"Latitude\"])\r\n orangelon200.append(obj[\"Longitude\"])\r\n elif (int(obj[\"TotalFatalInjuries\"]) > 100):\r\n yellowlat100.append(obj[\"Latitude\"])\r\n yellowlon100.append(obj[\"Longitude\"])\r\n else:\r\n bluelat.append(obj[\"Latitude\"])\r\n bluelon.append(obj[\"Longitude\"])\r\n\r\n\r\n\r\nfig = go.Figure()\r\n\r\nfig.add_trace(go.Scattermapbox(\r\n lat = redlat300,\r\n lon = redlon300,\r\n mode = \"markers\", \r\n marker = {\"size\": 10, \"color\": \"red\"}\r\n )\r\n )\r\n\r\nfig.add_trace(go.Scattermapbox(\r\n lat = orangelat200,\r\n lon = orangelon200,\r\n mode = \"markers\", \r\n marker = {\"size\": 10, \"color\": \"orange\"}\r\n )\r\n ) \r\n\r\nfig.add_trace(go.Scattermapbox(\r\n lat = yellowlat100,\r\n lon = yellowlon100,\r\n mode = \"markers\", \r\n marker = {\"size\": 10, \"color\": \"yellow\"}\r\n )\r\n )\r\n\r\nfig.add_trace(go.Scattermapbox(\r\n lat = bluelat,\r\n lon = bluelon,\r\n mode = \"markers\", \r\n marker = {\"size\": 10, \"color\": \"blue\"}\r\n )\r\n ) \r\n\r\n\r\nfig.update_layout(\r\n autosize=True,\r\n hovermode='closest',\r\n mapbox=go.layout.Mapbox(\r\n accesstoken=token,\r\n bearing=0,\r\n style=mapbox_style,\r\n center=go.layout.mapbox.Center(\r\n lat=33.930828,\r\n lon=-98.484879\r\n ),\r\n pitch=0,\r\n zoom=1\r\n ))\r\n\r\nplotly.offline.plot(fig, filename='plane_crash.html')", "repo_name": "NeeharikaK/5303-DB-DataVisualization", "sub_path": "A08/arm3.py", "file_name": "arm3.py", "file_ext": "py", "file_size_in_byte": 3209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 45, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 45, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scattermapbox", "line_number": 47, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 47, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scattermapbox", "line_number": 55, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 55, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scattermapbox", "line_number": 63, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 63, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scattermapbox", "line_number": 71, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 71, "usage_type": "name"}, {"api_name": "plotly.graph_objects.layout.Mapbox", "line_number": 83, "usage_type": "call"}, {"api_name": "plotly.graph_objects.layout", "line_number": 83, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 83, "usage_type": "name"}, {"api_name": "plotly.graph_objects.layout.mapbox.Center", "line_number": 87, "usage_type": "call"}, {"api_name": "plotly.graph_objects.layout", "line_number": 87, "usage_type": "attribute"}, {"api_name": "plotly.graph_objects", "line_number": 87, "usage_type": "name"}, {"api_name": "plotly.offline.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 95, "usage_type": "attribute"}]} +{"seq_id": "1297095333", "text": "# -*- coding: utf-8 -*-\nfrom itertools import chain\nfrom operator import itemgetter\n\nfrom cgi import escape\nfrom dateutil.parser import parse\nfrom dateutil.tz import gettz, UTC\nfrom flask_babelex import lazy_gettext as _\nfrom sqlalchemy import Integer, and_, false, or_, true\nfrom sqlalchemy.dialects.postgresql import array\nfrom wtforms import Form, fields, widgets\nfrom wtforms.compat import text_type\nfrom wtforms.widgets import html_params, HTMLString\nfrom wtforms_alchemy.fields import QuerySelectField\n\nfrom apollo import models\nfrom apollo.core import BooleanFilter, CharFilter, ChoiceFilter, FilterSet\nfrom apollo.helpers import _make_choices\nfrom apollo.settings import TIMEZONE\nfrom apollo.submissions.models import FLAG_CHOICES\nfrom apollo.submissions.qa.query_builder import (\n build_expression, generate_qa_query\n)\n\nAPP_TZ = gettz(TIMEZONE)\n\n\nclass TagLookupFilter(ChoiceFilter):\n def __init__(self, *args, **kwargs):\n self.contains = kwargs.pop('contains', None)\n super().__init__(*args, **kwargs)\n\n def filter(self, query, value, **kwargs):\n if value:\n if value == 'NULL':\n condition = (models.Submission.data[self.name] == None) # noqa\n elif value == 'NOT_NULL':\n condition = (models.Submission.data[self.name] != None) # noqa\n else:\n condition = (models.Submission.data[self.name] == value)\n\n return (condition, None)\n elif self.contains:\n condition = (models.Submission.data[self.name] != None) # noqa\n return (condition, None)\n\n return (None, None)\n\n\ndef make_submission_sample_filter(\n participant_set_id, filter_on_locations=False):\n class SubmissionSampleFilter(ChoiceFilter):\n def __init__(self, *args, **kwargs):\n sample_choices = models.Sample.query.filter_by(\n participant_set_id=participant_set_id\n ).order_by(\n models.Sample.name\n ).with_entities(models.Sample.id, models.Sample.name).all()\n self.participant_set_id = participant_set_id\n self.filter_on_locations = filter_on_locations\n\n kwargs['choices'] = _make_choices(sample_choices, _('Sample'))\n super().__init__(*args, **kwargs)\n\n def filter_by_locations(self, query, value):\n joined_classes = [\n mapper.class_ for mapper in query._join_entities]\n if models.Location in joined_classes:\n query1 = query\n else:\n query1 = query.join(models.Submission.location)\n\n sample_locations = models.Participant.query.filter_by(\n participant_set_id=participant_set_id\n ).join(\n models.Participant.samples\n ).filter(\n models.Sample.participant_set_id == participant_set_id,\n models.Sample.id == value\n ).with_entities(\n models.Participant.location_id\n )\n\n query2 = query1.filter(\n models.Submission.location_id.in_(sample_locations)\n )\n return query2\n\n def filter_by_participants(self, query, value):\n participants_in_sample = models.Participant.query.join(\n models.Participant.samples\n ).filter(\n models.Participant.participant_set_id == participant_set_id,\n models.Sample.id == value\n )\n participant_ids = list(\n chain(*participants_in_sample.with_entities(\n models.Participant.id).all()))\n query2 = query.filter(\n models.Submission.participant_id.in_(participant_ids))\n\n return query2\n\n def queryset_(self, query, value, **kwargs):\n if value:\n if self.filter_on_locations:\n return self.filter_by_locations(query, value)\n else:\n return self.filter_by_participants(query, value)\n\n return query\n\n return SubmissionSampleFilter\n\n\ndef make_participant_role_filter(participant_set_id):\n class ParticipantRoleFilter(ChoiceFilter):\n def __init__(self, *args, **kwargs):\n role_choices = models.ParticipantRole.query.filter_by(\n participant_set_id=participant_set_id\n ).order_by(\n models.ParticipantRole.name\n ).with_entities(\n models.ParticipantRole.id, models.ParticipantRole.name).all()\n self.participant_set_id = participant_set_id\n\n kwargs['choices'] = _make_choices(\n role_choices, _('Participant Role'))\n\n super().__init__(*args, **kwargs)\n\n def queryset_(self, query, value, **kwargs):\n if value:\n return query.filter(models.Participant.role_id == value)\n\n return query\n\n return ParticipantRoleFilter\n\n\ndef make_submission_location_group_filter(location_set_id):\n class SubmissionLocationGroupFilter(ChoiceFilter):\n def __init__(self, *args, **kwargs):\n group_choices = models.LocationGroup.query.filter_by(\n location_set_id=location_set_id\n ).order_by(\n models.LocationGroup.name\n ).with_entities(\n models.LocationGroup.id, models.LocationGroup.name\n ).all()\n self.location_set_id = location_set_id\n\n kwargs['choices'] = _make_choices(group_choices, _('Group'))\n super().__init__(*args, **kwargs)\n\n def queryset_(self, query, value, **kwargs):\n if value:\n location_ids = models.Location.query.join(\n models.Location.groups\n ).filter(\n models.Location.location_set_id == self.location_set_id,\n models.LocationGroup.id == value,\n ).with_entities(models.Location.id)\n\n return query.filter(\n models.Submission.location_id.in_(location_ids))\n\n return query\n\n return SubmissionLocationGroupFilter\n\n\ndef make_base_submission_filter(event, filter_on_locations=False):\n class BaseSubmissionFilterSet(FilterSet):\n sample = make_submission_sample_filter(\n event.participant_set_id,\n filter_on_locations=filter_on_locations\n )()\n location_group = make_submission_location_group_filter(\n event.location_set_id\n )()\n\n return BaseSubmissionFilterSet\n\n\nclass IncidentStatusFilter(ChoiceFilter):\n def __init__(self, *args, **kwargs):\n kwargs['choices'] = (\n ('', _('All Incidents')),\n ('NULL', _('Unmarked Incidents')),\n ('confirmed', _('Confirmed Incidents')),\n ('rejected', _('Rejected Incidents')),\n ('citizen', _('Citizen Report Incidents')),\n )\n super().__init__(*args, **kwargs)\n\n def filter(self, query, value, **kwargs):\n if value:\n if value == 'NULL':\n return (models.Submission.incident_status == None, None) # noqa\n\n return (models.Submission.incident_status == value, None)\n\n return (None, None)\n\n\ndef make_submission_analysis_filter(event, form, filter_on_locations=False):\n attributes = {}\n if form.form_type == 'INCIDENT':\n attributes['status'] = IncidentStatusFilter(default='confirmed')\n\n return type(\n 'SubmissionAnalysisFilterSet',\n (make_base_submission_filter(\n event, filter_on_locations=filter_on_locations),),\n attributes\n )\n\n\ndef make_incident_location_filter(event, form, tag, filter_on_locations=False):\n base_filter_class = make_submission_analysis_filter(\n event, form, filter_on_locations=filter_on_locations)\n\n attributes = {\n tag: TagLookupFilter(\n choices=(('NOT_NULL', ''),),\n contains=True,\n default='NOT_NULL',\n widget=widgets.HiddenInput()\n )\n }\n\n return type(\n 'CriticalIncidentLocationFilterSet',\n (base_filter_class,),\n attributes)\n\n\nclass FormGroupFilter(ChoiceFilter):\n def __init__(self, *args, **kwargs):\n self.formobj = kwargs.pop('form')\n self.group = kwargs.pop('group')\n super().__init__(*args, **kwargs)\n\n def filter(self, query, value, **kwargs):\n group_tags = self.formobj.get_group_tags(self.group['name'])\n\n if value == '1':\n # Partial\n if group_tags:\n constraint = and_(\n ~models.Submission.data.has_all(array(group_tags)),\n models.Submission.data.has_any(array(group_tags))\n )\n else:\n constraint = false()\n elif value == '2':\n # Missing\n if group_tags:\n constraint = or_(\n ~models.Submission.data.has_any(array(group_tags)),\n models.Submission.data == None # noqa\n )\n else:\n constraint = true()\n elif value == '3':\n # Complete\n if group_tags:\n constraint = models.Submission.data.has_all(array(group_tags))\n else:\n constraint = false()\n elif value == '4':\n # Conflict\n if group_tags:\n query_params = [\n models.Submission.conflicts.has_key(tag) # noqa\n for tag in group_tags\n ]\n constraint = or_(*query_params)\n else:\n constraint = false()\n else:\n constraint = None\n\n if constraint is None:\n return (None, None)\n else:\n form_ = kwargs['form']\n if form_.data and form_.data.get('conjunction') is True:\n # OR conjunction\n return (None, constraint)\n else:\n # AND conjunction\n return (constraint, None)\n\n\nclass FieldOptionFilter(ChoiceFilter):\n def filter(self, query, value, **kwargs):\n if value:\n return (\n models.Submission.data[self.name].astext.cast(\n Integer) == int(value),\n None\n )\n\n return (None, None)\n\n\nclass FieldValueFilter(CharFilter):\n pass\n\n\nclass ParticipantIDFilter(CharFilter):\n def filter(self, query, value, **kwargs):\n if value:\n return (\n models.Participant.participant_id == value,\n None\n )\n\n return (None, None)\n\n\nclass SubmissionQuarantineStatusFilter(ChoiceFilter):\n def filter(self, query, value, **kwargs):\n if value and value == 'N':\n return (\n or_(\n models.Submission.quarantine_status == None, # noqa\n models.Submission.quarantine_status == ''),\n None\n )\n elif value in ('A', 'R'):\n return (models.Submission.quarantine_status == value, None)\n elif value == 'AR':\n return (\n or_(\n models.Submission.quarantine_status == 'A',\n models.Submission.quarantine_status == 'R'\n ),\n None\n )\n else:\n return (None, None)\n\n\nclass SubmissionSenderVerificationFilter(ChoiceFilter):\n def filter(self, query, value, **kwargs):\n if value and value == '1':\n return (\n models.Submission.sender_verified == True, # noqa\n None\n )\n elif value:\n return (\n models.Submission.sender_verified == False, # noqa\n None\n )\n\n return (None, None)\n\n\nclass OnlineStatusFilter(ChoiceFilter):\n def filter(self, query, value, **kwargs):\n if value and value == '1':\n return (\n models.Submission.unreachable == True, # noqa\n None\n )\n elif value:\n return (\n models.Submission.unreachable == False, # noqa\n None\n )\n\n return (None, None)\n\n\nclass DateFilter(CharFilter):\n def filter(self, query, value, **kwargs):\n if value:\n try:\n dt = parse(value, dayfirst=True)\n except (OverflowError, ValueError):\n return (None, None)\n\n dt = dt.replace(tzinfo=APP_TZ)\n upper_bound = dt.replace(hour=23, minute=59, second=59).astimezone(\n UTC).replace(tzinfo=None)\n lower_bound = dt.replace(hour=0, minute=0, second=0).astimezone(\n UTC).replace(tzinfo=None)\n\n return (\n and_(\n models.Submission.participant_updated >= lower_bound,\n models.Submission.participant_updated <= upper_bound\n ),\n None\n )\n\n return (None, None)\n\n\nclass LocationSelectWidget(widgets.Select):\n @classmethod\n def render_option(cls, value, label, selected, **kwargs):\n options = dict(kwargs, value=value)\n if selected:\n options['selected'] = True\n if hasattr(label, 'location_type'):\n return HTMLString('' % (\n html_params(**options),\n escape(text_type(label.name)),\n escape(text_type(label.location_type))))\n else:\n return HTMLString('' % (\n html_params(**options),\n escape(text_type(label))))\n\n\nclass ParticipantSelectWidget(widgets.Select):\n @classmethod\n def render_option(cls, value, label, selected, **kwargs):\n options = dict(kwargs, value=value)\n if selected:\n options['selected'] = True\n if hasattr(label, 'participant_id'):\n return HTMLString('' % (\n html_params(**options),\n escape(text_type(label.participant_id)),\n escape(text_type(label.name))))\n else:\n return HTMLString('' % (\n html_params(**options),\n escape(text_type(label))))\n\n\nclass LocationQuerySelectField(QuerySelectField):\n widget = LocationSelectWidget()\n\n def process_formdata(self, valuelist):\n if valuelist and valuelist[0] and valuelist[0] != '__None':\n self.query = models.Location.query.filter(\n models.Location.id == valuelist[0])\n return super(LocationQuerySelectField, self).process_formdata(\n valuelist)\n\n\nclass ParticipantQuerySelectField(QuerySelectField):\n widget = ParticipantSelectWidget()\n\n def process_formdata(self, valuelist):\n if valuelist and valuelist[0] and valuelist[0] != '__None':\n self.query = models.Participant.query.filter(\n models.Participant.id == valuelist[0])\n return super(ParticipantQuerySelectField, self).process_formdata(\n valuelist)\n\n\nclass FormSerialNumberFilter(CharFilter):\n def filter(self, query, value, **kwargs):\n if value:\n return (\n models.Submission.serial_no == value,\n None\n )\n\n return (None, None)\n\n\nclass AJAXLocationFilter(ChoiceFilter):\n field_class = LocationQuerySelectField\n\n def __init__(self, *args, **kwargs):\n kwargs['query_factory'] = lambda: []\n kwargs['get_pk'] = lambda i: i.id\n\n return super(AJAXLocationFilter, self).__init__(*args, **kwargs)\n\n def queryset_(self, queryset, value, **kwargs):\n if value:\n location_query = models.Location.query.with_entities(\n models.Location.id\n ).join(\n models.LocationPath,\n models.Location.id == models.LocationPath.descendant_id\n ).filter(models.LocationPath.ancestor_id == value.id)\n\n return queryset.filter(\n models.Submission.location_id.in_(location_query))\n\n return queryset\n\n\nclass QualityAssuranceFilter(ChoiceFilter):\n field_class = fields.FormField\n\n def __init__(self, form, qa_form, *args, **kwargs):\n kwargs['form_class'] = form\n self.qa_form = qa_form\n super(QualityAssuranceFilter, self).__init__(*args, **kwargs)\n\n def queryset_(self, query, value):\n if (\n 'criterion' in value and 'condition' in value and\n value['criterion'] and value['condition']\n ):\n if value['criterion'] == 'A':\n # find all records for which any match the\n # following condition\n condition = value['condition']\n\n qa_subqueries = []\n if self.qa_form.quality_checks:\n for check in self.qa_form.quality_checks:\n qa_expr = build_expression(check)\n single_qa_query, tags = generate_qa_query(\n qa_expr, self.qa_form)\n uses_null = 'null' in qa_expr.lower()\n\n if tags and uses_null is False:\n null_query = or_(*[\n models.Submission.data[tag] == None # noqa\n for tag in tags])\n else:\n null_query = false()\n\n filter_query = None\n\n if condition == FLAG_CHOICES[3][0]:\n # verified: checklists that fail QA,\n # have all fields verified, and none are missing\n term1 = (single_qa_query == True) # noqa\n if tags:\n term2 = models.Submission.verified_fields.has_all( # noqa\n array(tags))\n else:\n term2 = false()\n filter_query = and_(term1, term2, null_query == False) # noqa\n elif condition == FLAG_CHOICES[1][0]:\n # missing: checklist has missing data\n filter_query = or_(\n null_query == True, single_qa_query == None) # noqa\n elif condition == FLAG_CHOICES[0][0]:\n # flagged: checklist fails QA, not all fields are\n # verified, and none of them are missing\n term1 = (single_qa_query == True) # noqa\n if tags:\n term2 = ~models.Submission.verified_fields.has_all( # noqa\n array(tags))\n else:\n term2 = true()\n\n filter_query = and_(term1, term2, null_query == False) # noqa\n elif condition == FLAG_CHOICES[2][0]:\n # ok: checklist passes QA and none of the fields are # noqa\n # missing\n filter_query = and_(\n single_qa_query == False, null_query == False) # noqa\n\n if filter_query is None:\n return query.filter(false())\n\n qa_subqueries.append(filter_query)\n else:\n return query.filter(false())\n\n return query.filter(or_(*qa_subqueries))\n\n try:\n index = int(value['criterion'])\n check = self.qa_form.quality_checks[index]\n except (IndexError, ValueError):\n return query\n\n qa_expr = build_expression(check)\n qa_subquery, tags = generate_qa_query(qa_expr, self.qa_form)\n question_codes = array(tags)\n uses_null = 'null' in qa_expr.lower()\n if tags:\n null_query = or_(*[\n models.Submission.data[tag] == None # noqa\n for tag in question_codes]) if not uses_null else false()\n else:\n null_query = false()\n\n condition = value['condition']\n if condition == FLAG_CHOICES[3][0]:\n # verified\n if tags:\n return query.filter(\n null_query == False, # noqa\n qa_subquery == True, # noqa\n models.Submission.verified_fields.has_all(\n question_codes))\n else:\n return query.filter(\n qa_subquery == True, # noqa\n false())\n elif condition == FLAG_CHOICES[1][0]:\n # missing\n if tags:\n term1 = null_query\n term2 = (qa_subquery == None) # noqa\n\n return query.filter(or_(term1, term2))\n\n return query.filter(qa_subquery == None) # noqa\n elif condition == FLAG_CHOICES[0][0]:\n # flagged\n term1 = (qa_subquery == True) # noqa\n if tags:\n term2 = ~models.Submission.verified_fields.has_all(\n question_codes)\n else:\n term2 = false()\n return query.filter(null_query == False, term1, term2) # noqa\n elif condition == FLAG_CHOICES[2][0]:\n # OK\n return query.filter(\n null_query == False, qa_subquery == False) # noqa\n return query\n\n\nclass SubmissionDateFilter(CharFilter):\n def queryset_(self, queryset, value):\n if value:\n try:\n timestamp = parse(value, dayfirst=True)\n except Exception:\n return queryset.filter(False)\n\n upper = timestamp.replace(hour=23, minute=59, second=59)\n lower = timestamp.replace(hour=0, minute=0, second=0)\n\n return queryset.filter(\n models.Submission.participant_updated >= lower,\n models.Submission.participant_updated <= upper\n )\n\n return queryset\n\n\ndef make_submission_location_filter(location_set_id):\n class AJAXLocationFilter(ChoiceFilter):\n field_class = LocationQuerySelectField\n\n def __init__(self, *args, **kwargs):\n kwargs['query_factory'] = lambda: []\n kwargs['get_pk'] = lambda i: i.id\n\n super().__init__(*args, **kwargs)\n\n def queryset_(self, query, value, **kwargs):\n if value:\n location_query = models.Location.query.with_entities(\n models.Location.id\n ).join(\n models.LocationPath,\n models.Location.id == models.LocationPath.descendant_id\n ).filter(models.LocationPath.ancestor_id == value.id)\n\n return query.filter(\n models.Submission.location_id.in_(location_query))\n\n return query\n\n return AJAXLocationFilter\n\n\ndef make_dashboard_filter(event, filter_on_locations=False):\n attributes = {}\n attributes['location'] = make_submission_location_filter(\n event.location_set_id)()\n attributes['sample'] = make_submission_sample_filter(\n event.participant_set_id, filter_on_locations=filter_on_locations)()\n attributes['location_group'] = make_submission_location_group_filter(\n event.location_set_id)()\n\n return type(\n 'SubmissionFilterSet',\n (make_base_submission_filter(\n event, filter_on_locations=filter_on_locations),),\n attributes)\n\n\ndef make_submission_list_filter(event, form, filter_on_locations=False):\n attributes = {}\n form._populate_field_cache()\n\n if form.data and form.data.get('groups'):\n if form.form_type == 'INCIDENT':\n option_fields = [\n f for f in form._field_cache.values()\n if f['type'] == 'select']\n for field in option_fields:\n choices = _make_choices(sorted(\n ((v, '{} - {}'.format(field['tag'], k)) for k, v in\n field['options'].items()),\n key=itemgetter(0)\n ), field['tag'])\n attributes[field['tag']] = FieldOptionFilter(choices=choices)\n\n for group in form.data.get('groups'):\n field_name = '{}__{}'.format(form.id, group['slug'])\n choices = [\n ('', _('%(group)s Status', group=group['name'])),\n ('1', _('%(group)s Partial', group=group['name'])),\n ('2', _('%(group)s Missing', group=group['name'])),\n ('3', _('%(group)s Complete', group=group['name'])),\n ('4', _('%(group)s Conflict', group=group['name']))\n ]\n attributes[field_name] = FormGroupFilter(\n choices=choices, form=form, group=group)\n\n if form.form_type == 'INCIDENT':\n attributes['status'] = IncidentStatusFilter()\n elif form.form_type in ['CHECKLIST', 'SURVEY']:\n attributes['quarantine_status'] = SubmissionQuarantineStatusFilter(\n choices=(\n ('', _('Quarantine Status')),\n ('A', _('Quarantine All')),\n ('R', _('Quarantine Results')),\n ('AR', _('Quarantine All + Results')),\n ('N', _('Quarantine None')),\n ), default='')\n attributes['sender_verification'] = SubmissionSenderVerificationFilter(\n choices=(\n ('', _('Phone Confirmation')),\n ('1', _('Phone Confirmed')),\n ('2', _('Phone Unconfirmed'))\n ))\n\n attributes['participant_id'] = ParticipantIDFilter()\n attributes['location'] = make_submission_location_filter(\n event.location_set_id)()\n attributes['conjunction'] = BooleanFilter(\n label=_('Optional Inclusion')\n )\n attributes['online_status'] = OnlineStatusFilter(\n choices=(\n ('', _('Signal Status')),\n ('0', _('Signal')),\n ('1', _('No Signal'))\n )\n )\n attributes['date'] = DateFilter()\n attributes['fsn'] = FormSerialNumberFilter()\n attributes['participant_role'] = make_participant_role_filter(\n event.participant_set_id)()\n\n return type(\n 'SubmissionFilterSet',\n (make_base_submission_filter(\n event, filter_on_locations=filter_on_locations),),\n attributes)\n\n\ndef generate_quality_assurance_filter(event, form):\n from apollo.submissions.filters import make_base_submission_filter\n\n quality_check_criteria = [\n ('', _('Quality Check Criterion')),\n ('A', _('Any Criterion'))\n ] + \\\n (\n [\n (str(idx), qc['description'])\n for idx, qc in enumerate(form.quality_checks)\n ]\n if form.quality_checks else []\n )\n quality_check_conditions = [('', _('Quality Check Condition'))] + \\\n list(FLAG_CHOICES)\n\n class QualityAssuranceConditionsForm(Form):\n criterion = fields.SelectField(choices=quality_check_criteria)\n condition = fields.SelectField(choices=quality_check_conditions)\n\n attributes = {}\n\n attributes['quality_check'] = QualityAssuranceFilter(\n QualityAssuranceConditionsForm, form)\n\n # quarantine status\n attributes['quarantine_status'] = SubmissionQuarantineStatusFilter(\n choices=(\n ('', _('Quarantine Status')),\n ('N', _('Quarantine None')),\n ('A', _('Quarantine All')),\n ('R', _('Quarantine Results'))\n ))\n\n attributes['online_status'] = OnlineStatusFilter(\n choices=(\n ('', _('Signal Status')),\n ('0', _('Signal')),\n ('1', _('No Signal'))\n )\n )\n\n # participant id and location\n attributes['participant_id'] = ParticipantIDFilter()\n attributes['location'] = AJAXLocationFilter()\n attributes['date'] = SubmissionDateFilter()\n attributes['fsn'] = FormSerialNumberFilter()\n attributes['participant_role'] = make_participant_role_filter(\n event.participant_set_id)()\n\n return type(\n 'QualityAssuranceFilterSet',\n (make_base_submission_filter(event),),\n attributes\n )\n", "repo_name": "nditech/apollo", "sub_path": "apollo/submissions/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 28648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "60", "api": [{"api_name": "dateutil.tz.gettz", "line_number": 25, "usage_type": "call"}, {"api_name": "apollo.settings.TIMEZONE", "line_number": 25, "usage_type": "argument"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 28, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 36, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 36, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 38, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 38, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 40, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 40, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 44, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 44, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 52, "usage_type": "name"}, {"api_name": "apollo.models.Sample.query.filter_by", "line_number": 54, "usage_type": "call"}, {"api_name": "apollo.models.Sample", "line_number": 54, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 54, "usage_type": "name"}, {"api_name": "apollo.models.Sample", "line_number": 57, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 57, "usage_type": "name"}, {"api_name": "apollo.models.Sample", "line_number": 58, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 58, "usage_type": "name"}, {"api_name": "apollo.helpers._make_choices", "line_number": 62, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 62, "usage_type": "call"}, {"api_name": "apollo.models.Location", "line_number": 68, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 68, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 71, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 71, "usage_type": "name"}, {"api_name": "apollo.models.Participant.query.filter_by", "line_number": 73, "usage_type": "call"}, {"api_name": "apollo.models.Participant", "line_number": 73, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 73, "usage_type": "name"}, {"api_name": "apollo.models.Participant", "line_number": 76, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 76, "usage_type": "name"}, {"api_name": "apollo.models.Sample", "line_number": 78, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 78, "usage_type": "name"}, {"api_name": "apollo.models.Sample", "line_number": 79, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 79, "usage_type": "name"}, {"api_name": "apollo.models.Participant", "line_number": 81, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 81, "usage_type": "name"}, {"api_name": "apollo.models.Submission.location_id.in_", "line_number": 85, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 85, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 85, "usage_type": "name"}, {"api_name": "apollo.models.Participant.query.join", "line_number": 90, "usage_type": "call"}, {"api_name": "apollo.models.Participant", "line_number": 90, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 90, "usage_type": "name"}, {"api_name": "apollo.models.Participant", "line_number": 91, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 91, "usage_type": "name"}, {"api_name": "apollo.models.Participant", "line_number": 93, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 93, "usage_type": "name"}, {"api_name": "apollo.models.Sample", "line_number": 94, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 94, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 97, "usage_type": "call"}, {"api_name": "apollo.models.Participant", "line_number": 98, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 98, "usage_type": "name"}, {"api_name": "apollo.models.Submission.participant_id.in_", "line_number": 100, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 100, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 100, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 117, "usage_type": "name"}, {"api_name": "apollo.models.ParticipantRole.query.filter_by", "line_number": 119, "usage_type": "call"}, {"api_name": "apollo.models.ParticipantRole", "line_number": 119, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 119, "usage_type": "name"}, {"api_name": "apollo.models.ParticipantRole", "line_number": 122, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 122, "usage_type": "name"}, {"api_name": "apollo.models.ParticipantRole", "line_number": 124, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 124, "usage_type": "name"}, {"api_name": "apollo.helpers._make_choices", "line_number": 127, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 128, "usage_type": "call"}, {"api_name": "apollo.models.Participant", "line_number": 134, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 134, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 142, "usage_type": "name"}, {"api_name": "apollo.models.LocationGroup.query.filter_by", "line_number": 144, "usage_type": "call"}, {"api_name": "apollo.models.LocationGroup", "line_number": 144, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 144, "usage_type": "name"}, {"api_name": "apollo.models.LocationGroup", "line_number": 147, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 147, "usage_type": "name"}, {"api_name": "apollo.models.LocationGroup", "line_number": 149, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 149, "usage_type": "name"}, {"api_name": "apollo.helpers._make_choices", "line_number": 153, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 153, "usage_type": "call"}, {"api_name": "apollo.models.Location.query.join", "line_number": 158, "usage_type": "call"}, {"api_name": "apollo.models.Location", "line_number": 158, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 158, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 159, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 159, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 161, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 161, "usage_type": "name"}, {"api_name": "apollo.models.LocationGroup", "line_number": 162, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 162, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 163, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 163, "usage_type": "name"}, {"api_name": "apollo.models.Submission.location_id.in_", "line_number": 166, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 166, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 166, "usage_type": "name"}, {"api_name": "apollo.core.FilterSet", "line_number": 174, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 186, "usage_type": "name"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 189, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 190, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 191, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 192, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 193, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 200, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 200, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 202, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 202, "usage_type": "name"}, {"api_name": "wtforms.widgets.HiddenInput", "line_number": 229, "usage_type": "call"}, {"api_name": "wtforms.widgets", "line_number": 229, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 239, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 251, "usage_type": "call"}, {"api_name": "apollo.models.Submission.data.has_all", "line_number": 252, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 252, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 252, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 252, "usage_type": "call"}, {"api_name": "apollo.models.Submission.data.has_any", "line_number": 253, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 253, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 253, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 253, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 256, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 260, "usage_type": "call"}, {"api_name": "apollo.models.Submission.data.has_any", "line_number": 261, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 261, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 261, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 261, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 262, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 262, "usage_type": "name"}, {"api_name": "sqlalchemy.true", "line_number": 265, "usage_type": "call"}, {"api_name": "apollo.models.Submission.data.has_all", "line_number": 269, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 269, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 269, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 269, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 271, "usage_type": "call"}, {"api_name": "apollo.models.Submission.conflicts.has_key", "line_number": 276, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 276, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 276, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 279, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 281, "usage_type": "call"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 297, "usage_type": "name"}, {"api_name": "sqlalchemy.Integer", "line_number": 302, "usage_type": "argument"}, {"api_name": "apollo.models.Submission", "line_number": 301, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 301, "usage_type": "name"}, {"api_name": "apollo.core.CharFilter", "line_number": 309, "usage_type": "name"}, {"api_name": "apollo.core.CharFilter", "line_number": 313, "usage_type": "name"}, {"api_name": "apollo.models.Participant", "line_number": 317, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 317, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 324, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 328, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 329, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 329, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 330, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 330, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 334, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 334, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 337, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 338, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 338, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 339, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 339, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 347, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 351, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 351, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 356, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 356, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 363, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 367, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 367, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 372, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 372, "usage_type": "name"}, {"api_name": "apollo.core.CharFilter", "line_number": 379, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 383, "usage_type": "call"}, {"api_name": "dateutil.tz.UTC", "line_number": 389, "usage_type": "argument"}, {"api_name": "dateutil.tz.UTC", "line_number": 391, "usage_type": "argument"}, {"api_name": "sqlalchemy.and_", "line_number": 394, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 395, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 395, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 396, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 396, "usage_type": "name"}, {"api_name": "wtforms.widgets.Select", "line_number": 404, "usage_type": "attribute"}, {"api_name": "wtforms.widgets", "line_number": 404, "usage_type": "name"}, {"api_name": "wtforms.widgets.HTMLString", "line_number": 411, "usage_type": "call"}, {"api_name": "wtforms.widgets.html_params", "line_number": 412, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 413, "usage_type": "call"}, {"api_name": "wtforms.compat.text_type", "line_number": 413, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 414, "usage_type": "call"}, {"api_name": "wtforms.compat.text_type", "line_number": 414, "usage_type": "call"}, {"api_name": "wtforms.widgets.HTMLString", "line_number": 416, "usage_type": "call"}, {"api_name": "wtforms.widgets.html_params", "line_number": 417, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 418, "usage_type": "call"}, {"api_name": "wtforms.compat.text_type", "line_number": 418, "usage_type": "call"}, {"api_name": "wtforms.widgets.Select", "line_number": 421, "usage_type": "attribute"}, {"api_name": "wtforms.widgets", "line_number": 421, "usage_type": "name"}, {"api_name": "wtforms.widgets.HTMLString", "line_number": 428, "usage_type": "call"}, {"api_name": "wtforms.widgets.html_params", "line_number": 429, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 430, "usage_type": "call"}, {"api_name": "wtforms.compat.text_type", "line_number": 430, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 431, "usage_type": "call"}, {"api_name": "wtforms.compat.text_type", "line_number": 431, "usage_type": "call"}, {"api_name": "wtforms.widgets.HTMLString", "line_number": 433, "usage_type": "call"}, {"api_name": "wtforms.widgets.html_params", "line_number": 434, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 435, "usage_type": "call"}, {"api_name": "wtforms.compat.text_type", "line_number": 435, "usage_type": "call"}, {"api_name": "wtforms_alchemy.fields.QuerySelectField", "line_number": 438, "usage_type": "name"}, {"api_name": "apollo.models.Location.query.filter", "line_number": 443, "usage_type": "call"}, {"api_name": "apollo.models.Location", "line_number": 443, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 443, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 444, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 444, "usage_type": "name"}, {"api_name": "wtforms_alchemy.fields.QuerySelectField", "line_number": 449, "usage_type": "name"}, {"api_name": "apollo.models.Participant.query.filter", "line_number": 454, "usage_type": "call"}, {"api_name": "apollo.models.Participant", "line_number": 454, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 454, "usage_type": "name"}, {"api_name": "apollo.models.Participant", "line_number": 455, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 455, "usage_type": "name"}, {"api_name": "apollo.core.CharFilter", "line_number": 460, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 464, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 464, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 471, "usage_type": "name"}, {"api_name": "apollo.models.Location.query.with_entities", "line_number": 482, "usage_type": "call"}, {"api_name": "apollo.models.Location", "line_number": 482, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 482, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 483, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 483, "usage_type": "name"}, {"api_name": "apollo.models.LocationPath", "line_number": 485, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 485, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 486, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 486, "usage_type": "name"}, {"api_name": "apollo.models.LocationPath", "line_number": 486, "usage_type": "attribute"}, {"api_name": "apollo.models.LocationPath", "line_number": 487, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 487, "usage_type": "name"}, {"api_name": "apollo.models.Submission.location_id.in_", "line_number": 490, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 490, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 490, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 495, "usage_type": "name"}, {"api_name": "wtforms.fields.FormField", "line_number": 496, "usage_type": "attribute"}, {"api_name": "wtforms.fields", "line_number": 496, "usage_type": "name"}, {"api_name": "apollo.submissions.qa.query_builder.build_expression", "line_number": 516, "usage_type": "call"}, {"api_name": "apollo.submissions.qa.query_builder.generate_qa_query", "line_number": 517, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 522, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 523, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 523, "usage_type": "name"}, {"api_name": "sqlalchemy.false", "line_number": 526, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 530, "usage_type": "name"}, {"api_name": "apollo.models.Submission.verified_fields.has_all", "line_number": 535, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 535, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 535, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 536, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 538, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 539, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 540, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 542, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 544, "usage_type": "name"}, {"api_name": "apollo.models.Submission.verified_fields.has_all", "line_number": 549, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 549, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 549, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 550, "usage_type": "call"}, {"api_name": "sqlalchemy.true", "line_number": 552, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 554, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 555, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 558, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 562, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 566, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 568, "usage_type": "call"}, {"api_name": "apollo.submissions.qa.query_builder.build_expression", "line_number": 576, "usage_type": "call"}, {"api_name": "apollo.submissions.qa.query_builder.generate_qa_query", "line_number": 577, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.array", "line_number": 578, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 581, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 582, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 582, "usage_type": "name"}, {"api_name": "sqlalchemy.false", "line_number": 583, "usage_type": "call"}, {"api_name": "sqlalchemy.false", "line_number": 585, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 588, "usage_type": "name"}, {"api_name": "apollo.models.Submission.verified_fields.has_all", "line_number": 594, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 594, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 594, "usage_type": "name"}, {"api_name": "sqlalchemy.false", "line_number": 599, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 600, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 606, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 609, "usage_type": "name"}, {"api_name": "apollo.models.Submission.verified_fields.has_all", "line_number": 613, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 613, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 613, "usage_type": "name"}, {"api_name": "sqlalchemy.false", "line_number": 616, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 618, "usage_type": "name"}, {"api_name": "apollo.core.CharFilter", "line_number": 625, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 629, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 637, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 637, "usage_type": "name"}, {"api_name": "apollo.models.Submission", "line_number": 638, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 638, "usage_type": "name"}, {"api_name": "apollo.core.ChoiceFilter", "line_number": 645, "usage_type": "name"}, {"api_name": "apollo.models.Location.query.with_entities", "line_number": 656, "usage_type": "call"}, {"api_name": "apollo.models.Location", "line_number": 656, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 656, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 657, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 657, "usage_type": "name"}, {"api_name": "apollo.models.LocationPath", "line_number": 659, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 659, "usage_type": "name"}, {"api_name": "apollo.models.Location", "line_number": 660, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 660, "usage_type": "name"}, {"api_name": "apollo.models.LocationPath", "line_number": 660, "usage_type": "attribute"}, {"api_name": "apollo.models.LocationPath", "line_number": 661, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 661, "usage_type": "name"}, {"api_name": "apollo.models.Submission.location_id.in_", "line_number": 664, "usage_type": "call"}, {"api_name": "apollo.models.Submission", "line_number": 664, "usage_type": "attribute"}, {"api_name": "apollo.models", "line_number": 664, "usage_type": "name"}, {"api_name": "apollo.helpers._make_choices", "line_number": 697, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 700, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 707, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 708, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 709, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 710, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 711, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 721, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 722, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 723, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 724, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 725, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 729, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 730, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 731, "usage_type": "call"}, {"api_name": "apollo.core.BooleanFilter", "line_number": 737, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 738, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 742, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 743, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 744, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 763, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 764, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 773, "usage_type": "call"}, {"api_name": "apollo.submissions.models.FLAG_CHOICES", "line_number": 774, "usage_type": "argument"}, {"api_name": "wtforms.Form", "line_number": 776, "usage_type": "name"}, {"api_name": "wtforms.fields.SelectField", "line_number": 777, "usage_type": "call"}, {"api_name": "wtforms.fields", "line_number": 777, "usage_type": "name"}, {"api_name": "wtforms.fields.SelectField", "line_number": 778, "usage_type": "call"}, {"api_name": "wtforms.fields", "line_number": 778, "usage_type": "name"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 788, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 789, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 790, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 791, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 796, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 797, "usage_type": "call"}, {"api_name": "flask_babelex.lazy_gettext", "line_number": 798, "usage_type": "call"}, {"api_name": "apollo.submissions.filters.make_base_submission_filter", "line_number": 812, "usage_type": "call"}]} +{"seq_id": "1139568705", "text": "import sys,os,glob,shutil,traceback\nfrom PyQt5 import QtWidgets,QtGui,QtCore\nfrom PyQt5.QtWidgets import QApplication,QMainWindow,QFileDialog,QInputDialog,QMessageBox,QTreeWidgetItem\nfrom ui import Ui_MainWindow\nfrom settings import DefaultSettings,CustomSettings\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super(MainWindow,self).__init__()\n self.ui=Ui_MainWindow()\n self.ui.setupUi(self)\n appIcon=QtGui.QIcon()\n appIcon.addFile('./resources/icon.ico',QtCore.QSize(64,64))\n self.setWindowIcon(appIcon)\n self.showMaximized()\n self.ui.statusbar.hide()\n #Interaction Widgets\n self.to_toggle_widgets=[self.ui.manage_btn,self.ui.add_btn,self.ui.remove_btn,self.ui.clear_btn]\n #Variables\n self.cleanup_directories=[]\n self.is_running=False\n #Initializations\n self.sting=CustomSettings()\n self.extensions=self.sting.load()\n self.ui.categories_comboBox.addItems(self.extensions.keys())\n self.ui.directory_listWidget.addItems(self.cleanup_directories)\n self.ui.directory_listWidget.setCurrentRow(0)\n self.settings_textBrowser()\n self.is_category_checked=False\n \n #Action Menu\n self.ui.actionAbout.triggered.connect(self.about_msg)\n self.ui.actionHelp.triggered.connect(self.help_msg)\n self.ui.actionExit.triggered.connect(self.exit_app)\n\n #Button Connections\n self.ui.add_btn.clicked.connect(self.add)\n self.ui.remove_btn.clicked.connect(self.remove)\n self.ui.clear_btn.clicked.connect(self.clear)\n self.ui.manage_btn.clicked.connect(self.manage)\n self.ui.addExt_Btn.clicked.connect(self.add_extension)\n self.ui.removeExt_Btn.clicked.connect(self.remove_extensions)\n self.ui.apply_setting_btn.clicked.connect(self.apply_settings)\n self.ui.reset_default_btn.clicked.connect(self.reset_defaults)\n self.ui.isCategoryChecked.stateChanged.connect(self.set_category_status)\n \n def add(self):\n \"Add directories to the listview for managing\"\n row=self.ui.directory_listWidget.currentRow()\n home=os.path.expanduser(\"~\")\n fileDialog=QFileDialog.getExistingDirectory(self,'Select CleanUp Directory',home,QFileDialog.ShowDirsOnly)\n if fileDialog=='':\n pass\n else:\n if fileDialog in self.cleanup_directories:\n QMessageBox.information(self,'Exists','Selected Directory has already been added.')\n return\n self.cleanup_directories.append(fileDialog)\n self.ui.directory_listWidget.insertItem(row,str(fileDialog))\n\n print(self.cleanup_directories)\n\n def remove(self):\n \"Remove Directories from the listview\"\n row=self.ui.directory_listWidget.currentRow()\n item=self.ui.directory_listWidget.item(row)\n if item is None:\n return\n item=self.ui.directory_listWidget.takeItem(row)\n self.cleanup_directories.remove(item.text())\n del item\n print(self.cleanup_directories)\n\n def clear(self):\n \"Clear the listview\"\n reply=QMessageBox.question(self,\"Clear List\",\"Are you sure you want to clear?\",QMessageBox.Yes|QMessageBox.No)\n if reply==QMessageBox.Yes:\n self.cleanup_directories.clear()\n self.ui.directory_listWidget.clear()\n self.ui.logs_textBrowser.clear()\n \n def manage(self):\n \"Function To Start Managing Directories\"\n self.is_running=True\n self.toggle(False)\n self.path_loop(self.cleanup_directories)\n self.after_manage()\n self.is_running=False \n def path_loop(self,directories:list):\n \"Function to Loop Through Directories and Passing it to main woker one at a time\"\n if(len(directories)<=0):\n return\n else:\n for directory in directories:\n self.path_Maintainer(directory)\n return\n def path_Maintainer(self,path):\n \"Worker function to Manage the file in single directory\"\n for key,val in self.extensions.items():\n grabbed=[] # Stores the list of files with extensions under one category and moves it and again stores another\n for ext in val:\n grabbed.extend(glob.glob(f'{path}/*.{ext}'))\n print(f'Path:{path} Grabbed:{grabbed}')\n for link in grabbed:\n print(f'Link:{link}')\n splitLink=path.split('/')\n curr_dir=splitLink[-1]\n if curr_dir in self.extensions.keys():\n exists_style=f'Exists in Suitable Directory:{link}
'\n self.ui.logs_textBrowser.append(exists_style)\n continue\n else:\n moving_style=f'Moving :{link}
'\n self.ui.logs_textBrowser.append(moving_style)\n self.__move(path,key,link) #Key Refering to Extension Name , Link Refering to File with that Extension \n\n def __move(self,path,dirname,link):\n \"Funtion to move the files\"\n exists=os.path.exists(f'{path}/{dirname}')\n if exists:\n dirFound_style=f'Existing Directory Found: Adding There
'\n self.ui.logs_textBrowser.append(dirFound_style)\n else:\n os.mkdir(f'{path}/{dirname}')\n try:\n shutil.move(link,f'{path}/{dirname}')\n moved_style=f'Moved File:{link}
'\n self.ui.logs_textBrowser.append(moved_style)\n except Exception as e:\n error_style=f'Error:{e}
'\n print(\"Exception Occured:\",e)\n self.ui.logs_textBrowser.append(error_style)\n \n def toggle(self,activate:bool):\n \"Function to toogle buttons/widgets\"\n for widget in self.to_toggle_widgets:\n widget.setEnabled(activate)\n\n def after_manage(self):\n \"Function to call after manage work has been done\"\n self.cleanup_directories.clear()\n self.ui.directory_listWidget.clear()\n self.toggle(True)\n\n\n def settings_textBrowser(self,defaultConfig=True,*args):\n \"Function to display current settings in textbrowser\"\n extensions=self.extensions\n if not defaultConfig:\n extensions=args[0]\n for key,val in extensions.items():\n title=f'
{key.upper()}'\n self.ui.textBrowser.append(title)\n for ext in val:\n exts=f'{ext}'\n self.ui.textBrowser.append(exts)\n def update_comboBox(self):\n self.ui.categories_comboBox.clear()\n self.ui.categories_comboBox.addItems(self.extensions.keys())\n\n def set_category_status(self):\n if self.ui.isCategoryChecked.isChecked():\n self.is_category_checked=True\n self.ui.categories_comboBox.setEnabled(False)\n else:\n self.is_category_checked=False\n self.ui.categories_comboBox.setEnabled(True)\n\n\n def redundancy_handler(self,check_data:list):\n for data in check_data:\n if self.is_category_checked:\n if data in self.extensions:\n QMessageBox.information(self,\"Exists\",f\"Category:{data} Exists\")\n return False\n else:\n for key in self.extensions:\n if data in self.extensions[key]:\n QMessageBox.information(self,\"Exists\",f\"Extension:{data} exists in {key}category\")\n return False\n return True\n\n\n def add_extension(self):\n \"Function to add extension to setting\"\n raw_data=self.ui.lineEdit.text()\n raw_data=raw_data.strip().lower()\n data_list=raw_data.split(',')\n\n no_redundancy=self.redundancy_handler(data_list)\n if no_redundancy:\n if not self.is_category_checked:\n extCat=self.ui.categories_comboBox.currentText()\n self.extensions[extCat].extend(data_list)\n self.sting.custom=self.extensions\n QMessageBox.information(self,\"Added\",\"Extensions Added\")\n else:\n for category in data_list:\n self.extensions[category]=[]\n self.sting.custom=self.extensions\n QMessageBox.information(self,\"Added\",\"Categories Added\")\n self.update_textBrowser()\n self.update_comboBox()\n print(self.extensions)\n\n def remove_extensions(self):\n raw_data=self.ui.lineEdit.text()\n raw_data=raw_data.strip().lower()\n data_list=raw_data.split(',')\n extCat=self.ui.categories_comboBox.currentText()\n removed=False\n if self.is_category_checked:\n reply=QMessageBox.question(self,\"Remove Category\",\"Are you sure you want to remove the categories?\",QMessageBox.Yes|QMessageBox.No)\n if reply==QMessageBox.Yes:\n for data in data_list:\n if data in self.extensions:\n self.extensions.pop(data)\n removed=True\n else:\n QMessageBox.information(self,\"Non Existent\",f\"Catgory:{data} not found\")\n else:\n reply=QMessageBox.question(self,\"Remove Extensions\",\"Are you sure you want to remove the extensions?\",QMessageBox.Yes|QMessageBox.No)\n if reply==QMessageBox.Yes:\n for data in data_list:\n if data in self.extensions[extCat]:\n self.extensions[extCat].remove(data)\n removed=True\n else:\n QMessageBox.information(self,\"Non Existent\",f\"Extension:{data} not found in Catgory:{extCat}\")\n self.sting.custom=self.extensions\n self.update_textBrowser()\n self.update_comboBox()\n if removed:\n QMessageBox.information(self,\"Removed\",\"Removed Successfully\")\n\n def apply_settings(self):\n self.sting.save()\n QMessageBox.information(self,\"Applied\",\"Settings Saved\")\n\n def reset_defaults(self):\n reply=QMessageBox.question(self,\"Reset\",\"Are you sure you want to reset to defaults?\",QMessageBox.Yes|QMessageBox.No)\n if reply==QMessageBox.Yes:\n self.sting.custom=DefaultSettings.default\n self.extensions=self.sting.custom\n self.sting.save()\n self.update_textBrowser()\n def update_textBrowser(self):\n self.ui.textBrowser.clear()\n self.settings_textBrowser()\n\n def about_msg(self):\n QMessageBox.about(self,\"About\",\"Designed and Developed by WolfTech\")\n \n def help_msg(self):\n howtouse=\"How to use:\\n1.Add Paths to Manage (Multiple Paths Can be added)\\n2.If want to remove select from list and click remove\\n3.Click clear to clear list and logs.\"\n QMessageBox.information(self,\"Help\",howtouse)\n \n def exit_app(self):\n reply=QMessageBox.question(self,\"Exit\",\"Are you sure you want to exit?\",QMessageBox.Yes|QMessageBox.No)\n if reply==QMessageBox.Yes:\n quit()\n\n\ndef excepthook(exc_type, exc_value, exc_tb):\n tb = \"\".join(traceback.format_exception(exc_type, exc_value, exc_tb))\n print(\"error catched!:\")\n print(\"error message:\\n\", tb)\n QApplication.quit()\n\n\n\nif __name__=='__main__':\n sys.excepthook=excepthook\n app=QApplication(sys.argv)\n window=MainWindow()\n window.show()\n sys.exit(app.exec_())\n", "repo_name": "kanchansapkota27/Simply-Manage", "sub_path": "simpleManager.py", "file_name": "simpleManager.py", "file_ext": "py", "file_size_in_byte": 11798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 7, "usage_type": "name"}, {"api_name": "ui.Ui_MainWindow", "line_number": 10, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 13, "usage_type": "name"}, {"api_name": "settings.CustomSettings", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.ShowDirsOnly", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 56, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 77, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 124, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 174, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 174, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 179, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 179, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 196, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 201, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 201, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 213, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 213, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 213, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 213, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 214, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 214, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 220, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 220, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 222, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 222, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 222, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 222, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 223, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 223, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 229, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 229, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 234, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 234, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 238, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 238, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 241, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 241, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 241, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 241, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 242, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 242, "usage_type": "name"}, {"api_name": "settings.DefaultSettings.default", "line_number": 243, "usage_type": "attribute"}, {"api_name": "settings.DefaultSettings", "line_number": 243, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 252, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 252, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 256, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 256, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 259, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 259, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 259, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 259, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 260, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 260, "usage_type": "name"}, {"api_name": "traceback.format_exception", "line_number": 265, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication.quit", "line_number": 268, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 268, "usage_type": "name"}, {"api_name": "sys.excepthook", "line_number": 273, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 274, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 274, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 277, "usage_type": "call"}]} +{"seq_id": "4239489688", "text": "# https://github.com/odysseusmax/animated-lamp/blob/master/bot/database/database.py\nimport motor.motor_asyncio\nfrom info import DATABASE_NAME, DATABASE_URI\n\n\nclass StreamFiles:\n def __init__(self, uri, database_name):\n self._client = motor.motor_asyncio.AsyncIOMotorClient(uri)\n self.db = self._client[database_name]\n self.col = self.db.stream_files\n\n def new_files(self, file_link):\n return dict(self=self, file_link=file_link)\n\n async def add_file(self, file_unique_id, file_link):\n file = self.new_files(file_unique_id, file_link)\n await self.col.insert_one(file)\n\n async def get_file(self, file_unique_id):\n return await self.col.find_one({\"file_unique_id\": file_unique_id})\n\n\nstream_file = StreamFiles(DATABASE_URI, DATABASE_NAME)\n", "repo_name": "Alex27ak/Emilia-Clarke-bot", "sub_path": "database/stream_files.py", "file_name": "stream_files.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "motor.motor_asyncio.motor_asyncio.AsyncIOMotorClient", "line_number": 8, "usage_type": "call"}, {"api_name": "motor.motor_asyncio.motor_asyncio", "line_number": 8, "usage_type": "attribute"}, {"api_name": "motor.motor_asyncio", "line_number": 8, "usage_type": "name"}, {"api_name": "info.DATABASE_URI", "line_number": 23, "usage_type": "argument"}, {"api_name": "info.DATABASE_NAME", "line_number": 23, "usage_type": "argument"}]} +{"seq_id": "16034246651", "text": "r\"\"\"Compare two algorithms on a set of fixed task initializations.\n\nRun:\n\n.. code-block:: bash\n\n python3 -m alf.bin.compare \\\n --root_dir1=~/tmp/ac_cart_pole \\\n --root_dir2=~/tmp/ddpg_cart_pole \\\n --alsologtostderr\n\nPrefix with ``DISPLAY= vglrun -d :7 `` if running remotely with virtual_gl.\nThe cleared DISPLAY env_var is so that gzclients are not created.\ngzclients are not being torn down after play and can occupy too many xserver\nconnections.\nSet the proper DISPLAY variable when recording video.\n\"\"\"\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\nimport collections\nimport heapq\nimport numpy as np\nimport os\nimport re\n\n\ndef _define_flags():\n flags.DEFINE_string('root_dir1', None, 'Root directory for algorithm one.')\n flags.DEFINE_string('root_dir2', None, 'Root directory for algorithm two.')\n flags.DEFINE_string('output_file', None, 'output html file.')\n flags.DEFINE_integer('num_runs', 10, 'Compare on so many runs.')\n flags.DEFINE_integer('start_from', 0, 'Start random seeds from here.')\n flags.DEFINE_string(\n 'common_gin', '', 'Common config for the two sides, '\n 'e.g. \"--gin_param=\\'GoalTask.random_range=5\\'\"')\n flags.DEFINE_integer('overwrite', 0, 'Overwrite cached files.')\n\n\nFLAGS = flags.FLAGS\n\nAVG_R_METRIC = \"AverageReturn\"\nAVG_R_DIFF = AVG_R_METRIC + \"_diff\"\n\n\ndef _return_diff(item):\n return abs(item[AVG_R_DIFF]) / (max(\n abs(float(item[\"alg1_\" + AVG_R_METRIC])),\n abs(float(item[\"alg2_\" + AVG_R_METRIC]))) + 1.e-5)\n\n\ndef _return_1_larger(item):\n return float(item[\"alg1_\" + AVG_R_METRIC]) > float(\n item[\"alg2_\" + AVG_R_METRIC])\n\n\ndef _file_exists(file):\n return (not FLAGS.overwrite and os.path.isfile(file)\n and os.stat(file).st_size > 100)\n\n\ndef _play_cmd(root_dir, seed):\n return (\"cd {root_dir} && \"\n \"python3 -m alf.bin.play \"\n \" --random_seed={seed} --num_episodes=1\"\n \" --verbosity=1 --root_dir=`pwd` --sleep_time_per_step=0\"\n \" --epsilon_greedy=0 {g}\").format(\n root_dir=root_dir, seed=seed, g=FLAGS.common_gin)\n\n\ndef _get_metric(pattern, buffer, log_file):\n match = re.search(pattern, buffer)\n assert match, \"{} not found in {}, remove and re-run?\".format(\n pattern, log_file)\n return \"{:.2f}\".format(float(match.group(1)))\n\n\ndef _get_avg(data, metric, i):\n vs = [float(v[\"alg{}_{}\".format(i + 1, metric)]) for v in data]\n return np.mean(vs)\n\n\ndef _create_html(data, all_data, metrics, abbr):\n \"\"\"Creates the comparison in html content and return as string.\"\"\"\n # Column ``AverageReturn_diff`` is after:\n # one seed column, two sets of metrics, two log_file paths\n avgreturn_index = 2 * len(metrics) + 2 + 1\n seed_index = 0\n\n html = r\"\"\"\n\n \n \n \n \n

Compare difference between algorithms

\n \"\"\"\n\n # Summary:\n html += \"\\n
Alg1: {}\\n\".format(FLAGS.root_dir1)\n    for m in metrics:\n        html += \"   |{}: {:.2f}\".format(\n            m, _get_avg(all_data, m, 0))\n    html += \"\\nAlg2: {}\\n\".format(FLAGS.root_dir2)\n    for m in metrics:\n        html += \"   |{}: {:.2f}\".format(\n            m, _get_avg(all_data, m, 1))\n    html += \"\\nnum_items: {}, have data for: {}\\n\".format(\n        FLAGS.num_runs, len(all_data))\n    percentiles = [.05, .1, .2, .4, .8, .99]\n    counts = [0] * len(percentiles)\n    wins = [0] * len(percentiles)\n    html += \"propotion_diffs:\\n\"\n    for item in data:\n        diff = _return_diff(item)\n        for i, p in enumerate(percentiles):\n            if diff > p:\n                counts[i] += 1\n                if _return_1_larger(item):\n                    wins[i] += 1\n    for i, p in enumerate(percentiles):\n        html += \"diff > {:.2f}: {:.2f}, W: {:2d} vs L: {:2d}\\n\".format(\n            p, counts[i] / len(all_data), wins[i], counts[i] - wins[i])\n\n    # Table:\n    html += \"\"\"

\n \n \n \"\"\"\n if data:\n for k, _ in data[0].items():\n for i, metric in enumerate(metrics):\n if metric in k:\n k = k.replace(metric, abbr[i])\n html += (\" \\n\"\n ).format(k)\n html += \"\"\"\n \n \n \\n\"\"\"\n for item in data:\n html += \" \\n\"\n for k, v in item.items():\n if k in [\"video1\", \"video2\"] and v != \"\":\n v = \"\"\"\"\"\".format(v)\n html += \" \\n\".format(v)\n html += \" \\n\"\n html += \"\"\"\n \n
{}
{}
\\n\"\"\"\n\n # Column header abbreviations:\n for i in range(len(metrics)):\n html += \"
-- \" + abbr[i] + \": \" + metrics[i]\n html += r\"\"\"\n \n\"\"\"\n return html\n\n\ndef _tokenize(s):\n s = s.replace(\"--gin_param=\", \"\")\n s = s.replace(\"'\", \"\")\n s = s.replace('\"', \"\")\n s = s.replace(\"=\", \"__\")\n s = s.replace(\" \", \"-\")\n s = s.replace(\"/\", \"_\")\n return s\n\n\ndef main(_):\n \"\"\"main function.\"\"\"\n # generate runs\n dirs = [FLAGS.root_dir1, FLAGS.root_dir2]\n metrics = [AVG_R_METRIC, \"AverageEpisodeLength\"]\n abbr = [\"R\", \"L\"]\n gin_str = \"\"\n if FLAGS.common_gin:\n gin_str = _tokenize(FLAGS.common_gin)\n gin_str = \"-\" + gin_str\n\n data = [] # used for displaying diffs in final HTML\n all_data = [] # used for computing average stats\n for seed in range(FLAGS.num_runs):\n seed += FLAGS.start_from\n item = collections.OrderedDict()\n item[\"seed\"] = seed\n vs = [\"\", \"\"]\n for i, root_dir in enumerate(dirs):\n mp4_f = root_dir + \"/play-seed_{}{}.mp4\".format(seed, gin_str)\n log_file = root_dir + \"/play-log-seed_{}{}.txt\".format(\n seed, gin_str)\n command = _play_cmd(root_dir,\n seed) + \" --record_file={} 2>> {}\".format(\n mp4_f, log_file)\n if not _file_exists(mp4_f):\n f = open(log_file, 'w')\n assert f, \"cannot write to \" + log_file\n f.write(command + \"\\n\")\n f.close()\n os.system(command)\n vs[i] = mp4_f\n\n # extract values\n f = open(log_file, 'r')\n assert f, log_file + \" cannot be read.\"\n lines = f.read().replace('\\n', ' ')\n f.close()\n for metric in metrics:\n value = _get_metric(r\"\\] \" + metric + r\": (\\S+)\", lines,\n log_file)\n item[\"alg{}_{}\".format(i + 1, metric)] = value\n item[\"logfile{}\".format(\n i + 1)] = 'log_file'.format(log_file)\n for metric in metrics:\n m1 = float(item[\"alg{}_{}\".format(1, metric)])\n m2 = float(item[\"alg{}_{}\".format(2, metric)])\n diff = m1 - m2\n item[\"{}_diff\".format(metric)] = diff\n item[\"video1\"] = vs[0]\n item[\"video2\"] = vs[1]\n all_data.append(item)\n if _return_diff(item) > 0.05: # >5% diff\n data.append(item)\n\n # Skipping this section as recording video affects the randomness in gazebo_base.\n # analyze results to record videos\n # abs_avg = [abs(v[AVG_R_DIFF]) for v in data]\n # idx = heapq.nlargest(FLAGS.num_videos, range(len(data)),\n # abs_avg.__getitem__)\n\n # create html\n output_file = FLAGS.output_file\n if not output_file:\n output_file = FLAGS.root_dir1 + \"/compare{}-{}.html\".format(\n gin_str, _tokenize(FLAGS.root_dir2))\n html = _create_html(data, all_data, metrics, abbr)\n f = open(output_file, 'w')\n assert f, \"Cannot write to \" + output_file\n f.write(html)\n f.close()\n print(\"Done: \", output_file)\n\n\nif __name__ == '__main__':\n _define_flags()\n logging.set_verbosity(logging.INFO)\n flags.mark_flag_as_required('root_dir1')\n flags.mark_flag_as_required('root_dir2')\n app.run(main)\n", "repo_name": "HorizonRobotics/alf", "sub_path": "alf/bin/compare.py", "file_name": "compare.py", "file_ext": "py", "file_size_in_byte": 9058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 266, "dataset": "github-code", "pt": "51", "api": [{"api_name": "absl.flags.DEFINE_string", "line_number": 30, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 30, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 31, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 31, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 32, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 32, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 33, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 33, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 34, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 34, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 35, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 35, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 38, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 38, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS", "line_number": 41, "usage_type": "attribute"}, {"api_name": "absl.flags", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 60, "usage_type": "call"}, {"api_name": "re.search", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 200, "usage_type": "call"}, {"api_name": "os.system", "line_number": 215, "usage_type": "call"}, {"api_name": "absl.logging.set_verbosity", "line_number": 261, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 261, "usage_type": "name"}, {"api_name": "absl.logging.INFO", "line_number": 261, "usage_type": "attribute"}, {"api_name": "absl.flags.mark_flag_as_required", "line_number": 262, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 262, "usage_type": "name"}, {"api_name": "absl.flags.mark_flag_as_required", "line_number": 263, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 263, "usage_type": "name"}, {"api_name": "absl.app.run", "line_number": 264, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 264, "usage_type": "name"}]} +{"seq_id": "1721502104", "text": "from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.forms import inlineformset_factory\nfrom django.core.paginator import Paginator\nfrom django.contrib import messages\nfrom django.contrib.auth.models import Group\nfrom django.contrib.auth.decorators import login_required\nfrom .decorators import unauthenticated, userPermission\nfrom .models import *\nfrom .forms import *\nfrom .filters import *\n# Create your views here.\n\n\n@unauthenticated # when signed in it restricts access to login page\ndef register(request):\n form = CreateUserForm()\n if request.method =='POST':\n form = CreateUserForm(request.POST)\n if form.is_valid():\n user = form.save()\n username = form.cleaned_data.get('username')\n group = Group.objects.get(name='customer')\n user.groups.add(group)\n messages.success(request, 'Account was created for ' + username)\n return redirect('login')\n\n context={'form':form}\n return render(request, 'accounts/register.html',context)\n\n\n@unauthenticated\ndef loginPage(request):\n if request.method == 'POST':\n username = request.POST.get('username')\n password = request.POST.get('password')\n\n user = authenticate(request, username=username, password=password)\n\n if user:\n login(request, user)\n return redirect('home')\n else:\n messages.info(request,'Username or password is incorrect')\n\n context = {}\n return render(request, 'accounts/login.html',context)\n\n\ndef logoutUser(request):\n logout(request)\n return redirect('login')\n\n\n@login_required(login_url='login')\n@userPermission\ndef home(request):\n orders = Order.objects.all()\n customers = Customer.objects.all()\n ordersCount = orders.count()\n delivered = orders.filter(status='Delivered').count()\n pending = orders.filter(status='Pending').count()\n delivery = orders.filter(status='Out for delivery').count()\n customerPaginator = Paginator(customers,5)\n customerPageNum = request.GET.get('page')\n customerPage = customerPaginator.get_page(customerPageNum)\n myFilter = OrderFilter(request.GET, queryset=orders) # filterforms\n orders = myFilter.qs\n orderPaginator = Paginator(orders,5)\n orderPageNum = request.GET.get('page')\n orderPage = orderPaginator.get_page(orderPageNum)\n context = {'orderPage': orderPage, 'customerPage':customerPage, 'ordersCount':ordersCount, 'delivered':delivered,\n 'pending':pending, 'delivery':delivery, 'myFilter':myFilter}\n return render(request, 'accounts/dashboard.html', context)\n\ndef userPage(request):\n context = {}\n return render(request, 'accounts/user.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef products(request):\n products = Product.objects.all()\n productPaginator = Paginator(products,10)\n productPageNum = request.GET.get('page')\n productPage = productPaginator.get_page(productPageNum)\n return render(request, 'accounts/products.html', {'productPage': productPage})\n\n\n@login_required(login_url='login')\n@userPermission\ndef customer(request, id):\n customer = Customer.objects.get(id=id)\n orders = customer.order_set.all()\n orderCount = orders.count()\n\n myCustomerFilter = CustomerOrderFilter(request.GET, queryset=orders)\n orders = myCustomerFilter.qs\n\n context = {\n 'customer':customer,\n 'orders':orders,\n 'orderCount':orderCount,\n 'myCustomerFilter':myCustomerFilter\n }\n return render(request, 'accounts/customer.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef createOrder(request, id):\n OrderFormSet = inlineformset_factory(Customer, Order, fields=('product', 'status'), extra=5)\n customer = Customer.objects.get(id=id)\n formset = OrderFormSet(queryset =Order.objects.none(), instance=customer) # queryset to display black on online forms\n # form = OrderForm(initial={'customer':customer})\n if request.method == 'POST':\n # form = OrderForm(request.POST)\n formset = OrderFormSet(request.POST, instance=customer)\n if formset.is_valid():\n formset.save()\n return redirect('/')\n\n context = {'formset': formset}\n return render(request, 'accounts/createOrder.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef updateOrder(request, id):\n order = Order.objects.get(id=id)\n\n form = OrderForm(instance=order) #pass in the instance value to modify the order instance picked by id\n if request.method == 'POST':\n form = OrderForm(request.POST, instance=order)\n if form.is_valid():\n form.save()\n return redirect('/')\n context = {'form':form}\n return render(request, 'accounts/createOrder.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef deleteOrder(request, id):\n order = Order.objects.get(id=id)\n if request.method =='POST':\n order.delete()\n return redirect('/')\n context = {'order': order}\n return render(request, 'accounts/deleteOrder.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef createCustomer(request):\n form = CustomerForm()\n if request.method == 'POST':\n form = CustomerForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('/')\n\n context = {'form':form, 'flag':1}\n return render(request, 'accounts/createCustomer.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef updateCustomer(request,id):\n customer = Customer.objects.get(id=id)\n form = CustomerForm(instance=customer)\n if request.method == 'POST':\n form = CustomerForm(request.POST, instance=customer)\n if form.is_valid():\n form.save()\n return redirect('/')\n\n context = {'form':form, 'flag':2}\n return render(request, 'accounts/createCustomer.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef createProduct(request):\n form = ProductForm()\n if request.method == 'POST':\n form = ProductForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('/')\n\n context = {'form': form, 'flag':1}\n return render(request, 'accounts/createProduct.html', context)\n\n\n@login_required(login_url='login')\n@userPermission\ndef updateProduct(request, id):\n product = Product.objects.get(id=id)\n form = ProductForm(instance=product)\n if request.method == 'POST':\n form = ProductForm(request.POST, instance=product)\n if form.is_valid():\n form.save()\n return redirect('/')\n context = {'form':form, 'flag':2}\n return render(request, 'accounts/createProduct.html', context)", "repo_name": "PrerithSubramanya/djangoCRM", "sub_path": "crm/accounts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6890, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "60", "api": [{"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 27, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "decorators.unauthenticated", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 46, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "decorators.unauthenticated", "line_number": 34, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 66, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 71, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 57, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 83, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 93, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 94, "usage_type": "name"}, {"api_name": "django.forms.inlineformset_factory", "line_number": 115, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 124, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 112, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 113, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 140, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 142, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 130, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 151, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 153, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 145, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 146, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 167, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 156, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 157, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 179, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 182, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 170, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 171, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 193, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 196, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 185, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 186, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 208, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 210, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 199, "usage_type": "call"}, {"api_name": "decorators.userPermission", "line_number": 200, "usage_type": "name"}]} +{"seq_id": "41061595042", "text": "import numpy as np\nimport pandas as pd\nfrom sklearn.utils import shuffle\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize\nimport sys\n\ntrainData = pd.read_csv(\"./data/train.csv\", header = None,names=['x1','x2','x3','x4','y'])\ntestData = pd.read_csv(\"./data/test.csv\", header = None,names=['x1','x2','x3','x4','y'])\ntrainData.insert(0,'ones',1)\ntestData.insert(0,'ones',1)\n\ndef data_shuffle(data):\n cols=data.shape[1]\n index_list=shuffle(list(range(data.shape[0])))\n dataNew=data.iloc[index_list]\n x = np.array(dataNew.iloc[:,0:cols-1].values)\n y = np.array(dataNew.iloc[:,-1].values)\n y[y==0]=-1\n return x,y\n\ndef sigmoid(z):\n y=1/(1+np.exp(-z))\n return y\n\n#-----------a three-layered NN---------#\ndef forward_propagate(data, theta1, theta2,theta3): \n cols=data.shape[1] \n x = np.array(data.iloc[:,0:cols-1].values)\n m = x.shape[0] \n a1=np.insert(x, 0, values=np.ones(m), axis=1) #m*N #input for L1\n z1 = sigmoid(a1 @ theta1.T) #theta1[h1*N],(h1+1) neurons in layer1.\n a2 = np.insert(z1, 0, values=np.ones(m), axis=1) #output of L1/input for L2\n z2 = sigmoid(a2 @ theta2.T) #(h2+1) neurons in L2. theta2[h2*h1]\n a3 = np.insert(z2, 0, values=np.ones(m), axis=1) \n yPre = a3 @ theta3.T \n return a1, z1, a2, z2, a3, yPre\n\n\ndef update_weights(theta, gradJ):\n r0=0.001 #0.0002\n d=0.001\n epochs=100\n for epoch in range(epochs):\n learningRate=r0/(1+r0/d*epoch)\n theta=theta-learningRate * gradJ\n\n return theta\n\n\ndef loss(yPre, data):\n y = np.array(data.iloc[:,-1].values)\n y[y==0]=-1\n return np.square(yPre - y) / 2\n\ndef run_backpropagation(weights, layers, y, prediction, activations):\n loss_deriv = prediction - y\n for i in range(layers):\n if i==layers-1:\n partial_z_w=partial_z_w()\n partial_z_lowerZ=partial_z_lowerZ()\n else:\n partial_z_w=partial_z_w()\n partial_z_lowerZ=partial_z_lowerZ()\n\n weight_derivs=0\n return weight_derivs\n\ndef partial_z_w():\n\n return partial_z_w\n\ndef partial_z_lowerZ():\n\n return partial_z_lowerZ", "repo_name": "xiaoyatang/CS6350-ML2022", "sub_path": "Neural_Networks/nn_trial.py", "file_name": "nn_trial.py", "file_ext": "py", "file_size_in_byte": 2113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "74629992158", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Nov 15 09:54:16 2017\n\n@author: remi\n\"\"\"\n\nfrom tkinter import Radiobutton, Toplevel, IntVar, Button, Frame, Tk, Label, Canvas, StringVar\nfrom tkinter import RIGHT, LEFT, TOP, GROOVE, BOTTOM\n#from tkinter import *\nfrom picture2word import picture2word, picture2word_\nfrom analyse_GUI import analyse_multi\nfrom word2picture import word2picture\nfrom PIL import Image, ImageTk\nimport numpy as np\nimport pickle as pk\nimport os\n\ntraining_set_size = 6000\n\ndef mouseDown(event) :\n global xc,yc\n xc,yc = event.x, event.y\n\ndef mouseMove(event) :\n global xc,yc\n xn,yn = event.x, event.y\n if xn > 97:\n xn = 97;\n elif xn < 1 :\n xn = 3;\n if yn > 97:\n yn = 99;\n elif yn < 3 :\n yn = 1;\n canvas1.create_line(xc,yc,xn,yn,width = 3,smooth = 1)\n canvas1.create_rectangle(xn-1,yn-1,xn+1,yn+1,fill='black')\n xc,yc = xn,yn\n\ndef predict() :\n global canvas1,text\n if len(base) >= 3 :\n canvas1.postscript(file = 'save.ps', colormode='color')\n picture = Image.open('save.ps')\n word,prediction,liste = analyse_multi(picture2word_(picture),base,label,3)\n os.remove('save.ps')\n word2picture(word)\n file = \"temp.png\"\n img = ImageTk.PhotoImage(file = file)\n canvas5.create_image(50, 50, image=img)\n canvas5.image = img\n os.remove(file)\n text.set(str(prediction))\n for i,can in enumerate([canvas2,canvas3,canvas4]) :\n word2picture(liste[i])\n file = \"temp.png\"\n img = ImageTk.PhotoImage(file = file)\n can.create_image(50, 50, image=img)\n can.image = img\n os.remove(file)\n\ndef erase() :\n for can in [canvas1,canvas2,canvas3,canvas4,canvas5] :\n can.delete(\"all\")\n text.set(\"\")\n\ndef save() : \n if database.get() == 1 :\n file = open('ourbase.pk', 'wb')\n pk.dump(base, file) \n file.close()\n\n file = open('ourlabel.pk', 'wb') \n pk.dump(label, file) \n file.close()\n print(\"our base saved\")\n elif database.get() == 2 :\n file = open('base_mnist_'+str(training_set_size)+'_custom.pk', 'wb') \n pk.dump(base, file) \n file.close()\n\n file = open('base_mnist_labels_'+str(training_set_size)+'_custom.pk', 'wb') \n pk.dump(label, file) \n file.close()\n print(\"mnist base custom saved\")\n elif database.get() == 3 :\n file = open('ourbase.pk', 'wb') \n pk.dump(base, file) \n file.close()\n\n file = open('ourlabel.pk', 'wb') \n pk.dump(label, file) \n file.close()\n print(\"our base saved\")\n else : \n file = open('base_mnist_'+str(training_set_size)+'_custom.pk', 'wb') \n pk.dump(base, file) \n file.close()\n\n file = open('base_mnist_labels_'+str(training_set_size)+'_custom.pk', 'wb') \n pk.dump(label, file) \n file.close()\n print(\"mnist base custom saved\")\n\ndef correct() :\n global toplevel\n toplevel = Toplevel()\n toplevel.title(\"Selection\")\n v = IntVar() \n b0 = Radiobutton(toplevel, text=\"0\", variable=v, value=0)\n b1 = Radiobutton(toplevel, text=\"1\", variable=v, value=1)\n b2 = Radiobutton(toplevel, text=\"2\", variable=v, value=2)\n b3 = Radiobutton(toplevel, text=\"3\", variable=v, value=3)\n b4 = Radiobutton(toplevel, text=\"4\", variable=v, value=4)\n b5 = Radiobutton(toplevel, text=\"5\", variable=v, value=5)\n b6 = Radiobutton(toplevel, text=\"6\", variable=v, value=6)\n b7 = Radiobutton(toplevel, text=\"7\", variable=v, value=7)\n b8 = Radiobutton(toplevel, text=\"8\", variable=v, value=8)\n b9 = Radiobutton(toplevel, text=\"9\", variable=v, value=9)\n b0.pack()\n b1.pack()\n b2.pack()\n b3.pack()\n b4.pack()\n b5.pack()\n b6.pack()\n b7.pack()\n b8.pack()\n b9.pack()\n button4 = Button(toplevel, text=\"Validate\", command=lambda x=v : validate_correct(x))\n button4.pack(side=RIGHT, padx=30, pady=30)\n\ndef add() :\n global toplevel2\n toplevel2 = Toplevel()\n toplevel2.title(\"Selection\")\n v2 = IntVar() \n b0 = Radiobutton(toplevel2, text=\"0\", variable=v2, value=0)\n b1 = Radiobutton(toplevel2, text=\"1\", variable=v2, value=1)\n b2 = Radiobutton(toplevel2, text=\"2\", variable=v2, value=2)\n b3 = Radiobutton(toplevel2, text=\"3\", variable=v2, value=3)\n b4 = Radiobutton(toplevel2, text=\"4\", variable=v2, value=4)\n b5 = Radiobutton(toplevel2, text=\"5\", variable=v2, value=5)\n b6 = Radiobutton(toplevel2, text=\"6\", variable=v2, value=6)\n b7 = Radiobutton(toplevel2, text=\"7\", variable=v2, value=7)\n b8 = Radiobutton(toplevel2, text=\"8\", variable=v2, value=8)\n b9 = Radiobutton(toplevel2, text=\"9\", variable=v2, value=9)\n b0.pack()\n b1.pack()\n b2.pack()\n b3.pack()\n b4.pack()\n b5.pack()\n b6.pack()\n b7.pack()\n b8.pack()\n b9.pack()\n button6 = Button(toplevel2, text=\"Validate\", command=lambda x=v2 : validate_add(x))\n button6.pack(side=RIGHT, padx=30, pady=30)\n\ndef validate_add(x) :\n global toplevel2\n canvas1.postscript(file = 'save.ps', colormode='color')\n picture = Image.open('save.ps')\n word = picture2word_(picture)\n os.remove('save.ps')\n base.append(word)\n label.append(x.get())\n erase()\n toplevel2.destroy()\n\ndef validate_correct(x) :\n global toplevel\n canvas1.postscript(file = 'save.ps', colormode='color')\n picture = Image.open('save.ps')\n word = picture2word_(picture)\n os.remove('save.ps')\n base.append(word)\n label.append(x.get())\n erase()\n toplevel.destroy()\n\ndef validate_database(x) :\n global base, label, distance_matrix\n base, label, distance_matrix = init(x.get(), training_set_size)\n\ndef init(database, training_set_size) :\n if database == 0 :\n try :\n file_base = open('base_mnist_'+str(training_set_size)+'.pk', 'rb')\n base = pk.load(file_base)\n file_base.close()\n \n file_labels = open('base_mnist_labels_'+str(training_set_size)+'.pk', 'rb')\n label = pk.load(file_labels)\n file_labels.close()\n print('base mnist loaded',len(base),len(label))\n except :\n print(\"Error in base loading : mnist\")\n base = []\n label = []\n \n try :\n file = open(\"distance_matrix.pk\", 'rb') \n distance_matrix = pk.load(file) \n file.close()\n except :\n distance_matrix = np.zeros((len(base),len(base)))\n \n return base, label, distance_matrix\n elif database == 1 :\n try :\n file_base = open('ourbase.pk', 'rb')\n base = pk.load(file_base)\n file_base.close()\n \n file_labels = open('ourlabel.pk', 'rb')\n label = pk.load(file_labels)\n file_labels.close()\n print('our base loaded',len(base),len(label))\n except :\n print(\"Error in base loading : our\")\n base = []\n label = []\n \n try :\n file = open(\"distance_matrix_custom.pk\", 'rb') \n distance_matrix = pk.load(file) \n file.close()\n except :\n distance_matrix = np.zeros((len(base),len(base)))\n \n return base, label, distance_matrix\n elif database == 2 :\n try :\n file_base = open('base_mnist_'+str(training_set_size)+'_custom.pk', 'rb')\n base = pk.load(file_base)\n file_base.close()\n \n file_labels = open('base_mnist_labels_'+str(training_set_size)+'_custom.pk', 'rb')\n label = pk.load(file_labels)\n file_labels.close()\n print('base mnist custom loaded',len(base),len(label))\n except :\n print(\"Error in base loading : mnist custom\")\n base = []\n label = []\n \n try :\n file = open(\"distance_matrix_custom.pk\", 'rb') \n distance_matrix = pk.load(file) \n file.close()\n except :\n distance_matrix = np.zeros((len(base),len(base)))\n \n return base, label, distance_matrix\n elif database == 3 :\n base = []\n label = []\n distance_matrix = np.zeros((len(base),len(base)))\n print('new base loaded',len(base),len(label))\n\n return base, label, distance_matrix\n else :\n print(\"Error in the choice of the database\")\n return [],[],np.zeros((0,0))\n\nwindow = Tk()\n\nwindow['bg']='dark gray'\nwindow.title(\"Hand Written Digit Recognition\")\n\n# frame 0\nframe0 = Frame(window, borderwidth=2, relief=GROOVE)\nframe0.pack(side=TOP, padx=5, pady=5)\n\n# frame 1\nframe1 = Frame(window, borderwidth=2, relief=GROOVE)\nframe1.pack(side=TOP, padx=30, pady=30)\nLabel(frame1, text=\"input\").pack(padx=10, pady=10)\n\n# frame 2\nframe2 = Frame(window, borderwidth=2, relief=GROOVE)\nframe2.pack(side=BOTTOM, padx=10, pady=10)\nLabel(frame2, text=\"output\").pack(padx=10, pady=10)\n\n# radiobutton in frame 0\ndatabase = IntVar(0)\ndatabase.set(0)\nb_0 = Radiobutton(frame0, text=\"Mnist Database\", variable=database, value=0, anchor='w')\nb_1 = Radiobutton(frame0, text=\"Our Database\", variable=database, value=1, anchor='w')\nb_2 = Radiobutton(frame0, text=\"Mnist Custumized\", variable=database, value=2, anchor='w')\nb_3 = Radiobutton(frame0, text=\"New Database\", variable=database, value=3, anchor='w')\nb_0.pack()\nb_1.pack()\nb_2.pack()\nb_3.pack()\nbutton5 = Button(frame0, text=\"Validate\", command=lambda x=database : validate_database(x))\nbutton5.pack(side=RIGHT, padx=30, pady=30)\n\n# canvas 1 in frame 1\ncanvas1 = Canvas(frame1, width=100, height=100, background='white')\ncanvas1.bind(\"\", mouseDown)\ncanvas1.bind(\"\", mouseMove)\ncanvas1.pack(side=LEFT, padx=30, pady=30)\n\n# buttons in frame 1\nbutton7 = Button(frame1, text=\"Save\", command=save)\nbutton7.pack(side=RIGHT, padx=30, pady=30)\n\nbutton3 = Button(frame1, text=\"Add\", command=add)\nbutton3.pack(side=RIGHT, padx=30, pady=30)\n\nbutton2 = Button(frame1, text=\"Correct\", command=correct)\nbutton2.pack(side=RIGHT, padx=30, pady=30)\n\nbutton1 = Button(frame1, text=\"Predict\", command=predict)\nbutton1.pack(side=RIGHT, padx=30, pady=30)\n\nbutton0 = Button(frame1, text=\"Erase\", command=erase)\nbutton0.pack(side=RIGHT, padx=30, pady=30)\n\n# canvas 5 in frame 2\ncanvas5 = Canvas(frame2, width=100, height=100, background='black')\ncanvas5.pack(side=LEFT, padx=30, pady=30)\n\n# canvas 2 in frame 2\ncanvas2 = Canvas(frame2, width=100, height=100, background='black')\ncanvas2.pack(side=LEFT, padx=30, pady=30)\n\n# canvas 3 in frame 2\ncanvas3 = Canvas(frame2, width=100, height=100, background='black')\ncanvas3.pack(side=LEFT, padx=30, pady=30)\n\n# canvas 4 in frame 2\ncanvas4 = Canvas(frame2, width=100, height=100, background='black')\ncanvas4.pack(side=LEFT, padx=30, pady=30)\n\n# frame 3 in frame 2\nframe3 = Frame(frame2, bg=\"white\", borderwidth=2, relief=GROOVE, width=100, height=100)\nframe3.pack(side=RIGHT, padx=5, pady=5)\ntext = StringVar()\ntext.set(\"\")\nLabel(frame3, textvariable = text,bg=\"white\").pack(padx=10, pady=10)\n\nbase, label, distance_matrix = init(database.get(), training_set_size)\n\nwindow.mainloop()\n\n", "repo_name": "koolok/mlp", "sub_path": "GUI.py", "file_name": "GUI.py", "file_ext": "py", "file_size_in_byte": 11165, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "PIL.Image.open", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "analyse_GUI.analyse_multi", "line_number": 46, "usage_type": "call"}, {"api_name": "picture2word.picture2word_", "line_number": 46, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 47, "usage_type": "call"}, {"api_name": "word2picture.word2picture", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 50, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "word2picture.word2picture", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 58, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 58, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 61, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 71, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 80, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 84, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 89, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 93, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 98, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 102, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 108, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 110, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 111, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 112, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 113, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 114, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 115, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 116, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 117, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 118, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 119, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 120, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 131, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 132, "usage_type": "name"}, {"api_name": "tkinter.Toplevel", "line_number": 136, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 138, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 139, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 140, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 141, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 142, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 143, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 144, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 145, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 146, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 147, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 148, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 159, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 160, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 165, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 165, "usage_type": "name"}, {"api_name": "picture2word.picture2word_", "line_number": 166, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 167, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 176, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 176, "usage_type": "name"}, {"api_name": "picture2word.picture2word_", "line_number": 177, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 178, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 192, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 196, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 209, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 215, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 219, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 232, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 238, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 242, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 267, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 269, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 275, "usage_type": "call"}, {"api_name": "tkinter.GROOVE", "line_number": 275, "usage_type": "name"}, {"api_name": "tkinter.TOP", "line_number": 276, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 279, "usage_type": "call"}, {"api_name": "tkinter.GROOVE", "line_number": 279, "usage_type": "name"}, {"api_name": "tkinter.TOP", "line_number": 280, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 281, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 284, "usage_type": "call"}, {"api_name": "tkinter.GROOVE", "line_number": 284, "usage_type": "name"}, {"api_name": "tkinter.BOTTOM", "line_number": 285, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 286, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 289, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 291, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 292, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 293, "usage_type": "call"}, {"api_name": "tkinter.Radiobutton", "line_number": 294, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 299, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 300, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 303, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 306, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 309, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 310, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 312, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 313, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 315, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 316, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 318, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 319, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 321, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 322, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 325, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 326, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 329, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 330, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 333, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 334, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 337, "usage_type": "call"}, {"api_name": "tkinter.LEFT", "line_number": 338, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 341, "usage_type": "call"}, {"api_name": "tkinter.GROOVE", "line_number": 341, "usage_type": "name"}, {"api_name": "tkinter.RIGHT", "line_number": 342, "usage_type": "name"}, {"api_name": "tkinter.StringVar", "line_number": 343, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 345, "usage_type": "call"}]} +{"seq_id": "25279983660", "text": "from selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nimport time\r\nimport telegram\r\n\r\ndriver = webdriver.Chrome()\r\ndriver.get(\"https://tomb.com/\")\r\nmy_api_key = \"5150330628:AAFDPyu7b-IxLXR_SbWOHX7CTOgvCHyLl2M\" #내 API 키 정보\r\nchat_room_id = -748747493 # 채팅방 ID\r\nmy_bot = telegram.Bot(my_api_key)\r\nwhile True:\r\n time.sleep(30)\r\n tvl = driver.find_elements_by_css_selector(\".text-2xl\") #TVL 불러오기\r\n title = list()\r\n for i in tvl:\r\n title.append(i.text.strip())\r\n con = title.pop(1)\r\n if len(con) == 12 :\r\n new_con = con[1:4] + con[5:8] + con[9:12]\r\n else: \r\n new_con = con[1] + con[3:6] + con[7:10] + con[11:14]\r\n new_con = int(new_con) #TVL 숫자화 완료\r\n\r\n\r\n peg = driver.find_elements_by_css_selector(\"span.text-white.text-4xl.mt-1\") #Peg 불러오기\r\n girl = list()\r\n for c in peg:\r\n girl.append(c.text.strip())\r\n conpeg = girl.pop(0)\r\n new_conpeg = conpeg[0] + conpeg[2:6]\r\n new_conpeg = float(new_conpeg) #Peg 숫자화 완료\r\n new_conpeg = new_conpeg * 0.0001 #Peg 원상복귀\r\n\r\n price = driver.find_elements_by_css_selector(\"span.text-white.text-md\") #Price 불러오기\r\n baby = list()\r\n for a in price:\r\n baby.append(a.text.strip())\r\n\r\n\r\n if new_con < 700000000 or new_conpeg < 1: #비교하기\r\n new_con = format(new_con, ',') #TVL 3자리 컴마\r\n my_bot.sendMessage(chat_id=chat_room_id, text=('Tomb status is precatious alert period 30m', 'TVL: %s'%(new_con), 'Peg: %s'%(new_conpeg), 'Tomb price:%s'%(baby.pop(0)), 'Tshare price:%s'%(baby.pop(0))))\r\n time.sleep(1800)\r\n \r\n \r\n else:\r\n new_con = format(new_con, ',') #TVL 3자리 컴마\r\n my_bot.sendMessage(chat_id=chat_room_id, text=('Tomb status is stable alert period 1h', 'TVL: %s'%(new_con), 'Peg: %s'%(new_conpeg), 'Tomb price:%s'%(baby.pop(0)), 'Tshare price:%s'%(baby.pop(0))))\r\n time.sleep(3570)\r\n \r\n\r\n \r\n\r\n#append 함수(멤버 메서드)를 이용하여 순차 보관\r\n# list1 = list() #비어있는 리스트 만들기\r\n# for i in range(1,30,3):\r\n# list1.append(i)\r\n# print(list1)\r\n# elem.send_keys(\"강선우\")\r\n# elem.send_keys(Keys.RETURN)\r\n# driver.find_elements_by_css_selector(\".rg_i.Q4LuWd\")[0].click()\r\n# assert \"Python\" in driver.title\r\n# elem = driver.find_element_by_name(\"q\")\r\n# elem.clear()\r\n# elem.send_keys(\"pycon\")\r\n# elem.send_keys(Keys.RETURN)\r\n# assert \"No results found.\" not in driver.page_source\r\n# driver.close()", "repo_name": "firstfistjang/mine", "sub_path": "tombinfo.py", "file_name": "tombinfo.py", "file_ext": "py", "file_size_in_byte": 2527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "telegram.Bot", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "5349001750", "text": "import argparse\nimport configparser\nimport copy\n\nimport logging\n\nimport numpy as np\nimport os\nimport time\nfrom pathlib import Path\n\nruns = 3\nproc_num = 10\nt_str = time.strftime(\"%Y%m%d%H%M%S\")\n\n\ndef currentDir():\n return os.path.dirname(os.path.realpath(__file__))\n\n\ndef parentDir(mydir):\n return str(Path(mydir).parent.absolute())\n\n\ndef init_logging(logfile):\n # debug, info, warning, error, critical\n # set up logging to file\n logging.shutdown()\n\n logger = logging.getLogger()\n logger.handlers = []\n\n logging.basicConfig(level=logging.INFO,\n format='%(asctime)s - %(levelname)s - %(message)s',\n filename=logfile,\n filemode='w')\n\n # create console handler and set level to debug\n ch = logging.StreamHandler()\n ch.setLevel(logging.CRITICAL)\n # add formatter to ch\n ch.setFormatter(logging.Formatter('%(message)s'))\n logging.getLogger().addHandler(ch)\n\n\ndef readConfigFile(configfile):\n parameters = {}\n # read parameters from config file\n config = configparser.ConfigParser()\n config.read(configfile)\n\n p_default = config['DEFAULT']\n parameters['data_path'] = parentDir(currentDir()) + os.sep + 'datasets' + os.sep + 'car_evaluation' + os.sep + p_default['DataFile'] #1\n parameters['num_target_features'] = p_default.getint('NumOfFeaturesToRecover')\n parameters['num_exps'] = p_default.getint('RunningTimes')\n\n # add time stamp to the name of log file\n logfile = p_default['LogFile']\n index = logfile.rfind('.')\n\n if index != -1:\n logfile = logfile[:index] + \"_\" + \"unknown_\" + t_str + logfile[index:]\n else:\n logfile = logfile + \"_\" + \"unknown_\" + t_str + \".log\"\n\n parameters['logpath'] = currentDir() + os.sep + \"log\" + os.sep + logfile\n\n return parameters\n\n\ndef load_data(path): #2\n # Solve iris\n import pandas as pd\n from sklearn.model_selection import train_test_split\n\n import category_encoders as ce\n # train validation test: 0.6 0.2 0.2\n # df = pd.read_csv(path, header=None)\n\n # # Column names\n # df.columns = ['SepalLength', 'SepalWidth',\n # 'PetalLength', 'PetalWidth', 'Class']\n\n # # Changes string to float\n # df.SepalLength = df.SepalLength.astype(float)\n # df.SepalWidth = df.SepalWidth.astype(float)\n # df.PetalLength = df.PetalLength.astype(float)\n # df.PetalWidth = df.PetalWidth.astype(float)\n\n # # Sets label name as Y\n # df = df.rename(columns={'Class': 'Y'})\n # x_tv, x_test, y_tv, y_test = train_test_split(df.drop(columns=['Y']).values, df.Y.values, test_size=0.2,\n # random_state=42)\n # x_train, x_val, y_train, y_val = train_test_split(x_tv, y_tv, test_size=0.25,\n # random_state=42)\n # return x_train, y_train, x_val, y_val, x_test, y_test\n\n df = pd.read_csv(path, header=None)\n\n df.replace('5more','5',inplace=True)\n\n # Column names\n df.columns = ['buying', 'maint',\n 'doors', 'persons', 'lug_boot', 'safety', 'Decision']\n\n # Changes string to float\n # df.SepalLength = df.SepalLength.astype(float)\n # df.SepalWidth = df.SepalWidth.astype(float)\n # df.PetalLength = df.PetalLength.astype(float)\n # df.PetalWidth = df.PetalWidth.astype(float)\n\n # Sets label name as Y\n df = df.rename(columns={'Decision': 'class'})\n def show(df):\n for i in df.columns[1:]:\n print(\"Feature: {} with {} Levels\".format(i,df[i].unique()))\n\n show(df)\n X = df.drop('class', axis = 1)\n y = df['class']\n # print(X,y)\n # x_train_array, x_test_array, y_train, y_test = train_test_split(X, y,\n # test_size=728,\n # random_state=42)\n # print(x_train_array.values, x_test_array.values, y_train, y_test)\n\n x_tv, x_test, y_tv, y_test = train_test_split(X, y, test_size=0.2,\n random_state=42)\n x_train, x_val, y_train, y_val = train_test_split(x_tv, y_tv, test_size=0.25,\n random_state=42)\n x_train, a, y_train, b = train_test_split(x_train, y_train, test_size=36,\n random_state=42) \n encoder = ce.OrdinalEncoder(cols=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n x_train = encoder.fit_transform(x_train)\n x_test = encoder.transform(x_test)\n x_val = encoder.transform(x_val)\n return x_train.values, y_train.values, x_val.values, y_val.values, x_test.values, y_test.values\n\n\nif __name__ == '__main__':\n # read parameters from config file\n configfile = 'config.ini'\n parameters = readConfigFile(configfile)\n\n # init logging\n init_logging(parameters['logpath'])\n # logging.info(\"This should be only in file\")\n # logging.critical(\"This shoud be in both file and console\")\n\n logging.critical('=================')\n logging.critical('dataset: %s', parameters['data_path'])\n logging.critical('=================')\n\n # load dataset\n x_tr, y_tr, x_val, y_val, x_test, y_test = load_data(parameters['data_path'])\n\n print(x_tr,y_tr,x_val,y_val)\n print(len(x_tr),len(y_tr))\n\n import sys\n\n sys.path.append('..')\n import sshap\n from sklearn.svm import SVC\n\n # sshap.reproduce(10)\n model = SVC(decision_function_shape='ovo')\n idxes = list(np.arange(len(y_tr)))\n\n perf_runs = []\n sshap.reproduce(seed=42)\n np.random.shuffle(idxes)\n num_shards = 5\n num_instance_in_shard = len(y_tr) // num_shards\n ls = sshap.ShardedStruct(depth=1, nl=[idxes[:num_instance_in_shard], idxes[num_instance_in_shard:num_instance_in_shard*2],\n idxes[num_instance_in_shard*2:num_instance_in_shard*3], idxes[num_instance_in_shard*3:num_instance_in_shard*4], \n idxes[num_instance_in_shard*4:num_instance_in_shard*5]])\n for c_run in range(runs):\n sshap.reproduce(seed=42 + c_run) \n # ls = sshap.ShardedStruct(depth=1, nl=[idxes[:len(y_tr) // 3], idxes[len(y_tr) // 3:len(y_tr) // 3 * 2],\n # idxes[len(y_tr) // 3 * 2:]])\n\n # ls = sshap.ShardedStruct(depth=1, nl=[idxes[:len(y_tr) // 2], idxes[len(y_tr) // 2:]])\n logging.critical('start valuation, current runs %d / %d' % (c_run + 1, runs))\n # cal different value\n loo = sshap.loo(x_tr, y_tr, x_val, y_val, model, ls)\n logging.critical('loo complete')\n sv, beta41, beta161, beta14, beta116 = sshap.monte_carlo_sv_beta(x_tr, y_tr, x_val, y_val, model, ls,\n m=10 * len(y_tr), proc_num=proc_num)\n logging.critical('sv and beta sv complete')\n ssv = sshap.monte_carlo_ssv(x_tr, y_tr, x_val, y_val, model, ls, m=10 * len(y_tr), proc_num=proc_num)\n logging.critical('ssv complete')\n rand_val = np.random.random(size=len(y_tr))\n logging.critical('random complete')\n\n np.savez('./result/' + f'{str(c_run)}_{str(runs)}_' + 'latest_car_evaluation_val.npz', loo=loo, rand_val=rand_val, sv=sv,\n ssv=ssv, beta161=beta161, beta41=beta41,\n beta14=beta14, beta116=beta116)\n np.savez('./result/' + t_str + f'_{str(c_run)}_{str(runs)}_' + 'car_evaluation_val.npz', loo=loo, rand_val=rand_val,\n sv=sv, ssv=ssv, beta161=beta161, beta41=beta41,\n beta14=beta14, beta116=beta116)\n\n # check the perf on test dataset\n perf_lists = []\n vales = np.load('./result/' + f'{str(c_run)}_{str(runs)}_' + 'latest_car_evaluation_val.npz')\n for j, alg in enumerate(['ssv', 'sv', 'beta161', 'beta41', 'beta116', 'beta14', 'loo', 'rand_val']):\n perf_lists.append([])\n l2h = np.argsort(vales[alg])\n for i in range(len(y_tr), 0, -1):\n tmp_ls = copy.deepcopy(ls)\n tmp_ls.idxes_available = l2h[:i]\n acc = sshap.eval_utility(x_tr, y_tr, x_test, y_test, model, tmp_ls)\n perf_lists[j].append(acc)\n np.savetxt('./result/' + f'{str(c_run)}_{str(runs)}_' + 'latest_perf_list.txt', np.asarray(perf_lists))\n np.savetxt('./result/' + t_str + f'_{str(c_run)}_{str(runs)}_' + 'perf_list.txt', np.asarray(perf_lists))\n perf_runs.append(perf_lists)\n np.save('./result/' + 'latest_perf_runs.npy', np.asarray(perf_runs))\n np.save('./result/' + t_str + 'perf_runs.npy', np.asarray(perf_runs))\n\n", "repo_name": "ZJU-DIVER/ValuationMeetsRTBF", "sub_path": "paper_exps/remove_car/main_car_evaluation.py", "file_name": "main_car_evaluation.py", "file_ext": "py", "file_size_in_byte": 8743, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "time.strftime", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.shutdown", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 33, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 49, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 127, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 131, "usage_type": "call"}, {"api_name": "category_encoders.OrdinalEncoder", "line_number": 133, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 150, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 151, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 152, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 162, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "sklearn.svm.SVC", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 168, "usage_type": "call"}, {"api_name": "sshap.reproduce", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 172, "usage_type": "attribute"}, {"api_name": "sshap.ShardedStruct", "line_number": 175, "usage_type": "call"}, {"api_name": "sshap.reproduce", "line_number": 179, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 184, "usage_type": "call"}, {"api_name": "sshap.loo", "line_number": 186, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 187, "usage_type": "call"}, {"api_name": "sshap.monte_carlo_sv_beta", "line_number": 188, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 190, "usage_type": "call"}, {"api_name": "sshap.monte_carlo_ssv", "line_number": 191, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 193, "usage_type": "attribute"}, {"api_name": "logging.critical", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 208, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 210, "usage_type": "call"}, {"api_name": "sshap.eval_utility", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 218, "usage_type": "call"}]} +{"seq_id": "28668670886", "text": "import pymongo\nfrom bson.objectid import ObjectId\nimport tornado.ioloop\nimport tornado.web\nimport tornado.websocket\nimport threading\nimport logging\nimport os\nimport sys\nfrom bson.json_util import dumps\nimport configparser\nimport json\nfrom datetime import datetime\nfrom watson_developer_cloud import VisualRecognitionV3\n\n# global variables\n_WEBSETTINGS = { \"static_path\": os.path.join(os.path.dirname(__file__)+\"Web/\", \"static\") }\n_clients = []\n\n# get config file settings\ncfg = configparser.ConfigParser()\ncfg.read('settings.cfg')\n\n# configure connection to mongodb\nconn = pymongo.MongoClient(cfg['DEFAULT']['_URI'])\ntry:\n conn.server_info()\nexcept Exception as e:\n logging.error(\"Unable to connect to {s}\".format(s=cfg['DEFAULT']['_URI']))\n conn = None\n sys.exit(1)\n\nhandle = conn[cfg['DEFAULT']['_DBNAME']][cfg['DEFAULT']['_COLNAME']]\nprint(\"Connected to Atlas!\")\n\n\n# configure connection to watson VisualRecognitionV3 api\nvisual_recognition = VisualRecognitionV3(\n cfg['DEFAULT']['_WATSONAPIVER'],\n iam_apikey=cfg['DEFAULT']['_WATSONAPIKEY'])\n\n\n\n#########\n# configure web interface\n#########\nclass MainHandler(tornado.web.RequestHandler):\n def get(self):\n self.render(\"Web/index.html\", title=\"Welcome\")\n\nclass WebSockHandler(tornado.websocket.WebSocketHandler):\n def open(self):\n print(\"New client connected\")\n _clients.append(self)\n self.write_message(\"You are connected\")\n\n def on_message(self, msg):\n print(msg)\n #self.write_message(msg)\n # oh man this is bad practice\n handle.insert_one({\"url\":msg, \"created\":datetime.now()})\n\n def on_close(self):\n print(\"Client disconnected\")\n\n def check_origin(self, origin):\n # who cares about security\n return True\n\n\n###########\n# Main loop\n##########\nif __name__ == \"__main__\":\n # start up the web servers as tornado applications\n application = tornado.web.Application([(r\"/\", MainHandler),], **_WEBSETTINGS)\n appsoc = tornado.web.Application([(r\"/\", WebSockHandler),],)\n\n # start a web server for sockets\n appsoc.listen(cfg['DEFAULT']['_WEBSOCKPORT'])\n\n # start a web server for index.html and run in background thread\n application.listen(cfg['DEFAULT']['_WEBPORT'])\n t = threading.Thread(target=tornado.ioloop.IOLoop.instance().start)\n t.daemon = True\n t.start()\n\n print(\"Listening...\")\n\n # connect to a change stream\n change_stream = handle.watch()\n # every change in the db\n for change in change_stream:\n # can be insert, update, replace (Compass)\n if change[\"operationType\"] == \"insert\":\n # make sure it had a URL attribute\n if \"url\" in change[\"fullDocument\"]:\n # boilerplate to prep watson api request\n image_url=change[\"fullDocument\"][\"url\"]\n resp = visual_recognition.classify(\n url=image_url,\n threshold='0.1',\n classifier_ids='default').get_result()\n subresp=resp['images'][0]['classifiers'][0]['classes']\n # If image does not exist then, send message on co\n if resp['images_processed'] == 1 :\n # odd formatting i dont have time for right now so just process it first\n labels = []\n #print(json.dumps(resp, indent=2))\n for label in subresp:\n obj = {}\n obj['description'] = label['class']\n obj['score'] = label['score']\n labels.append(obj)\n\n # update mongodb record with response from IBM watson\n handle.update_one({'_id':ObjectId(change[\"fullDocument\"][\"_id\"])}, {\"$set\": {\"watsonlabels\":labels}})\n else:\n logging.warning(\"API error on URl: {a}\".format(a=resp.error.message))\n logging.warning(change[\"fullDocument\"][\"url\"])\n\n # print to screen\n print(dumps(change))\n print(\"\")\n\n for c in _clients:\n # fix disconnecting clients symptom rather than fixings\n try:\n c.write_message(dumps(change))\n except:\n pass\n", "repo_name": "gianpag/IBM_VisionApi_lab", "sub_path": "IBM_runner.py", "file_name": "IBM_runner.py", "file_ext": "py", "file_size_in_byte": 4251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 21, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "watson_developer_cloud.VisualRecognitionV3", "line_number": 38, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 47, "usage_type": "name"}, {"api_name": "tornado.ioloop.websocket", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "tornado.ioloop.web.Application", "line_number": 76, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 76, "usage_type": "name"}, {"api_name": "tornado.ioloop.web.Application", "line_number": 77, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 77, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 84, "usage_type": "call"}, {"api_name": "tornado.ioloop.ioloop.IOLoop.instance", "line_number": 84, "usage_type": "call"}, {"api_name": "tornado.ioloop.ioloop", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 84, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 119, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 120, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 123, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "4610101793", "text": "# utils.timer\n# Provides timing functionality.\n#\n# Created:\n# Author:\n#\n# ID: timer.py [] allen.leis@gmail.com $\n\n\"\"\"\nProvides timing functionality for the Hecate application.\n\"\"\"\n\n##########################################################################\n## Imports\n##########################################################################\n\nimport time\n\nfrom functools import wraps\nfrom datetime import timedelta\n\nfrom dateutil.relativedelta import relativedelta\n\n##########################################################################\n## Decorator\n##########################################################################\n\ndef timeit(func, wall_clock=True):\n \"\"\"\n Appends the return with a Timer object recording function execution time.\n \"\"\"\n @wraps(func)\n def timer_wrapper(*args, **kwargs):\n \"\"\"\n Inner function that uses the Timer context object\n \"\"\"\n with Timer(wall_clock) as timer:\n result = func(*args, **kwargs)\n\n return result, timer\n return timer_wrapper\n\n\n##########################################################################\n## Timer functions\n##########################################################################\n\ndef humanizedelta(*args, **kwargs):\n \"\"\"\n Wrapper around dateutil.relativedelta (same construtor args) and returns\n a humanized string representing the delta in a meaningful way.\n \"\"\"\n if 'milliseconds' in kwargs:\n sec = kwargs.get('seconds', 0)\n msec = kwargs.pop('milliseconds')\n kwargs['seconds'] = sec + (float(msec) / 1000.0)\n\n delta = relativedelta(*args, **kwargs)\n attrs = ('years', 'months', 'days', 'hours', 'minutes', 'seconds')\n parts = [\n '%d %s' % (getattr(delta, attr), getattr(delta, attr) > 1 and attr or attr[:-1])\n for attr in attrs if getattr(delta, attr)\n ]\n\n return \" \".join(parts)\n\nclass Timer(object):\n \"\"\"\n A context object timer. Usage:\n >>> with Timer() as timer:\n ... do_something()\n >>> print timer.elapsed\n \"\"\"\n\n def __init__(self, wall_clock=True):\n \"\"\"\n If wall_clock is True then use time.time() to get the number of\n actually elapsed seconds. If wall_clock is False, use time.clock to\n get the process time instead.\n \"\"\"\n self.wall_clock = wall_clock\n self.time = time.time if wall_clock else time.clock\n\n # Stubs for serializing an empty timer.\n self.started = None\n self.finished = None\n self.elapsed = 0.0\n\n def __enter__(self):\n self.started = self.time()\n return self\n\n def __exit__(self, type, value, tb):\n self.finished = self.time()\n self.elapsed = self.finished - self.started\n\n def __str__(self):\n return humanizedelta(seconds=self.elapsed)\n\n @property\n def timedelta(self):\n return timedelta(seconds=self.elapsed)\n\n def serialize(self):\n return {\n 'started': self.started,\n 'finished': self.finished,\n 'elapsed': humanizedelta(seconds=self.elapsed),\n }\n", "repo_name": "looselycoupled/hecate", "sub_path": "hecate/utils/timer.py", "file_name": "timer.py", "file_ext": "py", "file_size_in_byte": 3100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "functools.wraps", "line_number": 32, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 82, "usage_type": "attribute"}, {"api_name": "time.clock", "line_number": 82, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "8851265962", "text": "import typing\n\nimport torch\n\nfrom torch_extras.layer import Layer\nfrom .common import StackRNNBase\n\nclass GrefenstetteRNN(StackRNNBase):\n\n def __init__(self,\n input_size: int,\n stack_embedding_size: int,\n controller: typing.Callable\n ):\n super().__init__(input_size, stack_embedding_size, controller)\n self.action_layer = Layer(\n self.controller.output_size(),\n 2,\n torch.nn.Sigmoid()\n )\n self.push_value_layer = Layer(\n self.controller.output_size(),\n stack_embedding_size,\n torch.nn.Tanh()\n )\n\n def initial_stack(self, batch_size, reading_size):\n return GrefenstetteStack(\n elements=[],\n bottom=torch.zeros((batch_size, reading_size), device=self.device)\n )\n\n class State(StackRNNBase.State):\n\n def compute_stack(self, hidden_state, stack):\n actions = self.rnn.action_layer(hidden_state)\n push_value = self.rnn.push_value_layer(hidden_state)\n return stack.next(actions, push_value), actions\n\nclass GrefenstetteStack:\n\n def __init__(self, elements, bottom):\n self.elements = elements\n self.bottom = bottom\n\n def reading(self):\n device = self.bottom.device\n batch_size = self.bottom.size(0)\n result = self.bottom\n strength_left = torch.ones((batch_size,), device=device)\n for value, strength in reversed(self.elements):\n result = result + value * torch.min(\n strength,\n torch.nn.functional.relu(strength_left)\n )[:, None]\n strength_left = strength_left - strength\n return result\n\n def next(self, actions, push_value):\n return GrefenstetteStack(\n self.next_elements(actions, push_value),\n self.bottom\n )\n\n def next_elements(self, actions, push_value):\n push_strength = actions[:, 0]\n pop_strength = actions[:, 1]\n result = []\n strength_left = pop_strength\n for value, strength in reversed(self.elements):\n result.append((\n value,\n torch.nn.functional.relu(\n strength -\n torch.nn.functional.relu(strength_left)\n )\n ))\n strength_left = strength_left - strength\n result.reverse()\n result.append((push_value, push_strength))\n return result\n", "repo_name": "bdusell/nondeterministic-stack-rnn", "sub_path": "src/stack_rnn_models/grefenstette.py", "file_name": "grefenstette.py", "file_ext": "py", "file_size_in_byte": 2500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "51", "api": [{"api_name": "common.StackRNNBase", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch_extras.layer.Layer", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch_extras.layer.Layer", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.Tanh", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "common.StackRNNBase.State", "line_number": 33, "usage_type": "attribute"}, {"api_name": "common.StackRNNBase", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "attribute"}]} +{"seq_id": "33601783879", "text": "import matplotlib\nimport matplotlib.pyplot as plt \nimport pandas as pd\n\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\n\nFILENAME = 'registrations_over_time.png'\n\ndef plot_registrations(dates, fn='plot.png'):\n\n df = pd.DataFrame({'xdata': dates, 'ydata': [n for (n,d) in enumerate(dates,1)]})\n plt.plot_date('xdata', 'ydata', data=df, xdate=True, ydate=False, marker='o', markerfacecolor='blue', markersize=8, color='skyblue', linewidth=3)\n\n plt.xlabel(\"Registration Date\")\n plt.ylabel(\"Registration Number\")\n\n yint = range(1, len(dates)+1, 10)\n plt.yticks(yint)\n\n plt.gcf().autofmt_xdate()\n\n plt.grid(True)\n\n plt.title('Registrations Over Time')\n\n plt.savefig(fn)\n\ndef plot_registration_dates(registration_dates, fn=FILENAME):\n dates = matplotlib.dates.date2num(registration_dates)\n plot_registrations(dates, fn)\n\n", "repo_name": "kimvanwyk/md410_2021_conv_website", "sub_path": "raw/registrees/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pandas.plotting.register_matplotlib_converters", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot_date", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.dates.date2num", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 30, "usage_type": "attribute"}]} +{"seq_id": "14033919574", "text": "import torch\nfrom torch import nn\nfrom timeit import default_timer as timer\n\nclass Projection(nn.Module):\n def __init__(self, K=None, h=None, w=None):\n super().__init__()\n if K is not None:\n self.K = K.view(-1,3,3).to(torch.float32)\n Ki = torch.inverse(K)\n\n #self.im_height = torch.tensor([h]).long()\n #self.im_width = torch.tensor([w]).long() \n im_height = h\n im_width = w\n # self.im_height = K[0,2].long()*2 \n # self.im_width = K[0,2].long()*2 \n # im_height = self.im_height.item()\n # im_width = self.im_width.item()\n\n # low-res ray\n i, j = torch.meshgrid(torch.linspace(0, im_width-1, im_width), torch.linspace(0, im_height-1, im_height)) # pytorch's meshgrid has indexing='ij'\n i = i.t().to(K.device)\n j = j.t().to(K.device)\n self.i = i\n self.j = j\n K = K.view(3,3)\n ray = torch.stack([(i-K[0][2])/K[0][0], (j-K[1][2])/K[1][1], torch.ones_like(i)], -1)\n self.ray = ray.reshape(1,-1,3).to(torch.float32)\n\n self.K = self.K.cuda()\n self.ray = self.ray.cuda()\n self.last_row = torch.tensor([0.,0.,0.,1]).reshape(1,1,4).float().cuda()\n\n self.ones = torch.ones(1,h*w, 1).float().cuda()\n self.depth_proj = torch.zeros(1,1,h,w).float().cuda()\n\n def get_K(self, im_height, im_width, offset, high_res=False):\n B = offset.shape[0]\n if high_res:\n K = self.K_highres.view(3,3)\n offset_ = offset * 4\n else:\n K = self.K.view(3,3)\n offset_ = offset\n K = K.to(offset.device).unsqueeze(0).repeat(B, 1,1)\n # K[:,0,2] = im_width / 2 - offset[:,0] * K[:,0,2] \n # K[:,1,2] = im_height / 2 - offset[:,1] * K[:,1,2]\n K[:,0,2] -= offset_[:,1] \n K[:,1,2] -= offset_[:,0] \n return K\n\n # def get_ray(self, im_height, im_width, offset, high_res=False):\n # K = self.get_K(im_height, im_width, offset, high_res)\n # B = offset.shape[0]\n # i, j = torch.meshgrid(torch.linspace(0, im_width-1, im_width), torch.linspace(0, im_height-1, im_height)) # pytorch's meshgrid has indexing='ij'\n # i = i.t()\n # j = j.t()\n # i = i.to(offset.device).unsqueeze(0).repeat(B,1,1)\n # j = j.to(offset.device).unsqueeze(0).repeat(B,1,1)\n # ray = torch.stack([(i-K[:,0,2].reshape(B,1,1))/K[:,0,0].reshape(B,1,1), (j-K[:,1,2].reshape(B,1,1))/K[:,1,1].reshape(B,1,1), torch.ones_like(i)], -1)\n # return ray.reshape(B,-1,3).to(torch.float32)\n\n def unproject(self, depth, pose, offset=None, high_res=False):\n #ray = self.get_ray(depth.shape[-2], depth.shape[-1], offset, high_res)\n ray = self.ray\n bs = depth.shape[0]\n\n xyz = depth.reshape(bs,-1,1) * ray.to(depth.device)\n\n # c2w\n if pose.shape[1]==3:\n pose = torch.cat((pose, torch.tensor([0.,0.,0.,1]).reshape(1,1,4).to(pose.dtype).to(pose.device)), 1)\n xyz = torch.cat((xyz, torch.ones_like(xyz[...,-1:])), -1)\n xyz = (pose @ xyz.transpose(1,2)).transpose(1,2)\n xyz = xyz[...,0:3]\n\n return xyz\n\n def project(self, xyz, pose, im_height, im_width, offset, high_res=False):\n # if not high_res:\n # K = self.K.to(xyz.device)\n # else:\n # K = self.K_highres.to(xyz.device)\n if pose.shape[1]==3:\n pose = torch.cat((pose, torch.tensor([0.,0.,0.,1]).reshape(1,1,4).to(pose.dtype).to(pose.device)), 1)\n #K = self.get_K(im_height, im_width, offset, high_res)\n K = self.K\n bs = xyz.shape[0]\n \n # w2c\n xyz = torch.cat((xyz, torch.ones_like(xyz[...,-1:])), -1)\n xyz = (torch.inverse(pose) @ xyz.transpose(1,2)).transpose(1,2)\n xyz = xyz[...,0:3]\n\n Kt = K.transpose(1,2)\n uv = torch.bmm(xyz, Kt.to(xyz.device))\n \n # import ipdb;ipdb.set_trace()\n d = uv[:,:,2:3]\n \n # avoid division by zero\n uv = uv[:,:,:2] / (torch.nn.functional.relu(d) + 1e-12)\n return uv, d\n\n def unproject_project(self, depth, pose, offset=None, high_res=False):\n #ray = self.get_ray(depth.shape[-2], depth.shape[-1], offset, high_res)\n ray = self.ray\n bs = depth.shape[0]\n\n xyz = depth.reshape(bs,-1,1) * ray\n\n # c2w\n #if pose.shape[1]==3:\n # pose = torch.cat((pose, torch.tensor([0.,0.,0.,1]).reshape(1,1,4).to(pose.dtype).to(pose.device)), 1)\n\n #xyz = torch.cat((xyz, torch.ones_like(xyz[...,-1:])), -1)\n xyz = torch.cat((xyz, self.ones), -1)\n xyz = (pose @ xyz.transpose(1,2)).transpose(1,2)\n xyz = xyz[...,0:3]\n\n K = self.K\n Kt = K.transpose(1,2)\n uv = torch.bmm(xyz, Kt.to(xyz.device))\n \n # import ipdb;ipdb.set_trace()\n d = uv[:,:,2:3]\n \n # avoid division by zero\n uv = uv[:,:,:2] / (torch.nn.functional.relu(d) + 1e-12)\n return uv, d\n\n def project(self, xyz, pose, im_height, im_width, offset, high_res=False):\n # if not high_res:\n # K = self.K.to(xyz.device)\n # else:\n # K = self.K_highres.to(xyz.device)\n if pose.shape[1]==3:\n pose = torch.cat((pose, torch.tensor([0.,0.,0.,1]).reshape(1,1,4).to(pose.dtype).to(pose.device)), 1)\n #K = self.get_K(im_height, im_width, offset, high_res)\n K = self.K\n bs = xyz.shape[0]\n \n # w2c\n xyz = torch.cat((xyz, torch.ones_like(xyz[...,-1:])), -1)\n xyz = (torch.inverse(pose) @ xyz.transpose(1,2)).transpose(1,2)\n xyz = xyz[...,0:3]\n\n Kt = K.transpose(1,2)\n uv = torch.bmm(xyz, Kt.to(xyz.device))\n \n # import ipdb;ipdb.set_trace()\n d = uv[:,:,2:3]\n \n # avoid division by zero\n uv = uv[:,:,:2] / (torch.nn.functional.relu(d) + 1e-12)\n return uv, d\n # project a previous frame to current frame\n # depth: 1x1xHxW\n # pose: 1x3x4 \n # pose_next: 1x3x4\n # offset: 1x2\n def forward(self, depth, pose, pose_next, offset=torch.zeros(1,2)):\n start = timer()\n _,_,H,W = depth.shape\n #xyz = self.unproject(depth, pose, offset=offset, high_res=False)\n if pose.shape[1]==3:\n pose = torch.cat((pose, torch.tensor([0.,0.,0.,1]).reshape(1,1,4).to(pose.dtype).to(pose.device)), 1)\n if pose_next.shape[1]==3:\n pose_next = torch.cat((pose_next, torch.tensor([0.,0.,0.,1]).reshape(1,1,4).to(pose.dtype).to(pose.device)), 1)\n pose_to_next = torch.inverse(pose_next) @ pose\n uv0,d0 = self.unproject_project(depth, pose_to_next, offset=offset, high_res=False)\n ## DEBUG\n if False:\n d0_upsampled = self.depth_zero_upsampling(depth[:,0]) \n xyz2 = self.unproject(d0_upsampled, pose[:,0], offset=offset, high_res=False)\n xyz_ = xyz.detach().cpu().numpy()\n xyz2_ = xyz2.detach().cpu().numpy()\n fig = plt.figure()\n ax = fig.add_subplot(projection='3d')\n ax.plot(xyz_[0,...,0], xyz_[0,...,1], xyz_[0,...,2],'r.')\n ax.plot(xyz2_[0,...,0], xyz2_[0,...,1], xyz2_[0,...,2],'b.')\n ax.set_xlabel('X Label')\n ax.set_ylabel('Y Label')\n ax.set_zlabel('Z Label')\n plt.show()\n import ipdb;ipdb.set_trace()\n #torch.cuda.synchronize()\n end = timer()\n print(f'+++++++ projector project, unproject ', (end-start)*1000)\n ###\n # project to frame 0\n #uv0, d0 = self.project(xyz, pose_next, H, W, offset=offset, high_res=False)\n uv0 = torch.round(uv0).to(torch.long)\n #u_mask = torch.logical_and(uv0[...,0]>=0 , uv0[...,0]=0 , uv0[...,1]=0 , uv0[...,0]=0 , uv0[...,1] B,C,H*W\n depth = depth.permute(0,2,1)\n depth_proj[0,:,uv_round[...,1],uv_round[...,0]] = depth[uv_mask[0:1]].T\n if False:\n import matplotlib.pyplot as plt\n plt.imshow(depth_proj[0,0].detach().cpu().numpy())\n plt.show()\n import ipdb;ipdb.set_trace()\n\n #torch.cuda.synchronize()\n end = timer()\n print(f'+++++++ projector post processing ', (end-start)*1000)\n\n return depth_proj \n", "repo_name": "JasonLSC/SteerNeRF_official", "sub_path": "models/project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 8842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "51", "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.float32", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.inverse", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.meshgrid", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.inverse", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.inverse", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 163, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.inverse", "line_number": 171, "usage_type": "call"}, {"api_name": "ipdb.set_trace", "line_number": 187, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.round", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 194, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.logical_and", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.logical_and", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "ipdb.set_trace", "line_number": 214, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "71400569118", "text": "import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport time\n\nclass use_unittest(unittest.TestCase):\n\tdef setUp(self):\n\t\tself.driver=webdriver.Chrome(\"chromedriver.exe\")\n\n\tdef test_cambiar_window(self):\n\t\tdriver=self.driver\n\t\tdriver.get(\"http://google.com.ec\")\n\t\ttime.sleep(3)\n\t\tdriver.execute_script(\"window.open('');\")\n\t\ttime.sleep(3)\n\t\tdriver.switch_to.window(driver.window_handles[1])\n\t\tdriver.get(\"http://xvideos.com\")\n\t\ttime.sleep(3)\n\t\tdriver.switch_to.window(driver.window_handles[0])\n\t\ttime.sleep(3)\n\nif _name=='main_':\n\tunittest.main()", "repo_name": "JeanCarlo96/FertilizantesBots", "sub_path": "WebScraping/erick_01.py", "file_name": "erick_01.py", "file_ext": "py", "file_size_in_byte": 590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 13, "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": 20, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "40060576145", "text": "# !/usr/bin/env python3\n# -*- coding:utf-8 -*-\nfrom py2neo import Graph, Node, Relationship\nfrom py2neo.database import Schema\nfrom itertools import islice\nimport pymysql\nfrom py2neo.ogm import GraphObject, Related, Property, RelatedTo, RelatedFrom, Label\nfrom py2neo.bulk import create_nodes, create_relationships\nfrom typing import List, Tuple, Dict, Callable, Iterable\nfrom ...config import neo4j_config\nfrom ...common import Experience\nimport datetime\n\nschool_mate = ['学习P', '学生P', '专业S']\n\n\ndef get_rank(user1, user2):\n leader_level = [\"部长P\", \"省长P\", \"副部长P\", \"副省长P\", \"厅长P\", \"局长P\", \"副厅长P\", \"副局长P\",\n \"处长P\", \"县长P\", \"副处长P\", \"副县长P\", \"科长P\", \"乡长P\", \"副科长P\", \"副乡长P\"]\n servant_level1 = [\"一级巡视员P\", \"二级巡视员P\", \"一级调研员P\", \"二级调研员P\", \"三级调研员P\", \"四级调研员P\",\n \"一级主任科员P\", \"二级主任科员P\", \"三级主任科员P\", \"四级主任科员P\", \"一级科员P\", \"二级科员P\"]\n servant_level2 = [\"巡视员P\", \"副巡视员P\", \"调研员P\", \"副调研员P\", \"主任科员P\", \"副主任科员P\", \"科员P\"]\n\n if user1 in servant_level1 and user2 in servant_level1:\n if servant_level1.index(user1) < servant_level1.index(user2):\n return 1\n else:\n return -1\n if user1 in servant_level2 and user2 in servant_level2:\n # print(user1,user2)\n if servant_level2.index(user1) < servant_level2.index(user2):\n return 1\n else:\n return -1\n if user1 in leader_level and user2 in leader_level:\n if leader_level.index(user1) // 2 < leader_level.index(user2) // 2:\n return 1\n else:\n return -1\n if user1 == user2[1:] and user2[0] == \"副\":\n return 1\n elif user2 == user1[1:] and user1[0] == \"副\":\n return -1\n else:\n return 0\n\n\ndef batch(iterable, n=1):\n l = len(iterable)\n for ndx in range(0, l, n):\n yield iterable[ndx:min(ndx + n, l)]\n\n\nclass UserNodeNEO(GraphObject):\n __primarylabel__ = 'YearUser'\n # 定义主键\n __primarykey__ = 'id' # id = year + user_id\n # 定义类的属性\n name = Property() # uid\n id = Property() # 2010156\n tag = Property() # 深圳市L罗湖区L教育局O人事科S干部P\n # 定义类的标签\n # leaf = Label()\n # 定义同事关系\n colleague = Related('UserNodeNEO', 'C')\n # 定义上下级关系\n subordinate = RelatedTo('UserNodeNEO', 'R')\n superior = RelatedFrom('UserNodeNEO', 'R')\n\n\nclass CareerSocialNetwork(object):\n def __init__(self, start, end):\n self.start = start\n self.end = end\n self.GraphDatabase = Graph(neo4j_config)\n self.schema = Schema(self.GraphDatabase)\n self.schema.create_index(\"YearUser\", \"id\") # much faster to build tree, very important index\n\n def get_node(self, id):\n return UserNodeNEO.match(self.GraphDatabase, id).first()\n\n def batch_node_insert(self, data: List, keys: List[str]) -> None:\n batch_size = 10000\n for b in batch(data, batch_size):\n create_nodes(self.GraphDatabase.auto(), b, labels={\"YearUser\"}, keys=keys)\n\n def batch_relation_insert(self, data: List, R: str) -> None:\n batch_size = 10000\n count = 1\n for b in batch(data, batch_size):\n count += 1\n create_relationships(self.GraphDatabase.auto(), b, R,\n start_node_key=(\"YearUser\", \"id\"),\n end_node_key=(\"YearUser\", \"id\"))\n\n def store_user_resume_nodes(self, exp_list: List[Experience], uid2name: Dict, init=True) -> None:\n \"\"\"\n store YearUser Nodes and Trajectory edges\n \"\"\"\n # if init:\n # self.GraphDatabase.delete_all() # clear\n # as with merge_nodes, also WriteBatch is another way(but only in newer version py2neo)\n keys = [\"id\", \"name\", \"uid\", \"tag\", \"raw\", \"interval\"]\n nodes_data = []\n career_track = []\n last_exp_uuid = {}\n last_splitnum={}\n exp_list_sorted = sorted(exp_list, key=lambda x:datetime.date.min if x.time_start is None else x.time_start)\n exp_list_sorted = sorted(exp_list_sorted, key=lambda x:\"0\" if x.person_uuid is None else x.person_uuid)\n '''Build career trajectory and store nodes'''\n for exp in exp_list_sorted:\n person_uuid = exp.person_uuid\n exp_uuid = exp.uuid\n splitnum = exp.splitnum\n interval = str(exp.time_start) + \"——\" + str(exp.time_end)\n id = str(exp_uuid) + '+' + str(splitnum)\n if exp.text:\n job = exp.text.strip().replace(\"MBA\", \"管理学硕士\").replace(\"EMBA\", \"管理学硕士\").replace(\"||\", \"|\")\n else:\n continue\n nodes_data.append([id, uid2name.get(person_uuid, \"null\"), person_uuid, exp.text_token, job, interval])\n if person_uuid not in last_exp_uuid:\n last_exp_uuid[person_uuid] = exp_uuid\n last_splitnum[exp_uuid] = str(splitnum)\n continue\n if last_exp_uuid[person_uuid] == exp_uuid: # uuid doesn't change\n if splitnum > 0:\n last_id = str(exp_uuid) + '+' + str(splitnum - 1)\n career_track.append(((last_id), {}, (id)))\n else: # uuid has changed\n last_id = last_exp_uuid[person_uuid] + '+' + last_splitnum[last_exp_uuid[person_uuid]]\n career_track.append(((last_id), {}, (id)))\n # record for track\n last_exp_uuid[person_uuid] = exp_uuid\n last_splitnum[exp_uuid] = str(splitnum)\n print(\"******** Inserting {} YearUser Nodes **************\".format(len(nodes_data)))\n self.batch_node_insert(nodes_data, keys=keys)\n print(\"******** Inserting {} Trajectory Rels *************\".format(len(career_track)))\n self.batch_relation_insert(career_track, \"trajectory\")\n\n def store_social_network(self, colleague_list, superior_list_same, superior_list_cross):\n relationship_col = []\n relationship_rank_same = []\n relationship_rank_cross = []\n\n for col in colleague_list:\n # print(col)\n mid, nid, year = col\n relationship_col.append(((mid), {\"period\": year}, (nid)))\n\n for col in superior_list_same:\n # print(col)\n mid, nid, m_rank, n_rank, year = col\n relationship_rank_same.append(((mid),\n {\"period\": year, \"subordinate\": n_rank, \"superior\": m_rank},\n (nid)))\n\n for col in superior_list_cross:\n # print(col)\n mid, nid, m_rank, n_rank, year = col\n relationship_rank_cross.append(((mid),\n {\"period\": year, \"subordinate\": n_rank, \"superior\": m_rank},\n (nid)))\n\n # print(len(relationship_col))\n # print(len(relationship_rank_same))\n # print(len(relationship_rank_cross))\n self.batch_relation_insert(relationship_col, \"Col\")\n self.batch_relation_insert(relationship_rank_same, \"Rank\")\n self.batch_relation_insert(relationship_rank_cross, \"Rank_cross\")\n", "repo_name": "kundtx/Career_Platform_with_Demo", "sub_path": "Career_Platform/career_platform/algorithm/network/csn.py", "file_name": "csn.py", "file_ext": "py", "file_size_in_byte": 7407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "51", "api": [{"api_name": "py2neo.ogm.GraphObject", "line_number": 54, "usage_type": "name"}, {"api_name": "py2neo.ogm.Property", "line_number": 59, "usage_type": "call"}, {"api_name": "py2neo.ogm.Property", "line_number": 60, "usage_type": "call"}, {"api_name": "py2neo.ogm.Property", "line_number": 61, "usage_type": "call"}, {"api_name": "py2neo.ogm.Related", "line_number": 65, "usage_type": "call"}, {"api_name": "py2neo.ogm.RelatedTo", "line_number": 67, "usage_type": "call"}, {"api_name": "py2neo.ogm.RelatedFrom", "line_number": 68, "usage_type": "call"}, {"api_name": "py2neo.Graph", "line_number": 75, "usage_type": "call"}, {"api_name": "config.neo4j_config", "line_number": 75, "usage_type": "argument"}, {"api_name": "py2neo.database.Schema", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "py2neo.bulk.create_nodes", "line_number": 85, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 87, "usage_type": "name"}, {"api_name": "py2neo.bulk.create_relationships", "line_number": 92, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 96, "usage_type": "name"}, {"api_name": "common.Experience", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 96, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 108, "usage_type": "attribute"}]} +{"seq_id": "12484178290", "text": "\"\"\"Circular Ensemble Module\n\nThis module contains the implementation of the Circular Ensemble.\nThis ensemble of random matrices contains mainly three sub-ensembles:\nCircular Orthogonal Ensemble (COE), Circular Unitary Ensemble (CUE)\nand Circular Symplectic Ensemble (CSE).\n\n\"\"\"\n\nfrom typing import Union, Sequence, Tuple\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import special\n\nfrom .base_ensemble import _Ensemble\n\n\n\ndef _sample_haar_mtx(size: int) -> np.ndarray:\n \"\"\"Samples Haar-distributed matrices.\n\n Samples Haar-distributed matrices that are useful to generate\n random matrices for COE, CUE and CSE ensembles.\n\n Args:\n n (int): matrix size.\n\n Returns:\n numpy array containing Haar-distributed random matrix.\n \"\"\"\n # n by n random complex matrix\n x_mtx = np.random.randn(size,size) + (0+1j)*np.random.randn(size,size)\n # orthonormalizing matrix using QR algorithm\n q_mtx, _ = np.linalg.qr(x_mtx)\n # the resulting Q is Haar-distributed\n return q_mtx\n\n\n#########################################################################\n### Circular Ensemble\n\nclass CircularEnsemble(_Ensemble):\n \"\"\"General Circular Ensemble class.\n\n This class contains common attributes and methods for all the\n Circular ensembles. Circular Ensembles are divided in:\n - Circular Orthogonal Ensemble (COE, beta=1): the distribution\n of the matrices of this ensemble are invariant under orthogonal\n conjugation, i.e., if X is in COE(n) and O is an orthogonal matrix,\n then O*X*O^T is equally distributed as X.\n - Circular Unitary Ensemble (CUE, beta=2): the distribution of\n the matrices of this ensemble are invariant under unitary\n conjugation, i.e., if X is in CUE(n) and O is an unitary matrix,\n then O*X*O^T is equally distributed as X.\n - Circular Symplectic Ensemble (CSE, beta=4): the distribution\n of the matrices of this ensemble are invariant under conjugation\n by the symplectic group.\n\n Attributes:\n matrix (numpy array): instance of the COE, CUE or CSE random\n matrix ensemble of size n times n if it is COE or CUE, or\n of size 2n times 2n if it is CSE.\n beta (int): descriptive integer of the gaussian ensemble type.\n For COE beta=1, for CUE beta=2, for CSE beta=4.\n n (int): random matrix size. Circular ensemble matrices are\n squared matrices. COE and CUE are of size n times n,\n and CSE are of size 2n times 2n.\n\n References:\n - Killip, R. and Zozhan, R.\n Matrix Models and Eigenvalue Statistics for Truncations of\n Classical Ensembles of Random Unitary Matrices.\n Communications in Mathematical Physics. 349 (2017): 991-1027.\n - \"Circular ensemble\". Wikipedia.\n en.wikipedia.org/wiki/Circular_ensemble\n\n \"\"\"\n\n def __init__(self, beta: int, n: int, random_state: int = None) -> None:\n \"\"\"Constructor for CircularEnsemble class.\n\n Initializes an instance of this class with the given parameters.\n\n Args:\n beta (int): descriptive integer of the Circular ensemble type.\n For COE beta=1, for CUE beta=2, for CSE beta=4.\n n (int): random matrix size. Circular ensemble matrices are\n squared matrices. COE and CUE are of size n times n,\n and CSE are of size 2n times 2n.\n random_state (int, default=None): random seed to initialize the pseudo-random\n number generator of numpy before sampling the random matrix instance. This \n has to be any integer between 0 and 2**32 - 1 (inclusive), or None (default).\n If None, the seed is obtained from the clock.\n\n \"\"\"\n super().__init__()\n # pylint: disable=invalid-name\n self.n = n\n self.beta = beta\n self._eigvals = None\n self.matrix = self.sample(random_state=random_state)\n\n def resample(self, random_state: int = None) -> np.ndarray:\n \"\"\"Re-samples new Circular Ensemble random matrix.\n\n It re-samples a new random matrix from the Circular ensemble. This is an alias\n for method ``sample``.\n\n Args:\n random_state (int, default=None): random seed to initialize the pseudo-random\n number generator of numpy. This has to be any integer between 0 and 2**32 - 1\n (inclusive), or None (default). If None, the seed is obtained from the clock.\n\n Returns:\n (ndarray) numpy array containing new matrix sampled.\n\n References:\n - Dumitriu, I. and Edelman, A. \"Matrix Models for Beta Ensembles\".\n Journal of Mathematical Physics. 43.11 (2002): 5830-5847.\n \"\"\"\n return self.sample(random_state=random_state)\n\n # pylint: disable=inconsistent-return-statements\n def sample(self, random_state: int = None) -> np.ndarray:\n \"\"\"Samples new Circular Ensemble random matrix.\n\n The sampling algorithm depends on the specification of\n beta parameter. If beta=1, COE matrix is sampled; if\n beta=2 CUE matrix is sampled and if beta=4\n CSE matrix is sampled.\n\n Args:\n random_state (int, default=None): random seed to initialize the pseudo-random\n number generator of numpy. This has to be any integer between 0 and 2**32 - 1\n (inclusive), or None (default). If None, the seed is obtained from the clock.\n\n Returns:\n (ndarray) numpy array containing new matrix sampled.\n\n References:\n - Killip, R. and Zozhan, R.\n Matrix Models and Eigenvalue Statistics for Truncations of\n Classical Ensembles of Random Unitary Matrices.\n Communications in Mathematical Physics. 349 (2017): 991-1027.\n - \"Circular ensemble\". Wikipedia.\n en.wikipedia.org/wiki/Circular_ensemble\n\n \"\"\"\n if random_state is not None:\n np.random.seed(random_state)\n\n if self.beta == 1:\n return self._sample_coe()\n if self.beta == 2:\n return self._sample_cue()\n if self.beta == 4:\n return self._sample_cse()\n\n def _sample_coe(self) -> np.ndarray:\n # sampling unitary Haar-distributed matrix\n u_mtx = _sample_haar_mtx(self.n)\n # mapping to Circular Orthogonal Ensemble\n self.matrix = np.matmul(u_mtx.transpose(), u_mtx)\n # setting array of eigenvalues to None to force re-computing them\n self._eigvals = None\n return self.matrix\n\n def _sample_cue(self) -> np.ndarray:\n # sampling unitary Haar-distributed matrix\n self.matrix = _sample_haar_mtx(self.n)\n # setting array of eigenvalues to None to force re-computing them\n self._eigvals = None\n return self.matrix\n\n def _sample_cse(self) -> np.ndarray:\n # sampling unitary Haar-distributed matrix of size 2n\n u_mtx = _sample_haar_mtx(2*self.n)\n # mapping to Circular Symplectic Ensemble\n j_mtx = self._build_j_mtx()\n # U_R = J * U^T * J^T\n u_r_aux = np.matmul(j_mtx, u_mtx.transpose())\n u_r_mtx = np.matmul(u_r_aux, j_mtx.transpose())\n # A = U^R * U\n self.matrix = np.matmul(u_r_mtx, u_mtx)\n # setting array of eigenvalues to None to force re-computing them\n self._eigvals = None\n return self.matrix\n\n def _build_j_mtx(self) -> np.ndarray:\n \"\"\"Creates an useful matrix to sample CSE matrices.\n\n Creates matrix J of zeros but with the upper-diagonal\n set to -1 and the lower-diagonal set to 1. This matrix\n is useful in the sampling algorithm of CSE matrices.\n\n Returns:\n numpy array containing J matrix.\n\n References:\n - Killip, R. and Zozhan, R.\n Matrix Models and Eigenvalue Statistics for Truncations of\n Classical Ensembles of Random Unitary Matrices.\n Communications in Mathematical Physics. 349 (2017): 991-1027.\n - \"Circular ensemble\". Wikipedia.\n en.wikipedia.org/wiki/Circular_ensemble\n \"\"\"\n size = 2*self.n\n j_mtx = np.zeros((size,size))\n # selecting indices\n inds = np.arange(size-1)\n # selecting upper-diagonal indices\n j_mtx[inds, inds+1] = -1\n # selecting lower-diagonal indices\n j_mtx[inds+1, inds] = 1\n return j_mtx\n\n def eigvals(self, normalize: bool = False) -> np.ndarray:\n \"\"\"Calculates the random matrix eigenvalues.\n\n Calculates the random matrix eigenvalues using numpy standard procedure.\n If the matrix ensemble is symmetric, a faster algorithm is used.\n\n Returns:\n numpy array with the calculated eigenvalues.\n\n \"\"\"\n norm_const = self.eigval_norm_const if normalize else 1.0\n if self._eigvals is not None:\n return norm_const * self._eigvals\n\n if self.beta == 1:\n # using eigvalsh because it's known all eigenvalues are real\n self._eigvals = np.linalg.eigvalsh(self.matrix)\n else:\n # using eigvals since some eigenvalues could be imaginary\n self._eigvals = np.linalg.eigvals(self.matrix)\n\n return norm_const * self._eigvals\n\n def plot_eigval_hist(\n self,\n bins: Union[int, Sequence] = 100,\n interval: Tuple = None,\n density: bool = False,\n normalize: bool = False,\n savefig_path: str = None,\n ) -> None: # pragma: no cover\n \"\"\"Computes and plots the histogram of the matrix eigenvalues.\n\n Calculates and plots the histogram of the current sampled matrix eigenvalues.\n It is important to underline that this function works with real and complex\n eigenvalues: if the matrix eigenvalues are complex, they are plotted in the\n complex plane next to a heap map to study eigenvalue density.\n\n Args:\n bins (int or sequence, default=100): If bins is an integer, it defines the number of\n equal-width bins in the range. If bins is a sequence, it defines the\n bin edges, including the left edge of the first bin and the right\n edge of the last bin; in this case, bins may be unequally spaced.\n interval (tuple, default=None): Delimiters (xmin, xmax) of the histogram.\n The lower and upper range of the bins. Lower and upper outliers are ignored.\n density (bool, default=False): If True, draw and return a probability\n density: each bin will display the bin's raw count divided by the total\n number of counts and the bin width, so that the area under the histogram\n integrates to 1. If set to False, the absolute frequencies of the eigenvalues\n are returned.\n normalize (bool, default=False): Whether to normalize the computed eigenvalues\n by the default normalization constant (see references). Defaults to False,\n i.e., the eigenvalues are normalized. Normalization makes the eigenvalues\n to be in the same support independently of the sample size.\n savefig_path (string, default=None): path to save the created figure. If it is not\n provided, the plot is shown at the end of the routine.\n\n References:\n - Killip, R. and Zozhan, R.\n Matrix Models and Eigenvalue Statistics for Truncations of\n Classical Ensembles of Random Unitary Matrices.\n Communications in Mathematical Physics. 349 (2017): 991-1027.\n\n \"\"\"\n # pylint: disable=arguments-differ\n if self.beta == 1:\n return super().plot_eigval_hist(\n bins=bins,\n interval=interval,\n density=density,\n normalize=normalize,\n savefig_path=savefig_path,\n )\n\n if (interval is not None) and not isinstance(interval, tuple):\n raise ValueError(\"interval argument must be a tuple (or None)\")\n\n eigvals = self.eigvals()\n xvals = eigvals.real\n yvals = eigvals.imag\n\n if interval is None:\n rang_val = self.beta/2\n rang_val += 0.1*rang_val\n rang = ((-rang_val, rang_val), (-rang_val, rang_val))\n extent = [-rang_val, rang_val, -rang_val, rang_val]\n else:\n rang = (interval, interval)\n extent = [interval[0], interval[1], interval[0], interval[1]]\n\n fig, axes = plt.subplots(nrows=1, ncols=2)\n fig.set_figheight(5)\n fig.set_figwidth(13)\n fig.subplots_adjust(hspace=.5)\n\n axes[0].set_xlim(rang[0][0], rang[0][1])\n axes[0].set_ylim(rang[1][0], rang[1][1])\n axes[0].plot(xvals, yvals, 'o')\n axes[0].set_aspect('equal', adjustable='box')\n axes[0].set_title('Complex plane')\n axes[0].set_xlabel('real')\n axes[0].set_ylabel('imaginary')\n\n h2d,_,_,img = axes[1].hist2d(xvals, yvals, range=rang,\n cmap=plt.cm.get_cmap('nipy_spectral'))\n fig.colorbar(img, ax=axes[1])\n axes[1].cla()\n axes[1].imshow(h2d.transpose(), origin='lower', interpolation=\"bilinear\", extent=extent)\n axes[1].set_title('Eigenvalue heatmap')\n axes[1].set_xlabel('real')\n axes[1].set_ylabel('imaginary')\n\n plt.suptitle(\"Complex eigenvalues histogram\", fontweight=\"bold\")\n\n # Saving plot or showing it\n if savefig_path:\n plt.savefig(savefig_path, dpi=600)\n else:\n plt.show()\n\n\n def joint_eigval_pdf(self, eigvals: np.ndarray = None) -> float:\n '''Computes joint eigenvalue pdf.\n\n Calculates joint eigenvalue probability density function given an array of\n eigenvalues. If the array of eigenvalues is not provided, the current random\n matrix sample (so its eigenvalues) is used. This function depends on beta,\n i.e., in the sub-Circular ensemble.\n\n Args:\n eigvals (np.ndarray, default=None): numpy array with the values (eigenvalues)\n to evaluate the joint pdf in.\n\n Returns:\n real number. Value of the joint pdf of the eigenvalues.\n\n References:\n - Killip, R. and Zozhan, R.\n Matrix Models and Eigenvalue Statistics for Truncations of\n Classical Ensembles of Random Unitary Matrices.\n Communications in Mathematical Physics. 349 (2017): 991-1027.\n - \"Circular ensemble\". Wikipedia.\n en.wikipedia.org/wiki/Circular_ensemble\n\n '''\n if eigvals is None:\n # calculating eigenvalues\n eigvals = self.eigvals()\n n_eigvals = len(eigvals)\n\n # calculating Circular eigval pdf constant depeding on beta\n const_beta = (2*np.pi)**self.n * \\\n special.gamma(1 + self.n*self.beta/2)/(special.gamma(1 + self.beta/2)**self.n)\n\n # calculating prod\n pdf = 1\n for k in range(n_eigvals):\n for i in range(k):\n complex_num = complex(0, np.exp(eigvals[i])) - complex(0,np.exp(eigvals[k]))\n pdf *= np.abs(complex_num)**self.beta\n # calculating Circular eigval pdf\n return (1/const_beta) * pdf\n", "repo_name": "AlejandroSantorum/scikit-rmt", "sub_path": "skrmt/ensemble/circular_ensemble.py", "file_name": "circular_ensemble.py", "file_ext": "py", "file_size_in_byte": 15452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 27, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.random.randn", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.linalg.qr", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 19, "usage_type": "attribute"}, {"api_name": "base_ensemble._Ensemble", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 189, "usage_type": "attribute"}, {"api_name": "numpy.linalg.eigvalsh", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 233, "usage_type": "attribute"}, {"api_name": "numpy.linalg.eigvals", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 236, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 217, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 242, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 242, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 307, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 307, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 321, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 333, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 335, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 335, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 338, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 368, "usage_type": "attribute"}, {"api_name": "scipy.special.gamma", "line_number": 369, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 369, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 376, "usage_type": "call"}]} +{"seq_id": "27419978315", "text": "import json\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.models import AnonymousUser\nfrom django.core import exceptions\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\nfrom django.db import IntegrityError\nfrom django.db.utils import Error\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render , resolve_url\nfrom django.urls import reverse\nfrom django.core import serializers\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Count\nfrom django.views.decorators.http import require_GET, require_POST\nfrom django.template.loader import render_to_string\n\nfrom django.core.paginator import Paginator\nfrom django.http.response import Http404, HttpResponse, HttpResponseBadRequest, JsonResponse\n\n\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom itertools import chain\nfrom operator import attrgetter\n\n\nfrom .models import Follow, User, Post, Like\n\ndef getPost(id):\n try:\n post = Post.objects.get(id = id)\n except Error:\n raise HttpResponseBadRequest\n else:\n return post\n\n\ndef getPostLikes(post):\n return Like.objects.filter(post = post).count()\n\n# check if user likes a specific post\ndef doesUserLike(user,post):\n try:\n Like.objects.get(post = post, user=user)\n except ObjectDoesNotExist:\n return False\n else:\n return True\n\n#check if user is following other\ndef isUserFollowing(user, other):\n try:\n Follow.objects.get(user= user, user_follow=other)\n except ObjectDoesNotExist:\n return False\n else:\n return True\n\n\n#get all posts\n@require_GET\ndef index(request):\n if request.GET.get('page'):\n #add annotation: create a variable for post likes that's more accesible from template\n posts = Post.objects.annotate(like_count = Count('likes')).order_by('-date').all() # in index it shows all posts in the app\n return getposts(request, posts)\n else:\n return render(request, \"network/index.html\")\n\n#get follower posts\n@login_required\ndef following(request):\n if request.GET.get('page'):\n posts = []\n for follow in request.user.following.all():\n posts = chain(posts, follow.user_follow.posts.all())\n\n posts = sorted(posts, key=attrgetter('date'), reverse= True)\n return getposts(request, posts)\n else:\n return render(request, \"network/following.html\")\n\n\n# the post parameter comes from the index and following functions.\n# it is the array of posts to get the posts from\ndef getposts(request, posts):\n p = Paginator(posts, 10)\n num_page = int(request.GET.get('page', 1))\n\n prev = 0\n next = 0\n\n if p.num_pages < num_page:\n raise HttpResponseBadRequest\n\n else:\n someposts = p.page(num_page)\n someposts_likes = []\n\n if request.user.is_authenticated:\n for post in someposts.object_list:\n if doesUserLike(request.user, post):\n someposts_likes.append(post.id)\n\n if num_page < p.num_pages:\n next = someposts.next_page_number()\n \n if num_page > 1:\n prev = someposts.previous_page_number()\n \n rendered_posts = render_to_string('network/posts.html', context={'page':someposts, 'userlikes': someposts_likes})\n return JsonResponse({\"posts\": rendered_posts, \"next\": next, \"prev\": prev, \"page\":num_page})\n\n\ndef getOnePost(request, id):\n post = Post.objects.get(id=id)\n\n if request.method == 'GET':\n data = {\n 'post': post,\n 'likes': getPostLikes(post),\n 'userlike': doesUserLike(request.user, post) if request.user.is_authenticated else False\n }\n return render(request,'network/onepost.html', data)\n\n elif request.method == 'PUT' and request.user == post.author and post.edited==False:\n data = json.loads(request.body)\n new_content = data.get('newcontent')\n post.content = new_content\n post.edited = True\n try:\n post.save()\n except:\n return Http404\n else:\n return HttpResponse(200)\n \n elif request.method == 'DELETE' and request.user == post.author:\n try:\n post.delete()\n except:\n return Http404()\n else:\n return HttpResponse(200)\n\n\ndef likeManager(request):\n if not request.user.is_authenticated:\n return HttpResponse(status=401)\n\n else:\n postid = json.loads(request.body).get('postid') #get the liked or disliked post id from the request\n post = getPost(postid)\n\n if request.method == 'PUT': # like - create new like object\n newLike = Like(post = post, user=request.user) \n try:\n newLike.full_clean() # see if everything is ok when creating object\n except ValidationError:\n raise HttpResponseBadRequest\n else:\n newLike.save()\n\n elif request.method == 'DELETE': # dislike - delete like object \n try:\n dislike = Like.objects.get(post= post, user=request.user)\n except ObjectDoesNotExist:\n raise HttpResponseBadRequest\n else:\n dislike.delete()\n\n return JsonResponse({'likes': getPostLikes(post) }, status=200)\n\n\n\n@require_POST\ndef newpost(request):\n if request.user.is_authenticated:\n data = json.loads(request.body)\n content = data.get('content')\n try: \n Post.objects.newPost(request.user,content)\n except ValidationError:\n HttpResponse(status=500, content={'message':'post unsuccessful'})\n else:\n return HttpResponse(200)\n else:\n return HttpResponse(status=401)\n\n\ndef followManager(user, other):\n if isUserFollowing(user, other):\n Follow.objects.unfollow(user, other)\n return JsonResponse({'follow':False}, status=200)\n else:\n Follow.objects.follow(user, other)\n return JsonResponse({'follow':True}, status=200)\n\n\ndef profile(request, username):\n user_profile = User.objects.get(username = username)\n\n if request.method == 'PUT': #follow user manager\n return followManager(request.user, user_profile) if request.user.is_authenticated else HttpResponse(status = 401)\n\n else:\n if request.GET.get('page'):\n posts = Post.objects.filter(author = user_profile)\n posts = posts.annotate(like_count = Count('likes')).order_by('-date')\n return getposts(request,posts)\n\n else: #just return the page\n profile = {\n 'username' : user_profile.username,\n 'followers_count' : user_profile.followers.count(),\n 'following_count' : user_profile.following.count(),\n 'userIsFollower' : isUserFollowing(request.user, user_profile) if request.user.is_authenticated else False,\n 'followsUser' : isUserFollowing(user_profile, request.user) if request.user.is_authenticated else False,\n 'posts_count' : user_profile.posts.count()\n }\n return render(request, 'network/profile.html', profile)\n\n\ndef login_view(request):\n if request.method == \"POST\":\n\n # Attempt to sign user in\n username = request.POST[\"username\"]\n password = request.POST[\"password\"]\n user = authenticate(request, username=username, password=password)\n\n # Check if authentication successful\n if user is not None:\n login(request, user)\n return HttpResponseRedirect(reverse(\"index\"))\n else:\n return render(request, \"network/login.html\", {\n \"message\": \"Invalid username and/or password.\"\n })\n else:\n return render(request, \"network/login.html\")\n\n\ndef logout_view(request):\n logout(request)\n return HttpResponseRedirect(reverse(\"index\"))\n\n\ndef register(request):\n if request.method == \"POST\":\n username = request.POST[\"username\"]\n email = request.POST[\"email\"]\n\n # Ensure password matches confirmation\n password = request.POST[\"password\"]\n confirmation = request.POST[\"confirmation\"]\n if password != confirmation:\n return render(request, \"network/register.html\", {\n \"message\": \"Passwords must match.\"\n })\n\n # Attempt to create new user\n try:\n user = User.objects.create_user(username, email, password)\n user.save()\n except IntegrityError:\n return render(request, \"network/register.html\", {\n \"message\": \"Username already taken.\"\n })\n login(request, user)\n return HttpResponseRedirect(reverse(\"index\"))\n else:\n return render(request, \"network/register.html\")\n", "repo_name": "PaulaUzca/Twitter-like-Network", "sub_path": "network/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8887, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "models.Post.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.utils.Error", "line_number": 33, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponseBadRequest", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Like.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Like.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Like", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Like.objects.get", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Like.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.Like", "line_number": 45, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Follow.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 54, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Post.objects.annotate", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 66, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 69, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_GET", "line_number": 62, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 77, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 79, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 72, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 88, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseBadRequest", "line_number": 95, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 112, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 117, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 125, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 128, "usage_type": "call"}, {"api_name": "django.http.response.Http404", "line_number": 135, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 137, "usage_type": "call"}, {"api_name": "django.http.response.Http404", "line_number": 143, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 145, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 150, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Like", "line_number": 157, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 160, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponseBadRequest", "line_number": 161, "usage_type": "name"}, {"api_name": "models.Like.objects.get", "line_number": 167, "usage_type": "call"}, {"api_name": "models.Like.objects", "line_number": 167, "usage_type": "attribute"}, {"api_name": "models.Like", "line_number": 167, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 168, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponseBadRequest", "line_number": 169, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 173, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 180, "usage_type": "call"}, {"api_name": "models.Post.objects.newPost", "line_number": 183, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 183, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 184, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 185, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 187, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 189, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 177, "usage_type": "name"}, {"api_name": "models.Follow.objects.unfollow", "line_number": 194, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 194, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 194, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 195, "usage_type": "call"}, {"api_name": "models.Follow.objects.follow", "line_number": 197, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 197, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 197, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 198, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 202, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 202, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 202, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 205, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 209, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 209, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 210, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 231, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 235, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 236, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 236, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 238, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 242, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 246, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 247, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 247, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 259, "usage_type": "call"}, {"api_name": "models.User.objects.create_user", "line_number": 265, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 265, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 265, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 267, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 268, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 271, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 272, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 272, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "27177240739", "text": "import os\n\nfrom flask import render_template, flash, redirect, session, url_for, request, jsonify, json\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask import send_file\nfrom io import BytesIO \n\nimport pdb #DEBUGING\n \n# FROM https://www.programmersought.com/article/15116850767/\nfrom flask_cors import CORS #Flask's cross-domain issues\n# FROM https://www.programmersought.com/article/15116850767/\n\nfrom flask import render_template, flash, redirect\nfrom flask_sqlalchemy import SQLAlchemy\n\n\nfrom config import basedir\nimport config\n\nfrom app import current_app, db\n\nfrom app.models import Ufile\nfrom app.models import General_txt\n\n\nfrom sqlalchemy import update\n\n#FROM https://github.com/realpython/discover-flask/blob/master/project/users/views.py\nfrom flask import Blueprint\nvue_bp = Blueprint(\n 'vue_client_bp', __name__,\n template_folder='templates'\n) \n#FROM https://github.com/realpython/discover-flask/blob/master/project/users/views.py\nfrom app.select.select import file_select2\n\nfrom app import *\n\nimport pdb # DEBUGING\n\n\n# sanity check route\n@vue_bp.route('/ping', methods=['GET'])\ndef ping_pong():\n return jsonify('pong!')\n \n\t\n@vue_bp.route('/add_file', methods=['GET', 'POST'])\ndef add_file():\n\n print(\" \")\n print(\" \")\n \n print(\"\")\n print(\"IN : add_file request.files.get('file') \", request.files.get('file'))\n print(\"\")\n print(\"\")\n \n \n file_obj = request.files.get('file')\n \n file_name = file_obj.filename\n file_data = file_obj.read()\n \n #pdb.set_trace()\n\n this_file_exist = Ufile.query.filter(Ufile.name == file_name).filter(Ufile.hide==False).first()\n if this_file_exist != None:\n flash(\"קובץ בשם זה כבר קיים במערכת \")\n return url_for('files.edit_files' )\n \n new_file = Ufile(file_name, file_data) #find out how to set file_data\n db.session.add(new_file) \n db.session.commit() \n \n save_file_res = retirect(url_for(files.save_file_to_upload_folder(new_file)))\n print(\"\")\n print(\"save_file_res: \", save_file_res)\n \n return \"File saved successfully\"\n\n\t\n@vue_bp.route('/del_file', methods=['GET', 'POST'])\ndef del_file():\n\n print(\" \")\n print(\" \")\n \n print(\"\")\n \n file_id = request.get_json().get('id')\n print(\"IN : dlete_file FILE-ID \", file_id)\n print(\"\")\n print(\"\")\n \n to_be_deleted_file = Ufile.query.filter(Ufile.id == file_id).first()\n if to_be_deleted_file == None:\n flash(\"אין קובץ כזה \")\n return \"No such file in system\"\n \n to_be_deleted_file.hide = True\n db.session.commit()\n \n return jsonify({\"status_msg\": \"File deleted successfully\" })\n\n \n# FROM https://www.programmersought.com/article/15116850767/\n@vue_bp.route('/get_file_from_vue', methods=['GET', 'POST'])\ndef get_file_from_vue():\n file_obj = request.files['file'] # Get files in Flask\n if file_obj is None:\n # Indicates that no file was sent\n return \"File not uploaded\"\n #save document\n file_path = os.path.join(app.config['UPLOAD_FOLDER'], \"1.jpg\") \n file_obj.save(file_path)\n return file_path\n print(\" \")\n print(\" \")\n \n print(\"\")\n print(\"IN : add_file request.files.get('file') \", request.files.get('file'))\n print(\"\")\n print(\"\")\n \n \n file_obj = request.files.get('file')\n \n file_name = file_obj.filename\n file_data = file_obj.read()\n \n #pdb.set_trace()\n\n this_file_exist = Ufile.query.filter(Ufile.name == file_name).filter(Ufile.hide==False).first()\n if this_file_exist != None:\n flash(\"קובץ בשם זה כבר קיים במערכת \")\n return url_for('files.edit_files' )\n \n new_file = Ufile(file_name, file_data) #find out how to set file_data\n db.session.add(new_file) \n db.session.commit() \n \n save_file_res = retirect(url_for(files.save_file_to_upload_folder(new_file)))\n print(\"\")\n print(\"save_file_res: \", save_file_res)\n \n return \"File saved successfully\"\n\n\n\n\n@vue_bp.route('/files_by_upload', methods=['GET', 'POST'])\ndef files_by_upload():\n\n print(\"\")\n print(\"\")\n print(\" IN files_by_upload\")\n \n files = Ufile.query.filter(Ufile.hide==False).all()\n \n FILE_NAMES = []\n for f in files:\n print(\"FILE: \", f)\n full_path = os.path.join(current_app.config['UPLOAD_FOLDER'], f.name)\n print(\"SENDING FILE FULL PATH: \", full_path)\n FILE_NAMES.append({'name': f.name, 'id': f.id})\n \n print(\"jsonify FILES\", jsonify(FILE_NAMES))\n \n #return send_file(BytesIO(f.data), attachment_filename=f.name, as_attachment=True) # SEND BINARY DATA\n \n return jsonify({\n 'status': 'success',\n 'FILES': FILE_NAMES\n }) \n \n\n@vue_bp.route('/files_by_data', methods=['GET', 'POST'])\ndef files_by_data():\n print(\"\")\n print(\"\")\n print(\" IN files_by_data\")\n \n files = Ufile.query.filter(Ufile.hide==False).all()\n\n for f in files:\n print(\"SENDIN BY DATA FILE: \", f)\n return send_file(BytesIO(f.data), attachment_filename=f.name, as_attachment=True) # SEND BINARY DATA\n\n\n \n@vue_bp.route('/prepare_for_jsonify', methods=['GET', 'POST'])\ndef prepare_for_jsonify(files):\n\n print(\"\")\n print(\"\")\n print(\"IN jsonify_fies\")\n \n files_arr = []\n for f in files:\n f_data = f.data\n tmp_arr = {} #JSON STYLE\n print(\"FILE: \", f.name)\n print(\"\")\n tmp_arr['name'] = f.name\n tmp_arr['body'] = f.body\n tmp_arr['data'] = f_data\n files_arr.append(tmp_arr)\n \n #return jsonify({'FILES': files_arr}) \n return files_arr \n", "repo_name": "yyyasmin/file_manager", "sub_path": "app/vue_client_bp/vue_client_bp.py", "file_name": "vue_client_bp.py", "file_ext": "py", "file_size_in_byte": 5691, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Blueprint", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.files.get", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.files.get", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "app.models.Ufile.query.filter", "line_number": 68, "usage_type": "call"}, {"api_name": "app.models.Ufile.query", "line_number": 68, "usage_type": "attribute"}, {"api_name": "app.models.Ufile", "line_number": 68, "usage_type": "name"}, {"api_name": "app.models.Ufile.name", "line_number": 68, "usage_type": "attribute"}, {"api_name": "app.models.Ufile.hide", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 71, "usage_type": "call"}, {"api_name": "app.models.Ufile", "line_number": 73, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 74, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 74, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 75, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "app.models.Ufile.query.filter", "line_number": 97, "usage_type": "call"}, {"api_name": "app.models.Ufile.query", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.models.Ufile", "line_number": 97, "usage_type": "name"}, {"api_name": "app.models.Ufile.id", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 99, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 103, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 103, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.config", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.request.files.get", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.request.files.get", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 128, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 128, "usage_type": "name"}, {"api_name": "app.models.Ufile.query.filter", "line_number": 135, "usage_type": "call"}, {"api_name": "app.models.Ufile.query", "line_number": 135, "usage_type": "attribute"}, {"api_name": "app.models.Ufile", "line_number": 135, "usage_type": "name"}, {"api_name": "app.models.Ufile.name", "line_number": 135, "usage_type": "attribute"}, {"api_name": "app.models.Ufile.hide", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 138, "usage_type": "call"}, {"api_name": "app.models.Ufile", "line_number": 140, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 141, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 141, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 141, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 142, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 142, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 144, "usage_type": "call"}, {"api_name": "app.models.Ufile.query.filter", "line_number": 160, "usage_type": "call"}, {"api_name": "app.models.Ufile.query", "line_number": 160, "usage_type": "attribute"}, {"api_name": "app.models.Ufile", "line_number": 160, "usage_type": "name"}, {"api_name": "app.models.Ufile.hide", "line_number": 160, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "app.current_app.config", "line_number": 165, "usage_type": "attribute"}, {"api_name": "app.current_app", "line_number": 165, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 173, "usage_type": "call"}, {"api_name": "app.models.Ufile.query.filter", "line_number": 185, "usage_type": "call"}, {"api_name": "app.models.Ufile.query", "line_number": 185, "usage_type": "attribute"}, {"api_name": "app.models.Ufile", "line_number": 185, "usage_type": "name"}, {"api_name": "app.models.Ufile.hide", "line_number": 185, "usage_type": "attribute"}, {"api_name": "flask.send_file", "line_number": 189, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "22779593693", "text": "import models.db_connection\nfrom tornado import gen\nimport queries\nimport datetime\nclass MonitorModel(models.db_connection.Connection):\n def __init__(self):\n super(MonitorModel,self).__init__()\n \n @gen.coroutine\n def get_beacons(self):\n results = yield self._session.query('SELECT * FROM departamento_beacon')\n result_array = []\n for row in results:\n row['posicion_geografica'] = list(eval(row['posicion_geografica']))\n result_array.append(row)\n results.free()\n return result_array\n \n @gen.coroutine\n def insert_beacon(self,beacons):\n inserted = 0\n updated = 0\n for beacon in beacons:\n try:\n insert = yield self._session.query(\"INSERT INTO departamento_beacon values('%s',%i, (point(%f,%f)), (SELECT current_timestamp))\" % (beacon['id_beacon'], beacon['fk_id_departamento'], beacon['x'], beacon['y']))\n inserted = inserted +1\n except:\n update = yield self._session.query(\"UPDATE departamento_beacon set fk_id_departamento = %i, posicion_geografica = (point(%f,%f)), fecha_sincronizacion = (SELECT current_timestamp) WHERE id_beacon = '%s'\" % (beacon['fk_id_departamento'], beacon['x'], beacon['y'], beacon['id_beacon']))\n updated = updated +1\n try:\n insert.free()\n update.free()\n except:\n pass\n return {'inserted': inserted, 'updated':updated} \n @gen.coroutine\n def get_stores(self):\n results = yield self._session.query('SELECT * FROM tienda')\n result_array = []\n for row in results:\n row['posicion_geografica'] = list(eval(row['posicion_geografica']))\n result_array.append(row)\n results.free()\n return result_array\n \n @gen.coroutine\n def get_beacon_info(self,beacon):\n result = yield self._session.query(\"SELECT tienda.nombre AS tienda, departamento.nombre AS departamento FROM tienda, departamento, departamento_beacon WHERE tienda.id_tienda = departamento.fk_id_tienda AND departamento.id_departamento = departamento_beacon.fk_id_departamento AND departamento_beacon.id_beacon = '%s' \" % beacon)\n result_to_return = result.as_dict()\n result.free()\n return result_to_return\n\n @gen.coroutine\n def get_store_by_id(self,store_id):\n store = yield self._session.query(\"SELECT tienda.nombre, direccion.* FROM tienda,direccion WHERE tienda.fk_id_direccion = direccion.id_direccion AND tienda.id_tienda = %s\" % store_id)\n store_to_return = store.as_dict()\n store.free()\n return store_to_return\n \n\n ", "repo_name": "RubMurga/tornado_example", "sub_path": "models/monitor_model.py", "file_name": "monitor_model.py", "file_ext": "py", "file_size_in_byte": 2472, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "models.db_connection.db_connection", "line_number": 5, "usage_type": "attribute"}, {"api_name": "models.db_connection", "line_number": 5, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 9, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 19, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 36, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 46, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "38138409616", "text": "#all_filted_data : not hmm train data\n\n\n\nimport numpy as np\nimport pandas\nimport os, sys\nimport pandas as pd\nfrom hmmlearn import hmm\nimport matplotlib.pyplot as plt\nfrom scipy import signal\nimport collections\nimport random\nimport statistics\nimport math\n\n#filtaring method\ndef lowpass(x, samplerate, fp, fs, gpass, gstop):\n\tfn = samplerate/2\n\twp = fp/fn\n\tws = fs/fn\n\tN,Wn = signal.buttord(wp,ws,gpass,gstop)\n\tb,a = signal.butter(N,Wn,\"low\")\n\ty = signal.filtfilt(b,a,x)\n\treturn y\n\n\n#read data method\ndef read_data(Data_Path,words,ch_names,one_data_len,one_state_num,b_num,samplerate=1000,fp=30,fs=50,gpass=3,gstop=40):\t\n\tall_filted_data = []\n\tword_num = len(words)\n\tprint(\"read end\")\n\tfor j in range(len(words)):\n\t\tfor k in range(len(ch_names)):\n\t\n\t\t\tdata = pd.read_csv(os.path.join(Data_Path,\"data_\" + words[j] +ch_names[k] +\".csv\")).values\n\t\t\n\t\t\tfilted_data = np.empty((0,data.shape[1]-100),float)\n\t\t\tfor i in range(data.shape[0]):\n\t\t\t\tfilted_data = np.vstack([filted_data,lowpass(data[i,:],samplerate,fp,fs,gpass,gstop)[50:-50]])\t\n\t\t\t\t\n\n\t\t\tfilted_data = (filted_data-filted_data.mean(axis=1).reshape(-1,1))/filted_data.std(axis=1).reshape(-1,1)\n\t\t\t\t\n\t\t\t\n\t\t\tif j == 0 and k == 0:\n\t\t\t\t(data_num, data_length) = filted_data.shape\t\n\t\t\t\tall_filted_data = np.zeros((word_num*b_num,data_num,data_length))\n\t\t\t####\n\t\t\tall_filted_data[j*b_num+k] = filted_data\t\n\tprint(\"read end\")\t\n\n\treturn all_filted_data\n\n#Data_Path = os.path.join(\"/\",\"mnt\",\"c\",\"Users\",\"Hirok\",\"Desktop\",\"M1\",\"1_word_HMM\",\"data\",\"covert\",\"vec_data\")\nData_Path = os.path.join(\"/\",\"mnt\",\"c\",\"Users\",\"Hirok\",\"Desktop\",\"M1\",\"1_word_HMM\",\"data\",\"overt\",\"div_data\")\n\n\nSave_Path = os.path.join(\"/\",\"mnt\",\"c\",\"Users\",\"Hirok\",\"Desktop\",\"M1\",\"1_word_HMM\",\"picture\")\n\n\ncolor = [\"#000000\",\"#44ffff\",\"#88ffff\",\"#bbffff\",\"#eeffff\",\"#ff44ff\",\"#ff88ff\",\"#ffbbff\",\"#ffeeff\",\"#ffff44\",\"#ffff88\",\"#ffffbb\",\"#ffffee\",\"#444444\",\"#888888\",\"#bbbbbb\",\"#eeeeee\",\"#44ff44\",\"#88ff88\",\"#bbffbb\",\"#eeffee\"]\nlabel_color = [\"red\",\"green\",\"blue\",\"#aaaaaa\",\"#555555\"]\nwords = [\"a\",\"i\",\"u\",\"e\",\"o\"]\nch_names = [\" F7-REF\",\" T7-REF\",\" Cz-REF\"]\n#ch_names = [\" F7-REF\"]\n\n#ch_names = [\"_F7-T7\",\"_T7-Cz\",\"_Cz-F7\"]\nfig_flag = 0\nb_num = len(ch_names)\n#b_num = 1\nword_num = len(words)\nall_data = np.empty((b_num,0))\none_data_len= 924\n#one_data_len= 400\n\none_state_num = 4\none_state_len = math.floor(one_data_len/one_state_num)\nstate_num = 1+len(words)*one_state_num\ndata_div_state =[[[[] for i in range(one_state_num)] for j in range(b_num)] for k in range(word_num)]\ndata_div_state_0 = [[]for i in range(b_num)]\nsum_data = np.empty((b_num,0))\nset_init = True\n#hmm_trainnum_rate = 0.3\n#classification_modelnum_rate = 0.4\n\n#test_num_rate = 0.7\nRestrict = 1\n#test_add_num = 10\nensemble_num = 5\n#test_add_nums = np.array([10,20,30,40,50,60,70])\ntest_add_nums = np.array([50,60,70,80,90,100])\nhmm_train_num_rates = False #set later\n\niteration = 10\nall_filted_data = []\nacc_list = []\n\n#read data\n#all_filted_data = read_data(Data_Path,words,ch_names,one_data_len,one_state_num,b_num,samplerate=1000,fp=30,fs=50,gpass=3,gstop=40)\nall_filted_data = read_data(Data_Path,words,ch_names,one_data_len,one_state_num,b_num,samplerate=1000,fp=30,fs=50,gpass=3,gstop=40)[:,:,0:one_data_len]\n\n(data_num,data_length) = all_filted_data[0].shape\n\nhmm_train_num_rates = (test_add_nums*ensemble_num)/data_num\ntest_num_rate = 1-hmm_train_num_rates\n\nhmm_train_num_rates = hmm_train_num_rates.tolist()\ntest_add_nums = test_add_nums.tolist()\ntest_num_rate = test_num_rate.tolist()\nplot_height = 0\n\nfor ver in range(len(hmm_train_num_rates)):\n\thmm_trainnum_rate = hmm_train_num_rates[ver]\n\ttest_num_rate = 1-hmm_trainnum_rate\n\ttest_add_num = test_add_nums[ver]\n\n\n\ttemp_acc_list =[]\n\thandle=[]\n\n\tfor it in range(iteration):\n\t\tfig_state = plt.figure(figsize=(20,10))\n\t\tax_state = fig_state.add_subplot(1,1,1)\n\t\tprint(\"iter : \",it)\n\n\n\t\ttrain_num = math.floor(data_num*hmm_trainnum_rate)\n\t\ttest_num = math.floor(data_num*test_num_rate)\n\t\ttrain_shuffle = random.sample(range(data_num),data_num) \n\t\tprint(f\"data_num:{data_num}\")\n\t\ttrain_data = np.zeros([word_num*b_num,train_num,data_length])\n\t\ttest_data = np.zeros([word_num*b_num,test_num,data_length])\n\n\t\ttrain_data = all_filted_data[:,train_shuffle[0:train_num],:]\n\t\ttest_data = all_filted_data[:,train_shuffle[train_num:data_num],:]\n\t\t#hmm_model = hmm.GaussianHMM(n_components=state_num,covariance_type=\"full\",init_params=\"\")\n\t\tif set_init == True:\n\t\t\tmodel = [hmm.GaussianHMM(n_components=one_state_num,covariance_type=\"full\",init_params=\"\") for i in range(word_num*ensemble_num)]\n\t\telse:\n\t\t\tmodel = [hmm.GaussianHMM(n_components=one_state_num,covariance_type=\"full\") for i in range(word_num*ensemble_num)]\n\n\t\t#init init_prob\n\t\tinit_prob = []\n\t\ttemp = []\n\t\tfor i in range(one_state_num):\n\t\t\ttemp.extend([np.random.rand()])\n\n\t\ttemp[0] += 1\t\n\t\tinit_prob.extend((temp/np.sum(temp)).tolist())\n\n\n\t\tans = []\n\t\tprint(\"train start\")\n\t\tone_train_num = train_num//ensemble_num\n\t\n\t\tfor i in range(word_num):\n\t\t\tfor e in range(ensemble_num):\n\t\t\t\tif set_init == True:\n\t\t\t\t\t#init a : transfer mat\n\t\t\t\t\ta = []\n\n\t\t\t\t\tfor s_j in range(one_state_num):\n\t\t\t\t\t\ttemp = []\n\t\t\t\t\t\tfor s_i in range(one_state_num):\t\n\t\t\t\t\t\t\ttemp.append(np.random.rand())\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\ta.append(temp)\n\n\t\t\t\t\ta = np.array(a)\n\n\t\t\t\t\ta[0,:] = 1.0/state_num\t\n\t\t\t\t\tfor j in range(one_state_num-1):\n\t\t\t\t\t\ta[j+1,j+1] += 1\n\n\t\t\t\t\t\t\n\t\t\t\t\tfor s_j in range(one_state_num):\n\t\t\t\t\t\ttemp = 0\n\t\t\t\t\t\tfor s_i in range(one_state_num):\n\t\t\t\t\t\t\ttemp += a[s_j,s_i]\t\n\t\t\t\t\t\ta[s_j,:] = a[s_j,:]/temp \n\n\t\t\t\t\ta = a.tolist()\n\n\n\t\t\t\t\t#init mu\n\n\t\t\t\t\ttrain_mu = np.zeros((one_state_num,b_num))\n\t\t\t\t\tfor s_i in range(one_state_num):\n\t\t\t\t\t\tfor s_j in range(b_num):\n\t\t\t\t\t\t\ttrain_mu[s_i,s_j] = train_data[i*b_num+s_j,e*one_train_num:(1+e)*one_train_num,s_i*one_state_len:(1+s_i)*one_state_len].mean()\n\t\t\t\t\t#init cov\n\t\t\t\t\ttrain_cov = np.tile(np.identity(b_num),(one_state_num,1,1))\n\t\t\t\t\tprint(\"train_cov.shape : \",train_cov.shape)\n\t\t\t\t\tfor s_i in range(one_state_num):\n\t\t\t\t\t\tif b_num ==1:\n\t\t\t\t\t\t\ttrain_cov[s_i] = np.cov(train_data[i,e*one_train_num:(1+e)*one_train_num,s_i*one_state_len:(1+s_i)*one_state_len].reshape(1,-1))\n\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\ttrain_cov[s_i] = np.cov(train_data[i*b_num:i*b_num+2,e*one_train_num:(1+e)*one_train_num,s_i*one_state_len:(1+s_i)*one_state_len].reshape((2,-1)),train_data[i*b_num+2,e*one_train_num:(1+e)*one_train_num,s_i*one_state_len:(1+s_i)*one_state_len].reshape((1,-1)),rowvar=1)\n\t\t\t\t\t\tfor b in range(b_num):\n\t\t\t\t\t\t\ttrain_cov[s_i,b,b]+=0.0001\n\t\t\t\t\tprint(i,\"-\",e)\n\n\t\t\t\t\t#train_cov[i*one_state_num+j+1] = np.cov(data_div_state[i,0:2,j],data_div_state[i,2,j],rowvar=1)\n\n\t\t\t\t\t\n\t\t\t\t\t#init params\n\t\t\t\t\n\t\t\t\t\tmodel[i*ensemble_num+e].startprob_ = init_prob\n\t\t\t\t\tmodel[i*ensemble_num+e].transmat_ = a\n\t\t\t\t\tmodel[i*ensemble_num+e].means_ = train_mu\n\t\t\t\t\tmodel[i*ensemble_num+e].covars_ = train_cov\n\t\t\t\t\t\n\t\t\t\ttemp = np.zeros((0,data_length))\n\t\t\t\t\n\t\t\t\tfor j in range(b_num):\n\t\t\t\t\ttemp = np.vstack([temp,train_data[i*b_num+j,e*one_train_num:(1+e)*one_train_num,:].sum(axis = 0)/one_train_num])\n\t\t\t\tprint(np.all(np.isnan(temp))==True)\t\n\t\t\t\tprint(\"fit\")\n\t\t\t\tmodel[i*ensemble_num+e].fit(temp.T)\n\t\t\t\t\n\t\t\t\n\t\tprint(\"train end\")\n\t\tprint(\"classification start\")\n\t\tprob_ans = np.zeros(word_num*ensemble_num)\n\t\tans_count = 0\n\t\tfig ,ax= plt.subplots(3,1,figsize=(20,10))\n\t\t\n\n\t\tfor i in range(word_num):\t\n\t\t\tflag_add_plot = 0\n\t\t\thandles = []\n\t\t\tprint(\"classification : \",i)\n\t\t\tfor j in range(math.floor(test_num/test_add_num)):\n\t\t\t\ttemp = np.zeros((0,data_length))\n\t\t\t\ttemp_ans = np.zeros(ensemble_num)\n\t\t\t\tfor k in range(b_num):\n\t\t\t\t\ttemp = np.vstack([temp,test_data[i*b_num+k,j*test_add_num:(1+j)*test_add_num,:].mean(axis=0)]) \n\t\t\t\tif j == 0:\n\t\t\t\t\tprint(temp.shape)\n\t\t\t\tfor k in range(word_num*ensemble_num):\n\t\t\t\t\t\n\t\t\t\t\tprob_ans[k] = model[k].score(temp.T)\n\t\t\t\tif j==0:\n\t\t\t\t\tprint(prob_ans)\n\t\t\t\t\"\"\"\t\n\t\t\t\tfor k in range(ensemble_num):\n\t\t\t\t\ttemp_ans[k] = np.argmax(prob_ans[k:word_num*ensemble_num:ensemble_num])\n\t\t\t\t\"\"\"\t\n\t\t\t\ttemp_ans_most_plob = np.argmax(prob_ans)\n\t\t\t\t\t\n\t\t\t\tstatelist = model[temp_ans_most_plob].predict(temp.T) \n\t\t\t\tx_plot = np.linspace(0,statelist.shape[0],statelist.shape[0])\n\t\t\t\ty_plot = np.full(statelist.shape[0],plot_height)\n\t\t\t\tcolor_list = [label_color[x] for x in statelist]\n\t\t\t\tax_state.scatter(x_plot,y_plot,label=words[i],color=color_list,marker=\".\")\t\n\t\t\t\t#ans.append(int(statistics.mode(temp_ans)))\t\t\n\t\t\t\tans.append(math.floor(temp_ans_most_plob/ensemble_num))\n\t\t\t\tax_state.scatter(statelist.shape[0]+1,plot_height,color=\"#000000\",marker=\"${}$\".format(words[ans[-1]]))\t\n\t\t\t\tplot_height +=1\t\n\t\n\t\t\t\tif ans[-1]==i:\n\t\t\t\t\tans_count +=1\n\t\t\t\t\tfor l in range(b_num):\n\n\t\t\t\t\t\tax[l].set_title(ch_names[l])\n\t\t\t\t\t\tline = ax[l].plot(x_plot,temp[l],label=words[i],color=label_color[i])\n\t\t\t\t\t\tif flag_add_plot == 0:\n\t\t\t\t\t\t\thandles.append(line)\t\t\t\t\n\t\t\t\t\t\t\tflag_add_plot = 1\t\t\n\t\t\ty_plot = np.full(statelist.shape[0],plot_height)\n\t\t\tax_state.scatter(x_plot,y_plot,label=words[i],color=\"#000000\",marker=\".\")\t\n\t\t\tplot_height +=1\t\n\n\t\t#plt.legend(handle,label_color)\n\t\tprint(\"classification end\")\n\t\tprint(\"ans : \",ans)\n\t\tprint(\"ans rate : \",ans_count/(word_num*test_num/test_add_num))\n\t\ttemp_acc_list.append(ans_count/(word_num*test_num/test_add_num))\n\t\tfig_state.show()\n\t\tplot_height = 0\n\t\tprint(\"check\")\n\t\tprint(len(handles))\n\t\tprint(len(label_color))\n\t\tif len(handles) == len(label_color):\t\n\t\t\tfor l in range(b_num):\t\n\t\t\t\tprint(\"legend set\")\n\t\t\t\tax[l].legend(handles[l,l+len(handles):b_num],label_color)\n\t\tfig.show()\n\t\tinput()\t\n\n\t#plt.legend(handle,label_color)\n\t#plt.show()\n\n\n\tacc_list.append(temp_acc_list)\nplt.figure(figsize=(20,10))\t\nplt.boxplot(acc_list)\nplt.show()\nprint(test_num)\n", "repo_name": "Hiroki-Maeda/M_lab_program", "sub_path": "state_change_plot.py", "file_name": "state_change_plot.py", "file_ext": "py", "file_size_in_byte": 9570, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "scipy.signal.buttord", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.signal.butter", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 23, "usage_type": "name"}, {"api_name": "scipy.signal.filtfilt", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 24, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 36, "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": "numpy.empty", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "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.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 73, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 128, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 129, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.GaussianHMM", "line_number": 139, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 139, "usage_type": "name"}, {"api_name": "hmmlearn.hmm.GaussianHMM", "line_number": 141, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 141, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 255, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.boxplot", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}]} +{"seq_id": "43780308146", "text": "# Original author: Glenn - 24/03/2021\n# Edited by Eric - Summer/2021\n\n#!/usr/bin/env python\n# coding: utf-8\n\nimport multiprocessing\nimport itertools, threading, time, sys, os, re\nimport pandas as pd\nimport shutil\nfrom functools import partial\nimport random\n\nprogress = True\nlog_file_folder = \"\"\n\n# Handles command line flags\nfor word in sys.argv:\n lowered = word.lower()\n\n if lowered == \"--no-progress\" or lowered == \"-np\":\n progress = False\n elif \"-log-dir\" in lowered or \"-ld\" in lowered:\n log_file_folder = word.split(\"=\",1)[1]\n#for\n\nif len(log_file_folder) == 0:\n print(\"\\n\\033[31mASSERT:\\033[m Must set a log folder path using the flag -log-dir=\\033[0m\")\n exit(-1)\n\n# Loading animation\ndone = False\nsuccess = True\ndef animate():\n # Loop through the animation cycles\n for c in itertools.cycle(['|', '/', '-', '\\\\']):\n # When the global variable is set at the eof\n # then the infinite loop will break\n if done:\n break\n # Using stdout makes it easier to deal with the newlines inherent in print()\n # '\\r' replaces the previous line so we don't crowd the terminal\n sys.stdout.write('\\r\\033[33mgenerating graphs ' + c + '\\033[0m')\n sys.stdout.flush()\n time.sleep(0.1)\n if success:\n sys.stdout.write('\\r\\033[1;32mDone. \\033[0m\\n')\n#animate()\n\ntry:\n def state_to_df(sim_time, region_state, num_boosters):\n percent_B = {}\n total_B = {}\n\n # Read the percentages of each state\n cell_population = region_state[0]\n percent_S = region_state[sIndex]\n percent_E = region_state[eIndex]\n percent_VD1 = region_state[vd1Index]\n percent_VD2 = region_state[vd2Index]\n percent_I = region_state[iIndex]\n percent_R = region_state[rIndex]\n percent_D = region_state[dIndex]\n\n percent_new_E = region_state[neweIndex]\n percent_new_I = region_state[newiIndex]\n percent_new_R = region_state[newrIndex]\n\n for boosters in range(0, num_boosters):\n percent_B[boosters] = region_state[bIndex+boosters]\n\n # Convert from percentages to cumulative totals\n total_S = round(cell_population*percent_S)\n total_E = round(cell_population*percent_E)\n total_VD1 = round(cell_population*percent_VD1)\n total_VD2 = round(cell_population*percent_VD2)\n total_I = round(cell_population*percent_I)\n total_R = round(cell_population*percent_R)\n total_D = round(cell_population*percent_D)\n\n for booster in range(0, num_boosters):\n total_B[booster] = round(cell_population*percent_B[booster])\n\n total_new_E = round(cell_population*percent_new_E)\n total_new_I = round(cell_population*percent_new_I)\n total_new_R = round(cell_population*percent_new_R)\n\n psum = percent_S + percent_E + percent_VD1 + \\\n percent_VD2 + percent_I + percent_R + percent_D + sum(percent_B.values())\n assert 0.995 <= psum < 1.005, (\"at time \" + str(curr_time))\n\n # Return the info in desired format\n state_percentages = [int(sim_time), percent_S, percent_E, percent_VD1, percent_VD2, percent_I, percent_R, percent_new_E, percent_new_I, percent_new_R, percent_D]\n state_totals = [int(sim_time), total_S, total_E, total_VD1, total_VD2, total_I, total_R, total_new_E, total_new_I, total_new_R, total_D]\n\n for booster in range(0, num_boosters):\n state_percentages.append(percent_B[booster])\n state_totals.append(total_B[booster])\n return state_totals, state_percentages\n #state_to_percent_df\n\n # Generates graph for one region_key\n def generate_graph(path, data_percents, data_totals, curr_states, global_data, region_key):\n import matplotlib.pyplot as plt # Needs this for multi-processing\n\n # Unpack Global Data\n COLORS = global_data[0]\n COLOR_BOOSTERS = global_data[1]\n LINE_STYLES = global_data[2]\n LINE_BOOSTERS = global_data[3]\n num_boosters = global_data[4]\n columns = global_data[5]\n\n # Make a folder for the region in that stats folder\n foldername = \"region_\" + region_key\n try:\n os.mkdir(path + \"/\" + foldername)\n except OSError as error:\n print('Region ID - ' + region_key + \": \")\n raise error\n\n percents_filename = foldername +\"_percentage_timeseries.csv\"\n totals_filename = foldername +\"_totals_timeseries.csv\"\n base_name = path + \"/\" + foldername + \"/\"\n percentages_filepath = base_name + percents_filename\n totals_filepath = base_name + totals_filename\n\n out_str = \"sim_time, S, E, VD1, VD2, I, R, New_E, New_I, New_R, D\"\n for booster in range(0, num_boosters):\n out_str += \", B\" + str(booster+1)\n out_str += \"\\n\"\n\n # write the timeseries percent file inside stats/region_id\n with open(percentages_filepath, \"w\") as out_file:\n out_file.write(out_str)\n for timestep in data_percents[region_key]:\n out_file.write(str(timestep).strip(\"[]\")+\"\\n\")\n\n # write the timeseries cumulative file inside stats/region_id\n with open(totals_filepath, \"w\") as out_file:\n out_file.write(out_str)\n for timestep in data_totals[region_key]:\n out_file.write(str(timestep).strip(\"[]\")+\"\\n\")\n\n # initialize graphing dfs (percents)\n df_vis_p = pd.DataFrame(data_percents[region_key], columns=columns)\n df_vis_p = df_vis_p.set_index(\"time\")\n\n # Determine whether to render vaccines\n vaccines = not (sum(df_vis_p['vaccinatedD1']) == 0 and sum(df_vis_p['vaccinatedD2']) == 0)\n\n # initialize graphing dfs (totals)\n df_vis_t = pd.DataFrame(data_totals[region_key], columns=columns)\n df_vis_t = df_vis_t.set_index(\"time\")\n\n x = list(df_vis_p.index)\n t = list(df_vis_t.index)\n\n ### --- SEIRD/SEVIRD --- ###\n fig, ax = plt.subplots(figsize=(15,6))\n\n ax.plot(x, 100*df_vis_p[\"susceptible\"], label=\"Susceptible\", color=COLORS[0], linestyle=LINE_STYLES[0])\n ax.plot(x, 100*df_vis_p[\"exposed\"], label=\"Exposed\", color=COLORS[2], linestyle=LINE_STYLES[2])\n ax.plot(x, 100*df_vis_p[\"infected\"], label=\"Infected\", color=COLORS[1], linestyle=LINE_STYLES[1])\n ax.plot(x, 100*df_vis_p[\"recovered\"], label=\"Recovered\", color=COLORS[5], linestyle=LINE_STYLES[3])\n ax.plot(x, 100*df_vis_p[\"deaths\"], label=\"Deaths\", color=COLORS[6], linestyle=LINE_STYLES[0])\n if vaccines:\n ax.plot(x, 100*df_vis_p[\"vaccinatedD1\"], label=\"Vaccinated 1 dose\", color=COLORS[3], linestyle=LINE_STYLES[0])\n ax.plot(x, 100*df_vis_p[\"vaccinatedD2\"], label=\"Vaccinated 2 dose\", color=COLORS[4], linestyle=LINE_STYLES[1])\n for booster in range(0, num_boosters):\n ax.plot(x, 100*df_vis_p[\"booster\"+str(booster)], label=\"Booster \"+str(booster+1),\n color=COLOR_BOOSTERS[booster],\n linestyle=LINE_BOOSTERS[booster])\n\n plt.title('Epidemic SEVIRD Percentages for ' + foldername + \" (pop=\" + str(int(curr_states[region_key][0])) + \")\")\n else:\n plt.title('Epidemic SEIRD Percentages for ' + foldername + \" (pop=\" + str(int(curr_states[region_key][0])) + \")\")\n plt.ylabel(\"Population (%)\")\n plt.legend(loc=\"upper right\")\n\n if vaccines:\n plt.savefig(base_name + \"SEVIRD.png\")\n else:\n plt.savefig(base_name + \"SEIRD.png\")\n plt.close(fig)\n\n # EID\n fig, ax = plt.subplots(figsize=(15,6))\n\n ax.plot(t, 100*df_vis_p[\"exposed\"], label=\"Exposed\", color=COLORS[2], linestyle=LINE_STYLES[2])\n ax.plot(t, 100*df_vis_p[\"infected\"], label=\"Infected\", color=COLORS[1], linestyle=LINE_STYLES[1])\n ax.plot(t, 100*df_vis_p[\"deaths\"], label=\"Deaths\", color=COLORS[6], linestyle=LINE_STYLES[0])\n plt.ylabel(\"Population (%)\")\n plt.title(\"Epidemic EID Percentages for \" + foldername + \" (pop=\" + str(int(curr_states[region_key][0])) + \")\")\n plt.legend(loc=\"upper right\")\n plt.savefig(base_name + \"EID.png\")\n plt.close(fig)\n\n # Vaccinated\n if vaccines and num_boosters > 0:\n fig, ax = plt.subplots(figsize=(15,6))\n\n ax.plot(x, 100*df_vis_p[\"vaccinatedD1\"], label=\"Vaccinated 1 dose\", color=COLORS[3], linestyle=LINE_STYLES[0])\n ax.plot(x, 100*df_vis_p[\"vaccinatedD2\"], label=\"Vaccinated 2 dose\", color=COLORS[4], linestyle=LINE_STYLES[1])\n for booster in range(0, num_boosters):\n ax.plot(x, 100*df_vis_p[\"booster\"+str(booster)], label=\"Booster \"+str(booster+1),\n color=COLOR_BOOSTERS[booster],\n linestyle=LINE_BOOSTERS[booster])\n\n plt.title('Epidemic Vaccine Percentages for ' + foldername + \" (pop=\" + str(int(curr_states[region_key][0])) + \")\")\n plt.ylabel(\"Population (%)\")\n plt.legend(loc=\"upper right\")\n plt.savefig(base_name + \"Vaccines.png\")\n plt.close(fig)\n #generate_graph()\n\n # Multiprocess each cell to produce their respective threads\n if __name__ == '__main__':\n # Don't forget to thread it!\n if progress:\n t = threading.Thread(target=animate)\n t.start()\n\n # Setup paths, filenames, and folders\n log_filename = log_file_folder + \"/pandemic_state.txt\"\n path = log_file_folder + \"/stats/per-region\"\n shutil.rmtree(path, ignore_errors=True)\n\n # Regex str to find underscore and one or more characters after the underscore (model id)\n regex_model_id = \"_\\w+\"\n # Regex str to read all state contents between <>\n regex_state = \"<.+>\"\n\n # State log structure\n sIndex = 1\n eIndex = 2\n vd1Index = 3\n vd2Index = 4\n iIndex = 5\n rIndex = 6\n neweIndex = 7\n newiIndex = 8\n newrIndex = 9\n dIndex = 10\n bIndex = 11\n\n COLORS = ['xkcd:blue', 'xkcd:red', 'xkcd:sienna', '#B91FDE', '#680D5A', 'xkcd:green', 'xkcd:black']\n COLOR_BOOSTERS = {}\n\n LINE_STYLES = ['-', '--', '-.', ':']\n LINE_BOOSTERS = {}\n\n curr_time = None\n curr_states = {}\n initial_pop = {}\n data_percents = {}\n data_totals = {}\n num_boosters = -1\n\n # Read the initial populations of all regions and their names in time step 0\n with open(log_filename, \"r\") as log_file:\n line_num = 0\n\n # For each line, read a line then:\n for line in log_file:\n # Strip leading and trailing spaces\n line = line.strip()\n\n # If a time marker is found that is not the current time\n if line.isnumeric() and line != curr_time:\n # Update new simulation time\n curr_time = line\n continue\n\n # Create an re match objects from the current line\n state_match = re.search(regex_state, line)\n id_match = re.search(regex_model_id, line)\n if not (state_match and id_match):\n continue\n\n # Parse the state and id and insert into initial_pop\n cid = id_match.group().lstrip('_')\n state = state_match.group().strip(\"<>\")\n state = state.split(\",\")\n initial_pop[cid] = float(state[0])\n\n # Initialize data strucutres with region keys\n if not cid in data_percents:\n data_percents[cid] = list()\n data_totals[cid] = list()\n\n state = state_match.group().strip(\"<>\")\n state = list(map(float, state.split(\",\")))\n curr_states[cid] = state\n\n if curr_states and num_boosters == -1:\n num_boosters = 0\n try:\n while True:\n list(curr_states.values())[\n 0][bIndex+num_boosters]\n num_boosters += 1\n except Exception:\n pass\n\n state_totals, state_percentages = state_to_df(curr_time, curr_states[cid], num_boosters)\n data_percents[cid].append(state_percentages)\n data_totals[cid].append(state_totals)\n\n line_num += 1\n #for\n #with\n\n columns = [\"time\", \"susceptible\", \"exposed\", \"vaccinatedD1\", \"vaccinatedD2\",\n \"infected\", \"recovered\", \"new_exposed\", \"new_infected\", \"new_recovered\", \"deaths\"]\n for booster in range(0, num_boosters):\n columns.append(\"booster\"+str(booster))\n COLOR_BOOSTERS[booster] = \"#%06x\" % random.randint(0, 0xFFFFFF)\n LINE_BOOSTERS[booster] = random.choice(LINE_STYLES)\n\n try:\n os.mkdir(path)\n except OSError as error:\n raise error\n\n global_data = [COLORS, COLOR_BOOSTERS, LINE_STYLES, LINE_BOOSTERS, num_boosters, columns]\n with multiprocessing.get_context('spawn').Pool() as pool:\n func = partial(generate_graph, path, data_percents, data_totals, curr_states, global_data)\n pool.map(func, data_percents)\n\n if not progress:\n print(\"\\033[1;32mDone.\\033[0m\")\n else:\n done = True\n t.join()\n #if\nexcept AssertionError as assertion:\n success = False\n done = True\n if progress:\n t.join()\n\n print(\"\\n\\033[31mASSERT:\\033[0m 0.995 <= psum < 1.005\", assertion)\n sys.exit(-1)\nexcept KeyboardInterrupt as interrupt:\n success = False\n done = True\n if progress:\n t.join()\n\n print(\"\\n\\033[33mStopped by user\\033[0m\")\n sys.exit(-1)\n# except Exception as error:\n# success = False\n# done = True\n# if progress:\n# t.join()\n\n# print(\"\\n\\033[31mException: \" + str(error) + \"\\033[0m\")\n# sys.exit(-1)\n", "repo_name": "muhammadbsalman/Geography-Based-SEIRDS-Vaccine-Booster-SYSC5104", "sub_path": "Scripts/Graph_Generator/graph_per_regions.py", "file_name": "graph_per_regions.py", "file_ext": "py", "file_size_in_byte": 14321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "itertools.cycle", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 44, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 146, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 221, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 227, "usage_type": "call"}, {"api_name": "re.search", "line_number": 276, "usage_type": "call"}, {"api_name": "re.search", "line_number": 277, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 318, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 319, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 322, "usage_type": "call"}, {"api_name": "multiprocessing.get_context", "line_number": 327, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 328, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 344, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 352, "usage_type": "call"}]} +{"seq_id": "4016015248", "text": "import json\n\nclass PlotData:\n\n def __init__(self, ra, dec, shape, size, color, label=None):\n self.ra = ra\n self.dec = dec\n self.shape = shape\n self.size = size\n self.color = color\n self.label = label\n\n self.ra_angle = None\n self.dec_angle = None\n\n self.x = None\n self.y = None\n\n\nclass PlotDataList:\n\n def __init__(self, input_data):\n list = json.loads(input_data)\n self.data = []\n self.min_x = self.max_x = self.min_y = self.max_y = None\n\n for element in list:\n ra = element['ra']\n dec = element['dec']\n size = element['size']\n shape = element['shape']\n color = element['color']\n label = element['label']\n\n self.data.append(PlotData(ra, dec, shape, size, color, label))\n\n\n", "repo_name": "nvermaas/MyAstroBase", "sub_path": "astrobase/starcharts_app/starchart/plot_data.py", "file_name": "plot_data.py", "file_ext": "py", "file_size_in_byte": 861, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "json.loads", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "41065640798", "text": "import sys\nimport os\nimport random\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Flatten, Dropout\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.callbacks import ModelCheckpoint\n\n\ndef get_image_training_data(path):\n '''Loads, scales and normalizes an image and returns the resulting data as a\n NumPy array\n '''\n image = Image.open(path)\n # Scale the image down to quarter resolution\n image = image.resize((image.width // 4, image.height // 4), Image.BILINEAR)\n image_data = np.array(image)\n # Return image data normalized to range [-0.5, 0.5]\n return image_data / 255.0 - 0.5\n\n\ndef read_driving_data(data_folder, include_side_images=False):\n '''Generates (image, steering) pairs from given driving data\n\n By default return only the center image and its steering angle, but if\n include_side_images is True, generates data also for the left and right\n image.\n '''\n log_path = os.path.join(data_folder, 'driving_log.csv')\n df = pd.read_csv(log_path)\n for i in tqdm(range(len(df))):\n record = df.iloc[i]\n steering_angle = record['steering']\n steering_angle *= 0.15\n image_path = os.path.join(data_folder, record.center.strip())\n image_data = get_image_training_data(image_path)\n yield image_data, steering_angle\n if include_side_images:\n for path in [record.left, record.right]:\n image_path = os.path.join(data_folder, path.strip())\n image_data = get_image_training_data(image_path)\n yield image_data, steering_angle * 1.5\n\n \ndef create_training_data(data_folder, include_side_images=False):\n '''Creates training data (X_train, y_train) from recorded simulator driving data'''\n print('Creating training data from {}'.format(data_folder))\n X_train = []\n y_train = []\n for image_data, steering_angle in read_driving_data(data_folder, include_side_images):\n X_train.append(image_data)\n y_train.append(steering_angle)\n print('{} total training samples'.format(len(X_train)))\n return np.array(X_train), np.array(y_train)\n\n\ndef show_layer_info(model):\n '''Shows Keras model layers and their dimensions'''\n print('Layer info for model:')\n for n, layer in enumerate(model.layers, 1):\n print(' Layer {:2} {:16} input shape {} output shape {}'.format(n, layer.name, layer.input_shape, layer.output_shape))\n\n\ndef train(model, X_train, y_train, nb_epoch=10):\n '''Trains a Keras model with Adam optimizer for given number of Epochs and saves\n the resulting model\n '''\n model.compile('adam', 'mse')\n # Use a callback to save the model with lowest validation score\n callbacks = [ModelCheckpoint('checkpoint.h5', monitor='val_loss', save_best_only=True, verbose=0)]\n model.fit(X_train, y_train, validation_split=0.2, nb_epoch=nb_epoch, verbose=2, callbacks=callbacks)\n model.save('model.h5')\n\n\ndef create_model():\n '''Creates the Keras model for behavioral cloning project\n\n The model is based on the NVidia model described in\n http://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf\n '''\n model = Sequential()\n #model.add(Convolution2D(24, 5, 5, border_mode='valid', subsample=(2, 2), input_shape=(80, 160, 3)))\n model.add(Convolution2D(24, 5, 5, border_mode='valid', input_shape=(40, 80, 3)))\n model.add(Activation('relu'))\n model.add(Convolution2D(36, 5, 5, border_mode='valid', subsample=(2, 2)))\n model.add(Activation('relu'))\n model.add(Convolution2D(48, 5, 5, border_mode='valid', subsample=(2, 2)))\n model.add(Activation('relu'))\n model.add(Convolution2D(64, 3, 3, border_mode='valid'))\n model.add(Activation('relu'))\n model.add(Convolution2D(64, 3, 3, border_mode='valid'))\n model.add(Activation('relu'))\n model.add(Flatten())\n model.add(Dense(100))\n model.add(Dropout(0.2))\n model.add(Activation('relu'))\n model.add(Dense(50))\n model.add(Activation('relu'))\n model.add(Dense(10))\n model.add(Activation('relu'))\n model.add(Dense(1))\n return model\n\n\nnb_epochs = int(sys.argv[1])\n\n# Support providing multiple data folders on the command line\nX_list = []\ny_list = []\nfor data_folder in sys.argv[2:]:\n X, y = create_training_data(data_folder, False)\n X_list.append(X)\n y_list.append(y)\n# Combine all the training sets\nX_train = np.concatenate(X_list)\ny_train = np.concatenate(y_list)\n \nmodel = create_model()\nshow_layer_info(model)\n\ntrain(model, X_train, y_train, nb_epochs)\n\n", "repo_name": "mqvist/CarND-Behavioral-Cloning", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "PIL.Image.open", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 21, "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": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 109, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "10635602224", "text": "from model import readconfig, read_yaml, SetUpTearDown, change_excel\nfrom common import importAnalysis, importdata, importContactData, customer_required, contact_required\nimport time, unittest\nfrom BeautifulReport import BeautifulReport\n\n\n# @unittest.skip\nclass TestImportContact(SetUpTearDown):\n customer, website, cname, mobile = change_excel()\n l1 = read_yaml()[0]['baseurl']\n token = readconfig('token')\n\n @unittest.skip(\"暂时跳过\") # 不执行该用例\n def test_importAnalysis(self): # 解析联系人、客户excel表\n \"\"\"解析联系人、客户excel表，接口地址:/api/scrm/importAnalysis\"\"\"\n l2 = read_yaml()[3]['url']\n url = self.l1 + l2\n\n t = time.time()\n if t % 2 == 0:\n result = importAnalysis(_url=url, _token=self.token, n=0) # 客户列表导入添加联系人 n=1和客户 n=0文件\n else:\n result = importAnalysis(_url=url, _token=self.token, n=1) # 联系人列表导入添加联系人 n=1 和客户 n=0文件\n self.assertEqual(result['code'], 0)\n\n def test_import_data(self): # 导入客户数据\n \"\"\"导入客户数据，接口地址:/api/scrm/importData\"\"\"\n l2 = read_yaml()[4]['url1']\n url = self.l1 + l2\n result = importdata(_url=url, token=self.token, a=self.customer, b=self.website)\n if result['code'] == 0:\n # 断言列表中total多了一个新加的客户\n a = int(readconfig(key='customer_total'))+1\n url2 = read_yaml()[2]['url']\n url3 = self.l + url2\n r = customer_required(_url=url3, _token=self.token)['page_info']['total']\n self.assertEqual(a, r)\n else:\n print(result['msg'])\n\n def test_importContactData(self): # 导入联系人数据\n \"\"\"导入联系人数据，接口地址:/api/scrm/importContactData\"\"\"\n l2 = read_yaml()[4]['url2']\n url = self.l1 + l2\n result = importContactData(_url=url, token=self.token, a=self.customer, b=self.cname, c=self.mobile)\n if result['code'] == 0:\n self.assertEqual(result['code'], 0)\n # 断言列表中total多了一个新加的联系人\n b = int(readconfig(key='contact_total'))+1\n url2 = read_yaml()[2]['url']\n url3 = self.l + url2\n r = contact_required(_url=url3, _token=self.token)['page_info']['total']\n self.assertEqual(b, r)\n else:\n print(result['msg'])\n\n\n\n\n\n\n\n", "repo_name": "jiangna123000/api_test_case", "sub_path": "Test_case/test_3import_contact.py", "file_name": "test_3import_contact.py", "file_ext": "py", "file_size_in_byte": 2499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "model.SetUpTearDown", "line_number": 8, "usage_type": "name"}, {"api_name": "model.change_excel", "line_number": 9, "usage_type": "call"}, {"api_name": "model.read_yaml", "line_number": 10, "usage_type": "call"}, {"api_name": "model.readconfig", "line_number": 11, "usage_type": "call"}, {"api_name": "model.read_yaml", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "common.importAnalysis", "line_number": 21, "usage_type": "call"}, {"api_name": "common.importAnalysis", "line_number": 23, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 13, "usage_type": "call"}, {"api_name": "model.read_yaml", "line_number": 28, "usage_type": "call"}, {"api_name": "common.importdata", "line_number": 30, "usage_type": "call"}, {"api_name": "model.readconfig", "line_number": 33, "usage_type": "call"}, {"api_name": "model.read_yaml", "line_number": 34, "usage_type": "call"}, {"api_name": "common.customer_required", "line_number": 36, "usage_type": "call"}, {"api_name": "model.read_yaml", "line_number": 43, "usage_type": "call"}, {"api_name": "common.importContactData", "line_number": 45, "usage_type": "call"}, {"api_name": "model.readconfig", "line_number": 49, "usage_type": "call"}, {"api_name": "model.read_yaml", "line_number": 50, "usage_type": "call"}, {"api_name": "common.contact_required", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "25904655457", "text": "import csv\nimport os\nimport re\nimport shutil\nimport sqlite3\n\nimport requests\nfrom requests.adapters import HTTPAdapter, Retry\nfrom urllib3.exceptions import ProtocolError\n\nDEFAULT_SIGNBANK_HOST = os.getenv(\"SIGNBANK_HOST\", \"https://signbank.nzsl.nz\")\nSIGNBANK_DATASET_ID = os.getenv(\"SIGNBANK_DATASET_ID\", 1)\nSIGNBANK_USERNAME = os.getenv(\"SIGNBANK_USERNAME\")\nSIGNBANK_PASSWORD = os.getenv(\"SIGNBANK_PASSWORD\")\n\n##\n# Start a requests session that is authenticated to Signbank\n\n\ndef signbank_session():\n s = requests.Session()\n s.get(\"%s/accounts/login/\" % DEFAULT_SIGNBANK_HOST)\n s.post(\"%s/accounts/login/\" % DEFAULT_SIGNBANK_HOST,\n data={'username': SIGNBANK_USERNAME, 'password': SIGNBANK_PASSWORD,\n 'csrfmiddlewaretoken': s.cookies['csrftoken']},\n headers={'Referer': DEFAULT_SIGNBANK_HOST})\n\n return s\n\ndef get_from_s3(key):\n \"\"\"\n Makes a GET request to S3, retrying a few times if it errors\n or if the connection is broken before giving up entirely\n\n :param key:\n :return:\n \"\"\"\n s = requests.Session()\n retries = Retry(total=5, backoff_factor=1,\n status_forcelist=[502, 503, 504])\n s.mount('https://', HTTPAdapter(max_retries=retries))\n\n # the above retry setup will handle occasional connection errors that are done\n # with a valid HTTP request, but they won't handle when the connection is just\n # broken, so we also have to explicitly catch protocol errors to attempt a retry\n try:\n return s.get(key)\n except ProtocolError:\n print(\"(had to retry get)\", end=\" \")\n return s.get(key)\n\n\n##########################\n# Dictionary data handling\n##########################\n\n\ndef fetch_gloss_export_file(filename):\n session = signbank_session()\n response = session.get(\"%s/dictionary/advanced/\" % DEFAULT_SIGNBANK_HOST,\n params={\"dataset\": SIGNBANK_DATASET_ID, \"published\": 'on', \"format\": 'CSV'})\n response.raise_for_status()\n with open(filename, \"wb\") as f:\n f.write(response.content)\n\n\ndef parse_signbank_csv(filename):\n with open(filename, 'r') as f:\n reader = csv.reader(f)\n headers = next(reader, None)\n return [{h: x for (h, x) in zip(headers, row)} for row in reader]\n\n\n##########################\n# Asset handling\n##########################\ndef fetch_gloss_asset_export_file(filename):\n session = signbank_session()\n video_response = session.get(\"%s/video/csv\" % DEFAULT_SIGNBANK_HOST)\n video_response.raise_for_status()\n with open(filename, \"wb\") as f:\n f.write(video_response.content)\n\n\ndef fetch_gloss_assets(data, database_filename, output_folder):\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n\n db = sqlite3.connect(database_filename)\n db.executescript(\n \"\"\"\n BEGIN;\n CREATE TABLE videos (\n word_id, video_type, filename, url, display_order\n );\n CREATE UNIQUE INDEX idx_word_videos ON videos (word_id, video_type, filename);\n COMMIT;\n \"\"\"\n )\n\n for entry in data:\n print(f\"{entry['Gloss']} ({entry['Video_type']})\", end=\" \")\n gloss_parts = entry['Gloss'].split(':')\n if (len(gloss_parts) < 2):\n print(f\"skipped - couldn't extract gloss ID\")\n continue\n\n gloss_id = gloss_parts[-1]\n video_type = entry['Video_type']\n url = entry['Videofile']\n\n basename = normalize_asset_filename(entry['Title'])\n filename = os.path.join(output_folder, basename)\n\n if filename.endswith(\".webm\"):\n print(\"skipped - webm video\")\n continue\n\n # We don't need to download videos, just know where they are\n if filename.endswith(\".png\"):\n if not os.path.exists(filename):\n asset_request = get_from_s3(entry['Videofile'])\n with open(filename, \"wb\") as asset_file:\n asset_file.write(asset_request.content)\n print(\"downloaded\", end=\", \")\n else:\n print(\"already downloaded\", end=\", \")\n else:\n print(\"not an image, skipping download\", end=\", \")\n\n # Update the words table with the picture, if this is an image and of type main\n if video_type == 'main' and filename.endswith('.png'):\n db.execute(\"UPDATE words SET picture = :basename WHERE id = :gloss_id\",\n {'basename': basename, 'gloss_id': gloss_id})\n print(\"assigned as main picture\", end=\", \")\n\n # Update the words table with the video URL, if this is a video and of type main\n if video_type == 'main' and filename.endswith('.mp4'):\n db.execute(\n \"UPDATE words SET video = :url WHERE id = :gloss_id\",\n {'gloss_id': gloss_id, 'url': url}\n )\n print(\"assigned as main video\", end=\", \")\n\n if video_type.startswith(\"finalexample\"):\n # finalexample{1,2,3,4} - this won't scale to double digits\n display_order = int(video_type[-1])\n db.execute(\"UPDATE examples SET video = :url WHERE word_id = :gloss_id AND display_order = :display_order\",\n {'display_order': display_order, 'gloss_id': gloss_id, 'url': url})\n\n # Insert the video data\n db.execute(\n \"\"\"\n INSERT INTO videos (word_id, video_type, filename, url, display_order)\n VALUES (:word_id, :video_type, :filename, :url, :display_order)\n ON CONFLICT DO NOTHING\n \"\"\", {\n 'word_id': gloss_id,\n 'video_type': video_type,\n 'filename': basename,\n 'url': url,\n 'display_order': entry['Version']\n }\n )\n db.commit()\n print(\"added to database\")\n\n# Modify filenames to match the Android requirements (lowercase a-z and _ only)\n# Since iOS uses the same underlying data, update iOS to use the same image names.\n\n\ndef normalize_asset_filename(filename):\n normalized_filename = filename.replace('-', '_').lower()\n num_of_periods = normalized_filename.count('.')\n if (num_of_periods > 1):\n normalized_filename = normalized_filename.replace(\n '.', '_', num_of_periods - 1)\n\n return normalized_filename\n else:\n return normalized_filename\n\n\n# The .dat file is used by the Android application, rather than SQLite, for\n# historical reasons. It only includes the data required by the application, not\n# including new data added to the SQLite database.\n\ndef write_datfile(database_filename, dat_file_filename):\n db = sqlite3.connect(database_filename)\n db.row_factory = sqlite3.Row\n with open(dat_file_filename, \"w\") as f:\n for row in db.execute(\"SELECT * FROM words\"):\n print(\"\\t\".join([\n row['gloss'] or '',\n row['minor'] or '',\n row['maori'] or '',\n row['picture'] or '',\n row['video'] or '',\n row['handshape'] or '',\n row['location']\n ]), file=f)\n\n# The SQLite database is used by all primary applications other than the Android application.\n# It includes a table containing the core dictionary data (words), and references to assets (videos).\n# Generally, vocabulary follows historical terminology rather than aligning with Signbank at this stage.\n\n\ndef write_sqlitefile(data, database_filename):\n if os.path.exists(database_filename):\n os.unlink(database_filename)\n db = sqlite3.connect(database_filename)\n db.execute(\n \"\"\"\n create table words (\n gloss, minor, maori, picture, video, handshape, location, location_identifier, variant_number, target, age_groups,\n contains_numbers boolean, hint, id PRIMARY KEY, inflection_manner_and_degree boolean, inflection_plural boolean,\n inflection_temporal boolean, is_directional boolean, is_fingerspelling boolean, is_locatable boolean,\n one_or_two_handed boolean, related_to, usage, usage_notes, word_classes, gloss_normalized,\n minor_normalized, maori_normalized\n )\n \"\"\"\n )\n\n for entry in data:\n target = \"{}|{}|{}\".format(\n normalise(entry['gloss_main']), normalise(entry['gloss_secondary']), normalise(entry['gloss_maori']))\n\n # Transform 'True'/'False' to boolean values - 0/1\n entry = {k: v if v not in (\"True\", \"False\") else (\n 1 if v == \"True\" else 0) for k, v in entry.items()}\n\n # Augment entry with additional attributes\n entry.update({\n \"target\": target,\n \"gloss_normalized\": normalise(entry[\"gloss_main\"]),\n \"minor_normalized\": normalise(entry[\"gloss_secondary\"]),\n \"maori_normalized\": normalise(entry[\"gloss_maori\"]),\n \"location_identifier\": entry[\"location_name\"],\n \"location\": normalize_location(entry[\"location_name\"])\n })\n db.execute(\n \"\"\"\n INSERT INTO words VALUES(\n :gloss_main,\n :gloss_secondary,\n :gloss_maori,\n '',\n '',\n :handshape,\n :location,\n :location_identifier,\n :variant_number,\n :target,\n :age_groups,\n :contains_numbers,\n :hint,\n :id,\n :inflection_manner_and_degree,\n :inflection_plural,\n :inflection_temporal,\n :is_directional,\n :is_fingerspelling,\n :is_locatable,\n :one_or_two_handed,\n :related_to,\n :usage,\n :usage_notes,\n :word_classes,\n :gloss_normalized,\n :minor_normalized,\n :maori_normalized\n ) ON CONFLICT DO NOTHING\n \"\"\", entry)\n add_examples(entry, db)\n add_topics(entry, db)\n db.commit()\n db.close()\n\n\ndef add_topics(entry, db):\n db.execute(\n \"CREATE TABLE IF NOT EXISTS topics (name varchar PRIMARY KEY UNIQUE)\")\n db.execute(\"CREATE TABLE IF NOT EXISTS word_topics (word_id, topic_name)\")\n for topic_name in entry[\"semantic_field\"].split(\"; \"):\n topic_name = topic_name.strip()\n if not topic_name:\n continue\n db.execute(\"INSERT INTO topics VALUES (:name) ON CONFLICT DO NOTHING\",\n {\"name\": topic_name})\n db.execute(\"INSERT INTO word_topics VALUES (:word_id, :topic_name) ON CONFLICT DO NOTHING\",\n {\"word_id\": entry[\"id\"], \"topic_name\": topic_name})\n\ndef add_examples(entry, db):\n db.execute(\n \"CREATE TABLE IF NOT EXISTS examples (word_id, display_order, sentence, translation, video)\")\n\n for i in [1, 2, 3, 4]:\n sentence = entry[f\"videoexample{i}\"]\n if not sentence:\n continue\n\n db.execute(\n \"INSERT INTO examples VALUES (:word_id, :display_order, :sentence, :translation, NULL)\",\n {\n \"word_id\": entry[\"id\"],\n \"display_order\": i,\n \"sentence\": sentence,\n \"translation\": entry[f\"videoexample{i}_translation\"]\n }\n )\n\ndef copy_images_to_one_folder(source, dest):\n if (os.path.isdir(dest)):\n shutil.rmtree(dest)\n os.makedirs(dest)\n\n # Warning: This is very shell-injectable\n os.system(f\"cp {source}/*.png {dest}/ 2>/dev/null\")\n\n# Helper functions\n\n\ndef normalize_location(location_str):\n return re.sub(r'\\A\\d{2} - ', '', location_str)\n\n##\n# Replace accented characters with their lowercased, unaccented equivalents.\n# Note that this is a non-exhaustive list, and there are more resilient ways to do this.\n\n\ndef normalise(s):\n return (s.lower()\n .replace(\"ā\", \"a\")\n .replace(\"ē\", \"e\")\n .replace(\"é\", \"e\")\n .replace(\"ī\", \"i\")\n .replace(\"ō\", \"o\")\n .replace(\"ū\", \"u\"))\n", "repo_name": "ODNZSL/nzsl-dictionary-scripts", "sub_path": "signbank.py", "file_name": "signbank.py", "file_ext": "py", "file_size_in_byte": 12050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.adapters.Retry", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib3.exceptions.ProtocolError", "line_number": 48, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 69, "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.makedirs", "line_number": 87, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 189, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 210, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 314, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 315, "usage_type": "call"}, {"api_name": "os.system", "line_number": 318, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 324, "usage_type": "call"}]} +{"seq_id": "29447525465", "text": "import pygame\nimport settings\n\nfrom node import Node\nfrom node_state_enum import NodeState\nfrom astar_pathfinding import astar_algorithm\n\npygame.init()\npygame.display.set_caption(\"A* pathfinding - SorenDev\")\nscreen: pygame.Surface = pygame.display.set_mode((settings.WIDTH, settings.HEIGHT))\n\ndef create_grid(r: int, w: int) -> list[list[Node]]:\n grid: list[list[Node]] = []\n gap: int = w // r\n \n for i in range(r):\n grid.append([])\n for j in range(r):\n node: Node = Node(i, j, gap, r)\n grid[i].append(node)\n \n return grid\n \ndef draw_grid(screen: pygame.Surface, r: int, w: int) -> None:\n gap: int = w // r\n \n for i in range(r):\n pygame.draw.line(screen, settings.BLACK, (0, i * gap), (settings.SIM_WIDTH, i * gap))\n for j in range(r + 1):\n pygame.draw.line(screen, settings.BLACK, (j * gap, 0), (j * gap, settings.SIM_WIDTH))\n \ndef draw(screen: pygame.Surface, grid: list[list[Node]], r: int, w: int) -> None:\n screen.fill(settings.WHITE)\n \n for row in grid:\n for node in row:\n node.draw(screen)\n \n draw_grid(screen, r, w)\n \n screen.blit(settings.RESET_TEXT, (settings.SIM_WIDTH + 20, settings.HEIGHT - settings.RESET_TEXT.get_height()))\n screen.blit(settings.RESET_GRID_TEXT, (settings.SIM_WIDTH + 20, settings.HEIGHT - 2 * settings.RESET_GRID_TEXT.get_height()))\n screen.blit(settings.START_TEXT, (settings.SIM_WIDTH + 20, settings.HEIGHT - 3 * settings.START_TEXT.get_height()))\n \n \n move_count_text = settings.H1_FONT.render(f\"Move count : {settings.MOVE_COUNT}\", True, settings.BLACK)\n move_length_text = settings.H2_FONT.render(f\"Total lenght : {round(settings.MOVE_LENGTH, 2)}\", True, settings.BLACK)\n screen.blit(move_count_text, (settings.SIM_WIDTH + 20, 0))\n screen.blit(move_length_text, (settings.SIM_WIDTH + 20, 45))\n \n pygame.display.update()\n \ndef get_clicked(pos: tuple[int, int], r: int, w: int) -> tuple[int, int]:\n gap: int = w // r\n \n x, y = pos\n \n row: int = x // gap\n col: int = y // gap\n \n return row, col\n\ndef reset_all_grid(grid: list[list[Node]]) -> None:\n for row in grid:\n for node in row:\n node.set_state(NodeState.WALKABLE)\n \ndef reset_grid(grid: list[list[Node]]) -> None:\n for row in grid:\n for node in row:\n if not node.state in (NodeState.START, NodeState.END, NodeState.BOUND):\n node.set_state(NodeState.WALKABLE)\n\ndef main(screen: pygame.Surface, w: int) -> None: \n rows: int = settings.ROWS\n \n grid: list[list[Node]] = create_grid(rows, w)\n \n start: Node = None\n end: Node = None\n \n started: bool = False\n while True: \n draw(screen, grid, rows, w)\n \n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n exit()\n \n if not started:\n if pygame.mouse.get_pressed()[0] and pygame.mouse.get_pos()[0] < settings.SIM_WIDTH and pygame.mouse.get_pos()[1] < settings.HEIGHT:\n r, c = get_clicked(pygame.mouse.get_pos(), rows, w)\n node: Node = grid[r][c]\n if not start:\n start = node\n start.set_state(NodeState.START)\n elif not end:\n end = node\n end.set_state(NodeState.END)\n elif not start == node and not end == node:\n node.set_state(NodeState.BOUND)\n elif pygame.mouse.get_pressed()[2]:\n r, c = get_clicked(pygame.mouse.get_pos(), rows, w)\n node: Node = grid[r][c]\n node.reset()\n \n if node == start:\n start = None\n if node == end:\n end = None\n \n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_r:\n start = None\n end = None\n reset_all_grid(grid)\n if event.key == pygame.K_t:\n reset_grid(grid)\n if event.key == pygame.K_SPACE and not started:\n reset_grid(grid)\n if start and end:\n for row in grid:\n for node in row:\n node.update_neighbors(grid)\n \n settings.MOVE_COUNT = 0\n settings.MOVE_LENGTH = 0\n \n settings.MOVE_COUNT, settings.MOVE_LENGTH = astar_algorithm(lambda: draw(screen, grid, rows, w), grid, start, end) \n \nif __name__ == \"__main__\":\n main(screen, settings.SIM_WIDTH)", "repo_name": "SorenDeveloppement/PathfindingVisualizer", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pygame.init", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "settings.WIDTH", "line_number": 10, "usage_type": "attribute"}, {"api_name": "settings.HEIGHT", "line_number": 10, "usage_type": "attribute"}, {"api_name": "node.Node", "line_number": 13, "usage_type": "name"}, {"api_name": "node.Node", "line_number": 19, "usage_type": "name"}, {"api_name": "node.Node", "line_number": 12, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 28, "usage_type": "attribute"}, {"api_name": "settings.BLACK", "line_number": 28, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 30, "usage_type": "attribute"}, {"api_name": "settings.BLACK", "line_number": 30, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 32, "usage_type": "attribute"}, {"api_name": "node.Node", "line_number": 32, "usage_type": "name"}, {"api_name": "settings.WHITE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "node.draw", "line_number": 37, "usage_type": "call"}, {"api_name": "settings.RESET_TEXT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "settings.HEIGHT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "settings.RESET_TEXT.get_height", "line_number": 41, "usage_type": "call"}, {"api_name": "settings.RESET_GRID_TEXT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "settings.HEIGHT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "settings.RESET_GRID_TEXT.get_height", "line_number": 42, "usage_type": "call"}, {"api_name": "settings.START_TEXT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.HEIGHT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.START_TEXT.get_height", "line_number": 43, "usage_type": "call"}, {"api_name": "settings.H1_FONT.render", "line_number": 46, "usage_type": "call"}, {"api_name": "settings.H1_FONT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "settings.MOVE_COUNT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "settings.BLACK", "line_number": 46, "usage_type": "attribute"}, {"api_name": "settings.H2_FONT.render", "line_number": 47, "usage_type": "call"}, {"api_name": "settings.H2_FONT", "line_number": 47, "usage_type": "attribute"}, {"api_name": "settings.MOVE_LENGTH", "line_number": 47, "usage_type": "attribute"}, {"api_name": "settings.BLACK", "line_number": 47, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 48, "usage_type": "attribute"}, {"api_name": "settings.SIM_WIDTH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 51, "usage_type": "attribute"}, {"api_name": "node.Node", "line_number": 63, "usage_type": "name"}, {"api_name": "node.set_state", "line_number": 66, "usage_type": "call"}, {"api_name": "node_state_enum.NodeState.WALKABLE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState", "line_number": 66, "usage_type": "name"}, {"api_name": "node.Node", "line_number": 68, "usage_type": "name"}, {"api_name": "node.state", "line_number": 71, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState.START", "line_number": 71, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState", "line_number": 71, "usage_type": "name"}, {"api_name": "node_state_enum.NodeState.END", "line_number": 71, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState.BOUND", "line_number": 71, "usage_type": "attribute"}, {"api_name": "node.set_state", "line_number": 72, "usage_type": "call"}, {"api_name": "node_state_enum.NodeState.WALKABLE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState", "line_number": 72, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 74, "usage_type": "attribute"}, {"api_name": "settings.ROWS", "line_number": 75, "usage_type": "attribute"}, {"api_name": "node.Node", "line_number": 77, "usage_type": "name"}, {"api_name": "node.Node", "line_number": 79, "usage_type": "name"}, {"api_name": "node.Node", "line_number": 80, "usage_type": "name"}, {"api_name": "pygame.event.get", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 92, "usage_type": "call"}, {"api_name": "settings.SIM_WIDTH", "line_number": 92, "usage_type": "attribute"}, {"api_name": "settings.HEIGHT", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 93, "usage_type": "attribute"}, {"api_name": "node.Node", "line_number": 94, "usage_type": "name"}, {"api_name": "node_state_enum.NodeState.START", "line_number": 97, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState", "line_number": 97, "usage_type": "name"}, {"api_name": "node_state_enum.NodeState.END", "line_number": 100, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState", "line_number": 100, "usage_type": "name"}, {"api_name": "node.set_state", "line_number": 102, "usage_type": "call"}, {"api_name": "node_state_enum.NodeState.BOUND", "line_number": 102, "usage_type": "attribute"}, {"api_name": "node_state_enum.NodeState", "line_number": 102, "usage_type": "name"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 104, "usage_type": "attribute"}, {"api_name": "node.Node", "line_number": 105, "usage_type": "name"}, {"api_name": "node.reset", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.K_r", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.K_t", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 120, "usage_type": "attribute"}, {"api_name": "node.update_neighbors", "line_number": 125, "usage_type": "call"}, {"api_name": "settings.MOVE_COUNT", "line_number": 127, "usage_type": "attribute"}, {"api_name": "settings.MOVE_LENGTH", "line_number": 128, "usage_type": "attribute"}, {"api_name": "settings.MOVE_COUNT", "line_number": 130, "usage_type": "attribute"}, {"api_name": "settings.MOVE_LENGTH", "line_number": 130, "usage_type": "attribute"}, {"api_name": "astar_pathfinding.astar_algorithm", "line_number": 130, "usage_type": "call"}, {"api_name": "settings.SIM_WIDTH", "line_number": 133, "usage_type": "attribute"}]} +{"seq_id": "17838892290", "text": "import os\nimport urllib\nfrom functools import lru_cache\nfrom enum import Enum\n\nimport boto3\n\nREGION = \"${AWS::Region}\"\nCLUSTER = \"${ClusterArn}\"\nDESIRED_COUNT = \"${DesiredCount}\"\nSTACK_ID = \"${AWS::StackId}\"\n\n\ndef env(k, default=None):\n if k in os.environ:\n ret = os.environ[k].strip()\n if len(ret) > 0:\n return ret\n if default:\n return default\n raise ValueError(f\"Required environment variable {k} not set\")\n\n\n# Check if we're in a test environment, and if so set the region from the\n# environment or use a default.\nif \"AWS::Region\" in REGION:\n REGION = env(\"AWS_DEFAULT_REGION\", \"us-east-1\")\n CLUSTER = env(\"CLUSTER_ARN\")\n STACK_ID = env(\"STACK_ID\")\n DESIRED_COUNT = 1\n print(\"Test environment detected, setting REGION to\", REGION)\nelse:\n print(\"REGION:\", REGION)\n DESIRED_COUNT = int(DESIRED_COUNT)\n\n\nECS = boto3.client(\"ecs\", region_name=REGION)\nELB = boto3.client(\"elbv2\", region_name=REGION)\nCFN = boto3.client(\"cloudformation\", region_name=REGION)\nSFN = boto3.client(\"stepfunctions\", region_name=REGION)\n\n\nclass Status(Enum):\n INITIAL = (0, \"Service startup requested\")\n STARTING = (1, \"Service starting\")\n LB_INITIAL = (2, \"Checking service health\")\n READY = (3, \"Service ready\")\n\n def __init__(self, order, label):\n self.order = order\n self.label = label\n\n\n@lru_cache\ndef get_starter_arn():\n outputs = CFN.describe_stacks(StackName=STACK_ID)[\"Stacks\"][0][\"Outputs\"]\n return [\n o[\"OutputValue\"] for o in outputs if o[\"OutputKey\"] == \"StarterStateMachineArn\"\n ][0]\n\n\ndef get_cluster_arn():\n return env(\"CLUSTER_ARN\")\n\n\ndef get_service_arn():\n return env(\"SERVICE_ARN\")\n\n\ndef get_refresh_seconds():\n return int(env(\"REFRESH_SECONDS\", 10))\n\n\ndef get_user_css():\n return env(\"USER_CSS\", \"\")\n\n\ndef get_title():\n return env(\"PAGE_TITLE\", \"${AWS::StackName}\")\n\n\ndef get_heading():\n return env(\"HEADING\", \"Please wait while the service starts...\")\n\n\ndef get_explanation():\n return env(\n \"EXPLANATION\",\n \"\"\"This service has been shut down due to inactivity. It is now being\n restarted and will be available again shortly.\"\"\",\n )\n\n\ndef starter_is_running():\n return (\n len(\n SFN.list_executions(\n stateMachineArn=get_starter_arn(), statusFilter=\"RUNNING\"\n )[\"executions\"]\n )\n > 0\n )\n\n\ndef get_tg_arns():\n return {\n lb[\"targetGroupArn\"]\n for lb in ECS.describe_services(\n cluster=get_cluster_arn(), services=[get_service_arn()]\n )[\"services\"][0][\"loadBalancers\"]\n }\n\n\ndef get_tg_healths():\n return [\n [\n h[\"TargetHealth\"][\"State\"]\n for h in ELB.describe_target_health(TargetGroupArn=tg_arn)[\n \"TargetHealthDescriptions\"\n ]\n ]\n for tg_arn in get_tg_arns()\n ]\n\n\ndef all_tgs_have_targets(tg_healths):\n for statuses in tg_healths:\n if len(statuses) < 1:\n return False\n return True\n\n\ndef all_tgs_have_healthy(tg_healths):\n for statuses in tg_healths:\n if \"healthy\" not in statuses:\n return False\n return True\n\n\ndef start_service():\n SFN.start_execution(stateMachineArn=get_starter_arn())\n\n\ndef get_service_status():\n if not starter_is_running():\n start_service()\n return Status.INITIAL\n\n tg_healths = get_tg_healths()\n # if all_tgs_have_healthy(tg_healths):\n # return Status.READY\n if all_tgs_have_targets(tg_healths):\n return Status.LB_INITIAL\n\n return Status.READY\n\n\ndef get_url(event):\n proto = event.get(\"headers\", {}).get(\"x-forwarded-proto\", \"https\")\n path = event.get(\"path\", \"/\")\n query = urllib.parse.urlencode(event.get(\"queryStringParameters\", {}))\n return urllib.parse.urlunsplit((proto, event[\"headers\"][\"host\"], path, query, \"\"))\n\n\ndef refresher_body(event, status):\n progress_pct = 100 / (len(Status.__members__) + 1) * (status.order + 1)\n # refresher_seconds = 1 if status == Status.READY else get_refresh_seconds()\n refresh_seconds = get_refresh_seconds()\n return f\"\"\"\n \n \n {get_title()}\n \n \n \n \n \n

\n
\n
\n

{get_heading()}

\n

{get_explanation()}

\n
\n
 
\n
\n
{status.label}
\n
\n
\n
\n \n \n \"\"\"\n\n\ndef lambda_handler(event, context):\n print(\"event:\", event)\n status = get_service_status()\n if event[\"httpMethod\"] != \"GET\":\n return {\n \"statusCode\": 100,\n \"statusDescription\": f\"100 {status.value.label}\",\n \"headers\": {\"Content-Type\": \"text/html\"},\n \"body\": status.label,\n }\n\n return {\n \"statusCode\": 200,\n \"statusDescription\": \"200 OK\",\n \"headers\": {\"Content-Type\": \"text/html\"},\n \"body\": refresher_body(event, status),\n }\n\n\nif __name__ == \"__main__\":\n import yaml\n\n event = {\"httpMethod\": \"GET\"}\n print(yaml.dump(lambda_handler(event, None)))\n", "repo_name": "sigdba/sig-shared-sceptre", "sub_path": "templates/EcsWebService/resources/WaiterLambda.py", "file_name": "WaiterLambda.py", "file_ext": "py", "file_size_in_byte": 6555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 37, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 38, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 39, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 40, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 43, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 54, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 161, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 161, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlunsplit", "line_number": 162, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 162, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 261, "usage_type": "call"}]} +{"seq_id": "9652197444", "text": "###############################################################################\n#\n# Author: Nathan Adelgren\n# Affiliation: Andlinger Center For Energy and the Environment\n# Princeton University\n#\n# Purpose: Perform matrix manipulations -- primarily principal pivots.\n# Original intention is for use as part of a solver for multi-\n# parametric Linear Complementarity Problems (mpLCP's).\n#\n################################################################################\n\nimport time\nfrom joblib import Parallel, delayed\nfrom joblib import wrap_non_picklable_objects\nfrom cython.parallel import prange, threadid\nimport multiprocessing\nfrom cypari2 import Pari\nfrom numba import njit, prange\n\n\n#@njit(parallel=True)\n#def prange_ok_result_outer_slice(x):\n# n = x.shape[0]\n# y = np.zeros(4)\n# z = y[:]\n# for i in prange(n):\n# z += x[i]\n# return y\n\n@njit(parallel=True, nopython=True)\ndef Div2(x, y):\n for i in prange(len(x)):\n x[i] = x[i]/y\n return x\n\n@delayed\n@wrap_non_picklable_objects\ndef Div(num, den):\n return num/den\n \ndef RowReduce(rowMod, rowStab, ind):\n temp = rowMod[ind]\n for i in prange(len(rowMod)):\n rowMod[i] -= temp*rowStab[i]\n return rowMod\n\n# Define Functions\n\n# Perform a pricipal pivot on the given matrix, i.e., perform elementary row \n# operations so that column j of M is an identity vector with a 1 in the i-th \n# position.\n#\n# Input: M -- the matrix to manipulate\n# i -- the row index\n# j -- the column index\n#\n# Output: M -- the updated matrix\ndef matrixPivot(M, i, j):\n oldM = M\n# t = time.time()\n temp = []\n temp.append(M[i][j])\n# for a in M[i]:\n# a = a/temp;\n# print(a);\n# for row in range(len(M)):\n# if row != i:\n# temp2 = M[row][j]\n# for col in range(len(M[row])):\n# M[row][col] -= temp2*M[i][col]\n# print(M[row][col])\n## for row in M:\n## print(row)\n# print(time.time() - t)\n\n t = time.time()\n M = oldM\n# M[i] = RowDiv(M[i], temp)\n# for row in prange(len(M)):\n# if row != i:\n# M[row] = RowReduce(M[row], M[i], j)\n# temp = M[i][j];\n# for a in prange(len(M[i])):\n# M[i][a] = M[i][a]/temp;\n# print(M[i][a]);\n# print(\"thread: \" + str(threadid()))\n# for row in prange(len(M)):\n# if row != i:\n# temp2 = M[row][j]\n# print(\"thread: \" + str(threadid()))\n# for col in prange(len(M[row])):\n# M[row][col] -= temp2*M[i][col]\n# print(M[row][col])\n# print(\"thread: \" + str(threadid()))\n\n print(M[i])\n print(Div2(M[i],temp))\n# random_vector = Parallel(n_jobs=-1)(Div(k, l) for k in M[i] for l in [3.])\n# print(random_vector)\n\n\n# for row in M:\n# print(row)\n print(time.time() - t)\n\n\n\n", "repo_name": "Nadelgren2/mpLCP_new_private", "sub_path": "matrix_manipulation_backup.py", "file_name": "matrix_manipulation_backup.py", "file_ext": "py", "file_size_in_byte": 2940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numba.prange", "line_number": 33, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 31, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 37, "usage_type": "name"}, {"api_name": "joblib.wrap_non_picklable_objects", "line_number": 38, "usage_type": "name"}, {"api_name": "numba.prange", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "37996235127", "text": "from abc import ABCMeta, abstractmethod\r\nfrom dataclasses import dataclass\r\nfrom datetime import datetime\r\nfrom typing import List, Optional, MutableMapping, Collection, Self, Any\r\n\r\nfrom syncify.abstract.misc import PrettyPrinter\r\nfrom syncify.enums.tags import TagName\r\nfrom syncify.utils import UnionList\r\n\r\n\r\n@dataclass\r\nclass Base:\r\n \"\"\"Generic Base class for all local/Spotify item/collections.\"\"\"\r\n clean_tags: MutableMapping[str, Any] = None\r\n _list_sep: str = \"; \"\r\n\r\n @property\r\n @abstractmethod\r\n def name(self) -> str:\r\n raise NotImplementedError\r\n\r\n\r\n@dataclass(repr=False, eq=False)\r\nclass Item(Base, PrettyPrinter, metaclass=ABCMeta):\r\n \"\"\"Generic class for storing an item.\"\"\"\r\n\r\n @property\r\n @abstractmethod\r\n def uri(self) -> Optional[str]:\r\n \"\"\"The URI associated with this item.\"\"\"\r\n raise NotImplementedError\r\n\r\n @property\r\n @abstractmethod\r\n def has_uri(self) -> Optional[bool]:\r\n \"\"\"Does this item have a valid URI.\"\"\"\r\n raise NotImplementedError\r\n\r\n def merge(self, item: Self, tags: UnionList[TagName] = TagName.ALL):\r\n \"\"\"Set the tags of this item equal to the given ``item``. Give a list of ``tags`` to limit which are set\"\"\"\r\n tag_names = set(TagName.to_tags(tags))\r\n\r\n for tag in tag_names: # merge on each tag\r\n if hasattr(item, tag):\r\n setattr(self, tag, item[tag])\r\n\r\n def __hash__(self):\r\n \"\"\"Uniqueness of an item is its URI + name\"\"\"\r\n return hash((self.uri, self.name))\r\n\r\n def __eq__(self, item):\r\n \"\"\"URI attributes equal if at least one item has a URI, names equal otherwise\"\"\"\r\n if self.has_uri or item.has_uri:\r\n return self.has_uri == item.has_uri and self.uri == item.uri\r\n else:\r\n return self.name == item.name\r\n\r\n def __ne__(self, item):\r\n return not self.__eq__(item)\r\n\r\n def __getitem__(self, key: str) -> Any:\r\n return getattr(self, key)\r\n\r\n def __setitem__(self, key: str, value: Any):\r\n if not hasattr(self, key):\r\n raise KeyError(f\"Given key is not a valid attribute of this item: {key}\")\r\n return setattr(self, key, value)\r\n\r\n\r\nclass Track(Item, metaclass=ABCMeta):\r\n \"\"\"Metadata/tags associated with a track.\"\"\"\r\n\r\n # metadata/tags\r\n title: Optional[str] = None\r\n artist: Optional[str] = None\r\n album: Optional[str] = None\r\n album_artist: Optional[str] = None\r\n track_number: Optional[int] = None\r\n track_total: Optional[int] = None\r\n genres: Optional[Collection[str]] = None\r\n year: Optional[int] = None\r\n bpm: Optional[float] = None\r\n key: Optional[str] = None\r\n disc_number: Optional[int] = None\r\n disc_total: Optional[int] = None\r\n compilation: Optional[bool] = None\r\n comments: Optional[List[str]] = None\r\n\r\n # images\r\n image_links: Optional[MutableMapping[str, str]] = None\r\n has_image: bool = False\r\n\r\n # properties\r\n length: Optional[float] = None\r\n rating: Optional[float] = None\r\n\r\n\r\nclass TrackProperties:\r\n \"\"\"Properties associated with a track.\"\"\"\r\n\r\n date_added: Optional[datetime] = None\r\n last_played: Optional[datetime] = None\r\n play_count: Optional[int] = None\r\n", "repo_name": "jor-mar/syncify", "sub_path": "syncify/abstract/item.py", "file_name": "item.py", "file_ext": "py", "file_size_in_byte": 3247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.MutableMapping", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 14, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 18, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 11, "usage_type": "name"}, {"api_name": "syncify.abstract.misc.PrettyPrinter", "line_number": 24, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 24, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 29, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 39, "usage_type": "name"}, {"api_name": "syncify.utils.UnionList", "line_number": 39, "usage_type": "name"}, {"api_name": "syncify.enums.tags.TagName", "line_number": 39, "usage_type": "name"}, {"api_name": "syncify.enums.tags.TagName.ALL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "syncify.enums.tags.TagName.to_tags", "line_number": 41, "usage_type": "call"}, {"api_name": "syncify.enums.tags.TagName", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 64, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 23, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Collection", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.MutableMapping", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 101, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 102, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "13720112241", "text": "from abc import ABC, abstractmethod\nfrom typing import Any, Generic, Optional, TypeVar\n\nfrom marvin._compat import BaseModel, Field, model_copy, model_dump\nfrom marvin.utilities.messages import Message\nfrom typing_extensions import Self\n\nfrom .handlers import Request, Response, Turn\n\nT = TypeVar(\n \"T\",\n bound=BaseModel,\n)\n\n\nclass Conversation(BaseModel, Generic[T], extra=\"allow\", arbitrary_types_allowed=True):\n turns: list[Turn[T]]\n model: Any\n\n def __getitem__(self, key: int) -> Turn[T]:\n return self.turns[key]\n\n @property\n def last_turn(self) -> Turn[T]:\n return self.turns[-1]\n\n @property\n def last_request(self) -> Optional[Request[T]]:\n return self.turns[-1][0] if self.turns else None\n\n @property\n def last_response(self) -> Optional[Response[T]]:\n return self.turns[-1][1] if self.turns else None\n\n @property\n def history(self) -> list[Message]:\n response: list[Message] = []\n if not self.turns:\n return response\n if self.last_request:\n response = self.last_request.messages or []\n if self.last_response:\n response.append(self.last_response.choices[0].message)\n return response\n\n def send(self, messages: list[Message], **kwargs: Any) -> Turn[T]:\n params = kwargs\n if self.last_request:\n params = model_dump(self.last_request, exclude={\"messages\"}) | kwargs\n\n turn = self.model.create(\n **params,\n messages=[\n *self.history,\n *messages,\n ],\n )\n self.turns.append(turn)\n return turn\n\n async def asend(self, messages: list[Message], **kwargs: Any) -> Turn[T]:\n params = kwargs\n if self.last_request:\n params = model_dump(self.last_request, exclude={\"messages\"}) | kwargs\n\n turn = await self.model.acreate(\n **params,\n messages=[\n *self.history,\n *messages,\n ],\n )\n self.turns.append(turn)\n return turn\n\n\nclass AbstractChatCompletion(\n BaseModel, Generic[T], ABC, extra=\"allow\", arbitrary_types_allowed=True\n):\n \"\"\"\n A ChatCompletion object is responsible for exposing a create and acreate method,\n and for merging default parameters with the parameters passed to these methods.\n \"\"\"\n\n defaults: dict[str, Any] = Field(default_factory=dict, exclude=True)\n\n def __call__(self: Self, **kwargs: Any) -> Self:\n \"\"\"\n Create a new ChatCompletion object with new defaults computed from\n merging the passed parameters with the default parameters.\n \"\"\"\n copy = model_copy(self)\n copy.defaults = self.defaults | kwargs\n return copy\n\n @abstractmethod\n def _serialize_request(self, request: Optional[Request[T]]) -> dict[str, Any]:\n \"\"\"\n Serialize the request.\n This should be implemented by derived classes based on their specific needs.\n \"\"\"\n pass\n\n @abstractmethod\n def _create_request(self, **kwargs: Any) -> Request[T]:\n \"\"\"\n Prepare and return a request object.\n This should be implemented by derived classes.\n \"\"\"\n pass\n\n @abstractmethod\n def _parse_response(self, response: Any) -> Any:\n \"\"\"\n Parse the response based on specific needs.\n \"\"\"\n pass\n\n def merge_with_defaults(self, **kwargs: Any) -> dict[str, Any]:\n \"\"\"\n Merge the passed parameters with the default parameters.\n \"\"\"\n return self.defaults | kwargs\n\n @abstractmethod\n def _send_request(self, **serialized_request: Any) -> Any:\n \"\"\"\n Send the serialized request to the appropriate endpoint/service.\n Derived classes should implement this.\n \"\"\"\n pass\n\n @abstractmethod\n async def _send_request_async(\n self, **serialized_request: Any\n ) -> Response[T]: # noqa\n \"\"\"\n Send the serialized request to the appropriate endpoint/service asynchronously.\n Derived classes should implement this.\n \"\"\"\n pass\n\n def create(\n self, response_model: Optional[type[T]] = None, **kwargs: Any\n ) -> Turn[T]:\n \"\"\"\n Create a completion synchronously.\n Derived classes can override this if they need to change the core logic.\n \"\"\"\n request = self._create_request(**kwargs, response_model=response_model)\n serialized_request = self._serialize_request(request=request)\n response_data = self._send_request(**serialized_request)\n response = self._parse_response(response_data)\n\n return Turn(\n request=Request(\n **serialized_request\n | self.defaults\n | model_dump(request, exclude_none=True)\n | ({\"response_model\": response_model} if response_model else {})\n ),\n response=response,\n )\n\n async def acreate(\n self, response_model: Optional[type[T]] = None, **kwargs: Any\n ) -> Turn[T]:\n \"\"\"\n Create a completion asynchronously.\n Similar to the synchronous version but for async implementations.\n \"\"\"\n request = self._create_request(**kwargs, response_model=response_model)\n serialized_request = self._serialize_request(request=request)\n response_data = await self._send_request_async(**serialized_request)\n response = self._parse_response(response_data)\n return Turn(\n request=Request(\n **serialized_request\n | self.defaults\n | model_dump(request, exclude_none=True)\n | ({\"response_model\": response_model} if response_model else {})\n ),\n response=response,\n )\n\n def chain(self, **kwargs: Any) -> Conversation[T]:\n \"\"\"\n Create a new Conversation object.\n \"\"\"\n with self as conversation:\n conversation.send(**kwargs)\n while conversation.last_turn.has_function_call():\n message = conversation.last_turn.call_function()\n conversation.send(\n message if isinstance(message, list) else [message],\n )\n\n return conversation\n\n async def achain(self, **kwargs: Any) -> Conversation[T]:\n \"\"\"\n Create a new Conversation object asynchronously.\n \"\"\"\n with self as conversation:\n await conversation.asend(**kwargs)\n while conversation.last_turn.has_function_call():\n message = conversation.last_turn.call_function()\n await conversation.asend(\n message if isinstance(message, list) else [message],\n )\n\n return conversation\n\n def __enter__(self: Self) -> Conversation[T]:\n \"\"\"\n Enter a context manager.\n \"\"\"\n return Conversation(turns=[], model=self)\n\n def __exit__(self: Self, *args: Any) -> None:\n \"\"\"\n Exit a context manager.\n \"\"\"\n pass\n", "repo_name": "PrefectHQ/marvin", "sub_path": "src/marvin/core/ChatCompletion/abstract.py", "file_name": "abstract.py", "file_ext": "py", "file_size_in_byte": 7122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3832, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.TypeVar", "line_number": 10, "usage_type": "call"}, {"api_name": "marvin._compat.BaseModel", "line_number": 12, "usage_type": "name"}, {"api_name": "marvin._compat.BaseModel", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 16, "usage_type": "name"}, {"api_name": "handlers.Turn", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 18, "usage_type": "name"}, {"api_name": "handlers.Turn", "line_number": 20, "usage_type": "name"}, {"api_name": "handlers.Turn", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "handlers.Request", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 32, "usage_type": "name"}, {"api_name": "handlers.Response", "line_number": 32, "usage_type": "name"}, {"api_name": "marvin.utilities.messages.Message", "line_number": 37, "usage_type": "name"}, {"api_name": "marvin.utilities.messages.Message", "line_number": 36, "usage_type": "name"}, {"api_name": "marvin.utilities.messages.Message", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 46, "usage_type": "name"}, {"api_name": "marvin._compat.model_dump", "line_number": 49, "usage_type": "call"}, {"api_name": "handlers.Turn", "line_number": 46, "usage_type": "name"}, {"api_name": "marvin.utilities.messages.Message", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 61, "usage_type": "name"}, {"api_name": "marvin._compat.model_dump", "line_number": 64, "usage_type": "call"}, {"api_name": "handlers.Turn", "line_number": 61, "usage_type": "name"}, {"api_name": "marvin._compat.BaseModel", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 78, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 85, "usage_type": "name"}, {"api_name": "marvin._compat.Field", "line_number": 85, "usage_type": "call"}, {"api_name": "typing_extensions.Self", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 87, "usage_type": "name"}, {"api_name": "marvin._compat.model_copy", "line_number": 92, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 97, "usage_type": "name"}, {"api_name": "handlers.Request", "line_number": 97, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 105, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 104, "usage_type": "name"}, {"api_name": "handlers.Request", "line_number": 105, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 113, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 112, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 126, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 135, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 133, "usage_type": "name"}, {"api_name": "handlers.Response", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 144, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 144, "usage_type": "name"}, {"api_name": "handlers.Turn", "line_number": 155, "usage_type": "call"}, {"api_name": "handlers.Request", "line_number": 156, "usage_type": "call"}, {"api_name": "marvin._compat.model_dump", "line_number": 159, "usage_type": "call"}, {"api_name": "handlers.Turn", "line_number": 145, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 166, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 166, "usage_type": "name"}, {"api_name": "handlers.Turn", "line_number": 176, "usage_type": "call"}, {"api_name": "handlers.Request", "line_number": 177, "usage_type": "call"}, {"api_name": "marvin._compat.model_dump", "line_number": 180, "usage_type": "call"}, {"api_name": "handlers.Turn", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 186, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 200, "usage_type": "name"}, {"api_name": "typing_extensions.Self", "line_number": 214, "usage_type": "name"}, {"api_name": "typing_extensions.Self", "line_number": 220, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 220, "usage_type": "name"}]} +{"seq_id": "5517775004", "text": "from typing import Generator\nimport pytest\nfrom playwright.sync_api import Playwright, Page, APIRequestContext, expect\n\n@pytest.fixture(scope=\"session\")\ndef api_request_context(playwright: Playwright) -> Generator[APIRequestContext, None, None]:\n request_context = playwright.request.new_context(base_url=\"https://jsonplaceholder.typicode.com/\")\n # typing.Generator -> declare types of variables, parameters and return values of a function upfront.\n # Checks the code before compiling and running\n yield request_context\n # Discard all stored responses\n request_context.dispose()\n\n\ndef test_post_todo(api_request_context: APIRequestContext) -> None:\n json_data = {\n \"userId\": 1,\n \"id\": 1,\n \"title\": \"delectus aut autem\",\n \"completed\": False,\n }\n response = api_request_context.post(\"/todos/1\", data=json_data)\n # Check new response code\n assert response.ok\n\n todos_response = response.json()\n print(\"\")\n print(f\"todo Var: {response}\")\n print(f\"todo_response Var: {todos_response}\")\n\n", "repo_name": "skdat6/playwright-tests", "sub_path": "API_sync_tests.py", "file_name": "API_sync_tests.py", "file_ext": "py", "file_size_in_byte": 1055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "playwright.sync_api.Playwright", "line_number": 6, "usage_type": "name"}, {"api_name": "playwright.sync_api.request.new_context", "line_number": 7, "usage_type": "call"}, {"api_name": "playwright.sync_api.request", "line_number": 7, "usage_type": "attribute"}, {"api_name": "playwright.sync_api", "line_number": 7, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 5, "usage_type": "call"}, {"api_name": "typing.Generator", "line_number": 6, "usage_type": "name"}, {"api_name": "playwright.sync_api.APIRequestContext", "line_number": 6, "usage_type": "name"}, {"api_name": "playwright.sync_api.APIRequestContext", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "73951217438", "text": "import asyncio\nimport random\nfrom datetime import timedelta\nfrom threading import Thread, Event\nfrom typing import Callable, Coroutine\n\nfrom mido import Message\nfrom simpleaudio import WaveObject\n\nfrom grid.pad_grid import PadGrid\nfrom midi.midi_controller import MidiController\n\n\nclass WhackAMole:\n def __init__(self, pad_grid: PadGrid, controller: MidiController, base_delay: timedelta, max_hits: int):\n self._pad_grid = pad_grid\n self._controller = controller\n self._base_delay = base_delay\n self._max_hits = max_hits\n\n def play(self) -> int:\n current_x = 0\n current_y = 0\n nb_hits = 0\n stop_event = Event()\n\n def sync_loop(call: Callable[[], Coroutine]):\n asyncio.run(call())\n\n async def strokes_loop():\n punch_sound = WaveObject.from_wave_file(\"media/punch.wav\")\n\n async def handle_stroke(msg: Message):\n nonlocal nb_hits\n\n coord = self._pad_grid.note_coordinate(msg.note)\n if coord == (current_x, current_y):\n punch_sound.play()\n nb_hits += 1\n\n [self._controller.bind_note_on(note, handle_stroke) for note in self._pad_grid.all_notes()]\n\n await self._controller.receive(stop_event)\n\n async def moles_loop():\n nonlocal current_x\n nonlocal current_y\n sent_hits = 0\n previous_x = -1\n previous_y = -1\n\n while sent_hits < self._max_hits:\n candidate_x = previous_x\n candidate_y = previous_y\n while candidate_x == previous_x and candidate_y == previous_y:\n candidate_x = random.randint(0, self._pad_grid.width - 1)\n candidate_y = random.randint(0, self._pad_grid.height - 1)\n\n previous_x = current_x = candidate_x\n previous_y = current_y = candidate_y\n self._controller.send_note_on(self._pad_grid.get_note(current_x, current_y))\n\n # Add a random delay\n delay = random.randint(-100, 200)\n await asyncio.sleep((self._base_delay + timedelta(milliseconds=delay)).total_seconds())\n self._controller.send_note_off(self._pad_grid.get_note(current_x, current_y))\n\n # Set to -1 to avoid hits to be counted during waiting time\n current_x = -1\n current_y = -1\n\n sent_hits += 1\n\n await asyncio.sleep(random.randint(500, 2500) / 1000.)\n\n moles_thread = Thread(target=lambda: sync_loop(moles_loop))\n strokes_thread = Thread(target=lambda: sync_loop(strokes_loop))\n\n self._pad_grid.reset(self._controller)\n strokes_thread.start()\n moles_thread.start()\n\n moles_thread.join()\n stop_event.set()\n strokes_thread.join()\n self._pad_grid.reset(self._controller)\n\n return nb_hits\n", "repo_name": "gray-matter/midi-games", "sub_path": "game/whack_a_mole.py", "file_name": "whack_a_mole.py", "file_ext": "py", "file_size_in_byte": 2977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "grid.pad_grid.PadGrid", "line_number": 15, "usage_type": "name"}, {"api_name": "midi.midi_controller.MidiController", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 15, "usage_type": "name"}, {"api_name": "threading.Event", "line_number": 25, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Coroutine", "line_number": 27, "usage_type": "name"}, {"api_name": "asyncio.run", "line_number": 28, "usage_type": "call"}, {"api_name": "simpleaudio.WaveObject.from_wave_file", "line_number": 31, "usage_type": "call"}, {"api_name": "simpleaudio.WaveObject", "line_number": 31, "usage_type": "name"}, {"api_name": "mido.Message", "line_number": 33, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 56, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 64, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 65, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 74, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 76, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "29073330202", "text": "# -*- coding: utf-8 -*-\nfrom django.http import HttpResponseRedirect\nfrom apps.acl.views import Acl as vAcl\nfrom sarv.local_settings import PROJECT_ADMINS\n\nclass PageRightsCheck(object):\n def process_request(self, request):\n if request.path in (\"/logout\", \"/login\", \"/\", \"\"): \n return None\n\n acl = vAcl()\n page = acl.get_page_from_url(request)\n userrights = acl.get_page_user_rights(request)\n \n if \"admin+\" in page:\n if page == \"admin+menu\":\n return None\n if hasattr(request, \"sarvuser\") \\\n and request.sarvuser.pk in PROJECT_ADMINS:\n request.__class__.acl = [True,True,True,True]\n elif \"admin+acl\" in page \\\n and \"acl\" in request.session \\\n and page in request.session[\"acl\"] \\\n and (userrights[3] == True \\\n or request.sarvuser.pk in PROJECT_ADMINS):\n pass\n else:\n return HttpResponseRedirect(\"/\")\n \n if not \"acl\" in request.session:\n return HttpResponseRedirect(\"/\")\n if \"acl\" in request.session \\\n and page in request.session[\"acl\"] \\\n and userrights[0] == False:\n return HttpResponseRedirect(\"/\")\n else:\n request.__class__.acl = userrights\n", "repo_name": "geocollections/sarv", "sub_path": "apps/acl/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "apps.acl.views.Acl", "line_number": 11, "usage_type": "call"}, {"api_name": "sarv.local_settings.PROJECT_ADMINS", "line_number": 19, "usage_type": "name"}, {"api_name": "sarv.local_settings.PROJECT_ADMINS", "line_number": 25, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "24593729688", "text": "import spacy\r\n\r\nNUM_TOPICS = 5 #use with api\r\n\r\nspacy.load('en')\r\nfrom spacy.lang.en import English\r\nparser = English()\r\ndef tokenize(text):\r\n lda_tokens = []\r\n tokens = parser(text)\r\n for token in tokens:\r\n if token.orth_.isspace():\r\n continue\r\n elif token.like_url:\r\n lda_tokens.append('URL')\r\n elif token.orth_.startswith('@'):\r\n lda_tokens.append('SCREEN_NAME')\r\n else:\r\n lda_tokens.append(token.lower_)\r\n return lda_tokens\r\n\r\nimport nltk\r\nnltk.download('wordnet')\r\nfrom nltk.corpus import wordnet as wn\r\ndef get_lemma(word):\r\n lemma = wn.morphy(word)\r\n if lemma is None:\r\n return word\r\n else:\r\n return lemma\r\n \r\nfrom nltk.stem.wordnet import WordNetLemmatizer\r\ndef get_lemma2(word):\r\n return WordNetLemmatizer().lemmatize(word)\r\n\r\nnltk.download('stopwords')\r\nen_stop = set(nltk.corpus.stopwords.words('english'))\r\n\r\ndef prepare_text_for_lda(text):\r\n tokens = tokenize(text)\r\n tokens = [token for token in tokens if len(token) > 4]\r\n tokens = [token for token in tokens if token not in en_stop]\r\n tokens = [get_lemma(token) for token in tokens]\r\n return tokens\r\n#load data\r\nimport random\r\ntext_data = []\r\nwith open('dataset.csv') as f:\r\n for line in f:\r\n tokens = prepare_text_for_lda(line)\r\n if random.random() > .99:\r\n print(tokens)\r\n text_data.append(tokens)\r\n\r\nfrom gensim import corpora\r\ndictionary = corpora.Dictionary(text_data)\r\ncorpus = [dictionary.doc2bow(text) for text in text_data]\r\nimport pickle\r\npickle.dump(corpus, open('corpus.pkl', 'wb'))\r\ndictionary.save('dictionary.gensim')\r\n\r\nimport gensim\r\n\r\nldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\r\nldamodel.save('model5.gensim')\r\ntopics = ldamodel.print_topics(num_words=4)\r\nfor topic in topics:\r\n print(topic)", "repo_name": "ckinateder/StoryGrab", "sub_path": "src/expML/hom.py", "file_name": "hom.py", "file_ext": "py", "file_size_in_byte": 1909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "51", "api": [{"api_name": "spacy.load", "line_number": 5, "usage_type": "call"}, {"api_name": "spacy.lang.en.English", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.morphy", "line_number": 26, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 26, "usage_type": "name"}, {"api_name": "nltk.stem.wordnet.WordNetLemmatizer", "line_number": 34, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 36, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 37, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 37, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 51, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 56, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 56, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 59, "usage_type": "call"}, {"api_name": "gensim.models.ldamodel.LdaModel", "line_number": 64, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 64, "usage_type": "attribute"}]} +{"seq_id": "4823932539", "text": "from sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nimport os\n\nfrom api.models.model import DetectionTime\nfrom sqlalchemy import and_\nimport api.schemas.detect_time as schema\nfrom fastapi import UploadFile\nfrom app.settings import SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH\n\n\nasync def get_detect_time(date, db: AsyncSession):\n stmt = (\n select(\n DetectionTime.id,\n DetectionTime.startTime,\n DetectionTime.endTime,\n DetectionTime.cageId,\n )\n .where(\n and_(\n DetectionTime.date == date,\n )\n )\n .order_by(DetectionTime.id)\n )\n\n result = await db.execute(stmt)\n detection_times = result.fetchall()\n\n formatted_detection_times = []\n for detection_time in detection_times:\n formatted_detection_times.append(\n {\n \"id\": detection_time.id,\n \"cageId\": detection_time.cageId,\n \"startTime\": detection_time.startTime,\n \"endTime\": detection_time.endTime,\n }\n )\n return formatted_detection_times\n\n\nasync def create_detect_time(video: UploadFile, db: AsyncSession):\n content = await video.read()\n filename = os.path.splitext(content.filename)[1]\n\n # ディレクトリが存在しない場合、作成する\n if not os.path.exists(SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH):\n os.makedirs(SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH)\n\n save_path = os.path.join(SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH, filename)\n with open(save_path, \"wb\") as f:\n f.write(content)\n\n # post = DetectionTime(\n # startTime=schema.startTime,\n # endTime=schema.endTime,\n # date=schema.date,\n # cageId=schema.cageId,\n # )\n # db.add(post)\n # db.commit()\n\n pass\n", "repo_name": "Al-Mikan/polor-detection", "sub_path": "backend/api/cruds/detect_time.py", "file_name": "detect_time.py", "file_ext": "py", "file_size_in_byte": 1842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 14, "usage_type": "call"}, {"api_name": "api.models.model.DetectionTime.id", "line_number": 15, "usage_type": "attribute"}, {"api_name": "api.models.model.DetectionTime", "line_number": 15, "usage_type": "name"}, {"api_name": "api.models.model.DetectionTime.startTime", "line_number": 16, "usage_type": "attribute"}, {"api_name": "api.models.model.DetectionTime", "line_number": 16, "usage_type": "name"}, {"api_name": "api.models.model.DetectionTime.endTime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "api.models.model.DetectionTime", "line_number": 17, "usage_type": "name"}, {"api_name": "api.models.model.DetectionTime.cageId", "line_number": 18, "usage_type": "attribute"}, {"api_name": "api.models.model.DetectionTime", "line_number": 18, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 21, "usage_type": "call"}, {"api_name": "api.models.model.DetectionTime.date", "line_number": 22, "usage_type": "attribute"}, {"api_name": "api.models.model.DetectionTime", "line_number": 22, "usage_type": "name"}, {"api_name": "api.models.model.DetectionTime.id", "line_number": 25, "usage_type": "attribute"}, {"api_name": "api.models.model.DetectionTime", "line_number": 25, "usage_type": "name"}, {"api_name": "fastapi.UploadFile", "line_number": 44, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "app.settings.SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH", "line_number": 49, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 50, "usage_type": "call"}, {"api_name": "app.settings.SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH", "line_number": 50, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "app.settings.SYSTEM_MEDIA_IMAGE_ANONYMOUS_POST_PATH", "line_number": 52, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}]} +{"seq_id": "18910426885", "text": "\"\"\"\nКогда баг починят, мы это узнаем, так как теперь тест будет отмечен как XPASS (“unexpectedly passing” —\nнеожиданно проходит). После этого маркировку xfail для теста можно удалить. Кстати, к маркировке xfail можно добавлять\nпараметр reason. Чтобы увидеть это сообщение в консоли, при запуске нужно добавлять параметр pytest -rx.\n\"\"\"\n\nimport pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\nurl_link = 'http://selenium1py.pythonanywhere.com/'\n\n@pytest.fixture(scope='function')\ndef browser():\n print('\\nСтарт браузера для теста..')\n browser = webdriver.Chrome()\n yield browser\n print('\\nЗакрытие браузера..')\n browser.quit()\n\nclass TestMainPage1():\n def test_guest_should_see_login_link(self, browser):\n print('\\nНачало 1 теста из класса')\n browser.get(url_link)\n browser.find_element(By.CSS_SELECTOR, '#login_link')\n\n def test_guest_should_see_basken_on_the_main_page(self, browser):\n print('\\nНачало 2теста из класса')\n browser.get(url_link)\n browser.find_element(By.CSS_SELECTOR, '.basket-mini .btn-group > a')\n\n # reason покажет указанное сообщение в консоли\n @pytest.mark.xfail(reason='исправлю эту ошибку прямо сейчас')\n def test_guest_should_see_search_button_on_the_main_page(self, browser):\n print('\\nНачало теста с маркером fail')\n browser.get(url_link)\n browser.find_element(By.CSS_SELECTOR, 'button.favorite')\n\n\"\"\"\nЗапустим наши тесты:\n\npytest -rx -v test_fixture10a.py\n\nСравните вывод в первом и во втором случае.\n\"\"\"\n", "repo_name": "D1mk0-0/stepik_auto_tests_course", "sub_path": "Lesson3/Lesson3.5.5_pytest_XFail_XPass/test_fixture10a.py", "file_name": "test_fixture10a.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 16, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 16, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 25, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 30, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}, {"api_name": "pytest.mark.xfail", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "18734283039", "text": "import sys, os, re, time\nimport pandas as pd\nimport pandas_ta as ta\nfrom kucoinapi import KucoinAPI\nfrom utilities import try_request\nfrom datetime import datetime\nfrom tradingview_ta import TA_Handler, Interval, Exchange\nfrom UpsideMomemtum import get_upside_momemtum, backfill_data, get_coinbase_data, get_binance_data\nfrom MongoDBHandle import CryptoDB\n\ndef get_etfs():\n\t# get kucoin tickers\n\ttickers = KucoinAPI().get_all_tickers()\n\n\t# generate reference\n\tkucoin_asset_reference = []\n\tfor t in tickers['data']['ticker']:\n\t\tif re.search('-USDT',t['symbol']) and t['symbol'] != 'XRP-USDT':\n\t\t\tkucoin_asset_reference.append(t['symbol'])\n\n\t# parse out etfs\n\tetf_assets = {}\n\tfor t in tickers['data']['ticker']:\n\t\tetf_search = re.search(\"(\\w+)(3[L|S])-USDT\",t['symbol'])\n\t\tif etf_search:\n\t\t\tname = etf_search.group(1)\n\t\t\tpos = etf_search.group(2)\n\t\t\tif f\"{name}-USDT\" in kucoin_asset_reference:\n\t\t\t\tif name not in etf_assets.keys():\n\t\t\t\t\tetf_assets[name] = {\n\t\t\t\t\t\t'kucoin':f\"{name}-USDT\",\n\t\t\t\t\t\t'long':None,\n\t\t\t\t\t\t'short':None,\n\t\t\t\t\t\t'coinbase':None,\n\t\t\t\t\t\t'binance':None\n\t\t\t\t\t}\n\n\t\t\t\tif pos == \"3L\":\n\t\t\t\t\tetf_assets[name]['long'] = f\"{name}3L-USDT\"\n\t\t\t\telif pos == \"3S\":\n\t\t\t\t\tetf_assets[name]['short'] = f\"{name}3S-USDT\"\n\n\t# crossreference coinbase assets\n\tresponse = try_request(\"https://api.exchange.coinbase.com/products\")\n\tfor asset in response.json():\n\t\tif asset['quote_currency'] == 'USD' and asset['base_currency'] in etf_assets.keys():\n\t\t\tetf_assets[asset['base_currency']]['coinbase'] = asset['id']\n\n\t# crossreference binance assets\n\tresponse = try_request(\"https://api.binance.us/api/v3/ticker/price\")\n\tfor asset in response.json():\n\t\ts = re.search(\"(\\w+)USD$\",asset['symbol'])\n\t\tif s:\n\t\t\tif s.group(1) in etf_assets.keys():\n\t\t\t\tetf_assets[s.group(1)]['binance'] = asset['symbol']\n\n\t# filter out incompatible etfs\n\tfor key in list(etf_assets):\n\t\tif etf_assets[key]['coinbase'] == None and etf_assets[key]['binance'] == None:\n\t\t\tdel etf_assets[key]\n\n\treturn etf_assets\n\ndef insert_update_etfs(client):\n\tetfs = get_etfs()\n\n\t# insert update the etfs into mongodb\n\trows = []\n\tfor k,v in etfs.items():\n\t\tv['asset'] = k\n\t\tv['um_15m'] = 0\n\t\tv['um_4h'] = 0\n\t\tv['volatility_28d'] = 0\n\t\tv['volatility_4h'] = 0\n\t\tv['atr_4h_pct'] = 0\n\t\tv['volume_24h'] = 0\n\t\tv['recc_buy'] = 0\n\t\tv['recc_sell'] = 0\n\t\tv['scanStatus'] = 'reset'\n\t\tv['lastUpdated'] = None\n\t\trows.append(v)\n\n\tdb = client.db\n\n\tfor r in rows:\n\t\tdb.etfs.update_one({'asset':r['asset']},{\"$set\":r},upsert=True)\n\ndef calculate_upside_momemtum(asset,datasource):\n\t# calculate upside momemtum on 15m and 4h time frames\n\t# get 15m candles from either coinbase or binance\n\tdata = []\n\tinterval = \"15m\"\n\tsuffix_15m = \"_15m\"\n\tsuffix_4h = \"_4h\"\n\trollnum = 16\n\tgranularity = 900\n\tif datasource == 'coinbase':\n\t\tdata = get_coinbase_data(asset,granularity)\n\telif datasource == 'binance':\n\t\tdata = get_binance_data(asset,interval)\n\n\t# calculate upside momemtum for both 15m and 4h, return tuple of latest value\n\tumdf = get_upside_momemtum(data,suffix_15m,return_df=True)\n\tumdf = backfill_data(umdf,rollnum,suffix_15m,suffix_4h)\n\n\trdf = pd.DataFrame(columns=umdf.columns)\n\n\tfor i in range(rollnum):\n\t\tfour_df = umdf.iloc[i::rollnum,:].copy().reset_index()\n\t\tumdf_4h = get_upside_momemtum(four_df,suffix=suffix_4h,return_df=True)\n\t\tumdf_4h.ta.atr(close=\"close_4h\",high=\"high_4h\",low=\"low_4h\",append=True,length=14)\n\t\tumdf_4h['atr_4h_pct'] = umdf_4h['ATRr_14'] / umdf_4h['ohlc4_4h'].rolling(14).mean() * 100 * 3\n\t\tumdf_4h.reset_index(inplace=True,drop=True)\n\t\tumdf_4h.set_index('index',inplace=True)\n\t\trdf = pd.concat([rdf,umdf_4h])\n\n\trdf = rdf.reset_index()\n\trdf = rdf.set_index('index').sort_index()\n\n\tupside_momemtum_15m = rdf.iloc[-1]['upside_momemtum_15m']\n\tlast_4h_idx = umdf[umdf['high_4h'].isna()].iloc[0].name - 1\n\tupside_momemtum_4h = rdf.iloc[last_4h_idx]['upside_momemtum_4h']\n\tstandard_4h_volatility = rdf.iloc[last_4h_idx]['ATRr_14']\n\tatr_4h_pct = rdf.iloc[last_4h_idx]['atr_4h_pct']\n\n\trdf['volume_24h'] = rdf.iloc[::-1].rolling(96)['volume_15m'].sum()\n\tlastidx = rdf[rdf['volume_24h'].isna()].iloc[0].name - 1\n\tvolume_24h = rdf.iloc[lastidx]['volume_24h']\n\treturn (upside_momemtum_15m,upside_momemtum_4h,volume_24h,standard_4h_volatility,atr_4h_pct)\n\ndef calculate_28d_volatility(asset,datasource):\n\t# calculate 28d volatility\n\t# get 28 daily candles, calculate ATR using a length of 28\n\tdata = []\n\tlength = 28\n\tif datasource == 'coinbase':\n\t\tdata = get_coinbase_data(asset,86400)\n\telif datasource == 'binance':\n\t\tdata = get_binance_data(asset,'1d')\n\tif len(data) < length:\n\t\tlength = len(data)\n\tdata.ta.atr(close=\"close_1d\",high=\"high_1d\",low=\"low_1d\",append=True,length=length)\n\treturn data.iloc[-1][f'ATRr_{length}']\n\ndef get_recommendation(asset,datasource):\n\t# get current trend (buy/sell, longs/shorts)\n\tbuy = 0\n\tsell = 0\n\tretry = True\n\tattempts = 0\n\txc = \"BinanceUS\"\n\tif datasource == \"coinbase\":\n\t\txc = \"Coinbase\"\n\tfor i in [\"1d\",\"4h\"]:\n\t\ttry:\n\t\t\thandler = TA_Handler(\n\t\t\t\tsymbol=f\"{asset}USD\",\n\t\t\t\texchange=xc,\n\t\t\t\tscreener=\"crypto\",\n\t\t\t\tinterval=i,\n\t\t\t\ttimeout=None\n\t\t\t)\n\t\t\twhile retry and attempts < 200:\n\t\t\t\ttry:\n\t\t\t\t\tanalysis = handler.get_analysis().summary\n\t\t\t\t\tretry = False\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tprint(f\"{asset},{i},{xc} - Error while getting tradingview data!\")\n\t\t\t\t\tattempts += 1\n\t\t\t\t\t# print(\"TA Connection error, retrying...\")\n\t\t\tbuy += analysis['BUY']\n\t\t\tsell += analysis['SELL']\n\t\texcept TypeError:\n\t\t\tcontinue\n\t\t# time.sleep()\n\t# if buy > sell:\n\t# \treturn 1\n\t# elif buy < sell:\n\t# \treturn -1\n\t# else:\n\t# \treturn 0\n\treturn (buy,sell)\n\t# potential \"bull \"score\" metric:\n\t\t# tradingview recommendation for both asset and bitcoin\n\t\t# ta recommendation on bitfinex shorts + longs for both asset and bitcoin\n\t\t# inverse 4h upside momemtum for both asset and bitcoin\n\ndef get_btcpair_recc(client,asset):\n\tasset_etf = client.db.etfs.find_one({'asset':asset})\n\tasset_buy = asset_etf['recc_buy']\n\tasset_sell = asset_etf['recc_sell']\n\tbtc_etf = client.db.etfs.find_one({'asset':'BTC'})\n\tbtc_buy = btc_etf['recc_buy']\n\tbtc_sell = btc_etf['recc_sell']\n\treturn (asset_buy + btc_buy - asset_sell - btc_sell)\n\ndef update_metrics(client,asset,assetpair,datasource):\n\t# print(f\"{assetpair} - Calculating upside momemtum\")\n\t(um15,um4,vol24h,atr_4h,atr_4h_pct) = calculate_upside_momemtum(assetpair,datasource)\n\ttostring = \"\\n----------------------------------------------------\\n\"\n\ttostring += f\"{assetpair} - um15: {um15}\\n\"\n\ttostring += f\"{assetpair} - um4: {um4}\\n\"\n\ttostring += f\"{assetpair} - vol24h: {vol24h}\\n\"\n\t# print(f\"{assetpair} - Calculating volatility\")\n\tv28d = calculate_28d_volatility(assetpair,datasource)\n\ttostring += f\"{assetpair} - v28d: {v28d}\\n\"\n\t# print(f\"{asset} - Getting recommendation\")\n\t(recc_buy,recc_sell) = get_recommendation(asset,datasource)\n\tbitcoin_pair_recc = get_btcpair_recc(client,asset)\n\n\ttostring += f\"{assetpair} - recc: {recc_buy - recc_sell}\"\n\ttostring += \"\\n----------------------------------------------------\\n\"\n\n\t# update database metrics for asset\n\tclient.updateETFMetrics(asset,{\n\t\t'um_15m':um15,\n\t\t'um_4h':um4,\n\t\t'volume_24h':vol24h,\n\t\t'volatility_28d':v28d,\n\t\t'volatility_4h':atr_4h,\n\t\t'atr_4h_pct':atr_4h_pct,\n\t\t'recc_buy':recc_buy,\n\t\t'recc_sell':recc_sell,\n\t\t'bitcoin_pair_recc':bitcoin_pair_recc,\n\t\t'datasource':datasource,\n\t\t'scanStatus':'updated',\n\t\t'lastUpdated':datetime.now()\n\t})\n\n\t# print(tostring)\n\ndef run(assets,datasource,client=None):\n\tif client == None:\n\t\tclient = CryptoDB()\n\tapp_status = client.getAppStatus()\n\twhile app_status == \"running\":\n\t\tupdate_metrics(client,'BTC','BTC-USD','coinbase')\n\t\tfor a,n in assets.items():\n\t\t\tif a != 'BTC':\n\t\t\t\tupdate_metrics(client,a,n,datasource)\n\t\t\t\ttime.sleep(0.4)\n\t\t\tapp_status = client.getAppStatus()\n\t\t\tif app_status != \"running\":\n\t\t\t\tbreak\n\tprint(f\"App status changed to: \\'{app_status}\\', shutting down scanner\")\n", "repo_name": "seann27/ETFTrader", "sub_path": "ScanETFS.py", "file_name": "ScanETFS.py", "file_ext": "py", "file_size_in_byte": 7794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "kucoinapi.KucoinAPI", "line_number": 13, "usage_type": "call"}, {"api_name": "re.search", "line_number": 18, "usage_type": "call"}, {"api_name": "re.search", "line_number": 24, "usage_type": "call"}, {"api_name": "utilities.try_request", "line_number": 44, "usage_type": "call"}, {"api_name": "utilities.try_request", "line_number": 50, "usage_type": "call"}, {"api_name": "re.search", "line_number": 52, "usage_type": "call"}, {"api_name": "UpsideMomemtum.get_coinbase_data", "line_number": 98, "usage_type": "call"}, {"api_name": "UpsideMomemtum.get_binance_data", "line_number": 100, "usage_type": "call"}, {"api_name": "UpsideMomemtum.get_upside_momemtum", "line_number": 103, "usage_type": "call"}, {"api_name": "UpsideMomemtum.backfill_data", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 106, "usage_type": "call"}, {"api_name": "UpsideMomemtum.get_upside_momemtum", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 115, "usage_type": "call"}, {"api_name": "UpsideMomemtum.get_coinbase_data", "line_number": 137, "usage_type": "call"}, {"api_name": "UpsideMomemtum.get_binance_data", "line_number": 139, "usage_type": "call"}, {"api_name": "tradingview_ta.TA_Handler", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 227, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 227, "usage_type": "name"}, {"api_name": "MongoDBHandle.CryptoDB", "line_number": 234, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 241, "usage_type": "call"}]} +{"seq_id": "33220395682", "text": "import os\nimport csv\nimport cv2\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten, SpatialDropout2D, ELU\nfrom keras.layers import Convolution2D, MaxPooling2D, Cropping2D\nfrom keras.layers.core import Lambda\n\nfrom keras.optimizers import SGD, Adam, RMSprop\nfrom keras.utils import np_utils\n\nfrom keras.callbacks import ModelCheckpoint\n\nfrom keras.models import model_from_json\n\n#1. Create generator\n\n# Images filepaths for generator\nsamples = []\n\n\ndef add_to_samples(csv_filepath, samples):\n with open(csv_filepath) as csvfile:\n reader = csv.reader(csvfile)\n for line in reader:\n samples.append(line)\n return samples\n\n\nsamples = add_to_samples('./data/driving_log.csv', samples)\n\nsamples = add_to_samples('./data/simulator_training_data/driving_log.csv', samples) # header already removed\n\n# Remove header, Udacity data comes with a header, simulator stores data without a header\nsamples = samples[1:]\n\nprint(\"Samples: \", len(samples))\n\n# Split samples into training and validation sets to reduce overfitting\n#10 perdent of the data points selected as validation samples\ntrain_samples, validation_samples = train_test_split(samples, test_size=0.1)\n\n\ndef generator(samples, batch_size=32):\n num_samples = len(samples)\n while 1: # Loop forever so the generator never terminates\n shuffle(samples)\n for offset in range(0, num_samples, batch_size):\n batch_samples = samples[offset:offset + batch_size]\n\n images = []\n steering_angles = []\n correction = 0.2 #Correction to be applied to the left and right images\n for batch_sample in batch_samples:\n name = './data/' + batch_sample[0]\n center_image = mpimg.imread(name)\n steering_angle = float(batch_sample[3])\n left_name = './data/' + batch_sample[1].lstrip() # had to strip the leading white space in the left image value from CSV\n left_image = mpimg.imread(left_name)\n right_name = './data/' + batch_sample[2].lstrip() # had to strip the leading white space in the right image value from CSV\n right_image = mpimg.imread(right_name)\n images.append(center_image)\n steering_angles.append(steering_angle)\n images.append(np.fliplr(center_image)) #flipped center image\n steering_angles.append(-1.0 * steering_angle)\n images.append(left_image)\n steering_angles.append(steering_angle + correction)\n images.append(np.fliplr(left_image)) #flipped left image\n steering_angles.append(-1.0 * (steering_angle + correction))\n images.append(right_image)\n steering_angles.append(steering_angle - correction)\n images.append(np.fliplr(right_image)) #flipped right image\n steering_angles.append(-1.0 * (steering_angle -correction))\n\n X_train = np.array(images)\n y_train = np.array(steering_angles)\n\n yield shuffle(X_train, y_train)\n\n\n# compile and train the model using the generator function\ntrain_generator = generator(train_samples, batch_size=32)\nvalidation_generator = generator(validation_samples, batch_size=32)\n\n\n#2. Preprocess Data\n\ndef resize_comma(image):\n import tensorflow as tf # This import is required to prevent pre-processing in drive.py, had to update tf in the starter kit environment\n return tf.image.resize_images(image,( 40, 160))\n\n\n#3. Model\n\n# Model adapted from Comma.ai model\n\nmodel = Sequential()\n\n# Crop 70 pixels from the top of the image to eliminate objects above the driving horizon and 25 from the bottom to eliminate the hood\nmodel.add(Cropping2D(cropping=((70, 25), (0, 0)),\n dim_ordering='tf', # default\n input_shape=(160, 320, 3)))\n\n# Resize the data\nmodel.add(Lambda(resize_comma))\n\n# Normalise and mean center by subtracting 0.5 the images as in the course lecture\nmodel.add(Lambda(lambda x: (x / 255.0) - 0.5))\n\n# Conv layer 1\nmodel.add(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode=\"same\"))\nmodel.add(ELU())\n\n# Conv layer 2\nmodel.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode=\"same\"))\nmodel.add(ELU())\n\n# Conv layer 3\nmodel.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode=\"same\"))\n\n#Flatten, dropout to prevent overfitting\nmodel.add(Flatten())\nmodel.add(Dropout(.2))\nmodel.add(ELU())\n\n# Fully connected layer 1\nmodel.add(Dense(512))\nmodel.add(Dropout(.5))\nmodel.add(ELU())\n\n# Fully connected layer 2\nmodel.add(Dense(50))\nmodel.add(ELU())\n\n#Final layer only one neuron as the steering wheel angle is continous\nmodel.add(Dense(1))\n\n#Adam optimizer to manage learning rate\nadam = Adam(lr=0.0001)\n\nmodel.compile(optimizer=adam, loss=\"mse\", metrics=['accuracy'])\n\nprint(\"Model summary:\\n\", model.summary())\n\n# 4. Training\nbatch_size = 32\nnb_epoch = 2\n\n# weights after every epoch\ncheckpointer = ModelCheckpoint(filepath=\"./tmp/v2-weights.{epoch:02d}-{val_loss:.2f}.hdf5\", verbose=1,\n save_best_only=False)\n\n# Train model using generator\nmodel.fit_generator(train_generator,\n samples_per_epoch=len(train_samples) * 3 * 2, # multiplier for flipped and left right images\n validation_data=validation_generator,\n nb_val_samples=len(validation_samples), nb_epoch=nb_epoch,\n callbacks=[checkpointer])\n\n# 5. Final Model\n\nmodel_json = model.to_json()\nwith open(\"model.json\", \"w\") as json_file:\n json_file.write(model_json)\n\nmodel.save(\"model.h5\")\nprint(\"Saved model to disk\")", "repo_name": "mgaonkar/Self_Driving_Car_Engineer_Behavioral_cloning", "sub_path": "model1.py", "file_name": "model1.py", "file_ext": "py", "file_size_in_byte": 5884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "csv.reader", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.image.imread", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.fliplr", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.image.resize_images", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 98, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Cropping2D", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers.core.Lambda", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.core.Lambda", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.ELU", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 123, "usage_type": "call"}, {"api_name": "keras.layers.ELU", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 127, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.layers.ELU", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.layers.ELU", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.layers.ELU", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 144, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "17051782194", "text": "import sys\n\nimport numpy as np\nfrom fastdtw import fastdtw\nfrom scipy import spatial, stats\nimport scipy.fftpack as spfft\nfrom scipy.signal import savgol_filter\n\n\"\"\" Utilities for signal reconstruction\nThis module contains functions that can downsample a signal, normalize signals\nor datasets, and measure the distance between 2 signals.\n\n- test\n\n\n\"\"\"\n\ndef fixed_downsample(signal,samples,ret_ind=True,last_ind=True):\n N = len(signal)\n gap = N//samples\n r = N%samples\n ind = []\n for i in range(samples):\n indx = i*gap\n ind.append(indx)\n if last_ind:\n ind[-1] = N-1\n ind.sort()\n\n ds = signal[ind]\n if ret_ind:\n return (ind,ds)\n else:\n return ds\n \ndef fixed_downsample2(signal,samples,ret_ind=True,last_ind=True):\n N = len(signal)\n gap = N/float(samples-1)\n #r = N%samples\n ind = []\n for i in range(samples):\n indx = int(round(i*gap))\n ind.append(indx)\n if last_ind:\n ind[-1] = N-1\n ind.sort()\n\n ds = signal[ind]\n if ret_ind:\n return (ind,ds)\n else:\n return ds\n\n\n\ndef fixed_downsampl_dep(\n signal, nb_pts, random=True, ret_ind=True,first_ind=True,last_ind=True):\n\n \"\"\" Applies a fixed length downsampling\n\n This function takes a signal as input, and will return a new signal with\n the number of points required. It can do this in 2 ways:\n\n - Uniform: the function will take equidistant points of the input signal\n\n - Random: the function will take random points of the input signal\n\n Parameters\n ----------\n signal : list\n the signal to downsample\n nb_pts : integer\n the number of points the downsampled signal will have\n random : boolean\n if True, returns random downsampling,\n if False, returns uniform downsampling\n ret_ind : boolean\n if True, the function returns the list of indices\n first_ind : boolean\n if True, the downsampled signal will contain the first point of\n the original signal\n last_ind : boolean\n if True, the downsampled signal will contain the last point of\n the original signal\n\n Returns\n -------\n ind : list\n indices that correspond to the position of the downsampled points\n y : numpy.ndarray\n the downsampled signal\n\n \"\"\"\n\n n = len(signal)\n ind = []\n\n if random:\n # uses a numpy function to randomly select indices of the original signal\n ind = np.random.choice(n, nb_pts, replace=False)\n ind.sort()\n ind = ind.tolist()\n else:\n # tries to find a uniform distance between the points\n # the euclidean division of n by nb_pts is the distance between the\n # points\n ratio = n // nb_pts\n # if remainder is not 0, we can not take equidistant pointw\n r = n % nb_pts\n sig = signal.copy()\n # this loop takes enough point on the extremities\n # to reduce the length of the signal and have a uniform distance\n for i in range(0, r):\n # if i is an even number\n if i%2 == 0:\n # take a point at the beginning of signal\n ind.append(i//2)\n # if i is odd\n else:\n # take a point at the end of signal\n ind.append(len(sig)-1 - (i//2))\n # once we remove enough points to have equidistant points, retain these\n # equidistant points\n for i in range((r+1)//2 + (r+1)%2 -1, nb_pts - (r+1)//2):\n # take points with ratio as a distance\n j = ratio*i\n ind.append(j)\n # sort the list to avoid any errors\n ind.sort()\n if first_ind:\n ind[0]=0\n if last_ind:\n ind[len(ind)-1] = len(signal)-1\n # take the values of the input signal at the indices chosen above\n y = np.array(signal[ind])\n if ret_ind:\n return (ind, y)\n else:\n return y\n\ndef var_downsample(signal, threshold=0.01, ret_ind=True, last_ind=True):\n # This function applies a variable length downsampling\n # Input :\n # signal (list) : the signal to downsample\n # threshold (float) : is the minimal variation needed to take a point\n # ret_ind (boolean) : if True,\n # the function returns the list of indices\n # last_ind (boolean) : if True, the downsampled signal will contain\n # the last point of the original signal\n # Output :\n # if ret_ind=True : - ind (list): indices that correspond to the\n # position of the downsampled points\n # - y (numpy array): the downsampled signal\n # if ret_ind = False : -y (numpy array): the downsampled signal\n \"\"\" Applies a variable length downsampling\n\n This function takes a signal as input, and will return a new signal with\n a variable number of points. This number of points depend on the threshold\n chosen. It works with a \"percentage rule\". That means that we retain a\n point only if the variation between this point and the last point is took\n is greater than the threshold.\n\n\n For example, we have taken the first point, with a value of 100.\n The values of the points between 1 and 5 are contained in the interval\n [99,101] so we don't take them. 6th point has a value of 102, so that makes\n a variation greater than 1% (the threshold we chose for the example), so\n we retain the point. We then compare the next points with the 6th point.\n\n Parameters\n ----------\n signal : list\n the signal to downsample\n threshold : float\n the minimal variation needed to take a point. The condition is\n\n ``if np.abs(next_point-last_point) > threshold * np.abs(last_point)``\n\n ``retain next_point``\n\n ret_ind : boolean\n if True, the function returns the list of indices\n last_ind : boolean\n if True, the downsampled signal will contain the last point of\n the original signal\n\n Returns\n -------\n ind : list\n indices that correspond to the position of the downsampled points\n y : numpy.ndarray\n the downsampled signal\n\n \"\"\"\n\n # we start with the first point\n ind = [0]\n # curr_val is the value of the last point we took\n curr_val = signal[0]\n # for all the remaining points in the signal\n for i in range(1, len(signal)-1):\n # check if the variation is greater than threshold\n #if np.abs((signal[i]-curr_val)) > threshold*np.abs(curr_val):\n if np.abs((signal[i]-curr_val)) > threshold:\n # if so, retain this point and take its value as curr_val\n ind.append(i)\n curr_val = signal[i]\n if last_ind:\n ind.append(len(signal)-1)\n # the output signal is the values of the input signal, at the indices we\n # chose above\n pass\n y = signal[ind]\n if ret_ind:\n return (ind, np.array(y))\n else:\n return np.array(y)\n\ndef adaptive_downsample(signal, threshold, distance, last_ind=True):\n\n \"\"\" Applies a combination of fixed and by percentage downsampling\n\n This downsampling method combines the by percentage rule, with a distance\n rule. We retain a point if one of these 2 conditions is met:\n\n - The variation is greater than the threshold\n - The distance between the last point taken and the point we compare is\n greater than the maximum distance chosen.\n\n This downsampling has the advantage of not missing important information\n such as sudden peaks, thanks to the percentage rule, and it avoids\n missing information if the signal is constant or varies very slowly, thanks\n to the distance rule downsampling.\n\n Parameters\n ----------\n signal : list\n the signal to downsample\n threshold : float\n the minimal variation needed to take a point. The condition is\n\n ``if np.abs(next_point-last_point) > threshold * np.abs(last_point)``\n ``retain next_point``\n distance : integer\n maximum distance between 2 points\n last_ind : boolean\n if True, the downsampled signal will contain the last point of\n the original signal\n\n Returns\n -------\n ind : list\n indices that correspond to the position of the downsampled points\n y : numpy.ndarray\n the downsampled signal\n labels : list\n list containing information about the type of downsampling for each\n point:\n\n - 0 if the point was chosen for its distance with the last point\n - 1 if the point was chosen because of its variation\n\n \"\"\"\n labels = [0]\n ind = [0]\n last_ind = 0\n for i in range(1, len(signal)):\n\n ###################\n # PERCENTAGE RULE #\n ###################\n\n # if last point has a value of 0,the next point will be more than 1%\n # of 0 so we make sure to take it and avoid a division by 0\n if signal[ind[last_ind]] == 0:\n # to be sure to take it, we make the variation greater than\n # threshold\n variation = threshold + 1\n else:\n variation = np.abs(\n (signal[i]-signal[ind[last_ind]])/signal[ind[last_ind]])\n if variation >= threshold:\n ind.append(i)\n labels.append(1)\n last_ind += 1\n\n else:\n\n #################\n # DISTANCE RULE #\n #################\n\n if i - ind[last_ind] >= distance:\n ind.append(i)\n labels.append(0)\n last_ind += 1\n else:\n continue\n if last_ind:\n # we choose a label of 0 if the last point was not already retained\n if ind[last_ind] != len(signal)-1:\n ind.append(len(signal)-1)\n last_ind += 1\n labels.append(0)\n y = signal[ind]\n return (ind, y, labels)\n\ndef distance(signal1, signal2, metric='Euclidean'):\n\n \"\"\" Measures the distance between 2 signals\n\n This function can measure the distance between 2 signals\n using different metrics :\n\n - 'Dtw' : Dynamic Time Warping distance\n - 'Euclidean' : the Euclidean distance or norm 2\n - 'Braycurtis' : the Bray-Curtis distance\n - 'Manhattan' : the Manhattan distance or norm 1\n - 'Spearmanr' : the Spearman rank-order correlation\n - 'Cosine' : the cosine similarity measure\n - 'Minkowski' : the Minkowski distance\n - 'Correlation' : the correlation distance\n - 'Jaccard' : the Jaccard similarity coefficient\n - 'Canberra' : the Canberra distance\n - 'Chebyshev' : the Chebyshev distance\n\n Parameters\n ----------\n signal1 : list\n input signal\n signal2 : list\n input signal\n metric : string\n the metric to use, see above for a list of all metrics available\n\n Returns\n -------\n dist : float\n the distance between the 2 signals\n\n \"\"\"\n if (np.array_equal(signal1-signal2, np.zeros(len(signal1)))):\n return 0\n else:\n if metric == 'Dtw':\n dist,path = fastdtw(signal1, signal2)\n elif metric == 'Euclidean':\n dist = spatial.distance.euclidean(signal1, signal2)\n elif metric == 'Braycurtis':\n dist = spatial.distance.braycurtis(signal1, signal2)\n elif metric == 'Manhattan':\n dist = spatial.distance.cityblock(signal1, signal2)\n elif metric == 'Spearmanr':\n prsn = stats.spearmanr(signal1, signal2)\n dist = prsn[0]\n elif metric == 'Cosine':\n dist = spatial.distance.cosine(signal1, signal2)\n elif metric == 'Minkowski':\n dist = spatial.distance.minkowski(signal1, signal2, p=2)\n elif metric == 'Correlation':\n dist = spatial.distance.correlation(signal1, signal2)\n elif metric == 'Jaccard':\n dist = spatial.distance.jaccard(signal1, signal2)\n #elif metric == 'Median':\n # dist = np.median(signal1, signal2)\n #elif metric == 'Levenshtein':\n # dist = levenshteinDistance(signal1, signal2)\n elif metric == 'Canberra':\n dist = spatial.distance.canberra(signal1, signal2)\n elif metric == 'Chebyshev':\n dist = spatial.distance.chebyshev(signal1, signal2)\n else:\n print('{0} is not implemented, returning euclidean distance'\n .format(metric))\n dist = distance(signal1, signal2)\n return dist\n\ndef rel_accuracy(or_sig, recon_sig, random, metric='Euclidean'):\n \"\"\" Gives a percentage of error of a reconstruction\n\n This function gives an idea of how good a reconstruction is by comparing\n the distance between the original signal and the reconstructed signal to\n the distance between the original signal and a randomly generated one.\n Then it assumes that the distance between random and original signal is\n 100% error, and with a rule of three, it deducts the percentage of error of\n the reconstruction.\n\n Sometimes, the percentage will be more than 100%, this means that the\n reconstruction was worse than a randomly generated signal.\n\n The randomly generated signal has to be given as input so it can be used\n to compare a full dataset and give consistent results.\n\n Parameters\n ----------\n or_sig : list\n the original signal\n recon_sig : list\n the reconstructed signal\n random : list\n the randomly generated signal.\n metric : string\n the metric to use to measure the distance. See ``distance``\n documentation for a list of all the metrics available\n\n Returns\n -------\n rel_acc : float\n the percentage of error of the reconstruction.\n\n \"\"\"\n # distance between original and random, supposed to be high\n high = accuracy(or_sig,random,metric=metric)\n # distance between original and reconstructed, supposed to be low\n low = accuracy(or_sig,recon_sig,metric=metric)\n # high is 100% or 1 error, so the rule of three gives rel_acc = 1*low/high\n rel_acc = low/high\n return rel_acc\n\ndef normalize(signal):\n \"\"\" Normalization of a signal\n\n This function normalizes a signal X, with the following formula:\n\n `norm = X-mean(X)/std(x)`\n where std is the standard deviation.\n\n Parameters\n ----------\n signal : list\n the signal to normalize\n\n Returns\n -------\n y : numpy.ndarray\n the normalized signal\n\n \"\"\"\n if np.std(signal) != 0:\n std = np.std(signal)\n else :\n std = 1\n y = np.array((signal-np.mean(signal))/std, dtype=np.float64)\n return y\n\ndef normalize_data(data, threshold=0, kind='whole'):\n\n \"\"\" Normalization of a dataset\n\n This function normalizes every signal of a dataset so they have values\n between -1 and 1.\n There are 3 ways of normalizing the dataset, see the parameter 'kind' for\n more information.\n\n Parameters\n ----------\n data : 2D array or equivalent\n the dataset to normalize\n threshold : float\n a value that will make the maximum value a bit larger to include new\n signals that may have a larger maximum value than the signals seen\n in the dataset\n kind : string\n - 'row':\n\n the row normalization normalizes every row by dividing all the\n values of the signal by the maximum, in absolute value, of the signal\n\n - 'whole_pos' :\n\n whole_pos stands for whole dataset with positive values.\n It makes sure the values are between 0 and 1. The normalization adds\n the smallest value to the whole dataset, if it is negative. Then it\n divides the dataset by the largest absolute value.\n\n - 'whole':\n\n normalizes the whole dataset, by dividing it by the largest\n absolute value of the dataset.\n\n Returns\n -------\n new_data : 2D array or equivalent\n the normalized dataset\n\n \"\"\"\n data_cop = data.copy()\n data_cop = np.array(data_cop)\n new_data = []\n if kind=='row_pos':\n for signal in data_cop:\n mini = np.min(signal)\n if mini < 0:\n signal = signal + np.abs(mini)\n if mini > 0:\n signal = signal - np.abs(mini)\n maxi = np.max(signal)\n if maxi == 0:\n new_sig = signal\n else:\n new_sig = np.array(signal) / maxi\n new_data.append(new_sig)\n return new_data\n elif kind=='row':\n for signal in data_cop:\n maxi = np.max(np.abs(signal))\n maxi = maxi + threshold*maxi\n if maxi == 0:\n new_sig = signal\n else:\n new_sig = np.array(signal) / maxi\n new_data.append(new_sig)\n return new_data\n elif kind == 'whole_pos':\n mini = np.min(data_cop)\n if mini < 0:\n data_cop = data_cop + np.abs(mini)\n maxi = np.max(data_cop)\n maxi = maxi + threshold*maxi\n new_data = data_cop / maxi\n return new_data\n else:\n maxi = np.max(data_cop)\n maxi = maxi + threshold*maxi\n new_data = data_cop / maxi\n return new_data\n\ndef smooth(signal, intensity=11, deg=3):\n \"\"\" Smoothes a signal, with a Savgol filter\n\n Parameters\n ----------\n signal : list\n the signal to smooth\n intensity : integer\n see scipy.signal.savgol_filter for information\n deg : integer\n see scipy.signal.savgol_filter for information\n\n Returns\n -------\n smoothed_signal : list\n the smoothed signal\n \"\"\"\n smoothed_signal = savgol_filter(signal, 11, 3)\n return smoothed_signal\n\ndef error_correction(y_original, ind, labels, threshold):\n\n \"\"\" Error correction algorithm for a reconstructed signal\n\n This function works only with signals that have been downsampled using\n the ``adaptive_downsample`` function and then reconstructed using any\n method.\n\n It uses the information given by the adaptive downsampling to make sure\n the reconstructed points are not larger than the threshold of the\n percentage rule. If it is the case, the point will be given the\n smallest (or largest) value acceptable.\n\n Parameters\n ----------\n y_original : list\n the reconstructed signal, after an adaptive downsampling\n ind : list\n indices that correspond to the position of the downsampled points\n labels : list\n the labels given by the ``adaptive_downsample`` function\n threshold : float\n the threshold value used for the downsampling\n\n Returns\n -------\n y : list\n the error corrected reconstructed signal\n\n \"\"\"\n\n# WARNING, this function has not been tested and may not improve the\n# accuracy of the reconstruction, use with caution\n\n y = y_original.copy()\n ind = np.array(ind)\n # gives the position of the points that have a label == 1\n ind_var = [i for i in range(len(ind)) if labels[i]==1]\n for i in ind_var:\n # last known point before the point with label==1\n ref_point = y[ind[i-1]]\n # infimum of interval where the original point should be\n inf = ref_point - np.abs(ref_point)*threshold\n # supremum of interval where the original point should be\n sup = ref_point + np.abs(ref_point)*threshold\n # position of reconstructed points to correct,\n # between the 2 known points\n x = np.arange(ind[i-1]+1, ind[i],1)\n if x.size == 0:\n continue\n else:\n for pt in x:\n if y[pt]< inf:\n y[pt] = ref_point*(1-threshold)\n elif y[pt]> sup:\n y[pt]= ref_point*(1+threshold)\n else :\n continue\n return y\n", "repo_name": "matt-bellucci/ZeLiC", "sub_path": "recon_utils.py", "file_name": "recon_utils.py", "file_ext": "py", "file_size_in_byte": 19774, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.random.choice", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 342, "usage_type": "call"}, {"api_name": "fastdtw.fastdtw", "line_number": 346, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 348, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 348, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 348, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.braycurtis", "line_number": 350, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 350, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 350, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.cityblock", "line_number": 352, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 352, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 352, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 354, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 354, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 357, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 357, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 357, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.minkowski", "line_number": 359, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 359, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 359, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.correlation", "line_number": 361, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 361, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 361, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.jaccard", "line_number": 363, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 363, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 363, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.canberra", "line_number": 369, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 369, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 369, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.chebyshev", "line_number": 371, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 371, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 371, "usage_type": "name"}, {"api_name": "numpy.std", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 443, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 443, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 443, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 494, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 501, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 506, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 506, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 515, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 517, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 523, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 583, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 592, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 595, "usage_type": "call"}]} +{"seq_id": "72833839838", "text": "import sqlite3\nfrom abc import ABC\nimport falcon\n\n\nclass Model(ABC):\n\n fields = []\n # Used for storing values that have a different db column name than is used in json representation\n # format: {name: db_name}\n alias_fields = {}\n # Used for fields that will come from the API but will not be written to the database. Typically due to additional\n # processing being necessary.\n extra_fields = set()\n # Used to get a subset of fields for returning results in List views (GET without an id)\n summary_fields = set()\n autoincrement_id = True\n table_name = \"\"\n exception_map = {}\n\n def __init__(self, **kwargs):\n for key in self.alias_fields.keys():\n if key not in self.fields and key not in self.extra_fields:\n raise ValueError(\"Invalid alias_field value: {} does not exist in class fields\".format(key))\n\n self._kvs = {}\n for each in self.fields + list(self.extra_fields):\n self._kvs[each] = None\n for k, v in kwargs.items():\n if k in self.fields or k in self.extra_fields:\n self._kvs[k] = v\n else:\n raise NameError(\"Invalid field name {}\".format(k))\n\n def __getattr__(self, item):\n return self._kvs[item]\n\n def __setattr__(self, key, value):\n if key in self.fields or key in self.extra_fields:\n self._kvs[key] = value\n else:\n super().__setattr__(key, value)\n\n @classmethod\n def has_external_file_name(cls):\n return \"external_file_name\" in cls.fields\n\n @classmethod\n def from_db(cls, row):\n kvs = {}\n for i, each in enumerate(row):\n kvs[cls.fields[i]] = each\n return cls(**kvs)\n\n @classmethod\n def from_media(cls, data: dict):\n return cls(**data)\n\n @classmethod\n def from_req(cls, req: falcon.Request):\n c = cls(**req.media)\n if \"campaign_id\" in c.fields:\n c.campaign_id = req.context[\"user\"][\"campaign\"]\n if \"creator_id\" in c.fields:\n c.creator_id = req.context[\"user\"][\"id\"]\n return c\n\n def to_dict(self) -> dict:\n return self._kvs\n\n def to_summary_dict(self) -> dict:\n d = {}\n for k, v in self._kvs.items():\n if k in self.summary_fields:\n d[k] = v\n return d\n\n def execute_sql(self, c: sqlite3.Cursor, sql: str, args):\n try:\n c.execute(sql, args)\n except Exception as e:\n if type(e) in self.exception_map:\n raise self.exception_map[type(e)]\n else:\n raise e\n\n def select_fields(self, index=0):\n columns = []\n for each in self.fields[index:]:\n if self._kvs[each] is None:\n continue\n if each in self.alias_fields:\n each = self.alias_fields[each]\n columns.append(\"[\" + each + \"]\")\n return columns\n\n def insert(self, db: sqlite3.Connection):\n c = db.cursor()\n columns = self.select_fields(1 if self.autoincrement_id else 0)\n values = \",\".join([\"?\"] * len(columns))\n columns = \",\".join(columns)\n sql = f\"INSERT INTO {self.table_name} ({columns}) VALUES ({values})\"\n self.execute_sql(c, sql, [self._kvs[each] for each in self.fields if self._kvs[each] is not None])\n if self.autoincrement_id:\n self._kvs[self.fields[0]] = c.lastrowid\n\n def update(self, db: sqlite3.Connection):\n c = db.cursor()\n columns = [f\"{field}=?\" for field in self.select_fields(1)]\n columns = \",\".join(columns)\n sql = f\"UPDATE [{self.table_name}] SET {columns} WHERE [{self.fields[0]}]=?\"\n self.execute_sql(c, sql, [self._kvs[each] for each in self.fields[1:] if self._kvs[each] is not None] + [self._kvs[self.fields[0]]])\n\n\nclass Relation:\n model: Model = None\n this: Model = None\n that: Model = None\n to_one = False\n\n def __init__(self):\n self.map_table_name = self.this.table_name + \"_\" + self.that.table_name\n\n def from_req(self, req: falcon.Request):\n instance = self.model.from_req(req)\n self.model = instance # This is probably a bad idea... but for now idc since this is all pretty rough\n\n def find_all(self, db: sqlite3.Connection, req: falcon.Request, column: str, id: str):\n c = db.cursor()\n this_id = self.this.table_name + \"_id\"\n that_id = self.that.table_name + \"_id\"\n user = req.context['user']['id']\n qualified_fields = [self.model.table_name + \".\" + each for each in self.model.fields]\n # In short, to see a relation table entry, the entry must be public, or the user must own BOTH sides\n # TODO allow DMs to see everything\n sql = f\"\"\"SELECT {','.join(qualified_fields)} FROM {self.map_table_name}\n JOIN {self.this.table_name} ON {self.this.table_name}.id = {self.model.table_name}.{this_id}\n JOIN {self.that.table_name} ON {self.that.table_name}.id = {self.model.table_name}.{that_id}\n WHERE {self.model.table_name}.{column}=? AND ({self.model.table_name}.is_public = 1 \n OR ({self.this.table_name}.creator_id = ? AND {self.that.table_name}.creator_id = ?))\"\"\"\n rows = c.execute(sql, (id, user, user)).fetchall()\n return rows\n\n def add(self, db: sqlite3.Connection):\n c = db.cursor()\n columns = [f\"[{field}]\" for field in self.model.fields]\n values = \",\".join([\"?\"] * len(columns))\n columns = \",\".join(columns)\n args = [self.model._kvs[each] for each in self.model.fields]\n c.execute(f\"INSERT INTO {self.map_table_name} ({columns}) VALUES ({values})\", args)\n", "repo_name": "qmulosoft/lorelog-api", "sub_path": "src/lore_log/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 5679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "abc.ABC", "line_number": 6, "usage_type": "name"}, {"api_name": "falcon.Request", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sqlite3.Cursor", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sqlite3.Connection", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sqlite3.Connection", "line_number": 107, "usage_type": "attribute"}, {"api_name": "falcon.Request", "line_number": 124, "usage_type": "attribute"}, {"api_name": "sqlite3.Connection", "line_number": 128, "usage_type": "attribute"}, {"api_name": "falcon.Request", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sqlite3.Connection", "line_number": 144, "usage_type": "attribute"}]} +{"seq_id": "17792236852", "text": "from flask import Flask, render_template\nimport datetime\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n print(\"XXX\")\n return \"Hello flask\"\n\n\n@app.route(\"/xxx\")\ndef xxx():\n return \"xxxxxxxxxxxxxxx\"\n\n\n# @app.route(\"/\")\n# def hello(name):\n# return f\"

Hello {name}

\"\n\n#\n@app.route(\"/index\")\ndef start():\n return render_template(\"index.html\")\n\n\n@app.route(\"/index2\")\ndef start2():\n headline = \"traytatatta\"\n return render_template(\"index2.html\", headline=headline)\n\n@app.route(\"/newyear\")\ndef newyear_checker():\n now = datetime.datetime.now()\n newyear = now.month == 1 and now.day == 1\n if newyear is False:\n a = \"Nie\"\n else:\n a = \"Tak!\"\n headline = \"Czy mamy Nowy Rok?\"\n return render_template(\"index2.html\", a=a, headline=headline)\n # return render_template(\"index3.html\", newyear=newyear, headline=headline) //mozna ifem w templatce\n\n@app.route(\"/xtend\")\ndef xtend():\n headline = \"traytatatta\"\n return render_template(\"xtend.html\", headline=headline)", "repo_name": "bart3k1/CS50", "sub_path": "CS_Flask/test02basicsflask.py", "file_name": "test02basicsflask.py", "file_ext": "py", "file_size_in_byte": 1040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "40310412584", "text": "from collections import defaultdict\nimport contextlib\nimport zlib\nimport datetime\nimport types\nimport operator\nfrom io import StringIO\nfrom time import perf_counter\n\n# Third-party modules\nimport bson\nimport cachetools\nimport orjson\nfrom pymongo import UpdateOne\nfrom typing import List, Dict, Any, Optional, Tuple\nfrom builtins import str, object\n\n# NOC modules\nfrom noc.core.scheduler.periodicjob import PeriodicJob\nfrom noc.core.models.problem import ProblemItem\nfrom noc.main.models.label import Label, MATCH_OPS\nfrom noc.sa.models.managedobject import ManagedObject\nfrom noc.inv.models.subinterface import SubInterface\nfrom noc.inv.models.interfaceprofile import InterfaceProfile\nfrom noc.core.debug import error_report\nfrom noc.core.log import PrefixLoggerAdapter\nfrom noc.inv.models.discoveryid import DiscoveryID\nfrom noc.inv.models.interface import Interface\nfrom noc.inv.models.link import Link\nfrom noc.core.mongo.connection import get_db\nfrom noc.core.service.error import RPCError, RPCRemoteError\nfrom noc.core.error import (\n ERR_CLI_AUTH_FAILED,\n ERR_CLI_NO_SUPER_COMMAND,\n ERR_CLI_LOW_PRIVILEGES,\n ERR_CLI_SSH_PROTOCOL_ERROR,\n ERR_CLI_CONNECTION_REFUSED,\n ERR_CLI_PASSWORD_TIMEOUT,\n)\nfrom noc.core.span import Span\nfrom noc.core.cache.base import cache\nfrom noc.core.perf import metrics\nfrom noc.core.comp import smart_bytes\nfrom noc.core.wf.interaction import Interaction\nfrom noc.core.wf.diagnostic import DiagnosticState, DiagnosticHub\n\n\nclass MODiscoveryJob(PeriodicJob):\n model = ManagedObject\n use_get_by_id = True\n use_offset = True\n # Name of umbrella class to cover discovery problems\n umbrella_cls = None\n # Job families\n is_box = False\n is_periodic = False\n # Get diagnostics with enabled discovery (Box/Periodic filtered)\n discovery_diagnostics = set()\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.out_buffer = StringIO()\n self.logger = PrefixLoggerAdapter(self.logger, \"\", target=self.out_buffer)\n self.check_timings = []\n self.problems: List[ProblemItem] = []\n self.caps = None\n self.has_fatal_error = False\n self.service = self.scheduler.service\n # Additional artefacts can be passed between checks in one session\n self.artefacts = {}\n\n def schedule_next(self, status):\n if self.check_timings:\n self.logger.info(\n \"Timings: %s\",\n \", \".join(\"%s = %.2fms\" % (n, t * 1000) for n, t in self.check_timings),\n )\n super().schedule_next(status)\n # Update alarm statuses\n # Clean up all open alarms as they has been disabled\n if self.get_umbrella_settings():\n self.update_alarms(self.problems, self.umbrella_cls)\n # Update diagnostics statuses\n self.update_diagnostics(self.problems)\n # Write job log\n key = \"discovery-%s-%s\" % (self.attrs[self.ATTR_CLASS], self.attrs[self.ATTR_KEY])\n problems = {}\n for p in list(self.problems):\n if not p.check:\n # Not Discovery problem\n continue\n path = \" | \".join(p.path)\n if p.check not in problems:\n problems[p.check] = defaultdict(str)\n if p.path:\n problems[p.check][path] = p.message\n else:\n # p[\"path\"] == \"\"\n problems[p.check][path] += f\"; {p.message}\"\n\n get_db()[\"noc.joblog\"].update_one(\n {\"_id\": key},\n {\n \"$set\": {\n \"log\": bson.Binary(zlib.compress(smart_bytes(self.out_buffer.getvalue()))),\n \"problems\": problems,\n }\n },\n upsert=True,\n )\n\n def get_running_policy(self):\n raise NotImplementedError\n\n def can_run(self):\n # Check object is managed\n if (\n Interaction.BoxDiscovery not in self.object.interactions\n and Interaction.PeriodicDiscovery not in self.object.interactions\n ):\n self.logger.info(\"Run Discovery on Object is not allowed. Skipping job\")\n return False\n return True\n\n @contextlib.contextmanager\n def check_timer(self, name):\n t = perf_counter()\n yield\n self.check_timings += [(name, perf_counter() - t)]\n\n def set_problem(\n self,\n check: Optional[str] = None,\n alarm_class: Optional[str] = None,\n path: Optional[List[str]] = None,\n message: Optional[str] = None,\n fatal: bool = False,\n diagnostic: Optional[str] = None,\n **kwargs,\n ):\n \"\"\"\n Set discovery problem\n :param check: Check name\n :param alarm_class: Alarm class instance or name\n :param path: Additional path\n :param message: Text message\n :param fatal: True if problem is fatal and all following checks\n must be disabled\n :param diagnostic: Diagnostic name affected by problem\n :param kwargs: Optional variables\n :return:\n \"\"\"\n self.logger.debug(\n \"[%s] Set problem: class=%s diagnostic=%s path=%s message=%s fatal=%s vars=%s\",\n check,\n alarm_class,\n diagnostic,\n path,\n message,\n fatal,\n kwargs,\n )\n self.problems += [\n ProblemItem(\n **{\n \"check\": check,\n \"alarm_class\": alarm_class,\n # in MongoDB Key must be string\n # \"path\": [str(path)] if path else [],\n \"labels\": [],\n \"diagnostic\": diagnostic,\n \"message\": message,\n \"fatal\": fatal,\n \"vars\": kwargs,\n }\n )\n ]\n if fatal:\n self.has_fatal_error = True\n\n def set_fatal_error(self):\n self.has_fatal_error = True\n\n def get_caps(self):\n \"\"\"\n Return object's capabilities\n :return:\n \"\"\"\n if self.caps is None:\n self.caps = self.object.get_caps()\n return self.caps\n\n def update_caps(self, caps, source):\n self.caps = self.object.update_caps(caps, source=source)\n\n def allow_sessions(self):\n r = self.object.can_cli_session()\n if r:\n self.object.get_profile().allow_cli_session(None, None)\n return r\n\n def load_diagnostic(self, is_box: bool = False, is_periodic: bool = False):\n r = set()\n for dc in self.object.iter_diagnostic_configs():\n if (is_box and not dc.discovery_box) or (is_periodic and not dc.discovery_periodic):\n continue\n # if dc.run_order != self.run_order:\n # continue\n if not dc.checks or dc.blocked:\n # Diagnostic without checks\n continue\n if dc.run_policy not in {\"A\", \"F\"}:\n continue\n r.add(dc.diagnostic)\n return r\n\n def update_diagnostics(self, problems: List[ProblemItem]):\n \"\"\"\n Syn problems to object diagnostic statuses\n :param problems:\n :return:\n \"\"\"\n #\n discovery_diagnostics = self.load_diagnostic(\n is_box=self.is_box, is_periodic=self.is_periodic\n )\n self.logger.debug(\"Updating diagnostics statuses: %s\", problems)\n if not discovery_diagnostics:\n self.logger.info(\"Discovered diagnostics not found\")\n return None\n now = datetime.datetime.now()\n processed = set()\n # Processed failed diagnostics\n with DiagnosticHub(self.object, sync_alarm=self.can_update_alarms()) as d:\n for p in problems:\n if p.diagnostic and p.diagnostic in discovery_diagnostics:\n d.set_state(\n p.diagnostic,\n state=DiagnosticState.failed,\n reason=p.message,\n changed_ts=now,\n )\n processed.add(p.diagnostic)\n # Set OK state\n # for diagnostic in discovery_diagnostics - processed:\n # self.object.set_diagnostic_state(diagnostic, state=True, changed_ts=now, bulk=bulk)\n # if bulk:\n # self.logger.info(\"Diagnostic changed: %s\", \", \".join(di.diagnostic for di in bulk))\n # self.object.save_diagnostics(self.object.id, bulk)\n # if self.can_update_alarms():\n # self.object.sync_diagnostic_alarm([d.diagnostic for d in bulk])\n\n def update_alarms(\n self, problems: List[ProblemItem], group_cls: str = None, group_reference: str = None\n ):\n \"\"\"\n Sync problems to alarm and use active_problems context variable\n for check active alarm group.\n\n * If empty problems and `group reference` not in active_problems- do nothing\n * If empty problems and `group reference` in active_problems - send empty ensure_group to dispose and remove it from context\n * If has problems - send ensure_group to dispose and save reference_group to active_problems context\n\n :param problems: List problems\n :param group_cls: Group Alarm Class\n :param group_reference: Group Reference\n :return:\n \"\"\"\n from noc.fm.models.alarmclass import AlarmClass\n\n self.logger.info(\"Updating alarm statuses\")\n group_cls: Optional[\"AlarmClass\"] = AlarmClass.get_by_name(group_cls or \"Group\")\n if not group_cls:\n self.logger.info(\"No umbrella alarm class. Alarm statuses not updated\")\n return\n\n group_reference = group_reference or f\"g:d:{self.object.id}:{group_cls.name}\"\n active_problems: Dict[str, List[str]] = self.context.get(\"active_problems\", {})\n if not problems and group_reference not in active_problems:\n # No money, no honey\n return\n details: List[Dict[str, Any]] = []\n now = datetime.datetime.now()\n for p in problems:\n if not p.alarm_class:\n continue\n ac = AlarmClass.get_by_name(p.alarm_class)\n if not ac:\n self.logger.info(\"Unknown alarm class %s. Skipping\", p.alarm_class)\n continue\n d_vars = {\"path\": \" | \".join(p.path), \"message\": p.message}\n if p.vars:\n d_vars.update(p.vars)\n labels = p.labels\n if p.fatal:\n labels += [\"noc::is_fatal::=\"]\n details += [\n {\n \"reference\": f\"d:{p.alarm_class}:{self.object.id}:{' | '.join(p.path)}\",\n \"alarm_class\": p.alarm_class,\n \"managed_object\": self.object.id,\n \"timestamp\": now,\n \"labels\": labels,\n \"vars\": d_vars,\n }\n ]\n msg = {\n \"$op\": \"ensure_group\",\n \"reference\": group_reference,\n \"alarm_class\": group_cls.name,\n \"alarms\": details,\n }\n stream, partition = self.object.alarms_stream_and_partition\n self.service.publish(\n orjson.dumps(msg),\n stream=stream,\n partition=partition,\n )\n self.logger.debug(\n \"Dispose: %s\", orjson.dumps(msg, option=orjson.OPT_INDENT_2).decode(\"utf-8\")\n )\n if not details and group_reference in active_problems:\n del active_problems[group_reference]\n else:\n active_problems[group_reference] = [d[\"reference\"] for d in details]\n self.context[\"active_problems\"] = active_problems\n\n def get_umbrella_settings(self) -> bool:\n \"\"\"\n Check enable Alarm for Discovery\n :param self:\n :return:\n \"\"\"\n prev_status = self.context.get(\"umbrella_settings\", False)\n current_status = self.can_update_alarms()\n\n self.context[\"umbrella_settings\"] = current_status\n\n if not prev_status and not current_status:\n return False\n return True\n\n def can_update_alarms(self):\n return False\n\n def get_fatal_alarm_weight(self):\n return 1\n\n def get_alarm_weight(self):\n return 1\n\n def set_artefact(self, name, value=None):\n \"\"\"\n Set artefact (opaque structure to be passed to following checks)\n :param name: Artefact name\n :param value: Opaque value\n :return:\n \"\"\"\n if not value:\n if name in self.artefacts:\n del self.artefacts[name]\n else:\n self.artefacts[name] = value or None\n\n def get_artefact(self, name):\n \"\"\"\n Get artefact by name\n :param name: artefact name\n :return: artefact\n \"\"\"\n return self.artefacts.get(name)\n\n def has_artefact(self, name):\n \"\"\"\n Check job has existing artefact\n :param name: artefact name\n :return: True, if artefact exists, False otherwise\n \"\"\"\n return name in self.artefacts\n\n\nclass DiscoveryCheck(object):\n name = None\n # If not none, check required script is available\n # before running check\n required_script = None\n # If not None, check object has all required capablities\n # from list\n required_capabilities = None\n # If not None, check job has all required artefacts\n required_artefacts = None\n #\n fatal_errors = {\n ERR_CLI_AUTH_FAILED,\n ERR_CLI_NO_SUPER_COMMAND,\n ERR_CLI_LOW_PRIVILEGES,\n ERR_CLI_CONNECTION_REFUSED,\n ERR_CLI_SSH_PROTOCOL_ERROR,\n ERR_CLI_PASSWORD_TIMEOUT,\n }\n # Error -> Alarm class mappings\n error_map = {\n ERR_CLI_AUTH_FAILED: \"Discovery | Error | Auth Failed\",\n ERR_CLI_PASSWORD_TIMEOUT: \"Discovery | Error | Auth Failed\",\n ERR_CLI_NO_SUPER_COMMAND: \"Discovery | Error | No Super\",\n ERR_CLI_LOW_PRIVILEGES: \"Discovery | Error | Low Privileges\",\n ERR_CLI_CONNECTION_REFUSED: \"Discovery | Error | Connection Refused\",\n ERR_CLI_SSH_PROTOCOL_ERROR: \"Discovery | Error | SSH Protocol\",\n }\n\n def __init__(self, job):\n self.service = job.service\n self.job = job\n self.object: ManagedObject = self.job.object\n self.logger = self.job.logger.get_logger(\"[%s\" % self.name)\n self.if_name_cache = {} # mo, name -> Interface\n self.if_mac_cache = {} # mo, mac -> Interface\n self.if_ip_cache = {}\n self.sub_cache = {}\n self.profile_cache = {}\n self.is_box = self.job.is_box\n self.is_periodic = self.job.is_periodic\n\n def is_enabled(self):\n checks = self.job.attrs.get(\"_checks\", set())\n return not checks or self.name in checks\n\n def has_fatal_error(self):\n return self.job.has_fatal_error\n\n def has_required_script(self):\n return not self.required_script or self.required_script in self.object.scripts\n\n def get_caps(self):\n return self.job.get_caps()\n\n def update_caps(self, caps, source):\n self.job.update_caps(caps, source)\n\n def has_capability(self, cap):\n return bool(self.get_caps().get(cap))\n\n def has_any_capability(self, caps):\n for c in caps:\n if self.has_capability(c):\n return True\n return False\n\n def has_required_capabilities(self):\n if not self.required_capabilities:\n return True\n caps = self.get_caps()\n for cn in self.required_capabilities:\n if cn not in caps:\n self.logger.info(\"Object hasn't required capability '%s'. \" \"Skipping\", cn)\n return False\n v = caps[cn]\n if not v:\n self.logger.info(\"Capability '%s' is disabled. Skipping\", cn)\n return False\n return True\n\n def has_required_artefacts(self):\n if not self.required_artefacts:\n return True\n for ra in self.required_artefacts:\n if not self.has_artefact(ra):\n self.logger.info(\"Job has not '%s' artefact. Skipping\", ra)\n return False\n return True\n\n def run(self):\n if not self.is_enabled():\n self.logger.info(\"Check is disabled. Skipping\")\n return\n if self.has_fatal_error():\n self.logger.info(\"Check is disabled due to previous fatal error. Skipping\")\n return\n if not self.has_required_artefacts():\n return\n with Span(server=\"discovery\", service=self.name) as span, self.job.check_timer(self.name):\n # Check required scripts\n if not self.has_required_script():\n self.logger.info(\"%s script is not supported. Skipping\", self.required_script)\n return\n # Check required capabilities\n if not self.has_required_capabilities():\n return\n # Run check\n try:\n self.handler()\n except RPCRemoteError as e:\n self.logger.error(\"RPC Remote error (%s): %s\", e.remote_code, e)\n if e.remote_code:\n message = f\"Remote error code {e.remote_code}\"\n else:\n message = f\"Remote error code {e.remote_code}, message: {e}\"\n self.set_problem(\n message=message,\n diagnostic=\"CLI\" if e.remote_code in self.error_map else None,\n fatal=e.remote_code in self.fatal_errors,\n )\n span.set_error_from_exc(e, e.remote_code)\n except RPCError as e:\n self.set_problem(\n message=f\"RPC Error: {e}\",\n diagnostic=\"CLI\" if e.default_code in self.error_map else None,\n fatal=e.default_code in self.fatal_errors,\n )\n self.logger.error(\"Terminated due RPC error: %s\", e)\n span.set_error_from_exc(e, e.default_code)\n except Exception as e:\n self.set_problem(message=f\"Unhandled exception: {e}\")\n error_report(logger=self.logger)\n span.set_error_from_exc(e)\n\n def handler(self):\n pass\n\n @staticmethod\n def build_effective_labels(obj) -> List[str]:\n \"\"\"\n Build object effective labels\n :param obj:\n :return:\n \"\"\"\n return [\n ll\n for ll in Label.merge_labels(obj.iter_effective_labels(obj))\n if obj.can_set_label(ll) or ll[-1] in MATCH_OPS or ll[-1] == \"*\"\n ]\n\n def update_if_changed(\n self,\n obj,\n values: Dict[str, Any],\n ignore_empty: List[str] = None,\n wait: bool = True,\n bulk: Optional[List[str]] = None,\n update_effective_labels: bool = False,\n ):\n \"\"\"\n Update fields if changed.\n :param obj: Document instance\n :type obj: Document\n :param values: New values\n :type values: dict\n :param ignore_empty: List of fields which may be ignored if empty\n :param wait: Wait for operation to complete. set write concern to 0 if False\n :param bulk: Execute as the bulk op instead\n :param update_effective_labels:\n :returns: List of changed (key, value)\n :rtype: list\n \"\"\"\n changes = []\n ignore_empty = ignore_empty or []\n for k, v in values.items():\n vv = getattr(obj, k)\n if hasattr(obj, \"extra_labels\") and k == \"extra_labels\":\n # Processed extra_labels\n sa_labels = obj.extra_labels.get(\"sa\", [])\n if v != sa_labels:\n remove_labels = set(sa_labels).difference(v)\n if remove_labels:\n obj.labels = [ll for ll in obj.labels if ll not in remove_labels]\n changes += [(\"labels\", obj.labels)]\n obj.extra_labels.update({\"sa\": v})\n changes += [(\"extra_labels\", {\"sa\": v})]\n continue\n if v != vv:\n if not isinstance(v, int) or not hasattr(vv, \"id\") or v != vv.id:\n if k in ignore_empty and (v is None or v == \"\"):\n continue\n setattr(obj, k, v)\n changes += [(k, v)]\n if update_effective_labels and hasattr(obj, \"effective_labels\"):\n el = self.build_effective_labels(obj)\n if set(el) != set(getattr(obj, \"effective_labels\", [])):\n changes += [(\"effective_labels\", el)]\n if changes:\n if bulk is not None:\n op = {\"$set\": dict(changes)}\n id_field = obj._fields[Interface._meta[\"id_field\"]].db_field\n bulk += [UpdateOne({id_field: obj.pk}, op)]\n else:\n kwargs = {}\n if not wait:\n kwargs[\"write_concern\"] = {\"w\": 0}\n obj.save(**kwargs)\n return changes\n\n def log_changes(self, msg: str, changes: List[Tuple[str, Any]]):\n \"\"\"\n Log changes\n :param msg: Message\n :type msg: str\n \"\"\"\n if changes:\n self.logger.info(\"%s: %s\" % (msg, \", \".join(\"%s = %s\" % (k, v) for k, v in changes)))\n\n def get_interface_by_name(self, name, mo=None):\n \"\"\"\n Returns Interface instance\n \"\"\"\n mo = mo or self.object\n try:\n name = mo.get_profile().convert_interface_name(name)\n except ValueError as e:\n self.logger.debug(\"Cannot convert remote port %s:%r, %r\", mo.name, name, e)\n return\n self.logger.debug(\"Searching port by name: %s:%s\", mo.name, name)\n key = (mo, name)\n if key not in self.if_name_cache:\n i = Interface.objects.filter(managed_object=mo, name=name).first()\n self.if_name_cache[key] = i\n return self.if_name_cache[key]\n\n def get_interface_by_mac(self, mac, mo=None):\n \"\"\"\n Returns Interface instance referred by MAC address\n \"\"\"\n mo = mo or self.object\n self.logger.debug(\"Searching port by MAC: %s:%s\", mo.name, mac)\n key = (mo, mac)\n if key not in self.if_mac_cache:\n i = Interface.objects.filter(managed_object=mo, mac=mac, type=\"physical\")[:2]\n if len(i) == 1:\n i = i[0]\n else:\n i = None # Non unique or not found\n self.if_mac_cache[key] = i\n return self.if_mac_cache[key]\n\n def get_interface_by_ip(self, ip, mo=None):\n \"\"\"\n Returns Interface instance referred by IP address\n \"\"\"\n mo = mo or self.object\n self.logger.debug(\"Searching port by IP: %s:%s\", mo.name, ip)\n key = (mo, ip)\n if key not in self.if_ip_cache:\n li = list(\n Interface.objects.filter(\n managed_object=self.object.id,\n ipv4_addresses__startswith=\"%s/\" % ip,\n type=\"physical\",\n )\n )\n if len(li) == 1:\n li = li[0]\n else:\n li = None # Non unique or not found\n self.if_ip_cache[key] = li\n return self.if_ip_cache[key]\n\n def set_interface(self, name, iface):\n \"\"\"\n Fill interface cache\n \"\"\"\n key = (self.object, name)\n self.if_name_cache[key] = iface\n\n def get_subinterface(self, interface, name):\n \"\"\"\n Returns Interface instance\n \"\"\"\n key = (str(interface.id), name)\n if key not in self.sub_cache:\n si = SubInterface.objects.filter(interface=interface.id, name=name).first()\n self.sub_cache[key] = si\n return self.sub_cache[key]\n\n def get_interface_profile(self, name):\n if name not in self.profile_cache:\n p = InterfaceProfile.objects.filter(name=name).first()\n self.profile_cache[name] = p\n return self.profile_cache[name]\n\n def clear_links(self):\n \"\"\"\n Clear all object's links\n \"\"\"\n self.logger.info(\"Cleaning links\")\n for i in Interface.objects.filter(\n managed_object=self.object.id, type__in=[\"physical\", \"aggregated\"]\n ):\n link = i.link\n if link:\n self.logger.info(\"Unlinking: %s\", link)\n try:\n i.unlink()\n except ValueError as e:\n self.logger.info(\"Failed to unlink: %s\", e)\n\n def set_problem(\n self,\n alarm_class: Optional[str] = None,\n path: Optional[List[str]] = None,\n message: Optional[str] = None,\n fatal: bool = False,\n diagnostic: Optional[str] = None,\n **kwargs,\n ):\n \"\"\"\n Set discovery problem\n :param alarm_class: Alarm class instance or name\n :param path: Additional path\n :param message: Text message\n :param fatal: True if problem is fatal and all following checks\n must be disabled\n :param diagnostic: Diagnostic name affected by problem\n :param kwargs: Dict containing optional variables\n :return:\n \"\"\"\n self.logger.info(\"Set path: %s\", path)\n self.job.set_problem(\n check=self.name,\n alarm_class=alarm_class,\n path=path,\n diagnostic=diagnostic,\n message=message,\n fatal=fatal,\n **kwargs,\n )\n\n def set_artefact(self, name, value=None):\n \"\"\"\n Set artefact (opaque structure to be passed to following checks)\n :param name: Artefact name\n :param value: Opaque value\n :return:\n \"\"\"\n self.job.set_artefact(name, value)\n\n def get_artefact(self, name: str) -> Optional[Any]:\n \"\"\"\n Get artefact by name\n :param name: artefact name\n :return: artefact\n \"\"\"\n return self.job.get_artefact(name)\n\n def has_artefact(self, name):\n \"\"\"\n Check job has existing artefact\n :param name: artefact name\n :return: True, if artefact exists, False otherwise\n \"\"\"\n return self.job.has_artefact(name)\n\n def invalidate_neighbor_cache(self, obj=None):\n \"\"\"\n Reset cached neighbors for object.\n\n NB: May be called by non-topology checks\n :param obj: Managed Object instance, jobs object if ommited\n :return:\n \"\"\"\n obj = obj or self.object\n if not obj.object_profile.neighbor_cache_ttl:\n # Disabled cache\n return\n keys = [\n \"mo-neighbors-%s-%s\" % (x, obj.id) for x in obj.segment.profile.get_topology_methods()\n ]\n if keys:\n self.logger.info(\"Invalidating neighor cache: %s\" % \", \".join(keys))\n cache.delete_many(keys, TopologyDiscoveryCheck.NEIGHBOR_CACHE_VERSION)\n\n def get_confdb(self):\n # Check cached value\n if hasattr(self, \"confdb\"):\n return self.confdb\n # Check artefact\n if self.has_artefact(\"confdb\"):\n self.confdb = self.get_artefact(\"confdb\")\n return self.confdb\n # Create\n self.logger.info(\"Building ConfDB\")\n self.confdb = self.object.get_confdb()\n self.set_artefact(\"confdb\", self.confdb)\n return self.confdb\n\n\nclass TopologyDiscoveryCheck(DiscoveryCheck):\n NEIGHBOR_CACHE_VERSION = 1\n # clean_interface settings\n aliased_names_only = False\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.neighbor_hostname_cache = {} # (method, id) -> managed object\n self.neighbor_ip_cache = {} # (method, ip) -> managed object\n self.neighbor_mac_cache = {} # (method, mac) -> managed object\n self.neighbor_id_cache = {}\n self.interface_aliases = {} # (object, alias) -> port name\n self._own_mac_check_cache = {}\n\n def handler(self):\n self.logger.info(\"Checking %s topology\", self.name)\n # Check object has interfaces\n if not self.has_capability(\"DB | Interfaces\"):\n self.logger.info(\"No interfaces has been discovered. Skipping topology check\")\n return\n # remote object -> [(local, remote), ..]\n candidates = defaultdict(set)\n loops = {} # first interface, second interface\n problems = {}\n # Check local side\n ln_key = \"mo-neighbors-%s-%s\" % (self.name, self.object.id)\n for li, ro, ri in self.cached_neighbors(self.object, ln_key, self.iter_neighbors):\n # Resolve remote object\n remote_object = self.get_neighbor(ro)\n if not remote_object:\n problems[li] = \"Remote object '%s' is not found\" % str(ro)\n self.logger.info(\"Remote object '%s' is not found. Skipping\", str(ro))\n continue\n # Resolve remote interface name\n remote_interface = self.get_remote_interface(remote_object, ri)\n if not remote_interface:\n problems[li] = \"Cannot resolve remote interface %s:%r. Skipping\" % (\n remote_object.name,\n ri,\n )\n self.logger.info(\n \"Cannot resolve remote interface %s:%r. Skipping\", remote_object.name, ri\n )\n continue\n else:\n self.logger.debug(\"Resolve remote interface as %s:%r\", remote_object.name, ri)\n # Detecting loops\n if remote_object.id == self.object.id:\n loops[li] = remote_interface\n if remote_interface in loops and loops[remote_interface] == li:\n self.logger.info(\n \"Loop link detected: %s:%s - %s:%s\",\n self.object.name,\n li,\n self.object.name,\n remote_interface,\n )\n self.confirm_link(self.object, li, remote_object, remote_interface)\n continue\n # Submitting candidates\n self.logger.info(\n \"Link candidate: %s:%s - %s:%s\",\n self.object.name,\n li,\n remote_object.name,\n remote_interface,\n )\n candidates[remote_object].add((li, remote_interface))\n\n # Checking candidates from remote side\n for remote_object in candidates:\n if self.required_script and self.required_script not in remote_object.scripts:\n self.logger.info(\n \"Remote object '%s' does not support %s script. \" \"Cannot confirm links\",\n remote_object.name,\n self.required_script,\n )\n continue\n try:\n rn_key = \"mo-neighbors-%s-%s\" % (self.name, remote_object.id)\n remote_neighbors = self.cached_neighbors(remote_object, rn_key, self.iter_neighbors)\n except Exception as e:\n self.logger.error(\n \"Cannot get neighbors from candidate %s: %s\", remote_object.name, e\n )\n self.set_problem(\n path=list(candidates[remote_object])[0][0],\n message=\"Cannot get neighbors from candidate %s: %s\" % (remote_object.name, e),\n )\n continue\n confirmed = set()\n for li, ro_id, ri in remote_neighbors:\n ro = self.get_neighbor(ro_id)\n if not ro or ro.id != self.object.id:\n self.logger.debug(\"Candidates check %s %s %s %s\" % (li, ro_id, ro, ri))\n continue # To other objects\n remote_interface = self.get_remote_interface(self.object, ri)\n if remote_interface:\n self.logger.debug(\"Resolve local interface as %s:%r\", self.object.name, ri)\n confirmed.add((remote_interface, li))\n self.logger.debug(\n \"Candidates: %s, Confirmed: %s\", candidates[remote_object], confirmed\n )\n for ll, rr in candidates[remote_object] - confirmed:\n problems[ll] = \"Pending link: %s - %s:%s\" % (ll, remote_object, rr)\n li = self.clean_interface(self.object, ll)\n if not li:\n self.logger.info(\"Cannot clean interface %s:%s. Skipping\", self.object, ll)\n continue\n ri = self.clean_interface(remote_object, rr)\n if not ri:\n self.logger.info(\"Cannot clean interface %s:%s. Skipping\", remote_object, rr)\n continue\n self.reject_link(self.object, li, remote_object, ri)\n for ll, rr in candidates[remote_object] & confirmed:\n li = self.clean_interface(self.object, ll)\n if not li:\n self.logger.info(\"Cannot clean interface %s:%s. Skipping\", self.object, ll)\n continue\n ri = self.clean_interface(remote_object, rr)\n if not ri:\n self.logger.info(\"Cannot clean interface %s:%s. Skipping\", remote_object, rr)\n continue\n self.confirm_link(self.object, li, remote_object, ri)\n if problems:\n for i in problems:\n self.set_problem(path=i, message=problems[i])\n\n def cached_neighbors(self, mo, key, iter_neighbors):\n \"\"\"\n Cache iter_neighbors results according to profile settings\n :param mo:\n :param key:\n :param iter_neighbors:\n :return:\n \"\"\"\n ttl = mo.object_profile.neighbor_cache_ttl\n if not ttl:\n # Disabled cache\n metrics[\"neighbor_cache_misses\"] += 1\n neighbors = iter_neighbors(mo)\n if isinstance(neighbors, types.GeneratorType):\n neighbors = list(iter_neighbors(mo))\n return neighbors\n # Cached version\n neighbors = cache.get(key, version=self.NEIGHBOR_CACHE_VERSION)\n if neighbors is None:\n self.logger.info(\"Neighbors cache is empty, getting from device...\")\n neighbors = iter_neighbors(mo)\n if isinstance(neighbors, types.GeneratorType):\n neighbors = list(iter_neighbors(mo))\n cache.set(key, neighbors, ttl=ttl, version=self.NEIGHBOR_CACHE_VERSION)\n if self.interface_aliases:\n alias_cache = {\n (mo.id, n[0]): self.interface_aliases[(mo.id, n[0])]\n for n in neighbors\n if (mo.id, n[0]) in self.interface_aliases\n }\n cache.set(\n \"%s-aliases\" % key, alias_cache, ttl=ttl, version=self.NEIGHBOR_CACHE_VERSION\n )\n metrics[\"neighbor_cache_misses\"] += 1\n else:\n self.logger.info(\"Use neighbors cache\")\n alias_cache = cache.get(\"%s-aliases\" % key, version=self.NEIGHBOR_CACHE_VERSION)\n self.logger.debug(\"Alias cache is %s\", alias_cache)\n if alias_cache:\n self.interface_aliases.update(alias_cache)\n metrics[\"neighbor_cache_hits\"] += 1\n return neighbors\n\n def iter_neighbors(self, mo):\n \"\"\"\n Generator yielding all protocol neighbors\n :param mo: Managed object reference\n :returns: yield (local interface, remote id, remote interface)\n \"\"\"\n return iter(())\n\n def get_neighbor_by_hostname(self, hostname):\n \"\"\"\n Resolve neighbor by hostname\n \"\"\"\n hostname = hostname.lower()\n if hostname not in self.neighbor_hostname_cache:\n hosts = DiscoveryID.objects.filter(hostname_id=hostname)[:2]\n n = None\n if len(hosts) == 1:\n n = hosts[0].object\n else:\n # Sometimes, domain part is truncated.\n # Try to resolve anyway\n m = list(\n DiscoveryID.objects.filter(\n hostname_id__istartswith=hostname + \".\" if \".\" not in hostname else hostname\n )\n )\n if len(m) == 1:\n n = m[0].object # Exact match\n self.neighbor_hostname_cache[hostname] = n\n if n:\n self.neighbor_id_cache[n.id] = n\n return self.neighbor_hostname_cache[hostname]\n\n get_neighbor = get_neighbor_by_hostname\n\n def get_neighbor_by_id(self, id):\n \"\"\"\n Resolve neighbor by managed object's id\n \"\"\"\n if id not in self.neighbor_id_cache:\n try:\n mo = ManagedObject.objects.get(id=id)\n self.neighbor_id_cache[id] = mo\n self.neighbor_hostname_cache[mo.name] = id\n except ManagedObject.DoesNotExist:\n self.neighbor_id_cache[id] = None\n return self.neighbor_id_cache[id]\n\n def get_neighbor_by_mac(self, mac):\n \"\"\"\n Resolve neighbor by hostname\n \"\"\"\n if mac not in self.neighbor_mac_cache:\n mo = DiscoveryID.find_object(mac=mac)\n self.neighbor_mac_cache[mac] = mo\n return self.neighbor_mac_cache[mac]\n\n def get_neighbor_by_ip(self, ip):\n \"\"\"\n Resolve neighbor by hostname\n \"\"\"\n if ip not in self.neighbor_ip_cache:\n mo = DiscoveryID.find_object(ipv4_address=ip)\n self.neighbor_ip_cache[ip] = mo\n return self.neighbor_ip_cache[ip]\n\n def get_remote_interface(self, remote_object: ManagedObject, remote_interface: str) -> str:\n \"\"\"\n Return normalized remote interface name\n May return aliases name which can be finally resolved\n during clean interface\n \"\"\"\n return remote_object.get_profile().convert_interface_name(remote_interface)\n\n def clean_interface(self, object, interface):\n \"\"\"\n Finaly clean interface name:\n * Check for interface alias\n * When aliased_names_only is not set - use local name\n :param object: ManagedObject instance\n :param interface: interface name\n :return: Interface name or None if interface cannot be cleaned\n \"\"\"\n i = self.interface_aliases.get((object.id, interface))\n if i:\n return i\n if self.aliased_names_only:\n return None\n return interface\n\n def confirm_link(\n self,\n local_object: ManagedObject,\n local_interface: str,\n remote_object: ManagedObject,\n remote_interface: str,\n ) -> None:\n self.logger.info(\n \"Confirm link: %s:%s -- %s:%s\",\n local_object,\n local_interface,\n remote_object,\n remote_interface,\n )\n # Get interfaces\n li = self.get_interface_by_name(mo=local_object, name=local_interface)\n if not li:\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. \" \"Interface %s:%s is not discovered\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n local_object.name,\n local_interface,\n )\n return\n ri = self.get_interface_by_name(mo=remote_object, name=remote_interface)\n if not ri:\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. \" \"Interface %s:%s is not discovered\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n remote_object.name,\n remote_interface,\n )\n return\n self.confirm_interface_link(li, ri)\n\n def confirm_interface_link(self, li: Interface, ri: Interface) -> None:\n \"\"\"\n Confirm links between interfaces\n \"\"\"\n local_object = li.managed_object\n local_interface = li.name\n remote_object = ri.managed_object\n remote_interface = ri.name\n # Check LAGs\n if (\n li.type == \"aggregated\"\n and ri.type != \"aggregated\"\n and not li.profile.allow_lag_mismatch\n ):\n self.logger.error(\n \"Cannot connect aggregated interface %s:%s to non-aggregated %s:%s\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n if (\n ri.type == \"aggregated\"\n and li.type != \"aggregated\"\n and not ri.profile.allow_lag_mismatch\n ):\n self.logger.error(\n \"Cannot connect aggregated interface %s:%s to non-aggregated %s:%s\",\n remote_object.name,\n remote_interface,\n local_object.name,\n local_interface,\n )\n return\n if ri.type == \"aggregated\" and li.type == \"aggregated\":\n lic = li.lag_members.count()\n ric = ri.lag_members.count()\n if lic != ric:\n self.logger.error(\"Cannot connect. LAG size mismatch: %s vs %s\", lic, ric)\n return\n # Get existing links\n llink = li.link\n rlink = ri.link\n # Check link is already exists\n if llink and rlink and llink.id == rlink.id:\n self.logger.info(\n \"Already linked: %s:%s -- %s:%s via %s\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n llink.discovery_method,\n )\n if llink.discovery_method != self.name and (\n llink.discovery_method is None\n or self.is_preferable_over(local_object, remote_object, llink)\n ):\n # Change disovery method\n self.logger.info(\"Remarking discovery method as %s\", self.name)\n llink.touch(self.name)\n else:\n # Change last seen\n llink.touch()\n return\n # Check method preferences\n if llink:\n if self.is_preferable_over(local_object, remote_object, llink):\n self.logger.info(\n \"Relinking %s: %s method is preferable over %s\",\n llink,\n self.name,\n llink.discovery_method,\n )\n else:\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. '%s' method is preferable over '%s'\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n llink.discovery_method,\n self.name,\n )\n return\n if rlink:\n if self.is_preferable_over(local_object, remote_object, rlink):\n self.logger.info(\n \"Relinking %s: %s method is preferable over %s\",\n rlink,\n self.name,\n rlink.discovery_method,\n )\n else:\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. '%s' method is preferable over '%s'\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n rlink.discovery_method,\n self.name,\n )\n return\n # Get interface discovery policies\n # Possible values are:\n # * I - Ignore links, all discovered links rejected\n # * O - Link created only if not exists\n # * R - Existing link will be replaced\n # * C - Link will be attached to cloud\n lpolicy = li.profile.discovery_policy\n rpolicy = ri.profile.discovery_policy\n self.logger.info(\"Interface linking policy: %s/%s\", lpolicy, rpolicy)\n # Check if either policy set to ignore\n if lpolicy == \"I\" or rpolicy == \"I\":\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. 'Ignore' interface discovery policy set\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n # Check if either side has *Create new* policy and\n # already linked\n if (lpolicy == \"O\" and llink) or (lpolicy == \"O\" and llink):\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. \"\n \"'Create new' interface discovery policy set and \"\n \"interface is already linked\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n # Do not allow merging clouds\n if lpolicy == \"C\" and rpolicy == \"C\":\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. Cloud merging is forbidden\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n # Get currently linked ends policies\n if llink:\n rri = [i for i in llink.interfaces if i.id != li.id][0]\n lrpolicy = rri.profile.discovery_policy\n else:\n lrpolicy = None\n if rlink:\n rri = [i for i in rlink.interfaces if i.id != ri.id][0]\n rrpolicy = rri.profile.discovery_policy\n else:\n rrpolicy = None\n # *Create new* policy blocks other side relinking\n if lrpolicy == \"O\" or rrpolicy == \"O\":\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. Blocked by 'Create new' policy on existing link\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n #\n if lpolicy in (\"O\", \"R\") and rpolicy in (\"O\", \"R\"):\n # Unlink when necessary\n if llink:\n try:\n li.unlink()\n except ValueError as e:\n self.logger.info(\"Failed to unlink %s: %s\" % (llink, e))\n return\n if rlink:\n try:\n ri.unlink()\n except ValueError as e:\n self.logger.info(\"Failed to unlink %s: %s\" % (llink, e))\n return\n self.logger.info(\n \"Linking: %s:%s -- %s:%s\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n try:\n li.link_ptp(ri, method=self.name)\n except ValueError as e:\n self.logger.info(\n \"Cannot link %s:%s -- %s:%s: %s\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n e,\n )\n return\n #\n if lpolicy == \"C\":\n if rlink:\n if rpolicy == \"O\":\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. \"\n \"Already linked. \"\n \"Connecting to cloud is forbidden by policy\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n else:\n self.logger.info(\"Unlinking %s\", ri)\n try:\n ri.unlink()\n except ValueError as e:\n self.logger.error(\"Failed to unlink %s: %s\", ri, e)\n return\n if llink:\n # Attach to existing cloud\n llink.interfaces = llink.interfaces + [ri]\n llink.save()\n else:\n # Create p2p link\n try:\n li.link_ptp(ri, method=self.name)\n except ValueError as e:\n self.logger.info(\n \"Cannot link %s:%s -- %s:%s: %s\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n e,\n )\n return\n if rpolicy == \"C\":\n if llink:\n if lpolicy == \"O\":\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. \"\n \"Already linked. \"\n \"Connecting to cloud is forbidden by policy\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n return\n else:\n self.logger.info(\"Unlinking %s\", li)\n li.unlink()\n if rlink:\n # Attach to existing cloud\n rlink.interfaces = rlink.interfaces + [li]\n rlink.save()\n else:\n # Create p2p link\n try:\n ri.link_ptp(li, method=self.name)\n except ValueError as e:\n self.logger.info(\n \"Cannot link %s:%s -- %s:%s: %s\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n e,\n )\n return\n #\n self.logger.info(\n \"Not linking: %s:%s -- %s:%s. \" \"Link creating not allowed\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n\n def reject_link(self, local_object, local_interface, remote_object, remote_interface):\n self.logger.info(\n \"Reject link: %s:%s -- %s:%s\",\n local_object,\n local_interface,\n remote_object,\n remote_interface,\n )\n # Get interfaces\n li = self.get_interface_by_name(mo=local_object, name=local_interface)\n if not li:\n self.logger.info(\n \"Cannot unlink: %s:%s -- %s:%s. Interface %s:%s is not discovered\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n local_object.name,\n local_interface,\n )\n return\n ri = self.get_interface_by_name(mo=remote_object, name=remote_interface)\n if not ri:\n self.logger.info(\n \"Cannot unlink: %s:%s -- %s:%s. Interface %s:%s is not discovered\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n remote_object.name,\n remote_interface,\n )\n return\n # Get existing links\n llink = li.link\n rlink = ri.link\n # Check link is already exists\n if llink and rlink and llink.id == rlink.id:\n if llink.discovery_method == self.name:\n self.logger.info(\n \"Unlinking: %s:%s -- %s:%s. \",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n llink.delete()\n else:\n self.logger.info(\n \"Cannot unlink: %s:%s -- %s:%s. Created by other discovery method (%s)\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n llink.discovery_method,\n )\n else:\n self.logger.info(\n \"Cannot unlink: %s:%s -- %s:%s. Not linked yet\",\n local_object.name,\n local_interface,\n remote_object.name,\n remote_interface,\n )\n\n def confirm_cloud(self, root_interface: Interface, interfaces: List[Interface]) -> None:\n \"\"\"\n Ensure `root_interface` and `interfaces` are connected to same cloud link\n\n :param root_interface: Root `Interface` of the cloud\n :param interfaces: List of `Interface`\n \"\"\"\n if not interfaces:\n return\n # get existing links\n links: Dict[Interface, Link] = {}\n for link in Link.objects.filter(\n interfaces__in=[root_interface.id] + [i.id for i in interfaces]\n ):\n for i in link.interfaces:\n links[i] = link\n # Get or create cloud\n root_link = links.get(root_interface)\n if root_link:\n if root_link.discovery_method != self.name:\n if not self.object.segment.profile.is_preferable_method(\n self.name, root_link.discovery_method\n ):\n self.logger.info(\n \"Cannot create cloud on %s:%s. Existing method '%s' is preferable over '%s'\",\n root_interface.managed_object.name,\n root_interface.name,\n root_link.discovery_method,\n self.name,\n )\n return\n self.logger.info(\n \"Changing cloud on %s:%s method to %s\",\n root_interface.managed_object.name,\n root_interface.name,\n self.name,\n )\n root_link.discovery_method = self.name\n else:\n self.logger.info(\n \"Using existent cloud on %s:%s\",\n root_interface.managed_object.name,\n root_interface.name,\n )\n else:\n # Create one\n self.logger.info(\n \"Creating cloud on %s:%s\", root_interface.managed_object.name, root_interface.name\n )\n root_link = Link(interfaces=[root_interface], discovery_method=self.name)\n # Check all interfaces\n for iface in interfaces:\n if_link = links.get(iface)\n if if_link:\n if if_link.id == root_link.id:\n self.logger.info(\n \"%s:%s is already linked\", iface.managed_object.name, iface.name\n )\n continue\n elif not self.object.segment.profile.is_preferable_method(\n self.name, if_link.discovery_method\n ):\n self.logger.info(\n \"Cannot unlink %s:%s. Method %s is preferable over %s\",\n iface.managed_object.name,\n iface.name,\n if_link.discovery_method,\n self.name,\n )\n continue\n else:\n self.logger.info(\n \"Relinking %s:%s to cloud %s:%s\",\n iface.managed_object.name,\n iface.name,\n root_interface.managed_object.name,\n root_interface.name,\n )\n iface.unlink()\n root_link.interfaces += [iface]\n else:\n self.logger.info(\n \"Linking %s:%s to cloud %s:%s\",\n iface.managed_object.name,\n iface.name,\n root_interface.managed_object.name,\n root_interface.name,\n )\n root_link.interfaces += [iface]\n root_link.save()\n\n def is_preferable_over(self, mo1, mo2, link):\n \"\"\"\n Check current discovery method is preferable over link's one\n :param mo1: Local managed object\n :param mo2: Remote managed object\n :param link: Existing ling\n :returns: True, if check's method is preferabble\n \"\"\"\n if mo1.segment == mo2.segment or mo2.segment.id not in mo1.segment.get_path():\n # Same segment, or mo1 is in upper segment. apply local segment policy\n return mo1.segment.profile.is_preferable_method(self.name, link.discovery_method)\n # mo2 is in upper segment, use remote segment policy\n return mo2.segment.profile.is_preferable_method(self.name, link.discovery_method)\n\n def set_interface_alias(self, object, interface_name, alias):\n \"\"\"\n Set interface alias\n Aliases will be finally resolved by clean_interface\n :param object:\n :param interface_name:\n :param alias:\n :return:\n \"\"\"\n self.interface_aliases[object.id, alias] = interface_name\n\n @cachetools.cachedmethod(operator.attrgetter(\"_own_mac_check_cache\"))\n def is_own_mac(self, mac):\n mr = DiscoveryID.macs_for_objects(self.object)\n return mr and any(1 for f, t in mr if f <= mac <= t)\n\n\nclass PolicyDiscoveryCheck(DiscoveryCheck):\n policy_name = None\n policy_map = {\n \"s\": [\"script\"],\n \"S\": [\"script\", \"confdb\"],\n \"C\": [\"confdb\", \"script\"],\n \"c\": [\"confdb\"],\n }\n\n def get_policy(self):\n \"\"\"\n Get effective policy\n :return:\n \"\"\"\n if self.policy_name:\n return getattr(self.object, self.policy_name)()\n raise NotImplementedError\n\n def get_data(self):\n \"\"\"\n Request data according to policy (Either from equipment of from ConfDB)\n :return:\n \"\"\"\n for method in self.policy_map[self.get_policy()]:\n check = getattr(self, \"can_get_data_from_%s\" % method)\n if not check():\n continue\n getter = getattr(self, \"request_data_from_%s\" % method)\n data = getter()\n if data is not None:\n return data\n return None\n\n def request_data_from_script(self):\n self.logger.info(\"Requesting data from device\")\n return self.get_data_from_script()\n\n def get_data_from_script(self):\n \"\"\"\n Actually get data from script. Should be overriden\n :return:\n \"\"\"\n return None\n\n def can_get_data_from_script(self):\n \"\"\"\n Check if object has all prerequisites to get data from script\n :return:\n \"\"\"\n if self.required_script not in self.object.scripts:\n self.logger.info(\"%s script is not supported. Skipping\", self.required_script)\n return False\n return True\n\n def request_data_from_confdb(self):\n # self.confdb is set by can_get_data_from_confdb\n return self.get_data_from_confdb()\n\n def get_data_from_confdb(self):\n \"\"\"\n Actually get data from ConfDB. Should be overriden\n :return:\n \"\"\"\n return None\n\n def can_get_data_from_confdb(self):\n \"\"\"\n Check if object has all prerequisites to get data from ConfDB\n :return:\n \"\"\"\n confdb = self.get_confdb()\n if confdb is None:\n self.logger.error(\"confdb artefact is not set. Skipping\")\n return False\n return True\n\n def has_required_script(self):\n return super().has_required_script() or self.get_policy() != [\"script\"]\n", "repo_name": "nocproject/noc", "sub_path": "services/discovery/jobs/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 60320, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 108, "dataset": "github-code", "pt": "51", "api": [{"api_name": "noc.core.scheduler.periodicjob.PeriodicJob", "line_number": 48, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 49, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 62, "usage_type": "call"}, {"api_name": "noc.core.log.PrefixLoggerAdapter", "line_number": 63, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 65, "usage_type": "name"}, {"api_name": "noc.core.models.problem.ProblemItem", "line_number": 65, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 94, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 94, "usage_type": "argument"}, {"api_name": "noc.core.mongo.connection.get_db", "line_number": 101, "usage_type": "call"}, {"api_name": "bson.Binary", "line_number": 105, "usage_type": "call"}, {"api_name": "zlib.compress", "line_number": 105, "usage_type": "call"}, {"api_name": "noc.core.comp.smart_bytes", "line_number": 105, "usage_type": "call"}, {"api_name": "noc.core.wf.interaction.Interaction.BoxDiscovery", "line_number": 118, "usage_type": "attribute"}, {"api_name": "noc.core.wf.interaction.Interaction", "line_number": 118, "usage_type": "name"}, {"api_name": "noc.core.wf.interaction.Interaction.PeriodicDiscovery", "line_number": 119, "usage_type": "attribute"}, {"api_name": "noc.core.wf.interaction.Interaction", "line_number": 119, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 127, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 129, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 125, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 133, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 133, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 134, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 134, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 135, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 135, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 135, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 136, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 138, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 138, "usage_type": "name"}, {"api_name": "noc.core.models.problem.ProblemItem", "line_number": 164, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 217, "usage_type": "name"}, {"api_name": "noc.core.models.problem.ProblemItem", "line_number": 217, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 231, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 231, "usage_type": "attribute"}, {"api_name": "noc.core.wf.diagnostic.DiagnosticHub", "line_number": 234, "usage_type": "call"}, {"api_name": "noc.core.wf.diagnostic.DiagnosticState.failed", "line_number": 239, "usage_type": "attribute"}, {"api_name": "noc.core.wf.diagnostic.DiagnosticState", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 254, "usage_type": "name"}, {"api_name": "noc.core.models.problem.ProblemItem", "line_number": 254, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 254, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 272, "usage_type": "name"}, {"api_name": "noc.fm.models.alarmclass.AlarmClass.get_by_name", "line_number": 272, "usage_type": "call"}, {"api_name": "noc.fm.models.alarmclass.AlarmClass", "line_number": 272, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 278, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 278, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 278, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 282, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 282, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 282, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 282, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 283, "usage_type": "attribute"}, {"api_name": "noc.fm.models.alarmclass.AlarmClass.get_by_name", "line_number": 287, "usage_type": "call"}, {"api_name": "noc.fm.models.alarmclass.AlarmClass", "line_number": 287, "usage_type": "name"}, {"api_name": "orjson.dumps", "line_number": 315, "usage_type": "call"}, {"api_name": "orjson.dumps", "line_number": 320, "usage_type": "call"}, {"api_name": "orjson.OPT_INDENT_2", "line_number": 320, "usage_type": "attribute"}, {"api_name": "builtins.object", "line_number": 382, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_AUTH_FAILED", "line_number": 394, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_NO_SUPER_COMMAND", "line_number": 395, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_LOW_PRIVILEGES", "line_number": 396, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_CONNECTION_REFUSED", "line_number": 397, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_SSH_PROTOCOL_ERROR", "line_number": 398, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_PASSWORD_TIMEOUT", "line_number": 399, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_AUTH_FAILED", "line_number": 403, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_PASSWORD_TIMEOUT", "line_number": 404, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_NO_SUPER_COMMAND", "line_number": 405, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_LOW_PRIVILEGES", "line_number": 406, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_CONNECTION_REFUSED", "line_number": 407, "usage_type": "name"}, {"api_name": "noc.core.error.ERR_CLI_SSH_PROTOCOL_ERROR", "line_number": 408, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 414, "usage_type": "name"}, {"api_name": "noc.core.span.Span", "line_number": 481, "usage_type": "call"}, {"api_name": "noc.core.service.error.RPCRemoteError", "line_number": 492, "usage_type": "name"}, {"api_name": "noc.core.service.error.RPCError", "line_number": 504, "usage_type": "name"}, {"api_name": "noc.core.debug.error_report", "line_number": 514, "usage_type": "call"}, {"api_name": "noc.main.models.label.Label.merge_labels", "line_number": 529, "usage_type": "call"}, {"api_name": "noc.main.models.label.Label", "line_number": 529, "usage_type": "name"}, {"api_name": "noc.main.models.label.MATCH_OPS", "line_number": 530, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 521, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 521, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 536, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 536, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 536, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 537, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 537, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 539, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 539, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 539, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface._meta", "line_number": 583, "usage_type": "attribute"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 583, "usage_type": "name"}, {"api_name": "pymongo.UpdateOne", "line_number": 584, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 592, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 592, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 592, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 592, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface.objects.filter", "line_number": 614, "usage_type": "call"}, {"api_name": "noc.inv.models.interface.Interface.objects", "line_number": 614, "usage_type": "attribute"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 614, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface.objects.filter", "line_number": 626, "usage_type": "call"}, {"api_name": "noc.inv.models.interface.Interface.objects", "line_number": 626, "usage_type": "attribute"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 626, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface.objects.filter", "line_number": 643, "usage_type": "call"}, {"api_name": "noc.inv.models.interface.Interface.objects", "line_number": 643, "usage_type": "attribute"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 643, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 667, "usage_type": "call"}, {"api_name": "noc.inv.models.subinterface.SubInterface.objects.filter", "line_number": 669, "usage_type": "call"}, {"api_name": "noc.inv.models.subinterface.SubInterface.objects", "line_number": 669, "usage_type": "attribute"}, {"api_name": "noc.inv.models.subinterface.SubInterface", "line_number": 669, "usage_type": "name"}, {"api_name": "noc.inv.models.interfaceprofile.InterfaceProfile.objects.filter", "line_number": 675, "usage_type": "call"}, {"api_name": "noc.inv.models.interfaceprofile.InterfaceProfile.objects", "line_number": 675, "usage_type": "attribute"}, {"api_name": "noc.inv.models.interfaceprofile.InterfaceProfile", "line_number": 675, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface.objects.filter", "line_number": 684, "usage_type": "call"}, {"api_name": "noc.inv.models.interface.Interface.objects", "line_number": 684, "usage_type": "attribute"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 684, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 697, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 697, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 698, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 698, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 698, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 699, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 699, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 701, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 701, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 735, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 735, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 735, "usage_type": "name"}, {"api_name": "noc.core.cache.base.cache.delete_many", "line_number": 768, "usage_type": "call"}, {"api_name": "noc.core.cache.base.cache", "line_number": 768, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 806, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 815, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 816, "usage_type": "call"}, {"api_name": "noc.core.perf.metrics", "line_number": 924, "usage_type": "name"}, {"api_name": "types.GeneratorType", "line_number": 926, "usage_type": "attribute"}, {"api_name": "noc.core.cache.base.cache.get", "line_number": 930, "usage_type": "call"}, {"api_name": "noc.core.cache.base.cache", "line_number": 930, "usage_type": "name"}, {"api_name": "types.GeneratorType", "line_number": 934, "usage_type": "attribute"}, {"api_name": "noc.core.cache.base.cache.set", "line_number": 936, "usage_type": "call"}, {"api_name": "noc.core.cache.base.cache", "line_number": 936, "usage_type": "name"}, {"api_name": "noc.core.cache.base.cache.set", "line_number": 943, "usage_type": "call"}, {"api_name": "noc.core.cache.base.cache", "line_number": 943, "usage_type": "name"}, {"api_name": "noc.core.perf.metrics", "line_number": 946, "usage_type": "name"}, {"api_name": "noc.core.cache.base.cache.get", "line_number": 949, "usage_type": "call"}, {"api_name": "noc.core.cache.base.cache", "line_number": 949, "usage_type": "name"}, {"api_name": "noc.core.perf.metrics", "line_number": 953, "usage_type": "name"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.objects.filter", "line_number": 970, "usage_type": "call"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.objects", "line_number": 970, "usage_type": "attribute"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID", "line_number": 970, "usage_type": "name"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.objects.filter", "line_number": 978, "usage_type": "call"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.objects", "line_number": 978, "usage_type": "attribute"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID", "line_number": 978, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject.objects.get", "line_number": 997, "usage_type": "call"}, {"api_name": "noc.sa.models.managedobject.ManagedObject.objects", "line_number": 997, "usage_type": "attribute"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 997, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject.DoesNotExist", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 1000, "usage_type": "name"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.find_object", "line_number": 1009, "usage_type": "call"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID", "line_number": 1009, "usage_type": "name"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.find_object", "line_number": 1018, "usage_type": "call"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID", "line_number": 1018, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 1022, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 1022, "usage_type": "name"}, {"api_name": "builtins.object.id", "line_number": 1039, "usage_type": "attribute"}, {"api_name": "builtins.object", "line_number": 1039, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 1048, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 1049, "usage_type": "name"}, {"api_name": "noc.sa.models.managedobject.ManagedObject", "line_number": 1050, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 1051, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 1087, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 1435, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 1435, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 1445, "usage_type": "name"}, {"api_name": "noc.inv.models.interface.Interface", "line_number": 1445, "usage_type": "name"}, {"api_name": "noc.inv.models.link.Link", "line_number": 1445, "usage_type": "name"}, {"api_name": "noc.inv.models.link.Link.objects.filter", "line_number": 1446, "usage_type": "call"}, {"api_name": "noc.inv.models.link.Link.objects", "line_number": 1446, "usage_type": "attribute"}, {"api_name": "noc.inv.models.link.Link", "line_number": 1446, "usage_type": "name"}, {"api_name": "noc.inv.models.link.Link", "line_number": 1484, "usage_type": "call"}, {"api_name": "builtins.object.id", "line_number": 1549, "usage_type": "attribute"}, {"api_name": "builtins.object", "line_number": 1549, "usage_type": "name"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID.macs_for_objects", "line_number": 1553, "usage_type": "call"}, {"api_name": "noc.inv.models.discoveryid.DiscoveryID", "line_number": 1553, "usage_type": "name"}, {"api_name": "cachetools.cachedmethod", "line_number": 1551, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 1551, "usage_type": "call"}]} +{"seq_id": "10452076892", "text": "import json\n\n\ndef get_status_code():\n file_handler = open(\"status_codes\", \"r\")\n lines = file_handler.readlines()\n count = 0\n dict_status_codes = {}\n current_key = \"\"\n for line in lines:\n line = line.strip()\n if len(line) == 0:\n continue\n if count == 3:\n count = 0\n current_key = \"\"\n if count == 0:\n dict_status_codes[line] = []\n current_key = line\n count += 1\n continue\n if count < 3:\n dict_status_codes[current_key].append(line)\n count += 1\n continue\n count = 0\n current_key = \"\"\n print(line)\n return dict_status_codes\n\n\nhandler_output = open(\"status_codes_dict\", \"w\")\ndict_status = get_status_code()\njson.dump(dict_status, handler_output, indent=4, sort_keys=True)\nhandler_output.close()\n", "repo_name": "cojocariumagda/ComIt", "sub_path": "Back/Helpers/get_status_code.py", "file_name": "get_status_code.py", "file_ext": "py", "file_size_in_byte": 877, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "json.dump", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "26057436780", "text": "from django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n # employee\n path('', views.index, name='home'),\n path('employee', views.employee, name='employee'),\n path('card', views.card, name='card'),\n path('attendance/employee', views.attendanceEmployee, name='attendance_employee'),\n path('attendance/student', views.attendanceStudent, name='attendance_student'),\n path('book-parental', views.bookParental, name='book_parental'),\n path('calender', views.calender, name='calender'),\n path('schedule', views.schedule, name='schedule'),\n path('learning', views.learning, name='learning'),\n path('raport', views.raport, name='raport'),\n path('exam', views.exam, name='exam'),\n\n # Student\n path('student', views.student, name='student'),\n path('student/calender', views.studentCalender, name='student_calender'),\n path('student/schedule', views.studentSchedule, name='student_schedule'),\n path('student/card', views.studentCard, name='student_card'),\n path('student/learning', views.studentLearning, name='student_learning'),\n path('student/raport', views.studentRaport, name='student_raport'),\n path('student/exam', views.studentExam, name='student_exam'),\n\n # Parent\n path('parent', views.parent, name='parent'),\n path('parent/parent-calender', views.calenderParent, name='parent_calender'),\n path('parent/parent-schedule', views.scheduleParent, name='parent_schedule'),\n path('parent/attendance-student', views.parentAttendanceStudent, name='parent_attendance_student'),\n path('parent/parent-card', views.Parentcard, name='parent_card'),\n path('parent/parent-raport', views.parentRaport, name='parent_raport'),\n\n # Owner\n path('owner', views.owner, name='owner'),\n path('owner/owner-calendar', views.ownerCalendar, name='owner_calendar'),\n path('owner/owner-attendance', views.ownerAttendance, name='owner_attendance'),\n path('owner/owner-payment', views.ownerPayment, name='owner_payment'),\n\n\n path('accounts/', include('django.contrib.auth.urls')),\n path('register/', views.register, name='register'),\n]", "repo_name": "arislaode/gis", "sub_path": "gis/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "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"}, {"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": 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": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.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": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "19452295156", "text": "from crispy_forms.helper import FormHelper\nfrom django import forms\nfrom whattoeat.models import MealRequirementsSet\n\n__author__ = 'michael'\n\nclass MealRequirementsSelectorForm(forms.Form):\n '''Form for selecting a meal profile to use in meal generation'''\n req_set = forms.ModelChoiceField(label='Choose a requirements set to use',\n queryset=MealRequirementsSet.objects.all(),\n empty_label=None) #only a placeholder, queryset must be\n #set in init method\n def __init__(self,*args,**kwargs):\n user = kwargs.pop('user',None)\n selected = kwargs.pop('selected',None)\n super(MealRequirementsSelectorForm,self).__init__(*args,**kwargs)\n\n if selected != None: #must come after super constructor\n self.fields['req_set'].initial = selected #make the selected field appear again when the form is loaded\n req_sets = user.profile.get_all_meal_requirements_sets()\n self.fields['req_set'].queryset = req_sets\n self.helper = FormHelper()\n self.helper.form_tag = False\n\n", "repo_name": "mjkilian/4thYearFinalProject", "sub_path": "Website/whattoeat/meals/generation/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.forms.Form", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "whattoeat.models.MealRequirementsSet.objects.all", "line_number": 10, "usage_type": "call"}, {"api_name": "whattoeat.models.MealRequirementsSet.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "whattoeat.models.MealRequirementsSet", "line_number": 10, "usage_type": "name"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "635634743", "text": "import mastodon \nimport xlsxwriter\nfrom mastodon import Mastodon\nimport textblob\nimport nltk\nimport time\n\n\n#language_translator = LanguageTranslator(\n# username='cfae4f65-e562-441e-9bca-f90751c59541',\n# password='fQqF7xiK1Mij')\n \n \n# Register app - only once!\n\n\n# Mastodon.create_app(\n # 'pytooterapp',\n # to_file = 'pytooter_clientcred.txt'\n# )\n\n\n# Log in - either every time, or use persisted\n\nmastodon = Mastodon(client_id = 'pytooter_clientcred.txt')\nmastodon.log_in(\n 'imranshaik@mail.usf.edu',\n 'imranshaik',\n to_file = 'pytooter_usercred.txt'\n)\n\n\n# # Create actual instance\nmastodon = Mastodon(\n client_id = 'pytooter_clientcred.txt',\n access_token = 'pytooter_usercred.txt'\n )\n#toot from python using the created app\n#mastodon.toot('Tooting from python!')\n\n\n#getting account details\nmastodon.account('440e674a4af6971d4f2b94ec90d219398097012eb105b2b1745a5583980bf2a5')\n\n\nworkbook = xlsxwriter.Workbook('mastodondata.xlsx')\nworksheet = workbook.add_worksheet()\nrow=0\ncol=0\n\n#get public timelines\ntimelines =list()\nfor i in range(1,20):\n\ttimelines.append(mastodon.timeline_public(max_id=None, since_id=None, limit=None))\n\ttime.sleep(5)\n\t\n#print('timeline',timelines)\n\npost=timelines[0]\nfor key in post[0].keys():\n\tif (key != 'account' and key !='application' and key!='media_attachments' and key!='mentions' and key!='tags'):\n\t\tworksheet.write(row,col,key)\n\t\tcol+=1\n\nrow+=1\ncol=0\nbadkeys=list()\n\nfor timeline in timelines:\n\tfor posts in timeline:\n\t\t#account=posts['account']\n\t\t#application=posts['application']\n\t\t\tfor key in posts:\n\t\t\t\tif (key != 'account' and key !='application' and key!='media_attachments' and key!='mentions' and key!='tags'):\n\t\t\t\t\tif type(posts[key])!='list' or type(posts[key])!='dict' or type(posts[key])!=None:\n\t\t\t\t\t\tvalue=posts[key]\n\t\t\t\t\t\tif key=='content':\n\t\t\t\t\t\t\t#posts[key]=posts[key][3:]\n\t\t\t\t\t\t\t#value=language_translator.translate(posts[key],source='ja', target='en')\n\t\t\t\t\t\t\tvalue=\"'\"+str(value)+\"'\"\n\t\t\t\t\t\t\tworksheet.write(row,col,value)\n\t\t\t\t\t\tworksheet.write(row,col,value)\n\t\t\t\t\t\t#outputcsv.write(row,cal,value)\n\t\t\t\t\t\tcol+=1\n\t\t\t\t\telse:\n\t\t\t\t\t\tvalue=''\n\t\t\t\t\t\tworksheet.write(row,col,value)\n\t\t\t\t\t\t#outputcsv.write(row,col,value)\n\t\t\t\t\t\tbadkeys.append(key)\n\t\t\t\t\t\tcol+=1\n\t\t\trow+=1\n\t\t\tcol=0\n\t\n\t\n\t\nworkbook.close()\nprint('badkeys',badkeys)\n\n\n#discovering attributes in post\n#print(timeline_public[0])\n\n\n", "repo_name": "imranshaikmuma/greyecosystem", "sub_path": "mastodon-hackathon/mastodondataextraction.py", "file_name": "mastodondataextraction.py", "file_ext": "py", "file_size_in_byte": 2365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "mastodon.Mastodon", "line_number": 25, "usage_type": "call"}, {"api_name": "mastodon.log_in", "line_number": 26, "usage_type": "call"}, {"api_name": "mastodon.Mastodon", "line_number": 34, "usage_type": "call"}, {"api_name": "mastodon.account", "line_number": 43, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 46, "usage_type": "call"}, {"api_name": "mastodon.timeline_public", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "72820783198", "text": "from PIL import Image\nfrom facenet_pytorch import MTCNN, InceptionResnetV1\n\n# If required, create a face detection pipeline using MTCNN:\nmtcnn = MTCNN()\n\n# Create an inception resnet (in eval mode):\nresnet = InceptionResnetV1(pretrained='vggface2').eval()\n\n\nimage_path = \"./test_2.jpg\"\n\nimg = Image.open(image_path)\n\n# Get cropped and prewhitened image tensor\nimg_cropped = mtcnn(img)\n\n# Calculate embedding (unsqueeze to add batch dimension)\nimg_embedding = resnet(img_cropped.unsqueeze(0))\n\n# print(img_embedding)\n", "repo_name": "dangxuanvuong98/face_recognition_001", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 516, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "facenet_pytorch.MTCNN", "line_number": 5, "usage_type": "call"}, {"api_name": "facenet_pytorch.InceptionResnetV1", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "22198994853", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 24 00:46:49 2019\n\n@author: GiaHanXinhDep\n\"\"\"\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n# Boost perfromance with jit. User can get rid of it.\nfrom numba import jit\nfrom scipy.sparse import spdiags \n\n@jit\ndef laplacian(N):\n \"\"\"Construct a sparse matrix that applies the 5-point discretization\"\"\"\n e=np.ones(N**2)\n e2=([1]*(N-1)+[0])*N\n e3=([0]+[1]*(N-1))*N\n A=spdiags([-4*e,e2,e3,e,e],[0,-1,1,-N,N],N**2,N**2)\n return A\n\n@jit\ndef main(M,N, Du, Dv, F, K):\n L = M\n u = np.ones((L, L))\n v = np.zeros((L, L))\n Lap = laplacian(L)\n\n h = L//2\n u += 0.02*np.random.random((L,L))\n v += 0.02*np.random.random((L,L))\n u[h-16:h+16, h-16:h+16] = 0.5\n v[h-16:h+16, h-16:h+16] = 0.25\n \n lu = u.reshape((L*L))\n lv = v.reshape((L*L))\n\n #evolve in time using Euler method\n for i in range(N):\n uvv = lu*lv*lv\n lu += (Du*Lap.dot(lu) - uvv + F *(1-lu))\n lv += (Dv*Lap.dot(lv) + uvv - (F+K)*lv )\n\n if i % 1000 == 0:\n filename = \"./data/gs_{:02d}.png\".format(i//1000)\n print(filename)\n plt.imshow(lu.reshape((L,L)), interpolation='bicubic',cmap=plt.cm.jet)\n plt.savefig(filename)\n\n@jit\ndef bonus(M,N, Du, Dv, F, K):\n L = M\n u = np.ones((L, L))\n v = np.zeros((L, L))\n Lap = laplacian(L)\n\n h = L//2\n u += 0.02*np.random.random((L,L))\n v += 0.02*np.random.random((L,L))\n u[h-16:h+16, h-16:h+16] = 0.5\n v[h-16:h+16, h-16:h+16] = 0.25\n \n lu = u.reshape((L*L))\n lv = v.reshape((L*L))\n\n #evolve in time using Euler method\n for i in range(N):\n uvv = lu*lv*lv\n lu += (Du*Lap.dot(lu) - uvv + F *(1-lu))\n lv += (Dv*Lap.dot(lv) + uvv - (F+K)*lv )\n\n u = lu\n v = lv\n f = plt.figure(figsize=(25, 10), dpi=400, facecolor='w', edgecolor='k');\n sp = f.add_subplot(1, 2, 1 );\n plt.pcolor(u.reshape((L, L)), cmap=plt.cm.jet)\n plt.axis('tight')\n\n sp = f.add_subplot(1, 2, 2 );\n plt.pcolor(v.reshape((L, L)), cmap=plt.cm.jet)\n plt.axis('tight')\n plt.savefig(\"bonus.png\")\n\nif __name__ == \"__main__\":\n# Constances\n M=256\n N=32000\n Du=0.14\n Dv=0.06\n F=0.035\n k=0.065\n \n# 0.14, 0.06, 0.035, 0.065\n# 0.16, 0.08, 0.060, 0.062 \n# 0.12, 0.08, 0.020, 0.050\n# 0.16, 0.08, 0.035, 0.060\n \n \n# Calculate Du, Dx (No need)\n \n# Uncomment to gennerate images.\n main(M,N,Du,Dv,F,k)\n \n # bonus(M,N,Du,Dv,F,k)", "repo_name": "ntuong196/Hannah", "sub_path": "p3.py", "file_name": "p3.py", "file_ext": "py", "file_size_in_byte": 2491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.ones", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.sparse.spdiags", "line_number": 21, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 49, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numba.jit", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 78, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 82, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "numba.jit", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "16196397115", "text": "import models\nfrom typing import Optional\nfrom fastapi import FastAPI, Request, Depends, BackgroundTasks\nfrom fastapi.templating import Jinja2Templates\nfrom database import SessionLocal, engine\nfrom sqlalchemy.orm import Session\nimport urllib.request\nimport json\nfrom models import Weather_data\n#import schedule\nimport sqlite3 as lite\nimport time\n\nKAIMAKLI = \"https://api.weather.com/v2/pws/observations/current?apiKey=6532d6454b8aa370768e63d6ba5a832e&stationId=INICOSIA31&numericPrecision=decimal&format=json&units=e\"\nUCY = \"https://api.weather.com/v2/pws/observations/current?apiKey=6532d6454b8aa370768e63d6ba5a832e&stationId=IAGLANDJ2&numericPrecision=decimal&format=json&units=e\"\n\ndef fahrenheit_to_celsius(tempF):\n return (tempF - 32) * 5/9\n\napp = FastAPI()\nmodels.Base.metadata.create_all(bind=engine)\n\ntemplates = Jinja2Templates(directory=\"templates\")\n\ndef get_db():\n db = SessionLocal()\n try:\n yield db\n finally:\n db.close()\n\ndef fetch_weather_data(db, link, weather):\n contents = json.loads(urllib.request.urlopen(link).read())\n data = contents[\"observations\"][0]\n\n tempF = float(data[\"imperial\"][\"temp\"])\n tempC = fahrenheit_to_celsius(tempF)\n\n heat_indexF = float(data[\"imperial\"][\"heatIndex\"])\n heat_indexC = fahrenheit_to_celsius(heat_indexF)\n\n data[\"imperial\"][\"temp\"] = round(tempC, 1)\n data[\"imperial\"][\"heatIndex\"] = round(heat_indexC, 1)\n\n weather.timestamp = data[\"obsTimeLocal\"]\n weather.neighborhood = data[\"neighborhood\"]\n weather.humidity = data[\"humidity\"]\n weather.windSpeed = data[\"imperial\"][\"windSpeed\"]\n weather.temperature = data[\"imperial\"][\"temp\"]\n weather.heatIndex = data[\"imperial\"][\"heatIndex\"]\n\n db.add(weather)\n db.commit()\n\n\n@app.get(\"/station/{station_name}\")\nasync def read_station_weather(station_name: str, background_tasks: BackgroundTasks , db: Session = Depends(get_db)):\n\n weather = Weather_data()\n link = UCY\n if station_name == \"kaimakli\":\n link = KAIMAKLI \n elif station_name == \"ucy\":\n link = UCY\n \n background_tasks.add_task(fetch_weather_data, db, link, weather)\n\n \n\n\n\n@app.get(\"/\")\ndef home(request: Request, db: Session = Depends(get_db)):\n data = db.query(models.Weather_data).all()\n \n print(type(data), data)\n # data=json.dumps(data)\n\n return templates.TemplateResponse(\"home.html\", {\n \"request\": request,\n \"data\": data\n })\n\n# while True:\n# db = SessionLocal()\n# read_station_weather(\"ucy\", db)\n# time.sleep(60)\n", "repo_name": "johnsvds/weather_station", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2525, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "fastapi.FastAPI", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Base.metadata.create_all", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Base", "line_number": 21, "usage_type": "attribute"}, {"api_name": "database.engine", "line_number": 21, "usage_type": "name"}, {"api_name": "fastapi.templating.Jinja2Templates", "line_number": 23, "usage_type": "call"}, {"api_name": "database.SessionLocal", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 33, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 33, "usage_type": "name"}, {"api_name": "fastapi.BackgroundTasks", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 57, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Weather_data", "line_number": 59, "usage_type": "call"}, {"api_name": "fastapi.Request", "line_number": 73, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 73, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Weather_data", "line_number": 74, "usage_type": "attribute"}]} +{"seq_id": "44043737107", "text": "\"\"\"\nChecks which perform remote requests\n\"\"\"\nfrom dataclasses import dataclass, field\nfrom http.client import HTTPResponse\nimport socket\nfrom typing import cast, Union\nfrom urllib.request import urlopen\nfrom urllib.error import HTTPError, URLError\n\nfrom ..constants import Status\nfrom ..utils import Timer\nfrom .base import Check\n\n\n# Default timeout, in seconds\nDEFAULT_TIMEOUT = 10\n\n# Error code for failed connection\n# This should be outside the standard HTTP error codes, so it is differentiated\nSTATUS_FAILED = 0\n\n\n@dataclass\nclass Response:\n \"\"\"\n Wrapper for an urllib Response\n \"\"\"\n raw: Union[HTTPResponse, HTTPError]\n _content: str = field(init=False, repr=False)\n\n @property\n def code(self):\n return self.raw.code\n\n @property\n def content(self):\n if self._content is None:\n self._content = self.raw.read().decode('utf-8')\n return self._content\n\n\nclass Web(Check):\n url: str\n timeout: float\n status_code: int = 200\n content_contains: Union[str, None]\n\n def __init__(\n self,\n url: str,\n label: str = None,\n timeout: float = DEFAULT_TIMEOUT,\n status_code: int = 200,\n content_contains: str = None,\n ):\n super().__init__(label)\n self.url = url\n self.timeout = timeout\n self.status_code = status_code\n self.content_contains = content_contains\n\n def update(self) -> None:\n # Clean data, assuming we're going to fail\n self.data = {\n 'status': STATUS_FAILED,\n }\n\n # Make the request\n timer = Timer()\n try:\n raw_response: HTTPResponse = cast(\n HTTPResponse,\n urlopen(\n self.url,\n timeout=self.timeout,\n ),\n )\n\n except HTTPError as e_response:\n # HTTP error - should be expected, analyse below\n response = Response(raw=e_response)\n\n except URLError as e:\n # Unable to connect - not expected, raise immediately\n self.status = Status.ERROR\n self.data['error'] = f'Could not reach server: {e.reason}'\n return\n\n except Exception as e:\n # Most likely here is socket.timeout for HTTPS\n self.status = Status.ERROR\n self.data['error'] = f'Could not reach server: {e}'\n return\n\n else:\n # Successful request\n response = Response(raw=raw_response)\n\n finally:\n # All checks log their elapsed time\n self.data['elapsed'] = timer.elapsed()\n\n # Update status code to that returned by server\n self.data['status'] = response.code\n\n if response.code != self.status_code:\n self.status = Status.ERROR\n self.data['error'] = (\n f'Expected status {self.status_code}, '\n f'instead found {response.code}.'\n )\n return\n\n if (\n self.content_contains and\n self.content_contains not in response.content\n ):\n self.status = Status.ERROR\n self.data['error'] = 'Expected content not found'\n return\n\n self.status = Status.OK\n", "repo_name": "radiac/disermo", "sub_path": "disermo/checks/remote.py", "file_name": "remote.py", "file_ext": "py", "file_size_in_byte": 3263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.Union", "line_number": 29, "usage_type": "name"}, {"api_name": "http.client.HTTPResponse", "line_number": 29, "usage_type": "name"}, {"api_name": "urllib.error.HTTPError", "line_number": 29, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 30, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 24, "usage_type": "name"}, {"api_name": "base.Check", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 47, "usage_type": "name"}, {"api_name": "utils.Timer", "line_number": 70, "usage_type": "call"}, {"api_name": "http.client.HTTPResponse", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 72, "usage_type": "call"}, {"api_name": "http.client.HTTPResponse", "line_number": 73, "usage_type": "argument"}, {"api_name": "urllib.request.urlopen", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 80, "usage_type": "name"}, {"api_name": "urllib.error.URLError", "line_number": 84, "usage_type": "name"}, {"api_name": "constants.Status.ERROR", "line_number": 86, "usage_type": "attribute"}, {"api_name": "constants.Status", "line_number": 86, "usage_type": "name"}, {"api_name": "constants.Status.ERROR", "line_number": 92, "usage_type": "attribute"}, {"api_name": "constants.Status", "line_number": 92, "usage_type": "name"}, {"api_name": "constants.Status.ERROR", "line_number": 108, "usage_type": "attribute"}, {"api_name": "constants.Status", "line_number": 108, "usage_type": "name"}, {"api_name": "constants.Status.ERROR", "line_number": 119, "usage_type": "attribute"}, {"api_name": "constants.Status", "line_number": 119, "usage_type": "name"}, {"api_name": "constants.Status.OK", "line_number": 123, "usage_type": "attribute"}, {"api_name": "constants.Status", "line_number": 123, "usage_type": "name"}]} +{"seq_id": "34648419880", "text": "# https://www.acmicpc.net/problem/2644\n\nimport sys\nfrom collections import deque\n\nn = int(sys.stdin.readline())\nstart, end = list(map(int, sys.stdin.readline().split()))\nm = int(sys.stdin.readline())\n\nrelationship = [[] for _ in range(n + 1)]\nfor _ in range(m):\n a, b = list(map(int, sys.stdin.readline().split()))\n relationship[a].append(b)\n relationship[b].append(a)\n\nvisited = [0 for _ in range(n + 1)]\n\ndef bfs(start):\n queue = deque([start])\n\n while queue:\n popped = queue.popleft()\n\n for adjacent in relationship[popped]:\n\n # 부모 혹은 자식은 1촌 추가\n if visited[adjacent] == 0:\n visited[adjacent] = visited[popped] + 1\n queue.append(adjacent)\n\n if adjacent == end:\n return visited[adjacent]\n \n # 촌수 계산 불가능한 경우\n return -1\n\nprint(bfs(start))", "repo_name": "desfox/algorithm", "sub_path": "boj/silver/2644.py", "file_name": "2644.py", "file_ext": "py", "file_size_in_byte": 893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sys.stdin.readline", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 12, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "11330437330", "text": "from tkinter import *\nfrom tkinter import filedialog\nfrom tkinter.colorchooser import askcolor\n\nimport os\nimport numpy as np\nimport torch\nfrom torchvision.transforms.functional import to_pil_image\nfrom PIL import Image, ImageTk\nimport webbrowser\n\nfrom test import demo_test\n\n\ndef resize(image, min_size):\n w, h = image.size\n if h > min_size and w > min_size:\n if h < w:\n factor = min_size / h\n else:\n factor = min_size / w\n image = image.resize((int(w * factor), int(h * factor)))\n return image\n\n\nclass Paint(object):\n DEFAULT_PEN_SIZE = 6.0\n DEFAULT_COLOR = ((0, 0, 0), '#000000')\n MAX_SIZE = (512, 512)\n SAVE_PATH = './examples/'\n\n def __init__(self, device='cuda'):\n self.device = device\n\n self.root = Tk()\n self.root.geometry('1400x700')\n self.root.title('Demo')\n\n self.warn_label = Label(self.root, text='This is a free open-source project by Lin')\n self.warn_label.place(y=650, x=1310, anchor=NE)\n\n self.github = Label(self.root, text='My Github Page', fg='blue', cursor='hand2')\n self.github.place(y=670, x=1310, anchor=NE)\n self.github.bind('', lambda x: self.open_url('https://github.com/CytrusL'))\n\n self.sketch_frame = LabelFrame(self.root, text='Sketch Image', width=540, height=550)\n self.sketch_frame.place(y=40, x=200)\n\n self.pred_frame = LabelFrame(self.root, text='Generated Image', width=540, height=550)\n self.pred_frame.place(y=40, x=770)\n\n self.select = Button(self.root, text=\"Select Image\", command=self.openImage)\n self.select.place(y=600, x=200, width=200, height=40)\n\n self.pen_button = Button(self.root, text='pen')\n self.pen_button.place(y=50, x=40, width=130, height=40)\n\n self.color_button = Button(self.root, text='color', command=self.choose_color)\n self.color_button.place(y=150, x=40, width=130, height=40)\n\n self.clear_button = Button(self.root, text='clear', command=self.clear)\n self.clear_button.place(y=250, x=40, width=130, height=40)\n\n self.choose_size_button = Scale(self.root, from_=1, to=12, orient=HORIZONTAL)\n self.choose_size_button.place(y=330, x=40, width=130, height=40)\n\n self.crop_var = IntVar()\n self.crop_button = Checkbutton(self.root, text='crop', variable=self.crop_var)\n self.crop_button.place(y=400, x=40, width=50, height=40)\n\n self.resize_var = IntVar()\n self.resize_button = Checkbutton(self.root, text='resize', variable=self.resize_var)\n self.resize_button.place(y=400, x=110, width=50, height=40)\n\n self.gen_button = Button(self.root, text='Generate', command=self.generate)\n self.gen_button.place(y=480, x=40, width=130, height=40)\n\n self.save_s_button = Button(self.root, text='Save Sketch', command=lambda: self.save_img('sketch'))\n self.save_s_button.place(y=600, x=590, width=150, height=40)\n\n self.save_g_button = Button(self.root, text='Save Generated', command=lambda: self.save_img('pred'))\n self.save_g_button.place(y=600, x=1160, width=150, height=40)\n\n self.setup()\n self.root.mainloop()\n\n def setup(self):\n self.canvas = None\n self.color_rec = []\n self.choose_size_button.set(self.DEFAULT_PEN_SIZE)\n self.im_size_label = None\n self.width = self.choose_size_button.get()\n self.color = self.DEFAULT_COLOR\n\n def clear(self):\n if self.color_rec:\n self.canvas.delete(*self.color_rec)\n self.strokes = np.ones((self.label_h, self.label_w, 3)) * 150\n self.mask = np.zeros((self.label_h, self.label_w))\n\n def choose_color(self):\n self.color = askcolor(color=self.color[1])\n\n def openImage(self):\n img_path = filedialog.askopenfilenames(initialdir='./')\n self.file_name = img_path[-1].split('/')[-1]\n print(self.file_name)\n\n if img_path:\n img_open = Image.open(img_path[-1]).convert('RGB')\n\n if self.resize_var.get():\n img_open = resize(img_open, 512)\n if self.crop_var.get():\n img_open = img_open.crop((0, 0, 512, 512))\n\n self.sketch = img_open\n\n w, h = self.sketch.size\n self.w, self.h = w, h\n\n self.sketch_label = self._resize(img_open, w, h, self.MAX_SIZE)\n self.label_w, self.label_h = self.sketch_label.size\n self.img_label = ImageTk.PhotoImage(self.sketch_label)\n\n self.clear()\n\n if self.canvas:\n self.canvas.destroy()\n if self.im_size_label:\n self.im_size_label.destroy()\n\n self.strokes = np.ones((self.label_h, self.label_w, 3)) * 150\n self.mask = np.zeros((self.label_h, self.label_w))\n\n self.canvas = Canvas(self.sketch_frame, width=self.label_w, height=self.label_h)\n self.canvas.place(y=260, x=270, anchor=CENTER)\n self.canvas.create_image(0, 0, image=self.img_label, anchor=NW)\n\n self.im_size_label = Label(self.root, text=f'{w}x{h}')\n self.im_size_label.place(y=610, x=500)\n\n self.canvas.bind('', self.paint)\n\n @staticmethod\n def _resize(img, w, h, size):\n scale = w / size[0] if h < w else h / size[1]\n\n w, h = int(w / scale), int(h / scale)\n img = img.resize((w, h), Image.BICUBIC)\n\n return img\n\n def paint(self, event):\n self.width = self.choose_size_button.get()\n offs = self.width // 2\n paint_color = self.color\n\n if self.width % 2 == 0:\n x1, x2 = event.x - offs, event.x + offs\n y1, y2 = event.y - offs, event.y + offs\n else:\n x1, x2 = event.x - offs, event.x + offs + 1\n y1, y2 = event.y - offs, event.y + offs + 1\n self.color_rec.append(self.canvas.create_rectangle(x1, y1,\n x2, y2,\n outline=paint_color[1],\n fill=paint_color[1]))\n self.strokes[y1:y2, x1:x2] = torch.tensor(paint_color[0], dtype=torch.uint8)\n self.mask[y1:y2, x1:x2] = 1\n\n def generate(self):\n strokes = Image.fromarray(self.strokes.astype(np.uint8)).resize((self.w, self.h), Image.NEAREST)\n mask = Image.fromarray(self.mask.astype(np.uint8)).resize((self.w, self.h), Image.NEAREST)\n strokes.save('./test.png')\n\n pred, pw, ph = demo_test(self.sketch, strokes, mask, device=self.device)\n self.pred = to_pil_image(pred[0])\n w, h = self.pred.size\n self.pred = self.pred.crop((0, 0, w-pw, h-ph))\n\n self.pred_label = self._resize(self.pred, w, h, self.MAX_SIZE)\n self.pred_img = ImageTk.PhotoImage(self.pred_label)\n\n self.pred_label = Label(self.pred_frame, image=self.pred_img)\n self.pred_label.place(y=260, x=270, anchor=CENTER)\n\n def save_img(self, type):\n if type == 'sketch':\n self.canvas.postscript(file=os.path.join(self.SAVE_PATH, 'sketch.eps'))\n img = Image.open(os.path.join(self.SAVE_PATH, 'sketch.eps'))\n img.save(os.path.join(self.SAVE_PATH, 'sketch.png'))\n elif type == 'pred':\n self.pred.save(os.path.join(self.SAVE_PATH, 'pred_'+self.file_name))\n\n def open_url(self, url):\n webbrowser.open_new(url)\n\n\nif __name__ == '__main__':\n Paint()\n", "repo_name": "CytrusL/colourisation", "sub_path": "old version [archive]/gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 7526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.ones", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "tkinter.colorchooser.askcolor", "line_number": 102, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilenames", "line_number": 105, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 105, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 110, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 124, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 150, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 169, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 173, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 173, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 173, "usage_type": "attribute"}, {"api_name": "PIL.Image.NEAREST", "line_number": 173, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 174, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 174, "usage_type": "attribute"}, {"api_name": "PIL.Image.NEAREST", "line_number": 174, "usage_type": "attribute"}, {"api_name": "test.demo_test", "line_number": 177, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional.to_pil_image", "line_number": 178, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 183, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 183, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 191, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 191, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"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": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "webbrowser.open_new", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "17925435011", "text": "from sqlalchemy import Column, DateTime, ForeignKey, Integer, String, Table\nfrom sqlalchemy.orm import relationship\nfrom glapg.database import Model\n\nbranches = Table('branch', Model.metadata,\n Column('cube_id', Integer, ForeignKey('cube.id')),\n Column('connection_id', Integer, ForeignKey('connection.id'))\n)\n\nclass Board(Model):\n __tablename__ = 'board'\n\n id = Column(Integer, primary_key=True)\n name = Column(String(63))\n\nclass Cube(Model):\n \"\"\"Represents one if the player's moves.\"\"\"\n __tablename__ = 'cube'\n\n id = Column(Integer, primary_key=True)\n board_id = Column(Integer, ForeignKey('board.id'))\n board = relationship('Board')\n # The sequential order in which this cube was placed.\n number = Column(Integer)\n state = Column(Integer)\n created = Column(DateTime)\n\nclass Plate(Model):\n \"\"\"Represents one of the 'plates' in the Glass Plate Game.\"\"\"\n __tablename__ = 'plate'\n\n id = Column(Integer, primary_key=True)\n board_id = Column(Integer, ForeignKey('board.id'))\n board = relationship('Board')\n # The plate's textual representation.\n name = Column(String(63))\n # The plate's visual representation.\n image_name = Column(String(63))\n\nclass Connection(Model):\n \"\"\"Represents a connection between multiple cubes.\"\"\"\n __tablename__ = 'connection'\n\n id = Column(Integer, primary_key=True)\n board_id = Column(Integer, ForeignKey('board.id'))\n board = relationship('Board')\n connection_next_id = Column(Integer, ForeignKey('connection.id'))\n connection_next = relationship('Connection')\n # Player's description for the connection.\n text = Column(String(1023))\n cubes_prev = relationship('Cube', secondary=branches)\n cube_next = Column(Integer, ForeignKey('cube.id'))\n\n'''class Train(Model):\n __tablename__ = 'train'\n\n id = Column(Integer, primary_key=True)\n color = Column(Integer)\n cube_initial = Column(Integer, ForeignKey('cube.id'))\n connections = relationship('cube', secondary='''\n", "repo_name": "glassplategame/glapg", "sub_path": "glapg/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "sqlalchemy.Table", "line_number": 5, "usage_type": "call"}, {"api_name": "glapg.database.Model.metadata", "line_number": 5, "usage_type": "attribute"}, {"api_name": "glapg.database.Model", "line_number": 5, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 6, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 7, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 7, "usage_type": "call"}, {"api_name": "glapg.database.Model", "line_number": 10, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 13, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 14, "usage_type": "call"}, {"api_name": "glapg.database.Model", "line_number": 16, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 20, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 24, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 25, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 26, "usage_type": "argument"}, {"api_name": "glapg.database.Model", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 32, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 33, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 38, "usage_type": "call"}, {"api_name": "glapg.database.Model", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 44, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 45, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 47, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 52, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "9465624300", "text": "import json\nimport time as t\nfrom datetime import datetime\nimport pandas as pd\nfrom pandas import json_normalize\nimport requests\nimport urllib3\n\n\ndef getMetadata(subscription_key, typeCode, freqCode, clCode, period, reporterCode, showHistory):\n baseURL = 'https://comtradeapi.un.org/data/v1/getMetadata/' + typeCode + '/' + freqCode + '/' + clCode\n PARAMS = dict(reporterCode=reporterCode, period=period)\n # add key\n PARAMS[\"subscription-key\"] = subscription_key\n # print(PARAMS)\n try:\n resp = requests.get(baseURL, params=PARAMS, timeout=120)\n # print(resp.text)\n # print(resp.url)\n if resp.status_code != 200:\n # This means something went wrong.\n jsonResult = resp.json()\n print('Error in calling API:', resp.url)\n try:\n print('Error code:', jsonResult['statusCode'])\n print('Error message:', jsonResult['message'])\n except:\n t.sleep(1)\n else:\n jsonResult = resp.json()\n df = json_normalize(jsonResult['data']) # Results contain the required data\n # Get the notes only\n FIELDS = ['notes']\n dt = df[FIELDS]\n dt = dt.explode('notes')\n df_final = (\n pd.DataFrame(dt[\"notes\"]\n .apply(pd.Series))\n )\n dt_final_latest = df_final[['datasetCode', 'publicationDate']].groupby(\"datasetCode\").max()\n dt_final_latest.loc[:, 'isLatestPublication'] = True\n df_final_merge = df_final.merge(dt_final_latest, on='publicationDate', how='left')\n if (showHistory == True):\n return df_final_merge\n else:\n return df_final_merge[df_final_merge.notnull()].query('isLatestPublication==True')\n except requests.exceptions.Timeout:\n # Maybe set up for a retry, or continue in a retry loop\n print('Request failed due to timeout')\n except requests.exceptions.TooManyRedirects:\n # Tell the user their URL was bad and try a different one\n print('Request failed due to too many redirects')\n except requests.exceptions.RequestException as e:\n # catastrophic error. bail.\n raise SystemExit(e)\n\ndef listReference(category=None):\n baseURL = 'https://comtradeapi.un.org/files/v1/app/reference/ListofReferences.json'\n try:\n resp = requests.get(baseURL, timeout=120)\n # print(resp.text)\n # print(resp.url)\n if resp.status_code != 200:\n # This means something went wrong.\n try:\n print('Error in calling API:', resp.url)\n except:\n t.sleep(1)\n else:\n resp.encoding = 'utf-8-sig'\n jsonResult = resp.json()\n df = json_normalize(jsonResult['results']) # Results contain the required data\n if category is not None:\n return df.query(\"category=='\" + category + \"'\")\n else:\n return df\n except requests.exceptions.Timeout:\n # Maybe set up for a retry, or continue in a retry loop\n print('Request failed due to timeout')\n except requests.exceptions.TooManyRedirects:\n # Tell the user their URL was bad and try a different one\n print('Request failed due to too many redirects')\n except requests.exceptions.RequestException as e:\n # catastrophic error. bail.\n raise SystemExit(e)\n\ndef getReference(category):\n try:\n baseURL = listReference(category).iloc[0]['fileuri']\n except:\n baseURL = ''\n print('Error in looking up the file URI for', category)\n if baseURL != '':\n try:\n resp = requests.get(baseURL, timeout=120)\n # print(resp.text)\n # print(resp.url)\n if resp.status_code != 200:\n # This means something went wrong.\n try:\n print('Error in calling API:', resp.url)\n except:\n t.sleep(1)\n else:\n resp.encoding = 'utf-8-sig'\n jsonResult = resp.json()\n df = json_normalize(jsonResult['results']) # Results contain the required data\n return df\n except requests.exceptions.Timeout:\n # Maybe set up for a retry, or continue in a retry loop\n print('Request failed due to timeout')\n except requests.exceptions.TooManyRedirects:\n # Tell the user their URL was bad and try a different one\n print('Request failed due to too many redirects')\n except requests.exceptions.RequestException as e:\n # catastrophic error. bail.\n raise SystemExit(e)\n\ndef convertCountryIso3ToCode(countryIsoCode):\n baseURL = 'https://comtradeapi.un.org/files/v1/app/reference/country_area_code_iso.json'\n resp = requests.get(baseURL, timeout=120)\n df = json_normalize(resp.json()['results'])\n df['country_area_code'] = df['country_area_code'].astype(str)\n delim = ','\n iso_string = countryIsoCode\n iso_list = iso_string.split(delim)\n code_list = df[df['iso3'].isin(iso_list)]['country_area_code'].tolist()\n code_string = delim.join(code_list)\n return code_string\n", "repo_name": "uncomtrade/comtradeapicall", "sub_path": "src/comtradeapicall/Metadata.py", "file_name": "Metadata.py", "file_ext": "py", "file_size_in_byte": 5293, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 38, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 47, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 50, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 53, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 72, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 77, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 80, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 83, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 107, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 109, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 112, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 115, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 121, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "16034265481", "text": "from absl import logging\nimport functools\nimport gin\nimport inspect\nfrom inspect import Parameter\nimport os\nimport pprint\nimport runpy\nimport shutil\n\n__all__ = [\n 'config',\n 'config1',\n 'configurable',\n 'define_config',\n 'get_all_config_names',\n 'get_config_value',\n 'get_handled_pre_configs',\n 'get_inoperative_configs',\n 'get_operative_configs',\n 'import_config',\n 'load_config',\n 'pre_config',\n 'reset_configs',\n 'validate_pre_configs',\n 'repr_wrapper',\n 'save_config',\n]\n\n\n@logging.skip_log_prefix\ndef config(prefix_or_dict, mutable=True, raise_if_used=True, **kwargs):\n \"\"\"Set the values for the configs with given name as suffix.\n\n Example:\n\n Assume we have the following decorated functions and classes:\n\n .. code-block:: python\n\n @alf.configurable\n def cool_func(param1, cool_arg1='a default value', cool_arg2=3):\n ...\n\n @alf.configurable\n def dumb_func(param1, a=1, b=2):\n ...\n\n @alf.configurable\n class Worker(obj):\n def __init__(self, job1=1, job2=2):\n ...\n\n @alf.configurable\n def func(self, a, b):\n ...\n\n We can config in the following ways:\n\n .. code-block::\n\n alf.config('cool_func', cool_arg1='new_value', cool_arg2='another_value')\n alf.config('Worker.func', b=3)\n alf.config('func', b=3) # 'Worker.func' can be uniquely identified by 'func'\n alf.config({\n 'dumb_func.b': 3,\n 'Worker.job1': 2 # now the default value of job1 for Worker() becomes 2.\n })\n\n\n Args:\n prefix_or_dict (str|dict): if a dict, each (key, value) pair in it\n specifies the value for a config with name key. If a str, it is used\n as prefix so that each (key, value) pair in kwargs specifies the\n value for config with name ``prefix + '.' + key``\n mutable (bool): whether the config can be changed later. If the user\n tries to change an existing immutable config, the change will be\n ignored and a warning will be generated. You can always change a\n mutable config. ``ValueError`` will be raised if trying to set a new\n immutable value to an existing immutable value.\n raise_if_used (bool): If True, ValueError will be raised if trying to\n config a value which has already been used.\n **kwargs: only used if ``prefix_or_dict`` is a str.\n \"\"\"\n if isinstance(prefix_or_dict, str):\n assert len(kwargs) > 0, (\"**kwargs should be provided when \"\n \"'prefix_or_dict' is a str\")\n prefix = prefix_or_dict\n configs = dict([(prefix + '.' + k, v) for k, v in kwargs.items()])\n elif isinstance(prefix_or_dict, dict):\n assert len(kwargs) == 0, (\"**kwargs should not be provided when \"\n \"'prefix_or_dict' is a dict\")\n configs = prefix_or_dict\n else:\n raise ValueError(\n \"Unsupported type for 'prefix_or_dict': %s\" % type(prefix_or_dict))\n for key, value in configs.items():\n config1(key, value, mutable, raise_if_used)\n\n\ndef get_all_config_names():\n \"\"\"Get the names of all configurable values.\"\"\"\n return sorted([name for name, config in _get_all_leaves(_CONF_TREE)])\n\n\ndef get_operative_configs():\n \"\"\"Get all the configs that have been used.\n\n A config is operative if a function call does not explicitly specify the value\n of that config and hence its default value or the value provided through\n alf.config() needs to be used.\n\n Returns:\n list[tuple[config_name, Any]]\n \"\"\"\n configs = [(name, config.get_effective_value())\n for name, config in _get_all_leaves(_CONF_TREE)\n if config.is_used()]\n return sorted(configs, key=lambda x: x[0])\n\n\ndef get_inoperative_configs():\n \"\"\"Get all the configs that have not been used.\n\n A config is inoperative if its value has been set through ``alf.config()``\n but its set value has never been used by any function calls.\n\n Returns:\n list[tuple[config_name, Any]]\n \"\"\"\n configs = [(name, config.get_value())\n for name, config in _get_all_leaves(_CONF_TREE)\n if config.is_configured() and not config.is_used()]\n return sorted(configs, key=lambda x: x[0])\n\n\ndef _get_all_leaves(conf_dict):\n \"\"\"\n Returns:\n list[tupe[path, _Config]]\n \"\"\"\n leaves = []\n for k, v in conf_dict.items():\n if not isinstance(v, dict):\n leaves.append((k, v))\n else:\n leaves.extend(\n [(name + '.' + k, node) for name, node in _get_all_leaves(v)])\n return leaves\n\n\nclass _Config(object):\n \"\"\"Object representing one configurable value.\"\"\"\n\n def __init__(self):\n self._configured = False\n self._used = False\n self._has_default_value = False\n self._mutable = True\n\n def set_default_value(self, value):\n self._default_value = value\n self._has_default_value = True\n\n def has_default_value(self):\n return self._has_default_value\n\n def get_default_value(self):\n return self._default_value\n\n def is_configured(self):\n return self._configured\n\n def set_mutable(self, mutable):\n self._mutable = mutable\n\n def is_mutable(self):\n return self._mutable\n\n def set_value(self, value):\n self._configured = True\n self._value = value\n\n def get_value(self):\n assert self._configured\n return self._value\n\n def get_effective_value(self):\n assert self._configured or self._has_default_value\n return self._value if self._configured else self._default_value\n\n def set_used(self):\n self._used = True\n\n def is_used(self):\n return self._used\n\n def reset(self):\n self._used = False\n self._configured = False\n self._mutable = True\n\n\n# _CONF_TREE is a suffix tree. For a name such as \"abc.def.ghi\", the corresponding\n# node can be found using _CONF_TREE['ghi']['def']['abc']\n_CONF_TREE = {}\n_PRE_CONFIGS = []\n_HANDLED_PRE_CONFIGS = []\n_DEFINED_CONFIGS = []\n_CONF_FILES = {} # key: file name, value: content\n_CONFIG_MODULES = {}\n_IMPORT_STACK = []\n_ROOT_CONF_FILE = None\n\n\ndef reset_configs():\n \"\"\"Reset all the configs to their initial states.\"\"\"\n\n def _reset_configs(tree):\n for child in tree.values():\n if isinstance(child, dict):\n _reset_configs(child)\n else:\n child.reset()\n\n _reset_configs(_CONF_TREE)\n for name in _DEFINED_CONFIGS:\n _remove_config_node(name)\n\n _DEFINED_CONFIGS.clear()\n _PRE_CONFIGS.clear()\n _HANDLED_PRE_CONFIGS.clear()\n _CONF_FILES.clear()\n _CONFIG_MODULES.clear()\n global _ROOT_CONF_FILE\n _ROOT_CONF_FILE = None\n\n\ndef _remove_config_node(config_name):\n \"\"\"Remove the _Config object corresponding to config_name.\"\"\"\n node = _CONF_TREE\n path = config_name.split('.')\n for name in reversed(path):\n tree = node\n if not isinstance(tree, dict) or name not in tree:\n raise ValueError(\"Cannot find config name %s\" % config_name)\n node = tree[name]\n\n assert isinstance(\n node, _Config), \"config_name is not a full path: %s\" % config_name\n del tree[name]\n\n\ndef _get_config_node(config_name):\n \"\"\"Get the _Config object corresponding to config_name.\"\"\"\n tree = _CONF_TREE\n path = config_name.split('.')\n for name in reversed(path):\n if not isinstance(tree, dict) or name not in tree:\n raise ValueError(\"Cannot find config name %s\" % config_name)\n tree = tree[name]\n\n if isinstance(tree, dict):\n leaves = _get_all_leaves(tree)\n if len(leaves) > 1:\n # only show at most 3 ambiguous choices\n leaves = leaves[:3]\n names = [name + '.' + config_name for name, node in leaves]\n raise ValueError(\"config name '%s' is ambiguous. There are %s\" %\n (config_name, names))\n\n assert len(leaves) == 1\n config_node = leaves[0][1]\n else:\n config_node = tree\n\n return config_node\n\n\n@logging.skip_log_prefix\ndef config1(config_name, value, mutable=True, raise_if_used=True):\n \"\"\"Set one configurable value.\n\n Args:\n config_name (str): name of the config\n value (any): value of the config\n mutable (bool): whether the config can be changed later. If the user\n tries to change an existing immutable config, the change will be\n ignored and a warning will be generated. You can always change a\n mutable config. ``ValueError`` will be raised if trying to set a new\n immutable value to an existing immutable value.\n raise_if_used (bool): If True, ValueError will be raised if trying to\n config a value which has already been used.\n \"\"\"\n config_node = _get_config_node(config_name)\n\n if (raise_if_used and config_node.is_used()\n and config_node.get_effective_value() != value):\n raise ValueError(\n \"Config '%s' has already been used. You should config \"\n \"its value before using it.\" % config_name)\n if config_node.is_configured():\n if config_node.get_value() != value:\n if config_node.is_mutable():\n logging.warning(\n \"The value of config '%s' has been configured to %s. It is \"\n \"replaced by the new value %s\" %\n (config_name, config_node.get_value(), value))\n config_node.set_value(value)\n config_node.set_mutable(mutable)\n else:\n logging.warning(\n \"The config '%s' has been configured to an immutable value \"\n \"of %s. The new value %s will be ignored\" %\n (config_name, config_node.get_value(), value))\n else:\n config_node.set_value(value)\n config_node.set_mutable(mutable)\n\n\n@logging.skip_log_prefix\ndef pre_config(configs):\n \"\"\"Preset the values for configs before the module defining it is imported.\n\n This function is useful for handling the config params from commandline,\n where there are no module imports and hence no config has been defined.\n\n The value is bound to the config when the module defining the config is\n imported later. ``validate_pre_configs()` should be called after the config\n file has been loaded to ensure that all the pre_configs have been correctly\n bound.\n\n Args:\n configs (dict): dictionary of config name to value\n \"\"\"\n for name, value in configs.items():\n try:\n config1(name, value, mutable=False)\n _HANDLED_PRE_CONFIGS.append((name, value))\n except ValueError:\n _PRE_CONFIGS.append((name, value))\n\n\ndef _handle_pre_configs(path, node):\n def _handle1(item):\n name, value = item\n parts = name.split('.')\n if len(parts) > len(path):\n return True\n for i in range(-len(parts), 0):\n if parts[i] != path[i]:\n return True\n node.set_value(value)\n node.set_mutable(False)\n _HANDLED_PRE_CONFIGS.append(item)\n return False\n\n global _PRE_CONFIGS\n _PRE_CONFIGS = list(filter(_handle1, _PRE_CONFIGS))\n\n\ndef validate_pre_configs():\n \"\"\"Validate that all the configs set through ``pre_config()`` are correctly bound.\"\"\"\n\n if _PRE_CONFIGS:\n raise ValueError((\n \"A pre-config '%s' was not handled, either because its config name \"\n +\n \"was not found, or there was some error when calling pre_config()\")\n % _PRE_CONFIGS[0][0])\n\n for (config_name, _) in _HANDLED_PRE_CONFIGS:\n _get_config_node(config_name)\n\n\ndef get_handled_pre_configs():\n \"\"\"Return a list of handled pre-config ``(name, value)``.\"\"\"\n return _HANDLED_PRE_CONFIGS\n\n\ndef get_config_value(config_name):\n \"\"\"Get the value of the config with the name ``config_name``.\n\n Args:\n config_name (str): name of the config or its suffix which can uniquely\n identify the config.\n Returns:\n Any: value of the config\n Raises:\n ValueError: if the value of the config has not been configured and it\n does not have a default value.\n \"\"\"\n config_node = _get_config_node(config_name)\n if not config_node.is_configured() and not config_node.has_default_value():\n raise ValueError(\n \"Config '%s' is not configured nor has a default value.\" %\n config_name)\n\n config_node.set_used()\n return config_node.get_effective_value()\n\n\ndef _make_config(signature, whitelist, blacklist):\n \"\"\"Create a dictionary of _Config for given signature.\n\n Args:\n signature (inspec.Signature): function signature\n whitelist (list[str]): allowed configurable argument names\n blacklist (list[str]): disallowed configurable argument names\n Returns:\n dict: name => _Config\n \"\"\"\n configs = {}\n for name, param in signature.parameters.items():\n if param.kind in (inspect.Parameter.VAR_POSITIONAL,\n inspect.Parameter.VAR_KEYWORD):\n continue\n if ((not blacklist and not whitelist)\n or (whitelist and name in whitelist)\n or (blacklist and name not in blacklist)):\n config = _Config()\n configs[name] = config\n if param.default is not inspect.Parameter.empty:\n config.set_default_value(param.default)\n\n return configs\n\n\ndef _add_to_conf_tree(module_path, func_name, arg_name, node):\n \"\"\"Add a config object to _CONF_TREE.\n\n Args:\n module_path (list[str]): module path of this function\n func_name (str): name of the function\n node (_Config): config object for this value\n arg_name: (str): name of the argument\n \"\"\"\n\n tree = _CONF_TREE\n path = module_path + func_name.split('.') + [arg_name]\n names = []\n for name in reversed(path[1:]):\n if not isinstance(tree, dict):\n raise ValueError(\"'%s' conflicts with existing config name '%s'\" %\n ('.'.join(path), '.'.join(names)))\n if name not in tree:\n tree[name] = {}\n tree = tree[name]\n names.insert(0, name)\n\n if not isinstance(tree, dict):\n raise ValueError(\"'%s' conflicts with existing config name '%s'\" %\n ('.'.join(path), '.'.join(names)))\n if path[0] in tree:\n if isinstance(tree[path[0]], dict):\n leaves = _get_all_leaves(tree)\n raise ValueError(\n \"'%s' conflicts with existing config name '%s'\" %\n ('.'.join(path), '.'.join([leaves[0][0]] + names)))\n else:\n raise ValueError(\"'%s' has already been defined.\" % '.'.join(path))\n\n tree[path[0]] = node\n\n _handle_pre_configs(path, node)\n\n\ndef _find_class_construction_fn(cls):\n \"\"\"Find the first __init__ or __new__ method in the given class's MRO.\n\n Adapted from gin-config/gin/config.py\n \"\"\"\n for base in type.mro(cls):\n if '__init__' in base.__dict__:\n return base.__init__\n if '__new__' in base.__dict__:\n return base.__new__\n\n\ndef _ensure_wrappability(fn):\n \"\"\"Make sure `fn` can be wrapped cleanly by functools.wraps.\n\n Adapted from gin-config/gin/config.py\n \"\"\"\n # Handle \"builtin_function_or_method\", \"wrapped_descriptor\", and\n # \"method-wrapper\" types.\n unwrappable_types = (type(sum), type(object.__init__),\n type(object.__call__))\n if isinstance(fn, unwrappable_types):\n # pylint: disable=unnecessary-lambda\n wrappable_fn = lambda *args, **kwargs: fn(*args, **kwargs)\n wrappable_fn.__name__ = fn.__name__\n wrappable_fn.__doc__ = fn.__doc__\n wrappable_fn.__module__ = '' # These types have no __module__, sigh.\n wrappable_fn.__wrapped__ = fn\n return wrappable_fn\n\n # Otherwise we're good to go...\n return fn\n\n\ndef _make_wrapper(fn, configs, signature, has_self):\n \"\"\"Wrap the function.\n\n Args:\n fn (Callable): function to be wrapped\n configs (dict[_Config]): config associated with the arguments of function\n ``fn``\n signature (inspect.Signature): Signature object of ``fn``. It is provided\n as an argument so that we don't need to call ``inspect.signature(fn)``\n repeatedly, whith is expensive.\n has_self (bool): whether the first argument is expected to be self but\n signature does not contains parameter for self. This should be True\n if fn is __init__() function of a class.\n Returns:\n The wrapped function\n \"\"\"\n\n @functools.wraps(fn)\n def _wrapper(*args, **kwargs):\n unspecified_positional_args = []\n unspecified_kw_args = {}\n num_positional_args = len(args)\n num_positional_args -= has_self\n\n for i, (name, param) in enumerate(signature.parameters.items()):\n config = configs.get(name, None)\n if config is None:\n continue\n elif i < num_positional_args:\n continue\n elif param.kind in (Parameter.VAR_POSITIONAL,\n Parameter.VAR_KEYWORD):\n continue\n elif param.kind == Parameter.POSITIONAL_ONLY:\n if config.is_configured():\n unspecified_positional_args.append(config.get_value())\n config.set_used()\n elif name not in kwargs and param.kind in (\n Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY):\n if config.is_configured():\n unspecified_kw_args[name] = config.get_value()\n config.set_used()\n\n return fn(*args, *unspecified_positional_args, **kwargs,\n **unspecified_kw_args)\n\n return _wrapper\n\n\ndef _decorate(fn_or_cls, name, whitelist, blacklist):\n \"\"\"decorate a function or class.\n\n Args:\n fn_or_cls (Callable): a function or a class\n name (str): name for the function. If None, ``fn_or_cls.__qualname__``\n will be used.\n whitelist (list[str]): A whitelisted set of kwargs that should be configurable.\n All other kwargs will not be configurable. Only one of ``whitelist`` or\n `blacklist` should be specified.\n blacklist (list[str]): A blacklisted set of kwargs that should not be\n configurable. All other kwargs will be configurable. Only one of\n ``whitelist` or ``blacklist`` should be specified.\n Returns:\n The decorated function\n \"\"\"\n signature = inspect.signature(fn_or_cls)\n configs = _make_config(signature, whitelist, blacklist)\n\n orig_name = name\n\n if name is None or '.' not in name:\n module_path = fn_or_cls.__module__.split('.')\n else:\n parts = name.split('.')\n module_path = parts[:-1]\n name = parts[-1]\n\n if name is None:\n name = fn_or_cls.__qualname__\n\n for arg_name, node in configs.items():\n _add_to_conf_tree(module_path, name, arg_name, node)\n\n if inspect.isclass(fn_or_cls):\n # cannot use _make_wrapper() directly on fn_or_cls. This is because\n # _make_wrapper() returns a function. But we want to return a class.\n construction_fn = _find_class_construction_fn(fn_or_cls)\n has_self = construction_fn.__name__ != '__new__'\n decorated_fn = _make_wrapper(\n _ensure_wrappability(construction_fn), configs, signature,\n has_self)\n if construction_fn.__name__ == '__new__':\n decorated_fn = staticmethod(decorated_fn)\n setattr(fn_or_cls, construction_fn.__name__, decorated_fn)\n else:\n fn_or_cls = _make_wrapper(fn_or_cls, configs, signature, has_self=0)\n\n if fn_or_cls.__module__ != '' and os.environ.get(\n 'ALF_USE_GIN', \"1\") == \"1\":\n # If a file is executed using runpy.run_path(), the module name is\n # '', which is not an acceptable name by gin.\n return gin.configurable(\n orig_name, whitelist=whitelist, blacklist=blacklist)(fn_or_cls)\n else:\n return fn_or_cls\n\n\ndef repr_wrapper(cls):\n \"\"\"A wrapper for automatically generating readable repr for an object.\n\n The presentation shows the arguments used to construct of object.\n It does not include the default arguments, nor the class members.\n\n To use it, simply use it to decorate an class.\n\n Example:\n\n .. code-block:: python\n\n @repr_wrapper\n class MyClass(object):\n def __init__(self, a, b, c=100, d=200):\n pass\n\n a = MyClass(1, 2)\n assert repr(a) == \"MyClass(1, 2)\"\n a = MyClass(3, 5, d=300)\n assert repr(a) == \"MyClass(1, 2, d=300)\"\n\n \"\"\"\n assert inspect.isclass(cls)\n signature = inspect.signature(cls)\n construction_fn = _find_class_construction_fn(cls)\n has_self = construction_fn.__name__ != '__new__'\n fn = _ensure_wrappability(construction_fn)\n defaults = {}\n for name, param in signature.parameters.items():\n if param.kind in (inspect.Parameter.VAR_POSITIONAL,\n inspect.Parameter.VAR_KEYWORD):\n continue\n if param.default is not inspect.Parameter.empty:\n defaults[name] = param.default\n\n setattr(cls, '__repr__', lambda self: self._repr_wrapper_str_)\n\n @functools.wraps(fn)\n def _wrapper(*args, **kwargs):\n ret = fn(*args, **kwargs)\n if has_self:\n self = args[0]\n else:\n self = ret\n\n s = []\n for val in args[has_self:]:\n s.append(pprint.pformat(val))\n for k, val in kwargs.items():\n if k not in defaults or val != defaults[k]:\n s.append(k + '=' + pprint.pformat(val))\n l = sum(map(len, s))\n multiline = l > 80 or any(map(lambda x: '\\n' in x, s))\n if multiline:\n s = [' ' + x for x in s]\n self._repr_wrapper_str_ = '%s(\\n%s)' % (cls.__qualname__,\n \",\\n\".join(s))\n else:\n self._repr_wrapper_str_ = '%s(%s)' % (cls.__qualname__,\n \", \".join(s))\n return ret\n\n decorated_fn = _wrapper\n if construction_fn.__name__ == '__new__':\n decorated_fn = staticmethod(decorated_fn)\n setattr(cls, construction_fn.__name__, decorated_fn)\n return cls\n\n\ndef configurable(fn_or_name=None, whitelist=[], blacklist=[]):\n \"\"\"Decorator to make a function or class configurable.\n\n This decorator registers the decorated function/class as configurable, which\n allows its parameters to be supplied from the global configuration (i.e., set\n through ``alf.config()``). The decorated function is associated with a name in\n the global configuration, which by default is simply the name of the function\n or class, but can be specified explicitly to avoid naming collisions or improve\n clarity.\n\n If some parameters should not be configurable, they can be specified in\n ``blacklist``. If only a restricted set of parameters should be configurable,\n they can be specified in ``whitelist``.\n\n The decorator can be used without any parameters as follows:\n\n .. code-block: python\n\n @alf.configurable\n def my_function(param1, param2='a default value'):\n ...\n\n In this case, the function is associated with the name\n 'my_function' in the global configuration, and both param1 and param2 are\n configurable.\n\n The decorator can be supplied with parameters to specify the configurable name\n or supply a whitelist/blacklist:\n\n .. code-block: python\n\n @alf.configurable('my_func', whitelist=['param2'])\n def my_function(param1, param2='a default value'):\n ...\n\n In this case, the configurable is associated with the name 'my_func' in the\n global configuration, and only param2 is configurable.\n\n Classes can be decorated as well, in which case parameters of their\n constructors are made configurable:\n\n .. code-block:: python\n\n @alf.configurable\n class MyClass(object):\n def __init__(self, param1, param2='a default value'):\n ...\n\n In this case, the name of the configurable is 'MyClass', and both `param1`\n and `param2` are configurable.\n\n The full name of a configurable value is MODULE_PATH.FUNC_NAME.ARG_NAME. It\n can be referred using any suffixes as long as there is no ambiguity. For\n example, assuming there are two configurable values \"abc.def.func.a\" and\n \"xyz.uvw.func.a\", you can use \"abc.def.func.a\", \"def.func.a\", \"xyz.uvw.func.a\"\n or \"uvw.func.a\" to refer these two configurable values. You cannot use\n \"func.a\" because of the ambiguity. Because of this, you cannot have a config\n name which is the strict suffix of another config name. For example,\n \"A.Test.arg\" and \"Test.arg\" cannot both be defined. You can supply a different\n name for the function to avoid conflict:\n\n .. code-block:: python\n\n @alf.configurable(\"NewTest\")\n def Test(arg):\n ...\n\n or\n\n .. code-block:: python\n\n @alf.configurable(\"B.Test\")\n def Test(arg):\n ...\n\n\n Note: currently, to maintain the compatibility with gin-config, all the\n functions decorated using alf.configurable are automatically configurable\n using gin. The values specified using ``alf.config()`` will override\n values specified through gin. Gin wrapper is quite convoluted and can make\n debugging more challenging. It can be disabled by setting environment\n varialbe ALF_USE_GIN to 0 if you are not using gin.\n\n Args:\n fn_or_name (Callable|str): A name for this configurable, or a function\n to decorate (in which case the name will be taken from that function).\n If not set, defaults to the name of the function/class that is being made\n configurable. If a name is provided, it may also include module components\n to be used for disambiguation. If the module components is provided,\n the original module name of the function will not be used to compose\n the full name.\n whitelist (list[str]): A whitelisted set of kwargs that should be configurable.\n All other kwargs will not be configurable. Only one of ``whitelist`` or\n ``blacklist`` should be specified.\n blacklist (list[str]): A blacklisted set of kwargs that should not be\n configurable. All other kwargs will be configurable. Only one of\n ``whitelist`` or ``blacklist`` should be specified.\n Returns:\n decorated function if fn_or_name is Callable.\n a decorator if fn is not Callable.\n Raises:\n ValueError: If a configurable with ``name`` (or the name of `fn_or_cls`)\n already exists, or if both a whitelist and blacklist are specified.\n \"\"\"\n\n if callable(fn_or_name):\n name = None\n else:\n name = fn_or_name\n\n if whitelist and blacklist:\n raise ValueError(\"Only one of 'whitelist' and 'blacklist' can be set.\")\n\n if not callable(fn_or_name):\n\n def _decorator(fn_or_cls):\n return _decorate(fn_or_cls, name, whitelist, blacklist)\n\n return _decorator\n else:\n return _decorate(fn_or_name, name, whitelist, blacklist)\n\n\ndef define_config(name, default_value):\n \"\"\"Define a configurable value with given ``default_value``.\n\n Its value can be retrieved by ``get_config_value(\"_CONFIG._USER.{name}\")``.\n\n Args:\n name (str): name of the configurable value\n default_value (Any): default value\n Returns:\n the configured value\n \"\"\"\n node = _Config()\n node.set_default_value(default_value)\n _add_to_conf_tree(['_CONFIG'], '_USER', name, node)\n _DEFINED_CONFIGS.append('_CONFIG._USER.' + name)\n return get_config_value(\"_CONFIG._USER.\" + name)\n\n\ndef _get_conf_file_full_path(conf_file):\n if os.path.isabs(conf_file):\n if os.path.exists(conf_file):\n return os.path.realpath(conf_file)\n if len(_IMPORT_STACK) == 0:\n # called from load_config()\n dir = os.getcwd()\n else:\n # callded from import_config()\n dir = os.path.dirname(_IMPORT_STACK[-1])\n candidate = os.path.join(dir, conf_file)\n if os.path.exists(candidate):\n return os.path.realpath(candidate)\n conf_path = os.environ.get(\"ALF_CONFIG_PATH\", None)\n conf_dirs = []\n if conf_path is not None:\n conf_dirs = conf_path.split(':')\n for dir in conf_dirs:\n candidate = os.path.join(dir, conf_file)\n if os.path.exists(candidate):\n return os.path.realpath(candidate)\n raise ValueError(f\"Cannot find conf file {conf_file}\")\n\n\ndef _add_conf_file(conf_file):\n if conf_file in _CONF_FILES:\n return\n with open(conf_file, \"r\") as f:\n _CONF_FILES[conf_file] = f.read()\n\n\ndef import_config(conf_file):\n \"\"\"Import the config from another file.\n\n Different from ``load_config()``, ``import_config()`` should only be used in\n config files. And it can be used multiple times inside your config files.\n\n If ``conf_file`` is a relative path, ``load_config()`` will first try to find it\n in the directory of the config file calling this function. If it cannot be found\n there, directories in the environment varianble ``ALF_CONFIG_PATH`` will be\n searched in order.\n\n Examples:\n\n 1. Suppose you have a config file ``~/code/my_conf.py``. You want to import\n another config file ``~/code/my_conf2.py``. You can use ``import_config(\"my_conf2.py\")``\n to import ``my_config2.py``.\n\n 2. Suppose you have a config file ``~/code/my_conf.py``. You want to import\n another config file ``~/code/base/my_conf2.py``. You can use ``import_config(\"base/my_conf2.py\")``\n to import ``my_config2.py``.\n\n 3. Suppose you have a config file ``~/code/my_conf.py``. You want to import\n another config file ``~/packages/my_conf2.py``. You need to set the environment\n variable as ``ALF_CONFIG_PATH=~/packages``. Then can use ``import_config(\"my_conf2.py\")``\n to import ``my_config2.py``.\n\n Args:\n conf_file\n Returns:\n the config module object, which can be used in a similar way as python\n imported module.\n \"\"\"\n if len(_IMPORT_STACK) == 0:\n raise ValueError(\"alf.import_config() can only be called inside a \"\n \"config file.\")\n conf_file = _get_conf_file_full_path(conf_file)\n return _import_config(conf_file)\n\n\nclass ConfigModule:\n pass\n\n\ndef _import_config(conf_file):\n if conf_file in _CONFIG_MODULES:\n return _CONFIG_MODULES[conf_file]\n _add_conf_file(conf_file)\n _IMPORT_STACK.append(conf_file)\n kv = runpy.run_path(conf_file)\n _IMPORT_STACK.pop()\n module = ConfigModule()\n for k, v in kv.items():\n setattr(module, k, v)\n _CONFIG_MODULES[conf_file] = module\n return module\n\n\ndef load_config(conf_file):\n \"\"\"Load config from a file.\n\n Different from ``import_config()``, ``load_config()`` should only be used by\n your main code to load the config. And it should be only called once unless\n ``reset_configs()`` is called to reset the configuration to default state.\n\n If ``conf_file`` is a relative path, ``load_config()`` will first try to find it\n in the current working directory. If it cannot be found there, directories in\n the environment varianble ``ALF_CONFIG_PATH`` will be searched in order.\n\n Args:\n conf_file\n Returns:\n the config module object, which can be used in a similar way as python\n imported module.\n \"\"\"\n global _ROOT_CONF_FILE\n if _ROOT_CONF_FILE is not None:\n raise ValueError(\n \"One process can only call alf.load_config() once. \"\n \"If you want to call it multiple times, you need to call \"\n \"alf.reset_configs() between the calls.\")\n conf_file = _get_conf_file_full_path(conf_file)\n _ROOT_CONF_FILE = conf_file\n return _import_config(conf_file)\n\n\ndef save_config(alf_config_file):\n \"\"\"Save config files.\n\n This will save config set using ``pre_config()``, the file loaded using\n ``load_config()`` and the files imported using ``import_config()`` if they\n are in the config root directory or its sub-directory, where the config root\n directory is the directory of the conf file loaded by ``load_config()``.\n\n \"\"\"\n if _ROOT_CONF_FILE is None:\n raise ValueError(\"alf.save_config() cannot be called before \"\n \"alf.load_config()\")\n config_dirname = \"config_files\"\n dir = os.path.join(os.path.dirname(alf_config_file), config_dirname)\n os.makedirs(dir, exist_ok=True)\n conf_file_name = os.path.basename(_ROOT_CONF_FILE)\n conf_root_dir = os.path.dirname(_ROOT_CONF_FILE)\n\n pre_configs = get_handled_pre_configs()\n config = ''\n config += \"import alf\\n\"\n if pre_configs:\n config += \"alf.pre_config({\\n\"\n for config_name, config_value in pre_configs:\n if isinstance(config_value, str):\n config += \" '%s': '%s',\\n\" % (config_name, config_value)\n else:\n config += \" '%s': %s,\\n\" % (config_name, config_value)\n config += \"})\\n\\n\"\n config += f\"config = alf.import_config('{config_dirname}/{conf_file_name}')\\n\"\n f = open(alf_config_file, 'w')\n f.write(config)\n f.close()\n\n for conf_file, content in _CONF_FILES.items():\n if conf_file.startswith(conf_root_dir):\n conf_rel_path = conf_file[len(conf_root_dir) + 1:]\n conf_rel_dir = os.path.dirname(conf_rel_path)\n if conf_rel_dir:\n os.makedirs(os.path.join(dir, conf_rel_dir), exist_ok=True)\n with open(os.path.join(dir, conf_rel_path), \"w\") as f:\n f.write(content)\n", "repo_name": "HorizonRobotics/alf", "sub_path": "alf/config_util.py", "file_name": "config_util.py", "file_ext": "py", "file_size_in_byte": 34248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 266, "dataset": "github-code", "pt": "51", "api": [{"api_name": "absl.logging.skip_log_prefix", "line_number": 31, "usage_type": "attribute"}, {"api_name": "absl.logging", "line_number": 31, "usage_type": "name"}, {"api_name": "absl.logging.warning", "line_number": 305, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 305, "usage_type": "name"}, {"api_name": "absl.logging.warning", "line_number": 312, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 312, "usage_type": "name"}, {"api_name": "absl.logging.skip_log_prefix", "line_number": 280, "usage_type": "attribute"}, {"api_name": "absl.logging", "line_number": 280, "usage_type": "name"}, {"api_name": "absl.logging.skip_log_prefix", "line_number": 321, "usage_type": "attribute"}, {"api_name": "absl.logging", "line_number": 321, "usage_type": "name"}, {"api_name": "inspect.Parameter", "line_number": 415, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 416, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 423, "usage_type": "attribute"}, {"api_name": "inspect.Parameter.VAR_POSITIONAL", "line_number": 532, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 532, "usage_type": "name"}, {"api_name": "inspect.Parameter.VAR_KEYWORD", "line_number": 533, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 533, "usage_type": "name"}, {"api_name": "inspect.Parameter.POSITIONAL_ONLY", "line_number": 535, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 535, "usage_type": "name"}, {"api_name": "inspect.Parameter.POSITIONAL_OR_KEYWORD", "line_number": 540, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 540, "usage_type": "name"}, {"api_name": "inspect.Parameter.KEYWORD_ONLY", "line_number": 540, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 519, "usage_type": "call"}, {"api_name": "inspect.signature", "line_number": 567, "usage_type": "call"}, {"api_name": "inspect.isclass", "line_number": 585, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 599, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 599, "usage_type": "attribute"}, {"api_name": "gin.configurable", "line_number": 603, "usage_type": "call"}, {"api_name": "inspect.isclass", "line_number": 632, "usage_type": "call"}, {"api_name": "inspect.signature", "line_number": 633, "usage_type": "call"}, {"api_name": "inspect.Parameter", "line_number": 639, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 640, "usage_type": "attribute"}, {"api_name": "inspect.Parameter", "line_number": 642, "usage_type": "attribute"}, {"api_name": "pprint.pformat", "line_number": 657, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 660, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 647, "usage_type": "call"}, {"api_name": "os.path.isabs", "line_number": 821, "usage_type": "call"}, {"api_name": "os.path", "line_number": 821, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 822, "usage_type": "call"}, {"api_name": "os.path", "line_number": 822, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 823, "usage_type": "call"}, {"api_name": "os.path", "line_number": 823, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 826, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 829, "usage_type": "call"}, {"api_name": "os.path", "line_number": 829, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 830, "usage_type": "call"}, {"api_name": "os.path", "line_number": 830, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 831, "usage_type": "call"}, {"api_name": "os.path", "line_number": 831, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 832, "usage_type": "call"}, {"api_name": "os.path", "line_number": 832, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 833, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 833, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 838, "usage_type": "call"}, {"api_name": "os.path", "line_number": 838, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 839, "usage_type": "call"}, {"api_name": "os.path", "line_number": 839, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 840, "usage_type": "call"}, {"api_name": "os.path", "line_number": 840, "usage_type": "attribute"}, {"api_name": "runpy.run_path", "line_number": 899, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 949, "usage_type": "call"}, {"api_name": "os.path", "line_number": 949, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 949, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 950, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 951, "usage_type": "call"}, {"api_name": "os.path", "line_number": 951, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 952, "usage_type": "call"}, {"api_name": "os.path", "line_number": 952, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 973, "usage_type": "call"}, {"api_name": "os.path", "line_number": 973, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 975, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 975, "usage_type": "call"}, {"api_name": "os.path", "line_number": 975, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 976, "usage_type": "call"}, {"api_name": "os.path", "line_number": 976, "usage_type": "attribute"}]} +{"seq_id": "26463839213", "text": "from typing import Optional\nclass Solution:\n def binaryTreePaths(self, root: Optional[TreeNode]) -> list[str]:\n # 34ms 73% 14MB 23%\n self.ans = []\n self.solve(root, \"\")\n return self.ans\n def solve(self, node, temp):\n if not node:\n return ;\n temp = temp + f\"{node.val}\"\n if not node.left and not node.right:\n self.ans.append(temp)\n return ;\n self.solve(node.left, temp + \"->\")\n self.solve(node.right, temp + \"->\")\n\n ", "repo_name": "kdm111/public_self_study_note", "sub_path": "LeetCode_Algorithm/257(Binary Tree Paths)/257.py", "file_name": "257.py", "file_ext": "py", "file_size_in_byte": 524, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.Optional", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "25666668596", "text": "import yaml\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\n\nfrom armada.exceptions import api_exceptions as err\nfrom armada.handlers.armada import Override\n\nLOG = logging.getLogger(__name__)\nCONF = cfg.CONF\n\nAPI_VERSION = 'v{}/{}'\n\n\nclass ArmadaClient(object):\n def __init__(self, session):\n self.session = session\n\n def _set_endpoint(self, version, action):\n return API_VERSION.format(version, action)\n\n def get_status(self, query, timeout=None):\n\n endpoint = self._set_endpoint('1.0', 'status')\n resp = self.session.get(endpoint, query=query, timeout=timeout)\n\n self._check_response(resp)\n\n return resp.json()\n\n def get_releases(self, query, timeout=None):\n\n endpoint = self._set_endpoint('1.0', 'releases')\n resp = self.session.get(endpoint, query=query, timeout=timeout)\n\n self._check_response(resp)\n\n return resp.json()\n\n def post_validate(self, manifest=None, timeout=None):\n\n endpoint = self._set_endpoint('1.0', 'validatedesign')\n # TODO(sh8121att) Look to update the UCP convention to\n # allow a list of hrefs\n req_body = {'href': manifest}\n\n resp = self.session.post(\n endpoint,\n data=req_body,\n headers={\n 'content-type': 'application/json'\n },\n timeout=timeout)\n\n self._check_response(resp)\n\n return resp.json()\n\n def post_apply(self,\n manifest=None,\n manifest_ref=None,\n values=None,\n set=None,\n query=None,\n timeout=None):\n \"\"\"Call the Armada API to apply a Manifest.\n\n If ``manifest`` is not None, then the request body will be a fully\n rendered set of YAML documents including overrides and\n values-files application.\n\n If ``manifest`` is None and ``manifest_ref`` is not, then the request\n body will be a JSON structure providing a list of references\n to Armada manifest documents and a list of overrides. Local\n values files are not supported when using the API with references.\n\n :param manifest: string of YAML formatted Armada manifests\n :param manifest_ref: valid file paths or URIs referring to Armada\n manifests\n :param values: list of local files containing values.yaml overrides\n :param set: list of single-value overrides\n :param query: explicit query string parameters\n :param timeout: a tuple of connect, read timeout (x, y)\n \"\"\"\n endpoint = self._set_endpoint('1.0', 'apply')\n\n if manifest:\n if values or set:\n document = list(yaml.safe_load_all(manifest))\n override = Override(\n document, overrides=set, values=values).update_manifests()\n manifest = yaml.dump(override)\n resp = self.session.post(\n endpoint,\n body=manifest,\n query=query,\n headers={\n 'content-type': 'application/x-yaml'\n },\n timeout=timeout)\n elif manifest_ref:\n req_body = {\n 'hrefs': manifest_ref,\n 'overrides': set or [],\n }\n resp = self.session.post(\n endpoint,\n data=req_body,\n query=query,\n headers={\n 'content-type': 'application/json'\n },\n timeout=timeout)\n\n self._check_response(resp)\n\n return resp.json()\n\n def get_test_release(self, release=None, query=None, timeout=None):\n\n endpoint = self._set_endpoint('1.0', 'test/{}'.format(release))\n resp = self.session.get(endpoint, query=query, timeout=timeout)\n\n self._check_response(resp)\n\n return resp.json()\n\n def post_test_manifest(self, manifest=None, query=None, timeout=None):\n\n endpoint = self._set_endpoint('1.0', 'tests')\n resp = self.session.post(endpoint, body=manifest, query=query,\n timeout=timeout)\n\n self._check_response(resp)\n\n return resp.json()\n\n def _check_response(self, resp):\n if resp.status_code == 401:\n raise err.ClientUnauthorizedError(\n \"Unauthorized access to %s, include valid token.\".format(\n resp.url))\n elif resp.status_code == 403:\n raise err.ClientForbiddenError(\"Forbidden access to %s\" % resp.url)\n elif not resp.ok:\n raise err.ClientError(\"Error - received %d: %s\" %\n (resp.status_code, resp.text))\n", "repo_name": "att-comdev/armada", "sub_path": "armada/common/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 4776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 78, "dataset": "github-code", "pt": "51", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 9, "usage_type": "name"}, {"api_name": "oslo_config.cfg.CONF", "line_number": 10, "usage_type": "attribute"}, {"api_name": "oslo_config.cfg", "line_number": 10, "usage_type": "name"}, {"api_name": "yaml.safe_load_all", "line_number": 89, "usage_type": "call"}, {"api_name": "armada.handlers.armada.Override", "line_number": 90, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 92, "usage_type": "call"}, {"api_name": "armada.exceptions.api_exceptions.ClientUnauthorizedError", "line_number": 140, "usage_type": "call"}, {"api_name": "armada.exceptions.api_exceptions", "line_number": 140, "usage_type": "name"}, {"api_name": "armada.exceptions.api_exceptions.ClientForbiddenError", "line_number": 144, "usage_type": "call"}, {"api_name": "armada.exceptions.api_exceptions", "line_number": 144, "usage_type": "name"}, {"api_name": "armada.exceptions.api_exceptions.ClientError", "line_number": 146, "usage_type": "call"}, {"api_name": "armada.exceptions.api_exceptions", "line_number": 146, "usage_type": "name"}]} +{"seq_id": "4752053047", "text": "from selenium import webdriver\r\nimport time\r\nfrom selenium.webdriver.common.by import By\r\n\r\nclass Step:\r\n def openHomePage(self, driver):\r\n print(\"enter openHomePage()\\n\")\r\n # driver = webdriver.Edge()\r\n driver.get('http://47.109.26.120/frontPage.html')\r\n time.sleep(1)\r\n if driver.find_element(by=By.CLASS_NAME, value='website_title'):\r\n return 0\r\n else:\r\n return 1\r\n\r\n def gotoSuccessPage(self, driver):\r\n print(\"enter successPage()\\n\")\r\n driver.find_element(by=By.ID, value='my').click()\r\n time.sleep(1)\r\n if driver.find_element(by=By.XPATH, value='/html/body/a').is_displayed():\r\n return 0\r\n else:\r\n return 1\r\n\r\nif __name__ == '__main__':\r\n t = Step()\r\n t.openHomePage()\r\n", "repo_name": "elber9898/UIAutoProject", "sub_path": "step/Step.py", "file_name": "Step.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 18, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "32857442045", "text": "\"\"\" Scrape Amazon for the weight of each car part in a given list of Amazon product links. \"\"\"\nimport scrapy\nimport os\nimport pandas as pd\nfrom .base_spider import BaseSpider\nfrom ..items import AmazonProductItem\nfrom ..itemloaders import AmazonProductItemLoader\n\n\nclass AmazonSpider(BaseSpider):\n name = 'amazon_spider'\n allowed_domains = ['proxy.scrapeops.io', 'amazon.com']\n custom_settings = {\n 'FEED_EXPORT_FIELDS': {\n 'partslink_number': 'partslink_number',\n 'weight': 'weight (pounds)',\n 'link':'link'\n },\n\n # Specify pipeline to use\n 'ITEM_PIPELINES': {'carpart_weight_scraper.pipelines.WeightConversionPipeline': 300},\n }\n\n\n def __init__(self, start=0, end=10, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.start = int(start)\n self.end = int(end)\n\n\n def start_requests(self):\n # List of dictionaries with amazon links to scrape\n amazon_links_data = self.fetch_amazon_links(self.start, self.end)\n\n for record in amazon_links_data:\n # Check if record is valid\n if not isinstance(record,dict) or 'link' not in record or not isinstance(record['link'], str):\n print('Invalid entry, no link provided:', record, type(record), '\\n')\n continue\n \n # Create request\n yield scrapy.Request(\n url=self.get_proxy_url(record['link']),\n callback=self.parse,\n meta={'partslink_number': record['partslink_number'], 'link': record['link']},\n )\n\n\n def parse(self, response):\n # Extract weight from page\n weight = response.xpath('//th[contains(text(), \"Item Weight\")]/following-sibling::td/text()').extract_first()\n if not weight:\n print('Weight not found for', response.meta['partslink_number'], 'at:', response.meta['link'])\n\n item_loader = AmazonProductItemLoader(item=AmazonProductItem(), response=response)\n item_loader.add_value('partslink_number', response.meta['partslink_number'])\n item_loader.add_value('link', response.meta['link'])\n item_loader.add_value('weight', weight)\n\n print(item_loader.load_item(), '\\n')\n yield item_loader.load_item()\n\n\n # ------------------------------------------------------- #\n # Helper Functions #\n # ------------------------------------------------------- #\n def fetch_amazon_links(self, start, end):\n \"\"\" Fetch a list of dictionaries with amazon links from a CSV file, and return a slice of the list \"\"\"\n amazon_links_file_path = 'data/in/amazon_links.csv'\n if not os.path.exists(amazon_links_file_path):\n raise FileNotFoundError(f'Input file not found: {amazon_links_file_path}')\n return pd.read_csv(amazon_links_file_path)[start:end].to_dict('records')\n", "repo_name": "jeremykc/CarPartWeightScraper", "sub_path": "carpart_weight_scraper/carpart_weight_scraper/spiders/amazon_spider.py", "file_name": "amazon_spider.py", "file_ext": "py", "file_size_in_byte": 2928, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "base_spider.BaseSpider", "line_number": 10, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 42, "usage_type": "call"}, {"api_name": "itemloaders.AmazonProductItemLoader", "line_number": 55, "usage_type": "call"}, {"api_name": "items.AmazonProductItem", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "36711410432", "text": "#import numpy as np\nimport pandas as pd\n#import seaborn as sns\n#import matplotlib.pyplot as plt\nimport matplotlib\nimport pathlib\nimport figurefirst\nimport tqdm\n#import subprocess\n\nfrom utils import fancyViz\nfrom utils import readSessions\nimport style\n\nstyle.set_context()\n\nendoDataPath = pathlib.Path(\"data\") / \"endoData_2019.hdf\"\noutputFolder = pathlib.Path(\"svg\")\ntemplateFolder = pathlib.Path(\"templates\")\n\nif not outputFolder.is_dir():\n outputFolder.mkdir()\n \nsvgName = \"continuity.svg\"\nlayout = figurefirst.FigureLayout(templateFolder / \"continuity.svg\")\nlayout.make_mplfigures()\nselection = pd.read_csv(\"continuitySelection.csv\", comment=\"#\")\nfancyVizs = {}\nsignals = {}\nlw = matplotlib.rcParams[\"axes.linewidth\"]\nuniqueSessions = selection.session.unique()\nwith tqdm.tqdm(total=len(uniqueSessions), desc=\"Loading data\") as t:\n for sess in readSessions.findSessions(endoDataPath, task=\"2choice\"):\n if str(sess) in uniqueSessions:\n k = str(sess)\n neurons = selection[selection.session == str(sess)].neuron\n fancyVizs[k] = fancyViz.SchematicIntensityPlot(sess, smoothing=7, linewidth=lw, splitReturns=False)\n signals[k] = sess.readDeconvolvedTraces(rScore=True)[neurons]\n t.update(1)\n\nfor i, (session, neuron) in selection.iterrows():\n if i>=14: break\n ax = layout.axes[\"s{}\".format(i+1)][\"axis\"]\n fancyVizs[session].draw(signals[session][neuron], ax=ax)\n genotype, animal, date = session.split(\"_\")\n ax.set_title(\"#{} ({})\\nneuron {}\".format(animal, date, neuron), fontsize=7)\n #if (i//6)%2 == 0:\n # ax.plot([0,0], [-2.75, -2.25], color=style.getColor(genotype), lw=2)\n #else:\n # ax.plot([0,0], [2.5, 2.0], color=style.getColor(genotype), lw=2)\n #axs.flat[i].set_title(\"{}, #{}\".format(session, neuron))\n\nlayout.insert_figures('target_layer_name')\nlayout.write_svg(outputFolder / svgName)\n\n#subprocess.check_call(['inkscape', '-f', outputFolder / svgName,\n# '-A', outputFolder / (svgName[:-3]+'pdf')])", "repo_name": "wegmor/striatum-2choice", "sub_path": "figureContinuity.py", "file_name": "figureContinuity.py", "file_ext": "py", "file_size_in_byte": 2047, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "style.set_context", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "figurefirst.FigureLayout", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.readSessions.findSessions", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.readSessions", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.fancyViz.SchematicIntensityPlot", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.fancyViz", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "32501694855", "text": "from django import forms\nfrom haystack.forms import SearchForm\n\nclass StyledSearchForm( SearchForm ):\n q = forms.CharField(\n required = False,\n label = 'Search',\n widget = forms.TextInput(attrs={\n 'class': 'form-control',\n 'placeholder': 'Title, Keyword, Case',\n 'autofocus': 'autofocus',\n }),\n )\n", "repo_name": "dwbond/legalseagull", "sub_path": "legalseagull/legalseagull/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "haystack.forms.SearchForm", "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.forms.TextInput", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "74387039197", "text": "from django.conf.urls import url\n\nfrom .views import BuildABoatView, BoatModelGroupListView, ModelPageHome, colorize, BoatLengthGroupDetailView, \\\n BoatListPerGroupCompare, BoatModelListView, BoatListCompare, BuiltBoatEmailCreateView, BuiltBoatShareCreateView, \\\n MotorsView, VideoView, AboutView, OptionalEquipmentView, DeckPlanView, FeaturesView\n\nurlpatterns = [\n url(r'^$', BoatModelGroupListView.as_view(), name=\"all_boats\"),\n\n # Use these with ajax!\n url(r'^boats/$', BoatModelListView.as_view(), name=\"boat_list\"),\n url(r'^boats/compare/$', BoatListCompare.as_view(), name=\"compare_all_boats\"),\n\n # Ajax per group!\n url(r'^boat_groups/(?P\\w+)/$', BoatLengthGroupDetailView.as_view(), name=\"boat_group\"),\n url(r'^boat_groups/(?P\\w+)/boats/compare/$', BoatListPerGroupCompare.as_view(), name=\"compare_boats\"),\n\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/$', ModelPageHome.as_view(), name=\"boat_detail\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/build_a_boat/$', BuildABoatView.as_view(),\n name=\"build_a_boat\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/built-boat-email/$', BuiltBoatEmailCreateView.as_view(),\n name=\"built_boat_email\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/built-boat-share/$', BuiltBoatShareCreateView.as_view(),\n name=\"built_boat_share\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/motors/$', MotorsView.as_view(), name=\"motors\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/video/$', VideoView.as_view(), name=\"video\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/about/$', AboutView.as_view(), name=\"about\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/optional-equipment/$', OptionalEquipmentView.as_view(),\n name=\"optional-equipment\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/deck-plan/$', DeckPlanView.as_view(), name=\"deck-plan\"),\n url(r'^boat_groups/(?P\\w+)/boats/(?P[-\\w]+)/features/$', FeaturesView.as_view(), name=\"features\"),\n url(r'^(?P[-\\w]+)/build_a_boat/(?P[-\\w]+)/color/(?P[-\\w]+)$', colorize, name=\"colorize\"),\n\n]\n", "repo_name": "elite0401/intrepidpowerboats", "sub_path": "intrepidboats/apps/boats/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2257, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "views.BoatModelGroupListView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.BoatModelGroupListView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "views.BoatModelListView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.BoatModelListView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "views.BoatListCompare.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.BoatListCompare", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "views.BoatLengthGroupDetailView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.BoatLengthGroupDetailView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "views.BoatListPerGroupCompare.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "views.BoatListPerGroupCompare", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "views.ModelPageHome.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "views.ModelPageHome", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "views.BuildABoatView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "views.BuildABoatView", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "views.BuiltBoatEmailCreateView.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "views.BuiltBoatEmailCreateView", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "views.BuiltBoatShareCreateView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "views.BuiltBoatShareCreateView", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "views.MotorsView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "views.MotorsView", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "views.VideoView.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "views.VideoView", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "views.AboutView.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "views.AboutView", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "views.OptionalEquipmentView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "views.OptionalEquipmentView", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "views.DeckPlanView.as_view", "line_number": 30, "usage_type": "call"}, {"api_name": "views.DeckPlanView", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "views.FeaturesView.as_view", "line_number": 31, "usage_type": "call"}, {"api_name": "views.FeaturesView", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "views.colorize", "line_number": 32, "usage_type": "argument"}]} +{"seq_id": "16391583439", "text": "#-------------------------------------------------------------------------------\n# Author: Lukasz Janyst \n# Date: 06.03.2018\n#-------------------------------------------------------------------------------\n# This file is part of PiPilot.\n#\n# PiPilot is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# PiPilot is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with PiPilot. If not, see .\n#-------------------------------------------------------------------------------\n\nimport json\nimport sys\nimport os\n\nfrom setuptools.command.build_py import build_py\nfrom distutils.spawn import find_executable\nfrom setuptools import setup\nfrom subprocess import Popen\nfrom PiPilot import __version__\nfrom shutil import copyfile, rmtree\n\n#-------------------------------------------------------------------------------\n# Package description\n#-------------------------------------------------------------------------------\nwith open('README.rst') as readme:\n long_description = readme.read()\n\n\n#-------------------------------------------------------------------------------\n# Run command\n#-------------------------------------------------------------------------------\ndef run_command(args, cwd):\n p = Popen(args, cwd=cwd)\n p.wait()\n return p.returncode\n\n\n#-------------------------------------------------------------------------------\n# Build the React web app\n#-------------------------------------------------------------------------------\nclass build_ui(build_py):\n def run(self):\n if not self.dry_run:\n\n #-------------------------------------------------------------------\n # Check and set the environment up\n #-------------------------------------------------------------------\n target_dir = os.path.join(self.build_lib, 'PiPilot', 'ui')\n\n if os.path.exists(target_dir):\n rmtree(target_dir)\n\n ui_path = os.path.join(os.getcwd(), 'ui')\n if not os.path.exists(ui_path):\n print('[!] The ui directory does not exist')\n sys.exit(1)\n\n npm = find_executable('npm')\n if npm is None:\n print('[!] You need to have node installed to build this app')\n sys.exit(1)\n\n #-------------------------------------------------------------------\n # Build the JavaScript code\n #-------------------------------------------------------------------\n ret = run_command([npm, 'install'], ui_path)\n if ret != 0:\n print('[!] Installation of JavaScript dependencies failed')\n sys.exit(1)\n\n ret = run_command([npm, 'run-script', 'build'], ui_path)\n if ret != 0:\n print('[!] Build of JavaScript artefacts failed')\n sys.exit(1)\n\n #-------------------------------------------------------------------\n # Create a list of artefacts\n #-------------------------------------------------------------------\n artefacts = [\n 'asset-manifest.json',\n 'favicon.png',\n 'index.html',\n 'manifest.json',\n 'service-worker.js'\n ]\n\n build_dir = 'ui/build'\n asset_manifest = os.path.join(build_dir, artefacts[0])\n if not os.path.exists(asset_manifest):\n print('[!] Asset manifest does not exist.')\n sys.exit(1)\n\n assets = json.loads(open(asset_manifest, 'r').read())\n for _, asset in assets.items():\n artefacts.append(asset)\n\n #-------------------------------------------------------------------\n # Copy the artefacts to the dist root\n #-------------------------------------------------------------------\n print('Copying JavaScript artefacts to', target_dir)\n for artefact in artefacts:\n source_file = os.path.join(build_dir, artefact)\n target_file = os.path.join(target_dir, artefact)\n target_prefix = os.path.dirname(target_file)\n if not os.path.exists(target_prefix):\n os.makedirs(target_prefix)\n copyfile(source_file, target_file)\n\n build_py.run(self)\n\n#-------------------------------------------------------------------------------\n# Setup\n#-------------------------------------------------------------------------------\nsetup(\n name = 'PiPilot',\n version = __version__,\n author = 'Lukasz Janyst',\n author_email = 'xyz@jany.st',\n url = 'https://github.com/ljanyst/pipilot',\n description = 'A software aircraft controller for RaspberryPi',\n long_description = long_description,\n license = 'GPL3 license',\n packages = ['PiPilot'],\n include_package_data = True,\n cmdclass={\n 'build_py': build_ui\n },\n package_data={\n '': ['*.conf'],\n },\n scripts=['pipilot'],\n classifiers = [\n 'Development Status :: 2 - Pre-Alpha',\n 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',\n 'Operating System :: Unix',\n 'Topic :: Scientific/Engineering :: Human Machine Interfaces',\n 'Intended Audience :: Developers',\n 'Intended Audience :: Science/Research',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Environment :: Console',\n 'Environment :: No Input/Output (Daemon)',\n 'Environment :: Web Environment'\n ],\n install_requires = [\n 'twisted', 'pyserial', 'autobahn'\n ]\n)\n", "repo_name": "ljanyst/pipilot", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 6158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "51", "api": [{"api_name": "subprocess.Popen", "line_number": 43, "usage_type": "call"}, {"api_name": "setuptools.command.build_py.build_py", "line_number": 51, "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.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 61, "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": "os.getcwd", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 66, "usage_type": "call"}, {"api_name": "distutils.spawn.find_executable", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 101, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 103, "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.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 116, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 117, "usage_type": "call"}, {"api_name": "setuptools.command.build_py.build_py.run", "line_number": 119, "usage_type": "call"}, {"api_name": "setuptools.command.build_py.build_py", "line_number": 119, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 124, "usage_type": "call"}, {"api_name": "PiPilot.__version__", "line_number": 126, "usage_type": "name"}]} +{"seq_id": "366147925", "text": "from typing import TYPE_CHECKING, Any, Iterable\n\nfrom synapse.replication.slave.storage._base import BaseSlavedStore\nfrom synapse.replication.slave.storage._slaved_id_tracker import SlavedIdTracker\nfrom synapse.replication.tcp.streams import GroupServerStream\nfrom synapse.storage.database import DatabasePool, LoggingDatabaseConnection\nfrom synapse.storage.databases.main.group_server import GroupServerWorkerStore\nfrom synapse.util.caches.stream_change_cache import StreamChangeCache\n\nif TYPE_CHECKING:\n from synapse.server import HomeServer\n\n\nclass SlavedGroupServerStore(GroupServerWorkerStore, BaseSlavedStore):\n def __init__(\n self,\n database: DatabasePool,\n db_conn: LoggingDatabaseConnection,\n hs: \"HomeServer\",\n ):\n super().__init__(database, db_conn, hs)\n\n self.hs = hs\n\n self._group_updates_id_gen = SlavedIdTracker(\n db_conn, \"local_group_updates\", \"stream_id\"\n )\n self._group_updates_stream_cache = StreamChangeCache(\n \"_group_updates_stream_cache\",\n self._group_updates_id_gen.get_current_token(),\n )\n\n def get_group_stream_token(self) -> int:\n return self._group_updates_id_gen.get_current_token()\n\n def process_replication_rows(\n self, stream_name: str, instance_name: str, token: int, rows: Iterable[Any]\n ) -> None:\n if stream_name == GroupServerStream.NAME:\n self._group_updates_id_gen.advance(instance_name, token)\n for row in rows:\n self._group_updates_stream_cache.entity_has_changed(row.user_id, token)\n\n return super().process_replication_rows(stream_name, instance_name, token, rows)\n", "repo_name": "matrix-org/synapse-dinsic", "sub_path": "synapse/replication/slave/storage/groups.py", "file_name": "groups.py", "file_ext": "py", "file_size_in_byte": 1695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "51", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 10, "usage_type": "name"}, {"api_name": "synapse.storage.databases.main.group_server.GroupServerWorkerStore", "line_number": 14, "usage_type": "name"}, {"api_name": "synapse.replication.slave.storage._base.BaseSlavedStore", "line_number": 14, "usage_type": "name"}, {"api_name": "synapse.storage.database.DatabasePool", "line_number": 17, "usage_type": "name"}, {"api_name": "synapse.storage.database.LoggingDatabaseConnection", "line_number": 18, "usage_type": "name"}, {"api_name": "synapse.replication.slave.storage._slaved_id_tracker.SlavedIdTracker", "line_number": 25, "usage_type": "call"}, {"api_name": "synapse.util.caches.stream_change_cache.StreamChangeCache", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 37, "usage_type": "name"}, {"api_name": "synapse.replication.tcp.streams.GroupServerStream.NAME", "line_number": 39, "usage_type": "attribute"}, {"api_name": "synapse.replication.tcp.streams.GroupServerStream", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "232266626", "text": "\"\"\"\nDjango settings for test_aws project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.7/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.7/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '*!r7y#wbbdm8pdmj-mxb-+v)c$i4he+gt)=+4-a@@d1^t%(d-!'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\nTEMPLATE_DEBUG = True\n\nALLOWED_HOSTS = []\n\n\n# Application definition\n\nINSTALLED_APPS = (\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 'sample_app',\n 'storages',\n)\n\nMIDDLEWARE_CLASSES = (\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.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'test_aws.urls'\n\nWSGI_APPLICATION = 'test_aws.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.7/ref/settings/#databases\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n }\n}\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.7/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.7/howto/static-files/\n\n\n\nimport dj_database_url\nDATABASES['default'] = dj_database_url.config()\n\nSECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n\nALLOWED_HOSTS = ['*']\nPROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))\nMEDIA_URL = \"/media/\"\nMEDIA_ROOT = os.path.join(PROJECT_ROOT, \"static\", *MEDIA_URL.strip(\"/\").split(\"/\"))\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nSTATIC_ROOT = 'staticfiles'\nSTATIC_URL = '/static/'\n\n\n\n\ntry:\n from local_settings import *\nexcept ImportError:\n pass\n\n\n", "repo_name": "northDacoder/aws_s3_django_guide", "sub_path": "test_aws/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 2598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "dj_database_url.config", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "41381742618", "text": "from loader import app, database, bots # database is already imported\nfrom userbot import UserBot\nfrom database import Channel, ChannelBotRelation # Import necessary models\nfrom pydantic.dataclasses import dataclass\nfrom typing import Optional\nfrom loader import logger\nfrom .check_public_channel import is_channel_public\nfrom database import Channel, ChannelBotRelation # Import necessary models and session\nfrom datetime import datetime\n@dataclass\nclass ChannelURL:\n url: str\n\n@app.post(\"/add_new_channel\")\nasync def add_new_channel(body: ChannelURL):\n try:\n if \"https://\" not in body.url:\n body.url = \"https://\"+body.url\n for bot in bots.values():\n try:\n channel_id = await bot.check_url(url=body.url)\n if channel_id:\n if database.is_channel_in_db(channel_id=channel_id):\n channel = await bot.get_channel_info(channel_link=channel_id)\n return {'status': \"ok\", 'channel': {'id': channel['id'], 'title': channel['title']}}\n except:\n pass\n\n filtered_bots = {bot_id: bot for bot_id, bot in bots.items()\n if bot.floodwait is None or bot.floodwait < datetime.now()}\n\n # Находим бота с наименьшим количеством каналов среди отфильтрованных ботов\n min_channels_bot = min(filtered_bots.values(), key=lambda x: len(database.get_channels_by_bot_id(x.bot_id)))\n try:\n new_channel_entity = await min_channels_bot.sub_to_channel(url=body.url)\n except Exception as e:\n if \"FLOOD\" in str(e):\n return {'status': \"failed\", 'error': f\"{str(e)}\"}\n else:\n return {'status': \"failed\", 'error': f\"Unable to access the channel: {str(e)}\"}\n\n if new_channel_entity:\n channel = Channel(\n telegram_id=new_channel_entity[\"id\"],\n username=new_channel_entity.get(\"username\"),\n name=new_channel_entity.get(\"title\"),\n date_added=datetime.now()\n )\n database.add_record(channel) # Добавление канала в БД\n\n # Создание и добавление связи между ботом и каналом в БД\n channel_bot_relation = ChannelBotRelation(\n bot_id=min_channels_bot.bot_id,\n channel_id=database.get_channel_id_by_tg_id(new_channel_entity[\"id\"])\n )\n database.add_record(channel_bot_relation)\n min_channels_bot.channels.append(new_channel_entity['id'])\n logger.info(f\"{min_channels_bot.phone}: Канал {new_channel_entity['title']} добавлен в базу данных.\")\n return {'status': \"ok\", 'channel': {'id': new_channel_entity['id'], 'title': new_channel_entity['title']}}\n\n else:\n return {'status': 'pending'}\n\n except Exception as e:\n return {\"status\": \"failed\", \"error\": str(e)}", "repo_name": "xdownedx/adv_buyer", "sub_path": "api/add_channel.py", "file_name": "add_channel.py", "file_ext": "py", "file_size_in_byte": 3080, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "pydantic.dataclasses.dataclass", "line_number": 10, "usage_type": "name"}, {"api_name": "loader.bots.values", "line_number": 19, "usage_type": "call"}, {"api_name": "loader.bots", "line_number": 19, "usage_type": "name"}, {"api_name": "loader.database.is_channel_in_db", "line_number": 23, "usage_type": "call"}, {"api_name": "loader.database", "line_number": 23, "usage_type": "name"}, {"api_name": "loader.bots.items", "line_number": 29, "usage_type": "call"}, {"api_name": "loader.bots", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "loader.database.get_channels_by_bot_id", "line_number": 33, "usage_type": "call"}, {"api_name": "loader.database", "line_number": 33, "usage_type": "name"}, {"api_name": "database.Channel", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "loader.database.add_record", "line_number": 49, "usage_type": "call"}, {"api_name": "loader.database", "line_number": 49, "usage_type": "name"}, {"api_name": "database.ChannelBotRelation", "line_number": 52, "usage_type": "call"}, {"api_name": "loader.database.get_channel_id_by_tg_id", "line_number": 54, "usage_type": "call"}, {"api_name": "loader.database", "line_number": 54, "usage_type": "name"}, {"api_name": "loader.database.add_record", "line_number": 56, "usage_type": "call"}, {"api_name": "loader.database", "line_number": 56, "usage_type": "name"}, {"api_name": "loader.logger.info", "line_number": 58, "usage_type": "call"}, {"api_name": "loader.logger", "line_number": 58, "usage_type": "name"}, {"api_name": "loader.app.post", "line_number": 14, "usage_type": "call"}, {"api_name": "loader.app", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "3086625673", "text": "import transformers\nimport torch.nn as nn\n\n\nclass BertBaseUncased(nn.Module):\n def __init__(self) -> None:\n super(BertBaseUncased, self).__init__()\n self.bert = transformers.BertModel.from_pretrained(\"bert-base-uncased\")\n self.bert_drop = nn.Dropout(0.3)\n self.out = nn.Linear(768, 1)\n\n def forward(self, ids, mask, token_type_ids):\n out1, out2 = self.bert(ids, attention_mask=mask, token_type_ids=token_type_ids)\n \"\"\"\n :: out1 => sequence of hidden states for each token for all batches\n if you have 512 tokens, then you'll have 512 vectors of size 768 for each batch\n :: out2 => contains the last layer hidden for the first cls token of the sequence\n \"\"\"\n bert_drop = self.bert_drop(out2)\n bert_out = self.out(bert_drop)\n return bert_out\n", "repo_name": "vrahul1997/nlp_from_scratch", "sub_path": "bert_based_nlp_abh_thk/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 861, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "transformers.BertModel.from_pretrained", "line_number": 8, "usage_type": "call"}, {"api_name": "transformers.BertModel", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "37925450473", "text": "import collections\r\nfrom fractions import Fraction\r\nfrom decimal import Decimal\r\n\r\np = []\r\nfor i in range(int(input())):\r\n a,b = map(int, input().split(\"/\"))\r\n p.append(Fraction(a,b))\r\n\r\nk = collections.Counter(p).most_common(1)[0]\r\ns1 = k[0].denominator\r\n\r\nprint('%s/%s' % (k[0],s1) if not str(k[0]).count('/') == 1 else k[0])", "repo_name": "PeterBeattie19/Programorama2017", "sub_path": "MostCommonFraction.py", "file_name": "MostCommonFraction.py", "file_ext": "py", "file_size_in_byte": 329, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "fractions.Fraction", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "22447693423", "text": "import numpy as np\r\nimport pandas as pd\r\nfrom scipy.linalg import expm\r\nfrom random import uniform\r\nfrom AFNSGlobal.fx_functions import calc_gamma0, calc_gamma1\r\n\r\n\r\ndef parameter_matrix_conv(n_currencies, a_parameters_priv):\r\n kappa_p = []\r\n theta_p = []\r\n sigma = []\r\n vlambda = []\r\n for i in range(n_currencies):\r\n kappa_p.append(np.zeros((3, 3)))\r\n theta_p.append(np.zeros(3))\r\n sigma.append(np.zeros((3, 3)))\r\n vlambda.append(np.zeros(1))\r\n # the part below has to be adapted to the number of currencies used\r\n # kappa_p\r\n np.fill_diagonal(kappa_p[0], a_parameters_priv[6:9])\r\n np.fill_diagonal(kappa_p[1], a_parameters_priv[[6, 17, 18]])\r\n kappa_p.append(np.zeros((1 + n_currencies * 2, 1 + n_currencies * 2)))\r\n np.fill_diagonal(kappa_p[n_currencies], a_parameters_priv[[6, 7, 8, 17, 18]])\r\n # theta_p\r\n theta_p[0] = a_parameters_priv[3:6]\r\n theta_p[1] = a_parameters_priv[[3, 15, 16]]\r\n theta_p.append(a_parameters_priv[[3, 4, 5, 15, 16]])\r\n # SIGMA\r\n np.fill_diagonal(sigma[0], a_parameters_priv[:3])\r\n np.fill_diagonal(sigma[1], a_parameters_priv[[0, 13, 14]])\r\n sigma.append(np.zeros((1 + n_currencies * 2, 1 + n_currencies * 2)))\r\n np.fill_diagonal(sigma[n_currencies], a_parameters_priv[[0, 1, 2, 13, 14]])\r\n # LAMBDA\r\n vlambda[0] = a_parameters_priv[9]\r\n vlambda[1] = a_parameters_priv[19]\r\n return sigma, theta_p, kappa_p, vlambda\r\n\r\n\r\n# INITIAL GUESS (must fit constraints), creates new random initial values until they fit the constraint\r\ndef con_kappa_p_eig(param):\r\n kappa_p = parameter_matrix_conv(2, param)[2]\r\n eig1 = np.linalg.eig(kappa_p[0])[0]\r\n eig2 = np.linalg.eig(kappa_p[1])[0]\r\n eig1 = np.real(np.amin(eig1))\r\n eig2 = np.real(np.amin(eig2))\r\n return np.amin([eig1, eig2])\r\n\r\n\r\n# INITIAL GUESS (must fit constraints), creates new random initial values within the boundaries until they fit the constraint\r\ndef init_guess(boundaries_priv):\r\n x = False\r\n while x is False:\r\n initial_guess = np.array([uniform(*boundaries_priv[i]) for i in range(len(boundaries_priv))])\r\n x = con_kappa_p_eig(initial_guess) > 0\r\n return initial_guess\r\n\r\n\r\n# Yield-Adjustment Term C/(T-t) - Double checked with EXCEL\r\ndef c_t_T(sigma_y, lambda_y, delta_t_y):\r\n c_a = sigma_y[0, 0] ** 2\r\n c_b = sigma_y[1, 1] ** 2\r\n c_c = sigma_y[2, 2] ** 2\r\n c_aux_a = delta_t_y ** 2 / 6\r\n c_aux_b = (1 / (2 * lambda_y ** 2)) - (1 / lambda_y ** 3) * (\r\n 1 - np.exp(-lambda_y * delta_t_y)) / delta_t_y + 1 / (\r\n 4 * lambda_y ** 3) * (1 - np.exp(-2 * lambda_y * delta_t_y)) / delta_t_y\r\n c_aux_c = (1 / (2 * lambda_y ** 2)) + (1 / lambda_y ** 2) * np.exp(-lambda_y * delta_t_y) - 1 / (\r\n 4 * lambda_y) * delta_t_y * np.exp(-2 * lambda_y * delta_t_y) - 3 / (4 * lambda_y ** 2) * np.exp(\r\n -2 * lambda_y * delta_t_y) - (2 / lambda_y ** 3) * (1 - np.exp(-lambda_y * delta_t_y)) / delta_t_y + 5 / (\r\n 8 * lambda_y ** 3) * (1 - np.exp(-2 * lambda_y * delta_t_y)) / delta_t_y\r\n c = c_a * c_aux_a + c_b * c_aux_b + c_c * c_aux_c\r\n return c\r\n\r\n\r\ndef ya_matrix(tenors_priv, sigma_priv, lambda_priv):\r\n ntenors = len(tenors_priv)\r\n n_currencies = 2\r\n yield_adj_matrix = []\r\n for n in range(n_currencies):\r\n for i in range(ntenors):\r\n yield_adj_matrix.append(c_t_T(sigma_priv[n], lambda_priv[n], tenors_priv[i]))\r\n return np.array(yield_adj_matrix)\r\n\r\n\r\ndef factor_loadings(tenors_priv, lambda_priv):\r\n n_tenors = len(tenors_priv)\r\n n_currencies = 2\r\n x_loadings = np.zeros((n_currencies * n_tenors, 1 + 2 * n_currencies))\r\n x_loadings[:, 0] = np.ones(n_currencies * n_tenors)\r\n x_loadings[:10, 1] = (np.ones(n_tenors) - np.exp(-lambda_priv[0] * tenors_priv)) / (lambda_priv[0] * tenors_priv)\r\n x_loadings[:10, 2] = (np.ones(n_tenors) - np.exp(-lambda_priv[0] * tenors_priv)) / (\r\n lambda_priv[0] * tenors_priv) - np.exp(\r\n -lambda_priv[0] * tenors_priv)\r\n x_loadings[10:20, 3] = (np.ones(n_tenors) - np.exp(-lambda_priv[1] * tenors_priv)) / (lambda_priv[1] * tenors_priv)\r\n x_loadings[10:20, 4] = (np.ones(n_tenors) - np.exp(-lambda_priv[1] * tenors_priv)) / (\r\n lambda_priv[1] * tenors_priv) - np.exp(\r\n -lambda_priv[1] * tenors_priv)\r\n return x_loadings\r\n\r\ndef create_sobs_matrix(parameters, ntenors):\r\n return_matrix = np.zeros((2 * ntenors, 2 * ntenors))\r\n sigma_obs_diag = np.array(\r\n 5 * [parameters[0]] + 3 * [parameters[1]] + 2 * [parameters[2]] + 5 * [parameters[0]] + 3 * [\r\n parameters[1]] + 2 * [parameters[2]])\r\n np.fill_diagonal(return_matrix, sigma_obs_diag)\r\n return return_matrix\r\n\r\n\r\n# the following functions extend the matrices for currency calib\r\ndef mod_factor_loadings(x_loadings, gamma0, gamma1, x):\r\n fl_fx = np.zeros((4, 5))\r\n fc_horizons = np.array([1/12, 3/12, 6/12, 1])\r\n x_d = x[:3].reshape((3, 1))\r\n x_f = x[[0, 3, 4]].reshape((3, 1))\r\n # part dA/dX\r\n psi21 = gamma1[0].T @ gamma0[0]\r\n psi21 = np.append(psi21, [0, 0])\r\n psi22 = gamma1[1].T @ gamma0[0]\r\n psi22 = np.insert(psi22, 1, [0, 0])\r\n psi23 = gamma1[0].T @ (gamma0[0] - gamma0[1])\r\n psi23 = np.append(psi23, [0, 0])\r\n psi24 = gamma1[0].T @ gamma1[0] @ x_d\r\n psi24 = np.append(psi24, [0, 0])\r\n psi25 = gamma1[0].T @ gamma1[0] @ x_d\r\n psi25 = np.append(psi25, [0, 0])\r\n psi26 = gamma1[0].T @ gamma1[1] @ x_f\r\n psi26 = np.append(psi26, [0, 0])\r\n psi27 = gamma1[1].T @ gamma1[0] @ x_d\r\n psi27 = np.insert(psi27, 1, [0, 0])\r\n psi2 = psi21 - psi22 + psi23 + psi24 + psi25 - psi26 - psi27\r\n fl_fx[0,:] = psi2 * fc_horizons[0]\r\n fl_fx[1, :] = psi2 * fc_horizons[1]\r\n fl_fx[2, :] = psi2 * fc_horizons[2]\r\n fl_fx[3, :] = psi2 * fc_horizons[3]\r\n mod_vec = np.concatenate((x_loadings, fl_fx))\r\n return mod_vec\r\n\r\n\r\ndef mod_yield_adj(ya_vector, gamma0, gamma1, x):\r\n ya_fx = np.array([1/12, 3/12, 6/12, 1]) #warning FC horizons are hardcoded\r\n x_d = x[:3].reshape((3, 1))\r\n x_f = x[[0, 3, 4]].reshape((3, 1))\r\n # part A\r\n psi11 = x_d[1] - x_f[1]\r\n psi12 = gamma0[0].T @ (gamma0[0] - gamma0[1])\\\r\n + gamma0[0].T @ gamma1[0] @ x_d\\\r\n - gamma0[0].T @ gamma1[1] @ x_f\\\r\n + x_d.T @ gamma1[0].T @ (gamma0[0] - gamma0[1])\\\r\n + x_d.T @ gamma1[0].T @ gamma1[0] @ x_d\\\r\n - x_d.T @ gamma1[0].T @ gamma1[1] @ x_f\r\n psi1 = psi11 + psi12\r\n # part dA/dX\r\n psi21 = gamma1[0].T @ gamma0[0]\r\n psi21 = np.append(psi21, [0,0])\r\n psi22 = gamma1[1].T @ gamma0[0]\r\n psi22 = np.insert(psi22, 1, [0,0])\r\n psi23 = gamma1[0].T @ (gamma0[0] - gamma0[1])\r\n psi23 = np.append(psi23, [0,0])\r\n psi24 = gamma1[0].T @ gamma1[0] @ x_d\r\n psi24 = np.append(psi24, [0,0])\r\n psi25 = gamma1[0].T @ gamma1[0] @ x_d\r\n psi25 = np.append(psi25, [0, 0])\r\n psi26 = gamma1[0].T @ gamma1[1] @ x_f\r\n psi26 = np.append(psi26, [0,0])\r\n psi27 = gamma1[1].T @ gamma1[0] @ x_d\r\n psi27 = np.insert(psi27, 1, [0,0])\r\n psi2 = psi21 - psi22 + psi23 + psi24 + psi25 - psi26 - psi27\r\n ya_fx = (-psi1 + psi2 @ x) * ya_fx\r\n mod_vec = np.concatenate((ya_vector, ya_fx.flatten()))\r\n return mod_vec\r\n\r\n\r\ndef mod_sobs_matrix(sobs_matrix): # extends the diagonal measurement error matrix by three values for fx with sobs1\r\n sobs_vector = np.diag(sobs_matrix)\r\n sobs_fx = np.zeros(4)\r\n sobs_fx.fill(sobs_vector[0])\r\n mod_matrix = np.concatenate((sobs_vector, sobs_fx))\r\n return np.diag(mod_matrix)\r\n\r\n\r\ndef kalman_afns(parameters, delta_t, tenors, rates, r_flag): # parameters, delta_time, tenors, rates\r\n\r\n # same tenors for both currencies\r\n ntenors = len(tenors)\r\n\r\n # same tenors for both currencies\r\n ntenors = len(tenors)\r\n n_observations = rates[\"eur\"].shape[0]\r\n n_currencies = 2\r\n # define parameters\r\n sigma, theta_p, kappa_p, vlambda = parameter_matrix_conv(n_currencies, parameters)\r\n sigma_obs = create_sobs_matrix(parameters[10:13], ntenors)\r\n sigma_obs = mod_sobs_matrix(sigma_obs)\r\n\r\n # create yield adjustment term for each maturity and currency\r\n yield_adj_matrix = ya_matrix(tenors, sigma, vlambda)\r\n\r\n # factor loadings\r\n factor_loadings_matrix = factor_loadings(tenors, vlambda)\r\n\r\n # transition - CORRECT\r\n phi_0 = (np.eye(1 + 2 * n_currencies) - expm(-kappa_p[n_currencies] * delta_t)) @ theta_p[n_currencies]\r\n phi_1 = expm(-kappa_p[n_currencies] * delta_t)\r\n\r\n # Gammas for FX, 0 domestic, 1 foreign\r\n gamma0 = [calc_gamma0(sigma[0], kappa_p[0], theta_p[0]), calc_gamma0(sigma[1], kappa_p[1], theta_p[1])]\r\n gamma1 = [calc_gamma1(sigma[0], vlambda[0], kappa_p[0]), calc_gamma1(sigma[1], vlambda[1], kappa_p[1])]\r\n\r\n # 1 initialize state vector with X=ThetaP, Sigma0 = ... (CDR, p. 10)\r\n\r\n x = theta_p[n_currencies]\r\n\r\n cap_sigma = np.diag(\r\n np.diag(sigma[n_currencies]) * np.diag(sigma[n_currencies]) / np.diag(2 * kappa_p[n_currencies]) * (\r\n np.diag(np.eye(1 + 2 * n_currencies) - expm(-2 * kappa_p[n_currencies] * delta_t))))\r\n\r\n loglikelihood = np.zeros(n_observations)\r\n list_factors = []\r\n\r\n for i in np.arange(n_observations):\r\n # 2 yt shape (10,), factor_loadings shape (10,3)\r\n if i == 0:\r\n # Q equals VAR(Zti..]/ModelVariance -> shape (10,10)\r\n factor_loadings_matrix_current = mod_factor_loadings(factor_loadings_matrix, gamma0, gamma1, x)\r\n yield_adj_matrix_current = mod_yield_adj(yield_adj_matrix, gamma0, gamma1, x)\r\n yt = factor_loadings_matrix_current @ x - yield_adj_matrix_current\r\n qt = factor_loadings_matrix_current @ cap_sigma @ factor_loadings_matrix_current.T + sigma_obs\r\n qt = (qt + qt.T) / 2\r\n else:\r\n factor_loadings_matrix_current = mod_factor_loadings(factor_loadings_matrix, gamma0, gamma1, x_predict)\r\n yield_adj_matrix_current = mod_yield_adj(yield_adj_matrix, gamma0, gamma1, x_predict)\r\n yt = factor_loadings_matrix_current @ x_predict - yield_adj_matrix_current\r\n qt = factor_loadings_matrix_current @ x_var_predict @ factor_loadings_matrix_current.T + sigma_obs\r\n qt = (qt + qt.T) / 2\r\n\r\n # shape v_error = (20,)\r\n rates_v = np.concatenate((rates[\"eur\"].iloc[i, :], rates[\"gbp\"].iloc[i, :], rates[\"fxgbpeur\"].iloc[i, :]))\r\n v_error = rates_v - yt\r\n\r\n # 3 Update\r\n if i == 0:\r\n kalman_gain = cap_sigma @ factor_loadings_matrix_current.T @ np.linalg.inv(qt)\r\n x_update = x + kalman_gain @ v_error\r\n x_var_update = (np.eye(1 + 2 * n_currencies) - kalman_gain @ factor_loadings_matrix_current) @ cap_sigma\r\n else:\r\n kalman_gain = x_var_predict @ factor_loadings_matrix_current.T @ np.linalg.inv(qt)\r\n x_update = x_predict + kalman_gain @ v_error\r\n x_var_update = (np.eye(1 + 2 * n_currencies) - kalman_gain @ factor_loadings_matrix_current) @ x_var_predict\r\n\r\n # 4 - equal to nextxmean and nextxvariance\r\n x_predict = phi_0 + phi_1 @ x_update\r\n x_var_predict = phi_1 @ x_var_update @ phi_1 + cap_sigma\r\n\r\n # 5\r\n llh = -0.5 * (n_currencies * ntenors) * np.log(2 * np.pi) - 0.5 * (\r\n np.linalg.slogdet(qt)[1] + v_error @ np.linalg.inv(\r\n qt) @ v_error)\r\n loglikelihood[i] = llh\r\n # state variables\r\n if r_flag:\r\n dict_factors = {\"Level G\": x_update[0], \"Slope D\": x_update[1],\r\n \"Curvature D\": x_update[2],\r\n \"Slope F\": x_update[3], \"Curvature F\": x_update[4]}\r\n list_factors.append(pd.DataFrame(dict_factors, index=[i]))\r\n if r_flag:\r\n df_factors_ts = pd.concat(list_factors)\r\n df_factors_ts.index = rates[\"eur\"].index\r\n df_factors_ts = df_factors_ts[[\"Level G\", \"Slope D\", \"Curvature D\", \"Slope F\", \"Curvature F\"]]\r\n return (-sum(loglikelihood)), df_factors_ts\r\n else:\r\n return (-sum(loglikelihood))\r\n", "repo_name": "tjdirks/afns-twoccy-ccycalib", "sub_path": "AFNS-TwoCurrency-CcyCalib/AFNSGlobal/kalman_filter_functions.py", "file_name": "kalman_filter_functions.py", "file_ext": "py", "file_size_in_byte": 12087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.linalg.eig", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.linalg.expm", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.linalg.expm", "line_number": 203, "usage_type": "call"}, {"api_name": "AFNSGlobal.fx_functions.calc_gamma0", "line_number": 206, "usage_type": "call"}, {"api_name": "AFNSGlobal.fx_functions.calc_gamma1", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.linalg.expm", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 246, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.linalg.slogdet", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 256, "usage_type": "attribute"}, {"api_name": "numpy.linalg.inv", "line_number": 256, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 264, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 266, "usage_type": "call"}]} +{"seq_id": "1648524734", "text": "import csv\nfrom itertools import islice\nfrom schema import ImpactArea, Blockchain, Topic, Web3, Organization\nfrom terminusdb_client import WOQLClient\nfrom datetime import datetime\nimport pytz\nimport re\nimport emoji\nimport json\nimport meilisearch\nimport ast\nimport hashlib\n\n\n# we keep all the information in dictionaries with Employee id as keys\norgs = {}\norgsjson = []\n\nclient = WOQLClient(\"https://cloud.terminusdb.com/Myseelia/\")\nclient.connect(db=\"play\", team=\"Myseelia\", use_token=True)\n\nclient1 = meilisearch.Client(\n 'https://ms-9ea4a96f02a8-1969.sfo.meilisearch.io', '117c691a34b21a6651798479ebffd181eb276958')\n\nindex = client1.index('orgs')\n\ndef get_emoji_regexp():\n # Sort emoji by length to make sure multi-character emojis are\n # matched first\n emojis = sorted(emoji.EMOJI_DATA, key=len, reverse=True)\n pattern = u'(' + u'|'.join(re.escape(u) for u in emojis) + u')'\n return re.compile(pattern)\n\n\ndef remove_emojis(string):\n return get_emoji_regexp().sub(r'', string)\n\ndef hash_string(string):\n sha256 = hashlib.sha256()\n sha256.update(string.encode('utf-8'))\n return sha256.hexdigest()\n\ndef to_json(obj):\n obj_dict = obj.__dict__\n if obj_dict['blockchainecosystem']:\n print(obj_dict['blockchainecosystem'])\n obj_dict['blockchainecosystem'] = [\n bc.name for bc in obj_dict['blockchainecosystem']]\n else:\n obj_dict['web3'] = None\n if obj_dict['impactarea']:\n print(obj_dict['impactarea'])\n obj_dict['impactarea'] = [ia.name for ia in obj_dict['impactarea']]\n else:\n obj_dict['web3'] = None\n if obj_dict['topic']:\n print(obj_dict['topic'])\n obj_dict['topic'] = [t.name for t in obj_dict['topic']]\n else:\n obj_dict['web3'] = None\n if obj_dict['web3']:\n print(obj_dict['web3'])\n obj_dict['web3'] = [w.name for w in obj_dict['web3']]\n else:\n obj_dict['web3'] = None\n print(obj_dict['datecreated'])\n obj_dict['datecreated'] = obj_dict['datecreated'].isoformat()\n print(\"here\")\n print(\"here\" + json.dumps(obj_dict))\n return json.dumps(obj_dict)\n\n\nwith open(\"Organizations.csv\") as file:\n csv_file = csv.reader(file)\n next(csv_file) # skiping header\n chunk_size = 1000\n while True:\n chunk = list(islice(csv_file, chunk_size))\n if not chunk:\n break\n # Process the chunk of rows here\n counter = 0\n for row in chunk:\n row = [remove_emojis(cell) for cell in row]\n assignee = row[0]\n impactArea = row[4].strip(\"{}\").split(\",\")\n impact_area_set = set()\n for value in impactArea:\n if value:\n value = value.strip().strip('\"')\n if value == \"Social justice\":\n impact_area_set.add(ImpactArea.SocialJustice)\n elif value in (\"Food & Agriculture\", \"Food & Ag.\"):\n impact_area_set.add(ImpactArea.FoodAg)\n elif value == \"Invest\":\n impact_area_set.add(ImpactArea.Politicsactivism)\n elif value == \"Politics & activism\":\n impact_area_set.add(ImpactArea.Investing)\n elif value == \"Innovate\":\n impact_area_set.add(ImpactArea.Innovation)\n else:\n impact_area_set.add(ImpactArea[value])\n blockchainEcosystem = row[1].strip(\"{}\").split(\",\")\n blockchainEcosystem_set = set()\n for value in blockchainEcosystem:\n if value:\n blockchain = value.strip()\n if blockchain == \"Binance Smart Chain\":\n blockchain = Blockchain.BinanceSmartChain\n elif blockchain == \"Regen Network\":\n blockchain = Blockchain.RegenNetwork\n elif blockchain == \"Energy Web Chain\":\n blockchain = Blockchain.EnergyWebChain\n elif blockchain == \"Hyperledger Fabric\":\n blockchain = Blockchain.HyperledgerFabric\n elif blockchain == \"Zero Carbon\":\n blockchain = Blockchain.ZeroCarbon\n elif blockchain == \"IXO\":\n blockchain = Blockchain.ixo\n elif blockchain in (\"Not found\", \"Not sure / still deciding\"):\n blockchain = Blockchain.Other\n elif blockchain == \"Not applicable\":\n break\n else:\n blockchain = Blockchain[blockchain]\n blockchainEcosystem_set.add(blockchain)\n web3 = row[15].strip(\"{}\")\n web3_set = set()\n for value in re.split(\",(?![^(]*\\))\", web3):\n if value:\n web3 = value.strip().strip('\"')\n # someone put \"Blockchain (L1,DAO\" which will match to \"Blockchain (L1\" since we strip the '\"'\n if web3 in (\"Blockchain (L1, L2)\", \"Blockchain (L1,L2)\", \"Blockchain (L1\"):\n web3 = Web3.Blockchain\n else:\n web3 = Web3[web3]\n web3_set.add(web3)\n topic = row[13].strip(\"{}\").split(\",\")\n topic_set = set()\n for value in topic:\n if value:\n topic = value.strip()\n if topic == \"inclusion and equality\":\n topic = Topic.inclusionequality\n elif topic == \"Circular Economy\":\n topic = Topic.CircularEconomy\n elif topic == \"Financial Inclusion\":\n topic = Topic.Financial_Inclusion\n elif topic == \"origin & trace\":\n topic = Topic.Traceability\n elif topic == \"Supply Chain\":\n topic = Topic.SupplyChain\n elif topic == \"Move-to-earn\":\n topic = Topic.Movetoearn\n elif topic == \"Work & Business\":\n topic = Topic.Work\n elif topic == \"Food Forests\":\n topic = Topic.FoodForests\n elif topic == \"Eco-Living\":\n topic = Topic.EcoLiving\n else:\n topic = Topic[topic]\n topic_set.add(topic)\n date_string = row[2]\n utc_date = datetime.min\n if date_string:\n date_format = \"%m/%d/%Y %I:%M %p\"\n parsed_date = datetime.strptime(date_string, date_format)\n # Convert the datetime object to UTC time\n utc_date = pytz.utc.normalize(pytz.utc.localize(parsed_date))\n preJan20thUpvotesstr = row[7]\n preJan20thUpvotesint = 0\n if preJan20thUpvotesstr.isdigit():\n preJan20thUpvotesint = int(preJan20thUpvotesstr)\n upvotesstr = row[7]\n upvotesint = 0\n if upvotesstr.isdigit():\n upvotesint = int(upvotesstr)\n\n org = Organization(\n assignee=row[0] if row[0] not in [None, ''] else None,\n blockchainecosystem=blockchainEcosystem_set if len(\n blockchainEcosystem_set) > 0 else None,\n description=row[3] if row[3] not in [None, ''] else None,\n logo=row[5] if row[5] not in [None, ''] else None,\n # name is the only mandatory field, so default it to \"\" if blank\n name=row[6] if row[6] not in [None, ''] else \"\",\n preJan20thUpvotes=preJan20thUpvotesint if preJan20thUpvotesint not in [\n 0] else None,\n reviewed=row[8] if row[3] not in [None, ''] else None,\n submittedbyemail=row[9] if row[9] not in [None, ''] else None,\n submittedbyname=row[10] if row[10] not in [None, ''] else None,\n submittedbyowner=row[11] if row[11] not in [\n None, ''] else None,\n subscribed=row[12] if row[12] not in [None, ''] else None,\n topic=topic_set if len(topic_set) > 0 else None,\n upvotes=upvotesint if upvotesint not in [0] else None,\n web3=web3_set if len(web3_set) > 0 else None,\n impactarea=impact_area_set if len(\n impact_area_set) > 0 else None,\n datecreated=utc_date if impact_area_set not in [\n datetime.min] else None,\n )\n\n orgs[counter] = org\n # print(to_json(org))\n # orgsjson.append(to_json(org))\n counter += 1\n inserted = client.insert_document(\n list(orgs.values()), commit_msg=\"Adding orgs\")\n documents = []\n for id in inserted:\n document = client.get_document(id)\n real_id = document['@id']\n num_id = real_id.split(\"/\")[-1]\n document = {k: json.dumps(v) for k, v in document.items() if k != '@id'}\n document.update({'id': num_id})\n documents.append(document)\n index.add_documents(documents)\n", "repo_name": "DarrenZal/Myseelia", "sub_path": "Terminus/insert_data.py", "file_name": "insert_data.py", "file_ext": "py", "file_size_in_byte": 9310, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "terminusdb_client.WOQLClient", "line_number": 19, "usage_type": "call"}, {"api_name": "meilisearch.Client", "line_number": 22, "usage_type": "call"}, {"api_name": "emoji.EMOJI_DATA", "line_number": 30, "usage_type": "attribute"}, {"api_name": "re.escape", "line_number": 31, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 32, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 74, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 78, "usage_type": "call"}, {"api_name": "schema.ImpactArea.SocialJustice", "line_number": 92, "usage_type": "attribute"}, {"api_name": "schema.ImpactArea", "line_number": 92, "usage_type": "name"}, {"api_name": "schema.ImpactArea.FoodAg", "line_number": 94, "usage_type": "attribute"}, {"api_name": "schema.ImpactArea", "line_number": 94, "usage_type": "name"}, {"api_name": "schema.ImpactArea.Politicsactivism", "line_number": 96, "usage_type": "attribute"}, {"api_name": "schema.ImpactArea", "line_number": 96, "usage_type": "name"}, {"api_name": "schema.ImpactArea.Investing", "line_number": 98, "usage_type": "attribute"}, {"api_name": "schema.ImpactArea", "line_number": 98, "usage_type": "name"}, {"api_name": "schema.ImpactArea.Innovation", "line_number": 100, "usage_type": "attribute"}, {"api_name": "schema.ImpactArea", "line_number": 100, "usage_type": "name"}, {"api_name": "schema.ImpactArea", "line_number": 102, "usage_type": "name"}, {"api_name": "schema.Blockchain.BinanceSmartChain", "line_number": 109, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 109, "usage_type": "name"}, {"api_name": "schema.Blockchain.RegenNetwork", "line_number": 111, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 111, "usage_type": "name"}, {"api_name": "schema.Blockchain.EnergyWebChain", "line_number": 113, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 113, "usage_type": "name"}, {"api_name": "schema.Blockchain.HyperledgerFabric", "line_number": 115, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 115, "usage_type": "name"}, {"api_name": "schema.Blockchain.ZeroCarbon", "line_number": 117, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 117, "usage_type": "name"}, {"api_name": "schema.Blockchain.ixo", "line_number": 119, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 119, "usage_type": "name"}, {"api_name": "schema.Blockchain.Other", "line_number": 121, "usage_type": "attribute"}, {"api_name": "schema.Blockchain", "line_number": 121, "usage_type": "name"}, {"api_name": "schema.Blockchain", "line_number": 125, "usage_type": "name"}, {"api_name": "re.split", "line_number": 129, "usage_type": "call"}, {"api_name": "schema.Web3.Blockchain", "line_number": 134, "usage_type": "attribute"}, {"api_name": "schema.Web3", "line_number": 134, "usage_type": "name"}, {"api_name": "schema.Web3", "line_number": 136, "usage_type": "name"}, {"api_name": "schema.Topic.inclusionequality", "line_number": 144, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 144, "usage_type": "name"}, {"api_name": "schema.Topic.CircularEconomy", "line_number": 146, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 146, "usage_type": "name"}, {"api_name": "schema.Topic.Financial_Inclusion", "line_number": 148, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 148, "usage_type": "name"}, {"api_name": "schema.Topic.Traceability", "line_number": 150, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 150, "usage_type": "name"}, {"api_name": "schema.Topic.SupplyChain", "line_number": 152, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 152, "usage_type": "name"}, {"api_name": "schema.Topic.Movetoearn", "line_number": 154, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 154, "usage_type": "name"}, {"api_name": "schema.Topic.Work", "line_number": 156, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 156, "usage_type": "name"}, {"api_name": "schema.Topic.FoodForests", "line_number": 158, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 158, "usage_type": "name"}, {"api_name": "schema.Topic.EcoLiving", "line_number": 160, "usage_type": "attribute"}, {"api_name": "schema.Topic", "line_number": 160, "usage_type": "name"}, {"api_name": "schema.Topic", "line_number": 162, "usage_type": "name"}, {"api_name": "datetime.datetime.min", "line_number": 165, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 165, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 168, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 168, "usage_type": "name"}, {"api_name": "pytz.utc.normalize", "line_number": 170, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pytz.utc.localize", "line_number": 170, "usage_type": "call"}, {"api_name": "schema.Organization", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 202, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 202, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "38971701679", "text": "import os\nimport json\nimport numpy as np\nimport torch\n\n\ndef mkdirs(paths):\n if isinstance(paths, list):\n for path in paths:\n if not os.path.exists(path):\n os.makedirs(path)\n else:\n if not os.path.exists(paths):\n os.makedirs(paths)\n\n\ndef invert_dict(d):\n return {v: k for k, v in d.items()}\n \n\ndef load_vocab(path):\n with open(path, 'r') as f:\n vocab = json.load(f)\n vocab['question_idx_to_token'] = invert_dict(vocab['question_token_to_idx'])\n vocab['program_idx_to_token'] = invert_dict(vocab['program_token_to_idx'])\n vocab['answer_idx_to_token'] = invert_dict(vocab['answer_token_to_idx'])\n # Sanity check: make sure , , and are consistent\n assert vocab['question_token_to_idx'][''] == 0\n assert vocab['question_token_to_idx'][''] == 1\n assert vocab['question_token_to_idx'][''] == 2\n assert vocab['program_token_to_idx'][''] == 0\n assert vocab['program_token_to_idx'][''] == 1\n assert vocab['program_token_to_idx'][''] == 2\n return vocab\n\n\ndef load_scenes(scenes_json):\n with open(scenes_json) as f:\n scenes_dict = json.load(f)['scenes']\n scenes = []\n for s in scenes_dict:\n table = []\n for i, o in enumerate(s['objects']):\n item = {}\n item['id'] = '%d-%d' % (s['image_index'], i)\n if '3d_coords' in o:\n item['position'] = [np.dot(o['3d_coords'], s['directions']['right']),\n np.dot(o['3d_coords'], s['directions']['front']),\n o['3d_coords'][2]]\n else:\n item['position'] = o['position']\n item['color'] = o['color']\n item['material'] = o['material']\n item['shape'] = o['shape']\n item['size'] = o['size']\n table.append(item)\n scenes.append(table)\n return scenes\n \n\ndef load_embedding(path):\n return torch.Tensor(np.load(path))", "repo_name": "kexinyi/ns-vqa", "sub_path": "reason/utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 248, "dataset": "github-code", "pt": "51", "api": [{"api_name": "os.path.exists", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 23, "usage_type": "call"}, {"api_name": "json.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "38282776617", "text": "\"\"\"\n Misc utils related to database functions.\n\"\"\"\nfrom flask import abort, current_app\nfrom sqlalchemy.exc import SQLAlchemyError, IntegrityError\nfrom ..models import (Analysis, RunStimulus, Predictor, PredictorEvent,\n ExtractedEvent, Stimulus, Report)\nfrom ..database import db\nfrom sqlalchemy.event import listens_for\nfrom sqlalchemy.dialects import postgresql\nimport shortuuid\n\n\ndef delete_analysis(analysis):\n analysis.runs = []\n\n # Delete reports\n Report.query.filter_by(analysis_id=analysis.hash_id).delete()\n\n db.session.delete(analysis)\n db.session.commit()\n\n\ndef create_pes(predictors, run_ids=None, stimulus_timing=False):\n \"\"\" Create PredictorEvents from EFs \"\"\"\n all_pes = []\n for pred in predictors:\n ef = pred.extracted_feature\n # For all instances for stimuli in this task's runs\n query = ExtractedEvent.query.filter_by(\n ef_id=ef.id).join(Stimulus).join(\n RunStimulus)\n\n if run_ids is not None:\n query = query.filter(RunStimulus.run_id.in_(run_ids))\n\n query = query.with_entities(\n 'extracted_event.onset', 'extracted_event.duration',\n 'extracted_event.value', 'extracted_event.object_id',\n 'extracted_event.stimulus_id', 'run_stimulus.run_id',\n 'run_stimulus.onset', 'run_stimulus.duration',\n 'stimulus.path')\n\n for (onset, dur, val, o_id, s_id, run_id, rs_onset, rs_dur, s_path) \\\n in query:\n if dur is None:\n dur = rs_dur\n s_path = s_path.split('/')[-1] if s_path else None\n\n res = dict(\n onset=(onset or 0) + rs_onset,\n duration=dur,\n value=val,\n run_id=run_id,\n predictor_id=pred.id,\n object_id=o_id,\n )\n\n if stimulus_timing:\n res.update(dict(\n stimulus_id=s_id,\n stimulus_onset=rs_onset,\n stimulus_duration=rs_dur,\n stimulus_path=s_path\n )\n )\n\n all_pes.append(res)\n return all_pes\n\n\ndef dump_pe(pes):\n \"\"\" Serialize PredictorEvents, with *SPEED*, using core SQL.\n Warning: relies on attributes being in correct order. \"\"\"\n statement = str(pes.statement.compile(dialect=postgresql.dialect()))\n params = pes.statement.compile(dialect=postgresql.dialect()).params\n res = db.session.connection().execute(statement, params)\n return [\n dict(\n zip(('id', 'onset', 'duration', 'value', 'object_id', 'run_id',\n 'predictor_id', 'stimulus_id'), r))\n for r in res\n ]\n\n\ndef dump_predictor_events(predictor_ids, run_ids=None, stimulus_timing=False):\n \"\"\" Query & serialize PredictorEvents, for both Raw and Extracted\n Predictors (which require creating PEs from EEs)\n \"\"\"\n\n # Query Predictors\n all_preds = Predictor.query.filter(Predictor.id.in_(predictor_ids))\n\n # Separate raw and extracted predictors\n raw_pred_ids = [p.id for p in all_preds.filter_by(ef_id=None)]\n ext_preds = Predictor.query.filter(\n Predictor.id.in_(set(predictor_ids) - set(raw_pred_ids)))\n\n # Query & dump raw PEs\n pes = PredictorEvent.query.filter(\n (PredictorEvent.predictor_id.in_(raw_pred_ids)))\n if run_ids is not None:\n pes = pes.filter((PredictorEvent.run_id.in_(run_ids)))\n\n pes = dump_pe(pes)\n\n # Create & dump Extracted PEs\n pes += create_pes(ext_preds, run_ids, stimulus_timing=stimulus_timing)\n return pes\n\n\n@listens_for(Analysis, \"after_insert\")\ndef update_hash(mapper, connection, target):\n analysis_table = mapper.local_table\n shortuuid.set_alphabet('23456789abcdefghijkmnopqrstuvwxyz')\n new_hash = shortuuid.random(length=5)\n res = connection.execute(\n f\"select id from analysis where hash_id='{new_hash}'\")\n\n # Check for clashes and generate new if there is one\n while res.rowcount > 0:\n print('trying again')\n new_hash = shortuuid.ShortUUID().random(length=6)\n res = connection.execute(\n f\"select id from analysis where hash_id='{new_hash}'\")\n\n connection.execute(\n analysis_table.update().\n values(\n hash_id=new_hash).where(analysis_table.c.id == target.id)\n )\n\n\ndef put_record(updated_values, instance, commit=True):\n try:\n for key, value in updated_values.items():\n setattr(instance, key, value)\n if commit is True:\n db.session.commit()\n return instance\n except IntegrityError as e:\n current_app.logger.error(e)\n db.session.rollback()\n abort(422, \"Error updating field\")\n except SQLAlchemyError as e:\n current_app.logger.error(e)\n db.session.rollback()\n abort(400, \"Error updating field\")\n\n\ndef get_or_create(model, commit=True, **kwargs):\n \"\"\" Checks to see if instance of model is in db.\n If not add and commit. If true, return all matches.\n Args:\n db_session: db session\n model: Model class\n **kwargs: columns to filter by\n\n Returns:\n (first matching or created instance, if instance is new)\n \"\"\"\n instance = db.session.query(model).filter_by(**kwargs).first()\n if instance:\n return instance, False\n else:\n instance = model(**kwargs)\n db.session.add(instance)\n\n if commit is True:\n db.session.commit()\n\n return instance, True\n", "repo_name": "neuroscout/neuroscout", "sub_path": "neuroscout/utils/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 5565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "51", "api": [{"api_name": "models.Report.query.filter_by", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Report.query", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Report", "line_number": 18, "usage_type": "name"}, {"api_name": "database.db.session.delete", "line_number": 20, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 20, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 20, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 21, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 21, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 21, "usage_type": "name"}, {"api_name": "models.RunStimulus", "line_number": 32, "usage_type": "argument"}, {"api_name": "models.Stimulus", "line_number": 31, "usage_type": "argument"}, {"api_name": "models.ExtractedEvent.query.filter_by", "line_number": 30, "usage_type": "call"}, {"api_name": "models.ExtractedEvent.query", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.ExtractedEvent", "line_number": 30, "usage_type": "name"}, {"api_name": "models.RunStimulus.run_id.in_", "line_number": 35, "usage_type": "call"}, {"api_name": "models.RunStimulus.run_id", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.RunStimulus", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.dialect", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 75, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.dialect", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 76, "usage_type": "name"}, {"api_name": "database.db.session.connection", "line_number": 77, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 77, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 77, "usage_type": "name"}, {"api_name": "models.Predictor.query.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "models.Predictor.query", "line_number": 92, "usage_type": "attribute"}, {"api_name": "models.Predictor", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Predictor.id.in_", "line_number": 92, "usage_type": "call"}, {"api_name": "models.Predictor.id", "line_number": 92, "usage_type": "attribute"}, {"api_name": "models.Predictor.query.filter", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Predictor.query", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.Predictor", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Predictor.id.in_", "line_number": 97, "usage_type": "call"}, {"api_name": "models.Predictor.id", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.Predictor", "line_number": 97, "usage_type": "name"}, {"api_name": "models.PredictorEvent.query.filter", "line_number": 100, "usage_type": "call"}, {"api_name": "models.PredictorEvent.query", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.PredictorEvent", "line_number": 100, "usage_type": "name"}, {"api_name": "models.PredictorEvent.predictor_id.in_", "line_number": 101, "usage_type": "call"}, {"api_name": "models.PredictorEvent.predictor_id", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.PredictorEvent", "line_number": 101, "usage_type": "name"}, {"api_name": "models.PredictorEvent.run_id.in_", "line_number": 103, "usage_type": "call"}, {"api_name": "models.PredictorEvent.run_id", "line_number": 103, "usage_type": "attribute"}, {"api_name": "models.PredictorEvent", "line_number": 103, "usage_type": "name"}, {"api_name": "shortuuid.set_alphabet", "line_number": 115, "usage_type": "call"}, {"api_name": "shortuuid.random", "line_number": 116, "usage_type": "call"}, {"api_name": "shortuuid.ShortUUID", "line_number": 123, "usage_type": "call"}, {"api_name": "sqlalchemy.event.listens_for", "line_number": 112, "usage_type": "call"}, {"api_name": "models.Analysis", "line_number": 112, "usage_type": "argument"}, {"api_name": "database.db.session.commit", "line_number": 139, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 139, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 139, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.IntegrityError", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.current_app.logger.error", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 142, "usage_type": "name"}, {"api_name": "database.db.session.rollback", "line_number": 143, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 143, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlalchemy.exc.SQLAlchemyError", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.current_app.logger.error", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 146, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 146, "usage_type": "name"}, {"api_name": "database.db.session.rollback", "line_number": 147, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 147, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 148, "usage_type": "call"}, {"api_name": "database.db.session.query", "line_number": 162, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 162, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 162, "usage_type": "name"}, {"api_name": "database.db.session.add", "line_number": 167, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 167, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 167, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 170, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 170, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 170, "usage_type": "name"}]} +{"seq_id": "73370076318", "text": "import logging\n\nfrom .utils import dict_map, dict_add, int_or_none\n\n@dict_map\ndef make_volumes(m):\n return dict(\n name=m[0],\n host=dict(\n sourcePath=m[1],\n ),\n )\n\n@dict_map\ndef make_port_mapping(m):\n return dict(\n containerPort=int(m['container_port']),\n hostPort=int(m['host_port']),\n protocol=m['protocol'],\n )\n\n@dict_map\ndef make_environment(m):\n return dict(\n name=m[0],\n value=str(m[1])\n )\n\n@dict_map\ndef make_mount_points(m):\n res = {}\n containerPath = m[0]\n if containerPath:\n res['containerPath'] = containerPath\n\n sourceVolume = m[1]['source_volume']\n if sourceVolume:\n res['sourceVolume'] = sourceVolume\n\n readOnly=m[1].get('read_only')\n if readOnly:\n res['readOnly'] = bool(readOnly)\n\n return res\n\n@dict_map\ndef make_volumes_from(m):\n res = {}\n sourceContainer = m[0]\n if sourceContainer:\n res['sourceContainer'] = sourceContainer\n\n readOnly = m[1].get('read_only')\n if readOnly:\n res['readOnly'] = bool(readOnly)\n\n return res\n\n@dict_map\ndef make_extra_hosts(m):\n return dict(\n hostname=m[0],\n ipAddress=m[1]\n )\n\n@dict_map\ndef make_ulimits(m):\n return dict(\n name=m[0],\n softLimit=int(m[1]['soft']),\n hardLimit=int(m[1]['hard'])\n )\n\ndef make_log_configuration(m):\n if not m:\n return None\n\n return dict(\n logDriver=m.get('log_driver', ''),\n options=m.get('options', {})\n )\n\n@dict_map\ndef make_container(m):\n name = m[0]\n c = m[1]\n\n container_map = dict(\n name=name,\n image=c['image'],\n cpu=int_or_none(c.get('cpu')),\n memory=int_or_none(c.get('memory')),\n memoryReservation=int_or_none(c.get('memory_reservation')),\n links=c.get(\"links\"),\n portMappings=make_port_mapping(c.get('port_mappings', {})),\n essential=c.get('essential'),\n entryPoint=c.get(\"entry_point\"),\n command=c.get(\"command\"),\n environment=make_environment(c.get('environment_variables', {})),\n mountPoints=make_mount_points(c.get('mount_points', {})),\n volumesFrom=make_volumes_from(c.get('volumes_from', {})),\n hostname=c.get('hostname'),\n user=c.get('user'),\n workingDirectory=c.get('working_directory'),\n disableNetworking=c.get('disable_networking'),\n privileged=c.get('privileged'),\n readonlyRootFilesystem=c.get('readonly_root_filesystem'),\n dnsServers=c.get('dns_servers'),\n dnsSearchDomains=c.get('dns_search_domains'),\n extraHosts=make_extra_hosts(c.get('extra_hosts', {})),\n dockerSecurityOptions=c.get('docker_security_options'),\n dockerLabels=c.get('docker_labels'),\n ulimits=make_ulimits(c.get('ulimits', {})),\n logConfiguration=make_log_configuration(c.get('log_configuration', {})),\n )\n\n container = {}\n\n for k,v in container_map.items():\n dict_add(container, k, v)\n\n return container\n\nclass TaskDefinition(object):\n def __init__(self, ecs, cfg):\n self.log = logging.getLogger(__name__)\n self.ecs = ecs\n self.cfg = cfg\n\n def get(self, revision):\n service = self.cfg['service']\n family = service['name']\n task_definition_name = ':'.join([family, revision])\n self.log.info(\"Task definition '%s'. GET\", task_definition_name)\n res = self.ecs.describe_task_definition(taskDefinition=task_definition_name)\n return res['taskDefinition']\n\n def register(self):\n cfg = self.cfg\n service = cfg['service']\n task = cfg['task']\n containers = task['containers']\n\n self.log.info(\"Task definition '%s'. REGISTER\", service['name'])\n\n container_definitions = make_container(containers)\n volumes = make_volumes(task.get('volumes', {}))\n placement_constraints = task.get('placement_constraints', [])\n\n res = self.ecs.register_task_definition(\n family=service['name'],\n taskRoleArn=task.get('task_role_arn', ''),\n networkMode=task.get('network_mode', 'bridge'),\n containerDefinitions=container_definitions,\n volumes=volumes,\n placementConstraints=placement_constraints,\n )\n\n return res['taskDefinition']\n", "repo_name": "adubkov/ecsdeploy", "sub_path": "ecsdeploy/task_definition.py", "file_name": "task_definition.py", "file_ext": "py", "file_size_in_byte": 4337, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "utils.dict_map", "line_number": 5, "usage_type": "name"}, {"api_name": "utils.dict_map", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.dict_map", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.dict_map", "line_number": 29, "usage_type": "name"}, {"api_name": "utils.dict_map", "line_number": 46, "usage_type": "name"}, {"api_name": "utils.dict_map", "line_number": 59, "usage_type": "name"}, {"api_name": "utils.dict_map", "line_number": 66, "usage_type": "name"}, {"api_name": "utils.int_or_none", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.int_or_none", "line_number": 92, "usage_type": "call"}, {"api_name": "utils.int_or_none", "line_number": 93, "usage_type": "call"}, {"api_name": "utils.dict_add", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.dict_map", "line_number": 83, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "13523758383", "text": "import numpy as np\n\nfrom CargaDeImagenes import CargaDeImagenes\n###Importar componentes de la red neuronal\nfrom keras.models import Sequential\nfrom keras.layers import InputLayer, Input, Conv2D, MaxPool2D, Reshape, Dense, Flatten, LeakyReLU, \\\n MaxPooling2D, Dropout, BatchNormalization, ReLU\n\nfrom sklearn.model_selection import KFold\n\n# Preparando para el entrenamiento\nancho = 256\nalto = 256\npixeles = ancho * alto\n# Imagen RGB -->3\nnumeroCanales = 1\nformaImagen = (ancho, alto, numeroCanales)\nnumeroCategorias = 5\n\ncantidaDatosEntrenamiento = [60, 60, 60, 60, 60]\n\n# Cargar las imágenes\ncargaDeImagenes = CargaDeImagenes(\"Dataset/ImagenesDeEntrenamiento/\")\nimagenes, probabilidades = cargaDeImagenes.cargarDatos(numeroCategorias, cantidaDatosEntrenamiento, ancho, alto)\n\n# Lista de modelos y las epocas de entrenamiento\nmodelosEntrenados = []\n\n\n# Modelo 1, tomado de: https://guru99.es/convnet-tensorflow-image-classification/\n# --------------Primer Modelo capas normales y no tiene nada de raro XD ---------------------------------------\nmodel1 = Sequential()\n# Capa entrada\nmodel1.add(InputLayer(input_shape=(pixeles,)))\nmodel1.add(Reshape(formaImagen))\n\n# Capas Ocultas\n# Capas convolucionales\nmodel1.add(Conv2D(kernel_size=5, strides=2, filters=16, padding=\"same\", activation=\"relu\", name=\"capa_1\"))\nmodel1.add(MaxPool2D(pool_size=2, strides=2))\n\nmodel1.add(Conv2D(kernel_size=3, strides=1, filters=36, padding=\"same\", activation=\"relu\", name=\"capa_2\"))\nmodel1.add(MaxPool2D(pool_size=2, strides=2))\n\n# Aplanamiento y capas densas\nmodel1.add(Flatten())\nmodel1.add(Dense(128, activation=\"relu\"))\n\n# Capa de salida\nmodel1.add(Dense(numeroCategorias, activation=\"softmax\"))\n\nmodel1.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n\nmodelosEntrenados.append((model1,10))\n\n\n#--------------------------------------------------------------------------------------------------\n#Modelo 2, tomado de: https://www.aprendemachinelearning.com/clasificacion-de-imagenes-en-python/\n#Este modelo cuenta con capas extras de activacion esta capa es una version con fugas de relu\n#f(x) = alpha * x if x < 0\n#f(x) = x if x >= 0\n#\n\nmodel2 = Sequential()\n\n# Capa entrada\nmodel2.add(InputLayer(input_shape=(pixeles,)))\nmodel2.add(Reshape(formaImagen))\n\n# Capas Ocultas\n# Capas convolucionales\nmodel2.add(Conv2D(filters=32, kernel_size=3,strides=1 ,activation='linear', padding='same', name=\"capa_1\"))\nmodel2.add(LeakyReLU(alpha=0.1))\nmodel2.add(MaxPooling2D(pool_size=2, padding='same'))\nmodel2.add(Dropout(0.5))\n# https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html\n# Aplanamiento y capas densas\nmodel2.add(Flatten())\nmodel2.add(Dense(32, activation='linear'))\nmodel2.add(LeakyReLU(alpha=0.1))\nmodel2.add(Dropout(0.5))\n\n#capa de salida\nmodel2.add(Dense(numeroCategorias, activation='softmax'))\n\nmodel2.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n\nmodelosEntrenados.append((model2,6))\n\n\n# --------------------------------------------------------------------------------------------------\n# Modelo 3\n#https://ichi.pro/es/reconocimiento-de-digitos-escritos-a-mano-usando-redes-neuronales\n# -convolucionales-cnn-en-el-conjunto-de-datos-mnist-p-30698561832603\n# En este modelo se uso el optimizador nadam que es una variacion del adam la razon es que es el que tiene\n# el segundo mejor corportamiento para la clasificacion de imagenes multiclase segun este estudio \n# https://velascoluis.medium.com/optimizadores-en-redes-neuronales-profundas-un-enfoque-pr%C3%A1ctico-819b39a3eb5\n\nmodel3 = Sequential()\n\n# Capa entrada\nmodel3.add(InputLayer(input_shape=(pixeles,)))\nmodel3.add(Reshape(formaImagen))\n\n# capas ocultas\nmodel3.add(Conv2D(kernel_size=5, strides=2, filters=30, padding=\"same\", activation=\"relu\", name=\"capa_1\"))\nmodel3.add(MaxPool2D(pool_size=2, strides=2))\n\nmodel3.add(Conv2D(kernel_size=3, strides=1, filters=15, padding=\"same\", activation=\"relu\", name=\"capa_2\"))\nmodel3.add(MaxPool2D(pool_size=2, strides=2))\n\n# Aplanamientpo y cpas densas\nmodel3.add(Flatten())\nmodel3.add(Dense(500, activation='relu'))\nmodel3.add(Dropout(0.5))\n\n# capa de salida\nmodel3.add(Dense(numeroCategorias, activation='softmax'))\n\nmodel3.compile(optimizer=\"nadam\", loss=\"categorical_crossentropy\",\n metrics=[\"accuracy\"])\nmodelosEntrenados.append((model3, 10))\n\n#------------------------------------------------------------------------------------------------------------\n#Modelo 4 se tomo como referencia https://medium.com/swlh/image-classification-for-playing-cards-26d660f3149e\n#se aumento el numero de capas convolucionales y densas ademas se uso la funcion de activacion rmsprop\n\nmodel4 = Sequential()\n\n# Capa entrada\nmodel4.add(InputLayer(input_shape=(pixeles,)))\nmodel4.add(Reshape(formaImagen))\n\n# capas ocultas\nmodel4.add(Conv2D(kernel_size=3, filters=64, padding=\"same\", activation=\"relu\", name=\"capa_1\"))\nmodel4.add(MaxPool2D(pool_size=2, strides=2))\n\nmodel4.add(Conv2D(kernel_size=3, filters=128, padding=\"same\", activation=\"relu\", name=\"capa_2\"))\nmodel4.add(MaxPool2D(pool_size=2, strides=2))\n\n\n# Aplanamientpo y cpas densas\nmodel4.add(Flatten())\nmodel4.add(Dropout(0.5))\nmodel4.add(Dense(512, activation='relu'))\n\n# capa de salida\nmodel4.add(Dense(numeroCategorias, activation='softmax'))\n\nmodel4.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\",\n metrics=[\"accuracy\"])\nmodelosEntrenados.append((model4, 8))\n\n\n#----------------------------------------------------------------------------------------------------\n# Modelo 5 tomado de\n# https://la.mathworks.com/help/deeplearning/ug/create-simple-deep-learning-network-for-classification.html\n# este modelo implementa una capa de normalizacion despues de cada capa convolucional+\n\nmodel5 = Sequential()\n\n\n# Capa entrada\nmodel5.add(InputLayer(input_shape=(pixeles,)))\nmodel5.add(Reshape(formaImagen))\n\n# capas ocultas\n\nmodel5.add(Conv2D(kernel_size=3, filters=32,activation=\"relu\", name=\"capa_1\"))\nmodel5.add(MaxPool2D(pool_size=2, strides=2))\nmodel4.add(Dropout(0.25))\n\nmodel5.add(Conv2D(kernel_size=3, filters=64,activation=\"relu\", name=\"capa_2\"))\nmodel5.add(Conv2D(kernel_size=3, filters=64,activation=\"relu\", name=\"capa_3\"))\nmodel4.add(Dropout(0.25))\n\nmodel5.add(Conv2D(kernel_size=3, filters=196,activation=\"relu\", name=\"capa_4\"))\nmodel4.add(Dropout(0.25))\n\n\n# Aplanamientpo y cpas densas\nmodel5.add(Flatten())\nmodel5.add(Dense(512, activation='relu'))\nmodel5.add(Dropout(0.5))\n\n# capa de salida\nmodel5.add(Dense(numeroCategorias, activation='softmax'))\n\nmodel5.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\",\n metrics=[\"accuracy\"])\nmodelosEntrenados.append((model5, 4))\n\n\n\n\n# Entrenando modelos\nnumero_modelo = 0\nfor (model, epocas) in modelosEntrenados:\n numero_modelo += 1\n # Entrenando modelo con validacion cruzada usando el 80% del dataset\n accuracy_fold = []\n loss_fold = []\n\n myFolds = KFold(n_splits=5, shuffle=True)\n\n i = 1\n for train, test in myFolds.split(imagenes, probabilidades):\n print(\"############Training fold \", i, \"########################\")\n model.fit(x=imagenes[train], y=probabilidades[train], epochs=epocas, batch_size=150)\n resultados = model.evaluate(x=imagenes[test], y=probabilidades[test])\n accuracy_fold.append(resultados[1])\n loss_fold.append(resultados[0])\n i += 1\n\n print(\"Accuracy validacion Cruzada modelo\" + str(numero_modelo) + \"=\", accuracy_fold)\n print(\"Accuracy mean validacion cruzada\", np.mean(accuracy_fold))\n\n # Guardar modelo\n ruta = \"Modelos_CNN/modelo\" + str(numero_modelo) + \".h5\"\n model.save(ruta)\n # Informe de estructura de la red\n print(\"Informe de estructura de la red del modelo\" + str(numero_modelo))\n model.summary()\n", "repo_name": "NixonBuritik/SI_ProyectoFinal_Servidor", "sub_path": "src/EntrenandoModelos.py", "file_name": "EntrenandoModelos.py", "file_ext": "py", "file_size_in_byte": 7751, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "51", "api": [{"api_name": "CargaDeImagenes.CargaDeImagenes", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.InputLayer", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.InputLayer", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.InputLayer", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 128, "usage_type": "call"}, {"api_name": "keras.layers.InputLayer", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 144, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 145, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.layers.InputLayer", "line_number": 164, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 165, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 169, "usage_type": "call"}, {"api_name": "keras.layers.MaxPool2D", "line_number": 170, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 173, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 174, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 175, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 177, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 178, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 182, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 183, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 184, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "27647783611", "text": "from typing import Union\n\nimport pytest\n\n\ndef test_access():\n from jobflow import OutputReference\n\n # test empty\n ref = OutputReference(\"123\")\n assert ref.attributes == ()\n\n # test bad init\n with pytest.raises(\n ValueError, match=\"Unrecognised attribute type 'x' for attribute '1'\"\n ):\n OutputReference(\"123\", ((\"x\", 1),))\n\n new_ref = ref.a\n assert new_ref.attributes == ((\"a\", \"a\"),)\n assert new_ref.uuid == \"123\"\n assert isinstance(new_ref, OutputReference)\n\n new_ref = ref[\"a\"]\n assert new_ref.attributes == ((\"i\", \"a\"),)\n assert new_ref.uuid == \"123\"\n assert isinstance(new_ref, OutputReference)\n\n new_ref = ref[1]\n assert new_ref.attributes == ((\"i\", 1),)\n assert new_ref.uuid == \"123\"\n assert isinstance(new_ref, OutputReference)\n\n # test filled\n ref = OutputReference(\"123\", ((\"a\", \"b\"),))\n\n new_ref = ref.a\n assert new_ref.attributes == ((\"a\", \"b\"), (\"a\", \"a\"))\n assert new_ref.uuid == \"123\"\n assert isinstance(new_ref, OutputReference)\n\n new_ref = ref[\"a\"]\n assert new_ref.attributes == ((\"a\", \"b\"), (\"i\", \"a\"))\n assert new_ref.uuid == \"123\"\n assert isinstance(new_ref, OutputReference)\n\n with pytest.raises(AttributeError):\n _ = ref.args\n\n with pytest.raises(AttributeError):\n _ = ref.__fake_variable\n\n\ndef test_get_set_attr():\n from jobflow import OutputReference\n\n ref = OutputReference(\"123\")\n\n # these should fail\n with pytest.raises(TypeError):\n ref[\"a\"] = 1\n\n with pytest.raises(TypeError):\n ref[1] = 1\n\n with pytest.raises(TypeError):\n ref.a = 1\n\n ref.uuid = 1\n assert ref.uuid == 1\n\n\ndef test_repr():\n from jobflow import OutputReference\n\n ref = OutputReference(\"123\")\n assert str(ref) == \"OutputReference(123)\"\n\n ref = OutputReference(\"123\", ((\"a\", \"a\"),))\n assert str(ref) == \"OutputReference(123, .a)\"\n\n ref = OutputReference(\"123\", ((\"a\", \"a\"), (\"i\", 1)))\n assert str(ref) == \"OutputReference(123, .a, [1])\"\n\n\ndef test_hash():\n from jobflow import OutputReference\n\n assert hash(OutputReference(\"123\")) == hash(OutputReference(\"123\"))\n assert hash(OutputReference(\"123\", ((\"i\", 1), (\"i\", 2)))) == hash(\n OutputReference(\"123\", ((\"i\", 1), (\"i\", 2)))\n )\n assert hash(OutputReference(\"123\", ((\"a\", \"b\"), (\"i\", 2)))) == hash(\n OutputReference(\"123\", ((\"a\", \"b\"), (\"i\", 2)))\n )\n assert hash(OutputReference(\"123\", ((\"a\", \"b\"), (\"i\", \"2\")))) == hash(\n OutputReference(\"123\", ((\"a\", \"b\"), (\"i\", \"2\")))\n )\n\n\ndef test_eq():\n from jobflow import OutputReference\n\n assert OutputReference(\"123\") == OutputReference(\"123\")\n assert OutputReference(\"123\") != OutputReference(\"1234\")\n assert OutputReference(\"123\", ((\"i\", 1),)) == OutputReference(\"123\", ((\"i\", 1),))\n assert OutputReference(\"123\", ((\"i\", \"a\"),)) == OutputReference(\n \"123\", ((\"i\", \"a\"),)\n )\n assert OutputReference(\"123\", ((\"a\", \"a\"),)) == OutputReference(\n \"123\", ((\"a\", \"a\"),)\n )\n assert OutputReference(\"123\", ((\"a\", \"a\"), (\"i\", \"b\"))) == OutputReference(\n \"123\", ((\"a\", \"a\"), (\"i\", \"b\"))\n )\n assert OutputReference(\"123\", ((\"i\", 1),)) != OutputReference(\"1234\", ((\"i\", 1),))\n assert OutputReference(\"123\", ((\"i\", 1),)) != OutputReference(\"123\", ((\"i\", 2),))\n assert OutputReference(\"123\", ((\"i\", 1),)) != OutputReference(\n \"123\", ((\"i\", 2), (\"i\", 3), (\"i\", 4))\n )\n assert OutputReference(\"123\", ((\"i\", 1),)) != \"OutputReference(123, [1])\"\n\n\ndef test_as_dict():\n from jobflow import OutputReference\n\n ref = OutputReference(\"123\")\n d = ref.as_dict()\n assert d[\"@class\"] == \"OutputReference\"\n assert d[\"@module\"] == \"jobflow.core.reference\"\n assert d[\"uuid\"] == \"123\"\n\n ref = OutputReference(\"123\", ((\"a\", \"a\"), (\"i\", \"b\")))\n d = ref.as_dict()\n assert d[\"@class\"] == \"OutputReference\"\n assert d[\"@module\"] == \"jobflow.core.reference\"\n assert d[\"uuid\"] == \"123\"\n assert d[\"attributes\"] == ((\"a\", \"a\"), (\"i\", \"b\"))\n\n\ndef test_set_uuid():\n from jobflow import OutputReference\n\n ref = OutputReference(\"123\")\n new_ref = ref.set_uuid(\"321\")\n assert ref.uuid == \"321\"\n assert new_ref.uuid == \"321\"\n\n ref = OutputReference(\"123\")\n new_ref = ref.set_uuid(\"321\", inplace=False)\n assert ref.uuid == \"123\"\n assert new_ref.uuid == \"321\"\n\n\ndef test_schema():\n from pydantic import BaseModel\n\n from jobflow import OutputReference\n\n class InnerSchema(BaseModel):\n n: float\n\n class MediumSchema(BaseModel):\n s: str\n nested: InnerSchema\n nested_opt: InnerSchema = None\n nested_u: Union[InnerSchema, dict] # noqa: FA100\n nested_l: list[InnerSchema]\n nested_d: dict[str, InnerSchema]\n\n class MySchema(BaseModel):\n number: int\n name: str\n nested: MediumSchema\n\n ref = OutputReference(\"123\", output_schema=MySchema)\n assert ref.attributes == ()\n\n # check valid schema access works\n new_ref = ref.number\n assert new_ref.uuid == \"123\"\n assert new_ref.output_schema is None\n\n new_ref = ref[\"name\"]\n assert new_ref.uuid == \"123\"\n assert new_ref.output_schema is None\n\n with pytest.raises(AttributeError):\n _ = ref.a.uuid\n\n with pytest.raises(AttributeError):\n _ = ref[\"a\"].uuid\n\n with pytest.raises(AttributeError):\n _ = ref[1].uuid\n\n # check valid nested schemas\n assert ref.nested.s.uuid == \"123\"\n with pytest.raises(AttributeError):\n _ = ref.nested.m.uuid\n\n assert ref.nested.nested.n.uuid == \"123\"\n with pytest.raises(AttributeError):\n _ = ref.nested.nested.m.uuid\n\n assert ref.nested.nested_opt.n.uuid == \"123\"\n with pytest.raises(AttributeError):\n _ = ref.nested.nested_opt.m.uuid\n\n # Union, List and Dict are currently not recognized by their inner type\n # but check that there is no problem with them\n assert ref.nested.nested_u.n.uuid == \"123\"\n assert ref.nested.nested_l[0].n.uuid == \"123\"\n assert ref.nested.nested_d[\"a\"].n.uuid == \"123\"\n\n\ndef test_resolve(memory_jobstore):\n from jobflow import OnMissing, OutputReference\n\n ref = OutputReference(\"123\")\n\n # fail if cache or store not provided\n with pytest.raises(TypeError):\n ref.resolve()\n\n # test on missing\n assert ref.resolve(memory_jobstore, on_missing=OnMissing.NONE) is None\n assert ref.resolve(memory_jobstore, on_missing=OnMissing.PASS) == ref\n\n with pytest.raises(ValueError, match=\"Could not resolve reference\"):\n ref.resolve(memory_jobstore, on_missing=OnMissing.ERROR)\n\n # resolve using store\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": 101})\n assert ref.resolve(store=memory_jobstore) == 101\n\n # resolve using store and empty cache\n cache = {}\n assert ref.resolve(store=memory_jobstore, cache=cache) == 101\n assert cache[\"123\"][1] == 101\n\n # check cache supersedes store\n cache = {\"123\": {1: \"xyz\"}}\n assert ref.resolve(store=memory_jobstore, cache=cache) == \"xyz\"\n assert cache[\"123\"][1] == \"xyz\"\n\n # test indexing\n ref = OutputReference(\"123\", ((\"i\", \"a\"), (\"i\", 1)))\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": {\"a\": [5, 6, 7]}})\n assert ref.resolve(memory_jobstore) == 6\n\n # test attribute access\n ref = OutputReference(\"123\", ((\"a\", \"__module__\"),))\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": OutputReference})\n assert ref.resolve(memory_jobstore) == \"jobflow.core.reference\"\n\n # test missing attribute throws error\n ref = OutputReference(\"123\", ((\"a\", \"b\"),))\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": [1234]})\n with pytest.raises(AttributeError):\n ref.resolve(memory_jobstore)\n\n # test missing index throws error\n ref = OutputReference(\"123\", ((\"i\", \"b\"),))\n with pytest.raises(TypeError):\n ref.resolve(memory_jobstore)\n\n\ndef test_resolve_references(memory_jobstore):\n from jobflow import OnMissing, OutputReference\n from jobflow.core.reference import resolve_references\n\n # resolve single using store\n ref = OutputReference(\"123\")\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": \"xyz\"})\n output = resolve_references([ref], memory_jobstore)\n assert len(output) == 1\n assert output[ref] == \"xyz\"\n\n # resolve multiple using cache\n ref1 = OutputReference(\"123\")\n ref2 = OutputReference(\"1234\")\n memory_jobstore.update({\"uuid\": \"1234\", \"index\": 1, \"output\": 101})\n output = resolve_references([ref1, ref2], memory_jobstore)\n assert len(output) == 2\n assert output[ref1] == \"xyz\"\n assert output[ref2] == 101\n\n # resolve group using cache\n ref1 = OutputReference(\"123\", ((\"i\", \"a\"),))\n ref2 = OutputReference(\"123\", ((\"i\", \"b\"),))\n ref3 = OutputReference(\"1234\")\n memory_jobstore.update(\n {\"uuid\": \"123\", \"index\": 1, \"output\": {\"a\": \"xyz\", \"b\": \"abc\"}}\n )\n output = resolve_references([ref1, ref2, ref3], memory_jobstore)\n assert len(output) == 3\n assert output[ref1] == \"xyz\"\n assert output[ref2] == \"abc\"\n assert output[ref3] == 101\n\n # test on missing\n ref1 = OutputReference(\"123\")\n ref2 = OutputReference(\"12345\")\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": \"xyz\"})\n output = resolve_references(\n [ref1, ref2], memory_jobstore, on_missing=OnMissing.PASS\n )\n assert len(output) == 2\n assert output[ref1] == \"xyz\"\n assert output[ref2] == ref2\n\n ref2 = OutputReference(\"12345\")\n with pytest.raises(ValueError, match=\"Could not resolve reference\"):\n resolve_references([ref1, ref2], memory_jobstore, on_missing=OnMissing.ERROR)\n\n # resolve using store and empty cache\n cache = {}\n output = resolve_references([ref], memory_jobstore, cache=cache)\n assert len(output) == 1\n assert output[ref] == \"xyz\"\n\n # check cache supersedes store\n cache = {\"123\": {1: 101}}\n output = resolve_references([ref], memory_jobstore, cache=cache)\n assert len(output) == 1\n assert output[ref] == 101\n\n # test attributes\n ref = OutputReference(\"123\", ((\"i\", \"a\"), (\"i\", 1)))\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": {\"a\": [5, 6, 7]}})\n output = resolve_references([ref], memory_jobstore)\n assert output[ref] == 6\n\n ref = OutputReference(\"123\", ((\"a\", \"__module__\"),))\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": OutputReference})\n output = resolve_references([ref], memory_jobstore)\n assert output[ref] == \"jobflow.core.reference\"\n\n\ndef test_find_and_get_references():\n from jobflow.core.reference import OutputReference, find_and_get_references\n\n ref1 = OutputReference(\"123\")\n ref2 = OutputReference(\"1234\", ((\"a\", \"a\"),))\n\n # test single reference\n assert find_and_get_references(ref1) == (ref1,)\n\n # test list and tuple of references\n assert find_and_get_references([ref1]) == (ref1,)\n assert set(find_and_get_references([ref1, ref2])) == {ref1, ref2}\n assert set(find_and_get_references((ref1, ref2))) == {ref1, ref2}\n\n # test dictionary dictionary values\n assert find_and_get_references({\"a\": ref1}) == (ref1,)\n assert set(find_and_get_references({\"a\": ref1, \"b\": ref2})) == {ref1, ref2}\n\n # test nested\n assert set(find_and_get_references({\"a\": [ref1, ref2]})) == {ref1, ref2}\n assert set(find_and_get_references([{\"a\": ref1}, {\"b\": ref2}])) == {ref1, ref2}\n\n\ndef test_find_and_resolve_references(memory_jobstore):\n from jobflow.core.reference import (\n OnMissing,\n OutputReference,\n find_and_resolve_references,\n )\n\n ref1 = OutputReference(\"123\")\n ref2 = OutputReference(\"1234\", ((\"i\", \"a\"),))\n memory_jobstore.update({\"uuid\": \"123\", \"index\": 1, \"output\": 101})\n memory_jobstore.update({\"uuid\": \"1234\", \"index\": 1, \"output\": {\"a\": \"xyz\", \"b\": 5}})\n\n # test no reference\n assert find_and_resolve_references(arg=True, store=memory_jobstore) is True\n assert find_and_resolve_references(\"xyz\", memory_jobstore) == \"xyz\"\n assert find_and_resolve_references([101], memory_jobstore) == [101]\n\n # test single reference\n assert find_and_resolve_references(ref1, memory_jobstore) == 101\n\n # test list and tuple of references\n assert find_and_resolve_references([ref1], memory_jobstore) == [101]\n assert find_and_resolve_references([ref1, ref2], memory_jobstore) == [101, \"xyz\"]\n\n # test dictionary dictionary values\n output = find_and_resolve_references({\"a\": ref1}, memory_jobstore)\n assert output == {\"a\": 101}\n output = find_and_resolve_references({\"a\": ref1, \"b\": ref2}, memory_jobstore)\n assert output == {\n \"a\": 101,\n \"b\": \"xyz\",\n }\n\n # test nested\n output = find_and_resolve_references({\"a\": [ref1, ref2]}, memory_jobstore)\n assert output == {\"a\": [101, \"xyz\"]}\n output = find_and_resolve_references([{\"a\": ref1}, {\"b\": ref2}], memory_jobstore)\n assert output == [\n {\"a\": 101},\n {\"b\": \"xyz\"},\n ]\n\n # test store, blank cache\n cache = {}\n output = find_and_resolve_references(\n {\"a\": [ref1, ref2]}, store=memory_jobstore, cache=cache\n )\n assert output == {\"a\": [101, \"xyz\"]}\n assert cache[\"123\"][1] == 101\n\n # test cache overrides store\n output = find_and_resolve_references(\n {\"a\": [ref1, ref2]}, store=memory_jobstore, cache={\"123\": {1: 1}}\n )\n assert output == {\"a\": [1, \"xyz\"]}\n\n # test on missing\n ref3 = OutputReference(\"12345\", ((\"i\", \"a\"),))\n output = find_and_resolve_references(\n [ref1, ref3], memory_jobstore, on_missing=OnMissing.PASS\n )\n assert output == [101, ref3]\n output = find_and_resolve_references(\n [ref1, ref3], memory_jobstore, on_missing=OnMissing.NONE\n )\n assert output == [101, None]\n\n with pytest.raises(ValueError, match=\"Could not resolve reference\"):\n find_and_resolve_references(\n [ref1, ref3], memory_jobstore, on_missing=OnMissing.ERROR\n )\n\n\ndef test_circular_resolve(memory_jobstore):\n from jobflow.core.reference import OutputReference\n\n # test catching circular resolve failure\n ref1 = OutputReference(\"12345\")\n task_data = {\"uuid\": ref1.uuid, \"index\": 1, \"output\": ref1}\n memory_jobstore.update(task_data)\n with pytest.raises(RuntimeError):\n ref1.resolve(memory_jobstore)\n\n\ndef test_reference_in_output(memory_jobstore):\n from jobflow.core.reference import OnMissing, OutputReference\n\n # test resolvable reference in job output\n ref1 = OutputReference(\"12345\")\n ref2 = OutputReference(\"56789\")\n task_data1 = {\"uuid\": ref1.uuid, \"index\": 1, \"output\": ref2}\n task_data2 = {\"uuid\": ref2.uuid, \"index\": 1, \"output\": \"xyz\"}\n memory_jobstore.update(task_data1)\n memory_jobstore.update(task_data2)\n assert ref1.resolve(memory_jobstore) == \"xyz\"\n\n # test missing reference in output\n ref1 = OutputReference(\"12345\")\n ref2 = OutputReference(\"999\")\n task_data = {\"uuid\": ref1.uuid, \"index\": 1, \"output\": ref2}\n memory_jobstore.update(task_data)\n assert ref1.resolve(memory_jobstore, on_missing=OnMissing.NONE) is None\n assert ref1.resolve(memory_jobstore, on_missing=OnMissing.PASS) == ref2\n with pytest.raises(ValueError, match=\"Could not resolve reference\"):\n ref1.resolve(memory_jobstore, on_missing=OnMissing.ERROR)\n\n\ndef test_not_iterable():\n from jobflow.core.reference import OutputReference\n\n ref = OutputReference(\"12345\")\n\n with pytest.raises(TypeError):\n next(ref)\n\n with pytest.raises(TypeError): # noqa: PT012\n for _ in ref:\n pass\n", "repo_name": "materialsproject/jobflow", "sub_path": "tests/core/test_reference.py", "file_name": "test_reference.py", "file_ext": "py", "file_size_in_byte": 15618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 58, "dataset": "github-code", "pt": "51", "api": [{"api_name": "jobflow.OutputReference", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 14, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 17, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 22, "usage_type": "argument"}, {"api_name": "jobflow.OutputReference", "line_number": 27, "usage_type": "argument"}, {"api_name": "jobflow.OutputReference", "line_number": 32, "usage_type": "argument"}, {"api_name": "jobflow.OutputReference", "line_number": 35, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 40, "usage_type": "argument"}, {"api_name": "jobflow.OutputReference", "line_number": 45, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 50, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 57, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 60, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 66, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 76, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 79, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 82, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 89, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 90, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 91, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 93, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 94, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 96, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 97, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 104, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 105, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 106, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 107, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 110, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 113, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 116, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 117, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 118, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 121, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 127, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 133, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 144, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 149, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 160, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 167, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 171, "usage_type": "name"}, {"api_name": "jobflow.OutputReference", "line_number": 176, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 188, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 191, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 194, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 199, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 203, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 207, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 220, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 223, "usage_type": "call"}, {"api_name": "jobflow.OnMissing.NONE", "line_number": 227, "usage_type": "attribute"}, {"api_name": "jobflow.OnMissing", "line_number": 227, "usage_type": "name"}, {"api_name": "jobflow.OnMissing.PASS", "line_number": 228, "usage_type": "attribute"}, {"api_name": "jobflow.OnMissing", "line_number": 228, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 230, "usage_type": "call"}, {"api_name": "jobflow.OnMissing.ERROR", "line_number": 231, "usage_type": "attribute"}, {"api_name": "jobflow.OnMissing", "line_number": 231, "usage_type": "name"}, {"api_name": "jobflow.OutputReference", "line_number": 248, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 253, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 254, "usage_type": "name"}, {"api_name": "jobflow.OutputReference", "line_number": 258, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 260, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 264, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 265, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 274, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 276, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 281, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 282, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 284, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 290, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 291, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 292, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 296, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 303, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 304, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 306, "usage_type": "call"}, {"api_name": "jobflow.OnMissing.PASS", "line_number": 307, "usage_type": "attribute"}, {"api_name": "jobflow.OnMissing", "line_number": 307, "usage_type": "name"}, {"api_name": "jobflow.OutputReference", "line_number": 313, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 314, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 315, "usage_type": "call"}, {"api_name": "jobflow.OnMissing.ERROR", "line_number": 315, "usage_type": "attribute"}, {"api_name": "jobflow.OnMissing", "line_number": 315, "usage_type": "name"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 319, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 325, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 330, "usage_type": "call"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 332, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 335, "usage_type": "call"}, {"api_name": "jobflow.OutputReference", "line_number": 336, "usage_type": "name"}, {"api_name": "jobflow.core.reference.resolve_references", "line_number": 337, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 344, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 345, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 348, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 351, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 352, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 353, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 356, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 357, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 360, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_get_references", "line_number": 361, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 371, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 372, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 377, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 378, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 379, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 382, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 385, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 386, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 389, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 391, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 398, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 400, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 408, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 415, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 421, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 422, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OnMissing.PASS", "line_number": 423, "usage_type": "attribute"}, {"api_name": "jobflow.core.reference.OnMissing", "line_number": 423, "usage_type": "name"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 426, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OnMissing.NONE", "line_number": 427, "usage_type": "attribute"}, {"api_name": "jobflow.core.reference.OnMissing", "line_number": 427, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 431, "usage_type": "call"}, {"api_name": "jobflow.core.reference.find_and_resolve_references", "line_number": 432, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OnMissing.ERROR", "line_number": 433, "usage_type": "attribute"}, {"api_name": "jobflow.core.reference.OnMissing", "line_number": 433, "usage_type": "name"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 441, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 444, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 452, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 453, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 461, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 462, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OnMissing.NONE", "line_number": 465, "usage_type": "attribute"}, {"api_name": "jobflow.core.reference.OnMissing", "line_number": 465, "usage_type": "name"}, {"api_name": "jobflow.core.reference.OnMissing.PASS", "line_number": 466, "usage_type": "attribute"}, {"api_name": "jobflow.core.reference.OnMissing", "line_number": 466, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 467, "usage_type": "call"}, {"api_name": "jobflow.core.reference.OnMissing.ERROR", "line_number": 468, "usage_type": "attribute"}, {"api_name": "jobflow.core.reference.OnMissing", "line_number": 468, "usage_type": "name"}, {"api_name": "jobflow.core.reference.OutputReference", "line_number": 474, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 476, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 479, "usage_type": "call"}]} +{"seq_id": "28340560873", "text": "#!/usr/bin/python\n\n\"\"\"\n\nبوت للعمل على ويكيبيانات أو ويكيبيديا\n\n\"\"\" \n#\n# (C) Ibrahem Qasim, 2022\n# \n#\n#--- \n\nimport traceback\n#import pywikibot\nimport re\nimport time\nimport urllib\nimport json\nimport codecs\nimport unicodedata\nimport sys\n#---\nimport pywikibot\n#---\nfrom datetime import datetime\n#---\nmenet = datetime.now().strftime(\"%Y-%b-%d %H:%M:%S\")\n#---\nfrom mdpy import printe\nfrom mdpy.bots import py_tools\n\n\n\n\n\n\n#---\n'''\n#---\nfrom mdpy.bots import wikidataapi\n# wikidataapi.Log_to_wiki(url=\"https://www.wikidata.org/w/api.php\" )\n# wikidataapi.post( params , apiurl = '' )\n# wikidataapi.Get_sitelinks_From_Qid( q )\n# wikidataapi.WD_Merge( q1, q2)\n# wikidataapi.Labels_API(Qid, label, lang, remove = False)\n# wikidataapi.sparql_generator_url(quary, printq = False, add_date = True)\n# wikidataapi.wbsearchentities(search, language)\n# wikidataapi.Claim_API_qid(qid, property, numeric)\n# wikidataapi.Claim_API_str(qid, property, string)\n# wikidataapi.\n# wikidataapi.\n#---\n'''\n#---\nimport requests\n#---\nfrom mdpy.bots import user_account_new\n#---\nusername = user_account_new.bot_username #user_account_new.my_username\npassword = user_account_new.bot_password #user_account_new.my_password #user_account_new.mdwiki_pass\n#---\nif 'workhimo' in sys.argv:\n username = user_account_new.my_username\n password = user_account_new.my_password\n#---\nyes_answer = [ \"y\" , \"a\" , \"\" , \"Y\" , \"A\", \"all\"]\n#---\nSS = {}\nr1_params = {\n 'format': 'json',\n 'action': 'query',\n 'meta': 'tokens',\n 'type': 'login',\n}\nr2_params = {\n #fz'assert': 'user',\n 'format': 'json',\n 'action': 'login',\n 'lgname': username,\n 'lgpassword': password,\n}\n#---\nSS[\"ss\"] = requests.Session()\n#---\ntimesleep = 0\n#---\nlogin_not_done = { 1 : True }\n#---\ndef Log_to_wiki(url = ''):\n #---\n if not login_not_done[1] : return ''\n #---\n printe.output( f\"wikidataapi.py: log to {url} user:{r2_params['lgname']}\" )\n SS[\"url\"] = url\n SS[\"ss\"] = requests.Session()\n #---\n if SS:\n #try:\n r11 = SS[\"ss\"].get(SS[\"url\"], params=r1_params)\n r11.raise_for_status()\n #except:\n #printe.output( \"wikidataapi.py: Can't log in . \")\n # log in\n r2_params['lgtoken'] = r11.json()['query']['tokens']['logintoken']\n r22 = SS[\"ss\"].post(SS[\"url\"], data= r2_params )\n #except:\n else:\n printe.output( \"wikidataapi.py: Can't log in . \")\n return False\n #---\n if r22.json()['login']['result'] != 'Success':\n printe.output(r22.json()['login']['reason'])\n #raise RuntimeError(r22.json()['login']['reason'])\n else:\n printe.output('wikidataapi.py login Success')\n #---\n # get edit token\n SS[\"r33\"] = SS[\"ss\"].get(SS[\"url\"], params={\n 'format': 'json',\n 'action': 'query',\n 'meta': 'tokens',\n })\n #---\n SS[\"url\"] = url\n #---\n SS[\"r3_token\"] = SS[\"r33\"].json()['query']['tokens']['csrftoken']\n #---\n #printe.output( ' r3_token:%s' % SS[\"r3_token\"] )\n #---\n login_not_done[1] = False\n #---\n#---\ndef get_status(req):\n try :\n st = req.status_code\n return st\n except:\n st = req.status\n return st\n#---\ndef post( params , apiurl='', token = True):\n #---\n Log_to_wiki(url = apiurl)\n #---\n # r4 = SS[\"ss\"].post(SS[\"url\"], data = params )\n # post to API without error handling\n #---\n if token :\n params[\"token\"] = SS[\"r3_token\"]\n #---\n params[\"format\"] = \"json\"\n #---\n jsone = {}\n try:\n r4 = SS[\"ss\"].post( SS[\"url\"] , data = params)\n jsone = r4.json()\n except Exception as e:\n pywikibot.output( 'Traceback (most recent call last):' )\n pywikibot.output(traceback.format_exc())\n pywikibot.output( params )\n pywikibot.output( 'CRITICAL:' )\n return {}\n #---\n status = get_status(r4)\n if status != 200:\n pywikibot.output( f\"<> wikidataapi.py: post error status: {str(status)}\" )\n return {}\n #---\n return jsone\n#---\ndef post_to_qs(data):\n menet = datetime.now().strftime(\"%Y-%b-%d %H:%M:%S\")\n #---\n r2 = requests.Session().post('https://quickstatements.toolforge.org/api.php', data={\n 'format': 'v1',\n 'action': 'import',#create\n #'type': 'item',\n 'compress': 1,\n 'submit': 1,\n 'batchname': menet,\n 'username': \"Mr. Ibrahem\",\n 'token': user_account_new.qs_token,\n 'data': data,\n })\n #---\n if not r2 or r2 == {}: return False\n #---\n print(\"QS_New_API: \" + str(r2.text) )\n #---\n return r2.json()\n#---\ndef QS_New_API(data2):\n #---\n CREATE = 'CREATE||'\n for ss in data2.get(\"sitelinks\",{}):\n dd = data2.get(\"sitelinks\",{})\n tit = dd[ss][\"title\"]\n wik = dd[ss][\"site\"]\n wik2 = dd[ss][\"site\"].replace(\"wiki\",\"\")\n CREATE += f'LAST|S{wik}|\"{tit}\"||'\n CREATE += f'LAST|L{wik2}|\"{tit}\"||'\n #---\n claims = data2.get(\"claims\",{})\n for Claim in claims:\n for P in claims[Claim]:\n value = P['mainsnak'][\"datavalue\"].get(\"value\",{}).get(\"id\",\"\")\n #value = P[\"datavalue\"].get(\"value\",{}).get(\"id\",\"\")\n if value != \"\":\n CREATE += f\"LAST|{P['mainsnak']['property']}|{value}||\"\n #---\n CREATE = CREATE + \"XX\"\n CREATE = CREATE.replace(\"||XX\",\"\")\n #---\n return post_to_qs(CREATE)\n#---\ndef Get_sitelinks_From_Qid( q ):\n params = {\n \"action\": \"wbgetentities\",\n \"format\": \"json\",\n \"props\": \"sitelinks\",\n \"ids\": q,\n \"utf8\": 1,\n }\n #---\n table = { \"sitelinks\" : {} ,\"q\" : \"\" }\n #---\n json1 = post(params, apiurl = \"https://www.wikidata.org/w/api.php\" )\n #---\n if json1:\n if 'success' in json1 and json1['success'] == 1:\n if 'entities' in json1:\n if \"-1\" not in json1['entities']:\n qli = [x for x in json1['entities'].keys() ]\n q2 = qli[0]\n #---\n if q2 in json1['entities']:\n table['q'] = q2\n ppe = json1['entities'][q2]\n #---\n if 'sitelinks' in ppe:\n for site in ppe['sitelinks'].keys():\n fsai = ppe['sitelinks'][site]\n table['sitelinks'][fsai['site']] = fsai['title']\n #---\n else:\n return {}\n #---\n return table\n#---\ndef WD_Merge( q1, q2):\n #---\n q11 = re.sub(r'Q' , '' , q1)\n q22 = re.sub(r'Q' , '' , q2)\n #---\n if q11.isdigit() and q22.isdigit():\n #---\n if int(q11) > int(q22):\n From = q1\n To = q2\n else:\n From = q2\n To = q1\n else:\n From = q2\n To = q1\n #---\n printe.output(f'from {From} to {To} ' )\n #---\n params = {\n \"action\": \"wbmergeitems\",\n \"fromid\": From,\n \"toid\": To,\n \"ignoreconflicts\": \"description\",\n \"summary\": \"\",\n }\n #---\n r4 = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token = True)\n #---\n if not r4: return False\n #---\n if 'success' in r4:\n if '\"redirected\":1' in r4:\n printe.output('<> ** true .. redirected.' )\n return True\n else:\n printe.output('<> ** true.' )\n #---\n pams2 = {\"action\": \"wbcreateredirect\",\"from\": From,\"to\": To,\"ignoreconflicts\": \"description\",\"summary\": \"\"}\n #---\n r5 = post(pams2, apiurl = \"https://www.wikidata.org/w/api.php\", token = True)\n #---\n if 'success' in r5:\n printe.output('<> **createredirect true.' )\n return True\n else:\n printe.output('<> r5' + str(r5))\n else:\n printe.output('<> r4' + str(r4))\n return False\n#---\ndef Labels_API(Qid, label, lang, remove=False):\n #---\n if Qid == '':\n printe.output( \"Labels_API Qid == '' \" )\n return False\n #---\n if label == \"\" and not remove:\n printe.output( \"Labels_API label == '' and remove = False \" )\n return False\n #---\n # save the edit\n out = f'{Qid} label:\"{lang}\"@{label}.'\n #---\n params = {\n \"action\": \"wbsetlabel\",\n \"id\": Qid,\n \"language\": lang,\n \"value\": label,\n }\n #---\n req = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token = True)\n #---\n if req:\n text = str(req)\n if ('using the same description text' in text) and ('associated with language code' in text):\n item2 = re.search(r'(Q\\d+)', str(req[\"error\"]['info'])).group(1)\n printe.output('<>API: same label item: ' + item2 )\n #---\n #outbot(text, fi = out, NoWait = nowait)\n #---\n if 'success' in req:\n printe.output('<> **Labels_API true.' )\n return True\n else:\n printe.output('<> r5' + str(req))\n #---\n return False\n#---\ndef get_redirects(liste):\n #---\n redirects = {}\n #---\n for i in range(0, len(liste), 50):\n #---\n # group = dict(list(liste.items())[i:i+50])\n group = liste[i:i+50]\n params = {\n \"action\": \"query\",\n \"format\": \"json\",\n \"titles\": '|'.join( group ),\n \"redirects\": 1,\n \"utf8\": 1,\n }\n #---\n json1 = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token = True)\n #---\n if json1:\n redd = json1.get(\"query\",{}).get(\"redirects\", [])\n for red in redd:\n redirects[ red[\"from\"] ] = red[\"to\"]\n #---\n return redirects\n#---\n'''\ndef Sitelink_API(Qid, title, wiki):\n #---\n if wiki.endswith(\"wiki\") : wiki = wiki[:-4]\n #---\n wiki = f\"{wiki}wiki\"\n #---\n print(' **Sitelink_API: Qid:\"%s\" %s:%s, lag:\"%s\"' % (Qid, wiki, title, FFa_lag[1]) )\n #---\n if Qid.strip() == \"\":\n printe.output('<> **Sitelink_API: False: Qid == \"\" %s:%s.' % (wiki, title) )\n return False\n #---\n paramse = {\n \"action\": \"wbsetsitelink\",\n \"id\": Qid,\n \"linktitle\": title,\n \"linksite\": wiki,\n }\n #---\n out = 'Added link to \"%s\" [%s]:\"%s\"' % ( Qid, wiki, title) \n #---\n r4 = post(paramse, apiurl = \"https://www.wikidata.org/w/api.php\", token = True)\n #---\n if not r4 or r4 == {}: return False\n #---\n if 'success' in str(r4).lower():\n printe.output( '<> true ' + out )\n return True\n #---\n return False\n#---\ndef Remove_Sitelink(Qid, wiki):\n #---\n if wiki.endswith(\"wiki\") : wiki = wiki[:-4]\n #---\n wiki = f\"{wiki}wiki\"\n #---\n out = 'remove \"%s\" link from \"%s\"' % ( wiki , Qid)\n #---\n params ={ \"action\": \"wbsetsitelink\", \"id\": Qid, \"linksite\": wiki}\n #---\n r4 = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token = True)\n #---\n if not r4 or r4 == {}: return False\n #---\n if 'success' in str(r4).lower():\n printe.output( '<> true ' + out )\n return True\n #---\n return False\n'''\n#---\ndef Claim_API_str(qid, property, string):\n #---\n printe.output(f'<> Claim_API_str: add claim to qid: {qid}, [{property}:{string}]')\n #---\n if string == '' or qid == '' or property == '' : return ''\n #---\n params = {\n \"action\": \"wbcreateclaim\",\n \"entity\": qid,\n \"snaktype\": \"value\",\n \"property\": property,\n \"value\": json.JSONEncoder().encode(string)\n }\n #---\n req = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token=True)\n #---\n if not req or req == {}:\n printe.output(f'req:str({req})')\n return False\n #--- \n if 'success' in req:\n printe.output('<> **Claim_API true.' )\n return True\n else:\n printe.output('<> req' + str(req))\n #---\n return False\n#---\ndef Delete_claim(claimid):\n #---\n params = { \"action\": \"wbremoveclaims\", \"claim\": claimid }\n #---\n req = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token=True)\n #---\n if not req or req == {}:\n printe.output(f'req:str({req})')\n return False\n #--- \n if 'success' in req:\n printe.output('<> **Claim_API true.' )\n return True\n else:\n printe.output('<> req' + str(req))\n #---\n return False\n#---\ndef Claim_API_qid(qid, property, numeric):\n #---\n printe.output(f'<> Claim_API_qid: add claim to qid: {qid}, [{property}:{numeric}]')\n #---\n # remove Q from numeric\n if 'Q' in numeric: numeric = numeric.replace('Q' , '')\n #---\n if numeric == '' or qid == '' or property == '' : return ''\n #---\n params = {\n \"action\": \"wbcreateclaim\",\n \"entity\": qid,\n \"snaktype\": \"value\",\n \"property\": property,\n \"value\": \"{\\\"entity-type\\\":\\\"item\\\",\\\"numeric-id\\\":\" + numeric + \"}\",\n }\n #---\n req = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token=True)\n #---\n if not req or req == {}:\n printe.output(f'req:str({req})')\n return False\n #--- \n if 'success' in req:\n printe.output('<> **Claim_API true.' )\n return True\n else:\n printe.output('<> req' + str(req))\n #---\n return False\n#---\ndef open_url(url, return_json = False):\n #---\n result = {} and return_json or \"\"\n #---\n # get the url\n req = False\n try:\n req = urllib.request.urlopen(url)\n except Exception as e:\n pywikibot.output( 'Traceback (most recent call last):' )\n pywikibot.output(traceback.format_exc())\n pywikibot.output( 'CRITICAL:' )\n #---\n if not req:\n printe.output( ' open_url no req ' )\n return result\n #---\n html = \"\"\n try:\n html = req.read().strip().decode('utf-8')\n except Exception as e:\n pywikibot.output( 'Traceback (most recent call last):' )\n pywikibot.output(traceback.format_exc())\n pywikibot.output( 'CRITICAL:' )\n return result \n #---\n jsontab = {}\n try:\n jsontab = json.loads(html)\n except Exception as e:\n pywikibot.output( f' open_url: Exception {e} ' )\n return result \n #---\n return jsontab\n#---\ndef sparql_generator_url(quary, printq = False, add_date = True):\n #---\n if add_date:\n quary = quary + '\\n#' + str(menet)\n #---\n if printq == True: printe.output(quary)\n #---\n fao = py_tools.quoteurl(quary)\n #---\n url = 'https://query.wikidata.org/bigdata/namespace/wdq/sparql?format=json&query=' + fao\n #---\n json1 = open_url(url, return_json = False)\n #---\n if json1 and 'head' in json1:\n var = [x for x in json1['head']['vars']]\n var.sort()\n #---\n qlist = []\n if json1:\n if 'results' in json1:\n results = json1['results']\n if 'bindings' in results:\n for result in json1['results']['bindings']:\n s = {}\n #for se in result: s[se] = result[se]['value']\n for vv in var:\n if vv in result:\n s[vv] = result[vv]['value']\n else:\n s[vv] = ''\n qlist.append(s)\n #---\n printe.output(f'#sparql_generator_url:<> {len(qlist)} items found. {menet}')\n return qlist\n#---\ndef wbsearchentities(search, language):\n params = {\n \"action\": \"wbsearchentities\",\n \"format\": \"json\",\n \"search\": search,\n \"language\": language,\n \"strictlanguage\": 1,\n \"type\": \"item\",\n \"utf8\": 1\n }\n #---\n req = post(params, apiurl = \"https://www.wikidata.org/w/api.php\")\n #---\n if not req or req == {}:\n printe.output( ' wbsearchentities no req ' )\n return False\n #---\n if 'success' not in req:\n printe.output('<> wbsearchentities: ' + str(req))\n return False\n #---\n table = {}\n #---\n if 'search' in req: \n search = req['search'] # list\n for s in search:\n ss = {\"id\": \"Q111587429\",\"title\": \"Q111587429\",\"pageid\": 106531075,\n \"display\": { \"label\": { \"value\": \"User:Mr. Ibrahem/Sodium nitrite (medical use)\", \"language\": \"en\" } },\n \"repository\": \"wikidata\", \n \"url\": \"//www.wikidata.org/wiki/Q111587429\",\n \"concepturi\": \"http://www.wikidata.org/entity/Q111587429\",\n \"label\": \"User:Mr. Ibrahem/Sodium nitrite (medical use)\",\n \"match\": {\n \"type\": \"label\",\n \"language\": \"en\",\n \"text\": \"User:Mr. Ibrahem/Sodium nitrite (medical use)\"\n }}\n #---\n id = s['id']\n table[id] = {}\n #---\n if s.get(\"display\",{}).get(\"label\",{}).get(\"value\",'') != '':\n table[id]['label'] = s['display']['label']['value']\n table[id]['lang'] = s['display']['label']['language']\n elif s.get(\"match\",{}).get(\"type\",'') == 'label':\n table[id]['label'] = s['match']['text']\n table[id]['lang'] = s['match']['language']\n else:\n table[id] = s\n #---\n #---\n return table\n#---\ndef Get_claim(q, property, get_claim_id=False):\n #---\n params = {\n \"action\": \"wbgetclaims\",\n \"entity\": q,\n \"property\": property,\n }\n #---\n json1 = post(params, apiurl = \"https://www.wikidata.org/w/api.php\", token=True)\n #---\n listo = []\n #---\n if not json1 or json1 == {}: return []\n #---\n claims_p = json1.get('claims',{}).get(property,{})\n #---\n for claims in claims_p:\n claim_id = claims.get('id','')\n datavalue = claims.get('mainsnak',{}).get('datavalue',{})\n Type = datavalue.get(\"type\", False)\n value = datavalue.get(\"value\",\"\")\n #---\n if type(value) == dict:\n if value.get(\"id\", False) :\n value = value.get(\"id\")\n #---\n if get_claim_id:\n listo.append({\"id\":claim_id, \"value\":value})\n else:\n listo.append(value)\n #---\n return listo\n#---\nif __name__ == '__main__':\n Log_to_wiki(url=\"https://www.wikidata.org/w/api.php\")\n#---\n#---", "repo_name": "MrIbrahem/mdwiki.toolforge.org", "sub_path": "md_core/mdpy/bots/wikidataapi.py", "file_name": "wikidataapi.py", "file_ext": "py", "file_size_in_byte": 18629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "51", "api": [{"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "mdpy.bots.user_account_new.bot_username", "line_number": 60, "usage_type": "attribute"}, {"api_name": "mdpy.bots.user_account_new", "line_number": 60, "usage_type": "name"}, {"api_name": "mdpy.bots.user_account_new.bot_password", "line_number": 61, "usage_type": "attribute"}, {"api_name": "mdpy.bots.user_account_new", "line_number": 61, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mdpy.bots.user_account_new.my_username", "line_number": 64, "usage_type": "attribute"}, {"api_name": "mdpy.bots.user_account_new", "line_number": 64, "usage_type": "name"}, {"api_name": "mdpy.bots.user_account_new.my_password", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mdpy.bots.user_account_new", "line_number": 65, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 84, "usage_type": "call"}, {"api_name": "mdpy.printe.output", "line_number": 94, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 94, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 96, "usage_type": "call"}, {"api_name": "mdpy.printe.output", "line_number": 109, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 109, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 113, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 113, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 116, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 116, "usage_type": "name"}, {"api_name": "pywikibot.output", "line_number": 159, "usage_type": "call"}, {"api_name": "pywikibot.output", "line_number": 160, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 160, "usage_type": "call"}, {"api_name": "pywikibot.output", "line_number": 161, "usage_type": "call"}, {"api_name": "pywikibot.output", "line_number": 162, "usage_type": "call"}, {"api_name": "pywikibot.output", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 173, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 173, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 175, "usage_type": "call"}, {"api_name": "mdpy.bots.user_account_new.qs_token", "line_number": 183, "usage_type": "attribute"}, {"api_name": "mdpy.bots.user_account_new", "line_number": 183, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 253, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 254, "usage_type": "call"}, {"api_name": "mdpy.printe.output", "line_number": 268, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 268, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 284, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 284, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 287, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 287, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 294, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 294, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 297, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 297, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 299, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 299, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 305, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 305, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 309, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 309, "usage_type": "name"}, {"api_name": "re.search", "line_number": 327, "usage_type": "call"}, {"api_name": "mdpy.printe.output", "line_number": 328, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 328, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 333, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 333, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 336, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 336, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 420, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 420, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 429, "usage_type": "call"}, {"api_name": "mdpy.printe.output", "line_number": 435, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 435, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 439, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 439, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 442, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 442, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 453, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 453, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 457, "usage_type": "call"}, {"api_name": "mdpy.printe", "line_number": 457, "usage_type": "name"}, {"api_name": "mdpy.printe.output", "line_number": 460, "usage_type": "call"}, {"api_name": "mdpy.printe", 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